BACKGROUND: Acute myocardial injury, cytokine storms, hypoxemia and pathogen-mediated damage were the major causes responsible for mortality induced by coronavirus disease 2019 (COVID-19)-related myocarditis. These need ECMO treatment. We investigated differentially expressed genes (DEGs) in patients with COVID-19-related myocarditis and ECMO prognosis.
METHODS: GSE150392 and GSE93101 were analyzed to identify DEGs. A Venn diagram was used to obtain the same transcripts between myocarditis-related and ECMO-related DEGs. Enrichment pathway analysis was performed and hub genes were identified. Pivotal miRNAs, transcription factors, and chemicals with the screened gene interactions were identified. The GSE167028 dataset and single-cell sequencing data were used to validate the screened genes.
RESULTS: Using a Venn diagram, 229 overlapping DEGs were identified between myocarditis-related and ECMO-related DEGs, which were mainly involved in T cell activation, contractile actin filament bundle, actomyosin, cyclic nucleotide phosphodiesterase activity, and cytokine-cytokine receptor interaction. 15 hub genes and 15 neighboring DEGs were screened, which were mainly involved in the positive regulation of T cell activation, integrin complex, integrin binding, the PI3K-Akt signaling pathway, and the TNF signaling pathway. Data in GSE167028 and single-cell sequencing data were used to validate the screened genes, and this demonstrated that the screened genes CCL2, APOE, ITGB8, LAMC2, COL6A3 and TNC were mainly expressed in fibroblast cells; IL6, ITGA1, PTK2, ITGB5, IL15, LAMA4, CAV1, SNCA, BDNF, ACTA2, CD70, MYL9, DPP4, ENO2 and VEGFC were expressed in cardiomyocytes; IL6, PTK2, ITGB5, IL15, APOE, JUN, SNCA, CD83, DPP4 and ENO2 were expressed in macrophages; and IL6, ITGA1, PTK2, ITGB5, IL15, VCAM1, LAMA4, CAV1, ACTA2, MYL9, CD83, DPP4, ENO2, VEGFC and IL32 were expressed in vascular endothelial cells.
CONCLUSIONS: The screened hub genes, IL6, ITGA1, PTK2, ITGB3, ITGB5, CCL2, IL15, VCAM1, GZMB, APOE, ITGB8, LAMA4, LAMC2, COL6A3 and TNFRSF9, were validated using GEO dataset and single-cell sequencing data, which may be therapeutic targets patients with myocarditis to prevent MI progression and adverse cardiovascular events.

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

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.

Cisplatin, a powerful chemotherapy medication, has long been a cornerstone in the fight against cancer due to chemotherapeutic failure. The mechanism of cisplatin resistance/failure is a multifaceted and complex issue that consists mainly of apoptosis inhibition through autophagy sensitization. Currently, researchers are exploring ways to regulate autophagy in order to tip the balance in favor of effective chemotherapy. Based on this notion, the current study primarily identifies the differentially expressed genes (DEGs) in cisplatin-treated autophagic ACHN cells through the Illumina Hi-seq platform. A protein-protein interaction network was constructed using the STRING database and KEGG. GO classifiers were implicated to identify genes and their participating biological pathways. ClueGO, David, and MCODE detected ontological enrichment and sub-networking. The network topology was further examined using 12 different algorithms to identify top-ranked hub genes through the Cytoscape plugin Cytohubba to identify potential targets, which established profound drug efficacy under an autophagic environment. Considerable upregulation of genes related to autophagy and apoptosis suggests that autophagy boosts cisplatin efficacy in malignant ACHN cells with minimal harm to normal HEK-293 growth. Furthermore, the determination of cellular viability and apoptosis by AnnexinV/FITC-PI assay corroborates with in silico data, indicating the reliability of the bioinformatics method followed by qRT-PCR. Altogether, our data provide a clear molecular insight into drug efficacy under starved conditions to improve chemotherapy and will likely prompt more clinical trials on this aspect.

Enrichment analysis (EA) is a common approach to gain functional insights from genome-scale experiments. As a consequence, a large number of EA methods have been developed, yet it is unclear from previous studies which method is the best for a given dataset. The main issues with previous benchmarks include the complexity of correctly assigning true pathways to a test dataset, and lack of generality of the evaluation metrics, for which the rank of a single target pathway is commonly used. We here provide a generalized EA benchmark and apply it to the most widely used EA methods, representing all four categories of current approaches. The benchmark employs a new set of 82 curated gene expression datasets from DNA microarray and RNA-Seq experiments for 26 diseases, of which only 13 are cancers. In order to address the shortcomings of the single target pathway approach and to enhance the sensitivity evaluation, we present the Disease Pathway Network, in which related Kyoto Encyclopedia of Genes and Genomes pathways are linked. We introduce a novel approach to evaluate pathway EA by combining sensitivity and specificity to provide a balanced evaluation of EA methods. This approach identifies Network Enrichment Analysis methods as the overall top performers compared with overlap-based methods. By using randomized gene expression datasets, we explore the null hypothesis bias of each method, revealing that most of them produce skewed P-values.

Ulcerative colitis (UC) is a chronic inflammatory disease that targets the colon and has seen an increasing prevalence worldwide. In our pursuit of new diagnostic and therapeutic approaches for UC, we undertook a sequencing of colons from UC mouse models. We focused on analyzing their differentially expressed genes (DEGs), enriching pathways, and constructing protein-protein interaction (PPI) and Competing Endogenous RNA (ceRNA) networks. Our analysis highlighted novel DEGs such as Tppp3, Saa3, Cemip, Pappa, and Nr1d1. These DEGs predominantly play roles in pathways like cytokine-mediated signaling, extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization. This suggests that the UC pathogenesis is intricately linked to the interactions between immune and non-immune cells with the extracellular matrix (ECM). To corroborate our findings, we also verified certain DEGs through quantitative real-time PCR. Within the PPI network, nodes like Stat3, Il1b, Mmp3, and Lgals3 emerged as significant and were identified to be involved in the crucial cytokine-mediated signaling pathway, which is central to inflammation. Our ceRNA network analysis further brought to light the role of the Smad7 Long non-coding RNA (lncRNA). Key MicroRNA (miRNAs) in the ceRNA network were pinpointed as mmu-miR-17-5p, mmu-miR-93-5p, mmu-miR-20b-5p, mmu-miR-16-5p, and mmu-miR-106a-5p, while central mRNAs included Egln3, Plagl2, Sema7a, Arrdc3, and Stat3. These insights imply that ceRNA networks are influential in UC progression and could provide further clarity on its pathogenesis. In conclusion, this research deepens our understanding of UC pathogenesis and paves the way for potential new diagnostic and therapeutic methods. Nevertheless, to solidify our findings, additional experiments are essential to confirm the roles and molecular interplay of the identified DEGs in UC.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, ‎we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.
Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI\'s gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.
The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.
The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.