Co-expression Network Analysis Research Articles

The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer’s disease patients

Alzheimer’s Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Construction and validation of a prognostic model of angiogenesis-related genes in multiple myeloma

BackgroundAngiogenesis is associated with tumour growth, infiltration, and metastasis. This study aimed to detect the mechanisms of angiogenesis-related genes (ARGs) in multiple myeloma (MM) and to construct a new prognostic model.MethodsMM research foundation (MMRF)-CoMMpass cohort, GSE47552, GSE57317, and ARGs were sourced from public databases. Differentially expressed genes (DEGs) in the tumour and control cohorts in GSE47552 were determined through differential expression analysis and were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was applied to derive modules linked to the ARG scores and obtain module genes in GSE47552. Differentially expressed ARGs (DE-ARGs) were selected for subsequent analyses by overlapping DEGs and module genes. Furthermore, prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Depending on the prognostic genes, a risk model was constructed, and risk scores were determined. Moreover, MM samples from MMRF-CoMMpass were sorted into high- and low-risk teams on account of the median risk score. Additionally, correlations among clinical characteristics, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), immune analysis, immunotherapy predictions and the mRNA‒miRNA‒lncRNA network were carried out.ResultsA total of 898 DEGs, 211 module genes, 24 DE-ARGs and three prognostic genes (AKAP12, C11orf80 and EMP1) were selected for this study. Enrichment analysis revealed that the DEGs were related to 86 GO terms, such as ‘cytoplasmic translation’, and 41 KEGG pathways, such as ‘small cell lung cancer’. A prognostic gene-based risk model was created in MMRF-CoMMpass and confirmed with the GSE57317 dataset. Moreover, a nomogram was established on the basis of independent prognostic factors that have proven to be good predictors. In addition, the immune cell infiltration results suggested that memory B cells were enriched in the high-risk group and that immature B cells were enriched in the low-risk group. Finally, the mRNA‒miRNA‒lncRNA network demonstrated that hsa-miR-508-5p was tightly associated with EMP1 and AKAP12. RT‒qPCR was used to validate the expression of the genes associated with prognosis.ConclusionA new prognostic model of MM associated with ARGs was created and validated, providing a new perspective for exploring the connection between ARGs and MM.

Multi-omic analysis identifies the molecular mechanism of hepatocellular carcinoma with cirrhosis

Hepatocellular carcinoma with cirrhosis promotes the advancement of malignancy and the development of fibrosis in normal liver tissues. Understanding the pathological mechanisms underlying the development of HCC with cirrhosis is important for developing effective therapeutic strategies. Herein, the RNA-sequencing (RNA-seq) data and corresponding clinical features of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) database using the University of California Santa Cruz (UCSC) Xena platform. The enrichment degree of hallmarkers for each TCGA-LIHC cohort was quantified by ssGSEA algorithm. Weighted gene co-expression network analysis (WGCNA) revealed two gene module eigengenes (MEs) associated with cirrhosis, namely, MEbrown and MEgreen. Analysis of these modules using AUCell showed that MEbrown had higher enrichment scores in all immune cells, whereas MEgreen had higher enrichment scores in malignant cells. The CellChat package revealed that both immune and malignant cells contributed to the fibrotic activity of myofibroblasts through diverse signaling pathways. Additionally, spatial transcriptomic data showed that hepatocytes, proliferating hepatocytes, macrophages, and myofibroblasts were located in closer proximity in HCC tissues. These cells may potentially participate in the process of stimulating myofibroblast fibrotic activity, which may be related to the development of liver fibrosis. In summary, we made full use of multi-omics data to explore gene networks and cell types that may be involved in the development and progression of cirrhosis in HCC.

Bioinformatics analysis of therapeutic targets for idiopathic pulmonary fibrosis and exploration of immune cell infiltration patterns

Idiopathic pulmonary fibrosis (IPF) is a severe progressive lung disease characterized by fibrotic changes in lung tissue, with limited treatment options. This study analyzed gene expression data from three gene expression omnibus datasets (GSE2052, GSE53845, and GSE110147) using R and LIMMA to identify differentially expressed genes (DEGs) in IPF samples. We identified 215 DEGs, comprising 106 upregulated and 109 downregulated genes. Weighted gene coexpression network analysis revealed five gene modules, with the module eigengene yellow showing the strongest correlation with IPF. Functional enrichment analysis of 40 consensus genes in this module indicated their significant involvement in extracellular matrix (ECM) organization. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed pathways related to protein digestion, cell adhesion molecules, and the advanced glycation end product–receptor for advanced glycation end product signaling pathway. Based on protein–protein interaction network analysis, collagen type XV alpha 1 chain (COL15A1) and collagen type VI alpha 3 chain (COL6A3) were identified as upregulated hub genes in IPF, which were regulated by microRNAs and transcription factors. Immune cell infiltration analysis showed significant changes in immune cell populations in IPF samples, with increases in memory B cells, plasma cells, and M0 macrophages and decreases in CD8 T cells, and resting natural killer cells. Potential drugs targeting COL15A1 and COL6A3 were predicted using multiple databases, revealing compounds such as (+)-JQ1, aristolochic acid I, and dexamethasone with promising binding potential. These findings suggest that COL15A1 and COL6A3 are central hub genes in IPF, are associated with ECM organization and immune response, and serve as therapeutic targets for IPF.

Integration WGCNA with LC-MS data for evaluating the processing status and transformation rules of Ligustri Lucidi Fructus: A novel strategy for evaluating the processing technology of traditional Chinese medicines

Ligustri Lucidi Fructus (LLF) is a traditional Chinese medicine (TCM) to treat hepatopathy and osteopathy. Wine-processed LLF (WLLF) was much more widely used than raw LLF (RLLF) in clinical practice, however, there is no consensus on processing time. To investigate the processing status of WLLF and transformation rules during processing, a UHPLC-Q-Orbitrap-MS method combined with data-independent acquisition (DIA) mode was firstly established and 227 compounds were identified or tentatively identified. Subsequently, a novel strategy using integration weighted gene co-expression network analysis (WGCNA) with LC-MS data was proposed. A total of 73 differential metabolites were screened out between RLLF and WLLF (wine steaming for 18 h). Meanwhile, the concentration of 11 differential compounds for WLLF was quantified. Finally, correlations between compounds were analyzed by WGCNA and the top five compounds negatively correlated with salidroside were validated, revealing that G13, specnuezhenide, oleuropein, acteoside, and neonuzhenide could be transformed into salidroside and its analogues during processing, respectively. The results indicated that our proposed strategy could be effectively employed to evaluate the processing status of TCMs.

Comparative Analyses of Dynamic Transcriptome Profile of Heart Highlight the Key Response Genes for Heat Stress in Zhikong Scallop Chlamys farreri.

Heat stress resulting from global climate change has been demonstrated to adversely affect growth, development, and reproduction of marine organisms. The Zhikong scallop (Chlamys farreri), an important economical mollusk in China, faces increasing risks of summer mortality due to the prolonged heat waves. The heart, responsible for transporting gas and nutrients, is vital in maintaining homeostasis and physiological status in response to environmental changes. In this study, the effect of heat stress on the cardiac function of C. farreri was investigated during the continuous 30-day heat stress at 27 °C. The results showed the heart rate of scallops increased due to stress in the initial phase of high temperature exposure, peaking at 12 h, and then gradually recovered, indicating an acclimatization at the end of the experiment. In addition, the levels of catalase (CAT), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC) exhibited an initial increase followed by recovery in response to heat stress. Furthermore, transcriptome analysis of the heart identified 3541 differentially expressed genes (DEGs) in response to heat stress. Subsequent GO and KEGG enrichment analysis showed that these genes were primarily related to signal transduction and oxidative stress, such as the phosphatidylinositol signaling system, regulation of actin cytoskeleton, MAPK signaling pathway, FoxO signaling pathway, etc. In addition, two modules were identified as significant responsive modules according to the weighted gene co-expression network analysis (WGCNA). The upregulation of key enzymes within the base excision repair and gap junction pathways indicated that the heart of C. farreri under heat stress enhanced DNA repair and maintained cellular integrity. In addition, the variable expression of essential signaling molecules and cytoskeletal regulators suggested that the heart of C. farreri modulated cardiomyocyte contraction, intracellular signaling, and heart rate through complex regulation of phosphorylation and calcium dynamics in response to heat stress. Collectively, this study enhances our understanding of cardiac function and provides novel evidence for unraveling the mechanism underlying the thermal response in mollusks.

Identification of key proteins in early-onset Alzheimer's disease based on WGCNA.

Early-onset Alzheimer's disease (EOAD) is sporadic, highly heterogeneous, and its underlying pathogenic mechanisms remain largely elusive. Proteomics research aims to uncover the biological processes and key proteins involved in disease progression. However, no proteomic studies to date have specifically focused on EOAD brain tissue. We integrated proteomic data from brain tissues of two Alzheimer's disease (AD) cohorts and constructed a protein co-expression network using weighted gene co-expression network analysis (WGCNA). We identified modules associated with EOAD, conducted functional enrichment analysis to understand the biological processes involved in EOAD, and pinpointed potential key proteins within the core modules most closely linked to AD pathology. In this study, we identified a total of 2,749 proteins associated with EOAD. Through protein co-expression network analysis, we discovered 41 distinct co-expression modules. Notably, the proteins within the core module most closely linked to AD pathology were significantly enriched in neutrophil degranulation. Additionally, we identified two potential key proteins within this core module that have not been previously reported in AD and validated their expression levels in 5xFAD mice. In summary, through a protein co-expression network analysis, we identified EOAD-related biological processes and molecular pathways, and screened and validated two key proteins, ERBB2IP and LSP1. These proteins may play an important role in the progression of EOAD, suggesting they could serve as potential therapeutic targets for the disease.

Amlexanox reduces new-onset atrial fibrillation risk in sepsis by downregulating S100A12: a Mendelian randomization study.

Sepsis is characterized by high morbidity and mortality rates, alongside limited therapeutic efficacy. Atrial fibrillation (AF), the most common arrhythmia, has been closely linked to sepsis in prior research. However, the specific mechanisms through which sepsis leads to new-onset AF remain poorly understood. This study focuses on identifying critical genes that are dysregulated in the development of new-onset AF within the context of sepsis, with the goal of uncovering new potential targets for its diagnosis and prevention. Our study began by applying Mendelian Randomization (MR) to assess the causal link between sepsis and AF. We then sourced sepsis and AF datasets from the Gene expression Omnibus (GEO) database. Using Weighted Gene Co-expression Network Analysis (WGCNA), we pinpointed key modules and genes associated with both sepsis and AF conditions. Protein-protein interaction (PPI) network was constructed. The Transcriptional Regulatory Relationships Unravelled by Sentence-based Text-mining (TRRUST) database helped build the transcription factor (TF) interaction network. Key genes were scrutinized through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to delve into their roles in new-onset AF's pathophysiology during sepsis. We employed the CIBERSORT algorithm to evaluate immune infiltration and the association between key genes and immune cells. The Connectivity Map (CMap) database facilitated the prediction of potential small molecule compounds targeting key genes. To culminate, an acute sepsis mouse model was developed to validate the implicated mechanisms of key genes involved in new-onset AF during sepsis, and to assess the prophylactic effectiveness of identified drug candidates. MR revealed potential independent risk factors for new-onset AF in sepsis. S100A12 was identified as a core interaction gene with elevated levels in sepsis and AF, underscoring its diagnostic and predictive significance. S100A12, along with associated genes, was mainly linked to immune and inflammatory response signaling pathways, correlating with immune cell levels. Targeting S100A12 identifies five potential small molecule therapeutics: amlexanox, balsalazide, methandriol, olopatadine, and tiboloe. In animal studies, acute sepsis increased S100A12 expression in serum and atrial tissues, correlating positively with inflammatory markers (IL-1β, IL-6, TNF-α) and negatively with heart rate, indicating a predisposition to AF. Early amlexanox administration can reduced S100A12 expression, dampened inflammation, and lessened new-onset AF risk in sepsis. This study demonstrates that sepsis may independently increase the risk of new-onset AF. We identified S100A12 as a key gene influencing the new-onset AF in sepsis through immune regulation, presenting considerable diagnostic and predictive value. Notably, amlexanox, by targeting S100A12 emerges as the most clinical relevant intervention for managing new-onset AF in sepsis patients.

Identification of molecular targets of Hypericumperforatum in blood for major depressive disorder: a machine-learning pharmacological study

BackgroundMajor depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericumperforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD.MethodsWe performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD.ResultsDifferential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking.ConclusionsThe active constituents of Hypericumperforatum can act on MDD and key targets and pathways of this action were identified.

Multiomics identification of ALDH9A1 as a crucial immunoregulatory molecule involved in calcific aortic valve disease

Mitochondrial dysfunction and immune cell infiltration play crucial yet incompletely understood roles in the pathogenesis of calcific aortic valve disease (CAVD). This study aimed to identify immune-related mitochondrial genes critical to the pathological process of CAVD using multiomics approaches. The CIBERSORT algorithm was employed to evaluate immune cell infiltration characteristics in CAVD patients. An integrative analysis combining weighted gene coexpression network analysis (WGCNA), machine learning, and summary data-based Mendelian randomization (SMR) was performed to identify key mitochondrial genes implicated in CAVD. Spearman’s rank correlation analysis was also performed to assess the relationships between key mitochondrial genes and infiltrating immune cells. Compared with those in normal aortic valve tissue, an increased proportion of M0 macrophages and resting memory CD4 T cells, along with a decreased proportion of plasma cells and activated dendritic cells, were observed in CAVD patients. Additionally, eight key mitochondrial genes associated with CAVD, including PDK4, LDHB, SLC25A36, ALDH9A1, ECHDC2, AUH, ALDH2, and BNIP3, were identified through the integration of WGCNA and machine learning methods. Subsequent SMR analysis, incorporating multiomics data, such as expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (mQTLs), revealed a significant causal relationship between ALDH9A1 expression and a reduced risk of CAVD. Moreover, ALDH9A1 expression was inversely correlated with M0 macrophages and positively correlated with M2 macrophages. These findings suggest that increased ALDH9A1 expression is significantly associated with a reduced risk of CAVD and that it may exert its protective effects by modulating mitochondrial function and immune cell infiltration. Specifically, ALDH9A1 may contribute to the shift from M0 macrophages to anti-inflammatory M2 macrophages, potentially mitigating the pathological progression of CAVD. In conclusion, ALDH9A1 represents a promising molecular target for the diagnosis and treatment of CAVD. However, further validation through in vivo and n vitro studies is necessary to confirm its role in CAVD pathogenesis and therapeutic potential.

Soil-Mulching Treatment Enhances the Content of Stilbene in Grape Berries: A Transcriptomic and Metabolomic Analysis

Soil mulching is a useful agronomic practice that promotes early fruit maturation and affects fruit quality. However, the regulatory mechanism of fruit metabolites under soil-mulching treatments remains unknown. In this study, variations in the gene sets and metabolites of grape berries after mulching (rice straw + felt + plastic film) using transcriptome and metagenomic sequencing were investigated. The results of the cluster analysis and orthogonal projection to latent structures discriminant analysis of the metabolites showed a difference between the mulching and control groups, as did the principal component analysis results for the transcriptome. In total, 36 differentially expressed metabolites were identified, of which 10 (resveratrol, ampelopsin F, piceid, 3,4′-dihydroxy-5-methoxystilbene, ε-viniferin, trans resveratrol, epsilon-viniferin, 3′-hydroxypterostilbene, 1-methyl-resveratrol, and pterostil-bene) were stilbenes. Their content increased after mulching, indicating that stilbene synthase activity increased after mulching. The weighted gene co-expression network analysis revealed that the turquoise and blue modules were positively and negatively related to stilbene compounds. The network analysis identified two seed genes (VIT_09s0054g00610, VIT_13s0156g00260) and two transcription factors (VIT_13s0156g00260, VIT_02s0025g04590). Overall, soil mulching promoted the accumulation of stilbene compounds in grapes, and the results provided key genetic information for further studies.

Genome-Wide Identification and Expression Analysis of the MADS-Box Gene Family in Cassava (Manihot esculenta)

The MADS-box gene family constitutes a vital group of transcription factors that play significant roles in regulating plant growth, development, and signal transduction processes. However, research on the MADS-box genes in cassava (Manihot esculenta) has been relatively limited. To gain deeper insights into the functions of the MADS-box genes in cassava development, in this study, we undertook a comprehensive genome-wide identification of the MADS-box gene family in cassava. We identified a total of 86 MADS-box genes with complete domains in the cassava genome, designated as MeMADS01 to MeMADS86. Through bioinformatic analyses, we investigated the basic physicochemical properties, conserved motifs, chromosomal locations, and phylogenetic relationships of the cassava MADS-box genes. The MADS-box gene family of cassava exhibited conservation, as well as species-specific characteristics, with intron loss being a predominant mode of evolution for the MADS-box genes. Expression pattern variations in the MeMADS genes across different tissues offer insights into their potential functions. Time-ordered gene co-expression network (TO-GCN), transcriptome data, and RT-qPCR analysis suggested the responsiveness of the MADS-box genes to drought stress. Meanwhile, MeMADS12 might be involved in regulating flowering under drought conditions via an ABA (abscisic acid)-dependent pathway. These findings provide valuable resources for a deeper understanding of the biological roles of the MADS-box genes in cassava.

Estradiol-mediated enhancement of the human ectocervical epithelial barrier correlates with desmoglein-1 expression in the follicular menstrual phase.

The cervicovaginal epithelial barrier is crucial for defending the female reproductive tract against sexually transmitted infections. Hormones, specifically estradiol and progesterone, along with their respective receptor expressions, play an important role in modulating this barrier. However, the influence of estradiol and progesterone on gene and protein expression in the ectocervical mucosa of naturally cycling women is not well understood. Mucosal and blood samples were collected from Kenyan female sex workers at high risk of sexually transmitted infections. All samples were obtained at two time points, separated by two weeks, aiming for the follicular and luteal phases of the menstrual cycle. Ectocervical tissue biopsies were analyzed by RNA-sequencing and in situ immunofluorescence staining, cervicovaginal lavage samples (CVL) were evaluated using protein profiling, and plasma samples were analyzed for hormone levels. Unsupervised clustering of RNA-sequencing data was performed using Weighted gene co-expression network analysis (WGCNA). In the follicular phase, estradiol levels positively correlated with a gene module representing epithelial structure and function, and negatively correlated with a gene module representing cell cycle regulation. These correlations were confirmed using regression analysis including adjustment for bacterial vaginosis status. Using WGCNA, no gene module correlated with progesterone levels in the follicular phase. In the luteal phase, no gene module correlated with either estradiol or progesterone levels. Protein profiling on CVL revealed that higher levels of estradiol during the follicular phase correlated with increased expression of epithelial barrier integrity markers, including DSG1. This contrasted to the limited correlations of protein expression with estradiol levels in the luteal phase. In situ imaging analysis confirmed that higher estradiol levels during the follicular phase correlated with increased DSG1 expression. We demonstrate that estradiol levels positively correlate with specific markers of ectocervical epithelial structure and function, particularly DSG1, during the follicular phase of the menstrual cycle. Neither progesterone levels during the follicular phase nor estradiol and progesterone levels during the luteal phase correlated with any specific sets of gene markers. These findings align with the expression of estradiol and progesterone receptors in the ectocervical epithelium during these menstrual phases.

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

Weighted gene co-expression network analysis and identification of ginsenoside biosynthesis candidate genes for ginseng adventitious roots under MeJA treatment.

Ginseng (Panax ginseng) is an herb with a long history and a wide range of applications. Ginsenoside is one of the most representative and active ginseng compounds, with various pharmacological effects. Therefore, the development of bioreactors using methyl jasmonate (MeJA) as an inducer for targeted ginsenoside production is of great commercial value. Combined with transcriptomic research tools, screenings to obtain candidate genes involved in ginsenoside biosynthesis are crucial for future discoveries about the molecular mechanism of MeJA-regulated ginsenoside biosynthesis. In our study, the ginsenoside content of ginseng adventitious roots treated with MeJA at different times was analyzed. Transcriptome analysis was performed to investigate the effects of MeJA on changes in ginsenoside content in ginseng adventitious roots. The MeJA could significantly increase changes in the content of pro-ginsenodiol ginsenosides as well as pro-triol ginsenosides Rg3, Re, and Rf in ginseng adventitious roots. Differential gene expression analysis showed that a total of 14,009 differentially expressed genes were obtained from the screening of the present study. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that differentially expressed genes were mainly enriched under GO terms in response to stimuli, metabolic processes, and the regulation of biological processes, with significant annotation to the metabolic terms of terpenoids and polyketides. Two expression modules of genes highly related to ginsenoside biosynthesis were obtained via WGCNA. Our study provides a reference system for the targeted ginsenoside production using MeJA as an inducer, and also provides genetic and gene resources for subsequently validating genes related to the regulation of ginsenoside biosynthesis using weighted gene co-expression network analysis (WGCNA).

Integrated Metabolomics and Transcriptomics Analyses Reveal the Regulatory Mechanisms of Anthocyanin and Carotenoid Accumulation in the Peel of Coffea arabica.

The color of coffee fruits is influenced by several factors, including cultivar, ripening stage, and metabolite composition. However, the metabolic accumulation of pigments and the molecular mechanisms underlying peel coloration during the ripening process of Coffea arabica L. remain relatively understudied. In this study, UPLC-MS/MS-based metabolomics and RNA sequencing (RNA-seq)-based transcriptomics were integrated to investigate the accumulation of anthocyanins and carotenoids in the peel of Coffea arabica at different ripening stages: green peel (GP), green-yellow peel (GYRP), red peel (RP), and red-purple peel (RPP). This integration aimed at elucidating the molecular mechanisms associated with these changes. A total of ten anthocyanins, six carotenoids, and thirty-five xanthophylls were identified throughout the ripening process. The results demonstrated a gradual decrease in the total carotenoid content in the peel with fruit maturation, while anthocyanin content increased significantly. Notably, the accumulation of specific anthocyanins was closely associated with the transition of peel colors from green to red. Integrated metabolomics and transcriptomics analyses identified the GYRP stage as critical for this color transition. A weighted gene co-expression network analysis (WGCNA) revealed that enzyme-coding genes such as 3AT, BZ1, and lcyE, along with transcription factors including MYB, NAC, and bHLH, which interact with PHD and SET TR, may regulate the biosynthesis of anthocyanins and carotenoids, thereby influencing peel pigmentation. These findings provide valuable insights into the molecular mechanisms underlying the accumulation of anthocyanins and carotenoids in Coffea arabica peel during fruit maturation.

Hormonal signaling regulates photosynthetic function of alfalfa (Medicago sativa L.) under NaHCO3 stress

The physiological and molecular mechanisms underlying salt-alkali tolerance in Medicago sativa are of significant importance for the development of animal husbandry on salt-alkali lands and the restoration of vegetation in such areas. This study utilized salt-alkali tolerance Medicago sativa 'Zhaodong' (ZD) and salt-alkali sensitive variety M. sativa 'Zhongmu No.1′ (ZM) as materials. Physiological analyses, transcriptomic sequencing, and hormone-targeted metabolomics techniques were employed to investigate the differential responses of the two alfalfa varieties to NaHCO3 stress in terms of morphology, photosynthetic functionality, and oxidative damage indicators. Additionally, weighted gene co-expression network analysis (WGCNA) was utilized to elucidate key mechanisms underlying salt-alkali tolerance in alfalfa. The results indicate that NaHCO3 stress leads to photosynthetic inhibition and oxidative damage in alfalfa leaves. Under NaHCO3 stress, PSI in alfalfa leaves exhibits higher stability compared to PSII. The salt-alkali tolerance alfalfa variety ZD demonstrates stronger tolerance compared to the salt-alkali sensitive variety ZM. Furthermore, differentially expressed genes (DEGs) between the two varieties under NaHCO3 stress are primarily enriched in KEGG pathways such as chlorophyll synthesis, photosynthesis, carbon fixation, and plant hormone synthesis and signaling. Weighted gene co-expression network analysis (WGCNA) was conducted based on physiological and transcriptomic data. Most differentially expressed genes (DEGs) in the top two modules with the highest correlation to physiological indicators such as photosynthesis are enriched in hormone synthesis and signal transduction pathways. Additionally, key transcription factors involved in hormone signal transduction were identified within these modules, such as MYC2 and ABI5, which regulate jasmonic acid (JA) and abscisic acid (ABA) signaling, respectively. These findings suggest that plant hormone signaling may play a critical role in regulating salt-alkali tolerance in alfalfa. Further analysis was conducted on plant hormone levels and gene expression involved in biosynthesis and signal transduction processes. The results indicate that NaHCO3 stress leads to significant accumulation of ABA and JA content in alfalfa leaves. The biosynthesis and signal transduction pathways of ABA and JA are activated under NaHCO3 stress. Additionally, the salt-alkali tolerance alfalfa variety ZD exhibits a more sensitive response to ABA and JA signals compared to ZM. Salicylic acid (SA) shows a positive response to NaHCO3 stress only in the ZD variety, which may be one of the key reasons for its stronger salt-alkali tolerance. Under NaHCO3 stress, overall growth-promoting hormones (IAA, GA, CK) are downregulated in ZD but upregulated in ZM, indicating that the salt-alkali tolerance alfalfa variety ZD mainly regulates the balance between growth and resistance by modulating the ratio of growth-promoting and stress-related hormones in response to NaHCO3 stress. This study reveals that hormone signaling plays a key role in regulating the photosynthetic function of alfalfa in response to salt-alkali stress, which provides theoretical basis and clues for the molecular breeding of alfalfa for salt-alkali tolerance.

Sequence-based analysis of the rice CAMTA family: haplotype and network analyses

The calmodulin-binding transcription activator (CAMTA) family contributes to stress responses in many plant species. The Oryza sativa ssp. japonica genome harbors seven CAMTA genes; however, intraspecific variation and functional roles of this gene family have not been determined. Here, we comprehensively evaluated the structure and characteristics of the CAMTA genes in japonica rice using bioinformatics approaches and RT-qPCR. Within the CAMTA gene and promoter sequences, 527 single nucleotide polymorphisms were retrieved from 3,024 rice accessions. The CAMTA genes could be subdivided into 5–14 haplotypes. Association analyses between haplotypes and phenotypic traits, such as grain weight and salt stress parameters, identified phenotypic differences between rice subpopulations harboring different CAMTA haplotypes. Co-expression analyses and the identification of CAMTA-specific binding motifs revealed candidate genes regulated by CAMTA. A Gene Ontology functional enrichment analysis of 690 co-expressed genes revealed that CAMTA genes have key roles in defense responses. An interaction analysis identified 30 putative CAMTA interactors. Three genes were identified in co-expression and interaction network analyses, suggesting that they are potentially regulated by CAMTAs. Based on all information obtained together with the phenotypes of the CRISPR-Cas9 knockout mutant lines of three OskCAMTA genes generated, CAMTA1 likely plays important roles in the response to salt stress in rice. Overall, our findings suggest that the CAMTA gene family is involved in development and the salt stress response and reveal candidate target genes, providing a basis for further functional characterization.

Physiological Parameters and Transcriptomic Levels Reveal the Response Mechanism of Maize to Deep Sowing and the Mechanism of Exogenous MeJA to Alleviate Deep Sowing Stress

Deep sowing, as a method to mitigate drought and preserve soil moisture and seedlings, can effectively mitigate the adverse effects of drought stress on seedling growth. The elongation of the hypocotyl plays an important role in the emergence of maize seeds from deep-sowing stress. This study was designed to explore the function of exogenous methyl jasmonate (MeJA) in the growth of the maize mesocotyl and to examine its regulatory network. The results showed that the addition of a 1.5 μ mol L−1 MeJA treatment significantly increased the mesocotyl length (MES), mesocotyl and coleoptile length (MESCOL), and seedling length (SDL) of maize seedlings. Transcriptome analysis showed that exogenous MeJA can alleviate maize deep-sowing stress, and the differentially expressed genes (DEGs) mainly include ornithine decarboxylase, terpene synthase 7, ethylene responsive transcription factor 11, and so on. In addition, candidate genes that may regulate the length of maize hypocotyls were screened by Weighted Gene Co-expression Network Analysis (WGCNA). These genes may be involved in the growth of maize hypocotyls through transcriptional regulation, histones, ubiquitin protease, protein binding, and chlorophyll biosynthesis and play an important role in maize deep-sowing tolerance. Our research findings may provide a theoretical basis for determining the tolerance of maize to deep-sowing stress and the mechanism of exogenous hormone regulation of deep-sowing stress.