Low-Grade Activation of the Extrinsic Coagulation Pathway in Patients with Ulcerative Colitis.

BACKGROUND: Ulcerative colitis (UC) increases the risk for venous thromboembolism. Tissue factor (TF) initiates the extrinsic coagulation pathway (ECP). AIMS: To investigate the correlation of UC severity with latent ECP activation and TF expression in primary colonic stromal cells (PCSC). METHODS: In plasma of 38 UC patients (31 males, disease duration 151 ± 25 months) and 28 healthy controls, exosomes and microparticles (EM) were counted. Moreover, TF protein concentration, activities of EM-bound TF (EM-TFa) and coagulation factor VII (FVIIa) were assessed. In PCSC in culture, TF mRNA (F3) from 12 patients with active UC and 7 controls was evaluated. RESULTS: UC patients had 4- and 3.7- times more exosomes and microparticles, respectively, than controls. TF protein in UC was correlated with several disease severity indices, such as partial Mayo score (pMs; r 0.443), albumin (- 0.362), ESR (0.353), PLT (0.575), and endoscopic Ms (eMs 0.468). EM-TFa was also significantly higher in UC and was correlated to SIBDQ (- 0.64), albumin (- 0.624), disease extent and eMs (0.422). Refractory-to-treatment patients had significantly higher TF protein, EM-TFa and FVIIa. Even within responders, the need for steroids or biologics correlated with a 2.2-times higher EM-TFa. PCSC from active UC maintained higher F3 than controls, which was correlated to pMs (0.56), albumin (- 0.543) and eMs. Treatment with cytokines further upregulated F3. P for all comparisons was < 0.05. CONCLUSION: Low-grade activation of the ECP associates with clinical, endoscopic UC activity and response to treatment. TF in PCSC mirrors its systemic activity and points to them as a source.

Next-Generation Sequencing of microRNAs in Small Abdominal Aortic Aneurysms: MiR-24 as a Biomarker.

BACKGROUND: Small abdominal aortic aneurysms (AAAs) are asymptomatic but can potentially lead to rupture if left undetected. To date, there is a lack of simple nonradiologic routine tests available for diagnosing AAAs. MicroRNAs (miRNAs) have been proven to be good-quality biomarkers in several diseases, including AAA. METHODS: An attempt to identify a panel of circulating miRNAs with differential expression in AAAs via next-generation sequencing (NGS) was performed in serum samples: small AAAs (n = 3), large AAAs (n = 3), and controls (n = 3). For miR-24, validation with real-time polymerase chain reaction (PCR) was undertaken in a larger group (n = 80). RESULTS: In the NGS study, 23 miRNAs were identified as differentially expressed (with statistical significance) in small AAAs in comparison with controls. Among them, miR-24 showed the largest upregulation with 23-fold change (log2FC 4.5, P = 0.024). For large AAAs compared with controls, and small AAAs compared with large AAAs, a panel of 33 and 131 miRNAs showed statistically significant differential expression, respectively. Based on the results of the NGS stage, a literature search was performed, and information regarding AAA pathogenesis, coronary artery disease, and peripheral arterial disease was documented where applicable: miR-24, miR-103, miR-193a, miR-486, miR-582, and miR-3663. Of these 6 miRNAs, miR-24 was chosen for further validation with real-time PCR. Additionally, in the NGS study analysis, 17 miRNAs were common between the small-large AAAs, small AAAs-controls, and large AAAs-controls comparisons: miR-7846, miR-3195, miR-486-2, miR-3194, miR-5589, miR-1538, miR-3178, miR-4771-1, miR-5695, miR-6504, miR-1908, miR-6823, miR-3159, miR-23a, miR-7853, miR-496, and miR-193a. Interestingly, in the validation stage with real-time PCR, miR-24 was found downregulated in small and large AAAs compared with controls (fold-changes: 0.27, P = 0.015 and 0.15, P = 0.005, respectively). No correlation was found between average Ct values, aneurysm diameter, and patients' age. CONCLUSIONS: Our findings further highlight the importance of miR-24 as a potential biomarker as well as a therapeutic target for abdominal aneurysmal disease. Future research and validation of a panel of miRNAs for AAA would aid in diagnosis and discrimination between diseases with overlapping pathogeneses.

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

INTRODUCTION: Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome. METHODS: We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. RESULTS: Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. CONCLUSIONS: Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

Novel clinical, molecular and bioinformatics insights into the genetic background of autism.

BACKGROUND: Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. METHOD: Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. RESULTS: Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. CONCLUSIONS: These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.

ZWA: Viral genome assembly and characterization hindrances from virus-host chimeric reads; a refining approach.

Viral metagenomics, also known as virome studies, have yielded an unprecedented number of novel sequences, essential in recognizing and characterizing the etiological agent and the origin of emerging infectious diseases. Several tools and pipelines have been developed, to date, for the identification and assembly of viral genomes. Assembly pipelines often result in viral genomes contaminated with host genetic material, some of which are currently deposited into public databases. In the current report, we present a group of deposited sequences that encompass ribosomal RNA (rRNA) contamination. We highlight the detrimental role of chimeric next generation sequencing reads, between host rRNA sequences and viral sequences, in virus genome assembly and we present the hindrances these reads may pose to current methodologies. We have further developed a refining pipeline, the Zero Waste Algorithm (ZWA) that assists in the assembly of low abundance viral genomes. ZWA performs context-depended trimming of chimeric reads, precisely removing their rRNA moiety. These, otherwise discarded, reads were fed to the assembly pipeline and assisted in the construction of larger and cleaner contigs making a substantial impact on current assembly methodologies. ZWA pipeline may significantly enhance virus genome assembly from low abundance samples and virus metagenomics approaches in which a small number of reads determine genome quality and integrity.

Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer.

Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders.

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

A main event and multiple introductions of SARS-CoV-2 initiated the COVID-19 epidemic in Greece.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Chains of infections starting from various countries worldwide seeded the outbreak of COVID-19 in Athens, capital city of Greece. A full-genome analysis of isolates from Athens' hospitals and other healthcare providers revealed the variety of SARS-CoV-2 that initiated the pandemic before lockdown and passenger flight restrictions. A dominant variant, encompassing the G614D amino acid substitution, spread through a major virus dispersal event, and sporadic introductions of rare variants characterized the local initiation of the epidemic. Mutations within the genome highlighted the genetic drift of the virus as rare variants emerged. An important variant contained a premature stop codon in orf7a leading to the truncation of a possibly important for viral pathogenesis domain. This study may serve as a reference for resolving future lines of infection in the area, especially after resumption of passenger flight connections to Athens and Greece during summer of 2020.

West Nile Virus Subgenomic RNAs Modulate Gene Expression in a Neuronal Cell Line.

Subgenomic flaviviral RNAs (sfRNAs) are small non-coding products of the incomplete degradation of viral genomic RNA. They accumulate during flaviviral infection and have been associated with many functional roles inside the host cell. Studies so far have demonstrated that sfRNA plays a crucial role in determining West Nile virus (WNV) pathogenicity. However, its modulatory role on neuronal homeostasis has not been studied in depth. In this study, we investigated the mechanism of sfRNA biosynthesis and its importance for WNV replication in neuronal cells. We found that sfRNA1 is functionally redundant for both replication and translation of WNV. However, the concurrent absence of sfRNA1 and sfRNA2 species is detrimental for the survival of the virus. Differential expression analysis on RNA-seq data from WT and ΔsfRNA replicon cell lines revealed transcriptional changes induced by sfRNA and identified a number of putative targets. Overall, it was shown that sfRNA contributes to the viral evasion by suppressing the interferon-mediated antiviral response. An additional differential expression analysis among replicon and control Neuro2A cells also clarified the transcriptional changes that support WNV replication in neuronal cells. Increased levels of translation and oxidative phosphorylation, post-translational modification processes, and activated DNA repair pathways were observed in replicon cell lines, while developmental processes such as axonal growth were deficient.

Dysbiosis Signature of Fecal Microbiota in Patients with Pancreatic Adenocarcinoma and Pancreatic Intraductal Papillary Mucinous Neoplasms.

Pancreatic cancer (PC) ranks as the seventh leading cause of cancer-related deaths, with approximately 500,000 new cases reported in 2020. Existing strategies for early PC detection primarily target individuals at high risk of developing the disease. Nevertheless, there is a pressing need to identify innovative clinical approaches and personalized treatments for effective PC management. This study aimed to explore the dysbiosis signature of the fecal microbiota in PC and potential distinctions between its Intraductal papillary mucinous neoplasm (IPMN) and pancreatic ductal adenocarcinoma (PDAC) phenotypes, which could carry diagnostic significance. The study enrolled 33 participants, including 22 diagnosed with PDAC, 11 with IPMN, and 24 healthy controls. Fecal samples were collected and subjected to microbial diversity analysis across various taxonomic levels. The findings revealed elevated abundances of Firmicutes and Proteobacteria in PC patients, whereas healthy controls exhibited higher proportions of Bacteroidota. Both LEfSe and Random Forest analyses indicated the microbiome's potential to effectively distinguish between PC and healthy control samples but fell short of differentiating between IPMN and PDAC samples. These results contribute to the current understanding of this challenging cancer type and highlight the applications of microbiome research. In essence, the study provides clear evidence of the gut microbiome's capability to serve as a biomarker for PC detection, emphasizing the steps required for further differentiation among its diverse phenotypes.

Power-law scaling in intratumoral microbiota of colorectal cancer.

It has recently been proposed that the study of microbial dynamics in humans may gain insights from island biogeographical theory. Here, we test whether the diversity of the intratumoral microbiota of colorectal cancer tumors (CRC) follows a power law with tumor size akin to the island species-area relationship. We confirm a direct correlation between the quantity of Amplicon Sequence Variants (ASVs) within CRC tumors and tumor sizes, following a (log)power model, explaining 47% of the variation. Understanding the processes involved, potentially through the analogy of tumors and islands, may ultimately contribute to future clinical and therapeutic strategies.

Exploring Microbial Metabolite Receptors in Inflammatory Bowel Disease: An In Silico Analysis of Their Potential Role in Inflammation and Fibrosis.

Metabolites produced by dysbiotic intestinal microbiota can influence disease pathophysiology by participating in ligand-receptor interactions. Our aim was to investigate the differential expression of metabolite receptor (MR) genes between inflammatory bowel disease (IBD), healthy individuals (HIs), and disease controls in order to identify possible interactions with inflammatory and fibrotic pathways in the intestine. RNA-sequencing datasets containing 643 Crohn's disease (CD) patients, 467 ulcerative colitis (UC) patients and 295 HIs, and 4 Campylobacter jejuni-infected individuals were retrieved from the Sequence Read Archive, and differential expression was performed using the RaNA-seq online platform. The identified differentially expressed MR genes were used for correlation analysis with up- and downregulated genes in IBD, as well as functional enrichment analysis using a R based pipeline. Overall, 15 MR genes exhibited dysregulated expression in IBD. In inflamed CD, the hydroxycarboxylic acid receptors 2 and 3 (HCAR2, HCAR3) were upregulated and were associated with the recruitment of innate immune cells, while, in the non-inflamed CD ileum, the cannabinoid receptor 1 (CNR1) and the sphingosine-1-phospate receptor 4 (S1PR4) were downregulated and were involved in the regulation of B-cell activation. In inflamed UC, the upregulated receptors HCAR2 and HCAR3 were more closely associated with the process of TH-17 cell differentiation, while the pregnane X receptor (NR1I2) and the transient receptor potential vanilloid 1 (TRPV1) were downregulated and were involved in epithelial barrier maintenance. Our results elucidate the landscape of metabolite receptor expression in IBD, highlighting associations with disease-related functions that could guide the development of new targeted therapies.

Landscape of Interactions between Stromal and Myeloid Cells in Ileal Crohn's Disease; Indications of an Important Role for Fibroblast-Derived CCL-2.

BACKGROUND AND AIMS: Monocyte recruitment in the lamina propria and inflammatory phenotype driven by the mucosal microenvironment is critical for the pathogenesis of inflammatory bowel disease. However, the stimuli responsible remain largely unknown. Recent works have focused on stromal cells, the main steady-state cellular component in tissue, as they produce pro-inflammatory chemokines that contribute to the treatment-resistant nature of IBD. METHODS: We studied the regulation of these processes by examining the communication patterns between stromal and myeloid cells in ileal Crohn's disease (CD) using a complete single-cell whole tissue sequencing analysis pipeline and in vitro experimentation in mesenchymal cells. RESULTS: We report expansion of S4 stromal cells and monocyte-like inflammatory macrophages in the inflamed mucosa and describe interactions that may establish sustained local inflammation. These include expression of CCL2 by S1 fibroblasts to recruit and retain monocytes and macrophages in the mucosa, where they receive signals for proliferation, survival, and differentiation to inflammatory macrophages from S4 stromal cells through molecules such as MIF, IFNγ, and FN1. The overexpression of CCL2 in ileal CD and its stromal origin was further demonstrated in vitro by cultured mesenchymal cells and intestinal organoids in the context of an inflammatory milieu. CONCLUSIONS: Our findings outline an extensive cross-talk between stromal and myeloid cells, which may contribute to the onset and progression of inflammation in ileal Crohn's disease. Understanding the mechanisms underlying monocyte recruitment and polarization, as well as the role of stromal cells in sustaining inflammation, can provide new avenues for developing targeted therapies to treat IBD.

Oncostatin M Induces a Pro-inflammatory Phenotype in Intestinal Subepithelial Myofibroblasts.

BACKGROUND: Oncostatin-M (OSM) is associated with antitumor necrosis factor (anti-TNF)-α resistance in inflammatory bowel disease (IBD) and fibrosis in inflammatory diseases. We studied the expression of OSM and its receptors (OSMR, gp130) on intestinal subepithelial myofibroblasts (SEMFs) and the effect of OSM stimulation on SEMFs. METHODS: The mRNA and protein expression of OSM, OSMR, gp130, and several fibrotic and chemotactic factors were studied in mucosal biopsies and isolated human intestinal SEMFs of patients with IBD and healthy controls (HCs) and in a model of human intestinal organoids (HIOs). Subepithelial myofibroblasts and HIOs were stimulated with OSM and interleukin (IL)-1α/TNF-α. RNAseq data of mucosal biopsies were also analyzed. RESULTS: Oncostatin-M receptors and gp130 were overexpressed in mucosal biopsies of patients with IBD (P < .05), especially in inflamed segments (P < .05). The expression of OSM, OSMR, and gp130 in SEMFs from HCs was increased after stimulation with IL-1α/TNF-α (P < .001; P < .01; P < .01). The expression of CCL2, CXCL9, CXCL10, and CXCL11 was increased in SEMFs from patients with IBD and HCs after stimulation with OSM in a dose-dependent manner (P < .001; P < .05; P < .001; P < .001) and was further increased after prestimulation with IL-1α/TNF-α (P < .01 vs OSM-alone). Similar results were yielded after stimulation of HIOs (P < .01). Oncostatin-M did not induce the expression of collagen I, III, and fibronectin. Oncostatin-M receptor expression was positively correlated with CCL2, CXCL9, CXCL10, and CXCL11 expression in mucosal biopsies (P < .001; P < .001; P = .045; P = .033). CONCLUSIONS: Human SEMFs overexpress OSMR in an inflammatory microenvironment. Oncostatin-M may promote inflammation in IBD via its stimulatory effects on SEMFs, which primarily involve chemoattraction of immune cells to the intestinal mucosa.

Oncostatin-M/OSMR show elevated expression on intestinal fibroblasts that is regulated by IBD-relevant pro-inflammatory stimuli. In turn, OSM induces a pro-inflammatory phenotype on primary intestinal fibroblasts, with prominent overexpression of chemotactic factors, without demonstrating a substantial profibrotic effect.

PepServe: a web server for peptide analysis, clustering and visualization.

Peptides, either as protein fragments or as naturally occurring entities are characterized by their sequence and function features. Many times the researchers need to massively manage peptide lists concerning protein identification, biomarker discovery, bioactivity, immune response or other functionalities. We present a web server that manages peptide lists in terms of feature analysis as well as interactive clustering and visualization of the given peptides. PepServe is a useful tool in the understanding of the peptide feature distribution among a group of peptides. The PepServe web application is freely available at http://bioserver-1.bioacademy.gr/Bioserver/PepServe/.

The Effect of Probiotic Supplements on Metabolic Parameters of People with Type 2 Diabetes in Greece-A Randomized, Double-Blind, Placebo-Controlled Study.

The role of probiotic supplementation in type 2 diabetes (T2D) treatment is controversial. The present study aimed to assess the effects of a multi-strain probiotic supplement (LactoLevureR (containing Lactobacillus acidophilus, Lactobacillus plantarum, Bifidobacterium lactis, and Saccharomyces boulardii)) over 6 months, primarily on glycemic control as well as on lipid levels and alterations in the gut microbiome, among individuals with T2D residing in Greece. A total of 91 adults with T2D (mean age [±SD] 65.12 ± 10.92 years, 62.6% males) were randomized to receive the probiotic supplement or a matching placebo capsule, once daily, for 6 months. Blood chemistries and anthropometric parameters were conducted every 3 months, and stool samples were collected at baseline and at 6 months. Significant reductions in HbA1c, fasting blood glucose, and total cholesterol were observed in participants treated with the probiotic supplement (n = 46) compared to the controls (n = 45), even after adjustment for a greater decrease in adiposity (waist circumference). Although there were no statistically significant differences in the diversity of the gut microbiome (α and ß diversity), the administration of probiotics did influence several genera, metabolites, and key enzymes associated with diabetes. Overall, the administration of the multi-strain probiotic LactoLevureR over a 6-month period in individuals with T2D was well-tolerated and had a positive impact on metabolic parameters, alongside improvements in indices of adiposity.

Identifying Microbiome Dynamics in Pediatric IBD: More than a Family Matter.

BACKGROUND: Pediatric inflammatory bowel disease (IBD) is a chronic inflammatory intestinal disease that affects both children and adolescents. Symptoms can significantly affect a child's growth, development, and quality of life, making early diagnosis and effective management crucial. This study focuses on treatment-naïve pediatric IBD patients and their immediate families to identify the role of the microbiome in disease onset. METHODS: Nine families with pediatric IBD were recruited, comprising seven drug-naïve Crohn's disease (CD) patients and two drug-naïve ulcerative colitis (UC) patients, as well as twenty-four healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing and bioinformatics analysis. RESULTS: We identified patterns of dysbiosis and hallmark microbial taxa among patients who shared ethnic, habitual, and dietary traits with themselves and their families. In addition, we examined the impact of the disease on specific microbial taxa and how these could serve as potential biomarkers for early detection. CONCLUSIONS: Our results suggest a potential role of maternal factors in the establishment and modulation of the early life microbiome, consistent with the current literature, which may have implications for understanding the etiology and progression of IBD.

Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia worldwide and is characterized by a high risk of thromboembolism, ischemic stroke, and fatality. The precise molecular mechanisms of AF pathogenesis remain unclear. The purpose of this study was to use bioinformatics tools to identify novel key genes in AF, provide deeper insights into the molecular pathogenesis of AF, and uncover potential therapeutic targets. Four publicly available raw RNA-Seq datasets obtained through the ENA Browser, as well as proteomic analysis results, both derived from atrial tissues, were used in this analysis. Differential gene expression analysis was performed and cross-validated with proteomics results to identify common genes/proteins between them. A functional enrichment pathway analysis was performed. Cross-validation analysis revealed five differentially expressed genes, namely FGL2, IGFBP5, NNMT, PLA2G2A, and TNC, in patients with AF compared with those with sinus rhythm (SR). These genes play crucial roles in various cardiovascular functions and may be part of the molecular signature of AF. Furthermore, functional enrichment analysis revealed several pathways related to the extracellular matrix, inflammation, and structural remodeling. This study highlighted five key genes that constitute promising candidates for further experimental exploration as biomarkers as well as therapeutic targets for AF.

Multi-omics integration to identify the genetic expression and protein signature of dilated and ischemic cardiomyopathy.

Introduction: Heart failure (HF) is a complex clinical syndrome leading to high morbidity. In this study, we aimed to identify the gene expression and protein signature of HF main causes, namely dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM). Methods: Omics data were accessed through GEO repository for transcriptomic and PRIDE repository for proteomic datasets. Sets of differentially expressed genes and proteins comprising DCM (DiSig) and ICM (IsSig) signatures were analyzed by a multilayered bioinformatics approach. Enrichment analysis via the Gene Ontology was performed through the Metascape platform to explore biological pathways. Protein-protein interaction networks were analyzed via STRING db and Network Analyst. Results: Intersection of transcriptomic and proteomic analysis showed 10 differentially expressed genes/proteins in DiSig (AEBP1, CA3, HBA2, HBB, HSPA2, MYH6, SERPINA3, SOD3, THBS4, UCHL1) and 15 differentially expressed genes/proteins in IsSig (AEBP1, APOA1, BGN, CA3, CFH, COL14A1, HBA2, HBB, HSPA2, LTBP2, LUM, MFAP4, SOD3, THBS4, UCHL1). Common and distinct biological pathways between DiSig and IsSig were retrieved, allowing for their molecular characterization. Extracellular matrix organization, cellular response to stress and transforming growth factor-beta were common between two subphenotypes. Muscle tissue development was dysregulated solely in DiSig, while immune cells activation and migration in IsSig. Discussion: Our bioinformatics approach sheds light on the molecular background of HF etiopathology showing molecular similarities as well as distinct expression differences between DCM and ICM. DiSig and IsSig encompass an array of "cross-validated" genes at both transcriptomic and proteomic level, which can serve as novel pharmacological targets and possible diagnostic biomarkers.

Inflammation and Immunity Gene Expression Patterns and Machine Learning Approaches in Association with Response to Immune-Checkpoint Inhibitors-Based Treatments in Clear-Cell Renal Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Despite the rapid evolution of targeted therapies, immunotherapy with checkpoint inhibition (ICI) as well as combination therapies, the cure of metastatic ccRCC (mccRCC) is infrequent, while the optimal use of the various novel agents has not been fully clarified. With the different treatment options, there is an essential need to identify biomarkers to predict therapeutic efficacy and thus optimize therapeutic approaches. This study seeks to explore the diversity in mRNA expression profiles of inflammation and immunity-related circulating genes for the development of biomarkers that could predict the effectiveness of immunotherapy-based treatments using ICIs for individuals with mccRCC. Gene mRNA expression was tested by the RT2 profiler PCR Array on a human cancer inflammation and immunity crosstalk kit and analyzed for differential gene expression along with a machine learning approach for sample classification. A number of mRNAs were found to be differentially expressed in mccRCC with a clinical benefit from treatment compared to those who progressed. Our results indicate that gene expression can classify these samples with high accuracy and specificity.