Gene co-expression analysis for functional classification and gene-disease predictions

Mesenchymal Stem/Stromal Cells Increase Cardiac miR-187-3p Expression in a Polymicrobial Animal Model of Sepsis

ABSTRACT

Sepsis-induced myocardial dysfunction (MD) is an important pathophysiological feature of multiorgan failure caused by a dysregulated host response to infection. Patients with MD continue to be managed in intensive care units with limited understanding of the molecular mechanisms controlling disease pathogenesis. Emerging evidences support the use of mesenchymal stem/stromal cell (MSC) therapy for treating critically ill septic patients. Combining this with the known role that microRNAs (miRNAs) play in reversing sepsis-induced myocardial-dysfunction, this study sought to investigate how MSC administration alters miRNA expression in the heart. Mice were randomized to experimental polymicrobial sepsis induced by cecal ligation and puncture (CLP) or sham surgery, treated with either MSCs (2.5 × 105) or placebo (saline). Twenty-eight hours post-intervention, RNA was collected from whole hearts for transcriptomic and microRNA profiling. The top microRNAs differentially regulated in hearts by CLP and MSC administration were used to generate a putative mRNA-miRNA interaction network. Key genes, termed hub genes, within the network were then identified and further validated in vivo. Network analysis and RT-qPCR revealed that septic hearts treated with MSCs resulted in upregulation of five miRNAs, including miR-187, and decrease in three top hit putative hub genes (Itpkc, Lrrc59, and Tbl1xr1). Functionally, MSC administration decreased inflammatory and apoptotic pathways, while increasing cardiac-specific structural and functional, gene expression. Taken together, our data suggest that MSC administration regulates host-derived miRNAs production to protect cardiomyocytes from sepsis-induced MD.

Enhancing performance of gene expression value prediction with cluster-based regression

BACKGROUND

The inherent correlations among gene expressions have received attention. Recently, it was reported that a set of approximately 1000 landmark genes can be utilized for prediction of expression of other genes (target genes).

OBJECTIVE

The objective of this study is to predict expression values of target genes based on expression values of landmark genes.

METHODS

A cluster-based regression method is proposed. In the proposed method, clusters are obtained from a set of training instances of a gene and an estimator is obtained per cluster. A test instance is assigned to one of clusters then a regression model corresponding to the cluster predicts expression value.

RESULTS

Performance of the proposed method is measured on the GEO (Gene Expression Omnibus) expression data and the GTEx (Genotype-Tissue Expression) expression data. In terms of mean absolute error averaged across target genes, the proposed method significantly outperforms previous approaches in the case of the GEO expression data.

CONCLUSIONS

The experimental results report that the combination of clustering and regression can outperform the state-of-the art methods such as generative adversarial networks and a gradient boosting based method.

Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

Amoeba Genome Reveals Dominant Host Contribution to Plastid Endosymbiosis

Eukaryotic photosynthetic organelles, plastids, are the powerhouses of many aquatic and terrestrial ecosystems. The canonical plastid in algae and plants originated >1 Ga and therefore offers limited insights into the initial stages of organelle evolution. To address this issue, we focus here on the photosynthetic amoeba Paulinella micropora strain KR01 (hereafter, KR01) that underwent a more recent (∼124 Ma) primary endosymbiosis, resulting in a photosynthetic organelle termed the chromatophore. Analysis of genomic and transcriptomic data resulted in a high-quality draft assembly of size 707 Mb and 32,361 predicted gene models. A total of 291 chromatophore-targeted proteins were predicted in silico, 208 of which comprise the ancestral organelle proteome in photosynthetic Paulinella species with functions, among others, in nucleotide metabolism and oxidative stress response. Gene coexpression analysis identified networks containing known high light stress response genes as well as a variety of genes of unknown function (“dark” genes). We characterized diurnally rhythmic genes in this species and found that over 49% are dark. It was recently hypothesized that large double-stranded DNA viruses may have driven gene transfer to the nucleus in Paulinella and facilitated endosymbiosis. Our analyses do not support this idea, but rather suggest that these viruses in the KR01 and closely related P. micropora MYN1 genomes resulted from a more recent invasion.

Transcriptomic coordination at hepatic steatosis indicates robust immune cell engagement prior to inflammation

BACKGROUND

Deregulation in lipid metabolism leads to the onset of hepatic steatosis while at subsequent stages of disease development, the induction of inflammation, marks the transition of steatosis to non-alcoholic steatohepatitis. While differential gene expression unveils individual genes that are deregulated at different stages of disease development, how the whole transcriptome is deregulated in steatosis remains unclear.

METHODS

Using outbred deer mice fed with high fat as a model, we assessed the correlation of each transcript with every other transcript in the transcriptome. The onset of steatosis in the liver was also evaluated histologically.

RESULTS

Our results indicate that transcriptional reprogramming directing immune cell engagement proceeds robustly, even in the absence of histologically detectable steatosis, following administration of high fat diet. In the liver transcriptomes of animals with steatosis, a preference for the engagement of regulators of T cell activation and myeloid leukocyte differentiation was also recorded as opposed to the steatosis-free livers at which non-specific lymphocytic activation was seen. As compared to controls, in the animals with steatosis, transcriptome was subjected to more widespread reorganization while in the animals without steatosis, reorganization was less extensive. Comparison of the steatosis and non-steatosis livers showed high retention of coordination suggesting that diet supersedes pathology in shaping the transcriptome's profile.

CONCLUSIONS

This highly versatile strategy suggests that the molecular changes inducing inflammation proceed robustly even before any evidence of steatohepatitis is recorded, either histologically or by differential expression analysis.

Application of weighted gene co-expression network analysis to identify the hub genes in H1N1

OBJECTIVE

Identifying the disease-associated interactions between different genes helps us to find novel therapeutic targets and predictive biomarkers.

METHODS

Gene expression data GSE82050 from H1N1 and control human samples were acquired from the NCBI GEO database. Highly co-expressed genes were grouped into modules. Through Person's correlation coefficient calculation between the module and clinical phenotype, notable modules were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted, and the hub genes within the module of interest were identified. Also, gene expression data GSE27131 were acquired from the GEO database to verify differential key gene expression analysis. The CIBERSORT was used to evaluate the immune cells infiltration and the GSVA was performed to identify the differentially regulated pathways in H1N1. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes.

RESULT

The black module was shown to have the highest correlation with the clinical phenotype, mainly functioning in the signaling pathways such as the mitochondrial inner membrane, DNA conformation change, DNA repair, and cell cycle phase transition. Through analysis of the black module, we found 5 genes that were highly correlated with the H1N1 phenotype. The H1N1 project from GSE27131 confirmed an increased expression of these genes.

CONCLUSION

By using the WGCNA we analyzed and predicted the key genes in H1N1. BRCA1, CDC20, MAD2L1, MCM2, and UBE2C were found to be the most relevant genes, which may be therapeutic targets and predictive biomarkers for H1N1 therapy.

Bioinformatics analysis of epigenetic and SNP-related molecular markers in systemic lupus erythematosus

We analyzed gene expression in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE) using public databases. The goal was to identify lupus biomarkers by determining whether differentially expressed genes are mediated by methylation, miRNA, or SNP. Two cDNA microarrays were subjected to integration analysis, and we calculated the mutually differentially expressed genes (|log2fold change (FC)| > 1, P < 0.05). These genes were analyzed using gene otology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. The differences in methylation sites for two methylation chips were calculated and the differentially methylated sites were annotated. These genes were compared to the differentially expressed genes. We obtained 135 differentially expressed microRNAs from the microRNA-chip results using PBMCs from SLE and healthy individuals. Predictive microRNA target genes were identified using GO, KEGG pathways, and PPI networks. The target genes identified were compared to the differentially expressed genes. We downloaded Chinese SLE genome-wide association study data from SLE-related literature, analyzed the loci with a P value < 0.05, and used annotated SLE-associated SNPs. We selected the genes corresponding to an SNP located on an exon and determined the intersection with the differentially expressed genes. We found 18 differentially expressed genes in both cDNA microarrays. The methylation chips had 50 corresponding methylation sites. On the basis of these results, we identified two genes, IFI44 and IFI44L. We further identified 135 differentially expressed microRNAs predicted to affect 5766 target genes. Two identified genes were in common with the differentially expressed genes. Finally, SNP annotated genes and cDNA chip genes overlap with identified MX1. Therefore, we used existing data to analyze the causes of differential gene expression in SLE, introducing new methods for determining biomarkers and therapeutic targets.

Intrauterine growth restriction followed by oxygen support uniquely interferes with genetic regulators of myelination

Intrauterine growth restriction (IUGR) and oxygen exposure in isolation and combination adversely affect the developing brain, putting infants at risk for neurodevelopmental disability including cerebral palsy. Rodent models of IUGR and postnatal hyperoxia have demonstrated oligodendroglial injury with subsequent white matter injury (WMI) and motor dysfunction. Here we investigate transcriptomic dysregulation in IUGR with and without hyperoxia exposure to account for the abnormal brain structure and function previously documented. We performed RNA sequencing and analysis using a mouse model of IUGR and found that IUGR, hyperoxia, and the combination of IUGR with hyperoxia (IUGR/hyperoxia) produced distinct changes in gene expression. IUGR in isolation demonstrated the fewest differentially expressed genes compared to control. In contrast, we detected several gene alterations in IUGR/hyperoxia; genes involved in myelination were strikingly downregulated. We also identified changes to specific regulators including TCF7L2, BDNF, SOX2, and DGCR8, through Ingenuity Pathway Analysis, that may contribute to impaired myelination in IUGR/hyperoxia. Our findings show that IUGR with hyperoxia induces unique transcriptional changes in the developing brain. These indicate mechanisms for increased risk for WMI in IUGR infants exposed to oxygen and suggest potential therapeutic targets to improve motor outcomes.Significance StatementThis study demonstrates that perinatal exposures of IUGR and/or postnatal hyperoxia result in distinct transcriptomic changes in the developing brain. In particular, we found that genes involved in normal developmental myelination, myelin maintenance, and remyelination were most dysregulated when IUGR was combined with hyperoxia. Understanding how multiple risk factors lead to WMI is the first step in developing future therapeutic interventions. Additionally, because oxygen exposure is often unavoidable after birth, an understanding of gene perturbations in this setting will increase our awareness of the need for tight control of oxygen use to minimize future motor disability.

Predicting candidate genes from phenotypes, functions and anatomical site of expression

Motivation

Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.

Results

We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression.

Availability and implementation

Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.

Supplementary information

Supplementary data are available at Bioinformatics online.

Systematic analysis of differentially expressed ZmMYB genes related to drought stress in maize

MYB transcription factors play pivotal roles in hormone conduction signaling and abiotic stress response. In this study, 54 differentially expressed ZmMYB genes were identified and comprehensive analyses were conducted including gene's structure, chromosomal localization, phylogenetic tree, motif prediction, cis-elements and expression patterns. The results showed that 54 genes were unevenly distributed on 10 chromosomes and classified into eleven main subgroups by phylogenetic analysis, supported by motif and exon/intron analyses. The mainly stress-related cis-elements were ABRE, ARE, MBS and DRE-core. In addition, 8 core ZmMYB genes were identified by co-expression network. qRT-PCR results showed that the 8 ZmMYB genes exhibited different expression levels under different abiotic stresses, indicating that they were responsive to various abiotic stress. These results will provide insight for further functional investigation of ZmMYB genes.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01013-2.

Transcriptomic meta-analysis of disuse muscle atrophy vs. resistance exercise-induced hypertrophy in young and older humans

BACKGROUND

Skeletal muscle atrophy manifests across numerous diseases; however, the extent of similarities/differences in causal mechanisms between atrophying conditions in unclear. Ageing and disuse represent two of the most prevalent and costly atrophic conditions, with resistance exercise training (RET) being the most effective lifestyle countermeasure. We employed gene-level and network-level meta-analyses to contrast transcriptomic signatures of disuse and RET, plus young and older RET to establish a consensus on the molecular features of, and therapeutic targets against, muscle atrophy in conditions of high socio-economic relevance.

METHODS

Integrated gene-level and network-level meta-analysis was performed on publicly available microarray data sets generated from young (18-35 years) m. vastus lateralis muscle subjected to disuse (unilateral limb immobilization or bed rest) lasting ≥7 days or RET lasting ≥3 weeks, and resistance-trained older (≥60 years) muscle.

RESULTS

Disuse and RET displayed predominantly separate transcriptional responses, and transcripts altered across conditions were mostly unidirectional. However, disuse and RET induced directly inverted expression profiles for mitochondrial function and translation regulation genes, with COX4I1, ENDOG, GOT2, MRPL12, and NDUFV2, the central hub components of altered mitochondrial networks, and ZMYND11, a hub gene of altered translation regulation. A substantial number of genes (n = 140) up-regulated post-RET in younger muscle were not similarly up-regulated in older muscle, with young muscle displaying a more pronounced extracellular matrix (ECM) and immune/inflammatory gene expression response. Both young and older muscle exhibited similar RET-induced ubiquitination/RNA processing gene signatures with associated PWP1, PSMB1, and RAF1 hub genes.

CONCLUSIONS

Despite limited opposing gene profiles, transcriptional signatures of disuse are not simply the converse of RET. Thus, the mechanisms of unloading cannot be derived from studying muscle loading alone and provides a molecular basis for understanding why RET fails to target all transcriptional features of disuse. Loss of RET-induced ECM mechanotransduction and inflammatory profiles might also contribute to suboptimal ageing muscle adaptations to RET. Disuse and age-dependent molecular candidates further establish a framework for understanding and treating disuse/ageing atrophy.

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND

Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.

RESULTS

Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions.

CONCLUSION

GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results.

Understanding the Molecular Drivers of Disease Heterogeneity in Crohn's Disease Using Multi-omic Data Integration and Network Analysis

Crohn's disease (CD), a form of inflammatory bowel disease (IBD), is characterized by heterogeneity along multiple clinical axes, which in turn impacts disease progression and treatment modalities. Using advanced data integration approaches and systems biology tools, we studied the contribution of CD susceptibility variants and gene expression in distinct peripheral immune cell subsets (CD14+ monocytes and CD4+ T cells) to relevant clinical traits. Our analyses revealed that most clinical traits capturing CD heterogeneity could be associated with CD14+ and CD4+ gene expression rather than disease susceptibility variants. By disentangling the sources of variation, we identified molecular features that could potentially be driving the heterogeneity of various clinical traits of CD patients. Further downstream analyses identified contextual hub proteins such as genes encoding barrier functions, antimicrobial peptides, chemokines, and their receptors, which are either targeted by drugs used in CD or other inflammatory diseases or are relevant to the biological functions implicated in disease pathology. These hubs could be used as cell type-specific targets to treat specific subtypes of CD patients in a more individualized approach based on the underlying biology driving their disease subtypes. Our study highlights the importance of data integration and systems approaches to investigate complex and heterogeneous diseases such as IBD.

Hippocampal CA3 transcriptional modules associated with granule cell alterations and cognitive impairment in refractory mesial temporal lobe epilepsy patients

In about a third of the patients with epilepsy the seizures are not drug-controlled. The current limitation of the antiepileptic drug therapy derives from an insufficient understanding of epilepsy pathophysiology. In order to overcome this situation, it is necessary to consider epilepsy as a disturbed network of interactions, instead of just looking for changes in single molecular components. Here, we studied CA3 transcriptional signatures and dentate gyrus histopathologic alterations in hippocampal explants surgically obtained from 57 RMTLE patients submitted to corticoamygdalohippocampectomy. By adopting a systems biology approach, integrating clinical, histopathological, and transcriptomic data (weighted gene co-expression network analysis), we were able to identify transcriptional modules highly correlated with age of disease onset, cognitive dysfunctions, and granule cell alterations. The enrichment analysis of transcriptional modules and the functional characterization of the highly connected genes in each trait-correlated module allowed us to unveil the modules’ main biological functions, paving the way for further investigations on their roles in RMTLE pathophysiology. Moreover, we found 15 genes with high gene significance values which have the potential to become novel biomarkers and/or therapeutic targets in RMTLE.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

Background

Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.

Results

The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively.

Conclusions

The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07643-w.

Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data

The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.

DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis

Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of variables from different omics datasets and incorporates prior biological information defined by users (such as signaling pathways and tissue networks). Benchmark experiments demonstrate that DeepOmix outperforms the other five cutting-edge prediction methods. Besides, Lower Grade Glioma (LGG) is taken as the case study to perform the prognosis prediction and illustrate the functional module nodes which are associated with the prognostic result in the prediction model.

Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

Identification of VIMP as a gene inhibiting cytokine production in human CD4+ effector T cells

Many players regulating the CD4+ T cell-mediated inflammatory response have already been identified. However, the critical nodes that constitute the regulatory and signaling networks underlying CD4 T cell responses are still missing. Using a correlation-network-guided approach, here we identified VIMP (VCP-interacting membrane protein), one of the 25 genes encoding selenoproteins in humans, as a gene regulating the effector functions of human CD4 T cells, especially production of several cytokines including IL2 and CSF2. We identified VIMP as an endogenous inhibitor of cytokine production in CD4 effector T cells via both the E2F5 transcription regulatory pathway and the Ca2+/NFATC2 signaling pathway. Our work not only indicates that VIMP might be a promising therapeutic target for various inflammation-associated diseases but also shows that our network-guided approach can significantly aid in predicting new functions of the genes of interest.

Resilience, plasticity and robustness in gene expression during aging in the brain of outbred deer mice

BACKGROUND

Genes that belong to the same network are frequently co-expressed, but collectively, how the coordination of the whole transcriptome is perturbed during aging remains unclear. To explore this, we calculated the correlation of each gene in the transcriptome with every other, in the brain of young and older outbred deer mice (P. leucopus and P. maniculatus).

RESULTS

In about 25 % of the genes, coordination was inversed during aging. Gene Ontology analysis in both species, for the genes that exhibited inverse transcriptomic coordination during aging pointed to alterations in the perception of smell, a known impairment occurring during aging. In P. leucopus, alterations in genes related to cholesterol metabolism were also identified. Among the genes that exhibited the most pronounced inversion in their coordination profiles during aging was THBS4, that encodes for thrombospondin-4, a protein that was recently identified as rejuvenation factor in mice. Relatively to its breadth, abolishment of coordination was more prominent in the long-living P. leucopus than in P. maniculatus but in the latter, the intensity of de-coordination was higher.

CONCLUSIONS

There sults suggest that aging is associated with more stringent retention of expression profiles for some genes and more abrupt changes in others, while more subtle but widespread changes in gene expression appear protective. Our findings shed light in the mode of the transcriptional changes occurring in the brain during aging and suggest that strategies aiming to broader but more modest changes in gene expression may be preferrable to correct aging-associated deregulation in gene expression.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava

Robustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.

Pentraxin 3 is a diagnostic and prognostic marker for ovarian epithelial cancer patients based on comprehensive bioinformatics and experiments

BACKGROUND

Ovarian epithelial cancer is one of the leading malignant tumors in gynecology and lacks effective diagnostic and prognostic markers. Our study aims to screen and verify ovarian epithelial cancer biomarkers.

METHODS

GSE18520 and GSE26712 were downloaded from the GEO database. The "limma" and "WGCNA" packages were used to explore hub genes. The Kaplan-Meier Plotter database was used for survival analysis of the hub genes. Immunohistochemical analysis was used to identify the expression level of Pentraxin 3 in ovarian epithelial cancer samples.

RESULTS

In this study, we integrated and analyzed two datasets, GSE18520 and GSE26712, and a total of 238 differentially expressed genes (DEGs) were screened out. Enrichment analysis showed that these DEGs were related to collagen-containing extracellular matrix and other pathways. Further application of WGCNA (weighted gene coexpression network analysis) identified 15 gene modules, with the purple module showing the highest correlation with ovarian epithelial cancer. Twenty-five genes were shared between the purple module and DEGs, 13 genes were related to the prognosis of ovarian epithelial cancer patients, and the PTX3 gene had the highest hazardous risk (HR) value. We performed immunohistochemical analyses on the 255 Pentraxin-3 (PTX3)-based clinical samples. PTX3 was found to be overexpressed in ovarian epithelial cancer and related to the degree of differentiation. The Cox proportional hazard model indicates that high PTX3 expression is an independent risk factor for the prognosis of ovarian epithelial cancer patients.

CONCLUSIONS

In conclusion, through WGCNA and a series of comprehensive bioinformatics analyses, PTX3 was first identified as a novel diagnostic and prognostic biomarker for ovarian epithelial cancer.

Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity

Macrophages are ubiquitous custodians of tissues, which play decisive role in maintaining cellular homeostasis through regulatory immune responses. Within tissues, macrophage exhibit extremely heterogeneous population with varying functions orchestrated through regulatory response, which can be further exacerbated in diverse genetic backgrounds. Gene regulatory networks (GRNs) offer comprehensive understanding of cellular regulatory behavior by unfolding the transcription factors (TFs) and regulated target genes. RNA-Seq coupled with ATAC-Seq has revolutionized the regulome landscape influenced by gene expression modeling. Here, we employ an integrative multi-omics systems biology-based analysis and generated GRNs derived from the unstimulated bone marrow-derived macrophages of five inbred genetically defined murine strains, which are reported to be linked with most of the population-wide human genetic variants. Our probabilistic modeling of a basal hemostasis pan regulatory repertoire in diverse macrophages discovered 96 TFs targeting 6279 genes representing 468,291 interactions across five inbred murine strains. Subsequently, we identify core and distinctive GRN sub-networks in unstimulated macrophages to describe the system-wide conservation and dissimilarities, respectively across five murine strains. Our study concludes that discrepancies in unstimulated macrophage-specific regulatory networks not only drives the basal functional plasticity within genetic backgrounds, additionally aid in understanding the complexity of racial disparity among the human population during stress.

High BLM Expression Predicts Poor Clinical Outcome and Contributes to Malignant Progression in Human Cholangiocarcinoma

Molecular mechanisms underlying the tumorigenesis of a highly malignant cancer, cholangiocarcinoma (CCA), are still obscure. In our study, the CCA expression profile data were acquired from The Cancer Genome Atlas (TCGA) database, and differentially expressed genes (DEGs) in the TCGA-Cholangiocarcinoma (TCGA-CHOL) data set were utilized to construct a co-expression network via weighted gene co-expression network analysis (WGCNA). The blue gene module associated with the histopathologic grade of CCA was screened. Then, five candidate hub genes were screened by combining the co-expression network with protein–protein interaction (PPI) network. After progression and survival analyses, bloom syndrome helicase (BLM) was ultimately identified as a real hub gene. Moreover, the receiver operating characteristic (ROC) curve analysis suggested that BLM had a favorable diagnostic and predictive recurrence value for CCA. The gene set enrichment analysis (GSEA) results for a single hub gene revealed the importance of cell cycle-related pathways in the CCA progression and prognosis. Furthermore, we detected the BLM expression in vitro, and the results demonstrated that the expression level of BLM was much higher in the CCA tissues and cells relative to adjacent non-tumor samples and normal bile duct epithelial cells. Additionally, after further silencing the BLM expression by small interfering RNA (siRNA), the proliferation and migration ability of CCA cells were all inhibited, and the cell cycle was arrested. Altogether, a real hub gene (BLM) and cell cycle-related pathways were identified in the present study, and the gene BLM may be involved in the CCA progression and could act as a reliable biomarker for potential diagnosis and prognostic evaluation.

Sequential progenitor states mark the generation of pancreatic endocrine lineages in mice and humans

The pancreatic islet contains multiple hormone⁺ endocrine lineages (α, β, δ, PP and ε cells), but the developmental processes that underlie endocrinogenesis are poorly understood. Here, we generated novel mouse lines and combined them with various genetic tools to enrich all types of hormone⁺ cells for well-based deep single-cell RNA sequencing (scRNA-seq), and gene coexpression networks were extracted from the generated data for the optimization of high-throughput droplet-based scRNA-seq analyses. These analyses defined an entire endocrinogenesis pathway in which different states of endocrine progenitor (EP) cells sequentially differentiate into specific endocrine lineages in mice. Subpopulations of the EP cells at the final stage (EP4^early and EP4^late) show different potentials for distinct endocrine lineages. ε cells and an intermediate cell population were identified as distinct progenitors that independently generate both α and PP cells. Single-cell analyses were also performed to delineate the human pancreatic endocrinogenesis process. Although the developmental trajectory of pancreatic lineages is generally conserved between humans and mice, clear interspecies differences, including differences in the proportions of cell types and the regulatory networks associated with the differentiation of specific lineages, have been detected. Our findings support a model in which sequential transient progenitor cell states determine the differentiation of multiple cell lineages and provide a blueprint for directing the generation of pancreatic islets in vitro.

Loss of Long Distance Co-Expression in Lung Cancer

Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.

Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

BACKGROUND

Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.

METHODS

Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/ ) was used for gene interaction relationship mapping.

RESULTS

We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway.

CONCLUSION

Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

The evolutionary conserved iron-sulfur protein TCR controls P700 oxidation in photosystem I

In natural habitats, plants have developed sophisticated regulatory mechanisms to optimize the photosynthetic electron transfer rate at the maximum efficiency and cope with the changing environments. Maintaining proper P700 oxidation at photosystem I (PSI) is the common denominator for most regulatory processes of photosynthetic electron transfers. However, the molecular complexes and cofactors involved in these processes and their function(s) have not been fully clarified. Here, we identified a redox-active chloroplast protein, the triplet-cysteine repeat protein (TCR). TCR shared similar expression profiles with known photosynthetic regulators and contained two triplet-cysteine motifs (CxxxCxxxC). Biochemical analysis indicated that TCR localizes in chloroplasts and has a [3Fe-4S] cluster. Loss of TCR limited the electron sink downstream of PSI during dark-to-light transition. Arabidopsis pgr5-tcr double mutant reduced growth significantly and showed unusual oxidation and reduction of plastoquinone pool. These results indicated that TCR is involved in electron flow(s) downstream of PSI, contributing to P700 oxidation.

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P700 oxidation at photosystem I is important for regulation of photosynthesis

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TCR is a redox active chloroplast protein harboring a 3Fe-4S iron-sulfur cluster

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TCR controls electron flow around photosystem I, contributing to P700 oxidation

High-pressure processing-induced transcriptome response during recovery of Listeria monocytogenes

Background

High-pressure processing (HPP) is a commonly used technique in the food industry to inactivate pathogens, including L. monocytogenes. It has been shown that L. monocytogenes is able to recover from HPP injuries and can start to grow again during long-term cold storage. To date, the gene expression profiling of L. monocytogenes during HPP damage recovery at cooling temperature has not been studied. In order identify key genes that play a role in recovery of the damage caused by HPP treatment, we performed RNA-sequencing (RNA-seq) for two L. monocytogenes strains (barotolerant RO15 and barosensitive ScottA) at nine selected time points (up to 48 h) after treatment with two pressure levels (200 and 400 MPa).

Results

The results showed that a general stress response was activated by SigB after HPP treatment. In addition, the phosphotransferase system (PTS; mostly fructose-, mannose-, galactitol-, cellobiose-, and ascorbate-specific PTS systems), protein folding, and cobalamin biosynthesis were the most upregulated genes during HPP damage recovery. We observed that cell-division-related genes (divIC, dicIVA, ftsE, and ftsX) were downregulated. By contrast, peptidoglycan-synthesis genes (murG, murC, and pbp2A) were upregulated. This indicates that cell-wall repair occurs as a part of HPP damage recovery. We also observed that prophage genes, including anti-CRISPR genes, were induced by HPP. Interestingly, a large amount of RNA-seq data (up to 85%) was mapped to Rli47, which is a non-coding RNA that is upregulated after HPP. Thus, we predicted that Rli47 plays a role in HPP damage recovery in L. monocytogenes. Moreover, gene-deletion experiments showed that amongst peptidoglycan biosynthesis genes, pbp2A mutants are more sensitive to HPP.

Conclusions

We identified several genes and mechanisms that may play a role in recovery from HPP damage of L. monocytogenes. Our study contributes to new information on pathogen inactivation by HPP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07407-6.

Gene Size Matters: An Analysis of Gene Length in the Human Genome

While it is expected for gene length to be associated with factors such as intron number and evolutionary conservation, we are yet to understand the connections between gene length and function in the human genome. In this study, we show that, as expected, there is a strong positive correlation between gene length, transcript length, and protein size as well as a correlation with the number of genetic variants and introns. Among tissue-specific genes, we find that the longest transcripts tend to be expressed in the blood vessels, nerves, thyroid, cervix uteri, and the brain, while the smallest transcripts tend to be expressed in the pancreas, skin, stomach, vagina, and testis. We report, as shown previously, that natural selection suppresses changes for genes with longer transcripts and promotes changes for genes with smaller transcripts. We also observe that genes with longer transcripts tend to have a higher number of co-expressed genes and protein-protein interactions, as well as more associated publications. In the functional analysis, we show that bigger transcripts are often associated with neuronal development, while smaller transcripts tend to play roles in skin development and in the immune system. Furthermore, pathways related to cancer, neurons, and heart diseases tend to have genes with longer transcripts, with smaller transcripts being present in pathways related to immune responses and neurodegenerative diseases. Based on our results, we hypothesize that longer genes tend to be associated with functions that are important in the early development stages, while smaller genes tend to play a role in functions that are important throughout the whole life, like the immune system, which requires fast responses.

Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.

Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma

CD4⁺ T helper (T_H) cells and regulatory T (T_reg) cells that respond to common allergens play an important role in driving and dampening airway inflammation in patients with asthma. Until recently, direct, unbiased molecular analysis of allergen-reactive T_H and T_reg cells has not been possible. To better understand the diversity of these T cell subsets in allergy and asthma, we analyzed the single-cell transcriptome of ~50,000 house dust mite (HDM) allergen-reactive T_H cells and T_reg cells from asthmatics with HDM allergy and from three control groups: asthmatics without HDM allergy and nonasthmatics with and without HDM allergy. Our analyses show that HDM allergen-reactive T_H and T_reg cells are highly heterogeneous and certain subsets are quantitatively and qualitatively different in individuals with HDM-reactive asthma. The number of interleukin-9 (IL-9)-expressing HDM-reactive T_H cells is greater in asthmatics with HDM allergy compared with nonasthmatics with HDM allergy, and this IL-9-expressing T_H subset displays enhanced pathogenic properties. More HDM-reactive T_H and T_reg cells expressing the interferon response signature (T_HIFNR and T_regIFNR) are present in asthmatics without HDM allergy compared with those with HDM allergy. In cells from these subsets (T_HIFNR and T_regIFNR), expression of TNFSF10 was enriched; its product, tumor necrosis factor-related apoptosis-inducing ligand, dampens activation of T_H cells. These findings suggest that the T_HIFNR and T_regIFNR subsets may dampen allergic responses, which may help explain why only some people develop T_H2 responses to nearly ubiquitous allergens.

Interleukin-1 receptor-associated kinase 4 as a potential biomarker: Overexpression predicts poor prognosis in patients with glioma

The undetectable onset of glioma and the difficulty of surgery lead to a poor prognosis. Appropriate biomarkers for diagnosis and treatment need to be identified. Interleukin-1 receptor-associated kinase 4 (IRAK4) is involved in the initiation and progression of cancer. However, up until now, no report has revealed the relationship between IRAK4 and glioma. The present study aimed to examine the expression of IRAK4 in glioma, and to determine if there was a relationship between IRAK4 expression and clinical outcomes or survival prognosis. Thousands of glioma tissue samples and corresponding clinical information were obtained from various databases. Then a series of bioinformatics methods were used to reveal the role of IRAK4 in glioma. Finally, reverse transcription-quantitative PCR technology was used to verify the bioinformatics results. The study found that the expression of IRAK4 was significantly increased in glioma compared with the control brain tissue samples, and IRAK4, as an independent prognostic factor, shortened the overall survival time of patients with glioma. Gene Set Enrichment Analysis showed that IRAK4 promoted the activation of cell signalling pathways, such as NOD-like and Toll-like receptor signalling pathways. Co-expression analysis showed that the expression of IRAK4 was correlated with CMTM6, MOB1A and other genes. The present study demonstrated the role of IRAK4 as an oncogene in the pathological process of glioma for the first time, and highlights the potential of IRAK4 as a biomarker for prognostic evaluation and treatment of glioma.

A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-related genes through type-II fuzzy data fusion

One of the effective missions of biology and medical science is to find disease-related genes. Recent research uses gene/protein networks to find such genes. Due to false positive interactions in these networks, the results often are not accurate and reliable. Integrating multiple gene/protein networks could overcome this drawback, causing a network with fewer false positive interactions. The integration method plays a crucial role in the quality of the constructed network. In this paper, we integrate several sources to build a reliable heterogeneous network, i.e., a network that includes nodes of different types. Due to the different gene/protein sources, four gene-gene similarity networks are constructed first and integrated by applying the type-II fuzzy voter scheme. The resulting gene-gene network is linked to a disease-disease similarity network (as the outcome of integrating four sources) through a two-part disease-gene network. We propose a novel algorithm, namely random walk with restart on the heterogeneous network method with fuzzy fusion (RWRHN-FF). Through running RWRHN-FF over the heterogeneous network, disease-related genes are determined. Experimental results using the leave-one-out cross-validation indicate that RWRHN-FF outperforms existing methods. The proposed algorithm can be applied to find new genes for prostate, breast, gastric, and colon cancers. Since the RWRHN-FF algorithm converges slowly on large heterogeneous networks, we propose a parallel implementation of the RWRHN-FF algorithm on the Apache Spark platform for high-throughput and reliable network inference. Experiments run on heterogeneous networks of different sizes indicate faster convergence compared to other non-distributed modes of implementation.

Cerebrum, liver, and muscle regulatory networks uncover maternal nutrition effects in developmental programming of beef cattle during early pregnancy

The molecular basis underlying fetal programming in response to maternal nutrition remains unclear. Herein, we investigated the regulatory relationships between genes in fetal cerebrum, liver, and muscle tissues to shed light on the putative mechanisms that underlie the effects of early maternal nutrient restriction on bovine developmental programming. To this end, cerebrum, liver, and muscle gene expression were measured with RNA-Seq in 14 fetuses collected on day 50 of gestation from dams fed a diet initiated at breeding to either achieve 60% (RES, n = 7) or 100% (CON, n = 7) of energy requirements. To build a tissue-to-tissue gene network, we prioritized tissue-specific genes, transcription factors, and differentially expressed genes. Furthermore, we built condition-specific networks to identify differentially co-expressed or connected genes. Nutrient restriction led to differential tissue regulation between the treatments. Myogenic factors differentially regulated by ZBTB33 and ZNF131 may negatively affect myogenesis. Additionally, nutrient-sensing pathways, such as mTOR and PI3K/Akt, were affected by gene expression changes in response to nutrient restriction. By unveiling the network properties, we identified major regulators driving gene expression. However, further research is still needed to determine the impact of early maternal nutrition and strategic supplementation on pre- and post-natal performance.

The role of gene to gene interaction in the breast's genomic signature of pregnancy

Full-term pregnancy at an early age confers long-term protection against breast cancer. Published data shows a specific transcriptomic profile controlling chromatin remodeling that could play a relevant role in the pregnancy-induced protection. This process of chromatin remodeling, induced by the breast differentiation caused by the first full-term pregnancy, has mainly been measured by the expression level of genes individually considered. However, genes equally expressed during the process of chromatin remodeling may behave differently in their interaction with other genes. These changes at the gene cluster level could constitute an additional dimension of chromatin remodeling and therefore of the pregnancy-induced protection. In this research, we apply Information and Graph Theories, Differential Co-expression Network Analysis, and Multiple Regression Analysis, specially designed to examine structural and informational aspects of data sets, to analyze this question. Our findings demonstrate that, independently of the changes in the gene expression at the individual level, there are significant changes in gene-gene interactions and gene cluster behaviors. These changes indicate that the parous breast, through the process of early full-term pregnancy, generates more modules in the networks, with higher density, and a genomic structure performing additional and more complex functions than those found in the nulliparous breast.

Identification of Key Pathways and Genes Related to the Development of Hair Follicle Cycle in Cashmere Goats

The development of hair follicle in cashmere goats shows significant periodic change, as with mice and humans. However, for cashmere goat with double-coat, the periodic change may be due to other regulatory molecules and signal pathways. To understand the mechanism of periodic development of hair follicle, we performed a weighted gene coexpression network analysis (WGCNA) to mine key genes and establish an interaction network by utilizing the NCBI public dataset. Ten coexpression modules, including 7689 protein-coding genes, were constructed by WGCNA, six of which are considered to be significantly related to the development of the hair follicle cycle. A functional enrichment analysis for each model showed that they are closely related to ECM- receptor interaction, focal adhesion, PI3K-Akt signaling pathway, estrogen signaling pathway, and so on. Combined with the analysis of differential expressed genes, 12 hub genes from coexpression modules were selected as candidate markers, i.e., COL1A1, C1QTNF6, COL1A2, AQP3, KRTAP3-1, KRTAP11-1, FA2H, NDUFS5, DERL2, MRPL14, ANTKMT and XAB2, which might be applied to improve cashmere production.

Gene co-expression network analysis of Trypanosoma brucei in tsetse fly vector

Background

Trypanosoma brucei species are motile protozoan parasites that are cyclically transmitted by tsetse fly (genus Glossina) causing human sleeping sickness and nagana in livestock in sub-Saharan Africa. African trypanosomes display digenetic life cycle stages in the tsetse fly vector and in their mammalian host. Experimental work on insect-stage trypanosomes is challenging because of the difficulty in setting up successful in vitro cultures. Therefore, there is limited knowledge on the trypanosome biology during its development in the tsetse fly. Consequently, this limits the development of new strategies for blocking parasite transmission in the tsetse fly.

Methods

In this study, RNA-Seq data of insect-stage trypanosomes were used to construct a T. brucei gene co-expression network using the weighted gene co-expression analysis (WGCNA) method. The study identified significant enriched modules for genes that play key roles during the parasite’s development in tsetse fly. Furthermore, potential 3′ untranslated region (UTR) regulatory elements for genes that clustered in the same module were identified using the Finding Informative Regulatory Elements (FIRE) tool.

Results

A fraction of gene modules (12 out of 27 modules) in the constructed network were found to be enriched in functional roles associated with the cell division, protein biosynthesis, mitochondrion, and cell surface. Additionally, 12 hub genes encoding proteins such as RNA-binding protein 6 (RBP6), arginine kinase 1 (AK1), brucei alanine-rich protein (BARP), among others, were identified for the 12 significantly enriched gene modules. In addition, the potential regulatory elements located in the 3′ untranslated regions of genes within the same module were predicted.

Conclusions

The constructed gene co-expression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. This will enhance understanding of the molecular mechanisms that underlie important biological processes during parasite’s development in tsetse fly. Ultimately, these findings will be key in the identification of potential molecular targets for disease control.

Navigating differential structures in complex networks

Structural changes in a network representation of a system, due to different experimental conditions, different connectivity across layers, or to its time evolution, can provide insight on its organization, function, and on how it responds to external perturbations. The deeper understanding of how gene networks cope with diseases and treatments is maybe the most incisive demonstration of the gains obtained through this differential network analysis point of view, which led to an explosion of new numeric techniques in the last decade. However, where to focus one's attention, or how to navigate through the differential structures in the context of large networks, can be overwhelming even for a few experimental conditions. In this paper, we propose a theory and a methodological implementation for the characterization of shared "structural roles" of nodes simultaneously within and between networks. Inspired by recent methodological advances in chaotic phase synchronization analysis, we show how the information about the shared structures of a set of networks can be split and organized in an automatic fashion, in scenarios with very different (i) community sizes, (ii) total number of communities, and (iii) even for a large number of 100 networks compared using numerical benchmarks generated by a stochastic block model. Then, we investigate how the network size, number of networks, and mean size of communities influence the method performance in a series of Monte Carlo experiments. To illustrate its potential use in a more challenging scenario with real-world data, we show evidence that the method can still split and organize the structural information of a set of four gene coexpression networks obtained from two cell types × two treatments (interferon-β stimulated or control). Aside from its potential use as for automatic feature extraction and preprocessing tool, we discuss that another strength of the method is its "story-telling"-like characterization of the information encoded in a set of networks, which can be used to pinpoint unexpected shared structure, leading to further investigations and providing new insights. Finally, the method is flexible to address different research-field-specific questions, by not restricting what scientific-meaningful characteristic (or relevant feature) of a node shall be used.

An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.

CCDC167 as a potential therapeutic target and regulator of cell cycle-related networks in breast cancer

According to cancer statistics reported in 2020, breast cancer constitutes 30% of new cancer cases diagnosed in American women. Histological markers of breast cancer are expressions of the estrogen receptor (ER), the progesterone receptor (PR), and human epidermal growth factor receptor (HER)-2. Up to 80% of breast cancers are grouped as ER-positive, which implies a crucial role for estrogen in breast cancer development. Therefore, identifying potential therapeutic targets and investigating their downstream pathways and networks are extremely important for drug development in these patients. Through high-throughput technology and bioinformatics screening, we revealed that coiled-coil domain-containing protein 167 (CCDC167) was upregulated in different types of tumors; however, the role of CCDC167 in the development of breast cancer still remains unclear. Integrating many kinds of databases including ONCOMINE, MetaCore, IPA, and Kaplan-Meier Plotter, we found that high expression levels of CCDC167 predicted poor prognoses of breast cancer patients. Knockdown of CCDC167 attenuated aggressive breast cancer growth and proliferation. We also demonstrated that treatment with fluorouracil, carboplatin, paclitaxel, and doxorubicin resulted in decreased expression of CCDC167 and suppressed growth of MCF-7 cells. Collectively, these findings suggest that CCDC167 has high potential as a therapeutic target for breast cancer.

GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Molecular Analysis of 14-3-3 Genes in Citrus sinensis and Their Responses to Different Stresses

14-3-3 proteins (14-3-3s) are among the most important phosphorylated molecules playing crucial roles in regulating plant development and defense responses to environmental constraints. No report thus far has documented the gene family of 14-3-3s in Citrus sinensis and their roles in response to stresses. In this study, nine 14-3-3 genes, designated as CitGF14s (CitGF14a through CitGF14i) were identified from the latest C. sinensis genome. Phylogenetic analysis classified them into ε-like and non-ε groups, which were supported by gene structure analysis. The nine CitGF14s were located on five chromosomes, and none had duplication. Publicly available RNA-Seq raw data and microarray databases were mined for 14-3-3 expression profiles in different organs of citrus and in response to biotic and abiotic stresses. RT-qPCR was used for further examining spatial expression patterns of CitGF14s in citrus and their temporal expressions in one-year-old C. sinensis “Xuegan” plants after being exposed to different biotic and abiotic stresses. The nine CitGF14s were expressed in eight different organs with some isoforms displayed tissue-specific expression patterns. Six of the CitGF14s positively responded to citrus canker infection (Xanthomonas axonopodis pv. citri). The CitGF14s showed expressional divergence after phytohormone application and abiotic stress treatments, suggesting that 14-3-3 proteins are ubiquitous regulators in C. sinensis. Using the yeast two-hybrid assay, CitGF14a, b, c, d, g, and h were found to interact with CitGF14i proteins to form a heterodimer, while CitGF14i interacted with itself to form a homodimer. Further analysis of CitGF14s co-expression and potential interactors established a 14-3-3s protein interaction network. The established network identified 14-3-3 genes and several candidate clients which may play an important role in developmental regulation and stress responses in this important fruit crop. This is the first study of 14-3-3s in citrus, and the established network may help further investigation of the roles of 14-3-3s in response to abiotic and biotic constraints.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Transcriptomic time-series analysis of cold- and heat-shock response in psychrotrophic lactic acid bacteria

BACKGROUND

Psychrotrophic lactic acid bacteria (LAB) species are the dominant species in the microbiota of cold-stored modified-atmosphere-packaged food products and are the main cause of food spoilage. Despite the importance of psychrotrophic LAB, their response to cold or heat has not been studied. Here, we studied the transcriptome-level cold- and heat-shock response of spoilage lactic acid bacteria with time-series RNA-seq for Le. gelidum, Lc. piscium, and P. oligofermentans at 0 °C, 4 °C, 14 °C, 25 °C, and 28 °C.

RESULTS

We observed that the cold-shock protein A (cspA) gene was the main cold-shock protein gene in all three species. Our results indicated that DEAD-box RNA helicase genes (cshA, cshB) also play a critical role in cold-shock response in psychrotrophic LAB. In addition, several RNase genes were involved in cold-shock response in Lc. piscium and P. oligofermentans. Moreover, gene network inference analysis provided candidate genes involved in cold-shock response. Ribosomal proteins, tRNA modification, rRNA modification, and ABC and efflux MFS transporter genes clustered with cold-shock response genes in all three species, indicating that these genes could be part of the cold-shock response machinery. Heat-shock treatment caused upregulation of Clp protease and chaperone genes in all three species. We identified transcription binding site motifs for heat-shock response genes in Le. gelidum and Lc. piscium. Finally, we showed that food spoilage-related genes were upregulated at cold temperatures.

CONCLUSIONS

The results of this study provide new insights on the cold- and heat-shock response of psychrotrophic LAB. In addition, candidate genes involved in cold- and heat-shock response predicted using gene network inference analysis could be used as targets for future studies.

Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Co-expression Networks in Predicting Transcriptional Gene Regulation

Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.