Systems genetics analysis of human body fat distribution genes identifies adipocyte processes.

Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.

Knockdown of SCN5A alters metabolic-associated genes and aggravates hypertrophy in the cardiomyoblast.

SCN5A mutations have been reported to cause various cardiomyopathies in humans. Most of the SCN5A mutations causes loss of function and thereby, alters the overall cellular function. Therefore, to understand the loss of SCN5A function in cardiomyocytes, we have knocked down the SCN5A gene (SCN5A-KD) in H9c2 cells and explored the cell phenotype and molecular behaviors in the presence and absence of isoproterenol (ISO), an adrenergic receptor agonist that induces cardiac hypertrophy. Expression of several genes related to hypertrophy, inflammation, fibrosis, and energy metabolism pathways were evaluated. It was found that the mRNA expression of hypertrophy-related gene, brain (B-type) natriuretic peptide (BNP) was significantly increased in SCN5A-KD cells as compared to 'control' H9c2 cells. There was a further increase in the mRNA expressions of BNP and ßMHC in SCN5A-KD cells after ISO treatment compared to their respective controls. Pro-inflammatory cytokine, tumor necrosis factor-alpha expression was significantly increased in 'SCN5A-KD' H9c2 cells. Further, metabolism-related genes like glucose transporter type 4, cluster of differentiation 36, peroxisome proliferator-activated receptor alpha, and peroxisome proliferator-activated receptor-gamma were significantly elevated in the SCN5A-KD cells as compared to the control cells. Upregulation of these metabolic genes is associated with increased ATP production. The study revealed that SCN5A knock-down causes alteration of gene expression related to cardiac hypertrophy, inflammation, and energy metabolism pathways, which may promote cardiac remodelling and cardiomyopathy.

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans.

Gene regulatory divergence between species can result from cis-acting local changes to regulatory element DNA sequences or global trans-acting changes to the regulatory environment. Understanding how these mechanisms drive regulatory evolution has been limited by challenges in identifying trans-acting changes. We present a comprehensive approach to directly identify cis- and trans-divergent regulatory elements between human and rhesus macaque lymphoblastoid cells using assay for transposase-accessible chromatin coupled to self-transcribing active regulatory region (ATAC-STARR) sequencing. In addition to thousands of cis changes, we discover an unexpected number (∼10,000) of trans changes and show that cis and trans elements exhibit distinct patterns of sequence divergence and function. We further identify differentially expressed transcription factors that underlie ∼37% of trans differences and trace how cis changes can produce cascades of trans changes. Overall, we find that most divergent elements (67%) experienced changes in both cis and trans, revealing a substantial role for trans divergence-alone and together with cis changes-in regulatory differences between species.

Temperature-driven coordination of circadian transcriptional regulation.

The circadian clock is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce circadian oscillations in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While previous studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome: genes are cycling in a temperature-specific manner, and the distributions of their phases also differ between the two conditions. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. We report that the phase differences between cycling genes increase as a function of geodesic distance in the low temperature condition, suggesting increased coordination of cycling on the gene regulatory network. Our results suggest a potential mechanism whereby the circadian clock mediates the fly's response to seasonal changes in temperature.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model.

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

Allele-Specific Regulation of the Candidate Autism Liability Gene RAI1 by the Enhancer Variant rs4925102 (C/G).

Retinoic acid-induced 1 (RAI1) is a dosage-sensitive gene that causes autistic phenotypes when deleted or duplicated. Observations from clinical cases and animal models also suggest that changes of RAI1 expression levels contribute to autism. Previously, we used a bioinformatic approach to identify several single nucleotide polymorphisms (SNPs) located within the 5'-region of RAI1 that correlate with RAI1 mRNA expression in the human brain. In particular, the SNP rs4925102 was identified as a candidate cis-acting regulatory variant, the genotype of which may affect the binding of transcription factors that influence RAI1 mRNA expression. In this study, we provide experimental evidence based on reporter gene, chromatin immunoprecipitation (ChIP), and chromatin conformation capture (3C) assays that rs4925102 regulates RAI1 mRNA expression in an allele-specific manner in human cell lines, including the neuroblastoma-derived cell line SH-SY5Y. We also describe a statistically significant association between rs4925102 genotype and autism spectrum disorder (ASD) diagnosis in a case-control study and near-statistically significant association in an Autism Genome Project (AGP) transmission disequilibrium (TDT) study using Caucasian subjects.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Trans-eQTL mapping in gene sets identifies network effects of genetic variants.

Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.

Analysis of ceRNA Network and Identification of Potential Treatment Target and Biomarkers of Endothelial Cell Injury in Sepsis.

Background: Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. Methods: The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an in vitro model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence in situ hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. Result: By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA ZFAS1 was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only SLC26A6 was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the ZFAS1/hsa-miR-449c-5p/SLC26A6 ceRNA regulatory axis may play a role in the progression of sepsis. Conclusion: The sepsis ceRNA network, especially the ZFAS1/hsa-miR-449c-5p/SLC26A6 regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung.

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

Genome-wide CRISPR activation screen identifies JADE3 as an antiviral activator of NF-kB-dependent IFITM3 expression.

The innate immune system features a web of interacting pathways that require exquisite regulation. To identify novel nodes in this immune landscape, we conducted a gain-of-function, genome-wide CRISPR activation screen with influenza A virus. We identified both appreciated and novel antiviral genes, including Jade family PHD zinc finger 3 (JADE3) a protein involved in directing the histone acetyltransferase histone acetyltransferase binding to ORC1 complex to modify chromatin and regulate transcription. JADE3 is both necessary and sufficient to restrict influenza A virus infection. Our results suggest a distinct function for JADE3 as expression of the closely related paralogs JADE1 and JADE2 does not confer resistance to influenza A virus infection. JADE3 is required for both constitutive and inducible expression of the well-characterized antiviral gene interferon-induced transmembrane protein 3 (IFITM3). Furthermore, we find JADE3 activates the NF-kB signaling pathway, which is required for the promotion of IFITM3 expression by JADE3. Therefore, we propose JADE3 activates an antiviral genetic program involving NF-kB-dependent IFITM3 expression to restrict influenza A virus infection.

Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.

Genome-wide association studies (GWAS) have mapped over 90% of disease- and quantitative-trait-associated variants within the non-coding genome. Non-coding regulatory DNA (e.g., promoters and enhancers) and RNA (e.g., 5' and 3' UTRs and splice sites) are essential in regulating temporal and tissue-specific gene expressions. Non-coding variants can potentially impact the phenotype of an organism by altering the molecular recognition of the cis-regulatory elements, leading to gene dysregulation. However, determining causality between non-coding variants, gene regulation, and human disease has remained challenging. Experimental and computational methods have been developed to understand the molecular mechanism involved in non-coding variant interference at the transcriptional and post-transcriptional levels. This review discusses recent approaches to evaluating disease-associated single-nucleotide variants (SNVs) and determines their impact on transcription factor (TF) binding, gene expression, chromatin conformation, post-transcriptional regulation, and translation.

Genetic variation drives differences in obesity-related gene regulation.

Heikkinen and colleagues recently demonstrated that genetic variation, rather than dietary changes, governs gene regulation in liver. This finding highlights the impact of noncoding variants on chromatin accessibility, histone modifications, transcription factor binding, and gene expression and has implications for future research directions in understanding the genetic basis of disease.

Formaldehyde regulates one-carbon metabolism and epigenetics.

Recently, Pham et al. used an array of model systems to uncover a role for the enzyme methionine adenosyltransferase (MAT)-1A, which is mainly expressed in liver, in both sensing formaldehyde and regulating transcriptional responses that protect against it. This provides a new lens for understanding the effects of formaldehyde on gene regulation.

Non-transcriptional IRF7 interacts with NF-κB to inhibit viral inflammation.

Interferon (IFN) regulatory factors (IRF) are key transcription factors in cellular antiviral responses. IRF7, a virus-inducible IRF, expressed primarily in myeloid cells, is required for transcriptional induction of interferon α and antiviral genes. IRF7 is activated by virus-induced phosphorylation in the cytoplasm, leading to its translocation to the nucleus for transcriptional activity. Here, we revealed a nontranscriptional activity of IRF7 contributing to its antiviral functions. IRF7 interacted with the pro-inflammatory transcription factor NF-κB-p65 and inhibited the induction of inflammatory target genes. Using knockdown, knockout, and overexpression strategies, we demonstrated that IRF7 inhibited NF-κB-dependent inflammatory target genes, induced by virus infection or toll-like receptor stimulation. A mutant IRF7, defective in transcriptional activity, interacted with NF-κB-p65 and suppressed NF-κB-induced gene expression. A single-action IRF7 mutant, active in anti-inflammatory function, but defective in transcriptional activity, efficiently suppressed Sendai virus and murine hepatitis virus replication. We, therefore, uncovered an anti-inflammatory function for IRF7, independent of transcriptional activity, contributing to the antiviral response of IRF7.

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation.

It is well known how sequencing technologies propelled cellular biology research in recent years, providing incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

Protein kinase C signaling "in" and "to" the nucleus: Master kinases in transcriptional regulation.

PKC is a multifunctional family of Ser-Thr kinases widely implicated in the regulation of fundamental cellular functions, including proliferation, polarity, motility, and differentiation. Notwithstanding their primary cytoplasmic localization and stringent activation by cell surface receptors, PKC isozymes impel prominent nuclear signaling ultimately impacting gene expression. While transcriptional regulation may be wielded by nuclear PKCs, it most often relies on cytoplasmic phosphorylation events that result in nuclear shuttling of PKC downstream effectors, including transcription factors. As expected from the unique coupling of PKC isozymes to signaling effector pathways, glaring disparities in gene activation/repression are observed upon targeting individual PKC family members. Notably, specific PKCs control the expression and activation of transcription factors implicated in cell cycle/mitogenesis, epithelial-to-mesenchymal transition and immune function. Additionally, PKCs isozymes tightly regulate transcription factors involved in stepwise differentiation of pluripotent stem cells toward specific epithelial, mesenchymal, and hematopoietic cell lineages. Aberrant PKC expression and/or activation in pathological conditions, such as in cancer, leads to profound alterations in gene expression, leading to an extensive rewiring of transcriptional networks associated with mitogenesis, invasiveness, stemness, and tumor microenvironment dysregulation. In this review, we outline the current understanding of PKC signaling "in" and "to" the nucleus, with significant focus on established paradigms of PKC-mediated transcriptional control. Dissecting these complexities would allow the identification of relevant molecular targets implicated in a wide spectrum of diseases.

Differential expression of follicular fluid exosomal microRNA in women with diminished ovarian reserve.

PURPOSE: Decreased ovarian reserve function is mainly characterized by female endocrine disorders and fertility decline. Follicular fluid (FF) exosomal microRNAs (miRNAs) have been shown to regulate the function of granulosa cells (GCs). The present study explored differentially expressed miRNAs (DEmiRNAs) in patients with diminished ovarian reserve (DOR). METHODS: FF was collected from 12 DOR patients and 12 healthy controls. DEmiRNAs between the two groups were identified and analyzed using high-throughput sequencing technology and validated by real-time quantitative PCR (RT-qPCR). RESULTS: A total of 592 DEmiRNAs were identified using high-throughput miRNA sequencing, of which 213 were significantly upregulated and 379 were significantly downregulated. The sequencing results were further validated by RT-qPCR. These DEmiRNA target genes were mainly involved in the cancer pathway, phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, regulation of actin cytoskeleton signaling pathway, and biological processes related to protein binding, nucleoplasm, cytoplasm, and cell membrane. CONCLUSION: FF exosomal miRNAs are significantly differentially expressed in DOR patients versus non-DOR patients, underscoring their crucial role in regulating the pathogenesis of DOR.

Accounting for isoform expression increases power to identify genetic regulation of gene expression.

A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.