Biology subjects

Assis, R.

Publications and source records attributed to Assis, R..

12 recordsLinked to original sources

bioRxiv2026

Ecological context structures duplication and mobilization of antibiotic and metal resistance genes in bacteria

Antibiotic resistance is a global challenge driven by the persistence and spread of resistance genes across ecological contexts. While mobile genetic elements (MGEs) facilitate horizontal gene transfer, gene duplication represents an additional mechanism through which resistance genes can be amplified, diversified, and maintained under selection. How these processes interact across environments remains poorly understood. Here, we examined genome-level patterns of resistance gene abundance, duplication, and mobilization across clinical, agricultural, and wastewater settings, focusing on both antibiotic resistance genes (ARGs) and metal resistance genes (MRGs). Resistance gene profiles were strongly structured by environment, with distinct duplication patterns emerging across sources. Duplicate genes were frequently associated with MGEs, although the strength of this relationship varied by resistance type and ecological context. Despite frequent co-occurrence of ARGs and MRGs, their duplication and mobilization dynamics were not uniformly coupled at the genome level. Together, these findings highlight gene duplication as a context-dependent contributor to resistance evolution and underscore the importance of ecological setting in shaping how resistance genes persist and spread across microbial communities.

bioinformatics↗

bioRxiv2026

Gene function predicts divergence across molecular levels

Genes can evolve through changes at their DNA, RNA, or protein levels. However, because these changes are measured using distinct and often incomparable metrics, their relative contributions to genic evolution remain unclear. Here, we address this challenge by developing a standardized framework for comparing evolutionary divergence in protein-coding sequences, multi-tissue expression profiles, and protein structures. Application of this approach to data from Drosophila, rodents, primates, and Arabidopsis reveals substantial variation in divergence patterns across taxa, with only sequence divergence matching expectations based on evolutionary theory. Moreover, sequences evolve slowest and protein structures fastest in all taxa, consistent with the idea that higher levels of biological organization are closer proxies for the functions on which selection acts. Yet, weak correlations among divergence measures suggest that targets of selection vary across genes, perhaps depending on their functions. Indeed, few genes exhibit similar evolutionary patterns across all three levels, and different functions are enriched in genes with low or high divergence in protein-coding sequences, gene expression profiles, and protein structures. Together, these findings support the hypothesis that evolutionary targets of genes depend on their functions, shedding light on how selection shapes different levels of biological organization across taxa.

evolutionary biology↗

bioRxiv2025

Taxonomic context and genomic architecture jointly shape expression divergence across animals

Gene expression divergence is a major source of phenotypic variation, yet the factors that shift regulatory optima remain incompletely understood. In particular, it is unclear how broad taxonomic differences and local genomic architecture interact to shape the tempo and mode of expression evolution. Here, we analyze single-copy orthologs between species pairs in Drosophila and mammals to quantify taxon-specific rates and magnitudes of expression divergence, assess the influence of genome architecture, and evaluate tissue-specific and functional patterns. Using a computational framework that predicts expression divergence and species-specific expression optima, we identify markedly higher divergence rates in Drosophila than in mammals, consistent with theoretical expectations and prior empirical work. Contrary to expectations, genes located in nested structures, in which one gene lies entirely within the intron of another, were no more likely to diverge than unnested genes in either taxon. However, when divergence occurred in Drosophila, nested genes exhibited larger shifts in expression optima, with the strongest effects among internal genes. Divergence rates were also higher among young than old nested genes in both taxa, although magnitudes of expression shifts were indistinguishable, suggesting that nesting contributes to early regulatory instability but does not typically trigger large regulatory changes. Tissue-level and functional analyses further revealed taxon-and architecture-specific signatures of expression divergence, including contrasting patterns across reproductive and neural tissues, as well as enrichment of core regulatory processes among unnested genes and enrichment of drug and xenobiotic metabolism among nested genes in Drosophila. Collectively, these findings demonstrate that taxonomic context and genome architecture shape expression evolution in distinct and measurable ways, giving rise to contrasting patterns of regulatory divergence.

bioinformatics↗

bioRxiv2025

Adaptive deletion of functional duplicate genes in Drosophila

Gene deletion is traditionally viewed as a nonadaptive mechanism that eliminates functional redundancy, yet emerging evidence indicates that it disproportionately affects tissue-specific duplicates with unique functions. Here, we test whether gene deletion preferentially removes weakly constrained, degenerating duplicates or instead eliminates functionally active duplicates as an adaptive genome streamlining process. To identify the evolutionary and functional factors that determine which duplicates are lost, we systematically analyzed 100 gene deletion events in Drosophila by integrating sequence, expression, interaction, and structural data. We uncovered a strong bias toward the loss of younger child copies among functionally unique duplicates, whereas no such bias was observed for redundant duplicates. Contrary to expectations under relaxed constraint, deleted unique genes evolve more slowly, show higher expression, engage in more protein-protein interactions, and do not exhibit elevated structural divergence or intrinsic disorder relative to redundant duplicates. When compared with single-copy genes, deleted unique genes display similar evolutionary rates, slightly lower expression, greater network connectivity, comparable structural divergence, and lower intrinsic disorder. These patterns suggest that deletion frequently targets functionally active rather than degenerate genes. Collectively, our results support the hypothesis that gene deletion in Drosophila can represent an adaptive process that removes transiently functional duplicates, promoting genome streamlining and regulatory stability. SIGNIFICANCEGene deletion is commonly assumed to be a nonadaptive consequence of redundancy or functional decay after gene duplication. This view has shaped how gene loss is interpreted across evolutionary studies. Using 100 gene deletion events in Drosophila, we show that this assumption can be misleading. Deletion strongly favors loss of the younger child copy among functionally unique duplicate genes, whereas redundant duplicates show no such bias. Deleted unique genes also lack signatures of degeneration: they evolve slowly, show relatively high expression, participate in extensive protein-protein interaction networks, exhibit typical levels of structural divergence, and tend to be structurally ordered. Together, these results support a model in which gene deletion can selectively remove transiently functional duplicates, contributing to genome streamlining and regulatory stability. More broadly, our study reframes gene deletion as a potentially adaptive force shaping gene content evolution.

evolutionary biology↗

bioRxiv2025

Genomic insights into ancestry and infectious disease in 17th-century colonial Brazil

During urban redevelopment in the historic district of a Brazilian metropolis, archaeologists uncovered a previously undocumented 17th-century cemetery, containing the closely spaced remains of over two dozen young adult males of military age. Historical records suggest the site once housed a makeshift hospital, raising questions about the origins and causes of death of those interred, particularly given the absence of skeletal evidence for violent or fatal trauma. The current study integrates bioarchaeological, historical, and genomic data to investigate the ancestry and putative disease-related mortality of nine individuals whose remains were recovered and subsequently sequenced. Population-genetic analyses revealed strong affinities with Northern European populations, particularly from Norway, Iceland, Estonia, and Czechoslovia, consistent with their likely roles as soldiers or laborers employed by the Dutch West India Company. To explore potential causes of death, we conducted a metagenomic screening with a novel pipeline optimized for degraded DNA, which revealed widespread presence of Klebsiella pneumoniae and Mycobacterium tuberculosis pathogens across all samples. Authenticity was confirmed through post-mortem damage patterns characteristic of historical samples. These findings, together with the absence of combat trauma and the collective burial context at the site, support the hypothesis of an epidemic-related mortality event. This study contributes to the growing field of historical pathogen genomics and offers a rare genomic perspective on life, mobility, and health during a period of colonial upheaval in South America.

evolutionary biology↗

bioRxiv2025

Discriminating models of trait evolution

AO_SCPLOWBSTRACTC_SCPLOWA central challenge in comparative biology is linking present-day trait variation across species with unobserved evolutionary processes that occurred in the past. In this endeavor, phylogenetic comparative methods are invaluable for fitting, comparing, and selecting evolutionary models of varying complexity and biological meaning. Traditionally, evolutionary studies have relied on conventional statistical approaches to assess model fit and identify the one that best explains variation in a given trait. Here we explore an alternative strategy by applying supervised learning to predict evolutionary models via discriminant analysis. We formally introduce Evolutionary Discriminant Analysis (EvoDA) as an addition to the biologists toolkit, offering a suite of new methods for studying trait evolution. We evaluate the performance of EvoDA alongside conventional model selection through a series of fungal phylogeny case studies, each targeting increasingly challenging analytical tasks. These results showcase the strengths of EvoDA, with substantial improvements over conventional approaches when studying traits subject to measurement error, which likely reflect realistic conditions in empirical datasets. To complement our simulation-based benchmarking, we explore the application of EvoDA for tackling a notoriously difficult task: predicting the mode and tempo of gene expression evolution. This empirical analysis suggests that stabilizing selection acts on a majority of genes, with bursts of expression evolution in a handful of genes related to stress, cellular transportation, and transcription regulation. Collectively, our findings illustrate the promise of EvoDA for predicting trait models across a range of evolutionary and experimental contexts, establishing a new methodological framework for the next era of comparative research. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWTo make sense of biodiversity, evolutionary studies have historically relied on conventional statistical procedures to evaluate competing hypotheses about the mode and tempo of trait evolution. Here, we introduce new supervised learning methods that substantially outperform traditional techniques for correctly assigning trait models across a range of evolutionary and experimental conditions. We find that these methods are highly robust to measurement noise expected from realistic trait data and offer new insights into a central question in comparative genomics: what are the evolutionary forces shaping variation in gene expression across species?

evolutionary biology↗

bioRxiv2024

A tale of too many trees: a conundrum for phylogenetic regression

AO_SCPLOWBSTRACTC_SCPLOWJust exactly which tree(s) should we assume when testing evolutionary hypotheses? This question has plagued comparative biologists for decades. Given a perfectly estimated tree (if this is even possible in practice), we seldom know with certainty whether such a tree is truly best (or even adequate) to represent the evolutionary history of our studied traits. Regardless of our certainty, choosing a tree is required for all phylogenetic comparative methods. Yet, phylogenetic conflict and error are ubiquitous in modern comparative biology, and we are still learning about their dangers when testing evolutionary hypotheses. Here we investigated the consequences of gene tree-species tree mismatch for phylogenetic regression in the presence of incomplete lineage sorting. Our simulation experiments reveal excessively high false positive rates for mismatched phylogenetic regression with both small and large trees, simple and complex traits, and known and estimated phylogenies. In some cases, we find evidence of a directionality of error: incorrectly assuming a species tree for traits that evolved according to a gene tree sometimes fares worse than the opposite. To explore difficult yet realistic regression scenarios, we also used estimated rather than known trees to conduct case studies, as well as an expansive gene expression dataset to investigate an arguably best-case scenario in which one may have a better chance to match tree with trait. Though never meant to be a panacea for all that may ail phylogenetic comparative methods, we found promise in the application of a robust estimator as a potential, albeit imperfect, solution to some issues raised by tree mismatch, perhaps offering a path forward. Collectively, our results emphasize the importance of careful study design for comparative methods, highlighting the need to fully appreciate the role of adequate phylogenetic modeling for testing evolutionary hypotheses.

evolutionary biology↗

bioRxiv2022

Robust Phylogenetic Regression

AO_SCPLOWBSTRACTC_SCPLOWModern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for combatting evolutionary pseudoreplication, which arises inherently from trait data sampled across related species. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for evolutionary pseudoreplication. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution--the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt, lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against pseudoreplication and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

evolutionary biology↗

bioRxiv2022

Predicting expression divergence and its evolutionary parameters between single-copy genes in two species

Predicting gene expression divergence and its evolutionary parameters is integral to understanding the emergence of new gene functions and associated traits. Whereas several sophisticated methods have been developed for these tasks, their applications are either limited to duplicate genes or require expression data from more than two species. Thus, here we present PiXi, the first machine learning framework for predicting expression divergence and its evolutionary parameters between single-copy genes in two species. In particular, PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for making predictions. We show that PiXi has high power and accuracy in predicting gene expression divergence and its underlying parameters across a wide range of evolutionary scenarios, with the globally best performance achieved by a multi-layer neural network. Moreover, application of our best performing PiXi predictor to empirical data from single-copy genes residing at different loci in two species of Drosophila reveals that expression divergence occurs in approximately 20% of these positionally relocated genes and is driven by a combination of neutral and selective forces. Further analysis shows that several of these genes are involved in the electron transport chain of the mitochondrial membrane, suggesting that new chromatin environments may impact energy production in Drosophila. Thus, by providing a toolkit for predicting expression divergence and its evolutionary parameters between single-copy genes in two species, PiXi can shed light on the origins of novel phenotypes across diverse biological processes and study systems.

evolutionary biology↗

bioRxiv2020

Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. To date, only one method--CDROM--has been developed with this goal in mind. In particular, CDROM employs gene expression distances as proxies for functional divergence, and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the underlying parameters of duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built upon a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents the best available method for classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby also highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

evolutionary biology↗

bioRxiv2019

Population-specific sequence and expression differentiation in Europeans

Much of the enormous phenotypic variation observed across human populations is thought to have arisen from events experienced as our ancestors peopled different regions of the world. However, little is known about the genes involved in these population-specific adaptations. Here we explore this problem by simultaneously examining population-specific sequence and expression differentiation in four human populations. In particular, we design a branch-based statistic to estimate population-specific differentiation in four populations, and apply this statistic to single nucleotide polymorphism (SNP) and RNA-seq data from Italian, British, Finish, and Yoruban populations. As expected, genome-wide estimates of sequence and expression differentiation each independently recapitulate the known demographic history of these four human populations, highlighting the utility of our statistic for identifying genic targets of population-specific adaptations. Application of our statistic reveals that genes containing large copy number variations (CNVs) have elevated levels of population-specific sequence and expression differentiation, consistent with the hypothesis that gene turnover is a key reservoir of adaptive variation. Further, European genes displaying population-specific sequence and expression differentiation are enriched for functions related to epigenetic regulation, immunity, and reproduction. Together, our findings illustrate that population-specific sequence and expression differentiation in humans may preferentially target genes with CNVs and play important roles in a diversity of adaptive and disease-related phenotypes.

evolutionary biology↗

bioRxiv2018

Rapid functional divergence of grass duplicate genes

Gene duplication has played an important role in the evolution and domestication of flowering plants. Yet little is known about how plant duplicate genes evolve and are retained over long timescales, particularly those arising from small-scale duplication (SSD) rather than whole-genome duplication (WGD) events. Here we address this question in the Poaceae (grass) family by analyzing gene expression data from nine tissues of Brachypodium distachyon, Oryza sativa japonica (rice), and Sorghum bicolor (sorghum). Consistent with theoretical predictions, expression profiles of most grass genes are conserved after SSD, suggesting that functional conservation is the primary outcome of SSD in grasses. However, we also uncover support for widespread functional divergence, much of which occurs asymmetrically via the process of neofunctionalization. Moreover, neofunctionalization preferentially targets younger (child) duplicate gene copies, is associated with RNA-mediated duplication, and occurs quickly after duplication. Further analysis reveals that functional divergence of SSD-derived genes is positively correlated with both sequence divergence and tissue specificity in all three grass species, and particularly with anther expression in B. distachyon. Therefore, as found in many animal species, SSD-derived grass genes often undergo rapid functional divergence that may be driven by natural selection on male-specific phenotypes.

evolutionary biology↗