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.

Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease.

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social and environmental factors. DNA methylation patterns reflect such multivariate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with multivariate sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing regions mapped to genes enriched for brain glial functions. Comethyl is expected to be useful in uncovering the multivariate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl with complete documentation and example analyses.

Integrative ATAC-seq and RNA-seq analysis associated with diabetic nephropathy and identification of novel targets for treatment by dapagliflozin.

Dapagliflozin (DAPA) are clinically effective in improving diabetic nephropathy (DN). However, whether and how chromatin accessibility changed by DN responds to DAPA treatment is unclear. Therefore, we performed ATAC-seq, RNA-seq, and weighted gene correlation network analysis to identify the chromatin accessibility, the messenger RNA (mRNA) expression, and the correlation between clinical phenotypes and mRNA expression using kidney from three mouse groups: db/m mice (Controls), db/db mice (case group), and those treated with DAPA (treatment group). RNA-Seq and ATAC-seq conjoint analysis revealed many overlapping pathways and networks suggesting that the transcriptional changes of DN and DAPA intervention largely occured dependently on chromatin remodeling. Specifically, the results showed that some key signal transduction pathways, such as immune dysfunction, glucolipid metabolism, oxidative stress and xenobiotic and endobiotic metabolism, were repeatedly enriched in the analysis of the RNA-seq data alone, as well as combined analysis with ATAC-seq data. Furthermore, we identified some candidate genes (UDP glucuronosyltransferase 1 family, Dock2, Tbc1d10c, etc.) and transcriptional regulators (KLF6 and GFI1) that might be associated with DN and DAPA restoration. These reversed genes and regulators confirmed that pathways related to immune response and metabolism pathways were critically involved in DN progression.

Atonal homolog 7 (ATOH7) confers neuroprotection for photoreceptor cells in glaucoma via inhibition of the notch pathway.

Single-cell RNA sequencing (scRNA-seq) technologies enable the profiling and analysis of the transcriptomes of single cells and hold promise for clarifying gene mechanisms at single-cell resolution. We based this study on scRNA-seq data to reveal glaucoma-related genes and downstream pathways with neuroprotection effects. The scRNA-seq datasets related to glaucoma of retinal tissue samples of human beings and Atonal Homolog 7 (ATOH7)-null mice were obtained from the GEO database. The 74 top marker genes and 20 cell clusters were obtained in human retinal tissue samples. The key gene ATOH7 was found after the intersection with genes from GeneCards data. In the ATOH7-null mouse retinal tissue samples, pseudotime inference demonstrated significant changes in cell differentiation. Moreover, mouse retinal photoreceptor cells (PRCs) were cultured and treated with lentivirus carrying oe-ATOH7 alone or in combination with Notch signaling pathway activator Jagged-1/FC, after which cell biological functions were determined. The involvement of ATOH7 in glaucoma was identified through regulating PRCs. Furthermore, ATOH7 conferred neuroprotection in PRCs in glaucoma by mediating the Notch signaling pathway. In vitro data confirmed that ATOH7 overexpression promoted the differentiation of PRCs and inhibited their apoptosis by suppressing the Notch signaling pathway. The evidence provided by our study highlighted the involvement of ATOH7 in the blockade of the Notch signaling pathway, resulting in the neuroprotection for PRCs in glaucoma.

Identification of biomarkers related to prognosis and diagnosis of periodontitis by bioinformatics based on public database.

OBJECTIVES: Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored. METHODS: GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi-array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver-operating characteristic curve analysis was used to verify the validity and agility of the hub genes-based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real-time polymerase chain reaction and western blotting. RESULTS: Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue. CONCLUSION: We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis.

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model.

BACKGROUND: Kawasaki disease (KD) is a febrile systemic vasculitis involvingchildren younger than five years old. However, the specific biomarkers and precise mechanisms of this disease are not fully understood, which can delay the best treatment time, hence, this study aimed to detect the potential biomarkers and pathophysiological process of KD through bioinformatic analysis. METHODS: The Gene Expression Omnibus database (GEO) was the source of the RNA sequencing data from KD patients. Differential expressed genes (DEGs) were screened between KD patients and healthy controls (HCs) with the "limma" R package. Weighted gene correlation network analysis (WGCNA) was performed to discover the most corresponding module and hub genes of KD. The node genes were obtained by the combination of the least absolute shrinkage and selection operator (LASSO) regression model with the top 5 genes from five algorithms in CytoHubba, which were further validated with the receiver operating characteristic curve (ROC curve). CIBERSORTx was employed to discover the constitution of immune cells in KDs and HCs. Functional enrichment analysis was performed to understand the biological implications of the modular genes. Finally, competing endogenous RNAs (ceRNA) networks of node genes were predicted using online databases. RESULTS: A total of 267 DEGs were analyzed between 153 KD patients and 92 HCs in the training set, spanning two modules according to WGCNA. The turquoise module was identified as the hub module, which was mainly enriched in cell activation involved in immune response, myeloid leukocyte activation, myeloid leukocyte mediated immunity, secretion and leukocyte mediated immunity biological processes; included type II diabetes mellitus, nicotinate and nicotinamide metabolism, O-glycan biosynthesis, glycerolipid and glutathione metabolism pathways. The node genes included ADM, ALPL, HK3, MMP9 and S100A12, and there was good performance in the validation studies. Immune cell infiltration analysis revealed that gamma delta T cells, monocytes, M0 macrophage, activated dendritic cells, activated mast cells and neutrophils were elevated in KD patients. Regarding the ceRNA networks, three intact networks were constructed: NEAT1/NORAD/XIST-hsa-miR-524-5p-ADM, NEAT1/NORAD/XIST-hsa-miR-204-5p-ALPL, NEAT1/NORAD/XIST-hsa-miR-524-5p/hsa-miR-204-5p-MMP9. CONCLUSION: To conclude, the five-gene signature and three ceRNA networks constructed in our study are of great value in the early diagnosis of KD and might help to elucidate our understanding of KD at the RNA regulatory level.

Identification of 8 Feature Genes Related to Clear Cell Renal Cell Carcinoma Progression Based on Co-Expression Analysis.

BACKGROUND: The aim of the study was to screen biomarkers related to clear cell renal cell carcinoma (ccRCC) progression and prognosis. METHODS: 1,026 ccRCC-related genes were dug from 494 ccRCC samples in TCGA based on weighted gene co-expression network analysis, and 7 modules were identified. Afterward, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on modules of interest. Genes in these modules were taken as the input to construct a protein-protein interaction network. Thereafter, 30 genes with the highest connectivity were taken as core genes. Univariate Cox regression, LASSO Cox regression, and multivariate Cox regression analyses were performed on core genes. Univariate and multivariate Cox regression analyses were performed on patients' clinical characteristics and risk scores. RESULTS: Stage displayed significantly strong correlations with green module and red module (p < 0.001). Genes in modules participated in biological functions including T-cell proliferation and regulation of lymphocyte activation. GSEA showed that high- and low-risk groups exhibited significant enrichment differences in pathways related to immunity, cell migration, and invasion. Immune infiltration analysis also presented a strong correlation between the expression of these 8 genes and immune cell infiltration in ccRCC samples. It was displayed that risk score could be an independent factor to assess patients' prognosis. CONCLUSION: We determined biomarkers relevant to ccRCC progression, offering candidate targets for ccRCC treatment.

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods.

Lung adenocarcinoma (LUAD) is one of the most common forms of lung cancer, with a very high mortality rate. Although the treatments available for LUAD have become more effective in recent years, significant improvement is still needed. Advances in sequencing technologies and bioinformatics analysis have enabled new approaches to be developed for identifying drug targets. In this work we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify hub genes related to LUAD through Weighted Gene Correlation Network Analysis (WGCNA) and other bioinformatics methods, with the goal of identifying new drug targets for cancer treatment.

Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation.

The human endometrium is a dynamic tissue that only is receptive to host the embryo during a brief time in the middle secretory phase, called the window of implantation (WOI). Despite its importance, regulation of the menstrual cycle remains incompletely understood. The aim of this study was to characterize the gene cooperation and regulation of menstrual cycle progression, to dissect the molecular complexity underlying acquisition of endometrial receptivity for a successful pregnancy, and to provide the scientific community with detailed gene co-expression information throughout the menstrual cycle on a user-friendly web-tool database. A retrospective gene co-expression analysis was performed based on the endometrial receptivity array (ERarray) gene signature from 523 human endometrial samples collected across the menstrual cycle, including during the WOI. Gene co-expression analysis revealed the WOI as having the significantly smallest proportion of negative correlations for transcriptional profiles associated with successful pregnancies compared to other cycle stages, pointing to a global transcriptional derepression being involved in acquisition of endometrial receptivity. Regulation was greatest during the transition between proliferative and secretory endometrial phases. Further, we prioritized nuclear hormone receptors as major regulators of this derepression and proved that some genes and transcription factors involved in this process were dysregulated in patients with recurrent implantation failure. We also compiled the wealth of gene co-expression data to stimulate hypothesis-driven single-molecule endometrial studies in a user-friendly database: Menstrual Cycle Gene Co-expression Network (www.menstrualcyclegcn.com). This study revealed a global transcriptional repression across the menstrual cycle, which relaxes when the WOI opens for transcriptional profiles associated with successful pregnancies. These findings suggest that a global transcriptional derepression is needed for embryo implantation and early development.

Elevation of LncRNA ENST00000453774.1 Prevents Renal Fibrosis by Upregulating FBN1, IGF1R, and KLF7.

INTRODUCTION: Transforming growth factor-ß (TGF-ß), a common outcome of various progressive chronic kidney diseases, can regulate and induce fibrosis. OBJECTIVE: The study aimed to identify downstream targets of lncRNA ENST00000453774.1 (lnc453774.1) and outline their functions on the development of renal fibrosis. METHODS: HK-2 cells were induced with 5 ng/mL TGF-ß1 for 24 h to construct a renal fibrosis cell model. Differentially expressed genes (DEGs) targeted by lnc453774.1 in TGF-ß1-induced renal fibrosis were identified using RNA sequencing. The dataset GSE23338 was employed to identify DEGs in 48-h TGF-ß1-stimulated human kidney epithelial cells, and these DEGs were intersected with genes in the key module using weighted gene co-expression network analysis to generate key genes associated with renal fibrosis. MicroRNAs (miRs) that had targeting relationship with keys genes and lnc453774.1 were predicted by using Miranda software, and important genes were intersected with key genes that had targeting relationship with these miRs. Key target genes by lnc453774.1 were identified in a protein-protein interaction network among lnc453774.1, important genes, and reported genes related to autophagy, oxidative stress, and cell adhesion. RESULTS: Key genes in the key module (turquoise) were intersected with DEGs in the dataset GSE23338 and yielded 20 key genes regulated by lnc453774.1 involved in renal fibrosis. Fourteen miRs had targeting relationship with lnc453774.1 and key genes, and 8 important genes targeted by these 14 miRs were identified. Fibrillin-1 (FBN1), insulin-like growth factor 1 receptor (IGF1R), and Kruppel-like factor 7 (KLF7) were identified to be involved in autophagy, oxidative stress, and cell adhesion and were elevated in the lnc453774.1-overexpressing TGF-ß1-induced cells. CONCLUSION: These results show FBN1, IGF1R, and KLF7 serve as downstream targets of lnc453774.1, and that lnc453774.1 may protect against renal fibrosis through competing endogenous miRs which target FBN1, IGF1R, and KLF7 mRNAs.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening.

BACKGROUND: Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. METHODS: The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. RESULTS: Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. CONCLUSIONS: Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer.

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R 2 = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors. In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms.

Four genes predict the survival of osteosarcoma patients based on TARGET database.

Osteosarcoma represents one of the most aggressive tumors of bone among adolescents and young adults. Despite improvements in treatment, osteosarcoma has a grave prognosis. The identification of prognostic factors is still in its infancy. Weighted gene correlation network analysis (WGCNA) was conducted on mRNA-sequencing and clinical information (gender, survival and metastasis) of osteosarcoma patients from the TARGET database to obtain genes in modules associated with metastasis of osteosarcoma. The Cox regression analysis was then performed on the gene expression profile from TARGET to screen genes associated with patients' survival. Known genes related to osteosarcoma were obtained by intersecting osteosarcoma-related genes from DisGeNET and DiGSeE, followed by the construction of PPI network of osteosarcoma-related genes and survival-related genes in modules. The screened key genes were subject to multi-factor Cox proportional hazards model, and osteosarcoma patients were classified into high- and low- risk groups according to the risk score to evaluate the potential of key genes to predict the survival of osteosarcoma patients. The WGCNA showed that 4 genes in tan and 19 genes in pink modules were related to the survival of osteosarcoma patients. Osteosarcoma-related known genes (9) were obtained in intersection of DisGeNET and DiGSeE. PPI network identified 4 key genes (KRT5, HIPK2, MAP3K5 and CD5) closely associated with survival of osteosarcoma patients. HIPK2, MAP3K5 and CD5 expression was inversely correlated with survival risk, while KRT5 expression was positively correlated with survival risk. These results show KRT5, HIPK2, MAP3K5 and CD5 serve as prognostic factors of osteosarcoma patients.

The key candidate genes in tubulointerstitial injury of chronic kidney diseases patients as determined by bioinformatic analysis.

Pathologic changes such as renal tubular atrophy and interstitial fibrosis are common in chronic kidney disease (CKD), which in turn, leads to loss of renal function. The aims of present study were to screen critical genes with tubulointerstitial lesion in CKD by weighted gene correlation network analysis (WGCNA). Gene expression data including 169 tubulointerstitial samples of CKD and 21 controls downloaded from Gene Expression Omnibus (GEO) database. Totally 294 differentially expressed genes (DEGs) were screened, including 180 upregulated and 114 downregulated genes. Meanwhile, 90 expression data of tubulointerstitial samples combined with clinic information were applied to explore the potential mechanisms of tubulointerstitial lesion. As a consequence, the blue, brown and yellow modules which included the most DEGs compared to the other modules and exhibited strongly association with eGFR, were significantly enriched in several signalling pathways that have been reported involved in pathogenesis of CKD. Furthermore, it was found that the four genes (PLG, ITGB2, CTSS and CCL5) was one of the DEGs which also be identified as hub genes according to Kwithin. Finally, the Nephroseq online tool showed that the tubulointerstitial expression levels of PLG significantly positively correlated with the estimated glomerular filtration rate (eGFR), while ITGB2, CTSS and CCL5 connected negatively to the eGFR of CKD patients. Taken together, WGCNA is an efficient approach to system biology. By this procedure, the present study enhanced the understanding of the transcriptome status of CKD and might shed a light on the further investigation on the mechanisms of renal tubulointerstitial injury in CKD. SIGNIFICANCE OF STUDY: Traditional molecular biology can only explain the local part of the biological system, and difficult to make comprehensive exploration of the whole biological system in the chronic kidney disease (CKD). In this study, we gave an explicit elucidation of dysregulated protein coding genes by the analysis of microarray datasets in GEO database. We have presented a novel approach using weighted gene correlation network analysis (WGCNA) to explore the DEGs which implicated in CKD process. In this study, we conducted WGCNA to explore the potential mechanisms of renal tubular damage, and provided novel biomarkers associated with the molecular mechanisms underlying renal tubulointerstitial injury in CKD.

Weighted Gene Correlation Network Analysis (WGCNA) Detected Loss of MAGI2 Promotes Chronic Kidney Disease (CKD) by Podocyte Damage.

BACKGROUND/AIMS: Podocyte damage is associated with proteinuria, glomerulosclerosis and decline of renal function. This study aimed to screen critical genes associated with podocyte injury in chronic kidney disease (CKD) by weighted gene correlation network analysis (WGCNA), and explore related functions. METHODS: GSE66107, GSE93798, GSE30528, GSE32591 gene expression data including podocyte injury models or glomeruli in CKD patients were downloaded from the GEO database. R was used for data analysis. Differentially expressed genes (DEGs) (FDR< 0.05 or |Fold Change|≥1.5) in GSE993395 were assessed by WGCNA. According to Gene Ontology (GO) and known podocyte standard genes (PSGs), podocyte injury-associated modules were defined, with hub genes selected based on average intramodular connectivity. The Cytoscape software was used for network visualization. Nephroseq was used to assess the clinical significance of hub genes. Small interfering RNA (siRNA) was used to evaluate the roles of hub genes in podocyte injury Results: Totally 7957 DEGs were screened, with 15 (co.DEGs) altered in all 4 datasets; 4031 DEGs were used for WGCNA, encompassing 12 modules. Green modules (most PSGs and co.DEGs) were significantly enriched in glomerular development, and considered podocyte injury-associated modules. Furthermore, MAGI2 (a hub gene) was also a co.DEG and PSG. Glomerular MAGI2 levels were reduced in various kidney diseases, and positively and negatively associated with glomerular filtration rate and urinary protein levels in CKD patients. Moreover, MAIG2 knockdown reduced NPHS2, CD2AP and SYNPO levels, and induced podocyte rearrangement and apoptosis. CONCLUSION: MAGI2 identified by WGCNA regulates cytoskeletal rearrangement in podocytes, with its loss predisposing to proteinuria and CKD.

Amino acid and lipid associated plasma metabolomic patterns are related to healthspan indicators with ageing.

Advancing age is associated with impairments in numerous physiological systems, leading to an increased risk of chronic disease and disability, and reduced healthspan (the period of high functioning healthy life). The plasma metabolome is thought to reflect changes in the activity of physiological systems that influence healthspan. Accordingly, we utilized an LC-MS metabolomics analysis of plasma collected from healthy young and older individuals to characterize global changes in small molecule abundances with age. Using a weighted gene correlation network analysis (WGCNA), similarly expressed metabolites were grouped into modules that were related to indicators of healthspan, including clinically relevant markers of morphology (body mass index, body fat, and lean mass), cardiovascular health (systolic/diastolic blood pressure, endothelial function), renal function (glomerular filtration rate), and maximal aerobic exercise capacity in addition to conventional clinical blood markers (e.g. fasting glucose and lipids). Investigation of metabolic classes represented within each module revealed that amino acid and lipid metabolism as significantly associated with age and indicators of healthspan. Further LC-MS/MS targeted analyses of the same samples were used to identify specific metabolites related to age and indicators of healthspan, including methionine and nitric oxide pathways, fatty acids, and ceramides. Overall, these results demonstrate that plasma metabolomics profiles in general, and amino acid and lipid metabolism in particular, are associated with ageing and indicators of healthspan in healthy adults.

Crucial genes of inflammatory bowel diseases explored by gene expression profiling analysis.

OBJECTIVES: This study aimed to uncover new potential genes associated with the inflammatory bowel diseases (IBDs). MATERIALS AND METHODS: The datasets GSE36807 and GSE9686 were obtained from Gene Expression Omnibus (GEO). Totally, 24 Crohn's disease (CD) samples, 20 ulcerative colitis (UC) samples and 15 healthy controls in the two datasets were used for our analysis. The differentially expressed genes (DEGs) were identified by limma package. Then, co-expression network was constructed by weighted gene correlation network analysis (WGCNA) package, and co-expression network modules were obtained via clustering method. The top 100 genes with the highest connectivity degrees were selected to construct a new co-expression network (CEN). Besides, pathway enrichment analysis for the genes in identified modules was conducted with the clusterProfiler package in R. RESULTS: Totally, 302 and 2276 DEGs were respectively identified in CD and UC samples, and 291 ones were both differentially expressed in the two subtypes. Five modules were identified from the CEN. In the new CEN consisted of the top 100 genes with the highest connectivity degrees, the up-regulated DEGs all belonged to module 5, and the down-regulated ones all belonged to module 1. Furthermore, pathway enrichment analysis showed that some DEGs were related to primary immunodeficiency (e.g., CD4, CD3D and CD40LG), complement and coagulation cascades (e.g., C2, C1QB and C7) and nitrogen metabolism (e.g., CA1, CA12 and CA2). CONCLUSION: The DEGs correlated with primary immunodeficiency, complement and coagulation cascades and nitrogen metabolism might be important for the development of IBD.

Deciphering ascorbic acid regulatory pathways in ripening tomato fruit using a weighted gene correlation network analysis approach.

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid (AsA), which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato (Solanum lycopersicum L.) fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty-four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.

Screening of key pathogenic genes of type 1 diabetes in children.

Background: The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. Methods: The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. Results: A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Conclusions: Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis.

BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood. MATERIAL/METHODS: Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers. RESULTS: WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells. CONCLUSION: Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.