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bioRxiv2026

Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Gene expression changes along cell lineages, but most single-cell RNA-seq analyses treat cells as independent snapshots and ignore their phylogenetic relationships. Here we present LaVOUS, a lineage-aware probabilistic framework for modeling sparse single-cell gene-expression counts on reconstructed lineage trees. LaVOUS couples Brownian motion and Ornstein-Uhlenbeck models of latent transcriptional dynamics with negative-binomial observation models and scalable variational inference, enabling likelihood-based tests for gene-expression heritability, branch-specific expression shifts, and ancestral expression reconstruction. In simulations, LaVOUS improved detection of lineage-associated expression changes over Gaussian phylogenetic models and accurately reconstructed expression histories across expression levels. Applied to lineage-resolved single-cell datasets from metastatic lung cancer, class-switching B cells, and the developing brain, LaVOUS identified expression changes associated with metastatic progression, isotype switching, and neuronal differentiation. LaVOUS provides a general framework for studying single-cell expression dynamics across development and disease.

bioinformatics↗

bioRxiv2026

Transposable elements modulate gene expression in response to oxidative stress in Drosophila suzukii

Transposable elements (TEs) are major contributors to genomic and regulatory variation and can influence the expression of nearby genes in response to environmental stress. The invasive fruit fly Drosophila suzukii, whose genome is composed of nearly 50% TE-derived sequences, provides an excellent model to investigate the regulatory impact of TEs on stress responses. Although previous studies found no global association between TEs and oxidative stress transcriptomic response in this species, the effects of individual TE insertions remain largely unexplored. Here, we investigated the role of two polymorphic intronic TE insertions, Mrp4-TE and inaE-TE, located within genes implicated in the oxidative stress response in D. melanogaster. Using wild-type strains of D. suzukii, we assessed the effect of these insertions on gene expression following oxidative stress induced by paraquat exposure. We found that their corresponding nearby genes Mrp4 and inaE were upregulated after paraquat treatment and that this response was significantly stronger in flies carrying the corresponding TE insertions. Allele-specific expression analyses further showed that the alleles containing the TE insertions exhibited a tendency towards higher expression levels than alleles lacking the insertions, both under control and oxidative stress conditions, supporting a cis-regulatory effect. In addition, Mrp4-TE was associated with increased sensitivity to oxidative stress. Together, our results provide the first evidence that TE insertions can modulate oxidative stress-responsive gene expression in D. suzukii. These findings highlight the importance of investigating individual TE insertions to understand the genetic and regulatory mechanism underlying stress responses, adaptation, and invasion success in this species.

genomics↗

bioRxiv2020

Differential gene expression reflects larval development and survival of monarch butterflies on different milkweed hosts

Second instar larvae of the monarch butterfly, Danaus plexippus, from a nonmigratory population in Irapuato, Mexico, were reared for twenty-four hours on three species of milkweed hosts: Asclepias curassavica, A. linaria, and Gomphocarpus physocarpus. We then measured larval growth and differential expression of coding genes and of microRNAs. Larval growth was similar on the two Asclepias species, while little growth was observed on G. physocarpus. The greatest differences in coding gene expression occurred in genes controlling growth and detoxification and were most extreme in comparisons between G. physocarpus and the two Asclepias. MicroRNAs are predicted to be involved as regulators of many of these processes, in particular miR-278, differentially expressed here, could be an important regulator of growth through Hippo signaling. The implications for survival of the monarch, especially in the context of environmental factors altering the availability of their favored milkweed species, are discussed.

genomics↗

bioRxiv2022

Engineered Serum Markers for Noninvasive Monitoring of Gene Expression in the Brain

Noninvasive efforts to map brain gene expression have been hampered by low sensitivity and limited access to the brain. Here, we introduce a new platform that enables multiplexed, noninvasive, and site-specific monitoring of brain gene expression through a novel class of engineered reporters called Released Markers of Activity (RMAs). Instead of detecting gene expression in the less accessible brain, RMA reporters exit from a known brain region into the blood, where they can be easily measured with biochemical techniques. Expressing RMAs at a single brain site, typically covering [~]1% of the brain volume, provides up to a 39,000-fold signal increase over the baseline in vivo. Further, expression of RMAs in as few as several hundred neurons was sufficient for their reliable detection. When placed under a promoter upregulated by neuronal activity, RMAs could be used to measure neuronal activity in specific brain regions with a simple blood draw. We found that chemogenetic activation of cells expressing Fos-responsive RMA increased serum levels of RMA over 4-fold compared to non-activated controls. By contrast, a control RMA expressed under a constitutive neuronal promoter did not show such upregulation, demonstrating multiplexed ratiometric measurement with RMAs and proving specificity of neuronal activity discrimination. Together, our study pioneers a new noninvasive paradigm for repeatable and multiplexed monitoring of gene expression in an intact brain with sensitivity that is currently unavailable through other noninvasive gene expression reporter systems.

neuroscience↗

bioRxiv2015

Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action

Increasing evidence shows that, like phenotypic mean, phenotypic variance is also genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e., genomic loci associated with gene expression variance, in humans. We discovered that common genetic variations may contribute to increasing gene expression variability via two distinct modes of action--epistasis and destabilization. Specifically, the epistasis model explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of \"third-party\" eQTLs that influence the level of gene expression in a fraction, rather than the entire set, of sampled individuals. The destabilization model explains the other three-quarters of evQTLs, which tend to be associated with mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and time-course stability of gene expression in single samples using repetitive qRT-PCR assays. The destabilizing evQTL SNPs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common SNPs may work interactively or independently to shape the variability of gene expression in humans. These findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variability-centered analytic methods for quantitative trait mapping.

Genetics↗

bioRxiv2026

PC-index, a composite metric for gene expression in single-cell RNA-seq

Single-cell RNA sequencing enables analysis of gene expression heterogeneity, but summarizing gene expression across large cell populations remains challenging. Gene expression is commonly described using average expression (counts per 10,000 transcripts, CP10K) and cellular prevalence (the fraction of cells expressing a gene), which can be difficult to interpret jointly. Here, we introduce the PC-index, a single, intuitive metric that integrates expression magnitude and prevalence. The PC-index is defined as the largest value X such that at least X% of cells express a gene at [≥]X/10 CP10K. Using human adipocytes from GTEx single-nucleus RNA-seq as an illustrative example, we show that adipocyte genes exhibit distinct PC-index profiles. For example, adiponectin has a PC-index of 21, indicating that at least 21% of adipocytes express it at [≥]2.1 CP10K. Conceptually, the PC-index reflects both percentage and CP10K, yielding a stable and interpretable single-number summary of gene expression behavior in single-cell data.

genomics↗

bioRxiv2022

PGC-1β maintains mitochondrial metabolism and restrains inflammatory gene expression

Metabolic programming of the innate immune cells known as dendritic cells (DCs) changes in response to different stimuli, influencing their function. While the mechanisms behind increased glycolytic metabolism in response to inflammatory stimuli are well-studied, less is known about the programming of mitochondrial metabolism in DCs. We used lipopolysaccharide (LPS) and interferon-{beta} (IFN-{beta}), which differentially stimulate the use of glycolysis and oxidative phosphorylation (OXPHOS), respectively, to identify factors important for mitochondrial metabolism. We found that the expression of peroxisome proliferator-activated receptor gamma coactivator 1{beta} (PGC-1{beta}), a transcriptional co-activator and known regulator of mitochondrial metabolism, decreases when DCs are activated with LPS, when OXPHOS is diminished, but not with IFN-{beta}, when OXPHOS is maintained. We examined the role of PGC-1{beta} in bioenergetic metabolism of DCs and found that PGC-1{beta} deficiency in DCs indeed impairs mitochondrial respiration. PGC-1{beta}-deficient DCs are more glycolytic compared to controls, likely to compensate for reduced OXPHOS. PGC-1{beta} deficiency also causes decreased capacity for ATP production at steady state and in response to IFN-{beta} treatment. Loss of PGC-1{beta} in DCs leads to increased expression of genes in inflammatory pathways, and reduced expression of genes encoding proteins important for mitochondrial metabolism and function. Collectively, these results demonstrate that PGC-1{beta} is a key positive regulator of mitochondrial metabolism in DCs.

immunology↗

bioRxiv2018

Transite: A computational motif-based analysis platform that identifies RNA-binding proteins modulating changes in gene expression

RNA-binding proteins (RBPs) play critical roles in regulating gene expression by modulating splicing, RNA stability, and protein translation. In response to various stimuli, alterations in RBP function contribute to global changes in gene expression, but identifying which specific RBPs are responsible for the observed changes in gene expression patterns remains an unmet need. Here, we present Transite a multi-pronged computational approach that systematically infers RBPs influencing gene expression changes through alterations in RNA stability and degradation. As a proof of principle, we applied Transite to public RNA expression data from human patients with non-small cell lung cancer whose tumors were sampled at diagnosis, or after recurrence following treatment with platinum-based chemotherapy. Transite implicated known RBP regulators of the DNA damage response and identified hnRNPC as a new modulator of chemotherapeutic resistance, which we subsequently validated experimentally. Transite serves as a generalizable framework for the identification of RBPs responsible for gene expression changes that drive cell-state transitions and adds additional value to the vast wealth of publicly-available gene expression data.

bioinformatics↗

bioRxiv2022

Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

To safeguard biodiversity in a changing climate, we require taxonomic information about species turnover and insights into the health of organisms. Environmental DNA approaches are increasingly used for species identification, but cannot provide functional insights. Transcriptomic methods reveal the physiological states of macroorganisms, but are currently species specific and require tissue sampling or animal sacrifice, making community-wide assessments challenging. Here, we test if broad functional information (expression level of the transcribed genes) can be harnessed from environmental RNA (eRNA), which includes extra-organismal RNA from macroorganisms along with whole microorganisms. We exposed Daphnia pulex as well as phytoplankton prey and microorganism colonizers to control (20 {degrees}C) and heat stress (28 {degrees}C) conditions for seven days. We sequenced eRNA from tank water (after complete removal of Daphnia) as well as RNA from Daphnia tissue, enabling comparisons of extra-organismal and organismal RNA based gene expression profiles. Both RNA types detected similar heat stress responses of Daphnia. Using eRNA, we identified 32 Daphnia genes to be differentially expressed following heat stress. Of these, 17 were also differentially expressed and exhibited similar levels of relative expression in organismal RNA. In addition to the extra-organismal Daphnia response, eRNA detected community-wide heat stress responses consisting of distinct functional profiles and 121 differentially expressed genes across 8 taxa. Our study demonstrates that environmental transcriptomics based on eRNA can non-invasively reveal gene expression responses of macroorganisms following environmental changes, with broad potential implications for the biomonitoring of ecological health across the trophic chain.

ecology↗

bioRxiv2024

Quantifying transcriptome turnover on phylogenies bymodeling gene expression as a binary trait

Changes in gene expression are a key driver of phenotypic evolution, leading to a persistent interest in the evolution of transcriptomes. Traditionally, gene expression is modeled as a continuous trait, leaving qualitative transitions largely unexplored. In this paper, we detail the development of new Bayesian inference techniques to study the evolutionary turnover of organ-specific transcriptomes, which we define as instances where orthologous genes gain or lose expression in a particular organ. To test these techniques, we analyze the transcriptomes of two male reproductive organs, testes and accessory glands, across 11 species of the Drosophila melanogaster species group. We first discretize gene expression states by estimating the probability that each gene is expressed in each organ and species. We then define a phylogenetic model of correlated transcriptome evolution in two or more organs and fit it to the expression state data. Inferences under this model show that many genes have gained and lost expression in each organ, and that the two organs experienced accelerated transcriptome turnover on different branches of the Drosophila phylogeny.

evolutionary biology↗

bioRxiv2023

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice--analogous to a map of elevation in a landscape--using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

bioinformatics↗

bioRxiv2020

Mitochondrial and insulin gene expression in single cells shape pancreatic beta cells' population divergence

Mitochondrial gene expression is pivotal to cell metabolism. Nevertheless, it is unknown whether it diverges within a given cell type. Here, we analysed single-cell RNA-seq experiments from [~]4600 human pancreatic alpha and beta cells, as well as [~]900 mouse beta cells. Cluster analysis revealed two distinct human beta cells populations, which diverged by mitochondrial (mtDNA) and nuclear DNA (nDNA)-encoded oxidative phosphorylation (OXPHOS) gene expression in healthy and diabetic individuals, and in newborn but not in adult mice. Insulin gene expression was elevated in beta cells with higher mtDNA gene expression in humans and in young mice. Such human beta cell populations also diverged in mt-RNA mutational repertoire, and in their selective signature, thus implying the existence of two previously overlooked distinct and conserved beta cell populations. While applying our approach to alpha cells, two sub-populations of cells were identified which diverged in mtDNA gene expression, yet these cellular populations did not consistently diverge in nDNA OXPHOS genes expression, nor did they correlate with the expression of glucagon, the hallmark of alpha cells. Thus, pancreatic beta cells within an individual are divided into distinct groups with unique metabolic-mitochondrial signature.

genomics↗

bioRxiv2025

Intra-population variability in genome-wide repressive histone marks underpins differential gene expression in a fungal wheat pathogen

Epigenetic modifications influence the expression of phenotypic traits by modulating gene expression and responses to environmental cues. In plant pathogens, the expression of virulence-associated genes such as effectors and gene clusters encoding the production of secondary metabolites are known to be regulated by epigenetic modifications. Modulating epigenetic patterns of such genes is considered a key adaptation for pathogens to successfully attack hosts. Gene expression variation within pathogen species are regulated by extensive cis- regulatory polymorphism and insertion activities of transposable elements. However, whether pathogens vary in epigenetic profiles among members of the same species remains largely unexplored. Here, we focus on the major fungal wheat pathogen Zymoseptoria tritici and establish histone methylation profiles for 45 isolates of an extensively characterized wheat field population. We analyzed the facultative heterochromatin mark H3K27me3, a histone methylation that is thought to regulate effector and gene cluster loci in the genome. H3K27m3 coverage was increased in transposable element rich regions, with newly inserted long-terminal repeat retrotransposons contributing to epigenetic variation among pathogen genotypes. Overall, nearly 20% of all genes showed within-population variation in H3K27me3 marks, which likely contributes to the substantial within-population variation in gene expression. Effector candidate genes and members of gene clusters showed higher than average variation in repressive histone marks among isolates. Taken together, our study provides among the first insights into intra-species epigenetic variation of a fungal pathogen. Such population-level variation in histone methylation patterns opens avenues to recapitulate epigenetic mechanisms of pathogen adaptation.

genomics↗

bioRxiv2017

The role of gene expression in the recent evolution of resistance in a model host parasite system

Damage by parasites is a perpetual challenge for hosts, often leading to the evolution of elaborate mechanisms of avoidance, immunity, or tolerance. Host resistance can evolve via changes in immune protein coding and/or expression. Heritable population differences in gene expression following infection can reveal mechanisms of immune evolution. We compared gene expression in infected and uninfected threespine stickleback (Gasterosteus aculeatus) from two natural populations that differ in their resistance to a native cestode parasite, Schistocephalus solidus. Genes in both the innate and adaptive immune system were differentially expressed as a function of host population, infection status, and their interaction. These genes were enriched for loci controlling immune functions that we independently verified differ between host populations, or in response to infection. For instance, populations differ strongly in reactive oxygen (ROS) production, and we observed corresponding differences in expression of ROS-affecting loci. Differentially expressed genes also were involved in fibroblast activation, B-cell activation, and leukocyte trafficking. Coexpression network analysis identified two distinct immune processes contributing to stickleback resistance; several modules of genes are correlated with parasite survival while a different set of modules are correlated with suppression of cestode growth. Comparison of networks between populations showed resistant fish have a dynamic expression profile while susceptible fish are static. In summary, recent evolutionary divergence between two vertebrate populations has generated population-specific gene expression responses to parasite infection, which reveal a few immune modules likely to separately affect cestode establishment, and growth.

evolutionary biology↗

bioRxiv2023

Cold-induced muscle atrophy in zebrafish: insights from swimming activity and gene expression analysis

The investigation into the effects of cold acclimation on fish skeletal muscle function and its potential implications for muscle atrophy is of great interest to us. This study examines how rearing zebrafish at low temperatures affects their locomotor activity and the expression of genes associated with muscle atrophy. Zebrafish were exposed to temperatures ranging from 10 {degrees}C to 25 {degrees}C, and their swimming distance was measured. The expression levels of important muscle atrophy genes, Atrogin-1 and MuRF1, were also evaluated. Our findings show that swimming activity significantly decreases when the water temperature ranges from 10 {degrees}C to 15 {degrees}C, indicating a decrease in voluntary movement. Additionally, gene expression analysis shows a significant increase in the expression of Atrogin-1 and MuRF1 at 10 {degrees}C. This up-regulation could lead to muscle atrophy caused by decreased activity in cold temperatures. To investigate the effects of exercise on reducing muscle atrophy, we subjected zebrafish to forced swimming at a temperature of 8 {degrees}C for ten days. This treatment significantly reduced the expression of Atrogin-1 and MuRF1, emphasizing the importance of muscle stimulation in preventing muscle atrophy in zebrafish. These findings suggest that zebrafish can serve as a valuable model organism for studying muscle atrophy and can be utilized in drug screening for muscle atrophy-related disorders. Cold-reared zebrafish provide a practical and ethical approach to inducing disuse muscle atrophy, providing valuable insights into potential therapeutic strategies for addressing skeletal muscle atrophy.

physiology↗

bioRxiv2023

DegCre: Probabilistic association of differential gene expression with regulatory regions

Differential gene expression in response to perturbations is mediated at least in part by changes in binding of transcription factors (TFs) and other proteins at specific genomic regions. Association of these cis-regulatory elements (CREs) with their target genes is a challenging task that is essential to address many biological and mechanistic questions. Many current approaches rely on chromatin conformation capture techniques that identify spatial proximity between genomic sites to establish CRE-to-gene associations. These methods can be effective but have limitations, including resolution, minimal detectable interaction distance, and cost. As an alternative, we have developed DegCre, a non-parametric method that evaluates correlations between measurements of perturbation-induced differential gene expression and differential regulatory signal at CREs to score possible CRE-to-gene associations. It has several unique features, including the ability to: use any type of CRE activity measurement; yield probabilistic scores for CRE-to-gene pairs; and assess CRE-to-gene pairings across a wide range of sequence distances. We apply DegCre to three data sets, each employing different perturbations and containing a variety of regulatory signal measurements, including chromatin openness, histone modifications, and TF occupancy. To test their efficacy, we compare DegCre associations to HiC loop calls and to CRISPR validated interactions, with both yielding good agreement. We demonstrate the identification of perturbation direct target genes with DegCre confirm the results with previous reports. DegCre is a novel approach to the association of CREs to genes from a perturbation-differential perspective, with strengths that are complementary to existing approaches and allow for new insights into gene regulation.

genomics↗

bioRxiv2018

Transposable elements and gene expression during the evolution of amniotes

BackgroundTransposable elements (TEs) are primarily responsible for the changes in genome sequences that occur over time within and between species. TEs themselves evolve, with clade specific LTR/ERV, LINEs and SINEs responsible for the bulk of species specific genomic features. Because TEs can contain regulatory motifs, they can be exapted as regulators of gene expression. While TE insertions can provide evolutionary novelty for the regulation of gene expression, their overall impact on the evolution of gene expression is unclear. Previous investigators have shown that tissue specific gene expression in amniotes is more similar across species than within species, supporting the existence of conserved developmental gene regulation. In order to understand how species specific TE insertions might affect the evolution/conservation of gene expression, we have looked at the association of gene expression in six tissues with TE insertions in six representative amniote genomes (human, opossum, platypus, anole lizard, bearded dragon and chicken).\n\nResultsWe have used a novel bootstrapping approach to minimise the conflation of effects of repeat types on gene expression. We compared the expression of orthologs containing different types of recent TE insertions to orthologs that contained older TE insertions and found significant differences in gene expression associated with TE insertions. Likewise, we compared the expression of non-ortholog genes containing different types of recent TE insertions to non-orthologs with older TE insertions and found significant differences in gene expression associated with TE insertions. As expected TEs were associated with species-specific changes in gene expression, but the magnitude and direction of change of expression changes were unexpected. Overall, orthologs containing clade specific TEs were associated with lower gene expression, while in non-orthologs, non clade-specific TEs were associated with higher gene expression. Exceptions were SINE elements in human and chicken, which had an opposite association with gene expression compared to other species.\n\nConclusionsOur observed species-specific associations of TEs with gene expression support a role for TEs in speciation/response to selection by species. TEs do not exhibit consistent associations with gene expression and observed associations can vary depending on the age of TE insertions. Based on these observations, it would be prudent to refrain from extrapolating these and previously reported associations to distantly related species.

evolutionary biology↗

bioRxiv2017

Impact of division rate and cell size on gene expression noise

The cell division rate, size, and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli d other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persistors cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding of circuits robustness across growth conditions is key for the effective design of synthetic biological systems.

systems biology↗