Co-expression Modules Research Articles

Single-cell RNA sequencing reveals the suppressive effect of PPP1R15A inhibitor Sephin1 in antitumor immunity

Protein phosphatase 1 regulatory subunit 15A (PPP1R15A) is an important factor in the integrated stress response (ISR) in mammals and may play a crucial role in tumorigenesis. In our studies, we found an inhibitor of PPP1R15A, Sephin1, plays a protumorigenic role in mouse tumor models. By analyzing the single-cell transcriptome data of the mouse tumor models, we found that in C57BL/6 mice, Sephin1 treatment could lead to higher levels of ISR activity and lower levels of antitumor immune activities. Specifically, Sephin1 treatment caused reductions in antitumor immune cell types and lower expression levels of cytotoxicity-related genes. In addition, Tcell receptor (TCR) repertoire analysis demonstrated that the clonal expansion of tumor-specific Tcells was inhibited by Sephin1. A special TCR+ macrophage subtype in tumor was identified to be significantly depleted upon Sephin1 treatment, implying its key antitumor role. These results suggest that PPP1R15A has the potential to be an effective target for tumor therapy.

Integrative systems immunology uncovers molecular networks of the cell cycle that stratify COVID-19 severity.

Several perturbations in the number of peripheral blood leukocytes, such as neutrophilia and lymphopenia associated with Coronavirus disease 2019 (COVID-19) severity, point to systemic molecular cell cycle alterations during severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, the landscape of cell cycle alterations in COVID-19 remains primarily unexplored. Here, we performed an integrative systems immunology analysis of publicly available proteome and transcriptome data to characterize global changes in the cell cycle signature of COVID-19 patients. We found significantly enriched cell cycle-associated gene co-expression modules and an interconnected network of cell cycle-associated differentially expressed proteins (DEPs) and genes (DEGs) by integrating the molecular data of 1469 individuals (981 SARS-CoV-2 infected patients and 488 controls [either healthy controls or individuals with other respiratory illnesses]). Among these DEPs and DEGs are several cyclins, cell division cycles, cyclin-dependent kinases, and mini-chromosome maintenanceproteins. COVID-19 patients partially shared the expression pattern of some cell cycle-associated genes with other respiratory illnesses but exhibited some specific differential features. Notably, the cell cycle signature predominated in the patients' blood leukocytes (B, T, and natural killercells) and was associated with COVID-19 severity and disease trajectories. These results provide a unique global understanding of distinct alterations in cell cycle-associated molecules in COVID-19 patients, suggesting new putative pathways for therapeutic intervention.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza. Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized. We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79. The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Activated autophagy of innate immune cells during the early stages of major trauma.

Trauma-induced immune dysfunction has been a major barrier to achieving reduced mortality, which is poorly understood. Autophagy is a crucial catabolic mechanism of immune cells during times of stress. Few studies have investigated the immune regulatory effects induced by autophagy after trauma. Here, we use single-cell transcriptomics analysis in a major trauma cohort to demonstrate the dominant role of autophagy in innate immune cells during the early stages of major trauma. Single-cell transcriptional profiling of peripheral blood mononuclear cells (PBMCs) was performed, which were sampled from three control participants and five major trauma patients within 6 hours of injury. In detail, after single-cell RNA-sequence data processing, cell type annotation and cluster marker identification were performed. A genetic toolbox with 604 autophagy-related genes was used to monitor the autophagy levels in immune cells. In addition, all transcriptome RNA sequencing data obtained from PBMCs in a cohort of 167 major trauma patients were downloaded from gene expression omnibus (GEO) datasets (GSE36809). Key deregulated biological processes and important autophagic hub genes involved in immune cells were identified by weighted gene co-expression network analysis and gene ontology enrichment analysis. A total of 20,445 differentially expressed genes were identified and five co-expression modules were constructed. Enrichment analysis indicated that activated autophagy is the most important biological process during the early stages of major trauma, and JMY (autophagy-related genes) were identified as hub genes. The single-cell transcriptional profiling of PBMCs demonstrated that all components of adaptive immune cells were significantly decreased, whereas components of innate immune cells (monocytes and neutrophils) were significantly increased in major trauma patients compared with control participants. Activated autophagy was detected in monocytes and neutrophils by monitoring the dynamic transcriptional signature of the autophagy-related genetic toolbox. Biological process analysis shows that antigen uptake, processing presentation, and major histocompatibility complex (MHC) class II protein complex assembly pathways were up-regulated in autophagy-positive monocytes, whereas antigen processing and presentation of endogenous antigen and type I interferon signaling pathways were up-regulated in autophagy-positive neutrophils during the early stages of major trauma. Our study demonstrated that autophagy is a biological process crucial to the development of immune disorders in the early stages of major trauma. Furthermore, the results of our study generated a comprehensive single-cell immune landscape for major trauma patients, in which we determined that autophagy profoundly affects the main functions of innate immune cells and provides insight into the cellular basis of immune dysregulation after major trauma.

Large-scale analyses of angiosperm Flowering Locus T genes reveal duplication and functional divergence in monocots.

FLOWERING LOCUS T (FT) are well-known key genes for initiating flowering in plants. Delineating the evolutionary history and functional diversity of FT genes is important for understanding the diversification of flowering time and how plants adapt to the changing surroundings. We performed a comprehensive phylogenetic analysis of FT genes in 47 sequenced flowering plants and the 1,000 Plant Transcriptomes (1KP) database with a focus on monocots, especially cereals. We revealed the evolutionary history of FT genes. The FT genes in monocots can be divided into three clades (I, II, and III), whereas only one monophyletic group was detected in early angiosperms, magnoliids, and eudicots. Multiple rounds of whole-genome duplications (WGD) events followed by gene retention contributed to the expansion and variation of FT genes in monocots. Amino acid sites in the clade II and III genes were preferentially under high positive selection, and some sites located in vital domain regions are known to change functions when mutated. Clade II and clade III genes exhibited high variability in important regions and functional divergence compared with clade I genes; thus, clade I is more conserved than clade II and III. Genes in clade I displayed higher expression levels in studied organs and tissues than the clade II and III genes. The co-expression modules showed that some of the FT genes might have experienced neofunctionalization and subfunctionalization, such as the acquisition of environmental resistance. Overall, FT genes in monocots might form three clades by the ancient gene duplication, and each clade was subsequently subjected to different selection pressures and amino acid substitutions, which eventually led to different expression patterns and functional diversification. Our study provides a global picture of FT genes' evolution in monocots, paving a road for investigating FT genes' function in future.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways arecrucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis.

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated. To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles. There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts. Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer. Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs. Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Weighted gene coexpression network analysis reveals negative regulation of hypertrophic cardiomyopathy by carboxylesterase 1 and cathepsin C.

Hypertrophic cardiomyopathy (HCM) is a primary cardiomyopathy characterized by hypertrophic cardiomyocytes. It is one of the leading causes of sudden death in adolescents. However, the molecular mechanism of HCM is not clear. In our study, ribonucleic acid (RNA) sequence data of myocardial tissue in HCM patients were extracted from the Gene Expression Omnibus (GEO) database (GSE130036) and analyzed by weighted gene coexpression network analysis (WGCNA). A total of 31 coexpression modules were identified. The coexpression black module significantly correlated with maximum left ventricular wall thickness (Maxi LVWT). We screened the differentially expressed mRNAs between normal tissues and HCM tissues using the dplyr and tidyr packages in R3.6.2. The genes in the black module and differentially expressed genes were further intersected. We found that the expression of carboxylesterase 1 (CES1) and cathepsin C (CTSC) was downregulated in HCM tissues and negatively correlated with Maxi LVWT. We further verified the expression of CES1 and CTSC was downregulated in HCM clinical blood and negatively correlated with Maxi LVWT. Finally, we demonstrated that overexpression of CTSC and CES1 could alleviate HCM in an HCM cell model. In summary, the study suggests that CES1 and CTSC negatively regulate the development of HCM and have potential as therapeutic and diagnostic targets for HCM.

Molecular endotypes of type 1 and type 2 SLE

ObjectiveTo character the molecular landscape of patients with type 1 and type 2 SLE by analysing gene expression profiles from peripheral blood.MethodsFull transcriptomic RNA sequencing was carried out on whole...

Identification of hub genes related to Duchenne muscular dystrophy by weighted gene co-expression network analysis

The study was aimed to analyze the potential gene modules and hub genes of Duchenne muscular dystrophy (DMD) by weighted gene co-expression network analysis. Based on the muscular dystrophy tissue expression profiling microarray GSE13608 from gene expression omnibus, gene co-expression modules were analyzed using weighted gene co-expression network analysis, gene modules related to DMD were screened, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed, and signature genes in the modules were screened. The protein-protein interaction network was constructed through Cytoscape, and hub genes were identified. The expression of hub genes in DMD versus normal muscle tissue was calculated in GSE6011. 12 co-expressed gene modules were identified, among which black module was significantly related to DMD. The characteristic genes in the module were enriched in the regulation of immune effector processes, immune response mediated by immunoglobulin, immune response mediated by B cells, etc. SERPING1, F13A1, C1S, C1R, and HLA-DPA1 were considered as hub genes in protein-protein interaction network. Analysis of GSE6011 shows that expression of SERPING1, F13A1, C1S, C1R, and HLA-DPA1 in tissues of DMD patients were higher than normal. SERPING1, F13A1, C1S, C1R, and HLA-DPA1 may participate in the development of DMD by regulating innate immunity and inflammation, and they are expected to be a potential biomarker and novel therapeutic targets for DMD.

Understanding water conservation vs. profligation traits in vegetable legumes through a physio-transcriptomic-functional approach.

Vegetable soybean and cowpea are related warm-season legumes showing contrasting leaf water use behaviors under similar root drought stresses, whose mechanisms are not well understood. Here we conducted an integrative phenomic-transcriptomic study on the two crops grown in a feedback irrigation system that enabled precise control of soil water contents. Continuous transpiration rate monitoring demonstrated that cowpea used water more conservatively under earlier soil drought stages, but tended to maintain higher transpiration under prolonged drought. Interestingly, we observed a soybean-specific transpiration rate increase accompanied by phase shift under moderate soil drought. Time-series transcriptomic analysis suggested a dehydration avoidance mechanism of cowpea at early soil drought stage, in which the VuHAI3 and VuTIP2;3 genes were suggested to be involved. Multifactorial gene clustering analysis revealed different responsiveness of genes to drought, time of day and their interactions between the two crops, which involved species-dependent regulation of the circadian clock genes. Gene network analysis identified two co-expression modules each associated with transpiration rate in cowpea and soybean, including a pair of negatively correlated modules between species. Module hub genes, including the ABA-degrading gene GmCYP707A4 and the trehalose-phosphatase/synthase gene VuTPS9 were identified. Inter-modular network analysis revealed putative co-players of the hub genes. Transgenic analyses verified the role of VuTPS9 in regulating transpiration rate under osmotic stresses. These findings propose that species-specific transcriptomic reprograming in leaves of the two crops suffering similar soil drought was not only a result of the different drought resistance level, but a cause of it.

Cell type characterization of spatiotemporal gene co-expression modules in Down syndrome brain

Down syndrome (DS) is the most common genetic cause of intellectual disability and increases the risk of other brain-related dysfunctions, like seizures, early-onset Alzheimer's disease, and autism. To reveal the molecular profiles of DS-associated brain phenotypes, we performed a meta-data analysis of the developmental DS brain transcriptome at cell type and co-expression module levels. In the DS brain, astrocyte-, microglia-, and endothelial cell-associated genes show upregulated patterns, whereas neuron- and oligodendrocyte-associated genes show downregulated patterns. Weighted gene co-expression network analysis identified cell type-enriched co-expressed gene modules. We present eight representative cell-type modules for neurons, astrocytes, oligodendrocytes, and microglia. We classified the neuron modules into glutamatergic and GABAergic neurons and associated them with detailed subtypes. Cell type modules were interpreted by analyzing spatiotemporal expression patterns, functional annotations, and co-expression networks of the modules. This study provides insight into the mechanisms underlying brain abnormalities in DS and related disorders.

Transcriptomic analysis of mid-secretory endometrium reveals essential immune factors associated with pregnancy after single euploid blastocyst transfer.

Implantation is a limiting factor for treatment success in assisted reproduction. Both embryonic and endometrial factors contribute to implantation. Embryonic factors have often been ignored in previous studies about the role of endometrium in implantation. In this study, we sought to identify the endometrial genes associated with negative pregnancy outcomes following the transfer of a single euploid blastocyst. Computational analyses of the transcriptomes of mid-secretory endometria from nine pregnant and seven non-pregnant patients in a cycle preceding the transfer of a single euploid blastocyst in a vitrified-warmed cycle were performed. Principal component analysis of two reported endometrial receptivity gene sets showed close clustering of the pregnant and non-pregnant samples. Differential gene expression analysis and co-expression module analysis identified 131 genes associated with the pregnancy status. The endometrial signatures identified highlight the importance of immune and metabolic regulation in pregnancy outcome. Network analysis identified 20 hub genes that could predict pregnancy outcomes with88.9% sensitivity and 85.7% specificity. Single-cell gene expression analysis highlighted the regulation of endometrial natural killer cells, T cells, and macrophages during embryo implantation. Immune cell abundance analysis supported the dysregulation of cytotoxic immune cells in the endometria of non-pregnant women. We reported the first endometrial gene signature associated with pregnancy after elimination of embryo aneuploidy and highlighted the importance of the endometrial immune microenvironment and metabolic status in pregnancy outcomes. This article is protected by copyright. All rights reserved.

HPV status represents dominant trait driving delineation of survival-associated gene co-expression networks in head and neck cancer

Integration of high-dimensional tumor gene expression data with clinicopathological data can increase our understanding of disease diversity, enable retrospective patient stratification, and identify new potential biomarkers and therapeutic targets. Using a systems biology approach, we provide a holistic overview of gene co-expression networks in head and neck squamous cell carcinomas (HNSCC). Weighted gene co-expression network analysis of HNSCC RNA sequencing data from 519 patients from The Cancer Genome Atlas (TCGA) was used to determine correlates of 5-year survival, using regression tree-based optimal threshold calculations. Survival-associated gene sets were transformed to gene set scores that were assessed for correlation with clinicopathological data. We identified 8 gene co-expression modules for HNSCC tumors, each of which contained co-expressed genes associated significantly with 5-year survival. Survival-associated co-expression gene signatures correlated dominantly with tumor HPV and p16 status. Network analysis identified that survival was associated with signaling networks of infection, immunity, epithelial-mesenchymal transition (EMT), hypoxia, glycolysis, focal adhesion, extracellular matrix, MYC signaling, autophagy and transcriptional regulation. EMT-associated gene signatures were expressed dominantly in fibroblasts, and cancer-associated fibroblasts were inversely correlated with immune activity. Interestingly, a high Immune Suppression Score based on expression of 21 genes associated with immune inhibition and including immune checkpoints, cytokines and regulatory T cell factors, was also associated with increased survival probability, and was significantly higher in HPV+ HNSCC. Networks associated with HNSCC survival were further associated with survival in cervical cancer, melanoma and lung cancer. This study defines 5129 genes associated with HNSCC survival, organized into co-expressed networks, their correlation with clinicopathological data, and with gene expression data from other malignant diseases, and provides a source for the discovery of biomarkers and novel therapies for HNSCC.

Transcriptome Comparison between Two Strains of Ustilago esculenta during the Mating.

Ustilago esculenta is a smut fungus that obligately infects Zizania latifolia and stimulates tissue swelling to form galls. Unlike T-type, MT-type U. esculenta can only proliferate within plant tissues and infect the offspring of their host. Production of telispores, haploid life, and plant cuticle penetration are not essential for it, which may lead to the degeneration in these processes. Transcriptome changes during the mating of T- and MT-type U. esculenta were studied. The functions of several secreted proteins were further confirmed by knock-out mutants. Our results showed that MT-type U. esculenta can receive environmental signals in mating and circumstance sensing as T-type does. However, MT-type U. esculenta takes a longer time for conjunction tube formation and cytoplasmic fusion. A large number of genes encoding secreted proteins are enriched in the purple co-expression module. They are significantly up-regulated in the late stage of mating in T-type U. esculenta, indicating their relationship with infecting. The knock-out of g6161 (xylanase) resulted in an attenuated symptom. The knock-out of g943 or g4344 (function unidentified) completely blocked the infection at an early stage. This study provides a comprehensive comparison between T- and MT-type during mating and identifies two candidate effectors for further study.

Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?

Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis. In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively. These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.

Transcriptome Dynamics of Common Bean Roots Exposed to Various Heavy Metals Reveal Valuable Target Genes and Promoters for Genetic Engineering

Understanding the gene regulatory basis of plant response to heavy metals (HMs) is fundamental for the management of food safety and security. However, a comprehensive and comparative view of the plant responses to different HMs is still lacking. Here, we compared root transcriptomes in common bean under 9 HM treatments at 50 μM for three time points each. Cd, Cr, Co, Ni, and Pb caused most severe morphological and/or biochemical retardations. A total of 448 genes were found to be responsive to all nine HMs, which were mostly involved in photosynthesis, oxidization-reduction, and ion binding. Cd and Cu triggered the greatest number of unique differentially expressed genes (DEG)s, which were predominantly related to cellular transport/localization in the former and RNA binding in the latter. Short-term and prolonged HM treatments shaped very different DEG patterns. Weighted gene co-expression network analysis identified six co-expression modules showing exceptionally high transcripts abundance in specific HM × time scenarios. We experimentally verified the promoter activity of the gene GIP1 and the novel function of XTH23 under Cu/Cd stress. Collectively, the transcriptomic atlas provides valuable resources for better understanding the common and unique mechanisms of plant response to different HMs and offers a mass of candidate target genes/promoters for genetic engineering.

Molecular signatures in the progression of COVID-19 severity

SARS-CoV-2 is the causative agent of COVID-19 that has infected over 642 million and killed over 6.6 million people around the globe. Underlying a wide range of clinical manifestations of this disease, from moderate to extremely severe systemic conditions, could be genes or pathways differentially expressing in the hosts. It is therefore important to gain insights into pathways involved in COVID-19 pathogenesis and host defense and thus understand the host response to this pathogen at the physiological and molecular level. To uncover genes and pathways involved in the differential clinical manifestations of this disease, we developed a novel gene co-expression network based pipeline that uses gene expression obtained from different SARS-CoV-2 infected human tissues. We leveraged the network to identify novel genes or pathways that likely differentially express and could be physiologically significant in the COVID-19 pathogenesis and progression but were deemed statistically non-significant and therefore not further investigated in the original studies. Our network-based approach aided in the identification of co-expression modules enriched in differentially expressing genes (DEGs) during different stages of COVID-19 and enabled discovery of novel genes involved in the COVID-19 pathogenesis, by virtue of their transcript abundance and association with genes expressing differentially in modules enriched in DEGs. We further prioritized by considering only those enriched gene modules that have most of their genes differentially expressed, inferred by the original studies or this study, and document here 7 novel genes potentially involved in moderate, 2 in severe, 48 in extremely severe COVID-19, and 96 novel genes involved in the progression of COVID-19 from severe to extremely severe conditions. Our study shines a new light on genes and their networks (modules) that drive the progression of COVID-19 from moderate to extremely severe condition. These findings could aid development of new therapeutics to combat COVID-19.

Vector-virus interaction affects viral loads and co-occurrence

BackgroundVector-borne viral diseases threaten human and wildlife worldwide. Vectors are often viewed as a passive syringe injecting the virus. However, to survive, replicate and spread, viruses must manipulate vector biology. While most vector-borne viral research focuses on vectors transmitting a single virus, in reality, vectors often carry diverse viruses. Yet how viruses affect the vectors remains poorly understood. Here, we focused on the varroa mite (Varroa destructor), an emergent parasite that can carry over 20 honey bee viruses, and has been responsible for colony collapses worldwide, as well as changes in global viral populations. Co-evolution of the varroa and the viral community makes it possible to investigate whether viruses affect vector gene expression and whether these interactions affect viral epidemiology.ResultsUsing a large set of available varroa transcriptomes, we identified how abundances of individual viruses affect the vector’s transcriptional network. We found no evidence of competition between viruses, but rather that some virus abundances are positively correlated. Furthermore, viruses that are found together interact with the vector’s gene co-expression modules in similar ways, suggesting that interactions with the vector affect viral epidemiology. We experimentally validated this observation by silencing candidate genes using RNAi and found that the reduction in varroa gene expression was accompanied by a change in viral load.ConclusionsCombined, the meta-transcriptomic analysis and experimental results shed light on the mechanism by which viruses interact with each other and with their vector to shape the disease course.