Biology subjects

Fagny, M.

Publications and source records attributed to Fagny, M..

15 recordsLinked to original sources

bioRxiv2026

How to be dispensable: genomic and transcriptomic determinants in maize genes

BackgroundPlant genomes harbour a substantial proportion of dispensable genes - present only in a subset of individuals - that differ from ubiquitously shared core genes in multiple genomic and expression features. While these differences have been repeatedly documented, the factors shaping gene dispensability remain poorly understood. ResultsWe assembled a pan-gene set from eight maize inbred lines from American and European germplasms, together with their transcriptomic profile across 22 tissues/conditions, revealing the genomic and transcriptomic determinants of maize gene dispensability. Multivariate analysis demonstrates that gene expression level and purifying selection - rather than gene size - are the primary factors distinguishing core from dispensable genes. Dispensable genes overlap Helitrons at 4.6 times the rate of core genes, implicating Helitron-mediated gene capture as a major mechanism of dispensable gene formation. Classifying genes into stably expressed, variably expressed, and on-off categories shows that all three classes contain dispensable genes, though in different proportions than for core genes. Contrary to previous assumptions, we show that dispensable genes can participate in basal biological functions just as core genes, and that gene duplication likely provides only a partial mechanism for functional complementation of accessory genes absence. ConclusionsOur results provide novel insights into the molecular and evolutionary factors distinguishing core from dispensable genes and into the biological mechanisms shaping gene dispensability in maize, and demonstrate that classifying genes by transcriptional patterns provides a powerful framework for understanding the biological functions and evolutionary dynamics of both core and dispensable genes.

plant biology↗

bioRxiv2025

Nuclear Myosin 1 links genomic architecture to adipose tissue remodeling, metabolic inflammation and obesity in mice

During adipogenesis, a metabolic shift from oxidative phosphorylation (OXPHOS) to aerobic glycolysis enables preadipocytes to meet the biosynthetic and energetic demands of differentiation. Nuclear myosin 1 (NM1), a chromatin-associated actomyosin motor that regulates transcription and chromatin accessibility, is essential for maintaining OXPHOS. Here, we identify NM1 as a key regulator of adipocyte differentiation and adipose tissue homeostasis. Integration of ATAC-seq and RNA-seq in NM1-deficient mouse embryonic fibroblasts revealed coordinated dysregulation of adipogenic genes (Insig1, Lipg, Fat1) and altered enhancer accessibility near Klf6, Foxo3, Smad5, and Gata4. NM1 knockout mesenchymal stem cells showed impaired adipogenic differentiation despite adipocyte hypertrophy. In vivo, NM1-deficient mice developed age-dependent visceral obesity with transcriptional reprogramming in white adipose tissue (WAT), including downregulation of adipogenesis and mitochondrial pathways, and activation of IFNG-, IL33-, and TNF-driven inflammation. Cross-species analysis revealed overlap with MYO1C-centered regulatory modules in human adipose tissue, implicating NM1/MYO1C in conserved chromatin-based control of adipose biology

cell biology↗

bioRxiv2024

Common DNA sequence variation influences epigenetic aging in African populations

Aging is associated with genome-wide changes in DNA methylation in humans, facilitating the development of epigenetic age prediction models. However, most of these models have been trained primarily on European-ancestry individuals, and none account for the impact of methylation quantitative trait loci (meQTL). To address these gaps, we analyzed the relationships between age, genotype, and CpG methylation in 3 understudied populations: central African Baka (n = 35), southern African {ddagger}Khomani San (n = 52), and southern African Himba (n = 51). We find that published prediction methods yield higher mean errors in these cohorts compared to European-ancestry individuals, and find that unaccounted-for DNA sequence variation may be a significant factor underlying this loss of accuracy. We leverage information about the associations between DNA genotype and CpG methylation to develop an age predictor that is minimally influenced by meQTL, and show that this model remains accurate across a broad range of genetic backgrounds. Intriguingly, we also find that the older individuals and those exhibiting relatively lower epigenetic age acceleration in our cohorts tend to carry more epigenetic age-reducing genetic variants, suggesting a novel mechanism by which heritable factors can influence longevity.

genetics↗

bioRxiv2024

Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network

Complex traits are determined by many loci--mostly regulatory elements--that, through combinatorial interactions, can affect multiple traits. Such high levels of epistasis and pleiotropy have been proposed in the omnigenic model and may explain why such a large part of complex trait heritability is usually missed by genome-wide association studies while raising questions about the possibility for such traits to evolve in response to environmental constraints. To explore the molecular bases of complex traits and understand how they can adapt, we systematically analyzed the distribution of SNP heritability for ten traits across 29 tissue-specific Expression Quantitative Trait Locus (eQTL) networks. We find that heritability is clustered in a small number of tissue-specific, functionally relevant SNP-gene modules and that the greatest heritability occurs in local "hubs" that are both the cornerstone of the networks modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. We confirm that this structure has allowed complex traits to evolve in response to environmental constraints, with the local "hubs" being the preferential targets of past and ongoing directional selection. Together, these results provide a conceptual framework for understanding complex trait architecture and evolution.

systems biology↗

bioRxiv2020

Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

Enhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.Significance Enhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

PUMA: PANDA Using MicroRNA Associations

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the mRNA transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with protein-protein interaction and target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression (GTEx) project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target gene regulatory interactions in the different network models. Furthermore, tissue-specific functions of miRNAs, which we identified by analyzing their regulatory profiles and which we made available through a Shiny app (https://kuijjer.shinyapps.io/puma_gtex/), are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific regulatory processes of miRNAs. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. PUMA is available in C++, MATLAB, and Python code on GitHub (https://github.com/kuijjerlab/PUMA and https://github.com/kuijjerlab/PyPuma).

systems biology↗

bioRxiv2019

Connectivity of variants in eQTL networks dictates reproducibility and functionality

Network analyses are a natural approach for identifying genetic variants and genes that work together to drive disease phenotypes. The relationship between SNPs and genes, captured in expression quantitative trait locus (eQTL) analysis, can be represented as a network with edges connecting SNPs and genes. Existing network methods treat such edges as fixed and known when they are most often thresholded estimates from eQTL regression. We consider various characterizations of an essential feature of nodes of eQTL networks, their degree centrality, that retains different data on eQTLs. We define the network metric of degree to be estimated by false discovery rates, test statistics, and p-values of the eQTL regressions in order to represent how central and potentially influential a SNP is to the network. We calculate degree metrics for data from 21 tissues from the GTEx project to assess the reproducibility, correlation across tissues, and, functional importance of degree.

systems biology↗

bioRxiv2018

Nongenic cancer-risk SNPs affect oncogenes, tumor suppressor genes, and immune function

Genome-wide associations studies (GWASes) have identified many germline genetic variants that are associated with an increased risk of developing cancer. However, how these single nucleotide polymorphisms (SNPs) alter biological function in a way that increases cancer risk is still largely unknown. We used a systems biology approach to analyze the regulatory role and functional associations of cancer-risk SNPs in thirteen distinct tissues. Using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis, keeping both cis- and trans-eQTLs, and representing those significant associations as edges in tissue-specific eQTL bipartite networks. We find that each network is organized into highly modular communities that group sets of SNPs together with functionally-related collections of genes. We mapped cancer-risk SNPs to each tissue-specific eQTL network. Although we find in each tissue that cancer-risk SNPs are distributed across the network, they are not uniformly distributed. Rather they are significantly over-represented in a small number of communities. This includes communities enriched for immune response processes as well as communities representing tissue-specific functions. Moreover, cancer-risk SNPs are over-represented in the central \"cores\" of communities, meaning they are more likely to influence the expression of many genes within the same community, thus affecting biological processes. And finally, we find that cancer-risk SNPs preferentially target oncogenes and tumor suppressor genes, suggesting non-genic mutations may still alter the effects of these key cancer-associated genes. This bipartite eQTL network approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

cancer biology↗

bioRxiv2018

Exploring the Genetic Basis of Human Population Differences in DNA Methylation and their Causal Impact on Immune Gene Regulation

DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, its genetic determinants, and their respective causal impact on immune gene regulation remain elusive. We report extensive population differences in DNA methylation between individuals of African and European descent -- detected in primary monocytes that were used as a model of a major innate immunity cell type. Most of these differences (~70%) were driven by DNA sequence variants nearby CpG sites (meQTLs), which account for ~60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

genomics↗

bioRxiv2017

Histopathological image QTL discovery of thyroid autoimmune disease variants

Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image Quantitative Trait Loci (iQTL) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype and Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling, and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data, and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.\n\nOne Sentence SummaryWe use a histopathological image QTL analysis to identify genomic variants associated with immune cell infiltration.

genomics↗

bioRxiv2017

Understanding Tissue-specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

genomics↗

bioRxiv2016

A network-based approach to eQTL interpretation and SNP functional characterization

Expression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only include cis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified both cis- and trans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.

genomics↗

bioRxiv2016

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.\n\nHighlightsO_LIOverview of assumptions used in preprocessing and normalization\nC_LIO_LIPipeline for preprocessing, quality control, and normalization of large heterogeneous data\nC_LIO_LIA Bioconductor package for the YARN pipeline and easy manipulation of count data\nC_LIO_LIPreprocessed GTEx data set using the YARN pipeline available as a resource\nC_LI

bioinformatics↗

bioRxiv2016

Transcriptional landscape of cell lines and their tissues of origin

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell lines transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models.\n\nHighlightsO_LICell lines differ from their source tissues in gene expression and regulation\nC_LIO_LIDistinct cell lines share altered patterns of cell cycle regulation\nC_LIO_LICell cycle genes are less strongly targeted by repressive TFs in cell lines\nC_LIO_LICell lines share expression with their source tissue, but at reduced levels\nC_LI

genomics↗

bioRxiv2016

Sexual dimorphism in gene expression and regulatory networks across human tissues

Sexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks.\n\nHighlightsO_LISexual dimorphism manifests in both gene expression and gene regulatory networks\nC_LIO_LISubstantial sexual dimorphism in regulatory networks was found in several tissues\nC_LIO_LIMany differentially regulated genes are not differentially expressed\nC_LIO_LISex hormone receptors do not exhibit sexually dimorphic expression in most tissues\nC_LI

genomics↗