Identification of Hub Genes and Key Pathways Associated With Bipolar Disorder Based on Weighted Gene Co-expression Network Analysis

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database

Bipolar disorder is a mood illness that affects many people. It has a high recurrence frequency and will cause significant damage to the patient's social function. At present, the pathogenesis of BD is not clear. The National Center for Biotechnology Information (NCBI) established and maintained the Gene Expression Omnibus (GEO) database, a gene expression database. For bioinformatics analysis, researchers can obtain expression data from the internet. At present, the samples of the dataset used in the research of BD are mostly from brain tissue, and the data containing blood samples are rarely used. GEO databases (GSE46416, GSE5388, and GSE5389) were used to retrieve public data, and utilizing the online tool GEO2R, differentially expressed genes (DEGs) were retrieved. The common DEGs between the samples of patients with BD and the samples of the normal population were screened by Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to perform functional annotation and pathway enrichment analysis of DEGs. A protein-protein interaction network (PPI) was built to investigate hub genes on this basis. There were 117 up-regulated DEGs and 38 down-regulated DEGs discovered, with two hub genes [SRC, CDKN1A] among the up-regulated DEGs. These two hub genes were also highly enriched in the oxytocin signaling pathway, proteoglycans in cancer and bladder cancer, according to KEGG analysis. The results of the receiver operating characteristic curve (ROC) of SRC and CDKN1A in the three datasets strongly suggested that SRC and CDKN1A were potential diagnostic markers of BD. The results strongly suggest that SRC and CDKN1A are related to the pathogenesis of BD.

Integrative analysis of the methylome and transcriptome of tomato fruit (Solanum lycopersicum L.) induced by postharvest handling

Tomato fruit ripening is triggered by the demethylation of key genes, which alters their transcriptional levels thereby initiating and propagating a cascade of physiological events. What is unknown is how these processes are altered when fruit are ripened using postharvest practices to extend shelf-life, as these practices often reduce fruit quality. To address this, postharvest handling-induced changes in the fruit DNA methylome and transcriptome, and how they correlate with ripening speed, and ripening indicators such as ethylene, abscisic acid, and carotenoids, were assessed. This study comprehensively connected changes in physiological events with dynamic molecular changes. Ripening fruit that reached 'Turning' (T) after dark storage at 20°C, 12.5°C, or 5°C chilling (followed by 20°C rewarming) were compared to fresh-harvest fruit 'FHT'. Fruit stored at 12.5°C had the biggest epigenetic marks and alterations in gene expression, exceeding changes induced by postharvest chilling. Fruit physiological and chronological age were uncoupled at 12.5°C, as the time-to-ripening was the longest. Fruit ripening to Turning at 12.5°C was not climacteric; there was no respiratory or ethylene burst, rather, fruit were high in abscisic acid. Clear differentiation between postharvest-ripened and 'FHT' was evident in the methylome and transcriptome. Higher expression of photosynthetic genes and chlorophyll levels in 'FHT' fruit pointed to light as influencing the molecular changes in fruit ripening. Finally, correlative analyses of the -omics data putatively identified genes regulated by DNA methylation. Collectively, these data improve our interpretation of how tomato fruit ripening patterns are altered by postharvest practices, and long-term are expected to help improve fruit quality.

Blueprint of differentially expressed genes reveals the dynamic gene expression landscape and the gender biases in long COVID

BACKGROUND

Long COVID has appeared as a significant global health issue and is an extra burden to the healthcare system. It affects a considerable number of people throughout the globe. However, substantial research gaps have been noted in understanding the mechanism and genomic landscape during the long COVID infection. A study has aimed to identify the differentially expressed genes (DEGs) in long COVID patients to fill the gap.

METHODS

We used the RNA-seq GEO dataset acquired through the GPL20301 Illumina HiSeq 4000 platform. The dataset contains 36 human samples derived from PBMC (Peripheral blood mononuclear cells). Thirty-six human samples contain 13 non-long COVID individuals' samples and 23 long COVID individuals' samples, considered the first direction analysis. Here, we performed two-direction analyses. In the second direction analysis, we divided the dataset gender-wise into four groups: the non-long COVID male group, the long COVID male group, the non-long COVID female group, and the long COVID female group.

RESULTS

In the first analysis, we found no gene expression. In the second analysis, we identified 250 DEGs. During the DEG profile analysis of the non-long COVID male group and the long COVID male group, we found three upregulated genes: IGHG2, IGHG4, and MIR8071-2. Similarly, the analysis of the non-long COVID female group and the long COVID female group reveals eight top-ranking genes. It also indicates the gender biases of differentially expressed genes among long COVID individuals. We found several DEGs involved in PPI and co-expression network formation. Similarly, cluster enrichment and gene list enrichment analysis were performed, suggesting several genes are involved in different biological pathways or processes.

CONCLUSIONS

This study will help better understand the gene expression landscape in long COVID. However, it might help the discovery and development of therapeutics for long COVID.

Brain and blood transcriptome profiles delineate common genetic pathways across suicidal ideation and suicide

Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to the severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with the presence of suicidal ideation were found within 18 co-expressed modules (p < 0.05), as well as in 3 co-expressed modules associated with suicidal ideation severity (p < 0.05, not explained by severity of depression). Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified and investigated using RNA-seq data from postmortem brain that revealed gene expression differences with moderate effect sizes in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity are associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.

Bioinformatics and system biology approaches to determine the connection of SARS-CoV-2 infection and intrahepatic cholangiocarcinoma

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide, which poses a severe threat to human health. COVID-19 is a systemic ailment affecting various tissues and organs, including the lungs and liver. Intrahepatic cholangiocarcinoma (ICC) is one of the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. Nonetheless, few studies have investigated the impact of COVID-19 on ICC patients.

METHODS

With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC, and searched for potential therapeutic drugs.

RESULTS

This study identified a total of 70 common differentially expressed genes (DEGs) shared by both diseases, shedding light on their shared functionalities. Enrichment analysis pinpointed metabolism and immunity as the primary areas influenced by these common genes. Subsequently, through protein-protein interaction (PPI) network analysis, we identified SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1 as hub genes. Additionally, 44 transcription factors (TFs) and 112 microRNAs (miRNAs) were forecasted to regulate the hub genes. Most importantly, several drug candidates (Periodate-oxidized adenosine, Desipramine, Quercetin, Perfluoroheptanoic acid, Tetrandrine, Pentadecafluorooctanoic acid, Benzo[a]pyrene, SARIN, Dorzolamide, 8-Bromo-cAMP) may prove effective in treating ICC and COVID-19.

CONCLUSION

This study is expected to provide valuable references and potential drugs for future research and treatment of COVID-19 and ICC.

Upregulated Genes in Atrial Fibrillation Blood and the Left Atrium

INTRODUCTION

Atrial fibrillation (AF) is a common arrhythmia associated with aging. Many known risk factors are associated with AF, but many senior individuals do not develop AF despite having multiple risk factors. This finding suggests that other factors may be involved in AF onset. This study aimed to identify upregulated genes in the peripheral blood and left atrium of patients with AF. These genes may serve as potential biomarkers to predict AF onset risk and its complications.

METHODS

Gene expression data were analyzed from blood (n = 3) and left atrial samples (n = 15) of patients with AF and sinus rhythm. We evaluated the significant genes identified using p value analysis of weighted average difference to confirm their rankings. We created figures for the genes using GeneMANIA and performed a functional analysis using Cytoscape3.10.1. Hub and bottleneck genes were identified based on degree and betweenness centrality. We used reference expression (RefEx) to confirm the organs in which the extracted genes were expressed. Heatmaps and Gene ontology term evaluation were performed to further elucidate the biological functions of the genes.

RESULTS

We identified 12 upregulated genes (CAST, ASAH1, MAFB, VCAN, DDIT4, FTL, HEXB, PROS1, BNIP3L, PABPC1, YBX3, and S100A6) in both the blood and left atrium of patients with AF. We analyzed the gene functions using GeneMANIA and Cytoscape. The identified genes were involved in a variety of pathways, including lysosomal function and lipid and sphingolipid catabolism. Next, we investigated whether the 12 identified genes identified were systemically expressed or had high organ specificity. Finally, RefEx was used to analyze the gene expression levels in various tissues. Four genes, FTL, ASAH1, S100A6, and PABPC1, were highly expressed in the normal heart tissue. Finally, we evaluated the expression levels of the 12 genes in the blood of patients with AF using a heatmap. Our findings suggest that the 12 genes identified in this study, especially the lysosome-related genes (FTL and ASAH1), may be involved in AF pathogenesis.

CONCLUSION

Lysosome-related genes may be important to understand the AF pathophysiology and to develop AF-related future studies.

Weighted gene co-expression network analysis reveals immune evasion related genes in Echinococcus granulosus sensu stricto

Cystic echinococcosis (CE) is a zoonotic disease caused by the tapeworm Echinococcus granulosus sensu lato (s.l). In the intermediate host, this disease is characterized by the growth of cysts in viscera such as liver and lungs, inside of which the parasite develops to the next infective stage known as protoscoleces. There are records that the infected viscera affect the development and morphology of E. granulosus s.l. protoscolex in hosts such as buffalo or humans. However, the molecular mechanisms that drive these differences remains unknown. Weighted gene co-expression network analysis (WGCNA) using a set of RNAseq data obtained from E. granulosus sensu stricto (s.s.) protoscoleces found in liver and lung cysts reveals 34 modules in protoscoleces of liver origin, of which 12 have differential co-expression from protoscoleces of lung origin. Three of these twelve modules contain hub genes related to immune evasion: tegument antigen, tegumental protein, ubiquitin hydrolase isozyme L3, COP9 signalosome complex subunit 3, tetraspanin CD9 antigen, and the methyl-CpG-binding protein Mbd2. Also, two of the twelve modules contain only hypothetical proteins with unknown orthology, which means that there are a group of unknown function proteins co-expressed inside the protoscolex of liver CE cyst origin. This is the first evidence of gene expression differences in protoscoleces from CE cysts found in different viscera, with co-expression networks that are exclusive to protoscoleces from liver CE cyst samples. This should be considered in the control strategies of CE, as intermediate hosts can harbor CE cysts in liver, lungs, or both organs simultaneously.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Transcriptomic profiling of early synucleinopathy in rats induced with preformed fibrils

Examination of early phases of synucleinopathy when inclusions are present, but long before neurodegeneration occurs, is critical to both understanding disease progression and the development of disease modifying therapies. The rat alpha-synuclein (α-syn) preformed fibril (PFF) model induces synchronized synucleinopathy that recapitulates the pathological features of Parkinson's disease (PD) and can be used to study synucleinopathy progression. In this model, phosphorylated α-syn (pSyn) inclusion-containing neurons and reactive microglia (major histocompatibility complex-II immunoreactive) peak in the substantia nigra pars compacta (SNpc) months before appreciable neurodegeneration. However, it remains unclear which specific genes are driving these phenotypic changes. To identify transcriptional changes associated with early synucleinopathy, we used laser capture microdissection of the SNpc paired with RNA sequencing (RNASeq). Precision collection of the SNpc allowed for the assessment of differential transcript expression in the nigral dopamine neurons and proximal glia. Transcripts upregulated in early synucleinopathy were mainly associated with an immune response, whereas transcripts downregulated were associated with neurotransmission and the dopamine pathway. A subset of 29 transcripts associated with neurotransmission/vesicular release and the dopamine pathway were verified in a separate cohort of males and females to confirm reproducibility. Within this subset, fluorescent in situ hybridization (FISH) was used to localize decreases in the Syt1 and Slc6a3 transcripts to pSyn inclusion-containing neurons. Identification of transcriptional changes in early synucleinopathy provides insight into the molecular mechanisms driving neurodegeneration.

Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation

Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.

Coexistent ARID1A-PIK3CA mutations are associated with immune-related pathways in luminal breast cancer

Up to 40% of luminal breast cancer patients carry activating mutations in the PIK3CA gene. PIK3CA mutations commonly co-occur with other mutations, but the implication of this co-occurrence may vary according to the specific genes involved. Here, we characterized a subgroup of luminal breast cancer expressing co-mutations in ARID1A and PIK3CA genes and identified their effect on important signaling pathways. Our study included 2609 primary breast cancer samples from the TCGA and METABRIC datasets that were classified based on tumor subtype and the existence of mutations in PIK3CA and ARID1A genes. Differential expression and WGCNA analyses were performed to detect molecular modules affected by the existence of the mutations. Our results reveal various evidence for the involvement of immune-related pathways in luminal tumors harboring ARID1A and PIK3CA mutations, as well as a unique Tumor-infiltrated immune cells composition. We also identified seven key hub genes in the ARID1A-PIK3CA mutated tumors associated with immune-related pathways: CTLA4, PRF1, LCK, CD3E, CD247, ZAP70, and LCP2. Collectively, these results indicate an immune system function that may contribute to tumor survival. Our data induced a hypothesis that ARID1A and PIK3CA mutations' co-occurrence might predict responses to immunotherapy in luminal BC and, if validated, could guide immunotherapy development.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co-expression network analysis

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange-fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress-specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co-expression networks under multiple abiotic stress conditions, weighted gene co-expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co-expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought-associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

The effects of maternal care on the developmental transcriptome and metatranscriptome of a wild bee

Maternal care acts as a strong environmental stimulus that can induce phenotypic plasticity in animals and may also alter their microbial communities through development. Here, we characterize the developmental metatranscriptome of the small carpenter bee, Ceratina calcarata, across developmental stages and in the presence or absence of mothers. Maternal care had the most influence during early development, with the greatest number and magnitude of differentially expressed genes between maternal care treatments, and enrichment for transcription factors regulating immune response in motherless early larvae. Metatranscriptomic data revealed fungi to be the most abundant group in the microbiome, with Aspergillus the most abundant in early larvae raised without mothers. Finally, integrative analysis between host transcriptome and metatranscriptome highlights several fungi correlating with developmental and immunity genes. Our results provide characterizations of the influence of maternal care on gene expression and the microbiome through development in a wild bee.

Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus

Drought stress limits woody species productivity and influences tree distribution. However, dissecting the molecular mechanisms that underpin drought responses in forest trees can be challenging due to trait complexity. Here, using a panel of 300 Chinese white poplar (Populus tomentosa) accessions collected from different geographical climatic regions in China, we performed a genome-wide association study (GWAS) on seven drought-related traits and identified PtoWRKY68 as a candidate gene involved in the response to drought stress. A 12-bp insertion and/or deletion and three nonsynonymous variants in the PtoWRKY68 coding sequence categorized natural populations of P. tomentosa into two haplotype groups, PtoWRKY68hap1 and PtoWRKY68hap2. The allelic variation in these two PtoWRKY68 haplotypes conferred differential transcriptional regulatory activities and binding to the promoters of downstream abscisic acid (ABA) efflux and signaling genes. Overexpression of PtoWRKY68hap1 and PtoWRKY68hap2 in Arabidopsis (Arabidopsis thaliana) ameliorated the drought tolerance of two transgenic lines and increased ABA content by 42.7% and 14.3% compared to wild-type plants, respectively. Notably, PtoWRKY68hap1 (associated with drought tolerance) is ubiquitous in accessions in water-deficient environments, whereas the drought-sensitive allele PtoWRKY68hap2 is widely distributed in well-watered regions, consistent with the trends in local precipitation, suggesting that these alleles correspond to geographical adaptation in Populus. Moreover, quantitative trait loci analysis and an electrophoretic mobility shift assay showed that SHORT VEGETATIVE PHASE (PtoSVP.3) positively regulates the expression of PtoWRKY68 under drought stress. We propose a drought tolerance regulatory module in which PtoWRKY68 modulates ABA signaling and accumulation, providing insight into the genetic basis of drought tolerance in trees. Our findings will facilitate molecular breeding to improve the drought tolerance of forest trees.

Identification of co-expressed gene networks promoting CD8+ T cell infiltration and having prognostic value in uveal melanoma

Current immunotherapies are unsatisfactory against uveal melanoma (UM); however, elevated CD8+ T cell infiltration level indicates poor prognosis in UM. Here, we aimed to identify co-expressed gene networks promoting CD8+ T cell infiltration in UM and created a prognostic hazard model based on the identified hub genes. Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Stromal-immune comprehensive score (ESTIMATE) was used to evaluate the immune-infiltration landscape of the tumor microenvironment. Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Correlation Network Analysis (WGCNA) were used to quantify CD8+ T cell infiltration level and identify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to analyze the biological processes. Least absolute shrinkage and selection operator (LASSO) Cox regression were used to establish a prognostic model, which was further validated. Finally, pan-cancer analysis evaluated these genes to be associated with CD8+ T cell infiltration in other tumors. In conclusion, the proposed four-gene (PTPN12, IDH2, P2RX4, and KDELR2) prognostic hazard model had satisfactory prognostic ability. These hub genes may promote CD8+ T cell infiltration in UM through antigen presentation, and CD8+ T cell possibly function as Treg, resulting in poor prognosis. These findings might facilitate the development of novel immunotherapies.

Network-based analysis identifies key regulatory transcription factors involved in skin aging

Skin aging is a complex process involving intricate genetic and environmental factors. In this study, we performed a comprehensive analysis of the transcriptional regulatory landscape of skin aging in canines. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify aging-related gene modules. We subsequently validated the expression changes of these module genes in single-cell RNA sequencing (scRNA-seq) data of human aging skin. Notably, basal cell (BC), spinous cell (SC), mitotic cell (MC), and fibroblast (FB) were identified as the cell types with the most significant gene expression changes during aging. By integrating GENIE3 and RcisTarget, we constructed gene regulation networks (GRNs) for aging-related modules and identified core transcription factors (TFs) by intersecting significantly enriched TFs within the GRNs with hub TFs from WGCNA analysis, revealing key regulators of skin aging. Furthermore, we demonstrated the conserved role of CTCF and RAD21 in skin aging using an H2O2-stimulated cell aging model in HaCaT cells. Our findings provide new insights into the transcriptional regulatory landscape of skin aging and unveil potential targets for future intervention strategies against age-related skin disorders in both canines and humans.

Functional Architecture of Brain and Blood Transcriptome Delineate Biological Continuity Between Suicidal Ideation and Suicide

Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with presence and severity of suicidal ideation were found within 18 and 3 co-expressed modules respectively (p < 0.05), not explained by severity of depression. Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified, and tested using RNA-seq data from postmortem brain that revealed gene expression differences in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity is associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.

Profiling of Differentially Expressed MicroRNAs in Human Umbilical Vein Endothelial Cells Exposed to Hyperglycemia via RNA Sequencing

Hyperglycemia is the hallmark of diabetes mellitus that results in oxidative stress, apoptosis, and diabetic vascular endothelial dysfunction. An increasing number of microRNAs (miRNAs) have been found to be involved in the pathogenesis of diabetic vascular complications. However, there is a limited number of studies that characterize the miRNA profile of endothelial cells exposed to hyperglycemia. Therefore, this study aims to analyze the miRNA profile of human umbilical-vein endothelial cells (HUVECs) exposed to hyperglycemia. HUVECs were divided into two groups: the control (treated with 5.5 mM glucose) and hyperglycemia (treated with 33.3 mM glucose) groups. RNA sequencing identified 17 differentially expressed miRNAs between the groups (p < 0.05). Of these, 4 miRNAs were upregulated, and 13 miRNAs were downregulated. Two of the most differentially expressed miRNAs (novel miR-1133 and miR-1225) were successfully validated with stem-loop qPCR. Collectively, the findings show that there is a differential expression pattern of miRNAs in HUVEC following exposure to hyperglycemia. These 17 differentially expressed miRNAs are involved in regulating cellular functions and pathways related to oxidative stress and apoptosis that may contribute to diabetic vascular endothelial dysfunction. The findings provide new clues on the role of miRNAs in the development of diabetic vascular endothelial dysfunction, which could be useful in future targeted therapy.

Comparative transcriptome profiling of near isogenic lines PBW343 and FLW29 to unravel defense related genes and pathways contributing to stripe rust resistance in wheat

Stripe rust (Sr), caused by Puccinia striiformis f. sp. tritici (Pst), is the most devastating disease that poses serious threat to the wheat-growing nations across the globe. Developing resistant cultivars is the most challenging aspect in wheat breeding. The function of resistance genes (R genes) and the mechanisms by which they influence plant-host interactions are poorly understood. In the present investigation, comparative transcriptome analysis was carried out by involving two near-isogenic lines (NILs) PBW343 and FLW29. The seedlings of both the genotypes were inoculated with Pst pathotype 46S119. In total, 1106 differentially expressed genes (DEGs) were identified at early stage of infection (12 hpi), whereas expressions of 877 and 1737 DEGs were observed at later stages (48 and 72 hpi) in FLW29. The identified DEGs were comprised of defense-related genes including putative R genes, 7 WRKY transcriptional factors, calcium, and hormonal signaling associated genes. Moreover, pathways involved in signaling of receptor kinases, G protein, and light showed higher expression in resistant cultivar and were common across different time points. Quantitative real-time PCR was used to further confirm the transcriptional expression of eight critical genes involved in plant defense mechanism against stripe rust. The information about genes are likely to improve our knowledge of the genetic mechanism that controls the stripe rust resistance in wheat, and data on resistance response-linked genes and pathways will be a significant resource for future research.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Gene co-expression modules behind the three-pistil formation in wheat

Multi-pistil trait in wheat is of great potential value in plant development research and crop breeding. Our previous studies identified the Pis1 locus that causes three pistils in wheat by genetic mapping using multiple DNA marker systems. However, there are still 26 candidate genes on the locus, and the causal gene remains to be found. In this study, we aimed to approach the molecular mechanism of multi-pistil formation. Comparative RNA sequencing (RNA-Seq) during the pistil formation was undertaken in four wheat lines: a three-pistil mutant TP, a single-pistil TILLING mutant of TP (SP), a three-pistil near-isogenic line CM28TP with the background of cultivar Chunmai 28 (CM28), and CM28. Electron microscopic analysis specified probable developmental stages of young spikes for the three-pistil formation. mRNA sequencing in the young spikes of the four lines represented 253 down-regulated genes and 98 up-regulated genes in both three-pistil lines, which included six potential genes for ovary development. Weighted gene co-expression analysis represented three-pistil trait-associated transcription factor-like genes, among which one hub gene, ARF5, was the most highlighted. ARF5 is on the Pis1 locus and an orthologue of MONOPTEROS which mediates tissue development in Arabidopsis. qRT-PCR validation implies that the deficiency of ARF5 underlies the three-pistil formation in wheat.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Chromosome groups 5, 6 and 7 harbor major quantitative trait loci controlling root traits in bread wheat (Triticum aestivum L.)

Identifying genomic regions for root traits in bread wheat can help breeders develop climate-resilient and high-yielding wheat varieties with desirable root traits. This study used the recombinant inbred line (RIL) population of Synthetic W7984 × Opata M85 to identify quantitative trait loci (QTL) for different root traits such as rooting depth (RD), root dry mass (RM), total root length (RL), root diameter (Rdia) and root surface areas (RSA1 for coarse roots and RSA2 for fine roots) under controlled conditions in a semi-hydroponic system. We detected 14 QTL for eight root traits on nine wheat chromosomes; we discovered three QTL each for RD and RSA1, two QTL each for RM and RSA2, and one QTL each for RL, Rdia, specific root length and nodal root number per plant. The detected QTL were concentrated on chromosome groups 5, 6 and 7. The QTL for shallow RD (Q.rd.uwa.7BL: Xbarc50) and high RM (Q.rm.uwa.6AS: Xgwm334) were validated in two independent F₂ populations of Synthetic W7984 × Chara and Opata M85 × Cascade, respectively. Genotypes containing negative alleles for Q.rd.uwa.7BL had 52% shallower RD than other Synthetic W7984 × Chara population lines. Genotypes with the positive alleles for Q.rm.uwa.6AS had 31.58% higher RM than other Opata M85 × Cascade population lines. Further, we identified 21 putative candidate genes for RD (Q.rd.uwa.7BL) and 13 for RM (Q.rm.uwa.6AS); TraesCS6A01G020400, TraesCS6A01G024400 and TraesCS6A01G021000 identified from Q.rm.uwa.6AS, and TraesCS7B01G404000, TraesCS7B01G254900 and TraesCS7B01G446200 identified from Q.rd.uwa.7BL encoded important proteins for root traits. We found germin-like protein encoding genes in both Q.rd.uwa.7BL and Q.rm.uwa.6AS regions. These genes may play an important role in RM and RD improvement. The identified QTL, especially the validated QTL and putative candidate genes are valuable genetic resources for future root trait improvement in wheat.

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.

The GRN concept as a guide for evolutionary developmental biology

Organismal phenotypes result largely from inherited developmental programs, usually executed during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and help determine the evolutionary trajectories of species. Modern evolutionary biology must, therefore, account for these mechanisms in both theory and in practice. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown in tandem with advances in "omic" technologies and experimental techniques. However, while the GRN concept is widely utilized, it is often less clear what practical implications it has for conducting research in evolutionary developmental biology. In this Perspective, we attempt to provide clarity by discussing how experiments and projects can be designed in light of the GRN concept. We first map familiar biological notions onto the more abstract components of GRN models. We then review how diverse functional genomic approaches can be directed toward the goal of constructing such models and discuss current methods for functionally testing evolutionary hypotheses that arise from them. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design. Taken together, the practical implications that we draw from the GRN concept provide a set of guideposts for studies aiming at unraveling the molecular basis of phenotypic diversity.

siVAE: interpretable deep generative models for single-cell transcriptomes

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia

Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data.

Bioinformatic Exploration of Hub Genes and Potential Therapeutic Drugs for Endothelial Dysfunction in Hypoxic Pulmonary Hypertension

Hypoxic pulmonary hypertension (HPH) is a fatal chronic pulmonary circulatory disease, characterized by hypoxic pulmonary vascular constriction and remodeling. Studies performed to date have confirmed that endothelial dysfunction plays crucial roles in HPH, while the underlying mechanisms have not been fully revealed. The microarray dataset GSE11341 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between hypoxic and normoxic microvascular endothelial cell, followed by Gene Ontology (GO) annotation/Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis, and protein-protein interaction (PPI) network construction. Next, GSE160255 and RT-qPCR were used to validate hub genes. Meanwhile, GO/KEGG and GSEA were performed for each hub gene to uncover the potential mechanism. A nomogram based on hub genes was established. Furthermore, mRNA-miRNA network was predicted by miRNet, and the Connectivity Map (CMAP) database was in use to identify similarly acting therapeutic candidates. A total of 148 DEGs were screened in GSE11341, and three hub genes (VEGFA, CDC25A, and LOX) were determined and validated via GSE160255 and RT-qPCR. Abnormalities in the pathway of vascular smooth muscle contraction, lysosome, and glycolysis might play important roles in HPH pathogenesis. The hub gene-miRNA network showed that hsa-mir-24-3p, hsa-mir-124-3p, hsa-mir-195-5p, hsa-mir-146a-5p, hsa-mir-155-5p, and hsa-mir-23b-3p were associated with HPH. And on the basis of the identified hub genes, a practical nomogram is developed. To repurpose known and therapeutic drugs, three candidate compounds (procaterol, avanafil, and lestaurtinib) with a high level of confidence were obtained from the CMAP database. Taken together, the identification of these three hub genes, enrichment pathways, and potential therapeutic drugs might have important clinical implications for HPH diagnosis and treatment.

Bioinformatics and systems-biology analysis to determine the effects of Coronavirus disease 2019 on patients with allergic asthma

Background

The coronavirus disease (COVID-19) pandemic has posed a significant challenge for global health systems. Increasing evidence shows that asthma phenotypes and comorbidities are major risk factors for COVID-19 symptom severity. However, the molecular mechanisms underlying the association between COVID-19 and asthma are poorly understood. Therefore, we conducted bioinformatics and systems biology analysis to identify common pathways and molecular biomarkers in patients with COVID-19 and asthma, as well as potential molecular mechanisms and candidate drugs for treating patients with both COVID-19 and asthma.

Methods

Two sets of differentially expressed genes (DEGs) from the GSE171110 and GSE143192 datasets were intersected to identify common hub genes, shared pathways, and candidate drugs. In addition, murine models were utilized to explore the expression levels and associations of the hub genes in asthma and lung inflammation/injury.

Results

We discovered 157 common DEGs between the asthma and COVID-19 datasets. A protein–protein-interaction network was built using various combinatorial statistical approaches and bioinformatics tools, which revealed several hub genes and critical modules. Six of the hub genes were markedly elevated in murine asthmatic lungs and were positively associated with IL-5, IL-13 and MUC5AC, which are the key mediators of allergic asthma. Gene Ontology and pathway analysis revealed common associations between asthma and COVID-19 progression. Finally, we identified transcription factor–gene interactions, DEG–microRNA coregulatory networks, and potential drug and chemical-compound interactions using the hub genes.

Conclusion

We identified the top 15 hub genes that can be used as novel biomarkers of COVID-19 and asthma and discovered several promising candidate drugs that might be helpful for treating patients with COVID-19 and asthma.

Integrated gene co-expression network analysis reveals unique developmental processes of Aurelia aurita

The typical life cycle of the moon jellyfish (Aurelia aurita) includes the planula, polyp, strobila, ephyra, and medusa developmental stages. These stages exhibit huge differences in both external morphology and internal physiological functions. However, the gene co-expression network involved in these post-embryonic developmental processes has not been studied yet. Here, based on 15 RNA sequencing samples covering all five stages of the A. aurita life cycle, we systematically analyzed the gene co-expression network and obtained 35 relevant modules. Furthermore, we identified the highly correlated modules and hub genes for each stage. These hub genes are implicated to play important roles in the developmental processes of A. aurita, which should help improve our understanding of the jellyfish life cycle.

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic workflow

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged.

Weighted Gene Correlation Network Analysis (WGCNA) of Arabidopsis Somatic Embryogenesis (SE) and Identification of Key Gene Modules to Uncover SE-Associated Hub Genes

Somatic embryogenesis (SE), which occurs naturally in many plant species, serves as a model to elucidate cellular and molecular mechanisms of embryo patterning in plants. Decoding the regulatory landscape of SE is essential for its further application. Hence, the present study was aimed at employing Weighted Gene Correlation Network Analysis (WGCNA) to construct a gene coexpression network (GCN) for Arabidopsis SE and then identifying highly correlated gene modules to uncover the hub genes associated with SE that may serve as potential molecular targets. A total of 17,059 genes were filtered from a microarray dataset comprising four stages of SE, i.e., stage I (zygotic embryos), stage II (proliferating tissues at 7 days of induction), stage III (proliferating tissues at 14 days of induction), and stage IV (mature somatic embryos). This included 1,711 transcription factors and 445 EMBRYO DEFECTIVE genes. GCN analysis identified a total of 26 gene modules with the module size ranging from 35 to 3,418 genes using a dynamic cut tree algorithm. The module-trait analysis revealed that four, four, seven, and four modules were associated with stages I, II, III, and IV, respectively. Further, we identified a total of 260 hub genes based on the degree of intramodular connectivity. Validation of the hub genes using publicly available expression datasets demonstrated that at least 78 hub genes are potentially associated with embryogenesis; of these, many genes remain functionally uncharacterized thus far. In silico promoter analysis of these genes revealed the presence of cis-acting regulatory elements, "soybean embryo factor 4 (SEF4) binding site," and "E-box" of the napA storage-protein gene of Brassica napus; this suggests that these genes may play important roles in plant embryo development. The present study successfully applied WGCNA to construct a GCN for SE in Arabidopsis and identified hub genes involved in the development of somatic embryos. These hub genes could be used as molecular targets to further elucidate the molecular mechanisms underlying SE in plants.

Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low‐grade glioma (LGG) and identify hub genes. A co‐expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co‐expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.

Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm

Purpose

The specific mechanisms and biomarkersunderlying the progression of stable coronary artery disease (CAD) to acute myocardial infarction (AMI) remain unclear. The current study aims to explore novel gene biomarkers associated with CAD progression by analyzing the transcriptomic sequencing data of peripheral blood monocytes in different stages of CAD.

Material and Methods

A total of 24 age- and sex- matched patients at different CAD stages who received coronary angiography were enrolled, which included 8 patients with normal coronary angiography, 8 patients with angiographic intermediate lesion, and 8 patients with AMI. The RNA from peripheral blood monocytes was extracted and transcriptome sequenced to analyze the gene expression and the differentially expressed genes (DEG). A Gene Oncology (GO) enrichment analysis was performed to analyze the biological function of genes. Weighted gene correlation network analysis (WGCNA) was performed to classify genes into several gene modules with similar expression profiles, and correlation analysis was carried out to explore the association of each gene module with a clinical trait. The dynamic network biomarker (DNB) algorithm was used to calculate the key genes that promote disease progression. Finally, the overlapping genes between different analytic methods were explored.

Results

WGCNA analysis identified a total of nine gene modules, of which two modules have the highest positive association with CAD stages. GO enrichment analysis indicated that the biological function of genes in these two gene modules was closely related to inflammatory response, which included T-cell activation, cell response to inflammatory stimuli, lymphocyte activation, cytokine production, and the apoptotic signaling pathway. DNB analysis identified a total of 103 genes that may play key roles in the progression of atherosclerosis plaque. The overlapping genes between DEG/WGCAN and DNB analysis identified the following 13 genes that may play key roles in the progression of atherosclerosis disease: SGPP2, DAZAP2, INSIG1, CD82, OLR1, ARL6IP1, LIMS1, CCL5, CDK7, HBP1, PLAU, SELENOS, and DNAJB6.

Conclusions

The current study identified a total of 13 genes that may play key roles in the progression of atherosclerotic plaque and provides new insights for early warning biomarkers and underlying mechanisms underlying the progression of CAD.

Constraint-Based, Score-Based and Hybrid Algorithms to Construct Bayesian Gene Networks in the Bovine Transcriptome

Simple Summary

In this study, we investigated and compared six different Bayesian network algorithms from three different categories to identify hub genes critical to gene expression networks activated in response to progesterone in the bovine uterus. We observed many common hub genes identified between constraint-based algorithms (CBAs) and hybrid algorithms (HAs), while it appeared that score-based algorithm (SBA) methods led to more accurate and relevant predictions of core genes. The results revealed that the identification of hub genes was affected by the type of network reconstruction and by the subsequently used topological parameters. Two identified genes known to have roles during pregnancy are ISG15 and DGAT2. The identified hub genes are associated with biological processes such as amino acid metabolism, hormonal signaling pathways and the immune system. Our analysis revealed a role for miRNAs in the regulation of this system. The biological and physiological roles (enzymatic and hormonal effects) of unannotated identified hub genes should be functionally validated by further studies.

Abstract

Bayesian gene networks are powerful for modelling causal relationships and incorporating prior knowledge for making inferences about relationships. We used three algorithms to construct Bayesian gene networks around genes expressed in the bovine uterus and compared the efficacies of the algorithms. Dataset GSE33030 from the Gene Expression Omnibus (GEO) repository was analyzed using different algorithms for hub gene expression due to the effect of progesterone on bovine endometrial tissue following conception. Six different algorithms (grow-shrink, max-min parent children, tabu search, hill-climbing, max-min hill-climbing and restricted maximum) were compared in three higher categories, including constraint-based, score-based and hybrid algorithms. Gene network parameters were estimated using the bnlearn bundle, which is a Bayesian network structure learning toolbox implemented in R. The results obtained indicated the tabu search algorithm identified the highest degree between genes (390), Markov blankets (25.64), neighborhood sizes (8.76) and branching factors (4.38). The results showed that the highest number of shared hub genes (e.g., proline dehydrogenase 1 (PRODH), Sam-pointed domain containing Ets transcription factor (SPDEF), monocyte-to-macrophage differentiation associated 2 (MMD2), semaphorin 3E (SEMA3E), solute carrier family 27 member 6 (SLC27A6) and actin gamma 2 (ACTG2)) was seen between the hybrid and the constraint-based algorithms, and these genes could be recommended as central to the GSE33030 data series. Functional annotation of the hub genes in uterine tissue during progesterone treatment in the pregnancy period showed that the predicted hub genes were involved in extracellular pathways, lipid and protein metabolism, protein structure and post-translational processes. The identified hub genes obtained by the score-based algorithms had a role in 2-arachidonoylglycerol and enzyme modulation. In conclusion, different algorithms and subsequent topological parameters were used to identify hub genes to better illuminate pathways acting in response to progesterone treatment in the bovine uterus, which should help with our understanding of gene regulatory networks in complex trait expression.

Identification of Metabolic Pathways Differentially Regulated in Somatic and Zygotic Embryos of Maritime Pine

Embryogenesis is a complex phase of conifer development involving hundreds of genes, and a proper understanding of this process is critical not only to produce embryos with different applied purposes but also for comparative studies with angiosperms. A global view of transcriptome dynamics during pine somatic and zygotic embryogenesis is currently missing. Here, we present a genome-wide transcriptome analysis of somatic and zygotic embryos at three developmental stages to identify conserved biological processes and gene functions during late embryogenesis. Most of the differences became more significant as the developmental process progressed from early to cotyledonary stages, and a higher number of genes were differentially expressed in somatic than in zygotic embryos. Metabolic pathways substantially affected included those involved in amino acid biosynthesis and utilization, and this difference was already observable at early developmental stages. Overall, this effect was found to be independent of the line (genotype) used to produce the somatic embryos. Additionally, transcription factors differentially expressed in somatic versus zygotic embryos were analyzed. Some potential hub regulatory genes were identified that can provide clues as to what transcription factors are controlling the process and to how the observed differences between somatic and zygotic embryogenesis in conifers could be regulated.

Multi-omics analysis predicts fibronectin 1 as a prognostic biomarker in glioblastoma multiforme

Glioblastoma (GBM) is one of the most malignant and intractable central nervous system tumors with high recurrence, low survival rate, and poor prognosis. Despite the advances of aggressive, multimodal treatment, a successful treatment strategy is still elusive, often leading to therapeutic resistance and fatality. Thus, it is imperative to search for and identify novel markers critically associated with GBM pathogenesis to improve the existing trend of diagnosis, prognosis, and treatment. Seven publicly available GEO microarray datasets containing 409 GBM samples were integrated and further data mining was conducted using several bioinformatics tools. A total of 209 differentially expressed genes (DEGs) were identified in the GBM tissue samples compared to the normal brains. Gene Ontology (GO) enrichment analysis of the DEGs revealed association of the upregulates genes with extracellular matrix (ECM), conceivably contributing to the invasive nature of GBM while downregulated DEGs were found to be predominantly related to neuronal processes and structures. Alongside, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway analyses described the involvement of the DEGs with various crucial contributing pathways (PI3K-Akt signaling pathway, p53 signaling pathway, insulin secretion, etc.) in GBM progression and pathogenesis. Protein-protein interaction (PPI) network containing 879 nodes and 1237 edges revealed 3 significant modules and consecutive KEGG pathway analysis of these modules showed a significant connection to gliomagenesis. Later, 10 hub genes were screened out based on degree and their expressions were externally validated. Surprisingly, only fibronectin 1 (FN1) high expression appeared to be related to poor prognosis. Subsequently, 109 transcription factors and 211 miRNAs were detected to be involved with the hub genes where FN1 demonstrated the highest number of interactions. Considering its high connectivity and potential prognostic value FN1 could be a novel biomarker providing new insights into the prognosis and treatment for GBM, although experimental validation is required.

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.

Perkinsus marinus suppresses in vitro eastern oyster apoptosis via IAP-dependent and caspase-independent pathways involving TNFR, NF-kB, and oxidative pathway crosstalk

The protozoan parasite Perkinsus marinus causes Dermo disease in eastern oysters, Crassostrea virginica, and can suppress apoptosis of infected hemocytes using incompletely understood mechanisms. This study challenged hemocytes in vitro with P. marinus for 1 h in the presence or absence of caspase inhibitor Z-VAD-FMK or Inhibitor of Apoptosis protein (IAP) inhibitor GDC-0152. Hemocytes exposure to P. marinus significantly reduced granulocyte apoptosis, and pre-incubation with Z-VAD-FMK did not affect P. marinus-induced apoptosis suppression. Hemocyte pre-incubation with GDC-0152 prior to P. marinus challenge further reduced apoptosis of granulocytes with engulfed parasite, but not mitochondrial permeabilization. This suggests P. marinus-induced apoptosis suppression may be caspase-independent, affect an IAP-involved pathway, and occur downstream of mitochondrial permeabilization. P. marinus challenge stimulated hemocyte differential expression of oxidation-reduction, TNFR, and NF-kB pathways. WGCNA analysis of P. marinus expression in response to hemocyte exposure revealed correlated protease, kinase, and hydrolase expression that could contribute to P. marinus-induced apoptosis suppression.

MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data

Motivation

Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

Results

We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

Availability and implementation

MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.

Machine learning approaches for diagnosing depression using EEG: A review

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.