BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.
OBJECTIVE: The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.
METHODS: Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.
RESULTS: We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.
CONCLUSIONS: This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.

The Ets domain transcription factors direct diverse biological processes throughout all metazoans and are implicated in development as well as in tumor initiation, progression and metastasis. The Drosophila Ets transcription factor Pointed (Pnt) is the downstream effector of the Epidermal growth factor receptor (Egfr) pathway and is required for cell cycle progression, specification, and differentiation of most cell types in the larval eye disc. Despite its critical role in development, very few targets of Pnt have been reported previously. Here, we employed an integrated approach by combining genome-wide single cell and bulk data to identify putative cell type-specific Pnt targets. First, we used chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) to determine the genome-wide occupancy of Pnt in late larval eye discs. We identified enriched regions that mapped to an average of 6,941 genes, the vast majority of which are novel putative Pnt targets. Next, we integrated ChIP-seq data with two other larval eye single cell genomics datasets (scRNA-seq and snATAC-seq) to reveal 157 putative cell type-specific Pnt targets that may help mediate unique cell type responses upon Egfr-induced differentiation. Finally, our integrated data also predicts cell type-specific functional enhancers that were not reported previously. Together, our study provides a greatly expanded list of putative cell type-specific Pnt targets in the eye and is a resource for future studies that will allow mechanistic insights into complex developmental processes regulated by Egfr signaling.

Mycobacterium tuberculosis, the causative agent of human tuberculosis (hTB), is a close evolutionary relative of Mycobacterium bovis, which causes bovine tuberculosis (bTB), one of the most damaging infectious diseases to livestock agriculture. Previous studies have shown that the pathogenesis of bTB disease is comparable to hTB disease, and that the bovine and human alveolar macrophage (bAM and hAM, respectively) transcriptomes are extensively reprogrammed in response to infection with these intracellular mycobacterial pathogens. In this study, a multi-omics integrative approach was applied with functional genomics and GWAS data sets across the two primary hosts (Bos taurus and Homo sapiens) and both pathogens (M. bovis and M. tuberculosis). Four different experimental infection groups were used: 1) bAM infected with M. bovis, 2) bAM infected with M. tuberculosis, 3) hAM infected with M. tuberculosis, and 4) human monocyte-derived macrophages (hMDM) infected with M. tuberculosis. RNA-seq data from these experiments 24 h post-infection (24 hpi) was analysed using three computational pipelines: 1) differentially expressed genes, 2) differential gene expression interaction networks, and 3) combined pathway analysis. The results were integrated with high-resolution bovine and human GWAS data sets to detect novel quantitative trait loci (QTLs) for resistance to mycobacterial infection and resilience to disease. This revealed common and unique response macrophage pathways for both pathogens and identified 32 genes (12 bovine and 20 human) significantly enriched for SNPs associated with disease resistance, the majority of which encode key components of the NF-κB signalling pathway and that also drive formation of the granuloma.

OBJECTIVE: Nonsyndromic craniosynostosis (nsCS), characterized by premature cranial suture fusion, is considered a primary skull disorder in which impact on neurodevelopment, if present, results from the mechanical hindrance of brain growth. Despite surgical repair of the cranial defect, neurocognitive deficits persist in nearly half of affected children. Therefore, the authors performed a functional genomics analysis of nsCS to determine when, where, and in what cell types nsCS-associated genes converge during development.
METHODS: The authors integrated whole-exome sequencing data from 291 nsCS proband-parent trios with 29,803 single-cell transcriptomes of the prenatal and postnatal neurocranial complex to inform when, where, and in what cell types nsCS-mutated genes might exert their pathophysiological effects.
RESULTS: The authors found that nsCS-mutated genes converged in cranial osteoprogenitors and pial fibroblasts and their transcriptional networks that regulate both skull ossification and cerebral neurogenesis. Nonsyndromic CS-mutated genes also converged in inhibitory neurons and gene coexpression modules that overlapped with autism and other developmental disorders. Ligand-receptor cell-cell communication analysis uncovered crosstalk between suture osteoblasts and neurons via the nsCS-associated BMP, FGF, and noncanonical WNT signaling pathways.
CONCLUSIONS: These data implicate a concurrent impact of nsCS-associated de novo mutations on cranial morphogenesis and cortical development via cell- and non-cell-autonomous mechanisms in a developmental nexus of fetal osteoblasts, pial fibroblasts, and neurons. These results suggest that neurodevelopmental outcomes in nsCS patients may be driven more by mutational status than surgical technique.

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.

Understanding drug resistance in cancer is paramount to improving patient outcomes, quality of life and reducing toxicities in patients receiving chemotherapy. Pharmacogenomic methods seek to understand the interaction of genomic variation and response to chemotherapeutic treatment. This chapter presents a workflow to interrogate multiple genomic inputs and individually assess their relationship with the phenotype of drug resistance using hierarchical clustering to determine the set of features that can best describe what features are associated with drug resistance. Then in a gene-centric manner regulatory features such as miRNAs, SNPs, or DNA methylation can be related back to the differential expression of genes to give understanding to the mechanism underlying resistance. In this chapter, we describe a computational method that can be adapted to a number of different diseases and phenotypes in which there are multiple genomic data types available with concordant phenotypic drug resistance information.

Ewing sarcoma (EwS) is a highly aggressive tumor of bone and soft tissues that mostly affects children and adolescents. The pathognomonic oncofusion EWSR1::FLI1 transcription factor drives EwS by orchestrating an oncogenic transcription program through de novo enhancers. By integrative analysis of thousands of transcriptomes representing pan-cancer cell lines, primary cancers, metastasis, and normal tissues, we identify a 32-gene signature (ESS32 [Ewing Sarcoma Specific 32]) that stratifies EwS from pan-cancer. Among the ESS32, LOXHD1, encoding a stereociliary protein, is the most highly expressed gene through an alternative transcription start site. Deletion or silencing of EWSR1::FLI1 bound upstream de novo enhancer results in loss of the LOXHD1 short isoform, altering EWSR1::FLI1 and HIF1α pathway genes and resulting in decreased proliferation/invasion of EwS cells. These observations implicate LOXHD1 as a biomarker and a determinant of EwS metastasis and suggest new avenues for developing LOXHD1-targeted drugs or cellular therapies for this deadly disease.

UNASSIGNED: Yoga is a multifaceted spiritual tool that helps in maintaining health, peace of mind, and positive thoughts. In the context of asana, yoga is similar to physical exercise. This study aims to construct a molecular network to find hub genes that play important roles in physical exercise and yoga.
UNASSIGNED: We combined differentially expressed genes (DEGs) in yoga and exercise using computational bioinformatics from publicly available gene expression omnibus (GEO) datasets and identified the codifferentially expressed mRNAs with GEO2R. The co-DEGs were divided into four different groups and each group was subjected to protein-protein interaction (PPI) network, pathways analysis, and gene ontology.
UNASSIGNED: Our study identified immunological modulation as a dominant target of differential expression in yoga and exercise. Yoga predominantly modulated genes affecting the Th1 and NK cells, whereas Cytokines, Macrophage activation, and oxidative stress were affected by exercise. We also observed that while yoga regulated genes for two main physiological functions of the body, namely Circadian Rhythm (BHLHE40) and immunity (LBP, T-box transcription factor 21, CEACAM1), exercise-regulated genes involved in apoptosis (BAG3, protein kinase C alpha), angiogenesis, and cellular adhesion (EPH receptor A1).
UNASSIGNED: The dissimilarity in the genetic expression patterns in Yoga and exercise highlights the discrete effect of each in biological systems. The integration and convergences of multi-omics signals can provide deeper and comprehensive insights into the various biological mechanisms through which yoga and exercise exert their beneficial effects and opens up potential newer research areas.

There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine.

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.