Understanding the sources of genetic diversity in Influenza A Virus (IAV) infections is crucial for understanding the mechanisms of viral evolution and immune escape. Whereas prior studies have characterized the effects of population bottlenecks during host-to-host transmission and intrahost tissue-to-tissue dissemination, the role of intracellular replication processes on IAV genetic diversity remains largely unexplored. In this study, we used stochastic mathematical modelling to simulate the replication of genetically distinct IAV strains within individual cells and tissues. Our results reveal significant bottleneck effects within a single infection cycle of individual cells. Intracellular bottleneck effects are driven by stochastic molecular processes and lead to the expansion or elimination of neutral variants creating large-scale differences between the initial and final frequencies of genetic variants in individual cells. By expanding our findings to a population-level model, we show that IAV intracellular replication reduces the effective population size, thereby diminishing the impact of selection and increasing the role of genetic drift. Our findings highlight the impact of intracellular replication processes on IAV genetic diversity.

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 Summary:We 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.

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.

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 and 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 to 220 generations. All aneuploid lineages showed rapid CNV loss and reversion to a single-copy genotype, whereas segmental amplifications were remarkably stable; one of the 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.

The spread of antibiotic resistance in pathogenic bacteria is one of the most pressing public health threats. 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 perform a small-scale study of the relationships between bacterial, fungal, and functional diversity with the distribution of two classes of ARGs (clinical and environmental) 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 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 significantly decreased as fungal diversity increased. Our results suggest that while bacterial diversity increases with distance from the road and stream, this diversity is correlated with the diversity of the environmental 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.IMPORTANCEAntibiotic resistance is often studied in hospitals and clinical settings, but much less is known about how resistance genes are distributed in everyday environments, such as agricultural soils. Hay fields are widespread, actively managed ecosystems that sit at the interface of natural microbial communities, farming practices, and food systems. In this study, we examine how antibiotic resistance genes are associated with broader patterns of bacterial diversity, fungal communities, and soil functional potential in a working hay field. By integrating resistance gene profiles with ecological measures of microbial diversity, we move beyond simply cataloging resistance and instead place it within a community and ecosystem context. This approach helps clarify whether resistance genes are linked to specific microbial groups, overall biodiversity, or functional traits related to soil processes. Our findings provide insight into how antibiotic resistance persists and is structured in low-input agricultural systems, contributing to a more complete understanding of environmental reservoirs of resistance and informing discussions about sustainable land management and public health risk.

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. ### Competing Interest Statement The authors have declared no competing interest.

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.

The effectiveness of the seasonal influenza vaccine varies between individuals. Specific risk groups with comorbidities, such as the obese, have an increased risk of infection even when vaccinated. It has been proposed that a heightened baseline inflammatory state and alterations in adaptive immune cell populations in obese individuals may adversely impact immune responses to pathogens and vaccines. Therefore, understanding differences in vaccine responses in obese individuals is critical for the development of improved influenza vaccine design and strategies. Previously, we identified a transcriptomic signature prior to seasonal influenza vaccination that is predictive of an individual's response to the influenza vaccine. To understand the dynamics of host responses following vaccination, we studied systemic gene expression from whole blood collected from 163 vaccinated individuals on days 3, 7, and 28 post-vaccination. Gene expression profiles were compared between high and low vaccine responders in both obese and non-obese groups. We detected characteristic changes in gene expression over time, with a peak at day 7 post-vaccination. A large subset of genes associated with the adaptive immune response exhibited significantly different expressions in high responders and non-responders to the vaccine. Distinct gene sets presented differing temporal patterns in different weight and response groups, indicative of molecular processes impacting the immune response to vaccination. IMPORTANCE:This study yields insights into how co-morbidities, such as obesity, impact the immune response to the seasonal influenza vaccine. By analyzing blood samples collected days and weeks after the vaccination of a cohort of subjects, we can examine changes in gene expression over time and how those changes are similar or different depending on risk factors. This provides clues into potential molecular mechanisms behind an effective immune response to vaccination.

Copy number variants (CNVs) are duplications and deletions of DNA sequences that contribute to genetic variation between individuals and underlie rapid adaptive evolution. Increases in gene copy number can confer a strong selective advantage in some circumstances, but CNVs can also incur fitness costs. Prior research has shown that experimental evolution of Saccharomyces cerevisiae in nutrient-limited chemostats recurrently selects for amplifications of nutrient transporter genes, including GAP1, MEP2, and PUT4 in glutamine, ammonium and proline limited conditions, respectively. However, the fate of these CNVs upon return of the organism to a non-selective environment, is unknown. To investigate the fitness costs and stability of CNVs upon reversal of the original selection pressure, we studied 15 unique CNV lineages that had been selected in different nitrogen-limited chemostats, including both segmental amplifications and whole-chromosome aneuploidies. We studied the stability of CNVs using a fluorescent CNV reporter system during propagation in nutrient-rich media using serial dilution of batch cultures for 110-220 generations. We found that only one-third of CNV lineages repeatedly underwent loss of CNVs and reversion to a single-copy genotype. All lineages containing aneuploidies showed rapid reversion dynamics, whereas lineages containing segmental amplifications were remarkably stable - only one of 11 strains reverted to a single copy. Pairwise competitive fitness assays revealed strong fitness defects associated with reverting CNVs in the nutrient-rich environment but minimal fitness defects in lineages that did not revert. Reversion of CNVs led to increased fitness. Using simulation-based inference to estimate reversion rates and fitness effects we find that negative selection is the primary driver of CNV loss although aneuploid reversion rates are high with estimates of 10-5 to 10-3. Whole-genome sequencing of clones isolated from evolved populations revealed that reversion of both aneuploids and a segmental amplification left no evidence of prior existence of the CNV, making the genomes of revertants indistinguishable from the single-copy ancestor. Our findings provide novel evidence of the low fitness cost and high stability of most segmental CNVs upon removal of the selection pressure, and that gene amplifications with large fitness costs are readily reversible, highlighting the significance of CNVs for genome evolution as well as rapid and reversible adaptation to transient selection pressures.

Sewage contamination of freshwater occurs in the form of raw waste or as effluent from wastewater treatment plants (WWTP's). While raw waste (animal and human) and under-functioning WWTP's can introduce live enteric bacteria to freshwater systems, most WWTP's, even when operating correctly, do not remove bacterial genetic material from treated waste, resulting in the addition of bacterial DNA, including antibiotic resistance genes, into water columns and sediment of freshwater systems. In freshwater systems with both raw and treated waste inputs, then, there will be increased interaction between live sewage-associated bacteria (untreated sewage) and DNA contamination (from both untreated and treated wastewater effluent). To evaluate this understudied interaction between DNA and bacterial contamination in the freshwater environment, we conducted a three-month field-based study of sewage-associated bacteria and genetic material in water and sediment in a freshwater tributary of the Hudson River (NY, USA) that supplies drinking water and receives treated and untreated wastewater discharges from several municipalities. Using both DNA and culture-based bacterial analyses, we found that both treated and untreated sewage influences water and sediment bacterial communities in this tributary, and water-sediment exchanges of enteric bacteria and genetic material. Our results also indicated that the treated sewage effluent on this waterway serves as a concentrated source of intI1 (antibiotic resistance) genes, which appear to collect in the sediments below the outfall along with fecal indicator bacteria. Our work also captured the environmental impact of a large rain event that perturbed bacterial populations in sediment and water matrices, independently from the outflow. This study suggests that large precipitation events are an important cause of bacterial and DNA contamination for freshwater tributaries, with runoff from the surrounding environment being an important factor.

Copy number variants (CNVs) are an important source of genetic variation underlying rapid adaptation and genome evolution. Whereas point mutation rates vary with genomic location and local DNA features, the role of genome architecture in the formation and evolutionary dynamics of CNVs is poorly understood. Previously, we found the GAP1 gene in Saccharomyces cerevisiae undergoes frequent amplification and selection in glutamine-limitation. The gene is flanked by two long terminal repeats (LTRs) and proximate to an origin of DNA replication (autonomously replicating sequence, ARS), which likely promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we evolved engineered strains lacking either the adjacent LTRs, ARS, or all elements in glutamine-limited chemostats. 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. Removal of local DNA elements significantly impacts the fitness effect of GAP1 CNVs and the rate of adaptation. In 177 CNV lineages, across all four strains, 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, distal ones mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination can mediate gene amplification following de novo retrotransposon events. Our study reveals that template switching during DNA replication is a prevalent source of adaptive CNVs.

Understanding the sources of genetic diversity in Influenza A Virus (IAV) infections is crucial for understanding the mechanisms of viral evolution and immune escape. Whereas prior studies have characterized the effects of population bottlenecks during host-to-host transmission and intrahost tissue-to-tissue dissemination, the role of intracellular replication processes on IAV genetic diversity remains largely unexplored. In this study, we used stochastic mathematical modeling to simulate the replication of genetically distinct IAV strains within individual cells and tissues. Our results reveal significant bottleneck effects within a single infection cycle of individual cells. Intracellular bottleneck effects are driven by stochastic molecular processes and lead to the expansion or elimination of neutral variants creating large-scale differences between the initial and final frequencies of genetic variants in individual cells. By expanding our findings to a population-level tissue model, we show that IAV intracellular replication reduces the effective population size, thereby diminishing the impact of selection and increasing the role of genetic drift. Our findings highlight the important contribution of intracellular replication processes to the generation of genetic diversity in IAV.

Copy number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas, CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. 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 gene 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- chemostats. We find CNV-amplified genes correlate with higher mRNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of gene expression by the abundance of the preceding step we were able to identify widespread differences in the efficiency of each level of 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 genome evolution shapes gene expression.

Background Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features. Results We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC). Conclusion Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

The activities of microbiomes in river sediments play an important role in sustaining ecosystem functions by driving many biogeochemical cycles. However, river ecosystems are frequently affected by anthropogenic activities, which may lead to microbial biodiversity loss and/or changes in ecosystem functions and related services. While parts of the Atlantic Forest biome stretching along much of the eastern coast of South America are protected by governmental conservation efforts, an estimated 89% of these areas in Brazil are under threat. This adds urgency to the characterization of prokaryotic communities in this vast and highly diverse biome. Here, we present prokaryotic sediment communities in the tropical Juliana River system at three sites, an upstream site near the river source in the mountains (Source) to a site in the middle reaches (Valley) and an estuarine site near the urban center of Ituberá (Mangrove). The diversity and composition of the communities were compared at these sites, along with environmental conditions, the former by using qualitative and quantitative analyses of 16S rRNA gene amplicons. While the communities included distinct populations at each site, a suite of core taxa accounted for the majority of the populations at all sites. Prokaryote diversity was highest in the sediments of the Mangrove site and lowest at the Valley site. The highest number of genera exclusive to a given site was found at the Source site, followed by the Mangrove site, which contained some archaeal genera not present at the freshwater sites. Copper (Cu) concentrations were related to differences in communities among sites, but none of the other environmental factors we determined was found to have a significant influence. This may be partly due to an urban imprint on the Mangrove site by providing organic carbon and nutrients via domestic effluents.

Nature has been a rich source of pharmaceutical compounds, producing 80% of our currently prescribed drugs. The feijoa plant, Acca sellowiana, is classified in the family Myrtaceae, native to South America, and currently grown worldwide to produce feijoa fruit. Feijoa is a rich source of bioactive compounds with anticancer, anti-inflammatory, antibacterial, and antifungal activities; however, the mechanism of action of these compounds is largely not known. Here, we used chemical genetic analyses in the model organism Saccharomyces cerevisiae to investigate the mechanism of action of a feijoa-derived ethanol adduct of vescalagin (EtOH-vescalagin). Genome-wide barcode sequencing analysis revealed yeast strains lacking genes in iron metabolism, zinc metabolism, retromer function, or mitochondrial function were hypersensitive to 0.3 µM EtOH-vescalagin. This treatment increased expression of iron uptake proteins at the plasma membrane, which was a compensatory response to reduced intracellular iron. Likewise, EtOH-vescalagin increased expression of the Cot1 protein in the vacuolar membrane that transports zinc into the vacuole to prevent cytoplasmic accumulation of zinc. Each individual subunit in the retromer complex was required for the iron homeostatic mechanism of EtOH-vescalagin, while only the cargo recognition component in the retromer complex was required for the zinc homeostatic mechanism. Overexpression of either retromer subunits or high-affinity iron transporters suppressed EtOH-vescalagin bioactivity in a zinc-replete condition, while overexpression of only retromer subunits increased EtOH-vescalagin bioactivity in a zinc-deficient condition. Together, these results indicate that EtOH-vescalagin bioactivity begins with extracellular iron chelation and proceeds with intracellular transport of zinc via the retromer complex. More broadly, this is the first report of a bioactive compound to further characterize the poorly understood interaction between zinc metabolism and retromer function.

Sarcoglycanopathies are among the most frequent and severe forms of autosomal recessive forms of limb-girdle muscular dystrophies (LGMDs) with childhood onset. Four subtypes are known: LGMDR3, LGMDR4, LGMDR5 and LGMDR6, which are caused, respectively, by mutations in the SGCA, SGCB, SGCG and SGCD genes. We present the clinical variability of LGMD 2C/R5 among a genetically homogeneous group of 57 patients, belonging to 35 pedigrees. Molecular genetic analysis showed that all 57 patients were homozygous for the C283Y variant. The muscles of the pelvic girdle and the trunk were affected early and were more severely affected, followed by the shoulder girdle. Macroglossia, hypertrophy of the calves, scapular winging and lumbar hyperlordosis were common in the ambulatory phase. A great intra and interfamilial variability in the clinical presentation of LGMD 2C/R5 was observed, despite having the same underlying molecular defect. Females demonstrated a relatively milder clinical course compared to males. Mean creatine phosphokinase (CK) CK levels were 20 times above normal values. Muscle computer tomography (CT) CT or MRIs showed earlier and more severe involvement of the flexor proximal limb muscles in comparison to extensor muscles.

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.

ABSTRACT Obesity is well established as a risk factor for many noncommunicable diseases; however, its consequences for infectious disease are poorly understood. Here, we investigated the impact of host obesity on influenza A virus (IAV) genetic variation using a diet-induced obesity ferret model and the A/Hong Kong/1073/1999 (H9N2) 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 mutations throughout the genome that were specific to obese hosts and that were preserved during transmission between hosts. Despite detection of obese-specific variants, the 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 and polymerase genes (PB2 and PB1). We also identified defective viral genomes (DVGs) that were found uniquely in either obese or lean hosts, but the overall DVG diversity and dynamics did not differ between the two groups. Our study suggests that obesity may result in a unique selective environment impacting intrahost IAV evolution, highlighting the need for additional genetic and functional studies to confirm these effects. IMPORTANCE 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. 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. 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.