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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

Genetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with independent chromatin data. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.

genetics↗

bioRxiv2023

Assessing tissue-specific gene expression of essential genes from human and mouse.

A gene satisfies the definition of essentiality when the loss of its function compromises an organisms viability. Identifying essential genes is useful for understanding the core components that regulate a biological system and ensure its survival. Advances in gene editing techniques like CRISPR-Cas9 generate the capacity to comprehensively interrogate a genome to elucidate what genes are essential. However, these techniques are often applied in the context of a single cell line and even when studies have collated essential gene sets across multiple cell lines, this information is rarely probed at a level that incorporates multiple cell and tissue types. The recent availability of large-scale single-cell RNA-sequencing (scRNA-seq) atlases provides an unprecedented opportunity to investigate the distribution of essential gene expression in cell and tissue types. Here, we leverage information contained in benchmarking datasets, single cell tissue atlases, and databases of essential genes, and develop a computational method, scEssentials, which uses a statistical framework to report on the robustness and specificity of essential genes in human and mouse across multiple cell types. Using scEssentials, both mouse and human models showed consistently high in expression and exhibit limited variably across more than 60 cell types. We also demonstrate a substantial number of significantly correlated gene pairs within scEssentials, which produce densely connected co-expression networks with functional annotation. Furthermore, we showed high frequencies of scEssentials across 200 pathways. Finally, we develop a score to quantify the relative essentiality of genes within scEssentials, which further validates with significant association with gene mutation frequency and chromatin accessibility. Using the heterogeneous ageing process, we demonstrate the application of scEssentials and their robust gene expression profile. Only one-fifth of scEssentials showed significant ageing-related differential expression among three age groups, occurring primarily in muscle satellite cells of varying tissue origins and highly interacting brain cells. Collectively, the robustness of scEssentials serves as a reference for analysing scRNA-seq data and provides insight into the heterogeneous nature of biological processes such as ageing.

bioinformatics↗

bioRxiv2021

Long-range promoter-enhancer contacts are conserved during evolution and contribute to gene expression robustness

Gene expression is regulated through complex molecular interactions, involving cis-acting elements that can be situated far away from their target genes. Data on long-range contacts between promoters and regulatory elements is rapidly accumulating. However, it remains unclear how these regulatory relationships evolve and how they contribute to the establishment of robust gene expression profiles. Here, we address these questions by comparing genome-wide maps of promoter-centered chromatin contacts in mouse and human. We show that there is significant evolutionary conservation of cis-regulatory landscapes, indicating that selective pressures act to preserve not only regulatory element sequences but also their chromatin contacts with target genes. The extent of evolutionary conservation is remarkable for long-range promoter-enhancer contacts, illustrating how the structure of regulatory landscapes constrains large-scale genome evolution. We show that the evolution of cis-regulatory landscapes, measured in terms of distal element sequences, synteny or contacts with target genes, is significantly associated with gene expression evolution.

evolutionary biology↗

bioRxiv2023

Identification of Differentially Expressed Genes in Wheat NILs in Response to Leaf 1 Rust Infection using In Silico Analysis

In this study, the molecular basis of leaf rust resistance was investigated by examining the differential gene expression of wheat cultivar Thatcher, both with and without a leaf rust resistance gene, in response to rust infection. unigenes/ESTs databases were utilized belonging to three near-isogenic lines (NILs) of Thatcher, including Thatcher, Thatcher + Lr10, and Thatcher + Lr1, to identify differentially expressed unigenes (DEUs) in response to leaf rust. Sixty-eight DEUs were identified using a data mining tool called Digital Differential Display (DDD). Among these DEUs were specific unigenes associated with Lr1 and/or Lr10-mediated resistance, as well as unigenes expressed exclusively in compatible interactions. Our results suggest that photosynthesis, lignin, and antifungal proteins play a role in the leaf rust resistance mechanism, as indicated by the overexpression of Ribulose-1,5-bisphosphate carboxylase/oxygenase, caffeic acid O-methyltransferase, and Glutathione S-transferase unigenes, respectively. Our findings demonstrate both common and distinctive genetic pathways involved in rust resistance in the Lr1 and Lr10 NILs. The expression of most DEUs was found to be high in leaves, moderate in sheath, stem, and inflorescence, and low in seed, root, flower, crown, callus, and cell culture, indicating their role in leaf rust. Using high-throughput microarray gene expression data, a subset of DEUs was validated, and as a result, eleven unigenes were mapped to deletion bins on individual wheat chromosomes. These key genes may be used as molecular markers for genome editing, which could facilitate the development of leaf rust-resistant wheat cultivars.

plant biology↗

bioRxiv2023

Environmental circadian disruption re-programs liver circadian gene expression

Circadian gene expression is fundamental to the establishment and functions of the circadian clock, a cell-autonomous and evolutionary-conserved timing system. Yet, how it is affected by environmental-circadian disruption (ECD) such as shiftwork and jetlag, which impact millions of people worldwide, are ill-defined. Here, we provided the first comprehensive description of liver circadian gene expression under normal and after ECD conditions. We found that post-transcription and post-translation processes are dominant contributors to whole-cell or nuclear circadian proteome, respectively. Furthermore, rhythmicity of 64% transcriptome, 98% whole-cell proteome and 95% nuclear proteome is re-written by ECD. The re-writing, which is associated with changes of circadian cis-regulatory elements, RNA-processing and protein trafficking, diminishes circadian regulation of fat and carbohydrate metabolism and persists after one week of ECD-recovery. One-Sentence SummaryEnvironmental-circadian disruption re-writes the circadian gene expression process which persists after one week of recovery.

molecular biology↗

bioRxiv2019

Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic and extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

bioinformatics↗

bioRxiv2016

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of pan-network and core-network representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.\n\nResearch AreaSystems and Synthetic Biology\n\nSummaryBy analyzing 134 microarray datasets for Arabidopsis, we found that gene coexpression networks are highly context-dependent.\n\nFinancial sourceWork at Brookhaven was supported by grants PM-031 from the Office of Biological Research of the U.S. Department of Energy.

Bioinformatics↗

bioRxiv2019

NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets, however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

bioinformatics↗

bioRxiv2020

Multi-faceted deregulation of gene expression and protein synthesis with age

Protein synthesis represents a major metabolic activity of the cell. However, how it is affected by aging and how this in turn impacts cell function remains largely unexplored. To address this question, herein we characterized age-related changes in both the transcriptome and translatome of mouse tissues over the entire lifespan. Expression of the majority of differentially expressed genes followed a U-shaped curve with the turning point around 3-months-old. We showed that transcriptome changes govern changes in the translatome and are associated with altered expression of genes involved in inflammation, extracellular matrix and lipid metabolism. We also identified genes that may serve as candidate biomarkers of aging. At the translational level, we uncovered sustained down-regulation of a set of 5 terminal oligopyrimidine (5TOP) transcripts encoding protein synthesis and ribosome biogenesis machinery and regulated by the mTOR pathway. For many of them, ribosome occupancy dropped 3-fold or even more. Moreover, with age, ribosome coverage gradually decreased in the vicinity of start codons and increased near stop codons, revealing complex age-related changes in the translation process. Taken together, our results reveal systematic and multi-dimensional deregulation in protein synthesis, showing how this major cellular process declines with age.

systems biology↗

bioRxiv2026

Age-associated increases in inter-individual gene expression variability across human tissues

Aging involves progressive physiological decline, yet the underlying transcriptomic patterns remain poorly understood. Although differentially expressed genes (DEGs) have been the primary focus of previous studies, here we investigate differentially variable genes (DVGs) using a novel Gene Stability Score (GSS). In 30 tissue types from nearly 1,000 individuals in the Genotype-Tissue Expression (GTEx) project, age- and sex-related DVGs account for approximately 15% of overall expression variability between samples of the same tissue, with age-related DVGs specifically contributing 7.7%. We further show that DEGs and DVGs affect distinct biological pathways, and that inter-individual instability is significantly correlated with cell-to-cell transcriptional noise. Moreover, gene regulatory network analysis reveals that this variability is not random but is shaped by local network architecture. Finally, we identify robust reference genes, including TBP, PUM1, and TMEM199, for RT-qPCR experiments in studying age-related gene expression changes in humans. Together, our findings suggest that aging involves both coordinated transcriptional programs and increased stochasticity across individuals and cells.

genomics↗

bioRxiv2026

Relationship Between Gene Expression and Drug Response in Triple-Negative Breast Cancer: Leveraging Single-Cell RNA Sequencing and Machine Learning to Identify Biomarker Profiles

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited therapeutic options and poor prognosis. To address these challenges, we combined single-cell RNA sequencing (scRNA-seq) data with advanced machine learning techniques to find biomarkers that predict treatment response. Using tumor and blood samples from TNBC patients treated with either paclitaxel alone or in combination with atezolizumab, we constructed cellular maps, identified differentially expressed genes, and analyzed co-expression networks. Copy number variation (CNV) profiling and weighted gene co-expression network analysis (WGCNA) identified immune-related genes, including IL7R, CD6, and TNFAIP3, as candidate biomarkers linked to therapeutic response. A feature selection using Random Forest followed by bootstrap-enhanced K-nearest neighbors (K-NN) classification achieved high predictive accuracy across all subgroups (AUC > 0.93). Local Interpretable Model-Agnostic Explanations (LIME) based interpretability further identified important factors influencing treatment sensitivity and resistance, including EGR1, MKI67, C1QA/B/C, GZMB, and PRF1. Notably, blood-derived biomarkers showed strong predictive potential, highlighting the variability of liquid biopsy approaches for non-invasive monitoring. Our results indicate that integration of scRNA-seq with interpretable machine learning facilitates the identification of reliable biomarkers and yields clinically actionable insights for personalized therapeutic strategies in TNBC.

bioinformatics↗

bioRxiv2019

The Gene Expression Deconvolution Interactive Tool: Accurate Cell Type Quantification from Gene Expression Data

The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming, and can introduce bias. Computational approaches that infer cell type abundance from expression data offer an alternate solution. While these methods have gained popularity, most are limited to predicting hematopoietic cell types and do not produce accurate predictions for stromal cell types. Many of these methods are also limited to particular platforms, whether RNA-seq or specific microarrays. We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a tool that overcomes these limitations, compares favorably with existing methods, and provides superior versatility. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include a variety of platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from eight sources spanning a wide variety of stromal and hematopoietic types in both human and mouse. This reference database allows the user to obtain estimates for a wide variety of tissue samples without having to provide their own data. GEDIT also accepts user submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data is available. Author SummaryThe Gene Expression Deconvolution Interactive Tool (GEDIT) is a robust and accurate tool that uses gene expression data to estimate cell type abundances. Extensive testing on a variety of tissue types and technological platforms demonstrates that GEDIT provides greater versatility than other cell type deconvolution tools. GEDIT utilizes reference data describing the expression profile of purified cell types, and we provide in the software package a library of reference matrices from various sources. GEDIT is also flexible and allows the user to supply custom reference matrices. A GUI interface for GEDIT is available at http://webtools.mcdb.ucla.edu/, and source code and reference matrices are available at https://github.com/BNadel/GEDIT.

bioinformatics↗

bioRxiv2023

DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes

Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developed DENetwork. This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model, DENetwork enhances the capabilities of traditional differential gene analysis tools, such as DESeq2. Our application of DENetwork to an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms. DENetwork offers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.

bioinformatics↗

bioRxiv2025

Detection of probable neuronal gene expression changes in skin biopsies from patients with paclitaxel-induced peripheral neuropathy

Our inability to obtain nerve samples from the vast majority of neuropathic pain patients impedes our ability to understand the disease, creates challenges in understanding mechanisms in specific patient populations, and limits our ability to make treatment decisions based on quantifiable molecular data. Fields like oncology have overcome these problems to take advantage of the insight that sequencing offers for understanding mechanisms of disease and have leveraged these molecular insights to dramatically change the treatment landscape in the past decade. Here we tested the hypothesis that skin biopsies could be used to gain insight into neuronal transcriptomic changes in patients with paclitaxel-induced peripheral neuropathy (PIPN). Our analysis reveals that hundreds of differentially expressed genes (DEGs) found through bulk RNA sequencing in these skin biopsies are likely contributed by dorsal root ganglion (DRG) neuronal axons and/or terminals. Up-regulated genes were representative of broad class of nociceptors whereas down-regulated genes were associated with putative injured DRG neurons expressing the PDIA2 gene. DEGs that could be confidently associated with specific subsets of skin cells were mostly expressed by keratinocytes supporting a growing literature tying keratinocyte-neuron communication abnormalities to pain in PIPN. We validated these findings by analyzing additional previously published datasets and through prospectively conducted spatial transcriptomics experiments on human skin biopsies. Our findings warrant further assessment of skin biopsies in additional neuropathic pain populations to gain insight into DRG neuron changes that have previously been thought to be inaccessible in routine clinical or scientific assessments in most patients.

neuroscience↗

bioRxiv2019

A resource for analyzing C. elegans’ gene expression data using transcriptional gene modules and module-weighted annotations

Identification of gene co-expression patterns (gene modules) is widely used for grouping functionally-related genes during transcriptomic data analysis. An organism-wide atlas of high quality fundamental gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method of independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially-expressed genes fail to do so. Using this resource, users can easily identify active modules in their gene expression data and access detailed descriptions of each module. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g. GO term). Analysis of \"module-weighted annotations\" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. Interactive access to the resource is provided at http://genemodules.org/.

bioinformatics↗

bioRxiv2021

Constitutive gene expression differs in three brain regions important for cognition in neophobic and non-neophobic house sparrows (Passer domesticus)

Neophobia (aversion to new objects, food, and environments) is a personality trait that affects the ability of wildlife to adapt to new challenges and opportunities. Despite the ubiquity and importance of this trait, the molecular mechanisms underlying repeatable individual differences in neophobia in wild animals are poorly understood. We evaluated wild-caught house sparrows (Passer domesticus) for neophobia in the lab using novel object tests. We then selected the most and least neophobic individuals (n=3 of each) and extracted RNA from four brain regions involved in learning, memory, threat perception, and executive function: striatum, dorsomedial hippocampus, medial ventral arcopallium, and caudolateral nidopallium (NCL). Our analysis of differentially expressed genes (DEGs) used 11,889 gene regions annotated in the house sparrow reference genome for which we had an average of 25.7 million mapped reads/sample. PERMANOVA identified significant effects of brain region, phenotype (neophobic vs. non-neophobic), and a brain region by phenotype interaction. Comparing neophobic and non-neophobic birds revealed constitutive differences in DEGs in three of the four brain regions examined: hippocampus (12% of the transcriptome significantly differentially expressed), striatum (4%) and NCL (3%). DEGs included important known neuroendocrine mediators of learning, memory, executive function, and anxiety behavior, including serotonin receptor 5A, dopamine receptors 1, 2 and 5 (downregulated in neophobic birds), and estrogen receptor beta (upregulated in neophobic birds). These results suggest that some of the behavioral differences between phenotypes may be due to underlying gene expression differences in the brain. The large number of DEGs in neophobic and non-neophobic birds also implies that there are major differences in neural function between the two phenotypes that could affect a wide variety of behavioral traits beyond neophobia.

animal behavior and cognition↗

bioRxiv2024

Gene expression asymmetry in Parkinson's Disease; variation of CCT and BEX gene expression levels are correlated with hemisphere specific severity

Parkinsons Disease (PD) develops unilaterally, which may be related to brain hemispheric differences in gene expression. Here we measured bulk RNA-seq levels in neuronal nuclei obtained from prefrontal cortex postmortem brain samples from males and females with PD and from healthy controls. Left and right hemispheres from each brain were related the side of symptom onset and compared. We employed two a priori approaches; first we identified genes differentially expressed between PD and controls and between left vs right PD brain hemispheres. Second, we examined the presence of, and correlates to, variable asymmetry seen in candidate PD differentially expressed genes. We found large variation among individuals with PD, and PD stratification by gene expression similarity was required for patterns of genetic asymmetry to emerge. For a subset of PD brains, hemispherical variation of CCT and BEX gene levels correlated with the side of PD symptom onset.

neuroscience↗

bioRxiv2019

Quantitative visualization of gene expression in Pseudomonas aeruginosa aggregates reveals peak expression of alginate in the hypoxic zone

It is well appreciated that oxygen- and nutrient-limiting gradients characterize microenvironments within chronic infections that foster bacterial tolerance to treatment and the immune response. However, determining how bacteria respond to these microenvironments has been limited by a lack of tools to study bacterial functions at the relevant spatial scales in situ. Here we report the application of the hybridization chain reaction (HCR) v3.0 to Pseudomonas aeruginosa aggregates as a step towards this end. As proof-of-principle, we visualize the expression of genes needed for the production of alginate (algD) and the dissimilatory nitrate reductase (narG). Using an inducible bacterial gene expression construct to calibrate the HCR signal, we were able to quantify algD and narG gene expression across microenvironmental gradients both within single aggregates and within aggregate populations using the Agar Block Biofilm Assay (ABBA). For the ABBA population, alginate gene expression was restricted to hypoxic regions within the environment (~40-200 M O2), as measured by an oxygen microelectrode. Within individual biofilm aggregates, cells proximal to the surface expressed alginate genes to a greater extent than interior cells. Lastly, mucoid biofilms consumed more oxygen than nonmucoid biofilms. These results establish that HCR has a sensitive dynamic range and can be used to resolve subtle differences in gene expression at spatial scales relevant to microbial assemblages. Because HCR v3.0 can be performed on diverse cell types, this methodological advance has the potential to enable quantitative studies of microbial gene expression in diverse contexts, including pathogen behavior in human chronic infections.\n\nImportanceThe visualization of microbial activities in natural environments is an important goal for numerous studies in microbial ecology, be the environment a sediment, soil, or infected human tissue. Here we report the application of the hybridization chain reaction (HCR) v3.0 to measure microbial gene expression in situ at single-cell resolution in aggregate biofilms. Using Pseudomonas aeruginosa with a tunable gene expression system, we show that this methodology is quantitative. Leveraging HCR v3.0 to measure gene expression within a P. aeruginosa aggregate, we find that bacteria just below the aggregate surface are the primary cells expressing genes that protect the population against antibiotics and the immune system. This observation suggests that therapies targeting bacteria growing with small amounts of oxygen may be most effective against these hard-to-treat infections. More generally, HCR v3.0 has potential for broad application into microbial activities in situ at small spatial scales.

microbiology↗