Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, along with repetitive behaviors. While genetic factors play a significant role in ASD, the precise genetic landscape remains complex and not fully understood, particularly in non-syndromic cases. The study performed an in silico comparison of three genetic databases. ClinVar, SFARI Gene, and AutDB were utilized to identify relevant gene subset and genetic variations associated with non-syndromic ASD. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis were conducted to elucidate the biological significance of the identified genes. The integrity of ASD-related gene subset and the distribution of their variations were statistically assessed. A subset of twenty overlapping genes potentially specific for non-syndromic ASD was identified. GSEA revealed enrichment of biological processes related to neuronal development and differentiation, synaptic function, and social skills, highlighting their importance in ASD pathogenesis. PPI network analysis demonstrated functional relationships among the identified genes. Analysis of genetic variations showed predominance of rare variants and database-specific distribution patterns. The results provide valuable insights into the genetic landscape of ASD and outline the genes and biological processes involved in the condition, while taking into account that the study relied exclusively on in silico analyses, which may be subject to biases inherent to database methodologies. Further research incorporating multi-omics data and experimental validation is warranted to enhance our understanding of non-syndromic ASD genetics and facilitate the development of targeted research, interventions and therapies.

OBJECTIVE: To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts.
METHODS: In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity.
RESULTS: By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus\'s role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity.
CONCLUSIONS: Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

UNASSIGNED: This study aims to address the substantive issue of lacking reliable prognostic biomarkers in hepatocellular carcinoma (HCC) by investigating the relationship between TP53-inducible glycolysis and apoptosis regulator (TIGAR) and HCC prognosis using The Cancer Genome Atlas database.
UNASSIGNED: (1) Integrated statistical analyses, including logistic regression, Wilcoxon signed-rank test, and Kruskal-Wallis test, were conducted to explore the association between TIGAR expression and clinical-pathological features of HCC. (2) The Kaplan-Meier method combined with univariate and multivariate Cox regression models underscored TIGAR as a prognostic factor in HCC. (3) Gene set enrichment analysis (GSEA) revealed key pathways associated with TIGAR, while single-sample gene set enrichment analysis (ssGSEA) determined its relevance to cancer immune infiltration.
UNASSIGNED: (1) Elevated TIGAR expression was significantly correlated with decreased survival outcomes in HCC patients. (2) GSEA highlighted the significant link between TIGAR and humoral immunity. (3) ssGSEA revealed a positive correlation between TIGAR expression and infiltration of Th1 and Th2 cells and a negative correlation with Th17 cell infiltration.
UNASSIGNED: TIGAR, as a potential prognostic biomarker for HCC, holds significant value in immune infiltration. Understanding the role of TIGAR could contribute to improved prognostic predictions and personalized treatment strategies for HCC patients.

UNASSIGNED: Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes.
UNASSIGNED: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.
UNASSIGNED: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).
UNASSIGNED: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.

OBJECTIVE: Despite the established antitumor effectiveness and synergistic interactions of melatonin with photon irradiation, its role in carbon-ion radiotherapy remains uncertain. This study aimed to elucidate the mechanisms and potential clinical advantages of combining exogenous melatonin therapy with carbon-ion radiotherapy.
METHODS: The investigation assessed the impact of combining exogenous melatonin with photon or carbon-ion irradiation on cell-cycle modulation and DNA-repair capability using the melanoma cell line B16F10. RNA sequencing and bioinformatics analysis were conducted to explore mechanisms and evaluate potential clinical benefits, with validation performed on the osteosarcoma cell line LM8.
RESULTS: Pre-treatment with melatonin reduced the survival fraction of B16F10 and LM8 cells upon exposure to photon and carbon-ion radiation. Mechanistically, melatonin was found to inhibit G2/M arrest, preserve DNA damage, and suppress key genes involved in DNA double-strand break repair after 8 Gy carbon-ion radiation. Furthermore, RNA sequencing and bioinformatics analysis revealed favorable changes in genes associated with survival and metastasis, highlighting potential clinical significance. LM8 cells treated with melatonin exhibited increased radiosensitivity and suppression of DNA-repair proteins.
CONCLUSIONS: The combination of exogenous melatonin not only heightened radiosensitivity and modulated hallmark tumor gene sets in vitro but also markedly suppressed the efficiency of DNA double-strand break-repair pathway, thus enhancing the cytotoxicity of carbon-ion radiotherapy.

Although sows do not directly enter the market, they play an important role in piglet breeding on farms. They consume large amounts of feed, resulting in a significant environmental burden. Pig farms can increase their income and reduce environmental pollution by increasing the litter size (LS) of swine. PCR-RFLP/SSCP and GWAS are common methods to evaluate single-nucleotide polymorphisms (SNPs) in candidate genes. We conducted a systematic meta-analysis of the effect of SNPs on pig LS. We collected and analysed data published over the past 30 years using traditional and network meta-analyses. Trial sequential analysis (TSA) was used to analyse population data. Gene set enrichment analysis and protein-protein interaction network analysis were used to analyse the GWAS dataset. The results showed that the candidate genes were positively correlated with LS, and defects in PCR-RFLP/SSCP affected the reliability of candidate gene results. However, the genotypes with high and low LSs did not have a significant advantage. Current breeding and management practices for sows should consider increasing the LS while reducing lactation length and minimizing the sows\' non-pregnancy period as much as possible.

Dyslipidemia and hepatic steatosis are the characteristics of the initial stage of nonalcohol fatty liver disease (NAFLD), which can be reversed by lifestyle intervention, including dietary supplementation. However, such commercial dietary supplements with solid scientific evidence and in particular clear mechanistic elucidation are scarce. Here, the health benefits of MHP, a commercial mulberry and Hippophae-based solid beverage, were evaluated in NAFLD rat model and the underlying molecular mechanisms were investigated. Histopathologic examination of liver and white adipose tissue found that MHP supplementation reduced hepatic lipid accumulation and adipocyte hypertrophy. Serum biochemical results confirmed that MHP effectively ameliorated dyslipidemia and decreased circulation-free fatty acid level. RNA-Seq-based transcriptomic analysis showed that MHP-regulated genes are involved in the inhibition of lipolysis of adipose tissue and thus may contribute to the reduction of hepatic ectopic lipid deposition. Furthermore, MHP upregulated ACSL1-CPT1a-CPT2 pathway, a canonical pathway that regulated mitochondrial fatty acid metabolism, and promoted liver and adipose tissue fatty acid β-oxidation. These results suggest that adipose tissue-liver crosstalk may play a key role in maintaining glucose and lipid metabolic hemostasis. In addition, MHP can also ameliorate chronic inflammation through regulating the secretion of adipokines. Our study demonstrates that MHP is able to improve dyslipidemia and hepatic steatosis through crosstalk between adipose tissue and liver and also presents transcriptomic evidence to support the underlying mechanisms of action, providing solid evidence for its health claims.

Ischemic stroke followed by reperfusion (IR) leads to extensive cerebrovascular injury characterized by neuroinflammation and brain cell death. Inhibition of matrix metalloproteinase-3 (MMP-3) emerges as a promising therapeutic approach to mitigate IR-induced stroke injury. We employed middle cerebral artery occlusion with subsequent reperfusion (MCAO/R) to model ischemic stroke in adult mice. Specifically, we investigated the impact of MMP-3 knockout (KO) on stroke pathophysiology using RNA sequencing (RNA-seq) of stroke brains harvested 48 h post-MCAO. MMP-3 KO significantly reduced brain infarct size following stroke. Notably, RNA-seq analysis showed that MMP-3 KO altered expression of 333 genes (252 downregulated) in male stroke brains and 3768 genes (889 downregulated) in female stroke brains. Functional pathway analysis revealed that inflammation, integrin cell surface signaling, endothelial- and epithelial-mesenchymal transition (EndMT/EMT), and apoptosis gene signatures were decreased in MMP-3 KO stroke brains. Intriguingly, MMP-3 KO downregulated gene signatures more profoundly in females than in males, as indicated by greater negative enrichment scores. Our study underscores MMP-3 inhibition as a promising therapeutic strategy, impacting multiple cellular pathways following stroke.

Domesticated animals have undergone significant changes in their behavior, morphology, and physiological functions during domestication. To identify the changes in gene expression associated with domestication, we collected the RNA-seq data of pigs, chickens, wild boars, and red junglefowl from public databases and performed a meta-analysis. Gene expression was quantified, and the expression ratio between domesticated animals and their wild ancestors (DW-ratio) was calculated. Genes were classified as \"upregulated\", \"downregulated\", or \"unchanged\" based on their DW-ratio, and the DW-score was calculated for each gene. Gene set enrichment analysis revealed that genes upregulated in pigs were related to defense from viral infection, whereas those upregulated in chickens were associated with aminoglycan and carbohydrate derivative catabolic processes. Genes commonly upregulated in pigs and chickens are involved in the immune response, olfactory learning, epigenetic regulation, cell division, and extracellular matrix. In contrast, genes upregulated in wild boar and red junglefowl are related to stress response, cell proliferation, cardiovascular function, neural regulation, and energy metabolism. These findings provide valuable insights into the genetic basis of the domestication process and highlight potential candidate genes for breeding applications.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model\'s efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.