Weighted Gene Co-expression Network Analysis Research Articles

Identification of potential biomarkers of gout through weighted gene correlation network analysis

BackgroundAlthough hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity.MethodsUsing the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT−qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments.ResultsAccording to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein–protein interaction (PPI) network. Using RT−qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins.ConclusionIn this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Volatiles profiles and factors affecting absorption of key odorants in Osmanthus black tea

Abstract Osmanthus black tea (OBT) is produced by reprocessing black tea while it absorbs the fragrance of Osmanthus fragrans (O. fragrans) and this scenting process is crucial for forming the unique aroma of OBT. In this study, more than eighty volatile compounds of OBT were analyzed by gas chromatography-mass spectrometry (GC-MS) and six compounds, α-ionone, β-ionone, γ-decalactone, dihydro-β-ionone, dihydro-β-ionol, and 6-pentyl-2-pyrone, were identified as the key volatiles of OBT by weighted gene co-expression network analysis (WGCNA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Variations in the amounts of these compounds occurred with different scenting process conditions such as scenting time (ST) and the ratio of flower to tea (RFT) were discussed. In addition, base teas with a high proportion of pekoes produced higher quality OBT by absorbing more key volatile compounds from O. fragrans. Comparing OBTs made from the fast-scenting process (FS) and the traditional scenting process (TS), showed that a higher aroma quality was obtained through repeated scenting rounds of TS. This study provides valuable insights into the volatile compounds and base tea requirements for an effective scenting process and a theoretical basis for improving scenting technology.

Identification of hub genes and diagnostic efficacy for triple-negative breast cancer through WGCNA and Mendelian randomization

ObjectiveTriple-negative breast cancer (TNBC) represents a particularly aggressive form of breast cancer with a poor prognosis due to a lack of targeted treatments resulting from limited a understanding of the underlying mechanisms. The aim of this study was the identification of hub genes for TNBC and assess their clinical applicability in predicting the disease.MethodsThis study employed a combination of weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) to identify new susceptible modules and central genes in TNBC. The potential functional roles of the central genes were investigated using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Furthermore, a predictive model and ROC curve were developed to assess the diagnostic performance of the identified central genes. The correlation between CCNB1 and immune cells proportion was also investigated. At last, a Mendelian randomization (MR) analysis utilizing Genome-Wide Association Study (GWAS) data was analyzed to establish the causal effect of CCNB1 level on TNBC.ResultsWGCNA was applied to determine gene co-expression maps and identify the most relevant module. Through a screening process, 1585 candidate hub genes were subsequently identified with WGCNA and DEGs. GO and KEGG function enrichment analysis indicated that these core genes were related to various biological processes, such as organelle fission, chromosome segregation, nuclear division, mitotic cell cycle phase transition, the cell cycle, amyotrophic lateral sclerosis, and motor proteins. Using STRING and Cytoscape, the top five genes with high degrees were identified as CDC2, CCNB1, CCNA2, TOP2A, and CCNB2. The nomogram model demonstrated good performance in predicting TNBC risk and was proven effective in diagnosis, as evidenced by the receiver operating characteristic (ROC) curve. Further investigation revealed a causal association between CCNB1 and immune cell infiltrates in TNBC. Survival analysis revealed high expression of the CCNB1 gene leads to poorer prognosis in TNBC patients. Additionally, analysis using inverse variance weighting revealed that CCNB1 was linked to a 2.8% higher risk of TNBC (OR: 1.028, 95% CI 1.002–1.055, p = 0.032).ConclusionWe established a co-expression network using the WGCNA methodology to detect pivotal genes associated with TNBC. This finding holds promise for advancing the creation of pre-symptomatic diagnostic tools and deepening our comprehension of the pathogenic mechanisms involved in TNBC risk genes.

Identification of a robust biomarker LAPTM4A for glioma based on comprehensive computational biology and experimental verification.

Glioma, a highly invasive and deadly form of human neoplasm, presents a pressing need for the exploration of potential therapeutic targets. While the lysosomal protein transmembrane 4A (LATPM4A) has been identified as a risk factor in pancreatic cancer patients, its role in glioma remains unexplored. The analysis of differentially expressed genes (DEG) was conducted from The Cancer Genome Atlas (TCGA) glioma dataset and the Genotype Tissue Expression (GTEx) dataset. Through weighted gene co-expression network analysis (WGCNA), the key glioma-related genes were identified. Among these, by using Kaplan-Meier (KM) analysis and univariate/multivariate COX methods, LAPTM4A emerged as the most influential gene. Moreover, the bioinformatics methods and experimental verification were employed to analyze its relationships with diagnosis, clinical parameters, epithelial-mesenchymal transition (EMT), metastasis, immune cell infiltration, immunotherapy, drug sensitivity, and ceRNA network. Our findings revealed that LAPTM4A was up-regulated in gliomas and was associated with clinicopathological features, leading to poor prognosis. Furthermore, functional enrichment analysis demonstrated that LATPM4A played a role in the immune system and cancer progression. In vitro experiments indicated that LAPTM4A may influence metastasis through the EMT pathway in glioma. Additionally, we found that LAPTM4A was associated with the tumor microenvironment (TME) and immunotherapy. Notably, drug sensitivity analysis revealed that patients with high LAPTM4A expression were sensitive to doxorubicin, which contributed to a reduction in LAPTM4A expression. Finally, we uncovered the FGD5-AS1-hsa-miR-103a-3p-LAPTM4A axis as a facilitator of glioma progression. In conclusion, our study identifies LATPM4A as a promising biomarker for prognosis and immune characteristics in glioma.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis

BackgroundThe immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.MethodsThe discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.ResultsThree signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.ConclusionThe present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Transcriptome and small RNA analysis unveils novel insights into the C4 gene regulation in sugarcane.

This study reveals miRNA indirect regulation of C4 genes in sugarcane through transcription factors, highlighting potential key regulators like SsHAM3a. C4 photosynthesis is crucial for the high productivity and biomass of sugarcane, however, the miRNA regulation of C4 genes in sugarcane remains elusive. We have identified 384 miRNAs along the leaf gradients, including 293 known miRNAs and 91 novel miRNAs. Among these, 86 unique miRNAs exhibited differential expression patterns, and we identified 3511 potential expressed targets of these differentially expressed miRNAs (DEmiRNAs). Analyses using Pearson correlation coefficient (PCC) and Gene Ontology (GO) enrichment revealed that targets of miRNAs with positive correlations are integral to chlorophyll-related photosynthetic processes. In contrast, negatively correlated pairs are primarily associated with metabolic functions. It is worth noting that no C4 genes were predicted as targets of DEmiRNAs. Our application of weighted gene co-expression network analysis (WGCNA) led to a gene regulatory network (GRN) suggesting miRNAs might indirectly regulate C4 genes via transcription factors (TFs). The GRAS TF SsHAM3a emerged as a potential regulator of C4 genes, targeted by miR171y and miR171am, and exhibiting a negative correlation with miRNA expression along the leaf gradient. This study sheds light on the complex involvement of miRNAs in regulating C4 genes, offering a foundation for future research into enhancing sugarcane's photosynthetic efficiency.

Disentangling the riddle of systemic lupus erythematosus with antiphospholipid syndrome: blood transcriptome analysis reveals a less-pronounced IFN-signature and distinct molecular profiles in venous versus arterial events

IntroductionSystemic lupus erythematosus with antiphospholipid syndrome (SLE-APS) represents a challenging SLE endotype whose molecular basis remains unknown.MethodsWe analysed whole-blood RNA-sequencing data from 299 patients with SLE (108 SLE-antiphospholipid antibodies (aPL)-positive,...

A delayed and unsynchronized ovary development as revealed by transcriptome of brain and pituitary of Coilia nasus

Coilia nasus is an anadromous fish that has been successfully domesticated in the last decade due to its high economic value. The fish exhibits a delayed ovary development during the reproductive season, despite breeding and selection for five to six offspring. The molecular mechanism of the delayed ovary development is still unknown, so the obstacles have not been removed in the large-scale breeding program. This study aims to investigate the key genes regulating ovarian development by comparing the transcriptomes of ovarian-stage IV and stage II brain/pituitary of Coilia nasus. Ovarian stages were validated by histological sections. A total of 75,097,641 and 66,735,592 high-quality reads were obtained from brain and pituitary transcriptomes, respectively, and alternatively spliced transcripts associated with gonadal development were detected. Compared to ovarian Ⅱ- brain, 515 differentially expressed genes (DEGs) were upregulated and 535 DEGs were downregulated in ovarian Ⅳ- brain, whereas 470 DEGs were upregulated and 483 DEGs were downregulated in ovarian Ⅳ- pituitary compared to ovarian Ⅱ- pituitary. DEGs involved in hormone synthesis and secretion and in the GnRH signaling pathway were screened. Weighted gene co-expression network analysis identified gene co-expression modules that were positively correlated with ovarian phenotypic traits. The hub genes Smad4 and TRPC4 in the modules were co-expressed with DEGs including Kiss1 receptor and JUNB, suggesting that ovarian development is controlled by a hypothalamic-pituitary-gonadal axis. Our results have provided new insights that advance our understanding of the molecular mechanism of C. nasus reproductive functions and will be useful for future breeding.

Identification of differentially expressed mRNA/lncRNA modules in acutely regorafenib-treated sorafenib-resistant Huh7 hepatocellular carcinoma cells.

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego.

Transcriptional dynamics reveals the asymmetrical events underlying graft union formation in pecan (Carya illinoinensis).

Grafting is a widely used technique for pecan propagation, however, the background molecular events underlying grafting are still poorly understood. In our study, the graft partners during pecan graft union formation were separately sampled for RNA-seq, and the transcriptional dynamics were described via weighted gene co-expression network analysis (WGCNA). To reveal the main events underlying grafting, the correlations between modules and grafting traits were analyzed. Functional annotation showed that during the entire graft process, signal transduction was activated in the scion, while mRNA splicing was induced in the rootstock. At 2 DAG, the main processes occurred in the scion were associated with protein synthesis and processing, while the primary processes happened in the rootstock were energy release-related. During the period of 7-14 DAG, defense response was a critical process worked in the scion, however, the main process functioned in the rootstock was photosynthesis. From 22 to 32 DAG, the principal processes taken place in the scion were jasmonic acid biosynthesis and defense response, whereas the highly activated processes associated with the rootstock were auxin biosynthesis and plant-type secondary cell wall biogenesis. Detection of hydrogen peroxide contents as well as peroxidase and β-1,3-glucanase activities showed that their levels were increased in the scion not the rootstock at certain time points after grafting. Our study reveals that the scion and rootstock might response asymmetrically to grafting in pecan, and the scion was likely associated with stress response, while the rootstock was probably involved in energy supply and xylem bridge differentiation during graft union formation.

Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM

BackgroundSkin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress.MethodsUtilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immune-related analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value.ResultsWe identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy.ConclusionThis study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity.

Transcriptome Profiling Unveils Key Genes Regulating the Growth and Development of Yangzhou Goose Knob

Goose is one of the most economically valuable poultry species and has a distinct appearance due to its possession of a knob. A knob is a hallmark of sexual maturity in goose (Anser cygnoides) and plays crucial roles in artificial selection, health status, social signaling, and body temperature regulation. However, the genetic mechanisms influencing the growth and development of goose knobs remain completely unclear. In this study, histomorphological and transcriptomic analyses of goose knobs in D70, D120, and D300 Yangzhou geese revealed differential changes in tissue morphology during the growth and development of goose knobs and the key core genes that regulate goose knob traits. Observation of tissue sections revealed that as age increased, the thickness of the knob epidermis, cuticle, and spinous cells gradually decreased. Additionally, fat cells in the dermis and subcutaneous connective tissue transitioned from loose to dense. Transcriptome sequencing results, analyzed through differential expression, Weighted Gene Co-expression Network Analysis (WGCNA), and pattern expression analysis methods, showed D70-vs.-D120 (up-regulated: 192; down-regulated: 423), D70-vs.-D300 (up-regulated: 1394; down-regulated: 1893), and D120-vs.-D300 (up-regulated: 1017; down-regulated: 1324). A total of 6243 differentially expressed genes (DEGs) were identified, indicating varied expression levels across the three groups in the knob tissues of D70, D120, and D300 Yangzhou geese. These DEGs are significantly enriched in biological processes (BP) such as skin morphogenesis, the regulation of keratinocyte proliferation, and epidermal cell differentiation. Furthermore, they demonstrate enrichment in pathways related to goose knob development, including ECM–receptor interaction, NF-kappa B, and PPAR signaling. Through pattern expression analysis, three gene expression clusters related to goose knob traits were identified. The joint analysis of candidate genes associated with goose knob development and WGCNA led to the identification of key core genes influencing goose knob development. These core genes comprise WNT4, WNT10A, TCF7L2, GATA3, ADRA2A, CASP3, SFN, KDF1, ERRFI1, SPRY1, and EVPL. In summary, this study provides a reference for understanding the molecular mechanisms of goose knob growth and development and provides effective ideas and methods for the genetic improvement of goose knob traits.

Metabolomic and Transcriptomic Analyses Provide New Insights into Health-Promoting Metabolites from Cannabis Seeds Growing in the Bama Region of China

Hemp seeds are consumed in “Bama longevity villages” in China and are believed to contribute to the locals’ longevity. However, the molecular breeding of hemp seeds is limited due to our lack of understanding of molecular regulation and metabolite accumulation at different maturity stages. Here, we conducted metabolomic and transcriptomic analyses of hemp seeds from the Bama region at four maturity stages (S1 to S4). In total, 1231 metabolites of 11 classes were detected in hemp seeds from S1–S4, including 233 flavonoids, 214 phenolic acids, 159 lipids, 118 amino acids and derivatives, 100 alkaloids, 100 organic acids, 71 nucleotides and derivatives, 43 lignans and coumarins, 14 terpenoids, 13 tannins, and 166 others. The metabolomic analysis of hemp seeds (from the Bama region) revealed a higher number of flavonoid metabolites compared with seven other varieties from other regions in China. Hemp seeds are rich in metabolites like cannflavin, trigonelline, citric acid, vitexin, choline alfoscerate, and choline, which may potentially contribute to the longevity of the Bama people. Through transcriptomic and metabolomic analyses, a gradual decrease in the overall expression pattern of genes and metabolite accumulation was observed during seed maturation. Weighted gene co-expression network analysis revealed that two genes (ncbi_115696993 and ncbi_115706674) are involved in regulating main metabolites, while transcription factor association analysis revealed that three transcription factor genes (MYB, NAC, and GRAS) are also involved in regulating the metabolites. The expression pattern of these five candidate genes was further verified by qPCR. Our study provides valuable insights into the metabolic substances during seed maturation and identifies candidate genes that could be utilized for future genetic engineering to enhance the endogenous biosynthesis of health-promoting metabolites in hemp seeds, potentially leading to improved nutritional and medicinal properties.

Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy.

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell-associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell-associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

Comparative physiology and transcriptome response patterns in cold-tolerant and cold-sensitive varieties of Solanum melongena

BackgroundClimate change has led to severe cold events, adversely impacting global crop production. Eggplant (Solanum melongena L.), a significant economic crop, is highly susceptible to cold damage, affecting both yield and quality. Unraveling the molecular mechanisms governing cold resistance, including the identification of key genes and comprehensive transcriptional regulatory pathways, is crucial for developing new varieties with enhanced tolerance.ResultsIn this study, we conducted a comparative analysis of leaf physiological indices and transcriptome sequencing results. The orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted peroxidase (POD) activity and soluble protein as crucial physiological indicators for both varieties. RNA-seq data analysis revealed that a total of 7024 and 6209 differentially expressed genes (DEGs) were identified from variety “A” and variety “B”, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of DEGs demonstrated that the significant roles of starch and sucrose metabolism, glutathione metabolism, terpenoid synthesis, and energy metabolism (sucrose and starch metabolism) were the key pathways in eggplant. Weighted gene co-expression network analysis (WGCNA) shown that the enrichment of numerous cold-responsive genes, pathways, and soluble proteins in the MEgrep60 modules. Core hub genes identified in the co-expression network included POD, membrane transporter-related gene MDR1, abscisic acid-related genes, growth factor enrichment gene DELLA, core components of the biological clock PRR7, and five transcription factors. Among these, the core transcription factor MYB demonstrated co-expression with signal transduction, plant hormone, biosynthesis, and metabolism-related genes, suggesting a pivotal role in the cold response network.ConclusionThis study integrates physiological indicators and transcriptomics to unveil the molecular mechanisms responsible for the differences in cold tolerance between the eggplant cold-tolerant variety “A” and the cold-sensitive variety “B”. These mechanisms include modulation of reactive oxygen species (ROS), elevation in osmotic carbohydrate and free proline content, and the expression of terpenoid synthesis genes. This comprehensive understanding contributes valuable insights into the molecular underpinnings of cold stress tolerance, ultimately aiding in the improvement of crop cold tolerance.

L-cysteine contributes to destructive activities of odontogenic cysts/tumor.

Odontogenic cysts/tumor can cause severe bone destruction, which affects maxillofacial function and aesthetics. Meanwhile, metabolic reprogramming is an important hallmark of diseases. Changes in metabolic flow affect all aspects of disease, especially bone-related diseases. At present, the researches on pathogenesis of odontogenic cysts/tumor are mainly focused on the level of gene regulation, but the effects of metabolic alterations on odontogenic cysts/tumor have still underexplored. Imaging analysis was used to evaluate the lesion size of different odontogenic lesions. Tartrate resistant acid phosphatase (TRAP) and immunohistochemistry (IHC) assays were utilized to detect the differences in bone destruction activity in odontogenic cysts and tumors. Furthermore, metabolomics and weighted gene co-expression network analysis (WGCNA) were conducted for the metabolomic features and key metabolite screening, respectively. The effect of ferroptosis inhibition on bone destruction was confirmed by IHC, immunofluorescence, and malondialdehyde colorimetric assay. The bone destruction activity of ameloblastoma (AM) was the strongest and the weakest in odontogenic cysts (OC). High-throughput targeted metabolomics was used to map the metabolomic profiles of OC, odontogenic keratocyst (OKC) and AM. WGCNA and differential analysis identified L-cysteine in OKC and AM. Cystathionine γ-lyase (CTH) was further screened by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The functions of L-cysteine were further validated. Finally, we confirmed that CTH affected destructive activities by regulating the sensitivity of epithelial cells to ferroptosis. High-throughput targeted metabolomics performed on diseased tissue confirmed the unique alteration of metabolic profiles in OKC and AM. CTH and its metabolite L-cysteine are the key factors regulating destructive activities.

Single-cell RNA sequencing and ATAC sequencing identify novel biomarkers for bicuspid aortic valve-associated thoracic aortic aneurysm

IntroductionBicuspid aortic valve (BAV) is the most prevalent congenital cardiovascular defect and known to cause thoracic aortic aneurysms (TAAs). To improve our understanding of BAV pathogenesis, we characterized the cellular composition of BAV tissues and identified molecular changes in each cell population.MethodsTissue samples from two patients with BAV and two heart transplant donors were analyzed using single-cell RNA sequencing, assay for transposase-accessible chromatin using sequencing, and weighted gene coexpression network analysis for differential gene analysis. TAA-related changes were evaluated by comparing the proportion of each cell type and gene expression profiles between TAA and control tissues. Further, by combining our single-cell RNA sequencing data with publicly available data from genome-wide association studies, we determined critical genes for BAV.ResultsWe found 20 cell subpopulations in TAA tissues, including multiple subtypes of smooth muscle cells, fibroblasts, macrophages, and T lymphocytes. This result suggested that these cells play multiple functional roles in BAV development. Several differentially expressed genes, including CD9, FHL1y, HSP90AA1, GAS6, PALLD, and ACTA2, were identified.DiscussionWe believe that this comprehensive assessment of the cellular composition of TAA tissues and the insights into altered gene expression patterns can facilitate identification of novel diagnostic biomarkers and therapeutic targets for BAV-associated TAA.

Comprehensive bioinformatics analysis unveils THEMIS2 as a carcinogenic indicator related to immune infiltration and prognosis of thyroid cancer.

The aim of this study was to identify biomarkers associated with the initiation and prognosis of thyroid cancer and elucidate the underlying pathogenic mechanisms. We obtained expression profiles and clinical information from the Cancer Genome Atlas (TCGA)-THCA and three datasets (GSE53157, GSE82208, and GSE76039). The three microarray datasets were combined using Perl and the sva package in R and termed 'merged dataset'. Weighted gene co-expression network analysis (WGCNA) identified 15 gene co-expression modules in the merged dataset and 235 hub genes. Venn diagram analysis revealed 232 overlapping genes between the merged and THCA datasets. Overlapping genes were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The least absolute shrinkage and selection operator (LASSO) regression identified THEMIS2 as a candidate hub gene. Cox, Kaplan-Meier (K-M) survival and gene set enrichment analysis (GSEA) confirmed the correlation of THEMIS2 with overall survival, its enrichment in immunologic processes, and its association with the p53 and JAK-STAT signaling pathways. Its expression was positively correlated with those of immune checkpoints and the infiltration level of immune cells. Receiver operating characteristic curve (ROC) analysis confirmed that THEMIS2, a diagnostic biomarker, could distinguish between tumor and normal specimens. The nomogram (ROC or DCA) model containing THEMIS2, age, and stage predicted favourable prognoses. Thus, THEMIS2 was a biomarker of immune infiltration and prognosis in thyroid cancer.

Prognostic value of CD8+T cells related genes and exhaustion regulation of Notch signaling pathway in hepatocellular carcinoma

Immunotherapy has emerged as the primary treatment modality for patients with advanced Hepatocellular carcinoma (HCC). However, its clinical efficacy remains limited, benefiting only a subset of patients, while most exhibit immune tolerance and face a grim prognosis. The infiltration of immune cells plays a pivotal role in tumor initiation and progression. In this study, we conducted an analysis of immune cell infiltration patterns in HCC patients and observed a substantial proportion of CD8+T cells. Leveraging the weighted gene co-expression network analysis (WGCNA), we identified 235 genes associated with CD8+T cell and constructed a risk prediction model. In this model, HCC patients were stratified into a high-risk and low-risk group. Patients in the high-risk group exhibited a lower survival rate, predominantly presented with intermediate to advanced stages of cancer, displayed compromised immune function, showed limited responsiveness to immunotherapy, and demonstrated elevated expression levels of the Notch signaling pathway. Further examination of clinical samples demonstrated an upregulation of the Notch1+CD8+T cell exhaustion phenotype accompanied by impaired cytotoxicity and cytokine secretion functions that worsened with increasing Notch activation levels. Our study not only presents a prognostic model but also highlights the crucial involvement of the Notch pathway in CD8+T cell exhaustion—a potential target for future immunotherapeutic interventions.