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At least 253 records · Page 14Linked to original sources

bioRxiv2024

Regulation of Sex-biased Gene Expression by the Ancestral X-Y Chromosomal Gene Pair Kdm5c-Kdm5d

Conventionally, Y-linked Sry is thought to drive sex differences by triggering differential hormone production. Ancestral X-Y gene pairs, however, are hypothesized to drive hormone-independent sex differences. Here, we show that the X-Y gene pair Kdm5c-Kdm5d regulates sex-biased gene expression in pluripotent mouse embryonic stem cells (ESCs). Wild-type (WT) XX female ESCs exhibit >2-fold higher expression of 409 genes relative to WT XY male ESCs. Conversely, WT XY male ESCs exhibit >2-fold higher expression of 126 genes compared to WT XX female ESCs. Loss of Kdm5c in female ESCs downregulates female-biased genes. In contrast, loss of either Kdm5c or Kdm5d in male ESCs upregulates female-biased genes and downregulates male-biased genes, effectively neutralizing sex-biased gene expression. KDM5C promotes the expression of Kdm5d and several other Y-linked genes in male ESCs. Moreover, ectopic Kdm5d expression in female ESCs is sufficient to drive male-biased gene expression. These results establish Kdm5c-Kdm5d as critical regulators of sex-biased gene expression.

genetics↗

bioRxiv2026

An information content principle explains regulatory patterns of gene expression across human tissues

Gene expression patterns range from broadly expressed housekeeping genes to highly tissue-specific ones. Notably, many genes exhibit intermediate specificity, characterized by elevated expression in some tissues while low or absent in others. Understanding how regulatory demands scale with tissue specificity offers a valuable opportunity to uncover fundamental principles of genome regulation. By analyzing cis-regulatory element (CRE) counts across human genes with varying tissue specificity, we observed a nonlinear pattern: genes with intermediate specificity harbor the highest CRE count, suggesting distinct regulatory strategies across the expression spectrum. Motivated by this observation, we used the Minimum Description Length (MDL) principle from information theory, together with a maximum parsimony approach from phylogenetics, to quantify regulatory demands across tissues. Our analysis revealed that MDL-based regulatory demand scales consistently with diverse regulatory features, including CRE count, transcription-factor and microRNA targeting, and gene structure. To test whether this scaling changes across the expression spectrum, we partitioned genes by expression breadth. Two patterns emerged: features scaling with MDL in selectively expressed genes tend to act as on/off switches, whereas those in ubiquitous genes serve as fine-tuning knobs. Evolutionary analysis revealed that these regulatory patterns vary with gene age, with alignment between MDL and CRE counts peaking in intermediate-aged genes. Collectively, these results establish MDL combined with maximum parsimony as a powerful framework linking regulatory architecture, expression specificity, and evolutionary age, offering novel insights into the organizational principles underlying genome regulation.

genomics↗

bioRxiv2014

Enhanced Transcriptome Maps from Multiple Mouse Tissues Reveal Evolutionary Constraint in Gene Expression for Thousands of Genes

We characterized by RNA-seq the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles obtained in human cell lines reveals substantial conservation of transcriptional programs, and uncovers a distinct class of genes with levels of expression across cell types and species, that have been constrained early in vertebrate evolution. This core set of genes capture a substantial and constant fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with strong and conserved epigenetic marking, as well as to a characteristic post-transcriptional regulatory program in which sub-cellular localization and alternative splicing play comparatively large roles.

Bioinformatics↗

bioRxiv2017

Hub connectivity, neuronal diversity, and gene expression in the C. elegans connectome

Studies of nervous system connectivity, in a wide variety of species and at different scales of resolution, have identified several highly conserved motifs of network organization. One such motif is a heterogeneous distribution of connectivity across neural elements, such that some elements act as highly connected and functionally important network hubs. These brain network hubs are also densely interconnected, forming a so-called rich-club. Recent work in mouse has identified a distinctive transcriptional signature of neural hubs, characterized by tightly coupled expression of oxidative metabolism genes, with similar genes characterizing macroscale inter-modular hub regions of the human cortex. Here, we sought to determine whether hubs of the neuronal C. elegans connectome also show tightly coupled gene expression. Using open data on the chemical and electrical connectivity of 279 C. elegans neurons, and binary gene expression data for each neuron across 948 genes, we computed a correlated gene expression score for each pair of neurons, providing a measure of their gene expression similarity. We demonstrate that connections between hub neurons are the most similar in their gene expression while connections between nonhubs are the least similar. Genes with the greatest contribution to this effect are involved in glutamatergic and cholinergic signalling, and other communication processes. We further show that coupled expression between hub neurons cannot be explained by their neuronal subtype (i.e., sensory, motor, or interneuron), separation distance, chemically secreted neurotransmitter, birth time, pairwise lineage distance, or their topological module affiliation. Instead, this coupling is intrinsically linked to the identity of most hubs as command interneurons, a specific class of interneurons that regulates locomotion. Our results suggest that neural hubs may possess a distinctive transcriptional signature, preserved across scales and species, that is related to the involvement of hubs in regulating the higher-order behaviors of a given organism.\n\nAuthor summarySome elements of neural systems possess many more connections than others, marking them as network hubs. These hubs are often densely interconnected with each other, forming a so-called rich-club that is thought to support integrated function. Recent work in the mouse suggests that connected pairs of hubs show higher levels of transcriptional coupling than other pairs of brain regions. Here, we show that hub neurons of the nematode C. elegans also show tightly coupled gene expression and that this effect cannot be explained by the spatial proximity or anatomical location of hub neurons, their chemical composition, birth time, neuronal lineage or topological module affiliation. Instead, we find that elevated coexpression is driven by the identity of most hubs of the C. elegans connectome as command interneurons, a specific functional class of neurons that regulate locomotion. These findings suggest that coupled gene expression is a highly conserved genomic signature of neural hubs that may be related to the specific functional role that hubs play in broader network function.

neuroscience↗

bioRxiv2021

An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression

Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of these datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive a similarity metric between genes called the Bayesian lead-lag R2 (LLR2). Importantly, the calculation of the LLR2 leverages biological databases that document known interactions amongst genes; this information is automatically used to define informative prior distributions on the ODE models parameters. As a result, the LLR2 is a biologically-informed metric that can be used to identify clusters or networks of functionally-related genes with co-moving or time-delayed expression patterns. We then derive data-driven shrinkage parameters from Steins unbiased risk estimate that optimally balance the ODE models fit to both data and external biological information. Using real gene expression data, we demonstrate that our methodology allows us to recover interpretable gene clusters and sparse networks. These results reveal new insights about the dynamics of biological systems.

genomics↗

bioRxiv2024

Evolved and plastic gene expression in adaptation to a novel niche

Gene expression, resulting from complex regulatory interactions, plays an important role in adaptation and speciation. While gene expression historically has been studied in the context of reproductive isolation in speciation research, a role for evolved differences in gene expression in adaptation to novel niches is increasingly appreciated. How gene expression evolves and enables divergent ecological adaptation, and how changes in gene expression relate to genomic architecture and genetic divergence are pressing questions in understanding the processes of adaptation and ecological speciation. Further, how plasticity in gene expression can both contribute to and be affected by the process of ecological adaptation is a crucial component in understanding gene expression evolution. To address these questions, we investigate the role of evolved and plastic gene expression differences in adaptation leveraging an established host plant shift in the peacock fly Tephritis conura. Using a cross-fostering design where larvae feed on either natal or alternate host plants, we uncover extensive evolved differences in gene expression between the ecotypes, strikingly in genes associated with processing of host plant chemicals. We find limited evidence for plasticity, with some indications of higher plasticity in the ancestral ecotype where the expression of three gene coexpression modules is altered when larvae are cross-fostered to the derived host plant. Interestingly, we find an enrichment of differentially expressed genes within a large, ecotype-specific inversion in the T. conura genome. This finding adds to evidence that inversions are important for enabling diversification in the face of gene flow and underscores that effects on gene expression may be key to understanding the role of inversions.

evolutionary biology↗

bioRxiv2025

NanoString Technologies Neuropathology Panel Produces Unreliable Measurements of Mouse Hippocampal Gene Expression

Technologies for measuring gene expression (i.e., the number of RNA transcripts) of large numbers of genes simultaneously in specific tissues have exploded in recent years. Current methods include high-throughput RNA sequencing (RNA-seq), transcript counting platforms like NanoStrings nCounter(R), and spatially resolved techniques based on fluorescent in situ hybridization (FISH). Several studies have evaluated the reliability of these different methods and performance in comparison to one another. Typically, technical reliability, as measured by Pearsons correlation of two measurements of the same sample, is usually well above 90%, and is statistically significant even for small sample sizes (e.g., 8 samples measured twice). We performed an experiment where we aimed to compare hippocampal gene expression between 3 groups (n=5 per group) of young adult male C57BL/6J mice. Before sampling, the groups were treated with either repeated injections of PBS (vehicle), extracellular vesicles taken from the blood plasma of sedentary mice (SedVs) or exercising mice (ExerVs). The hippocampus was dissected, and RNA purified using standard methods. The samples were analyzed using the NanoString Neuropathology panel, that measures 770 genes simultaneously. To estimate reliability, we measured 8 of the samples twice in two separate assays. Surprisingly, only 85 genes showed a significant Pearsons correlation (p<0.05), and none of these met false discovery significance (all q<0.05). To confirm that no errors were made transferring labels, the individual samples were permuted to see whether a different assignment could recover a greater number of positive correlations. Results showed that the original assignment was best suggesting no errors in sample assignments were made. We conclude that the Nanostring neuropathology panel produces unreliable data for mouse hippocampal gene expression.

neuroscience↗

bioRxiv2024

Spatio-temporal Reconstruction of Gene Expression Patterns in Developing Mice

Understanding gene regulation in organism development is crucial in biology. Techniques like whole-mount in situ hybridization can reveal spatial gene expression in organs and tissues. However, capturing time-lapse images of gene expression dynamics in embryos developing in utero, such as mice, remains technically challenging beyond the early stages. To address this, we present a method to integrate static snapshots of gene expression patterns across limb developmental stages, creating a continuous 2D reconstruction of gene expression patterns over time. This method interpolates small tissue regions over time to create smooth temporal trajectories of gene expression. We successfully applied it to a number of key genes in limb development, including Sox9, Hand2, and Bmp2. This approach enables a detailed spatio-temporal mapping of gene expression, providing insights into developmental mechanisms. By estimating Gene Expression Patterns at previously unobserved time points, it facilitates the comparison of these patterns across samples. The reconstructed trajectories offer high-quality data that will be useful to guide computational modeling and machine learning, advancing the study of developmental biology in systems where real-time imaging is technically difficult or impossible.

developmental biology↗

bioRxiv2019

Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony?

Studying relationships among gene-products by gene expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed correlation of transcript and protein expression levels. All these efforts partook in the current understanding of signaling network models and expanded the signaling databases. In fact, due to the unavailability or high-cost of the experiments, most of the studies do not usually look for direct interactions, and some parts of these networks are contradictory. Besides, it is now a standard step to accomplish enrichment analysis on biological annotations, to make claims about the potentially implicated biological pathways in any perturbation. Explicitly, upon identifying differentially expressed genes, they are spontaneously presumed the corresponding dysregulated pathways. Then, molecular mechanistic insights are proposed for disease etiology and drug discovery based on statistically enriched biological processes. In this study, using four common and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level and the causal relationships among the gene pairs. We illustrated that the signaling network was not more consistent or coherent with the recorded expression profile compared to the random relationships. Finally, we provided the pieces of evidence and concluded that gene-product expression data, especially at the transcript level, are not reliable or at least insufficient to infer causal biological relationships among genes and in turn, describe cellular behavior.

systems biology↗

bioRxiv2019

Analysis of Genetically Regulated Gene Expression identifies a trauma type specific PTSD gene, SNRNP35

PTSD has significant genetic heritability; however, it is unclear how genetic risk influences tissue-specific gene expression. We used brain and non-brain transcriptomic imputation models to impute genetically regulated gene expression (GReX) in 9,087 PTSD-cases and 23,811 controls and identified thirteen significant GReX-PTSD associations. The results suggest substantial genetic heterogeneity between civilian and military PTSD cohorts. The top study-wide significant PTSD-association was with predicted downregulation of the Small Nuclear Ribonucleoprotein U11/U12 Subunit 35 (SNRNP35) in the BA9 region of the prefrontal cortex (PFC) in military cohorts. In peripheral leukocytes from 175 U.S. Marines, the observed PTSD differential gene expression correlated with the predicted blood GReX differences for these individuals, and deployment stress downregulated SNRNP35 expression, primarily in Marines with post-deployment PTSD. SNRNP35 is a subunit of the minor spliceosome complex and SNRNP35 knockdown in cells validated its functional importance in U12-intron splicing. Finally, mimicking acute activation of the endogenous stress axis in mice downregulated PFC Snrnp35 expression.

genetics↗

bioRxiv2017

Rational Design Of RNA Structures That Predictably Tune Eukaryotic Gene Expression

Predictable tuning of gene expression is essential for engineering genetic circuits and for optimising enzyme levels in metabolic engineering projects. In bacteria, gene expression can be tuned at the stage of transcription, by exchanging the promoter, or at stage of translation by altering the ribosome binding site sequence. In eukaryotes, however, only promoter exchange is regularly used, as the tools to modulate translation are lacking. Working in S. cerevisiae yeast, we here describe how hairpin RNA structures inserted into the 5 untranslated region (5UTR) of mRNAs can be used to tune expression levels by altering the efficiency of translation initiation. We demonstrate a direct link between the calculated free energy of folding in the 5UTR and protein abundance, and show that this enables rational design of hairpin libraries that give predicted expression outputs. Our approach is modular, working with different promoters and protein coding sequences, and it outperforms promoter mutation as a way to predictably generate a library where a protein is induced to express at a range of different levels. With this tool, computational RNA sequence design can be used to predictably fine-tune protein production, providing a new way to modulate gene expression in eukaryotes.

synthetic biology↗

bioRxiv2022

Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids

The enhanced performance of hybrids though heterosis remains a key aspect in plant breeding, however the underlying mechanisms are still not fully elucidated. To investigate the potential role of transcriptomic and epigenomic patterns in early expression of hybrid vigour, we investigated gene expression, small RNA abundance and genome-wide methylation in hybrids from two distant Brassica napus ecotypes during seed and seedling developmental stages using next-generation sequencing technologies. A total of 71217, 773, 79518 and 31825 differentially expressed genes, microRNAs, small interfering RNAs and differentially methylated regions were identified, respectively. Approximately 70% of the differential expression and methylation patterns observed could be explained due to parental dominance levels. Via gene ontology enrichment and microRNA-target association analyses during seed development we found copies of reproductive, developmental and meiotic genes with transgressive and paternal dominance patterns. Interestingly, maternal dominance was more prominent in hypermethylated and downregulated features during seed formation. This contrasts to the general maternal gamete demethylation reported during gametogenesis in most plant species. Associations between methylation and gene expression allowed identification of putative epialleles with diverse pivotal biological functions during seed formation. Furthermore, most differentially methylated regions, differentially expressed siRNAs and transposable elements were found in regions flanking genes that had no differential expression. This suggests that differential expression and methylation of epigenomic features may help maintain expression of pivotal genes in a hybrid context. Differential expression and methylation patterns during seed formation in an F1 hybrid provide novel insight into genes and mechanisms with a potential role in early heterosis. Key messageTranscriptomic and epigenomic profiling of gene expression and small RNAs during seed and seedling development reveals expression and methylation dominance levels with implications on early stage heterosis in oilseed rape.

plant biology↗

bioRxiv2026

Circadian rhythm disruption alters rhythmic properties of clock and memory gene expression in the hippocampi of rats

Our modern environment - with its artificial lighting, irregular work hours, and frequent travel -- often disrupts our circadian rhythms, which can lead to health problems, particularly in learning and memory. This is especially concerning given the aging population and the rising prevalence of dementia. Yet, the biological mechanisms linking circadian disruption to cognitive impairment remain poorly understood. At the molecular level, genetic techniques have been used to attenuate or abolish expression of key genes involved in circadian rhythms and these manipulations have detrimental effects on memory function. However, whether environmentally induced circadian disruption, impairs memory via changes in overall gene expression levels in the hippocampus or rather via changes in the coordinated rhythmic patterns of circadian expression across groups of genes is less known. Here, we examined how environmental circadian disruption affects the expression of genes involved in the circadian clock and memory in the hippocampus of rats using a forced desynchrony model. Circadian disruption changed the rhythmic properties of gene expression in most genes assessed but had no measurable effect on average expression levels across the day. These findings suggest that the inability to maintain circadian synchrony rather than overall expression may underlie the cognitive deficits observed in circadian-related disorders.

neuroscience↗

bioRxiv2018

A system for gene expression noise control in yeast

Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a \"noise tuner\" which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate whereas mean expression can be independently adjusted by mRNA stability. This noise tuner enables twofold changes in gene expression noise over a fivefold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.

synthetic biology↗

bioRxiv2020

Predicting the evolution of sexual dimorphism in gene expression

Sexual dimorphism in gene expression is likely to be the underlying source of dimorphism in a variety of traits. Many analyses implicitly make the assumption that dimorphism only evolves when selection favors different phenotypes in the two sexes, although theory makes clear that it can also evolve as an indirect response to other kinds of selection. Furthermore, previous analyses consider the evolution of a single transcript or trait at a time, ignoring the genetic covariance with other transcripts and traits. We first show which aspects of the genetic-variance covariance matrix, G, affect dimorphism when these assumptions about selection are relaxed. We then reanalyze gene expression data from Drosophila melanogaster with these predictions in mind. Dimorphism of gene expression for individual transcripts shows the signature of both direct selection for dimorphism and indirect responses to selection. To account for the effect of measurement error on evolutionary predictions, we estimated a G matrix for eight linear combinations of expression traits. Sex-specific genetic variances in female- and male-biased transcription, as well as one relatively unbiased combination were quite unequal, ensuring that most forms of selection on these traits will have large effects on dimorphism. Predictions of response to selection based on the whole G matrix showed that sexually concordant and antagonistic selection are equally capable of changing sexual dimorphism. In addition, the indirect responses of dimorphism due to cross-trait covariances were quite substantial. The assumption that sexual dimorphism in transcription is an adaptation is likely to be incorrect in many specific cases.

evolutionary biology↗

bioRxiv2026

Decoding universal principles of codon-mediated regulation of gene expression

Codon usage determines gene expression levels, yet its universal principles remain elusive. Here, we developed a regression-based model to derive "codon weights" from transcriptomic data, enabling improved prediction of mRNA and protein abundance across diverse taxa, including plants, mammals, insects, and microbes. Ribosome profiling (Ribo-seq) data analysis revealed that these codon weights correlate with Ribo-seq-weighted cumulative codon frequencies specifically in ribosome-unoccupied regions, rather than at stalling sites, across all seven model species. Experimental validation using species-specific optimization confirmed that our method effectively modulates gene expression in Escherichia coli and terrestrial plants. These findings demonstrate that species-specific environments for gene expression are encoded in codon weights, which can be deduced through a universal, species-independent framework, providing a new foundation for synthetic biology.

genomics↗

bioRxiv2023

Exploiting fluctuations in gene expression to detect causal interactions between genes

Characterizing and manipulating cellular behaviour requires a mechanistic understanding of the causal interactions between cellular components. We present an approach to detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. We show how this relation can, in theory, be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established "dual reporter assay". While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to demonstrate the real-world applicability of our approach in certain circumstances. Our experimental data suggest that the method can detect causal interactions in specific synthetic gene regulatory circuits in E. coli confirming our theoretical result in a narrow set of controlled experimental settings. Further work is needed to show that the approach is practical on a large scale, with naturally occurring gene regulatory networks, or in organisms other than E. coli.

systems biology↗

bioRxiv2019

Short residence times of DNA-bound transcription factors can reduce gene expression noise and increase the transmission of information in a gene regulation system

Gene expression noise is not just ubiquitous but also variable, and we still do not understand some of the most elementary factors that affect it. Among them is the residence time of a transcription factor (TF) on DNA, the mean time that a DNA-bound TF remains bound. Here, we use a stochastic model of transcriptional regulation to study how this residence time affects gene expression. We find that the effect of residence time on gene expression depends on the level of induction of the gene. At high levels of induction, residence time has no effect on gene expression. However, as the level of induction decreases, short residence times reduce gene expression noise. The reason is that fast on-off TF binding dynamics prevent long periods where proteins are predominantly synthesized or degraded, which can cause excessive fluctuations in gene expression. As a consequence, short residence times can help a gene regulation system acquire information about the cellular environment it operates in. Our predictions are consistent with the observation that experimentally measured residence times are usually modest and lie between seconds to minutes.

systems biology↗