Gene Co-expression Network Research Articles

Identification of Common Angiogenesis Marker Genes in Chronic Lung Diseases and Their Relationship with Immune Infiltration Based on Bioinformatics Approaches

Objective: This study aims to explore the role of angiogenesis-related genes in chronic lung diseases (ILD and COPD) using bioinformatics methods, with the goal of identifying novel therapeutic targets to slow disease progression and prevent its deterioration into fibrosis or pulmonary artery hypertension. Methods: The research methods encompassed differential analysis, WGCNA (Weighted Gene Co-expression Network Analysis), and multiple machine learning approaches to screen for key genes. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to assess related biological functions and pathways. Additionally, immune cell infiltration was analyzed to evaluate the immune status of the disease and the correlation between genes and immunity. Results: COPD and ILD are closely associated with pathways related to angiogenesis, immune responses, and others, with differential genes in both groups linked to inflammation-related signaling pathways. The study established a chronic lung disease-related gene set comprising 171 genes and further screened out 21 genes related to angiogenesis. Ultimately, four key genes—COL10A1, EDN1, MMP1, and RRAS—were identified through machine learning methods. These four genes are closely related to angiogenesis and immune processes, and clustering analysis based on them can reflect different disease states and variations in immune cell infiltration. Conclusions: COL10A1, EDN1, MMP1, and RRAS represent potential therapeutic targets for slowing the progression of chronic lung diseases and preventing their deterioration. Furthermore, monocytes exhibited consistent infiltration patterns across disease and control groups, as well as among different subgroups, suggesting their potential significant role in the development of chronic lung diseases.

Identification of PIF1 as a Ferroptosis-Related Prognostic Biomarker Correlated with Immune Infiltration in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a primary liver malignancy characterized by high morbidity and mortality. Recently, ferroptosis has been recognized as an important factor in regulating cell growth in HCC. However, the role of ferroptosis-related genes in HCC remains unclear. The SRP119173 dataset from the Sequence Read Archive database was used to screen differentially expressed genes (DEGs) related to ferroptosis. Meanwhile, weighted gene co-expression network analysis was conducted to identify the HCC-related gene modules in the TCGA-liver hepatocellular carcinoma (LIHC) cohort. Next, the candidate genes related to HCC progression and ferroptosis were identified by Venn analysis. Kaplan-Meier, multivariate COX regression, and CIBERSORT analyses were then performed. Our results found that the levels of PIF1 5'-to-3' DNA helicase (PIF1) were notably elevated in HCC tissues relative to normal tissues. Additionally, HCC patients with high PIF1 expression had worse overall survival outcomes than patients with low PIF1 expression. Additionally, the PIF1 gene could independently predict HCC patients' prognosis. Meanwhile, HCC patients with high PIF1 levels had a higher proportion of regulatory T cells (Tregs) and M0 macrophages, as well as higher expression of immune checkpoints such as PD-1 (PDCD1) and PD-L1 (CD274), compared with patients with low PIF1 levels. Our data suggested that a ferroptosis-related gene PIF1 may serve as a potential biomarker for predicting prognosis in HCC patients.

Differences and driving factors of leaf functional traits between old tree and mature tree of Pinus tabulaeformis in the Loess Plateau

BackgroundStudy the leaf functional traits is highly important for understanding the survival strategies and climate adaptability of old trees. In this study, the old (over 100 years old) and mature trees (about 50 years old) of Pinus tabulaeformis in the Loess Plateau were studied, and the variation of 18 leaf functional traits (6 economic, 4 anatomical, 2 photosynthetic and 6 physiological traits) was analyzed to understand the differences of survival strategies between old and mature trees. Combined with transcriptome and simple sequence repeats (SSR) techniques, the effects of soil property factors and genetic factors on leaf functional traits and the potential molecular mechanisms of traits differences were studied.ResultsCompared with mature trees, old trees presented greater economic traits (except leaf phosphorus content), anatomical traits (except the stomatal density), and physiological traits (except superoxide dismutase activity) and lower photosynthetic traits, and their survival strategies were more conservative. The difference was mainly driven by soil property and genetic factors (common explanation rate was 67.89%), and the independent effect of genetic factors (10.09%) was slightly higher than that of soil property factors (2.88%). In addition, by constructing weighted gene co-expression networks analysis WGCNA), this research identified 24 candidate hub genes that regulate leaf functional traits, most of which are related to plant growth and development and the stress response, and can be used for further regulatory mechanism analysis.ConclusionsIn conclusion, this study is helpful to understand the ecological adaptability of P. tabuliformis under the background of climate change in the Loess Plateau, and provides a theoretical basis related to leaf functional traits and molecular regulation for the protection of old trees.

Global identification and regulatory network analysis reveal the significant roles of lncRNAs during anther and pollen development in Arabidopsis.

A high-throughput sequencing identified 1283 lncRNAs in anthers at different stages in Arabidopsis and their relationship with protein-coding genes and miRNAs during anther and pollen development were analyzed. Long non-coding RNAs (lncRNAs) are important regulatory molecules involved in various biological processes. However, their roles in male reproductive development and interactions with miRNAs remained elusive. In this study, a high-throughput sequencing of anthers at different developmental stages in Arabidopsis identified 1283 lncRNAs including 524 differentially expressed lncRNAs (DELs). Most of these DELs exhibited positive correlations with the expression patterns of adjacent protein-coding genes. Weighted gene co-expression network analysis (WGCNA) revealed that protein-coding genes targeted by DELs in four modules related to the tetrad stage were associated with functions such as pollen wall formation, pollen germination, or pollen tube growth, respectively. Furthermore, five, 10, and 11 lncRNAs were predicted as miRNAs' endogenous target mimics (eTMs), precursors, and natural antisense transcripts of pri-miRNA, respectively. Remarkably, the lncRNA, host gene of ath-miR167a (ath-miR167aHG), predicted as the precursor of miR167a, was selected for function validation. Its overexpression resulted in the up-regulation of miR167a and the subsequent down-regulation of miR167a's target genes ARF6 and ARF8, demonstrating a functional interaction between ath-miR167aHG and miR167a. The transgenic plants showed delayed flowering, shorter filaments, abnormal anther dehiscence, and undeveloped siliques ultimately, suggesting a role of ath-miR167aHG in male reproductive development. Collectively, our research shed new light on the functions of lncRNAs in male reproductive development and uncovered the unique interactions between lncRNAs and miRNAs.

Vitamin A family suppresses periodontitis by restoring mitochondrial metabolic reprogramming in macrophages through JAK-STAT pathway

ObjectiveMitochondrial metabolic reprogramming in macrophages is crucial in the development and progression of inflammation. Given vitamin A’s antioxidant properties and its therapeutic effects on inflammation, this study aims to elucidate how vitamin A influences mitochondrial metabolic reprogramming in inflammatory states, specifically in periodontitis, through genetic bioinformatics and experimental methods.MethodThe study utilized the GSE16134 dataset from the Gene Expression Omnibus (GEO) database, focusing on human periodontitis. Vitamin A-targeted genes (ATGs) were identified and analyzed using CIBERSORT to explore their role in inflammation. Cluster analysis revealed two phenotypes associated with ATGs, showing differential expression of genes like COX1, IL-1β, and STAT3, and immune activation patterns. Weighted Gene Co-expression Network Analysis (WGCNA) identified 145 markers correlated with ATG-guided phenotypes and inflammation. Machine learning models, combined with Gene Set Variation Analysis (GSVA), identified five key genes (RGS1, ACAT2, KDR, TUBB2A, TDO2) linked to periodontitis. Cell Type-Specific Enrichment Analysis (CSEA) highlighted macrophages as critical in metabolic reprogramming, validated by external datasets with an AUC of 0.856 in GSE10334 and 0.750 in GSE1730678. Experimental validation showed vitamin A’s role in suppressing endoplasmic reticulum stress and altering mitochondrial dynamics, as well as metabolic reprogramming influencing inflammation via the STAT3 pathway in RAW 264.7 cells.ResultsThe study identified 13 differentially expressed ATGs in periodontitis, showing strong correlations with inflammation, particularly in plasma cells, macrophages, dendritic cells, neutrophils, and mast cells. Two ATG-guided phenotypes were identified, differing in gene expression and immune activation. WGCNA and machine learning models identified 145 markers and five key genes associated with periodontitis. GSVA and CSEA analyses highlighted the JAK-STAT pathway and macrophage involvement in metabolic reprogramming. Experimental data confirmed vitamin A’s effects on mitochondrial dynamics and metabolic reprogramming through the STAT3 pathway.ConclusionThe study demonstrates that vitamin A’s therapeutic effect on periodontitis is mediated through JAK-STAT pathway-guided mitochondrial metabolic reprogramming in macrophages. It identifies two genetic and immune-related phenotypes and five genetic identifiers associated with periodontitis risk.

Combined Bulked Segregant Analysis-Sequencing and Transcriptome Analysis to Identify Candidate Genes Associated with Cold Stress in Brassica napus L

Cold tolerance in rapeseed is closely related to its growth, yield, and geographical distribution. However, the mechanisms underlying cold resistance in rapeseed remain unclear. This study aimed to explore cold resistance genes and provide new insights into the molecular mechanisms of cold resistance in rapeseed. Rapeseed M98 (cold-sensitive line) and D1 (cold-tolerant line) were used as parental lines. In their F2 population, 30 seedlings with the lowest cold damage levels and 30 with the highest cold damage levels were selected to construct cold-tolerant and cold-sensitive pools, respectively. The two pools and parental lines were analyzed using bulk segregant sequencing (BSA-seq). The G’-value analysis indicated a single peak on Chromosome C09 as the candidate interval, which had a 2.59 Mb segment with 69 candidate genes. Combined time-course and weighted gene co-expression network analyses were performed at seven time points to reveal the genetic basis of the two-parent response to low temperatures. Twelve differentially expressed genes primarily involved in plant cold resistance were identified. Combined BSA-seq and transcriptome analysis revealed BnaC09G0354200ZS, BnaC09G0353200ZS, and BnaC09G0356600ZS as the candidate genes. Quantitative real-time PCR validation of the candidate genes was consistent with RNA-seq. This study facilitates the exploration of cold tolerance mechanisms in rapeseed.

Renal and Peripheral Blood Transcriptome Signatures That Predict Treatment Response in Proliferative Lupus Nephritis-A Prospective Study.

Mechanisms contributing to non-response to treatment in lupus nephritis (LN) are unclear. We characterised the transcriptome of paired peripheral blood mononuclear cells (PBMCs) and renal tissues in LN before and after cyclophosphamide (CYC) treatment and identified markers that predicted treatment response. Total RNA isolated from paired PBMCs (n = 32) and renal tissues (n = 25) of 16 proliferative LN before CYC treatment, 6 months post-treatment, and during renal flare, was sequenced on Illumina Novaseq-6000 platform. Post-treatment, eight patients were clinical responders (CR), of whom four flared (FL), and eight were non-responders (NR). Comparative transcriptomic analyses before and after treatment within CR, NR, and FL groups was performed using DESeq2. Weighted gene co-expression network analysis (WGCNA) and ROC analysis was performed to identify and validate hub genes predictive of treatment response. Based on this, we observed that pathways such as degradation of cell cycle proteins, expression of G0 and G1 phase proteins, and apoptosis, were upregulated in CR PBMCs post-treatment, while IFN-γ signalling and ECM organisation were downregulated. In NR PBMCs, ECM molecules, neddylation and BCR signalling were upregulated post-CYC treatment, while in NR renal tissue, TLR, IFN and NF-κB signalling pathways were upregulated. In FL PBMCs, neutrophil degranulation and ROS and RNS production in phagocytes were downregulated following treatment, whereas, in the corresponding renal tissue, cell-ECM interactions and ISG15 antiviral mechanism were downregulated. After WGCNA and subsequent ROC analysis, TENM2, NLGN1 and AP005230.1 from PBMCs each predicted NR (AUC-0.91; p = 0.03), while combined model improved prediction (AUC-0.94; p = 0.02). AP005230.1 from renal tissue also predicted non-response (AUC-0.94; p = 0.01) and AC092436.3 from PBMCs predicted renal flare (AUC-0.81; p = 0.04). Our study identified significant DEGs/pathways specific to different treatment outcomes and hub genes that predicted non-response and renal flare.

Integrated transcriptomic analysis of human induced pluripotent stem cell-derived osteogenic differentiation reveals a regulatory role of KLF16.

Osteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses, KLF16 emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression of Klf16 in vitro inhibited osteogenic differentiation and mineralization, while Klf16 +/- mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such as KLF16 , which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.

Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relationship between autophagy and LN remains unelucidated. We analyzed differentially expressed genes (DEGs) in kidney tissues from 14 LN patients and 7 normal controls using the GSE112943 dataset. Key modules and their contained genes were identified utilizing weighted gene co-expression network analysis (WGCNA). Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. The area under the receiver operating characteristic (ROC) curves was used to assess the accuracy of the signature genes. We also analyzed immune cell infiltration in LN. Additionally, the association between key genes and kidney diseases was predicted. Finally, key genes expression in kidney was verified by clinical samples and animal experiments. A total of 10304 DEGs were identified in GSE1129943 and 29 modules were identified in WGCNA. Among them, the brown module and coral 2 module exhibited significant correlation with LN (cor = 0.86, -0.84, p<0.001). Machine learning techniques identified 5 signature genes, but only 2 were validated in the external dataset GSE32591, namely MAP1LC3B (AUC = 0.920) and TNFSF10 (AUC = 0.937), which are involved in autophagy and apoptosis. Immune infiltration analysis suggested that these key genes may be associated with immune cell infiltration in LN. In addition, these genes have been linked to a variety of renal diseases, and their expression was verified in kidney tissues in LN patients and lupus mice. MAP1LC3B and TNFSF10 may be key autophagy-related genes in LN. These key genes have the potential to provide new insights into the molecular diagnosis and treatment of LN.

A quantitative prediction method utilizing whole omics data for biosensing

Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker. Our benchmark test using a transcriptome dataset revealed that OmicSense achieves accurate and robust prediction against background noise without overfitting. Weighted gene co-expression network analysis revealed that OmicSense preferentially utilized hub nodes of the network, indicating the interpretability of the method. Application of OmicSense to single-cell transcriptome, metabolome, and microbiome datasets confirmed high prediction performance (r > 0.8), suggesting applicability to diverse scientific fields. Given the recent rapidly expanding availability of omics data, the developed prediction tool OmicSense, can accelerate the use of omics data as a “biosensor” based on an assemblage of potential biomarkers.

Identification and validation of prognostic biomarkers in ccRCC: immune-stromal score and survival prediction

BackgroundThe tumor microenvironment (TME) is integral to tumor progression. However, its prognostic implications and underlying mechanisms in clear cell renal cell carcinoma (ccRCC) are not yet fully elucidated. This study aims to examine the prognostic significance of genes associated with immune-stromal scores and to explore their underlying mechanisms in ccRCC.MethodsData from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were subjected to analysis to compute immune and stromal scores utilizing the ESTIMATE algorithm. The weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with these scores. Differentially expressed genes were assessed using the limma package. Prognostic biomarkers were subsequently identified through univariate, LASSO, and multivariate Cox regression analyses, culminating in the development of a risk score model. Gene expression was confirmed in ccRCC cell lines (786-O, Caki-1) and tumor tissues. Functional assays, such as wound healing and Transwell assays, were employed to evaluate tumor invasion and migration. The prognostic accuracy was assessed through ROC curve analysis, and a nomogram integrating risk scores with clinical variables was constructed. Analyses of immune infiltration, human leukocyte antigen (HLA) expression, immune checkpoint expression, immunophenoscore (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and responses to six targeted therapies were conducted across different risk groups.ResultsTwelve critical prognostic markers, including CAPRIN1, CXCR3, FERMT3, HAPLN3, HBP1, MACF1, MPEG1, OSCAR, STAT1, UBA7, VAMP1, and VSIG4, were identified. The risk score model exhibited a high degree of predictive accuracy for survival outcomes in ccRCC. Immune profiling revealed significant differences in the TME between risk groups, with high-risk patients displaying elevated expression of HLA and immune checkpoints. Drug sensitivity analyses suggested that high-risk patients had a better response to erlotinib, temsirolimus, axitinib, and sunitinib, whereas low-risk patients demonstrated greater sensitivity to pazopanib. Variability in immunotherapy responsiveness between groups was observed based on IPS and TIDE analyses.ConclusionThis study highlights the prognostic value and TME-related mechanisms of immune-stromal score signatures in ccRCC, developing a risk score model and nomogram for predicting patient prognosis.

Strigolactones regulate Bambusa multiplex sheath senescence by promoting chlorophyll degradation.

Culm sheaths are capable of photosynthesis and are an important class of non-leaf organs in bamboo plants. The source-sink interaction mechanism has been found to play an important role in the interaction between culm sheaths and internodes in Bambusa multiplex. Research on the regulatory mechanisms of culm sheath senescence is important for the study of internode growth, but reports in this regard are limited. In this study, a weighted gene co-expression network analysis was performed on transcriptome data of B. multiplex culm sheaths at different developmental stages and identified some gene modules significantly related to the typical senescence stages (SS3 and SS4). Among these modules, one module significantly associated with both SS3 and SS4 was identified, and its hub gene (BmCCD8) was a key gene of the strigolactones (SLs) synthesis pathway. To verify the relationship between SLs and culm sheath senescence, we performed experiments such as detection of endogenous hormone, treatment with exogenous hormones, transmission electron microscopic observation and detection of gene expression levels. A positive relationship was found between the SL content and the degree of sheath senescence. Treatment with the artificial SL analog GR24 resulted in a significant decrease in chlorophyll content in the sheath, while treatment with the SL synthesis inhibitor Tis108 led to a significant increase in chlorophyll content. A different response pattern to exogenous GR24 and Tis108 was also observed in genes related to the chlorophyll degradation pathway. Chloroplasts were also found to begin degradation one day after the end of exogenous GR24 treatment. Thus, we concluded that SLs may regulate culm sheath senescence by promoting chlorophyll degradation.

Transcriptome-Wide Analysis of N6-Methyladenosine-Modified Long Noncoding RNAs in Particulate Matter-Induced Lung Injury

Background: N6-methyladenosine (m6A) modification plays a crucial role in the regulation of diverse cellular processes influenced by environmental factors. Nevertheless, the involvement of m6A-modified long noncoding RNAs (lncRNAs) in the pathogenesis of lung injury induced by particulate matter (PM) remains largely unexplored. Methods: Here, we establish a mouse model of PM-induced lung injury. We utilized m6A-modified RNA immunoprecipitation sequencing (MeRIP-seq) to identify differentially expressed m6A peaks on long non-coding RNAs (lncRNAs). Concurrently, we performed lncRNA sequencing (lncRNA-seq) to determine the differentially expressed lncRNAs. The candidate m6A-modified lncRNAs in the lung tissues of mice were identified through the intersection of the data obtained from these two sequencing approaches. Results: A total of 664 hypermethylated m6A peaks on 644 lncRNAs and 367 hypomethylated m6A peaks on 358 lncRNAs are confirmed. We use bioinformatic tools to analyze the potential functions and pathways of these m6A-modified lncRNAs, revealing their involvement in regulating inflammation, immune response, and metabolism-related pathways. Three key m6A-modified lncRNAs (lncRNA NR_003508, lncRNA uc008scb.1, and lncRNA ENSMUST00000159072) are identified through a joint analysis of the MeRIP-seq and lncRNA-seq data, and their validation is carried out using MeRIP-PCR and qRT-PCR. Analysis of the coding-non-coding gene co-expression network reveals that m6A-modified lncRNAs NR_003508 and uc008scb.1 participate in regulating pathways associated with inflammation and immune response. Conclusions: This study first provides a comprehensive transcriptome-wide analysis of m6A methylation profiling in lncRNAs associated with PM-induced lung injury and identifies three pivotal candidate m6A-modified lncRNAs. These findings shed light on a novel regulatory mechanism underlying PM-induced lung injury.

The Regulation of Messenger RNAs and Biological Pathways by Long Non-Coding RNAs and Circular RNAs in Ischemic Stroke.

Our aim was to evaluate the regulation of messenger RNAs (mRNAs) and biological pathways by long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in ischemic stroke. We employed weighted gene co-expression network analysis (WGCNA) to construct two co-expression networks for mRNAs with circRNAs and lncRNAs, respectively, to investigate their association with ischemic stroke. We compared the overlap of mRNAs and biological pathways in the stroke-associated modules of the two networks. Furthermore, we validated the association of key non-coding RNAs with the risk of ischemic stroke and poor prognosis using quantitative real-time polymerase chain reaction. Ischemic stroke patients exhibited lower eigengene expression in the turquoise module associated with lncRNAs and mRNAs, as well as in the turquoise, red, and greenyellow modules associated with circRNAs and mRNAs in ischemic stroke. In the lncRNA-mRNA network and circRNA-mRNA network, we observed a significant overlap of the 5126 mRNAs (P < 0.001) and 51 biological pathways (P < 0.001), respectively. Among the ten key non-coding RNAs, lnc-TPRG1-AS1, lnc-GUK1, and hsa_circ_RELL1 were significantly increased (P < 0.05), while hsa_circ_ZBTB20 and hsa_circ_ERBB2 were significantly decreased (P < 0.05) in ischemic stroke. Additionally, ischemic stroke patients with poor functional outcome had significantly lower levels of hsa_circ_ZBTB20 and hsa_circ_ERBB2 compared to those with favorable prognosis (P < 0.05). Our findings suggest lncRNAs and circRNAs display similar biological functions in ischemic stroke. Key non-coding RNAs may be associated with the risk and clinical prognosis of ischemic stroke. These results warrant further validation in the future studies.

Widely Targeted Metabolomics and Transcriptomics Analysis of the Response and Adaptation Mechanisms of Trifolium ambiguum to Low-Temperature Stress

Caucasian clover (Trifolium ambiguum M.Bieb.) is a perennial legume known for its exceptional cold tolerance, commonly used in agriculture and ecosystems in cold climates. Given the impact of climate change, enhancing the cold adaptation of Caucasian clover is crucial for sustaining agricultural productivity. This study employs metabolomics, transcriptomics, and Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the molecular mechanisms of Caucasian clover’s response to low-temperature stress. Metabolomic analysis showed that low-temperature stress triggered the accumulation of fatty acids, amino acids, and antioxidants, which are critical for maintaining membrane stability and antioxidant capacity, thus protecting the plant from oxidative damage. Transcriptomic analysis revealed significant upregulation of genes involved in cold adaptation, particularly those related to antioxidant defense, membrane lipid repair, and signal transduction, including genes in the ABA signaling pathway and antioxidant enzymes, thereby improving cold tolerance. WGCNA identified gene modules closely linked to cold adaptation, especially those involved in antioxidant defense, fatty acid metabolism, signal transduction, and membrane repair. These modules function synergistically, with coordinated gene expression enhancing cold resistance. This study also investigated the isoflavonoid biosynthesis pathway under low-temperature stress, highlighting its role in enhancing antioxidant capacity and cold tolerance. Low-temperature stress induced upregulation of key enzyme genes, such as Isoflavone Synthase (IFS) and Isoflavone-7-O-Glucosyltransferase (IF7GT), promoting antioxidant metabolite accumulation and further enhancing the plant’s cold adaptation. Overall, this study offers novel molecular insights into the cold tolerance mechanisms of Caucasian clover and provides valuable theoretical support for breeding cold-resistant crops in cold climates.

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.

Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses. Bioinformatics technologies based on high-throughput data provide a crucial foundation for comprehensively understanding the biological characteristics of ischemic stroke and discovering effective predictive targets. In this study, we evaluated gene expression data from ischemic stroke patients retrieved from the Gene Expression Omnibus (GEO) database, conducting differential expression analysis and functional analysis. Through weighted gene co-expression network analysis (WGCNA), we characterized gene modules associated with ischemic stroke. To screen candidate core genes, three machine learning algorithms were applied, including Least Absolute Shrinkage and Selection Operator (LASSO), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), ultimately identifying five candidate core genes: MBOAT2, CKAP4, FAF1, CLEC4D, and VIM. Subsequent validation was performed using an external dataset. Additionally, the immune infiltration landscape of ischemic stroke was mapped using the CIBERSORT method, investigating the relationship between candidate core genes and immune cells in the pathogenesis of ischemic stroke, as well as the key pathways associated with the core genes. Finally, the key gene VIM was further identified and preliminarily validated through four machine learning algorithms, including generalized linear model (GLM), Extreme Gradient Boosting (XGBoost), RF, and SVM-RFE. This study contributes to advancing our understanding of biomarkers for ischemic stroke and provides a reference for the prediction and diagnosis of ischemic stroke.

Uncovering the molecular mechanisms of Acer fabri in adjusting to low-temperature stress through integrated physiological and transcriptomic analysis

Acer fabri is an excellent color-leaf tree species, with high ornamental value. Low temperatures are known to limit the growth and geographical distribution of A. fabri. The molecular mechanism of A. fabri in response to low-temperature stress was rarely reported. To understand the molecular mechanism of A. fabri in response to low-temperature stress, relevant physiological changes were identified and the transcriptome sequencing was conducted under different stress durations. The results showed that the proline, the soluble sugar (SS) and the soluble protein (SP) content increased in A. fabri leaves under low-temperature stress, while the peroxidase (POD) and activating superoxide dismutase (SOD) activity increased first and then decreased. It was also found by the OPLS-DA analysis that SOD is the most important physiological indicator of A. fabri in response to low-temperature stress. By transcriptome sequencing, a total of 56,732 genes were identified, including 832 transcription factors (TFs). Differentially expressed genes (DEGs) were significantly enriched in metabolic pathways, phytohormone signaling, and plant mitogen-activated protein kinase (MAPK) signaling pathways. Moreover, the analysis of gene co-expression networks, specifically weighted gene co-expression network analysis (WGCNA), indicates that Af0048792 and Af0026061 could be significant in the response to stress from low temperatures. Furthermore, it was observed that NAC (Af0033429) and MIKC (Af0004917) might have interactions with Af0048792, and MIKC (Af0004917) may additionally interact with Af0026061. These findings could enhance our understanding of the molecular mechanisms of A. fabri in response to low-temperature stress.

Identification of retinol dehydrogenase 10 as a shared biomarker for metabolic dysfunction-associated steatotic liver disease and type 2 diabetes mellitus

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is an independent risk factor for type 2 diabetes mellitus (T2DM), and its early identification and intervention offer opportunities for reversing diabetes mellitus.MethodsIn this study, we identified biomarkers for the MASLD dataset (GSE33814, GSE48452) and the T2DM dataset (GSE76895 and GSE89120) by bioinformatics analysis. Next, we constructed weighted gene co-expression network (WGCNA) for disease module analysis to screen out shared genes strongly associated with diseases. We also analyzed the enriched pathways of shared genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Next, hub gene validation was performed using the least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curves. Finally, we used RT-qPCR, immunofluorescence, Western blotting and Elisa to validate hub gene expression in MASLD and T2DM mouse models.ResultsThis analysis identified 20 genes shared by MASLD and T2DM that were enriched in the bile secretion, phototransduction, cancer, carbohydrate digestion and absorption, cholesterol/glycerol metabolism, and retinol metabolism. The LASSO algorithm and ROC curve identified Retinol Dehydrogenase 10 (RDH10) as the best diagnostic gene for MASLD and T2DM. Immunofluorescence and Western blot showed that RDH10 expression was reduced in the liver and pancreatic islets of MASLD and T2DM model mice. Similarly, serum levels of RDH10 were significantly lower in MASLD and T2DM model mice and humans than in controls.ConclusionOur study suggests that RDH10 is a common diagnostic marker for MASLD and T2DM and provides new research directions for the prevention and treatment of MASLD and T2DM.

Insights from the single-cell level: lineage trajectory and somatic-germline interactions during spermatogenesis in dwarf surfclam Mulinia lateralis

BackgroundSpermatogenesis is a complex process of cellular differentiation that commences with the division of spermatogonia stem cells, ultimately resulting in the production of functional spermatozoa. However, a substantial gap remains in our understanding of the molecular mechanisms and key driver genes that underpin this process, particularly in invertebrates. The dwarf surfclam (Mulinia lateralis) is considered an optimal bivalve model due to its relatively short generation time and ease of breeding in laboratory settings.ResultsIn this study, over 4,600 testicular cells from various samples were employed to identify single-cell heterogeneity on a more comprehensive scale. The four germ cell populations (spermatogonia, primary spermatocytes, secondary spermatocytes, and round spermatids/spermatozoa) and three somatic populations (follicle cell, hemocyte, and nerve cell) were characterized. The four types of germ cells exhibited disparate cell cycle statuses and an uninterrupted developmental trajectory, progressing from spermatogonia to spermatids/spermatozoa. Pseudotime analysis indicates that gene expression, translation, ATP metabolic process, and microtubule-based process are involved in the transition of germ cell types. Weighted gene coexpression network analysis (WGCNA) identified four modules corresponding to the four types of germ cells, as well as key transcription factors (e.g., MYC, SREBF1, SOXH) that may play a critical role in these cell types. Furthermore, our findings revealed that there is extensive bidirectional communication between the somatic cells and the germline cells, including the FGF and TGF-β signaling pathways, as well as other ligand-receptor pairs, such as NTN1-NEO1 and PLG-PLGRKT.ConclusionsThis study provides a comprehensive single-cell transcriptome landscape of the gonad, which will contribute to the understanding of germ cell fate transition during spermatogenesis, and the development of germ cell manipulation technologies in mollusks.

Exploring markers in nursing care of prostate cancer.

Prostate cancer is epithelial malignant prostate hyperplasia caused by a tumor. We found prostate cancer GSE141551 and GSE200879 profiles from gene expression omnibus database, followed by differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis, protein-protein interaction analysis, gene function enrichment analysis, and comparative toxicology database analysis. Finally, the gene expression heat map was drawn, and miRNA information regulating core DEGs was retrieved. A total of 1151 DEGs were found, most of them focusing on systematic development, cell development, cell differentiation, regulation of multicellular biological processes, anatomical morphogenesis, MAPK signaling pathway, proteoglycans in cancer, fluid shear stress, and atherosclerosis. The core genes (MYL9, TAGLN, SMTN, CNN1, MYH11, MYLK, MYOCD, ACTC1, LMOD1, and TPM2) obtained in end are all lowly expressed in prostate cancer samples and are associated with hypertension, tumor metastasis, prostate tumors, and tumor aggressiveness. LMOD1 and SMTN are lowly expressed in prostate cancer and may be used as markers in prostate cancer nursing.