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Behavior Genetics

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54th Annual BGA Meeting, London, England

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Founded in 1970, the Behavior Genetics Association (BGA) is an international society. Behavior genetics has a strong tradition in twin and family study designs, and more recently gene-finding studies.

The purpose of the BGA is to promote the scientific study of the interrelationship of genetic mechanisms and behavior, both human and animal; to encourage and aid the education and training of research workers in the field of behavior genetics; and to aid in the dissemination and interpretation to the general public of knowledge concerning the interrelationship of genetics and behavior, and its implications for health and human development and education.

We welcome scientists who share our interest in the interrelationships between genes and traits/behavior, both in humans and non-human animals.

** In memoriam of Professor Peter McGuffin CBE FMedSci **

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IBG's mission is to conduct interdisciplinary research and training that examines the nature and origins of individual differences in behavior. Current research at IBG uses large scale family, twin, adoption, and molecular genetic studies in humans, and behavioral and molecular genetic studies in model organisms, in order to understand behaviors of societal and health relevance, such as aging and dementia, drug use and abuse, cognitive abilities, personality, learning disabilities, and psychopathology.

We are living through one of the great scientific revolutions of our time. The advances in genetics are breathtaking, and these advances are reshaping the field of behavioral genetics. We are excited by the ways that IBG can harness the energy in the field to grow in new and important ways. 

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EDITORIAL article

Editorial: the genetics and epigenetics of mental health.

• 1 Instituto de Pesquisa Pelé Pequeno Príncipe, Curitiba, Brazil
• 2 Faculdades Pequeno Príncipe, Curitiba, Brazil
• 3 Department of Genetics, Federal University of Parana, Post-graduation Program in Genetics, Curitiba, Brazil
• 4 Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, Biomedical Research Institute of Lleida (IRBLleida), Lleida, Spain
• 5 CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain

Editorial on the Research Topic The genetics and epigenetics of mental health

Mental health conditions cover a broad spectrum of disturbances, including neurological and substance use disorders, suicide risk, and associated psychosocial, cognitive, and intellectual disabilities (WHO, 2022). Despite a substantial amount of evidence, the interaction of genetic variants, epigenetic mechanisms, and environmental risk factors involved in mental health is poorly understood. Through distinct perspectives and different experimental approaches, the genetics and epigenetics of mental health were addressed in seven relevant articles included in this Research Topic, briefly summarized below.

Stress has severe consequences on the epigenome, but the timing of its occurrence, as well as the intensity and number of events, are critical for the severity of mental health symptoms. In particular, Serpeloni et al. demonstrated that stress generated in the form of intimate partner violence (IPV) during and/or after pregnancy impacts the offspring’s epigenome, shaping its resilience. They observed that individuals exposed to maternal IPV after birth presented psychiatric issues similar to their mothers, with different outcomes if the exposure to maternal IPV occurred both prenatally and postnatally. Prenatal IPV was associated with differential methylation in CpG sites in the genes encoding the glucocorticoid receptor ( NR3C1 ) and its repressor FKBP51 ( FKBP5 ), associated with the ability to terminate hormonal stress responses. Also considering early-life experiences and data from 2008 to 2016 of the Health and Retirement Study, Shin et al. concluded that early life experiences and relationships have a significant influence, attenuating or exacerbating the risk of suffering from mental health problems among individuals with a higher polygenic risk score predisposing to autism.

Environmental and developmental factors are also strongly linked to obsessive-compulsive disorder (OCD). They may explain the apparent discrepancy between the relatively high heritability scores and the inconsistent results found in genetic association studies, owing to their impact on gene expression and regulation. Based on this, Deng et al. stratified OCD patients by the age of disease onset. The findings revealed associations between the early onset and variants of genes whose products play a role in neural development, corroborating the age-associated genetic heterogeneity of OCD.

Further exploring environmental and genetic etiological clues, Li et al. used genome-wide association study (GWAS) data to calculate polygenic risk scores for salivary and tongue dorsum microbiomes associated with anxiety and depression. Additionally, causal relationships between the oral microbiome, anxiety, and depression were detected through Mendelian randomization, unraveling potential pathogenic mechanisms and interventional targets. Constructing a similar line of evidence, Becerra et al. found associations between the epigenetic regulation of inflammatory processes, the composition of gut microbiome, and modified Rosenberg self-esteem scores in samples from the Native Hawaiian and other Pacific Islander (NHPI) populations, which present a high prevalence and mortality from chronic and immunometabolic diseases, as well as mental health problems. This warrants further investigation into the relationship of microbiota to brain activity and mental health.

There is a lot of debate regarding suicidal behavior and its relationship with psychiatric disorders, but the extent to which they share the same genetic architecture is unknown. This Research Topic was investigated by Kootbodien et al. through the use of genomic structural equation modeling and Mendelian randomization with a large genomic dataset. The authors observed a strong genetic correlation between suicidal ideation, attempts, and self-harm, as well as a moderate to strong genetic correlation between suicidal behavioral traits and a range of psychiatric disorders, most notably major depressive disorder, involving pathways related to developmental biology, signal transduction, and RNA degradation. In conclusion, the study provided evidence of a shared etiology between suicidal behavior and psychiatric disorders, with overlapping pathophysiological pathways.

Malekpour et al. , in their investigation of psychogenic non-epileptic seizures (PNES), also uncovered shared pathways with psychiatric conditions. PNES, the most prevalent non-epileptic disorder among patients referring to epilepsy centers, carries a mortality rate akin to drug-resistant epilepsy. Employing a systems biology approach, the authors pinpointed several key components influencing the disease pathogenesis network. These include brain-derived neurotrophic factor (BDNF), cortisol, norepinephrine, proopiomelanocortin (POMC), neuropeptide Y (NPY), the growth hormone receptor signaling pathway, phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling, and the neurotrophin signaling pathway.

In general, these studies have some limitations: small sample sizes, leading to low statistical power in some cases, environmental confounding factors (such as diet and physical activity), which were not considered in the microbiome studies, incomplete phenotype descriptions, and partial coverages of human genetic diversity. Childhood adversities and adult comorbidities are among the variables that were not controlled for as possible causes of the investigated psychiatric and neurological disorders, and some results still claim for functional studies to be validated. Thus, the findings brought more elaborated questions, each of which shed some light on knowledge gaps that remain very difficult to fill. How do early-life epigenetic processes regulate our mental health resilience and disease resistance? What is the role of the microbiome in this process and how do genetic variants influence its composition? How does the impact of all these elements shape the resistance of human populations to psychiatric and neurological diseases and, most importantly, translate into public health measures in the future? We hope to engage more researchers in the pursuit of these answers.

Author contributions

GCK: Conceptualization, Data curation, Writing–original draft, Writing–review and editing. ABWB: Writing–original draft, Writing–review and editing. ADST: Conceptualization, Data curation, Writing–original draft, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Empresa Brasileira de Serviços Hospitalares (Ebserh) grant numbers 423317/2021-0 and 313741/2021-2 (8520137521584230), Research for the United Health SUS System (PPSUS-MS), CNPq, Fundação Araucária and SESA-PR, Protocol N°: SUS2020131000106. ABWB receives CNPq research productivity scholarships (protocols 313741/2021). ADST receives financial support from Instituto de Salud Carlos III (Miguel Servet, 2023: CP23/00095), co-funded by Fondo Social Europeo Plus (FSE+).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: methylation, GWAS-genome-wide association study, microbiome & dysbiosis, poligenic risk score, neurological conditions, epigenome, genome

Citation: Kretzschmar GC, Boldt ABW and Targa ADS (2024) Editorial: The genetics and epigenetics of mental health. Front. Genet. 15:1402495. doi: 10.3389/fgene.2024.1402495

Received: 17 March 2024; Accepted: 26 March 2024; Published: 09 April 2024.

Edited and reviewed by:

Copyright © 2024 Kretzschmar, Boldt and Targa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabriela Canalli Kretzschmar, [email protected] ; Angelica Beate Winter Boldt, [email protected] ; Adriano D. S. Targa, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Behavioral Genetics and Genomics

Image Credit: Jeffrey Rhoades

Research in Behavioral Genetics and Genomics at MIT combines experimental and computational approaches to quantify animal behaviors and elucidate the underlying genes, gene networks and neural circuits.

Sinisa Hrvatin

Brandon (brady ) weissbourd, sara prescott, li-huei tsai, olivia corradin, gut-brain connection signals worms to alter behavior while eating.

Study may lead to a better understanding of the digestive tract’s nervous system.

When a hungry worm encounters a rich food source, it immediately slows down so it can devour the feast. Once the worm is full, or the food runs out, it will begin roaming again.

School of Science appoints eight faculty members to named professorships

Biologists discover function of gene linked to familial als.

Study in worms reveals gene loss can lead to accumulation of waste products in cells.

New technique scours the genome for genes that combat disease

Biological engineers identify genes that protect against protein linked to Parkinson's disease.

Using a modified version of the CRISPR genome-editing system, MIT researchers have developed a new way to screen for genes that protect against specific diseases.

CRISPR is normally used to edit or delete genes from living cells. However, the MIT team adapted it to randomly turn on or off distinct gene sets across large populations of cells, allowing the researchers to identify genes that protect cells from a protein associated with Parkinson’s disease.

Steven Flavell

Concerns of genetic determinism of behavior by linking environmental influences, genetic research

New genomic research provides strong rationale against genetic determinism for behavior. / Matt Hudson

It has long been known that there is a complex interplay between genetic factors and environmental influences in shaping behavior. Recently it has been found that genes governing behavior in the brain operate within flexible and contextually responsive regulatory networks. However, conventional genome-wide association studies (GWAS) often overlook this complexity, particularly in humans where controlling environmental variables poses challenges.

In a new perspective article publishing February 27th in the open-access journal PLOS Biology by researchers from the University of Illinois Urbana-Champaign and Rutgers University, US, the importance of integrating environmental effects into genetic research is underscored. The authors discuss how failure to do so can perpetuate deterministic thinking in genetics, as historically observed in the justification of eugenics movements and, more recently, in cases of racially motivated violence.

The authors propose expanding GWAS by incorporating environmental data, as demonstrated in studies on aggression in fruit flies, in order to get a broader understanding of the intricate nature of gene-environment interactions. Additionally, they advocate for better integration of insights from animal studies into human research. Animal experiments reveal how both genotype and environment shape brain gene regulatory networks and subsequent behavior, and these findings could better inform similar experiments with people.  

“Advances in genomic technology have really illustrated how changes in the environment lead to changes not only in behavior, but in the expression of genes, in a way that’s not determined just by heredity,” said co-author Matthew Hudson (CABBI/GNDP), professor of crop sciences at Illinois. “We now understand that even the same genes can function very differently across individuals depending on their expression.”

Furthermore, the authors stress the importance of multidisciplinary collaboration to understand the roots of behavior, especially among the animal and human research communities. Co-author Rina Bliss, professor of sociology at Rutgers, added, “We really need these kinds of collaborations among social scientists and biologists to illuminate the complexity of gene-environment interactions, especially as they relate to human behavior.” The article also suggests that emerging technologies such as brain organoids and new forms of brain imaging will be necessary to elucidate the molecular mechanisms linking genetic and environmental influences on behavior.

Ultimately, the authors stress that a paradigm shift is needed in human social and behavioral genomics towards a nuanced comprehension of gene-environment interactions. “Studying the roots of behavior holds great potential for insights that can help better understand brain function, in health and disease. We hope this article helps researchers to make the most of the opportunities while avoiding reductionist pitfalls,” said coauthor Gene Robinson (GNDP), IGB Director and professor of entomology and neuroscience at Illinois.

The authors suggest that a holistic perspective and fostering interdisciplinary collaboration could help researchers navigate the complexities of human behavior, while mitigating the risks associated with deterministic thinking in genetics.

Celebrating a Century of Research in Behavioral Genetics

• Open access
• Published: 20 January 2023
• Volume 53 , pages 75–84, ( 2023 )

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A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to slow down in the 1990s, molecular genetics made it possible to assess DNA variation directly. From a rocky start with candidate gene association research, by 2005 the technological advance of DNA microarrays enabled genome-wide association studies, which have successfully identified some of the DNA variants that contribute to the ubiquitous heritability of behavioral traits. The ability to aggregate the effects of thousands of DNA variants in polygenic scores has created a DNA revolution in the behavioral sciences by making it possible to use DNA to predict individual differences in behavior from early in life.

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Introduction

Although the history of heredity and behavior can be traced back to ancient times (Loehlin 2009 ), the first human behavioral genetic research was reported in the 1920s, which applied quantitative genetic twin and adoption designs to assess genetic influence on newly developed measures of intelligence. The 1920s also marked the beginning of single-gene research that led to molecular genetics. The goal of this review is to outline 100 years of progress in quantitative genetic and molecular genetic research on behavior, a whistle-stop tour of a few of the major milestones in the journey. The review focuses on human research even though non-human animal research played a major role in the first 50 years (Maxson 2007 ). It uses intelligence as a focal example because intelligence was the target of much human research, even though a similar story could be told for other areas of behavioral genetics such as psychopathology.

The Two Worlds of Genetics

The most important development during this century of behavioral genetic research has been the synthesis of the two worlds of genetics, quantitative genetics and molecular genetics. Quantitative genetics and molecular genetics both have their origins in the 1860s with Francis Galton (Galton 1865 , 1869 ) and Gregor Mendel (Mendel 1866 ), respectively. Not much happened until the 1900s when Galton’s insights led to methods to study genetic influence on complex traits and when Mendel’s work was re-discovered. The two worlds clashed as Mendelians looked for 3:1 segregation ratios indicative of single-gene traits, whereas Galtonians assumed that Mendel’s laws of heredity were specific to pea plants because they knew that complex traits are distributed continuously.

Antipathy between the two worlds of genetics followed because of the different goals of Mendelians and Galtonians. Mendelians, the predecessors of molecular geneticists, wanted to understand how genes work, which led to the use of induced mutations and a focus on dichotomous traits that were easily assessed such as physical characteristics rather than behavioral traits. In contrast, Galtonians, whose descendants are quantitative geneticists, used genetics as a tool to understand the etiology of naturally occurring variation in complex traits selected for their intrinsic interest and importance, with behavioral traits, especially intelligence, high on the list. The resolution to the conflict could be seen in Ronald Fisher’s 1918 paper, which showed that Mendelian inheritance is compatible with quantitative traits if the assumption is made that several genes affect a trait (Fisher 1918 ). Nonetheless, the two worlds of genetics went their own way for most of the century.

The synthesis of the two worlds of genetics began in the 1980s with the technological advances of DNA sequencing, polymerase chain reaction, and DNA microarrays that enabled genome-wide association (GWA) studies of complex traits. In addition to finding DNA variants associated with complex traits, GWA genotypes led to three far-reaching advances in genetic research. First, GWA genotypes were used to estimate directly the classical quantitative genetic parameters of heritability and genetic correlation, which could be called quantitative genomics . Second, the results of GWA studies were used to create polygenic scores that predict individual differences for complex traits. Third, GWA genotypes facilitated new approaches to causal modeling of the interplay between genes and environment. Together, when applied to behavioral traits, these advances could be called behavioral genomics . This synthesis of the two worlds of genetics, the journey from behavioral genetics to behavioral genomics, is the overarching theme of this whistle-stop tour celebrating a century of research in behavioral genetics. (See Fig.  1 .) The itinerary begins with milestones in quantitative genetics and then molecular genetics, concluding with behavioral genomics.

Synthesis of the two worlds of genetics: from behavioral genetics to behavioral genomics.

Quantitative Genetics

The first 50 years of quantitative genetic research, from 1920 to 1970, started off well with family studies (Jones 1928 ; Thorndike 1928 ), twin studies (Holzinger 1929 ; Lauterbach 1925 ; Merriman 1924 ; Tallman 1928 ) and adoption studies (Burks 1928 ; Freeman et al. 1928 ) using the recently devised IQ test. However, this nascent research was squelched with the emergence of Nazi eugenic policies (McGue 2008 ). The void was filled with behaviorism (Watson 1930 ), which led to environmentalism, the ‘blank slate’ view that we are what we learn (Pinker 2003 ).

Nonetheless, a few studies of IQ appeared in the 1930 and 1940 s, such as the first study of identical twins reared apart (Newman et al. 1937 ) and the first adoption study that assessed birth parents (Skodak and Skeels 1949 ). Both indicated substantial genetic influence on IQ, as did a review of all available IQ data (Woodworth 1941 ).

In 1960, the field-defining book, Behavior Genetics (Fuller and Thompson 1960 ), was published. It mostly reviewed research on nonhuman animals. In their preface, the authors noted that “we considered omitting human studies completely” (p. vi); even their chapter on cognitive abilities primarily reviewed nonhuman research. An earlier influential review began by saying, “In the writer’s opinion, the genetics of behavior must be worked out on species that can be subjected to controlled breeding. At the present time this precludes human subjects” (Hall 1951 ).

In 1963, a milestone review was published in Science of 52 family, twin and adoption studies of IQ (Erlenmeyer-Kimling and Jarvik 1963 ). Although the studies were very small by modern standards and heritability was not calculated, the average results from the different designs suggested substantial heritability. For example, the average MZ and DZ twin correlations were 0.87 and 0.53, respectively, suggesting a heritability of 68%. However, despite being published in Science , the paper was largely ignored; it was cited only 22 times in five years.

The pace of behavioral genetic research picked up in the 1960s, once again primarily research on non-human animals (Lindzey et al. 1971 ; McClearn 1971 ), although some twin studies on cognitive abilities were also published (Nichols 1965 ; Schoenfeldt 1968 ). However, the first 50 years of quantitative genetic research ended badly with the publication in 1969 of Arthur Jensen’s paper, How Much Can We Boost IQ and Scholastic Achievement? (Jensen 1969 ). The paper touched on ethnic differences, which made it one of the most controversial papers in the behavioral sciences, with 900 citations in the first five years and more than 6200 citations in total.

1970 was a watershed year marking the second 50 years of behavioral genetic research. It was the year that the Behavior Genetics Association was launched and the first issue of its journal, Behavior Genetics , was published. Another 1970 milestone was the publication of the foundational paper for model-fitting analysis of quantitative genetic designs (Jinks and Fulker 1970 ).

The 1970s and 1980s yielded most of the major discoveries for quantitative genetics as applied to behavioral traits, discoveries that are listed as landmarks in the following paragraphs. Nonetheless, in the aftermath of Jensen’s 1969 paper, behavioral genetic research, especially on intelligence, was highly controversial (Scarr and Carter-Saltzman 1982 ). Most notably, Leon Kamin severely criticized the politics as well as science of behavioral genetic research on intelligence in his book, The Science and Politics of I.Q. (Kamin 1974 ). He concluded that “There exist no data which should lead a prudent man to accept the hypothesis that I.Q. test scores are in any degree heritable” (p. 1). The book was cited more than 2000 times and stoked antipathy towards genetic research. It also impugned the motivation of genetic researchers, saying that they are ‘committed to the view that those on the bottom are genetically inferior victims of their own immutable defects’ (p. 2).

All Traits are Heritable

Despite this hostility, genetic research grew exponentially in the 1970s and created a seismic shift from the prevailing view that behavioral traits like intelligence are not “in any degree heritable”. In 1978, a review of 30 twin studies of intelligence yielded an average heritability estimate of 46% (Nichols 1978 ). Moreover, the conclusion began to emerge that all traits show substantial heritability. This conclusion, which has been called the first law of behavioral genetics (Turkheimer 2000 ), was first observed in 1976 in a twin study of cognitive data for 3000 twin pairs, which also included extensive data on personality and interests for 850 twin pairs (Loehlin and Nichols 1976 ). The authors noted “the curious uniformity of identical-fraternal differences both within and across trait domains” (p. 89). A 2015 meta-analysis of all published twin studies showed that behavioral traits are about 50% heritable on average (Polderman et al. 2015 ). Demonstrating the ubiquitous importance of genetics was the fundamental accomplishment of behavioral genetics.

No Traits are 100% Heritable

The flip side of the finding of 50% heritability was just as important: no traits are 100% heritable. It is ironic that, after a century of environmentalism, genetic research provided the strongest evidence for the importance of the environment; previous environmental research was confounded because it ignored genetics. Moreover, investigating environmental influences in genetically sensitive designs led to two of the most important discoveries about the environment: nonshared environment and the nature of nurture.

Nonshared Environment

Quantitative genetic research showed that environmental influences work very differently from the way they were assumed to work. A second discovery by Loehlin and Nichols ( 1976 ) was that salient environmental influences are not shared by twins growing up in the same family: “Environment carries substantial weight in determining personality – it appears to account for at least half the variance – but that environment is one for which twin pairs are correlated close to zero” (p. 92). This phenomenon has come to be known as nonshared environment (Plomin and Daniels 1987 ).

Loehlin and Nichols suggested that cognitive abilities are an exception to the rule that environmental influences make children in a family different from, not similar to, one another. Their twin study suggested that about 25% of the variance of cognitive abilities could be attributed to shared environment. A direct test of shared environmental influence is the correlation between adoptive siblings, genetically unrelated children adopted into the same family. Seven small studies of adoptive siblings yielded an average IQ correlation of 0.25, which seemed to precisely confirm the twin estimate (McGue et al. 1993 ).

However, in 1978, a study of 100 pairs of adoptive siblings reported an IQ correlation of -0.03 (Scarr and Weinberg 1978 ). This is a good example of the progressive nature of behavioral genetic research (Urbach 1974 ). Scarr and Weinberg noted that previous studies involved children, whereas theirs was the first study of post-adolescent adoptive siblings aged 16 to 22, and they hypothesized that the effect of shared environmental influence on cognitive development diminishes after adolescence as young adults make their own way in the world. Their hypothesis was confirmed in two additional studies of post-adolescent adoptive siblings that yielded an average IQ correlation of -0.01 (McGue et al. 1993 ). Evidence that shared environmental influence declines after adolescence to negligible levels for cognitive abilities has also emerged from twin studies (Briley and Tucker-Drob 2013 ; Haworth et al. 2010 ). However, one of the biggest mysteries about nonshared environment remains: what are these environmental influences that make children growing up in the same family so different (Plomin 2011 )?

The Nature of Nurture

Another milestone was the revelation that environmental measures widely used in the behavioral sciences, such as parenting, social support, and life events, show genetic influence (Plomin and Bergeman 1991 ), with heritabilities of about 25% on average (Kendler and Baker 2007 ). This finding emerged in the 1980s as measures of the environment were included in quantitative genetic designs, which also led to the discovery that associations between environmental measures and psychological traits are significantly mediated genetically (Plomin et al. 1985 ). The nature of nurture is one of the major directions for research in behavioral genomics, as discussed later.

Heritability Increases During Development

Another milestone in the 1970s was the Louisville Twin Study in which mental development of 500 pairs of twins was assessed longitudinally and showed that the heritability of intelligence increases from infancy to adolescence (Wilson 1983 ). In light of the replication crisis in science (Ritchie 2021 ), a cause for celebration is that this counterintuitive finding of increasing heritability of intelligence – from about 40% in childhood to more than 60% in adulthood -- has consistently replicated, as seen in cross-sectional (Haworth et al. 2010 ) and longitudinal (Briley and Tucker-Drob 2013 ) mega-analyses.

In 1977, a landmark paper showed how univariate analysis of variance can be extended to multivariate analysis of covariance in a model-fitting framework (Martin and Eaves 1977 ). They applied their approach to cognitive abilities and found an average genetic correlation of 0.52, indicating that many genes affect diverse traits, called pleiotropy . Subsequent studies also yielded genetic correlations greater than 0.50 between diverse cognitive abilities (Plomin and Kovas 2005 ).

In the 1970s and 1980s, bigger and better studies made most of the major quantitative genetic discoveries, going far beyond merely estimating heritability. But it was not all smooth sailing. Most notably, The Bell Curve resurrected many of the issues that followed Jensen’s 1969 paper (Herrnstein and Murray 1996 ). Nonetheless, by the 1990s, quantitative genetic research had convinced most scientists of the importance of genetics for behavioral traits, including intelligence (Snyderman and Rothman 1990 ). One symbol of this change was that the 1992 Centennial Conference of the American Psychological Association chose behavioral genetics as one of two themes that best represented the past, present, and future of psychology (Plomin and McClearn 1993 ). Then, just as quantitative genetic discoveries began to slow, the synthesis with molecular genetics began, which led to the DNA revolution and behavioral genomics.

Molecular Genetics

During its first 50 years, molecular genetics focused on single-gene disorders. In 1933, a Nobel prize was awarded to Thomas Hunt Morgan for mapping genes responsible for single-gene mutations in fruit flies (Morgan et al. 1923 ), but human mapping was stymied because only a few single-gene markers such as blood types were available – variants in DNA itself were not available for another fifty years. Research on single-gene effects discovered in pedigree studies only incidentally involved behavioral traits. For example, phenylketonuria, the most common single-gene metabolic disorder, was discovered in 1934 (Folling 1934 ) and shown to be responsible for 1% of the population institutionalized for severe intellectual disability.

In the 1940s, it became clear that DNA is the mechanism of heredity, culminating in the most famous paper in biology which proposed the double-helix structure of DNA (Watson and Crick 1953 ). An important milestone for human behavioral genetics was the discovery in 1959 that the most common form of intellectual disability, Down syndrome, was due to a trisomy of chromosome 21 (Lejeune et al. 1959 ).

In 1961, the genetic code was cracked showing that three-letter sequences of the four-letter alphabet of DNA coded for the 20 amino acids (Crick et al. 1961 ). Just as with quantitative genetics, the 1970s was a watershed decade that ushered in the second 50 years, the genomics era.

The Genomics Era

The era of genomics began in the 1970s when methods were developed to sequence DNA’s nucleotide bases (Sanger et al. 1977 ). In 2003, fifty years after the discovery of the double helix structure of DNA, the Human Genome Project identified the sequence of 92% of the three billion nucleotide bases in the human genome (Collins et al. 2003 ).

In the 1980s, the first common variants in DNA itself were discovered, restriction fragment length polymorphisms (RFLPs) (Botstein et al. 1980 ). RFLPs enabled linkage mapping for single-gene disorders and were the basis for DNA fingerprinting, which revolutionized forensics (Jeffreys 1987 ). Polymerase chain reaction (PCR) was also developed which facilitated genotyping by rapidly amplifying DNA fragments (Mullis et al. 1986 ). In the 1980s, these developments increased the pace of linkage mapping of single-gene disorders, many of which had cognitive consequences, such as phenylketonuria (Woo et al. 1983 ) and Huntington disease (Gusella et al. 1983 ). In the 1990s, DNA sequencing revealed thousands of single-nucleotide polymorphisms (SNPs), the most common DNA variant (Collins et al. 1997 ).

In the 1990s, linkage was also attempted for complex traits that did not show single-gene patterns of transmission, such as reading disability (Cardon et al. 1994 ), but these were unsuccessful because linkage, which traces chromosomal recombination between disease genes and DNA variants within families, is unable to detect small effect sizes (Plomin et al. 1994 ). Researchers then pivoted towards allelic association in unrelated individuals, which is much more powerful in detecting DNA variants of small effect size. An early example of association was an allele of the apolipoprotein E gene on chromosome 19 that was found in 40% of individuals with late-onset Alzheimer disease as compared to 15% in controls (Corder et al. 1993 ).

The downside of allelic association is that an association can only be detected if a DNA variant is itself the functional gene or very close to it. For this reason, and because genotyping each DNA variant was slow and expensive, the 1990s became the decade of candidate gene studies in which thousands of studies reported associations between complex behavioral traits and a few ‘candidate’ genes, typically neurotransmitter genes thought to be involved in behavioral pathways. However, these candidate-gene associations failed to replicate because these studies committed most of the sins responsible for the replication crisis (Ioannidis 2005 ). For example, when 12 candidate genes reported to be associated with intelligence were tested in three large samples, none replicated (Chabris et al. 2012 ).

Genome-wide Association

In 1996, an idea emerged that was the opposite of the candidate-gene approach: using thousands of DNA variants to systematically assess associations across the genome in large samples of unrelated individuals (Risch and Merikangas 1996 ). However, genome-wide association (GWA) seemed a dream because genotyping was slow and expensive.

The problem of genotyping each DNA variant in large samples was solved in the 2000s by the commercial availability of DNA microarrays, called SNP chips , which genotype hundreds of thousands of SNPs for an individual quickly, accurately, and inexpensively. SNP chips paved the way for GWA analyses. In 2007, the first major GWA analysis included 2000 cases for each of seven major disorders and compared SNP allele frequencies for these cases with controls (The Wellcome Trust Case Control Consortium 2007 ). Replicable associations were found but they were few in number and extremely small in effect size. Hundreds of GWA reports appeared in the next decade with similarly small effect sizes across the behavioral and biological sciences (Visscher et al. 2017 ), including cognitive traits such as educational attainment (Rietveld et al. 2013 ) and intelligence in childhood (Benyamin et al. 2014 ) and adulthood (Davies et al. 2011 ).

These GWA studies led to the realization that the biggest effect sizes were much smaller than anyone anticipated. For case-control studies, risk ratios were less than 1.1, and for dimensional traits, variance explained was less than 0.001. This meant that huge sample sizes would be needed to detect these miniscule effects, and thousands of these associations would be needed to account for heritability, which is usually greater than 50% for cognitive traits. Ever larger GWA samples scooped up more of these tiny effects. Most recently, a GWA meta-analysis with a sample size of 3 million netted nearly four thousand independent significant associations after correction for multiple testing, but the median effect size of these SNPs accounted for less than 0.0001 of the variance (Okbay et al. 2022 ).

A century after Fisher’s 1918 paper, the discovery of such extreme polygenicity (Boyle et al. 2017 ; Visscher et al. 2021 ) was a turning point in the voyage from behavioral genetics to behavioral genomics. GWA genotypes brought the two worlds of genetics together by making it possible to use GWA genotypes to create three sets of tools to investigate highly polygenic traits: quantitative genomics, polygenic scores, and causal modeling (see Fig.  1 ). When applied to behavioral traits, these tools constitute the new field of behavioral genomics.

Quantitative Genomics

What good are SNP associations that account for such tiny effects? The molecular genetic goal of tracking effects from genes to brain to behavior is daunting when the effects are so small. However, in contrast to this bottom-up approach from genes to behavior, the top-down perspective of behavioral genetics answered this question by using GWA genotypes to estimate quantitative genetic parameters of heritability and genetic correlations, which could be called quantitative genomics . The journey picked up speed as quantitative genomics led to three new milestones.

Genome-wide Complex Trait Analysis (GCTA). In 2011, the first new method was devised to estimate heritability and genetic correlations since twin and adoption designs in the early 1900s. GCTA (originally called GREML) uses GWA genotypes for large samples of unrelated individuals to compare overall SNP similarity to phenotypic similarity pair by pair for all pairs of individuals (Yang et al. 2011 ). The extent to which SNP similarity explains trait similarity is called SNP heritability because it is limited to heritability estimated by the SNPs on the SNP chip. Genetic correlations are estimated by comparing each pair’s SNP similarity to their cross-trait phenotypic similarity.

SNP heritability estimates are about half the heritability estimated by twin studies (Plomin and von Stumm 2018 ). This ‘missing heritability’ occurs because SNP heritability is limited to the common SNPs genotyped on current SNP chips, which also creates a ceiling for discovery in GWA research. Most SNPs are not common, and rare SNPs appear to be responsible for much of the missing heritability, at least for height (Wainschtein et al. 2022 ). Importantly, quantitative genomic estimates of genetic correlations are not limited in this way and thus provide estimates of genetic correlations similar to those from twin studies (Trzaskowski et al. 2013 ).

Linkage Disequilibrium Score (LDSC) Regression. In 2015, a second quantitative genomic method, LDSC, was published which estimates heritability and genetic correlations from GWA summary effect size statistics for each SNP, corrected for linkage disequilibrium between SNPs (Bulik-Sullivan et al. 2015 ). LDSC estimates of heritability and genetic correlations are similar to GCTA estimates, although GCTA estimates are generally more accurate (Evans et al. 2018 ; Ni et al. 2018 ). The advantage of LDSC is that it can be applied to published GWA summary statistics in contrast to GCTA which requires access to GWA data for individuals in the GWA study.

Genomic Structural Equation Modeling (Genomic SEM). In 2019, a third quantitative genomic analysis completed the arc from quantitative genetics to quantitative genomics by combining quantitative genetic structural equation model-fitting, routinely used in twin analyses, to LDSC heritabilities and genetic correlations (Grotzinger et al. 2019 ). Genomic SEM provides insights into the multivariate genetic architecture of cognitive traits (Grotzinger et al. 2019 ) and psychopathology (Grotzinger et al. 2022 ).

The second answer to the question about what to do with SNP associations that have such small effect sizes is the creation of polygenic scores.

Polygenic Scores

A milestone that marks the spot where the DNA revolution began to transform the behavioral sciences is polygenic scores. Rather than using GWA genotypes to estimate SNP heritabilities and genetic correlations, polygenic scores use GWA genotypes to create a single score for each individual that aggregates, across all SNPs on a SNP chip, an individual’s genotype for each SNP (0, 1 or 2) weighted by the SNP’s effect size on the target trait as indicated by GWA summary statistics. In 2001, polygenic scores were introduced in plant and animal breeding (Meuwissen et al. 2001 ) and later in cognitive abilities (Harlaar et al. 2005 ) and psychopathology (Purcell et al. 2009 ). GWA summary statistics needed to create polygenic scores are now publicly available for more than 500 traits, including dozens for psychiatric disorders and other behavioral traits including cognitive traits (PGS Catalog 2022 ).

The most predictive polygenic scores in the behavioral sciences are for cognitive traits, especially educational attainment and intelligence. Early GWA studies of cognitive traits were underpowered to detect the small effects that we now know are responsible for heritability (Plomin and von Stumm 2018 ). In 2013, a landmark was a GWA study of educational attainment with a sample size exceeding 100,000 (Rietveld et al. 2013 ). A polygenic score derived from its GWA summary statistics predicted 2% of the variance of educational attainment in independent samples. The finding that the biggest effects accounted for only 0.0002 of the variance of educational attainment made it clear that much larger samples would be needed to scoop up more of the tiny effects responsible for the twin heritability estimate of about 40%. In the past decade, the predictive power of polygenic scores for educational attainment has increased with increasing sample sizes from 2% (Rietveld et al. 2013 ) to 5% (Okbay et al. 2016 ) to 10% (Lee et al. 2018 ) to 14% in a GWA study with a sample size of three million (Okbay et al. 2022 ). The current polygenic score for intelligence, derived from a GWA study with a sample of 280,000, predicted 4% of the variance (Savage et al. 2018 ), but, together, the polygenic scores for educational attainment and intelligence predicted 10% of the variance of intelligence test scores (Allegrini et al. 2019 ).

The next milestone will be to narrow the gap between heritability explained by polygenic scores and SNP heritability. A more daunting challenge will be to break through the ceiling of SNP heritability to reach the heritability estimated by twin studies. Reaching both of these destinations will be facilitated by even larger GWA studies and whole-genome sequencing (Wainschtein et al. 2022 ).

Polygenic scores are unique predictors because inherited DNA variations do not change systematically during life – there is no backward causation in the sense that nothing in the brain, behavior or environment changes inherited differences in DNA sequence. For this reason, polygenic scores can predict behavioral traits from early in life without knowing anything about the intervening pathways between genes, brain, and behavior.

Polygenic scores have brought behavioral genetics to the forefront of research in many areas of the life sciences because polygenic scores can be created in any sample of unrelated individuals for whom GWA genotype data are available. No special samples of twins or adoptees are needed, nor is it necessary to assess behavioral traits in order to use polygenic scores to predict them.

Although the implications and applications of polygenic scores derive from its power to predict behavioral traits without regard to explanation (Plomin and von Stumm 2022 ), another milestone on the road to behavioral genomics has been the leverage provided by GWA genotypes for causal modeling.

Causal Modeling

A final milestone on the journey from behavioral genetics to behavioral genomics is a suite of new approaches that use GWA genotypes in causal models that attempt to dissect sources of genetic influence on behavioral traits (Pingault et al. 2018 ). Although traditional quantitative genetic models are causal models, GWA genotypes have enhanced causal modeling in research on assortative mating (Border et al. 2021 ; Yengo et al. 2018 ), population stratification (Abdellaoui et al. 2022 ; Lawson et al. 2020 ), and Mendelian randomization (Richmond and Davey Smith 2022 ).

An explosion of research on genotype-environment correlation was ignited by a 2018 paper in Science on the topic of the nature of nurture (Kong et al. 2018 ). The study included both parent and offspring GWA genotypes and showed that a polygenic score computed from non-transmitted alleles from parent to offspring influenced offspring educational attainment; these indirect effects were dubbed genetic nurture . GCTA has also been used to investigate genotype-environment correlation (Eilertsen et al. 2021 ). Although a great strength of behavioral genomics is its ability to investigate genetic influence in samples of unrelated individuals, combining GWA genotypes with traditional quantitative genetic designs has also enriched causal modeling (McAdams et al. 2022 ), for example, by comparing results within and between families (Brumpton et al. 2020 ; Howe et al. 2022 ).

This whistle-stop tour has highlighted some of the milestones in a century of research in behavioral genetics. The progress is unmatched in the behavioral sciences and its discoveries have been transformative. The most exciting development is the synthesis of quantitative genetics and molecular genetics into behavioral genomics. The energy from this fusion will propel the field far into the future.

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This work was supported in part by the UK Medical Research Council (MR/V012878/1 and previously MR/M021475/1).

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Plomin, R. Celebrating a Century of Research in Behavioral Genetics. Behav Genet 53 , 75–84 (2023). https://doi.org/10.1007/s10519-023-10132-3

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Published : 20 January 2023

Issue Date : March 2023

DOI : https://doi.org/10.1007/s10519-023-10132-3

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Genetic association study opens up new treatment avenues for Pick's disease, a rare form of early-onset dementia

by Mayo Clinic

Pick's disease, a neurodegenerative disease of unknown genetic origin, is a rare type of frontotemporal dementia that affects people under the age of 65. The condition causes changes in personality, behavior and sometimes language impairment.

In patients with the disease, tau proteins build up and form abnormal clumps called Pick bodies, which restrict nutrients to the brain and cause neurodegeneration. The only way to diagnose the disease is by looking at brain tissue under a microscope after a person dies.

Researchers at Mayo Clinic in Florida, University College London in England and collaborators worldwide have established the Pick's Disease International Consortium to study a specific MAPT gene variation known as MAPT H2 that makes the tau protein and acts as a driver of disease. They investigated a connection between the gene and disease risk, age at onset, and duration of Pick's disease. Their findings are reported in The Lancet Neurology .

Mayo Clinic researchers identified the first MAPT gene mutations for a behavioral form of dementia in 1998, and other genetic changes associated with related dementias in 2001, which paved the way to understanding the mechanisms of tau-related disease. This new study confirms a tau genetic factor linked specifically to Pick's disease and opens up new avenues of therapeutic design.

"Our research could have profound implications for the development of therapies for Pick's disease and other related neurodegenerative diseases, including Alzheimer's disease and progressive supranuclear palsy ," says Owen Ross, Ph.D., a Mayo Clinic neuroscientist and senior author of the paper. The consortium hosts a database of clinical, pathological and demographic information about patients with the disease who donated their brain tissue for science.

To conduct the study, researchers investigated brain samples of 338 patients confirmed to have Pick's disease to compare with blood samples from 1,312 neurologically healthy individuals. Patients confirmed to have the disease came from 35 brain banks and hospitals in North America, Europe and Australia between 2020 and 2023. The Mayo Clinic Brain Bank was among the sites in the study that provided the largest collection of samples.

Analyzing DNA from the blood samples and brain tissue, the research team recorded baseline information on study participants, including age at disease onset, age at death for those with Pick's disease, and sex and age at blood collection for the control group.

Disease duration was calculated by the difference between age at Pick's disease onset and age at death. In addition, the researchers looked at clinical characteristics such as clinical diagnosis, impairment in behavior and language.

"We found that the MAPT H2 genetic variant is associated with an increased risk of Pick's disease in people of European descent," says Dr. Ross. "We were only able to determine that because of the global consortium, which greatly increased the sample size of pathology cases to study with Pick's disease."

The team's next steps are to expand the consortium to the Middle East, Asia, Africa and Latin America, further resolve the genetic architecture of the disease, and assess this specific genetic variant as a biomarker or test for clinical diagnosis of Pick's disease. There is currently no clinical test or diagnosis available for Pick's disease. For the first time, the creation of the consortium may allow for the development of a clinical test.

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Metacognitive abilities like reading the emotions and attitudes of others may be more influenced by environment than genetics

Twin studies have proven invaluable for teasing out the effects of both genetics and the environment on human biology. In a study published April 2 in Cell Reports , researchers studied pairs of twins to look at how the interplay of genetics and environment affect cognitive processing -- the way that people think. They found that some cognitive abilities appear to be regulated more by environmental factors than by genetics.

"Past research has suggested that general intelligence -- often referred to as intelligence quotient or IQ -- has a heritability ranging from 50% to 80%," says senior and corresponding author Xiaohong Wan of Beijing Normal University in China. "Our study may be the first to demonstrate that a different kind of cognitive ability, known as metacognition and mentalizing, might be much more influenced by environment."

Cognitive functions such as perception, attention, memory, language, and planning are considered to be the basis for general intelligence. These functions regulate the way that people organize and process new information. By contrast, metacognition looks at how well people understand and control their cognitive processes.

Metacognition is important for developing learning strategies and is believed to be a predictor of an individual's successes in school and social achievements. Mentalizing describes the process of recognizing and understanding mental states like emotions and attitudes, both in ourselves and in other people.

For this research, the investigators recruited 57 pairs of adult monozygotic (identical) twins and 48 pairs of dizygotic (fraternal) twins from the Beijing Twin Study (BeTwiSt). This is an ongoing, long-term study established in 2006 that includes extensive data like brain images and psychological surveys, as well as genetic information, on pairs of twins.

The twins were asked to perform tasks related to metacognition. These tasks consisted of watching a cluster of moving dots on a screen and making a perceptual judgement on the net direction of the dots. They were also asked to rate their confidence about their decisions. To measure mentalizing, the participants were asked to evaluate a partner's confidence in their decision-making abilities.

The investigators found that pairs of twins who had parents with higher levels of education and higher family incomes have similar results to each other, regardless of whether they were identical or fraternal. These observations suggested that familial environment was more likely to influence metacognitive abilities than genetics.

"Our findings were outside our expectations," Wan says. "Decades of extensive research utilizing the classical twin paradigm have consistently demonstrated the heritability of nearly all cognitive abilities so far investigated. Our findings emphasize that these shared family environmental factors, such as parental nurturing and the transmission of cultural values, likely play a significant role in shaping the mental state representations in metacognition and mentalizing."

The researchers acknowledge that there are limitations to this research and that many more studies are needed. They plan to continue their research in this area, including using population studies to further investigate what kind of specific parental nurturing factors and sociocultural values affect individuals' metacognitive and mentalizing abilities.

This research was supported by the Ministry of Science and Technology of the People's Republic of China, the National Natural Science Foundation of China, the Interdisciplinary Innovation Team of the Chinese Academy of Sciences, and the BeTwiSt of Institute of Psychology, Chinese Academy of Sciences.

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• Shaohan Jiang, Fanru Sun, Peijun Yuan, Yi Jiang, Xiaohong Wan. Distinct genetic and environmental origins of hierarchical cognitive abilities in adult humans . Cell Reports , 2024; 43 (4): 114060 DOI: 10.1016/j.celrep.2024.114060

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Comparison of biosimilar Tigerase and Pulmozyme in long-term symptomatic therapy of patients with cystic fibrosis and severe pulmonary impairment (subgroup analysis of a Phase III randomized open-label clinical trial (NCT04468100))

Affiliations.

• 1 State Budgetary Healthcare Facility of Moscow City D.D. Pletnyov City Clinical Hospital of the Department of Healthcare of Moscow, Moscow, Russia.
• 2 JSC GENERIUM, Moscow, Russia.
• 3 Research Centre for Medical Genetics, Moscow, Russia.
• 4 Department of Hospital Therapy, Federal State Budgetary Educational Institution of Higher Education Kazan State Medical University of the Ministry of Health of the Russian Federation, Kazan, Russia.
• 5 State Healthcare Institution of Yaroslavl Region Children's Clinical Hospital No. 1, Cystic Fibrosis Centre, Yaroslavl, Russia.
• 6 State Autonomous Healthcare Institution of Yaroslavl Region Clinical Hospital No. 2, Yaroslavl, Russia.
• 7 State Budgetary Healthcare Institution G.G. Kuvatov Republican Clinical Hospital, Republic of Bashkortostan, Russia.
• 8 State Budgetary Healthcare Institution of Novosibirsk Region State Novosibirsk Regional Clinical Hospital, Novosibirsk, Russia.
• 9 State Budgetary Healthcare Institution of Sverdlovsk Region Sverdlovsk Regional Clinical Hospital No. 1, Yekaterinburg, Russia.
• 10 State Budgetary Healthcare Institution Chelyabinsk Regional Clinical Hospital, Chelyabinsk, Russia.
• 11 Krai State Budgetary Healthcare Institution Krai Clinical Hospital (KSBHI Krai Clinical Hospital), Barnaul, Russia.
• 12 State Budgetary Healthcare Institution Municipal Multidisciplinary Hospital No. 2, Saint Petersburg, Russia.
• 13 Federal State Budgetary Scientific Institution Tomsk National Research Medical Centre of the Russian Academy of Sciences (Tomsk NRMC), Clinic of Genetics, Research Scientific Institute of Medical Genetics, Tomsk, Russia.
• 14 Research Medical Centre for General Therapy and Pharmacology, Limited Liability Company, Stavropol, Russia.
• 15 Department of Clinical Pharmacology and Intensive Care, Volgograd State Medical University, Volgograd, Russia.
• PMID: 34941914
• PMCID: PMC8699637
• DOI: 10.1371/journal.pone.0261410

Background: Patients with cystic fibrosis (CF) need costly medical care and adequate therapy with expensive medicinal products. Tigerase® is the first biosimilar of dornase alfa, developed by the lead Russian biotechnology company GENERIUM. The aim of the manuscript to present post hoc sub-analysis of patients' data with cystic fibrosis and severe pulmonary impairment of a larger comparative study (phase III open label, prospective, multi-centre, randomized study ( NCT04468100 )) of a generic version of recombinant human DNase Tigerase® to the only comparable drug, Pulmozyme®.

Methods: In the analyses included subgroup of 46 severe pulmonary impairment patients with baseline FEV1 level 40-60% of predicted (23 patients in each treatment group) out of 100 patients registered in the study phase III open label, prospective, multi-center, randomized study ( NCT04468100 ), and compared efficacy endpoints (FEV1, FVC, number and time of exacerbations, body weight, St.George's Respiratory Questionnaire) as well as safety parameters (AEs, SAEs, anti-drug antibody) within 24 treatment weeks.

Results: All outcomes were comparable among the studied groups. In the efficacy dataset, the similar mean FEV1 and mean FVC changes for 24 weeks of both treatment groups were observed. The groups were also comparable in safety, all the secondary efficacy parameters and immunogenicity.

Conclusions: The findings from this study support the clinical Tigerase® biosimilarity to Pulmozyme® administered in CF patients with severe impairment of pulmonary function.

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• Clinical Trial, Phase III
• Randomized Controlled Trial
• Research Support, Non-U.S. Gov't
• Biosimilar Pharmaceuticals / chemical synthesis
• Biosimilar Pharmaceuticals / therapeutic use*
• Cystic Fibrosis / complications
• Cystic Fibrosis / drug therapy*
• Cystic Fibrosis / physiopathology
• Deoxyribonuclease I / chemistry
• Deoxyribonuclease I / metabolism
• Deoxyribonuclease I / therapeutic use*
• Deoxyribonucleases / therapeutic use*
• Expectorants / therapeutic use
• Forced Expiratory Volume
• Lung / physiopathology
• Lung Diseases / drug therapy
• Lung Diseases / physiopathology
• Middle Aged
• Mucociliary Clearance
• Prospective Studies
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• Recombinant Proteins / metabolism
• Recombinant Proteins / pharmacology
• Recombinant Proteins / therapeutic use
• Biosimilar Pharmaceuticals
• Expectorants
• Recombinant Proteins
• Deoxyribonucleases
• Deoxyribonuclease I
• dornase alfa

Associated data

• ClinicalTrials.gov/NCT04468100

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Challenges Faced by Behavioral Genetic Studies: Researchers Perspective from the MENA Region

Omar f. khabour.

1 Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan;

Ahmed A. Abu-Siniyeh

2 Department of Clinical Laboratory Sciences, Faculty of Science, The University of Jordan, Amman, Jordan;

Karem H. Alzoubi

3 Department of Pharmacy Practice and Pharmacotherapeutics, University of Sharjah, Sharjah, UAE;

4 Department of Clinical Pharmacy, Jordan University of Science and Technology, Irbid, 22110 Jordan;

Nihaya A. Al-Sheyab

5 Faculty of Nursing, Maternal and Child Health Department, Jordan University of Science and Technology, Irbid, 22110, Jordan;

6 Charles Perkins Centre and Faculty of Nursing and Midwifery, The University of Sydney, Sydney, Australia

Associated Data

Data will be made available upon reasonable request through email to the corresponding author [KHA].

Behavioral genetic studies are important for the understanding of the contribution of genetic variations to human behavior. However, such studies might be associated with some ethical concerns.

In the current study, ethical challenges related to studies of genetic variations contributing to human behavior were examined among researchers. To achieve the study purpose, the Middle East and North Africa (MENA) region researchers were taken as an example, where the after- mentioned ethical challenges were discussed among a group of researchers, who were the participants of an online forum. Discussions and responses of the participants were monitored and were later qualitatively analyzed.

Discussions revealed that several ethical challenges, including subjects’ recruitment, the difficulty of obtaining informed consents, and issues of privacy and confidentiality of obtained data as information leakage, in this case, will lead to social stigma and isolation of the participants and their immediate family members. Jordanian social and cultural norms, faith, and the tribal nature of the population were raised as a major challenge that might face conducting behavioral genetic studies in the Arab populations of the MENA. The lack of regulation related to the conduction of genetic studies, misunderstanding, and misuse of genetic information are other challenges. A full explanation of genetic research and the current and future possible benefits/risks of such research could be potential solutions.

In conclusion, the MENA populations are tackled with major challenges in relation to conducting research studies in genetics/antisocial behavior field/s. Establishment of guidelines related to genetic studies, capacity building, increasing public awareness about the importance of genetic testing, and enhancing responsible conduct of research will facilitate the conduct of such sensitive studies in the future in the region.

1. INTRODUCTION

Antisocial behaviors are defined as angry, hostile, or aggressive acts that harm and lack consideration for others' comfort or violate their rights in which the aggressor usually has no regret for what he or she has committed [ 1 , 2 ]. Such kind of condition has a variety of symptoms, which begins in preschool-aged children and continues through adulthood [ 3 ]. It has been found that around 50 percent of the adults who suffer from antisocial behavior symptoms have acquired these behaviors since they were in elementary school, and those symptoms persisted until adulthood. Symptoms included unashamed disrepute, disregard for others, scarcity of ethical behavior, offensive to others without a sign of bother for the other, irritability, and aggression [ 4 ].

The etiology of antisocial behavior is heterogeneous, so that each condition is different from the other and the notion of the causes of antisocial behavior relies on different factors such as environmental, biological, and genetic factors [ 5 ]. Different biological factors have an implication in developing antisocial behavior, such as brain damage during pregnancy, brain hypoxia in the womb or birth, or neuropsychological dysfunction and psychosocial influences [ 6 ]. A number of studies also showed that environmental factors have a major role in the creation of antisocial behavior, especially during childhood [ 7 ]. Examples of such factors include; the person exposed to domestic violence and abused in his home, his parents being drug users, was abused during his childhood either sexually, physically, or emotionally or an unstable home environment [ 5 , 7 ].

As shown by various studies, genes have an impact on childhood antisocial and aggressive behavior during childhood, and scientists have recognized specific genetic associations with antisocial behavior [ 8 , 9 ]. Even with an increasing understanding of the genetic bases of human behavior, a cautious approach is warranted either in making inferences about a given individual or in considering changes to the legal system that might now take a defendant's experience and disposition into account [ 10 , 11 ]. In fact, antisocial behavior had a clear genetic component and was shown to be influenced by certain genetic polymorphisms [ 12 ]. Polymorphisms in four genes have been found to be associated with an increased vulnerability for antisocial and impulsive behavior in response to aversive environmental conditions [ 13 ]. These genes include 1) MAOA that codes for monoamine oxidase A enzyme, which has an important implication in dopamine, noradrenaline, and serotonin metabolism [ 14 ]; 2) SLC6A4 gene codes for the serotonin transporter SLC6A4, which plays an important role in controlling serotonin levels in the synaptic cleft [ 15 ]; 3) COMT gene codes for catechol-O-methyltransferase), which is the main regulator of dopamine levels in synapses [ 16 ]; 4) DRD4 gene codes for the dopamine D4 receptor, which is a G protein-coupled receptor that is highly expressed in the cerebellum and plays a significant role in dopaminergic synapses [ 17 ]. In a genome-wide study of antisocial behavior in a large combined sample, it has been shown that a large number of genetic variants play a role in antisocial behavior and several variants show gender-specific effects on antisocial behavior in males and females [ 18 ]. In the current study, the ethical challenges face by genetic studies that examine genetic variations contributing to antisocial behavior were discussed in this study by taking the Middle East and North Africa (MENA) researchers as an example.

2.1. Study Design

In the current study, a descriptive qualitative approach was used to explore the research ethics of genetic variations contributing to antisocial behavior by taking MENA researchers as an example. Researchers from MENA participated in the study as a part of the Responsible Conduct of Research (RCR) training program, which was hosted by the online learning platforms of Jordan University of Science and Technology and the University of California at San Diego, CA, USA. This program was funded by The National Institute of Health/Fogarty International Center to enhance the RCR in the MENA region. Twenty-eight researchers were the study participants, including 18 from Jordan, 2 from Tunisia, 2 from Morocco, and one from each of the following countries: Egypt, Yemen, Iraq, Sudan, Algeria, and Gaza-Strip/Palestinian Authority. The majority of participants were faculty members (PhD holders, n=24) and 4 were MSc research assistants.

The opinions of the scientists (n=28) were collected through an online discussion forum. The forum was opened for a total of two weeks, with enough time provided by the moderator for each of the questions to motivate online discussion. The posts in the discussion (n=118) were used as a tool for qualitative analysis and were categorized into six ethical themes 1) subjects recruitment, 2) informed consent process, 3) privacy and confidentiality, 4) nature of the population (culture, norms, and regulations), 5) interpretation of the findings, and 6) Risks and benefits. An expert qualitative researcher monitored forum discussions. This process included promoting the study participants to be involved in the discussion using some provocative statements and probe questions such as “good point that needs more elaboration ….”, “could you explain more…”, etc. Such a monitoring process is effective in improving the validity of the collected data and study findings [ 19 ]. To reduce bias, a different researcher other than the one who monitored the discussion forum transcribed the collected data into different domains. In detail , the moderator was responsible for posting the main questions and probing when needed, and then he directed and facilitated the online discussion. Participation in the online discussions was voluntary and participants were informed that they have the right to withdraw at any point without penalties. All discussions in the forum were transcribed verbatim for analysis. Researchers conducted the analysis process in its original English to maintain fidelity of the results, which could be lost by early and inaccurate translation. Preliminary analysis was conducted after the end of the online discussion on the forum to get a general impression of the data. Analysis of the transcribed data was undertaken manually through the coding process and generating categories and themes by two independent researchers, who then met to overcome any inconsistency in the categorization of themes and subthemes and reach a consensus.

2.2. Ethical Approval

The Institutional Review Board (IRB) at the Jordan University of Science and Technology (IRB-JUST) approved this study. The IRB applies the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Participants gave informed consent before they participated in the forum.

Participants were asked about their opinion concerning ethical challenges for studies that examine genetic variations contributing to antisocial behavior in the Arabic populations. A total of 6 challenges were raised/discussed by the participants (Table ​ 1 1 ). The order of challenges according to number of participants who raised/discussed them is privacy and confidentiality > risks and benefits > culture/norms/regulations > informed consent > interpretations of the findings > Subjects’ recruitments (Table ​ 1 1 ). Details about challenges are presented below:

Main challenges of conduction of antisocial genetic research in the MENA region

3.1. Subjects' Recruitment

Several participants pointed to a challenge in recruiting subjects for this kind of research due to the sensitivity of the topic and the lack of awareness and knowledge of such studies and their test results.

Among suggested solutions to this challenge are creating more awareness about genetic studies, proper genetic counseling, and highlighting potential benefits.

One participant stated that “participants always concentrate on the direct benefit of research”.

Another participant said, “The lack of interest of the participants in such studies will pose an obstacle in recruiting”. However, one participant suggested a solution for the recruitment challenge: “Development of effective interventions based on genetic testing could promote motivation to participate.”

3.2. Informed Consent Process

Participants discussed the reasons why informed consent in genetic studies in Arab populations is difficult to obtain, and whether this difficulty (if any) applies only to antisocial studies or for all types of research, and how this problem can be solved?

Three members agreed that “the hardest part is achieving the consent form”.

One participant stated that “……. As the information gained from genetic testing will be retraced to the whole family, thus, submitting the consent will be very hard to achieve …”.

Most members also stressed that informed consent is required and participants need to have a full explanation of the current and future possible benefits/risks of such research in order to convince the population of signing the consent form.

For example, one member stated, “I think genetic counseling is one way to deliver information. I think an explanation and understanding of some of the human behavior by participants would encourage them to accept informed consent and participation in such studies.”

Another member said “The main ethical challenge in antisocial studies is the high risk of stigma and its devastating consequences that need to be stated in the consent form.”

3.3. Privacy and Confidentiality

Others highlighted matter of privacy and confidentiality of medical information of the study participants as the leakage in most cases would lead to stigmatizing related individuals, including socially isolating them and creating negative psychological effects on them.

A female member (S.A) said “Confidentiality breach and disclosure of such information generated by genetic studies can associate some families with some genetic diseases (even if rare) in the form of the social stigma that can lead to isolation and all psychological consequences……”

Such information might be used inappropriately by private insurance companies by depriving/ refuse to cover persons from stigmatized families.”

One male member pointed to the shared nature of genetic information and highlighted that breaching confidentiality can also harm the relatives of participants.

The member stated “….. Being afraid of breaching confidentiality and privacy…..These genetic findings might affect negatively the carrier (relatives of the person who has the genetic condition.”

3.4. Nature of the Population: Culture, Norms, and Regulations

Two participants stressed the importance of Jordanian social norms, culture, faith, and the tribal nature of the population, which might form an obstacle in conducting antisocial genetic-related studies in the Arab populations, including Jordan.

One member stated, “The culture and norms of the Jordanian community could be a major barrier for such genetic tests as people not prepared and are threatened by antisocial consequences”.

Another example: “societies in the Middle East should be prepared to accept the results of genetic studies, this needs a time that is not short. Researchers should play a great effective role in this, simplified lectures, debates, and discussions involving different spectra of the society, implanting the culture of “no discrimination” and even “Drama” and social media can participate in changing the ideas of the societies.”

Another two members stated that “If the study holds conflict with participants’ religious beliefs then it will be almost impossible to recruit the required population”.

Examples related to the lack of regulation related to the conduction of genetic study as an obstacle are “The question of who should be entitled to have the right to access and use such stigmatizing information/records related to families in the population”.

“There is a need to have regulations monitoring the process of such studies due to the fear of having a social misunderstanding of the real purpose behind conducting such studies.”

3.5. Interpretation of the Findings

Misunderstanding and misuse of genetic information are highlighted by several as major challenge s .

For example:

“If such studies proved a direct relation of the “warrior gene” to antisocial behavior then violent individuals will try to get away with punishment by blaming it all on their bad genes”. “Authorities might blame the genes for all antisocial behavior and crimes and disregard other criminating factors.”

“Members/families (in Jordan) that would be diagnosed carrying variants/genes related to causing antisocial behavior will be highly stigmatized, due to the fact that antisocial behavior is always connected to mental illness in Jordanian society”.

“Such research will end up being a tool for destroying others’ lives by stigmatizing them with a specific genetic trait which states that this person has the potential possibility of committing a specific antisocial behavior, consequently condemning them for what they might do and not for what they did”.

3.6. Risks and Benefits

Major discussions related to antisocial genetic research were focused on issues related to benefits/risks from the conduction of such studies. Two members shared opposite opinions concerning whether genetic polymorphisms would increase the possibility of an individual to have an untreatable genetic disorder. Meanwhile, one member recommended that it is not worth it to tell the subject that they hold a good possibility to an untreatable genetic disorder, whereas the other said that knowing such a possibility will create extra awareness within that subject, who in turn will avoid specific behavior which will prevent further and future complications.

One member stated, “If a patient is susceptible to develop an untreatable disease, s/he may benefit from avoiding other factors that could contribute to increasing the risk, the management can be provided early and some complications may be prevented.”

Another member stated, “Do you think that research aiming at genetic testing for the susceptibility for behavioral and cognitive disorders is a waste of money and resources or could it lead to preventative approaches?”

Two members stated that, “Financials should be aimed towards the other affecting factors such as environmental factors and dysfunctional families for that they are easier to identify”, One member mentioned that “Genetic testing of the behavioral disorders is not a waste of money for that preventive measures can be taken should the genetic susceptibility be identified”.

More examples: “Genetic testing for study purposes could reveal certain genetic predispositions for some disease which predisposes participants to social stigma, social isolation, and psychological problems in eastern communities ( e.g. Jordan)”.

“Revealing information about the existence and relation of a gene to the antisocial behavior among a specific population will create a stigmatizing effect which in turn will negatively affect such population, and therefore researchers should think twice when it comes to conducting such research in Jordan”.

4. DISCUSSION

The behavioral genetics is an important topic in genetics, which focuses on investigating the link between genes and criminal and aggressive behaviors and their relations with the surrounding environment [ 20 ]. During the last years, growing pieces of evidence exposed the significant impact of both genes and the environment on an individual’s antisocial behavior that changed the views toward antisocial behavior concepts. Moreover, understanding the causes of antisocial behavior would set the stage for prevention interventions that could significantly reduce the crime rate [ 21 ]. In the current study, a group of MENA researchers examined ethical challenges related to studies of genetic variations contributing to antisocial behavior in the Arab populations. The main challenges discussed included the recruitment of subjects, informed consent process, risks and benefits, privacy and confidentiality, and interpretations of findings and culture.

The first challenge discussed by the researchers is related to recruitments of subjects due to the sensitivity of the topic and lack of awareness about genetic testing in the Arab populations. In a study that was conducted on genetic factors that contribute to epilepsy, the decline-to-participate rate among eligible subjects was about 84%, which has been attributed to confidentiality and lack of compensation [ 22 ]. The recruitment of subjects to cancer genetic studies and clinical trials for genetic diseases was also a challenge [ 23 , 24 ]. Thus, enhancing the awareness of the MENA populations about the importance of genetic studies can improve the recruitment of participants in such studies.

The second main challenge was a violation of a patient’s confidentiality and privacy by genetic testing. In a study that investigated the attitudes of healthcare professionals toward behavioral genetic testing for antisocial behavior, the majority of study participants were against genetic testing unless treatment was obtainable. Participants were worried about probable harm, such as exposing patient’s confidentiality that may lead to social stigma and racial discrimination [ 25 ]. Similar findings were reported in previous studies that highlighted the potential of genetic data in exposing personal/family information such as one's parent, sibling, and children [ 26 - 28 ]. Maintaining participants’ confidentiality and privacy will enhance the trust in researchers and will ultimately increase participation in genetic studies.

A different challenge that the researchers raised is related to the risks and benefits of the study. Some of the researchers doubted concerning applications of the findings and if interventions are available for subjects at risk of antisocial behavior. In a previous study, disagreement regarding the usefulness of such studies was reported. Some participants indicated that genetic testing could provide an early indication for parents about the problems children may face in the near future, which enables them to take their preventative decision early. On the other hand, some participants were concerned about potential risks for such testing that include misinterpretation of the findings, false positives, false negatives, and the danger of stigmatization that potentially comes along with a positive test result [ 29 , 30 ]. With respect to the interpretation of data, which was also discussed by researchers in the current study, it is worth to mention that environmental risk factors have also been identified in influencing antisocial behavior. Actually, environmental factors tend to contribute largely to antisocial outcomes when compared to genetic ones. This is because variations in the environment seem to significantly affect an individual's gene expression via mechanisms that involve epigenetic changes. This will complicate the understanding and prediction of an individual's behavior [ 31 , 32 ].

Social norms, culture, faith, and the tribal nature of the Arab populations of the MENA might be a major difficulty in conducting antisocial genetic related studies. It is not an easy task to convince people to accept the results of genetic studies, especially if the study holds conflict with participants’ religious beliefs that will complicate participation in such a study. Another obstacle that may face genetic studies is stigmatizing the family diagnosed by carrying variants/genes related to causing antisocial behavior because antisocial behavior is always linked to psychological illness among the Jordanian community. The informed consent process was also among the challenges discussed by the researchers. The informed consent was also highlighted as a challenge in several previous studies [ 33 , 34 ]. There is a necessity to comprehend people’s beliefs, awareness, and responses toward genetic testing, which may fill the gap by explaining and clearing vague, concern, and knowledge in such research [ 35 ]. The shortage of expertise in the area of genetic counseling and the absence of regulations in most of the countries in the region will pose difficulties in resolving such a challenge.

The MENA populations are tackled with major challenges in providing comprehensive and up-to-date health services in the field of genetics [ 36 ]. These obstacles include lack of resources, a limited number of trained persons in the area of genetic testing and counseling, a misperception that genetic testing is too expensive to conduct, lack of regulations, and fear of families who have been diagnosed with a certain genetic disorder to be stigmatized within their community. Establishment of guidelines related to genetic studies, capacity building, increasing public awareness about the importance of genetic testing, and enhancing responsible conduct of research will facilitate the conduction of such sensitive studies in the future in the region.

Social behaviors are expected to be affected by strong environmental factors. In fact, the impact of the environment on gene expression via epigenetic mechanisms is well established [ 37 - 40 ]. In recent reviews, epigenetic regulation of genes involved in neuroendocrine, serotonergic and oxytocinergic pathways and their role in modulating personality and vulnerability to proactive and reactive aggressive behavior were discussed [ 41 , 42 ]. Thus, behavioral genetics and epigenetics are important areas in research that help uncover the fine interaction between genes and environment and the subsequent molecular pathways that contribute to aggression in the populations.

Among the limitations of the current study is that the majority of participants were from Jordan. The study was part of a fellowship that was conducted in Jordan. It is worth mentioning that most of the MENA countries share language, culture, religions… etc. , and these countries suffer from the same issues regarding scientific research as explained above. Thus, the study represents the MENA region to some extent. However, expanding the study to have good representations of MENA countries is strongly recommended.

The MENA populations are tackled with major challenges in relation to conducting research studies in genetics/antisocial behavior field/s. Establishment of guidelines related to genetic studies, capacity building, increasing public awareness about the importance of genetic testing, and enhancing responsible conduct of research will facilitate the conduct of such sensitive studies in the future in the region.

ACKNOWLEDGEMENTS

Declared none.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The Institutional Review Board (IRB) at the Jordan University of Science and Technology, Jordan (IRB-JUST) approved this study.

HUMAN AND ANIMAL RIGHTS

No animals were used in this study. For the proceduces on humans the IRB applies the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

CONSENT FOR PUBLICATION

Participants gave informed consent before they participated in the forum.

AVAILABILITY OF DATA AND MATERIALS

Work on this project was supported by grant # 5R25TW010026-02 from the Fogarty International Center of the U.S. National Institutes of Health.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

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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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Begmatov, S., Beletsky, A.V., Dorofeev, A.G. et al. Metagenomic insights into the wastewater resistome before and after purification at large‑scale wastewater treatment plants in the Moscow city. Sci Rep 14 , 6349 (2024). https://doi.org/10.1038/s41598-024-56870-0

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DOI : https://doi.org/10.1038/s41598-024-56870-0

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