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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Large scale differential gene expression analysis identifies genes associated with Bipolar Disorder

Background and purposeBipolar disorder (BD) is a common psychiatric disorder with high morbidity and mortality. Several polymorphisms have been found to be implicated in the pathogenesis of BD, however, these loci have small effect sizes that fail to explain the high heritability of the disease. Here, we provide more insights into the genetic basis of BD by identifying the differentially expressed genes (DEGs) and their associated pathways and biological processes in post-mortem brain tissues of patients with BD.\n\nMethodsEight datasets were eligible for the differential expression analysis. We used six datasets for the discovery of the gene signature and used the other two for independent validation. We performed the multi-cohort analysis by a random-effect model using R and MetaIntegrator package.\n\nResultsThe initial analysis resulted in the identification of 126 DEGs (30 up-regulated and 94 down-regulated). We refined this initial signature by a forward search process and resulted in the identification of 22 DEGs (6 up-regulated and 16 down-regulated). We validated the final gene signature in the independent datasets and resulted in an Area Under the ROC Curve (AUC) of 0.756 and 0.76, respectively. We performed gene set enrichment analysis (GSEA) which identified several biological processes and pathways related to BD including Ca transport, inflammation and DNA damage response.\n\nConclusionOur findings support the previous findings that link BD pathogenesis to abnormalities in glial inflammation and calcium transport and also identify several other biological processes not previously reported to be associated with the development of the disease. Such findings will improve our understanding of the genetic basis underlying BD and may have future clinical implications.

bioinformatics↗

bioRxiv2026

Environmental Regulation and Gene-by-Environment Interaction Influence RAP1 Activity and its Impact on Gene Expression

Gene-by-environment (GxE) interactions play a major role in shaping both phenotypic and molecular variation, with important implications for human health and disease. In this study, we used the Doxycycline (Dox) regulated, tetracycline-responsive (Tet-Off) promoter system to sequentially reduce or titrate gene expression levels of the essential yeast transcription factor Repressor Activator Protein 1 (RAP1) similar to a hypomorph allele series, across three distinct environments: Yeast Peptone Dextrose (YPD) media, YPD media with Heat Shock (HS), and Yeast Peptone Acetate (YPAC) media. We then performed RNA sequencing (RNA Seq) to assess global transcriptional responses to RAP1 reduction in these different growth environments. Our analysis first focused on the independent effects of varying RAP1 expression levels within and across environments. We then explored GxE interactions, revealing a subset of genes with significant consequences of reduced levels of RAP1 and environment-specific expression patterns. Notably, many genes exhibited opposite effects of RAP1 titration on gene expression when yeast were grown in YPAC media compared to YPD media and/or HS, suggesting environment-dependent regulatory architecture. This design reveals how cells integrate internal transcriptional and regulatory changes with external environmental cues, providing a deeper view of GxE architecture. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified co-regulated gene modules, and by combining this with transcription factor motif enrichment tests, our study identified candidate regulators driving their dynamics. Our findings demonstrate that gene regulatory networks can vary dramatically depending on the environmental context an organism experiences, which can then influence the specific phenotypes produced by a particular genetic perturbation. This illustrates the complexity of genotype-environment interactions and the importance of studying gene function in multiple environments to gain a truly comprehensive understanding of a genes sometimes numerous and diverse functions.

genomics↗

bioRxiv2023

The quantitative genetics of gene expression in Mimulus guttatus

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population of Mimulus guttatus and scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05) and that cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTL make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of "gene expression modules", which has important implications for the evolution of gene expression and also how it is studied by geneticists. Author summaryMimulus guttatus (yellow monkeyflower) is a model for the study of quantitative trait evolution in natural populations. Research has focused mainly on whole organism traits like flower size or herbivore resistance, but the level of expression of a gene is also a quantitative trait. In this study, we dissect leaf transcriptome variation using a breeding design that estimates the contribution of individual loci to expression variation (eQTLs). We find at least rough agreement to the "oligogenic model" where a major locus (the cis regulatory region) generates much of the genetic variation in the population. While associations studies routinely characterize genetic effects as binary (the two alternatives at a single nucleotide polymorphism or "SNP"), this description proves insufficient for Mimulus. Most loci exhibit multiple, and in some cases, a continuum of alleles. We find that trans eQTLs have different features than cis eQTLs, both in terms of the diversity and genetic dominance of alleles. These genetic features of eQTLs are critical determinants of the "G matrix", the genetic variances and covariances among all genes. The G matrix determines how gene expression will evolve under selection in response to changing environmental conditions. Our finding of large effect sizes and high allelic diversity suggests that the G matrix may be surprisingly malleable, even on ecological timescales.

evolutionary biology↗

bioRxiv2020

Co-expression networks from gene expression variability between genetically identical seedlings can reveal novel regulatory relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this we used transcriptomes that were generated from genetically identical individual plants that were grown in the same conditions and for the same amount of time. Twelve time points were used to cover the 24h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcription factors GI, PIF4 and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild. Author summaryPlant development and response to changes in the environment are strongly regulated at the level of gene expression. That is why understanding how gene expression is regulated is key, and transcriptome approaches have allowed the analysis of transcription for all genes of the genome. Extracting useful information from the high amount of data generated by transcriptomes is a challenge, and gene co-expression networks are a powerful tool to do this. The principle is to find genes that co-vary in expression in different conditions and to pair them together. Communities of genes that are more closely linked are then identified and this is the starting point to look for their implication in the same pathway. Co-expression networks have been used to identify new regulation and function of genes involved in plant development and their response to the environment. They were constructed using transcriptomes generated on plants experiencing genetic or environmental perturbation. We show that co-expression networks can in fact be constructed in the absence of genetic and environmental perturbation. Our work will allow further exploration of gene co-regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

plant biology↗

bioRxiv2024

MAGE: Monte Carlo method for Aberrant Gene Expression

Identifying genes that are aberrantly expressed is an important first step in the diagnosis and treatment of many diseases. Conventionally, differential expression (DE) analysis is used to screen gene expression profiles to identify functionally associated genes. DE often relies on the variance and fold change in expression from individual genes, which does not consider the expression of all other genes within the profile. When the overall gene expression is skewed, DE does not capture outliers in gene expression. To address this, we have developed a non-parametric DE method based on the probability density for an entire expression profile to select genes that deviate from the global distribution between two gene expression profiles with multiple replicates. Rather than assuming a particular distribution of expression per gene, our method assumes that aberrantly expressed genes (AEGs) will exhibit expression patterns distinguishable from non-AGEs which make up the majority of the profile. Here we introduce our nonparametric method (MAGE: Monte Carlo method for aberrant gene expression) and demonstrate that MAGE can identify AEGs that are not found by conventional DE analyses. The main feature of MAGE is (1) identifying outliers based on the expression profile of all genes rather than performing DE analyses on a per-gene basis and (2) consideration of the variance in expression between two different conditions. We also compared our results with traditional DE analysis as well as density-based clustering methods. MAGE produces consistent results in a variety of conditions and performs conservatively with the addition of noise. We also applied MAGE to single-cell RNA-seq samples and demonstrated that the analysis is robust with subsampling.

bioinformatics↗

bioRxiv2020

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

bioinformatics↗

bioRxiv2018

Genome-wide gene expression profiling reveals that cuticle alterations and P450 detoxification are associated with pyrethroid resistance in Anopheles arabiensis populations from Ethiopia

BACKGROUNDVector control is the main intervention in malaria control and elimination strategies. However, the development of insecticide resistance is one of the major challenges for controlling malaria vectors. Anopheles arabiensis populations in Ethiopia showed resistance against both DDT and the pyrethroid deltamethrin. Although a L1014F target-site resistance mutation was present in the voltage gated sodium channel of investigated populations, the levels of resistance and biochemical studies indicated the presence of additional resistance mechanisms. In this study, we used genome-wide transcriptome profiling by RNAseq to assess differentially expressed genes between three deltamethrin and DDT resistant An. arabiensis field populations (Tolay, Asendabo, Chewaka) and two susceptible strains (Sekoru and Mozambique).\n\nRESULTSBoth RNAseq analysis and RT-qPCR showed that a glutathione-S-transferase, gstd3, and a cytochrome P450 monooxygenase, cyp6p4, were significantly overexpressed in the group of resistant populations compared to the susceptible strains, suggesting that the enzymes they encode play a key role in metabolic resistance against deltamethrin or DDT. Furthermore, a gene ontology enrichment analysis showed that expression changes of cuticle related genes were strongly associated with insecticide resistance, although this did not translate in increased thickness of the procuticle.\n\nCONCLUSIONOur transcriptome sequencing of deltamethrin/DDT resistant An. arabiensis populations from Ethiopia suggests non-target site resistance mechanisms and pave the way for further investigation of the role of cuticle composition in resistance.

molecular biology↗

bioRxiv2022

A Model-Based Clustering via Mixture of Bayesian Hierarchical Models with Covariate Adjustment for Detecting Differentially Expressed Genes from Paired Design

The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts in whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. Model-based clustering, such as mixtures of Bayesian hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. However, not all known confounding factors can be controlled in a paired/match design. To the best of our knowledge, no clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of Bayesian hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method

genomics↗

bioRxiv2025

Gene expression divergence following gene and genome duplications in spatially resolved plant transcriptomes

Gene and genome duplications are key drivers of plant genome evolution, expanding genetic repertoires and facilitating functional innovation. However, genes originating from different duplication mechanisms undergo different outcomes, especially at the expression level. Previous studies have demonstrated that segmental or whole-genome duplications generate duplicates with similar and somewhat redundant expression profiles across multiple tissues, while other duplicates display increased divergence, ultimately leading to functional innovations. However, little is known about how duplicates diverge in expression across cell types in a single tissue. Here, we used high-resolution spatial transcriptomic data from five species (Arabidopsis thaliana, Glycine max, Phalaenopsis aphrodite, Zea mays, and Hordeum vulgare) to investigate the evolution of gene expression following gene duplications. We found that genes originating from segmental or whole-genome duplications display increased expression levels, expression breadths, spatial variability, and number of coexpression partners. Duplication mechanisms that preserve cis-regulatory landscapes typically generate paralogs with more preserved expression profiles, but such differences by duplication mode disappear over time. Expression divergence also depends on gene functions, with dosage-sensitive gene families displaying highly preserved expression profiles, while families involved in more specialized processes (e.g., flowering and phytohormone biosynthesis) display increased divergence. Paralogs originating from large-scale (including whole-genome) duplications display redundant and/or overlapping expression profiles, indicating functional redundancy and/or subfunctionalization, while small-scale duplicates diverge asymmetrically, indicating neofunctionalization. Collectively, our findings provide new insights into the tempo and mode of gene expression evolution, helping understand how gene and genome duplications shape cell identities.

evolutionary biology↗

bioRxiv2017

An automated model test system for systematic development and improvement of gene expression models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

synthetic biology↗

bioRxiv2018

GEESE: Metabolically driven latent space learning for gene expression data

Gene expression microarrays provide a characterisation of the transcriptional activity of a particular biological sample. Their high dimensionality hampers the process of pattern recognition and extraction. Several approaches have been proposed for gleaning information about the hidden structure of the data. Among these approaches, deep generative models provide a powerful way for approximating the manifold on which the data reside.\n\nHere we develop GEESE, a deep learning based framework that provides novel insight into the manifold learning for gene expression data, employing a metabolic model to constrain the learned representation. We evaluated the proposed framework, showing its ability to capture biologically relevant features, and encoding that features in a much simpler latent space. We showed how using a metabolic model to drive the autoencoder learning process helps in achieving better generalisation to unseen data. GEESE provides a novel perspective on the problem of unsupervised learning for biological data.\n\nAvailabilitySource code of GEESE is available at https://bitbucket.org/mbarsacchi/geese/.

systems biology↗

bioRxiv2018

Parameter inference with analytical propagators for stochastic models of autoregulated gene expression

Stochastic gene expression in regulatory networks is conventionally modelled via the Chemical Master Equation (CME). As explicit solutions to the CME, in the form of so-called propagators, are oftentimes not readily available, various approximations have been proposed. A recently developed analytical method is based on a separation of scales that assumes significant differences in the lifetimes of mRNA and protein in the network, allowing for the efficient approximation of propagators from asymptotic expansions for the corresponding generating functions. Here, we showcase the applicability of that method to simulated data from a telegraph model for gene expression that is extended with an autoregulatory mechanism. We demonstrate that the resulting approximate propagators can be applied successfully for Bayesian parameter inference in the non-regulated model; moreover, we show that, in the extended autoregulated model, autoactivation or autorepression may be refuted under certain assumptions on the model parameters. Our results indicate that the method showcased here may allow for successful parameter inference and model identification from longitudinal single cell data.

cell biology↗

bioRxiv2024

Extracellular electron transfer genes expressed by candidate flocking bacteria in cable bacteria sediment

Cable bacteria, filamentous sulfide oxidizers that live in sediments, are at times associated with large flocks of swimming bacteria. It has been proposed that these flocks of bacteria transport electrons extracellularly to cable bacteria via an electron shuttle intermediate, but the identity and activity of these bacteria in freshwater sediment remains mostly uninvestigated. We coupled metagenomics and metatranscriptomics to 16S rRNA amplicon-based correlations with cable bacteria from two time series experiments up to 155 days. We identified bacteria expressing genes for extracellular electron transfer and motility, including synthesis genes for potential extracellular electron shuttles: phenazines and flavins. Of the 85 high quality MAGs (Metagenome Assembled Genomes >90% complete and <5% contaminated), 56 had genes encoding flagellar proteins, and of these 22 had genes encoding extracellular electron transport proteins. The candidate flockers constituted 21.4% of all MAGs and 42.1% of the proposed flocking bacteria expressed extracellular electron transfer genes. The proposed flockers belonged to a large variety of taxonomic groups: 18 genera spread across 9 phyla. Our data suggest that electric relationships in freshwater sediments between cable bacteria and other microbes likely help to generate and/or sustain cryptic element cycling and deep oxygen breathing, affecting more element cycles than sulfur, like metal- and in particular iron- and carbon cycles.

microbiology↗

bioRxiv2022

From Multi-Allele Fish to Non-Standard Environments, How ZFIN Assigns Phenotypes, Human Disease Models, and Gene Expression Annotations to Genes

Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN) is the central repository for genetic, genomic, and phenotypic data that result from research using Danio rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity.

bioinformatics↗

bioRxiv2018

Multidimensional analysis of Gammaherpesvirus RNA expression reveals unexpected heterogeneity of gene expression

Virus-host interactions are frequently studied in bulk cell populations, obscuring cell-to-cell variation. Here we investigate endogenous herpesvirus gene expression at the single-cell level, combining a sensitive and robust fluorescent in situ hybridization platform with multiparameter flow cytometry, to study the expression of gammaherpesvirus non-coding RNAs (ncRNAs) during lytic replication, latent infection and reactivation in vitro. This method allowed robust detection of viral ncRNAs of murine gammaherpesvirus 68 ({gamma}HV68), Kaposis sarcoma associated herpesvirus and Epstein-Barr virus, revealing variable expression at the single-cell level. By quantifying the inter-relationship of viral ncRNA, viral mRNA, viral protein and host mRNA regulation during {gamma}HV68 infection, we find heterogeneous and asynchronous gene expression during latency and reactivation, with reactivation from latency identified by a distinct gene expression profile within rare cells. Further, during lytic replication with {gamma}HV68, we find many cells have limited viral gene expression, with only a fraction of cells showing robust gene expression, dynamic RNA localization, and progressive infection. These findings, powered by single-cell analysis integrated with automated clustering algorithms, suggest inefficient or abortive {gamma}HV infection in many cells, and identify substantial heterogeneity in viral gene expression at the single-cell level.\n\nAUTHOR SUMMARY\n\nThe gammaherpesviruses are a group of DNA tumor viruses that establish lifelong infection. How these viruses infect and manipulate cells has frequently been studied in bulk populations of cells. While these studies have been incredibly insightful, there is limited understanding of how virus infection proceeds within a single cell. Here we present a new approach to quantify gammaherpesvirus gene expression at the single-cell level. This method allows us to detect cell-to-cell variation in the expression of virus non-coding RNAs, an important and understudied class of RNAs which do not encode for proteins. By examining multiple features of virus gene expression, this method further reveals significant variation in infection between cells across multiple stages of infection. These studies emphasize that gammaherpesvirus infection can be surprisingly heterogeneous when viewed at the level of the individual cell. Because this approach can be broadly applied across diverse viruses, this study affords new opportunities to understand the complexity of virus infection within single cells.

microbiology↗

bioRxiv2020

Modeling gene expression evolution with EvoGeneX uncovers differences in evolution of species, organs and sexes

While DNA sequence evolution has been well studied, the expression of genes is also subject to evolution, yet the evolution of gene expression is currently not well understood. Recently, new tissue/organ-specific gene expression datasets spanning several organisms across the tree of life, have become available, providing the opportunity to study gene expression evolution in more detail. While a theoretical model to study the evolution of continuous traits exists, in practice computational methods often cannot confidently distinguish between alternative evolutionary scenarios. This lack of power has been attributed to modest numbers of species considered in these studies. We hypothesised that biological replicates can be used to increase predictive power of these models. With this in mind, we introduce EvoGeneX, a computationally efficient method to uncover the mode of gene expression evolution based on the Ornstein-Uhlenbeck process. Importantly, in addition to modelling expression variations between species, EvoGeneX models within-species variation. Furthermore, to facilitate comparative analysis of gene expression evolution, we introduce a formal approach, based on Michaelis-Menten equation, to measure the dynamics of evolutionary divergence of a group of genes in terms of groups asymptotic divergence level and rate. Finally, we used these tools to preform the first analysis the evolution of gene expression across different body parts, species, and sexes of the Drosophila genus. Our analysis revealed that neutral expression evolution can be confidently rejected in favor of purifying selection in nearly half of the genes. In addition, we quantified differences in the evolutionary dynamics of male and female gonads and uncovered interesting examples of adaptive gene expression evolution.

evolutionary biology↗

bioRxiv2019

Leveraging tissue specific gene expression regulation to identify genes associated with complex diseases

To increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN (Transcriptome-mediated identification of disease-associated Gens with Epigenetic aNnotation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7 % to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimers disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci. Author summaryTWAS-like methods have been widely applied to understand disease etiology using eQTL data and GWAS results. However, it is still challenging to discriminate the true disease-associated genes from those in strong LD with true genes, which is largely due to the misidentification of eQTLs. Here we introduce a novel statistical method named T-GEN to identify disease-associated genes considering epigenetic information. Compared to current TWAS methods, T-GEN can not only identify eQTLs with higher CADD scores and function potentials in gene-expression imputation models, but also identify more disease-associated genes across 207 traits and more genes with high (>0.99) pLI scores. Applying T-GEN in late-onset Alzheimers disease identified 96 genes at 15 loci with two novel loci. Among 96 identified genes, 50 genes were further replicated in an independent GWAS.

genetics↗

bioRxiv2024

Transcription factors SlMYB41, SlMYB92 and SlWRKY71 regulate gene expression in tomato exodermis

Root barrier cell types, like the endodermis and exodermis, are crucial for plant acclimation to environmental stresses. Deposition of suberin, a hydrophobic polymer, in these cell layers restricts the movement of molecules and plays a vital role in stress responses. This study investigates the role of SlMYB41, SlMYB92 and SlWRKY71 transcription factors (TFs) in regulating suberin biosynthesis in the tomato (Solanum lycopersicum) root exodermis by genetic perturbation. Genetic perturbation of these TFs altered exodermal suberin deposition patterns, indicating the SlMYBs as positive and SlWRKY71 negative regulators of suberization. RNA sequencing revealed a significant overlap between differentially expressed genes regulated by these TFs, suggesting a shared regulatory network. Gene set enrichment analyses highlighted their role in lipid and suberin biosynthesis as well as overrepresentation of exodermis-enriched transcripts. Furthermore, transactivation assays demonstrated that these two MYBs promote the expression of suberin-related genes, while SlWRKY71 represses them. These results indicate a complex antagonistic relationship, advancing our understanding of the regulatory mechanisms controlling exodermis suberization in tomato roots. HighlightMYB and WRKY transcription factors collaboratively regulate suberin biosynthesis in the tomato root exodermis. Antagonistic interactions may fine-tune suberization or act as a break to stop overaccumulation.

plant biology↗