scholarly journals Gene expression variability in human and chimpanzee populations share common determinants

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Benjamin Jung Fair ◽  
Lauren E Blake ◽  
Abhishek Sarkar ◽  
Bryan J Pavlovic ◽  
Claudia Cuevas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Comparative Analysis ◽  
Individual Variation ◽  
Disease Susceptibility ◽  
Expression Data ◽  
Cell Type ◽  
Orthologous Genes ◽  
Expression Variability ◽  
Gene Regulatory

Inter-individual variation in gene expression has been shown to be heritable and is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

scholarly journals Gene expression variability in human and chimpanzee populations share common determinants

2020 ◽  
Author(s):  
Benjamin J. Fair ◽  
Lauren E. Blake ◽  
Abhishek Sarkar ◽  
Bryan J. Pavlovic ◽  
Claudia Cuevas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Comparative Analysis ◽  
Individual Variation ◽  
Disease Susceptibility ◽  
Expression Data ◽  
Cell Type ◽  
Orthologous Genes ◽  
Expression Variability ◽  
Gene Regulatory

AbstractInter-individual variation in gene expression has been shown to be heritable and it is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

scholarly journals Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia D. van Asten ◽  
Ji Won Oh ◽  
Arantza Farina-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

AbstractDeconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

scholarly journals BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data

2020 ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia van Asten ◽  
Ji-won Oh ◽  
Arantza Fariña-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

Abstract High-resolution deconvolution of bulk gene expression profiles is pivotal to characterize the complex cellular make-up of tissues, such as tumor microenvironment. Single-cell RNA-seq provides reliable prior knowledge for deconvolution, however, a comprehensive statistical model is required for efficient utilization due to the inherently variable nature of gene expression. We introduce BLADE (Bayesian Log-normAl Deconvolution), a comprehensive probabilistic framework to estimate both cellular make-up and gene expression profiles of each cell type in each sample. Unlike previous comprehensive statistical approaches, BLADE can handle >20 cell types thanks to the efficient variational inference. Throughout an intensive evaluation using >700 datasets, BLADE showed enhanced robustness against gene expression variability and better completeness than conventional methods, in particular to reconstruct gene expression profiles of each cell type. All-in-all, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems based on standard bulk gene expression data.

scholarly journals Causal gene regulatory network inference using enhancer activity as a causal anchor

2018 ◽  
Author(s):  
Deepti Vipin ◽  
Lingfei Wang ◽  
Guillaume Devailly ◽  
Tom Michoel ◽  
Anagha Joshi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Causal Gene ◽  
High Confidence ◽  
Enhancer Activity ◽  
Enhancer Rna ◽  
Gene Regulatory

AbstractMotivationTranscription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data has been widely used to infer the cellular regulatory networks, the methods mainly infer correlations rather than causality. We propose that a causal inference framework successfully used for eQTL data can be extended to infer causal regulatory networks using enhancers as causal anchors and enhancer RNA expression as a readout of enhancer activity.ResultsWe developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukemia overlapped significantly with experimentally validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types.AvailabilityMethods have been implemented in the Findr software, available at https://github.com/lingfeiwang/[email protected], [email protected]

scholarly journals Gene regulatory network inference from perturbed time-series expression data via ordered dynamical expansion of non-steady state actors

2014 ◽  
Author(s):  
Mahdi Zamanighomi ◽  
Mostafa Zamanian ◽  
Michael J Kimber ◽  
Zhengdao Wang
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Regulation ◽  
Steady State ◽  
Gene Expression Data ◽  
Network Inference ◽  
Optimization Problems ◽  
Expression Data ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are often difficult to scale, are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, present an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Inferring gene regulation dynamics from static snapshots of gene expression variability

2021 ◽  
Vol 104 (4) ◽  
Author(s):  
Euan Joly-Smith ◽  
Zitong Jerry Wang ◽  
Andreas Hilfinger
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Expression Variability
Sign in / Sign up

Export Citation Format

Share Document