Disparity of gene expression in coronary artery disease: insights from MEIS1, HIRA, and Myocardin.

INTRODUCTION: Coronary artery disease (CAD) in young adults can have devastating consequences. The cardiac developmental gene MEIS1 plays important roles in vascular networks and heart development. This gene effects on the regeneration capacity of the heart. Considering role of MEIS1 in cardiac tissue development and the progression of myocardial infarction this study investigated the expression levels of the MEIS1, HIRA, and Myocardin genes in premature CAD patients compared to healthy subjects and evaluated the relationships between these genes and possible inflammatory factors. METHODS AND RESULTS: The study conducted a case-control design involving 35 CAD patients and 35 healthy individuals. Peripheral blood mononuclear cells (PBMCs) were collected, and gene expression analysis was performed using real-time PCR. Compared with control group, the number of PBMCs in the CAD group exhibited greater MEIS1 and HIRA gene expression, with fold changes of 2.45 and 3.6. The expression of MEIS1 exhibited a negative correlation with IL-10 (r= -0.312) expression and positive correlation with Interleukin (IL)-6 (r = 0.415) and tumor necrosis factor (TNF)-α (r = 0.534) gene expression. Moreover, there was an inverse correlation between the gene expression of HIRA and that of IL-10 (r= -0.326), and a positive correlation was revealed between the expression of this gene and that of the IL-6 (r = 0.453) and TNF-α (r = 0.572) genes. CONCLUSION: This research demonstrated a disparity in expression levels of MEIS1, HIRA, and Myocardin, between CAD and healthy subjects. The results showed that, MEIS1 and HIRA play significant roles in regulating the synthesis of proinflammatory cytokines, namely, TNF-α and IL-6.

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.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

The art of modeling gene regulatory circuits.

The amazing complexity of gene regulatory circuits, and biological systems in general, makes mathematical modeling an essential tool to frame and develop our understanding of their properties. Here, we present some fundamental considerations to develop and analyze a model of a gene regulatory circuit of interest, either representing a natural, synthetic, or theoretical system. A mathematical model allows us to effectively evaluate the logical implications of our hypotheses. Using our models to systematically perform in silico experiments, we can then propose specific follow-up assessments of the biological system as well as to reformulate the original assumptions, enriching both our knowledge and our understanding of the system. We want to invite the community working on different aspects of gene regulatory circuits to explore the power and benefits of mathematical modeling in their system.

A Bidirectional Non-Coding RNA Promoter Mediates Long-Range Gene Expression Regulation.

Recent evidence suggests that human gene promoters display gene expression regulatory mechanisms beyond the typical single gene local transcription modulation. In mammalian genomes, genes with an associated bidirectional promoter are abundant; bidirectional promoter architecture serves as a regulatory hub for a gene pair expression. However, it has been suggested that its contribution to transcriptional regulation might exceed local transcription initiation modulation. Despite their abundance, the functional consequences of bidirectional promoter architecture remain largely unexplored. This work studies the long-range gene expression regulatory role of a long non-coding RNA gene promoter using chromosome conformation capture methods. We found that this particular bidirectional promoter contributes to distal gene expression regulation in a target-specific manner by establishing promoter-promoter interactions. In particular, we validated that the promoter-promoter interactions of this regulatory element with the promoter of distal gene BBX contribute to modulating the transcription rate of this gene; removing the bidirectional promoter from its genomic context leads to a rearrangement of BBX promoter-enhancer interactions and to increased gene expression. Moreover, long-range regulatory functionality is not directly dependent on its associated non-coding gene pair expression levels.

Flexible modeling of regulatory networks improves transcription factor activity estimation.

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

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.

Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.

The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.

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.

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.

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.

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.

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.

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.

Non-canonical functions of enhancers: regulation of RNA polymerase III transcription, DNA replication, and V(D)J recombination.

Enhancers are the key regulators of other DNA-based processes by virtue of their unique ability to generate nucleosome-depleted regions in a highly regulated manner. Enhancers regulate cell-type-specific transcription of tRNA genes by RNA polymerase III (Pol III). They are also responsible for the binding of the origin replication complex (ORC) to DNA replication origins, thereby regulating origin utilization, replication timing, and replication-dependent chromosome breaks. Additionally, enhancers regulate V(D)J recombination by increasing access of the recombination-activating gene (RAG) recombinase to target sites and by generating non-coding enhancer RNAs and localized regions of trimethylated histone H3-K4 recognized by the RAG2 PHD domain. Thus, enhancers represent the first step in decoding the genome, and hence they regulate biological processes that, unlike RNA polymerase II (Pol II) transcription, do not have dedicated regulatory proteins.

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.

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.

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.

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.

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.