Co-expression Modules Research Articles

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

The molecular mechanism underlying dermatomyositis related interstitial lung disease: evidence from bioinformatic analysis and in vivo validation

BackgroundDermatomyositis (DM) is an autoimmune and inflammatory disease that can affect the lungs, causing interstitial lung diseases (ILD). However, the exact pathophysiological mechanisms underlying DM-ILD are unknown. Idiopathic pulmonary fibrosis (IPF) belongs to the broader spectrum of ILD and evidence shows that common pathologic pathways might lie between IPF and DM-ILD.MethodsWe retrieved gene expression profiles of DM and IPF from the Gene Expression Omnibus (GEO) and utilized weighted gene co-expression network analysis (WGCNA) to reveal their co-expression modules. We then performed a differentially expressed gene (DEG) analysis to identify common DEGs. Enrichment analyses were employed to uncover the hidden biological pathways. Additionally, we conducted protein-protein interaction (PPI) networks analysis, cluster analysis, and successfully found the hub genes, whose levels were further validated in DM-ILD patients. We also examined the relationship between hub genes and immune cell abundance in DM and IPF. Finally, we conducted a common transcription factors (TFs)-genes network by NetworkAnalyst.ResultsWGCNA revealed 258 intersecting genes, while DEG analysis identified 66 shared genes in DM and IPF. All of these genes were closely related to extracellular matrix and structure, cell-substrate adhesion, and collagen metabolism. Four hub genes (POSTN, THBS2, COL6A1, and LOXL1) were derived through intersecting the top 30 genes of the WGCNA and DEG sets. They were validated as active transcripts and showed diagnostic values for DM and IPF. However, ssGSEA revealed distinct infiltration patterns in DM and IPF. These four genes all showed a positive correlation with immune cells abundance in DM, but not in IPF. Finally, we identified one possible key transcription factor, MYC, that interact with all four hub genes.ConclusionThrough bioinformatics analysis, we identified common hub genes and shared molecular pathways underlying DM and IPF, which provides valuable insights into the intricate mechanisms of these diseases and offers potential targets for diagnostic and therapeutic interventions.

Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia.

The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.

The mRNA-lncRNA landscape of multiple tissues uncovers key regulators and molecular pathways that underlie heterosis for feed intake and efficiency in laying chickens

BackgroundHeterosis is routinely exploited to improve animal performance. However, heterosis and its underlying molecular mechanism for feed intake and efficiency have been rarely explored in chickens. Feed efficiency continues to be an important breeding goal trait since feed accounts for 60 to 70% of the total production costs in poultry. Here, we profiled the mRNA-lncRNA landscape of 96 samples of the hypothalamus, liver and duodenum mucosa from White Leghorn (WL), Beijing-You chicken (YY), and their reciprocal crosses (WY and YW) to elucidate the regulatory mechanisms of heterosis.ResultsWe observed negative heterosis for both feed intake and residual feed intake (RFI) in YW during the laying period from 43 to 46 weeks of age. Analysis of the global expression pattern showed that non-additivity was a major component of the inheritance of gene expression in the three tissues for YW but not for WY. The YW-specific non-additively expressed genes (YWG) and lncRNA (YWL) dominated the total number of non-additively expressed genes and lncRNA in the hypothalamus and duodenum mucosa. Enrichment analysis of YWG showed that mitochondria components and oxidation phosphorylation (OXPHOS) pathways were shared among the three tissues. The OXPHOS pathway was enriched by target genes for YWL with non-additive inheritance of expression in the liver and duodenum mucosa. Weighted gene co-expression network analysis revealed divergent co-expression modules associated with feed intake and RFI in the three tissues from WL, YW, and YY. Among the negatively related modules, the OXPHOS pathway was enriched by hub genes in the three tissues, which supports the critical role of oxidative phosphorylation. Furthermore, protein quantification of ATP5I was highly consistent with ATP5I expression in the liver, which suggests that, in crossbred YW, non-additive gene expression is down-regulated and decreases ATP production through oxidative phosphorylation, resulting in negative heterosis for feed intake and efficiency.ConclusionsOur results demonstrate that non-additively expressed genes and lncRNA involved in oxidative phosphorylation in the hypothalamus, liver, and duodenum mucosa are key regulators of the negative heterosis for feed intake and RFI in layer chickens. These findings should facilitate the rational choice of suitable parents for producing crossbred chickens.

Pre-pregnancy gene expression signatures are associated with subsequent improvement/worsening of rheumatoid arthritis during pregnancy

BackgroundWhile many women with rheumatoid arthritis (RA) improve during pregnancy and others worsen, there are no biomarkers to predict this improvement or worsening. In our unique RA pregnancy cohort that includes a pre-pregnancy baseline, we have examined pre-pregnancy gene co-expression networks to identify differences between women with RA who subsequently improve during pregnancy and those who worsen.MethodsBlood samples were collected before pregnancy (T0) from 19 women with RA and 13 healthy women enrolled in our prospective pregnancy cohort. RA improvement/worsening between T0 and 3rd trimester was assessed by changes in the Clinical Disease Activity Index (CDAI). Pre-pregnancy expression profiles were examined by RNA sequencing and differential gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules correlated with the improvement/worsening of RA during pregnancy and to assess their functional relevance.ResultsOf the 19 women with RA, 14 improved during pregnancy (RAimproved) while 5 worsened (RAworsened). At the T0 baseline, however, the mean CDAI was similar between the two groups. WGCNA identified one co-expression module related to B cell function that was significantly correlated with the worsening of RA during pregnancy and was significantly enriched in genes differentially expressed between the RAimproved and RAworsened groups. A neutrophil-related expression signature was also identified in the RAimproved group at the T0 baseline.ConclusionThe pre-pregnancy gene expression signatures identified represent potential biomarkers to predict the subsequent improvement/worsening of RA during pregnancy, which has important implications for the personalized treatment of RA during pregnancy.

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis.

Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

Concurrent impact of de novo mutations on cranial and cortical development in nonsyndromic craniosynostosis.

Nonsyndromic craniosynostosis (nsCS), characterized by premature cranial suture fusion, is considered a primary skull disorder in which impact on neurodevelopment, if present, results from the mechanical hindrance of brain growth. Despite surgical repair of the cranial defect, neurocognitive deficits persist in nearly half of affected children. Therefore, the authors performed a functional genomics analysis of nsCS to determine when, where, and in what cell types nsCS-associated genes converge during development. The authors integrated whole-exome sequencing data from 291 nsCS proband-parent trios with 29,803 single-cell transcriptomes of the prenatal and postnatal neurocranial complex to inform when, where, and in what cell types nsCS-mutated genes might exert their pathophysiological effects. The authors found that nsCS-mutated genes converged in cranial osteoprogenitors and pial fibroblasts and their transcriptional networks that regulate both skull ossification and cerebral neurogenesis. Nonsyndromic CS-mutated genes also converged in inhibitory neurons and gene coexpression modules that overlapped with autism and other developmental disorders. Ligand-receptor cell-cell communication analysis uncovered crosstalk between suture osteoblasts and neurons via the nsCS-associated BMP, FGF, and noncanonical WNT signaling pathways. These data implicate a concurrent impact of nsCS-associated de novo mutations on cranial morphogenesis and cortical development via cell- and non-cell-autonomous mechanisms in a developmental nexus of fetal osteoblasts, pial fibroblasts, and neurons. These results suggest that neurodevelopmental outcomes in nsCS patients may be driven more by mutational status than surgical technique.

Weighted gene co-expression network analysis of key targets and interventional mechanism of Milkvetch root in diabetic nephropathy.

This work aimed to explore the key targets and intervention mechanisms of Huangqi (Astragalus membranaceus) in diabetic nephropathy using weighted gene co-expression network analysis (WGCNA). The findings will provide references for identifying critical therapeutic targets for diabetic nephropathy. The GSE1009 dataset was selected from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI) for analysis. WGCNA network was constructed to identify differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analysis were performed on the DEGs. There were 752 downregulated DEGs and 1,547 upregulated DEGs in the diabetic nephropathy samples. Genes such as PLCE1, CLIC5, PTPRO, HSPA12A, AIF1, GMDS, and SEMA5A were significantly suppressed in the diabetic nephropathy samples, while genes such as CEP152, LUNAR1, and SLC9A1 were significantly upregulated. The optimal soft threshold for the WGCNA network was determined as 12. Hierarchical clustering analysis was conducted to detect co-expression modules with corresponding color assignments, and a total of 9 modules were identified. Clinical characteristics showed a high correlation with the gray, blue, green, and brown modules of the WGCNA. GO analysis and KEGG pathway enrichment analysis revealed that the blue module DEGs were mainly enriched in immune response, inflammatory response, signal transduction, plasma membrane, extracellular region, cell surface, extracellular matrix, and proteinaceous extracellular matrix. The green module DEGs were mainly enriched in mitochondrial elongation, mitochondrial mutation termination, translation, mitochondrial inner membrane, mitochondrion, ATP biosynthetic process, mitochondrial large ribosomal subunit, mitochondrial intermembrane space, nucleolus, and ribosome. Visualization analysis of the bioactive components of Huangqi showed compounds such as quercetin, resveratrol, 7-O-methylisomucronulatol, and isoquercetin, which had more targets. Differentially expressed genes in diabetic nephropathy were mainly enriched in immune response and inflammatory response. Various components of Huangqi have positive application value in the treatment of diabetic nephropathy and can be considered for clinical promotion.

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co-expression network analysis.

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange-fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress-specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co-expression networks under multiple abiotic stress conditions, weighted gene co-expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co-expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought-associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

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

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

B cell subsets-related biomarkers and molecular pathways for systemic lupus erythematosus by transcriptomics analyses

BackgroundSystemic lupus erythematosus (SLE), an autoimmune disease, is characterised by B-cell abnormalities and a loss of tolerance that can produce autoantibody. However, the imperative genes and molecular pathways involved in the change of B cell populations remain unclear. MethodsThe expression of B cell subsets between SLE and healthy controls (HCs) was detected based on micro-array transcriptome data. The Weighted Gene Co-Expression Network Analysis (WGCNA) further revealed the co-expression modules of naïve and memory B cells. Whereafter, we performed the functional enrichment analysis, Protein-protein interaction (PPI) networks construction and feature selection to screen hub genes. Ultimately, we recruited SLE patients and HCs from the Second Hospital of Shanxi Medical University and further verified these genes in transcriptome sequencing samples. ResultsTotal of 1087 SLE patients and 86 HCs constituted in the study. Compared to HCs, the levels of peripheral naïve B cells of SLE patients decreased, while memory B cells increased. WGCNA identified two modules with the highest correlation for the subsequent analysis. The purple module was primarily in connection with naïve B cells, and the GO analysis indicated that these genes were mainly abundant in B cell activation. The blue module relevant to memory B cells was most significantly enriched in the “defence response to virus” correlation pathway. Then we screened six hub genes by PPI and feature selection. Finally, four biomarkers (IFI27, IFITM1, MX2, IRF7) were identified by transcriptome sequencing verification. ConclusionOur study identified hub genes and key pathways associated with the naïve and memory B cells respectively, which may offer novel insights into the behaviours of B cells and the pathogenesis of SLE.

Integrated study of systemic and local airway transcriptomes in asthma reveals causal mediation of systemic effects by airway key drivers

BackgroundSystemic and local profiles have each been associated with asthma, but parsing causal relationships between system-wide and airway-specific processes can be challenging. We sought to investigate systemic and airway processes in asthma and their causal relationships.MethodsThree hundred forty-one participants with persistent asthma and non-asthmatic controls were recruited and underwent peripheral blood mononuclear cell (PBMC) collection and nasal brushing. Transcriptome-wide RNA sequencing of the PBMC and nasal samples and a series of analyses were then performed using a discovery and independent test set approach at each step to ensure rigor. Analytic steps included differential expression analyses, coexpression and probabilistic causal (Bayesian) network constructions, key driver analyses, and causal mediation models.ResultsAmong the 341 participants, the median age was 13 years (IQR = 10–16), 164 (48%) were female, and 200 (58.7%) had persistent asthma with mean Asthma Control Test (ACT) score 16.6 (SD = 4.2). PBMC genes associated with asthma were enriched in co-expression modules for NK cell-mediated cytotoxicity (fold enrichment = 4.5, FDR = 6.47 × 10−32) and interleukin production (fold enrichment = 2.0, FDR = 1.01 × 10−15). Probabilistic causal network and key driver analyses identified NK cell granule protein (NKG7, fold change = 22.7, FDR = 1.02 × 10−31) and perforin (PRF1, fold change = 14.9, FDR = 1.31 × 10−22) as key drivers predicted to causally regulate PBMC asthma modules. Nasal genes associated with asthma were enriched in the tricarboxylic acid (TCA) cycle module (fold enrichment = 7.5 FDR = 5.09 × 10−107), with network analyses identifying G3BP stress granule assembly factor 1 (G3BP1, fold change = 9.1 FDR = 2.77 × 10−5) and InaD-like protein (INADL, fold change = 5.3 FDR = 2.98 × 10−9) as nasal key drivers. Causal mediation analyses revealed that associations between PBMC key drivers and asthma are causally mediated by nasal key drivers (FDR = 0.0076 to 0.015).ConclusionsIntegrated study of the systemic and airway transcriptomes in a well-phenotyped asthma cohort identified causal key drivers of asthma among PBMC and nasal transcripts. Associations between PBMC key drivers and asthma are causally mediated by nasal key drivers.

Integrated co-expression analysis of regulatory elements (miRNA, lncRNA, and TFs) in bovine monocytes induced by Str. uberis

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis-induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

Exploring shared pathways and the shared biomarker ERRFI1 in Obstructive sleep apnoea and atherosclerosis using integrated bioinformatics analysis

Obstructive sleep apnea (OSA) is an upper airway disorder occurring during sleep and is associated with atherosclerosis (AS). AS is a cardiovascular disease caused by environmental and genetic factors, with a high global mortality rate. This study investigated common pathways and potential biomarkers of OSA and AS. Microarray data were downloaded from the Gene Expression Omnibus (GEO) database and used to screen for differentially expressed genes (DEGs) in the OSA and AS datasets. A weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules of OSA and AS. The least absolute shrinkage and selection operators (LASSO) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of OSA and AS. Results revealed that differentially expressed genes may be involved in inflammatory processes, chemokine signaling pathways, and molecular changes in cell adhesion. ERBB receptor feedback inhibitor 1 (ERRFI1) was the best-shared biomarker for OSA and AS. Immune infiltration analysis showed that ERRFI1 expression was correlated with immune cell changes. Changes in immune pathways, inflammatory processes, and cell adhesion molecules may underlie the pathogenesis of both diseases, and ERRFI1 may be a potential diagnostic marker for patients with OSA and AS.

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis.

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein-protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-β1 (TGF-β1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future.

Genome-wide identification of PYL/PYR-PP2C (A)-SnRK2 genes in Eutrema and their co-expression analysis in response to ABA and abiotic stresses

ABA signaling core components PYR/PYL, group A PP2C and SnRK2 play important roles in various environmental stress responses of plants. This study identified 14 PYR/PYL, 9 PP2C (A), and 10 SnRK2 genes from halophytic Eutrema. Phylogenetic analysis showed 4 EsPYR/PYL, 4 EsPP2C (A) and 3 EsSnRK2 subfamilies characterized, which was supported by their gene structures and protein motifs. Large-scale segmental duplication event was demonstrated to be a major contributor to expansion of the EsPYL-PP2C (A)-SnRK2 gene families. Synteny relationship analysis revealed more orthologous PYL-PP2C (A)-SnRK2 gene pairs located in collinear blocks between Eutrema and Brassica than that between Eutrema and Arabidopsis. RNA-seq and qRT-PCR revealed EsABI1, EsABI2 and EsHAL2 showed a significantly up-regulated expression in leaves and roots in response to ABA, NaCl or cold stress. Three markedly co-expression modules of ABA/R-brown, NaCl/L-lightsteelblue1 and Cold/R-lightgreen were uncovered to contain EsPYL-PP2C (A)-SnRK2 genes by WGCNA analysis. GO and KEGG analysis indicated that the genes of ABA/R-brown module containing EsHAB1, EsHAI2 and EsSnRK2.6 were enriched in proteasome pathway. Further, EsHAI2-OE transgenic Arabidopsis lines showed significantly enhanced seeds germination and seedlings growth. This work provides a new insight for elucidating potential molecular functions of PYL-PP2C (A)-SnRK2 responding to ABA and abiotic stresses.

Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis.

Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC. We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis. WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644. This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.

ScGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.

Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types. In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells. We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset. Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.

Integrated Bioinformatic Analyses Reveal Immune Molecular Markers and Regulatory Networks for Cerebral Ischemia-Reperfusion.

Cerebral ischemia-reperfusion injury (CIR) following a stroke results in secondary damage and is a leading cause of adult disability. The present study aimed to identify hub genes and networks in CIR to explore potential therapeutic agents for its treatment. Differentially expressed genes based on the GSE23163 dataset were identified, and weighted gene co-expression network analysis was performed to explore co-expression modules associated with CIR. Hub genes were identified by intersecting immune gene profiles, differentially expressed genes, and modular genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and transcription factor-microRNA-gene regulatory network analyses were then conducted in selected crucial modules. Subsequently, their expression levels in animal models were verified using real-time quantitative polymerase chain reaction and Western blotting. Finally, potential drug molecules were screened for, and molecular docking simulations were performed to identify potential therapeutic targets. Seven hub genes-namely, Ccl3, Ccl4, Ccl7, Cxcl1, Hspa1a, Cd14, and Socs3-were identified. Furthermore, we established a protein interaction network using the STRING database and found that the core genes selected through the cytohubba plugin remained consistent. Animal experiments showed that at the transcriptional level, all seven genes showed significant differences (p < 0.001, fold change vs sham, 5-200). At the translational level, however, only Ccl3, Ccl4, Ccl7, Hspa1a, and Socs3 showed significant differences, while Cxcl1 and Cd14 did not. Nifedipine, with the highest predicted score, was identified as a therapeutic agent and successfully docked with the protein encoded by the hub genes. The expression of Ccl3, Ccl4, Ccl7, Hspa1a, and Socs3 was significantly different in CIR tissues compared to normal tissues both at the transcriptional and translational levels. Systems biology approaches indicated that these could be possible CIR marker genes, providing a stepping stone for further experimental studies.

A chromosome-scale Rhubarb (Rheum tanguticum) genome assembly provides insights into the evolution of anthraquinone biosynthesis

Rhubarb is the collective name for various perennial plants from the genus Rheum L. and the Polygonaceae family. They are one of the most ancient, commonly used, and important herbs in traditional Chinese medicine. Rhubarb is a major source of anthraquinones, but how they are synthesized remains largely unknown. Here, we generate a genome sequence assembly of one important medicinal rhubarb R. tanguticum at the chromosome level, with 2.76 Gb assembled into 11 chromosomes. The genome is shaped by two recent whole-genome duplication events and recent bursts of retrotransposons. Metabolic analyses show that the major anthraquinones are mainly synthesized in its roots. Transcriptomic analysis reveals a co-expression module with a high correlation to anthraquinone biosynthesis that includes key chalcone synthase genes. One CHS, four CYP450 and two BGL genes involved in secondary metabolism show significantly upregulated expression levels in roots compared with other tissues and clustered in the co-expression module, which implies that they may also act as candidate genes for anthraquinone biosynthesis. This study provides valuable insights into the genetic bases of anthraquinone biosynthesis that will facilitate improved breeding practices and agronomic properties for rhubarb in the future.