Immune landscape of hepatocellular carcinoma: The central role of TP53-inducible glycolysis and apoptosis regulator.

Objective: 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. Methods: (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. Results: (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. Conclusion: 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.

Integrative multi-omics characterization of hepatocellular carcinoma in hispanic patients.

BACKGROUND: The incidence and mortality rates of hepatocellular carcinoma (HCC) among Hispanic individuals in the United States are much higher than in non-Hispanic white people. We conducted multi-omics analyses to elucidate molecular alterations in HCC among Hispanic patients. METHODS: Paired tumor and adjacent non-tumor samples were collected from 31 Hispanic HCCs in South Texas (STX-Hispanic) for genomic, transcriptomic, proteomic, and metabolomic profiling. Serum lipids were profiled in 40 Hispanic and non-Hispanic patients with or without clinically diagnosed HCC. RESULTS: Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1 in STX Hispanic HCCs, suggesting a predominant activation of the Wnt/ß-catenin pathway. TERT promoter mutations were also significantly more frequent in the Hispanic cohort (Fisher's exact test, p < .05). Cell cycles and liver function were positively and negatively enriched, respectively, with gene set enrichment analysis. Gene sets representing specific liver metabolic pathways were associated with dysregulation of corresponding metabolites. Negative enrichment of liver adipogenesis and lipid metabolism corroborated with a significant reduction in most lipids in serum samples of HCC patients (paired t-test, p < .0001). Two HCC subtypes from our Hispanic cohort were identified and validated with the TCGA liver cancer cohort. Patients with better overall survival showed higher activity of immune and angiogenesis signatures, and lower activity of liver function-related gene signatures. They also had higher levels of immune checkpoint and immune exhaustion markers. CONCLUSIONS: Our study revealed specific molecular features of Hispanic HCC and potential biomarkers for therapeutic management. It provides a unique resource for studying Hispanic HCC.

Exploring the role of candidalysin in the pathogenicity of Candida albicans by gene set enrichment analysis and evolutionary dynamics.

AIMS: 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. CONCLUSION: 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.

Gestational Diabetes Mellitus-Induced Milk Fat Globule Membrane Protein Changes of Human Mature Milk Based on TMT Proteomic Analysis.

Breastfeeding by mothers with gestational diabetes mellitus (GDM) has been shown to reduce maternal insulin demands and diminish the risks of diabetes in infants, leading to improved long-term health outcomes. Milk fat globule membrane (MFGM) proteins play a crucial role in influencing the immunity and cognitive development of infants. Understanding the alterations in MFGM proteins in breastmilk from mothers with GDM is essential for enhancing their self-efficacy and increase breastfeeding rates. The objective of this study is to investigate and compare MFGM proteins in milk from mothers with GDM and without based on tandem mass tag (TMT) labeling and liquid chromatography tandem mass spectrometry (LC-MS) techniques. A total of 5402 proteins were identified, including 4 upregulated proteins and 24 downregulated proteins. These significantly altered proteins were found to be associated with human diseases, cellular processes, and metabolism pathways. Additionally, the oxidative phosphorylation pathway emerged as the predominant pathway through Gene Set Enrichment Analysis (GSEA) involving all genes.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues. RESULTS: Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology. CONCLUSIONS: This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

Exploring the Biomarkers and Potential Mechanisms of Botulinum Toxin Type A in the Treatment of Microglial Inflammatory Activation through P2X7 Receptors based on Transcriptome Sequencing.

AIMS: This study aims to explore the potential mechanism by which Botulinum toxin type A (BoNT/ A) inhibits microglial inflammatory activation through P2X7 receptors (P2X7R). BACKGROUND: BoNT/A is a promising analgesic drug, and previous studies have established that it alleviates Neuropathic Pain (NP) by inhibiting microglial inflammatory activation. This study examined the biomarkers and potential mechanisms by which BoNT/A relieves neuropathic pain by mediating microglial P2X7R and analyzing transcriptome sequencing data from mouse BV-2 microglial cells. OBJECTIVE: The P2X7R agonist Bz-ATP was used to induce microglial inflammatory activation, whilst RNAseq technology was used to explore the biomarkers and potential mechanisms through which BoNT/A suppresses microglial inflammation. METHODS: RNA sequencing was performed on three BV-2 cell samples treated with a P2X7R specific activator (Bz-ATP) and three BV-2 cell samples pre-treated with BoNT/A. Only data that successfully passed quality control measures were included in subsequent analysis. Initially, Differentially Expressed Genes (DEGs) were identified from BoNT/A and control samples, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Biomarkers were then identified by constructing a Protein- Protein Interaction (PPI) network and utilizing the CytoHubba plug-in in Cytoscape software. Lastly, enrichment analysis and regulatory network analysis were performed to elucidate the potential mechanism of BoNT/A in the treatment of NP. RESULTS: 93 DEGs related to the "cell component size regulation" GO term and enriched in the "axon guidance" KEGG pathway were identified. Subsequently, 6 biomarkers were identified, namely PTPRF, CHDH, CKM, Ky, Sema3b, and Sema3f, which were enriched in pathways related to biosynthesis and metabolism, disease progression, signal transduction, and organelle function, including the "ribosome" and "Wnt signaling pathway." Finally, a competing endogenous RNA (ceRNAs) network was constructed from 6 mRNAs, 66 miRNAs, and 31 lncRNAs, forming a complex relationship network. CONCLUSION: Six genes (PTPRF, Sema3b, Sema3f, CHDH, CKM, and Ky) were identified as biomarkers of microglial inflammatory activation following BoNT/A treatment. This finding may provide a valuable reference for the relief and treatment of neuropathic pain.

Periprostatic adipose tissue inhibits tumor progression by secreting apoptotic factors: A natural barrier induced by the immune response during the early stages of prostate cancer.

Prostate cancer (PCa) is the second most prevalent malignancy in men worldwide. The risk factors for PCa include obesity, age and family history. Increased visceral fat has been associated with high PCa risk, which has prompted previous researchers to investigate the influence of body composition and fat distribution on PCa prognosis. However, there is a lack of studies focusing on the mechanisms and interactions between periprostatic adipose tissue (PPAT) and PCa cells. The present study investigated the association between the composition of pelvic adipose tissue and PCa aggressiveness to understand the role played by this tissue in PCa progression. Moreover, PPAT-conditioned medium (CM) was prepared to assess the influence of the PPAT secretome on the pathophysiology of PCa. The present study included 50 patients with localized PCa who received robot-assisted radical prostatectomy. Medical records were collected, magnetic resonance imaging scans were analyzed and body compositions were calculated to identify the associations between adipose tissue volume and clinical PCa aggressiveness. In addition, CM was prepared from PPAT and perivesical adipose tissue (PVAT) collected from 25 patients during surgery, and its effects on the PCa cell lines C4-2 and LNCaP, and the prostate epithelial cell line PZ-HPV-7, were investigated using a cell proliferation assay and RNA sequencing (RNA-seq). The results revealed that the initial prostate-specific antigen level was significantly correlated with pelvic and periprostatic adipose tissue volumes. In addition, PPAT volume was significantly higher in patients with extracapsular tumor extension. PCa cell proliferation was significantly reduced when the cells were cultured in PPAT-CM compared with when they were cultured in control- and PVAT-CM. RNA-seq revealed that immune responses, and the cell death and apoptosis pathways were enriched in PPAT-CM-cultured cells indicating that the cytokines or other factors secreted from PPAT-CM induced PCa cell apoptosis. These findings revealed that the PPAT secretome may inhibit PCa cell proliferation by activating immune responses and promoting cancer cell apoptosis. This mechanism may act as a first-line defense during the early stages of PCa.

Discerning Endoscopic Severity of Inflammatory Bowel Disease by Scoping the Peripheral Blood Transcriptome.

Background and Aims: Ulcerative colitis (UC) and Crohn's disease (CD) are chronic inflammatory bowel diseases (IBDs) with an incompletely understood etiology and pathogenesis. Identification of suitable drug targets and assessment of disease severity are crucial for optimal management. Methods: Using RNA sequencing, we investigated differential gene expression in peripheral blood samples from IBD patients and non-inflamed controls, analyzed pathway enrichment, and identified genes whose expression correlated with endoscopic disease severity. Results: Neutrophil degranulation emerged as the most significant pathway across all IBD sample types. Signaling by interleukins was prominent in patients with active intestinal inflammation but also enriched in CD and UC patients without intestinal inflammation. Nevertheless, genes correlated to endoscopic disease severity implicated the primary cilium in CD patients and translation and focal adhesion in UC patients. Moreover, several of these genes were located in genome-wide associated loci linked to IBD, cholesterol levels, blood cell counts, and levels of markers assessing liver and kidney function. These genes also suggested connections to intestinal epithelial barrier dysfunction, contemporary IBD drug treatment, and new actionable drug targets. A large number of genes associated with endoscopic disease severity corresponded to noncoding RNAs. Conclusion: This study revealed biological pathways associated with IBD disease state and endoscopic disease severity, thus providing insights into the underlying mechanisms of IBD pathogenesis as well as identifying potential biomarkers and therapies. Peripheral blood might constitute a suitable noninvasive diagnostic sample type, in which gene expression profiles might serve as indicators of ongoing mucosal inflammation, and thus guide personalized treatment decisions.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Database-assisted screening of autism spectrum disorder related gene set.

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.

Resource: A curated database of brain-related functional gene sets (Brain.GMT).

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Mulberry and Hippophae-based solid beverage attenuate hyperlipidemia and hepatic steatosis via adipose tissue-liver axis.

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 ß-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.

Effect of SNPs on Litter Size in Swine.

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.

Synergistic Effects of Melatonin on Radiosensitization in Carbon-ion Radiotherapy.

BACKGROUND/AIM: 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. MATERIALS AND 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. CONCLUSION: 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.

An integrated bioinformatics analysis to identify the shared biomarkers in patients with obstructive sleep apnea syndrome and nonalcoholic fatty liver disease.

Background: 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. Methods: 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. Results: 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). Conclusion: 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.

Transcriptional Signatures of Domestication Revealed through Meta-Analysis of Pig, Chicken, Wild Boar, and Red Junglefowl Gene Expression Data.

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.

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

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.

Identification of genomic regions and genes associated with subclinical ketosis in periparturient dairy cows.

Subclinical ketosis (SCK) is a prevalent metabolic disorder that occurs during the transition to lactation period. It is defined as a high blood concentration of ketone bodies (beta-hydroxybutyric acid f ≥ 1.2 mmol/L) within the first few weeks of lactation, and often presents without clinical signs. SCK is mainly caused by negative energy balance (NEB). The objective of this study is to identify single nucleotide polymorphisms (SNPs) associated with SCK using genome-wide association studies (GWAS), and to predict the biological functions of proximal genes using gene-set enrichment analysis (GSEA). Blood samples were collected from 112 Holstein cows between 5 and 18 days postpartum to determine the incidence of SCK. Genomic DNA extracted from both SCK and healthy cows was examined using the Illumina Bovine SNP50K BeadChip for genotyping. GWAS revealed 194 putative SNPs and 163 genes associated with those SNPs. Additionally, GSEA showed that the genes retrieved by Database for Annotation, Visualization, and Integrated Discovery (DAVID) belonged to calcium signaling, starch and sucrose, immune network, and metabolic pathways. Furthermore, the proximal genes were found to be related to germ cell and early embryo development. In summary, this study proposes several feasible SNPs and genes associated with SCK through GWAS and GSEA. These candidates can be utilized in selective breeding programs to reduce the genetic risk for SCK and subfertility in high-performance dairy cows.

MMP-3 Knockout Induces Global Transcriptional Changes and Reduces Cerebral Infarction in Both Male and Female Models of Ischemic Stroke.

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.

Application of Graph Models to the Identification of Transcriptomic Oncometabolic Pathways in Human Hepatocellular Carcinoma.

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.