homework plant systems interactions

Plant System Interactions

• Word Document File

Description

We have always found it hard to connect how different plant organ systems interacted with each other. We created this worksheet (with answer key) to help students understand the intricacies of plant systems. They will analyze how the vascular, reproductive, shoot, and root system are connected to perform tasks.

Questions & Answers

• We're hiring
• Help & FAQ
• Privacy policy
• Student privacy
• Terms of service
• Tell us what you think

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Xylem and Phloem – Plant Vascular System

The vascular system of plants consists of the xylem and phloem. They are somewhat like blood vessels in animals, but plants transport materials using two tissues rather than one. Here is a look at what xylem and phloem are, what they transport, and how they work.

What are Xylem and Phloem?

Xylem and phloem are the two types of transport tissue found in vascular plants. They form a complex network running throughout the plant, carrying resources to different parts and disposing of waste products.

• Xylem primarily transports water and mineral nutrients from the roots to the rest of the plant, and it also plays a role in physical support.
• Phloem transports organic substances, such as sugars produced during photosynthesis, from the leaves to other parts of the plant.

Importance of the Vascular System in Plants

The vascular system allows plants to grow taller and larger, enabling them to inhabit a wide variety of environments. Without these conduits, plants only grow to a small size. Non-vascular plants, such as mosses and liverworts, lack xylem and phloem and rely on diffusion and osmosis for the distribution of nutrients. Vascular plants, including trees, flowering plants, and ferns, use xylem and phloem to efficiently transport nutrients, even against gravity.

The term “xylem” comes from the Greek word “xylon,” which means “wood.” This reflects the role of xylem tissue in contributing to the structural strength of plants, particularly woody ones.

Function and Structure of Xylem

Xylem transports water and dissolved minerals absorbed from the soil by the roots to the above-ground parts of the plant. The plant uses the water transported by the xylem photosynthesis and transpiration. Additionally, the xylem also provides structural support to the plant.

The xylem tissue consists of four main types of cells: tracheids, vessel elements, xylem parenchyma, and xylem fibers. The vessel elements and tracheids are the water-conducting cells. Vessel elements are wider and shorter than tracheids and connect together at the ends. The ends have perforation plates that permit water transfer between cells. Tracheids are long, thin, and tapered at the ends. The secondary cell walls of the tracheids contain lignin. The parenchyma stores food and helps in the repair and growth of xylem, while xylem fibers provide support.

In most plants, the xylem is in the center of the stem, forming a core of rigid, woody material. Mature xylem consists of dead vessel element and tracheid cells connected by hollow ends.

Transportation in Xylem

The mechanism of water transport in xylem primarily involves a process known as cohesion-tension theory. Here, the evaporative pull of transpiration from the leaves creates a tension or negative pressure that pulls water upward from the roots through the xylem tissue. Also, root pressure also plays a role. Here, water enters roots from the soil via osmosis, generating a positive pressure that forces water upward into the plant.

The term “phloem” comes from the Greek word “phloios,” meaning “bark.” This name is fitting, as phloem is often found just beneath the bark in trees.

Function and Structure of Phloem

Phloem transports organic nutrients, particularly sugars synthesized during photosynthesis, from the leaves to all other cells of the plant, including the roots.

Phloem tissue is composed of sieve-tube elements, companion cells, phloem fibers, and phloem parenchyma. The sieve-tube elements, along with their companion cells, primarily control the transportation of food. Phloem fibers provide support, and phloem parenchyma assists with food storage and the secretion of plant resins.

In most plants, the phloem is towards the exterior of the plant, just below the bark in stems and roots. The sieve-tube cells are alive, but they lack a nucleus and have less cytoplasm than other plant cells . The companion cells are living cells with a normal composition.

Transportation in Phloem

The transport mechanism in phloem is known as translocation. It involves an active process where sugars load into sieve tubes in the leaves (source) and unload where they are needed (sink), such as roots or developing shoots. This differential in sugar concentration results in water moving from xylem to phloem, building a pressure that drives the sap down the plant.

Differences in Xylem and Phloem in Monocots and Dicots

Monocots and dicots differ in the arrangement and structure of their xylem and phloem.

In dicot plants, the vascular system is organized in a ring, with the xylem typically inside, surrounded by phloem. There is often a region of meristematic cambium cells, which divide to produce more xylem or phloem cells, allowing the stem or root to increase in diameter.

In monocot plants, the xylem and phloem are paired into bundles scattered throughout the stem. Monocots do not have a vascular cambium, meaning they typically do not increase in diameter after growth.

Girdling and Its Impacts

Girdling is a practice that removes a ring of bark (the phloem layer) from around the entire circumference of a tree or plant stem. This disrupts the downward transportation of sugars and other metabolites from the leaves through the phloem. Girdling can cause the death of a tree because it interrupts the supply of food from leaves to the roots, essentially starving the plant.

However, girdling also has a deliberate use in horticulture. It encourages the plant to produce larger fruits or to direct the plant’s energy towards certain branches. By disrupting the flow of nutrients, the plant overcompensates in the remaining portions, often leading to increased yield or size of the produce.

• Lucas, William; et al. (2013). “”The Plant Vascular System ” Evolution, Development and Functions”. Journal of Integrative Plant Biology . 55 (4): 294–388. doi: 10.1111/jipb.12041
• McCulloh, Katherine A.; John S. Sperry; Frederick R. Adler (2003). “Water transport in plants obeys Murray’s law”. Nature . 421 (6926): 939–942. doi: 10.1038/nature01444
• Raven, Peter A.; Evert, Ray F.; Eichhorn, Susan E. (1999). Biology of Plants . W.H. Freeman and Company. ISBN 978-1-57259-611-5.
• Roberts, Keith (ed.) (2007). Handbook of Plant Science . Vol. 1 (Illustrated ed.). John Wiley & Sons. ISBN 9780470057230.
• Slewinski, Thomas L.; Zhang, Cankui; Turgeon, Robert (2013-07-05). “Structural and functional heterogeneity in phloem loading and transport”. Frontiers in Plant Science . 4: 244. doi: 10.3389/fpls.2013.00244

Related Posts

23.6 Plant Sensory Systems and Responses

Learning objectives.

In this section, you will explore the following questions:

• How do red and blue light affect plant growth and metabolic activities?
• What is gravitropism?
• What are examples of plant hormones, and how do they affect plant growth and development?
• What are the differences between thigmotropism, thigmonastism, and thigmogenesis?
• How do plants defend themselves against predators and respond to wounds?

Connection for AP® Courses

Connection for ap ® courses.

Why do some flowering plants bloom in the spring, whereas others bloom in the summer or fall? Why do the stems of plants grow upward and bend toward light, while roots grow downward? Why do the leaves of deciduous plants in northern climates turn colors in the fall and eventually fall off, while evergreens keep their needles all year around? Is it true that putting an unripe avocado in a paper bag will hasten the ripening process? Can the same tactic work on bananas?

Like most other organisms, plants have the ability to detect and respond to environmental change; this ability is an adaptation favored by natural selection. Flowering plants react to light, gravity, infection by pathogens, drought, and, as with the Venus flytrap, touch. Animals have two systems on which they can rely to detect and respond to stimuli: the nervous and endocrine systems. Plants, however, only have chemical control mechanisms at their disposal. In addition to the phytochromes, plants evolved hormones that allow them to respond to environmental changes. Like animal hormones, plant hormones are chemical signaling molecules that trigger a cellular response through a signal transduction pathway.

Although you do not have to memorize a laundry list of plant hormones and their activities, you should understand the basic mechanism of how they trigger a response. Examples that we’ll learn about in this chapter include auxins, which cause plant stems to grow and bend toward light, and gibberellins, which stimulate cell growth and breaks seed dormancy. Plants also produce other chemicals to protect against biotic stimuli, including herbivory and parasitism. The fact that plants produce chemicals should not be surprising; our pharmaceutical industry depends on these substances. For example, species of the foxglove plant produce digitalis, a powerful heart medicine, whereas the opium from the poppy is the source of many narcotics.

Information presented and the examples highlighted in the section support concepts outlined in Big Idea 2 and Big Idea 3 of the AP ® Biology Curriculum Framework. The AP ® Learning Objectives listed in the Curriculum Framework provide a transparent foundation for the AP ® Biology course, an inquiry-based laboratory experience, instructional activities, and AP ® exam questions. A learning objective merges required content with one or more of the seven science practices.

The Science Practices Assessment Ancillary contains additional test questions for this section that will help you prepare for the AP exam. These questions address the following standards:

• [APLO 2.29]
• [APLO 2.30]

Animals can respond to environmental factors by moving to a new location. Plants, however, are rooted in place and must respond to the surrounding environmental factors. Plants have sophisticated systems to detect and respond to light, gravity, temperature, and physical touch. Receptors sense environmental factors and relay the information to effector systems—often through intermediate chemical messengers—to bring about plant responses.

Plant Responses to Light

Plants have a number of sophisticated uses for light that go far beyond their ability to photosynthesize low-molecular-weight sugars using only carbon dioxide, light, and water. Photomorphogenesis is the growth and development of plants in response to light. It allows plants to optimize their use of light and space. Photoperiodism is the ability to use light to track time. Plants can tell the time of day and time of year by sensing and using various wavelengths of sunlight. Phototropism is a directional response that allows plants to grow towards, or even away from, light.

The sensing of light in the environment is important to plants; it can be crucial for competition and survival. The response of plants to light is mediated by different photoreceptors, which are comprised of a protein covalently bonded to a light-absorbing pigment called a chromophore . Together, the two are called a chromoprotein.

The red/far-red and violet-blue regions of the visible light spectrum trigger structural development in plants. Sensory photoreceptors absorb light in these particular regions of the visible light spectrum because of the quality of light available in the daylight spectrum. In terrestrial habitats, light absorption by chlorophylls peak in the blue and red regions of the spectrum. As light filters through the canopy and the blue and red wavelengths are absorbed, the spectrum shifts to the far-red end, shifting the plant community to those plants better adapted to respond to far-red light. Blue-light receptors allow plants to gauge the direction and abundance of sunlight, which is rich in blue–green emissions. Water absorbs red light, which makes the detection of blue light essential for algae and aquatic plants.

The Phytochrome System and the Red/Far-Red Response

The phytochromes are a family of chromoproteins with a linear tetrapyrrole chromophore, similar to the ringed tetrapyrrole light-absorbing head group of chlorophyll. Phytochromes have two photo-interconvertible forms: Pr and Pfr. Pr absorbs red light (~667 nm) and is immediately converted to Pfr. Pfr absorbs far-red light (~730 nm) and is quickly converted back to Pr. Absorption of red or far-red light causes a massive change to the shape of the chromophore, altering the conformation and activity of the phytochrome protein to which it is bound. Pfr is the physiologically active form of the protein; therefore, exposure to red light yields physiological activity. Exposure to far-red light inhibits phytochrome activity. Together, the two forms represent the phytochrome system ( Figure 23.38 ).

The phytochrome system acts as a biological light switch. It monitors the level, intensity, duration, and color of environmental light. The effect of red light is reversible by immediately shining far-red light on the sample, which converts the chromoprotein to the inactive Pr form. Additionally, Pfr can slowly revert to Pr in the dark, or break down over time. In all instances, the physiological response induced by red light is reversed. The active form of phytochrome (Pfr) can directly activate other molecules in the cytoplasm, or it can be trafficked to the nucleus, where it directly activates or represses specific gene expression.

Once the phytochrome system evolved, plants adapted it to serve a variety of needs. Unfiltered, full sunlight contains much more red light than far-red light. Because chlorophyll absorbs strongly in the red region of the visible spectrum, but not in the far-red region, any plant in the shade of another plant on the forest floor will be exposed to red-depleted, far-red-enriched light. The preponderance of far-red light converts phytochrome in the shaded leaves to the Pr—inactive—form, slowing growth. The nearest non-shaded or even less-shaded areas on the forest floor have more red light; leaves exposed to these areas sense the red light, which activates the Pfr form and induces growth. In short, plant shoots use the phytochrome system to grow away from shade and towards light. Because competition for light is so fierce in a dense plant community, the evolutionary advantages of the phytochrome system are obvious.

In seeds, the phytochrome system is not used to determine direction and quality of light—shaded versus unshaded. Instead, is it used merely to determine if there is any light at all. This is especially important in species with very small seeds, such as lettuce. Because of their size, lettuce seeds have few food reserves. Their seedlings cannot grow for long before they run out of fuel. If they germinated even a centimeter under the soil surface, the seedling would never make it into the sunlight and would die. In the dark, phytochrome is in the Pr—inactive form—and the seed will not germinate; it will only germinate if exposed to light at the surface of the soil. Upon exposure to light, Pr is converted to Pfr and germination proceeds.

Plants also use the phytochrome system to sense the change of season. Photoperiodism is a biological response to the timing and duration of day and night. It controls flowering, setting of winter buds, and vegetative growth. Detection of seasonal changes is crucial to plant survival. Although temperature and light intensity influence plant growth, they are not reliable indicators of season because they may vary from one year to the next. Day length is a better indicator of the time of year.

As stated above, unfiltered sunlight is rich in red light but deficient in far-red light. Therefore, at dawn, all the phytochrome molecules in a leaf quickly convert to the active Pfr form, and remain in that form until sunset. In the dark, the Pfr form takes hours to slowly revert back to the Pr form. If the night is long as in winter, all of the Pfr form reverts. If the night is short as in summer, a considerable amount of Pfr may remain at sunrise. By sensing the Pr/Pfr ratio at dawn, a plant can determine the length of the day/night cycle. In addition, leaves retain that information for several days, allowing a comparison between the length of the previous night and the preceding several nights. Shorter nights indicate springtime to the plant; when the nights become longer, autumn is approaching. This information, along with sensing temperature and water availability, allows plants to determine the time of the year and adjust their physiology accordingly. Short-day, long-night plants use this information to flower in the late summer and early fall, when nights exceed a critical length—often eight or fewer hours. Long-day, short-night plants flower during the spring, when darkness is less than a critical length—often eight to 15 hours. Not all plants use the phytochrome system in this way. Flowering in day-neutral plants is not regulated by daylength.

Career Connection

Horticulturist.

The word horticulturist comes from the Latin words for garden— hortus —and culture— cultura . This career has been revolutionized by progress made in the understanding of plant responses to environmental stimuli. Growers of crops, fruit, vegetables, and flowers were previously constrained by having to time their sowing and harvesting according to the season. Now, horticulturists can manipulate plants to increase leaf, flower, or fruit production by understanding how environmental factors affect plant growth and development.

Greenhouse management is an essential component of a horticulturist’s education. To lengthen the night, plants are covered with a blackout shade cloth. Long-day plants are irradiated with red light in winter to promote early flowering. For example, fluorescent, cool white light high in blue wavelengths encourages leafy growth and is excellent for starting seedlings. Incandescent lamps—standard light bulbs—are rich in red light, and promote flowering in some plants. The timing of fruit ripening can be increased or delayed by applying plant hormones. Recently, considerable progress has been made in the development of plant breeds that are suited to different climates and resistant to pests and transportation damage. Both crop yield and quality have increased as a result of practical applications of the knowledge of plant responses to external stimuli and hormones.

Horticulturists find employment in private and governmental laboratories, greenhouses, botanical gardens, and in the production or research fields. They improve crops by applying their knowledge of genetics and plant physiology. To prepare for a horticulture career, students take classes in botany, plant physiology, plant pathology, landscape design, and plant breeding. To complement these traditional courses, horticulture majors add studies in economics, business, computer science, and communications.

The Blue Light Responses

Phototropism—the directional bending of a plant toward or away from a light source—is a response to blue wavelengths of light. Positive phototropism is growth towards a light source ( Figure 23.39 ), while negative phototropism, also called skototropism, is growth away from light.

The aptly-named phototropins are protein-based receptors responsible for mediating the phototropic response. Like all plant photoreceptors, phototropins consist of a protein portion and a light-absorbing portion, called the chromophore. In phototropins, the chromophore is a covalently-bound molecule of flavin; hence, phototropins belong to a class of proteins called flavoproteins.

Other responses under the control of phototropins are leaf opening and closing, chloroplast movement, and the opening of stomata. However, of all responses controlled by phototropins, phototropism has been studied the longest and is the best understood.

In their 1880 treatise The Power of Movements in Plants , Charles Darwin and his son Francis first described phototropism as the bending of seedlings toward light. Darwin observed that light was perceived by the tip of the plant—the apical meristem—but that the response—bending—took place in a different part of the plant. They concluded that the signal had to travel from the apical meristem to the base of the plant.

In 1913, Peter Boysen-Jensen demonstrated that a chemical signal produced in the plant tip was responsible for the bending at the base. He cut off the tip of a seedling, covered the cut section with a layer of gelatin, and then replaced the tip. The seedling bent toward the light when illuminated. However, when impermeable mica flakes were inserted between the tip and the cut base, the seedling did not bend. A refinement of the experiment showed that the signal traveled on the shaded side of the seedling. When the mica plate was inserted on the illuminated side, the plant did bend towards the light. Therefore, the chemical signal was a growth stimulant because the phototropic response involved faster cell elongation on the shaded side than on the illuminated side. We now know that as light passes through a plant stem, it is diffracted and generates phototropin activation across the stem. Most activation occurs on the lit side, causing the plant hormone indole acetic acid (IAA) to accumulate on the shaded side. Stem cells elongate under influence of IAA.

Cryptochromes are another class of blue-light absorbing photoreceptors that also contain a flavin-based chromophore. Cryptochromes set the plants 24-hour activity cycle, also know as its circadian rhythem, using blue light cues. There is some evidence that cryptochromes work together with phototropins to mediate the phototropic response.

Link to Learning

Watch this video showing plant growth and movement in response to various stimuli.

• Green light is absorbed by the plant. The seedling would not bend, but it would grow tall and spindly as if grown in the dark.
• Green light is not absorbed by the plant. The seedling would not bend, but it would grow tall and spindly as if grown in the dark.
• Green light is not absorbed by the plant. The seedling would not grow tall and spindly, but it would bend.
• Green light is absorbed by the plant. The seedling would not grow tall and spindly, but it would bend.

Plant Responses to Gravity

Whether or not they germinate in the light or in total darkness, shoots usually sprout up from the ground, and roots grow downward into the ground. A plant laid on its side in the dark will send shoots upward when given enough time. Gravitropism ensures that roots grow into the soil and that shoots grow toward sunlight. Growth of the shoot apical tip upward is called negative gravitropism , whereas growth of the roots downward is called positive gravitropism .

Amyloplasts , also known as statoliths , are specialized plastids that contain starch granules and settle downward in response to gravity. Amyloplasts are found in shoots and in specialized cells of the root cap. When a plant is tilted, the statoliths drop to the new bottom cell wall. A few hours later, the shoot or root will show growth in the new vertical direction.

The mechanism that mediates gravitropism is reasonably well understood. When amyloplasts settle to the bottom of the gravity-sensing cells in the root or shoot, they physically contact the endoplasmic reticulum (ER), causing the release of calcium ions from inside the ER. This calcium signaling in the cells causes polar transport of the plant hormone IAA to the bottom of the cell. In roots, a high concentration of IAA inhibits cell elongation. The effect slows growth on the lower side of the root, while cells develop normally on the upper side. IAA has the opposite effect in shoots, where a higher concentration at the lower side of the shoot stimulates cell expansion, causing the shoot to grow up. After the shoot or root begin to grow vertically, the amyloplasts return to their normal position. Other hypotheses—involving the entire cell in the gravitropism effect—have been proposed to explain why some mutants that lack amyloplasts may still exhibit a weak gravitropic response.

Growth Responses

A plant’s sensory response to external stimuli relies on chemical messengers—hormones. Plant hormones affect all aspects of plant life, from flowering to fruit setting and maturation, and from phototropism to leaf fall. Potentially every cell in a plant can produce plant hormones. They can act in their cell of origin or be transported to other portions of the plant body, with many plant responses involving the synergistic or antagonistic interaction of two or more hormones. In contrast, animal hormones are produced in specific glands and transported to a distant site for action, and they act alone.

Plant hormones are a group of unrelated chemical substances that affect plant morphogenesis. Five major plant hormones are traditionally described: auxins, particularly IAA, cytokinins, gibberellins, ethylene, and abscisic acid. In addition, other nutrients and environmental conditions can be characterized as growth factors.

The term auxin is derived from the Greek word auxein , which means to grow . Auxins are the main hormones responsible for cell elongation in phototropism and gravitropism. They also control the differentiation of meristem into vascular tissue, and promote leaf development and arrangement. While many synthetic auxins are used as herbicides, IAA is the only naturally occurring auxin that shows physiological activity. Apical dominance—the inhibition of lateral bud formation—is triggered by auxins produced in the apical meristem. Flowering, fruit setting and ripening, and inhibitionx of abscission —leaf falling—are other plant responses under the direct or indirect control of auxins. Auxins also act as a relay for the effects of the blue light and red/far-red responses.

Commercial use of auxins is widespread in plant nurseries and for crop production. IAA is used as a rooting hormone to promote growth of adventitious roots on cuttings and detached leaves. Applying synthetic auxins to tomato plants in greenhouses promotes normal fruit development. Outdoor application of auxin promotes synchronization of fruit setting and dropping to coordinate the harvesting season. Fruits such as seedless cucumbers can be induced to set fruit by treating unfertilized plant flowers with auxins.

The effect of cytokinins was first reported when it was found that adding the liquid endosperm of coconuts to developing plant embryos in culture stimulated their growth. The stimulating growth factor was found to be cytokinin , a hormone that promotes cytokinesis, or cell division. Almost 200 naturally occurring or synthetic cytokinins are known to date. Cytokinins are most abundant in growing tissues, such as roots, embryos, and fruits, where cell division is occurring. Cytokinins are known to delay senescence in leaf tissues, promote mitosis, and stimulate differentiation of the meristem in shoots and roots. Many effects on plant development are under the influence of cytokinins, either in conjunction with auxin or another hormone. For example, apical dominance seems to result from a balance between auxins that inhibit lateral buds, and cytokinins that promote bushier growth.

Gibberellins

Gibberellins (GAs) are a group of about 125 closely related plant hormones that stimulate shoot elongation, seed germination, and fruit and flower maturation. GAs are synthesized in the root and stem apical meristems, young leaves, and seed embryos. In urban areas, GA antagonists are sometimes applied to trees under power lines to control growth and reduce the frequency of pruning.

GAs break dormancy—a state of inhibited growth and development—in the seeds of plants that require exposure to cold or light to germinate. Other effects of GAs include gender expression, seedless fruit development, and the delay of senescence in leaves and fruit. Seedless grapes are obtained through standard breeding methods and contain inconspicuous seeds that fail to develop. Because GAs are produced by the seeds, and because fruit development and stem elongation are under GA control, these varieties of grapes would normally produce small fruit in compact clusters. Maturing grapes are routinely treated with GA to promote larger fruit size, as well as looser bunches—longer stems—which reduces the instance of mildew infection ( Figure 23.40 ).

Abscisic Acid

The plant hormone abscisic acid (ABA) was first discovered as the agent that causes the abscission or dropping of cotton bolls. However, more recent studies indicate that ABA plays only a minor role in the abscission process. ABA accumulates as a response to stressful environmental conditions, such as dehydration, cold temperatures, or shortened day lengths. Its activity counters many of the growth-promoting effects of GAs and auxins. ABA inhibits stem elongation and induces dormancy in lateral buds.

ABA induces dormancy in seeds by blocking germination and promoting the synthesis of storage proteins. Plants adapted to temperate climates require a long period of cold temperature before seeds germinate. This mechanism protects young plants from sprouting too early during unseasonably warm weather in winter. As the hormone gradually breaks down over winter, the seed is released from dormancy and germinates when conditions are favorable in spring. Another effect of ABA is to promote the development of winter buds; it mediates the conversion of the apical meristem into a dormant bud. Low soil moisture causes an increase in ABA, which causes stomata to close, reducing water loss in winter buds.

Ethylene is associated with fruit ripening, flower wilting, and leaf fall. Ethylene is unusual because it is a volatile gas (C 2 H 4 ). Hundreds of years ago, when gas street lamps were installed in city streets, trees that grew close to lamp posts developed twisted, thickened trunks and shed their leaves earlier than expected. These effects were caused by ethylene volatilizing from the lamps.

Aging tissues, especially senescing leaves, and nodes of stems produce ethylene. The best-known effect of the hormone, however, is the promotion of fruit ripening. Ethylene stimulates the conversion of starch and acids to sugars. Some people store unripe fruit, such as avocadoes, in a sealed paper bag to accelerate ripening; the gas released by the first fruit to mature will speed up the maturation of the remaining fruit. Ethylene also triggers leaf and fruit abscission, flower fading and dropping, and promotes germination in some cereals and sprouting of bulbs and potatoes.

Ethylene is widely used in agriculture. Commercial fruit growers control the timing of fruit ripening with application of the gas. Horticulturalists inhibit leaf dropping in ornamental plants by removing ethylene from greenhouses using fans and ventilation.

Nontraditional Hormones

Recent research has discovered a number of compounds that also influence plant development. Their roles are less understood than the effects of the major hormones described so far.

Jasmonates play a major role in defense responses to herbivory. Their levels increase when a plant is wounded by a predator, resulting in an increase in toxic secondary metabolites. They contribute to the production of volatile compounds that attract natural enemies of predators. For example, chewing of tomato plants by caterpillars leads to an increase in jasmonic acid levels, which in turn triggers the release of volatile compounds that attract predators of the pest.

Oligosaccharins also play a role in plant defense against bacterial and fungal infections. They act locally at the site of injury, and can also be transported to other tissues. Strigolactones promote seed germination in some species and inhibit lateral apical development in the absence of auxins. Strigolactones also play a role in the establishment of mycorrhizae, a mutualistic association of plant roots and fungi. Brassinosteroids are important to many developmental and physiological processes. Signals between these compounds and other hormones, notably auxin and GAs, amplifies their physiological effect. Apical dominance, seed germination, gravitropism, and resistance to freezing are all positively influenced by hormones. Root growth and fruit dropping are inhibited by steroids.

Plant Responses to Wind and Touch

The shoot of a pea plant winds around a trellis, while a tree grows on an angle in response to strong prevailing winds. These are examples of how plants respond to touch or wind.

The movement of a plant subjected to constant directional pressure is called thigmotropism , from the Greek words thigma meaning touch , and tropism implying direction . Tendrils are one example of this. The meristematic region of tendrils is very touch sensitive; light touch will evoke a quick coiling response. Cells in contact with a support surface contract, whereas cells on the opposite side of the support expand ( Figure 23.14 ). Application of jasmonic acid is sufficient to trigger tendril coiling without a mechanical stimulus.

A thigmonastic response is a touch response independent of the direction of stimulus Figure 23.24 . In the Venus flytrap, two modified leaves are joined at a hinge and lined with thin fork-like tines along the outer edges. Tiny hairs are located inside the trap. When an insect brushes against these trigger hairs, touching two or more of them in succession, the leaves close quickly, trapping the prey. Glands on the leaf surface secrete enzymes that slowly digest the insect. The released nutrients are absorbed by the leaves, which reopen for the next meal.

Everyday Connection for AP® Courses

The mimosa plant is also known as the sensitive plant, because its leaves are sensitive to touch and will fold inward and droop. Leaves in their normal state are shown on the left.

Thigmomorphogenesis is a slow developmental change in the shape of a plant subjected to continuous mechanical stress. When trees bend in the wind, for example, growth is usually stunted and the trunk thickens. Strengthening tissue, especially xylem, is produced to add stiffness to resist the wind’s force. Researchers hypothesize that mechanical strain induces growth and differentiation to strengthen the tissues. Ethylene and jasmonate are likely involved in thigmomorphogenesis.

Use the menu at the left to navigate to three short movies: a Venus fly trap capturing prey, the progressive closing of sensitive plant leaflets, and the twining of tendrils.

A Venus fly trap response is triggered by touching the leaves leaves ______.

• anywhere, because the whole surface of the leaf responds to touch
• only on the margins of the leaves where insects usually land
• in the center of the leaf, where the touch-sensitive hairs are located
• on the petiole followed by the center of leaf which signal the presence of a wandering insect

Defense Responses against Herbivores and Pathogens

Plants face two types of enemies: herbivores and pathogens. Herbivores both large and small use plants as food, and actively chew them. Pathogens are agents of disease. These infectious microorganisms, such as fungi, bacteria, and nematodes, live off of the plant and damage its tissues. Plants have developed a variety of strategies to discourage or kill attackers.

The first line of defense in plants is an intact and impenetrable barrier. Bark and the waxy cuticle can protect against predators. Other adaptations against herbivory include thorns, which are modified branches, and spines, which are modified leaves. They discourage animals by causing physical damage and inducing rashes and allergic reactions. A plant’s exterior protection can be compromised by mechanical damage, which may provide an entry point for pathogens. If the first line of defense is breached, the plant must resort to a different set of defense mechanisms, such as toxins and enzymes.

Secondary metabolites are compounds that are not directly derived from photosynthesis and are not necessary for respiration or plant growth and development. Many metabolites are toxic, and can even be lethal to animals that ingest them. Some metabolites are alkaloids, which discourage predators with noxious odors, such as the volatile oils of mint and sage, or repellent tastes, like the bitterness of quinine. Other alkaloids affect herbivores by causing either excessive stimulation—caffeine is one example—or the lethargy associated with opioids. Some compounds become toxic after ingestion; for instance, glycol cyanide in the cassava root releases cyanide only upon ingestion by the herbivore.

Mechanical wounding and predator attacks activate defense and protection mechanisms both in the damaged tissue and at sites farther from the injury location. Some defense reactions occur within minutes: others over several hours. The infected and surrounding cells may die, thereby stopping the spread of infection.

Long-distance signaling elicits a systemic response aimed at deterring the predator. As tissue is damaged, jasmonates may promote the synthesis of compounds that are toxic to predators. Jasmonates also elicit the synthesis of volatile compounds that attract parasitoids, which are insects that spend their developing stages in or on another insect, and eventually kill their host. The plant may activate abscission of injured tissue if it is damaged beyond repair.

Science Practice Connection for AP® Courses

Think about it.

• Owners and managers of plant nurseries have to plan a lighting schedule for a long-day plant that will flower in February. What lighting periods will be most effective?
• Storage facilities for fruits and vegetables are usually refrigerated and well ventilated. Why are these conditions advantageous?
• Stomata close in response to bacterial infection. Create a diagram to illustrate how this is a defense mechanism for the plant.

This section may include links to websites that contain links to articles on unrelated topics.  See the preface for more information.

Copy and paste the link code above.

Related Items

Plant Systems

Loading ad...

Plant systems

• Google Classroom
• Microsoft Teams
• Download PDF

Module 7: Plant Structure and Function

Plant sensory systems and responses, learning outcomes.

• Identify common sensory systems and responses in plants

Animals can respond to environmental factors by moving to a new location. Plants, however, are rooted in place and must respond to the surrounding environmental factors. Plants have sophisticated systems to detect and respond to light, gravity, temperature, and physical touch. Receptors sense environmental factors and relay the information to effector systems—often through intermediate chemical messengers—to bring about plant responses.

Plant Responses to Light

Plants have a number of sophisticated uses for light that go far beyond their ability to photosynthesize low-molecular-weight sugars using only carbon dioxide, light, and water. Photomorphogenesis is the growth and development of plants in response to light. It allows plants to optimize their use of light and space. Photoperiodism is the ability to use light to track time. Plants can tell the time of day and time of year by sensing and using various wavelengths of sunlight. Phototropism is a directional response that allows plants to grow towards, or even away from, light. Positive phototropism is growth towards a light source (Figure 1), while negative phototropism (also called skototropism) is growth away from light.

Figure 1. Azure bluets ( Houstonia caerulea ) display a phototropic response by bending toward the light. (credit: Cory Zanker)

The sensing of light in the environment is important to plants; it can be crucial for competition and survival. The response of plants to light is mediated by different photoreceptors, which are comprised of a protein covalently bonded to a light-absorbing pigment called a chromophore . Together, the two are called a chromoprotein.

The red/far-red and violet-blue regions of the visible light spectrum trigger structural development in plants. Sensory photoreceptors absorb light in these particular regions of the visible light spectrum because of the quality of light available in the daylight spectrum. In terrestrial habitats, light absorption by chlorophylls peaks in the blue and red regions of the spectrum. As light filters through the canopy and the blue and red wavelengths are absorbed, the spectrum shifts to the far-red end, shifting the plant community to those plants better adapted to respond to far-red light. Blue-light receptors allow plants to gauge the direction and abundance of sunlight, which is rich in blue–green emissions. Water absorbs red light, which makes the detection of blue light essential for algae and aquatic plants.

Horticulturalist

The word “horticulturist” comes from the Latin words for garden ( hortus ) and culture ( cultura ). This career has been revolutionized by progress made in the understanding of plant responses to environmental stimuli. Growers of crops, fruit, vegetables, and flowers were previously constrained by having to time their sowing and harvesting according to the season. Now, horticulturists can manipulate plants to increase leaf, flower, or fruit production by understanding how environmental factors affect plant growth and development.

Greenhouse management is an essential component of a horticulturist’s education. To lengthen the night, plants are covered with a blackout shade cloth. Long-day plants are irradiated with red light in winter to promote early flowering. For example, fluorescent (cool white) light high in blue wavelengths encourages leafy growth and is excellent for starting seedlings. Incandescent lamps (standard light bulbs) are rich in red light, and promote flowering in some plants. The timing of fruit ripening can be increased or delayed by applying plant hormones. Recently, considerable progress has been made in the development of plant breeds that are suited to different climates and resistant to pests and transportation damage. Both crop yield and quality have increased as a result of practical applications of the knowledge of plant responses to external stimuli and hormones.

Horticulturists find employment in private and governmental laboratories, greenhouses, botanical gardens, and in the production or research fields. They improve crops by applying their knowledge of genetics and plant physiology. To prepare for a horticulture career, students take classes in botany, plant physiology, plant pathology, landscape design, and plant breeding. To complement these traditional courses, horticulture majors add studies in economics, business, computer science, and communications.

Plant Responses to Gravity

Whether or not they germinate in the light or in total darkness, shoots usually sprout up from the ground, and roots grow downward into the ground. A plant laid on its side in the dark will send shoots upward when given enough time. Gravitropism ensures that roots grow into the soil and that shoots grow toward sunlight. Growth of the shoot apical tip upward is called negative gravitropism , whereas growth of the roots downward is called positive gravitropism .

Amyloplasts (also known as statoliths ) are specialized plastids that contain starch granules and settle downward in response to gravity. Amyloplasts are found in shoots and in specialized cells of the root cap. When a plant is tilted, the statoliths drop to the new bottom cell wall. A few hours later, the shoot or root will show growth in the new vertical direction.

The mechanism that mediates gravitropism is reasonably well understood. When amyloplasts settle to the bottom of the gravity-sensing cells in the root or shoot, they physically contact the endoplasmic reticulum (ER), causing the release of calcium ions from inside the ER. This calcium signaling in the cells causes polar transport of the plant hormone IAA to the bottom of the cell. In roots, a high concentration of IAA inhibits cell elongation. The effect slows growth on the lower side of the root, while cells develop normally on the upper side. IAA has the opposite effect in shoots, where a higher concentration at the lower side of the shoot stimulates cell expansion, causing the shoot to grow up. After the shoot or root begin to grow vertically, the amyloplasts return to their normal position. Other hypotheses—involving the entire cell in the gravitropism effect—have been proposed to explain why some mutants that lack amyloplasts may still exhibit a weak gravitropic response.

Plant Responses to Wind and Touch

The shoot of a pea plant winds around a trellis, while a tree grows on an angle in response to strong prevailing winds. These are examples of how plants respond to touch or wind.

The movement of a plant subjected to constant directional pressure is called thigmotropism , from the Greek words thigma meaning “touch,” and tropism implying “direction.” Tendrils are one example of this. The meristematic region of tendrils is very touch sensitive; light touch will evoke a quick coiling response. Cells in contact with a support surface contract, whereas cells on the opposite side of the support expand. Application of jasmonic acid is sufficient to trigger tendril coiling without a mechanical stimulus.

A thigmonastic response is a touch response independent of the direction of stimulus. In the Venus flytrap, two modified leaves are joined at a hinge and lined with thin fork-like tines along the outer edges. Tiny hairs are located inside the trap. When an insect brushes against these trigger hairs, touching two or more of them in succession, the leaves close quickly, trapping the prey. Glands on the leaf surface secrete enzymes that slowly digest the insect. The released nutrients are absorbed by the leaves, which reopen for the next meal.

Thigmomorphogenesis is a slow developmental change in the shape of a plant subjected to continuous mechanical stress. When trees bend in the wind, for example, growth is usually stunted and the trunk thickens. Strengthening tissue, especially xylem, is produced to add stiffness to resist the wind’s force. Researchers hypothesize that mechanical strain induces growth and differentiation to strengthen the tissues. Ethylene and jasmonate are likely involved in thigmomorphogenesis.

Defense Responses against Herbivores and Pathogens

Plants face two types of enemies: herbivores and pathogens. Herbivores both large and small use plants as food, and actively chew them. Pathogens are agents of disease. These infectious microorganisms, such as fungi, bacteria, and nematodes, live off of the plant and damage its tissues. Plants have developed a variety of strategies to discourage or kill attackers.

The first line of defense in plants is an intact and impenetrable barrier. Bark and the waxy cuticle can protect against predators. Other adaptations against herbivory include thorns, which are modified branches, and spines, which are modified leaves. They discourage animals by causing physical damage and inducing rashes and allergic reactions. A plant’s exterior protection can be compromised by mechanical damage, which may provide an entry point for pathogens. If the first line of defense is breached, the plant must resort to a different set of defense mechanisms, such as toxins and enzymes.

Secondary metabolites are compounds that are not directly derived from photosynthesis and are not necessary for respiration or plant growth and development. Many metabolites are toxic, and can even be lethal to animals that ingest them. Some metabolites are alkaloids, which discourage predators with noxious odors (such as the volatile oils of mint and sage) or repellent tastes (like the bitterness of quinine). Other alkaloids affect herbivores by causing either excessive stimulation (caffeine is one example) or the lethargy associated with opioids. Some compounds become toxic after ingestion; for instance, glycol cyanide in the cassava root releases cyanide only upon ingestion by the herbivore.

Mechanical wounding and predator attacks activate defense and protection mechanisms both in the damaged tissue and at sites farther from the injury location. Some defense reactions occur within minutes: others over several hours. The infected and surrounding cells may die, thereby stopping the spread of infection.

Long-distance signaling elicits a systemic response aimed at deterring the predator. As tissue is damaged, jasmonates may promote the synthesis of compounds that are toxic to predators. Jasmonates also elicit the synthesis of volatile compounds that attract parasitoids, which are insects that spend their developing stages in or on another insect, and eventually kill their host. The plant may activate abscission of injured tissue if it is damaged beyond repair.

• Introduction to Plant Sensory Systems and Responses. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution
• Biology. Provided by : OpenStax CNX. Located at : http://cnx.org/contents/[email protected] . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

30.1: The Plant Body - Plant Tissues and Organ Systems

• Last updated
• Save as PDF
• Page ID 13743

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

Learning Objectives

• Differentiate among the types of plant tissues and organs

Plant Tissues

Plants are multicellular eukaryotes with tissue systems made of various cell types that carry out specific functions. Plant tissue systems fall into one of two general types: meristematic tissue and permanent (or non-meristematic) tissue. Cells of the meristematic tissue are found in meristems, which are plant regions of continuous cell division and growth. Meristematic tissue cells are either undifferentiated or incompletely differentiated; they continue to divide and contribute to the growth of the plant. In contrast, permanent tissue consists of plant cells that are no longer actively dividing.

Meristematic tissues consist of three types, based on their location in the plant. Apical meristems contain meristematic tissue located at the tips of stems and roots, which enable a plant to extend in length. Lateral meristems facilitate growth in thickness or girth in a maturing plant. Intercalary meristems occur only in monocots at the bases of leaf blades and at nodes (the areas where leaves attach to a stem). This tissue enables the monocot leaf blade to increase in length from the leaf base; for example, it allows lawn grass leaves to elongate even after repeated mowing.

Meristems produce cells that quickly differentiate, or specialize, and become permanent tissue. Such cells take on specific roles and lose their ability to divide further. They differentiate into three main types: dermal, vascular, and ground tissue. Dermal tissue covers and protects the plant. Vascular tissue transports water, minerals, and sugars to different parts of the plant. Ground tissue serves as a site for photosynthesis, provides a supporting matrix for the vascular tissue, and helps to store water and sugars.

Plant tissues are either simple (composed of similar cell types) or complex (composed of different cell types). Dermal tissue, for example, is a simple tissue that covers the outer surface of the plant and controls gas exchange. Vascular tissue is an example of a complex tissue. It is made of two specialized conducting tissues: xylem and phloem. Xylem tissue transports water and nutrients from the roots to different parts of the plant. It includes three different cell types: vessel elements and tracheids (both of which conduct water) and xylem parenchyma. Phloem tissue, which transports organic compounds from the site of photosynthesis to other parts of the plant, consists of four different cell types: sieve cells (which conduct photosynthates), companion cells, phloem parenchyma, and phloem fibers. Unlike xylem-conducting cells, phloem-conducting cells are alive at maturity. The xylem and phloem always lie adjacent to each other. In stems, the xylem and the phloem form a structure called a vascular bundle; in roots, this is termed the vascular stele or vascular cylinder.

Plant Organ Systems

In plants, just as in animals, similar cells working together form a tissue. When different types of tissues work together to perform a unique function, they form an organ; organs working together form organ systems. Vascular plants have two distinct organ systems: a shoot system and a root system. The shoot system consists of two portions: the vegetative (non-reproductive) parts of the plant, such as the leaves and the stems; and the reproductive parts of the plant, which include flowers and fruits. The shoot system generally grows above ground, where it absorbs the light needed for photosynthesis. The root system, which supports the plants and absorbs water and minerals, is usually underground.

• There are two types of plant tissues: meristematic tissue found in plant regions of continuous cell division and growth, and permanent (or non-meristematic) tissue consisting of cells that are no longer actively dividing.
• Meristems produce cells that differentiate into three secondary tissue types: dermal tissue which covers and protects the plant, vascular tissue which transports water, minerals, and sugars and ground tissue which serves as a site for photosynthesis, supports vascular tissue, and stores nutrients.
• Vascular tissue is made of xylem tissue which transports water and nutrients from the roots to different parts of the plant and phloem tissue which transports organic compounds from the site of photosynthesis to other parts of the plant.
• The xylem and phloem always lie next to each other forming a structure called a vascular bundle in stems and a vascular stele or vascular cylinder in roots.
• Parts of the shoot system include the vegetative parts, such as the leaves and the stems, and the reproductive parts, such as the flowers and fruits.
• meristem : the plant tissue composed of totipotent cells that allows plant growth
• parenchyma : the ground tissue making up most of the non-woody parts of a plant
• xylem : a vascular tissue in land plants primarily responsible for the distribution of water and minerals taken up by the roots; also the primary component of wood
• phloem : a vascular tissue in land plants primarily responsible for the distribution of sugars and nutrients manufactured in the shoot
• tracheid : elongated cells in the xylem of vascular plants that serve in the transport of water and mineral salts

Contributions and Attributions

• OpenStax College, Biology. October 17, 2013. Provided by : OpenStax CNX. Located at : http://cnx.org/content/m44700/latest...ol11448/latest . License : CC BY: Attribution
• Secondary growth. Provided by : Wikipedia. Located at : en.Wikipedia.org/wiki/Secondary_growth . License : CC BY-SA: Attribution-ShareAlike
• xylem. Provided by : Wiktionary. Located at : en.wiktionary.org/wiki/xylem . License : CC BY-SA: Attribution-ShareAlike
• parenchyma. Provided by : Wiktionary. Located at : en.wiktionary.org/wiki/parenchyma . License : CC BY-SA: Attribution-ShareAlike
• phloem. Provided by : Wiktionary. Located at : en.wiktionary.org/wiki/phloem . License : CC BY-SA: Attribution-ShareAlike
• tracheid. Provided by : Wikipedia. Located at : en.Wikipedia.org/wiki/tracheid . License : CC BY-SA: Attribution-ShareAlike
• meristem. Provided by : Wiktionary. Located at : en.wiktionary.org/wiki/meristem . License : CC BY-SA: Attribution-ShareAlike
• OpenStax College, The Plant Body. October 17, 2013. Provided by : OpenStax CNX. Located at : http://cnx.org/content/m44700/latest..._30_01_02f.jpg . License : CC BY: Attribution
• OpenStax College, The Plant Body. October 17, 2013. Provided by : OpenStax CNX. Located at : http://cnx.org/content/m44700/latest...e_30_01_01.jpg . License : CC BY: Attribution

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 28 May 2024

Physiochemical interaction between osmotic stress and a bacterial exometabolite promotes plant disease

• Felix Getzke 1   na1 ,
• Lei Wang 2   na1 ,
• Guillaume Chesneau 1 ,
• Nils Böhringer 2 , 3 ,
• Fantin Mesny   ORCID: orcid.org/0000-0002-3044-1398 1   nAff6 ,
• Nienke Denissen 1 ,
• Hidde Wesseler 1 ,
• Priscilla Tijesuni Adisa 1 ,
• Michael Marner   ORCID: orcid.org/0000-0002-1024-1567 4 ,
• Paul Schulze-Lefert   ORCID: orcid.org/0000-0002-8978-1717 1 , 5 ,
• Till F. Schäberle   ORCID: orcid.org/0000-0001-9947-8079 2 , 3 , 4 &
• Stéphane Hacquard   ORCID: orcid.org/0000-0003-2293-3525 1 , 5  

Nature Communications volume  15 , Article number:  4438 ( 2024 ) Cite this article

1106 Accesses

75 Altmetric

Metrics details

• Bacterial pathogenesis
• Plant immunity

Various microbes isolated from healthy plants are detrimental under laboratory conditions, indicating the existence of molecular mechanisms preventing disease in nature. Here, we demonstrated that application of sodium chloride (NaCl) in natural and gnotobiotic soil systems is sufficient to induce plant disease caused by an otherwise non-pathogenic root-derived Pseudomonas brassicacearum isolate (R401). Disease caused by combinatorial treatment of NaCl and R401 triggered extensive, root-specific transcriptional reprogramming that did not involve down-regulation of host innate immune genes, nor dampening of ROS-mediated immunity. Instead, we identified and structurally characterized the R401 lipopeptide brassicapeptin A as necessary and sufficient to promote disease on salt-treated plants. Brassicapeptin A production is salt-inducible, promotes root colonization and transitions R401 from being beneficial to being detrimental on salt-treated plants by disturbing host ion homeostasis, thereby bolstering susceptibility to osmolytes. We conclude that the interaction between a global change stressor and a single exometabolite from a member of the root microbiome promotes plant disease in complex soil systems.

Similar content being viewed by others

Impact of root-associated strains of three Paraburkholderia species on primary and secondary metabolism of Brassica oleracea

Sucrose triggers a novel signaling cascade promoting Bacillus subtilis rhizosphere colonization

Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease

Introduction.

Subterranean and aerial plant tissues are colonized by complex microbial communities that are referred to as the root and shoot microbiota, respectively 1 , 2 , 3 , 4 . Recent efforts to systematically isolate and characterise the microbiota of healthy-looking Arabidopsis thaliana grown in natural soils revealed that few bacteria (<5%) can cause disease under specific laboratory conditions 5 , 6 , 7 . These include for example Pseudomonas brassicacearum Root401 (referred to as R401), Streptomyces spp . Root107 (R107), Xanthomonas spp . Leaf131 and Leaf148 (L131, L148), or multiple Pseudomonas viridiflava isolates that were retrieved from symptomless plant tissues 2 , 5 , 8 , 9 , 10 . The identification of these so-called opportunistic pathogens suggests the existence of molecular mechanisms that suppress detrimental phenotypes in nature whilst facilitating infection under laboratory conditions.

Stevens (1960) 11 advanced the concept of the ‘disease triangle’, by which environmental factors contribute to the establishment of plant diseases. In this triangle, abiotic conditions can influence the host, the microbiota, or the interaction between the two, facilitating or inhibiting pathogen progression 12 , 13 , 14 . Similar to pathogen perception, e.g., the sensing of salt stress results in signalling cascades involving cytoplasmic Ca 2+ influx, ROS production and the accumulation of plant hormones, primarily abscisic acid 15 , 16 , 17 , 18 (ABA). ABA mediates closure of stomata to restrict transpirational water loss 18 and inhibits the expression of the salicylic acid (SA) biosynthetic gene ISOCHORISMATE SYNTHASE1 ( ICS1/SID2 ) in A. thaliana , thereby suppressing SA-dependent immunity 19 , 20 . This ABA-SA cross-talk was recently shown to be leaf-age dependent and controlled by the SNAC-A transcription factor cascade 21 . Collectively, abiotic stress signalling and host innate immunity processes are tightly connected and likely contribute to disease emergence in nature 14 , 22 , 23 .

Here, we focus on a dominant member of the bacterial root microbiota called P. brassicacearum R401 that was previously shown to be detrimental in mono-association experiments with A. thaliana in an agar matrix-based gnotobiotic system 6 . This strain was also recently shown to deploy unrelated inhibitory exometabolites that co-function to keep bacterial competitors at bay and promote strain colonization success in roots 24 . We report here that R401 is nonpathogenic on plants grown in natural or gnotobiotic peat-based soil systems and that NaCl treatment promotes R401 disease symptoms in these soil-grown plants, providing evidence for environmental conditions that conditionally promote plant disease. In R401, we identify a biosynthetic gene cluster (BGC) homologous to the syp-syr BGC that is responsible for syringopeptin biosynthesis in P. syringae B728a 25 and we demonstrate that this locus confers root colonization capability to the strain and is sufficient to transition R401 from being beneficial to being detrimental on salt-treated plants. We further report that the produced exometabolite is necessary and sufficient to promote susceptibility to salt stress, thereby bolstering disease (here referred to as the combined detrimental effect of a biotic and an abiotic factor). We conclude that the physiochemical interaction between a bacterial exometabolite and NaCl promotes host susceptibility to osmotic stress and alters host health in complex soil-microbiome systems.

R401 is detrimental on soil-grown plants facing salt stress

Inoculation of R401 on A. thaliana plants grown on ½ Murashige-Skoog (½ MS) agar matrix plates was shown to cause extensive plant growth inhibition and anthocyanin accumulation in shoots 6 and we were able to reproduce similar phenotypes (Fig.  1a ). However, in the sterile peat matrix of the gnotobiotic Flowpot system 26 or in natural, non-sterile Cologne agricultural soil (CAS), R401 inoculation did not cause disease (Fig.  1b,c ). Salt stress has been shown to facilitate Pseudomonas infections in tomato 27 ( Solanum lycopersicum , Solanaceae) and cucumber 28 ( Cucumis sativus , Cucurbitaceae). We therefore speculated that salt stress may facilitate the detrimental effects of P. brassicacearum R401 on A. thaliana (Brassicaceae). In axenic conditions (Fig.  1b ) and in the presence of a complex, natural soil microbiota (Fig.  1c ), the application of 100 mM NaCl negatively affected plant growth (HK 0 mM NaCl vs. HK 100 mM NaCl; p  < 0.001, Kruskal-Wallis followed by Dunn’s post-hoc test, Fig.  1b,c ). Co-inoculation of R401 and 100 mM NaCl further aggravated this effect in both soil systems, leading to highly stunted and chlorotic plants, reminiscent of R401 effects in ½ MS agar plates (R401 0 mM NaCl vs. R401 100 mM NaCl; p  < 0.001 and p  < 0.01 for Fig.  1 b and c , respectively, Fig.  1a-c ). Collectively, this indicates that in complex soil systems, the combined action of the opportunistic pathogen R401 and salt stress is required to cause disease on A. thaliana .

a log2-transformed shoot fresh weight of A. thaliana plants grown axenically on ½ MS agar plates for 19 dpi. At 14 dpi plants were flushed with either heat-killed (HK) or live wild-type (WT) R401 cells. Five minutes after flushing plants were transferred to new sterile plates and grown for five days ( n  = 5 plants). b log2-transformed shoot fresh weight of A. thaliana plants grown in the gnotobiotic Flowpot system for 28 dpi in the presence or absence of 100 mM NaCl and either heat-killed (HK) or live wild-type (WT) R401 cells ( n  = 30 plants). c log2-transformed shoot fresh weight of A. thaliana plants grown in the non-sterile Cologne agricultural soil (CAS) in the greenhouse for 28 dpi in the presence or absence of 100 mM NaCl and either heat-killed (HK) or live wild-type (WT) R401 ( n  = 16 plants). a– c Representative images illustrating the respective plant phenotypes are shown below each plot. Within each figure panel, all images are to scale. Statistical significance was determined by Kruskal-Wallis followed by Dunn’s post-hoc test and Benjamini-Hochberg adjustment. Significance compared to HK is indicated by black asterisks ( ∗ ∗ and ∗ ∗ ∗ indicate p  < 0.01, and 0.001, respectively; ns, not significant). Statistical comparisons were conducted between WT and HK samples for each NaCl treatment separately. Boxplots show 25 th percentile, median, and 75 th percentile.

Salt-treated plants down-regulate immune processes in shoots but not in roots

We hypothesized that salt stress response in A. thaliana comes at the cost of dampening immune sectors in roots, which in turn promotes R401 infection. We sequenced the root and shoot transcriptome of A. thaliana 28 days post treatment with either heat-killed (HK) or live R401 wild-type (WT) strains mono-inoculated in the absence or presence of NaCl (0 vs. 100 mM) in the Flowpot system (Fig.  1b , Supplementary Data  1 ). Inoculation of R401 in the absence of salt stress had marginal effects on both root and shoot transcriptomes compared to control samples (0 mM NaCl + R401 HK), with 2 and 14 differentially expressed genes identified (DEGs), respectively ( p  < 0.05, −1 <Log2FC > 1, Fig.  2a ). Salt stress alone also triggered subtle transcriptional reprogramming in roots (85 DEGs, Fig.  2a and Supplementary Fig.  1a ), but not in shoots (1,192 DEGs, Fig.  2a and Supplementary Fig.  1b ). Based on PANTHER functional annotation analyses 29 , 30 , 31 , up-regulated genes in roots of salt-treated plants can be categorized into the GO-terms ‘response to abscisic acid’, ‘response to osmotic stress’, and ‘response to water deprivation’, while in shoots the most enriched GO-terms included ‘anthocyanin-containing compound biosynthetic process’, ‘hyperosmotic salinity response’ and ‘abscisic acid-activated signaling pathway’. Collectively, these data confirmed that salt stress as applied in our experimental setup activated the stereotypical salt stress response and interfered with plant photosynthesis 32 , 33 , 34 , 35 . Notably, combinatorial treatment of R401 and salt had the most extensive effect on the host transcriptome, with 2,824 and 2,150 DEGs identified in roots and shoots, respectively (Fig.  2a and Supplementary Fig.  1a,b ). However, only in roots did R401 cause major additive transcriptional reprogramming compared to salt stress alone, with 1,922 newly identified DEGs (Fig.  2a ) . Although this activation was associated with disease symptoms, no increased proliferation of R401 was noted in roots of salt-treated compared to control plants, indicated that bacterial over-proliferation at roots is likely not the direct cause driving disease (Supplementary Fig.  1c ). Cluster-based GO-category enrichment analysis 36 of DEGs responding to salt stress or combinatorial treatment of salt and R401 revealed 14 GO-term clusters involved in primary metabolism or biotic and abiotic stresses (Supplementary Fig.  1d ). Top 10 GO-terms that showed the strongest down-regulation are dominated by innate immune processes such as ‘response to SA’, ‘innate immune process’, ‘systemic acquired resistance’, or ‘indole glucosinolate metabolic processes’ (Fig.  2b ). However, these terms were specifically down-regulated in shoots but not in roots in response to salt, indicating that salt-induced dampening of immune response was shoot-specific.

a Transcriptomic analysis of A. thaliana shoots and roots (28 dpi) grown in the gnotobiotic Flowpot system in the presence of R401, 100 mM NaCl or 100 mM NaCl and R401 compared to the control condition (0 mM NaCl with Heat-Killed (HK) R401), n = three biological replicates each comprising 10 plants. The number of differentially-enriched genes (DEGs) as compared to the control condition is indicated above the graphs and are shown in black in the volcano plots. b Significantly enriched GO-categories in NaCl or NaCl + R401 treated shoots or roots. All significantly up-or down-regulated genes ( p.adj  ≤ 0.05 and fold change ≤ 0.5 or ≥ 2) as compared to control conditions (0 mM NaCl + HK R401) were selected for the analysis. Cluster-based GO-category enrichment analysis was performed using Metascape 36 in multiple gene lists-mode for significantly down- and upregulated genes separately. Spheres size indicates the number of genes (in %) that are significantly up-regulated for each GO-category. The absence of a sphere indicates that the respective GO-category was not significantly enriched in the respective condition. p -values were calculated based on the cumulative hypergeometric distribution and are indicated in unfilled spheres.

Mutation of host NADPH oxidase RBOHD does not promote R401 detrimental activity

We next tested whether salt stress-mediated suppression of immunity in shoots could promote pathogenicity of other opportunistic pathogens, such as Streptomyces sp . R107 and Xanthomonas sp . L131 6 , 7 . Resistance to Xanthomonas L131, but also another closely related L148 opportunistic pathogen was recently shown to fully depend on the host NADPH/respiratory burst oxidase homologue D 7 , 9 , 10 (RBOHD), which was approx. 51-fold down-regulated by salt stress in A. thaliana shoots in our dataset ( p.adj  < 0.05, Supplementary Data  1 ). Therefore, we hypothesized that salt stress might bolster infection of opportunistic pathogens by dampening ROS-mediated immunity in A. thaliana . However, while R401 and R107 became more detrimental on salt-treated A. thaliana than on control plants, this was not the case for L131 (Supplementary Fig.  2a ). In contrast—and consistent with previous work 7 —inoculation of L131 on an immunocompromised A. thaliana rbohD mutant promoted disease ( p  < 0.05). However, this infection facilitation phenotype was not observed for R401, indicating that R401 detrimental activity did not require impairment of RBOHD-dependent ROS production in A. thaliana (Supplementary Fig.  2b ). Taken together, our results suggest that disease caused by combinatorial treatment of R401 and salt is not associated with bacterial overgrowth in roots, nor with downregulation of root immunity or dampening of RBOHD-dependent ROS-mediated protective immunity.

R401 brpC is required for detrimental activity in salt-treated plants

Inspection of the genome of R401 revealed the absence of genes encoding the type-III secretion system 37 , 38 , 39 , indicating that intracellular delivery of bacterial effectors via this machinery is likely not the cause of R401 detrimental activity in salt-treated plants. Therefore, we hypothesized that the detrimental activity of R401 on salt-treated plants is mediated by the production of specialized metabolites. AntiSMASH-based prediction 40 of BGCs revealed a > 140 kb BGC with high similarity to the syp-syr BGC of Pseudomonas syringae B728a involved in syringopeptin and syringomycin biosynthesis 25 (Fig.  3a and Supplementary Data  2 ). The two molecules are commonly co-regulated and required for virulence in Pseudomonas syringae strains 41 . R401 has been phylogenetically assigned to the P. brassicacearum species 24 ; therefore, we termed the putatively produced specialized metabolites ‘brassicapeptin’ and ‘brassicamycin’. To test whether the syp-syr BGC contributes to R401 disease symptoms on salt-treated A. thaliana , we generated a marker-free knockout mutant ( ∆brpC ) lacking brpC , one of the core biosynthetic genes putatively involved in brassicapeptin biosynthesis (Fig.  3a ). Loss of brassicapeptin production in the ∆brpC mutant was further validated by liquid chromatography-mass spectrometry (LC-MS, Fig.  3b ). We then mono-inoculated heat-killed (HK) or live R401 wild type (WT) or ∆brpC mutant cells into the gnotobiotic Flowpot system across a gradient of NaCl concentrations (0–150 mM NaCl). R401 WT was again able to promote disease on salt-treated plants, and this effect was NaCl-concentration dependent (Fig.  3c ), indicating a dose-dependent relationship between increased salt concentrations and R401 detrimental activity (Fig.  3d ). Remarkably, the detrimental effect of R401 WT on salt-treated plants was fully abolished in the ∆brpC mutant, which was even able to partly rescue salt stress-induced plant growth inhibition (Fig.  3c ). Therefore, mutation of a single bacterial gene involved in the production of a specialized exometabolite was sufficient to turn this detrimental isolate into a beneficial plant growth-promoting isolate under salt stress. Notably, brpC -dependent detrimental activity of R401 under salt stress was retained in plants co-cultured with a representative, yet simplified 15-member synthetic microbial community in the FlowPot system (Supplementary Fig.  3 , see methods). In salt-treated plants, brpC is a disease determinant that dominantly functions irrespective of the absence or presence of microbial competitors.

a Schematic overview of the genomic context of the fragmented syp-syr -operon which encodes genes for syringopeptin and syringomycin biosynthesis in Pseudomonas syringae B728a and likely for related specialized metabolites in Pseudomonas brassicacearum R401, thereby termed brassicapeptin and brassicamycin. Genes within the biosynthetic gene cluster (BGC) are colored in grey, R401 brpC is highlighted in green. brpC is likely involved in brassicapeptin biosynthesis in R401. A ∆brpC knockout mutant has been generated in R401, lacking the full-length brpC coding region. Gene prediction and annotation was performed using antiSMASH 40 for B728a and R401 separately, the figure was assembled in Adobe Illustrator. b Extracted ion chromatograms for R401 brassicapeptin (EICs: C 96 H 161 N 23 O 26 [M + (1-3)H] (1-3)+ ± 0.01  m/z ) of the WT and mutant extracts, confirming complete lack of brassicapeptin production in the tested mutant. c log2-transformed shoot fresh weight of A. thaliana plants grown in the gnotobiotic Flowpot system for 28 dpi in the presence of increasing concentrations of NaCl (0, 50, 100, or 150 mM NaCl) and either heat-killed (HK), live wild-type (WT) or live ∆brpC R401 cells. Letters indicate statistically significant differences as determined by Kruskal-Wallis followed by Dunn’s post hoc test and Benjamini-Hochberg adjustment with p  < 0.05 ( n  = 30 plants). Statistical comparisons were conducted for each salt treatment separately. d Correlation analysis of the mean effect of either R401 WT or ∆brpC on A. thaliana shoot fresh weight, normalized by the respective HK control and the applied salt concentrations. Data derives from ( b ). p -values and R 2 derive from a linear regression; ns, not significant. e Log2-transformed Colony Forming Units (CFUs) of R401 WT and ∆brpC R401 per gram of Arabidopsis thaliana roots. Plants were grown in a gnotobiotic Flowpot system for 28 days in two concentrations of NaCl (0 and 100 mM NaCl). Stars (***) indicate statistically significant differences between WT and ∆brpC ( n  = 12 biologically independent samples each comprising 5 roots), as determined by Student’s t-test with p  < 0.001. Statistical comparisons were conducted for each salt treatment separately. f Growth curves of R401 WT and ∆brpC mutant in artificial root exudate (ARE) liquid medium or ARE medium supplemented with 100 mM NaCl; n  = 10 biologically-independent samples. c, e Boxplots show 25 th p e rcentile, median, and 75 th percentile.

Quantification of CFU of R401 WT and ∆ brpC in A. thaliana roots (0 and 100 mM NaCl) revealed impaired root colonization of the mutant, irrespective of the salt conditions (Fig.  3e ). Notably, this growth defect was not observed in axenic liquid medium (Fig.  3f ), indicating that brpC is a root colonization determinant. Notably, the growth of R401 WT was insensitive to 100 mM NaCl in liquid medium (Fig.  3f ) and did not differ in roots of control and NaCl-treated-plants (Fig.  3e ), corroborating our earlier observation (Supplementary Fig.  2c ) that disease was not associated with bacterial overgrowth in salt-treated A. thaliana . However, in vitro experiments in a liquid medium revealed that the production of brassicapeptin is salt-inducible (Supplementary Fig  4 ), raising the possibility that disease symptoms under salt are exacerbated by brassicapeptin overproduction at roots, rather than bacterial over proliferation.

R401 brpC drives root colonization and promotes disease in salt-treated tomato

We first tested whether detrimental activity of R401 WT also occurred in the context of other abiotic stresses, such as drought or low photosynthetically active radiation (low PAR 42 ). While low PAR had more severe effects on shoot fresh weight compared to drought, this treatment did not facilitate R401-induced disease symptoms. In contrast, R401 in conjunction with drought stress—mimicked by the application of 5% polyethylene glycol (PEG8000)—led to disease, demonstrating that the detrimental activity of R401 is potentiated by hyperosmotic stresses ( p  < 0.05, Supplementary Fig.  5 ). Next, we assessed whether R401 brpC also act as a root colonization and disease determinant in other evolutionary distant plant species. Salt- and brpC -dependent detrimental activity of R401 was recapitulated in Solanum lycopersicum cv . Micro-Tom (Micro-Tom, p  < 0.05), but not in Lotus japonicus Gifu (Gifu) (Supplementary Fig.  6a,b ). This is potentially explained by the fact that the latter plant exhibited high tolerance to the applied salt conditions (Supplementary Fig.  6b ) and/or is not affected by brassicapeptin production. We next harvested roots and shoots of both Micro-Tom and Gifu seedlings and quantified colonisation capability of R401 WT and ∆brpC mutant in the presence or absence of NaCl. While colonisation of R401 WT remained stable, irrespective of the salt treatment or the host plant, the ∆brpC mutant showed reduced root colonization in Micro-Tom plants ( p  < 0.01), which was not observed in roots of Gifu plants (Supplementary Fig.  6c,d ). Unlike for A. thaliana (Fig.  3e ), defect in root colonization observed for the ∆brpC mutant was more pronounced in roots of salt-treated Micro-Tom than in roots of control Micro-Tom plants, Supplementary Fig.  6c ), suggesting that interaction between brassicapeptin and salt is needed to drive R401 colonization in Micro-Tom. Irrespective of this difference, our data suggest a conserved functioning of brassicapeptin in salt-stressed A. thaliana and Micro-Tom roots, irrespective of the 112 million years of reproductive isolation between these plants 43 .

Isolation and structural characterisation of R401 brassicapeptin

Next, we aimed to isolate the R401 brassicapeptin and elucidate its structure. Therefore, a 70 L fermentation of R401 was performed and extracted using ethyl acetate. The therefrom-resulting organic crude extract was further fractionated and purified using column chromatography ( e.g ., medium pressure (flash) and high-performance liquid chromatography (HPLC) to finally yield the natural products brassicapeptin A as the major compound of this class, and brassicapeptin B in minor amounts. Additionally, the high-resolution electro spray ionization (HR-ESI)-MS/MS data analysis indicated two further minor derivatives, brassicapeptin C and D (Supplementary Note  1 and Supplementary Fig.  7 ). Brassicapeptin A and B were obtained as white amorphous powders and subsequently analysed by mass spectrometry (MS). The HR-ESI-MS spectrum of brassicapeptin A indicated a molecular weight of 2052.2004, suggesting a molecular formula of C 96 H 161 N 23 O 26 (Supplementary Fig.  7a ) and of C 94 H 157 N 23 O 26 for brassicapeptin B ([M + 2H] 2+ m/z 1027.1080 and m/z 1005.0940, respectively) (Supplementary Fig.  7b ). Comparison of the HR-ESI-MS/MS fragmentation patterns of brassicapeptin A and B gave first insights into the amino acid composition and revealed a high similarity between the molecules ( i.e ., from b 1 -b 7 and from y 1 -y 10 , except for differences from b 8 -b 11 and from y 11 -y 14 ) (Fig.  4a,b ). To fully resolve their structures, nuclear magnetic resonance (NMR) experiments and Marfey’s analysis were performed (for details about structure elucidation see Supplementary Note  1 and Supplementary Fig.  7 ). A combination of 1D and 2D experiments ( 1 H, 13 C, HMBC, HSQC, COSY, TOCSY and ROESY) revealed the brassicapeptins to consist of 22 amino acid residues plus a fatty acid chain (Fig.  4c,d ). The latter is six carbons in length, including a carbonyl group; the amino acid chain is cyclized by an intramolecular connection between threonine (Thr 17 ) and isoleucine (Ile 22 ) (Fig.  4c ). Brassicapeptin A and B differ only by one amino acid, which is a homoserine (Hse 9 ) in brassicapeptin A and a glycine (Gly 9 ) in B, respectively (Fig.  4c ). These newly discovered brassicapeptins represent large cyclic lipopeptides. Comparison to the previously described syringopeptins 44 , 45 , 46 , which are also produced by Pseudomonas strains, revealed notable structural differences, including a different fatty acid starter unit and a smaller ring structure that is formed intramolecularly between the six C-terminal amino acid residues (Supplementary Fig.  8 ); thereby suggesting that brassicapaptins represent a novel sub-group of cyclic syringopeptin-type lipopeptides. They also show high similarity to the reported cichopeptins and corpeptin 47 , 48 , which possess a macrocycle formed by the five C-terminal amino acids. In brassicapeptins, this macrocycle is extended by one amino acid, ranging from the C-terminal Ile 22 towards Thr 17 (Supplementary Fig.  8 ).

a, b HR-ESI-MS/MS fragments of brassicapeptin A ( a ) and brassicapeptin B ( b ). c Chemical structures of brassicapeptin A and B detected from ESI-MS/MS fragmentations and nuclear magnetic resonance (NMR) analysis. Brassicapeptin A and B differ only by one amino acid, which is a homoserine (Hse 9 ) in brassicapeptin A and a glycine (Gly 9 ) in B, respectively. d 1 H- 1 H Correlated Spectroscopy (COSY; blue), Total Correlation Spectroscopy (TOCSY; blue), Heteronuclear Multiple Bond Correlation (HMBC; red) and Rotating-frame Nuclear Overhauser Effect Spectroscopy (ROESY; pink) revealed the complete structure of brassicapeptin A.

Brassicapeptin A and NaCl additively contribute to plant disease

Because R401 is detrimental on A. thaliana grown on ½ Murashige-Skoog (½ MS) agar plates 6 (Fig.  1a ), we first tested whether brpC also drives R401 pathogenicity in this system. Inoculation of R401 WT and ∆brpC mutant, followed by shoot fresh weight measurements (14 dpi) revealed that brpC contributes, yet only partially, to R401 detrimental activity in this agar-based gnotobiotic system ( p  < 0.05), suggesting that other bacterial genetic determinants are necessary to drive full pathogenicity (Supplementary Fig.  9 ). We next assessed the putative phytotoxic activity of brassicapeptin A in this reductionist system. We transplanted seven-day-old A. thaliana seedlings to ½ MS agar plates containing increasing concentrations of purified brassicapeptin A solubilized in dimethyl sulfoxide (DMSO), in the presence or absence of 100 mM NaCl. Within 14 days, brassicapeptin A showed a concentration-dependent effect on root and shoot growth, suggesting that the molecule alone in the absence of salt stress is already sufficient to induce a stunted growth phenotype (Fig.  5a-c ). This is consistent with the fact that brpC contributes to R401 detrimental activity in this gnotobiotic system in the absence of salt stress (Supplementary Fig.  9 ). However, plants exposed to 1 µg/mL brassicapeptin A and 100 mM NaCl died immediately after transfer, whereas those exposed to 1 µg/mL brassicapeptin A or 100 mM NaCl alone remained alive and did not show leaf bleaching and severely inhibited root growth phenotypes (Fig.  5a-c ). Given the phytotoxic effect of brassicapeptin A, we further assessed its potential mode of action. We observed that brassicapeptin A not only induced ion leakage in A. thaliana leaf discs after 16 h of incubation ( p  < 0.05, Fig.  5d ), but also compromised viability of A. thaliana cells based on protoplast transfection assays with a luciferase construct 49 ( p  < 0.05, Fig.  5e ). Our results suggest that this cyclic lipopeptide likely inserts into host plasma membranes to disrupt ion homeostasis, which is consistent with earlier work on syringopeptin, a structurally-related compound that functions as a pore-forming molecule 50 , 51 , 52 . Therefore, brassicapeptin A-induced disruption of ion homeostasis, combined with increased osmotic pressure in the root environment, are likely jointly contributing to R401-induced disease symptoms in salt-stressed plants. Taken together, our data support the hypothesis that brassicapeptin production does not only promote R401 colonization at roots but also enhanced host susceptibility to osmotic stress, thereby leading to disease.

a– c Shoot fresh weight ( a ), root length ( b ) and images of representative phenotypes ( c ) of A. thaliana plants grown axenically on ½ MS agar plates supplemented with increasing concentration of brassicapeptin A and either 0 or 100 mM NaCl ( n  = 72 plants). Sterile A. thaliana seeds were pre-germinated on ½ MS agar plates for seven days and then transferred to new plates containing NaCl and/or brassicapeptin A for another 14 days; black, blue, and green markings in ( c ) indicate root length 0, 7 and 14 days after seedling transfer, respectively. d Ion leakage assay of A. thaliana leaf discs (n = 8 samples each comprising 5 leaf discs), 16 h after treatment with increasing concentrations of brassicapeptin A. DMSO or brassicapeptin A was taken up in sterile miliQ water. All solutions were measured before the experiment resulting in measurements of 2 µS/cm. a– d Brassicapeptin was solubilized in DMSO. Concentrations indicate final brassicapeptin A concentrations in the agar or miliQ water. a, b, d Letters indicate statistically significant differences as determined by Kruskal-Wallis followed by Dunn’s post-hoc test and Benjamini-Hochberg adjustment with p  < 0.05. Statistical comparisons were conducted for each salt treatment separately. e Luciferase activity was measured in Arabidopsis thaliana protoplasts inoculated with water, DMSO, or two concentrations of Brassicapeptin A (1 ng/mL and 1 μg/mL). Protoplasts were transfected with a LUC reporter assay (+LUC) or not transfected (-LUC). All protoplasts were incubated with water, DMSO or Brassicapeptin A for 16 hours before measuring Luciferase activity. DMSO was used as a control since Brassicapeptin A is solubilized in DMSO. Letters indicate statistically significant differences between conditions ( n  = 24 biologically independent samples), as determined by Kruskall-Wallis followed by Dunn’s post-hoc test with p  < 0.05 and Bonferroni adjustment. ( a, b, d, e ) Boxplots show 25 th p e rcentile, median, and 75 th percentile.

Brassicapeptin A displays moderate antimicrobial activity against microbes from different kingdoms of life

Given the antimicrobial activity of known cyclic lipopeptides 53 , we hypothesised that brassicapeptin contributes to the previously described remarkable inhibitory activity of R401 24 . We used the ∆brpC mutant and also generated a novel R401 triple mutant impaired in the production of pyoverdine, DAPG, and brassicapeptin ( ∆pvdY∆phlD∆brpC , Fig.  6a ) in order to abolish the dominant inhibitory activity of DAPG and pyoverdine, which co-explained >70% of R401 antagonistic activity based on previous measurements of R401 inhibitory halos 24 . Using eight microbes belonging to different taxonomic groups of the core root microbiota 4 , 54 (Fig.  6b ), we observed—using the ∆pvdY∆phlD mutant background—that brpC mildly contributed to microbial growth inhibition for half of the tested target bacterial and fungal strains (R83D2, R16D2, R31D1, F80, p  < 0.05). The inhibitory activity of purified brassicapeptin A was further assessed against the same eight target strains as well as seven other environmental pathogens by micro-broth-dilution assays to determine the minimum inhibitory concentration (MIC) (Fig.  6c and Supplementary Data  3 ). Brassicapeptin A MICs displayed the lowest values for bacterial target strains Listeria monocytogenes DSM20600 (2 µg/mL), R16D2 (8-4 µg/mL), Mycobacterium smegmatis ATCC607 (8 µg/mL), R420 (16 µg/mL), Staphylococcus aureus ATCC25923 (16 µg/mL), R483D2 (32 µg/mL), R431 (32 µg/mL), and for fungal target strains Colletotrichum coccodes DSM62126 (32-16 µg/mL) and F80 (32 µg/mL), thereby validating the weak, yet multi-kingdom antimicrobial activity of this cyclic lipopeptide (Fig.  6c and Supplementary Data  3 ). Finally, we assessed the prevalence of the syp-syr containing BGC in plant-associated Pseudomonas strains by scrutinizing the genomes of several commensal culture collections encompassing 1,567 isolates retrieved from roots or leaves of A. thaliana or roots of the legume host Lotus japonicus . We observed that the syp-syr containing BGC is rare in these Pseudomonas genomes 2 , 5 , 55 , 56 , contrasting with the higher prevalence of BGCs involved in pyoverdine and DAPG biosynthesis (Supplementary Fig.  10 ). Our results indicate that acquisition of this rare BGC by Pseudomonas isolates has nonetheless likely broad implication, not only for root colonization, disruption of host ion homeostasis and plant health under osmotic stress, but also for strain competitiveness via direct microbe-microbe competition.

a Genomic map of the main chromosome of R401, illustrating the locations of three biosynthetic gene clusters (BGC) involved in brassicapeptin A (green), 2,4-Diacetylphloroglucinol (DAPG, yellow) and pyoverdine (red) production in R401. Further indicated are respective mutants for each BGC (∆brpC , ∆phlD , ∆pvdY , respectively). R401 ∆phlD and ∆pvdY have been previously characterized 24 . b Balloon plot depicting the inhibitory activity of R401 WT, ∆brpC (single), ∆phlD∆pvdY (double), or ∆phlD∆pvd Y ∆brpC (triple) mutants, against six taxonomically diverse bacteria and two fungi belonging to the core microbiota of A. thaliana . Inhibitory activity was measured as halo of inhibition size (cm) by a modified Burkholder assay as described before 24 . ‘.’ indicates no detectable halo formation for the respective interaction. Statistical significance was determined by Wilcoxon test between R401 WT and ∆brpC and between ∆phlD∆pvdY and ∆phlD∆pvdY∆brpC ( p  < 0.05, n  = 9 biologically independent samples). Target strains are coloured based on the bacterial or fungal classes. c Minimum inhibitory concentrations (MIC) of purified brassicapeptin A against the same core root microbiota of A. thaliana and environmental pathogens. Micro-broth-dilution assays were carried out to test a triplicated dilution series of brassicapeptin A (64–0.03 µg/mL). The potency of the compound is depicted as black circles (•) if an inhibitory effect could be observed, while black triangles (▲) indicate that no inhibition was observed (>64 µg/mL, see also. Supplementary Data  3 ).

Here, we report that salt-mediated dampening of host innate immunity likely contributes little to R401-mediated disease in soil-grown plants. Instead, we identify a bacterial exometabolite—with predicted pore-forming activity—that is sufficient to drive disease in salt-treated plants and that dominantly functions, irrespective of the presence or absence of microbial competitors in soil. We report that brassicapeptin production drives root colonization and enhances susceptibility to osmotic stress, thereby contributing to disease emergence of soil-grown plants facing salt stress.

After 28 days of chronic salt treatment, the response of A. thaliana to salt stress was largely shoot-specific with photosynthesis-related genes being extensively downregulated, likely due to accumulation of Cl - and Na + ions in leaves that disrupt photosynthetic machinery 32 , 57 , 58 . In roots, only subtle transcriptional reprogramming was observed after 28 days of continuous salt stress, which can be explained by previous observation reporting a gradual decline of salt stress response over time in A. thaliana roots 34 . The effect of R401 in the absence of salt stress was also minor in both shoots and roots, while in the presence of hyperosmotic NaCl concentrations and of R401, extensive and root-specific transcriptional reprogramming was observed. This suggests that host response to disease induced by the combinatorial presence of salt and R401 primarily occurs in roots. Given that salt stress-induced dampening of immunity occurs in leaves, but not in roots, we propose that R401-induced stunted plant phenotypes under salt stress is largely immunity-independent. This is corroborated by our observation that R401 does not become detrimental on an immunocompromised rbohD A. thaliana mutant and by the fact that purified brassicapeptin A and salt are sufficient to induce disease-like symptoms in R401-free plant growth assays. Furthermore, low PAR treatment was shown to dampen SA- and JA-dependent immunity sectors in A. thaliana roots and shoots, which promotes infection by Botrytis cinearea and Pseudomonas syringae pv. tomato DC3000 42 . However, R401 did not cause disease under such conditions. Although we cannot fully exclude the possibility that salt-induced dampening of immunity still contributes to R401 infection, our results suggest that this effect remains marginal.

R401 encodes a 140 kb BGC that is responsible for the production of the non-ribosomal peptides brassicapeptin A and B and the corresponding brassicamycin; however, only brassicapeptins could be detected in culture extracts. A R401 mutant lacking brpC, one of the core biosynthetic genes required for the production of brasscapeptin, showed impaired root colonization whilst retaining its WT-like growth in liquid media. In A. thaliana , impaired root colonization of the mutant was observed largely independently from the salt stress condition, indicating that brpC is a root colonization determinant. This same mutant also lost its ability to cause disease on salt-stressed plants in the FlowPot gnotobiotic system, demonstrating a requirement of brassicapeptin production for the detrimental activity of R401 in salt-treated plants. However, we report that disease symptoms caused by the combinatorial presence of R401 and salt stress was not associated with increased bacterial abundance at roots, suggesting that bacterial load is not causally linked to disease appearance in salt-stressed A. thaliana . Notably, structurally related compounds have been demonstrated to intercalate plasma membranes, thereby destabilising them and causing pore formation disrupting cell integrity, eventually leading to uncontrolled diffusion of cell solutes into the surrounding medium 45 , 59 , 60 . Although we could not test for similar effects of brassicapeptin in roots due to technical constraints, we nonetheless observed leakage of cellular solutes from A. thaliana leaf discs that were treated with brassicapeptin A, as well as brassicapeptin A-induced death of A. thaliana leaf protoplast, supporting the assumption that the molecule inserts into plant plasma membranes. Given that brassicapeptin production is needed for R401 proliferation at roots, we speculate that brassicapeptin promotes nutrient leaching from the root endosphere that fuel bacterial growth. Our results also suggest that brassicapeptin-induced ion homeostasis disruption is more damageable for plants facing high osmotic stress, thereby leading to disease. Consistently, we observed a linear dose-response relationship between applied NaCl concentrations and the detrimental effect of R401 on A. thaliana . We also showed brpC -dependent detrimental activity occurs under both salt and drought stress, but not under light stress, confirming that co-occurrence of hyperosmotic conditions and R401 brassicapeptin is required for disease in soil-grown plants. Finally, we report that brassicapeptin production is salt-inducible, at least in vitro. Altogether, these data indicate that plants colonized by R401 are more susceptible to salt stress than germ-free plants, which is also consistent with a root-specific transcriptional reprogramming observed after combinatorial treatment with R401 and NaCl.

Unlike in the FlowPot system, R401 was already detrimental for A. thaliana growth in the absence of salt stress in an ½ MS agar-based gnotobiotic plant system. In agar plates, the R401 mutant lacking brpC became less pathogenic than the WT strain but nonetheless retained some detrimental activity, indicating that brpC only partially contributes to R401 pathogenicity in this system. Our observation that brpC is contributing to R401 pathogenicity in agar plates is corroborated by the fact that purified brassicapeptin A was sufficient to negatively impact shoot and root phenotypes already in the absence of salt stress. Therefore, nutrient concentrations in ½ MS agar medium are likely sufficient to impose hyperosmotic stress on brassicapeptin A-treated plants. Consistent with our earlier observation showing that R401 brpC and salt are both needed to promote disease, we demonstrated that co-treatment with purified brassicapeptin A and 100 mM NaCl exacerbated the effect observed for brassicapeptin A alone, thereby leading to dead plants. Collectively, this indicates that the interaction between a specialized bacterial exometabolite and high osmotic concentrations in the environment is sufficient to explain R401 detrimental activity on salt-sensitive plants.

R401, R131 and R107 have been isolated from healthy A. thaliana plants; however, under favourable conditions they can become detrimental to plant health – in the case of R401 even in the context of natural or synthetic microbial communities. Groundwater-derived Pseudomonas sp . N2C3 (N2C3) also contains the syp-syr BGC and has been demonstrated to cause syp -dependent stunting of A. thaliana root and shoot growth in ½ MS agar medium 61 . In natural soil, N2C3 does not cause any detrimental phenotypes, even after inoculation of high bacterial titres 62 (1 ×10 6 cells per gram soil, a phenotype that is reminiscent of the herein described effects of R401). Using computational analyses, convergent gain and loss of the syp-syr BGC has been demonstrated for the Pseudomonas fluorescence -clade, which comprises P. brassicacearum 61 . Loss of brpC in R401 was sufficient to turn this detrimental strain into a plant growth-promoting strain under salt stress. While the mechanisms of growth promotion of the ∆brpC R401 mutant remain elusive, it is conceivable that the acquisition of the syp-syr BGC in this strain – and likely in other Pseudomonas spp. isolates - might overwrite their plant growth-promoting capabilities.

High soil salinity is one of the main constraints for agricultural performance worldwide and arises through frequent irrigation and fertilisation, which results in the accumulation of nutrient salts in agricultural soils. This accumulation will become more problematic due to an increasing demand for field irrigation owing to climate change 33 , 35 , 63 , 64 , 65 . Our data provide first line of evidence indicating that the interplay between the host, its microbiota, and the osmotic environment can conditionally lead to disease due to the presence of specific bacterial taxa that employed exometabolites that likely disrupt ion homeostasis and promote nutrient leakage at the host interface. Given that the disease phenotype conferred by R401 under salt stress is retained in a microbial community context and in the natural CAS soil, it reflects an important phenomenon that has physiological relevance for plant disease emergence in natural environment. While R401 likely fulfils extensive biocontrol activities due to its diverse repertoire of antibacterial and antifungal specialized exometabolites 24 , favourable abiotic conditions allow for disease development by brassicapeptin-producing R401. Our results indicate that acquisition of brassicapeptin production capability has likely provided a competitive advantage for R401 not only for root colonization but also for microbe-microbe competition. Therefore, we delineate how a single bacterial molecule can have multiple independent effects on organisms that evolved in at least three kingdoms of life (plants, bacteria, fungi), thereby contributing to bacterial competitiveness at roots and promoting plant susceptibility to osmotic stress. It is also tempting to speculate that membrane-intercalating exometabolites outside of the genus Pseudomonas , such as surfactin produced by Bacillus spp . may cause similar detrimental activity as R401 brassicapeptin A 66 , 67 , 68 , 69 , 70 . Taken together, our data provides a mechanistic explanation for the emergence of a disease in the plant microbiome that requires a single bacterial exometabolite and adequate abiotic stress conditions. Our work also defines an ecological framework to understand the conditional detrimental activity of R401 and likely other Pseudomonas spp . isolates in complex soil environments.

All primers used in this study can be found in Supplementary Data  4 .

Microorganisms

The bacterial and fungal strains used in this study have been initially isolated from unplanted soil, A. thaliana roots or shoots 2 , 3 and are summarized in Supplementary Data  5 . The R401 ∆brpC mutant has been deposited in the bacterial culture collection of the Department of Plant Microbe Interactions at the Max Planck Institute for Plant Breeding Research in Cologne, Germany, and are available upon request from Stéphane Hacquard ([email protected]).

Plant species

A. thaliana ecotype Columbia-0 (Col-0), Lotus japonicus ecotype Gifu B-129, and Solanum lycopersicum cv . Micro-Tom were used as wild-types. A. thaliana rbohd contains a dSpm transposon in the fifth exon of AtrbohD 71 (AT5G47910)

Microbial culture conditions

Bacteria were streaked from glycerol stocks (25% glycerol) on TSA plates (15 g/L Tryptic Soy Broth, Sigma Aldrich; with 10 g/l Bacto Agar, Duchefa Biochemie) and grown at 25 °C. Single colonies were inoculated in liquid 50% TSB (15 g/L Tryptic Soy broth, Sigma Aldrich) and grown until dense at 25 °C with 180 rpm agitation. Dense cultures were then stored at 4 °C and diluted 1 to 10 in TSB the day before the experiment and cultured at 25 °C with 180 rpm agitation overnight to ensure sufficient cell densities for slow- and rapidly-growing bacteria. Glycerol stocks were stored at -80 °C and kept on dry ice when transported. Individual pieces of fungal mycelium were transferred to potato dextrose agar (PDA; Sigma-Aldrich) Petri dishes from glycerol stocks (approx. 30 pieces of fungal mycelium in 25% sterile glycerol, stored at -80 °C). Fungi were grown at 25 °C in the dark for 14 days.

Seed sterilisation

A. thaliana and S. lycopersicum Micro-Tom seeds were sterilized using 70% ethanol and bleach. Seeds were submerged in 70% ethanol and left shaking at 40 rpm for 14 minutes. Ethanol was removed before the seeds were submerged in 8.3% sodium hypochlorite (Roth) containing 1 µL of Tween 20 (Sigma-Aldrich) and left shaking at 40 rpm for 4 minutes. Under sterile conditions, the seeds were washed 7 times and finally taken up with sterile 10 mM MgCl 2 . Seeds were left for stratification at 4 °C for 3 days. Seed sterility was confirmed by plating approx. 100 seeds on a 50% TSA plate. The seed coat of L. japonicus Gifu seeds was first abraded using sanding paper, then seeds were incubated for 20 min in diluted bleach, followed by five-times washing in sterile water. Sterilized Gifu seeds were pregerminated on sterile, water-soaked Whatman paper for 7 days.

Gnotobiotic Flowpot experiments

Flowpot assembly was performed according to Kremer and colleagues 26 with minor adjustments 24 . A 2:1 mixture of peat potting mix and vermiculite was used as a matrix. The matrix was sterilized two times (25 min liquid cycle (121 °C) and 45 min solid cycle (134 °C)) and stored at 60 °C until completely dry. Prior to Flowpot assembly, the matrix was rehydrated with sterile MiliQ water. Flowpots were assembled by adding a layer of glass beads to the conical end of a truncated syringe, followed by a layer of the rehydrated, sterile substrate, subsequently covered with a sterile mesh secured by a cable tie. Assembled Flowpots were sterilized on a 25 min liquid cycle, stored at 60 °C overnight and sterilized twice on a 45 min solid cycle. For Micro-Tom and Gifu, big Flowpots fitting 50 mL soil were used, as described by Wippel and colleagues 55 . Microbes were grown and inocula were prepared as described above. Each Flowpot was inoculated with 50 mL half-strength Murashige and Skoog medium with vitamins (½ MS; 2.2 g/L, Duchefa Biochemie, 0.5 g/L MES, pH 5.7). For bacteria, a final OD 600 of 0.0025 in 50 ml ½ MS were inoculated per Flowpot. For salt stress treatment ½ MS contained 50, 100 or 150 mM NaCl. For drought treatment, ½ MS contained 5% polyethylene glycol (PEG8000; Sigma-Aldrich). Per Flowpot, five or three surface-sterilized and stratified A. thaliana or Micro-Tom seeds were inoculated, respectively. For Gifu, 7 days old, pregerminated seedlings with similar developmental stages were carefully transferred to the Flowpots. Microboxes were then incubated in a light cabinet under short day conditions (10 h light at 21 °C, 14 h dark at 19 °C) for 28 days and randomized every 2–3 days. For low PAR treatments, microboxes were partly covered in cardboard boxes, as described in Hou et al. 42 , with a photosynthetic photon flux density of 27.91 μ mol m −2 s −1 . The light condition was measured by Spectral PAR meter PG100N (UPRtek).

Natural soil experiments

Cologne agricultural soil (CAS) was obtained from the Max Planck Institute for Plant Breeding Research in Cologne, Germany. R401 WT was cultured as described above. CAS was placed in squared pots with an edge length of 9 cm and flush inoculated with 100 mL sterile water or 100 mM NaCl containing either live or heat-killed R401 WT cells at an OD 600 of 0.0025. Four surface-sterilized and stratified Col-0 seeds were placed per pot. Pots where then placed in trays in the greenhouse for 28 days. The temperature was set at 22 °C during day and 18 °C during night, with a relative humidity at 65% and 16 h of light.

Agar plate experiments

For the experiment depicted in Fig.  1a and Supplementary Fig.  9 , 24 surface-sterilized and stratified A. thaliana seeds were placed in two rows per 12 cm square plate containing ½ MS medium with 10 g/L Bacto-Agar (Duchefa Biochemie). Agar plates were sealed and incubated in a light cabinet under short day conditions (10 h light at 21 °C, 14 h dark at 19 °C) for 14 days. At day 14, plants were flushed with 15 mL 10 mM MgCl 2 containing either live or heat-killed R401 WT cells at an OD 600 of 0.0005 for 5 mins. Plants were transferred to new plates and grown for another 5 days for a total of 19 days. Shoot fresh weight was measured as a proxy to determine bacterial detrimental activity.

RNA Seq experiments

Total RNA was extracted from A. thaliana roots and shoots by RNeasy Plant Mini Kit (Qiagen). RNA-Seq libraries were prepared by the Max Planck Genome-centre Cologne with NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® and then sequenced on a NextSeq 2000 in 2 ×150 paired-end read mode. RNA-Seq read quality was observed with FastQC v0.11.9, then reads were trimmed with Trimmomatic PE v0.38 72 using parameters TRAILING:20 AVGQUAL:20 MINLEN:100. Trimmed reads were then mapped on the reference A. thaliana genome TAIR10 using Hisat2 v2.2.1 73 , taking into consideration exon and splicing sites locations (according to annotation file TAIR10_GFF3_genes.gff downloaded from arabidopsis.org in October 2022). The number of fragments (pair of reads) mapped on each gene was then quantified using featureCounts v2.0.0 74 (parameter -p, default settings). Resulting data were used to calculate FPKM (fragments per kilobase of transcript per million fragments mapped) values for each gene in each sample: (1) Scaling factor: SF=Total number of mapped reads / 1e6; (2) Fragments per million: FPM=Number of reads mapped on one gene / SF; (3) FPKM = FPM / (Gene length / 1000). Numbers of mapped reads on each gene were also used to perform differential gene expression analysis with DESeq2 v1.24.0 75 and functions estimateSizeFactor, estimateDispersions and nbinomWaldTest. log2FoldChanges values were then corrected with shrinkage algorithm apeglm v1.6.0 76 . One R401 WT and 100 mM NaCl-treated shoot sample (sample ID: 5642.W) was highly contaminated with brown trout ( Salmo trutta ) reads, likely arising during library preparation. This sample was therefore excluded from the analysis.

antiSMASH 40 predictions are derived from Getzke et al. 24 . Bacterial genomes were downloaded from “ www.at-sphere.com ” or NCBI and submitted to https://antismash.secondarymetabolites.org/ version 6.0. Only high-quality genomes, as assessed by CheckM with ≥90% completeness and ≤5% contamination ratio were used for the analysis. For R401, the PacBio-sequenced high-quality genome was used for BGC prediction using antiSMASH.

∆brpC mutant generation

R401 ∆ brpC mutant generation was conducted as described in Getzke. 24 and Vannier et al. 77 . All utilized primers can be found in Supplementary Data  4 . Marker-free knockouts in R401 were generated through homologous recombination using the cloning vector pK18mobsacB (GenBank accession: FJ437239), which encodes the kanR and sacB genes conferring resistance to kanamycin and susceptibility to sucrose, respectively. In this method, upstream and downstream sequences of the gene to be deleted are integrated into the pKl8mobsacB suicide plasmid by Gibson assembly 78 . The resulting plasmid is transformed into BW29427 E. coli cells and subsequently conjugated into R401. The plasmid is then integrated into the chromosome by homologous recombination and deletion mutants are generated by a second sucrose counter-selection-mediated homologous recombination event 79 .

Generation of pK18mobsaB-derived plasmid containing flanking regions of the gene of interest

Primers were designed to amplify a 750-bp DNA sequence ( i.e ., flanking region) directly upstream and downstream of the target region, sharing terminal sequence overlaps to the linearized pK18mobsacB vector and the other respective flanking region using Geneious Prime. R401 genomic DNA was isolated from 6 μl dense R401 culture in 10 μl of buffer I (pH 12) containing 25 mM NaOH, 0.2 mM EDTA at 95 °C for 30 min, before the pH was readjusted using 10 μl of buffer II (pH 7.5) containing 40 mM Tris-HCl. The R401 genomic DNA was used for amplification of the flanking regions through PCR using the respective flanking region-specific primer combinations (Supplementary Data  4 ). PCR was conducted with 0.2 µl Phusion Hot Start High-Fidelity DNA polymerase (New England Biolabs) in 20-µl reactions containing 4 µl 5x Phusion HF buffer (New England Biolabs), 0.4 µl 10 mM dNTPs, 1 µl of 10 µM forward primer, 1 µl of 10 µM reverse primer, 2 µl of R401 genomic DNA as template, filled up to 20 µ1 with nuclease-free water. The tubes were placed into a preheated (98 °C) thermal cycler set at the following program: 98 °C for 30 s, 35 cycles of 98 °C for 7 s, 60 °C for 20 s, 72 °C for 15 s, then a final extension at 72 °C for 7 min. Five microliters of the PCR product were combined with 1 µl Orange DNA Loading Dye (6x; New England Biolabs), loaded on 1% agarose gels containing 0.05% EtBr, and run at 110 mV. After confirmation of successful amplification, the PCR product was purified using AMPure XP (Beckman-Coulter) and subsequently quantified using Nanodrop (Thermo Fisher Scientific). Plasmid purification was performed on an E. coli culture containing plasmid pK18mobsacB using the QIAprep Spin Miniprep Kit for plasmid DNA purification (QIAGEN) following the manufacturer’s instructions. The pkl8mobsacB vector was then amplified and linearized through PCR using the pk18mobsac_F (PKSF) and pk18mobsac_R (PKSR) primers (Supplementary Data  4 ). PCR was conducted with 0.2 µl Phusion Hot Start High-Fidelity DNA polymerase (New England Biolabs) in 20-µl reactions, largely as described above with 1 µl 0.1 ng/µl pkl8mobsac as a template. Annealing temperature was decreased to 55 °C and extension time increased to 150 s for each cycle. Template DNA was digested by DpnI (New England Biolabs) in 50-µl reactions containing 1 µl DpnI, 1 µg DNA, 5 µl Cutsmart buffer (New England Biolabs) and filled up to 50 µl with nuclease-free water. The tubes were then incubated at 37 °C for 15 min followed by heat inactivation at 80 °C for 20 min. Five microliters of the DpnI-digested plasmid were combined with 1 µl Orange DNA Loading Dye and analysed by DNA agarose electrophoresis. Upon successful verification of amplification and digestion, the remaining sample was purified using AMPure XP and subsequently quantified using Nanodrop. Linearized pK18mobsacB and both flanking regions were mixed in a molar ratio of 1:3:3 into a 10-µl total volume, added to 10 µl 2X Gibson Assembly® Master Mix (New England Biolabs) and incubated at 50 °C for 1 h.

Transformation into chemically competent E. coli BW29427 cells

The vector was transformed into 50 µl chemically competent BW29427 E. coli cells according to the following heat shock protocol: 2 µl of the vector were gently mixed with 50 µl of competent cells, and the resulting mixture was incubated on ice for 30 min. The mixture was transferred to a water bath at 42 °C for 1 min and put back on ice for 2 min. Then, 1 ml of 50% TSB with 50 µg/ml diaminopimelic acid (DAP; Sigma-Aldrich) was added to the heat-shocked cells, the mixture was left to regenerate at 37 °C for 1 h and then plated on 50% TSA containing 25 µg/ml Kanamycin (Kan) and 50 µg/ml DAP. The plates were incubated at 37 °C overnight. The resulting colonies were validated by colony PCR using the M13F and M13R primers. Colony PCR was performed on at least four separate colonies with 0.4 µl DFS-Taq polymerase (BIORON) in 25 µl reactions containing 2.5 µl l0x incomplete buffer (BIORON), 0.5 10 mM MgC12, 0.5 µl 10 mM dNTPs, 0.75 µl 10 µM forward primer, 0.75 µl 10 µM reverse primer, a small fraction of a colony and filled up to 25 µl with nuclease-free water. The tubes were placed in a thermocycler set at the following program: 94 °C for 2 min, 35 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 2 min, then a final extension at 72 °C for 10 min. Five microliters of the PCR product were combined with 1 µl Orange DNA Loading Dye and analysed by DNA agarose electrophoresis. Positive colonies were purified by streaking on new 50% TSA plates containing 25 µg/ml Kan and 50 µg/ml DAP and further verified by Sanger sequencing (Eurofins Scientific) following the manufacturer’s protocol.

Conjugation of E. coli and R401 and selection for first homologous recombination event

E. coli BW29427 cells containing the plasmid and R401 were inoculated into 4 ml of 50% TSB containing 25 µg/ml Kan and 50 µg/ml DAP or 50% TSB and incubated overnight at 37 °C with 180 rpm agitation or 25 °C with 180 rpm agitation, respectively. Cells were harvested by centrifugation at 8000 rpm for 2 min at room temperature, then washed 3x and subsequently resuspended in 1 ml of 50% TSB followed by centrifugation, after which the supernatant was discarded. After quantifying OD 600 , both cultures were mixed to equal parts and approx. 10x concentrated by centrifugation. The bacterial suspension was plated on 50% TSA plates containing 50 µg/ml DAP and incubated at 25 °C overnight to allow for conjugation events. The mating patches were scraped of the plate and resuspended in 1 ml 50% TSB. Then, 100 µl were spread on 50% TSA plates containing 25 µg/ml Kan and 50 µg/ml Nitrofurantoin (Nitro; Sigma-Aldrich; to counter-select E. coli ) and incubated at 25 °C. Colonies were validated for successful genomic insertion of the plasmid via colony PCR using a primer specific to the genomic DNA approx. 150 bp upstream of the upward flanking region (upup) and the plasmid specific M13F primer. Colony PCR was performed on at least 15 separate colonies and a WT control with 0.4 µl DFS-Taq polymerase in 25-µl reactions as described previously, but with an annealing temperature of 60 °C. Five microliters of the PCR product were combined with 1 ml Orange DNA Loading Dye and analysed by DNA agarose electrophoresis followed by Sanger sequencing following the manufacturer’s protocol.

Sucrose counter-selection to induce the second homologous recombination event

A R401 colony with a successful genomic insertion of the plasmid was resuspended from a plate into 1 ml of 50% TSB. The cell density in the medium was then measured using the Multisizer 4e Coulter Counter (Beckman Coulter) following the manufacturer’s protocol. One hundred microliters of 500 cells/µl, 5,000 cells/µl and 50,000 cells/µl dilutions were spread on three separate 50% TSA plates containing 300 mM sucrose. The plates were incubated at 25 °C for approx. 48 h. At least 30 colonies were examined by colony PCR using the respective upup and dwdw primers. Colony PCR was performed with 0.4 µl DFS-Taq polymerase in 25-µl reactions as described previously with an annealing temperature of 60 °C. Five microliters of the PCR product were combined with 1 µl Orange DNA Loading Dye and analysed by DNA agarose electrophoresis. Positive colonies were purified by streaking on new 50% TSA plates and further verified by Sanger sequencing (Eurofins Scientific) following the manufacturer’s protocol. They were also streaked on 50% TSA containing 25 µg/ml Kan to verify loss of the plasmid. A second colony PCR was performed on positive colonies and a wt control to validate the absence of the GOI, using a forward (inF) and reverse (inR) primer inside the GOI. Colony PCR was performed with 0.4 µl DFS-Taq polymerase in 25 µl reactions as described previously. Five microliters of the PCR product were combined with 1 ml Orange DNA Loading Dye and analysed by DNA agarose electrophoresis. Upon successful verification, 4 ml of 50% TSB were inoculated with a positive colony and grown overnight at 25 °C at 180 rpm. Finally, 750 µl of the overnight culture were added to 750 µl of 50% glycerol in an internally threaded 1.8 ml Nunc CryoTube, gently mixed, and stored at -80 °C.

Quantification of brassicapeptin production

Abolishment of brassicapeptin production in the deletion mutant was corroborated by HR-UPLC-MS measurement. Therefore, R401 WT and R401 ∆ brpC were grown in 50% TSB for 3 days; subsequently samples were taken and analysed as described in Getzke et al. 24 . (Fig.  3b ).

Quantification of R401 load on plant roots and shoots

Col-0, Gifu and Micro-Tom roots and Gifu and Micro-Tom shoot (28 days old, grown in FlowPot gnotobiotic system, See Fig.  3e , Supplementary Fig.  1c and 6c,d ) were carefully cleaned, dried and collected in pre-weighed, sterile 2 mL tubes containing 1 steel bead (3 mm diameter). Tubes were weighed again to assess the root or shoot fresh weight. Subsequently, samples were ground in a Precellys 24 TissueLyser (Bertin Technologies) for 2 ×30 s at 6,200 rpm with 15 s intervals. Then, 150 µL of sterile 10 mM MgCl 2 were added to each tube and roots were ground again under the same conditions. The homogenate was serially diluted in 10 mM MgCl 2 . Undiluted samples and each dilution were plated on 50% TSA square plates, dried and left to grow at 25 °C until single colonies appeared. Colonization was expressed as Log2 CFU per mg of roots or shoot. Pictures were taken and single colonies were counted blinded.

Microbial growth rates validation

Assessment of microbial growth rates was conducted as described before 24 . Either artificial root exudates (ARE) or ARE supplemented with 100 mM NaCl were inoculated with R401 WT or ∆ brpC cells to a final OD 600 0.01.

Isolation of R401 brassicapeptin

R401 was precultured in 300 mL flasks containing 100 mL TSB medium for 2 days at 30 °C and 160 rpm. 80 mL preculture were added to 1 L M19 medium (casein peptone 20 g/l, D-mannitol 20 g/L) in 5 L flasks. This procedure was carried out 70-times. All flasks were incubated at 30 °C and 160 rpm for 24 hours, followed by an extraction using EtOAc (volume ratio 1:1) for three times, yielding 16.24 g crude extract. Twenty-one fractions were collected from reversed phase flash chromatography (Interchim Puriflash 4125 chromatography system with Puriflash C18-AQ30 μm F0120 column) with an elution gradient starting from 10% MeOH/H 2 O to 100% MeOH over 4 h. Fraction 19 (143.8 mg) was further subjected to semi-preparative HPLC (semipreparative purification column: VP 250/10 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel, Flow: 3 mL/min; Gradient: 0–20 min, gradient increased from 40% to 100% MeOH; 20–32 min, 100% MeOH) to give two subfractions (fractions 19.1 and 19.2). Fraction 20 (96.7 mg) was also subjected to semi-preparative HPLC (semipreparative purification column: VP 250/10 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel; Flow: 3 mL/min; Gradient: 0–20 min, gradient increased from 40% to 100% MeOH; 20–32 min, 100% MeOH) to give two subfractions (fractions 20.1 and 20.2). Subfraction 19.2 (6.7 mg) and 20.2 (6.7 mg) were further purified by semi-preparative HPLC (analysis column: EC 250/4.6 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel; Flow: 1 mL/min; Gradient: 0–40 min, gradient increased from 40% to 100% MeOH; 40–50 min, 100% MeOH) to yield brassicapeptin A (6.1 mg, t R  = 38.4 min). Fraction 18 (108.6 mg) was subjected to semi-preparative HPLC (semipreparative purification column: VP 250/10 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel; Flow: 3 mL/min; Gradient: 0–20 min, gradient increased from 40% to 100% MeOH; 20–32 min, 100% MeOH) to give three subfractions (fractions 18.1–18.3). Subfraction 18.3 (8 mg) was further purified by semi-preparative HPLC (analysis column: EC 250/4.6 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel; Flow: 1 mL/min; Gradient: 0–3 min, 10% MeCN; 3–58 min, gradient increased from 10% to 92.5% MeCN; 58–65 min, 100% MeCN) to yield brassicapeptin A (3 mg, t R  = 56.4 min) and B (0.9 mg, t R  = 52.3 min).

Structure elucidation of R401 brassicapeptin

The planar structure of the isolated compounds was elucidated by analysis of NMR data, LC-HR-MS and LC-HR-MS/MS data. The 1D and 2D NMR spectra were recorded in CD 3 OD or DMSO- d 6 using Bruker Avance II 600 MHz spectrometers equipped with a Prodigy cryoprobe (Bruker, Ettlingen, Germany) and Bruker Avance Neo 700 MHz spectrometer equipped with a 5 mm CryoProbe Prodigy TCI ( 1 H, 15 N, 13 C Z-GRD) (Bruker). The NMR data can be found in Supplementary Data  6 , all 1D and 2D NMR spectra can be found in Supplementary Fig.  7c-i . The LC-HR-MS and MS/MS data were recorded on a quadrupole time-of-flight spectrometer (LC-QTOF maXis II, Bruker Daltonik) equipped with an electrospray ionization source in line with an Agilent 1290 infinity LC system (Agilent). C18 RP-UHPLC (ACQUITY UPLC BEH C18 column; 130 Å, 1.7 µm, 2.1 × 100 mm) was performed at 45 °C with the following linear gradient: 0 min: 95% A; 0.30 min: 95% A; 18.00 min: 4.75% A; 18.10 min: 0% A; 22.50 min: 0% A; 22.60 min: 95% A; 25.00 min: 95% A (A: H2O, 0.1% HCOOH; B: CH3CN, 0.1% HCOOH; flow rate: 0.6 mL/min). Mass spectral data were acquired using a 50 to 2,000  m/z scan range at 1 Hz scan rate. MS/MS experiments were performed with 6 Hz and the top five most intense ions in each full MS spectrum were targeted for fragmentation by higher-energy collisional dissociation at 25 eV or 55 eV using N2 at 10–2 mbar. Precursors were excluded after two spectra, released after 0.5 min, and reconsidered if the intensity of an excluded precursor increased by factor 1.5 or more. The HR-ESI-MS data can be found in Supplementary Fig.  7a,b,k,l , the HR-ESI-MS/MS data can be found in Supplementary Data  7 . The absolute configuration of isolated compounds was elucidated by Marfey assay. A 5 mM stock solution in H 2 O was prepared from the reference amino acids. 20 µL 1 M NaHCO 3 and 50 µL 7 mM L FDVA (Sigma Aldrich) in acetone was added to 50 µL stock solution of the reference amino acids. The mixture was stirred at 40 °C for 3 h and then quenched by adding 20 µL of 1 M HCl. After evaporation, the residue was dissolved in 40 µl DMSO and analysed by UPLC HRMS (maXis II). brassicapeptin A (0.5 mg) and B (0.3 mg) were dissolved in 200 µL of 6 M DCl in D 2 O and stirred at 160 °C for 7 h. After concentrating the solution under reduced pressure, the residue was dissolved in 200 µl H 2 O, and 100 µL of 1 M NaHCO 3 and 200 µL of 7 mM L FDVA in acetone were added. After stirring for 3 h at 40 °C, the solution was quenched by adding 100 µL of 1 M HCl. After evaporation to dryness, the residue was dissolved in 50 µL DMSO and analysed by UPLC HRMS (maXis II). The results of the Marfey analysis can be found in Supplementary Fig.  7j .

In planta activity test of R401 brassicapeptin A

Surface sterilized and stratified A. thaliana seeds were pregerminated on ½ MS agar plates. After seven days, seedlings were transferred to ½ MS agar plates supplemented with either 1 ng/µL, 1 µg/µL brassicapeptin A solubilized in DMSO or DMSO as negative control and either 0 mM or 100 mM NaCl. After 14 days agar plates for seven days and then transferred to new plates containing NaCl and/or brassicapeptin A. Agar plates were incubated in a light cabinet under short day conditions (10 h light at 21 °C, 14 h dark at 19 °C) for additional 14 days and randomized every 2–3 days.

Ion leakage assay

Five discs with 3 mm diameter of approx. 28 days old A. thaliana Col-0 leaves were transferred to wells of a 24-well plate, filled with sterile MiliQ water supplemented with either 1 ng/µl, 1 µg/µL brassicapeptin A solubilized in DMSO or DMSO as negative control. Before the transfer of leaf discs and after 16 h, ion leakage measurements were taken using the Twin Cond conductivity meter B-173 (HORIBA).

Protoplast transfection assay

Arabidopsis thaliana Col-0, were grown on ½ MS agar for 2 weeks at 22 °C during the day (16 hours) and 18 °C during the night (8 hours). Protoplasts were isolated from leaves and transfected following a protocol adapted from 49 . Briefly, leaves were chopped and mixed with an enzyme solution (1.5% Celulase R10 and 0.5% Maceroenzymes R10) for 3 hours in darkness, then filtered through a 100 μm nylon mesh. The concentration of protoplasts was estimated using a hemocytometer and adjusted to 5.10 5 cells/mL. Protoplast solutions (300 μL of 5.10 5 cells/mL) were transfected with 6 μg of the luciferase (LUC) reporter construct (pZmUBQ:LUC) 49 . Transfected protoplasts were treated with either water or DMSO as controls and two concentrations of Brassicapeptin A diluted in DMSO at 1 ng/mL and 1 μg/mL. Additionally, non-transfected protoplasts inoculated with water were used as controls. All protoplast conditions were incubated for 16 hours at 21 °C in the dark and harvested by centrifugation at 1000 x g. The supernatant was removed, and protoplasts were lysed by adding 250 μL of 2X lysis reagent (Promega, E1531). The LUC activity of samples was measured in a luminometer (Centro, LB960) for 1 second/sample using a 96-well plate in which 50 μL of protoplast lysate were mixed with 50 μL of the LUC substrate (Promega, E1501). For each condition, we 4 biological replicates and 4 technical replicates were included.

Modified Burkholder assays

Modified Burkholder assays to determine the antagonistic potential of R401 and its mutants against 8 core microbiota members of Arabidopsis roots, including 6 bacteria and two fungi. The screen was carried out as described in Getzke et al. 24 . Strains were cultivated in 50% TSB until turbidity, stored at 4 °C and diluted 1:10 in 50% TSB one day before the experiment. Bacterial cultures were pelleted at 4000 rpm for 15 min. The resulting bacterial pellets were subsequently washed 3 times and resuspended in 1 ml 10 mM MgCl 2 . OD 600 were measured and set depending on the strain. One hundred microliters bacterial culture were inoculated per 50 ml 25% TSA. After drying, up to nine different 3 µl droplets of bacterial suspensions with 0.4 OD 600 were applied with equal distances. For all experiments, plates were incubated at 25 °C for up to 96 hours and photographs were taken thereafter for quantitative image analysis. The size of the halo of inhibition was measured using ImageJ with up to five separate measurements, which were subsequently averaged to reduce variation. For Fusarium oxysporum F212 and Macrophomina phaseolina F80, pieces of 7-14 days old mycelium were transferred to pre-weighed sterile 2 mL screw cap tubes containing one and approx. 15 steel beads of 3 mm and 1 mm diameter, respectively. Per 50 mg harvested fungal mycelium, 1000 µL of sterile 10 mM MgCl 2 were added. The mycelium was subsequently grinded in a paint shaker at approx. 600 rpm for at least 10 min until homogeneous. The resulting slurry was used to inoculate 100% Potato Glucose Agar medium to a final concentration of 50 µg/ml.

Minimum inhibitory concentration

Determination of the minimum inhibitory concentration (MIC) of brassicapeptin A was carried out by micro-broth-dilution assays in 96 well plates as previously reported 80 . Briefly, brassicapeptin A was dissolved in dimethyl sulfoxide (DMSO, Carl Roth GmbH + Co., Karlsruhe, Germany) with a concentration of 30 μM and tested in a triplicated 1:2-fold dilution series (64 to 0.03 μg/mL). Dilution series of rifampicin, tetracycline, and gentamicin (all Sigma -Aldrich, St. Louis, MO, USA) at the same concentrations were prepared as positive controls for Escherichia coli ATCC25922, Staphylococcus aureus ATCC25923 and Listeria monocytogenes DSM20600. Instead of gentamicin, isoniazid was used for Mycobacterium smegmatis ATCC607 assays at the same concentration range. M. smegmatis assays were incubated for 48 h (37 °C, 180 rpm, 85% rel. humidity r.H.) before cell viability was measured using BacTiter-Glo™ (BTG) according to the manufacturer’s recommendations (Promega, Walldorf, Germany). For Septoria tritici MUCL45408, Botrytis cinerea HAG001286 and Colletotrichum coccodes DSM62126 tebuconazole (Cayman Chemical Company, Ann Arbor, MI, USA), amphotericin B (Sigma- Aldrich) and nystatin (Sigma Aldrich) were used as positive controls. For fungal assays, a previously prepared spore solution was diluted to 1 × 10 5 spores/mL in potato dextrose medium (Sigma-Aldrich). Septoria assay plates were incubated for 4 days, while Botrytis and Colletotrichum assays were only incubated for 48 h (all at 25 °C, 180 rpm and 85% r.H.) before cell viability was assessed by ATP quantification using BTG.

Additionally, we assessed the potency of brassicapeptin A against 8 microorganisms isolated from the rhizosphere of A. thaliana . Bacterial isolates were screened in tryptic soy broth using an assay inoculum of 5 ×10 5 cells/mL. Microtiter plates were incubated at 28 °C for 1-3 days and reference antibiotics were ceftazidime (LKT Laboratories, Inc., St. Paul, Minnesota), ciprofloxacin (Cayman Chemical Company) and gentamicin. Either cell viability (BTG) or turbidity measurements were used to determine growth inhibition. Fungal strains F212 and F80 were screened in potato glucose broth at 25 °C for 2 days (F212) or 3 days (F80). For strain F212 a spore solution was prepared and used for the assay inoculation as described above. The assay inoculum of strain F80 was prepared by diluting the homogenized pre-culture 1:4800. For both fungi the same reference antimycotics (tebuconazole, amphotericin B, nystatin) were used. Read-out was done by cell viability (F212) or turbidity assessment (F80). The determined MICs for brassicapeptin A can be found in Supplementary Data  3 .

Statistical analyses

All statistical analyses were conducted in R 4.1.2. Data visualisation was conducted using the ggplot2 package (as part of the Tidyverse) or the ComplexHeatmap package. As nonparametric tests, Kruskal-Wallis followed by Dunn’s post-hoc test and Benjamini-Hochberg (BH) adjustment for multiple comparisons from the PMCMRplus package (Pohlert, 2022) were used. The respective statistical tests are indicated in each figure description. Significance was indicated by significance group ( p  ≤ 0.05). No statistical methods were used to pre-determine sample sizes. Halo size quantification in modified Burkholder experiments, and root length measurements were performed blinded using the Fiji package of ImageJ. Colony counts of R401 were performed blinded. RNA sequencing data processed as described above and further analysed and visualized as previously described 6 . GO Term enrichment was conducted as indicated in the respective figure description or results section. Figures were assembled in Adobe Illustrator.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

RNAseq read data are available at GEO accession: GSE242479 . All included data is also accessible from the link in the code availability section. A spreadsheet with all exact p -values is provided in the Source Data file.  Source data are provided with this paper.

Code availability

All codes and respective data generated for this study are available at https://github.com/scriptsFG/Getzke-Wang-et-al-2023.git .

Agler, M. T. et al. Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation. PLoS Biol. 14 , e1002352 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528 , 364 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Duran, P. et al. Microbial Interkingdom Interactions in Roots Promote Arabidopsis Survival. Cell 175 , 973 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Thiergart, T. et al. Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nat. Ecol. Evol. 4 , 122 (2020).

Article   PubMed   Google Scholar  

Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas Pathogens Exhibit Stable Associations over Evolutionary Timescales. Cell Host Microbe 24 , 168 (2018).

Ma, K. W. et al. Coordination of microbe-host homeostasis by crosstalk with plant innate immunity. Nat. Plants 7 , 814 (2021).

Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol 6 , 852 (2021).

Shalev, O. et al. Commensal Pseudomonas strains facilitate protective response against pathogens in the host plant. Nat. Ecol. Evol. 6 , 383 (2022).

Pfeilmeier, S. et al. Leaf microbiome dysbiosis triggered by T2SS-dependent enzyme secretion from opportunistic Xanthomonas pathogens. Nat. Microbiol . 9 , 136–149 (2024).

Entila, F., Han, X., Mine, A., Schulze-Lefert, P. & Tsuda, K. Commensal lifestyle regulated by a negative feedback loop between Arabidopsis ROS and the bacterial T2SS. Nat. Commun . 15 , 456 (2024).

Stevens R. B. in: Plant Pathology, an Advanced Treatise, Vol. 3. J. G. Horsfall and A. E. Dimond, eds. Academic Press, NY., 357-429 (1960).

Cheng, Y. T., Zhang, L. & He, S. Y. Plant-Microbe Interactions Facing Environmental Challenge. Cell Host Microbe 26 , 183–192 (2019).

Arnault, G., Mony, C. & Vandenkoornhuyse, P. Plant microbiota dysbiosis and the Anna Karenina Principle. Trends Plant Sci. 28 , 18–30 (2023).

Article   CAS   PubMed   Google Scholar  

Mesny, F., Hacquard, S. & Thomma, B. P. Co-evolution within the plant holobiont drives host performance. Embo Rep. 24 , e57455 (2023).

Hua, D. P. et al. A Plasma Membrane Receptor Kinase, GHR1, Mediates Abscisic Acid- and Hydrogen Peroxide-Regulated Stomatal Movement in Arabidopsis. Plant Cell 24 , 2546–2561 (2012).

Zhang, Y. Y. et al. Phospholipase D alpha 1 and Phosphatidic Acid Regulate NADPH Oxidase Activity and Production of Reactive Oxygen Species in ABA-Mediated Stomatal Closure in Arabidopsis. Plant Cell 21 , 2357–2377 (2009).

Zhu, J. K. Salt and drought stress signal transduction in plants. Annu Rev. Plant Biol. 53 , 247–273 (2002).

Zhu, J. K. Abiotic Stress Signaling and Responses in Plants. Cell 167 , 313–324 (2016).

Pieterse, C. M. J., Van der Does, D., Zamioudis, C., Leon-Reyes, A. & Van Wees, S. C. M. Hormonal Modulation of Plant Immunity. Annu Rev. Cell Dev. Bi 28 , 489–521 (2012).

Article   CAS   Google Scholar  

Yasuda, M. et al. Antagonistic interaction between systemic acquired resistance and the abscisic acid-mediated abiotic stress response in Arabidopsis. Plant Cell 20 , 1678–1692 (2008).

Berens, M. L. et al. Balancing trade-offs between biotic and abiotic stress responses through leaf age-dependent variation in stress hormone cross-talk. P Natl Acad. Sci. USA 116 , 2364–2373 (2019).

Article   ADS   CAS   Google Scholar  

Fan, J., Hill, L., Crooks, C., Doerner, P. & Lamb, C. Abscisic Acid Has a Key Role in Modulating Diverse Plant-Pathogen Interactions. Plant Physiol. 150 , 1750–1761 (2009).

Jiang, C. J. et al. Abscisic Acid Interacts Antagonistically with Salicylic Acid Signaling Pathway in Rice-Magnaporthe grisea Interaction. Mol. Plant Microbe 23 , 791–798 (2010).

Getzke, F. et al. Cofunctioning of bacterial exometabolites drives root microbiota establishment. P Natl Acad. Sci. USA 120 , e2221508120 (2023).

Feil, H. et al. Comparison of the complete genome sequences of Pseudomonas syringae pv. syringae B728a and pv. tomato DC3000. P Natl Acad. Sci. USA 102 , 11064–11069 (2005).

Kremer, J. M. et al. Peat-based gnotobiotic plant growth systems for Arabidopsis microbiome research. Nat. Protoc. 16 , 2450–2470 (2021).

Dimartino, M. et al. Occurrence and Pathogenicity of Pseudomonas Fluorescens and P. Putida on Tomato Plants in Italy. J. Plant Pathol. 93 , 79–87 (2011).

Google Scholar  

Chojak-Kozniewska, J., Linkiewicz, A., Sowa, S., Radzioch, M. A. & Kuzniak, E. Interactive effects of salt stress and Pseudomonas syringae pv. lachrymans infection in cucumber: Involvement of antioxidant enzymes, abscisic acid and salicylic acid. Environ. Exp. Bot. 136 , 9–20 (2017).

Mi, H. Y., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8 , 1551–1566 (2013).

Mi, H. Y., Muruganujan, A., Ebert, D., Huang, X. S. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res 47 , D419–D426 (2019).

Carbon, S. et al. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res 45 , D331–D338 (2017).

Chaves, M. M., Flexas, J. & Pinheiro, C. Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Ann. Bot.-Lond. 103 , 551–560 (2009).

Eynard, A., Lal, R. & Wiebe, K. Crop response in salt-affected soils. J. Sustain Agr. 27 , 5–50 (2005).

Article   Google Scholar  

Geng, Y. et al. A Spatio-Temporal Understanding of Growth Regulation during the Salt Stress Response in Arabidopsis. Plant Cell 25 , 2132–2154 (2013).

Hanin, M., Ebel, C., Ngom, M., Laplaze, L. & Masmoudi, K. New Insights on Plant Salt Tolerance Mechanisms and Their Potential Use for Breeding. Front Plant Sci. 7 , 1787 (2016).

Zhou, Y. Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10 , 1523 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Cunnac, S., Lindeberg, M. & Collmer, A. Pseudomonas syringae type III secretion system effectors: repertoires in search of functions. Curr. Opin. Microbiol 12 , 53–60 (2009).

Guo, M., Tian, F., Wamboldt, Y. & Alfano, J. R. The Majority of the Type III Effector Inventory of Pseudomonas syringae pv. tomato DC3000 Can Suppress Plant Immunity. Mol. Plant Microbe 22 , 1069–1080 (2009).

Wagner, S. et al. Bacterial type III secretion systems: a complex device for the delivery of bacterial effector proteins into eukaryotic host cells. Fems Microbiol Lett. 365 , fny201 (2018).

Blin, K. et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res 49 , W29–W35 (2021).

Scholz-Schroeder, B. K., Hutchison, M. L., Grgurina, I. & Gross, D. C. The contribution of syringopeptin and syringomycin to virulence of Pseudomonas syringae pv. syringae strain B301D on the basis of sypA and syrB1 biosynthesis mutant analysis. Mol. Plant Microbe 14 , 336–348 (2001).

Hou, S. et al. A microbiota-root-shoot circuit favours Arabidopsis growth over defence under suboptimal light. Nat. Plants 7 , 1078 (2021).

Ku, H. M., Vision, T., Liu, J. P. & Tanksley, S. D. Comparing sequenced segments of the tomato and Arabidopsis genomes: Large-scale duplication followed by selective gene loss creates a network of synteny. P Natl Acad. Sci. USA 97 , 9121–9126 (2000).

Grgurina, I. et al. A new syringopeptin produced by bean strains of Pseudomonas syringae pv. syringae. Bba-Protein Struct. M 1597 , 81–89 (2002).

Grgurina, I. et al. Novel cyclic lipodepsipeptide from Pseudomonas syringae pv. lachrymans strain 508 and syringopeptin antimicrobial activities. Antimicrob. Agents Ch 49 , 5037–5045 (2005).

Isogai, A. et al. Structural-Analysis of New Syringopeptins by Tandem Mass-Spectrometry. Biosci. Biotech. Bioch 59 , 1374–1376 (1995).

Huang, C. J. et al. Characterization of Cichopeptins, New Phytotoxic Cyclic Lipodepsipeptides Produced by Pseudomonas cichorii SF1-54 and Their Role in Bacterial Midrib Rot Disease of Lettuce. Mol. Plant Microbe 28 , 1009–1022 (2015).

Emanuele, M. C. et al. Corpeptins, new bioactive lipodepsipeptides from cultures of. Febs Lett. 433 , 317–320 (1998).

Saur, I. M. L., Bauer, S., Lu, X. L. & Schulze-Lefert, P. A cell death assay in barley and wheat protoplasts for identification and validation of matching pathogen AVR effector and plant NLR immune receptors. Plant Methods 15 , 118 (2019).

Bender, C. L., Alarcon-Chaidez, F. & Gross, D. C. Pseudomonas syringae phytotoxins: Mode of action, regulation, and biosynthesis by peptide and polyketide synthetases. Microbiol Mol. Biol. R. 63 , 266 (1999).

Carpaneto, A., Dalla Serra, M., Menestrina, G., Fogliano, V. & Gambale, F. The phytotoxic lipodepsipeptide syringopeptin 25A from Pseudomonas syringae pv. syringae forms ion channels in sugar beet vacuoles. J. Membr. Biol. 188 , 237–248 (2002).

Agner, G. et al. Membrane-permeabilizing activities of cyclic lipodepsipeptides, syringopeptin 22A and syringomycin E from Pseudomonas syringae pv. syringae in human red blood cells and in bilayer lipid membranes. Bioelectrochemistry 52 , 161–167 (2000).

Geudens, N. & Martins, J. C. Cyclic Lipodepsipeptides From Pseudomonas spp. - Biological Swiss-Army Knives. Front Microbiol 9 , 1867 (2018).

Mesny, F. et al. Genetic determinants of endophytism in the root mycobiome. Nat. Commun. 12 , 7227 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Wippel, K. et al. Host preference and invasiveness of commensal bacteria in the Lotus and Arabidopsis root microbiota. Nat. Microbiol 6 , 1150 (2021).

Levy, A. et al. Genomic features of bacterial adaptation to plants. Nat. Genet 50 , 138 (2018).

Acosta-Motos, J. R. et al. Plant Responses to Salt Stress: Adaptive Mechanisms. Agronomy-Basel 7 , 18 (2017).

Ashraf, M. & Harris, P. J. C. Potential biochemical indicators of salinity tolerance in plants. Plant Sci. 166 , 3–16 (2004).

Dalla Serra, M. et al. The interaction of lipodepsipeptide toxins from Pseudomonas syringae pv. syringae with biological and model membranes: A comparison of syringotoxin, syringomycin, and two syringopeptins. Mol. Plant Microbe 12 , 391–400 (1999).

Hutchison, M. L. & Gross, D. C. Lipopeptide phytotoxins produced by Pseudomonas syringae pv syringae: Comparison of the biosurfactant and ion channel-forming activities of syringopeptin and syringomycin. Mol. Plant Microbe 10 , 347–354 (1997).

Melnyk, R. A., Hossain, S. S. & Haney, C. H. Convergent gain and loss of genomic islands drive lifestyle changes in plant-associated Pseudomonas. Isme J. 13 , 1575–1588 (2019).

Song, Y. et al. FERONIA restricts Pseudomonas in the rhizosphere microbiome via regulation of reactive oxygen species. Nat. Plants 7 , 644 (2021).

Corwin, D. L. Climate change impacts on soil salinity in agricultural areas. Eur. J. Soil Sci. 72 , 842–862 (2021).

Eswar, D., Karuppusamy, R. & Chellamuthu, S. Drivers of soil salinity and their correlation with climate change. Curr. Opin. Env Sust. 50 , 310–318 (2021).

Mukhopadhyay, R., Sarkar, B., Jat, H. S., Sharma, P. C. & Bolan, N. S. Soil salinity under climate change: Challenges for sustainable agriculture and food security. J. Environ. Manag. 280 , 111736 (2021).

Bionda, N., Pitteloud, J. P. & Cudic, P. Cyclic lipodepsipeptides: a new class of antibacterial agents in the battle against resistant bacteria. Future Med Chem. 5 , 1311–1330 (2013).

Kuiper, I. et al. Characterization of two Pseudomonas putida lipopeptide biosurfactants, putisolvin I and II, which inhibit biofilm formation and break down existing biofilms. Mol. Microbiol 51 , 97–113 (2004).

Neu, T. R., Hartner, T. & Poralla, K. Surface-Active Properties of Viscosin - a Peptidolipid Antibiotic. Appl Microbiol Biot. 32 , 518–520 (1990).

Peypoux, F., Bonmatin, J. M. & Wallach, J. Recent trends in the biochemistry of surfactin. Appl Microbiol Biot. 51 , 553–563 (1999).

Raaijmakers, J. M., de Bruijn, I., Nybroe, O. & Ongena, M. Natural functions of lipopeptides from Bacillus and Pseudomonas: more than surfactants and antibiotics. Fems Microbiol Rev. 34 , 1037–1062 (2010).

Torres, M. A., Dangl, J. L. & Jones, J. D. G. Arabidopsis gp91(phox) homologues AtrbohD and AtrbohF are required for accumulation of reactive oxygen intermediates in the plant defense response. P Natl Acad. Sci. USA 99 , 517–522 (2002).

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114–2120 (2014).

Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37 , 907 (2019).

Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30 , 923–930 (2014).

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550 (2014).

Zhu, A. Q., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35 , 2084–2092 (2019).

Vannier, N. et al. Genome-resolved metatranscriptomics reveals conserved root colonization determinants in a synthetic microbiota. Nat. Commun. 14 , 8274 (2023).

Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6 , 343–U341 (2009).

Kvitko, B. H. & Collmer, A. Construction of pv. tomato DC3000 Mutant and Polymutant Strains. Methods Mol. Biol. 712 , 109–128 (2011).

Wang, L. et al. Discovery and Biosynthesis of Antimicrobial Phenethylamine Alkaloids from the Marine Flavobacterium Tenacibaculum discolor sv11. J. Nat. Prod. 85 , 1039–1051 (2022).

Download references

Acknowledgements

This work was supported by funds to S.H. from European Research Council starting and consolidator grants (MICRORULES 758003 and MICROBIOSIS 101089198). It includes also funds to S.H. and P.S.-L. from the Max Planck Society, the Cluster of Excellence on Plant Sciences (CEPLAS) and the Priority Programme: Deconstruction and Reconstruction of the Plant Microbiota (SPP DECRyPT 2125; project P.S.-L.: SCHU 799/8-1; project S.H.: HA 8169/2-2), both funded by the Deutsche Forschungsgemeinschaft. Work in the Schäberle lab was supported by the German Federal Ministry of Education and Research (BMBF). L.W. was funded by the China Scholarship Council (CSC NO. 201908080177). We thank the Max Planck-Genome-Centre Cologne for advising and performing the RNA sequencing. We also thank Brigitte Pickel for her support in halo size quantifications and Flowpot harvest and Maria Patras for her support in Marfey´s analysis. Finally, thanks to Neysan Donnelly for editing this manuscript.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Fantin Mesny

Present address: Institute for Plant Sciences, University of Cologne, 50674, Cologne, Germany

These authors contributed equally: Felix Getzke, Lei Wang.

Authors and Affiliations

Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany

Felix Getzke, Guillaume Chesneau, Fantin Mesny, Nienke Denissen, Hidde Wesseler, Priscilla Tijesuni Adisa, Paul Schulze-Lefert & Stéphane Hacquard

Institute for Insect Biotechnology, Justus-Liebig-University Giessen, 35392, Giessen, Germany

Lei Wang, Nils Böhringer & Till F. Schäberle

German Center for Infection Research (DZIF), Partner Site Giessen-Marburg-Langen, 35392, Giessen, Germany

Nils Böhringer & Till F. Schäberle

Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Branch for Bioresources, 35392, Giessen, Germany

Michael Marner & Till F. Schäberle

Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany

Paul Schulze-Lefert & Stéphane Hacquard

You can also search for this author in PubMed   Google Scholar

Contributions

F.G., S.H., T.F.S. and P.S.L. initiated the project. S.H. and T.F.S. supervised the project. F.G. and S.H. designed the experiments. L.W., N. B. and T.F.S. isolated and elucidated brassicapeptins. N.B. performed in vitro salt experiments with WT R401. F.G, N.D and G.C. performed Flowpot experiments and determined bacterial load on plant tissues. F.G. and H.W. performed CAS and the initial agar plate experiment. F.M. performed initial RNA sequencing data analysis including differential gene expression analysis. F.G. performed all further RNA sequencing analyses. F.G. and G.C. performed brassicapeptin A experiments. P.T.A. performed modified Burkholder assay screen. M.M. provided MIC data. F.G., L.W., G.C, P.T.A. and S.H. generated the figures. L.W. provided the original draft on brassicapeptin structure elucidation. F.G. and S.H. wrote the manuscript, with inputs from all co-authors.

Corresponding authors

Correspondence to Till F. Schäberle or Stéphane Hacquard .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Marc Ongena and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1, supplementary data 2, supplementary data 3, supplementary data 4, supplementary data 5, supplementary data 6, supplementary data 7, reporting summary, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Getzke, F., Wang, L., Chesneau, G. et al. Physiochemical interaction between osmotic stress and a bacterial exometabolite promotes plant disease. Nat Commun 15 , 4438 (2024). https://doi.org/10.1038/s41467-024-48517-5

Download citation

Received : 08 September 2023

Accepted : 01 May 2024

Published : 28 May 2024

DOI : https://doi.org/10.1038/s41467-024-48517-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Open access
• Published: 28 May 2024

Winter survival in red clover: experimental evidence for interactions among stresses

• Åshild Ergon 1 &
• Helga Amdahl 2  

BMC Plant Biology volume  24 , Article number:  467 ( 2024 ) Cite this article

28 Accesses

Metrics details

There is a lack of knowledge on the combined effects of different stresses on plants, in particular different stresses that occur during winter in temperate climates. Perennial herbaceous plants in temperate regions are exposed to many different stresses during winter, but except for the fact that cold temperatures induce resistance to a number of them, very little is known about their interaction effects. Knowledge about stress interactions is needed in order to predict effects of climate change on both agricultural production and natural ecosystems, and to develop adaptation strategies, e.g., through plant breeding. Here, we conducted a series of experiments under controlled conditions to study the interactions between cold (low positive temperature), clover rot infection (caused by Sclerotinia trifoliorum ) and freezing, in red clover ( Trifolium pratense ) accessions. We also compared our results with winter survival in field experiments and studied associations between stress and shoot growth.

Exposure to low positive temperatures (cold acclimation) induced resistance to clover rot. There was a clear negative interaction effect between freezing stress and clover rot infection, resulting in up to 37% lower survival rate compared to what would have been expected from the additive effect of freezing and infection alone. Freezing tolerance could continue to improve during incubation under artificial snow cover at 3 °C in spite of darkness, and we observed compensatory shoot growth following freezing after prolonged incubation. At the accession level, resistance to clover rot was negatively correlated with growth in the field during the previous year at a Norwegian location. It was also negatively correlated with the shoot regrowth of control plants after incubation. Clover rot resistance tests under controlled conditions showed limited correlation with clover rot resistance observed in the field, suggesting that they may reveal variation in more specific resistance mechanisms.

Conclusions

We here demonstrate, for the first time, a strong negative interaction between freezing and infection with a winter pathogen. We also characterize the effects of cold acclimation and incubation in darkness at different temperatures on winter stress tolerance, and present data that support the notion that annual cycles of growth and stress resistance are associated at the genetic level.

Peer Review reports

The ongoing climate change has led to a need to predict the effect of future climatic conditions on plant survival, growth and reproduction, as well as to breed agricultural crop varieties that are optimized for future conditions. These needs have made it clear that, while we have some knowledge about the effects of individual stresses, we know very little about the effects of simultaneous or sequential combinations of stresses. Plants are exposed to a range of abiotic and biotic stresses during their lifetime and have, in addition to constitutive mechanisms, evolved responses that enable them to cope with these stresses. Signalling pathways eliciting such responses interact in an extensive signalling network that integrates external (environmental) and internal (developmental) stimuli and governs the allocation of resources to protective responses, storage, growth and development. Interacting effects of stresses on plants can occur at many different levels from gene to physiology and morphology. These effects may also vary depending on other environmental factors, plant developmental stage, individual stress levels and durations, and on whether the stresses occur simultaneously or sequentially. The effects of stress combinations, and particularly the activation of signalling pathways, have attracted some attention in the past decade and is the subject of several review papers [ 1 , 2 , 3 ].

Overwintering herbaceous plants face a number of different abiotic stresses during the winter, along with psychrophilic pathogens [ 4 ]. A snow cover established before the ground is deeply frozen will promote fungal disease but protects plants against freezing, which will be more harmful if temperatures drop without a snow cover in place. During the winter a snow cover may come and go due to variations in weather conditions and thus plants may be exposed to different stresses at different times during the winter period.

Winter survival is a major limitation for plant species at high latitudes, including agricultural biennial or perennial crops. Overwintering species adapted to cold climates have evolved a cold acclimation response, whereby they go through a developmental and physiological reprogramming in response to lower temperatures and shorter photoperiods in the autumn [ 5 , 6 , 7 ]. The cold acclimation is necessary for these plants to survive winter. Not only freezing tolerance is substantially improved by cold acclimation, but also resistance to other winter stresses, such as winter pathogens [ 8 , 9 , 10 , 11 ].

Due to a number of complicating factors, there are large uncertainties regarding the effects of climate change on winter stresses and plant survival and climate change can both worsen and alleviate winter stresses [ 12 , 13 , 14 ]. For example, higher winter temperatures may lead to a reduction in the presence of a protective snow cover, resulting in more freezing stress, and more precipitation in winter and unstable weather conditions is expected to result in more ice cover, anoxia and more frequent freeze-thaw events. However, in cold areas where winter temperatures will still remain below 0 °C, more precipitation may lead to deeper and longer-lasting snow cover. Indirect effects can also occur, for example, a shift of the cold acclimation period to a time of the year with less light may have a negative effect on the cold acclimation process, depending on the importance of light for the cold acclimation process of the species in question [ 13 ].

Improved winter survival is a major breeding goal in most red clover breeding programs [ 15 , 16 ]. Clover rot, caused by Sclerotinia trifoliorum infection of the root and crown during winter, is regarded as an important factor leading to winter mortality [ 17 , 18 ]. There is, however, strong genotype x environment interactions on red clover winter survival, and in some locations and years freezing tolerance plays a role [ 19 ]. Genetic variation in winter survival, freezing tolerance and clover rot resistance has been described [ 20 , 21 , 22 , 23 , 24 ]. Red clover has been shown to be more resistant to clover rot after a period of cold acclimation than before, both when incubated under humid conditions in a greenhouse [ 22 , 25 ] or under an artificial snow cover in darkness at a low positive temperature [ 17 ]. To our knowledge the effect of the length of the cold acclimation period on resistance to clover rot has so far not been investigated. Apart from the knowledge that a mild cold stress induces some resistance to several different winter stresses, there is generally very limited information about interactions among different winter stresses on plants. In theory, such interactions could have both positive and negative effects on winter survival, as one type of stress may either elicit responses that protect against other types of stress, or cause damage that renders plants more vulnerable to other types of stresses. Besides being informative for development of plant breeding strategies, knowledge on winter stress interactions and the physiology behind could help improving prediction models of grassland growth and overwintering [ 26 ].

This research includes experiments aimed at answering the following two main questions: (i) does a longer period of cold acclimation increase resistance to clover rot? and (ii) is there an interaction between clover rot and freezing stress? For this, the responses of several European red clover accessions were tested. In addition, by including results from previous studies similar red clover material we investigated associations between resistance to clover rot and shoot growth potential.

Four experiments were performed under controlled conditions. In experiment 1 and 2, we investigated the effects of various factors on resistance to clover rot, namely cold acclimation (both experiments), length of cold acclimation (both experiments) and plant age (experiment 1) prior to freezing and/or inoculation with clover rot, as well as length of incubation and incubation temperature under artificial snow cover following clover rot inoculation (experiment 2). In experiment 3 and 4 we studied the interactions between freezing stress and clover rot. All experiments except experiment 2 included non-inoculated, but incubated controls in order to also study the effect of incubation stress per se. Experiment 3 and 4 included treatments with freezing stress applied either before or after inoculation and/or incubation, in order to study if the order of the stress treatments mattered. See Table  1 , Supplementary Table 1 and Supplementary Fig. 1 for a comparative overview of the growth, inoculation, incubation and freezing treatments in the different experiments, and the Methods section for details.

Experiment 1 and 2: variation in resistance to clover rot

A period with low positive temperatures (cold acclimation) had a strong positive effect on survival of inoculated plants in experiment 2 (Fig.  1 B and C) and a weaker effect in experiment 1 (Fig.  1 A). There was no clear effect of plant age, and a longer cold acclimation treatment only improved survival at the high incubation temperature (16 °C), and not at the low incubation temperature (3 °C).

Survival rate of red clover from different growth treatments after inoculation with Sclerotinia trifoliorum and incubation under artificial snow cover at 3 °C in experiment 1 ( A ) and at 16 °C ( B ) or 3 °C ( C ) in experiment 2. Averages of 9 (A) and 12 (B, C) populations are shown (see Table  2 for an overview). NA-YOUNG, NA and NA-OLD; non-acclimated plants that were 6, 7 or 9 weeks old, respectively. CA-SHORT and CA-LONG; 6 weeks old plants cold acclimated for one or three additional weeks, respectively. Least Square Means generated in analyses of variance (Supplementary Tables 3 and 3) are shown. Values within panels and time points that are not labelled with the same letter are significantly different ( P  < 0.05)

Some differences in survival of inoculated plants among accessions could be detected, but only across, and not within, the applied growth treatments. NGB2487 had a significantly higher survival rate than Sangria, NGB4089, Vltavín, Trubadur and Karim in experiment 1 (Table  2 A). NGB2487 also had a significantly higher relative regrowth (inoculated vs. non-inoculated) than Trubadur. In experiment 2, a significant effect of population was seen at 16 °C only, with SWÅ RK09093 (Åke) having better survival than Niederwangen_262 (Table  3 B). Survival rate at 3 °C was only moderately correlated with survival rate at 16 °C ( R  = 0.53, P  < 0.0001).

We also noted a strong effect of plant age and/or cold acclimation on regrowth of non-inoculated plants after incubation under artificial snow cover; on average across populations, 9 weeks old plants (either cold acclimated or not) had better regrowth than 6–7 weeks old plants (Fig.  2 ).

Differences in regrowth among non-inoculated plants from different growth treatments after incubation under artificial snow cover at 3 °C in experiment 1. NA-YOUNG; non-acclimated 6 weeks old plants, NA-OLD; non-acclimated 9 weeks old plants, CA-SHORT; 6 weeks old plants cold acclimated for an additional week, CA-LONG, 6 weeks old plants cold acclimated for an additional 3 weeks. LS means across populations and incubation length generated in analyses of variance (Supplementary Table 2B) are shown. Values that are not labelled with the same letter are significantly different ( P  < 0.05)

Experiment 3 and 4: interaction effects between clover rot and freezing stress

When applied as single stresses, clover rot and freezing had small but significant effects on survival rates in experiment 3. Clover rot reduced survival by 11% and freezing at the lowest temperature (-6 °C) prior to incubation reduced survival by 14% (Table  3 A). The two stresses had a stronger effect on the dry matter of the regrowth than on survival; when applied as single stresses they reduced the average dry weight per plant by 21% and 17–43% after 6 weeks incubation, respectively (Table  3 B). In experiment 4, plants were on average more stress susceptible than in experiment 3 and there were significant effects of clover rot and freezing treatment on survival after both 3 and 4.5 weeks long incubation. In most cases, survival and dry weight of the regrowth was lower after 4.5 weeks incubation in experiment 4 than after 6 weeks incubation in experiment 3 (Table  3 ).

The combination of clover rot and freezing before inoculation reduced survival significantly more than what would have been expected from additive effects of the two stresses applied separately, both in experiment 3 and 4 (Table  3 A, 4 ). In experiment 3, this was also the case for the combination of clover rot and freezing after inoculation, while it was not in experiment 4. In experiment 3, pre- and re-growth occurred in a greenhouse under somewhat different conditions. In particular, the natural light must have given higher light intensities than in the growth chambers used in experiment 4, where in fact long petioles were noted after pre-growth, and this may have influenced the results (see Discussion). Combining stresses had a very different effect on the dry weight of the regrowth after incubation than it had on survival. In contrast to the effect on survival, the plants exposed to both clover rot and freezing stress had a slightly higher regrowth than what would have been expected from additive effects of the two stresses applied separately (Table  3 B, 4 ).

In experiment 4, freezing before incubation had a stronger negative effect on survival and regrowth than freezing after incubation, both in non-inoculated and inoculated plants (Table  3 ; Fig.  3 ). After the 4.5 weeks incubation period, freezing of non-inoculated plants after incubation, even down to -7.5 °C, did not affect survival at all (Table  3 A), suggesting an induction of freezing tolerance during incubation. In fact, regrowth of plants given a freezing treatment after incubation was higher in the 4.5 weeks incubation treatment (0.35–0.40 g DW plant −1 ) than in the 3 weeks incubation treatment (0.13–0.23 g DW plant −1 ) (Table  3 B, Fig.  3 B), even though the regrowth capacity of the control plants was reduced from 0.65 g DW plant −1 to 0.39 g DW plant −1 over the same incubation period (Table  3 B), suggesting a stimulation of shoot growth induced by freezing.

Interactions between freezing time (before or after incubation under artificial snow cover at low temperature, and averaged across freezing temperatures), inoculation with Sclerotinia trifoliorum and incubation length on red clover survival ( A ) and regrowth ( B ) after incubation in experiment 4. Values within panels that are not labelled with the same letter are significantly different ( P  < 0.05). See Supplementary Table 7 for the statistical analysis

Comparison with results from other studies of the EUCLEG red clover collection

In the set of 110 EUCLEG accessions, clover rot resistance measured in the field was correlated with the clover rot resistance of non-cold acclimated plants under controlled conditions reported by Frey et al. [ 24 ], but to a limited extent ( R  = 0.34, P  = 0.0003, Supplementary Table 8 A). In the smaller sets of accessions tested in experiment 1 and 2 there were higher correlation coefficients between survival rate of inoculated plants (across all treatments) and the clover rot resistance reported by Frey et al. [ 24 ] ( R  = 0.61–0.76, P  < 0.04), but no correlation with clover rot resistance in the field (Supplementary Table 8B and C).

Interestingly, shoot growth during the establishment year in the field experiment was negatively associated with clover rot resistance, particularly in the field experiment itself (|R|= 0.47–0.59, P  < 0.0001, Supplementary Table 8 A), but also in the test under controlled conditions by Frey et al. [ 24 ] (|R|= 0.37–0.50, P  < 0.0001, Fig. 4 A). Shoot growth was also to a limited extent negatively correlated with freezing tolerance (|R|= 0.29–0.34, P  < 0.002). Correlations were significant and of similar magnitude not only for canopy height in both late September and late October, but also for the difference in height between these two time points, showing that at least some of the variation in growth is due to late autumn growth. This variation was associated with geographic origin of the accessions, with Nordic material being more resistant to clover rot and having less shoot growth in the establishment year (Fig. 4 B). Regrowth of non-inoculated NA-OLD plants measured in experiment 1 was also positively correlated with shoot growth in the establishment year and negatively correlated with clover rot resistance in the field experiment (|R|=0.68–0.77, P  < 0.04; Supplementary Table 8B, Fig. 5 ).

Principal component analysis of traits recorded in 110 accessions from the EUCLEG red clover panel, grouped into four regions of origin. Canopy height in late October and clover rot resistance expressed during the following winter was recorded in the Norwegian EUCLEG field experiment [ 23 , 27 ], clover rot resistance data are from Frey et al. [ 24 ] and freezing tolerance data from Zanotto et al. [ 28 ]. Correlation coefficients between traits are given in Supplementary Table 8A

Correlation between regrowth of non-acclimated and non-inoculated (but incubated) plants in experiment 1 with canopy height in late October and clover rot resistance expressed during the following winter in the Norwegian EUCLEG field experiment [ 23 , 27 ]. Correlation coefficients between traits are given in Supplementary Table 8B

The effect of cold acclimation on resistance to clover rot

Our results indicate that cold acclimation before infection increases resistance to clover rot during prolonged incubation with the fungus (Fig.  1 ), as previously observed [ 17 , 22 , 25 ]. The results also show that the higher resistance in cold acclimated plants is not simply due to avoidance of a “cold shock” upon incubation at low temperature, since the effect of cold acclimation was very clear under incubation at 16 °C. The length of cold acclimation mattered only when the inoculated plants were incubated at 16 °C rather than at 3 °C, possibly due to a higher amount of carbohydrate reserves that would be needed for respiration under the dark and warm conditions, and accumulation of such compounds during cold acclimation [ 29 ]. Thus, the results indicate that the cold acclimation-induced resistance to clover rot expressed at around 3 °C (similar to natural conditions), is at least partly relying on relatively rapid cold-induced responses, such as the expression of pathogenesis-related proteins observed in many species [ 10 , 30 , 31 ], and less on slower responses that builds up over several weeks, such as accumulation of organic reserves in the crown tissue. It should be noted, however, that the result might have been different with a higher light intensity during CA.

Clover rot resistance is associated with lower shoot growth potential

The negative correlation between growth in the establishment year and clover rot resistance both in the field experiment and under controlled conditions (Fig.  4 A), the correlation between clover rot resistance in the field and shoot regrowth after incubation in experiment 1 (Fig.  5 ), as well as the association of these traits with the latitudinal origin of the accessions (Fig.  4 B), suggest that adaptation to seasonal climatic variation and appropriate regulation of growth and allocation is important for clover rot resistance under field conditions at Nordic latitudes, although the traits may simply be co-inherited and not necessarily functionally related. Similarly, negative phenotypic correlations between growth in the establishment year and winter survival in two of three locations, as well as with freezing tolerance, were found in Nordic red clover gene bank material [ 23 ]. This is probably a reflection of the growth-stress tolerance trade-off which is observed in many perennial forage species [ 13 , 32 , 33 , 34 ], but that may be at least partly genetically uncoupled from more specific stress resistance mechanisms, as shown for lucerne [ 35 , 36 , 37 ] and cocksfoot [ 32 ]. We found relatively low correlations or no significant correlation at all between resistance measured in the field experiment and resistance measured in several experiments under controlled conditions. It is likely that the latter reveals variation in more specific resistance mechanisms, while the former reveals the variation that is associated with shoot growth potential, in addition to variation in other winter stresses or other factors like competition, soil conditions etc. Therefore, screening of resistance under controlled conditions can supplement screening in field trials.

Low positive temperatures can induce freezing tolerance in the absence of light

In experiment 4, freezing before incubation under artificial snow cover had a much bigger negative effect on both survival and regrowth than freezing after incubation, independently of whether plants had been inoculated with clover rot prior to incubation or not (Table  3 ). This was very clear after the 4.5 weeks long incubation but there was also a tendency after 3 weeks. At the same time, there was no synergistic effect between clover rot infection and freezing after incubation in this experiment. In fact, plants tolerated freezing much better after incubation in darkness at a low positive temperature (almost no mortality among non-inoculated plants, even at the lowest freezing temperature), suggesting that the cold acclimation process continued during the low temperature incubation and improved freezing tolerance further. Even of cold acclimation to some extent depends on the presence of light [ 13 , 38 ], our results indicate that light-independent cold acclimation can also increase freezing tolerance in red clover. Whether this has any role under natural conditions is yet to be shown. With climate change it is increasingly relevant at high latitudes where the cold acclimation period is shifting towards a much darker part of the year [ 13 ]. It is puzzling that we did not observe the same phenomenon in experiment 3. The likely higher light intensities in the greenhouse used in experiment 3 compared to the growth chambers used in experiment 4 may have provided plants in experiment 3 with more organic reserves after pre-growth than in experiment 4, which may have affected stress tolerance positively and sufficiently to mask a positive effect of incubation on freezing tolerance. This explanation is supported by the fact that inoculation had a much more detrimental effect on survival in experiment 4 than in experiment 3, especially when taking the shorter incubation length into account (Table  3 A).

Freezing stress increases susceptibility to subsequent clover rot infection in a synergistic manner

We observed a clearly negative interaction between freezing stress and subsequent clover rot infection on survival (Tables  3 and 4 ), suggesting that under field conditions, the presence of both will exacerbate winter mortality. Being a necrotrophic pathogen, S. trifoliorum likely benefits from the cell and tissue damage that freezing can generate, as this will make nutrients and entries for infection available for the fungus.

Freezing can induce compensatory shoot growth

The strong negative synergistic effect of clover rot and freezing on survival rate was not seen in the regrowth data (Table  4 ). There was, in fact, an opposite effect seen in experiment 4. A partial explanation may be that there is more growth per surviving plant due to less competition for light in treatments with high mortality. This could compensate for the synergistic effect between freezing and infection, but not the significant overcompensation that was found in experiment 4 (Fig.  3 ). Such overcompensation can be explained by a stress-induced stimulation of subsequent shoot growth. This is what we observed in plants that were frozen after incubation in experiment 4; they had more regrowth than the control plants. Compensatory growth following freezing stress has also been observed in timothy [ 39 ].

We have demonstrated several interaction effects among different winter stress factors in red clover: (i) A low positive temperature prior to infection improves resistance to clover rot while (ii) freezing prior to infection results in increased susceptibility in a synergistic manner. (iii) Freezing tolerance can improve over several weeks in darkness at low positive temperatures. Moreover, we have identified associations between stresses and shoot growth, supporting the notion that annual cycles of growth and stress resistance are linked: (i) During prolonged incubation in darkness at low positive temperatures, and/or in response to freezing, subsequent compensatory shoot growth (when exposed to normal growing conditions) can be stimulated. (ii) In a diverse collection of accessions, clover rot resistance measured in the field is associated with less shoot growth prior to winter (under Nordic conditions) and immediately after a simulated winter. Finally, we found that measurements of resistance under controlled conditions were only moderately correlated with resistance measured in the field, and may therefore, in line with the above results, have revealed variation in other and more specific resistance mechanisms that are independent of annual growth cycles. Our results improve the current knowledge on winter physiology of red clover and provide information that can be used in modelling of climate effects on winter survival and productivity, as well as breeding for winter survival and climate adaptation in red clover.

Experiments under controlled conditions

Plant material, pre-growth and cold acclimation.

The populations used in the experiments (Supplementary Table 2) were all diploid and belonged to the set of populations characterized in the EUCLEG project ( www.eucleg.eu ) [ 27 ]. The populations were selected to represent a broad range from susceptible to resistant to clover rot according to observations in the Norwegian EUCLEG field trial. Seeds were scarified with sandpaper and sown in a peat soil. After germination, individual young seedlings were transplanted into a small volume (28 cm 3 ) of peat soil. In experiment 1 and 3, plants were grown in a greenhouse (59°40’ N, 10°47’ E, from November 2019) at 16 °C with natural light supplied with metal halide lamps (approximately 150 µmol m −2 s −1 PAR) for a photoperiod of 12 h. Plants in experiment 2 and 4 were pre-grown in a growth chamber with approximately 100 µmol m −2  s −1 PAR. Six weeks old plants were cold acclimated for one or three weeks (CA-SHORT and CA-LONG) in a growth chamber at 3°C, 12 h photoperiod and approximately 100 µmol m −2  s −1 PAR from metal halide lamps. Six and nine weeks old non-acclimated plants (NA-SHORT and NA-LONG) or seven weeks old non-acclimated plants (NA) were included in experiment 1 and 2, respectively (see Table  1 ).

Inoculation and incubation

Sclerotia of S. trifoliorum were collected in the Norwegian EUCLEG field trial (located at Arneberg; 60°45’ N, 11°12’ E) in the spring of 2019. Individual sclerotia were surface sterilised, cut in slices, and allowed to produce mycelium on potato dextrose agar (PDA). After forming new sclerotia, the isolates were stored on PDA at a low positive temperature. Prior to each inoculation event, PDA plates were inoculated and placed at room temperature to initiate growth. Flasks with potato dextrose broth (PDB) were then inoculated with a few plugs with actively growing mycelium and kept at 9 °C. After one week, the medium was filtered away and the mycelium was homogenized in a 0.2% gelatine solution. Five isolates (named 202,887–202,891 and stored at the isolate collection of Norwegian Institute of Bioeconomy Research) were mixed in equal amounts and after dilution the resulting optical density at 430 nm was 0.5 in experiment 1, 3 and 4 and 0.15 in experiment 2. One ml was applied to the base of the petioles and surrounding soil. Non-inoculated controls (in experiment 1, 3 and 4) were mock inoculated with 1 ml of 0.2% gelatine. Plants were placed under an artificial snow cover consisting of layers of wet cellulose paper covered by a plastic sheet and incubated in darkness at 3 °C (all experiments) and 16 °C (experiment 2). Inoculated and non-inoculated plants were placed under separate covers. Artificial snow covers were removed prior to subsequent freezing treatment or regrowth.

Freezing treatments

Plants were exposed to freezing stress either before inoculation and incubation, or after. For this purpose, they were placed in programmed freezing chambers initially set at 2 °C. The temperature was first lowered from 2 °C to -3 °C at 1 °C h −1 and kept at this level for 12 h to ensure even freezing, after which the temperature was lowered again by 1 °C h −1 down to the assigned test temperature (-4.5 and − 6 °C in experiment 3 and − 4.5, -6 and − 7.5 °C in experiment 4). When the temperature reached the test temperature, it was kept there for 1 h before the temperature was raised, again by 1 °C h −1 , up to 2 °C.

Recovery and measurements of survival and regrowth

After the designated incubation time the artificial snow covers were removed and plants were placed in a greenhouse (experiment 1 and 3) or a growth chamber (experiment 2 and 4) with the same conditions as during the pre-growth and allowed to recover and regrow. After four weeks, survival was recorded, and above-ground biomass was collected from surviving plants. Biomass from plants of the same population within each block was bulked, dried at 60 °C, and weighed. Average dry weight per tested plant (including dead ones) were calculated. In addition to the survival rate and dry matter of the regrowth, the relative regrowth was calculated for inoculated plants in experiment 1 as the dry matter divided by the dry matter of the non-inoculated plants of the same population receiving the same incubation and freezing treatments and averaged over replicates.

Statistical analysis of survival and regrowth

The experiments had a split-plot design, with inoculation and incubation length applied to main plots (i.e., tables on which all the plants were covered by an artificial snow cover), and freezing treatment applied to sub-plots. Within each sub-plot, populations were organized in randomly placed rows with five, eight, three or six plants per population (experiment 1–4, respectively). Survival rate, dry matter of the regrowth and relative regrowth were subjected to analyses of variance using PROC MIXED in SAS Enterprise Guide 7.1 (SAS Institute Inc., Cary, NC, USA). The statistical models are presented in Supplementary Tables 3, 4, 5, 6 and 7. The response variable values of the different treatment combinations were calculated as Least Square Means and contrasts among them were estimated using the Tukey-Kramer test implemented in PROC MIXED. Interaction effects between clover rot and freezing stress were studied further by testing the difference between the reduction in survival rate or regrowth in treatments involving both stresses and the reduction expected from additive effects only using PROC TTEST.

Analysis and comparison with results from other studies of the EUCLEG red clover collection

Larger sets of the EUCLEG red clover collection have been phenotyped in several field experiments [ 27 ] and in experiments under controlled conditions, including phenotyping of clover rot resistance of non-cold acclimated plants [ 24 ] and freezing tolerance of cold acclimated plants [ 28 ]. In the Norwegian field experiment, including 110 of the EUCLEG accessions [ 23 ], shoot growth in the establishment year was recorded with a plate meter at five points per plot in late September and in late October, in 2018. The following spring the field was naturally heavily infested with S. trifoliorum and the disease in each plot was recorded on a scale from 1 (all plants dead) to 9 (no symptoms).

Trait variables for which there was a significant effect of population in experiment 1 and 2 were included in correlation and principal component analysis (PCA), including data on shoot growth in the establishment year (two time points as well as the difference between them, which represents late autumn growth) and clover rot resistance as measured in the Norwegian field experiment, as well as freezing tolerance data from Zanotto et al. [ 28 ] and clover rot resistance data from Frey et al. [ 24 ]. Pearson correlation coefficients were calculated with the CORR procedure in SAS and PCA was performed in MiniTab v21.3.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Zhang H, Sonnewald U. Differences and commonalities of plant responses to single and combined stresses. Plant J. 2017;90:839–55. https://doi.org/10.1111/tpj.13557 .

Article   CAS   PubMed   Google Scholar  

Zarattini M, Farjad M, Launay A, Cannella D, Soulié M-C, Bernacchia G, Fagard M. Every cloud has a silver lining: how abiotic stresses affect gene expression in plant-pathogen interactions. J Exp Bot. 2021;72:1020–33. https://doi.org/10.1093/jxb/eraa531 .

Zandalinas SI, Mittler R. Plant responses to multifactorial stress combination. New Phytol. 2022;234:1161–7. https://doi.org/10.1111/nph.18087 .

Article   PubMed   Google Scholar  

Bélanger G, Castonguay Y, Bertrand A, Dhont C, Rochette P, Couture L, Drapeau R, Mongrain D, Chalifour FP, Michaud R. Winter damage to perennial forage crops in eastern Canada: causes, mitigation, and prediction. Can J Plant Sci. 2006;86:33–47.

Article   Google Scholar  

Theocharis A, Clément C, Barka EA. Physiological and molecular changes in plants grown at low temperatures. Planta. 2012;235:1091–105. https://doi.org/10.1007/s00425-012-1641-y .

John R, Anjum NA, Sopory SK, Akram NA, Ashraf M. Some key physiological and molecular processes of cold acclimation. Biol Plant. 2016;60:603–18. https://doi.org/10.1007/s10535-016-0648-9 .

Article   CAS   Google Scholar  

Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Dynamics of plant metabolism during cold acclimation. Int J Mol Sci. 2019;20:5411.

Article   PubMed   PubMed Central   Google Scholar  

Årsvoll K. Effects of hardening, plant age, and development in Phleum pratense and Festuca pratensis on resistance to snow mould fungi. Sci Rep Agric Univ Norway. 1977;56:1–14.

Google Scholar  

Tronsmo AM. Predisposing effects of low temperature on resistance to winter stress factors in grasses. Acta Agric Scand. 1984;34:210–20.

Kuwabara C, Imai R. Molecular basis of disease resistance acquired through cold acclimation in overwintering plants. J Plant Biol. 2009;52:19–26. https://doi.org/10.1007/s12374-008-9006-6 .

Kuwabara C, Sasaki K, Umeki N, Hoshino T, Saburi W, Matsui H, Imai R. A model system for studying plant–microbe interactions under snow. Plant Physiol. 2021;185:1489–94. https://doi.org/10.1093/plphys/kiab027 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Rapacz M, Ergon Å, Höglind M, Jørgensen M, Jurczyk B, Østrem L, Rognli OA, Tronsmo AM. Overwintering of herbaceous plants in a changing climate. Still more questions than answers. Plant Sci. 2014;225:34–44. https://doi.org/10.1016/j.plantsci.2014.05.009 .

Ergon Å. Optimal regulation of the balance between productivity and overwintering of perennial grasses in a warmer climate. Agronomy. 2917;7:19. https://doi.org/10.3390/agronomy7010019 .

Ergon Å, Seddaiu G, Korhonen P, Virkajärvi P, Bellocchi G, Jørgensen M, Østrem L, Reheul D, Volaire F. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change? Eur J Agron. 2018;92:97–106. https://doi.org/10.1016/j.eja.2017.09.016 .

Taylor NL. A century of clover breeding developments in the United States. Crop Sci. 2008;48:1–13. https://doi.org/10.2135/cropsci2007.08.0446 .

Boller B, Schubiger FX, Kölliker R. Red clover. In Boller B, Posselt UK, Veronesi F, editors. Fodder crops and amenity grasses, handbook of plant breeding. Berlin: Springer; 2010. p. 439–455.

Ӧhberg H, Ruth P, Bång U. Effect of ploidy and flowering type of red clover cultivars and of isolate origin on severity of clover rot, Sclerotinia trifoliorum . J Phytopathol. 2005;153:505–11.

Vleugels T, De Riek J, Heungens K, van Bockstaele E, Baert J. Genetic diversity and population structure of Sclerotinia species from red clover crops. J Plant Pathol. 2012;94:493–504.

Zanotto S, Palmé A, Helgadóttir Á, Daugstad K, Isolahti M, Öhlund L, Marum P, Moen MA, Veteläinen M, Rognli OA, Ergon Å. Trait characterization of genetic resources reveals useful variation for the improvement of cultivated Nordic red clover. J Agron Crop Sci. 2021;207:492–503. https://doi.org/10.1111/jac.12487 .

Herrmann D, Boller B, Studer B, Widmer F, Kolliker R. Improving persistence in red clover: insights form QTL analysis and comparative phenotypic evaluation. Crop Sci. 2008;48:269–77. https://doi.org/10.1007/s00122-005-0158-1 .

Klimenko I, Razgulajeva N, Gau M, Okomura K, Nakaya A, Tabata S, Kozlov N, Isobe S. Mapping candidate QTLs related to plant persistency in red clover. Theor Appl Gen. 2010;120:1253–63. https://doi.org/10.1007/s00122-009-1253-5 .

Vleugels T. Breeding for resistance to clover rot ( Sclerotinia spp.) in red clover ( Trifolium pratense L.). Belgium: PhD Thesis, Faculty of Bioscience Engineering, Ghent University; 2013.

Zanotto S, Amdahl H, Ergon Å, et al. Red clover adaptation to a Nordic climate. In: Hartmann S, et al., editors. Exploiting genetic diversity of forages to fulfill their economic and environmental roles. Proceedings of the 34th Meeting of the EUCARPIA Fodder Crops and Amenity Grasses Section in cooperation with the EUCARPIA Festulolium Working Group. Olomouc: Palacký University Press; 2021. https://doi.org/10.5507/vup.21.24459677.22 . Freising 6–8 September.

Chapter   Google Scholar  

Frey LA, Vleugels T, Ruttink T, Schubiger FX, Pégard M, Skøt L, Grieder C, Studer B, Roldan–Ruiz I, Kölliker R. Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover. Theor Appl Gen. 2022;135:4337–49. https://doi.org/10.1007/s00122-022-04223-8 .

Arseniuk E. Effect of induced autotetraploidy on response to Sclerotinia clover rot in Trifolium pratense L. Plant Breed. 1989;103:310–8. https://doi.org/10.1111/j.1439-0523.1989.tb00391.x .

Höglind M, Van Oijen M, Cameron D, Persson T. Process-based simulation of growth and overwintering of grassland using the BASGRA model. Ecol Model. 2016;335:1–15. https://doi.org/10.1016/j.ecolmodel.2016.04.024 .

Nay MM, Grieder C, Frey LA, Amdahl H, Radovic J, Jaluvka L, Palmé A, Skøt L, Ruttink T, Kölliker R. Multi-location trials and population-based genotyping reveal high diversity and adaptation to breeding environments in a large collection of red clover. Front Plant Sci. 2023;14:1128823.

Zanotto S, Ruttink T, Pegard M, Skøt L, Grieder C, Kӧlliker R, Ergon Å. A genome-wide association study of freezing tolerance in red clover ( Trifolium pratense L.) germplasm of European origin. Front Plant Sci. 2023;14:1189662. https://doi.org/10.3389/fpls.2023.1189662 .

Bertrand A, Rocher S, Claessens A, Bipfubusa M, Papadopoulos Y, Castonguay Y. Biochemical and molecular responses during overwintering of red clover populations recurrently selected for improved freezing tolerance. Plant Sci. 2020;292:110388.

Ergon Å, Klemsdal SS, Tronsmo AM. Interactions between cold hardening and Microdochium nivale infection on expression of pathogenesis-related genes in winter wheat. Physiol Mol Plant Pathol. 1998;53:301–10. https://doi.org/10.1006/pmpp.1998.0182 .

Gaudet DA, Laroche A, Frick M, Davoren J, Puchalski B, Ergon Å. Expression of plant defence-related (PR-protein) transcripts during hardening and dehardening of winter wheat. Physiol Mol Plant Pathol. 2000;57:15–24. https://doi.org/10.1006/pmpp.2000.0275 .

Bristiel P, Gillespie L, Østrem L, Balachowski J, Violle C, Volaire F. Experimental evaluation of the robustness of the growth–stress tolerance trade-off within the perennial grass Dactylis glomerata . Funct Ecol. 2018;32:1944–58. https://doi.org/10.1111/1365-2435.13112 .

Gillespie LM, Volaire F. Are winter and summer dormancy symmetrical seasonal adaptive strategies? The case of temperate herbaceous perennials. Ann Bot. 2017;119:311–23. https://doi.org/10.1093/aob/mcw264 .

Keep T, Sampoux JP, Barre P, Blanco-Pastor JL, Dehmer KJ, Durand JL, Hegarty M, Ledauphin T, Muylle H, Roldán-Ruiz I, Ruttink T, Surault F, Willner E, Volaire F. To grow or survive: which are the strategies of a perennial grass to face severe seasonal stress? Funct Ecol. 2021;35:1145–58. https://doi.org/10.1111/1365-2435.13770 .

Brummer EC, Shah MM, Luth D. Reexamining the relationship between fall dormancy and winter hardiness in alfalfa. Crop Sci. 2000;40:971–7.

Castonguay Y, Laberge S, Brummer EC, Volenec JJ. Alfalfa winter hardiness: a research retrospective and integrated perspective. Adv Agron. 2006;90:203–65. https://doi.org/10.1016/S0065-2113(06)90006-6 .

Claessens A, Bertrand A, Thériault M, Baron V, Lajeunesse J, Schellenberg M, Rocher S. Agronomical evaluation of low dormancy alfalfa populations selected by an indoor screening method. Crop Sci. 2022;62:1797–806. https://doi.org/10.1002/csc2.20779 .

Kovi MR, Ergon Å, Rognli OA. Freezing tolerance revisited — effects of variable temperatures on gene regulation in temperate grasses and legumes. Curr Opin Plant Biol. 2016;33:140–6. https://doi.org/10.1016/j.pbi.2016.07.006 .

Østrem L, Rapacz M, Jørgensen M, Höglind M. Impact of frost and plant age on compensatory growth in timothy and perennial ryegrass during winter. Grass Forage Sci. 2010;65:15–22. https://doi.org/10.1111/j.1365-2494.2009.00715.x .

Download references

Acknowledgements

We acknowledge the efforts of the EUCLEG project coordinator Bernadette Julier, work package leader Isabel Roldán-Ruiz and red clover leader Roland Kölliker. Christoph Grieder, Agroscope, provided seeds from the EUCLEG red clover panel. We are grateful to Elisa Gauslaa and Norwegian Institute of Bioeconomy Research for preparing isolates and inoculum of S. trifoliorum , and to Øyvind Jørgensen, Liv Berge and Maria Sviggum Ahlin, Norwegian University of Life Sciences (NMBU), for technical assistance. Experiments under controlled climate were conducted at the Centre for Plant Research in Controlled Climate (SKP), NMBU.

This work was funded by EUCLEG (EU Project no. 737212).

Author information

Authors and affiliations.

Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, Ås, N-1432, Norway

Åshild Ergon

Graminor AS, Hommelstadvegen 60, Ridabu, 2322, Norway

Helga Amdahl

You can also search for this author in PubMed   Google Scholar

Contributions

ÅE designed and conducted all experiments under controlled conditions, analyzed the data and wrote the paper. HA was in charge of the field experiment. ÅE recorded autumn growth and clover rot resistance in the field experiment. HA reviewed the manuscript, gave critical comments and approved the final version.

Corresponding author

Correspondence to Åshild Ergon .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Ergon, Å., Amdahl, H. Winter survival in red clover: experimental evidence for interactions among stresses. BMC Plant Biol 24 , 467 (2024). https://doi.org/10.1186/s12870-024-05167-5

Download citation

Received : 31 August 2023

Accepted : 17 May 2024

Published : 28 May 2024

DOI : https://doi.org/10.1186/s12870-024-05167-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cold acclimation
• Sclerotinia trifoliorum
• Stress interactions
• Trifolium pratense

BMC Plant Biology

ISSN: 1471-2229

• General enquiries: [email protected]

Root growth and belowground interactions in spring wheat /faba bean intercrops

• Research Article
• Open access
• Published: 27 May 2024

Cite this article

You have full access to this open access article

• Sofia Hadir   ORCID: orcid.org/0009-0007-0352-2815 1 ,
• Thomas F. Döring 2 ,
• Eric Justes 3 ,
• Dereje T. Demie 1 ,
• Madhuri Paul 2 ,
• Nicole Legner 4 ,
• Roman Kemper 2 ,
• Thomas Gaiser 1 ,
• Odette Weedon 5 ,
• Frank Ewert 1 , 6 &
• Sabine J. Seidel 1  

154 Accesses

Explore all metrics

Background and aims

Intercrops offer multiple advantages over sole crops. The aim of our study was to characterize root growth and interactions in spring wheat/faba bean intercrops to better understand belowground interactions that govern resource capture.

Materials and methods

A field experiment was conducted with one faba bean cultivar and two spring wheat cultivars sown at three sowing densities, defining three intercropping designs. Destructive root coring was conducted (0–100 cm) in the intercrops and sole crops at two development stages. FTIR spectroscopy was used to discriminate the species’ root masses. The plant-plant interaction index was calculated to represent the belowground interactions.

A negative impact of intercropping on total root mass was observed in the treatment with high sowing density in both stages. For the fully and partial replacement design treatments, plant-plant facilitation was more pronounced than competition in all layers. Competition dominated root growth in the treatment with high sowing density in both stages. Lower sowing densities encouraged deep root growth of wheat (both cultivars) in intercropping. The early root growth in depth and in density of one spring wheat cultivar impacted negatively faba bean root growth. Intercropping resulted in a grain yield advantage in both fully and only one partial replacement design treatment.

In the intercrops, total root mass and plant-plant interactions were affected more by sowing density than by the spring wheat cultivar. Understanding the effect of sowing density on root growth in intercropping can help to support the design of sustainable intercropping systems.

Similar content being viewed by others

Impact of combined organic amendments and chemical fertilizers on soil microbial limitations, soil quality, and soybean yield

Optimising physiochemical control of invasive japanese knotweed.

Bottom-up perspective – The role of roots and rhizosphere in climate change adaptation and mitigation in agroecosystems

Avoid common mistakes on your manuscript.

Introduction

Crop mixtures or intercrop or intercropping is the practice of cultivating two or more crops with different rooting abilities, canopy structure, height, and nutrient requirements simultaneously (Hauggaard-Nielsen et al. 2008 ; Lithourgidis et al. 2011 ). To study interactions in intercrops, different experimental designs can be applied. A common one is the replacement (substitutive) design, in which the densities of the partners relative to the respective densities of the sole crops add up to 100% (Snaydon 1991 ). In the additive design, the intercrop is formed by adding the plants of both species in the same densities as in their sole crops; as a result, the total density of the intercrop is higher than the density of sole crops (Snaydon 1991 ).

The mixture mechanisms that affect intercrop performance are (resource use) complementarity (e.g. through different rooting habits/structures), competition (for light, soil water, and nutrients), and facilitation (e.g. of phosphorus and micronutrient acquisition via root-root interactions) (Vandermeer 1989 ; Brooker et al. 2015 ; Stomph et al. 2020 ; Zhang et al. 2021 ). So, the behavior and performance of intercrops is governed by complex interactions. According to Justes et al. ( 2021 ), Competition occurs when one species has a greater ability to use limiting resources (e.g., nutrients, water, space, light) than others. Complementarity occurs when intercropped plants have different requirements for abiotic resources in space, time, or form. Cooperation (or facilitation) is observed when the modification of the environment by one specie is beneficial to the other(s). Compensation occurs when the failure of one specie is compensated by the other(s) because they differ in their sensitivity to abiotic or biotic stress (Justes et al. 2021 ; Döring and Elsalahy 2022 ). The review on interspecific root-root interactions in competition-based and facilitation-based intercropping systems by Yu et al. ( 2022 ) describes in detail the mechanisms that drive interspecific below-ground competition (e.g. driven by resource depletion) and facilitation (e.g. due to nutrient or water enrichment or enrichment of beneficial microbiome) in intercropping. Due to the mentioned interactions, intercrops offer the possibility of increasing the productivity of a defined piece of land (Lithourgidis et al. 2011 ), limiting the use of synthetic fertilizers (Jensen et al. 2020 ), suppressing weeds (Den Hollander et al. 2007 ), as well as increasing biodiversity and maintaining and regenerating ecosystem services (Kremen and Miles 2012 ). Intercrops also minimize risks related to volatile market prices, drought, and/or floods (Brooker et al. 2015 ; Bedoussac et al. 2015 ). Further ecosystem services offered by intercrops include belowground biomass advantage which is directly linked to better nitrogen (N) mineralization and carbon (C) sequestration (Cong et al. 2015 ) and soil stability which decreases soil erosion (Obalum and Obi 2010 , Sharma et al. 2017 ).

To optimize the intercrop cultivation (e.g. choice of partners, sowing density) and to enhance ecosystem services (e.g. root-based C input for enhanced C sequestration), a better understanding of the underlying mechanisms responsible for belowground growth and interactions in species mixtures and of other ecosystem services is needed (Li et al. 2006 ; Tosti and Thorup-Kristensen 2010 ; Bargaz et al. 2015 ; Brooker et al. 2015 ; Shao et al. 2019 ). As root studies are generally laborious, particularly in (in-row) species mixtures, little is known about the effect of intercrop management practices on belowground growth especially under field conditions and in temperate climatic zones. Several methods for root species identification in mixtures have been applied. Methods based on DNA, 13 C, or root morphology are time-consuming and need extensive training (Rewald et al. 2012 ). The monolith excavation method combined with visual distinction (Li et al. 2011 ; Yu et al. 2022 ) is rather simple and cheap but less accurate. Infrared spectroscopy has been proven to be a fast tool to discriminate roots of different species such as corn-soybean (White et al. 2011 ), pea-oat (Naumann et al. 2010 ), pea-oat and maize-barnyard grass (Legner et al. 2018 ), faba bean-wheat (Streit et al. 2019 ), and blue lupin-winter rye (Kemper et al. 2022 ). Fourier transform infrared (FTIR) spectroscopy can be applied to separate roots of species in mixtures and can also give an estimation of the species specific proportions within a root sample (Meinen and Rauber 2015 ; Streit et al. 2019 ; Kemper et al. 2022 ). In these studies, mean root mass LER (over differential depths) ranged from 0.52 to 1.50 depending on the experimental year and the species (Streit et al. 2019 ; Kemper et al. 2022 ).

One important aspect in studying intercrop performance and the linkage of root traits in species mixtures is to understand the effect of management practices such as sowing density and cultivar ( cv. ) selection as a way to improve intercrop design and cultivation (Demie et al. 2022 ; Yu et al. 2022 ). The sowing density is important because it dictates the number of intraspecific and interspecific neighbors (Homulle et al. 2022 ). Sowing density affects aboveground productivity mainly through intra- and interspecific competition for resources capture (Yu et al. 2016 ). Belowground, studies on the impact of sowing density on root growth are still scarce, especially when sowing densities of both species are varied. To the best of our knowledge, only Wang et al. ( 2018 ) evaluated the effect of increasing total sowing density in a maize/spring wheat strip intercropping system on root growth. They found that with increasing sowing density of maize in species mixtures, root growth of the intercropped maize was increased significantly in comparison to the maize sole crop.

Shao et al. ( 2019 ) found that genotypes with less variation in root size, as well as medium root size, medium to broad root system, and more inter-row root distribution, help to reduce root-to-root competition and tend to have higher yield at high planting densities in a strip intercropping system. Hence, the genotype plays an essential role in determining the root traits and eventually the complementarity and/or competition between intercropped species.

Currently, knowledge of the root systems contribution to intercrop yield advantage and the related effects of cultivar choice and sowing density is scarce. Specific belowground processes between the species should be considered to improve interspecific facilitation in future species mixture designs (Yu et al. 2022 ). The aim of this study was therefore to investigate the effect of faba bean and spring wheat intercropping on root and shoot growth as a first step to understand root interactions in intercrops and to study the effects of different sowing densities and cultivars on belowground growth and interactions.

Site description, field design, and crop management

The research facility Campus Klein-Altendorf (CKA) of the University of Bonn, Germany, is located in Rheinbach near Bonn (50° 37’ 31’’ N, 6° 59’ 21’’ E). The soil at the experimental station was classified as Haplic Luvisol, derived from loess and characterised by a silty-loamy texture with clay accumulation in the subsoil between about 45 and 95 cm soil depth (Barej et al. 2014 ). The climate at the experimental station can be described as moderately humid with maritime influences. The mean annual air temperature and precipitation are 10.3 °C and 669 mm (1991 to 2020), respectively. In 2021, an in-row mixture trial of spring wheat ( Triticum aestivum L.) and faba bean ( Vicia faba L.) with two spring wheat and one faba bean cv . and three total sowing densities ( TSD ) representing three types of intercropping designs was established. Each cultivar was also sown as a sole crop. In a subset of these plots, the presented root observations were conducted (Table  1 ). The sowing densities of sole crops considered in this study are higher than the usually applied densities in Germany, but as the emergence rate is not well known we kept them to better reflect the interactions in intercrops. The sowing densities in grain/m2 and in % are given in Table  2 .

The experiment presented in this study of a large in-row mixture experiment. Due to a sowing error, the intended field design could not be fully implemented and there were therefore less than four field replicates available for the current study (Table S1 ). Therefore, root sampling was repeated four times in the selected plots (one plot for each treatment). The plot size was 15m 2 (1.5 × 10 m) with a row distance of 21 cm and 6 rows per plot.

The preceding crop in 2020 was spring barley. On 30/03/2021, the soil was harrowed to 10 cm soil depth. Soil mineral N was 98 kg ha −1 (16 kg ha −1 from 0 to 30, 27 kg ha −1 from 30 to 60 cm and 55 kg ha −1 from 60 to 90 cm) on 17/02/2021. Spring wheat cultivars SU Ahab and Anabel and faba bean cultivar Fanfare were sown on 30–31/03/2021. The cultivars are described in Paul et al. ( 2024 ). Spring wheat emerged mid-April (BBCH 11/12 on 19/04/2021) and faba bean emerged about one week later. Hand harvest took place on 13/08/2021 (BBCH 99) and machine harvest on 25 August 2021, when both crops were fully ripened. No fertilizers or pesticides were applied.

Root sampling

Root samples were taken with a soil auger with an inner diameter of 9 cm down to 100 cm soil depth in the selected plots on 09/06/2021 and on 05–06/07/2021. The root sampling in the intercrop treatments covered always one faba bean and one wheat plant and the core was placed not exactly above a row but next to the row (from the row to 1.5 cm from the middle of the row) (see Fig. S1 ). On 09/06/2021, the BBCH stages of wheat and faba bean were 39 (end of shooting) and 63 (full flowering), respectively. On 05–06/07/2021, the BBCH stages of wheat and faba bean were 69 (end of flowering) and 71 (approx. 10% of the pods have a species or variety-specific size achieved), respectively. Samples were taken in eight plots (three sole crops and five intercrops) replicated four times per plot (Table  1 ). Soil cores were split into ten centimetre sections and stored separately in plastic bags and dried under a plastic crop tunnel before sample preparation and evaluation performed at the University of Göttingen, Germany.

Quantification of root biomass, root carbon and nitrogen contents

The root samples were washed in a root washing machine (custom made, mesh size 1 mm) and cleaned of soil residues and non-root particular organic matter manually. The root samples were frozen in a tea bag between different cleaning, scanning, and drying steps. Roots were scanned with a flat-bed scanner (Expression 12000XL, Epson, Suwa, Japan) and analysed with WinRhizo 2016a software (Régent Instruments Inc., Quebec, QC; Canada) to estimate the root length density (RLD, cm cm −3 soil). After scanning, all roots were oven-dried at 40 °C for 48 h and weighted. The samples were ground with an ultra-centrifugal mill (Retsch, ZM 200, Haan, Germany) and stored in glass vials for the next analysis (see  Discrimination between species ).

Due to low absolute weights in deeper soil layers, the root mass samples of the subsoil layers were pooled for weighing and for the C and N content determination (after the FTIR analyses) resulting in samples soil depths of 0–10 cm, 10–20 cm, 20–30 cm, 30–60 cm, and 60–100 cm. Root C and N were measured according to ISO 13,878 and ISO 10,694 standards with an elemental analyzer VarioMAX cube (Elementar Analysensysteme GmbH, Langenselbold, Germany).

Discrimination between species

Fourier transform infrared spectroscopy (ftir).

The roots of the sole crops of the two spring wheat cultivars (SU Ahab and Anabel) and one faba bean cultivar (Fanfare) were used to evaluate the species’ root proportion in the intercrop samples. Absorption spectra of the ground root samples of the sole crops, as well as of the intercrops, were measured by the FTIR-ATR spectrometer (Alpha-P with a diamond crystal attenuated total reflection (ATR) device, Bruker Optics, Ettlingen, Germany) with a resolution of 4 cm −1 and 32 scans in the spectral range of 4000 –400 cm −1 . Each sample was measured 3 to 5 times. The evaluation of the FTIR-ATR spectra was conducted with the Opus software Quant 2 (version 7.2, Bruker Optics, Ettlingen, Germany). The FTIR spectra of the sole crop sample species were used for a cluster analysis (Opus software, version 7.2, Bruker Optics) to allow for species discrimination. For the cluster analyses, the spectra were pre-processed by second derivative and vector normalization, the frequency range was reduced and the Euclidian’s distance and Ward’s algorithm was applied (Fig. S2 , S3 and S4 ). The interspecific heterogeneity for both species was higher than the intraspecific heterogeneity permitting a separation of the two species. Both spring wheat cultivars separately but also combined were clearly separable from faba bean via cluster analysis (Fig. S5 ). Since the average FTIR spectra of both spring wheat cultivars were very similar, both spring wheat cultivars were combined for the second sampling date analyses (Fig. S5 and S6 ).

Model establishment

For the quantification of the root proportion of each species in the intercrops root samples, the FTIR spectra of the single species samples were used to generate a model. For establishing a two-species model, a calibration set of 35 “artificial mixtures” was generated in 3% steps from 0 to 100% for spring wheat and faba bean, respectively. These mixtures covered the complete calibration range. 20 additional “artificial mixtures” with known species composition were generated to be used for external calibration of the model. With the FTIR spectra of these calibration mixtures, a model was calculated on the basis of multivariate calibrations with the method of partial least square (PLS) regression using the software Quant 2 (Opus, version 7.2, Bruker Optics, Ettlingen, Germany). The absorption of infrared radiation is correlated to the concentration of compounds in a multi-compound system. The established model was evaluated by an internal validation (cross validation) and was subsequently optimized by the Quant 2 software. This optimization process detected the best data preparation and the best frequency range to explain the actual mixtures of the calibration samples. Six to eight of the proposed optimized models were verified by an external calibration (20 additional “artificial mixtures”). Both internal validation and external calibration were compared with the calculated statistical parameters of each calibration. For the first sampling date for each wheat cultivar, a separate model was generated. The statistical parameters of the model (calibration/internal validation and external calibration) are shown in Tables S2 (first sampling date, 09/06/2021) and S3 (second sampling date, 05-06/07/2021). With the chosen model, the FTIR spectra of the mixed species samples were evaluated with the associated model. The output of this evaluation was the percent share of each species within the mixed species root mass samples which were used for further calculations. Values outside the calibration range (below 0% or above 100%) were corrected to 0% and 100%.

Data analysis and statistics

Root parameters and indexes.

Root length density (RLD, in cm cm −3 ) per layer was calculated using the following equation:

The soil volume of each layer is equal to 636 cm 3 (core diameter: 9 cm, sample height: 10 cm).

Root mass (t ha −1 ) was calculated according to the Eq. ( 2 ):

The surface area of cylinder (core auger) is equal to 63.6 cm 2 .

Specific root length (SRL; m g −1 ) was calculated as follows:

The FTIR method used in this study to separate between the intercropped species allows only to determine the root mass of the two species, separately. Thus, the RLD and SRL in this study refer to the whole intercropping system rather than to the specific crop species.

Various terminologies for characterizing the yield advantages in intercrops exist in the literature, namely, ‘overyielding’ (Li et al. 2013 ; Streit et al. 2019 ; Nelson et al. 2021 ; Yang et al. 2022 ) or ‘Relative Yield Total’ (Willey and Osiru 1972 ), which is identical to ‘Land Equivalent Ratio’ (LER) defined by De Wit and Van den Bergh ( 1965 ). In the context of our study, we use also the term root mass advantage to characterize the positive effect of intercrops on root biomass.

So, the LER for the faba bean and spring wheat mixtures was calculated for aboveground biomass (LER AGB ) at the two growing stages and at harvest as well as for belowground biomass (LER Root ) according to Eqs.  4 – 6 . The LER was only calculated for the treatments with fully replacement design. The LER for bean and wheat in intercrops is the sum of the partial LER for bean (pLER Bean ) and wheat (pLER Wheat ):

The expected values of grain yield, root mass, RLD and SRL were estimated based on the Eq. ( 7 ):

Where p is the sowing density of the species in the intercrop divided by the sowing density in the sole crop and M is either the grain yield, root mass, SRL or the RLD of the sole crop.

We applied an adapted version of the 4 C approach of Justes et al. ( 2021 ) to find out when and where facilitation or competition dominates. Here, instead of using the pLER as presented in Justes et al. ( 2021 ), the calculation is being adapted by dividing the root biomass by the ratio of plant density DR (Eqs. 8 – 10 ). The novel index is called plant-plant interaction index (PPII), where:

and ratio of plant density DR (with density in plants per m²);

If PPII= 1, neutral effect. If PPII < 1, net competition. If PPII > 1, net facilitation.

This approach has the advantage of giving the information on the net effect of plant-plant interactions, expressed by plant density.

Statistical analyses

The statistical analyses were performed using the programme R version 4.2.1 (23/06/2022) (R Core Team 2018 ).

Shoot biomass, root mass and RLD were analysed by a one-factorial analysis of variance (Anova) (factor treatment), as well as two-factorial analysis of variance (factors cultivar and sowing density) for all treatments. Mean values of treatments were compared with a Tukey post-hoc test at a significance level of α = 0.05. Outliers were detected for each of the response variables (root mass, RLD, FTIR predictions) using the package rstatix in the programme R. Values above- Q3 + 1.5 x IQR or below Q1–1.5 x IQR were considered as outliers and were deleted. Q1 and Q3 are the first and third quartile, respectively. IQR is the interquartile range (IQR = Q3 - Q1). A one-sample t-test against 1 was used to test the significance of LER root and one sample t-test against 0.5 was used to test the significance of pLER Wheat and pLER Bean . For the calculation of PPII, infinite values induced by 0 when dividing root masses were deleted and not considered in the calculation of the means. Also, we considered the mean across replicates.

Shoot sampling, soil water, and nutrient derivation

Shoot biomass, plant height, number of plants per m 2 and volumetric soil water content at 0, 30, 45, 60 and 90 cm soil depth were measured in the days preceding the two dates when the root sampling took place. Shoot samples for estimation of shoot dry weight were collected destructively with one sample per plot on 06.06 and 06–08/07/2021. Hand harvest of 2 row meters took place on 13/08/2021 in which 1 m from both the 3rd and 4th rows (2m in total per plot) were harvested and ensuring that cuts were made a minimum of 1 m from the plot boundary to reduce boundary effects. Wheat and faba bean were separated manually in case of intercrop treatments. The fresh biomass samples were weighed and (in case of large samples only aliquots) then oven-dried (105° C) until constant weight was reached and weighed again to estimate shoot, straw or grain dry matter. Due to lack of replicates regarding shoot biomass and yield at harvest, the aboveground dataset is only presented as supplementary (Table S4 ).

The soil water content was measured at soil depth of 0, 30, 45, 60, and 90 cm with a mobile FDR probe (ThetaProbe ML3, ecoTech Umwelt-Meßsysteme GmbH, Bonn, Germany) on 07/06/2021 and 05/07/2021. Soil samples from 0 to 30, 30–60, and 60–90 cm soil depth were collected to estimate soil mineral nitrogen (Nmin) before sowing (17/02/2021, pooled samples over field) and one day after harvest (26/08/2021, pooled samples per plot) using a Pürckhauer auger. Nitrate-N and ammonium-N were determined photometrically using a continuous flow analyser (Seal QuAAtro 39, Norderstedt, Germany) after K 2 SO4 extraction of the soil sample.

General characteristics of the growth period

The growing season in 2021 can be characterized as chilly in April and May with a normal rainfall pattern, however, a storm with a heavy rainfall occurred on 14–15/07/2021 with about 120 mm of rainfall. In the growth period from 30/03/2021 to 25/08/2021, total rainfall was 395 mm and the mean air temperature was 14 °C (Fig. S7 ).

Aboveground overyielding in intercrops

Total dry matter grain yield in intercrops varied from 4.5 t ha-1 to 5.6 t ha-1 (Table S4 ). In intercrops with cv . SU Ahab, the grain yield attained values were higher for the treatments of the partial replacement design and fully replacement design but lower that for the additive design (Table  3 ). For intercrops with the cv. Anabel the lower sowing density of the partial replacement treatment ( TSD  = 66%) resulted in grain yield value lower than the expected one. However, for that same cultivar, a value of grain yield attained higher than the expected one was found under fully replacement design (FB_50_SW_Ana_50, TSD  = 100%).

In intercrops, LER could only be calculated for the fully replacement design treatments (FB_50_SW_Ana_50 and FB_50_SW_SUAh_50), the shoot LER values ranged from 1.03 to 1.42 (Table S5 ) with a mean across both varieties of 1.28 ± 0.20 at the first sampling date and 1.10 ± 0.10 at the second sampling date. At harvest, the wheat contributed less (lower pLER Bean ) than the faba bean to the positive grain yield overyielding (1.27 ± 0.28, mean across both cultivars). The comparison between both wheat varieties revealed that the grain yield LER of the intercrops with  cv. SU Ahab was higher than in intercrops with   cv.  Anabel. The cv. SU Ahab seems to be more advantageous for mixtures (higher LER for grains and higher absolute grain yield in mixture) than the cultivar Anabel (Table S5 ).

Root growth in intercrops

Characterisation of root mass.

The cumulated root mass over the soil profile (all soil depths measured) increased from the first to second date by 19% (mean of the two cultivars) for the sole crop wheat and 34% for the sole crop faba bean (Table S6 ). For the intercrops, the greatest increase between the two sampling dates were estimated in treatments FB_50_SW_Ana_50 (46%) and FB_100_SW_SUAh_100 (41%) and the lowest were estimated for the treatments FB_50_SW_SUAh_50 (21%) and FB_33_SW_Ana_33 (20%). On sampling date one (09/06/2021), the significantly highest mean values of total root mass (0–1 m) were observed in the intercrop with wheat cv. SU Ahab with TSD = 66% (FB_33_SW_SUAh_33) and 100% (FB_50_SW_SUAh_50) TSD with 2.11 t ha-1 and 2.03 t ha-1, respectively (Table S6 ).

At the first sampling date (Fig.  1 ), the lowest root mass values in the topsoil (0–30 cm) were determined for the wheat sole crops. The highest sowing density ( TSD =  200%) showed lower total root mass as compared to the two other sowing densities in intercropping. For the upper subsoil (30–60 cm), the sole wheat root mass was significantly higher than all intercrop treatments. The intercropping of faba bean with the wheat cv . Anabel at the lowest sowing density achieved the lowest root mass value, while the faba bean sole crop achieved the second lowest total root mass at this soil depth. For the deeper subsoil layers (60–100 cm), the faba bean sole crop presented the lowest value. At the first sampling date, spring wheat cv . Anabel developed more roots in deeper soil layers as a sole crop and in intercropping in comparison to cv. SU Ahab (Fig.  1 ).

Mean ( n  = 4) total root mass (sum of both crops) in t ha −1 at the first sampling date (09/06/2021) for three soil layers. Different letters indicate significant differences (Anova and Tukey post-hoc test, α = 0.05). Error bars refer to the standard deviation. Treatment abbreviations: FB_100 = Sole crop faba bean Fanfare, SW_SUAh_100 = Sole crop spring wheat SU Ahab, SW_Ana_100 = Sole crop spring wheat Anabel, FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

At the second sampling date, no significant differences between the treatments with regard to topsoil root mass were observed (Fig.  2 ). The intercrops with low sowing density (FB_33_SW_SUAh_33 and FB_33_SW_Ana_33) achieved the significantly lowest values of root mass cultivars in the upper subsoil (30–60 cm). In the deeper soil layer (60–100 cm), faba bean reached the lowest root mass. Results of a two-way Anova (α = 0.05) indicated that the cultivar choice had no significant effect on root mass but sowing density had. Also, no significant interactions between the sowing density and cultivar for root mass were found (Table S7 ).

Total root mass (sum of both crops) in t ha −1 of the second sampling date (05–06/07/2021) for three soil layers. Different letters indicate significant differences (Anova and Tukey post-hoc test, α = 0.05). Error bars refer to the standard deviation. Treatment abbreviations: FB_100 = Sole crop faba bean Fanfare, SW_SUAh_100 = Sole crop spring wheat SU Ahab, SW_Ana_100 = Sole crop spring wheat Anabel, FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

Proportion of faba bean and spring wheat root in intercrops

The results of discrimination between species using the FTIR showed that wheat root mass dominated in the subsoil (20/30–100 cm, Fig.  3 ). In general, there were no significant differences in faba bean root mass proportions between the different treatments. Only in the first sampling date significant differences in 0–10 cm (the very high sowing density led to low faba bean root proportions) and in 60–100 cm depth (the intercrop treatments with wheat cv. Anabel had low faba bean root proportions) were observed. The quick and deep rooting ability of the cv. Anabel in comparison to cv. SU Ahab is illustrated by the greater proportion of faba in intercrops with cv. SU Ahab in the deeper soil depths (60–100 cm) at both sampling dates (although the differences were only significant at the first sampling date).

Mean values ( n  = 4) of species proportion of root mass (%) of spring wheat and faba bean in five intercrops. Different letters indicate significant differences (Anova and Tukey post-hoc test, α = 0.05) between proportion of root mass of faba bean within each soil layer (0–10 cm, 10–20 cm, 20–30 cm, 30–60 cm, 60–90 cm) in 09/06/2021 (top panel) and 05/07/2021 (bottom panel). Treatment abbreviations: FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%)

Root mass advantage in intercropping

At the first sampling date (09/06/2021) in the topsoil and upper subsoil layers (0–40 cm) for intercrops with wheat cv. Anabel and 0–30 cm for intercrops with wheat cv. SU Ahab), a positive root mass LER was observed (Table  4 ). A the second sampling date (05/07/2021), the root mass LER was above one for the layers 0–20 cm for the intercrop with cv. SU Ahab and above one from the layers 0–60 cm for the intercrops with cv. Anabel (Table  4 ).

Effect of sowing density on root mass of intercrops

The analysis based on the comparison between the attained and the expected values of root mass revealed that, on both sampling dates, under high sowing density ( TSD  = 200%, additive design) the expected values of root mass in 0–1 m soil depth were higher than the attained values (Fig.  4 ). In contrast, for the lower sowing densities ( TSD  = 66%, partial replacement design and TSD  = 100%, full replacement design), the attained values were higher than the expected one.

Expected vs. attained values of mean root mass (t ha −1 , n  = 4) over 0–1 m soil depth in intercrops on 09/06/2021 (top panels) and on 5/07/2021 (bottom panels). The error bars refer to the standard deviation. Treatment abbreviations: FB_33_SW_SUAh_33  = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

Root length density

On both sampling dates and in all soil layers, the RLD of the tap rooted sole faba bean was lowest (Figs.  5 and 6 ). In the upper subsoil (30–60 cm), mostly significant differences were found between RLD of faba bean and spring wheat in sole cropping. For the mixed cropping treatments, the RLD in the upper subsoil was higher for the fully replacement treatments ( TSD  = 100%) as compared to the partial replacement ones ( TSD  = 66%) and vice versa in the deeper subsoil 0–100 cm). Thus, lower sowing densities encouraged deep rooting in mixtures.

No significant differences in RLD were observed for the wheat cv. SU Ahab for all sowing densities on either sampling date in any soil layer. For the wheat cv. Anabel, RLD in the upper subsoil was significantly higher in the 50%-50% treatment as compared to the 33–33% treatment (both dates).

For deep subsoil (60–100 cm) and for all treatments, the RLD decreased with soil depth. However, the mean RLD for the subsoil (60–100 cm) was found to be highest in the 33%-33% mixture with the wheat cv. SU Ahab. Additionally, in both treatments with TSD 66%, the mean RLD from 60 to 100 cm was higher in comparison to the mean RLD of 30–60 cm. Both the intercrops and the spring wheat sole crops attained slightly higher cumulative RLD values than the faba bean, with a mean value over all intercrops and sole crop spring wheat treatments of around 18 cm cm −3 compared to 5 cm cm −3 for the faba bean (0–1 m soil depth) (Table S8 ).

Mean values ± standard error ( n  = 4) of root length density (RLD, not crop-specific) in cm cm −3 , for sole faba bean and sole spring wheat, as well as for the mixtures treatments for cumulated three soil layers in 09/06/2021. Different letters indicate significant differences at each soil depth (Anova and Tukey post-hoc test, α = 0.05). Error bars refer to the standard deviation. Treatment abbreviations: FB_100 = Sole crop faba bean Fanfare, SW_SUAh_100 = Sole crop spring wheat SU Ahab, SW_Ana_100 = Sole crop spring wheat Anabel, FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

Mean values ± standard error ( n  = 4) of root length density (not crop-specific) in cm cm −3 (RLD), for sole faba bean and sole spring wheat, as well as for the mixtures treatments for cumulated three soil layers in 05/07/2021. Different letters indicate significant differences (Anova and Tukey post-hoc test, α = 0.05). Error bars refer to the standard deviation. Treatment abbreviations: FB_100 = Sole crop faba bean Fanfare, SW_SUAh_100 = Sole crop spring wheat SU Ahab, SW_Ana_100 = Sole crop spring wheat Anabel, FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

Specific root length

On both sampling dates, the mean SRL (all depths) was lower in faba bean compared to spring wheat (Table S9 ). An enhanced SRL (more fine roots in 0–100 cm) in intercrops as compared with the expected SRL from sole crops was observed. A trend for decreasing mean SLR values with increasing TSD in the mixtures was observed.

Belowground interactions in intercrops

Generally, the mean PPII decreased from the topsoil to the subsoil. The analysis of PPII showed that under fully replacement design ( TSD  = 100%) and partial replacement design ( TSD  = 66%), the facilitation were the most dominant interaction. In contrast, the competition between the species was more pronounced in the additive design (Fig.  7 ), in both growing stages.

The mean PPII is shown for each soil depth. The area where PPII > 1 indicates facilitation between the two species. The area where PPII < 1 indicates competition between the two species. The red line shows PPII= 1, indicating a neutral effect. The mean PPII was calculated as the mean of PPII across treatment´s replicates ( n  = 4) for the sampling dates in 09/06/2021 (left panel) and 05/07/2021 (right panel). X axis was cut in the value 25, data points > 25 are shown directly after the value 25. Treatment abbreviations: FB_100 = Sole crop faba bean Fanfare, SW_SUAh_100 = Sole crop spring wheat SU Ahab, SW_Ana_100 = Sole crop spring wheat Anabel, FB_33_SW_SUAh_33 = Intercrop Fanfare (SD = 33%) x SU Ahab (SD = 33%), FB_33_SW_Ana_33 = Intercrop Fanfare (SD = 33%) x Anabel (SD = 33%), FB_50_SW_SUAh_50 = Intercrop Fanfare (SD = 50%) x SU Ahab (SD = 50%), FB_50_SW_Ana_50 = Intercrop Fanfare (SD = 50%) x Anabel (SD = 50%), FB_100_SW_SUAh_100 = Intercrop Fanfare (SD = 100%) x SU Ahab (SD = 100%))

Root carbon content

The root C content, calculated as C concentrations (mean: 45%) multiplied by root dry matter, did not change significantly across the treatments for both sampling dates. However there was a trend of higher root C contents in the intercrop treatments compared with the sole crops, with the exception of the treatment with TSD  = 200% (Fig. S8 ). For the intercrop treatments with wheat cv. SU Ahab, there was a decrease of root C content with increasing TSD . The opposite trend was observed for the wheat cv. Anabel.

Root nitrogen content

The mean root N content were 2.3% (sole faba bean), 0.7% (sole wheat), and 1.2% (intercrop). As expected, the lowest values of root N content were estimated in sole spring wheat treatments (Fig. S9 ). Root N content in several intercrop treatments was comparable to the sole crop faba bean treatment. On the second sampling date, no significant differences were observed between the intercropping treatments and sole faba bean. However, in faba bean, the root N content was also found to be higher in the deeper soil layers (20–60 cm).

Soil mineral N

Before the establishment of the crops, the initial Nmin was 16 kg ha −1 in the topsoil (0–30 cm), 27 kg ha −1 in the upper subsoil (30–60 cm) and 55 kg ha −1 in the deeper soil (60–90 cm). After harvest, lower Nmin values over the whole soil layers were found in the spring wheat sole crop treatments. The topsoil Nmin values were lower in sole cropping (wheat and bean) as compared to the intercropping treatments (Fig. S10 ). The highest topsoil value (25 kg ha −1 ) was determined in the treatment FB_100_SW_SUAh_100. In the upper subsoil 30–60 cm, the lowest value of 7.7 kg ha −1 was measured in the intercropping treatment with highest total grain yield and with lowest sowing density (FB_33_SW_Ana_33) followed by both spring wheat sole treatments. Again, the highest value in 30–60 cm soil depth of 18 kg ha −1 was measured in the treatment with the highest sowing density FB_100_SW_SUAh_100. In the deeper subsoil (60–90 cm), soil Nmin was lowest in the intercrop treatments FB_100_SW_SUAh_100 and FB_50_SW_SUAh_50.

Higher topsoil N but low subsoil N were observed in the intercrop treatments with wheat cultivar SU Ahab (slower root growth) as compared to the intercrop treatments with cv. Anabel (fast early root growth). Especially in the upper soil layers there was a trend for a higher N depletion (lower Nmin values) in the low sowing density as compared to the high density intercrop treatments.

Soil volumetric water content

In general, the soil volumetric water content around the flowering of spring wheat in July (second sampling date) was higher than at the early sampling date in June (first sampling date). Soil volumetric water content for the spring wheat cultivar Anabel, which indicates the potential to root quickly and deeply, was lower in the sole crop treatment and in mixtures compared to the cv. SU Ahab at the second sampling date, particularly at deeper soil depths (Fig. S11 ). However, in the treatment with the cv. SU Ahab as a sole crop and as intercrop ( TSD  = 100%) the lowest soil water content values were measured at 30–60 cm soil depth. In general, soil water depletion was lower for the low density (FB_33_SW_SUAh_33) as compared to the very high density intercrop treatment (FB_100_SW_SUAh_100) (second sampling date, 30–90 cm).

Root mass, root length density and belowground interactions

Although calculating root biomass in t ha −1 based on soil auger data is a common practice (Chirinda et al. 2012 ; Streit et al. 2019 ), we want to emphasize that this approach involves certain uncertainties since the root samples can only represent the root mass in a given soil volume.

Root system extension of wheat often exceeds the one of legumes like faba bean (Gregory et al. 1995 ; Turpin et al. 2002 ), though under field conditions, factors such as phenology, sampling technique and sampling depth may influence root growth. The faba bean root mass at flowering (2.3 t ha-1) observed in our study is higher than the values reported in the studies from Rengasamy and Reid ( 1993 ), who reported average root mass over years and treatments of approximately 1.4 t ha −1 for a sampling depth of 70 cm. These values are also higher than the values reported by Streit et al. ( 2019 ) who found values of around 0.7 t ha −1 for a sampling depth up to 60 cm. This difference can be attributed to the higher sowing density considered in our study for the sole cropping treatments and also the sampling technique as we always considered a faba bean in the soil core which overrepresented the faba bean compared to the study of Streit et al. ( 2019 ), for instance. Literature revealed high variability for spring wheat root masses ranging from 0.8 t ha −1 to 1.4 t ha −1 at flowering (Wechsung et al. 1995 ; Gan et al. 2009 ). In our study, a spring wheat root mass of 1.4 t ha −1 was reached at flowering over the soil depth of 0 to 1 m. This rather high value can be partly attributed to the enhanced sowing density considered for the sole crops compared to the optimal sowing density recommended for spring wheat.

Cereals are generally considered as strong competitors compared to legumes, mainly due to a larger root system and deeper root distribution (Gregory et al. 1995 ; Hauggaard-Nielsen et al. 2001 ; Corre-Hellou and Crozat 2005 ; Bedoussac et al. 2015 ). Many studies reported that intercrops produce significantly higher root masses as compared to their sole cropping equivalents (Ma and Chen 2016 ). Root mass advantage was observed in faba bean-maize (Xia et al. 2013 ) and faba bean-winter wheat intercrops (Streit et al. 2019 ). In our study, the mean topsoil root LER was above one indicating a root mass advantage in intercropping versus sole cropping. In the upper subsoil, it depended on the spring wheat cultivar, but LER root was always below one in the deeper subsoil (60–100 cm).

A combination of tap rooted and fibrous rooted crops is widely recognized as being one of the mechanisms of overyielding in intercrops due to belowground complementarity which may increase water and nutrient acquisition by niche differentiation and due to resource partitioning (Yu et al. 2022 ). In line with this finding, the attained values of root mass in the intercrop treatments for both wheat and faba bean (0–1 m soil depth) were mostly higher than the expected values (Fig.  4 ). This applied for both the low density ( TSD  = 66%) and the nearly optimal sowing density ( TSD  = 100%), but not for the very high sowing density ( TSD  = 200%).

It is assumed that belowground biomass advantage during vegetative stages fosters higher resource availability, as well as shoot and grain overyielding. This was especially reported under stress conditions (Fargione and Tilmann, 2005 ; Hector et al.  2002 ). The enhanced root growth and development partially compensated competition for light (Amossé et al. 2013 ), carbon dioxide (Shili-Touzi et al. 2010 ) and other resources (Wang et al. 2018 ). The results of aboveground overyielding and intercations in intercrops as described by the plant-plant interaction index (PPII) showed a positive correlation between facilitation, enhanced root growth, facilitation process and overyielding especially for intercrops with the spring cv. SU Ahab. However, due to lack of real field replicates, a clear relationship between belowground root interactions and aboveground overyielding could not be statistically tested. Also, the favorable growing conditions characterizing our experimental site and year combination (fertile soil, favorable soil moisture due to plenty of rain) could be a reason behind these observations. Similar studies in contrasting environments should be performed to better assess the relationship between belowground root advantage and aboveground overyielding.

Sowing density effect on root growth advantage and facilitation and competition

The spatial arrangement in intercropping is an important factor for the above- and belowground growth (Wang et al. 2018 ; Homulle et al. 2022 ). In our study, the spatial arrangement was represented by the sowing density that characterized the designs considered in the study, as well as by the completely mixed design or adjacent row design which permitted a high interaction between the species (Homulle et al. 2022 ; Li et al. 2006 ). The high sowing density in the additive design resulted in low root biomass over the whole soil profile (Table S6 ). and enhanced plant-plant competition between faba bean and spring wheat in both growing stages.

In a sole cropped spring wheat experiment, Hecht et al. ( 2016 ) found that RLD increased with increasing sowing density in the topsoil (0–10 cm), partly due to greater production of fine roots. The authors argued that light competition forced plants to grow more shoot mass at the cost of investment into roots, in our study an increased sowing density fostered RLD only at the first sampling date and only in 0–10 cm soil depth. However, for the second date there was a decrease of total RLD with increasing TSD . Bulson et al. ( 1997 ) reported a significant decrease in resource complementarity with increasing wheat and faba bean sowing density. The presented low attained root mass compared to the expected values in the high sowing density treatment (additive design, TSD  = 200%) indicates high competition under the high sowing density of the additive design.

Cultivar effect on belowground growth and interactions in intercrops

Although statistically there was no significant effect of the cultivar on the root mass, we observed a difference in rooting ability between both spring wheat cultivars (Figs.  1 , 2 , 5 and 6 ). The ability of cv. Anabel to root quickly and deeply around faba bean flowering as compared to cv. SU Ahab resulted in lower root mass proportions of faba bean intercropped with cv. Anabel compared to intercropped with cv. SU Ahab. Moreover, comparing the root growth patterns in intercrops and sole crops in two different growth stages (flowering of wheat and flowering of bean), permitted to better understand the cultivar effect of root growth dynamics in intercrops. Other studies only considered studying root growth around flowering (Streit et al. 2019 ), where it is assumed that the species reach their maximum root mass (Chirinda et al. 2012 ). In our study, we found that the early dominance of one spring wheat cultivar ( cv. Anabel) impacted negatively faba bean root growth in intercrops.

Soil mineral N, soil water, and root carbon and nitrogen in sole crops and intercrops

Soil mineral N below the faba bean at harvest time are usually higher than below cereals (Neugschwandtner et al. 2015 ), this was not confirmed in our study. For the upper soil layer (0–30), the Nmin in sole crop treatments was higher below faba bean than below spring wheat (both cultivars). However, in the subsoil layers (60–100), Nmin below faba bean sole crops was higher than the one below spring wheat sole crops (both cultivars). This could be attributed to the low RLD of faba bean in deeper layers which decreased the N uptake (Kage 1997 ). In intercrops, the mineral N content in the topsoil after harvest was greater than in both sole crops, indicating a difference in N uptake rate between intercrops and sole crops. In a long-term experiment, an increase of topsoil organic N content by 11% was observed in intercropping as compared to sole cropping, indicating that increased biological N fixation contributed to increased soil N content (Cong et al. 2015 ). Moreover, it is widely recognized that N uptake is mainly performed by the fine roots (McCormack et al. 2017 ). This was also indicated by our study where for the low density treatments with high SRL (higher fine roots compared to the high TSD treatment), the N uptake was greater than in the high density treatments.

Plant diversity also affects soil organic C stocks in deeper soil which is more stable and difficult to access for microbes (Chen et al. 2020 ). Hence, root-based C inputs in deeper soil layers is the major source of soil organic carbon (Yu et al. 2022 ). We observed no significant effect of intercropping on root C in the deep soil layer (30–100 cm, date 05/07/2021, Fig. S12 ). In the deeper soil layers (30–100 cm), total C in roots in the mixtures was on average 22% greater than the average root C in sole faba bean and 18% lower than average root C in sole spring wheat (mean of both cultivars), providing a possible mechanism for the divergence in soil C sequestration between sole crops and intercropping systems. Similar trends were observed by Cong et al. ( 2015 ).

Characterization of soil water depletion at different soil layers below the root zone is important in evaluating water use pattern and its linkage to the RLD (Moroke et al. 2005 ). Our results didn´t confirm the positive correlation between RLD and soil water depletion already reported in other studies (Moroke et al. 2005 ; Zhang et al. 2020 ).This can be explained by the non-significant differences between the intercrop treatments in term of RLD, found in our study (Figs. 5 and 6 ).

Implication of the results to better understand intercrops and their belowground interactions

In our study, LER in the fully replacement design revealed that intercropping was favorable to increase the aboveground biomass and yield. The overyielding in terms of yield and aboveground biomass found in this study was already reported in many other contexts. Many studies argue the importance of studying roots in intercrops to better understand the belowground mechanisms that increase their productivity and allow a better resource capture (Ma et al. 2019 ; Homulle et al. 2022 ). We demonstrated that high sowing densities of the additive design led to decreased root mass, RLD and SRL and also to competition between the intercropped species which resulted in lower grain yield value compared to the expected one. The early root dominance of spring wheat cultivar was not beneficial for the grain yield. When resources such as soil water become scarce, this may lead to a decreased resource capture. We found that lower sowing densities (i) led to a lower depletion of soil water in the deeper soil layers, (ii) fostered deeper rooting, (iii) led to a depletion of more N in the upper soil layers, and (iv) fostered higher SRL and thus potentially enhanced root N uptake as compared to high density intercrops. Comparing intercropping with sole cropping but also different sowing densities within the intercropping system revealed that there were different depth-dependent processes occurring belowground that affected not only root biomass but also soil mineral N and soil water content and thus their plant availability. Thus, an improved understanding of the effects of the species (or cultivar) combination and the crop management on root growth are essential for better understanding interactions and productivity in intercrops.

In our study, belowground root growth and interactions varied with the different intercropping designs and spring wheat cultivars considered in the study. On both sampling stages, the belowground intercrop advantage decreased under high sowing density due to plant-plant competition. Intercropping of faba bean with a spring wheat cultivar characterized by a rather small root system during faba bean flowering fostered a higher belowground intercrop advantage, as facilitation dominated the plant-plant interactions in intercrops under lower and optimal sowing densities in both growing stages. Further research should focus on finding the optimal sowing density that can enhance aboveground root advantage and improve the facilitation process permitting optimal resource capture and depletion. The effect of spring wheat cultivar choice, although insignificant in this study, seems to have an effect on the total root mass and belowground interactions in intercrops, a generalization of the results should be further researched in the frame of breeding experiments.

Also, we suggest to conduct a similar study under limited growth conditions and with several sampling dates to better assess the relationship between above- and belowground overyielding and support the generalization of the obtained results. Moreover, there is a need to explore the effects of mixtures on soil C and N sequestration to mitigate climate change.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Amossé C, Jeuffroy M-H, David C (2013) Relay intercropping of legume cover crops in organic winter wheat: effects on performance and resource availability. Field Crops Res 145:78–87

Article   Google Scholar  

Barej J, Pätzold S, Perkons U, Amelung W (2014) Phosphorus fractions in bulk subsoil and its biopore systems. Eur J Soil Sci 65:553–561

Article   CAS   Google Scholar  

Bargaz A, Isaac ME, Jensen ES, Carlsson G (2015) Intercropping of faba bean with wheat under low water availability promotes faba bean nodulation and root growth in deeper soil layers. Procedia Environ Sci 29:111–112. https://doi.org/10.1016/j.proenv.2015.07.188

Bedoussac L, Journet E-P, Hauggaard-Nielsen H et al (2015) Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agron Sustain Dev 35:911–935. https://doi.org/10.1007/s13593-014-0277-7

Brooker RW, Bennett AE, Cong W et al (2015) Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. New Phytol 206:107–117. https://doi.org/10.1111/nph.13132

Article   PubMed   Google Scholar  

Bulson H, Snaydon R, Stopes C (1997) Effects of plant density on intercropped wheat and field beans in an organic farming system. J Agric Sci 128:59–71

Chen X, Chen HY, Chen C et al (2020) Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol Rev 95:167–183

Chirinda N, Olesen JE, Porter JR (2012) Root carbon input in organic and inorganic fertilizer-based systems. Plant Soil 359:321–333

Cong W-F, Hoffland E, Li L et al (2015) Intercropping enhances soil carbon and nitrogen. Glob Change Biol 21:1715–1726. https://doi.org/10.1111/gcb.12738

Corre-Hellou G, Crozat Y (2005) Assessment of root system dynamics of species grown in mixtures under field conditions using herbicide injection and 15 N natural abundance methods: a case study with pea, barley and mustard. Plant Soil 276:177–192

De Wit C, Van den Bergh J (1965) Competition between herbage plants. J Agric Sci 13:212–221

Google Scholar  

Demie DT, Döring TF, Finckh MR, van der Werf  W, Enjalbert J, Seidel SJ (2022) Mixture× genotype effects in cereal/legume intercropping. Front Plant Sci 13:846720

Den Hollander NG, Bastiaans L, Kropff MJ (2007) Clover as a cover crop for weed suppression in an intercropping design: II. Competitive ability of several clover species. Eur J Agron 26:104–112

Döring TF, Elsalahy H (2022) Quantifying compensation in crop mixtures and monocultures. Eur J Agron 132:126408. https://doi.org/10.1016/j.eja.2021.126408

Fargione JE, Tilman D (2005) Diversity decreases invasion via both sampling and complementarity effects. Ecol Lett 8(6):604–611

Gan YT, Campbell CA, Janzen HH et al (2009) Root mass for oilseed and pulse crops: growth and distribution in the soil profile. Can J Plant Sci 89:883–893

Gregory PJ, Palta JA, Batts GR (1995) Root systems and root: mass ratio-carbon allocation under current and projected atmospheric conditions in arable crops. Plant Soil 187:221–228

Hauggaard-Nielsen H, Ambus P, Jensen ES (2001) Temporal and spatial distribution of roots and competition for nitrogen in pea-barley intercrops–a field study employing 32P technique. Plant Soil 236:63–74

Hauggaard-Nielsen H, Jørnsgaard B, Kinane J, Jensen ES (2008) Grain legume–cereal intercropping: the practical application of diversity, competition and facilitation in arable and organic cropping systems. Renew Agric Food Syst 23:3–12

Hecht VL, Temperton VM, Nagel KA et al (2016) Sowing density: a neglected factor fundamentally affecting Root distribution and biomass allocation of field grown spring barley (Hordeum Vulgare L). Front Plant Sci 7. https://doi.org/10.3389/fpls.2016.00944

Hector A, Bazeley‐White E, Loreau M, Otway S, Schmid B (2002) Overyielding in grassland communities: testing the sampling effect hypothesis with replicated biodiversity experiments. Ecol Lett 5(4):502–511

Homulle Z, George TS, Karley AJ (2022) Root traits with team benefits: understanding belowground interactions in intercropping systems. Plant Soil 471:1–26. https://doi.org/10.1007/s11104-021-05165-8

Jensen ES, Carlsson G, Hauggaard-Nielsen H (2020) Intercropping of grain legumes and cereals improves the use of soil N resources and reduces the requirement for synthetic fertilizer N: a global-scale analysis. Agron Sustain Dev 40:1–9

Justes E, Bedoussac L, Dordas C et al (2021) The 4 C approach as a way to understand species interactions determining intercropping productivity. Front Agric Sci Eng 8:3

Kage H (1997) Is low rooting density of faba beans a cause of high residual nitrate content of soil at harvest? Plant Soil 190:47–60

Kemper R, Döring TF, Legner N et al (2022) Root traits in cover crop mixtures of blue lupin and winter rye. Plant Soil 475:309–328. https://doi.org/10.1007/s11104-022-05366-9

Kremen C, Miles A (2012) Ecosystem services in biologically diversified versus conventional farming systems: benefits, externalities, and trade-offs. Ecol Soc 17(4)

Legner N, Meinen C, Rauber R (2018) Root differentiation of agricultural plant cultivars and proveniences using FTIR spectroscopy. Front Plant Sci 9:748

Article   PubMed   PubMed Central   Google Scholar  

Li L, Sun J, Zhang F et al (2006) Root distribution and interactions between intercropped species. Oecologia 147:280–290

Li L, Sun J, Zhang F (2011) Intercropping with wheat leads to greater root weight density and larger below-ground space of irrigated maize at late growth stages. Soil Sci Plant Nutr 57:61–67. https://doi.org/10.1080/00380768.2010.548307

Li L, Zhang L, Zhang F (2013) Crop mixtures and the mechanisms of overyielding. Encycl Biodivers 2:382–395

Lithourgidis AS, Dordas CA, Damalas CA, Vlachostergios DN (2011) Annual intercrops: an alternative pathway for sustainable agriculture. Aust J Crop Sci 5(4):396–410

Ma Z, Chen HY (2016) Effects of species diversity on fine root productivity in diverse ecosystems: a global meta-analysis. Glob Ecol Biogeogr 25:1387–1396

Ma L, Li Y, Wu P et al (2019) Effects of varied water regimes on root development and its relations with soil water under wheat/maize intercropping system. Plant Soil 439:113–130

McCormack ML, Guo D, Iversen CM et al (2017) Building a better foundation: improving root-trait measurements to understand and model plant and ecosystem processes. New Phytol 215:27–37. https://doi.org/10.1111/nph.14459

Meinen C, Rauber R (2015) Root discrimination of closely related crop and weed species using FT MIR-ATR spectroscopy. Front Plant Sci 6:765

Moroke T, Schwartz R, Brown K, Juo A (2005) Soil water depletion and root distribution of three dryland crops. Soil Sci Soc Am J 69:197–205

Naumann A, Heine G, Rauber R (2010) Efficient discrimination of oat and pea roots by cluster analysis of fourier transform infrared (FTIR) spectra. Field Crops Res 119:78–84

Nelson WCD, Siebrecht-Schöll DJ, Hoffmann MP et al (2021) What determines a productive winter bean-wheat genotype combination for intercropping in central Germany? Eur J Agron 128:126294

Neugschwandtner R, Ziegler K, Kriegner S et al (2015) Nitrogen yield and nitrogen fixation of winter faba beans. Acta Agric Scand Sect B—Soil Plant Sci 65:658–666

CAS   Google Scholar  

Obalum SE, Obi ME (2010) Physical properties of a sandy loam Ultisol as affected by tillage-mulch management practices and cropping systems. Soil Tillage Res 108(1-2):30–36

Paul MR, Demie DT, Seidel SJ, Döring TF (2024) Evaluation of multiple spring wheat cultivars in diverse intercropping systems. Eur J Agron 152:127024

R Core Team (2018) R: eine sprache und umgebung für statistisches rechnen. R stiftung für statistisches rechnen, Wien. https://www.R-project.org

Rengasamy JI, Reid JB (1993) Root system modification of faba beans (Vicia faba L.), and its effects on crop performance. 1. Responses of root and shoot growth to subsoiling, irrigation and sowing date. Field Crops Res 33(3):175–196

Rewald B, Meinen C, Trockenbrodt M et al (2012) Root taxa identification in plant mixtures–current techniques and future challenges. Plant Soil 359:165–182

Shao H, Shi D, Shi W, Ban X, Chen Y, Ren W, Chen F, Mi G (2019) Genotypic difference in the plasticity of root system architecture of field-grown maize in response to plant density. Plant Soil 439:201–217

Sharma N, Singh RJ, Mandal D et al (2017) Increasing farmer’s income and reducing soil erosion using intercropping in rainfed maize-wheat rotation of Himalaya, India. Agric Ecosyst Environ 247:43–53

Shili-Touzi I, De Tourdonnet S, Launay M, Doré T (2010) Does intercropping winter wheat (Triticum aestivum) with red fescue (Festuca rubra) as a cover crop improve agronomic and environmental performance? A modeling approach. Field Crops Res 116:218–229

Snaydon RW (1991) Replacement or additive designs for competition studies? J Appl Ecol 930–946

Stomph T, Dordas C, Baranger A, de Rijk J, Dong B, Evers J, Gu C, Li L, Simon J, Jensen ES, Wang Q, Wang Y, Wang Z, Xu H, Zhang C, Zhang L, Zang WP, Bedoussac L, van der Werf W (2020) Designing intercrops for high yield, yield stability and efficient use of resources: are there principles? Adv Agron 1–50

Streit J, Meinen C, Nelson WCD et al (2019) Above- and belowground biomass in a mixed cropping system with eight novel winter faba bean genotypes and winter wheat using FTIR spectroscopy for root species discrimination. Plant Soil 436:141–158. https://doi.org/10.1007/s11104-018-03904-y

Tosti G, Thorup-Kristensen K (2010) Using coloured roots to study root interaction and competition in intercropped legumes and non-legumes. J Plant Ecol 3:191–199. https://doi.org/10.1093/jpe/rtq014

Turpin JE, Herridge DF, Robertson MJ (2002) Nitrogen fixation and soil nitrate interactions in field-grown chickpea (Cicer arietinum) and fababean (Vicia faba). Aust J Agric Res 53:599–608

Vandermeer JH (1989) The ecology of intercropping. Cambridge University Press, Cambridge

Wang Y, Qin Y, Chai Q et al (2018) Interspecies interactions in relation to root distribution across the rooting profile in wheat-maize intercropping under different plant densities. Front Plant Sci 9:483

Wechsung G, Wechsung F, Wall GW et al (1995) Biomass and growth rate of a spring wheat root system grown in free-air CO2 enrichment (FACE) and ample soil moisture. J Biogeogr 22:623–634

White KE, Reeves JB III, Coale FJ (2011) Mid-infrared diffuse reflectance spectroscopy for the rapid analysis of plant root composition. Geoderma 167:197–203

Willey R, Osiru D (1972) Studies on mixtures of maize and beans (Phaseolus vulgaris) with particular reference to plant population. J Agric Sci 79:517–529

Xia H-Y, Zhao J-H, Sun J-H et al (2013) Dynamics of root length and distribution and shoot biomass of maize as affected by intercropping with different companion crops and phosphorus application rates. Field Crops Res 150:52–62

Yang H, Xu H, Zhang W et al (2022) Overyielding is accounted for partly by plasticity and dissimilarity of crop root traits in maize/legume intercropping systems. Funct Ecol 36:2163–2175

Yu Y, Stomph T-J, Makowski D et al (2016) A meta-analysis of relative crop yields in cereal/legume mixtures suggests options for management. Field Crops Res 198:269–279. https://doi.org/10.1016/j.fcr.2016.08.001

Yu R-P, Yang H, Xing Y et al (2022) Belowground processes and sustainability in agroecosystems with intercropping. Plant Soil 476:263–288

Zhang X, Whalley P, Ashton R et al (2020) A comparison between water uptake and root length density in winter wheat: effects of root density and rhizosphere properties. Plant Soil 451:345–356

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhang W, Gao S, Li Z et al (2021) Shifts from complementarity to selection effects maintain high productivity in maize/legume intercropping systems. J Appl Ecol 58:2603–2613. https://doi.org/10.1111/1365-2664.13989

Download references

Acknowledgements

We would like to thank all student assistants that helped on the field and in the labs. Furthermore we would like to thank Prof. Stefan Siebert (University of Göttingen) who allowed us to use his root lab. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the REA can be held responsible for them.

Open Access funding enabled and organized by Projekt DEAL. The presented study has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy-EXC 2070-390732324 (PhenoRob), the German Federal Ministry of Education and Research (BMBF) (project “Sustainable Subsoil Management-Soil3”, Grant 031B0151A), the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) (grant number 2822ABS010), as well as by the European Union (EU horizon project IntercropValuES, grant agreement No 101081973).

Author information

Authors and affiliations.

Crop Science, Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, 53115, Bonn, Germany

Sofia Hadir, Dereje T. Demie, Thomas Gaiser, Frank Ewert & Sabine J. Seidel

Agroecology and Organic Farming, Institute of Crop Science and Resource Conservation, University of Bonn, Auf dem Hügel 6, 53121, Bonn, Germany

Thomas F. Döring, Madhuri Paul & Roman Kemper

Persyst Department, CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement), Montpellier, F-34398, France

Eric Justes

Division of Agronomy, Department of Crop Sciences, University of Goettingen, Goettingen, Germany

Nicole Legner

Department of Ecological Plant Protection, University of Kassel, Witzenhausen, Germany

Odette Weedon

Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany

Frank Ewert

You can also search for this author in PubMed   Google Scholar

Contributions

S.S and T.D. conceived the idea, planned the research and designed the experiments. S.S., D.D and M.P conducted the experiment in the field, S.H. and S.S collected the root samples. N.L. processed the root samples and performed the FTIR analysis. S.H. analyzed the data and wrote the article. O.W., T.G., N.L., F.E, E.J., R.K., M.P., and S.S. contributed to data interpretation, writing and editing of the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sofia Hadir .

Ethics declarations

Competing interests.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Communicated by Rui-Peng Yu.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Hadir, S., Döring, T.F., Justes, E. et al. Root growth and belowground interactions in spring wheat /faba bean intercrops. Plant Soil (2024). https://doi.org/10.1007/s11104-024-06742-3

Download citation

Received : 17 July 2023

Accepted : 13 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1007/s11104-024-06742-3

The Unique Burial of a Child of Early Scythian Time at the Cemetery of Saryg-Bulun (Tuva)

<< Previous page

Pages:  379-406

In 1988, the Tuvan Archaeological Expedition (led by M. E. Kilunovskaya and V. A. Semenov) discovered a unique burial of the early Iron Age at Saryg-Bulun in Central Tuva. There are two burial mounds of the Aldy-Bel culture dated by 7th century BC. Within the barrows, which adjoined one another, forming a figure-of-eight, there were discovered 7 burials, from which a representative collection of artifacts was recovered. Burial 5 was the most unique, it was found in a coffin made of a larch trunk, with a tightly closed lid. Due to the preservative properties of larch and lack of air access, the coffin contained a well-preserved mummy of a child with an accompanying set of grave goods. The interred individual retained the skin on his face and had a leather headdress painted with red pigment and a coat, sewn from jerboa fur. The coat was belted with a leather belt with bronze ornaments and buckles. Besides that, a leather quiver with arrows with the shafts decorated with painted ornaments, fully preserved battle pick and a bow were buried in the coffin. Unexpectedly, the full-genomic analysis, showed that the individual was female. This fact opens a new aspect in the study of the social history of the Scythian society and perhaps brings us back to the myth of the Amazons, discussed by Herodotus. Of course, this discovery is unique in its preservation for the Scythian culture of Tuva and requires careful study and conservation.

Keywords: Tuva, Early Iron Age, early Scythian period, Aldy-Bel culture, barrow, burial in the coffin, mummy, full genome sequencing, aDNA

Information about authors: Marina Kilunovskaya (Saint Petersburg, Russian Federation). Candidate of Historical Sciences. Institute for the History of Material Culture of the Russian Academy of Sciences. Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail: [email protected] Vladimir Semenov (Saint Petersburg, Russian Federation). Candidate of Historical Sciences. Institute for the History of Material Culture of the Russian Academy of Sciences. Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail: [email protected] Varvara Busova  (Moscow, Russian Federation).  (Saint Petersburg, Russian Federation). Institute for the History of Material Culture of the Russian Academy of Sciences.  Dvortsovaya Emb., 18, Saint Petersburg, 191186, Russian Federation E-mail:  [email protected] Kharis Mustafin  (Moscow, Russian Federation). Candidate of Technical Sciences. Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected] Irina Alborova  (Moscow, Russian Federation). Candidate of Biological Sciences. Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected] Alina Matzvai  (Moscow, Russian Federation). Moscow Institute of Physics and Technology.  Institutsky Lane, 9, Dolgoprudny, 141701, Moscow Oblast, Russian Federation E-mail:  [email protected]

Shopping Cart Items: 0 Cart Total: 0,00 € place your order

Price pdf version

student - 2,75 € individual - 3,00 € institutional - 7,00 €

Copyright В© 1999-2022. Stratum Publishing House

Follow Puck Worlds online:

• Follow Puck Worlds on Twitter

Site search

Filed under:

• Kontinental Hockey League

Gagarin Cup Preview: Atlant vs. Salavat Yulaev

Share this story.

• Share this on Facebook
• Share this on Twitter
• Share this on Reddit
• Share All sharing options

Share All sharing options for: Gagarin Cup Preview: Atlant vs. Salavat Yulaev

Gagarin cup (khl) finals:  atlant moscow oblast vs. salavat yulaev ufa.

Much like the Elitserien Finals, we have a bit of an offense vs. defense match-up in this league Final.  While Ufa let their star top line of Alexander Radulov, Patrick Thoresen and Igor Grigorenko loose on the KHL's Western Conference, Mytischi played a more conservative style, relying on veterans such as former NHLers Jan Bulis, Oleg Petrov, and Jaroslav Obsut.  Just reaching the Finals is a testament to Atlant's disciplined style of play, as they had to knock off much more high profile teams from Yaroslavl and St. Petersburg to do so.  But while they did finish 8th in the league in points, they haven't seen the likes of Ufa, who finished 2nd. 

This series will be a challenge for the underdog, because unlike some of the other KHL teams, Ufa's top players are generally younger and in their prime.  Only Proshkin amongst regular blueliners is over 30, with the work being shared by Kirill Koltsov (28), Andrei Kuteikin (26), Miroslav Blatak (28), Maxim Kondratiev (28) and Dmitri Kalinin (30).  Oleg Tverdovsky hasn't played a lot in the playoffs to date.  Up front, while led by a fairly young top line (24-27), Ufa does have a lot of veterans in support roles:  Vyacheslav Kozlov , Viktor Kozlov , Vladimir Antipov, Sergei Zinovyev and Petr Schastlivy are all over 30.  In fact, the names of all their forwards are familiar to international and NHL fans:  Robert Nilsson , Alexander Svitov, Oleg Saprykin and Jakub Klepis round out the group, all former NHL players.

For Atlant, their veteran roster, with only one of their top six D under the age of 30 (and no top forwards under 30, either), this might be their one shot at a championship.  The team has never won either a Russian Superleague title or the Gagarin Cup, and for players like former NHLer Oleg Petrov, this is probably the last shot at the KHL's top prize.  The team got three extra days rest by winning their Conference Final in six games, and they probably needed to use it.  Atlant does have younger regulars on their roster, but they generally only play a few shifts per game, if that. 

The low event style of game for Atlant probably suits them well, but I don't know how they can manage to keep up against Ufa's speed, skill, and depth.  There is no advantage to be seen in goal, with Erik Ersberg and Konstantin Barulin posting almost identical numbers, and even in terms of recent playoff experience Ufa has them beat.  Luckily for Atlant, Ufa isn't that far away from the Moscow region, so travel shouldn't play a major role. 

I'm predicting that Ufa, winners of the last Superleague title back in 2008, will become the second team to win the Gagarin Cup, and will prevail in five games.  They have a seriously well built team that would honestly compete in the NHL.  They represent the potential of the league, while Atlant represents closer to the reality, as a team full of players who played themselves out of the NHL. 

• Atlant @ Ufa, Friday Apr 8 (3:00 PM CET/10:00 PM EST)
• Atlant @ Ufa, Sunday Apr 10 (1:00 PM CET/8:00 AM EST)
• Ufa @ Atlant, Tuesday Apr 12 (5:30 PM CET/12:30 PM EST)
• Ufa @ Atlant, Thursday Apr 14 (5:30 PM CET/12:30 PM EST)

Games 5-7 are as yet unscheduled, but every second day is the KHL standard, so expect Game 5 to be on Saturday, like an early start. 

Loading comments...

• Yekaterinburg
• Novosibirsk
• Vladivostok

• Tours to Russia
• Practicalities
• Russia in Lists

Rusmania • Deep into Russia

Out of the Centre

Savvino-storozhevsky monastery and museum.

Zvenigorod's most famous sight is the Savvino-Storozhevsky Monastery, which was founded in 1398 by the monk Savva from the Troitse-Sergieva Lavra, at the invitation and with the support of Prince Yury Dmitrievich of Zvenigorod. Savva was later canonised as St Sabbas (Savva) of Storozhev. The monastery late flourished under the reign of Tsar Alexis, who chose the monastery as his family church and often went on pilgrimage there and made lots of donations to it. Most of the monastery’s buildings date from this time. The monastery is heavily fortified with thick walls and six towers, the most impressive of which is the Krasny Tower which also serves as the eastern entrance. The monastery was closed in 1918 and only reopened in 1995. In 1998 Patriarch Alexius II took part in a service to return the relics of St Sabbas to the monastery. Today the monastery has the status of a stauropegic monastery, which is second in status to a lavra. In addition to being a working monastery, it also holds the Zvenigorod Historical, Architectural and Art Museum.

Belfry and Neighbouring Churches

Located near the main entrance is the monastery's belfry which is perhaps the calling card of the monastery due to its uniqueness. It was built in the 1650s and the St Sergius of Radonezh’s Church was opened on the middle tier in the mid-17th century, although it was originally dedicated to the Trinity. The belfry's 35-tonne Great Bladgovestny Bell fell in 1941 and was only restored and returned in 2003. Attached to the belfry is a large refectory and the Transfiguration Church, both of which were built on the orders of Tsar Alexis in the 1650s.  

To the left of the belfry is another, smaller, refectory which is attached to the Trinity Gate-Church, which was also constructed in the 1650s on the orders of Tsar Alexis who made it his own family church. The church is elaborately decorated with colourful trims and underneath the archway is a beautiful 19th century fresco.

Nativity of Virgin Mary Cathedral

The Nativity of Virgin Mary Cathedral is the oldest building in the monastery and among the oldest buildings in the Moscow Region. It was built between 1404 and 1405 during the lifetime of St Sabbas and using the funds of Prince Yury of Zvenigorod. The white-stone cathedral is a standard four-pillar design with a single golden dome. After the death of St Sabbas he was interred in the cathedral and a new altar dedicated to him was added.

Under the reign of Tsar Alexis the cathedral was decorated with frescoes by Stepan Ryazanets, some of which remain today. Tsar Alexis also presented the cathedral with a five-tier iconostasis, the top row of icons have been preserved.

Tsaritsa's Chambers

The Nativity of Virgin Mary Cathedral is located between the Tsaritsa's Chambers of the left and the Palace of Tsar Alexis on the right. The Tsaritsa's Chambers were built in the mid-17th century for the wife of Tsar Alexey - Tsaritsa Maria Ilinichna Miloskavskaya. The design of the building is influenced by the ancient Russian architectural style. Is prettier than the Tsar's chambers opposite, being red in colour with elaborately decorated window frames and entrance.

At present the Tsaritsa's Chambers houses the Zvenigorod Historical, Architectural and Art Museum. Among its displays is an accurate recreation of the interior of a noble lady's chambers including furniture, decorations and a decorated tiled oven, and an exhibition on the history of Zvenigorod and the monastery.

Palace of Tsar Alexis

The Palace of Tsar Alexis was built in the 1650s and is now one of the best surviving examples of non-religious architecture of that era. It was built especially for Tsar Alexis who often visited the monastery on religious pilgrimages. Its most striking feature is its pretty row of nine chimney spouts which resemble towers.

Plan your next trip to Russia

Ready-to-book tours.

Your holiday in Russia starts here. Choose and book your tour to Russia.

REQUEST A CUSTOMISED TRIP

Looking for something unique? Create the trip of your dreams with the help of our experts.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Specific features of the ecological functioning of urban soils in Moscow and Moscow region

Eurasian Soil Science

Urban soils (constructozems) were studied in Moscow and several cities (Dubna, Pushchino, and Serebryanye Prudy) of Moscow oblast. The soil sampling from the upper 10-cm-thick layer was performed in the industrial, residential, and recreational functional zones of these cities. The biological (the carbon of the microbial biomass carbon, Cmic and the microbial (basal) respiration, BR) and chemical (pHwater and the contents of Corg, heavy metals, and NPK) indices were determined in the samples. The ratios of BR to Cmic (the microbial respiration quotient, qCO2) and of Cmic to Corg were calculated. The Cmic varied from 120 to 738 μg C/g soil; the BR, from 0.39 to 1.94 μg CO2-C/g soil per hour; the Corg, from 2.52 to 5.67%; the qCO2, from 1.24 to 5.28 μg CO2-C/mg Cmic/g soil per h; and the Cmic/Corg, from 0.40 to 1.55%. Reliable positive correlations were found between the Cmic and BR, the Cmic and Cmic/Corg, and the Cmic and Corg values (r = 0.75, 0.95, and 0.61, respectively), as well...

Related Papers

N.D. Ananyeva

Journal of Mining Institute

Alexey Alekseenko

Soils and plants of Saint Petersburg are under the constant technogenic stress caused by human activity in in-dustrial, residential, and recreational landscapes of the city. To assess the transformed landscapes of various functional zones, we studied utility, housing, and park districts with a total area of over 7,000 hectares in the southern part of the city during the summer seasons of 2016-2018. Throughout the fieldwork period, 796 individual pairs of soil and plant samples were collected. A complex of consequent laboratory studies performed in an accredited laboratory allowed the characterization of key biogeochemical patterns of urban regolith specimens and herbage samples of various grasses. Chemical analyses provided information on the concentrations of polluting metals in soils and plants of different land use zones. Data interpretation and calculation of element accumulation factors revealed areas with the most unfavorable environmental conditions. We believe that a high pollution level in southern city districts has led to a significant degree of physical, chemical, and biological degradation of the soil and vegetation cover. As of today, approximately 10 % of the Technosols in the study area have completely lost the ability to biological self-revitalization, which results in ecosystem malfunction and the urgent need for land remediation.

András Bidló

The main purpose of the present study was to monitor actual contamination levels and execute a comparative assessment of results in a mid-sized Hungarian city for two different years. The first citywide soil investigations were completed in 2011. In 2018, the most prominent properties (pH, CaCO3, texture, and trace metals Cd, Co, Cu, Ni, Pb, and Zn) were reanalyzed and were supplemented with mesofauna on selected sites. The available trace metal elements of urban soils showed the following tendency in 2011: Zn &gt; Cu &gt; Pb &gt; Cd &gt; Cr = Ni = Co. In 2018, the previous order changed to Zn &gt; Pb &gt; Cu &gt; Cr &gt; Cd = Ni = Co. Cd and Pb enrichments were found, especially near the M7 motorway. The comparison between 2011 and 2018 revealed soil contamination was, on average, higher in 2011. Soil microarthropod communities were sampled and assessed using abundance data and diversity measurements. Soil biological quality was evaluated with the help of the Soil Biological Qualit...

Mikhail Reshetnikov , Ngun Clement

A soil diagnosis of an urban territory Stepnoe (Saratov region) was conducted within the framework of soil research monitoring of inhabited localities with low levels of anthropogenic impact using chemical and microbiological analysis. Excess over maximum permissible concentration (MPC) of mobile forms of Cr, Zn and Cd were not observed within the researched territory. A universal excess over MPC of mobile forms of Ni, Cu and Pb was established which is most likely connected with anthropogenic contamination. It was discovered that, at the territory of the Stepnoe settlement, mobile forms of heavy metals compounds (HM) in most cases formed paragenetic associations with high correlation coefficient and despite this, an excess over MPC was not significant. This point to a common mineralogical origin of the elements inherited from the parent rock. The values of the total index of chemical contamination were not above 16, which puts the researched samples in a category with permissible contamination. The indices of the total number of heterotrophic bacteria, iron-oxidizing and hydrocarbon-oxidizing bacteria in most samples corresponded to normal indices for chestnut solonetsous and saline soils. In some samples, a deviation from the normal indices was observed justifying the impact of specific contaminants on the soil.

Soil Science Annual

Lidia Oktaba

The objective of the study was to determine properties of soils located within a city, and to assess the effect of anthropopressure on the accumulation of carbon and nitrogen in soils of Pruszków . a medium sized town in central Poland. Surface soil layers (0.20 cm) were collected at 36 sites. A total of 12 samples from lawns, 11 from allotment gardens, 9 from fields and 4 from fallow lands were subject to analysis. Lawns and allotment gardens were treated as central zone I . under strong pressure of anthropogenic factors, fields and fallow lands were treated as zone II . with potentially low level of anthropogenic influence. The statistical analysis showed significantly higher (p=0.008) amount of organic carbon (Corg) in lawns (mean 20.5 g·kg

For the first time, the quantitative geochemical data are given for urban soils of several groups of cities which differ in population. The content of chemical elements is considered as well as the specific ecological significance of soil contamination by these elements. The figures were established by authors on the base of average concentrations of chemical elements in the soils of more than 300 cities and settlements. The major part of data (sampling, analyses, and their statistical treatment) was obtained directly by authors as a result of special studies conducted for more than 15 years. The sufficiently numerous published materials of different researchers were also used. The greatest elements accumulation comparing with the Earth’s soils (tens of thousands of tons per 1 km2) is associated with an increase in the content of Ca and Mg. Considering the environmental significance of chemical elements accumulation in soils, we note the primary role of Pb and Zn in all groups of cities. Out from the rest pollutants it is necessary, first of all, to note As, Cu, and Cl, which are the main contaminants in four of six cities groups. In two groups of settlements, Cd and Co are important soil pollutants. In three groups, a considerable increase in the Ca content significantly modifies ecological–geochemical state of soils.

Richard Pouyat

Journal of Central European Agriculture

Marcos Francos

Soil Science

Heikki Setälä

RELATED PAPERS

Bilqis Bilqis Tsuroyya

Brendan Howe

Revista Nacional de Odontología

Renata De La Hoz

Tijdschrift voor rechtsgeschiedenis

Tammo Wallinga

Arthritis Research &amp; Therapy

Barbara Colombo

The American Journal of Occupational Therapy

Rene Padilla

Calcified Tissue International

Zvi Schwartz

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Arslan Basharat

Nuclear Physics B

Andre Martin

faruk yagci

Población y Salud en Mesoamérica

Maria Del Carmen Bisi Molina

Nanang Suhendar

Teologia w Polsce

Marcin Walczak

MARGARITA MARIA GOMEZ GOMEZ

Reproductive Toxicology

Fábio Seiva

Jurnal Fitofarmaka Indonesia

Mamat Pratama

Anais do 11º Congresso Brasileiro de Pesquisa e Desenvolvimento em Design

Helena Bastos

Médecine du Sommeil

Mohamed AKKARI

Journal of Hazardous Materials

Mounir Jaidann

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024