Gene Co-expression Research Articles

Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding, exerting small regulatory effects on downstream genes. However, which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. By contrast, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. Here we demonstrate that common and rare genetic diseases can be linked by studying how common disease-associated variants influence gene co-expression in 57 different tissues and cell types. We implemented this method in a framework called Downstreamer and applied it to 88 GWAS traits. We find that predicted downstream “genes” are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers–Danlos syndromes. 78% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Based on our findings, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve the identification of these highly connected genes linked to common traits and diseases.

Identification of SPP1+ macrophages as an immune suppressor in hepatocellular carcinoma using single-cell and bulk transcriptomics

IntroductionMacrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown.MethodsTwo bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC.ResultsIn this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1+ macrophages as an unfavorable cell type, while VCAN+ macrophages, C1QA+ macrophages, and CD8+ T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1+ macrophages, inhibits CD8+ T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype.DiscussionThis study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

Transcriptomic analysis delineates potential regulatory network as therapeutic alternatives in chronic myeloid leukemia

BackgroundChronic myeloid leukemia (CML) relapse and progression after discontinuation of tyrosine kinase inhibitors (TKI) therapy in the chronic phase (CP) are attributed to primitive quiescent leukemic progenitor cells (LPCs). This study aimed to identify key differentially expressed genes (DEGs) involved in molecular pathways alternative to BCR-ABL, which could lead to the eradication of leukemic stem/progenitor cells.MethodsmRNA expression profiling dataset comprising newly diagnosed untreated CP CML leukemic progenitor cell samples n=5 and normal hematopoietic progenitor cell samples n=5 were selected for DEGs identification. A weighted gene co-expression network was constructed to identify significant hub modules. Protein–protein interaction (PPI) network construction and pathway enrichment analysis provided additional information on the biological pathways involved. The genes involved in the resultant PPI network were subjected to a gene transcriptional regulatory network based on transcription factors (TFs) and microRNAs (miRNAs) involved in the target genes.ResultsA total of 709 genes that were differentially expressed among those corresponding to p<0.05 and log2FC>0.5 0.5$$\\end{document}]]>. The WGCNA showed a significant hub module comprising 267 DEGs. The major pathways of the hub genes involved in the PPI network at the highest confidence score >0.9 0.9$$\\end{document}]]> were related to mitochondrial translational elongation. The integrated gene transcriptional regulatory network revealed a TF (ETS1), two miRNAs (miR-34a-5p and miR-205-5p) targeting a hub gene (GFM1) as the highest-order motif or subnetwork in the three-node miRNA feed-forward loop.ConclusionsThe study suggests that the regulatory feed-forward loop may provide a potential therapeutic target in CML. Consequently, the mitochondrial translation pathway may be explored as an alternative to BCR-ABL pathways, which could aid in eliminating or increasing sensitization of CML progenitor/stem cells to TKI treatment.

Transcriptome Reveals Molecular Mechanisms of Neuroendocrine Regulation of Allometric Growth in the Red Swamp Crayfish Procambarus clarkii.

Allometric growth is a typical characteristic of crustaceans, which mainly occurs among individuals, life stages, tissues, and between sexes. The red swamp crayfish Procambarus clarkii is an economically important crustacean species in the world. To date, the molecular regulatory mechanisms of neuroendocrine system in the allometric growth of P. clarkii remain unclear. In this study, P. clarkii exhibiting significant allometric growth among individuals were sampled from three full-sibling families. The brain, eyestalk, nerve cord, and Y-organ were dissected for transcriptome analysis. Key functional genes were identified by random forest and DESeq2 methods. The gene pathways were enriched utilizing Kyoto Encyclopedia Genes and Genomes (KEGG) analysis. Gene topological analysis was established through weighted gene co-expression network analysis (WGCNA), and hub genes were screened by protein-protein interaction (PPI) networks. Transcriptomic analysis results were validated via qRT-PCR. RNA-Seq identified 31 differentially expressed genes (DEGs) (7 up- and 24 downregulated); 301 DEGs (23 up- and 278 downregulated); 1308 DEGs (474 up- and 834 downregulated); and 64 DEGs (52 up- and 12 downregulated) in the brain, eyestalk, Y-organ, and nerve cord, respectively. Crucial functional genes such as CHIA in the brain and perlucin-like in the eyestalk were notably identified. WGCNA revealed two hub modules, while PPI networks identified neuroendocrine regulators module which hub genes mainly including CP1876-like and cuticle protein AM1199-like, and structural components module which hub genes mainly including CUB& CCP Domain-Containing Protein, ARRDC, and E3 Ubiquitin protein ligase MCYCBP2-like. Correspondingly, the significant gene pathways such as amino sugar and nucleotide sugar metabolism (pcla00520) and insect hormone biosynthesis (pcla00981) were enriched. The results revealed the complex interactions and regulatory relationships of hub genes within hub modules to coordinate molting and growth. The results of RNA-Seq analysis were validated by the consistency of gene expression in qRT-PCR. In present study, key functional genes in the neuroendocrine system regulating allometric growth among individuals were identified, and significant pathways mainly include hormone synthesis were screened, thus constructing a neuroendocrine molecular regulatory network for the allometric growth of P. clarkii. Building on these investigations, a comprehensive mechanism whereby neuroendocrine regulators interact with structural components to coordinate molting and growth was proposed. The result would provide valuable insights into the molecular regulatory mechanisms of allometric growth, highlighting the interplay between the neuroendocrine system and relevant tissues.

Identification and Analysis of Autophagy-Related Genes as Diagnostic Markers and Potential Therapeutic Targets for Tuberculosis Through Bioinformatics.

According to the World Health Organization, Mycobacterium tuberculosis infections affect approximately 25% of the world's population. There is mounting evidence linking autophagy and immunological dysregulation to tuberculosis (TB). As a result, this research set out to discover TB-related autophagy-related biomarkers and prospective treatment targets. We used five autophagy databases to get genes linked to autophagy and Gene Expression Omnibus databases to get genes connected to TB. Then, functional modules associated with autophagy were obtained by analyzing them using weighted gene co-expression network analysis. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to examine the autophagy-related genes (ATGs) of important modules. Limma was used to identify differentially expressed ATGs (DE-ATGs), and the external datasets were used to further confirm their identification. We used DE-ATGs and a protein-protein interaction network to search the hub genes. CIBERSORT was used to estimate the kinds and amounts of immune cells. After that, we built a drug-gene interaction network and a network that included messenger RNA, small RNA, and DNA. At last, the differential expression of hub ATGs was confirmed by RT-qPCR, immunohistochemistry, and western blotting. The diagnostic usefulness of hub ATGs was evaluated using receiver operating characteristic curve analysis. Including 508 ATGs, four of the nine modules strongly linked with TB were deemed essential. Interleukin 1B (IL1B), CAPS1, and signal transducer and activator of transcription 1 (STAT1) were identified by intersection out of 22 DE-ATGs discovered by differential expression analysis. Research into immune cell infiltration found that patients with TB had an increased proportion of plasma cells, CD8 T cells, and M0 macrophages. A competitive endogenous RNA network utilized 10 long non-coding RNAs and 2 miRNAs. Then, the IL1B-targeted drug Cankinumad was assessed using this network. During bioinformatics analysis, three hub genes were validated in mouse and macrophage infection models. We found that IL1B, CASP1, and STAT1 are important biomarkers for TB. As a result, these crucial hub genes may hold promise as TB treatment targets.

Integrated miRNA-seq and functional analyses reveal the regulatory role of sha-miR-92a_L + 2R + 4 via targeting vegfaa in rainbow trout (Oncorhynchus mykiss) responding to acute hypoxia and reoxygenation stress

BackgroundHypoxia negatively affects the behavior, growth, reproduction and survival of fish, causing serious economic losses to aquaculture. Rainbow trout (Oncorhynchus mykiss), an important economic fish worldwide, belongs to a hypoxia-sensitive fish species, however, little is known about the regulatory mechanism of microRNAs (miRNAs) under hypoxia stress.ResultsRainbow trout were subjected to hypoxia stress for 3 h (H3h_L), 12 h (H12h_L), 24 h (H24h_L) and 3 h reoxygenation (R3h_L) to systemically evaluate the changes of miRNA expression profiles in liver, and functions of sha-miR-92a_L + 2R + 4 were investigated. We found 17, 144, 57 and 55 differentially expressed (DE) miRNAs in the H3h_L vs. control (N_L), H12h_L vs. N_L, H24h_L vs. N_L and R3h_L vs. N_L comparisons, respectively. Enrichment analysis revealed that the targets of DE miRNAs were significantly enriched in HIF signaling pathway, VEGF signaling pathway, FoxO signaling pathway and glycolysis/gluconeogenesis. Through miRNA-mRNA interaction and weighted gene co-expression network analysis (WGCNA), five key DE miRNAs (sha-miR-92a_L + 2R + 4, ssa-miR-128-3p, ssa-miR-101b-3p_R + 1, ola-miR-199a-5p_R + 2 and tni-miR-199_1ss18CG) were identified, which can target at least two hypoxia-responsive genes, such as vegfaa, ho, glut1a and junb. Functional analysis found that sha-miR-92a_L + 2R + 4 directly regulated vegfaa expression by targeting its 3′-UTR, overexpression of sha-miR-92a_L + 2R + 4 significantly decreased vegfaa expression in rainbow trout liver cells, while opposite results were obtained after transfection of sha-miR-92a_L + 2R + 4 inhibitor. Furthermore, overexpressed sha-miR-92a_L + 2R + 4 promoted rainbow trout liver cell proliferation and inhibited apoptosis.ConclusionThis study deepens our understanding of the crucial roles of miRNAs under hypoxia stress in rainbow trout. These results can contribute to devise strategies for improving rainbow trout survival rate and aquaculture production during hypoxia stress and help speeding up the selective breeding of hypoxia-tolerant rainbow trout.

5-Aminolevulinic Acid (5-ALA)-Induced Drought Resistance in Maize Seedling Root at Physiological and Transcriptomic Levels

Drought stress seriously affects the growth, development, yield, and quality of maize. This study aimed to investigate the effects of exogenous 5-ALA on root morphology and physiological changes in maize seedlings and to detect its regulatory network. The results showed that adding 25 mg/L 5-ALA accelerated root morphogenesis (root average diameter, main root length, total root length, and root surface area) and promoted dry matter accumulation and free radical removal. Transcriptome analysis showed that after applying exogenous 5-ALA, differently expressed genes (DEGs) were mainly involved in histidine metabolism, amino acid biosynthesis, plasma membrane components, secondary active sulfate transmembrane transporter activity, and anion reverse transporter activity. Two inbred lines specifically responded to organelle and structural molecular activity, and 5-ALA may regulate maize roots to achieve drought tolerance through these two pathways. In addition, candidate genes that may regulate maize root growth were screened by weighted gene co-expression network analysis (WGCNA). These genes may play important roles in alleviating drought stress through lignin synthesis, heat shock proteins, iron storage and transport, calcium binding proteins, and plasma membrane regulation of exogenous regulator 5-ALA. Our results may provide a theoretical basis for clarifying the response of maize seedling roots to drought and the mechanism of exogenous hormones in alleviating drought.

Proteomic analysis of plasma and duodenal tissue in celiac disease patients reveals potential noninvasive diagnostic biomarkers

The pathogenesis of celiac disease (CeD) remains incompletely understood. Traditional diagnostic techniques for CeD include serological testing and endoscopic examination; however, they have limitations. Therefore, there is a need to identify novel noninvasive biomarkers for CeD diagnosis. We analyzed duodenal and plasma samples from CeD patients by four-dimensional data-dependent acquisition (4D-DIA) proteomics. Differentially expressed proteins (DEPs) were identified for functional analysis and to propose blood biomarkers associated with CeD diagnosis. In duodenal and plasma samples, respectively, 897 and 140 DEPs were identified. Combining weighted gene co-expression network analysis(WGCNA) with the DEPs, five key proteins were identified across three machine learning methods. FGL2 and TXNDC5 were significantly elevated in the CeD group, while CHGA expression showed an increasing trend, but without statistical significance. The receiver operating characteristic curve results indicated an area under the curve (AUC) of 0.7711 for FGL2 and 0.6978 for TXNDC5, with a combined AUC of 0.8944. Exploratory analysis using Mfuzz and three machine learning methods identified four plasma proteins potentially associated with CeD pathological grading (Marsh classification): FABP, CPOX, BHMT, and PPP2CB. We conclude that FGL2 and TXNDC5 deserve exploration as potential sensitive, noninvasive diagnostic biomarkers for CeD.

Divergence in MiRNA targeting of AchAco and its role in citrate accumulation in kiwifruit

BackgroundMicroRNA (miRNA) is a crucial post-transcriptional regulatory factor in plant growth and development. Duplicated genes often exhibit functional divergence due to competition for the identical miRNA binding sites. Kiwifruit (Actinidia spp.) is an economically significant horticultural crop renowned for its distinctive flavor, which is largely attributable to elevated citrate levels during fruit development. However, the mechanisms through which miRNA-targeted modules evolve following duplication events and regulate citrate biosynthesis, thereby influencing the unique flavor profile of kiwifruits, remain poorly understood.ResultsIn this study, we examined the expression patterns of miRNAs and interactions with their targets in kiwifruit fruit samples from various pulp tissues and developmental stages. Our analysis identified 46 miRNAs, comprising 44 known miRNAs and two novel/kiwifruit-specific miRNAs, which targeted a total of 1,474 genes. Correlation analysis revealed weak relationships between the expression levels of miRNAs and their target genes. Among these targets, 27 tandemly duplicated genes, and 782 whole genome duplication (WGD) genes exhibited a loss of miRNA binding sites in one of their duplicated copies. Furthermore, weighted gene co-expression network analysis demonstrated that most duplicated genes clustered into distinct gene modules. These findings suggest that the loss of miRNA targets following duplications contributes to expression divergence among gene duplicates, thereby facilitating stable gene expression within the miRNA-targeted network. For instance, the aconitate hydratase genes AchAco4 and AchAco6 were each targeted by different miRNAs, ach-miR-3774 and ach-miR-10371, respectively. Notably, these genes exhibited distinct expression patterns compared to their duplicated counterparts.ConclusionsThis study enhances our understanding of how the miRNA-AchAco module regulates citrate content and provides insights into the molecular network that influences the flavor profile of kiwifruit.Clinical trial numberNot applicable.

Transcriptome Profiling Implicates Non-Coding RNAs Involved in Flowering and Floral Organ Development in Water Lily

In this study, we performed small RNA and whole-transcriptome sequencing of different tissues of Nymphaea minuta to systematically investigate the roles and regulatory mechanisms of miRNA, lncRNA, and circRNA in the regulation of flowering-related target genes. Fifteen samples were sequenced using the Illumina platform, with strict data quality control to ensure the reliability of the analysis. By applying multiple bioinformatics tools, miRNA, lncRNA, and circRNA were comprehensively identified, annotated, and functionally analyzed, with a focus on screening non-coding RNAs closely related to the flowering process. The results showed significant differential expression of these miRNAs and lncRNAs across different tissues, which influenced the expression of flowering-related genes through specific regulatory networks. The constructed gene co-expression network further revealed the central roles of these non-coding RNAs in flowering regulation. This study provides new insights into the flowering regulatory mechanisms of N. minuta, highlights the potential of this species for studying aquatic plant flowering mechanisms, and provides an important theoretical basis for gene function research in aquatic plants.

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms. Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking. The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes. A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method. 8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma. Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

Identification of key protein-coding genes, lncRNAs and their regulatory network associated with the progression of lung adenocarcinoma in humans

Lung adenocarcinoma (LUAD) is an aggressive subtype of non-small cell lung cancer (NSCLC) known for its high propensity for early metastasis and rapid progression, often leading to late-stage diagnosis and poor prognosis. This cancer type frequently spreads to distant organs, including the brain, liver, and bones, contributing to its high mortality rate among lung cancer subtypes. Therefore, effective strategies need to be developed for early detection and prognosis of LUAD. Transcriptomic advancements have helped in better prognosis by analyzing the complex interplay among protein-coding and non-coding genes across various cancers. However, the regulatory mechanisms and the interactions between the coding and non-coding elements involved in LUAD progression is poorly understood. In this quest, we used weighted gene co-expression network analysis (WGCNA) approach on RNA-Seq data and identified a set of protein-coding genes (PCGs) and lncRNAs that are crucial for the development of LUAD. Further, we derived the networks of PCGs with lncRNAs at the mRNA (i.e., transcriptional) and protein (i.e., post-transcriptional) levels. Our analysis revealed 405 PCGs as the candidate biomarkers among which ADAMTS8, PECAM1, RGCC, and TCF21 were detected as key PCGs. Gene ontology and pathway analysis suggested angiogenesis as the crucial pathway regulated by the candidate biomarkers. BANCR was the only lncRNA among those analyzed that showed both differential expression in tumor tissues and a significant association with better survival, making it a promising candidate for further investigation as a prognostic biomarker in LUAD. Further, we identified MALAT1-TCF21-NORAD-SELP-BANCR-KLF2, as the key regulatory mechanism through which BANCR might be regulating the LUAD progression.

Effect of COL11A1 on oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is the most common malignant tumor of the oral cavity, which is mainly a series of atypical hyperplasia of oral epithelial cells, and the overall prognosis remains poor. MethodsGSE37991 and GSE38517 were downloaded from gene expression omnibus (GEO) to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs was performed, weighted gene co-expression network analysis (WGCNA) was used. Protein-protein interaction (PPI) network was constructed. Core gene expression was visualized using a heatmap. Comparative toxicogenomics database (CTD) and miRNA analyses identified related diseases and regulatory miRNAs. Western blot (WB) was conducted to examine expression of COL11A1 and TGF-SMAD signaling components in OSCC samples. Results5163 DEGs were identified. DEGs were enriched in metabolic processes and signaling pathways, including TGF-β/SMAD and PI3K-Akt. WGCNA identified 11 core modules. PPI network analysis revealed five core genes: COL11A1, AURKA, MELK, CCNA2, and BUB1. Heatmap analysis showed that COL11A1 is highly expressed in OSCC. CTD analysis indicated that COL11A1 is associated with OSCC. miRNA prediction identified potential regulatory factors. Western blot analysis demonstrated that COL11A1 is overexpressed in OSCC and is associated with TGF-SMAD signaling, inflammation, and cell cycle progression. ConclusionCOL11A1 is highly expressed in OSCC and may serve as a target gene interacting with the TGF-SMAD signaling pathway.

Integration of transcriptional and epigenetic regulation of TFEB reveals its dual functional roles in Pan-cancer.

Transcription factor EB (TFEB) mainly regulates the autophagy-lysosomal pathway, associated with many diseases, including cancer. However, the role of TFEB in pan-cancer has not been investigated systematically. In this study, we comprehensively analyzed TFEB targets under three stresses in Hela cells by cross-validation of RNA-seq and ChIP-seq. 1712 novel TFEB targets have not been reported in the Gene Set Enrichment Analysis and ChIP Enrichment Analysis databases. We further investigated their distributions and roles among the pan-cancer co-expression networks across 32 cancers constructed by multiscale embedded gene co-expression network analysis (MEGENA) based on the Cancer Genome Atlas (TCGA) cohort. Specifically, TFEB might serve as a hidden player with multifaceted functions in regulating pan-cancer risk factors, e.g. CXCL2, PKMYT1and BUB1, associated with cell cycle and immunosuppression. TFEB might also regulate protective factors, e.g. CD79A, related to immune promotion in the tumor microenvironment. We further developed a Shiny app website to present the comprehensive regulatory targets of TFEB under various stimuli, intending to support further research on TFEB functions. Summarily, we provided references for the TFEB downstream targets responding to three stresses and the dual roles of TFEB and its targets in pan-cancer, which are promising anticancer targets that warrant further exploration.

Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Unveiling Immunotherapy Evasion in Lung Cancer: The Role of Fanconi Anemia and Stemness Genes in Shaping an Immunosuppressive Microenvironment.

The study aimed to investigate the fanconi anemia (FA)-related and stemness-related genes in lung cancer (LC) patients. Firstly, we identified stemness-related genes through weighted gene co-expression network analysis combined with TCGA database. Further combined stemness-related genes with FA-related genes to screen for prognostic-related genes. Risk score was constructed from the screened genes and comprehensive bioinformatics analyses were performed. Finally, single-cell data and in vitro experiment were used to validate our results. We screened a total of eight genes to construct a risk score. The risk score was an independent prognostic factor for LC. The validation results of multiple GEO databases were consistent with our results. Functional and pathway enrichment analysis showed that risk score was associated with cell cycle, DNA replication, DNA damage repair, and immune-related pathways. The results showed to be related to the stem cell self-renewal and proliferation. Besides, we also found that patients with higher risk scores had lower immune activity and function, and the effectiveness of immunotherapy might be poorer, with a higher rate of immune escape. Finally, our results revealed that SLC2A1 had the highest correlation with B cells in single-cell data analysis, and we validated its correlation with B cells and its expression with FA-related genes, tumor invasiveness, stemness, and drug sensitivity. Our research constructed a risk score based on FA-related and tumor stemness-related specific genes. In addition to accurately predicting the prognosis of patients with LC, the risk score may also serve as an innovative and viable predictor of immunotherapy response.

Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data.

To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

Deletion of Stimulator of Interferons Genes Aggravated Cardiac Dysfunction in Physiological Aged Mice

BackgroundStimulator of interferons genes (STING) is crucial for innate immune response. It has been demonstrated that cGAS-STING pathway was the driver of aging-related inflammation. However, whether STING is involved in cardiac dysfunction during the physiological aging process remains unclear. MethodsGene expression profiles were obtained from the Gene Expression Omnibus database, followed by weighted gene co-expression network analysis, gene ontology analysis and protein network interaction analysis to identify key pathway and genes associated with aging. The effects of STING on cardiac function, glucose homeostasis, inflammation, and autophagy in physiological aging were investigated with STING knockout mice. ResultsBioinformatics analysis revealed STING emerged as a hub gene of interest. Subsequent experiments demonstrated the activation of STING pathway in the heart of aged mice. Knockout of STING alleviated the inflammation in aged mice. However, Knockout of STING impaired glucose tolerance, inhibited autophagy, enhanced oxidative stress and aggravated cardiac dysfunction in aged mice. ConclusionAlthough reducing inflammation, long-term STING inhibition by genetic ablation exacerbated cardiac dysfunction in aged mice. Given the multifaceted nature of aging and the diverse cellular functions of STING beyond immune regulation, the negative effects of targeting STING as a strategy to mitigate aging phenotype should be fully considered.

Inhibitory mechanism of theaflavins on intestinal fluoride transport: a new insight from network pharmacology integrated transcriptomic analysis

Water and some foods are important sources of fluoride (F-) exposure, therefore, it is necessary to study intestinal F- absorption and its influencing factors to assess the benefits or risks of F- exposure. Our previous study found that theaflavins including theaflavin (TF), theaflavin-3-gallate (TF3G), theaflavin-3′-gallate (TF3′G) and theaflavin-3,3′-digallate (TFDG) could all significantly inhibit F- transport in Caco-2 monolayer models. However, information on its related mechanisms is still scarce. In this study, its related mechanisms were revealed by network pharmacology integrated transcriptomic analysis. A total of 27 candidate target proteins were screened by network pharmacology analysis for theaflavins inhibiting F- transport. Weighted gene co-expression network analysis (WGCNA) of transcriptome data identified three co-expression modules including 1422 genes. Further, 53 hub genes were screened by protein-protein interaction (PPI) network. Notably, MAPK8 (also called JNK1, c-Jun N-terminal kinase 1) was both a candidate target in network pharmacology analysis and a hub gene in WGCNA. The GO and KEGG results suggested that theaflavins inhibiting F- transport may be related to the MAPK signaling pathway. Further experiment showed that TF could reverse F--mediated activation in the JNK MAPK signaling pathway activation and increase in intracellular reactive oxygen species (ROS). In addition, we demonstrated that TF could restore the F--mediated in decreased expression and disorder of tight junction proteins, which ultimately inhibited F- transport. In conclusion, the inhibition of F- transport in Caco-2 monolayer models by theaflavins may be mediated via the ROS/JNK MAPK/tight junction signaling pathway.

The effect of Polyethylene Terephthalate (PET) Microplastic stress on the composition and gene regulatory network of amino acid in Capsicum annuum

Polyethylene Terephthalate (PET) is a widely used plastic in daily life. The extensive accumulation of PET microplastics (PET-MPs) in the environment adversely affects plant growth in multiple ways. However, the impact of PET-MPs exposure on the plant metabolism and the underlying molecular mechanisms are largely unexplored. To address this gap, we employed metabolomics and transcriptomics combination analyses to investigate the effects of PET-MPs exposure, varying in particle size and concentration, on the amino acid content and composition in pepper, as well as the underlying genes regulatory network. A total of 282 amino acids and their derivatives were identified, including 8 essential amino acids. Significant changes in differentially accumulated amino acids (DAAs) and differentially expressed genes (DEGs) were observed across different treatments, indicating that PET-MPs exposure affects amino acid metabolism in peppers, with these effects closely related to the size and concentration of PET-MPs. Ten DAAs with significant variable importance were identified through OPLS-DA. Weighted gene co-expression network analysis (WGCNA) revealed that the red module was significantly correlated with most of the DAAs indicators, highlighting the essential roles of HMSI, BCAT, and 12 transcription factor (TF) genes in regulating amino acid synthesis under PET-MPs exposure. Furthermore, correlation and redundancy analysis (RDA) identified three candidate genes, HSMI, PROC, and FHM, involved in amino acid biosynthesis pathways. This study enhances our understanding of MPs pollution and provides novel insights into the impact of MPs on crop growth and nutrition.