Function of miRNA-145-5p in the pathogenesis of human disorders.

miR-145-5p is a microRNA whose role in diverse disorders has been verified. This miRNA is encoded by MIR145 gene on chromosome 5. This miRNA is mainly considered as a tumor suppressor miRNA in diverse types of cancers, including bladder cancer, breast cancer, cervical cancer, cholangiocarcinoma, renal cancer, and gastrointestinal cancers. However, few studies have reported up-regulation of this miRNA in some cancers. Moreover, it has been shown to affect pathogenesis of a number of non-malignant conditions such as aplastic anemia, asthma, cerebral ischemia/reperfusion injury, diabetic nephropathy, rheumatoid arthritis and Sjögren syndrome. In the current review, we summarize the available literature about the role of miR-145-5p in these conditions.

Transcript Expression Profiles and MicroRNA Regulation Indicate an Upregulation of Processes Linked to Oxidative Stress, DNA Repair, Cell Death, and Inflammation in Type 1 Diabetes Mellitus Patients.

Type 1 diabetes (T1D) arises from autoimmune-mediated destruction of insulin-producing ß-cells leading to impaired insulin secretion and hyperglycemia. T1D is accompanied by DNA damage, oxidative stress, and inflammation, although there is still scarce information about the oxidative stress response and DNA repair in T1D pathogenesis. We used the microarray method to assess mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) of 19 T1D patients compared to 11 controls and identify mRNA targets of microRNAs that were previously reported for T1D patients. We found 277 differentially expressed genes (220 upregulated and 57 downregulated) in T1D patients compared to controls. Analysis by gene sets (GSA and GSEA) showed an upregulation of processes linked to ROS generation, oxidative stress, inflammation, cell death, ER stress, and DNA repair in T1D patients. Besides, genes related to oxidative stress responses and DNA repair (PTGS2, ATF3, FOSB, DUSP1, and TNFAIP3) were found to be targets of four microRNAs (hsa-miR-101, hsa-miR148a, hsa-miR-27b, and hsa-miR-424). The expression levels of these mRNAs and microRNAs were confirmed by qRT-PCR. Therefore, the present study on differential expression profiles indicates relevant biological functions related to oxidative stress response, DNA repair, inflammation, and apoptosis in PBMCs of T1D patients relative to controls. We also report new insights regarding microRNA-mRNA interactions, which may play important roles in the T1D pathogenesis.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.

BACKGROUND: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. METHODS: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. RESULTS: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. CONCLUSIONS: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2.

scDesign2 is a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. This article shows how to download and install the scDesign2 R package, how to fit probabilistic models (one per cell type) to real data and simulate synthetic data from the fitted models, and how to use scDesign2 to guide experimental design and benchmark computational methods. Finally, a note is given about cell clustering as a preprocessing step before model fitting and data simulation.

A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis.

BACKGROUND: Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. PURPOSE: To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. METHODS: CHD patients' samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. RESULTS: The violet module was the most significantly associated with smoking (r = -0.28, p = 0.006). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. CONCLUSION: This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker's CHD risk.

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis.

PURPOSE: Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients' prognosis remains poor due to tumor invasion and metastasis. MATERIALS AND METHODS: We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. RESULT: By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. CONCLUSION: In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

Identification of Therapeutic Targets and Prognostic Biomarkers among Genes from the Mediator Complex Family in the Hepatocellular Carcinoma Tumour-Immune Microenvironment.

BACKGROUND: Hepatocellular carcinoma (HCC) is predominant among all types of primary liver cancers characterised by high morbidity and mortality. Genes in the mediator complex (MED) family are engaged in the tumour-immune microenvironment and function as regulatory hubs mediating carcinogenesis and progression across diverse cancer types. Whereas research studies have been conducted to examine the mechanisms in several cancers, studies that systematically focused on the therapeutic and prognostic values of MED in patients with HCC are limited. METHODS: The online databases ONCOMINE, GEPIA, UALCAN, GeneMANIA, cBioPortal, OmicStudio, STING, Metascape, and TIMER were used in this study. RESULTS: The transcriptional levels of all members of the MED family in HCC presented an aberrant high expression pattern. Significant correlations were found between the MED1, MED6, MED8, MED10, MED12, MED15, MED17, MED19, MED20, MED21, MED22, MED23, MED24, MED25, MED26, and MED27 expression levels and the pathological stage in the patients with HCC. The patients with high expression levels of MED6, MED8, MED10, MED17, MED19, MED20, MED21, MED22, MED24, and MED25 were significantly associated with poor prognosis. Functional enrichment analysis revealed that the members of the MED family were mainly enriched in the nucleobase-containing compound catabolic process, regulation of chromosome organisation, and transcriptional regulation by TP53. Significant correlations were found between the MED6, MED8, MED10, MED17, MED19, MED20, MED21, MED22, MED24, and MED25 expression levels and all types of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells). B cells and MED8 were independent predictors of overall survival. We found significant correlations between the somatic copy number alterations of the MED6, MED8, MED10, MED20, MED21, MED22, MED24, and MED25 molecules and the abundance of immune infiltrates. CONCLUSIONS: Our study delineated a thorough landscape to investigate the therapeutic and prognostic potentials of the MED family for HCC cases, which yielded promising results for the development of immunotherapeutic drugs and construction of a prognostic stratification model.

Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA).

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.

Current expression quantification methods suffer from a fundamental but undercharacterized type of error: the most likely estimates for transcript abundances are not unique. This means multiple estimates of transcript abundances generate the observed RNA-seq reads with equal likelihood, and the underlying true expression cannot be determined. This is called nonidentifiability in probabilistic modeling. It is further exacerbated by incomplete reference transcriptomes where reads may be sequenced from unannotated transcripts. Graph quantification is a generalization to transcript quantification, accounting for the reference incompleteness by allowing exponentially many unannotated transcripts to express reads. We propose methods to calculate a "confidence range of expression" for each transcript, representing its possible abundance across equally optimal estimates for both quantification models. This range informs both whether a transcript has potential estimation error due to nonidentifiability and the extent of the error. Applying our methods to the Human Body Map data, we observe that 35%-50% of transcripts potentially suffer from inaccurate quantification caused by nonidentifiability. When comparing the expression between isoforms in one sample, we find that the degree of inaccuracy of 20%-47% transcripts can be so large that the ranking of expression between the transcript and other isoforms from the same gene cannot be determined. When comparing the expression of a transcript between two groups of RNA-seq samples in differential expression analysis, we observe that the majority of detected differentially expressed transcripts are reliable with a few exceptions after considering the ranges of the optimal expression estimates.

COVID-19 and vertical transmission: assessing the expression of ACE2/TMPRSS2 in the human fetus and placenta to assess the risk of SARS-CoV-2 infection.

BACKGROUND: Pregnant women have been identified as a potentially at-risk group concerning COVID-19 infection, but little is known regarding the susceptibility of the fetus to infection. Co-expression of ACE2 and TMPRSS2 has been identified as a prerequisite for infection, and expression across different tissues is known to vary between children and adults. However, the expression of these proteins in the fetus is unknown. METHODS: We performed a retrospective analysis of a single cell data repository. The data were then validated at both gene and protein level by performing RT-qPCR and two-colour immunohistochemistry on a library of second-trimester human fetal tissues. FINDINGS: TMPRSS2 is present at both gene and protein level in the predominantly epithelial fetal tissues analysed. ACE2 is present at significant levels only in the fetal intestine and kidney, and is not expressed in the fetal lung. The placenta also does not co-express the two proteins across the second trimester or at term. INTERPRETATION: This dataset indicates that the lungs are unlikely to be a viable route of SARS-CoV2 fetal infection. The fetal kidney, despite presenting both the proteins required for the infection, is anatomically protected from the exposure to the virus. However, the gastrointestinal tract is likely to be susceptible to infection due to its high co-expression of both proteins, as well as its exposure to potentially infected amniotic fluid. TWEETABLE ABSTRACT: This work provides detailed mechanistic insight into the relative protection & vulnerabilities of the fetus & placenta to SARS-CoV-2 infection by scRNAseq & protein expression analysis for ACE2 & TMPRSS2. The findings help to explain the low rate of vertical transmission.

Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model.

BACKGROUND: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. METHODS: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. RESULTS: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. CONCLUSIONS: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

Thyroid nodule molecular profiling: The clinical utility of Afirma Xpression Atlas for nodules with Afirma Genomic Sequencing Classifier-suspicious results.

BACKGROUND: The Afirma Genomic Sequencing Classifier uses whole transcriptome RNA sequencing to identify thyroid nodules as benign or suspicious. The Afirma Xpression Atlas became available in 2018 and reports findings across 593 genes, including 905 variants and 235 fusions. When an alteration is identified, its risk of malignancy and associated neoplasm type is listed. We report the results of Afirma Xpression Atlas testing at our institution during its first 2 years of clinical use. METHODS: All patient charts with indeterminate thyroid nodules and Afirma Xpression Atlas results at our institution were reviewed. Thyroid nodule characteristics, cytology, Afirma Genomic Sequencing Classifier results, Afirma Xpression Atlas results, and final histopathology were reported. RESULTS: Afirma Xpression Atlas was performed on 136 indeterminate nodules since May 2018, and 103 met inclusion criteria. Forty-three nodules had positive Afirma Xpression Atlas results, and of these, 83.7% were follicular cell-derived thyroid cancer on surgical histopathology. This is similar to the overall 82.5% positive predictive value among Afirma Genomic Sequencing Classifier-suspicious indeterminate nodules during the same time period. Of the 60 nodules with negative Afirma Xpression Atlas, 73.3% were follicular cell-derived thyroid cancer on surgical histopathology. CONCLUSION: Afirma Xpression Atlas positivity is predictive of follicular cell-derived thyroid cancer, but its positive predictive value is similar to that of Genomic Sequencing Classifier-suspicious results alone at our institution, which is higher than previously published. Specific mutations likely predict follicular cell-derived thyroid cancer with higher accuracy, but our current sample size of any given mutation is too small to evaluate this further. Larger studies are needed to determine whether Afirma Xpression Atlas results predictably inform the risk of malignancy and tumor characteristics in thyroid nodules.

Pregnancy-associated plasma protein A mRNA expression as a marker for differentiated thyroid cancer: results from a "surgical" and a "cytological" series.

PURPOSE: Pregnancy-associated plasma protein A (PAPPA) is a metalloproteinase initially described for its role during pregnancy. PAPPA regulates IGF ligands 1 (IGF1) bioavailability through the degradation of IGF-binding protein 4 (IGFBP4). After the cleavage of IGFBP4, free IGF1 is able to bind IGF1 receptors (IGF1R) triggering the downstream signaling. Recently, PAPPA expression has been linked with development of several cancers. No data have been published on thyroid cancer, yet. METHODS: We evaluated PAPPA, insulin-like growth factor (IGF1), IGF1 receptors (IGF1R) and IGF-binding protein 4 (IGFBP4) mRNA expression levels in a "Surgical series" of 94 thyroid nodules (64 cancers, 16 follicular adenomas and 14 hyperplastic nodules) and in a "Cytological series" of 80 nodules from 74 patients underwent to fine-needle aspiration cytology (FNAC). In tissues, PAPPA was also evaluated by western blot. RESULTS: We found that PAPPA expression was increased in thyroid cancer specimen at mRNA and protein levels and that, adenomas and hyperplastic nodules had an expression similar to normal tissues. When applied on thyroid cytologies, PAPPA expression was able to discriminate benign from malignant nodules contributing to pre-surgical classification of the nodules. We calculated a cut-off with a good specificity (91%) which reached 100% when combined with molecular biology. CONCLUSION: These results show that PAPPA could represent a promising diagnostic marker for differentiated thyroid cancer.

Single-cell transcriptome atlas of human mesenchymal stem cells exploring cellular heterogeneity.

BACKGROUND: The heterogeneity of mesenchymal stem cells (MSCs) is poorly understood, thus limiting clinical application and basic research reproducibility. Advanced single-cell RNA sequencing (scRNA-seq) is a robust tool used to analyse for dissecting cellular heterogeneity. However, the comprehensive single-cell atlas for human MSCs has not been achieved. METHODS: This study used massive parallel multiplexing scRNA-seq to construct an atlas of > 130 000 single-MSC transcriptomes across multiple tissues and donors to assess their heterogeneity. The most widely clinically utilised tissue resources for MSCs were collected, including normal bone marrow (n = 3), adipose (n = 3), umbilical cord (n = 2), and dermis (n = 3). RESULTS: Seven tissue-specific and five conserved MSC subpopulations with distinct gene-expression signatures were identified from multiple tissue origins based on the high-quality data, which has not been achieved previously. This study showed that extracellular matrix (ECM) highly contributes to MSC heterogeneity. Notably, tissue-specific MSC subpopulations were substantially heterogeneous on ECM-associated immune regulation, antigen processing/presentation, and senescence, thus promoting inter-donor and intra-tissue heterogeneity. The variable dynamics of ECM-associated genes had discrete trajectory patterns across multiple tissues. Additionally, the conserved and tissue-specific transcriptomic-regulons and protein-protein interactions were identified, potentially representing common or tissue-specific MSC functional roles. Furthermore, the umbilical-cord-specific subpopulation possessed advantages in immunosuppressive properties. CONCLUSION: In summary, this work provides timely and great insights into MSC heterogeneity at multiple levels. This MSC atlas taxonomy also provides a comprehensive understanding of cellular heterogeneity, thus revealing the potential improvements in MSC-based therapeutic efficacy.

Dysregulation of Circadian Clock Genes as Significant Clinic Factor in the Tumorigenesis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide due to its asymptomatic onset and poor survival rate. This highlights the urgent need for developing novel diagnostic markers for early HCC detection. The circadian clock is important for maintaining cellular homeostasis and is tightly associated with key tumorigenesis-associated molecular events, suggesting the so-called chronotherapy. An analysis of these core circadian genes may lead to the discovery of biological markers signaling the onset of the disease. In this study, the possible functions of 13 core circadian clock genes (CCGs) in HCC were systematically analyzed with the aim of identifying ideal biomarkers and therapeutic targets. Profiles of HCC patients with clinical and gene expression data were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium. Various bioinformatics methods were used to investigate the roles of circadian clock genes in HCC tumorigenesis. We found that patients with high TIMELESS expression or low CRY2, PER1, and RORA expressions have poor survival. Besides, a prediction model consisting of these four CCGs, the tumor-node-metastasis (TNM) stage, and sex was constructed, demonstrating higher predictive accuracy than the traditional TNM-based model. In addition, pathway analysis showed that these four CCGs are involved in the cell cycle, PI3K/AKT pathway, and fatty acid metabolism. Furthermore, the network of these four CCGs-related coexpressed genes and immune infiltration was analyzed, which revealed the close association with B cells and nTreg cells. Notably, TIMELESS exhibited contrasting effects against CRY2, PER1, and RORA in most situations. In sum, our works revealed that these circadian clock genes TIMELESS, CRY2, PER1, and RORA can serve as potential diagnostic and prognostic biomarkers, as well as therapeutic targets, for HCC patients, which may promote HCC chronotherapy by rhythmically regulating drug sensitivity and key cellular signaling pathways.

MGEnrichment: A web application for microglia gene list enrichment analysis.

Gene expression analysis is becoming increasingly utilized in neuro-immunology research, and there is a growing need for non-programming scientists to be able to analyze their own genomic data. MGEnrichment is a web application developed both to disseminate to the community our curated database of microglia-relevant gene lists, and to allow non-programming scientists to easily conduct statistical enrichment analysis on their gene expression data. Users can upload their own gene IDs to assess the relevance of their expression data against gene lists from other studies. We include example datasets of differentially expressed genes (DEGs) from human postmortem brain samples from Autism Spectrum Disorder (ASD) and matched controls. We demonstrate how MGEnrichment can be used to expand the interpretations of these DEG lists in terms of regulation of microglial gene expression and provide novel insights into how ASD DEGs may be implicated specifically in microglial development, microbiome responses and relationships to other neuropsychiatric disorders. This tool will be particularly useful for those working in microglia, autism spectrum disorders, and neuro-immune activation research. MGEnrichment is available at https://ciernialab.shinyapps.io/MGEnrichmentApp/ and further online documentation and datasets can be found at https://github.com/ciernialab/MGEnrichmentApp. The app is released under the GNU GPLv3 open source license.

Optimizing network propagation for multi-omics data integration.

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a 'topology bias' caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

Skeletal muscle methylome and transcriptome integration reveals profound sex differences related to muscle function and substrate metabolism.

Nearly all human complex traits and diseases exhibit some degree of sex differences, with epigenetics being one of the main contributing factors. Various tissues display sex differences in DNA methylation; however, this has not yet been explored in skeletal muscle, despite skeletal muscle being among the tissues with the most transcriptomic sex differences. For the first time, we investigated the effect of sex on autosomal DNA methylation in human skeletal muscle across three independent cohorts (Gene SMART, FUSION, and GSE38291) using a meta-analysis approach, totalling 369 human muscle samples (222 males and 147 females), and integrated this with known sex-biased transcriptomics. We found 10,240 differentially methylated regions (DMRs) at FDR < 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and substrate metabolism. We then investigated biological factors underlying DNA methylation sex differences and found that circulating hormones were not associated with differential methylation at sex-biased DNA methylation loci; however, these sex-specific loci were enriched for binding sites of hormone-related transcription factors (with top TFs including androgen (AR), estrogen (ESR1), and glucocorticoid (NR3C1) receptors). Fibre type proportions were associated with differential methylation across the genome, as well as across 16% of sex-biased DNA methylation loci (FDR < 0.005). Integration of DNA methylomic results with transcriptomic data from the GTEx database and the FUSION cohort revealed 326 autosomal genes that display sex differences at both the epigenome and transcriptome levels. Importantly, transcriptional sex-biased genes were overrepresented among epigenetic sex-biased genes (p value = 4.6e-13), suggesting differential DNA methylation and gene expression between male and female muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in antioxidant metabolism, displays male-biased expression as well as lower methylation in males across the three cohorts. In conclusion, we uncovered 8420 genes that exhibit DNA methylation differences between males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.