Biology subjects

Mehta, P.

Publications and source records attributed to Mehta, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Dose-additive and dose-dependent fetal testis toxicity of a nine-phthalate mixture in the rat

Phthalates (ortho-phthalic acid diesters) are used in a variety of manufacturing processes, particularly as non-covalent plasticizers in flexible polyvinyl chloride. Phthalates are male reproductive toxicants that disrupt the development of the fetal testis, resulting in malformed seminiferous cords, multinucleated germ cells (MNGs), and decreased testosterone production. We previously reported that MNGs were a sensitive, dose-dependent marker of dipentyl phthalate (DPEP) germ cell toxicity. To test whether germ cell effects of phthalates are dose-additive, we measured the induction of MNGs by a phthalate mixture. We treated timed pregnant Sprague Dawley rats with an equipotent mixture of nine phthalates, spanning a two-log dose range, from GD 17-21. We found that the phthalate mixture effects on morphogenesis of the testis, assessed through histological analysis of MNGs and seminiferous cord diameter were dose-additive. Consistent with prior results, we also found that the testosterone-reducing effect of the phthalate mixture was dose-additive and dose-dependent. Induction of MNGs had the lowest effective dose of any endpoint that we measured, with a benchmark dose (BMD10) of 9.402 (6.093, 13.13) mg ({sum} all phthalates)/kg/d, which is equivalent to a BMD10 of 3.517 (2.275, 4.919) mg DPEP/kg/d, based on relative potency factors. Quantitative analysis of phthalate metabolites in dam serum showed that the metabolites were present in approximately the same relative concentrations as the parent compounds in the dosing mixture, but with lower levels of monocyclohexyl phthalate and monobenzyl phthalate. Proteomic and spatial transcriptomic analyses showed significant decreases in expression of Leydig cell genes and proteins involved in steroid hormone biosynthesis, as well as enrichment of proteins related to metabolism of lipids and amino acids. Proteomic BMD analysis produced a lowest BMDmedian of 2.156 (0.204, 27.830) mg ({sum} all phthalates)/kg/d, which was within a 4.36-fold margin of the lowest apical endpoint BMD.

pharmacology and toxicology↗

bioRxiv2026

Multi-omics reveals a monocyte-macrophage-fibroblast axis in post-COVID-19 fibroinflammatory lung remodelling

Post-COVID-19 residual lung abnormalities (RLA) are associated with persistent respiratory symptoms and radiological changes, yet the underlying mechanisms remain unclear. We performed integrated multi-omic profiling of paired bronchoalveolar lavage and blood samples from patients with post-COVID-19 RLA and healthy controls, combining single-cell RNA sequencing, CITE-seq, single-cell T cell receptor sequencing, bronchoalveolar lavage fluid proteomics and functional fibroblast assays. In post-COVID-19 RLA lungs, we identified an increased abundance of profibrotic SPP1hi monocyte-derived alveolar macrophages, arising from an expanded circulating HLA-DRlowCD163+PDE4Dhi classical monocyte progenitor population, supporting a blood-lung myeloid axis. Cell-cell communication modelling positioned macrophages as central hubs of immune-stromal crosstalk, promoting monocyte recruitment with profibrotic priming, and fibroblast activation. Proteomic analysis of bronchoalveolar lavage fluid from post-COVID-19 RLA and idiopathic pulmonary fibrosis, compared with healthy controls, revealed shared and distinct signatures. These alveolar proteins in post-COVID-19 RLA were predominantly attributed to myeloid cells and predicted to engage fibroblast receptors. Bronchoalveolar lavage fluid induced fibroblast proliferation, differentiation and collagen deposition in vitro, with proliferation attenuated by the antifibrotic drug nintedanib. We also identified compartment-specific lymphoid dysregulation, including depletion of mucosal-associated invariant T (MAIT) cells in both the lung and blood, decreased natural killer (NK) cells with oligoclonal T cell expansion in the lung, and expansion of regulatory and cytotoxic T cells in the blood. These findings support a persistent monocyte-macrophage-fibroblast axis linking immune dysregulation to fibroproliferative remodelling after COVID-19 and highlights candidate therapeutic targets for post-viral lung fibrosis. We provide a publicly available atlas (on publication).

systems biology↗

bioRxiv2026

Colon-to-hind paw cross-organ sensitization is partially mediated by TrkB.T1-facilitated spinal neuroinflammation to activate lumbar DRG neurons

Patients with bowel disease can develop referred somatic pain at a later time with unknown molecular mechanisms. Using experimental mice with colitis induced by intracolonic installation of 2,4,6-Trinitrobenzenesulfonic acid (TNBS), we find an increase in the percentage of hind paw primary afferent neurons expressing Piezo2 or calcitonin gene-related peptide (CGRP), which are attenuated by TrkB.T1 knockout (KO). Concomitantly, TrkB.T1 KO also attenuates colitis-induced hind paw mechanical hypersensitivity and pain. Next, we find that TrkB.T1 is expressed in spinal cord astrocytes and its expression level is increased by colitis. TrkB.T1 KO reduces colitis-induced upregulation of Tumor necrosis factor-alpha (TNF-) mRNA but not upregulation of interleukin (IL)-6 mRNA in the spinal cord. Using calcium (Ca2+) imaging and ex vivo approaches, we find that TNF- elicits Ca2+ transients in capsaicin-sensitive as well as capsaicin-insensitive DRG neurons, and increases Piezo2 and CGRP expression in DRG neurons via distinct signaling pathways. Notably, TNF-or colitis-induced Piezo2 upregulation in L4 DRG neurons is mediated by or associated with the PI3K/Akt pathway that does not participate in CGRP upregulation. In contrast, CGRP upregulation in hind paw primary afferent neurons is associated with an upregulation of phosphorylated cAMP response element binding protein (p-CREB). Finally, we find that TrkB.T1 KO does not change the expression level of transient receptor potential cation channel subfamily V member 1 (TrpV1) in L4 DRG in colitis, explaining the ineffectiveness of TrkB.T1 KO on colitis-induced hind paw thermal hyperalgesia. These results suggest complex and distinct molecular pathways in colitis-induced somatic pain modalities and provide information for specific pain modality management.

neuroscience↗

bioRxiv2026

Testosterone Administration Modulates Competitive Choice via Ventral Striatum and TPJ

How does testosterone influence decisions and choice-related neural computations in competitive environments? To address this question, we administered testosterone or placebo to female participants (n = 54) in a double-blind, randomized design. Following drug treatment, participants competed in a dot estimation task that manipulated opponent status (lower, equal, or higher) and outcome feedback (win or loss), after which they decided whether to compete against the same opponent again. All participants adjusted their behavior based on opponent status and outcome feedback. Participants who received testosterone, however, showed significantly greater sensitivity to outcome feedback: they were more willing to compete after winning and less willing after losing, and made those decisions faster - suggesting that testosterone increases the weighting of immediate, salient outcome information in competitive decision-making. At the neural level, a network comprising ventral striatum, vmPFC, bilateral TPJ and ACC processed outcome-related signals during the feedback period. Critically, neural prediction analyses at the trial-level revealed that activity in left ventral striatum and TPJ predicted subsequent decisions to compete, but only in participants who received testosterone. The direction of these effects mirrored the behavioral results: striatal activity amplified the tendency to re-compete after winning, whereas TPJ activity predicted renewed competition after losing. Together, these findings demonstrate that testosterone biases competitive decisions by amplifying the influence of outcome-related activity in reward and social cognition circuits.

neuroscience↗

bioRxiv2026

Emergent frequency-dependent selection predicts mutation outcomes in complex ecological communities

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the framework of population genetics largely neglects interactions from a species-rich community. Here, we bridge this gap by using dynamical mean-field theory to integrate community ecology into classical population genetics models. We show that ecological interactions result in emergent frequency-dependent selection between parents and mutants, characterized by a single parameter measuring the strength of ecological feedbacks. This result generalizes classical population genetics models to highly diverse communities and enables predictions of mutation outcomes in these eco-evolutionary settings. We derive an analytic expression for fixation probability that extends Kimuras formula and reveals that ecological interactions strongly suppress the fixation of moderately beneficial mutations. This suppression arises because frequency-dependent selection leads to prolonged coexistence between parent and mutant lineages, which acts as a barrier to fixation. The strength of these effects increases with effective population size and the number of open niches in the ecosystem. Our study establishes a framework for integrating ecological interactions into population genetics, showing that evolutionary outcomes can be predicted using simple models even in the presence of complex community feedbacks.

evolutionary biology↗

bioRxiv2026

Ecology of metagenomes: incorporating genotype-to-phenotype maps into ecological models

A major theoretical problem in community ecology is to understand how genes, organisms, and environments combine to shape the structure and diversity of ecological communities. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. This limitation is particularly acute in microbial ecology, where the widespread use of sequencing technologies allows researchers to directly measure the genomic and metagenomic properties of communities. Here, we bridge this gap by incorporating genotype-to-phenotype maps into classical ecological models, including the generalized Lotka-Volterra model (GLV) and consumer resource models (CRMs). We focus on the case where genotype-to-phenotype maps are linear, which provides a tractable yet powerful framework for analyzing complex traits. Even in this simple setting, the resulting ecological dynamics give rise to novel gene-level ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. We find that ecological interactions between genes lead to pervasive "metagenomic hitchhiking" -- low-fitness genes can survive in the ecosystem because they are integrated into genomes of high-fitness species. We also show that phylogenetic relationships between species mold the ability of closely related strains to stably coexist in complex communities. This highlights how lineage structure and competitive interactions jointly shape community composition. Our framework provides a principled foundation for interpreting metagenomic data through the lens of ecological theory. Author summaryRecent advances in sequencing technologies have transformed our ability to characterize microbial communities at the genomic level. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. Here, we address this gap by extending classical ecological models to explicitly include genotype-to-phenotype maps. We focus on complex traits where the genotype-to-phenotype map is approximately linear. We show that the resulting ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. Our framework provides a novel perspective for interpreting metagenomic data through the lens of ecological theory.

ecology↗

bioRxiv2026

From genes to collective modes: biological constraints shape metabolic evolution

Population genetics has been successful in explaining how drift, selection, and mutation shape allele frequencies. However, we still lack an understanding of how biological and evolutionary constraints arising from genotype-phenotype maps shape evolutionary dynamics. To explore this question, we developed a new framework for simulating the evolution of an inherently polygenic and epistatic trait - metabolism - combining population genetics with flux balance analysis. We found that the evolution of metabolic networks exhibits surprisingly simple, reproducible dynamics. We identify evolutionary collective modes (EvCMs) - linear combinations of genes with high, constant selective pressure - as organizers of the long-term dynamics and the origin of this simplicity. EvCMs arise through the interaction of physical constraints, evolvability, and the requirements for growth. We developed a theoretical framework to predict EvCMs from large-scale metabolic models and found quantitative agreement between theory and simulation of E. coli central metabolism. Inspired by these results, we re-analyzed mutational data from Lenskis long-term evolution experiment and found compelling evidence for the existence of EvCMs. This work suggests that biological constraints encoded in genotype-phenotype maps play an important role in shaping evolutionary dynamics, offering a new perspective on complex trait evolution -- one that shifts focus from individual genes to collective modes.

evolutionary biology↗

bioRxiv2026

Parameter-free representations outperform single-cell foundation models on downstream benchmarks

Single-cell RNA sequencing (scRNA-seq) data exhibit strong and reproducible statistical structure. This has motivated the development of large-scale foundation models, such as TranscriptFormer, that use transformer-based architectures to learn a generative model for gene expression by embedding genes into a latent vector space. These embeddings have been used to obtain state-of-the-art (SOTA) performance on downstream tasks such as cell-type classification, disease-state prediction, and cross-species learning. Here, we ask whether similar performance can be achieved without utilizing computationally intensive deep learning-based representations. Using simple, interpretable pipelines that rely on careful normalization and linear methods, we obtain SOTA or near SOTA performance across multiple benchmarks commonly used to evaluate single-cell foundation models, including outperforming foundation models on out-of-distribution tasks involving novel cell types and organisms absent from the training data. Our findings highlight the need for rigorous benchmarking and suggest that the biology of cell identity can be captured by simple linear representations of single cell gene expression data.

bioinformatics↗

bioRxiv2026

Bidirectional fibrogenic cross-talk revealed in a human iPSC-derived epithelial-mesenchymal co-culture model of pulmonary fibrosis

Pulmonary fibrosis (PF) can arise from mutations in alveolar epithelial type 2 (AT2) cell-specific genes, but manifests in fibrotic activation of mesenchymal cells, thus involving fibrogenic epithelial-mesenchymal crosstalk. The ligand-receptor interactions underlying the onset and early progression of PF remain poorly understood. Induced pluripotent stem cell (iPSC)-derived models are powerful tools to study respiratory diseases, yet are currently limited to reductionist single lineage epithelial models or multi-lineage systems that lack purity and lung-specificity of the mesenchyme. Here we generate a human iPSC line carrying both a lung mesenchyme-specific reporter (TBX4-LERtdTomato) and a reporter for mesenchymal activation/differentiation (ACTA2GFP). Applying this line, we develop a directed differentiation protocol capable of generating cells that express key molecular and functional features of primary human developing lung mesenchyme across multiple iPSC genetic backgrounds. We then establish co-cultures of these iPSC-derived lung mesenchymal cells (iLM) with patient-specific iPSC-derived alveolar epithelial type 2 cells (iAT2s) carrying an SFTPCI73T mutation as a model for PF. We find increased expression of fibrotic markers in co-cultures with mutant iAT2s as compared to co-cultures with gene-corrected iAT2s. Moreover, mutant iAT2s express markers of alveolar-basal intermediate (ABI) cells only in the presence of iLM, suggesting that bidirectional crosstalk promotes this aberrant cell state. We identify ligand-receptor pairs enriched in co-cultures with mutant iAT2s, including TGF{beta}, multiple integrins, and additional genes that have not been previously linked to PF. Finally, we show that small molecule-mediated inhibition of TGF{beta} or integrins V{beta}1/V{beta}6 attenuates both fibrotic mesenchymal activation and the presence of ABI cells in iLM/iAT2 co-cultures. Thus, we have established a human iPSC-derived co-culture system that recapitulates key molecular hallmarks of bidirectional fibrogenic epithelial-mesenchymal crosstalk in pulmonary fibrosis, and enables the identification and study of potentially druggable pathways involved in disease initiation and progression.

molecular biology↗

bioRxiv2025

What's next for Indian Ornithology? 101 key research questions

India has a rich history of ornithology, and bird research in this field has expanded considerably in recent decades, spurred by a growing number of birdwatchers and ornithologists. Despite this progress, critical gaps remain. This paper highlights key areas where further research is needed and identifies pressing questions that will shape Indian ornithology in the coming years. Drawing from diverse inputs, we present a curated list of 101 research questions spanning across the various disciplines in ornithology - Natural History, Physiology and Disease Ecology, Behaviour, Population and Community Ecology, Habitat Ecology, Macroecology and Biogeography, Population Genetics and Evolution, Applied/Economic Ornithology and Conservation. The list was compiled through a multi-stage process, starting with a public open call for questions, followed by review and curation by a smaller panel of subject specialists. Each question was independently scored by a panel of experts based on three criteria: generality, novelty, and relevance. To account for variation in scoring styles, scores were normalised using Z-scores. The top 101 research questions were then selected based on these standardised scores. Each question is accompanied by an annotation that describes the significance of the question, and highlights opportunities to address it. While our list of research questions highlights significant research priorities, it is not intended to be exhaustive. Rather, it reflects the perspectives of those involved in its curation, who deemed these questions particularly relevant and impactful towards advancing Indian ornithology. We expect these questions to spark new project ideas among students, researchers, and citizen scientists, while guiding funders, managers, and policymakers toward priority research areas.

ecology↗

bioRxiv2025

Rlim coordinates diurnal regulation of food intake and thermogenesis

Energy homeostasis in mice is maintained through coordinated activity among hypothalamic nuclei that regulate food intake and thermogenesis. These processes must adapt to the sleep-wake cycle, yet the underlying pathways, cell types, and molecular mechanisms governing their diurnal regulation remain poorly understood. We show that mice lacking the E3 ubiquitin ligase Rlim are lean and resistant to diet-induced obesity, owing to reduced food intake and enhanced brown adipose tissue (BAT) thermogenesis. We identify GABAergic neurons in the suprachiasmatic nucleus (SCN)--components of the central circadian clock--as mediators of these effects. Specifically, Rlim in RIP-Cre+ neurons governs daily thermogenic rhythms, while Rlim in vasoactive intestinal peptide (VIP)-expressing neurons modulates diurnal feeding behavior. Thus, Rlim is a key regulator of diurnal rhythms controlling energy balance.

physiology↗

bioRxiv2025

Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana, we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

plant biology↗

bioRxiv2025

Finding signatures of low-dimensional geometric landscapes in high-dimensional cell fate transitions

Multicellular organisms develop a wide variety of highly-specialized cell types. The consistency and robustness of developmental cell fate trajectories suggests that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. A complementary set of works draws on the theory of dynamical systems to argue that cell fate transitions can be categorized into universal decision-making classes. However, the theory connecting geometric landscapes and decision-making classes to high-dimensional gene expression space is still in its infancy. Here, we introduce a phenomenological model that allows us to identify gene expression signatures of decision-making classes from single-cell RNA-sequencing time-series data. Our model combines low-dimensional gradient-like dynamical systems and high-dimensional Hopfield networks to capture the interplay between cell fate, gene expression, and signaling pathways. We apply our model to the maturation of alveolar cells in mouse lungs to show that the transient appearance of a mixed alveolar type 1/type 2 state suggests the triple cusp decision-making class. We also analyze lineage-tracing data on hematopoetic differentiation and show that bipotent neutrophil-monocyte progenitors likely undergo a heteroclinic flip bifurcation. Our results suggest it is possible to identify universal decision-making classes for cell fate transitions directly from data.

biophysics↗

bioRxiv2025

Inferring genotype-phenotype maps using attention models

Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pair-wise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.

genetics↗

bioRxiv2025

A theory of ecological invasions and its implications for eco-evolutionary dynamics

Predicting the outcomes of species invasions is a central goal of ecology, a task made especially challenging due to ecological feedbacks. To address this, we develop a general theory of ecological invasions applicable to a wide variety of ecological models: including Lotka-Volterra models, consumer resource models, and models with cross feeding. Importantly, our framework remains valid even when invading evolved (non-random) communities and accounts for invasion-driven species extinctions. We derive analytical expressions relating invasion fitness to invader abundance, shifts in the community, and extinction probabilities. These results can be understood through a new quantity we term "dressed invasion fitness", which augments the traditional notion of invasion fitness by incorporating ecological feedbacks. We apply our theory to analyze short-term evolutionary dynamics through a series of invasions by mutants whose traits are correlated with an existing parent. We demonstrate that, generically, mutants and parents can coexist, often by driving the extinction of low-abundance species. We validate theoretical predictions against experimental datasets spanning ecosystems from plants to microbial protists. Our work highlights the central role of ecological feedbacks in shaping community responses to invasions and mutations, suggesting that parent-mutant coexistence is widespread in eco-evolutionary dynamics.

ecology↗

bioRxiv2024

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

biophysics↗

bioRxiv2024

A universal niche geometry governs the response of ecosystems to environmental perturbations

How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment. Here, we introduce a theoretical framework for calculating the linear response of ecosystems to environmental perturbations in generalized consumer-resource models. Our construction is applicable to a wide class of systems, including models with non-reciprocal interactions, cross-feeding, and non-linear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces and ecological interactions are represented by geometric transformations between these spaces. We show that near a steady state, such geometric transformations directly map environmental perturbations - in resource availability and mortality rates - to shifts in niche structure. We illustrate these ideas in a variety of settings including a minimal model for pH-induced toxicity in bacterial denitrification.

ecology↗

bioRxiv2023

Metabolic similarity and the predictability of microbial community assembly

AbstractWhen microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

ecology↗