Biology subjects

Gresham, D.

Publications and source records attributed to Gresham, D..

At least 19 recordsLinked to original sources

bioRxiv2026

A high aneuploidy supply rate underlies parallel evolution of fluconazole resistance in Saccharomyces cerevisiae

Copy number variants (CNVs) of ERG11 play a major role in acquisition of resistance to the commonly used azole family of antifungal drugs in many pathogenic yeast species. However, the conditions in which these CNVs arise, and the factors that underlie their emergence and selection in evolving populations, remain poorly understood. Here, we studied the dynamics of de novo CNVs at the ERG11 locus in Saccharomyces cerevisiae to determine the effect of different drug concentrations and temperatures on CNV formation and selection. We found that ERG11 CNVs emerge reproducibly at fluconazole concentrations around the IC50. With increases in temperature, CNVs emerged more rapidly and had a higher tendency towards fixation. Evolved ERG11 CNV strains provided a significant growth advantage across a narrow range of fluconazole concentrations near the IC50 of the wildtype strain. Whole genome sequencing of 35 independent evolved lineages revealed that all ERG11 CNVs are aneuploidies of chromosome VIII. Relocation of ERG11 to chromosome XI also results exclusively in selection for chromosome XI aneuploids. We show that fluconazole does not increase the frequency at which aneuploids are generated. Therefore, we conclude that a high spontaneous rate of aneuploidy formation underlies recurrent acquisition of resistance to fluconazole in a temperature and drug concentration dependent manner. Author SummaryCopy number variations (CNV), defined as duplications or deletions of genomic regions, are pervasive throughout all forms of life. Although CNVs can provide a route toward rapid adaptation, they can also be associated with high fitness costs. In pathogenic yeast species, CNVs with diverse genetic structures are known to play a role in antifungal drug resistance. However, the conditions that favor CNV formation and selection are unclear. We applied a fluorescent CNV reporter system to track newly formed CNVs and found that they arise reproducibly at a narrow range of drug concentrations, and that increases in temperature generally lead to more rapid emergence of CNVs. DNA sequencing revealed that the evolved CNVs contained duplications of the entire chromosome, rather than just the region surrounding the gene under selection. Thus, aneuploidy - gain or loss of an entire chromosome - is a mechanism for rapid evolution of drug resistance, potentially without the fitness costs thought to be associated with this mechanism.

evolutionary biology↗

bioRxiv2026

Quiescence improves Candida albicans survival of fungicidal drug exposure

Quiescence, defined as the reversible exit from mitotic division and proliferative growth, is the predominant state of all microbes. Despite its prevalence, the properties and consequences of quiescence in Candida albicans, an opportunistic fungal pathogen, remain largely unexplored. In this study, we characterized the morphological, molecular, and biophysical properties of quiescent C. albicans cells and assessed the effects of quiescence on antifungal drug efficacy. Quiescent cells that were induced via carbon starvation in rich and minimal media underwent distinct morphological changes upon entry into quiescence; this included an increase in cell buoyant density, altered fluidity of the cytoplasm and nucleus, and remodeling of mitochondria. Most C. albicans cells arrested in an unbudded G1/G0 state, although a significant fraction of cells had budded morphologies and 4N DNA content, indicating that they arrested at other cell cycle phases. Both budded and unbudded quiescent cells efficiently re-entered the cell cycle upon nutrient replenishment, with time-to-quiescence exit varying depending on the total nutritional quality of the medium. Quiescence was associated with large-scale gene expression remodeling, including downregulation of ribosomal biogenesis genes and upregulation of autophagy and stress response pathways. Notably, a greater proportion of quiescent cells than proliferative cells survived exposure to the commonly used antifungal drugs micafungin, caspofungin, and amphotericin B in genetically diverse strains. Thus, quiescence is a distinct cellular state with important implications for antifungal drug efficacy in C. albicans. Author SummaryWe show that Candida albicans, a common fungal pathogen, can enter a reversible, non-dividing state when starved of carbon. Starved cells become smaller and denser, reorganize their mitochondria, change how densely packed the inside of the cell and its nucleus are, and switch on stress-protection and internal recycling programs while reducing protein synthesis activity. Most cells have ceased to actively divide, but many retained budded shapes and could restart growth when nutrients returned; the timing of recovery depended on the nutritional environment in which quiescence was initiated. Critically, quiescent cells from laboratory and clinical strains exhibited greater survival than proliferative cells when exposed to widely used fungicidal drugs including micafungin, caspofungin, and amphotericin B. These findings indicate that quiescence is an active, adaptive physiological state that helps Candida albicans survive hostile environmental conditions such as temperature stress and drug exposure. Accounting for the metabolic state of fungal cells in diagnostics and drug development may improve treatment outcomes.

microbiology↗

bioRxiv2026

Collective Posterior Inference from Highly Variable Empirical Replicates

High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime without incurring prohibitive computational costs or careful hyperparameter tuning. Here, we introduce a new method for fast and robust collective posterior inference from multiple independent replicates using a robust product-of-experts aggregation scheme that automatically mitigates the influence of outliers. Evaluating it on synthetic and empirical evolutionary datasets, we find it achieves state-of-the-art estimation accuracy and computational efficiency, including inference from noisy observations. Our method is compatible with any SBI framework, providing a scalable, plug-and-play solution for inference from noisy multiple-replicate datasets.

bioinformatics↗

bioRxiv2025

Dynamic Gene Regulatory Network Inference with Interpretable, Biophysically-Motivated Neural ODEs

Gene Regulatory Networks (GRNs) are complex dynamical systems that modulate gene expression and drive transitions between phenotypic cell states. Determining these networks is crucial in understanding how gene dysregulation can lead to phenotypic variation and perturbation responses. We present a novel biophysically-motivated neural ordinary differential equation (ODE) model framework with a biologically interpretable deep learning architecture that leverages dynamic single-cell data: in-CAHOOTTS (gene regulatory network Inference with Context Aware Hybrid neural-ODEs on Transcriptional Time-series Systems). Our approach combines accurate prediction with mechanistic interpretability, decomposing gene expression dynamics into fundamental biophysical processes (mRNA transcription and degradation) while inferring regulatory network structure. We validate in-CAHOOTTS by learning the Saccharomyces cerevisiae response to rapamycin treatment, and learning the dynamic cell cycle progression. The trained model accurately predicts gene expression trajectories and identifies biologically relevant rapamycin response regulators. The framework achieves stable long-term predictions with time frames extending 30 times beyond training data, maintaining realistic oscillatory dynamics for over 40 hours in cell cycle modeling, demonstrating that neural ODEs can capture true biological attractors rather than merely fitting data. We argue that interpretable neural ODEs can successfully model complex biological dynamics while revealing mechanistic insights essential for understanding living systems. By bridging extensible machine learning frameworks and mechanistic biology, in-CAHOOTTS represents a significant advance toward deep learning systems that both predict biological outcomes and explain the mechanisms driving them.

systems biology↗

bioRxiv2025

Investigating the distribution of antibiotic resistance genes in relation to bacterial, fungal, and functional diversity in a hay field.

The spread of antibiotic resistance in pathogenic bacteria is one of the most pressing public health threats of the 21st century, with at least 5 million deaths currently associated with it. While recent work has shown the importance of environmental reservoirs in the emergence of antibiotic resistance genes (ARGs), it is unclear which features of microbial diversity relate to ARGs of clinical relevance. Here, we investigate the role of bacterial, fungal, and functional diversity on the distribution of clinical and environmental ARGs along a single transect located in an aging hay field on an otherwise active farm. This transect spans a length of several hundred meters, increasing in distance from an agricultural access road and stream. We use 16S rRNA and ITS amplicon sequencing to measure bacterial and fungal diversity respectively, in combination with whole-genome sequencing (WGS) to characterize functional and ARG diversity. We find increasing bacterial and functional diversity along the transect, as well as distinct community structures for both bacteria and fungi. While we find that the diversity of environmental ARGs is significantly correlated with both bacterial and fungal diversity, clinical ARG diversity only significantly decreased as fungal diversity increased. Our results suggest that while microbial diversity increases with distance from the road and stream; and this diversity tends to determine the diversity of the majority of ARGs; this trend is not observed for ARGs of clinical relevance, which appear to be largely driven by the variety of fungal groups in the environment.

ecology↗

bioRxiv2025

Copy number variation facilitates rapid toggling between ecological strategies

A central question in evolutionary biology is how different types of mutations shape ecological strategy. Whereas both single nucleotide variants (SNVs) and copy number variants (CNVs) can alter gene dosage, it remains unclear whether CNVs confer unique adaptive advantages. Here, we show that CNVs do not mediate adaptive tracking in fluctuating environments, but instead act as reversible toggles between ecological strategies. Using a dual-fluorescent CNV reporter system in Saccharomyces cerevisiae, we tracked CNV dynamics at two transporter loci during long-term evolution under static and fluctuating nitrogen limitation. CNVs arose with high repeatability in static conditions but showed dampened or divergent dynamics in fluctuating environments, suggesting they do not track environmental change. Instead, we found that the ratio of copy number between the two loci, but not copy number at either locus alone, predicted ecological strategy: imbalanced CNV ratios defined specialists, while balanced ratios defined generalists. Evolution in static environments favored specialists whereas fluctuating environments favored generalists. Applying this framework to over 3,000 sequenced yeast genomes, we found generalist CNV signatures in both wild and domesticated strains, but specialist signatures exclusively in domesticated strains. These findings introduce a generalizable framework for predicting ecological strategy from genome structure, positioning CNV ratios as a molecular signature of niche breadth across evolutionary and ecological contexts.

evolutionary biology↗

bioRxiv2025

Segmental copy number amplifications are stable in the absence of selection

Copy number variants (CNVs) are DNA duplications and deletions that cause genetic variation, underlying rapid adaptive evolution. CNVs often confer selective advantages, but can also incur fitness costs. Evolution of Saccharomyces cerevisiae in nutrient-limited chemostats recurrently selects for amplifications of nutrient transporter genes. However, their fate upon return to a non-selective environment remains unknown. To investigate CNV fitness and stability upon removing the original selection pressure, we studied 15 CNV lineages (11 segmental, 4 whole-chromosomal amplifications) selected in nitrogen-limited chemostats. CNV stability was monitored using fluorescent reporters during propagation in nutrient-rich batch cultures for 110-220 generations. All aneuploid lineages showed rapid CNV loss and reversion to a single-copy genotype, whereas segmental amplifications were remarkably stable- one of 11 strains reverted. Pairwise fitness competitions in rich media revealed strong fitness defects associated solely with CNVs that reverted; reversion led to increased fitness. Using simulation-based inference to estimate reversion rates and fitness effects, we determined negative selection as the primary driver of CNV loss. Whole-genome sequencing revealed that reversion of aneuploids and a segmental amplification left no evidence of prior CNV existence, rendering revertant genomes indistinguishable from the single-copy ancestor. Detailed characterization of a partial revertant identified chromosomal translocation, suggesting that extant CNVs can undergo structural diversification. Our findings provide novel evidence that most segmental CNVs adapted to nitrogen limitation are stable upon removal of selection, but costly gene amplifications are readily reversible. Together, these highlight the importance of CNVs in both long-term genome evolution and rapid, reversible adaptation to transient selection.

evolutionary biology↗

bioRxiv2024

DNA replication errors are a major source of adaptive gene amplification

Copy number variants (CNVs)--gains and losses of genomic sequences--are an important source of genetic variation underlying rapid adaptation and genome evolution. However, despite their central role in evolution little is known about the factors that contribute to the structure, size, formation rate, and fitness effects of adaptive CNVs. Local genomic sequences are likely to be an important determinant of these properties. Whereas it is known that point mutation rates vary with genomic location and local DNA sequence features, the role of genome architecture in the formation, selection, and the resulting evolutionary dynamics of CNVs is poorly understood. Previously, we have found that the GAP1 gene in Saccharomyces cerevisiae undergoes frequent and repeated amplification and selection under long-term experimental evolution in glutamine-limiting conditions. The GAP1 gene has a unique genomic architecture consisting of two flanking long terminal repeats (LTRs) and a proximate origin of DNA replication (autonomously replicating sequence, ARS), which are likely to promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we performed experimental evolution in glutamine-limited chemostats using engineered strains lacking either the adjacent LTRs, ARS, or all elements. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. We find that although GAP1 CNVs repeatedly form and sweep to high frequency in strains with modified genome architecture, removal of local DNA elements significantly impacts the rate and fitness effect of CNVs and the rate of adaptation. We performed genome sequence analysis to define the molecular mechanisms of CNV formation for 177 CNV lineages. We find that across all four strain backgrounds, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, a distal ARS can mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination mechanisms still mediate gene amplification following de novo insertion of retrotransposon elements at the locus. Our study demonstrates the remarkable plasticity of the genome and reveals that template switching during DNA replication is a frequent source of adaptive CNVs.

evolutionary biology↗

bioRxiv2023

Post-transcriptional mechanisms modulate the consequences of adaptive copy number variation

Copy-number variants (CNVs) are an important class of recurrent variants that mediate adaptive evolution. While CNVs can increase the relative fitness of the organism, they can also incur a cost. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus. To understand the role that expression plays in adaptation, both in relation to the adaptation of the organism to the selective condition, and as a consequence of the CNV, we measured the transcriptome, translatome, and proteome of 4 strains of evolved yeast, each with a unique CNV, and their ancestor in Gln-conditions. We find CNV-amplified genes correlate with higher RNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of expression by the abundance of the preceding step we were able to identify widespread divergence in the efficiency of each step in the gene in the efficiency of each step in gene expression. Genes with significantly different translational efficiency were enriched for potential regulatory mechanisms including either upstream open reading frames, RNA binding sites for SSD1, or both. Genes with lower protein expression efficiency were enriched for genes encoding proteins in protein complexes. Taken together, our study reveals widespread changes in gene expression at multiple regulatory levels in lineages containing adaptive CNVs highlighting the diverse ways in which adaptive evolution shapes gene expression.

genomics↗

bioRxiv2023

Parallel proteomics and phosphoproteomics defines starvation signal specific processes in cell quiescence

Cells arrest growth and enter a quiescent state upon nutrient deprivation. However, the molecular processes by which cells respond to different starvation signals to regulate exit from the cell division cycle and initiation of quiescence remains poorly understood. To study the role of protein expression and signaling in quiescence we combined temporal profiling of the proteome and phosphoproteome using stable isotope labeling with amino acids in cell culture (SILAC) in Saccharomyces cerevisiae (budding yeast). We find that carbon and phosphorus starvation signals activate quiescence through largely distinct remodeling of the proteome and phosphoproteome. However, increased expression of mitochondrial proteins is associated with quiescence establishment in response to both starvation signals. Deletion of the putative quiescence regulator RIM15, which encodes a serine-threonine kinase, results in reduced survival of cells starved for phosphorus and nitrogen, but not carbon. However, we identified common protein phosphorylation roles for RIM15 in quiescence that are enriched for RNA metabolism and translation. We also find evidence for RIM15-mediated phosphorylation of some targets, including IGO1, prior to starvation consistent with a functional role for RIM15 in proliferative cells. Finally, our results reveal widespread catabolism of amino acids in response to nitrogen starvation, indicating widespread amino acid recycling via salvage pathways in conditions lacking environmental nitrogen. Our study defines an expanded quiescent proteome and phosphoproteome in yeast, and highlights the multiple coordinated molecular processes at the level of protein expression and phosphorylation that are required for quiescence.

cell biology↗

bioRxiv2023

Host obesity impacts genetic variation in influenza A viral populations

Obesity is a chronic health condition characterized by excess adiposity leading to a systemic increase in inflammation and dysregulation of metabolic hormones and immune cell populations. Obesity is well established as a risk factor for many noncommunicable diseases; however, its consequences for infectious disease are poorly understood. Influenza A virus (IAV) is a highly infectious pathogen responsible for seasonal and pandemic influenza. Host risk factors, including compromised immunity and pre-existing health conditions, can contribute to increased infection susceptibility and disease severity. During viral replication in a host, the negative sense single stranded RNA genome of IAV accumulates genetic diversity that may have important consequences for viral evolution and transmission. Here, we investigated the impact of host obesity on IAV genetic variation using a diet induced obesity ferret model. We infected obese and lean male ferrets with the A/Hong Kong/1073/1999 (H9N2) IAV strain. Using a co-caging study design, we investigated the maintenance, generation, and transmission of intrahost IAV genetic variation by sequencing viral genomic RNA obtained from nasal wash samples over multiple days of infection. We found evidence for an enhanced role of positive selection acting on de novo mutations in obese hosts that led to nonsynonymous changes that rose to high frequency. In addition, we identified numerous cases of recurrent low-frequency mutations throughout the genome that were specific to obese hosts. Despite these obese-specific variants, overall viral genetic diversity did not differ significantly between obese and lean hosts. This is likely due to the high supply rate of de novo variation and common evolutionary adaptations to the ferret host regardless of obesity status, which we show are mediated by variation in the hemagglutinin (HA) and polymerase genes (PB2 and PB1). As with single nucleotide variants, we identified a class of defective viral genomes (DVGs) that were found uniquely in either obese or lean hosts, but overall DVG diversity and dynamics did not differ between the two groups. Our study provides the first insight into the consequences of host obesity on viral genetic diversity and adaptation, suggesting that host factors associated with obesity alter the selective environment experienced by a viral population, thereby impacting the spectrum of genetic variation.

microbiology↗

bioRxiv2023

mRNA condensation fluidizes the cytoplasm

The intracellular environment is packed with macromolecules of mesoscale size, and this crowded milieu significantly influences cell physiology. When exposed to stress, mRNAs released after translational arrest condense with RNA binding proteins, resulting in the formation of membraneless RNA protein (RNP) condensates known as processing bodies (P-bodies) and stress granules (SGs). However, the impact of the assembly of these condensates on the biophysical properties of the crowded cytoplasmic environment remains unclear. Here, we find that upon exposure to stress, polysome collapse and condensation of mRNAs increases mesoscale particle diffusivity in the cytoplasm. Increased mesoscale diffusivity is required for the efficient formation of Q-bodies, membraneless organelles that coordinate degradation of misfolded peptides that accumulate during stress. Additionally, we demonstrate that polysome collapse and stress granule formation has a similar effect in mammalian cells, fluidizing the cytoplasm at the mesoscale. We find that synthetic, light-induced RNA condensation is sufficient to fluidize the cytoplasm, demonstrating a causal effect of RNA condensation. Together, our work reveals a new functional role for stress-induced translation inhibition and formation of RNP condensates in modulating the physical properties of the cytoplasm to effectively respond to stressful conditions.

cell biology↗

bioRxiv2023

Increased mesoscale diffusivity in response to acute glucose starvation

Macromolecular crowding is an important parameter that impacts multiple biological processes. Passive microrheology using single particle tracking is a powerful means of studying macromolecular crowding. Here we monitored the diffusivity of self-assembling fluorescent nanoparticles (NS) in response to acute glucose starvation. mRNP diffusivity was reduced upon glucose starvation as previously reported. In contrast, we observed increased diffusivity of NS particles. Our results suggest that, upon glucose starvation, mRNP granule diffusivity may be reduced due to changes in physical interactions, while global crowding in the cytoplasm may be reduced.

biophysics↗

bioRxiv2022

Copy number variants alter local and global mutational tolerance

Copy number variants (CNVs), duplications and deletions of genomic content, contribute to evolutionary adaptation, but can also confer deleterious effects, and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that have CNVs of variable structures but with multiple copies of the gene GAP1. Although beneficial in glutamine-limited chemostats, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genetic interactions with CNVs. We find that CNVs confer novel mutational tolerance in amplified essential genes and novel genetic interactions. We validated a novel genetic interaction with BMH1. CNV strains have increased mutational tolerance in genes related to translation, and reduced mutational tolerance in genes related to mitochondrial function. We performed RNAseq and found that transcriptional dosage compensation does not affect the majority of genes amplified by CNVs. Furthermore, we do not find that CNV strains exhibit previously described transcriptional signatures of aneuploidy. Instead, CNV strains exhibit downregulation of genes involved in cellular respiration, nucleoside biosynthetic processes, and small molecule metabolism, and upregulation of genes involved in transposition, nucleic acid metabolic processes, and siderophore transport. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV associated diseases, such as cancer.

genetics↗

bioRxiv2022

Influenza A virus reassortment is strain dependent

RNA viruses can exchange genetic material during coinfection, an interaction that creates novel strains with implications for viral evolution and public health. Influenza A viral genetic exchange occurs when genome segments from distinct strains reassort in coinfection. Predicting potential reassortment between influenza strains is a longstanding goal. Experimental coinfection studies have shed light on factors that limit or promote reassortment. However, determining the reassortment potential between diverse Influenza A strains has remained an elusive goal. To fill this gap, we developed a high throughput genotyping approach to quantify reassortment among a diverse panel of human influenza virus strains, encompassing 41 years of epidemics, multiple geographic locations, and both circulating human subtypes A/H1N1 and A/H3N2. We found that the reassortment rate (proportion of reassortants) is an emergent property of a pair of strains where strain identity is a predictor of the reassortment rate. We show little evidence that antigenic subtype drives reassortment as intersubtype (H1N1xH3N2) and intrasubtype reassortment rates were, on average, similar. Instead, our data suggest that certain strains bias the reassortment rate up or down, independently of the coinfecting partner. We also observe that viral productivity is an emergent property of coinfections and that it is not correlated to reassortment rate, thus affecting the total number of reassortant progeny produced. Assortment of individual segments among progeny, and pairwise segment combinations within progeny, were not random and generally favored homologous combinations. This outcome was not related to strain similarity or shared subtype. Reassortment rate was closely correlated to both the proportion of unique genotypes and the proportion of progeny with heterologous pairwise segment combinations. We provide experimental evidence that viral genetic exchange is potentially an individual social trait subject to natural selection, which implies the propensity for reassortment is not evenly shared among strains. This study highlights the need for research incorporating diverse strains to discover the traits that shift the reassortment potential as we work towards the goal of predicting influenza virus evolution resulting from segment exchange.

microbiology↗

bioRxiv2021

Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10-4.7 -10-4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.

evolutionary biology↗

bioRxiv2021

Diversity and selection of SARS-CoV-2 minority variants in the early New York City outbreak

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller, and use of replicate sequencing have the most significant impact on single nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false negative rates. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intrahost viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intrahost variation, viral diversity, and viral evolution. IMPORTANCEWhen viruses replicate inside a host, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus, nor strongly beneficial, can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in inclusion of false positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant calling tools. We used simulated and synthetic data to test their performance against a true set of variants, and then used these studies to inform variant identification in data from clinical SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

microbiology↗

bioRxiv2021

Single-cell gene regulatory network inference atscale: The Inferelator 3.0

MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AvailabilityThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

systems biology↗