Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function

Methylome and proteome integration in human skeletal muscle uncover group and individual responses to high-intensity interval training

Exercise is a major beneficial contributor to muscle metabolism, and health benefits acquired by exercise are a result of molecular shifts occurring across multiple molecular layers (i.e., epigenome, transcriptome, and proteome). Identifying robust, across-molecular level targets associated with exercise response, at both group and individual levels, is paramount to develop health guidelines and targeted health interventions. Sixteen, apparently healthy, moderately trained (VO2 max = 51.0 ± 10.6 mL min-1  kg-1 ) males (age range = 18-45 years) from the Gene SMART (Skeletal Muscle Adaptive Responses to Training) study completed a longitudinal study composed of 12-week high-intensity interval training (HIIT) intervention. Vastus lateralis muscle biopsies were collected at baseline and after 4, 8, and 12 weeks of HIIT. DNA methylation (~850 CpG sites) and proteomic (~3000 proteins) analyses were conducted at all time points. Mixed models were applied to estimate group and individual changes, and methylome and proteome integration was conducted using a holistic multilevel approach with the mixOmics package. A total of 461 proteins significantly changed over time (at 4, 8, and 12 weeks), whilst methylome overall shifted with training only one differentially methylated position (DMP) was significant (adj.p-value < .05). K-means analysis revealed cumulative protein changes by clusters of proteins that presented similar changes over time. Individual responses to training were observed in 101 proteins. Seven proteins had large effect-sizes >0.5, among them are two novel exercise-related proteins, LYRM7 and EPN1. Integration analysis showed bidirectional relationships between the methylome and proteome. We showed a significant influence of HIIT on the epigenome and more so on the proteome in human muscle, and uncovered groups of proteins clustering according to similar patterns across the exercise intervention. Individual responses to exercise were observed in the proteome with novel mitochondrial and metabolic proteins consistently changed across individuals. Future work is required to elucidate the role of these proteins in response to exercise.

Construction and Analysis of the lncRNA-miRNA-mRNA Network Based on Competing Endogenous RNA in Atrial Fibrillation

Background

Accumulated studies have revealed that long non-coding RNAs (lncRNAs) play critical roles in human diseases by acting as competing endogenous RNAs (ceRNAs). However, functional roles and regulatory mechanisms of lncRNA-mediated ceRNA in atrial fibrillation (AF) remain unknown. In the present study, we aimed to construct the lncRNA-miRNA-mRNA network based on ceRNA theory in AF by using bioinformatic analyses of public datasets.

Methods

Microarray data sets of GSE115574 and GSE79768 from the Gene Expression Omnibus database were downloaded. Twenty-one AF right atrial appendage (RAA) samples and 22 sinus rhythm (SR) subjects RAA samples were selected for subsequent analyses. After merging all microarray data and adjusting for batch effect, differentially expressed genes were identified. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. A ceRNA network was constructed.

Result

A total of 8 lncRNAs and 43 mRNAs were significantly differentially expressed with fold change >1.5 (p < 0.05) in RAA samples of AF patients when compared with SR. GO and KEGG pathway analysis showed that cardiac muscle contraction pathway were involved in AF development. The ceRNA was predicted by co-expressing LOC101928304/ LRRC2 from the constructional network analysis, which was competitively combined with miR-490-3p. The expression of LOC101928304 and LRRC were up-regulated in myocardial tissue of patients with AF, while miR-490-3p was down-regulated.

Conclusion

We constructed the LOC101928304/miR-490-3p/LRRC2 network based on ceRNA theory in AF in the bioinformatic analyses of public datasets. The ceRNA network found from this study may help improve our understanding of lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of AF.

Investigating the role of EGF-CFC gene family in recurrent pregnancy loss through bioinformatics and molecular approaches

Recurrent pregnancy loss (RPL) is the most common reproductive failure, reaching 1-5% of women throughout their lives, and having unknown etiology in 50% of the cases. In humans, EGF-CFC1 (Epidermal Growth Factors & Cripto/FRL-1/Cryptic) gene family is composed by TDGF1 and CFC1, two developmental genes. The aim of this study was to investigate the role of EGF-CFC on RPL. To this, multiple approaches were performed; we conducted an expression analysis of TDGF1 and CFC1 using publicly available data from Gene Omnibus Expression (GEO), systems biology analyses and functional prediction; and a molecular analysis carried out in a case-control study. Our GEO analysis showed a decrease in TDGF1 expression in the endometrium (p=0.049) and CFC1 expression in placenta (p=0.015) of women with RPL. Network analysis, gene ontology and literature pointed to a strong connection between EGF-CFC1 gene family to pathways that play key roles during pregnancy, including TGF-β, c-Src/MAPK/AKT, Notch, TNFα, IFNγ and IL-6. A pathogenicity score developed for this gene family showed that the c.-14+1429T>C (rs3806702) variant in the TDGF1 and the p.Arg47Gln (rs201431919) variant in CFC1 gene would be the ones with the highest deleterious effect for RPL. In the case-control study, which involved 149 women with RPL and 159 controls, no statistical difference was observed in the allele and genotype distributions of the variants studied in the two groups. In this study, we performed extensive bioinformatics analysis for biomarker prioritization followed by experimental validation of proposed selected markers. Although there is no statistical difference in the frequencies of these variants between RPL and controls, the expression analysis results suggest that TDGF1 and CFC1 genes might play a role in RPL. In addition, systems biology analyzes raise the hypothesis that genes in other signaling pathways that may be related to RPL as good candidates for future studies.Abbreviations RPL: recurrent pregnancy loss; EGF-CFC1: Epidermal Growth Factors - Cripto/FRL-1; GEO: Gene Omnibus Expression; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

MOTIVATION

The evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers' attention.

METHODS

In this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).

RESULTS

This method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some "dark genes" that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

Epigenetics of Skeletal Muscle-Associated Genes in the ASB, LRRC, TMEM, and OSBPL Gene Families

Much remains to be discovered about the intersection of tissue-specific transcription control and the epigenetics of skeletal muscle (SkM), a very complex and dynamic organ. From four gene families, Leucine-Rich Repeat Containing (LRRC), Oxysterol Binding Protein Like (OSBPL), Ankyrin Repeat and Socs Box (ASB), and Transmembrane Protein (TMEM), we chose 21 genes that are preferentially expressed in human SkM relative to 52 other tissue types and analyzed relationships between their tissue-specific epigenetics and expression. We also compared their genetics, proteomics, and descriptions in the literature. For this study, we identified genes with little or no previous descriptions of SkM functionality (ASB4, ASB8, ASB10, ASB12, ASB16, LRRC14B, LRRC20, LRRC30, TMEM52, TMEM233, OSBPL6/ORP6, and OSBPL11/ORP11) and included genes whose SkM functions had been previously addressed (ASB2, ASB5, ASB11, ASB15, LRRC2, LRRC38, LRRC39, TMEM38A/TRIC-A, and TMEM38B/TRIC-B). Some of these genes have associations with SkM or heart disease, cancer, bone disease, or other diseases. Among the transcription-related SkM epigenetic features that we identified were: super-enhancers, promoter DNA hypomethylation, lengthening of constitutive low-methylated promoter regions, and SkM-related enhancers for one gene embedded in a neighboring gene (e.g., ASB8-PFKM, LRRC39-DBT, and LRRC14B-PLEKHG4B gene-pairs). In addition, highly or lowly co-expressed long non-coding RNA (lncRNA) genes probably regulate several of these genes. Our findings give insights into tissue-specific epigenetic patterns and functionality of related genes in a gene family and can elucidate normal and disease-related regulation of gene expression in SkM.

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes

Anthracycline-induced cardiotoxicity (ACT) is a key limiting factor in setting optimal chemotherapy regimes, with almost half of patients expected to develop congestive heart failure given high doses. However, the genetic basis of sensitivity to anthracyclines remains unclear. We created a panel of iPSC-derived cardiomyocytes from 45 individuals and performed RNA-seq after 24 hr exposure to varying doxorubicin dosages. The transcriptomic response is substantial: the majority of genes are differentially expressed and over 6000 genes show evidence of differential splicing, the later driven by reduced splicing fidelity in the presence of doxorubicin. We show that inter-individual variation in transcriptional response is predictive of in vitro cell damage, which in turn is associated with in vivo ACT risk. We detect 447 response-expression quantitative trait loci (QTLs) and 42 response-splicing QTLs, which are enriched in lower ACT GWAS [Formula: see text]-values, supporting the in vivo relevance of our map of genetic regulation of cellular response to anthracyclines.