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Perspective

Perspective: Dimensions of the scientific method

* E-mail: [email protected]

Affiliation Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America

• Eberhard O. Voit

Published: September 12, 2019

• https://doi.org/10.1371/journal.pcbi.1007279
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The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

Citation: Voit EO (2019) Perspective: Dimensions of the scientific method. PLoS Comput Biol 15(9): e1007279. https://doi.org/10.1371/journal.pcbi.1007279

Editor: Jason A. Papin, University of Virginia, UNITED STATES

Copyright: © 2019 Eberhard O. Voit. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported in part by grants from the National Science Foundation ( https://www.nsf.gov/div/index.jsp?div=MCB ) grant NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs) and the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/ ) grant NIH-2P30ES019776-05 (PI: Carmen Marsit). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

The traditional scientific method: Hypothesis-driven deduction

Research is the undisputed core activity defining science. Without research, the advancement of scientific knowledge would come to a screeching halt. While it is evident that researchers look for new information or insights, the term “research” is somewhat puzzling. Never mind the prefix “re,” which simply means “coming back and doing it again and again,” the word “search” seems to suggest that the research process is somewhat haphazard, that not much of a strategy is involved in the process. One might argue that research a few hundred years ago had the character of hoping for enough luck to find something new. The alchemists come to mind in their quest to turn mercury or lead into gold, or to discover an elixir for eternal youth, through methods we nowadays consider laughable.

Today’s sciences, in stark contrast, are clearly different. Yes, we still try to find something new—and may need a good dose of luck—but the process is anything but unstructured. In fact, it is prescribed in such rigor that it has been given the widely known moniker “scientific method.” This scientific method has deep roots going back to Aristotle and Herophilus (approximately 300 BC), Avicenna and Alhazen (approximately 1,000 AD), Grosseteste and Robert Bacon (approximately 1,250 AD), and many others, but solidified and crystallized into the gold standard of quality research during the 17th and 18th centuries [ 1 – 7 ]. In particular, Sir Francis Bacon (1561–1626) and René Descartes (1596–1650) are often considered the founders of the scientific method, because they insisted on careful, systematic observations of high quality, rather than metaphysical speculations that were en vogue among the scholars of the time [ 1 , 8 ]. In contrast to their peers, they strove for objectivity and insisted that observations, rather than an investigator’s preconceived ideas or superstitions, should be the basis for formulating a research idea [ 7 , 9 ].

Bacon and his 19th century follower John Stuart Mill explicitly proposed gaining knowledge through inductive reasoning: Based on carefully recorded observations, or from data obtained in a well-planned experiment, generalized assertions were to be made about similar yet (so far) unobserved phenomena [ 7 ]. Expressed differently, inductive reasoning attempts to derive general principles or laws directly from empirical evidence [ 10 ]. An example is the 19th century epigram of the physician Rudolf Virchow, Omnis cellula e cellula . There is no proof that indeed “every cell derives from a cell,” but like Virchow, we have made the observation time and again and never encountered anything suggesting otherwise.

In contrast to induction, the widely accepted, traditional scientific method is based on formulating and testing hypotheses. From the results of these tests, a deduction is made whether the hypothesis is presumably true or false. This type of hypotheticodeductive reasoning goes back to William Whewell, William Stanley Jevons, and Charles Peirce in the 19th century [ 1 ]. By the 20th century, the deductive, hypothesis-based scientific method had become deeply ingrained in the scientific psyche, and it is now taught as early as middle school in order to teach students valid means of discovery [ 8 , 11 , 12 ]. The scientific method has not only guided most research studies but also fundamentally influenced how we think about the process of scientific discovery.

Alas, because biology has almost no general laws, deduction in the strictest sense is difficult. It may therefore be preferable to use the term abduction, which refers to the logical inference toward the most plausible explanation, given a set of observations, although this explanation cannot be proven and is not necessarily true.

Over the decades, the hypothesis-based scientific method did experience variations here and there, but its conceptual scaffold remained essentially unchanged ( Fig 1 ). Its key is a process that begins with the formulation of a hypothesis that is to be rigorously tested, either in the wet lab or computationally; nonadherence to this principle is seen as lacking rigor and can lead to irreproducible results [ 1 , 13 – 15 ].

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The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

https://doi.org/10.1371/journal.pcbi.1007279.g001

Going further, the prominent philosopher of science Sir Karl Popper argued that a scientific hypothesis can never be verified but that it can be disproved by a single counterexample. He therefore demanded that scientific hypotheses had to be falsifiable, because otherwise, testing would be moot [ 16 , 17 ] (see also [ 18 ]). As Gillies put it, “successful theories are those that survive elimination through falsification” [ 19 ]. Kelley and Scott agreed to some degree but warned that complete insistence on falsifiability is too restrictive as it would mark many computational techniques, statistical hypothesis testing, and even Darwin’s theory of evolution as nonscientific [ 20 ].

While the hypothesis-based scientific method has been very successful, its exclusive reliance on deductive reasoning is dangerous because according to the so-called Duhem–Quine thesis, hypothesis testing always involves an unknown number of explicit or implicit assumptions, some of which may steer the researcher away from hypotheses that seem implausible, although they are, in fact, true [ 21 ]. According to Kuhn, this bias can obstruct the recognition of paradigm shifts [ 22 ], which require the rethinking of previously accepted “truths” and the development of radically new ideas [ 23 , 24 ]. The testing of simultaneous alternative hypotheses [ 25 – 27 ] ameliorates this problem to some degree but not entirely.

The traditional scientific method is often presented in discrete steps, but it should really be seen as a form of critical thinking, subject to review and independent validation [ 8 ]. It has proven very influential, not only by prescribing valid experimentation, but also for affecting the way we attempt to understand nature [ 18 ], for teaching [ 8 , 12 ], reporting, publishing, and otherwise sharing information [ 28 ], for peer review and the awarding of funds by research-supporting agencies [ 29 , 30 ], for medical diagnostics [ 7 ], and even in litigation [ 31 ].

A second dimension of the scientific method: Data-mining–inspired induction

A major shift in biological experimentation occurred with the–omics revolution of the early 21st century. All of a sudden, it became feasible to perform high-throughput experiments that generated thousands of measurements, typically characterizing the expression or abundances of very many—if not all—genes, proteins, metabolites, or other biological quantities in a sample.

The strategy of measuring large numbers of items in a nontargeted fashion is fundamentally different from the traditional scientific method and constitutes a new, second dimension of the scientific method. Instead of hypothesizing and testing whether gene X is up-regulated under some altered condition, the leading question becomes which of the thousands of genes in a sample are up- or down-regulated. This shift in focus elevates the data to the supreme role of revealing novel insights by themselves ( Fig 2 ). As an important, generic advantage over the traditional strategy, this second dimension is free of a researcher’s preconceived notions regarding the molecular mechanisms governing the phenomenon of interest, which are otherwise the key to formulating a hypothesis. The prominent biologists Patrick Brown and David Botstein commented that “the patterns of expression will often suffice to begin de novo discovery of potential gene functions” [ 32 ].

Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.

https://doi.org/10.1371/journal.pcbi.1007279.g002

This data-driven, discovery-generating approach is at once appealing and challenging. On the one hand, very many data are explored simultaneously and essentially without bias. On the other hand, the large datasets supporting this approach create a genuine challenge to understanding and interpreting the experimental results because the thousands of data points, often superimposed with a fair amount of noise, make it difficult to detect meaningful differences between sample and control. This situation can only be addressed with computational methods that first “clean” the data, for instance, through the statistically valid removal of outliers, and then use machine learning to identify statistically significant, distinguishing molecular profiles or signatures. In favorable cases, such signatures point to specific biological pathways, whereas other signatures defy direct explanation but may become the launch pad for follow-up investigations [ 33 ].

Today’s scientists are very familiar with this discovery-driven exploration of “what’s out there” and might consider it a quaint quirk of history that this strategy was at first widely chastised and ridiculed as a “fishing expedition” [ 30 , 34 ]. Strict traditionalists were outraged that rigor was leaving science with the new approach and that sufficient guidelines were unavailable to assure the validity and reproducibility of results [ 10 , 35 , 36 ].

From the view point of philosophy of science, this second dimension of the scientific method uses inductive reasoning and reflects Bacon’s idea that observations can and should dictate the research question to be investigated [ 1 , 7 ]. Allen [ 36 ] forcefully rejected this type of reasoning, stating “the thinking goes, we can now expect computer programs to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles… In contrast with this view, many are convinced that no purely logical process can turn observation into understanding.” His conviction goes back to the 18th century philosopher David Hume and again to Popper, who identified as the overriding problem with inductive reasoning that it can never truly reveal causality, even if a phenomenon is observed time and again [ 16 , 17 , 37 , 38 ]. No number of observations, even if they always have the same result, can guard against an exception that would violate the generality of a law inferred from these observations [ 1 , 35 ]. Worse, Popper argued, through inference by induction, we cannot even know the probability of something being true [ 10 , 17 , 36 ].

Others argued that data-driven and hypothesis-driven research actually do not differ all that much in principle, as long as there is cycling between developing new ideas and testing them with care [ 27 ]. In fact, Kell and Oliver [ 34 ] maintained that the exclusive acceptance of hypothesis-driven programs misrepresents the complexities of biological knowledge generation. Similarly refuting the prominent rule of deduction, Platt [ 26 ] and Beard and Kushmerick [ 27 ] argued that repeated inductive reasoning, called strong inference, corresponds to a logically sound decision tree of disproving or refining hypotheses that can rapidly yield firm conclusions; nonetheless, Platt had to admit that inductive inference is not as certain as deduction, because it projects into the unknown. Lander compared the task of obtaining causality by induction to the problem of inferring the design of a microprocessor from input-output readings, which in a strict sense is impossible, because the microprocessor could be arbitrarily complicated; even so, inference often leads to novel insights and therefore is valuable [ 39 ].

An interesting special case of almost pure inductive reasoning is epidemiology, where hypothesis-driven reasoning is rare and instead, the fundamental question is whether data-based evidence is sufficient to associate health risks with specific causes [ 31 , 34 ].

Recent advances in machine learning and “big-data” mining have driven the use of inductive reasoning to unprecedented heights. As an example, machine learning can greatly assist in the discovery of patterns, for instance, in biological sequences [ 40 ]. Going a step further, a pithy article by Andersen [ 41 ] proffered that we may not need to look for causality or mechanistic explanations anymore if we just have enough correlation: “With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories.”

Of course, the proposal to abandon the quest for causality caused pushback on philosophical as well as mathematical grounds. Allen [ 10 , 35 ] considered the idea “absurd” that data analysis could enhance understanding in the absence of a hypothesis. He felt confident “that even the formidable combination of computing power with ease of access to data cannot produce a qualitative shift in the way that we do science: the making of hypotheses remains an indispensable component in the growth of knowledge” [ 36 ]. Succi and Coveney [ 42 ] refuted the “most extravagant claims” of big-data proponents very differently, namely by analyzing the theories on which machine learning is founded. They contrasted the assumptions underlying these theories, such as the law of large numbers, with the mathematical reality of complex biological systems. Specifically, they carefully identified genuine features of these systems, such as nonlinearities, nonlocality of effects, fractal aspects, and high dimensionality, and argued that they fundamentally violate some of the statistical assumptions implicitly underlying big-data analysis, like independence of events. They concluded that these discrepancies “may lead to false expectations and, at their nadir, even to dangerous social, economical and political manipulation.” To ameliorate the situation, the field of big-data analysis would need new strong theorems characterizing the validity of its methods and the numbers of data required for obtaining reliable insights. Succi and Coveney go as far as stating that too many data are just as bad as insufficient data [ 42 ].

While philosophical doubts regarding inductive methods will always persist, one cannot deny that -omics-based, high-throughput studies, combined with machine learning and big-data analysis, have been very successful [ 43 ]. Yes, induction cannot truly reveal general laws, no matter how large the datasets, but they do provide insights that are very different from what science had offered before and may at least suggest novel patterns, trends, or principles. As a case in point, if many transcriptomic studies indicate that a particular gene set is involved in certain classes of phenomena, there is probably some truth to the observation, even though it is not mathematically provable. Kepler’s laws of astronomy were arguably derived solely from inductive reasoning [ 34 ].

Notwithstanding the opposing views on inductive methods, successful strategies shape how we think about science. Thus, to take advantage of all experimental options while ensuring quality of research, we must not allow that “anything goes” but instead identify and characterize standard operating procedures and controls that render this emerging scientific method valid and reproducible. A laudable step in this direction was the wide acceptance of “minimum information about a microarray experiment” (MIAME) standards for microarray experiments [ 44 ].

A third dimension of the scientific method: Allochthonous reasoning

Parallel to the blossoming of molecular biology and the rapid rise in the power and availability of computing in the late 20th century, the use of mathematical and computational models became increasingly recognized as relevant and beneficial for understanding biological phenomena. Indeed, mathematical models eventually achieved cornerstone status in the new field of computational systems biology.

Mathematical modeling has been used as a tool of biological analysis for a long time [ 27 , 45 – 48 ]. Interesting for the discussion here is that the use of mathematical and computational modeling in biology follows a scientific approach that is distinctly different from the traditional and the data-driven methods, because it is distributed over two entirely separate domains of knowledge. One consists of the biological reality of DNA, elephants, and roses, whereas the other is the world of mathematics, which is governed by numbers, symbols, theorems, and abstract work protocols. Because the ways of thinking—and even the languages—are different in these two realms, I suggest calling this type of knowledge acquisition “allochthonous” (literally Greek: in or from a “piece of land different from where one is at home”; one could perhaps translate it into modern lingo as “outside one’s comfort zone”). De facto, most allochthonous reasoning in biology presently refers to mathematics and computing, but one might also consider, for instance, the application of methods from linguistics in the analysis of DNA sequences or proteins [ 49 ].

One could argue that biologists have employed “models” for a long time, for instance, in the form of “model organisms,” cell lines, or in vitro experiments, which more or less faithfully reflect features of the organisms of true interest but are easier to manipulate. However, this type of biological model use is rather different from allochthonous reasoning, as it does not leave the realm of biology and uses the same language and often similar methodologies.

A brief discussion of three experiences from our lab may illustrate the benefits of allochthonous reasoning. (1) In a case study of renal cell carcinoma, a dynamic model was able to explain an observed yet nonintuitive metabolic profile in terms of the enzymatic reaction steps that had been altered during the disease [ 50 ]. (2) A transcriptome analysis had identified several genes as displaying significantly different expression patterns during malaria infection in comparison to the state of health. Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [ 51 ]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible. Speculation regarding possible explanations led to the hypothesis that many parasite-harboring red blood cells might “hide” from circulation and therewith from detection in the blood stream. While experimental testing of the feasibility of the hypothesis would have been expensive, a dynamic model confirmed that such a concealment mechanism could indeed quantitatively explain the apparently very high growth rates [ 52 ]. In all three cases, the insights gained inductively from computational modeling would have been difficult to obtain purely with experimental laboratory methods. Purely deductive allochthonous reasoning is the ultimate goal of the search for design and operating principles [ 53 – 55 ], which strives to explain why certain structures or functions are employed by nature time and again. An example is a linear metabolic pathway, in which feedback inhibition is essentially always exerted on the first step [ 56 , 57 ]. This generality allows the deduction that a so far unstudied linear pathway is most likely (or even certain to be) inhibited at the first step. Not strictly deductive—but rather abductive—was a study in our lab in which we analyzed time series data with a mathematical model that allowed us to infer the most likely regulatory structure of a metabolic pathway [ 58 , 59 ].

A typical allochthonous investigation begins in the realm of biology with the formulation of a hypothesis ( Fig 3 ). Instead of testing this hypothesis with laboratory experiments, the system encompassing the hypothesis is moved into the realm of mathematics. This move requires two sets of ingredients. One set consists of the simplification and abstraction of the biological system: Any distracting details that seem unrelated to the hypothesis and its context are omitted or represented collectively with other details. This simplification step carries the greatest risk of the entire modeling approach, as omission of seemingly negligible but, in truth, important details can easily lead to wrong results. The second set of ingredients consists of correspondence rules that translate every biological component or process into the language of mathematics [ 60 , 61 ].

This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.

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Once the system is translated, it has become an entirely mathematical construct that can be analyzed purely with mathematical and computational means. The results of this analysis are also strictly mathematical. They typically consist of values of variables, magnitudes of processes, sensitivity patterns, signs of eigenvalues, or qualitative features like the onset of oscillations or the potential for limit cycles. Correspondence rules are used again to move these results back into the realm of biology. As an example, the mathematical result that “two eigenvalues have positive real parts” does not make much sense to many biologists, whereas the interpretation that “the system is not stable at the steady state in question” is readily explained. New biological insights may lead to new hypotheses, which are tested either by experiments or by returning once more to the realm of mathematics. The model design, diagnosis, refinements, and validation consist of several phases, which have been discussed widely in the biomathematical literature. Importantly, each iteration of a typical modeling analysis consists of a move from the biological to the mathematical realm and back.

The reasoning within the realm of mathematics is often deductive, in the form of an Aristotelian syllogism, such as the well-known “All men are mortal; Socrates is a man; therefore, Socrates is mortal.” However, the reasoning may also be inductive, as it is the case with large-scale Monte-Carlo simulations that generate arbitrarily many “observations,” although they cannot reveal universal principles or theorems. An example is a simulation randomly drawing numbers in an attempt to show that every real number has an inverse. The simulation will always attest to this hypothesis but fail to discover the truth because it will never randomly draw 0. Generically, computational models may be considered sets of hypotheses, formulated as equations or as algorithms that reflect our perception of a complex system [ 27 ].

Impact of the multidimensional scientific method on learning

Almost all we know in biology has come from observation, experimentation, and interpretation. The traditional scientific method not only offered clear guidance for this knowledge gathering, but it also fundamentally shaped the way we think about the exploration of nature. When presented with a new research question, scientists were trained to think immediately in terms of hypotheses and alternatives, pondering the best feasible ways of testing them, and designing in their minds strong controls that would limit the effects of known or unknown confounders. Shaped by the rigidity of this ever-repeating process, our thinking became trained to move forward one well-planned step at a time. This modus operandi was rigid and exact. It also minimized the erroneous pursuit of long speculative lines of thought, because every step required testing before a new hypothesis was formed. While effective, the process was also very slow and driven by ingenuity—as well as bias—on the scientist’s part. This bias was sometimes a hindrance to necessary paradigm shifts [ 22 ].

High-throughput data generation, big-data analysis, and mathematical-computational modeling changed all that within a few decades. In particular, the acceptance of inductive principles and of the allochthonous use of nonbiological strategies to answer biological questions created an unprecedented mix of successes and chaos. To the horror of traditionalists, the importance of hypotheses became minimized, and the suggestion spread that the data would speak for themselves [ 36 ]. Importantly, within this fog of “anything goes,” the fundamental question arose how to determine whether an experiment was valid.

Because agreed-upon operating procedures affect research progress and interpretation, thinking, teaching, and sharing of results, this question requires a deconvolution of scientific strategies. Here I proffer that the single scientific method of the past should be expanded toward a vector space of scientific methods, with spanning vectors that correspond to different dimensions of the scientific method ( Fig 4 ).

The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.

https://doi.org/10.1371/journal.pcbi.1007279.g004

Obviously, all three dimensions have their advantages and drawbacks. The traditional, hypothesis-driven deductive method is philosophically “clean,” except that it is confounded by preconceptions and assumptions. The data-mining–inspired inductive method cannot offer universal truths but helps us explore very large spaces of factors that contribute to a phenomenon. Allochthonous, model-based reasoning can be performed mentally, with paper and pencil, through rigorous analysis, or with a host of computational methods that are precise and disprovable [ 27 ]. At the same time, they are incomparable faster, cheaper, and much more comprehensive than experiments in molecular biology. This reduction in cost and time, and the increase in coverage, may eventually have far-reaching consequences, as we can already fathom from much of modern physics.

Due to its long history, the traditional dimension of the scientific method is supported by clear and very strong standard operating procedures. Similarly, strong procedures need to be developed for the other two dimensions. The MIAME rules for microarray analysis provide an excellent example [ 44 ]. On the mathematical modeling front, no such rules are generally accepted yet, but trends toward them seem to emerge at the horizon. For instance, it seems to be becoming common practice to include sensitivity analyses in typical modeling studies and to assess the identifiability or sloppiness of ensembles of parameter combinations that fit a given dataset well [ 62 , 63 ].

From a philosophical point of view, it seems unlikely that objections against inductive reasoning will disappear. However, instead of pitting hypothesis-based deductive reasoning against inductivism, it seems more beneficial to determine how the different methods can be synergistically blended ( cf . [ 18 , 27 , 34 , 42 ]) as linear combinations of the three vectors of knowledge acquisition ( Fig 4 ). It is at this point unclear to what degree the identified three dimensions are truly independent of each other, whether additional dimensions should be added [ 24 ], or whether the different versions could be amalgamated into a single scientific method [ 18 ], especially if it is loosely defined as a form of critical thinking [ 8 ]. Nobel Laureate Percy Bridgman even concluded that “science is what scientists do, and there are as many scientific methods as there are individual scientists” [ 8 , 64 ].

Combinations of the three spanning vectors of the scientific method have been emerging for some time. Many biologists already use inductive high-throughput methods to develop specific hypotheses that are subsequently tested with deductive or further inductive methods [ 34 , 65 ]. In terms of including mathematical modeling, physics and geology have been leading the way for a long time, often by beginning an investigation in theory, before any actual experiment is performed. It will benefit biology to look into this strategy and to develop best practices of allochthonous reasoning.

The blending of methods may take quite different shapes. Early on, Ideker and colleagues [ 65 ] proposed an integrated experimental approach for pathway analysis that offered a glimpse of new experimental strategies within the space of scientific methods. In a similar vein, Covert and colleagues [ 66 ] included computational methods into such an integrated approach. Additional examples of blended analyses in systems biology can be seen in other works, such as [ 43 , 67 – 73 ]. Generically, it is often beneficial to start with big data, determine patterns in associations and correlations, then switch to the mathematical realm in order to filter out spurious correlations in a high-throughput fashion. If this procedure is executed in an iterative manner, the “surviving” associations have an increased level of confidence and are good candidates for further experimental or computational testing (personal communication from S. Chandrasekaran).

If each component of a blended scientific method follows strict, commonly agreed guidelines, “linear combinations” within the 3D space can also be checked objectively, per deconvolution. In addition, guidelines for synergistic blends of component procedures should be developed. If we carefully monitor such blends, time will presumably indicate which method is best for which task and how the different approaches optimally inform each other. For instance, it will be interesting to study whether there is an optimal sequence of experiments along the three axes for a particular class of tasks. Big-data analysis together with inductive reasoning might be optimal for creating initial hypotheses and possibly refuting wrong speculations (“we had thought this gene would be involved, but apparently it isn’t”). If the logic of an emerging hypotheses can be tested with mathematical and computational tools, it will almost certainly be faster and cheaper than an immediate launch into wet-lab experimentation. It is also likely that mathematical reasoning will be able to refute some apparently feasible hypothesis and suggest amendments. Ultimately, the “surviving” hypotheses must still be tested for validity through conventional experiments. Deconvolving current practices and optimizing the combination of methods within the 3D or higher-dimensional space of scientific methods will likely result in better planning of experiments and in synergistic blends of approaches that have the potential capacity of addressing some of the grand challenges in biology.

Acknowledgments

The author is very grateful to Dr. Sriram Chandrasekaran and Ms. Carla Kumbale for superb suggestions and invaluable feedback.

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Introduction

There is a problem: data from the field, how should hypothesis & prediction be defined, hypothesis generation in biology : a science teaching challenge & potential solution.

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Paul K. Strode; Hypothesis Generation in Biology : A Science Teaching Challenge & Potential Solution . The American Biology Teacher 1 September 2015; 77 (7): 500–506. doi: https://doi.org/10.1525/abt.2015.77.7.4

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Helping students understand and generate appropriate hypotheses and test their subsequent predictions – in science in general and biology in particular – should be at the core of teaching the nature of science. However, there is much confusion among students and teachers about the difference between hypotheses and predictions. Here, I present evidence of the problem and describe steps that scientists actually follow when employing scientific reasoning strategies. This is followed by a proposed solution for helping students effectively explore this important aspect of the nature of science.

I taught high school biology and chemistry for 8 years before beginning a doctoral program in ecology and environmental science at the University of Illinois. Graduate school revealed that, while I had been effective in teaching science content to my students, I had mostly failed in teaching them the nature of science (NOS). Indeed, I had even promoted several of the myths of science outlined by McComas (1996) – most blatantly that “a hypothesis is an educated guess” and “science is procedural more than creative.” I had even failed at understanding and teaching the hypothetico-deductive method of science that so many science teachers (this author included) mislead their students into thinking is the only way to practice science : formulate a hypothesis, deduce its consequences (make a prediction), and observe those consequences (perform an experiment and collect data).

For example, in my second year of graduate school, a chance conversation in the woods with one of my committee members revealed my own shortfalls. When pressed for the hypothesis I was testing with my research, I delivered the prediction that if we had an average spring warm-up, then the timing of leaf growth, caterpillar hatching, and bird migration would be synchronized, but if we had an early or late spring, there would be a mismatch in one or more of the trophic levels. I had given my committee member an “educated guess,” an “If…, then…” statement exactly in the form I had learned in my science classes and identical to how I had taught my high school students to write hypotheses. While I may have based my prediction on some overarching patterns or underlying mechanisms that were already known for the community interactions I was studying, I certainly could not verbalize them.

Since returning to teaching high school biology after graduate school, I work to help my students hone the scientific reasoning strategies of abduction (ingenuity, or borrowing an idea from earlier studies), deduction, and induction. But with such an NOS focus in my classroom on these reasoning skills, I have become somewhat hypersensitive to moments when students get it wrong – for example, when students inappropriately marry a method with the tail end of a deductive statement ( If I do X, then Y will happen ) and call it a “hypothesis.”

Most commonly in scientific research, a hypothesis is a tentative, testable, and falsifiable statement that explains some observed phenomenon in nature. We more specifically call this kind of statement an explanatory hypothesis . However, as we will see, a hypothesis can also be a statement that describes an observed pattern in nature. We call this kind a generalizing hypothesis .

In the sections that follow, I present evidence that students, teachers, textbooks, and even practicing scientists confuse predictions with hypotheses. I then discuss the ways the terms are defined and used in the logical practice of scientific reasoning. Finally, I provide some simple ideas for how we can improve the teaching of NOS in the classroom.

In 2006, I chaperoned a group of high school students presenting precollege research at the Intel International Science and Engineering Fair (Intel ISEF) in Indianapolis. Upon inspection of a wide range of student poster presentations, I observed that several students had written predictions on their posters but labeled them “hypotheses.” In the interest of quantifying this misconception, I quickly designed a small survey and randomly sampled all non-engineering and non-math projects with project numbers ending in 1, 4, or 7 (n = 127). In this initial survey, 78 (80%) of 98 student posters reviewed had incorrectly identified a prediction as a hypothesis.

Where had these students gone wrong or been misled during their formal science education or in their science-fair preparation work? Indeed, it is human nature to formulate explanations for observed natural phenomena ( Brewer et al., 1998 ; Lawson, 2004 ). Cognitive scientists sometimes argue that children are themselves “little scientists.” For example, children with little or no formal training in the process of science can propose functional hypotheses to explain a natural event ( Vosniadou & Brewer, 1992 ) and causal hypotheses to explain how one event in nature may affect another ( Samarapungavan & Wiers, 1997 ). Have we, the science educators, excised reasoning skill from our students?

For the Intel ISEF Indianapolis survey and other surveys I report next, I followed the definitions of hypotheses described above, as candidate explanations or generalizations for observations seen in nature. If a proposed explanation or generalization of a pattern is valid, then we can anticipate (predict) a particular outcome from an experiment or that we will see the pattern elsewhere in nature. Therefore, a scientific hypothesis can lead to predictions ( Singer, 2007 ; Campbell et al., 2008 ) but is not, itself, “just a prediction” (a very common misconception).

My interest in student misunderstanding of the hypothesis was piqued at the 2006 Intel ISEF, so colleagues and I have now surveyed 1864 student projects at eight Intel ISEF competitions (2006, 2008–2014; Table 1 ). Students in the sample identified hypotheses on 1448 (78%) of these projects but wrote predictions 81.2% of the time; they wrote candidate explanations or generalizations on only 272 (18.8%) of the projects ( Table 2 ). Failure to write hypotheses was consistently greater than success across years, and the two groups were statistically distinguishable (paired t-test: t = 20.55, df = 7, P < 0.001). Informal interviews with students revealed that while some could explain their research as hypothesis-driven, these students could not avoid predictive statements (e.g., “If I do X, then Y will happen”).

In addition to the surveys conducted at Intel ISEF, I analyzed 66 current middle school, high school, and college science textbooks by assessing all NOS chapters, all laboratory prompts, and glossaries. Fifty-four of the 66 science textbooks included instruction for understanding the hypothesis; 12 (18%) did not contain any mention of the hypothesis. Forty-two percent of textbooks that mentioned the hypothesis failed by confusing it with a prediction in either (1) the definition of the hypothesis, (2) an example hypothesis, or (3) a lab prompt (e.g., “Propose a hypothesis about what will happen…”) (for more examples, see Table 3 ). The largest proportion (13 of 17; 76%) of textbooks with this confused definition and/or use of the term hypothesis was in the middle school sample. Six (17%) of the 35 high school science textbooks failed in at least one of the assessed categories. The 14 textbooks designed for the college market (and used in our upper-level, IB, and AP classes) fared best; only one (7%), a biology textbook, failed to teach the hypothesis as distinct from the prediction.

I surveyed 17 preservice science teachers in a graduate-level teacher preparation course focused on NOS at the University of Colorado; and 59 biology teachers, selected at random (a convenience sample), at the 2011 annual meeting of the National Association of Biology Teachers (NABT). I gave both groups (on the first day of the term for the students in the science education course) a “pop quiz” on paper that asked them to (1) write a definition of the hypothesis in science; and, after reading a set of observations, (2) write a hypothesis about the observations that could be tested with an experiment. In the science education course, 5 of the 17 teacher-candidates (29%) showed mastery of the hypothesis, while 12 (71%) confused the hypothesis with the prediction. Less than half of all responders (27/59; 45%) at the NABT meeting exhibited a genuine understanding of scientific hypotheses. Thirteen (48%) of the 27 responders with correct understanding were biology teachers with Ph.D. degrees.

As a comparison, Lawson (2002) reported that in a sample of preservice middle and high school biology teachers, 96% “confused hypotheses with predictions and agreed with the statement that a hypothesis is an educated guess of what will be observed under certain conditions.” If this situation is not addressed explicitly, teachers are likely to pass this misunderstanding on to their students.

I analyzed 300 peer-reviewed, published scientific papers that are part of a teaching collection I have accumulated over several years of teaching various biology courses. The papers are mostly from fields of biology in which hypothesis testing is common, but other fields of science are also represented, as well as science education papers (including several papers published in The American Biology Teacher ). Sixty-two percent (186/300) of the scientific papers analyzed use some form of the term ( hypothesis , hypotheses , hypothesize , or hypothesized ), and 12.3% (23/186) mislabel predictions as hypotheses. Again, see Table 2 for examples of incorrectly and correctly written hypotheses from students, textbooks, teachers, scientists, and science educators.

Many textbooks oversimplify the definition of the hypothesis to an educated guess . But as McComas (1996) asks, “An educated guess about what?” Some textbooks do better; in their popular upper-level textbook, Biology , Campbell et al. (2008) define the hypothesis as “A tentative answer to a well-framed question – an explanation on trial” (p. 19) ( Table 3 ). However, getting to that tentative answer or explanation is not as easy as it seems, and many scholars have written about it.

Generalizing & Explanatory Hypotheses

McComas (1996 , 2004 , 2015 ) explains that observations of natural phenomena can produce two strands of hypothetical reasoning: generalizations and explanations. We often use generalizing hypotheses to summarize patterns we observe in nature, and we can refer to these types of hypotheses as immature laws . If the generalizations hold true over and over again, they become established laws of nature. We then use explanatory hypotheses to provide reasons for the generalizations. Explanatory hypotheses can also be referred to as immature theories , because if the explanations survive various angles of rigorous testing they become established theories. Thus, theories can explain laws but never become laws.

As an example, consider Harvard University evolutionary biologist Jonathan Losos, who, with his colleagues, studies the Anolis lizards of the Caribbean Islands. One specific pattern the researchers have consistently observed is that some anoles (e.g., Anolis valencienni ) living on narrow twigs in their forest habitats have short legs ( Losos & Schneider, 2009 ). This observed pattern produces the generalization (generalizing hypothesis or immature law ) that particular body shapes and sizes in anoles are linked to particular habitats, and we can predict that anoles discovered living on twigs in forests on other islands will also have short legs. Losos and his colleagues proposed that adaptation to their twig habitats by way of natural selection was a likely explanation (explanatory hypothesis or immature theory ) for the pattern of short-legged anoles living on twigs. In one experiment to test the twig adaptation hypothesis, small breeding populations of long-legged trunk anoles ( A. sangrei ) were placed on small anole-free islands with only small-twigged bushes as habitat ( Losos et al., 2001 ). The prediction that follows the twig adaptation hypothesis is that, after several generations, the surviving anole population would have shorter legs as the environment and natural selection sift out the individuals with longer legs that are unable to use the twiggy habitat efficiently. Indeed, later generations of the anoles had significantly shorter legs than their ancestors. Figure 1 illustrates how these ideas are applied to the Anolis lizard example. Teachers might use a figure like this one in direct instruction to explain the situation – or ask students to create one after reading a scientific paper, to check for understanding.

Two pathways to theories and laws by way of explanatory hypotheses and generalizing hypotheses. Note that both types can generate predictions and that explanatory hypotheses and their resulting theories can provide explanations for generalizing hypotheses and their resulting laws, respectively.

Abduction, Deduction, & Induction

In the above example, Losos and his colleagues moved through several levels of logic that have been summarized by Lawson (2010) . These levels form the basic inferences of scientific reasoning, argumentation, and discovery – they are abduction, deduction, and induction. In noticing the short legs on twig anoles and that they moved easily in their twig habitat, the researchers proposed that the short legs were an adaptation driven by the uniqueness of the twig habitat. Proposing that the twig habitat may have driven the twig anoles to evolve short legs required some imagination and ingenuity on the part of Losos and his colleagues – a logical strategy in science called abduction and also known as the “creative leap” ( Langley, 1999 ). However, sometimes the abductive strategy involves literally abducting (figuratively stealing) an idea from the results of an earlier study. Indeed, adaptation had already been shown as an explanation for traits in other species. For example, different beak shapes and sizes of the Galápagos finches (e.g., the medium ground finch, Geospiza fortis ) function as adaptations to different food resources. Perhaps Losos and his colleagues saw the connection between the short legs of the anoles and their twig habitats as an analogy to the small beaks of the medium and small ground finches and the soft seeds the birds eat. In short, abductive reasoning produces explanatory hypotheses , sometimes through leaps of creativity.

If adaptation by natural selection is a reasonable hypothesis for the short legs on the twig anoles, then a logical consequence is that long-legged anoles placed in habitats with only twigs as perches would evolve shorter legs. This second logical strategy is called deduction – the researchers deduced an outcome of an experiment, a prediction, given the “adaptation by natural selection” hypothesis. Thus, deductive reasoning tests ideas with predictions .

When Losos and his colleagues looked at the results of their experiment, they found that the long-legged anoles had evolved shorter legs. They thus logically concluded that the result was in support of their twig habitat hypothesis and was also in support of established natural selection theory. This final logical step is called induction : if the observed result matches the predicted outcome, then the hypothesis is supported.

The process described above is often referred to in textbooks as the hypothetico-deductive strategy of “the scientific method.” It is important to point out here that hypothetico-deductive reasoning, coupled with induction, is not without problems. First, a logical fallacy of induction is affirming the hypothesis without considering other explanations – there may be other hypotheses that explain the observed result. The case may simply be that females prefer to mate with short-legged males. Indeed, false hypotheses can produce true predictions. A second problem with induction is that in designing and carrying out our experiments and affirming our hypotheses, we may unknowingly be making several assumptions, also called auxiliary hypotheses , that if violated throw doubt on our conclusions. For example, Losos and his colleagues assume that leg length in anoles is a strongly heritable trait, similar to beak size in finches. If the trait is not heritable, they will not see their predicted result.

Solving the Problem of “Hypothesis” in the Science Classroom

The results of the various surveys reported here are evidence that many of our students are not learning how to formulate and propose hypotheses to drive their scientific studies. Even our best science students, those who qualify for the Intel ISEF, are generating predictions but calling them “hypotheses.” These mistakes likely arise from several correctable teaching approaches. First – and perhaps the most commonly observed error in teaching hypothesis writing – is having students write “if…, then…” statements, where the if phrase is actually an experimental method, and the then phrase is a specific prediction. For example, a textbook, a teacher, or a student may propose the prediction, “ If fertilizer is added to the soil, then the plants will grow taller ,” but call it a hypothesis. Textbooks, teachers, students, and scientists who propose predictions in place of explanations are skipping abduction and analogical reasoning and proceeding directly to making predictions ( Lawson, 2004 ).

The if–then mistake is correctable. For example, when my students verbalize or write predictions and call or label them “hypotheses,” I point out the mistake, but then ask them how or why they are able to make those predictions. Students invariably begin their answers with “Because…” and often end up stating something close to the hypothesis they are testing. Using this strategy, we can guide our students toward a generalizing hypothesis or help them work through analogical reasoning and abduct an explanatory hypothesis. An additional strategy to help students delineate the hypothesis from the prediction is to have students write predictions and label them as predictions when they are planning their investigations. Perhaps the most critical component of this pedagogical strategy is that students become focused on keeping their explanations (generalizing or explanatory hypotheses) as completely separate statements from their predictions.

A second, egregious, and all too common practice is when teachers require students to write hypotheses for “canned” lab activities, the likely objective of which is simply to make determinations, such as the value of a physical constant ( Yip, 2007 ). In these cases, teachers can help students write generalizing hypotheses that explain patterns, but only after students have made some observations and recorded some data. In all cases, teachers may consider providing students a flow chart, similar to Figure 1 , that helps them move through the two strands of generating explanatory and generalizing hypotheses and their related predictions.

Finally, teachers are advised to take a close look at the textbooks they are using and carefully assess how the textbooks define and use hypothesis . They may indeed be using a textbook that confuses students on some level about what hypotheses are.

Correcting this confusion – between the hypothesis and the prediction in particular, and about NOS in general – will not happen overnight, or even within the next few weeks, but it does begin with teachers like you .

Science is an essential course in a student's formal education, but many have demonstrated that misunderstanding of NOS by students and teachers can be a major challenge. Perhaps the most important goal of science education in a democracy is to produce a future consensus of public policy makers and an informed electorate who have a scientific understanding of the natural world. Indeed, a lack of understanding of NOS has made it far too easy today for science denial and pseudoscience to influence personal and public decision making ( Flammer, 2006 ). Science educators must teach students how to use the logical strategies of scientific reasoning and how to employ the procedures for obtaining meaningful and credible knowledge through scientific results that will contribute to scientific knowledge and to the formation of effective, evidence-based public policy ( Dias et al., 2004 ; Forrest, 2011 ). The public must understand how science works, and I am convinced that we can produce a more scientifically literate public if we commit to a greater focus in science education on the nature of science, and starting with the hypothesis.

The author thanks the Boulder Valley School District and Cordon-Pharma Colorado for financial support for several trips to the Intel International Science and Engineering Fairs. H. Ayi-Bonte, K. Donley, H. Petach, and A. Smith contributed to data collection at various Intel ISEF events. Two anonymous reviewers and H. Ayi-Bonte, H. Petach, J. S. Levine, H. Quinn, S. M. Zerwin, J. M. Strode, and W. F. McComas provided invaluable comments on the manuscript.

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Biomedical informatics, Stanford

Mark A. Musen, M.D., Ph.D., is Professor of Biomedical Informatics at Stanford University, where he is Director of the Stanford Center for Biomedical Informatics Research. Dr. Musen conducts research related to intelligent systems, reusable ontologies, metadata for publication of scientific data sets, and biomedical decision support. His group developed Protégé, the world’s most widely used technology for building and managing terminologies and ontologies. He is principal investigator of the National Center for Biomedical Ontology, one of the original National Centers for Biomedical Computing created by the U.S. National Institutes of Heath (NIH). He is principal investigator of the Center for Expanded Data Annotation and Retrieval (CEDAR). CEDAR is a center of excellence supported by the NIH Big Data to Knowledge Initiative, with the goal of developing new technology to ease the authoring and management of biomedical experimental metadata. Dr. Musen chairs the Health Informatics and Modeling Topic Advisory Group for the World Health Organization’s revision of the International Classification of Diseases (ICD-11) and he directs the WHO Collaborating Center for Classification, Terminology, and Standards at Stanford University.

Cameron Neylon

Scholarly Communications, Consultant (formerly PLoS)

Cameron Neylon is a freelance researcher, consultant and is an advocate of open research practice who has always worked in interdisciplinary areas of research. He has previously been Advocacy Director at PLOS (the Public Library of Science), a Senior Scientist at the STFC Isis Neutron and Muon Facility and tenured faculty at the University of Southampton. Along his earlier work in structural biology and biophysics his research and writing focuses on the interface of web technology with science and the successful (and unsuccessful) application of generic and specially designed tools in the academic research environment. He is a co-author of the Panton Principles for Open Data in Science and the Altmetrics Manifesto, and writes regularly on the social, technical, and policy issues of open research at his blog, Science in the Open.

Karen Skinner

Pharmacology, National Institutes of Health (Retired)

Dr. Skinner retired from the National Institutes of Health in 2012, where she served as the Deputy Director for Science and Technology Development at the National Institute on Drug Abuse. Dr. Karen Skinner joined the NIH in 1989 as a program officer in Developmental Neurogenetics at the National Institute on Neurological Disorders and Stroke. She moved to the National Institute on Drug Abuse (NIDA) in 1991, and currently serves as the Deputy Director for Science and Technology Development in the Division of Neuroscience and Behavior Research at NIDA. Her current program activities at NIDA include resource discovery and sharing, informatics, computation and emerging technologies. She serves as Project Officer for the Neuroscience Information Framework, a project of the NIH Neuroscience Blueprint Initiative, and as Program Officer for the NIH Roadmap National Center for Integrative Biomedical Informatics. She also belongs to the BISTI consortium, and serves on the Trans-NIH BioMedical Informatics Coordinating Committee, and the NIH Blueprint NITRC project team.

Ben Goldacre

Medicine/Pharma,University of Oxford

Ben Michael Goldacre is a physician, academic and science writer. As of March 2015, he is a Senior Clinical Research Fellow at the Centre for Evidence-Based Medicine based at Nuffield Department of Primary Care Health Science, University of Oxford.[2] He is a founder of the AllTrials campaign to require open science practices in clinical trials. Goldacre is known in particular for his “Bad Science” column in The Guardian, which he wrote between 2003 and 2011, and is the author of three books: Bad Science (2008), a critique of irrationality and certain forms of alternative medicine; Bad Pharma (2012), an examination of the pharmaceutical industry, its publishing and marketing practices, and its relationship with the medical profession, and I Think You’ll Find It’s a Bit More Complicated Than That, a collection of his journalism. Goldacre frequently delivers free talks about bad science—he describes himself as a “nerd evangelist”.

Elba Serrano

Neuroscience, New Mexico State University

Elba Serrano’s biomedical research focuses on disorders of hearing and balance, sensory organ formation, and nanobiotechnology. Her research has been furthered by grants and awards from the National Institutes of Health, NASA, NSF, the Ford Foundation, and the Whitehall Foundation. Serrano is an advocate of interdisciplinary research and education and she collaborates with scientists and engineers at UCSD, the Center for Integrated Nanotechnologies (CINT), Harvard, MIT and NMSU. Serrano is the Principal Investigator and Program Director of NMSU’s NIH funded BP-ENDURE Building Research Achievement in Neuroscience (BRAiN) and Research Initiative for Scientific Enhancement (RISE) student training programs. She has offered courses, workshops, and lectures on science, ethics, and society for over 15 years. She is a current member of the Advisory Board to the NIH Director, the NIH NIDCD Council and the Health Sciences Advisory Council for the Hispanic Association of Colleges and Universities (HACU). Formerly, Serrano has served on the CINT User’s Advisory Board, the International Neuroethics SocietyProgram Committee, the Society for Neuroscience Professional Development Committee, and the Advisory Board for the Annual Biomedical Research Conference for Minority Students (ABRCMS).

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Biological explanations of criminal behavior

Shichun ling.

a Department of Criminology, University of Pennsylvania, Philadelphia, PA 19104, USA

Rebecca Umbach

b Department of Psychology, Columbia University, New York, NY, USA

c Behavioral Sciences Training in Drug Abuse Research, NYU Rory Meyers College of Nursing, New York, NY, USA

Adrian Raine

d Departments of Criminology, Psychiatry, and Psychology, University of Pennsylvania, Philadelphia, PA, USA

There is a growing literature on biological explanations of antisocial and criminal behavior. This paper provides a selective review of three specific biological factors – psychophysiology (with the focus on blunted heart rate and skin conductance), brain mechanisms (with a focus on structural and functional aberrations of the prefrontal cortex, amygdala, and striatum), and genetics (with an emphasis on gene-environment and gene-gene interactions). Overall, understanding the role of biology in antisocial and criminal behavior may help increase the explanatory power of current research and theories, as well as inform policy and treatment options.

A growing body of literature has indicated the importance of considering neurobiological factors in the etiology of antisocial and criminal behavior. Behaviors, including criminality, are the result of complex, reciprocally influential interactions between an individual’s biology, psychology, and the social environment ( Focquaert, 2018 ). As research progresses, the misconception that biology can predetermine criminality is being rectified. Elucidating the biological underpinnings of criminal behavior and broader, related outcomes such as antisocial behavior can provide insights into relevant etiological mechanisms. This selective review discusses three biological factors that have been examined in relation to antisocial and criminal behavior: psychophysiology, brain, and genetics.

Psychophysiology, or the levels of arousal within individuals, has become an important biological explanation for antisocial and criminal behavior. Two common psychophysiological measures are heart rate and skin conductance (i.e. sweat rate). Both capture autonomic nervous system functioning; skin conductance reflects sympathetic nervous system functioning while heart rate reflects both sympathetic and parasympathetic nervous system activity. Blunted autonomic functioning has been associated with increased antisocial behavior, including violence ( Baker et al., 2009 ; Choy, Farrington, & Raine, 2015 ; Gao, Raine, Venables, Dawson, & Mednick, 2010 ; Portnoy & Farrington, 2015 ). Longitudinal studies have found low resting heart rate in adolescence to be associated with increased risk for criminality in adulthood ( Latvala, Kuja-Halkola, Almqvist, Larsson, & Lichtenstein, 2015 ; Raine, Venables, & Williams, 1990 ). However, there is likely a positive feedback loop whereby blunted autonomic functioning may lead to increased antisocial/criminal behavior, which in turn may reinforce disrupted physiological activity. For example, males and females who exhibited high rates of proactive aggression (an instrumental, predatory form of aggression elicited to obtain a goal or reward) in early adolescence were found to have poorer skin conductance fear conditioning in late adolescence ( Gao, Tuvblad, Schell, Baker, & Raine, 2015 ; Vitiello & Stoff, 1997 ).

Theories have been proposed to explain how blunted autonomic functioning could increase antisociality. The fearlessness hypothesis suggests that antisocial individuals, due to their blunted autonomic functioning, are not deterred from criminal behavior because they do not experience appropriate physiological responses to risky or stressful situations nor potential aversive consequences ( Portnoy et al., 2014 ; Raine, 2002 ). Alternatively, the sensation-seeking hypothesis suggests that blunted psychophysiology is an uncomfortable state of being, and in order to achieve homeostasis, individuals engage in antisocial behavior to raise their arousal levels ( Portnoy et al., 2014 ; Raine, 2002 ).

Another mechanism that could connect disrupted autonomic functioning to antisocial behavior is the failure to cognitively associate physiology responses with emotional states. Appropriately linking autonomic conditions to emotional states is important in socialization processes such as fear conditioning, which is thought to contribute to the development of a conscience. The somatic marker hypothesis ( Bechara & Damasio, 2005 ) suggests that ‘somatic markers’ (e.g. sweaty palms) may reflect emotional states (e.g. anxiety) that can inform decision-making processes. Impairments in autonomic functioning could lead to risky or inappropriate behavior if individuals are unable to experience or label somatic changes and connect them to relevant emotional experiences. Indeed, psychopathic individuals exhibit somatic aphasia (i.e. the inaccurate identification and recognition of one’s bodily state; Gao, Raine, & Schug, 2012 ). Moreover, blunted autonomic functioning impairs emotional intelligence, subsequently increasing psychopathic traits ( Ling, Raine, Gao, & Schug, 2018a ). Impaired autonomic functioning and reduced emotional intelligence may impede the treatment of psychopathy ( Polaschek & Skeem, 2018 ) and disrupt development of moral emotions such as shame, guilt, and empathy ( Eisenberg, 2000 ). Such moral dysfunction, a strong characteristic of psychopaths, may contribute to their disproportionate impact on the criminal justice system ( Kiehl & Hoffman, 2011 ).

While there is evidence that antisocial/criminal individuals typically exhibit abnormal psychophysiological functioning, it is important to acknowledge that there are different antisocial/criminal subtypes, and they may not share the same deficits. Whereas individuals who are high on proactive aggression may be more likely to exhibit blunted autonomic functioning, individuals who are high on reactive aggression (an affective form of aggression that is elicited as a response to perceived provocation) may be more likely to exhibit hyperactive autonomic functioning ( Hubbard, McAuliffe, Morrow, & Romano, 2010 ; Vitiello & Stoff, 1997 ). This may have implications for different types of offenders, with elevated autonomic functioning presenting in reactively aggressive individuals who engage in impulsive crimes and blunted autonomic functioning presenting in proactively aggressive offenders engaging in more premediated crimes. Similarly, psychopaths who are ‘unsuccessful’ (i.e. convicted criminal psychopaths) exhibit reduced heart rate during stress while those who are ‘successful’ (i.e. non-convicted criminal psychopaths) exhibit autonomic functioning similar to non-psychopathic controls ( Ishikawa, Raine, Lencz, Bihrle, & LaCasse, 2001 ). Despite differences among subgroups, dysfunctional autonomic functioning generally remains a reasonably well-replicated and robust correlate of antisocial and criminal behavior.

There has been increasing interest in the role of the brain in antisocial/criminal behavior. In general, research suggests that antisocial/criminal individuals tend to exhibit reduced brain volumes as well as impaired functioning and connectivity in key areas related to executive functions ( Alvarez & Emory, 2006 ; Meijers, Harte, Meynen, & Cuijpers, 2017 ; Morgan & Lilienfeld, 2000 ), emotion regulation ( Banks, Eddy, Angstadt, Nathan, & Phan, 2007 ; Eisenberg, 2000 ), decision-making ( Coutlee & Huettel, 2012 ; Yechiam et al., 2008 ), and morality ( Raine & Yang, 2006 ) while also exhibiting increased volumes and functional abnormalities in reward regions of the brain ( Glenn & Yang, 2012 ; Korponay et al., 2017 ). These prefrontal and subcortical regions that have been implicated in antisocial/criminal behavior are the selective focus of this review.

Conventional criminal behavior has typically been associated with prefrontal cortex (PFC) structural aberrations and functional impairments ( Brower & Price, 2001 ; Yang & Raine, 2009 ). The PFC is considered the seat of higher-level cognitive processes such as decision-making, attention, emotion regulation, impulse control, and moral reasoning ( Sapolsky, 2004 ). In healthy adults, larger prefrontal structures have been associated with better executive functioning ( Yuan & Raz, 2014 ). However, structural deficits and functional impairments of the PFC have been observed in antisocial and criminal individuals, suggesting that PFC aberrations may underlie some of the observed behaviors.

While many studies on brain differences related to criminal behavior have consisted of correlational analyses, lesion studies have provided some insight into causal neural mechanisms of antisocial/criminal behavior. The most well-known example of the effects of prefrontal lobe lesions is the case of Phineas Gage, who was reported to have a dramatic personality change after an iron rod was shot through his skull and damaged his left and right prefrontal cortices ( Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994 ; Harlow, 1848 , 1868 ). Empirical studies suggest that prefrontal lesions acquired earlier in life disrupt moral and social development ( Anderson, Bechara, Damasio, Tranel, & Damasio, 1999 ; Taber-Thomas et al., 2014 ). A study of 17 patients who developed criminal behavior following a brain lesion documented that while these lesions were in different locations, they were all connected functionally to regions activated by moral decisionmaking ( Darby, Horn, Cushman, & Fox, 2018 ), suggesting that disruption of a neuromoral network is associated with criminality. Nevertheless, while lesion studies have implicated specific brain regions in various psychological processes such as moral development, generalizability is limited because of the heterogeneity of lesion characteristics, as well as subjects’ characteristics that may moderate the behavioral effects of the lesion.

In recent years, non-invasive neural interventions such as transcranial magnetic stimulation and transcranial electric stimulation have been used to manipulate activity within the brain to provide more direct causal evidence of the functions of specific brain regions with regard to behavior. These techniques involve subthreshold modulation of neuronal resting membrane potential ( Nitsche & Paulus, 2000 ; Woods et al., 2016 ). Using transcranial electric stimulation, upregulation of the PFC has been found to decrease criminal intentions and increase perceptions of moral wrongfulness of aggressive acts ( Choy, Raine, & Hamilton, 2018 ), providing support for the causal influence of the PFC on criminal behavior.

Importantly, there is evidence of heterogeneity within criminal subgroups. Successful psychopaths and white-collar offenders do not seem to display these prefrontal deficits ( Raine et al., 2012 ; Yang et al., 2005 ). While unsuccessful psychopaths exhibit reduced PFC gray matter volume compared to successful psychopaths and non-offender controls, there are no prefrontal gray matter volume differences between successful psychopaths and non-offender controls ( Yang et al., 2005 ). Similarly, while prefrontal volume deficits have been found in conventional criminals (i.e. blue-collar offenders), white-collar offenders do not exhibit frontal lobe reductions ( Brower & Price, 2001 ; Ling et al., 2018b ; Raine et al., 2012 ) and in fact may exhibit increased executive functioning compared to blue-collar controls ( Raine et al., 2012 ). Lastly, antisocial offenders with psychopathy exhibited reduced gray matter volumes in the prefrontal and temporal poles compared to antisocial offenders without psychopathy and non-offenders ( Gregory et al., 2012 ). It is therefore important to acknowledge that there are various types of antisocial and criminal behavior that may have different neurobiological etiologies.

The amygdala is an important brain region that has been implicated in emotional processes such as recognition of facial and auditory expressions of emotion, especially for negative emotions such as fear ( Fine & Blair, 2000 ; Murphy, Nimmo-Smith, & Lawrence, 2003 ; Sergerie, Chochol, & Armony, 2008 ). Normative amygdala functioning has been thought to be key in the development of fear conditioning ( Knight, Smith, Cheng, Stein, & Helmstetter, 2004 ; LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998 ; Maren, 2001 ), and appropriate integration of the amygdala and PFC has been argued to underlie the development of morality ( Blair, 2007 ). The amygdala is thought to be involved in stimulus-reinforcement learning that associates actions that harm others with the aversive reinforcement of the victims’ distress and in recognizing threat cues that typically deter individuals from risky behavior. However, amygdala maldevelopment can lead to a diminished ability to recognize distress or threat cues; disrupting the stimulus-reinforcement learning that discourages antisocial/criminal behavior ( Blair, 2007 ; Sterzer, 2010 ). Indeed, while reduced amygdala volume in adulthood has been associated with increased aggressive and psychopathic characteristics from childhood to early adulthood, it is also associated with increased risk for future antisocial and psychopathic behavior ( Pardini, Raine, Erickson, & Loeber, 2014 ).

Although the amygdala has been implicated in criminal behavior, there may be important differences between subtypes of offenders. Whereas psychopathic antisocial individuals may be more likely to exhibit cold, calculating forms of aggression, non-psychopathic antisocial individuals may be more likely to engage in impulsive, emotionally-reactive aggression ( Glenn & Raine, 2014 ). Research suggests the former may exhibit amygdala hypoactivity and the latter, amygdala hyperactivity ( Raine, 2018a ). Indeed, violent offenders have been found to exhibit increased amygdala reactivity in response to provocations ( da Cunha-Bang et al., 2017 ). Spousal abusers have also been found to exhibit increased amygdala activation when responding to aggressive words compared to nonabusers ( Lee, Chan, & Raine, 2008 ). In a community sample of healthy adults, psychopathy scores were negatively related to amygdala reactivity while antisocial personality disorder scores were positively associated with amygdala reactivity after adjusting for overlapping variance between psychopathy and antisocial personality disorder ( Hyde, Byrd, Votruba-Brzal, Hariri, & Manuck, 2014 ). Nevertheless, more research is needed to determine whether the presence of callous-unemotional traits (e.g. lack of guilt; Lozier, Cardinale, VanMeter, & Marsh, 2014 ; Viding et al., 2012 ) or severity of antisocial behavioral traits ( Dotterer, Hyde, Swartz, Hariri, & Williamson, 2017 ; Hyde et al., 2016 ) are most relevant to the observed amygdala hypo-reactivity.

The striatum has recently garnered more attention as a region that could be implicated in the etiology of criminal behavior because of its involvement in reward and emotional processing ( Davidson & Irwin, 1999 ; Glenn & Yang, 2012 ). Dysfunction in the striatum has been hypothesized to be a neural mechanism that underlies the impulsive/antisocial behavior of criminals. Indeed, individuals with higher impulsive/antisocial personality traits have been found to exhibit increased activity in the striatum ( Bjork, Chen, & Hommer, 2012 ; Buckholtz et al., 2010 ; Geurts et al., 2016 ). Psychopathic individuals, compared to non-psychopathic individuals, demonstrate a 9.6% increase in striatal volumes ( Glenn, Raine, Yaralian, & Yang, 2010 ). Moreover, striatal enlargement and abnormal functional connectivity of the striatum has specifically been associated with the impulsive/antisocial dimension of psychopathy ( Korponay et al., 2017 ), suggesting this dimension of psychopathy is related to reward processes ( Hare, 2017 ).

While much of the literature on striatal abnormalities in antisocial individuals has focused on psychopathic individuals, there is some evidence that offenders in general exhibit striatal abnormalities. Increased volume ( Schiffer et al., 2011 ) and increased reactivity to provocations ( da Cunha-Bang et al., 2017 ) have both been found in violent offenders as compared to non-offendersMoreover, weak cortico-striatal connectivity has been associated with increased frequency of criminal convictions ( Hosking et al., 2017 ). In contrast, one study found reduced striatal activity to be associated with antisocial behavior ( Murray, Shaw, Forbes, & Hyde, 2017 ). While more research is needed, current literature suggests that striatal deviations are linked to criminal behavior. One important consideration for future studies is to determine a consistent operationalization for the striatum, as some studies examine the dorsal striatum (i.e. putamen and caudate; Yang et al., 2015 ), others assess the corpus striatum (i.e. putamen, caudate, and globus pallidus; Glenn et al., 2010 ), and still others analyze the role of the ventral striatum (i.e. nucleus accumbens and olfactory tubercle; Glenn & Yang, 2012 ) in relation to antisocial/criminal behavior.

Abnormalities in brain regions other than the PFC, amygdala, and striatum are also associated with antisocial behavior. The neuromoral theory of antisocial behavior, first proposed by Raine and Yang (2006) , argued that the diverse brain regions impaired in offenders overlap significantly with brain regions involved in moral decision-making. A recent update of this theory ( Raine, 2018b ) argues that key areas implicated in both moral decision-making and the spectrum of antisocial behaviors include frontopolar, medial, and ventral PFC regions, and the anterior cingulate, amygdala, insula, superior temporal gyrus, and angular gyrus/temporoparietal junction. It was further hypothesized that different manifestations of antisocial behavior exist on a spectrum of neuromoral dysfunction, with primary psychopathy, proactive aggression, and life-course persistent offending being more affected, and secondary psychopathy, reactive aggression, and crimes involving drugs relatively less affected. Whether the striatum is part of the neural circuit involved in moral decision-making is currently unclear, making its inclusion in the neuromoral model debatable. Despite limitations, the neuromoral model provides a way of understanding how impairments to different brain regions can converge on one concept – impaired morality – that is a common core to many different forms of antisocial behaviors.

One implication of the model is that significant impairment to the neuromoral circuit could constitute diminished criminal responsibility. Given the importance of a fully developed emotional moral capacity for lawful behavior, moral responsibility would appear to require intactness of neuromoral circuity. To argue that the brain basis to moral thinking and feeling are compromised in an offender comes dangerously close to challenging moral responsibility, a concept which in itself may be just a short step removed from criminal responsibility.

There is increasing evidence fora genetic basis of antisocial/criminal behavior. Behavioral genetic studies of twins and adoptees have been advantageous because such designs can differentiate the effects of genetics and environment within the context of explaining variance within a population ( Glenn & Raine, 2014 ). Additionally, a variety of psychological and psychiatric constructs associated with antisociality/criminality, such as intelligence, personality, and mental health disorders, have been found to be heritable ( Baker, Bezdjian, & Raine, 2006 ). While individual study estimates vary, meta-analyses have suggested the level of heritability of antisocial behavior is approximately 40–60% ( Raine, 2013 ). Shared environmental factors have been estimated to explain approximately 11–14% of the variance in antisocial/criminal behavior and non-shared environmental influences approximately 31–37% ( Ferguson, 2010 ; Gard, Dotterer, & Hyde, 2019 ). However, the heritability of antisocial/criminal behaviors vary in part based upon the specific behaviors examined ( Burt, 2009 ; Gard et al., 2019 ).

Inspired by prominent theories of the neurobiology of aggression, there have been several candidate genes implicated in the serotonergic and catecholaminergic neurobiological systems that have been examined in relation to antisocial/criminal behavior ( Tiihonen et al., 2015 ). However, a meta-analysis of genetic variants related to antisocial/criminal behavior yielded null results at the 5% significance level ( Vassos, Collier, & Fazel, 2014 ). Nevertheless, genes do not operate in isolation, thus it is important to consider the context in which genes are activated.

Gene-environment (G x E) interactions have garnered increasing attention over the years, as these can increase risk for antisocial behavior and/or produce epigenetic changes within individuals. Longitudinal studies and meta-analyses have documented the moderating effect of the monoamine oxidase A (MAOA) gene on the relationship between maltreatment and antisocial behaviors, with the maltreatment-antisocial behavior relationship being stronger for individuals with low MAOA than high MAOA ( Byrd & Manuck, 2014 ; Caspi et al., 2002 ; Fergusson, Boden, & Horwood, 2011 ; Kim-Cohen et al.,2006 ). Similarly, in a large study of African-American females, having the A1 allele of the DRD2 gene or a criminal father did not individually predict antisocial outcomes, but having both factors increased risk for serious delinquency, violent delinquency, and police contacts ( Delisi, Beaver, Vaughn, & Wright, 2009 ). This type of G x E interaction reflects how genotypes can influence individuals’ sensitivity to environmental stressors. However, there may be important subgroup differences to consider when examining genetic risk for criminal behavior. For example, low-MAOA has been associated with higher risk for violent crime in incarcerated Caucasian offenders but not incarcerated non-Caucasian offenders ( Stetler et al., 2014 ). Additionally, high-MAOA may protect abused and neglected Caucasians from increased risk of becoming violent or antisocial, but this buffering effect was not found for abused and neglected non-Caucasians ( Widom & Brzustowicz, 2006 ). Thus, while the MAOA gene has been associated with antisocial/criminal behavior, there are still nuances of this relationship that should be considered ( Goldman & Rosser, 2014 ).

Another way in which G x E interactions manifest themselves is when environmental stressors result in epigenetic changes, thus becoming embedded in biology that result in long-term symptomatic consequences. For example, females exposed to childhood sex abuse have exhibited alterations in the methylation of the 5HTT promoter region, which in turn has been linked to subsequent antisocial personality disorder symptoms ( Beach, Brody, Todorov, Gunter, & Philibert, 2011 ). There has been a growing body of work on such epigenetic mechanisms involved in the biological embedding of early life stressors and transgenerational trauma ( Kellermann, 2013 ; Provencal & Binder, 2015 ). Thus, just as biological mechanisms can influence environmental responses, environmental stressors can affect biological expressions.

While genes may interact with the environment to produce antisocial/criminal outcomes, they can also interact with other genes. There is evidence that dopamine genes DRD2 and DRD4 may interact to increase criminogenic risk ( Beaver et al., 2007 ; Boutwell et al., 2014 ). The effect of the 7-repeat allele DRD4 is strengthened in the presence of the A1 allele of DRD2, and has been associated with increased odds of committing major theft, burglary, gang fighting, and conduct disorder ( Beaver et al., 2007 ; Boutwell et al., 2014 ). However, there is some evidence that DRD2 and DRD4 do not significantly affect delinquency abstention for females ( Boutwell & Beaver, 2008 ). Thus there may be demographic differences that moderate the effect of genetic interactions on various antisocial outcomes ( Dick, Adkins, & Kuo, 2016 ; Ficks & Waldman, 2014 ; Rhee & Waldman, 2002 ; Salvatore & Dick, 2018 ), and such differences warrant further research.

Importantly, biological correlates of antisocial and criminal behavior are inextricably linked in dynamical systems, in which certain processes influence others through feedback loops. While a detailed summary is beyond the scope of this review, some interactions between biological mechanisms are briefly illustrated here. Within the brain, the PFC and amygdala have reciprocal connections, with the PFC often conceptualized as monitoring and regulating amygdala activity ( Gillespie, Brzozowski, & Mitchell, 2018 ). Disruption of PFC-amygdala connectivity has been linked to increased antisocial/criminal behavior, typically thought to be due to the impaired top-down regulation of amygdala functioning by the PFC. Similarly, the brain and autonomic functioning are linked ( Critchley, 2005 ; Wager et al., 2009 ); output from the brain can generate changes in autonomic functioning by affecting the hypothalamic-pituitary-adrenal axis, but autonomic functions also provide input to the brain that is essential for influencing behavioral judgments and maintaining coordinated regulation of bodily functions ( Critchley, 2005 ). While not comprehensive, these examples illustrate that biological systems work together to produce behavior.

While biological processes can contribute to antisocial/criminal behavior, these do not guarantee negative outcomes. Considering that many of the aforementioned biological risk factors are significantly influenced by social environment, interventions in multiple spheres may help mitigate biological risks for antisocial behavior.

With regard to psychophysiological correlates of antisocial behavior, research suggests differential profiles of arousal impairment depending on the type of antisocial behavior ( Hubbard et al., 2010 ; Vitiello & Stoff, 1997 ). Treatments designed to address the issues associated with psychophysiological differences are typically behavioral in nature, targeted at associated symptoms. Studies of mindfulness have suggested its utility in improving autonomic functioning ( Delgado-Pastor, Perakakis, Subramanya, Telles, & Vila, 2013 ) and emotion regulation ( Umbach, Raine, & Leonard, 2018 ), which may better help individuals with reactive aggression and hyperarousal. Hypo-arousal has been associated with impaired emotional intelligence ( Ling et al., 2018a ), but emotional intelligence training programs have shown some promise in reducing aggression and increasing empathy among adolescents and increasing emotional intelligence among adults ( Castillo, Salguero, Fernandez-Berrocal, & Balluerka, 2013 ; Hodzic, Scharfen, Ropoll, Holling, & Zenasni, 2018 ), and in reducing recidivism ( Megreya, 2015 ; Sharma, Prakash, Sengar, Chaudhury, & Singh, 2015 ).

Regarding healthy neurodevelopment, research has supported a number of areas to target. Poor nutrition, both in utero and in early childhood, have been associated with negative and criminal outcomes ( Neugebauer, Hoek, & Susser, 1999 ). Deficits of omega-3 fatty acids have been linked with impaired neurocognition and externalizing behavior ( Liu & Raine, 2006 ; McNamara & Carlson, 2006 ). The opposite relationship is also supported; increased intake of omega-3 fatty acids has been associated with a variety of positive physical and mental health outcomes ( Ruxton, Reed, Simpson, & Millington, 2004 ), increased brain volume in regions related to memory and emotion regulation ( Conklin et al.,2007 ), and reduction in behavioral problems in children ( Raine, Portnoy, Liu, Mahoomed, & Hibbeln, 2015 ). Studies examining the effect of nutritional supplements have suggested that reducing the amount of sugar consumed by offenders can significantly reduce offending during incarceration ( Gesch, Hammond, Hampson, Eves, & Crowder, 2002 ; Schoenthaler, 1983 ). Thus, nutritional programs show some promise in reducing antisocial and criminal behavior.

A healthy social environment is also crucial for normative brain development and function. Early adversity and childhood maltreatment have been identified as significant risk factors for both neurobiological and behavioral problems ( Mehta et al., 2009 ; Teicher et al., 2003 ; Tottenham et al., 2011 ). A review of maltreatment prevention programs supports the efficacy of nurse-family partnerships and programs that integrate early preschool with parent resources in reducing childhood maltreatment ( Reynolds, Mathieson, & Topitzes, 2009 ). Promoting healthy brain development in utero and in crucial neurodevelopmental periods is likely to reduce externalizing behaviors, as well as other psychopathology.

Knowing that the social context could help to buffer biological risks is promising because it suggests that changing an individual’s environment could mitigate biological criminogenic risk. Rather than providing a reductionist and deterministic perspective of the etiology of criminal behavior, incorporating biological factors in explanations of antisocial/criminal behaviors can highlight the plasticity of the human genome ( Walsh & Yun, 2014 ). They can also provide a more holistic understanding of the etiologies of such behavior. For example, sex differences in heart rate have been found to partially explain the gender gap in crime ( Choy, Raine, Venables, & Farrington, 2017 ). Social interventions that aim to provide an enriched environment can be beneficial for all, but may be particularly important for individuals at higher biological risk for antisocial behavior. While biological explanations of antisocial and criminal behavior are growing, they are best thought of as complementary to current research and theories, and a potential new avenue to target with treatment options.

Disclosure statement

No potential conflict of interest was reported by the authors.

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• Published: 15 March 2024

Metagenomic insights into the wastewater resistome before and after purification at large‑scale wastewater treatment plants in the Moscow city

• Shahjahon Begmatov 1 ,
• Alexey V. Beletsky 1 ,
• Alexander G. Dorofeev 2 ,
• Nikolai V. Pimenov 2 ,
• Andrey V. Mardanov 1 &
• Nikolai V. Ravin 1  

Scientific Reports volume  14 , Article number:  6349 ( 2024 ) Cite this article

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• Antimicrobials
• Microbial communities
• Industrial microbiology

Wastewater treatment plants (WWTPs) are considered to be hotspots for the spread of antibiotic resistance genes (ARGs). We performed a metagenomic analysis of the raw wastewater, activated sludge and treated wastewater from two large WWTPs responsible for the treatment of urban wastewater in Moscow, Russia. In untreated wastewater, several hundred ARGs that could confer resistance to most commonly used classes of antibiotics were found. WWTPs employed a nitrification/denitrification or an anaerobic/anoxic/oxic process and enabled efficient removal of organic matter, nitrogen and phosphorus, as well as fecal microbiota. The resistome constituted about 0.05% of the whole metagenome, and after water treatment its share decreased by 3–4 times. The resistomes were dominated by ARGs encoding resistance to beta-lactams, macrolides, aminoglycosides, tetracyclines, quaternary ammonium compounds, and sulfonamides. ARGs for macrolides and tetracyclines were removed more efficiently than beta-lactamases, especially ampC , the most abundant ARG in the treated effluent. The removal efficiency of particular ARGs was impacted by the treatment technology. Metagenome-assembled genomes of multidrug-resistant strains were assembled both for the influent and the treated effluent. Ccomparison of resistomes from WWTPs in Moscow and around the world suggested that the abundance and content of ARGs depend on social, economic, medical, and environmental factors.

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Introduction.

The spread of antimicrobial resistance (AMR) in the environmental microbiome has become one of the frequently noted problems in recent years, along with global climate change, food security and other technological challenges. Numerous studies show that from year to year, in addition to increasing the cost of hospitalization and treatment of patients infected with multidrug-resistant bacteria, the number of deaths of such patients is growing 1 , 2 . Understanding the mechanisms underlying the emergence, selection and dissemination of AMR, and antibiotic resistance genes (ARGs), is required for the development of sustainable strategies to control and minimize this threat. The dissemination of antibiotic resistant bacteria (ARB) and ARGs occurs differently and this process is more active in urban territories rather than in rural ones. The rate of spread of ARGs and ARB in urban areas is obviously determined by the high population density and, as a rule, wastewater which flows from these areas contains both ARG and ARB. Most antibiotics used in medicine, agriculture and the food industry, as well as resistant bacteria, end up in wastewater. Wastewater treatment plants (WWTPs) therefore could provide a comprehensive overview of ARG abundance, diversity and genomic backgrounds in particular region 3 . Moreover, wastewater and WWTPs are places where ARGs and ARB are particularly abundant and are often considered “hotspots” for the formation of strains with multiple resistance and one of the main sources of the spread of AMR in the environment 4 .

Despite numerous studies on the role of WWTPs in resistome diversity and dissemination, each new study is, in terms of time and geography, unique, as many urban areas and countries have not yet been studied. In addition, some studies are dedicated to explore only one component of the wastewater treatment system, such as wastewater, activated sludge or treated effluent, and there is a lack of research that would give a comprehensive view of the diversity and change in the composition of the resistome at different stages of water cleaning, from wastewater to treated effluent, released into the environment.

Usually, wastewater treatment in large facilities takes place in three stages. The first stage includes physical methods of water cleaning, the second stage is microbiological treatment in bioreactors with activated sludge (AS), and the third stage is the final treatment of water and its disinfection. At the second stage, than could be performed using several technologies, microorganisms of AS are used to remove organic matter, ammonium, and (in more complex processes) phosphorus 5 . At this stage, the removal of microorganisms present in the wastewater, including ARB, occurs due to their adsorption on AS particles, which are removed along with excess AS. The efficiency of this process differs for various bacteria and depends on the purification technology used. Therefore, purification technologies directly affect the removal of particular ARGs and ARB, however, this issue was poorly studied 6 .

ARGs representing all known resistance mechanisms have been found in WWTP environments 7 . ARGs for beta-lactams, macrolides, quinolones, tetracyclines, sulfonamides, trimethoprim, and multidrug efflux pump genes have been found in the incoming wastewater, AS, and treated effluent in various countries 7 , 8 . Recently, Munk and coauthors (2022) using metagenomics methods characterized resistomes of 757 urban wastewater samples from 243 cities in 101 countries covering 7 major geographical regions. They reported regional patterns in wastewater resistomes that differed between subsets corresponding to drug classes and were partly driven by taxonomic variation 3 . Although this study did not analyzes the composition of the wastewater resistome after treatment, there are numerous evidences that the prevalence of ARB and ARG in rivers may increase downstream from the point of discharge of treated wastewater into them 9 , 10 . In a study of WWTPs in Germany, 123 types of clinically significant antibiotic resistance genes were found in treated wastewater discharged into water bodies 11 . An analysis of the presence of 30 ARGs at different stages of wastewater treatment at WWTPs in Northern China showed that the content of most ARGs in the treated effluent was lower compared to the influent entering the treatment, although an increase in the abundance of some ARGs and their release into the environment was also observed 12 . A metagenomic analysis of WWTP in Hong Kong revealed seasonal changes in the content of several types of ARG and its decrease in the treated effluent 13 , 14 . Most ARGs were reduced by more than 98% in the treated effluent compared to the wastewater entering the treatment 14 . Some other studies have also reported a decrease in ARGs after wastewater treatment 15 , 16 , 17 . However, in other studies, no changes in the ARG content or even an increase were observed 17 , 18 , 19 . Although there are numerous studies of resistomes in WWTP-related environments the distribution of samples was geographically biased and covered mostly North America, Western Europe, Eastern Asia (mostly China), Australasia, and few places in South America/Caribbean and Sub-Saharan Africa 3 .

In order to expand the geographical coverage and our knowledge about global resistome abundance and diversity, we analyzed resistomes of wastewater before and after treatment at large-scale WWTPs in the city of Moscow (Russia). Although Moscow WWTPs are among the largest in the world and may play an important role in the spread of antibiotic resistance, the resistomes of municipal wastewaters in Moscow have not previously been studied by modern molecular genetic methods. Previously we performed 16S rRNA gene profiling of AS microbial communities at large-scale WWTPs responsible for the treatment of municipal wastewater ion Moscow 5 . Comparison of microbial communities of AS samples from WWTPs in Moscow and worldwide revealed that Moscow samples clustered together indicating the importance of influent characteristics, related to local social and environmental factors, for wastewater microbiomes 5 . For example, due to the relatively low cost of water for household consumption, wastewater in Moscow has a relatively low content of organic matter. Apparently the presence of ARB and ARGs in communal wastewater depends on the frequency of antibiotic use and the range of drugs used. These factors differ in different countries and cities. Therefore, the characterization of the resistome and the role of Moscow WWTPs in the dispersion of ARGs is an important goal. Of particular interest is also the assessment of the impact of wastewater treatment technology on the composition of the resistome and the degree of ARG removal.

Here we present the first metagenomic overview of the composition of resistome of influent wastewater, AS and treated effluent released into the environment at two Moscow WWTPs employing different treatment technologies.

Characteristics of WWTPs and water chemistry

The Lyuberetskiy WWTP complex of JSC “Mosvodokanal” carry out the treatment of wastewater in the city of Moscow with a capacity of about 2 million m 3 per day. This complex consists of several wastewater treatment units (hereafter referred to as WWTPs). They purify the same inflow wastewater but otherwise are independent installations between which there is no transfer and mixing of AS. Two WWTPs implementing different technologies for wastewater treatment were chosen as the objects of study. The first one (LOS) is operated using anaerobic/anoxic/oxic process, also known as the University of Cape Town (UCT) technology. There the sludge mixture first enters the anaerobic zone, where phosphate-accumulating microorganisms (PAO) consume easily degradable organics, then to the anoxic zone, where denitrification and accumulation of phosphates by denitrifying PAO occur, and finally to the aerobic zone, where organic matter and ammonium are oxidized while PAO accumulate large quantities of polyphosphate. The second WWTP (NLOS2) uses a simpler nitrification–denitrification technology (N-DN). In the aerobic zones organics and ammonium are oxidized, while in the anoxic zone nitrate is reduced to gaseous nitrogen. This treatment technology removes organic matter and nitrogen, but was not specially aimed to remove phosphates. The production capacity of LOS is approximately 2 times more than that of NLOS2; there are no other important differences between these WWTPs besides treatment technology.

Sampling and chemical analysis

Wastewater and AS samples were collected in September 2022 and kindly provided by “Mosvodokanal” JSC. The temperature of water samples was about 24 °C. Samples of AS from bioreactors of two WWTPs were taken in 50 ml Falcon tubes (BD Biosciences). Wastewater samples (influent and effluents from two WWTPs) were taken in 5 L plastic bottles. The cells were collected by centrifugation at 3000 g for 20 min at 4 °C.

Wastewater quality values, namely, biochemical oxygen demand (five days incubation) (BOD 5 ), chemical oxygen demand (COD), total suspended solids (TSS), sludge volume index (SVI), ammonium nitrogen (N-NH 4 ), nitrate nitrogen (N-NO 3 ), nitrite nitrogen (N-NO 2 ) and phosphorus (P-PO 4 ) in the influent and effluents of two WWTPs were measured by the specialized laboratory “MSULab” according to the Federal inspection of environmental management’s protocols for chemical analyses of water.

DNA isolation, 16S rRNA gene sequencing and analysis

Total genomic DNA was isolated using a Power Soil DNA isolation kit (Qiagen, Germany). DNA for each sample was isolated in four parallel replicates, which were then pooled. PCR amplification of 16S rRNA gene fragments comprising the V3–V4 variable regions was performed using the universal primers 341F (5′-CCTAYG GGDBGCWSCAG) and 806R (5′-GGA CTA CNVGGG THTCTAAT) 20 . The obtained PCR fragments were bar-coded and sequenced on Illumina MiSeq (2 × 300 nt reads). Pairwise overlapping reads were merged using FLASH v.1.2.11 21 . All sequences were clustered into operational taxonomic units (OTUs) at 97% identity using the USEARCH v.11 program 22 . Low quality reads were removed prior to clustering, chimeric sequences and singletons were removed during clustering by the USEARCH algorithms. To calculate OTU abundances, all reads obtained for a given sample were mapped to OTU sequences at a 97% global identity threshold by USEARCH. The taxonomic assignment of OTUs was performed by searching against the SILVA v.138 rRNA sequence database using the VSEARCH v. 2.14.1 algorithm 23 .

The diversity indices at a 97% OTU cut-off level were calculated using USEARCH v.11 22 . To avoid sequencing depth bias, the numbers of reads for each sample were randomly sub-sampled to the size of the smallest set.

Sequencing of metagenomic DNA, contigs assembly and binning of MAGs

Metagenomic DNA was sequenced using the Illumina HiSeq2500 platform according to the manufacturer’s instructions (Illumina Inc., San Diego, CA, USA). The sequencing of a paired-end (2 × 150 bp) NEBNext Ultra II DNA Library prep kit (NEB) generated from 145 to 257 million read pairs per sample. Adapter removal and trimming of low-quality sequences (Q < 30) were performed using Cutadapt v.3.4 24 and Sickle v.1.33 ( https://github.com/najoshi/sickle ), respectively. The resulting Illumina reads were de novo assembled into contigs using SPAdes v.3.15.4 in metagenomic mode 25 .

The obtained contigs were binned into metagenome-assembled genomes (MAGs) using 3 different programs: MetaBAT v.2.2.15 26 , MaxBin v.2.2.7 27 and CONCOCT v.1.1.0 28 . The results of the three binning programs were merged into an optimized set of MAGs using DAS Tool v.1.1.4 29 . The completeness of the MAGs and their possible contamination (redundancy) were estimated using CheckM v.1.1.3 30 with lineage-specific marker genes. The assembled MAGs were taxonomically classified using the Genome Taxonomy Database Toolkit (GTDB-Tk) v.2.0.0 31 and Genome Taxonomy database (GTDB) 32 .

ARG identification

Open reading frames (ORFs) were predicted in assembled contigs using Prodigal v.2.6.3 33 . ARGs were predicted using the NCBI AMRFinderPlus v.3.11.4 ( https://github.com/ncbi/amr/wiki ) command line tool and its associated database 34 . The predicted protein sequences of all ORFs were analyzed in this tool with parameter “-p”.

Efficiency of wastewater treatment

Two wastewater treatment technologies were used in the investigated WWTPs,—nitrification/denitrification at NLOS2 and more advanced anaerobic/anoxic/oxic UCT process at LOS. LOS removed more than 99.5% of organic matter (according to the BOD5 data) and more than 99.9% of ammonium while the performance of NLOS2 was poorer (Table 1 ). Particularly noticeable differences were observed in nitrate and nitrite concentrations in the effluents suggesting the lower efficiency of denitrification in the NLOS2. Interestingly, although the NLOS2 unit was not designed to remove phosphorus, the concentration of phosphates in the treated effluent at this WWTP is only slightly higher than at LOS. The treated influent at LOS contains fewer solids consistently with lower SVI. Overall, the technology used at LOS plant is more efficient.

Microbiomes of the influent wastewater, activated sludge and treated effluent

The 16S rRNA gene profiling of microbial communities revealed 1013 species-level OTUs (97% identity) in the influent and 1.2–1.7 times more OTUs in the AS and treated effluent samples (Supplemental Table S1 ). The Shannon diversity indices correlated with the number of detected OTUs and increased in the series “influent” – “activated sludge” – “effluent” at each WWTP (Supplemental Table S2 ).

Analysis of the microbiome of wastewater supplied for biological treatment showed that that the most numerous phyla in the microbial community were Firmicutes (28.4% of all 16S rRNA gene sequences), Campylobacterota (28.0%), Proteobacteria (20.9%), and Bacteroidota (10.5%) (Fig.  1 ). These were mainly representatives of the fecal microbiota, which are often found in wastewater. The phylum Firmicutes was dominated by Streptococcaceae (9.7%, mostly S treptococcus sp.), Lachnospiraceae (5.9%), Ruminococcaceae (3.0%), Carnobacteriaceae (1.7%), Peptostreptococcaceae (1.6%) and Veillonellaceae (1.4%). Most of Campylobacterota belonged to the family Arcobacteraceae (26.8%) of the genera Arcobacter (19.9%), Pseudarcobacter (2.5%) and uncultured lineage (4.3%), as well as by sulfur-oxidizing Sulfurospirillum (1.0%). Among the Proteobacteria the most abundant genera were Acinetobacter (7.8%) , Aeromonas (1.8%) and Pseudomonas (1.1%). Most of the identified Bacteroidota were typical fecal contaminants such as members of the genera Bacteroides (2.6%), Macellibacteroides (1.5%), Prevotella (1.4%), and Cloacibacterium (1.2%).

Microbial community composition in the influent, AS and treated effluent samples according to 16S rRNA gene profiling. The composition is displayed at the phylum level. INFL, influent wastewater; AS-LOS, AS at LOS plant; CW-LOS, treated effluent at LOS plant; AS-NLOS2, AS at NLOS2 plant; CW-NLOS2, treated effluent at NLOS2 plant.

Activated sludge of WWTP bioreactors is a complex microbial community consisting of physiologically and phylogenetically heterogeneous groups of microorganisms involved in the removal of major contaminants from wastewater. The composition of AS microbiomes was very different from the microbiome of incoming wastewater (Fig.  1 ). The phyla Campylobacterota (less than 0.5%) and Firmicutes (2–4%) were much less abundant in AS microbiomes. Proteobacteria was the dominant group in the microbiomes of AS (23–40%), but its composition differed from the microbiome of influent wastewater: instead of the fecal microflora (Enterobacterales and others) the AS community harbored lineages involved in the purification processes ( Competibacteraceae , Rhodocyclaceae , Nitrosomonadaceae , etc.). Likewise, Bacteroidota were among the most numerous phyla in AS microbiomes at both LOS (6.5%) and NLOS2 (14.1%), but instead of Bacteroidales mostly comprised Chitinophagales and Sphingobacteriales typical for AS communities. The numerous groups of AS community also included Chloroflexi (22% and 10% in LOS and NLOS2, respectively), Patescibacteria (1.8% and 9.9%), Nanoarchaeota (4.3% and 9.1%), Nitrospirota (3.9% and 7.3%), Verrucomicrobiota and Myxococcota (about 4% in both WWTPs). Bacteria that play an important role in the removal of nitrogen ( Nitrospira and Nitrosomonas ) and phosphorus ( Dechloromonas ), as well as glycogen-accumulating Ca . Competibacter, have been found in large numbers. The abundance of these functional groups is consistent with the high efficiency of nitrogen and phosphorus removal.

The main source of microorganisms in treated effluent is the AS, from which they are washed out; bacteria from the influent water may also be present in minor amounts. Therefore, as expected, the microbiome composition of treated wastewater was similar to that of activated sludge. Consistently, compositions of microbiomes of treated effluent were similar to that of AS samples. However, some differences were observed, in particular, the microbiomes of the treated effluent contained many Cyanobacteria (7.74% and 3.49% for LOS and NLOS2, respectively) which were found in minor amounts both in the influent water and in the ASs (< 0.5%). Probably, these light-dependent bacteria proliferate in the final clarifier and then can be easily washed out with the effluent.

Diversity of resistomes

The results of metagenomic analysis of incoming wastewater revealed 544 ARGs in the assembled contigs, classified into 33 AMR gene families (Table 2 and Supplemental Table S3 ). Among the most numerous were classes A, C, D and metallo- beta-lactamases, rifampin ADP-ribosyltransferase, Erm 23S ribosomal RNA methyltransferase, aminoglycoside nucleotidyl-, acetyl- and phospho-transferases, the ABC-F type ribosomal protection proteins, chloramphenicol acetyltransferase, trimethoprim-resistant dihydrofolate reductase, quaternary ammonium compound efflux SMR transporters, lincosamide nucleotidyltransferases, tetracycline efflux MFS transporters and tetracycline resistance ribosomal protection proteins (Table 2 ). These genes may enable antibiotic inactivation (373 genes), target protection (85 genes), efflux (44 genes) and target replacement (25 genes).

The abovementioned genes confer resistance to most of commonly used drugs: beta-lactams (198 genes), macrolides (74 genes), rifamycin (60 genes), aminoglycosides (51 genes), tetracycline (27 genes), phenicols (27 genes), diaminopyrimidines (19 genes), quaternary ammonium compounds (16 genes), glycopeptides (15 genes), lincosamide (13 genes), fosfomycine (12 genes) and drugs of 11 others classes (Fig.  2 ).

ARGs identified in wastewater and AS samples categorized by drug classes.

About twice less ARGs were identified in AS samples from both WWTPs. Like in the influent, beta-lactamases of classes A, D, and metallo-beta-lactamases were the most numerous, while only a few genes for class C enzymes were found (Table 2 ). Other families of ARGs, numerous in the influent, were also numerous in AS microbiomes. A notable difference between the resistomes of the AS samples is the greater number of rifampin-ADP-ribosyltransferase genes ( arr ) in NLOS2 compared to LOS (63 vs 33). The largest number of arr genes was assigned to Bacteroidota, and the lower relative abundance of this phylum in AS at LOS likely explains these differences. Like in the wastewater, resistance to beta-lactams, macrolides, rifamycin, aminoglycosides, and tetracyclines was the most common (Fig.  2 ). On the contrary, genes for some drug classes were underrepresented in AS resistomes, especially for diaminopyrimidines (3 and 2 genes for LOS and NLOS2, respectively) and glycopeptide antibiotics (2 and 0 genes).

The results of metagenomic analysis of treated effluent showed that the diversity of these resistomes was only slightly higher than that of the corresponding AS samples. This result was expected since the main source of microorganisms in the effluent is activated sludge, from which they are partially washed. However, resistomes of treated effluent at both WWTPs contains about twice more class A beta-lactamase genes than AS samples suggesting less efficient absorption of their host bacteria at AS particles (Table 2 ).

Quantitative analysis of antibiotic resistance genes of WWTP

The results described above provide information on the diversity of resistance genes, but not on their abundance in the metagenomes, which depends on the abundance of corresponding bacterial hosts. To quantify the shares of individual ARGs in the metagenome and resistome, the amounts of metagenomic reads mapped to the corresponding ARGs in contigs were determined. In total, the resistome accounted for about 0.05% of the metagenome of wastewater supplied for treatment, while the shares of resistomes in the metagenomes of AS and treated effluent samples were 0.02% and 0.014% at the LOS and NLOS2 WWTPs, respectively.

Quantitative analysis of the content of individual ARGs in metagenomes showed that the structure of the influent resistome was very different from that of AS and treated effluent. The relative content of ARGs accounting for more than 1% in at least one analyzed resistome is shown in Fig.  3 . The LOS and NLOS2 WWTPs differed significantly from each other, and the differences between the AS and effluent resistomes at each WWTP were much less pronounced.

The relative abundancies of particular ARGs in the resistomes. Only ARGs with shares greater than 1% in at least one sample are shown, all other ARGs are shown as “others”.

The resistome of the influent was not only the most diverse, but also the most even in composition. The shares of none of the ARGs exceeded 5% of the resistome, and the 23 most common ARGs accounted for a half of the resistome. The most abundant ten ARGs were qacE, sul1, ampC, blaOXA, msr(E), erm(B), mph(E), tet(C), aph(3'')-Ib and aph(6)-Id, conferring resistance to antiseptics, sulfonamides, beta-lactams, macrolides, aminoglycosides (streptomycin), and tetracyclines.

AS and treated effluent at LOS plant was strongly dominated by a single AGR type, class C beta-lactamase ampC , accounting for about 45% of their resistomes. This gene was also the most abundant one in the resistomes of AS and effluent at NLOS2 (14.8% and 18.2%, respectively). Apparently it originates from the influent wastewater supplied for treatment where its share in the resistome was 3.2%. AmpC β-lactamases are considered clinically important cephalosporinases encoded on the chromosomes and plasmids of various bacteria (especially Enterobacteriaceae ), where they mediate resistance to cephalothin, cefazolin, cefoxitin and most penicillins 35 . Close homologues of this gene, with a nucleotide sequence identity of 99.8–100%, have been found in plasmids and chromosomes of various Proteobacteria ( Thauera, Sphingobium, Aeromonas etc.). Since in all samples ampC was found in short contigs with very high coverage, it is likely widespread in the genomes of various bacteria in different genetic contexts.

The second most abundant ARG in the resistomes of AS samples was sulfonamide-resistant dihydropteroate synthase ( sul1 ). It accounted for 4–5% of AS and treated effluent resistomes in LOS and for about 11% in NLOS2, while its share in the influent water resistome was about 5%. The sul1 gene is usually found in class 1 integrons being linked to other resistance genes, including qacE 36 . Consistently, sul1 and qacE were found in one contig assembled for the influent water samples and assigned to Gammaproteobacteria. Another sulfonamide-resistance gene, sul2 , was also numerous, accounting for about 2% of the resistomes in the influent and LOS samples, and for about 4% in the AS and water treated at NLOS2.

Since ARGs entering the activated sludge and then into the treated effluent originate mostly from wastewater supplied for treatment, the absolute majority of ARGs present in the influent in significant amounts (more than 0.2% resistome) in were also found in AS and effluent samples. The only exception macrolide 2′-phosphotransferase gene mph(B) accounting for 0.51% in the influent resistome. Likewise, all ARGs accounting for more than 0.2% of resistomes in the treated effluent were present also in the influent.

Potential multidrug resistant strains

One of the most important public health problems is the spread of multidrug resistant pathogens (MDR), which refers to resistance to at least one agent in three or more chemical classes of antibiotic (e.g. a beta-lactam, an aminoglycoside, a macrolide) 37 . Such strains can arrive with wastewater entering the treatment, and also form in AS communities. AS are dense and highly competitive microbial communities, which, along with the presence of sublethal concentrations of antibiotics and other toxicants in wastewater, creates ideal conditions not only for the selection of resistant strains, but also for the formation of multiple resistance through horizontal gene transfer 4 . To identify MDR bacteria, we binned metagenomic contigs into metagenome-assembled genomes (MAGs) and looked for MAGs comprising several ARGs. Only MAGs with more than 70% completeness and less than 15% contamination were selected for analysis: 117, 56, 72, 94 and 121 for influent, AS of LOS, effluent of LOS, AS of NLOS2 and effluent of NLOS2, respectively. Five MAGs of MDR bacteria were identified in the metagenome of the influent, one—in AS of LOS, two—in the LOS effluent and one in the NLOS2 effluent (Table 3 ). These MAGs were assigned to unclassified genus-level lineages of Ruminococcaceae and Cyclobacteriaceae, Phocaeicola vulgatus, Streptococcus parasuis, Ancrocorticia sp., Enterococcus sp., Bacillus cereus and Undibacterium sp.

Disscussion

We characterized the composition of microbial communities and the resistomes of influent wastewater, activated sludge and treated effluent from two WWTPs in city of Moscow, where various biological water treatment technologies are used. Among the predominant bacteria in the influent wastewater we found mainly fecal contaminants of the genera Collinsella , Bacteroides , Prevotella , Arcobacter , Arcobacteraceae , Blautia , Faecalibacterium, Streptococcus , Acinetobacter , Aeromonas and Veillonella 38 , 39 , 40 , 41 , 42 , 43 . Previously, we performed 16S rRNA gene profiling of wastewater before and after treatment at one WWTP (LOS) and revealed that all abovementioned potential pathogens were efficiently removed and their relative abundance in the water microbiome decreased by 50‒100 times 44 . Similar pattern of removal of potential pathogenic bacteria was observed here for NLOS2 where another water treatment technology is used.

An important indicator of the dissemination of ARG is the proportion of the resistome in the entire metagenome before and after wastewater treatment. In the influent, the resistome accounted for about 0.05% of the metagenome, which corresponds to approximately two ARGs per bacterial genome. Approximately the same values are typical for most countries 3 . After treatment, the fraction of the resistome in the wastewater metagenomes decreases, but, surprisingly, only by 2–4 times. However, since the total concentration of microorganisms in treated effluent is approximately two orders of magnitude lower than in raw wastewater, it is likely that the total abundance of ARGs in the treated effluent is significantly reduced.

Apparently, fecal contaminants effectively removed during treatment are not the only carriers of ARG in wastewater, which are also found in bacteria characteristic of activated sludge and thus appearing in the effluents. Unfortunately, due to the high diversity of microbiomes and the tendency of ARG to be present in multiple copies in different genomic environments, most of the contigs containing ARG turned out to be short, which did not allow to define their taxonomic affiliation.

The resistome of influent water includes 26 ARGs, the share of which is more than 1%. Among of them the prevalence of ampC, aadA, qacE, bla, qacF and qacL is specific for Moscow WWTPs, since these genes were not among the 50 most common ARGs according to the results of a worldwide analysis of wastewater resistomes in large cities 3 . Different ARGs were most “evenly” represented in the influent wastewater while in the AS and treated effluent, a clear selection of particular types of ARGs was observed, which obviously reflects a change in the composition of microbiomes. A vivid example is the increase in the proportion of ampC in the resistomes, especially at LOS.

The discovered ARGs can confer resistance to most classes of antibiotics and among the resistomes of the studied WWTPs in the city of Moscow, genes conferring resistance to beta-lactam antibiotics were the most common, they accounted for about 26% of the resistome in the water supplied for treatment (Fig.  4 ). Similar values have been observed for wastewater in some other countries, particularly in Eastern Europe and Brazil, where 20 to 25% of reads were assigned to ARGs conferring resistance to beta-lactams 3 . According to data for 2021, beta lactams accounted for about 40% of the total antibiotic consumption in Russia in the medical sector 45 .

The relative abundancies of ARGs in the resistomes categorized by drug classes.

Like in most wastewater resistomes in different countries, ARGs conferring resistance to macrolides, aminoglycosides and tetracycline were also among the most abundant in wastewater from Moscow (Fig.  4 ). Resistance to macrolides, rather than beta-lactams, was most common in wastewater from most countries in Europe and North America, while in Moscow ARGs to macrolide were the second most common. Macrolides and tetracyclines are also widely used in medicine in Russia (20% and 5% of total antibiotic consumption in 2021, respectively). On the contrary, medical consumption of aminoglycosides in Russia is rather low (< 1% of the total), therefore, the high abundance of relevant ARGs was unexpected. The opposite pattern was observed for quinolones, which make up about 22% of the antibiotics used in medicine, but their ARGs accounted for only about 1% of the resistome. However the main mechanisms of resistance to quinolones, mutations in the target enzymes, DNA gyrase and DNA topoisomerase IV, and increased drug efflux 46 , were not addressed in our study.

A peculiar feature of Moscow wastewater resistome was the high content of resistance genes to sulfonamides (about 9%), which were not among the major genes in wastewater resistomes worldwide 3 . Sulfonamides are synthetic antimicrobial agents that currently have limited use in the human medicine, alone or mainly in combination with trimethoprim (a dihydrofolate reductase inhibitor), in the treatment of uncomplicated respiratory, urinary tract and chlamydia infections 7 , 47 . Different sulfonamide ARGs ( sul1, sul2 and sul3 ) were detected in the wastewater in the some countries, including Denmark, Canada, Spain and China, applying culture dependent, independent and qPCR methods 7 . The opposite picture was observed for streptogramin resistance genes, which were among the ARGs in the majority of resistomes worldwide, but in Moscow wastewater they accounted for less than 1%. This is probably due to the limited use of this drug in Russia.

Another distinguishing feature of the resistome of wastewater in Moscow is the high content of ARGs conferring resistance to quaternary ammonium compounds (QAC), about 9%. It can be explained by the frequent use of these antiseptics in medicine. QACs are active ingredients in more than 200 disinfectants currently recommended for inactivation the SARS-CoV-2 (COVID-19) virus 48 . A recent study showed that the number of QACs used to inactivate the virus in public facilities, hospitals and households increased during the COVID-19 pandemic 49 . Indeed, the results of a study dedicated to the study of wastewater resistome worldwide 3 did not reveal the presence of QAC ARGs in the wastewater, since the samples for this study were collected before the pandemic.

An important issue is the extent to which different water treatment technologies remove ARGs. The effective removal of ARG was primary due to a decrease in the concentration of microorganisms in treated effluent, since the share of resistome in the metagenome after treatment decreased by only 2.6 –3.7 times and the NLOS2 plant appeared to be more effective in this respect. However, compared to LOS, treated effluent at NLOS2 contains approximately twice as much suspended solids, probably due to poorer settling characteristics of the sludge indicated by the higher SVI. Therefore, the overall efficiency of removing ARGs from wastewater at two WWTPs may be similar.

Considering the relative abundances of ARGs in the resistomes, genes conferring resistance to macrolides and tetracyclines were removed more efficiently than beta lactamases, especially ampC , and rifampin ADP-ribosyltransferase genes. The low efficiency of removal of the ampC gene and the increase in its abundance in the resistome after wastewater treatment were previously reported for WWTPs in Germany 50 . Efficient removal of ARGs to macrolides ( ermB, ermF, mph(A), mef(A) ) and tetracyclines ( tet(A), tet(C), tet(Q), tet(W) ) has been reported in a number of studies worldwide 51 . ARGs enabling resistance to sulfonamides, tetracyclines and chloramphenicol were more efficiently removed at LOS than at NLOS2, while the opposite was observed for beta lactamases (Fig.  4 ). The later became the most abundant class of ARGs in the treated effluent.

Metagenomic analysis not only identified resistance genes, but also revealed probable MDR strains based on the analysis of assembled MAGs. We identified 9 such strains in both influent, AS and treated effluent. The real number of MDR strains is probably higher, since only a small fraction of all metagenomic contigs was included in the assembled high quality MAGs.

Phocaeicola vulgatus , (formerly Bacteroides vulgatus ), is a mutualistic anaerobic bacteria commonly found in the human gut microbiome and frequently involved in human infections. The results of whole genome analysis showed presence of blaTEM-1 and blaCMY-2 ARGs, which confers resistant to beta-lactams 52 , 53 . P. vulgatus was also identified as potential host for the transmission of tetracycline ARGs 54 . Streptococcus parasuis is an important zoonotic pathogen that causes primarily meningitis, sepsis, endocarditis, arthritis, and pneumonia in both pigs and humans 55 . A variety of MDR strains of this bacterium have been described. For instance, S. parasuis strain H35 was isolated from a lung sample of a pig in China; several ARGs, including optrA , catQ , erm(B), lsa(E), msr(D), mef(A), mdt(A), tet(M), lnu(B), aadE and two copies of aacA-aphD , were found in the chromosome and cfr(D) was detected on plasmid pH35-cfrD 56 . MDR strain of Bacillus cereus was identified in the effluent water microbiome. This bacterium is known as human pathogen and a common cause of food poisoning with toxin-producing property 57 . Bacillus cereus was isolated from drinking water treatment plant in China and antimicrobial susceptibility testing revealed that it was resistant to cefoxitin, penicillin tetracycline 58 , macrolide-lincosamide-streptogramin (MLSB), aminoglycoside and tetracycline antibiotics 59 . Assembled MAG B.cereus from effluent water contained ARGs conferring to macrolides, beta-lactams, fosfomycin and streptogramin and may be considered as MDR strain. Genomes of members of the genera Streptococcus (AS of LOS) and Enterococcus (influent), not identified at the species level, were found to contain multiple ARGs. Most of species of these genera are opportunistic and true pathogens known for their drug resistance 60 , 61 . One MAG from the influent water metagenome was assigned to uncultured lineage of the family Ruminococcaceae. Members of this family are typical non-pathogenic gut inhabitants, although genomes of some strains could harbor ARGs 62 .

Three MAGs retrieved from influent wastewater microbiome ( Ancrocorticia ) and treated effluent water ( Cyclobacteriaceae and Undibacterium ) were found to contain several ARGs. However, we found no evidences about pathogenic and MDR strains in these taxa. It is possible that these environmental bacteria acquired ARGs via horizontal gene from outside their lineages. WWTPs are an ideal environment for horizontal gene transfer (HGT), since when bacteria are exposed to strong selective pressures, such as the presence of antimicrobials, the horizontal acquisition of ARGs enables genetic diversification and create the potential for rapid gains in fitness 63 .

Conclusions

Metagenome sequencing of the raw wastewater, activated sludge and treated wastewater at two large WWTPs of the Moscow city revealed several hundreds of ARGs that could confer resistance to most commonly used classes of antibiotics.

Resistome accounted for about 0.05% of the wastewater metagenome and after wastewater treatment its share decreased by 3–4 times.

The resistomes were dominated by ARGs encoding resistance to beta-lactams, macrolides, aminoglycosides, tetracycline, QAC, and sulfonamides. A peculiar feature of Moscow wastewater resistome was the high content of ARGs to sulfonamides and limited occurrence of resistance to streptogramins.

ARGs for macrolides and tetracyclines were removed more efficiently than ARGs for beta-lactamases.

A comparison of wastewater resistomes from Moscow and around the world suggested that the abundance and content of ARG in wastewater depend on social, medical, and environmental factors.

Data availability

The raw data generated from 16S rRNA gene sequencing and metagenome sequencing have been deposited in the NCBI Sequence Read Archive (SRA) and are available via the BioProject PRJNA945245.

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Acknowledgements

This work was partly supported by the Russian Science Foundation (Project 22-74-00022 to S.B.).

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S.B. and N.V.R. designed and supervised the research project; A.G.D. collected the samples and analysed chemical composition of wastewater; A.V.M. performed 16S rRNA gene profiling and metagenome sequencing; S.B., A.V.B., N.V.P., and N.V.R. analysed the sequencing data; S.B. and N.V.R. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

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Synthetic droplets cause a stir in the primordial soup: Chemotaxis research answers questions about biological movement

by Okinawa Institute of Science and Technology

Our bodies are made up of trillions of different cells, each fulfilling their own unique function to keep us alive. How do cells move around inside these extremely complicated systems? How do they know where to go? And how did they get so complicated to begin with? Simple yet profound questions like these are at the heart of curiosity-driven basic research, which focuses on the fundamental principles of natural phenomena. An important example is the process by which cells or organisms move in response to chemical signals in their environment, also known as chemotaxis.

A group of researchers from three different research units at the Okinawa Institute of Science and Technology (OIST) came together to answer basic questions about chemotaxis by creating synthetic droplets to mimic the phenomena in the lab, allowing them to precisely isolate, control and study the phenomena.

Their results, which help answer questions about the principles of movement in simple biological systems, have been published in the Journal of The American Chemical Society .

"We have shown that it is possible to make protein droplets migrate through simple chemical interactions," says Alessandro Bevilacqua, Ph.D. student in the Protein Engineering and Evolution Unit and co-first author on the paper. Professor Paola Laurino, head of the unit and senior author. Laurino adds that they "have created a simple system that mimics a very complex phenomenon, and which can be modulated through enzymatic activity."

Tensions on the surface

While the process of creating droplets might not sound like the most complicated task, mimicking biological processes as close to reality as possible while keeping accurate control over all the variables certainly is. The synthetic, membrane-less droplets contain a very high concentration of the bovine protein BSA to mimic the crowded conditions inside cells, as well as urease, an enzyme that catalyzes the breakdown of urea into ammonia.

Ammonia is basic, meaning it has a high pH-value. As the enzyme gradually catalyzes the production of ammonia, it diffuses into the solution, creating a 'halo' of higher pH around the droplet, which in turn enables droplets to detect other droplets and migrate towards each other.

The researchers found that the key to understanding the chemotaxis of the droplets is the pH-gradient, as it facilitates the Marangoni effect, which describes how molecules flow from areas of high surface tension to low.

Surface tension is the measure of energy required to keep molecules at the surface together, like glue. When pH increases, this glue weakens, causing molecules to spread out and lowering surface tension, which in turn makes it easier for molecules to move. You can see this by adding soap, which has a high pH, to one end of a bathtub of still water: the water will flow towards the end with soap because of the Marangoni effect.

When two synthetic droplets are close enough, their halos interact, raising the pH in the environment between them, which makes them move together. Because the surface tension is still strong on the opposite ends of the droplets, they keep their shape until the surfaces touch, and the cohesive forces within the droplets overcome the surface tension, causing them to merge. As larger droplets both produce more ammonia and have a larger surface area (which decreases surface tension), they attract droplets smaller than themselves.

Collaborating on ancient soup and future biotech

Thanks to the development of these droplets, the researchers have made headway in answering basic questions about biological movement—and in doing so, they have gained insight into the directed movement of the earliest forms of life in the primordial soup billions of years ago, as well as a lead on creating new biologically inspired materials.

Our knowledge of life as it looked billions of years ago is fuzzy at best. A prominent hypothesis is that life originated in the oceans, as organic molecules gradually assembled and became more sophisticated in a 'primordial soup'—and this could have been facilitated by chemotaxis through the Marangoni effect.

"It would have been beneficial for droplets to have this mechanism of migration in the hypothetical origin of life scenario," as Professor Laurino puts it. This migration could have triggered the formation of primitive metabolic pathways whereby enzymes catalyze a variety of substances that ultimately produce a chemical gradient that drives the droplets together, leading to larger and more sophisticated communities.

The research also points ahead in time, providing leads on new technology. "One example is the creation of responsive materials inspired by biology," suggests Alessandro Bevilacqua. "We have shown how simple droplets can migrate thanks to a chemical gradient. A future application of this could be technologies that sense or react to chemical gradients, for example in micro-robotics or drug delivery."

The project began during the coronavirus pandemic, when a member of the Protein Engineering and Evolution Unit was in quarantine with a member of the Complex Fluids and Flows Unit. The two began talking, and though the two units are from two disparate fields—biochemistry and mechanics, respectively—the project evolved in tandem. Eventually, members from the Micro/Bio/Nanofluidics Unit joined the project with sophisticated measurements of the droplets' surface tension .

The unique non-disciplinary research environment at OIST catalyzed the collaboration. As Professor Laurino puts it, "This project could never have existed if we were separated by departments. It hasn't been an easy collaboration, because we communicate our field in very different ways—but being physically close made it significantly easier."

Alessandro Bevilacqua adds, "The coffee factor has been very important. Being able to sit down with other unit members made the process much faster and more productive." Their cooperation doesn't stop here—rather, this paper is the beginning of a fruitful partnership between the three units.

"We see a lot of synergy in our work, and we work effectively and efficiently together. I don't see a reason why we should stop," say Professor Laurino. It's thanks to the combined efforts of the three units that we now know more about the minute movements of life at the smallest, earliest, and possibly future scale.

Journal information: Journal of the American Chemical Society

Provided by Okinawa Institute of Science and Technology

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Yeast Communities of the Moscow City Soils

• Experimental Articles
• Published: 02 June 2018
• Volume 87 , pages 407–415, ( 2018 )

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• A. N. Tepeeva 1 ,
• A. M. Glushakova 1 &
• A. V. Kachalkin 1  

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Yeast abundance and diversity were studied in the soils (topsoil) of Moscow city: urban soils under lawn vegetation and close to the areas of household waste disposal, as well as in zonal soddy-podzolic soils (retisols) in parks (Losiny Ostrov and Izmailovo). The numbers of soil yeasts were similar in all studied urban biocenoses (on average ~3.5 × 10 3 CFU/g). From all studied soils, 54 yeast species were isolated. The highest yeast diversity was found in the soils adjacent to the areas of household waste storage. Soils from different urban sites were found to have different ratios of ascomycetous and basidiomycetous yeasts: basidiomycetes predominated in urban soils under lawn vegetation, while in the areas close to the waste disposal sites their share was considerably lower. The differences between the studied urban soils were also found in the structure of soil yeast complexes. In urban soils with high anthropogenic impact, the isolation frequency of clinically important yeast species ( Candida parapsilosis , C. tropicalis , Diutina catenulata , and Pichia kudriavzevii ) was as high as 35% of all studied samples, while its share in the community was 17%. The factors responsible for development of specific features of yeast communities in various urban soils are discussed in the paper.

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A. N. Tepeeva, A. M. Glushakova & A. V. Kachalkin

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Original Russian Text © A.N. Tepeeva, A.M. Glushakova, A.V. Kachalkin, 2018, published in Mikrobiologiya, 2018, Vol. 87, No. 3.

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Tepeeva, A.N., Glushakova, A.M. & Kachalkin, A.V. Yeast Communities of the Moscow City Soils. Microbiology 87 , 407–415 (2018). https://doi.org/10.1134/S0026261718030128

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Received : 06 September 2017

Published : 02 June 2018

Issue Date : May 2018

DOI : https://doi.org/10.1134/S0026261718030128

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