Weighted Gene Correlation Network Analysis Applied to Identify the Immune Cell-related Hub Genes in ANCA Nephritis

Background: Antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is the most common reason caused rapidly progressive glomerulonephritis worldwide. But the molecular mechanisms of ANCA - associated nephritis (AAN) have not been thoroughly expounded. So that，we aim to seek the potential molecular pathogenesis of AAN by bioinformatic.Result: Finally, four hub genes, PBK, CEP55, CCNB1 and BUB1B, were identified. These four hub geneswas verified higher in AAN than normal.Conclusion: Those four genes identified by integrated bioinformatics analysis may play a critical role in AAN. May offering a new insights and potential therapeutic to the AAN

A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing

Background Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells. Results We observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96. Conclusions Our results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr.

LCK and CD3E Orchestrate the Tumor Microenvironment and Promote Immunotherapy Response and Survival of Muscle-Invasive Bladder Cancer Patients

Background: Studies have demonstrated the significance of multiple biomarkers for bladder cancer. Here, we attempt to present biomarkers potentially predictive of the prognosis and immunotherapy response of muscle-invasive bladder cancer (MIBC).Method: Immune and stromal scores were calculated for MIBC patients from The Cancer Genome Atlas (TCGA). Core differential expression genes (DEGs) with prognostic value were identified and validated using an independent dataset GSE31684. The clinical implications of prognostic genes and the inter-gene correlation were presented. The distribution of tumor-infiltrating immune cells (TICs), the correlation with tumor mutation burden (TMB), and the expression of eight immune checkpoint–relevant genes and CD39 were accordingly compared. Two bladder cancer cohorts (GSE176307 and IMvigor210) receiving immunotherapy were recruited to validate the prognostic value of LCK and CD3E for immunotherapy.Results: 361 MIBC samples from TCGA revealed a worse overall survival for higher stromal infiltration (p = 0.009) but a better overall survival for higher immune infiltration (p = 0.042). CD3E and LCK were independently validated by TCGA and GSE31684 to be prognostic for MIBC. CD3E was the most correlative gene of LCK, with a coefficient of r = 0.86 (p < 0.001). CD8+ T cells and macrophage M1 are more abundant in favor of a higher expression of CD3E and LCK in MIBC and across pan-cancers. Immune checkpoints like CTLA4, CD274 (PD-1), and PDCD1 (PD-L1) were highly expressed in high-CD3E and high-LCK groups for MIBC and also for pan-cancers, except for thymoma. LCK and CD3E had a moderate positive correlation with CD39 expression. Importantly, high-LCK and high-CD3E groups had a higher percentage of responders than the low-expression groups both in GSE176307 (LCK: 22.73vs. 13.64%, CD3E: 22.00 vs. 13.16%) and IMvigor210 cohorts (LCK: 28.19 vs. 17.45%, CD3E: 25.50 vs. 20.13%).Conclusion: CD3E and LCK were potential biomarkers of MIBC. CD3E and LCK were positively correlated with several regular immunotherapy biomarkers, which is supported by real-world outcomes from two immunotherapy cohorts.

Bioengineering the ameloblastoma tumour to study its effect on bone nodule formation

Ameloblastoma is a benign, epithelial cancer of the jawbone, which causes bone resorption and disfigurement to patients affected. The interaction of ameloblastoma with its tumour stroma drives invasion and progression. We used stiff collagen matrices to engineer active bone forming stroma, to probe the interaction of ameloblastoma with its native tumour bone microenvironment. This bone-stroma was assessed by nano-CT, transmission electron microscopy (TEM), Raman spectroscopy and gene analysis. Furthermore, we investigated gene correlation between bone forming 3D bone stroma and ameloblastoma introduced 3D bone stroma. Ameloblastoma cells increased expression of MMP-2 and -9 and RANK temporally in 3D compared to 2D. Our 3D biomimetic model formed bone nodules of an average surface area of 0.1 mm2 and average height of 92.37 $$\pm $$ ± 7.96 μm over 21 days. We demonstrate a woven bone phenotype with distinct mineral and matrix components and increased expression of bone formation genes in our engineered bone. Introducing ameloblastoma to the bone stroma, completely inhibited bone formation, in a spatially specific manner. Multivariate gene analysis showed that ameloblastoma cells downregulate bone formation genes such as RUNX2. Through the development of a comprehensive bone stroma, we show that an ameloblastoma tumour mass prevents osteoblasts from forming new bone nodules and severely restricted the growth of existing bone nodules. We have identified potential pathways for this inhibition. More critically, we present novel findings on the interaction of stromal osteoblasts with ameloblastoma.

GLRX5 as a Differential Diagnosis Marker for Kashin-Beck Disease and Osteoarthritis and Its Clinical Significance

Background: Kashin-Beck disease (KBD) is currently an endemic form of osteoarthritis. In this study, we explored novel KBD diagnostic biomarkers.Methods: The GSE59446 dataset was used to conduct Weighted Gene Co-expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis with peripheral blood samples of 100 healthy individuals and 100 KBD patients. As part of the gene ontology pathway enrichment analysis, genes related to SONFH and DEGs were selected from the extraction module. Then, central DEGs were selected for LASSO analysis, and, based on SVM-RFE and DEG results, overlapping genes were identified as key KBD genes. Next, we analyzed the correlations between the selected genes and age, gender, and other factors to eliminate their influences on gene expression. Finally, we evaluated the diagnostic value of key KBD genes using case information collected by us.Results: Seven gene co-expression modules were created using WGCNA. The turquoise module was identified as a KBD key module since it showed the highest correlation to KBD. The functional enrichment analysis revealed that the genes associated with this key module were mainly involved in mitochondrial reactions, protein heterooligomerization, and negatively regulating cysteine-type endopeptidase-dependent apoptotic processes. Additionally, 12 key genes were identified using the LASSO analysis, 5 major genes using SVM-RFE analysis, and 36 DEGs were screened through the "limma" R package. The GLRX5 gene - pivotal in DEGs, LASSO, and SVM-RFE - was further aggregated as the key KBD gene. Correlation analyses confirmed the GLRX5 diagnostic value for KBD and that it was not related to age, gender, and other factors. Finally, data from our patients demonstrated that GLRX5 can be a KBD diagnostic biomarker.Conclusions: We demonstrated that the target gene GLRX5 can be a KBD non-invasive diagnosis biomarker.

Generation and network analysis of an RNA-seq transcriptional atlas for the rat.

The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and down-loaded 7700 rat RNA-seq datasets from public repositories, down-sampled them to a common depth and quantified expression. Data from 590 rat tissues and cells, averaged from each Bioproject, can be visualised and queried at http://biogps.org/ratatlas. Gene correlation network (GCN) analysis revealed clusters of transcripts that were tissue or cell-type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species whilst some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell-type specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single cell RNA-seq data.

Transcriptomics Integrated with Metabolomics Reveals the Effect of Cluster Thinning on Monoterpene Biosynthesis in ‘Muscat Hamburg’ Grape

Monoterpenes are crucial to floral and fruit aromas in grapes and wines. Cluster thinning is a common practice for improving grape quality. Using Vitis vinifera cv. Muscat Hamburg, the effects of three cluster-thinning regimes on the biosynthesis and accumulation of monoterpenes from véraison to harvest were investigated at the transcriptomics and targeted metabolomics levels. It was observed that more intense thinning produced higher concentrations of total monoterpenes, particularly in their bound forms. The numbers of differentially expressed genes among the three treatments were 193, 200, and 238 at the three developmental stages. In total, 10 modules were identified from a weighted gene correlation network analysis, and one module including 492 unigenes was associated with monoterpene metabolism. These findings provide new insights into the molecular basis of the relationship between cluster thinning and monoterpene biosynthesis in Muscat Hamburg grape. Cluster thinning could be carefully considered for its application in production.

Identification of Key Exosome Gene Signature in Mediating Coronary Heart Disease by Weighted Gene Correlation Network Analysis

Background. Coronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD. Method. Six CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples. Result. We obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients. Conclusion. The underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD.

Vegetarian diet duration’s influence on women’s gut environment

Background Nutrient composition of vegetarian diets is greatly different from that of omnivore diets, which may fundamentally influence the gut microbiota and fecal metabolites. The interactions between diet pattern and gut environment need further illustration. This study aims to compare the difference in the gut microbiota and fecal metabolites between vegetarian and omnivore female adults and explore associations between dietary choices/duration and gut environment changes. Methods In this study, investigations on the fecal metabolome together with the gut microbiome were performed to describe potential interactions with quantitative functional annotation. In order to eliminate the differences brought by factors of gender and living environment, 80 female adults aged 20 to 48 were recruited in the universities in Beijing, China. Quantitative Insights Into Microbial Ecology (QIIME) analysis and Ingenuity Pathway Analysis (IPA) were applied to screen differential data between groups from gut microbiota and fecal metabolites. Furthermore, weighted gene correlation network analysis (WGCNA) was employed as the bioinformatics analysis tool for describing the correlations between gut microbiota and fecal metabolites. Moreover, participants were further subdivided by the vegetarian diet duration for analysis. Results GPCR-mediated integration of enteroendocrine signaling was predicted to be one of the regulatory mechanisms of the vegetarian diet. Intriguingly, changes in the gut environment which occurred along with the vegetarian diet showed attenuated trend as the duration increased. A similar trend of returning to “baseline” after a 10-year vegetarian diet was detected in both gut microbiota and fecal metabolome. Conclusions The vegetarian diet is beneficial more than harmful to women. Gut microbiota play roles in the ability of the human body to adapt to external changes.