Over-representation Analysis Research Articles

Target-based drug repositioning in papillary renal cell carcinoma with TFE3 fusion

Papillary renal cell carcinoma (PRCC) is the second most common subtype of renal cell carcinoma. Recurrent transcription factor E3 (TFE3) fusions have been identified in PRCC. Although patients with TFE3 fusions show relatively aggressive phenotypes, no effective targeted therapies have been developed. In this study, we aimed to identify putative therapeutic targets through systematic bioinformatic analysis of PRCC with TFE3 fusion. We identified TFE3 fusion cases (n = 6) and controls (n = 282) in The Cancer Genome Atlas database. A total of 1,314 differentially expressed genes were extracted. An overrepresentation analysis revealed cancer-related signaling pathways, which were categorized to 13 cancer-related pathways, including nuclear factor-erythroid factor 2-related factor 2 (NRF2)-, G protein-coupled receptor (GPCR) signaling-, inflammatory response-, development-, and insulin-like growth factor-related pathways. A network analysis and literature review were performed using drug-target databases to identify putative applicable drugs. We identified several promising drug candidates for PRCC with TFE3 fusion, including amrubicin, BCL6 inhibitors (79-6, BI-3812), cetuximab, epirubicin, gemcitabine, ipilimumab, linsitinib, olaparib, Orencia, paclitaxel, panitumumab, sorafenib, and tamoxifen. Our suggested strategy can help bridge the gap between transcriptome studies and the choice of therapeutic agents. Further experimental validation is needed to improve the efficacy of precision medicine for PRCC.

N-acetyltransferase 10 as a novel prognostic biomarker in papillary renal cell carcinoma: a machine learning and experimental validation study.

N-acetyltransferase 10 (NAT10) is a lysine acetyltransferase known for catalyzing the N4-acetylcytidine (ac4C) modifications on RNAs. Recent studies have associated NAT10 with the pathogenesis of various cancers. However, its specific function and prognostic significance in papillary renal cell carcinoma (pRCC) remain poorly understood. This study aimed to explore NAT10's prognostic value and mechanisms in pRCC. NAT10 expression and prognostic associations in pancancer were analyzed using The Cancer Genome Atlas (TCGA). Single-cell RNA (scRNA) sequencing data from a previous study were used to characterize NAT10 expression at the single-cell level in pRCC. Pathway enrichment analysis, including gene set variance analysis (GSVA) and overrepresentation analysis, was conducted to investigate the potential mechanisms through which NAT10 exerts its effects. Immune cell infiltration analysis, conducted through the ESTIMATE and CIBERSORT algorithms, was performed to examine the association of NAT10 with the tumor microenvironment (TME). A NAT10-related prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression on genes that were both positively correlated with NAT10 and were identified as NAT10-mediated ac4C targets by ac4C RNA immunoprecipitation sequencing The model's performance was validated in the TCGA training set (n=285), with 42 events (deaths) and 243 censored cases, and in the GSE2748 external validation set (n=28), with 13 events (deaths) and 15 censored cases. The association between NAT10-related risk scores and immunotherapy response was assessed via the IMvigor210 cohort. Finally, the aberrant expression of NAT10 was confirmed through immunohistochemistry data from the Human Protein Atlas database and our experimental validations from quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analyses. NAT10 is upregulated in multiple cancers, including pRCC, and higher NAT10 expression correlates with advanced stages and poorer prognosis. Single-cell RNA-sequencing data confirmed elevated NAT10 expression in malignant pRCC cells. Pathway enrichment and immune cell infiltration analyses indicated that NAT10 is associated with malignancy-related pathways and a disordered TME. A prognostic model was constructed using LASSO Cox regression, with 18 core genes being identified, and demonstrated high predictive accuracy for survival. The model achieved AUC values of 0.97, 0.93, and 0.82 for 1-, 3-, and 5-year survival in the TCGA training set, respectively, while all AUC values in the GSE2748 external validation set were 1. Higher NAT10-related risk scores were linked to poorer immunotherapy response in the IMvigor210 cohort. NAT10's prognostic significance was validated across various cancers, with elevated expression at both the messenger RNA and protein levels confirmed through immunohistochemistry and experimental validation in RCC cell lines. Our findings suggest that NAT10 is aberrantly expressed in pRCC, is associated with poor prognosis, and contributes to pRCC progression through multiple pathways, offering new insights into the personalized treatment of patients with pRCC.

A hypothesis of mesenchymal stem cell osteogenic differentiation mediated by chelidonic acid through the calcium import: original research and computer simulation.

Chelidonic acid (ChA) is small molecule capable of inducing the differentiation of mesenchymal stem cells (MSCs) into osteoblasts and the formation of mineralized bone matrix (MBM) both in vitro and in vivo. However, the molecular mechanisms underlying these effects are unknown. Therefore, in silico modelling of potential molecular targets of ChA was performed. ChA was isolated from Saussurea controversa. The ability of ChA to induce in vitro differentiation MSCs into osteoblasts synthesizing MBM was detected using alizarin red staining. ChA osteogenic activity was studied in mice by in situ test of ectopic osteogenesis, using the subcutaneous implantation of syngeneic bone marrow on the calcium phosphate coated titanium plates. DIGEP-Pred web service was used to simulate in silico the effect of ChA on gene expression, and overrepresentation analysis to search for common ontologies and pathways. ChA linearly increased the number of single (R2 = 0.92, p = 0.039) and the total areas of MBM sites (R2 = 0.96, p = 0.019) in a 21-day MSC culture. Oral administration of ChA led to two to three times improved bone and bone marrow formation in situ. In silico modelling identified 306 genes (including 7 calcium import genes) and 9 signalling pathways potentially involved in ChA osteogenic effect and calcium metabolism in MSCs. In silico analysis revealed a list of key signalling pathways and genes for calcium influx into MSCs and their differentiation into osteoblasts as the first target candidates for studying real gene expression and molecular mechanisms of the ChA osteogenic effects.

PALS1-dependent modulations of mRNA profiles in MDCK II cells grown in non-confluent monolayers and three-dimensional cysts

In epithelia, apicobasal cell polarization is closely linked to cell-cell contact formation, both controlled by the conserved Crumbs (CRB) complex, which includes the transmembrane protein Crumbs (CRB3a) and adapter proteins PALS1, PATJ, and LIN7c. In MDCK II cells, a model for cell polarization, depletion of PALS1 - which binds to all CRB components - leads to defective cell polarization and improper distribution of tight junction proteins, resulting in severe epithelial barrier defects in 3D cyst models. This study investigated whether this phenotype is associated with transcriptional changes by analyzing wildtype (WT) and PALS1 knockout (KO) MDCK II cell lines grown under non-confluent conditions and in 3D cyst cultures. Our results indicate that the transition from non-confluent cells to 3D cysts involves numerous differentially expressed genes (DEGs) in both WT and KO cells. Importantly, the analyses revealed significant overlaps between WT and KO cells in their maturation processes, suggesting that most identified DEGs are linked to differentiation from non-confluent to polarized MDCK cells and likely not a result of PALS1 deficiency. Gene Ontology (GO) enrichment and over-representation analyses using REACTOME and KEGG databases confirmed these similarities. In contrast, the direct comparison of WT and KO cells at the two stages showed fewer DEGs and overlaps in associated biological processes and signaling pathways. DEGs associated with the 3D stage, in which the phenotype manifests, contain DEGs and pathways that were predominantly linked to cell cycle linked processes, centromere assembly, or DNA replication. Furthermore, the transcription of genes encoding key junction proteins, additional polarity proteins, and cell-substrate interaction proteins is less affected by the loss of PALS1, indicating that PALS1 influences the transcriptional profiles in epithelial cells as a modulating factor.

Immuno-oncologic profiling by stage-dependent transcriptome and proteome analyses of spontaneously regressing canine cutaneous histiocytoma.

Canine cutaneous histiocytoma (CCH) is a tumor that originates from dermal Langerhans cells and affects particularly young dogs. The common spontaneous regression of CCH makes it an interesting model in comparative oncology research. Previous studies have indicated that anti-tumor immune responses may be involved, but details remain speculative to date. Here, we asked which specific immuno-oncological dynamics underlie spontaneous regression of CCH on mRNA and protein levels. QuantSeq 3' mRNA sequencing with functional over-representation analysis and an nCounter RNA hybridization assay were employed on 21 formalin-fixed, paraffin-embedded CCH samples representing three different tumor stages (dataset information: GSE261387-Immuno-Oncologic Profiling by Stage-Dependent Transcriptome and Proteome Analyses of Spontaneously Regressing Canine Cutaneous Histiocytoma-OmicsDI). Nine additional samples were subjected to matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Surprisingly, only minor stage-specific differences were found. When we investigated expression of B7 family ligands and CD28 family receptors holding co-stimulatory and -inhibitory functions, respectively, we found a higher abundance of CD80, CD86, CTLA4 and CD28, which may trigger a balanced activation of lymphocyte-mediated immune responses. CD80 and CD86 expressing cells were further quantified by in situ hybridization and compared with data from three cases of canine histiocytic sarcoma (HS), a malignant tumor variant originating from antigen-presenting interstitial dendritic cells. A stage-specific increase of CD80 expressing cells was recorded in CCH from the tumor bottom to the top, while CD86 was continuously and homogenously expressed at high levels. Overall expression of CD80 in CCH was similar to that in HS (73.3 ± 37.4% vs 62.1 ± 46.4%), while significantly more CD86 expressing tumor cells were found in CCH (94.7 ± 10.3%) when compared to HS (57.6 ± 11.0%). Our data suggest that major immuno-oncological pathways are not regulated during regression of CCH on the mRNA or protein levels as detectable by the methods used. Instead, our data provide further evidence supporting previous hypotheses towards a role of immune stimulatory B7 family ligands and CD28 family receptors in the regression of CCH.

XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic–mesenchymal cell states in neuroblastoma cancer.

Co-expression gene module analysis in response to attenuated cercaria vaccine reveals a critical role for NK cells in protection against Schistosoma mansoni.

Despite decades of research, an effective schistosomiasis vaccine remains elusive. The radiation-attenuated (RA) cercarial vaccine remains the best model for eliciting high levels of protection. We have recently explored this model in mice to identify potentially protective pathways by examining gene expression patterns in peripheral blood mononuclear cells (PBMC). Herein, we reanalyzed the transcriptomic data from PBMC obtained from vaccinated and infected C57BL/6 mice in three timepoints (Days 7 and 17 after infection or vaccination and Day 7 post-challenge). In addition, we generated new data on PBMC collected 35days after infection. Deconvolution analysis was performed to estimate immune cell composition by CIBERSORTx. Gene co-expression networks and over-representation analysis (ORA) were performed using the CEMiTool package. Protein-protein interaction networks were constructed using STRING, and the hub proteins for each module were identified using Cytoscape. Co-expression network analysis identified a module (M2) associated with the infection process, grouping genes related to a Th2 immune response, and a second module (M6) associated with the vaccination process, displaying pathways related to a Th1 response, CD8 + T cells and NK cells. Within each module, five hub proteins were identified based on protein-protein interaction networks. The M2 infection module revealed Chil3, Il4, Cx3cr1, Emr1 and Ccl2 as hubs, while module M6, associated with vaccination, disclosed Prf1, Klrc1, IFN-γ, Ncr1 and Tbx21 as hub proteins. Our data point to the potentiald role of NK cells that may contribute to the RA vaccine response through the production of IFN-γ orchestrated by the T-bet transcription factor (Tbx21).

Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

Genes of DLK1-DIO3 Locus and miR-379/656 Cluster is a Potential Diagnostic and Prognostic Marker in Patients With Hepatocellular Carcinoma: A Systems Biology Study

BackgroundHepatocellular carcinoma is the sixth most common malignancy reported globally. This highlights the need for reliable biomarkers that can be employed for diagnostic and prognostic applications. The present study aimed to classify and characterize the clinical potential of DLK1-DIO3 and miR-379/656 cluster genes in hepatocellular carcinoma. MethodsWe extensively studied the clinical potential of DLK1-DIO3 genes through a comprehensive systems biology approach and assessed the diagnostic and prognostic potential of the genes associated with the region. Additionally, we have predicted the gene targets of the miR-379/656 cluster associated with the locus and have identified the gene ontology, pathway, and disease associations. ResultsWe report this region as a potential biomarker for hepatocellular carcinoma. About thirty clustered miRNAs, a long-non-coding RNA, and two coding genes of the region were underexpressed in tumors. ROC analysis identified 11 clustered miRNAs with diagnostic potential. Survival analyses identified MEG3 and the miR-379/656 cluster as prognostically significant. Further, the Random-Forest model predicted that the miRNA cluster classifies patients according to TNM staging. Furthermore, overrepresentation analysis identified several key pathways, molecular functions, and biological processes associated with the cluster gene targets. ConclusionOur study suggests that DLK1-DIO3 genes, miR-379/656 cluster, and its target gene network might be potential diagnostic and prognostic markers for HCC management and therapy.

Abstract 4123872: Unsupervised clustering analysis of large-scale proteomics data identifies molecular phenogroups of heart failure in a population-based cohort

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.

Transcriptional Profiling of Tofacitinib Treatment in Juvenile Idiopathic Arthritis: Implications for Treatment Response Prediction.

To assess changes in gene expression following tofacitinib treatment and investigate transcription patterns as potential predictors of treatment response in patients with active juvenile idiopathic arthritis (JIA). Whole blood samples were collected from JIA patients at baseline and after 18 weeks of open-label tofacitinib treatment (clinical trial NCT02592434). Patients who achieved a JIA-American College of Rheumatology (ACR) response of 70 or above at week 18 were classified as treatment-responders (TR) while those with at most a JIA-ACR30 response were classified as poor-responders (PR). Differential gene expression and gene ontology (GO) over-representation analyses were performed to compare RNA expression between week 18 and baseline samples, as well as between PR and TR samples at baseline. Samples from 67 patients at baseline and 60 at week 18 were analyzed. Following 18 weeks of tofacitinib treatment across all JIA subjects, 883 genes showed significant differential expression (week 18 - baseline). The most strongly downregulated genes were over-represented within IL-7, type I, and type II interferon pathways, while upregulated genes were enriched in ontologies related to neuronal cell processes and cell signaling. Comparing PR and TR at baseline, 663 genes showed differential expression. Upregulated genes were over-represented within ontologies including activation of MAPK activity (p=9.40x10-5), myeloid cell development (p=8.13 x10-5), activation of GTPase activity (p=0.00015), and organelle transport along microtubules (p=0.00021). Tofacitinib treatment in JIA downregulated genes in interferon and IL-7 signaling pathways regardless of effectiveness. Furthermore, baseline upregulation of MAPK signaling may predict poor response to tofacitinib treatment in JIA.

Proteome characterization of extracellular vesicles from human milk: Uncovering the surfaceome by a lipid-based protein immobilization technology.

Breast milk is an essential source of nutrition and hydration for the infant. In addition, this highly complex fluid is rich in extracellular vesicles (EVs). Here, we have applied a microfluidic technology, lipid-based protein immobilization (LPI) and liquid chromatography with tandem mass spectrometry (LC-MS/MS) to characterize the proteome of human milk EVs. Mature milk from six mothers was subjected to EV isolation by ultracentrifugation followed by size exclusion chromatography. Three of the samples were carefully characterized; suggesting a subset enriched by small EVs. The EVs were digested by trypsin in an LPI flow cell and in-solution digestion, giving rise to two fractions of peptides originating from the surface proteome (LPI fraction) or the complete proteome (in-solution digestion). LC-MS/MS recovered peptides corresponding to 582 proteins in the LPI fraction and 938 proteins in the in-solution digested samples; 400 of these proteins were uniquely found in the in-solution digested samples and were hence denoted "cargo proteome". GeneOntology overrepresentation analysis gave rise to distinctly different functional predictions of the EV surfaceome and the cargo proteome. The surfaceome tends to be overrepresented in functions and components of relevance for the immune system, while the cargo proteome primarily seems to be associated with EV biogenesis.

Epigenetic insights into creep-feeding: methylation profiling of Longissimus thoracis muscle at weaning in crossbred cattle

IntroductionThis study investigated the impact of creep-feeding supplementation on the genome methylation of the Longissimus thoracis (LT) muscle in crossbred beef cattle (Bos taurus × Bos indicus).MethodsThe experiment involved 48 uncastrated F1 Angus-Nellore males (half-siblings), which were divided into two groups: NCF – no creep-feeding (n = 24) and CF – creep-feeding (n = 24). After weaning at 210 days, all animals were feedlot finished for 180 days under identical conditions. LT muscle biopsies were collected at weaning for genomic DNA methylation analysis by reduced representation bisulfite sequencing (RRBS).Results and discussionThe groups differed significantly (CF > NCF: p < 0.05) to weaning weight (243.57±5.70 vs. 228.92±5.07kg), backfat thickness (12.96±0.86 vs. 10.61±0.42mm), LT muscle marbling score (366.11±12.39 vs. 321.50±13.65), and LT intramuscular fat content (5.80±0.23 vs. 4.95±0.20%). The weights at the beginning of the experiment and at slaughter (390 days) did not differ significantly. Mean methylation levels were higher in CF with 0.18% more CpG, 0.04% CHG, and 0.03% CHH. We identified 974 regions with differential methylation (DMRs: > 25% and q < 0.05), which overlapped with 241 differentially methylated genes (DMGs). Among these genes, 108 were hypermethylated and 133 were hypomethylated in CF group. Notably, 39 of these DMGs were previously identified as differentially expressed genes (DEGs: log2 fold change [0.5]) in the same animal groups. Over-representation analysis highlighted epigenetic regulations related to muscle growth, PPAR signaling, adipogenesis, insulin response, and lipid metabolism. Key DMGs/DEGs included: ACAA1, SORBS1, SMAD3, TRIM63, PRKCA, DNMT3A, RUNX1, NRG3, and SLC2A8. These epigenetic changes improved the performance of supplemented animals up to weaning and enhanced meat quality traits, particularly higher intramuscular fat. The results provided insights into the intricate interplay between nutrition, epigenetics, gene expression and phenotypes in beef cattle production.

Integrated multi-omics predictive analysis of atherosclerosis: a sub-study from the Mineralocorticoid Receptor Antagonism in Diabetic Atherosclerosis (MAGMA) trial

Abstract Background/Introduction Mineralocorticoid receptor (MR) antagonists (MRA) are beneficial in cardiorenal outcomes in randomized controlled trials but the mechanisms are unclear. MAGMA was an NHLBI sponsored randomized, double-blind, placebo controlled, 12-month trial comparing Spironolactone (n = 37) vs. placebo (n = 42) in Type 2 diabetics with CKD stages 3-4 on maximal renin-angiotensin system (RAS) blockade and a prior atherosclerotic event and/or left ventricular (LV) hypertrophy. The primary outcome of percent (%) change in total aortic wall volume (TWV) at 12 months, measured by magnetic resonance imaging (MRI), was significantly reduced by Spironolactone. Purpose/Originality The purpose of this analysis was to understand mechanistic pathways of MRAs using a multi-omics approach that could help tease out molecular mechanisms of benefit. Revealing for the first time which of these integrated pathways are predictive of the primary outcome will help design treatments for targeted interventions. Methods Plasma and peripheral blood mononuclear cells from patients randomized to Spironolactone or placebo at baseline and 3-months were measured for aptamer-based proteomic biomarkers (7,596 proteins) and 10-X platform-based single-cell RNA-sequencing (scRNAseq), respectively. Pre-selected candidate predictors were used as inputs of a predictive model of changes of TWV. We fit a Mixed-Multivariate Random Forest (RF) model, a variation of the RF supervised tree-based machine learning method to take the experimental design into account in the regression formulation. The two sources of "omics predictors" were integrated into a supervised multi-omics model to jointly explain the outcome using an extension of Sparse Generalized Canonical Correlation Analysis (SGCCA). Integrated multiome functional analyses with graphical visualizations were carried out by statistical enrichment analysis using Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) against databases of gene ontologies, biological pathways, putative regulatory motifs, proteins, or disease annotations. Results The plasma proteome in response to Spironolactone revealed downregulation of MR targets including fibrosis, immune activation/inflammation, leukocyte activation, proliferation and pathways involved in cytokine stimulation. scRNAseq pathways revealed negative regulation of cytokines production such as IL-2 and redistribution of multiple cell types. Predictors of plaque progression involved cytokine-receptor, complement-coagulation, cell adhesion and axonal guidance targets. Multiome integrated functional analyses results of predictive pathways will be presented. Conclusions The changes in plasma proteomic profile with Spironolactone were consistent with the phenotype of reduced atherosclerosis and downregulation of multiple inflammatory, immune response and profibrotic pathways.

Circulating metabolites in patients with chronic heart failure are related to increased lipid utilisation but not to gut microbiota alterations

Abstract Background The gut microbiota produces numerous metabolites that can enter the circulation and exert their effects outside the gut. Some have been implicated in the pathogenesis of chronic heart failure (HF). Several studies have reported altered gut microbiota composition and circulating metabolites in patients with chronic HF compared to healthy controls (HC). However, limited data is available on the potential interplay between the dysbiotic features of the gut microbiota and the altered circulating metabolome in these patients. Purpose To examine differences in circulating metabolites between patients with chronic HF and HC, and their association with cardiac function and gut dysbiosis. Methods We included 123 patients, aged 18-75 years, with stable chronic HF and left ventricular ejection fraction (LVEF) <40%, and 51 HC. We collected fasting blood samples for metabolomic and lipidomic analyses, which were performed using liquid chromatography tandem mass spectrometry. A web-based platform was utilised for data preprocessing, filtering, pathway analysis, and metabolite annotation. Principal component analysis and orthogonal partial least squares discriminant analysis were used to explore differences in plasma metabolic profiles. Over-representation analysis was performed to identify the pathways in which relevant metabolites were involved. Stool samples were sequenced using 16S ribosomal RNA gene amplification. We calculated a dysbiosis index for each sample based on different abundances of microbial taxa in patients vs. controls. Results After adjusting for age and sex, we identified 73 enriched metabolites and 27 enriched lipids (odds ratio≥2 and p<0.05), and 124 depleted metabolites and 6 depleted lipids (odds ratio≤0.5 and p<0.05) in HF patients compared to HC. The enriched metabolites were involved in pathways related to lipid metabolism (beta oxidation, glycerolipid, sex hormone, and fatty acid metabolism), pyrimidine metabolism, Warburg effect, citric acid cycle, and pentose phosphate pathway. The depleted metabolites were involved in pathways related to lipid biosynthesis (fatty acids, steroids, bile acids), amino acid metabolism (glycine, serine, taurine, hypotaurine, cysteine, selenoamino acids), polyamine biosynthesis (spermidine, spermine), sulphate/sulphite metabolism, propanoate and pyruvate metabolism, carbohydrate metabolism (galactose, amino sugars, glycolysis), transfer of acetyl groups into mitochondria, urea cycle, and beta oxidation of very long chain fatty acids (Figure 1). LVEF, N-terminal pro B-type natriuretic peptide, and the dysbiosis index were not significantly related to any metabolite. Conclusions In patients with chronic HF, energy metabolism is significantly altered compared to HC, with a notable shift towards lipid utilisation. However, the alterations in circulating metabolites were not related to markers of cardiac function and appear to be unrelated to gut dysbiosis.Figure 1

Large-scale proteomic profiling reveals characteristic HIV-specific druggable protein signatures associated with incident heart failure

Abstract Background Compared with the general population, persons with HIV suffer from an augmented risk of both systolic and diastolic heart failure despite effective antiretroviral therapy [1]. While inflammatory and coagulation markers are thought to be involved in this increased risk, other contributing mechanisms, such as pro-inflammatory protein networks, remain to be elucidated. Purpose To identify novel protein predictors of incident heart failure events in the longitudinal Veterans Aging Cohort Study Biomarker Cohort of United States Veterans with HIV (PLWH; n = 1525) and without HIV (PWoH; n = 853) and to assess the functional relatedness of these proteins in PLWH. Methods We characterized the plasma proteome of PLWH and PWoH using an aptamer-based platform and assessed univariable associations with incident heart failure. Druggable proteins were identified using the Therapeutic Target Database [2]. We built a protein interaction network querying significant proteins (q-value <0.05) using Cytoscape for the Retrieval of Interacting Genes/Proteins (STRING) database [3]. To focus on densely connected core proteins, we used the mcl clustering method and identified a subnetwork of approximately 77 proteins. Over-representation analysis was performed for the core network by identifying enriched gene sets using Gene Ontology biological processes and WikiPathways. Final results are filtered based on a false discovery rate (FDR) threshold of 0.05. Results Of 4926 plasma proteins, 441 proteins in all PLWH and 706 in those with well-controlled HIV (viral load <200 copies/mL) were significantly associated with incident heart failure, compared with only 142 among PWoH (Figure 1A). Of the top 50 proteins most significantly associated with heart failure, 21 were druggable in PLWH and 14 in PWoH; only 5 druggable markers were shared, while 16 were unique to HIV (Table 1). Pleiotrophin, a pro-angiogenic, pro-inflammatory cytokine that induces tumor necrosis factor- (TNF) α, interleukin- (IL) 1β, and IL-6 expression and has been implicated in cardiomyocyte apoptosis [4,5], had the highest hazard ratio (HR) for incident heart failure (HR = 4.7; p = 1.6E-20; q = 1.7E-18) in PLWH. The most significant enriched protein pathway included 36 genes involved in chemotaxis (FDR = 1.3E-30); 3 of the 21 druggable targets (pleiotrophin, ephrin-A5, and ephrin type-A receptor 2) were represented in this pathway (Figure 1B). Ephrin signaling pathways, which are involved in cell adhesion, differentiation, and proliferation [6], have not previously been implicated in heart failure, nor in HIV. Subnetwork analysis of druggable proteins demonstrated that ephrin-signaling, IL-15, and TNF pathways were functionally interconnected (Figure 1C). Conclusions Our findings indicate that there may be unique pro-inflammatory pathways characteristic of HIV-associated cardiomyopathy that merit further study as dedicated pharmacologic targets and/or risk markers in this population.

Mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data.

Oral cancer, representing 2-4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein-Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.

Unveiling the Molecular Effects of Replacement and Legacy PFASs: Transcriptomic Analysis of Zebrafish Embryos Reveals Surprising Similarities and Potencies.

The prevalence of per- and poly fluoroalkyl substances (PFASs) in the environment has prompted restrictions on legacy PFASs due to their recognized toxic effects. Consequently, alternative "replacement" PFASs have been introduced and are prevalent in environmental matrices. Few studies have investigated the molecular effects of both legacy and replacement PFASs under short-term exposures. This study aimed to address this by utilizing transcriptomic sequencing to compare the molecular impacts of exposure to concentrations 0.001-5 mg/L of the legacy PFOS and two of its replacements, PFECHS and FBSA. Using zebrafish embryos, the research assessed apical effects (mortality, morphology, and growth), identified differentially expressed genes (DEGs) and enriched pathways, and determined transcriptomic points of departure (tPoDs) for each compound. Results indicated that PFOS exhibited the highest relative potency, followed by PFECHS and then FBSA. While similarities were observed among the ranked DEGs across all compounds, over-representation analysis revealed slight differences. Notably, PFOS demonstrated the lowest tPoD identified to date. These findings raise concerns regarding the safety of emerging replacement PFASs and challenge assumptions about PFAS toxicity solely resulting from their accumulative potential. As replacement PFASs proliferate in the environment, this study underscores the need for heightened scrutiny of their effects and questions current regulatory thresholds.

Gene expression changes in mouse lung induced by subacute inhalation of PM10-rich particulate matter

Introduction Particulate matter (PM) air pollution is associated with an increased incidence of lung diseases, but the underlying mechanisms have not been fully elucidated. In this study, a mouse model of subacute lung inflammation was employed to investigate the cellular responses and gene expression changes induced by exposure to natural ambient air pollution. Methods C57BL/6J mice were exposed to road dust (primarily PM10) at 150 µg/m³ for 21 days (8 h/day) through a nose-only inhalation exposure system. Lung tissues were analyzed for the expression of proinflammatory signaling, oxidative stress, and fibrosis markers. RNA-sequencing analysis was conducted to identify differentially expressed genes (DEGs). A gene ontology over-representation analysis was performed to identify the altered genetic pathways. Results Elevated levels of proinflammatory cytokines, including IL-1β, IL-6, and TNF-α, and an increase in phosphorylated MAPK were determined in the road dust exposure group compared to the control group. Histopathological examinations revealed more severe lung inflammation and damage in the exposed mice, including fibrosis and bronchiolar hyperplasia. Gene expression profiling identified 108 DEGs, with decreases in most except genes such as Krt15 and Reg3g. The protein–protein interaction network analysis together with text-mining identified 18 key hub genes, associated with fatty acid oxidation, lipid metabolism, and peroxisomes. Conclusion This study identified key genes, signaling pathways, and cellular responses in mouse lung affected by road dust exposure. These findings contribute to a deeper understanding of the transcriptional and cellular responses induced by subacute exposure to the PM in road dust.