Hub genes identification and validation of ferroptosis in SARS-CoV-2 induced ARDS: perspective from transcriptome analysis.

Introduction: Acute Respiratory Distress Syndrome (ARDS) poses a significant health challenge due to its high incidence and mortality rates. The emergence of SARS-CoV-2 has added complexity, with evidence suggesting a correlation between COVID-19 induced ARDS and post-COVID symptoms. Understanding the underlying mechanisms of ARDS in COVID-19 patients is crucial for effective clinical treatment. Method: To investigate the potential role of ferroptosis in SARS-CoV-2 induced ARDS, we conducted a comprehensive analysis using bioinformatics methods. Datasets from the Gene Expression Omnibus (GEO) were utilized, focusing on COVID-19 patients with varying ARDS severity. We employed weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and single-cell sequencing to identify key genes associated with ferroptosis in ARDS. Hub genes were validated using additional GEO datasets and cell experiment. Result: The analysis discerned 916 differentially expressed genes in moderate/severe ARDS patients compared to non-critical individuals. Weighted Gene Co-expression Network Analysis (WGCNA) unveiled two modules that exhibited a positive correlation with ARDS, subsequently leading to the identification of 15 hub genes associated with ferroptosis. Among the noteworthy hub genes were MTF1, SAT1, and TXN. Protein-protein interaction analysis, and pathway analysis further elucidated their roles. Immune infiltrating analysis highlighted associations between hub genes and immune cells. Validation in additional datasets confirmed the upregulation of MTF1, SAT1, and TXN in SARS-CoV-2-induced ARDS. This was also demonstrated by qRT-PCR results in the BEAS-2B cells vitro model, suggesting their potential as diagnostic indicators. Discussion: This study identifies MTF1, SAT1, and TXN as hub genes associated with ferroptosis in SARS-CoV-2-induced ARDS. These findings provide novel insights into the molecular mechanisms underlying ARDS in COVID-19 patients and offer potential targets for immune therapy and targeted treatment. Further experimental validation is warranted to solidify these findings and explore therapeutic interventions for ARDS in the context of COVID-19.

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning.

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signalling in OS and further constructed novel hypoxia-related gene signatures for OS prediction and prognosis. This study employed Gene Set Enrichment Analysis (GSEA), Weighted correlation network analysis (WGCNA) and Least absolute shrinkage and selection operator (LASSO) analyses to identify Stanniocalcin 2 (STC2) and Transmembrane Protein 45A (TMEM45A) as the diagnostic biomarkers, which further assessed by Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves in training and test dataset. Univariate and multivariate Cox regression analyses were used to construct the prognostic model. STC2 and metastasis were devised to forge the OS risk model. The nomogram, risk score, Kaplan Meier plot, ROC, DCA, and calibration curves results certified the excellent performance of the prognostic model. The expression level of STC2 and TMEM45A was validated in external datasets and cell lines. In immune cell infiltration analysis, cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. And the immune infiltration of CAFs was negatively associated with the expression of STC2 (P < 0.05). Pan-cancer analysis revealed that the expression level of STC2 was significantly higher in Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Lung squamous cell carcinoma (LUSC), and Stomach adenocarcinoma (STAD). Additionally, the higher expression of STC2 was associated with the poor outcome in those cancers. In summary, this study identified STC2 and TMEM45A as novel markers for the diagnosis and prognosis of osteosarcoma, and STC2 was shown to correlate with immune infiltration of CAFs negatively.

Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy.

Background: In the context of head-and-neck squamous cell carcinoma (HNSCC), dendritic cells (DCs) assume pivotal responsibilities, acting as architects of antigen presentation and conductors of immune checkpoint modulation. In this study, we aimed to identify hub genes associated with DCs in HNSCC and explore their prognostic significance and implications for immunotherapy. Methods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA)-HNSCC and GSE65858 cohorts underwent meticulous analysis. Employing weighted gene co-expression network analysis (WGCNA), we delineated candidate genes pertinent to DCs. Through the application of random survival forest and least absolute shrinkage and selection operator (LASSO) Cox's regression, we derived key genes of significance. Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) highlighted transcription factors, with Dual-luciferase assays confirming their regulatory role. Furthermore, immunotherapeutic sensitivity was assessed utilizing the Tumor Immune Dysfunction and Exclusion online tool. Results: This study illuminated the functional intricacies of HNSCC DC subsets to tailor innovative therapeutic strategies. We leveraged clinical data from the TCGA-HNSCC and GSE65858 cohorts. We subjected the data to advanced analysis, including WGCNA, which revealed 222 DC-related candidate genes. Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox's regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival. Differential gene expression analysis between ACP2 + and ACP2 - DC cells revealed 208 differential expressed genes. Lisa analysis identified the top five significant transcription factors as STAT1, SPI1, SMAD1, CEBPB, and IRF1. The correlation between STAT1 and ACP2 was confirmed through quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Dual-luciferase assays in HEK293T cells. Additionally, TP53 and FAT1 mutations were more common in high-risk DC subgroups. Importantly, the sensitivity to immunotherapy differed among the risk clusters. The low-risk cohorts were anticipated to exhibit favorable responses to immunotherapy, marked by heightened expressions of immune system-related markers. In contrast, the high-risk group displayed augmented proportions of immunosuppressive cells, suggesting a less conducive environment for immunotherapeutic interventions. Conclusions: Our research may yield a robust DC-based prognostic system for HNSCC; this will aid personalized treatment and improve clinical outcomes as the battle against this challenging cancer continues.

Identification of pivotal genes and crucial pathways in liver fibrosis through WGCNA analysis.

BACKGROUND: Liver fibrosis is a progressive liver disease with increasing incidence, yet its underlying pathogenic mechanisms remain incompletely understood. OBJECTIVE: : This study aims to explore potential therapeutic targets for liver fibrosis using weighted gene co-expression network analysis (WGCNA) and experimental validation. METHODS: We retrieved the microarray data (GSE174099) from the GEO database and performed differential expression analysis and WGCNA to identify co-expression modules associated with liver fibrosis. A module with the highest correlation to liver fibrosis was selected for further analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to investigate the biological functions and signaling pathways of the identified genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The correlation between core genes and immune cells was analyzed with the CIBERSORT algorithm. Additionally, pathological and molecular biology experiments were performed to validate the expression levels of core genes in liver tissue, including HE and Masson staining, immunohistochemistry, RT-qPCR, and Western blotting. RESULTS: We identified a total of 86 intersecting genes from the differential expression analysis and WGCNA. GO enrichment analysis revealed that these genes were involved in processes such as cellular response to cAMP, collagen-containing extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis highlighted the involvement of these genes in pathways like Cell Adhesion Molecules and the PI3K-Akt signaling pathway. Using Cytoscape software, we identified four core genes: Cftr, Cldn4, Map2, and Spp1. Pathological examinations showed that the experimental group exhibited significant fibrous tissue proliferation compared to the control group. Immunohistochemistry, RT-qPCR, and Western blotting analyses confirmed that these core genes were significantly upregulated in the experimental group (P< 0.05). CONCLUSION: This study identified four key genes (Cftr, Cldn4, Map2, Spp1) that are significantly associated with liver fibrosis. These genes are upregulated in liver fibrosis and could potentially as biomarkers for diagnosis and targets for therapeutic interventions.

mir-744-5p inhibits cell growth and angiogenesis in osteosarcoma by targeting NFIX.

BACKGROUND: Osteosarcoma (OS) is a malignant bone tumor that commonly occurs in children and adolescents under the age of 20. Dysregulation of microRNAs (miRNAs) is an important factor in the occurrence and progression of OS. MicroRNA miR-744-5p is aberrantly expressed in various tumors. However, its roles and molecular targets in OS remain unclear. METHODS: Differentially expressed miRNAs in OS were analyzed using the Gene Expression Omnibus dataset GSE65071, and the potential hub miRNA was identified through weighted gene co-expression network analysis. Quantitative real-time PCR (qRT-PCR) was used to detect the expression of miR-744-5p in OS cell lines. In vitro experiments, including CCK-8 assays, colony formation assays, flow cytometry apoptosis assays, and tube formation assays, were performed to explore the effects of miR-744-5p on OS cell biological behaviors. The downstream target genes of miR-744-5p were predicted through bioinformatics, and the binding sites were validated by a dual-luciferase reporter assay. RESULTS: The lowly expressed miRNA, miR-744-5p, was identified as a hub miRNA involved in OS progression through bioinformatic analysis. Nuclear factor I X (NFIX) was confirmed as a direct target for miR-744-5p in OS. In vitro studies revealed that overexpression of miR-744-5p could restrain the growth of OS cells, whereas miR-744-5p inhibition showed the opposite effect. It was also observed that treatment with the conditioned medium from miR-744-5p-overexpressed OS cells led to poorer proliferation and angiogenesis in human umbilical vein endothelial cells (HUVECs). Furthermore, NFIX overexpression restored the suppression effects of miR-744-5p overexpression on OS cell growth and HUVECs angiogenesis. CONCLUSION: Our results indicated that miR-744-5p is a potential tumor-suppressive miRNA in OS progression by targeting NFIX to restrain the growth of OS cells and angiogenesis in HUVECs.

Metabolome and Transcriptome Unveil the Correlated Metabolites and Transcripts with 2-acetyl-1-pyrroline in Fragrant Rice.

Fragrance is a valuable trait in rice varieties, with its aroma significantly influencing consumer preference. In this study, we conducted comprehensive metabolome and transcriptome analyses to elucidate the genetic and biochemical basis of fragrance in the Shangsixiangnuo (SSXN) variety, a fragrant indica rice cultivated in Guangxi, China. Through sensory evaluation and genetic analysis, we confirmed SSXN as strongly fragrant, with an 806 bp deletion in the BADH2 gene associated with fragrance production. In the metabolome analysis, a total of 238, 233, 105 and 60 metabolic compounds exhibited significant changes at the seedling (S), reproductive (R), filling (F), and maturation (M) stages, respectively. We identified four compounds that exhibited significant changes in SSXN across all four development stages. Our analyses revealed a significant upregulation of 2-acetyl-1-pyrroline (2AP), the well-studied aromatic compound, in SSXN compared to the non-fragrant variety. Additionally, correlation analysis identified several metabolites strongly associated with 2AP, including ethanone, 1-(1H-pyrrol-2-yl)-, 1H-pyrrole, and pyrrole. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) analysis highlighted the magenta and yellow modules as particularly enriched in aroma-related metabolites, providing insights into the complex aromatic compounds underlying the fragrance of rice. In the transcriptome analysis, a total of 5582, 5506, 4965, and 4599 differential expressed genes (DEGs) were identified across the four developmental stages, with a notable enrichment of the common pathway amino sugar and nucleotide sugar metabolism in all stages. In our correlation analysis between metabolome and transcriptome data, the top three connected metabolites, phenol-, 3-amino-, and 2AP, along with ethanone, 1-(1H-pyrrol-2-yl)-, exhibited strong associations with transcripts, highlighting their potential roles in fragrance biosynthesis. Additionally, the downregulated expression of the P4H4 gene, encoding a procollagen-proline dioxygenase that specifically targets proline, in SSXN suggests its involvement in proline metabolism and potentially in aroma formation pathways. Overall, our study provides comprehensive insights into the genetic and biochemical mechanisms underlying fragrance production in rice, laying the foundation for further research aimed at enhancing fragrance quality in rice breeding programs.

Exploring the common mechanisms and biomarker ST8SIA4 of atherosclerosis and ankylosing spondylitis through bioinformatics analysis and machine learning.

Background: Atherosclerosis (AS) is a major contributor to cerebrovascular and cardiovascular events. There is growing evidence that ankylosing spondylitis is closely linked to AS, often co-occurring with it; however, the shared pathogenic mechanisms between the two conditions are not well understood. This study employs bioinformatics approaches to identify common biomarkers and pathways between AS and ankylosing spondylitis. Methods: Gene expression datasets for AS (GSE100927, GSE28829, GSE155512) and ankylosing spondylitis (GSE73754, GSE25101) were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes for AS and ankylosing spondylitis were identified using the Limma R package and weighted gene co-expression network analysis (WGCNA) techniques, respectively. The machine learning algorithm SVM-RFE was applied to pinpoint promising biomarkers, which were then validated in terms of their expression levels and diagnostic efficacy in AS and ankylosing spondylitis, using two separate GEO datasets. Furthermore, the interaction of the key biomarker with the immune microenvironment was investigated via the CIBERSORT algorithm, single-cell analysis was used to identify the locations of common diagnostic markers. Results: The dataset GSE100927 contains 524 DEGs associated with AS, whereas dataset GSE73754 includes 1,384 genes categorized into modules specific to ankylosing spondylitis. Analysis of these datasets revealed an overlap of 71 genes between the DEGs of AS and the modular genes of ankylosing spondylitis. Utilizing the SVM-RFE algorithm, 15 and 24 central diagnostic genes were identified in datasets GSE100927 and GSE73754, respectively. Further validation of six key genes using external datasets confirmed ST8SIA4 as a common diagnostic marker for both conditions. Notably, ST8SIA4 is upregulated in samples from both diseases. Additionally, ROC analysis confirmed the robust diagnostic utility of ST8SIA4. Moreover, analysis through CIBERSORT suggested an association of the ST8SIA4 gene with the immune microenvironment in both disease contexts. Single-cell analysis revealed that ST8SIA4 is primarily expressed in Macrophages, Monocytes, T cells, and CMPs. Conclusion: This study investigates the role of ST8SIA4 as a common diagnostic gene and the involvement of the lysosomal pathway in both AS and ankylosing spondylitis. The findings may yield potential diagnostic biomarkers and offer new insights into the shared pathogenic mechanisms underlying these conditions.

Insights into the ameliorative effect of ZnONPs on arsenic toxicity in soybean mediated by hormonal regulation, transporter modulation, and stress responsive genes.

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils.

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis.

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

Prognostic Model and Immune Response of Clear Cell Renal Cell Carcinoma Based on Co-Expression Genes Signature.

BACKGROUND: The identification of reliable prognostic markers is crucial for optimizing patient management and improving clinical outcomes in clear cell renal cell carcinoma (ccRCC). METHODS: We used the GSE89563 dataset from the GEO database and the Kidney Clear Cell Carcinoma (KIRC) dataset from the TCGA database to develop a prognostic model based on weighted gene co-expression network analysis (WGCNA) and non-negative matrix factorization (NMF) to predict disease progression and prognosis in ccRCC. RESULT: We utilized WGCNA to identify risk genes and applied NMF to stratify high-risk populations in ccRCC. We characterized the immune gene features of these high-risk groups and ultimately developed a risk prediction model for ccRCC patients using a Lasso regression approach. The risk score was calculated as follows: Risk score = SUM (-0.136394797 ANK3 + 0.004238138 BIVM_ERCC5 - 0.046248451 C4orf19 - 0.036013206 F2RL3 - 0.125531316 GNG7 - 0.012698109 METTL7A + 0.078462369 MSTO1 - 0.050450656 PINK1 - 0.059446590 SLC16A12 - 0.039883686 SLC2A9 + 0.083310722 TLCD1 - 0.059801739 WDR72 + 0.071430088 ZNF117). CONCLUSION: We develop a prognostic model for clear cell renal cell carcinoma and analyzed immune response in subgroups and confirmed protein-level expression concordance.

A m6A regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma.

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

A novel histone deacetylase inhibitor W2A-16 improves the barrier integrity in brain vascular endothelial cells.

The maturation of brain microvascular endothelial cells leads to the formation of a tightly sealed monolayer, known as the blood-brain barrier (BBB). The BBB damage is associated with the pathogenesis of age-related neurodegenerative diseases including vascular cognitive impairment and Alzheimer's disease. Growing knowledge in the field of epigenetics can enhance the understanding of molecular profile of the BBB and has great potential for the development of novel therapeutic strategies or targets to repair a disrupted BBB. Histone deacetylases (HDACs) inhibitors are epigenetic regulators that can induce acetylation of histones and induce open chromatin conformation, promoting gene expression by enhancing the binding of DNA with transcription factors. We investigated how HDAC inhibition influences the barrier integrity using immortalized human endothelial cells (HCMEC/D3) and the human induced pluripotent stem cell (iPSC)-derived brain vascular endothelial cells. The endothelial cells were treated with or without a novel compound named W2A-16. W2A-16 not only activates Wnt/ß-catenin signaling but also functions as a class I HDAC inhibitor. We demonstrated that the administration with W2A-16 sustained barrier properties of the monolayer of endothelial cells, as evidenced by increased trans-endothelial electrical resistance (TEER). The BBB-related genes and protein expression were also increased compared with non-treated controls. Analysis of transcript profiles through RNA-sequencing in hCMEC/D3 cells indicated that W2A-16 potentially enhances BBB integrity by influencing genes associated with the regulation of the extracellular microenvironment. These findings collectively propose that the HDAC inhibition by W2A-16 plays a facilitating role in the formation of the BBB. Pharmacological approaches to inhibit HDAC may be a potential therapeutic strategy to boost and/or restore BBB integrity.

Weighted Gene Co-expression Network Analysis (WGCNA) of Wnt Signaling Related to Periodontal Ligament Formation: A Bioinformatics-Based Analysis.

Introduction The Wnt signaling pathway is crucial for tooth development, odontoblast differentiation, and dentin formation. It interacts with epithelial cadherin (E-cadherin) and beta-catenin in tooth development and periodontal ligament (PDL) formation. Dysregulation of Wnt signaling is linked to periodontal diseases, requiring an understanding of therapeutic interventions. Weighted gene co-expression network analysis (WGCNA) can identify co-expressed gene modules. Our study aims to identify hub genes in WGCNA analysis of Wnt signaling-based PDL formation. Methods The study used a microarray dataset GSE201313 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus to analyze the impact of DMP1 expression on XLH dental pulp cell differentiation and PDL formation. The standardized dataset was used for WGCNA analysis, which generated a co-expression network by calculating pairwise correlations between genes and constructing an adjacency matrix. The topological overlap matrix (TOM) was transformed into a hierarchical clustering tree and then cut into modules or clusters of highly interconnected genes. The module eigengene (ME) was calculated for each module, and the genes within this module were identified as hub genes. Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) was used for WGCNA analysis. Results The study used the WGCNA package to analyze 1,000 differentially expressed genes, constructing a gene co-expression network and generating a hierarchical clustering tree and TOM. The analysis reveals a scale-free topology fitting index R2 and mean connectivity for various soft threshold powers, with an R2 value of 5. COL6A1, MMP3, BGN, COL1A2, and FBN2 are hub genes implicated in PDL development. Conclusion The study identified key hub genes, including COL6A1, MMP3, BGN, and FBN2, crucial for PDL formation, tissue remodeling, and cell-matrix interactions, guiding future therapeutic strategies.

Gene coexpression network analysis reveals the genes and pathways in pectoralis major muscle and liver associated with wooden breast in broilers.

Wooden breast (WB) is a myopathy mainly affecting pectoralis major (PM) muscle in modern commercial broiler chickens, causing enormous economic losses in the poultry industry. Recent studies have observed hepatic and PM muscle injury in broilers affected by WB, but the relationships between WB and the 2 tissues are mostly unclear. In the current study, the RNA-seq raw data of PM muscle and liver were downloaded from GSE144000, and we constructed the gene coexpression networks of PM muscle and liver to explore the relationships between WB and the 2 tissues using the weighted gene coexpression network analysis (WGCNA) method. Six and 2 gene coexpression modules were significantly correlated with WB in the PM muscle and liver networks, respectively. TGF-beta signaling, Toll-like receptor signaling and mTOR signaling pathways were significantly enriched in the genes within the 6 gene modules of PM muscle network. Meanwhile, mTOR signaling pathway was significantly enriched in the genes within the 2 gene modules of liver network. In the consensus gene coexpression network across the 2 tissues, salmon module (r = -0.5 and p = 0.05) was significantly negatively correlated with WB, in which Toll-like receptor signaling, apoptosis, and autophagy pathways were significantly enriched. The genes related with the 3 pathways, myeloid differentiation primary response 88 (MYD88), interferon regulatory factor 7 (IRF7), mitogen-activated protein kinase 14 (MAPK14), FBJ murine osteosarcoma viral oncogene homolog (FOS), jun proto-oncogene (JUN), caspase-10, unc-51 like autophagy activating kinase 2 (ULK2) and serine/threonine kinase 11 (LKB1), were identified in salmon module. In this current study, we found that the signaling pathways related with cell inflammation, apoptosis and autophagy might influence WB across 2 tissues in broilers.

Identification of deregulated lncRNAs in Alzheimer's disease: an integrated gene co-expression network analysis of hippocampus and fusiform gyrus RNA-seq datasets.

Introduction: The deregulation of lncRNAs expression has been associated with neuronal damage in Alzheimer's disease (AD), but how or whether they can influence its onset is still unknown. We investigated 2 RNA-seq datasets consisting, respectively, of the hippocampal and fusiform gyrus transcriptomic profile of AD patients, matched with non-demented controls. Methods: We performed a differential expression analysis, a gene correlation network analysis (WGCNA) and a pathway enrichment analysis of two RNA-seq datasets. Results: We found deregulated lncRNAs in common between hippocampus and fusiform gyrus and deregulated gene groups associated to functional pathways related to neurotransmission and memory consolidation. lncRNAs, co-expressed with known AD-related coding genes, were identified from the prioritized modules of both brain regions. Discussion: We found common deregulated lncRNAs in the AD hippocampus and fusiform gyrus, that could be considered common signatures of AD pathogenesis, providing an important source of information for understanding the molecular changes of AD.

Identification of candidate genes and molecular mechanisms related to asthma progression using bioinformatics.

BACKGROUND: Asthma is a heterogeneous disorder. This study aimed to identify changes in gene expression and molecular mechanisms associated with moderate to severe asthma. METHODS: Differentially expressed genes (DEGs) were analyzed in GSE69683 dataset among moderate asthma and its controls as well as between severe asthma and moderate asthma. Key module genes were identified via co-expression analysis, and the molecular mechanism of the module genes was explored through enrichment analysis and gene set enrichment analysis (GSEA). GSE89809 was used to verify the characteristic genes related to moderate and severe asthma. RESULTS: Accordingly, 2540 DEGs were present between moderate asthma and the control group, while 6781 DEGs existed between severe asthma and moderate asthma. These genes were identified into 14 co-expression modules. Module 7 had the highest positive correlation with severe asthma and was recognized to be a key module by STEM. Enrichment analysis demonstrated that the module genes were mainly involved in oxidative stress-related signaling pathways. The expression of HSPA1A, PIK3CG and PIK3R6 was associated with moderate asthma, while MAPK13 and MMP9 were associated with severe asthma. The AUC values were verified by GSE89809. Additionally, 322 drugs were predicted to target five genes. CONCLUSION: These results identified characteristic genes related to moderate and severe asthma and their corresponding molecular mechanisms, providing a basis for future research.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness.

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Characterizing defective lipid metabolism in the lateral septum of mice treated with olanzapine: implications for its side effects.

Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Apoa1, Apoc3, and Apoh. These findings suggest that olanzapine may influence body weight and adiposity through its impact on lipid metabolism-related genes in the LS. Therefore, the neural circuits connecting the LS and LH, along with the accompanying alterations in lipid metabolism, are likely crucial factors contributing to the weight gain and metabolic side effects associated with olanzapine treatment.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis.

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.