Weighted Gene Co-expression Network Research Articles

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.

Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma.

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC. Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets. LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses. The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Abstract 4346: Deciphering the landscape of extrachromosomal DNA in gastric cancer and its potential impact on cancer dynamics

Abstract Extrachromosomal DNA (ecDNA) drives substantial copy number increases in focal regions harboring oncogenes and its non-Mendelian inheritance during mitosis contributes to cancer heterogeneity and a poor prognosis across multiple cancer types. However, the prevalence and function of ecDNA in gastric cancer (GC) are still largely unknown. Here, we profiled ecDNA by whole-genome sequencing analysis from 76 GC patients and explored its functional attributes through whole-transcriptome sequencing from the same tissues. Focal somatic copy number amplification (fSCNA) was detected in 27 patients (35.5%), of which 17 (22.4%) had at least one circular form of focal amplification (ecDNA+/fSCNA+). Most of the patients carrying one or more ecDNA were classified as TCGA-CIN subtype (n=12; CIN: chromosomal instability). The fSCNA hotspots were observed at 17q12 and 19q12, where ERBB2 and CCNE1 are located, respectively. Amplifications at both loci occurred in seven patients and were formed into ecDNA in four and two patients, respectively. The ecDNA amplicons carried more oncogenes with significantly higher copy numbers than other amplicons (one-tailed Mann-Whitney U test, p=3.9e-03), and 44.4% (12 of 27) of ecDNA amplicons regions were overlapped with tandem duplication or chromothripsis, known causes of ecDNA formation. Oncogenes such as ERBB2 and MIEN1, residing on ecDNA, exhibited significantly increased expression compared to matched normal tissue as well as patients with the corresponding genes located on their chromosome only, suggesting that the ecDNA-induced copy number increases contribute to elevated expression levels. On the other hand, MYC showed higher expression in the ecDNA+/fSCNA+ group, irrespective of its fSCNA status (padj=0.028 and 4.24e-06 against the ecDNA-/fSCNA+ and ecDNA-/fSCNA- group, respectively). We further explored gene clusters that were associated with the ecDNA+/fSCNA+ group using weighted gene co-expression network (WGCNA) analysis. One module was distinct from ecDNA- patients, consisting of genes associated with the “protein processing in endoplasmic reticulum” and “spliceosome” pathways, inferring that transcription and translation are more active in ecDNA+ patients. Moreover, the overall survival analysis of 76 GC patients revealed that ecDNA+/fSCNA+ patients were significantly associated with poor prognosis (log-rank test, p=0.014). In conclusion, ecDNA may be a main source of high expression of oncogenes that influence GC development, and subsequently upregulated global transcription activity will make GC more aggressive. Citation Format: Donghyeok Seol, Seunghyun Kang, Sanghyun Kim, Jieun Lee, Mira Yoo, Duyeong Hwang, So Hyun Kang, Chanmi Bang, Young Suk Park, Sang-Hoon Ahn, Hyung-Ho Kim, Hoon Kim, Yun-Suhk Suh. Deciphering the landscape of extrachromosomal DNA in gastric cancer and its potential impact on cancer dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4346.

Bi-level gene selection of cancer by combining clustering and sparse learning

The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

Identification of Salt-Sensitive and Salt-Tolerant Genes through Weighted Gene Co-Expression Networks across Multiple Datasets: A Centralization and Differential Correlation Analysis.

Salt stress is a significant challenge that severely hampers rice growth, resulting in decreased yield and productivity. Over the years, researchers have identified biomarkers associated with salt stress to enhance rice tolerance. However, the understanding of the mechanism underlying salt tolerance in rice remains incomplete due to the involvement of multiple genes. Given the vast amount of genomics and transcriptomics data available today, it is crucial to integrate diverse datasets to identify key genes that play essential roles during salt stress in rice. In this study, we propose an integration of multiple datasets to identify potential key transcription factors. This involves utilizing network analysis based on weighted co-expression networks, focusing on gene-centric measurement and differential co-expression relationships among genes. Consequently, our analysis reveals 86 genes located in markers from previous meta-QTL analysis. Moreover, six transcription factors, namely LOC_Os03g45410 (OsTBP2), LOC_Os07g42400 (OsGATA23), LOC_Os01g13030 (OsIAA3), LOC_Os05g34050 (OsbZIP39), LOC_Os09g29930 (OsBIM1), and LOC_Os10g10990 (transcription initiation factor IIF), exhibited significantly altered co-expression relationships between salt-sensitive and salt-tolerant rice networks. These identified genes hold potential as crucial references for further investigation into the functions of salt stress response in rice plants and could be utilized in the development of salt-resistant rice cultivars. Overall, our findings shed light on the complex genetic regulation underlying salt tolerance in rice and contribute to the broader understanding of rice's response to salt stress.

A novel transcription factor FnMYB4 regulates pigments metabolism of yellow leaf mutants in Fragaria nilgerrensis

The strawberry species Fragaria nilgerrensis Schlechtendal ex J. Gay, renowned for its distinctive white, fragrant peach-like fruits and strong disease resistance, is an exceptional research material. In a previous study, an ethyl methane sulfonate (EMS) mutant library was established for this species, resulting in various yellow leaf mutants. Leaf yellowing materials are not only the ideal materials for basic studies on photosynthesis mechanism, chloroplast development, and molecular regulation of various pigments, but also have important utilization value in ornamental plants breeding. The present study focused on four distinct yellow leaf mutants: mottled yellow leaf (MO), yellow green leaf (YG), light green leaf (LG), and buddha light leaf (BU). The results revealed that the flavonoid content and carotenoid-to-chlorophyll ratio exhibited a significant increase among these mutants, while experiencing a significant decrease in chlorophyll and carotenoid contents compared to the wild type (WT). To clarify the regulatory mechanisms and network relationships underlying these mutants, the RNA-seq and weighted gene co-expression network (WGCNA) analyses were employed. The results showed flavonoid metabolism pathway was enriched both in MO and YG mutants, while the chlorophyll biosynthesis pathway and carotenoid degradation pathway were only enriched in MO and YG mutants, respectively. Subsequently, key structural genes and transcription factors were identified on metabolic pathways of three pigments through correlation analyses and quantitative experiments. Furthermore, a R2R3-MYB transcription factor, FnMYB4, was confirmed to be positively correlated with flavonoid synthesis through transient overexpression, virus-induced gene silencing (VIGS), and RNA interference (RNAi), accompanying by reoccurrence and attenuation of mutant phenotype. Finally, dual-luciferase (LUC) and yeast one-hybrid assays confirmed the binding of FnMYB4 to the FnFLS and FnF3H promoters, indicating that FnMYB4 positively regulates flavonoid synthesis. In addition, correlation analyses suggested that FnMYB4 also might be involved in chlorophyll and carotenoid metabolisms. These findings demonstrated the pivotal regulatory role of FnMYB4 in strawberry leaf coloration.

Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis

Background and aimsThis study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS). Methods and resultsFour microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein–protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases. ConclusionWe performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning.

Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC. We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC. Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo. Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

Transcriptome profile analysis in spinal cord injury rats with transplantation of menstrual blood-derived stem cells.

Menstrual blood-derived stem cells (MenSCs) are vital in treating many degenerative and traumatic disorders. However, the underlying molecular mechanisms remain obscure in MenSCs-treating spinal cord injury (SCI) rats. MenSCs were adopted into the injured sites of rat spinal cords at day 7 post surgery and the tissues were harvested for total RNA sequencing analysis at day 21 after surgery to investigate the expression patterns of RNAs. The differentially expressed genes (DEGs) were analyzed with volcano and heatmap plot. DEGs were sequentially analyzed by weighted gene co-expression network, functional enrichment, and competitive endogenous RNAs (ceRNA) network analysis. Next, expression of selected miRNAs, lncRNAs, circRNAs and mRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatics packages and extra databases were enrolled to scoop the genes functions and their interaction relationships. A total of 89 lncRNAs, 65 circRNAs, 120 miRNAs and 422 mRNAs were significantly upregulated and 65 lncRNAs, 72 circRNAs, 74 miRNAs, and 190 mRNAs were significantly downregulated in the MenSCs treated rats compared to SCI ones. Current investigation revealed that MenSCs treatment improve the recovery of the injured rats and the most significantly involved pathways in SCI regeneration were cell adhesion molecules, nature killer cell mediated cytotoxicity, primary immunodeficiency, chemokine signaling pathway, T cell receptor signaling pathway and B cell receptor signaling pathway. Moreover, the lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA ceRNA network of SCI was constructed. Finally, the protein-protein interaction (PPI) network was constructed using the top 100 DE mRNAs. The constructed PPI network included 47 nodes and 70 edges. In summary, the above results revealed the expression profile and potential functions of differentially expressed (DE) RNAs in the injured spinal cords of rats in the MenSCs-treated and SCI groups, and this study may provide new clues to understand the mechanisms of MenSCs in treating SCI.

In-silico and in-vitro evidence suggest LINC01405 as a sponge for miR-29b and miR-497-5p, and a potential regulator of Wnt, PI3K, and TGFB signaling pathways in breast carcinoma.

Carcinoma of the breast, a prevailing factor in female mortality worldwide, involves dysregulation of lncRNAs and microRNAs. The main goal of this research was to predict and experimentally examine the LINC01405 expression status in breast cancer subtypes, along with investigation of its interaction with miR-29b and miR-497-5p that results in regulating PI3-Kinase, WNT, and TGF-beta signaling pathways. We performed a meta-analysis of five GEO datasets, encompassing microarray and RNA-seq data, to identify differentially expressed genes. The Cancer Genome Atlas transcriptome dataset was also analyzed to determine essential gene modules, associated with different stages of breast cancer by weighted gene co-expression networks. In addition, networks of drug-gene interactions were constructed to explore potential treatment options. LINC01405 as a microRNA sponge was chosen and examined. furthermore, downstream target genes were discovered. Experimental validation consisted of plasmid constructs used in cell culture experiments, RT-qPCR for expression analysis, and cell cycle assays. Our bioinformatics findings showed higher LINC01405 expression in Basal-like triple-negative breast carcinoma. In contrast, lower expression in Luminal samples was observed compared with normal samples, which was consistently observed in both breast cancer tissues and cell lines. LINC01405 expression level was correlated with miR-29b and miR-497 levels. The MDA-MB-231 cell line demonstrated higher LINC01405 expression and lower miR-29b and miR-497 expression levels. However, SKBR3 and MCF7 cells had lower LINC01405 expression and higher miR-29b and miR-497 levels, suggesting a regulatory role for LINC01405 as a competing endogenous RNA. This was experimentally confirmed when LINC01405 was overexpressed in SKBR3 cells, and the common target genes of miR-29b and miR-497 were upregulated. Additionally, LINC01405 upregulation led to the increased cell populations, proliferation, and upregulation of critical cancer-related genes, including AKT1, AKT3, mTOR, WNT3A, SMAD3, CYCLIN D1, CYCLIN D2, BCL2, and GSK3B. We revealed the differential expression of LINC01405 in several types of breast cancer and its role in regulating signaling pathways, potentially via scavenging miRNAs. These findings clarified the role of LINC01405 in breast cancer development and identified potential therapeutic targets.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations.

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Key genes of inflammation and Crohn’s disease severity identified via bioinformatics and clinical specimen analysis

Crohn’s disease (CD) is a nonspecific inflammatory disease of digestive tract with limited known biomarkers. We aimed to identify potential molecules involved in CD severity. Weighted gene co-expression network and differentially expressed gene analyses were used to explore hub genes associated with inflammation and involved in pathological processes of CD in the GSE83448 cohort. The expression and relationship of genes with clinical characteristics in normal and CD intestinal mucosa were validated in GSE208303 and our research cohorts. Five genes related to disease and inflammation were identified in the clinically significant modules that were differentially expressed in healthy controls and CD patients. Among them, solute carrier family 39 member 5 (SLC39A5) expression was reduced in CD tissues compared with that in normal tissues; low SLC39A5 expression was associated with CD-simplifying endoscopic score and stenosis. Furthermore, we broadened the bioinformatic threshold with qRT-PCR verification and western blot, and found that the expressions of cytochrome P450 family 2 subfamily S member 1 (CYP2S1), fatty acid 2-hydroxylase (FA2H), fatty acid binding protein 1 (FABP1), and Rho family GTPase 3 (RND3) were reduced in CD tissues. Therefore, SLC39A5, CYP2S1, FA2H, FABP1, and RND3 may be novel biomarkers for CD and associated with severity. Highlights SLC39A5 expression was reduced in CD tissues. Low SLC39A5 expression was associated with CD-simplifying endoscopic score and stenosis. CYP2S1, FA2H, FABP1, and RND3 were reduced in CD tissues.

Objective quantification technique and widely targeted metabolomic reveal the effect of drying temperature on sensory attributes and related non-volatile metabolites of black tea

Drying temperature (DT) considerably affects the flavor of black tea (BT); however, its influence on non-volatile metabolites (NVMs) and their correlations remain unclear. In this study, an objective quantification technique and widely targeted metabolomics were applied to explore the effects of DT (130 °C, 110 °C, 90 °C, and 70 °C) on BT flavor and NVMs conversion. BT with a DT of 90 °C presented the highest umami, sweetness, overall taste, and brightness color values. Using the weighted gene co-expression network and multiple factor analysis, 455 sensory trait-related NVMs were explored across six key modules. Moreover, 169 differential NVMs were screened, and flavonoids, phenolic acids, amino acids, organic acids, and lipids were identified as key differential NVMs affecting the taste and color attributes of BT in response to DT. These findings enrich the BT processing theory and offer technical support for the precise and targeted processing of high-quality BT.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BackgroundIn recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.MethodsSLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.ResultsFive key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.ConclusionIn summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

Oil candidate genes in seeds of cotton (Gossypium hirsutum L.) and functional validation of GhPXN1

BackgroundCottonseed oil is a promising edible plant oil with abundant unsaturated fatty acids. However, few studies have been conducted to explore the characteristics of cottonseed oil. The molecular mechanism of cottonseed oil accumulation remains unclear.ResultsIn the present study, we conducted comparative transcriptome and weighted gene co-expression network (WGCNA) analysis for two G. hirsutum materials with significant difference in cottonseed oil content. Results showed that, between the high oil genotype 6053 (H6053) and the low oil genotype 2052 (L2052), a total of 412, 507, 1,121, 1,953, and 2,019 differentially expressed genes (DEGs) were detected at 10, 15, 20, 25, and 30 DPA, respectively. Remarkably, a large number of the down-regulated DEGs were enriched in the phenylalanine metabolic processes. Investigation into the dynamic changes of expression profiling of genes associated with both phenylalanine metabolism and oil biosynthesis has shed light on a significant competitive relationship in substrate allocation during cottonseed development. Additionally, the WGCNA analysis of all DEGs identified eight distinct modules, one of which includes GhPXN1, a gene closely associated with oil accumulation. Through phylogenetic analysis, we hypothesized that GhPXN1 in G. hirsutum might have been introgressed from G. arboreum. Overexpression of the GhPXN1 gene in tobacco leaf suggested a significant reduction in oil content compared to the empty-vector transformants. Furthermore, ten other crucial oil candidate genes identified in this study were also validated using quantitative real-time PCR (qRT-PCR).ConclusionsOverall, this study enhances our comprehension of the molecular mechanisms underlying cottonseed oil accumulation.

Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs

BackgroundFeed efficiency is a research priority to support a sustainable meat production. It is recognized as a complex trait that integrates multiple biological pathways orchestrated in and by various tissues. This study aims to determine networks between biological entities to explain inter-individual variation of feed efficiency in growing pigs.ResultsThe feed conversion ratio (FCR), a measure of feed efficiency, and its two component traits, average daily gain and average daily feed intake, were obtained from 47 growing pigs from a divergent selection for residual feed intake and fed high-starch or high-fat high-fiber diets during 58 days. Datasets of transcriptomics (60 k porcine microarray) in the whole blood and metabolomics (1H-NMR analysis and target gas chromatography) in plasma were available for all pigs at the end of the trial. A weighted gene co-expression network was built from the transcriptomics dataset, resulting in 33 modules of co-expressed molecular probes. The eigengenes of eight of these modules were significantly (P le 0.05) or tended to be (0.05 < P le 0.10) correlated to FCR. Great homogeneity in the enriched biological pathways was observed in these modules, suggesting co-expressed and co-regulated constitutive genes. They were mainly enriched in genes participating to immune and defense-related processes, and to a lesser extent, to translation, cell development or learning. They were also generally associated with growth rate and percentage of lean mass. In the whole network, only one module composed of genes participating to the response to substances, was significantly associated with daily feed intake and body adiposity. The plasma profiles in circulating metabolites and in fatty acids were summarized by weighted linear combinations using a dimensionality reduction method. Close association was thus found between a module composed of co-expressed genes participating to T cell receptor signaling and cell development process in the whole blood and related to FCR, and the circulating concentrations of polyunsaturated fatty acids in plasma.ConclusionThese systemic approaches have highlighted networks of entities driving key biological processes involved in the phenotypic difference in feed efficiency between animals. Connecting transcriptomics and metabolic levels together had some additional benefits.

Brain Gene Co-Expression Network Analysis Identifies 22q13 Region Genes Associated with Autism, Intellectual Disability, Seizures, Language Impairment, and Hypotonia.

Phelan-McDermid syndrome (PMS) is a rare genetic neurodevelopmental disorder caused by 22q13 region deletions or SHANK3 gene variants. Deletions vary in size and can affect other genes in addition to SHANK3. PMS is characterized by autism spectrum disorder (ASD), intellectual disability (ID), developmental delays, seizures, speech delay, hypotonia, and minor dysmorphic features. It is challenging to determine individual gene contributions due to variability in deletion sizes and clinical features. We implemented a genomic data mining approach for identifying and prioritizing the candidate genes in the 22q13 region for five phenotypes: ASD, ID, seizures, language impairment, and hypotonia. Weighted gene co-expression networks were constructed using the BrainSpan transcriptome dataset of a human brain. Bioinformatic analyses of the co-expression modules allowed us to select specific candidate genes, including EP300, TCF20, RBX1, XPNPEP3, PMM1, SCO2, BRD1, and SHANK3, for the common neurological phenotypes of PMS. The findings help understand the disease mechanisms and may provide novel therapeutic targets for the precise treatment of PMS.

Identification of oxidative stress-related biomarkers associated with the development of acute-on-chronic liver failure using bioinformatics

Acute-on-chronic liver failure (ACLF) is a serious stage of chronic liver disease with high short-term mortality and no definitely effective treatment. Oxidative stress (OS) is involved in the development of ACLF. OS-related genes targeted therapy may provide additional assistance for the treatment of ACLF. ACLF related gene sets and oxidative stress-related genes (OSGs) were respectively downloaded from gene expression omnibus (GEO) database and GeneCards database for integrated bioinformatics analyses (functional enrichment, weighted gene co-expression network and immune cells infiltration). Immune-related differentially expressed oxidative stress-related genes (DEOSGs) in ACLF were used for construction of protein–protein interaction (PPI) network in which hub genes were screened out. Hub genes with consistently good diagnostic or prognostic value for ACLF in four gene sets were named as key genes. DEOSGs were significantly enriched in biological process and signaling pathways related to inflammation, immune response and oxidative stress. Six key genes (MPO, CCL5, ITGAM, TLR2, TLR4, and TIMP1) were identified and found to be highly correlated with immune response and metabolic process. This study deepened our understanding of the impact of oxidative stress on the pathogenesis and prognosis of ACLF and provided more insights into the prediction of prognosis and molecular targeted therapy in ACLF.

Phenomic and transcriptomic analyses reveal the sequential synthesis of Fe3O4 nanoparticles in Acidithiobacillus ferrooxidans BYM.

Understanding the molecular mechanism of magnetite (Fe3O4) nanoparticle synthesis in Acidithiobacillus ferrooxidans BYM is particularly important for the commercial development of biogenic Fe3O4 nanoparticles. The phenomic parameters such as intracellular iron content and number and size of Fe3O4 nanoparticles were significantly affected by different treatment conditions, i.e., FeSO4·7H2O concentrations (0, 40, and 80 g/L), growth times (12, 36, and 50 h), and magnetic field intensities (0.05, 3.5, and 15 mT) (P < 0.01). Transcriptome analysis revealed that 2,164, 1,587, and 1,061 differentially expressed genes (DEGs) were accordingly detected, and 24 significant expression profiles were identified in A. ferrooxidans BYM under the three treatment conditions. The construction of gene regulatory networks for Fe3O4 nanoparticle synthesis indicated that DEGs mainly enrich ion transport, oxidation-reduction process, membrane structure, signal transduction, and quorum sensing. The four modules were found to be significantly associated with Fe3O4 nanoparticle phenomic parameters using a weighted gene co-expression network. Ten hub genes significantly correlated with Fe3O4 nanoparticle phenomic parameters (P < 0.01) were finally selected from 24 eigengenes related to iron metabolism screened from these models. On the basis of the previous research results and the present study findings, we provide a hypothetical molecular model for Fe3O4 nanoparticle synthesis mediated by these hub genes in A. ferrooxidans BYM comprising membrane formation, iron uptake and transport, iron redox, and crystal maturity. Our results will enable in-depth studies of Fe3O4 nanoparticle synthesis in non-magnetotactic magnetosome-producing bacteria. IMPORTANCE As the most important non-magnetotactic magnetosome-producing bacteria, Acidithiobacillus ferrooxidans only requires very mild conditions to produce Fe3O4 nanoparticles, thus conferring greater flexibility and potential application in biomagnetic nanoparticle production. However, the available information cannot explain the mechanism of Fe3O4 nanoparticle formation in A. ferrooxidans. In this study, we applied phenomic and transcriptomic analyses to reveal this mechanism. We found that different treatment condition factors notably affect the phenomic data of Fe3O4 nanoparticle in A. ferrooxidans. Using transcriptomic analyses, the gene network controlling/regulating Fe3O4 nanoparticle biogenesis in A. ferrooxidans was proposed, excavating the candidate hub genes for Fe3O4 nanoparticle formation in A. ferrooxidans. Based on this information, a sequential model for Fe3O4 nanoparticle synthesis in A. ferrooxidans was hypothesized. It lays the groundwork for further clarifying the feature of Fe3O4 nanoparticle synthesis.

Identification of formononetin as the active compound of CR-SR in hepatocellular carcinoma treatment: An integrated approach combining network pharmacology and weighted gene co-expression networks.

Hepatocellular carcinoma (HCC) is a life-threatening disease for which there is no cure. Traditional Chinese medicine is a treasure trove of Medicinals that has been used for thousands of years. In China, the traditional herb pair, Curcumae Rhizoma and Sparganii Rhizoma (CR-SR) represent a classic herbal combination used for the treatment of HCC. However, the drug targets and pharmacological mechanism of action of CR-SR in the treatment of HCC are unclear. To address this, we screened the active components and drug targets of CR-SR from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and a high-throughput experiment- and reference-guided database of traditional Chinese medicines (HERB database). Combined with the weighted co-expression network analysis of dataset GSE76427, we constructed an active component-target-disease regulatory network. It was found that CR-SR's active components for HCC treatment included trans-gondoic acid, beta-sitosterol, stigmasterol, hederagenin, and formononetin. These compounds specifically targeted the genes Estrogen Receptor 1 (ESR1), Cyclin A2 (CCNA2), Checkpoint Kinase 1 (CHEK1), and Nuclear Receptor Coactivator 2 (NCOA2). ESR1, CCNA2, and CHEK1 genes showed significant differences in survival prognosis, expression levels, and statistical significance during the pathological stage. Moreover, their high affinity for formononetin was determined through molecular docking analysis. Cell assays and high-throughput sequencing were performed to reveal that the inhibitory effect of formononetin on HepG2 cell proliferation was related to hepatocyte metabolism and cell cycle regulation-related pathways. This study provides insights into potential HCC treatments.