Comprehensive analysis of disulfidptosis-related genes reveals the effect of disulfidptosis in ulcerative colitis.

Ulcerative colitis (UC) is a chronic inflammatory condition of the intestinal tract. Various programmed cell death pathways in the intestinal mucosa are crucial to the pathogenesis of UC. Disulfidptosis, a recently identified form of programmed cell death, has not been extensively reported in the context of UC. This study evaluated the expression of disulfidptosis-related genes (DRGs) in UC through public databases and assessed disulfide accumulation in the intestinal mucosal tissues of UC patients and dextran sulfate sodium (DSS)-induced colitis mice via targeted metabolomics. We utilized various bioinformatics techniques to identify UC-specific disulfidptosis signature genes, analyze their potential functions, and investigate their association with immune cell infiltration in UC. The mRNA and protein expression levels of these signature genes were confirmed in the intestinal mucosa of DSS-induced colitis mice and UC patients. A total of 24 DRGs showed differential expression in UC. Our findings underscore the role of disulfide stress in UC. Four UC-related disulfidptosis signature genes-SLC7A11, LRPPRC, NDUFS1, and CD2AP-were identified. Their relationships with immune infiltration in UC were analyzed using CIBERSORT, and their expression levels were validated by quantitative real-time PCR and western blotting. This study provides further insights into their potential functions and explores their links to immune infiltration in UC. In summary, disulfidptosis, as a type of programmed cell death, may significantly influence the pathogenesis of UC by modulating the homeostasis of the intestinal mucosal barrier.

Integrated Analysis Reveals COL4A3 as a Novel Diagnostic and Therapeutic Target in UV-Related Skin Cutaneous Melanoma.

Background: High levels of UV exposure are a significant factor that can trigger the onset and progression of SKCM. Moreover, this exposure is closely linked to the malignancy of the tumor and the prognosis of patients. Our objective is to identify a tumor biomarker database associated with UV exposure, which can be utilized for prognostic analysis and diagnosis and treatment of SKCM. Methods: This study used the weighted gene co-expression network analyses (WGCNA) and gene mutation frequency analyses to screen for UV-related target genes using the GSE59455 and the cancer genome atlas databases (TCGA). The prognostic model was created using Cox regression and least absolute shrinkage and selection operator analyses (LASSCO). Furthermore, in vitro experiments further validated that the overexpression or knockdown of COL4A3 could regulate the proliferation and migration abilities of SKMEL28 and A357 melanoma cells. Results: A prognostic model was created that included six genes with a high UV-related mutation in SKCM: COL4A3, CHRM2, DSC3, GIMAP5, LAMC2, and PSG7. The model had a strong patient survival correlation (P˂0.001, hazard ratio (HR) = 1.57) and significant predictor (P˂0.001, HR = 3.050). Furthermore, the model negatively correlated with immune cells, including CD8+ T cells (Cor=-0.408, P˂0.001), and M1-type macrophages (Cor=-0.385, P˂0.001), and immune checkpoints, including programmed cell death ligand-1. Moreover, we identified COL4A3 as a molecule with significant predictive functionality. Overexpression of COL4A3 significantly inhibited the proliferation, migration, and invasion abilities of SKMEL28 and A357 melanoma cells, while knockdown of COL4A3 yielded the opposite results. And overexpression of COL4A3 enhanced the inhibitory effects of imatinib on the proliferation, migration, and invasion abilities of SKMEL28 and A357 cells. Conclusion: The efficacy of the prognostic model was validated by analyzing the prognosis, immune infiltration, and immune checkpoint profiles. COL4A3 stands out as a novel diagnostic and therapeutic target for SKCM, offering new strategies for small-molecule targeted drug therapies.

Unraveling chemical changes associated with the sensory quality of Chinese steamed bread as altered by wheat flour type.

Chinese steamed bread (CSB) is an important staple of the Chinese people, and its flavor profile is mostly affected by wheat varieties among others. This study selected wheat flour made from three different wheat varieties and investigated their contribution to the CSB flavor profile in terms of metabolism. Thirteen aroma-active compounds identified by GC-O were determined as the main contributors to the different aroma profiles of three CSBs. 350 sensory trait-related metabolites were obtained from five key modules via weighted gene co-expression network analysis. It was found that the sensory characteristics of CSBs made of different wheat flour were significantly different. The higher abundance of lipids in Yongliang No.4 (YL04) wheat flour was converted to large number of fatty acids in fermented dough, which led to the bitterness of CSB. Besides, the abundance in organic acids and fatty acids contributed to the sour, milky, wetness and roughness attributes of YL04-CSB. More fatty amides and flavonoids in Jiangsu Red Durum wheat flour contributed to the fermented and winey attributes of CSB. Carbohydrates with higher abundance in Canadian wheat flour was involved in sugar-amine reaction and glucose conversion, which enhanced the sweetness of CSB. In addition, fatty acids, organic acids, amino acids, and glucose were crucial metabolites which can further formed into various characteristic compounds such as hexanal, hexanol, 2,3-butanediol, acetoin, and 2,3-butanedione and thus contributed to the winey, fresh sweet, and green aroma properties. This study is conductive to better understand the evolution of the compounds that affect the quality and aroma of CSBs.

Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1ß were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms.

Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co-expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein-protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK-specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.

Alternaria solani effectors AsCEP19 and AsCEP20 reveal novel functions in pathogenicity and conidiogenesis.

Previous work identified a pair of specific effectors AsCEP19 and AsCEP20 in Alternaria solani as contributors to the virulence of A. solani. Here, we constructed AsCEP19 and AsCEP20 deletion mutants in A. solani strain HWC168 to further reveal the effects of these genes on the biology and pathogenicity of A. solani. Deletion of AsCEP19 and AsCEP20 did not affect vegetative growth but did affect conidial maturation, with an increase in the percentage of abnormal conidia produced. Furthermore, we determined the expression patterns of genes involved in the conidiogenesis pathway and found that the regulatory gene abaA was significantly upregulated and chsA, a positive regulator for conidiation, was significantly downregulated in the mutant strains compared to the wild-type strain. These results suggest that AsCEP19 and AsCEP20 indirectly affect the conidial development and maturation of A. solani. Pathogenicity assays revealed significantly impaired virulence of ΔAsCEP19, ΔAsCEP20, and ΔAsCEP19 + AsCEP20 mutants on potato and tomato plants. Moreover, we performed localization assays with green fluorescent protein-tagged proteins in chili pepper leaves. We found that AsCEP19 can specifically localize to the chloroplasts of chili pepper epidermal cells, while AsCEP20 can localize to both chloroplasts and the plasma membrane. Weighted gene co-expression network analysis revealed enrichment of genes of this module in the photosynthesis pathway, with many hub genes associated with chloroplast structure and photosynthesis. These results suggest that chloroplasts are the targets for AsCEP19 and AsCEP20. IMPORTANCE: Alternaria solani is an important necrotrophic pathogen causing potato early blight. Previous studies have provide preliminary evidence that specific effectors AsCEP19 and AsCEP20 contribute to virulence, but their respective functions, localization, and pathogenic mechanisms during the infection process of A. solani remain unclear. Here, we have systematically studied the specific effectors AsCEP19 and AsCEP20 for the first time, which are essential for conidial maturation. The deletion of AsCEP19 and AsCEP20 can significantly impair fungal pathogenicity. Additionally, we preliminarily revealed that AsCEP19 and AsCEP20 target the chloroplasts of host cells. Our findings further enhance our understanding of the molecular mechanisms underlying the virulence of necrotrophic pathogens.

Construction of a Novel Mitochondria-Associated Gene Model for Assessing ESCC Immune Microenvironment and Predicting Survival.

Esophageal squamous cell carcinoma (ESCC) is among the most common malignant tumors of the digestive tract, with the sixth highest fatality rate worldwide. The ESCC-related dataset, GSE20347, was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene co-expression network analysis was performed to identify genes that are highly correlated with ESCC. A total of 91 transcriptome expression profiles and their corresponding clinical information were obtained from The Cancer Genome Atlas database. A mitochondria-associated risk (MAR) model was constructed using the least absolute shrinkage and selection operator Cox regression analysis and validated using GSE161533. The tumor microenvironment and drug sensitivity were explored using the MAR model. Finally, in vitro experiments were performed to analyze the effects of hub genes on the proliferation and invasion abilities of ESCC cells. To confirm the predictive ability of the MAR model, we constructed a prognostic model and assessed its predictive accuracy. The MAR model revealed substantial differences in immune infiltration and tumor microenvironment characteristics between high- and low-risk populations and a substantial correlation between the risk scores and some common immunological checkpoints. AZD1332 and AZD7762 were more effective for patients in the low-risk group, whereas Entinostat, Nilotinib, Ruxolutinib, and Wnt.c59 were more effective for patients in the high-risk group. Knockdown of TYMS significantly inhibited the proliferation and invasive ability of ESCC cells in vitro. Overall, our MAR model provides stable and reliable results and may be used as a prognostic biomarker for personalized treatment of patients with ESCC.

Transcriptomic Insights into Different Stimulation Intensity of Electroacupuncture in Treating COPD in Rat Models.

Background: Electroacupuncture (EA), with varying stimulation intensities, has demonstrated therapeutic potentials in both animal and clinical studies for the treatment of chronic obstructive pulmonary disease (COPD). However, a comprehensive investigation of the intensity-related effects, particularly 1mA and 3mA of EA, and the underlying mechanisms remains lacking. Methods: A COPD rat model was established by prolonged exposure to cigarette smoke and intermittent intratracheal instillation of lipopolysaccharide. EA treatment was administered at acupoints BL13 (Feishu) and ST36 (Zusanli), 20 minutes daily for 2 weeks, with intensities of 1mA and 3mA. EA effectiveness was evaluated by pulmonary function, histopathological change, serum level of inflammatory cytokines, and level of oxidative stress markers in serum and lung tissues. Transcriptome profiling and weighted gene co-expression network analysis (WGCNA) were performed to reveal gene expression patterns and identify hub genes. Real-time quantitative PCR (RT-qPCR) and Western blot (WB) were performed to detect the mRNA and protein expression levels, respectively. Results: EA at both 1mA and 3mA exerted differing therapeutic effects by improving lung function and reducing inflammation and oxidative stress in COPD rats. Transcriptome analysis revealed distinct expression patterns between the two groups, functionally corresponding to shared and intensity-specific (1mA and 3mA) enriched pathways. Eight candidate genes were identified, including Aqp9, Trem1, Mrc1, and Gpnmb that were downregulated by EA and upregulated in COPD. Notably, Msr1 and Slc26a4 exclusively downregulated in EA-1mA, while Pde3a and Bmp6 upregulated solely in EA-3mA. WGCNA constructed 5 key modules and elucidated the module-trait relationship, with the aforementioned 8 genes being highlighted. Additionally, their mRNA and protein levels were validated by RT-qPCR and WB. Conclusion: Our results demonstrated that 1mA and 3mA intensities induce distinct gene expression patterns at the transcriptional level, associated with shared and 1mA vs 3mA-specific enriched pathways. Genes Mrc1, Gpnmb, Trem1, and Aqp9 emerge as promising targets, and further studies are needed to elucidate their functional consequences in COPD.

Transglutaminase 2 serves as a pathogenic hub gene of KRAS mutant colon cancer based on integrated analysis.

BACKGROUND: Colon cancer is acknowledged as one of the most common malignancies worldwide, ranking third in United States regarding incidence and mortality. Notably, approximately 40% of colon cancer cases harbor oncogenic KRAS mutations, resulting in the continuous activation of epidermal growth factor receptor signaling. AIM: To investigate the key pathogenic genes in KRAS mutant colon cancer holds considerable importance. METHODS: Weighted gene co-expression network analysis, in combination with additional bioinformatics analysis, were conducted to screen the key factors driving the progression of KRAS mutant colon cancer. Meanwhile, various in vitro experiments were also conducted to explore the biological function of transglutaminase 2 (TGM2). RESULTS: Integrated analysis demonstrated that TGM2 acted as an independent prognostic factor for progression-free survival. Immunohistochemical analysis on tissue microarrays revealed that TGM2 was associated with an elevated probability of perineural invasion in patients with KRAS mutant colon cancer. Additionally, biological roles of the key gene TGM2 was also assessed, suggesting that the downregulation of TGM2 attenuated the proliferation, invasion, and migration of the KRAS mutant colon cancer cell line. CONCLUSION: This study underscores the potential significance of TGM2 in the progression of KRAS mutant colon cancer. This insight not only offers a theoretical foundation for therapeutic approaches but also highlights the need for additional clinical trials and fundamental research to support our preliminary findings.

Screening of key immune-related gene in Parkinson's disease based on WGCNA and machine learning. / 基于WGCNAå’Œæœºå™¨å­¦ä¹ ç­›é€‰å¸•é‡‘æ£®ç— å ç–«ç›¸å ³å ³é”®åŸºå› .

OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning. METHODS: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. RESULTS: A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation. CONCLUSIONS: This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics.

Background: Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. Methods: In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. Results: The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. Conclusion: This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Elucidating the molecular mechanisms of pterostilbene against cervical cancer through an integrated bioinformatics and network pharmacology approach.

Pterostilbene (PTE), a natural phenolic compound, has exhibited promising anticancer properties in the preclinical treatment of cervical cancer (CC). This study aims to comprehensively investigate the potential targets and mechanisms underlying PTE's anticancer effects in CC, thereby providing a theoretical foundation for its future clinical application and development. To accomplish this, we employed a range of methodologies, including network pharmacology, bioinformatics, and computer simulation, with specific techniques such as WGCNA, PPI network construction, ROC curve analysis, KM survival analysis, GO functional enrichment, KEGG pathway enrichment, molecular docking, MDS, and single-gene GSEA. Utilizing eight drug target prediction databases, we have identified a total of 532 potential targets for PTE. By combining CC-related genes from the GeneCards disease database with significant genes derived from WGCNA analysis of the GSE63514 dataset, we obtained 7915 unique CC-related genes. By analyzing the intersection of the 7915 CC-related genes and the 2810 genes that impact overall survival time in CC, we identified 690 genes as crucial for CC. Through the use of a Venn diagram, we discovered 36 overlapping targets shared by PTE and CC. We have constructed a PPI network and identified 9 core candidate targets. ROC and KM curve analyses subsequently revealed IL1B, EGFR, IL1A, JUN, MYC, MMP1, MMP3, and ANXA5 as the key targets modulated by PTE in CC. GO and KEGG pathway enrichment analyses indicated significant enrichment of these key targets, primarily in the MAPK and IL-17 signaling pathways. Molecular docking analysis verified the effective binding of PTE to all nine key targets. MDS results showed that the protein-ligand complex between MMP1 and PTE was the most stable among the nine targets. Additionally, GSEA enrichment analysis suggested a potential link between elevated MMP1 expression and the activation of the IL-17 signaling pathway. In conclusion, our study has identified key targets and uncovered the molecular mechanism behind PTE's anticancer activity in CC, establishing a firm theoretical basis for further exploration of PTE's pharmacological effects in CC therapy.

Objective Quantification Technique and Widely Targeted Metabolomics-Based Analysis of the Effects of Different Saccharidation Processes on Preserved French Plums.

Vacuum saccharification significantly affected the flavor and color of preserved French plums. However, the correlation between color, flavor, and metabolites remains unclear. Metabolites contribute significantly to enhancing the taste and overall quality of preserved French plums. This study aimed to investigate the distinctive metabolites in samples from various stages of the processing of preserved French plums. The PCF4 exhibited the highest appearance, overall taste, and chroma. Furthermore, utilizing UPLC and ESI-Q TRAP-MS/MS, a comprehensive examination of the metabolome in the processing of preserved French plums was conducted. A total of 1776 metabolites were analyzed. Using WGCNA, we explored metabolites associated with sensory features through 10 modules. Based on this, building the correlation of modules and objective quantification metrics yielded three key modules. After screening for 151 differentiated metabolites, amino acids, and their derivatives, phenolic acids, flavonoids, organic acids, and other groups were identified as key differentiators. The response of differential metabolites to stress influenced the taste and color properties of preserved prunes. Based on these analyses, six important metabolic pathways were identified. This study identified changes in the sensory properties of sugar-stained preserved prunes and their association with metabolite composition, providing a scientific basis for future work to improve the quality of prune processing.

Contribution of FOS in neutrophils to venous thromboembolism via miR-144 based on bioinformatic prediction and validation.

The Finkel-Biskis-Jinkins Osteosarcoma (c-Fos; encoded by FOS) plays an important role in several cardiovascular diseases, including atherosclerosis and stroke. However, the relationship between FOS and venous thromboembolism (VTE) remains unknown. We identified differentially expressed genes in Gene Expression Omnibus dataset, GSE48000, comprising VTE patients and healthy individuals, and analysed them using CIBERSORT and weighted co-expression network analysis (WGCNA). FOS and CD46 expressions were significantly downregulated (FOS p = 2.26E-05, CD64 p = 8.83E-05) and strongly linked to neutrophil activity in VTE. We used GSE19151 and performed PCR to confirm that FOS and CD46 had diagnostic potential for VTE; however, only FOS showed differential expression by PCR and ELISA in whole blood samples. Moreover, we found that hsa-miR-144 which regulates FOS expression was significantly upregulated in VTE. Furthermore, FOS expression was significantly downregulated in neutrophils of VTE patients (p = 0.03). RNA sequencing performed on whole blood samples of VTE patients showed that FOS exerted its effects in VTE via the leptin-mediated adipokine signalling pathway. Our results suggest that FOS and related genes or proteins can outperform traditional clinical markers and may be used as diagnostic biomarkers for VTE.

De novo FRMD5 Missense Variants in Patients with Childhood-Onset Ataxia, Prominent Nystagmus, and Seizures.

BACKGROUND: FRMD5 variants were recently identified in patients with developmental delay, ataxia, and eye movement abnormalities. OBJECTIVES: We describe 2 patients presenting with childhood-onset ataxia, nystagmus, and seizures carrying pathogenic de novo FRMD5 variants. Weighted gene co-expression network analysis (WGCNA) was performed to gain insights into the function of FRMD5 in the brain. METHODS: Trio-based whole-exome sequencing was performed in both patients, and CoExp web tool was used to conduct WGCNA. RESULTS: Both patients presented with developmental delay, childhood-onset ataxia, nystagmus, and seizures. Previously unreported findings were diffuse choreoathetosis and dystonia of the hands (patient 1) and areas of abnormal magnetic resonance imaging signal in the white matter (patient 2). WGCNA showed that FRMD5 belongs to gene networks involved in neurodevelopment and oligodendrocyte function. CONCLUSIONS: We expanded the phenotype of FRMD5-related disease and shed light on its role in brain function and development. We recommend including FRMD5 in the genetic workup of childhood-onset ataxia and nystagmus. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Transcriptome-Based Weighted Gene Co-Expression Network Analysis Reveals the Photosynthesis Pathway and Hub Genes Involved in Promoting Tiller Growth under Repeated Drought-Rewatering Cycles in Perennial Ryegrass.

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought-rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought-rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought-rewatering (D2R), and one cycle of drought-rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson's correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding.

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

MAIN CONCLUSION: 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.

SIRT1/P53 in retinal pigment epithelial cells in diabetic retinopathy: a gene co-expression analysis and He-Ying-Qing-Re formula treatment.

Objective: Diabetic retinopathy (DR) is a severe diabetic complication that leads to severe visual impairment or blindness. He-Ying-Qing-Re formula (HF), a traditional Chinese medicinal concoction, has been identified as an efficient therapy for DR with retinal vascular dysfunction for decades and has been experimentally reported to ameliorate retinal conditions in diabetic mice. This study endeavors to explore the therapeutic potential of HF with key ingredients in DR and its underlying novel mechanisms. Methods: Co-expression gene modules and hub genes were calculated by weighted gene co-expression network analysis (WGCNA) based on transcriptome sequencing data from high-glucose-treated adult retinal pigment epithelial cell line-19 (ARPE-19). The chromatographic fingerprint of HF was established by ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-Q-TOF-MS). The molecular affinity of the herbal compound was measured by molecular docking. Reactive oxygen species (ROS) was measured by a DCFDA/H2DCFDA assay. Apoptosis was detected using the TUNEL Assay Kit, while ELISA, Western blot, and real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used for detecting the cytokine, protein, and mRNA expressions, respectively. Results: Key compounds in HF were identified as luteolin, paeoniflorin, and nobiletin. For WGCNA, ME-salmon ("protein deacetylation") was negatively correlated with ME-purple ("oxidative impairment") in high-glucose-treated ARPE-19. Luteolin has a high affinity for SIRT1 and P53, as indicated by molecular docking. Luteolin has a hypoglycemic effect on type I diabetic mice. Moreover, HF and luteolin suppress oxidative stress production (ROS and MDA), inflammatory factor expression (IL-6, TNF-α, IL1-ß, and MCP-1), and apoptosis, as shown in the in vivo and in vitro experiments. Concurrently, treatment with HF and luteolin led to an upregulation of SIRT1 and a corresponding downregulation of P53. Conclusion: Using HF and its active compound luteolin as therapeutic agents offers a promising approach to diabetic retinopathy treatment. It primarily suppressed protein acetylation and oxidative stress via the SIRT1/P53 pathway in retinal pigment epithelial cells.

Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer.

Background: Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified. Methods: Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). Results: The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor. Conclusion: Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.

Identification of key mRNAs and signaling pathways in obsessive compulsive disorder based on weighted gene co-expression network analysis and cytoHubba plugin.

PURPOSE: Obsessive-compulsive disorder (OCD) is a complex psychiatric disorder. Genetic and broad environmental factors are common risk factors for OCD. The purpose of this study is to explore the molecular mechanism of OCD and to find new molecular targets for the diagnosis and management of OCD. METHODS: All data were downloaded from public dataset. Key modules and candidate key mRNAs were identified based on weighted gene co-expression network analysis (WGCNA). The "limma" R package was used for differential expression analysis of mRNAs. Subsequently, functional enrichment analysis of differentially expressed mRNAs (DEmRNAs) was also carried out. In addition, a diagnostic model was constructed. Finally, the infiltration level of immune cells in OCD and its correlation with multicentric key DEmRNAs were analyzed. RESULTS: Green and red modules were selected as the hub modules. A total of 447 mRNAs were considered candidate key mRNAs according to GS > 0.2 and MM > 0.3. A total of 26 DEmRNAs in the same direction were identified in the GSE60190 and GSE78104 datasets. A total of 26 DEmRNAs were intersected with candidate key mRNAs in WGCNA to obtain 10 intersection DEmRNAs (HSPB1, ITPK1, CBX7, PPP1R10, TAOK1, PISD, MKNK2, RWDD1, PPA1, and RELN). However, only four DEmRNAs (HSPB1, TAOK1, MKNK2, and PPA1) predicted related drugs. Subsequently, receiver operating characteristic analysis shows that the diagnostic model has high diagnostic value. Moreover, six multicentric key DEmRNAs (SNRPF, SNRNP70, PRPF8, NOP56, EPRS, and CCT2) were screened by UpSet package. Finally, six multicentric key DEmRNAs were found to be associated with immune cells. CONCLUSION: The key molecules obtained in this study lay a foundation for further research on the molecular mechanism of OCD.