Gene Co-expression Network Research Articles

The shifts in microbial interactions and gene expression caused by temperature and nutrient loading influence Raphidiopsis raciborskii blooms

Climate change and the trophic status of water bodies are important factors in global occurrence of cyanobacterial blooms. The aim of this study was to explore the cyanobacteria‒bacterial interactions that occur during Raphidiopsis raciborskii (R. raciborskii) blooms by conducting microcosm simulation experiments at different temperatures (20 °C and 30 °C) and with different phosphorus concentrations (0.01 mg/L and 1 mg/L) using an ecological model of microbial behavior and by analyzing microbial self-regulatory strategies using weighted gene coexpression network analysis (WGCNA). Three-way ANOVA revealed significant effects of temperature and phosphorus on the growth of R. raciborskii (P < 0.001). The results of a metagenomics-based analysis of bacterioplankton revealed that the synergistic effects of both climate and trophic changes increased the ability of R. raciborskii to compete with other cyanobacteria for dominance in the cyanobacterial community. The antagonistic effects of climate and nutrient changes favored the occurrence of R. raciborskii blooms, especially in eutrophic waters at approximately 20 °C. The species diversity and richness indices differed between the eutrophication treatment group at 20 °C and the other treatment groups. The symbiotic bacterioplankton network revealed the complexity and stability of the symbiotic bacterioplankton network during blooms and identified the roles of key species in the network. The study also revealed a complex pattern of interactions between cyanobacteria and non-cyanobacteria dominated by altruism, as well as the effects of different behavioral patterns on R. raciborskii bloom occurrence. Furthermore, this study revealed self-regulatory strategies that are used by microbes in response to the dual pressures of temperature and nutrient loading. These results provide important insights into the adaptation of microbial communities in freshwater ecosystems to environmental change and provide useful theoretical support for aquatic environmental management and ecological restoration efforts.

Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke

This study employs machine learning and single-cell transcriptome sequencing (scRNA-seq) analysis to unearth novel biomarkers and delineate the immune characteristics of ischemic stroke (IS), thereby contributing fresh insights into IS treatment strategies.Our research leverages gene expression data sourced from the GEO database. We undertake weighted gene co-expression network analysis (WGCNA) to filter pertinent genes and subsequently employ machine learning algorithms for the identification of feature genes. Concurrently, we rigorously execute quality control measures, dimensionality reduction techniques, and cell annotation on the scRNA-seq data to pinpoint differentially expressed genes (DEGs). The identification of core genes, denoted as Hub genes, among the feature genes and DEGs, is achieved through meticulous overlapping analysis. We illuminate the immune characteristics of these Hub genes using a suite of analytical tools, encompassing CIBERSORT, MCPcounter, and pseudotemporal analysis, all based on immune cell annotations and single-cell transcriptome data.Subsequently, we harness the CMap database to prognosticate potential therapeutic drugs and scrutinize their associations with the identified Hub genes. Our findings unveil robust linkages between three pivotal Hub genes—namely, RNF13, VASP, and CD163—and specific immune cell types such as T cells and neutrophils. These Hub genes predominantly manifest in macrophages and microglial cells within the scRNA-seq immune cell population, exhibiting variances across different stages of cellular differentiation. In conclusion, this study unearths highly pertinent biomarkers for IS diagnosis and elucidates IS-induced immune infiltration characteristics, thus providing a firm foundation for a comprehensive exploration of potential immune mechanisms and the identification of novel therapeutic targets for IS.

Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention

Collagen VI-related dystrophies (COL6RD) are a group of rare muscle disorders caused by mutations in specific genes responsible for type VI collagen production. It affects muscles, joints, and connective tissues, leading to weakness, joint problems, and structural issues. Currently, there is no effective treatment for COL6RD; its management typically addresses symptoms and complications. Therefore, it is essential to decipher the disease’s molecular mechanisms, identify drug targets, and develop effective treatment strategies to treat COL6RD. In this study, we employed differential gene expression analysis, weighted gene co-expression network analysis, and genome-scale metabolic modeling to investigate gene expression patterns in COL6RD patients, uncovering key genes, significant metabolites, and disease-related pathophysiological pathways. First, we performed differential gene expression and weighted gene co-expression network analyses, which led to the identification of 12 genes (CHCHD10, MRPS24, TRIP10, RNF123, MRPS15, NDUFB4, COX10, FUNDC2, MDH2, RPL3L, NDUFB11, PARVB) as potential hub genes involved in the disease. Second, we utilized a drug repurposing strategy to identify pharmaceutical candidates that could potentially modulate these genes and be effective in the treatment. Next, we utilized context-specific genome-scale metabolic models to compare metabolic variations between healthy individuals and COL6RD patients. Finally, we conducted reporter metabolite analysis to identify reporter metabolites (e.g., phosphatidates, nicotinate ribonucleotide, ubiquinol, ferricytochrome C). In summary, our analysis revealed critical genes and pathways associated with COL6RD and identified potential targets, reporter metabolites, and candidate drugs for therapeutic interventions.

New insight into the development of synpolydactyly caused by expansion of HOXD13 polyalanine based on weighted gene co-expression network analysis

BackgroundSynpolydactyly (SPD) is mainly caused by mutations of polyalanine expansion (PAE) in the transcription factor gene HOXD13 and the involved cell types and signal pathway are still not clear possible pathways and single-cell expression characteristics of limb bud in HOXD13 PAE mice was analyzed in this study.MethodWe investigated a previous study of a mouse model with SPD and conducted weighted gene co-expression network analysis (WGCNA) using a single-cell RNA sequencing dataset from limb bud cells of SPD mouse model of HOXD13 + 7A heterozygote.ResultsAnalysis of WGCNA revealed that synpolydactyly-associated Hoxd13 PAEs alter the immune response and osteoclast differentiation, and enhance DNA replication. Bmp4, Hand2, Hoxd12, Lnp, Prrx1, Gmnn, and Cdc6 were found to play potentially key roles in synpolydactyly.ConclusionsThese findings evaluated the main genes related to SPD with PAE mutations in HOXD13 and advance our understanding of human limb development.

Combined Transcriptome and Metabolome Analyses Provide New Insights into the Changes in the Flesh Color of Anthocyanins in Strawberry (Fragaria × ananassa (Weston) Duchesne ex Rozier)

Background: Strawberries are bright in color, sweet and sour in taste, and rich in nutrients and flavonoid compounds such as anthocyanins and proanthocyanidins. The synthesis and accumulation of anthocyanins are the decisive factors that make strawberries appear bright red. From the perspective of plant breeding, a change in flesh color is an important goal. Methods: In this study, two strawberry plants with different flesh colors were selected, and transcriptome and metabolome analyses were performed during the color change period (S1) and ripening period (S2). Results: RNA-seq revealed a total of 13,341 differentially expressed genes (DEGs) between and within materials, which were clustered into 5 clusters. A total of 695 metabolites were detected via metabolome analysis, and 243 differentially regulated metabolites (DRMs) were identified. The anthocyanin biosynthesis, starch and sucrose metabolism and glycolysis/gluconeogenesis pathways were determined to be important regulatory pathways for changes in strawberry flesh color through a joint analysis of RNA-seq data and the metabolome. The leucoanthocyanidin reductase (LAR) and chalcone synthase (CHS) gene is a key gene related to anthocyanins, cinnamic acid, and phenylalanine. In addition, through joint RNA-seq and metabolome analyses combined with weighted gene co-expression network analysis (WGCNA), we identified 9 candidate genes related to strawberry flesh color. Conclusions: Our research findings have laid the groundwork for a more comprehensive understanding of the molecular mechanisms governing the color transformation in strawberry flesh. Additionally, we have identified novel genetic resources that can be instrumental in advancing research related to strawberry color change.

Alteration of the metabolite interconversion enzyme in sperm and Sertoli cell of non-obstructive azoospermia: a microarray data and in-silico analysis

Numerous variables that regulate the metabolism of Sertoli cells and sperm have been identified, one of which is sex steroid hormones. These hormones play a vital role in maintaining energy homeostasis, influencing the overall metabolic balance of the human body. The proper functioning of the reproductive system is closely linked to energy status, as the reproductive axis responds to metabolic signals. The aim of this study was to investigate the gene expression patterns of metabolite interconversion enzymes in testicular cells (Sertoli cells and spermatogonia) of non-obstructive azoospermia (NOA) patients, as compared to normal controls, to understand the molecular mechanisms contributing to NOA. We used microarray and bioinformatics techniques to analyze 2912 genes encoding metabolite interconversion enzymes, including methyltransferase, monooxygenase, transmembrane reductase, and phosphohydrolase, in both testicular cells and normal samples. In sperm, the upregulation of MOXD1, ACAD10, PCYT1A, ARG1, METTL6, GPLD1, MAOA, and CYP46A1 was observed, while ENTPD2, CPT1C, ADC, and CYB5B were downregulated. Similarly, in the Sertoli cells of three NOA patients, RPIA, PIK3C3, LYPLA2, CA11, MBOAT7, and HDHD2 were upregulated, while NAA25, MAN2A1, CYB561, PNPLA5, RRM2, and other genes were downregulated. Using STRING and Cytoscape, we predicted the functional and molecular interactions of these proteins and identified key hub genes. Pathway enrichment analysis highlighted significant roles for G1/S-specific transcription, pyruvate metabolism, and citric acid metabolism in sperm, and the p53 signaling pathway and folate metabolism in Sertoli cells. Additionally, Weighted Gene Co-expression Network Analysis (WGCNA) and single-cell RNA sequencing (scRNA-seq) were performed to validate these findings, revealing significant alterations in gene expression and cellular distribution in NOA patients. Together, these results provide new insights into the molecular mechanisms underlying NOA and identify potential therapeutic targets.

IGSF1: a biomarker for predicting prognosis, immunotherapy response, and drug candidates in COVID-19 combined hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis and a common cause of cancer-related death worldwide, and despite ongoing therapeutic breakthroughs, patient survival benefits are limited. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19) and poses a major threat to humanity worldwide. As the epidemic continues to develop, more and more people are infected with SARS-CoV-2, including patients with HCC. However, the relationship between COVID-19 and HCC has not yet been fully elucidated. Our study aimed to identify the shared genetic characteristics and molecular mechanisms between COVID-19 and HCC. The data involved in this study come from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression(GTEx), and Cancer Cell Line Encyclopedia(CCLE) databases. We used differentially expressed genes to perform enrichment analysis to reveal the biological landscape of COVID-19 combined with HCC. In addition, weighted gene co-expression network analysis (WGCNA) was used to study the co-expression network related to COVID-19 and HCC. We then combined the validation datasets to screen out immunoglobulin superfamily member 1 (IGSF1) as the most important core gene. Finally, we extensively studied the functional expression of IGSF1 in tumor samples, normal tissues, and cancer cell lines. The molecular mechanisms related to COVID-19 and HCC are rarely studied. Our study identifies IGSF1 as a potential therapeutic target and immune-related biomarker for patients with COVID-19 and HCC.

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients.

Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension

Abstract Background Despite its functional importance in various fundamental bioprocesses, studies of N6-methyladenosine (m6A) in the pulmonary arterial hypertension (PAH) are lacking. Here we studied the potential relevance of m6A RNA methylation and immune response in PAH development. Methods We constructed a monocrotaline (MCT) induced PAH rat model and performed Methylated RNA immunoprecipitation sequencing (MeRIP-Seq). The 18 idiopathic PAH (IPAH) microarray data obtained from the GEO database was used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). CIBERSORT was used to investigate the effect of m6A methylation on immune cell infiltration during PAH. Results A differential pattern of m6A abundance, mainly up-methylation, was observed in the lung tissues of rats with MCT induced PAH. By WGCNA, multi-list pathway enrichment analysis and protein-protein interaction (PPI) analysis, we found that m6A methylation modification may play important roles in mediating immune response during PAH. CYBERSORT algorithm indicated that the m6A methylation can drive monocyte to form M1 macrophage, which may be mediated by CCR5 and CXCL9. Conclusion Collectively, m6A landscape is altered in PAH. We summarize newly discovered m6A in controlling immune response, which caused activation of M1 macrophage during PAH. It’s provided a novel insight into the therapeutic mechanisms of PAH.

Combined Transcriptome and Metabolome Analysis Reveals That Carbon Catabolite Repression Governs Growth and Pathogenicity in Verticillium dahliae.

Carbon catabolite repression (CCR) is a common transcriptional regulatory mechanism that microorganisms use to efficiently utilize carbon nutrients, which is critical for the fitness of microorganisms and for pathogenic species to cause infection. Here, we characterized two CCR genes, VdCreA and VdCreC, in Verticillium dahliae that cause cotton Verticillium wilt disease. The VdCreA and VdCreC knockout mutants displayed slow growth with decreased conidiation and microsclerotium production and reduced virulence to cotton, suggesting that VdCreA and VdCreC are involved in growth and pathogenicity in V. dahliae. We further generated 36 highly reliable and stable ΔVdCreA and ΔVdCreC libraries to comprehensively explore the dynamic expression of genes and metabolites when grown under different carbon sources and CCR conditions. Based on the weighted gene co-expression network analysis (WGCNA) and correlation networks, VdCreA is co-expressed with a multitude of downregulated genes. These gene networks span multiple functional pathways, among which seven genes, including PYCR (pyrroline-5-carboxylate reductase), are potential target genes of VdCreA. Different carbon source conditions triggered entirely distinct gene regulatory networks, yet they exhibited similar changes in metabolic pathways. Six genes, including 6-phosphogluconolactonase and 2-ODGH (2-oxoglutarate dehydrogenase E1), may serve as hub genes in this process. Both VdCreA and VdCreC could comprehensively influence the expression of plant cell wall-degrading enzyme (PCWDE) genes, suggesting that they have a role in pathogenicity in V. dahliae. The integrated expression profiles of the genes and metabolites involved in the glycolysis/gluconeogenesis and pentose phosphate pathways showed that the two major sugar metabolism-related pathways were completely changed, and GADP (glyceraldehyde-3-phosphate) may be a pivotal factor for CCR under different carbon sources. All these results provide a more comprehensive perspective for further analyzing the role of Cre in CCR.

Identification of genes associated with sex expression and sex determination in hemp (Cannabis sativa L.).

Dioecy in flowering plants has evolved independently many different times, and thus the genetic mechanisms underlying sex determination are diverse. In hemp (Cannabis sativa), sex is controlled by a pair of sex chromosomes (XX for females and XY for males). In an attempt to understand the molecular mechanism responsible for sex expression in hemp plants, we carried out RNA-Seq of male and female plants at different developmental stages. Using a pipeline involving differential gene expression analysis and weighted gene co-expression network analysis, we identified genes important for male and female flower development. We also demonstrate that sex-biased expression is already established at very early vegetative stages, before the onset of reproductive development, and several genes encoding transcription factors of the REM, bZIP and MADS family as candidate sex determination genes in hemp. Our findings demonstrate that the gene regulatory networks governing male and female development in hemp diverge already at a very early stage, leading to profound morphological differences in male and female hemp plants.

Evaluation on the Efficacy of Farrerol in Inhibiting Shoot Blight of Larch (Neofusicoccum laricinum).

Neofusicoccum laricinum is the causal agent of larch shoot blight, a fungal disease affecting several species of larch. It causes severe damage, including stunting and mortality. This study aims to address the severe impact of larch shoot blight by evaluating the effect of farrerol on the inhibition of Neofusicoccum laricinum in Larix olgensis. We used LC-MS/MS and weighted gene co-expression network analysis to investigate farrerol's effects on Neofusicoccum laricinum and identify associated genes in resistant and susceptible larch. Our study identified significant differences in metabolite profiles between resistant and susceptible cultivars, with higher concentrations of farrerol showing complete inhibition of N. laricinum. Additionally, specific genes associated with farrerol content were up-regulated in resistant larch. Farrerol at higher concentrations completely inhibited N. laricinum, showing a strong correlation with increased disease resistance. This research suggests that farrerol enhances disease resistance in larch and provides a foundation for developing disease-resistant larch varieties based on antifungal metabolite traits.

Screening of m6A-associated ferroptosis-related genes in atherosclerosis based on WGCNA.

N6-methyladenosine (m6A) has been shown to mediate ferroptosis but its role in atherosclerosis (AS) is unclear. Differentially expressed m6A-associated ferroptosis-related genes (DE-m6A-Ferr-RGs) were obtained using differential expression analysis and Pearson correlation analysis. Weighted gene co-expression network analysis (WGCNA) was also performed. The intersection of the module genes and the DE-m6A-Ferr-RGs were recorded as candidate m6A-Ferr-related signature genes. Finally, the m6A-Ferr-related signature genes were screened using least absolute shrinkage and selection operator (LASSO) analysis. Expression validation, receiver operating characteristic ( mapping, and immune correlation analysis were also performed based on the m6A-Ferr-related signature genes. The expression of m6A-Ferr-related signature genes was further validated using a real-time polymerase chain reaction (RT-qPCR). In total, 6,167 differentially expressed genes were intersected with 24 m6A- and 259 ferroptosis-related genes, respectively, resulting in 113 DE-m6A-Ferr-RGs obtained using Pearson's correlation analysis. The module genes obtained from the WGCNA and the 113 DE-m6A-Ferr-RGs were intersected to obtain 48 candidate m6A-Ferr-related signature genes. LASSO analysis was performed and six m6A-Ferr-related signature genes were screened. In addition, the area under the curve values of all six m6A-Ferr-related signature genes were greater than 0.7, indicating that they had potential diagnostic value. Furthermore, the RT-qPCR results revealed that the expression of SLC3A2, NOX4, and CDO1 was consistent with the transcriptome level. Moreover, there was a significant difference in two types of immune cells between the AS and control groups. Naive B cells, CD8+ T cells, regulatory T cells, and activated natural killer cells were positively correlated with CDO1 and NOX4 but negatively correlated with ATG7, CYBB, and SLC3A2. In total, three m6A-Ferr-related signature genes (NOX4, CDO1, and SLC3A2) were obtained through a series of bioinformatics analyses and an RT-qPCR.

Consensus gene co-expression analysis across multiple intestinal tissues to identify key genes and pathways associated with abdominal fat deposition in broilers

ABSTRACT 1. Abdominal fat deposition (AFD) is regulated by multiple intestinal tissues, and changes in the function of intestinal tissues are associated with AFD. Currently, integration of transcriptomic data across multiple intestinal tissues to explore excessive AFD has rarely been reported in broilers. 2. In this study, a consensus gene co-expression network across the duodenum, jejunum, ileum and caecum of high- and low-abdominal fat broiler lines (HL and LL) was constructed using a publicly available transcriptomic data set. Combining the results of functional enrichment analyses and differential gene expression analyses, this investigated the genes and biological pathways across the four intestinal tissues that might influence AFD. 3. In one expression module, NDUFA5, NDUFS6, NDUFA4, NDUFS4, ATP5H, ATP5J and ATP5C1 were significantly enriched in the oxidative phosphorylation pathway, with GPX2 and GSR significantly enriched in the glutathione metabolism pathway. These genes were significantly downregulated in the four intestinal tissues of the HL compared to LL chickens, which may be associated with AFD by increasing intestinal permeability. 4. Lipid metabolism relevant genes were identified in other modules (ALDH7A1, ACSBG1, THEM4 and DECR1), which may be linked to AFD through regulation of lipid metabolism. Interestingly, in the first module, 12 genes were significantly enriched in the proteasome pathway and significantly downregulated in the four intestinal tissues in HL birds compared to LL birds, indicating a link between the proteasome and AFD.

Time-Course Transcriptome Analysis Reveals Distinct Transcriptional Regulatory Networks in Resistant and Susceptible Grapevine Genotypes in Response to White Rot.

Grapevine (Vitis vinifera L.) is a globally significant economic crop. However, its widely cultivated varieties are highly susceptible to white rot disease. To elucidate the mechanisms of resistance in grapevine against this disease, we utilized time-ordered gene co-expression network (TO-GCN) analysis to investigate the molecular responses in the grapevine varieties 'Guifeimeigui' (GF) and 'Red Globe' (RG). An assessment of their resistance demonstrated that GF is highly resistant to white rot, whereas RG is highly susceptible. We conducted transcriptome sequencing and a TO-GCN analysis on leaf samples from GF and RG at seven time points post-infection. Although a significant portion of the differentially expressed genes related to disease resistance were shared between GF and RG, the GF variety rapidly activated its defense mechanisms through the regulation of transcription factors during the early stages of infection. Notably, the gene VvLOX3, which is a key enzyme in the jasmonic acid biosynthetic pathway, was significantly upregulated in GF. Its upstream regulator, Vitvi08g01752, encoding a HD-ZIP family transcription factor, was identified through TO-GCN and yeast one-hybrid analyses. This study provides new molecular insights into the mechanisms of grapevine disease resistance and offers a foundation for breeding strategies aimed at enhancing resistance.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes.

The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis (PBC) and finding relevant biomarkers for diagnosis and therapeutic evaluation. To determine PBC-associated hub genes and assess their clinical utility for disease prediction. PBC expression data were obtained from the Gene Expression Omnibus database. Overlapping genes from differential expression analysis and weighted gene co-expression network analysis (WGCNA) were identified as key genes for PBC. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes. Hub genes were identified in protein-protein interaction (PPI) networks using the Degree algorithm in Cytoscape software. The relationship between hub genes and immune cells was investigated. Finally, a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC. We identified 71 overlapping key genes using differential expression analysis and WGCNA. These genes were primarily enriched in pathways related to cytokine-cytokine receptor interaction, and Th1, Th2, and Th17 cell differentiation. We utilized Cytoscape software and identified five hub genes (CD247, IL10, CCL5, CCL3, and STAT3) in PPI networks. These hub genes showed a strong correlation with immune cell infiltration in PBC. However, inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk. Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment, thereby offering significant clinical utility.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

BackgroundGene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis.ResultsIn this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data.ConclusionThe proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein.

Unveiling the genetic link and pathogenesis between psoriasis and IgA nephropathy based on Mendelian randomization and transcriptome data analyses.

It has been reported that many people with psoriasis have been diagnosed with secondary IgA nephropathy (IgAN). However, the mechanisms behind the association between psoriasis and IgAN have not been well clarified. The connection between psoriasis and IgAN deserves deeper exploration. Mendelian randomization (MR) analysis would be employed to explore the link of causality between IgAN and psoriasis, psoriasis vulgaris, other and unspecified psoriasis, guttate psoriasis, and arthropathic psoriasis. Transcriptomic analyses were carried out against the Gene Expression Omnibus databases. We identified crosstalk genes through the analysis of Differentially expressed genes and weight gene co-expression network analysis. Functional annotations were enriched for these crosstalk genes. Subsequently, we established a protein-protein interaction network, and candidate genes would be discovered through the utilization of the MCODE and CytoHubba plug-in applications. Lastly, the predictive efficacy of these genes was examined via creating receiver operating characteristic curves. The MR analysis suggested that psoriasis vulgaris patients were at a higher risk for IgAN. [OR = 1.040, 95%CI (1.005,1.076), p = 0.026 < 0.05]. Additionally, arthropathic psoriasis may augment the incidence of IgAN [OR = 1.081, 95%CI (1.040-1.124), p < 0.01] in the European population. Through the analysis of DEGs and WGCNA, we identified 12 significant genes (NETO2, RRM2, SLAMF7, GBP1, KIF20A, CCL4, MMP1, IL1β, NDC80, CXCL9, C15orf48, GSTA3), which may be potential crosstalk genes between the two diseases. Then, the functional annotation results indicated that the crosstalk genes seemed primarily involved in immune and inflammatory responses. By establishing the PPI network, we further discovered that CXCL9, IL1β, CCL4, and MMP1 play a vital part in psoriasis and IgAN, and all have good diagnostic values. Our MR analysis provided evidence that genetic vulnerability to IgAN may be associated with an elevated risk of psoriasis vulgaris and arthropathic psoriasis respectively among Europeans. Doctors should be aware of these associations when patients with psoriasis present with renal dysfunction, especially those with psoriasis vulgaris and arthropathic psoriasis. Chronic inflammation, drug effects, and immunity may contribute to the generation and development of both diseases. IL1β, CXCL9, CCL4, and MMP1 may be core biomarkers for psoriasis and IgAN.

Investigating the Role of Hub Calcification Proteins in Atherosclerosis via Integrated Transcriptomics and Network-Based Approach

Atherosclerosis (AS) is a chronic inflammatory condition of the arteries, characterized by plaque formation that can restrict blood flow and lead to potentially fatal cardiovascular events. Given that AS is responsible for a quarter of global deaths, this study aimed to develop a systematic bioinformatics approach to identify biomarkers and regulatory targets involved in plaque development, with the goal of reducing cardiovascular disease risk. AS-specific mRNA expression profiles were retrieved from a publicly accessible database, followed by differentially expressed genes (DEGs) identification and AS-specific weighted gene co-expression network (WGCN) construction. Thereafter, calcification and atherosclerosis-specific (CASS) DEGs were utilized for protein–protein interaction network (PPIN) formation, followed by gene ontology (GO) term and pathway enrichment analyses. Lastly, AS-specific 3-node miRNA feed-forward loop (FFL) construction and analysis was performed. Microarray datasets GSE43292 and GSE28829 were obtained from gene expression omnibus (GEO). A total of 3785 and 6176 DEGs were obtained in case of GSE28829 and GSE43292; 3256 and 5962 module DEGs corresponding to GSE28829 and GSE43292 were obtained from WGCN. From a total of 54 vascular calcification (VC) genes, 20 and 29 CASS-DEGs corresponding to GSE28829 and GSE43292 were overlapped. As observed from FFL centrality measures, the highest-order subnetwork motif comprised one TF (SOX7), one miRNA (miR-484), and one mRNA (SPARC) in the case of GSE28829. Also, in the case of GSE43292, the highest-order subnetwork motif comprised one TF (ESR2), one miRNA (miR-214-3p), and one mRNA (MEF2C). These findings have important implications for developing new therapeutic strategies for AS. The identified TFs and miRNAs may serve as potential therapeutic targets for treating atherosclerotic plaques, offering insights into the molecular mechanisms underlying the pathogenesis and highlighting new avenues for research and treatment.

Analysis of Alternative Splicing Events and Identification of Key Genes in Brassica napus Leaves Infected by Leptosphaeria biglobosa

Alternative splicing (AS) is a prevalent post-transcriptional regulatory mechanism in eukaryotes and plays a crucial role in plant disease resistance. Here, we used the Illumina Novaseq sequencing platform to conduct transcriptome sequencing on canola (Brassica napus) leaves infected with the blackleg pathogen (Leptosphaeria biglobosa strain nm−1) at 0 h, 72 h, 120 h, and 168 h post-inoculation to investigate the mechanism of AS coordination with transcriptional regulation in canola’s response to blackleg disease. The rMATS software (4.1.0) was employed to analyze different AS events in samples taken at 72 h, 120 h, and 168 h. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to elucidate the biological functions of differentially spliced genes at various time points, while Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify key modules and hub genes. As a result, our analysis reveals 16908 AS events across three time points, with 221 being differently spliced. Intron retention (RI) was the most common AS event, accounting for approximately 55% of all events, while alternative 5′ splice site events were least common, comprising only 2%. Furthermore, a total of 213 significantly differentially spliced genes were identified, which were enriched in functions related to protein kinase activity, transferase activity, and pathways such as MAPK signaling pathway—plant and plant hormone signal transduction. WGCNA identified three key modules and ten hub genes, including calcium-binding transcription activator 1, LRR class receptor serine/threonine protein kinase FEI 2, PLATZ transcription factor family proteins, serine/threonine protein kinase PRP4, and E3 ubiquitin ligase SUD1, all of which are associated with canola resistance to L. biglobosa. Thus, this study provides a theoretical basis for identifying disease-resistance genes involved in AS and for exploring the functions of AS gene isoforms in canola.