KEGG Pathway Enrichment Analysis Research Articles

Exploring the anti-metastatic potential of sunitinib and novel analogs in colorectal cancer: insights into HIF-1α mediated metastasis

IntroductionColorectal cancer (CRC) is a prevalent malignancy worldwide with high mortality rate. Metastasis, the primary cause of cancer-related deaths, is attributed to various factors including tumor hypoxia. Due to the urgent demand for potent anti-metastatic agents, we aimed to determine the effects of sunitinib and novel analogs on the metastatic behavior of human CRC cells in hypoxic condition for the first time.MethodsFor in silico analyses, pathogenic targets of metastatic CRC were identified, PPI network was constructed and KEGG pathway enrichment analysis was conducted. The expression of HIF1A was evaluated in seven CRC cell lines, and computational modeling was carried out to define the interaction of sunitinib with HIF-1α. For in vitro studies, analogs of sunitinib were synthesized, and cells were assessed for viability, migration, invasion, MMPs activity and gene expression in hypoxic condition.Results and DiscussionComputational analyses highlighted the importance of HIF-1α as a crucial mediator of metastasis in CRC. Molecular docking and dynamics simulations demonstrated favorable and stable interaction of sunitinib and three novel analogs with HIF-1α PAS-B domain. Volcano plots indicated upregulation of HIF1A in LoVo cells compared to six other CRC cell lines. Findings of in vitro studies revealed considerable inhibitory effects of sunitinib and analogs on LoVo cell migration and invasion in hypoxic condition. Gelatin zymography and qPCR analysis indicated decreased activity of MMP-2 and MMP-9, along with downregulation of EMT transcription factors in hypoxic condition. Current study reports promising anti-metastatic effects of sunitinib and novel analogs on CRC cells, providing foundation for further investigation to combat cancer metastasis.

Combination of Metabolomics and Bioinformatics to Reveal the Mechanism of Luteolin in the Treatment of Cervical Cancer.

The incidence of cervical cancer is high among women globally. The potential therapeutic efficacy of luteolin in the treatment of cervical cancer has been identified. Therefore, we aim to elucidate the mechanism of action of luteolin in the treatment of cervical cancer through a comprehensive approach that integrates metabolomics with bioinformatics. The first step involved the identification of differential metabolites through UHPLC-Q-Orbitrap-MS, which were then utilized for enrichment analysis of metabolic pathways and to determine the targets associated with these differential metabolites. Subsequently, the differential analysis and WGCNA were employed to identify DEGs and functional module genes respectively. The common targets were obtained by intersecting the results from the aforementioned three analyses, followed by conducting GO and KEGG pathway enrichment analysis on these targets. Subsequently, PPI networks were constructed using these common targets, and key targets were identified utilizing the MCC, EPC, Degree, Closeness Centrality, Betweenness Centrality, and Bottleneck algorithms in the CytoHubba plug-in. The subsequent steps involved the analysis of key genes for constructing a nomogram, conducting a ROC curve, examining content expression and survival analysis, and ultimately employing molecular docking to investigate the interaction between luteolin and crucial targets. The metabolomics analysis revealed the identification of a total of 45 distinct metabolites in this study, primarily associated with amino acid and nucleotide metabolism. The intersection of 773 differential metabolite targets, 3493 cervical cancer differential genes, and 3245 WGCNA-associated module genes yielded a set of 32 target genes associated with luteolin therapy for cervical cancer. The GO and KEGG pathway enrichment analysis also revealed that these targets were primarily associated with amino acid metabolism and nucleotide metabolism. The CytoHubba plug-in was utilized to identify three key genes (DMNT1, EZH2, and GMPS) through the application of multiple algorithms. Additionally, the datasets GSE63514, GSE67522, and GEPIA2 revealed a significant upregulation of all three genes in tumor tissue. ROC analysis demonstrated the good predictive ability of these three hub genes. Finally, the molecular docking results demonstrated the high binding affinity of luteolin towards DMNT1, EZH2, and GMPS. In conclusion, this study has unveiled the potential of luteolin in modulating amino acid and nucleotide metabolism for the treatment of cervical cancer, thereby providing a theoretical foundation for further investigation into the intricate association between luteolin and cervical cancer.

Exploring the Mechanism of Curcumin-mediated Photodynamic Therapy for Systemic Lupus Erythematosus Based on Network Pharmacology

ObjectiveTo explore the potential therapeutic targets, this study explored possible molecular mechanism of curcumin photodynamic therapy for systemic lupus erythematosus using network pharmacology. MethodsThe potential targets for systemic lupus erythematosus upon curcumin and photodynamic treatment were predicted using Venny2.1 online analysis platform, followed by GO functional annotation and KEGG enrichment analyses being performed to screen the possible underlying mechanisms. The core targets were then obtained through construction of protein-protein interaction (PPI) network maps using Cytoscape software. ResultsThe network pharmacology analyses showed that 73 common targets for curcumin, photodynamics, and systemic lupus erythematosus. The GO enrichment analysis and KEGG pathway enrichment analysis further highlighted the association between identified targets with 200 biological processes, 25 cellular components, 37 molecular functions and 125 pathways. The core targets including AKT1, ALB, TNF, EGFR, CASP3, HSP90AA1, STAT3, HRAS, SRC, and MMP9 were further predicted through PPI network analysis. ConclusionThis study provided a basis for the in-depth exploration of the mechanism of curcumin photodynamic therapy for systemic lupus erythematosus.

Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Primary Ovarian Insufficiency

Background & aimsPrimary ovarian insufficiency (POI) is a significant clinical syndrome that leads to female infertility, and its incidence continues to increase. We used metabolome-specific Mendelian randomization (MR) to identify causally associated metabolites and explore the relationship between candidate metabolites and upstream genetic variations. MethodsThe primary MR analysis utilized the inverse variance weighted (IVW) method as the primary approach to assess the causal relationship between exposure and POI. Multiple sensitivity analyses included MR-Egger, weighted median, and weighted mode methods. ResultsAfter using genetic variants as probes, we identified 27 metabolites of 278 that are associated with the risk of POI, including dodecanedioate (OR 0.052, 95% CI 0.010 – 0.265; P < 0.001), adrenate (OR 0.113, 95% CI 0.016 – 0.822; P = 0.031), indolepropionate (OR 0.174, 95% CI 0.051 – 0.593; P = 0.005), homocitrulline (OR 0.194, 95% CI 0.051 – 0.741; P = 0.016), and 3-methylhistidine (OR 0.404, 95% CI 0.193 – 0.848; P = 0.017). Our study indicated the presence of heterogeneity; therefore, we employed the IVW random-effects model as the primary approach. KEGG pathway enrichment analysis identified six significant metabolic pathways, primarily including biosynthesis of unsaturated fatty acids, phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, linoleic acid metabolism, valine, leucine and isoleucine biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis. ConclusionsBy integrating genomics and metabolomics, this study provides novel insights into the causal relationship linking circulating metabolites and the onset of POI.

Combining Single-Cell RNA Sequencing and Mendelian Randomization to Explore Novel Drug Targets for Parkinson's Disease.

Neuroinflammation is a key pathological factor of PD, and T cells play a central role in neuroinflammatory progression. However, the causal effect of T cell-related genes on the risk of PD is still unclear. We explored single-cell RNA sequencing (scRNA-Seq) datasets of the peripheral blood T cells of PD patients and healthy controls, and screened the differentially expressed genes (DEGs) in the cytotoxic CD4 + T cells relative to the other T cell subsets. Pseudo-time series analysis, cell-cell communication analysis, and metabolic pathway analysis was performed for the cytotoxic CD4 + T cells. The DEGs were also functionally annotated through GO and KEGG pathway enrichment analyses. The MR approach was used to establish causal effects of the DEGs (exposure) on PD risk (outcome), and explore new drug targets for PD. The findings of MR analysis were further validated by Steiger filtering, bidirectional MR, Bayesian colocalization analysis, and phenotype scanning, and the GWAS data from an independent PD case-control cohort was used for external validation of the results. Finally, differences in gene expression between PD patients and healthy controls were further validated in scRNA-Seq and bulk transcriptome sequencing data. We found that increased expression of IL-32, GNLY, MT2A, and ARPC2 was significantly associated with a higher risk of PD. In contrast, the increase in ARRB2 was closely related to a lower risk of PD. IL32, GNLY, MT2A, ARRB2, and ARPC2 are the causal genes and potential drug targets of PD. Cytotoxic CD4 + T cells are likely the key effectors of PD-related neuroinflammation. These findings provide new insights into the pathogenesis and treatment options for PD, and further research and clinical trials based on the five potential drug targets and neuroinflammation are necessary.

P-1980. Decoding the Complexity of Post COVID Conditions: Transcriptomic Analyses of Machine-Learning-Based Chronic Symptom Phenotypes

Abstract Background Post-COVID conditions (PCC, ‘Long COVID’) remain a military health concern, and symptom heterogeneity limits understanding of PCC pathogenesis. To address this, we used machine learning to define PCC phenotypes in Military Health System (MHS) beneficiaries. Here, we extend these findings to identify early transcriptomic profiles which may predict the development of these distinct symptom-based PCC phenotypes. (A) Machine learning techniques identified three groups of participants based on chronic symptom-clustering. Radial charts display the distribution of post-COVID condition (PCC) symptoms. Each plot’s axes represent different symptoms, with the length of each spoke indicating symptom prevalence. Shorter spokes indicate lower prevalence, while longer spokes indicate higher prevalence. (B) Clinical and demographic characteristics of 154 military health system beneficiaries by identified symptom-clusters. (C) Schematic representation of pathway enrichment analysis using the differentially expressed signature and pathway databases. (D-E) Jaccard similarity analysis identifies the similarity of leading-edge pathways within each symptom-cluster phenotype, with a result close to 1 indicating high similarity and close to 0 indicating significant differences. (F) Network analysis depicts pathway interactions between each symptom-cluster phenotype, with larger nodes representing clusters and smaller nodes indicating individual pathways. Grey lines indicate associations between clusters and pathways, revealing both overlapping and distinct pathway sets. Upregulated and downregulated pathways are highlighted in red and blue, respectively. (G) Non-overlapping leading-edge pathways are identified, with the size of each pathway node corresponding to the normalized enrichment score (NES), with an upward arrow and red color indicates upregulation, while a downward arrow and blue color denotes downregulation. Methods The EPICC cohort study evaluates SARS-CoV-2 infection in MHS beneficiaries. PCC phenotypes were identified using symptom data; transcriptome profiling was performed on early post-infection blood samples from participants who did and did not develop 3-month symptom PCC phenotypes. Participants with pre-COVID comorbidities were excluded to delineate gene responses for PCC from pre-existing illnesses. Comparison of transcriptomic signatures associated with symptom clusters involved utilizing Reactome, KEGG, BioCarta, and Hallmark pathway enrichment analysis. Hypergeometric p-values and the Jaccard coefficient (JC) assess pathway distinctiveness between symptom clusters and those with no PCC. Results We analyzed 154 participants with no reported comorbidities (Fig. 1), who completed a symptom survey 3-months after a SARS-CoV-2 positive test, and who had an early blood sample (median: 36 days post-infection). Clustering analysis stratified 3 distinct PCC symptom clusters: Sensory, Fatigue/difficulty thinking, and Difficulty breathing/exercise intolerance. Transcriptomic analysis noted post-infection signatures which predicted the development of each cluster (JC: 0.18–0.28). Leading-edge pathways included cytokine signaling in the Sensory cluster (Normalized Enrichment Score, NES=16.16, P &amp;lt; 0.001), nervous system development in the Fatigue/difficulty thinking cluster (NES=10.23, P &amp;lt; 0.001), and MYC Targets V1 in the Difficulty thinking/exercise intolerance cluster (NES=8.89, P &amp;lt; 0.001). Conclusion Distinct symptom-based PCC phenotypes are associated with different early post-infection gene expression pathways. These findings inform mechanistic hypotheses and the development of PCC prediction tools. These methods may be applied for prediction of post-acute sequelae in other pathogens. Disclosures Timothy Burgess, MD, MPH, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response Simon Pollett, MBBS, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response

Compound identification of Shuangxinfang and its potential mechanisms in the treatment of myocardial infarction with depression: insights from LC-MS/MS and bioinformatic prediction

BackgroundPatients with myocardial infarction (MI) have a high incidence of depression, which deteriorates the cardiac function and increases the risk of cardiovascular events. Shuangxinfang (Psycho-cardiology Formula, PCF) was proved to possess antidepressant and cardioprotective effects post MI. However, the compounds of PCF remain unidentified, and the pertinent mechanism is still not systematic. The purpose of this study is to determine the ingredients of PCF, further to probe the underlying mechanism for MI with depression.MethodsThe compounds of PCF were qualitatively identified by LC-MS/MS. The optimal dosage for lavage with the PCF solution in rats was determined to be 1 mL/100 g/day for a duration of 5 days. We also detected the PCF components migrating to blood in the control and model rats. Then the targets of PCF compounds were searched on Swiss target database, and the targets of depression and MI were predicted on TTD, OMIM, GeneCards, DrugBank and PharmGkb database. All the targets were intersected to construct the Protein-Protein Interaction (PPI) network on Metascape platform and the herb-compound-target (HCT) network on Cytoscape, to identify the hub targets. GO and KEGG pathway enrichment analysis were conducted on DAVID platform. Molecular docking was modeled on AutoDock Vina software.ResultsThere were 142 bioactive compounds from PCF acting on 270 targets in a synergistic way. And a total of seven components migrating to blood were identified, including Miltionone I, Neocryptotanshinone, Danshenxinkun A, Ferulic acid, Valerophenone, Vanillic acid and Senkyunolide D. Then SRC and MAPK3 were obtained as the hub proteins by degree value in PPI network, and P2RY12 was picked out as seed proteins ranked by scores from MCODES. Further analysis of biological process and signaling pathways also revealed the significance of ERK/MAPK. Statistical analyses (e.g., GO and KEGG pathway enrichment, PPI network analysis) demonstrated the significance of the identified targets and pathways (p &amp;lt; 0.05). Molecular docking results showed that the binding energies were all less than −5 kcal/mol. The stability of Neocryptotanshinone possessed the lowest binding energy to MAPK3.ConclusionWe identified PCF’s bioactive compounds and predicted its therapeutic mechanism for MI with depression using LC-MS/MS and bioinformatics. Key targets SRC, MAPK3, and seed protein P2RY12 were crucial for PCF’s cardio-neuroprotective effects. Neocryptotanshinone showed the strongest binding to MAPK3, suggesting it as a pivotal active ingredient. These findings offer new insights and targets for future research on PCF.

Mechanism of Radix Bupleuri and Hedysarum Multijugum Maxim drug pairs on liver fibrosis based on network pharmacology, bioinformatics and molecular dynamics simulation.

A number of studies demonstrate the therapeutic effectiveness of Radix Bupleuri (RB) and Hedysarum Multijugum Maxim (HMM) in treating liver fibrosis, but the exact molecular mechanisms remain unclear. This study aims to explore the mechanism of RB-HMM drug pairs in treating liver fibrosis by using network pharmacology, bioinformatics, molecular docking, molecular dynamics simulation technology and in vitro experiments. Totally, 155 intersection targets between RB-HMM and liver fibrosis were identified. In the protein-protein interaction (PPI) network, the top 10 hub targets with the highest node connection values were TNF, IL-6, AKT1, EGFR, HIF1A, PPARG, CASP3, SRC, MMP9 and HSP90AA1. GO functional and KEGG pathway enrichment analysis involved 335 biological processes, 39 cellular components, 78 molecular functions, and 139 signaling pathways. The bioinformatics analysis indicated that TNF, IL-6, PPARG and MMP9 were promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver fibrosis. Moreover, the molecular docking and molecular dynamic simulation of 50 ns well complemented the binding affinity and strong stability between the three common compounds MOL000098 (quercetin), MOL000354 (isorhamnetin) and MOL000422 (kaempferol) and four final hub targets (TNF, IL-6, PPARG and MMP9). Calculation of binding free energy and decomposition free energy using MM_PBSA and MM_GBSA also validated the strong binding affinity and stability of 12 systems. MOL000098 (quercetin) was selected via MTT assay and western blot assay verified MOL000098 (quercetin) treatments remarkably decreased the protein levels of TNF and IL-6 in TGFβ stimulated LX2 cells. In conclusion, RB-HMM drug pairs can affect the progression of liver fibrosis through multiple components, multiple targets and multiple pathways, and treat liver fibrosis possibly through anti-inflammatory and affecting cell apoptosis.

Different expression of circulating microRNA profile in tibetan OSAHS with metabolic syndrome patients

Recent empirical investigations reinforce the understanding of a profound interconnection between metabolic functions and Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS). This study identifies distinctive miRNA signatures in OSAHS with Metabolic Syndrome (Mets) patients from healthy subjects, that could serve as diagnostic biomarkers or describe differential molecular mechanisms with potential therapeutic implications. In this study, OSAHS with MetS patients showed significantly higher Apnea Hyponea Index(AHI), but lower oxygen desaturation index(ODI 4/h) and minimum pulse oxygen saturation(SpO2). A total of 33 differentially expressed miRNAs by Limma method, and 31 differentially expressed miRNAs by DEseq2 method were screened. In addition, GO enrichment analysis of target genes associated with differentially expressed miRNAs revealed significant enrichment in metabolic processes, suggesting that the differential expression of OSAHS-induced miRNAs may contribute to the progression of metabolic disorders through the regulation of metabolic pathways. Furthermore, KEGG pathway enrichment analysis revealed significant enrichment in the p53 signaling pathway and several other pathways. Notably, the Wnt signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, and AMPK signaling pathway are implicated in the metabolic processes of glucose dysregulation and lipid homeostasis, as well as the pathogenesis of hypertension associated with OSAHS. We identified IKBKB, PIK3R1, and MAP2K1 as the target genes most associated with Mets pathogenesis in OSAHS, regulated by miR-503-5p, miR-497-5p, and miR-497-5p, respectively. Additionally, the target genes of differentially expressed miRNAs between Tibetan OSAHS patients with MetS and healthy individuals are regulated by transcription factors such as NR2C1, STAT3, STAT5a, HIF1a, ETV4, NANOG, RELA, SP1, E2F1, NFKB1, AR, and MYC. In conlusion, we found differentially expressed miRNAs in Tibetan OSAHS patients with Metabolic Syndrome for the first time. Enrichment analysis results suggest that differentially expressed miRNAs may involved in the development of OSAHS-related metabolic disorders by regulating metabolic pathways. We also revealed that IKBKB, PIK3R1, and MAP2K1 are mostly associated with metabolic disorder in OSAHS, and miR-503-5p and miR-497-5p may regulate the development of MetS associated with OSAHS by modulating IKBKB, PIK3R1, and MAP2K1.

P0029 Abnormal glycerophospholipid metabolism in CCL2+DPP4+ mesenchymal stem cells may contributes to creeping fat fibrosis in Crohn's Disease

Abstract Background Creeping fat (CF), an aberrant mesenteric adipose tissue (MAT), is characterized by abnormal lipid metabolism, inflammatory cell infiltration, and extracellular matrix (ECM) deposition in the intestines affected by Crohn's disease (CD). Our previous single-cell RNA-seq analysis identified a CCL2+DPP4+ subset of mesenchymal stem cells (MSCs) that accelerates the abnormal formation of CF. Building on these findings, this study investigates the role of CCL2+DPP4+ MSCs in CF fibrosis formation. Methods We employed H&amp;E and Masson staining to assess histopathological changes and collagen deposition. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was conducted on CCL2+DPP4+ MSCs isolated from CF biopsies of CD patients (n=5) and MAT from controls without CD (n=5). Results We observed ECM deposition in CF and an upregulation of fibrosis-related genes (COL1A1, COL1A2, and FN1)（Figure1）. Analysis of our previous single-cell data revealed that CCL2+DPP4+ MSCs play a significant role in the abnormal accumulation of the ECM (Figure2). Lipidomic profiling revealed differential lipid levels between the CF and healthy MAT（H-MAT） groups, with downregulation of specific lipids in CCL2+DPP4+ MSCs derived from CF, including PE (20:5_16:1), PC (14:0_22:6), and PE (16:1_14:0). KEGG pathway enrichment analysis highlighted significant enrichment in Glycerophospholipid metabolism. Phospholipids (PLs), crucial for membrane structure and function, were found to be regulated by key enzymes PISD(Phosphatidylserine decarboxylase) and LPCAT3(Lysophosphatidylcholine acyltransferase 3), which convert PS（phosphatidylserine） to PE（phosphatidylethanolamine） and integrate polyunsaturated fatty acids into lysophospholipids, respectively. Quantitative real-time PCR confirmed reduced expression of these enzymes and increased fibrosis gene expression in CF-derived CCL2+DPP4+ MSCs(Figure3). Conclusion Our findings suggest that the CCL2+DPP4+ MSC subset in CF exhibits abnormal glycerophospholipid metabolism, which is associated with decreased PISD and LPCAT3 expression. This may contribute to enhanced ECM formation and promotion of CF fibrosis. Understanding these mechanisms could lead to novel therapeutic approaches for CD.

P0005 Transcriptomic analysis reveals molecular differences between intestinal tuberculosis and ileal Crohn's Disease

Abstract Background Intestinal tuberculosis (ITB) and ileal Crohn's disease (iCD) are common chronic granulomatous diseases of the intestine, exhibiting similarities in clinical and histopathological features, which makes differential diagnosis challenging. We aim to explore the transcriptomic signature of ITB and iCD and identify potential biomarkers for differential diagnosis. Methods We prospectively enrolled seven iCD patients, seven ITB patients, and nine healthy donors. All participants underwent laboratory tests and colonoscopies. Biopsy samples from the ulcer margins of the ileocecal region were collected for bulk transcriptome analysis. Differentially expressed genes (DEGs; P-value&amp;lt;0.05, fold change&amp;gt;2) were selected for further analysis. Results Patients with ITB and iCD showed no statistical differences in routine blood tests, inflammatory markers, or nutritional indicators. Transcriptome analysis revealed 2,690 and 1,849 differentially expressed genes (DEGs) in ITB and iCD, respectively, compared to healthy controls. KEGG pathway enrichment analysis of DEGs that changed commonly or specifically in two diseases revealed shared activation of the IL-17 signaling pathway and upregulation of cytokine-cytokine receptor interactions in the iCD and ITB groups. Distinct changes in the iCD group included upregulation of virus-related responses, neuroactive ligand-receptor interaction, efferocytosis, and downregulation of bile secretion and cholesterol metabolism. In contrast, the ITB group showed activation of chemokine signaling and complement-related pathways and downregulation of tyrosine and retinol metabolism pathways. Further comparison of inflammatory cytokines, chemokines, and extracellular matrix (ECM) related genes among the three groups revealed that ITB patients exhibited a more robust pro-inflammatory response and more pronounced ECM remodeling than iCD patients. To identify diagnostic markers for ITB and iCD, we analyzed the DEGs that changed specifically in iCD or ITB, selecting the top 20 genes with the most tremendous fold changes. We found that genes such as CCL17, PDZK1, PLA2G12B, PRRX1, and ZNF488 showed significant differences in expression between the iCD and ITB groups. Receiver Operating Characteristic (ROC) Curve analysis demonstrated strong predictive value for these five genes in distinguishing ITB from iCD patients, with AUC values of 0.8214, 0.8367, 0.8750, 0.8571, and 0.9643, respectively. Conclusion Our study advances the molecular understanding of ITB and iCD, emphasizing distinct transcriptomic responses in the mucosal tissues of these conditions. Notably, five genes—PRRX1, PDZK1, PLA2G12B, ZNF488, and CCL17—exhibited strong diagnostic potential for distinguishing ITB from iCD.

Reduced Glutathione Promoted Growth Performance by Improving the Jejunal Barrier, Antioxidant Function, and Altering Proteomics of Weaned Piglets.

Reduced glutathione (GSH) is a main nonenzymatic antioxidant, but its effects and underlying mechanisms on growth and intestinal health in weaned piglets still require further assessment. A total of 180 weaned piglets were randomly allotted to 5 groups: a basal diet (CON), and a basal diet supplemented with antibiotic chlortetracycline (ABX), 50 (GSH1), 65 (GSH2), or 100 mg/kg GSH (GSH3). Results revealed that dietary GSH1, GSH2, and ABX improved body weight and the average daily gain of weaned piglets, and ABX decreased albumin content but increased aspartate aminotransferase (AST) activity and the ratio of AST to alanine transaminase levels in plasma. GSH2 significantly decreased glucose content but increased the content of triglyceride and cholesterol in the plasma. Both GSH1 and GSH2 improved the jejunal mucosa architecture (villus height, crypt depth, and the ratio of villus height to crypt depth), tight junction protein (ZO-1 and Occludin), and antioxidant capacity (CAT and MDA), and the effects were superior to ABX. Dietary GSH improved the jejunal barrier by probably inhibiting the myosin light chain kinas pathway to up-regulate the transcript expression of tight junction protein (ZO-1 and Occludin) and Mucins. Through the proteomics analysis of the jejunal mucosa using 4D-DIA, the KEGG pathway enrichment analysis showed that differentiated proteins were significantly enriched in redox homeostasis-related pathways such as glutathione metabolism, cytochrome P450, the reactive oxygen species metabolic pathway, the oxidative phosphorylation pathway, and the phosphatidylinositol 3-kinase-serine/threonine kinase pathway in GSH2 vs. CON and in GSH2 vs. ABX. The results of proteomics and qRT-PCR showed that GSH supplementation might dose-dependently promote growth performance and that it alleviated the weaning stress-induced oxidative injury of the jejunal mucosa in piglets by activating SIRTI and Akt pathways to regulate GPX4, HSP70, FoxO1. Therefore, diets supplemented with 50-65 mg/kg GSH can promote the growth of and relieve intestinal oxidative injury in weaned piglets.

Clinical significance and pro-oncogenic function of DBF4 in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common malignant urological tumor, and regrettably, and is insensitive to chemotherapy and radiotherapy, resulting in poor patient outcomes. DBF4 plays a critical role in DNA replication and participates in various biological functions, making it an attractive target for cancer treatment. However, its significance in ccRCC has not yet been explored.MethodsWe utilized external datasets and bioinformatics analyses to investigate the significance of DBF4 in ccRCC. We analysed its expression patterns, prognostic and diagnostic value, and potential mechanisms. We subsequently validated our findings through an immunohistochemistry (IHC) assay of ccRCC clinical samples. We further investigated the impact of DBF4 on the progression of ccRCC cells. Various assays, including assessments of cell proliferation, apoptosis, the cell cycle, cell migration and invasion, and colony formation, and xenograft tumor models were subsequently performed following to the knockdown of DBF4 expression via shRNA.ResultsBioinformatics analyses revealed that DBF4 is significantly overexpressed in ccRCC tissues compared with adjacent normal tissues. This overexpression was confirmed by IHC analysis of 75 pairs of clinical ccRCC tumor and adjacent tissues. Kaplan-Meier analysis revealed that high DBF4 expression was associated with a significantly lower five-year overall survival rate. Moreover, DBF4 expression was identified as an independent risk factor in multivariate Cox regression analysis. GO and KEGG pathway enrichment analyses revealed a substantial enrichment of terms associated with cell division, whereas gene set enrichment analysis (GSEA) revealed correlations between increased DBF4 expression and the activation of cell cycle-related pathways. Subsequent in vitro and in vivo experiments demonstrated that DBF4 knockdown in ccRCC cells not only suppressed proliferation and migration in vitro but also significantly inhibited tumor growth in xenograft mice by arresting the cell cycle at the G1/G0 phase, which was mediated by the inhibition of MCM2 phosphorylation and cyclin D1 and CDK4 expression.ConclusionThe current study revealed that DBF4 overexpression is a significant factor associated with malignant features and poor prognosis in patients with ccRCC. Therefore, it was proposed that DBF4 could serve as a novel potential prognostic biomarker and molecular target for ccRCC.Clinical trial numberNot applicable.

Construction and validation of a diagnostic model for cholangiocarcinoma based on tumor-educated platelet RNA expression profiles

Abstract Objectives We aim to explore the diagnostic value of platelet-based “liquid biopsy” technology for cholangiocarcinoma (CCA), seeking reliable methods for early cancer diagnosis to improve patient prognosis. Methods Bioinformatics methods were utilized to analyze the GEO databases (GSE183635) and (GSE68086), identifying differentially expressed genes and constructing a diagnostic model of CCA using tumor-educated platelet (TEP) RNA expression profiles. GO and KEGG pathway enrichment analysis were performed. Additionally, platelet RNA from CCA patients and controls totaling 60 cases was extracted by qRT-PCR experiments to validate the diagnostic reliability of candidate genes, further confirmed through in vitro experiments. Results A diagnostic model comprising seven platelet genes (CRYM, IFI27, EED, METAP1, RASGRP1, SEC11A, and WDR82) effectively distinguished CCA from controls. Area under curve (AUC) values were 0.862 (training set), 0.875 (internal validation), 0.865 (total internal), and 0.954 (external validation). GO analysis highlighted “non-coding RNA processing,” “nuclear envelope,” and “catalytic activity, acting on RNA.” KEGG pathways included “Ribosome biogenesis in eukaryotes”, “RNA translocation” and “Spliceosome”. qRT-PCR experiments revealed significant differences (p&lt;0.05) in METAP1, SEC11A, WDR82, RASGRP1, and EED gene expression in CCAs, consistent with bioinformatics predictions. CRYM showed significant differences (p&lt;0.001) compared to healthy individuals. WDR82 and CRYM had high diagnostic efficiency (AUC 0.939 and 0.942), surpassing conventional tumor markers (AFP, CEA, and CA19-9). Joint receiver operating characteristics (ROC) analysis yielded an AUC of 0.806, sensitivity of 1.000, and accuracy of 0.833. Conclusions Based on the GEO database, we identified seven TEP RNAs (CRYM, IFI27, METAP1, SEC11A, WDR82, RASGRP1, EED) with strong discriminative ability for CCA, suggesting their potential as reliable non-invasive biomarkers.

Integrating 16S rRNA Gene Sequencing and Metabolomics Analysis to Reveal the Mechanism of L-Proline in Preventing Autism-like Behavior in Mice.

Autism spectrum disorder (ASD) is characterized by impaired social interaction and repetitive stereotyped behavior. Effective interventions for the core autistic symptoms are currently limited. This study employed a valproic acid (VPA)-induced mouse model of ASD to assess the preventative effects of L-proline supplementation on ASD-like behaviors. The method of 16S rRNA sequencing and untargeted metabolomics analyses were conducted to investigate the modulation of gut microbiota and gut metabolites by L-proline. The results indicated that L-proline supplementation significantly prevented ASD-like behavioral disorders, including alleviating social communication deficits and reducing repetitive behavior in the ASD mice. The 16S rRNA sequencing analysis revealed that L-proline regulated the composition and structure of gut microbiota. L-Proline supplementation enhances the abundance of the Verrucomicrobia at the phylum level and the Akkermansia at the genus level, while concurrently reducing the abundance of the Patescibacteria at the phylum level, as well as the Ileibacterium, Candidatus_Saccharimonas, and Lachnospiraceae_UCG-006 at the genus level in the VPA-induced mouse model for ASD. Additionally, the untargeted metabolomics results indicated that L-proline also modified the gut metabolite profiles. Functional analysis of the gut microbiota and KEGG pathway enrichment analysis of differential metabolites between the L-proline-supplemented and VPA groups corroborated that L-proline decreased pathways related to nucleotide metabolism, taurine and hypotaurine metabolism, and pyruvate metabolism, while increasing pathways involved in alpha-linolenic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. The integrative metabolomic and microbiome analyses showed strong connections between the gut metabolites and gut microbiota affected by L-proline. These findings suggest that the modulatory effects of L-proline on gut microbiota and its metabolites may play a crucial role in preventing autism in mice. These findings suggest that dietary L-proline may represent a viable, effective option for preventing the physiological and behavioral deficits associated with ASD in mice.

Proteomic Characterization of Liver Cancer Cells Treated with Clinical Targeted Drugs for Hepatocellular Carcinoma.

Background/Objectives: Hepatocellular carcinoma (HCC) remains a significant global health concern, primarily due to the limited efficacy of targeted therapies, which are often compromised by drug resistance and adverse side effects. Methods: In this study, we utilized a Tandem Mass Tag (TMT)-based quantitative proteomic approach to analyze global protein expression and serine/threonine/tyrosine (S/T/Y) phosphorylation modifications in HepG2 cells following treatment with three clinically relevant hepatocellular carcinoma-targeted agents: apatinib, regorafenib, and lenvatinib. Results: Utilizing KEGG pathway enrichment analysis, biological process enrichment analysis, and protein interaction network analysis, we elucidated the common and specific metabolic pathways, biological processes, and protein interaction regulatory networks influenced by three liver cancer therapeutics. The study additionally proposed potential combinational treatment strategies, highlighting a possible synergistic interaction between HCC-targeted drugs and the DNA methyltransferase inhibitor. Furthermore, through the integration of clinical phosphorylation site data, we identified several phosphorylation sites that exhibited higher abundance in tumor tissues compared to adjacent non-tumor tissues. These sites were associated with poor prognosis and elevated functional scores. Conclusions: In summary, this study conducted an in-depth analysis of the molecular alterations in proteins and phosphorylation modifications induced by clinical HCC-targeted drugs, predicting drug combination strategies and therapeutic targets.

Investigation of the Potential Pharmacological Substance Basis and Mechanism of Action of Xuantu Granules in Treating Diabetic Kidney Disease Based on UHPLC-Q-Exactive-HRMS and Bioinformatics.

The objective of this study is to analyze and identify the main chemical components and blood-absorbed components of Xuantu Granules and predict their pharmacological substance basis and mechanism in the treatment of DKD. A DKD rat model was established by feeding SD rats a high-fat and high-sugar diet and administering intraperitoneal injections of streptozotocin (STZ). The therapeutic effect of Xuantu granules was evaluated. Drug-containing serum was prepared after gavage, and the major chemical components of Xuantu Granules and the drug-containing serum were detected using UHPLC-Q-Exactive-HRMS. Blood-absorbed components were identified based on retention time, mass-to-charge ratio, and MS/MS spectrum. Blood-absorbed components' target proteins were searched using the CTD, SwissTarget, BindingDB, and TargetNet databases. DKD disease target genes were screened from the GEO database using WGCNA. A "bioactive blood-absorbed component-target-disease" PPI network was constructed using Cytoscape software, and the key clustering subnetworks were identified by MCODE plugin. GO functional analysis and KEGG pathway enrichment analysis were performed on subnetworks. Xuantu Granules lowered fasting blood glucose, improved renal function, reduced proteinuria, and improved renal tissue pathological changes in DKD rats. 36 chemical components were identified, among which 12 compounds, including β -Carboline-1-propionic acid, Morin, Afzelin, Schizandrin, Gomisin A were identified as blood-absorbed components. Bioinformatics analysis indicated that AKT1, TNF, TP53, IL6, SRC, IL1B, EGFR, JUN, BCL2, and CASP3 might be the main therapeutic targets. The involved pathways included the IL-17 signaling pathway, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications and so on. Xuantu Granules may exert therapeutic effects on DKD through multiple targets and pathways.

Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2, and ND4L, were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.

FGF21 ameliorates diabetic nephropathy through CDK1-dependently regulating the cell cycle.

Diabetic nephropathy (DN) is a prevalent global renal illness and one of the main causes of end-stage renal disease (ESRD). FGF21 has been shown to ameliorate diabetic nephropathy, and in addition FGF-21-treated mice impeded mitogenicity, whereas it is unclear whether FGF21 can influence DN progression by regulating the cell cycle in diabetic nephropathy. In order to create a diabetic model, STZ injections were given to C57BL/6J mice for this investigation. Then, FGF21 was administered, and renal tissue examination and pathological observation were combined with an assessment of glomerular injury, inflammation, oxidative stress, and the fibrinogen system in mice following the administration of the intervention. Furthermore, we used db/db mice and FGF21 direct therapy for 8weeks to investigate changes in fasting glucose and creatinine expression as well as pathological changes in glomeruli glycogen deposition, fibrosis, and nephrin expression. To investigate the mechanism of action of FGF21 in the treatment of glycolytic kidney, transcriptome sequencing of renal tissues and KEGG pathway enrichment analysis of differential genes were performed. The study's findings demonstrated that FGF21 intervention increased clotting time, decreased oxidative stress and inflammation, and avoided thrombosis in addition to considerably improving glomerular filtration damage. After 8weeks of FGF21 treatment, glomerular glycogen deposition, fibrosis, and renin expression decreased in db/db mice. Moreover, there was a notable reduction of creatinine and fasting blood glucose levels. Additionally, the CDK1 gene, a key player in controlling the cell cycle, was discovered through examination of the transcriptome sequencing data. It was also shown that FGF21 dramatically reduces the expression of CDK1, which may help diabetic nephropathy by averting mitotic catastrophe and changing the renal cell cycle. In short, FGF21 improved the development of diabetic nephropathy in diabetic nephropathy-affected animals by reducing glomerular filtration damage, inflammation, and oxidative stress, inhibiting the formation of thrombus, and controlling the cell cycle through CDK1.

Transcript Changes of Placental Tissue in Gestational Diabetes Mellitus Based on Transcriptome Sequencing.

This study aims to identify key genes that may be involved in the pathogenesis of gestational diabetes mellitus and to preliminarily elucidate the underlying mechanisms. High-throughput transcriptome sequencing was employed to identify Differentially expressed genes (DEGs) in placental tissue samples of GDM and normal pregnant women. Functional and pathway analyses of these DEGs were conducted using bioinformatics databases. Significant DEGs were validated through real-time quantitative PCR in conjunction with relevant literature. In comparison to the normal pregnancy group, 435 DEGs were identified in the GDM group, comprising 128 upregulated and 307 downregulated genes. GO enrichment analysis revealed that DEGs were primarily associated with biological processes, such as cellular processes, biological regulation, regulation of biological processes, and response to stimuli. Cell component enrichment analysis indicated their association with cellular anatomical entities and protein-containing complexes. Molecular function enrichment analysis highlighted their roles in binding and catalytic activities. KEGG pathway enrichment analysis indicated the involvement of DEGs in signalling pathways related to PI3K-Akt signaling pathway and ECM-receptor interaction. qRT-PCR validation of five randomly selected DEGs confirmed consistent expression trends with RNA-Seq quantification. YWHAB, LEP, CCL21, PAPPA2, and SFN may be potential biological markers for the diagnosis of GDM, involved in the occurrence and development of GDM, and have certain value for disease prediction and diagnosis.