Evaluation of the gut microbiome associated with COVID-19.

Introduction: In 2019, a new virus from the coronavirus family called SARS-CoV-2, infected populations throughout the world. Coronavirus disease 2019 (COVID-19), an illness induced by this virus, attacks vital organs in the body, such as the respiratory system and the gastrointestinal tract. Recent studies have confirmed changes in the gut microbiome caused by the COVID-19 disease. We examined the alteration of the gut microbiome in COVID-19 patients compared to healthy individuals. Materials and methods: in this study, the 16s metagenomics dataset, publicly available in the Sequence Read Archive (SRA) database, was used for analysis (accession number PRJNA636824). The analysis processes were performed using the CLC Microbial Genomics Module 20.1.1 (Qiagen). At first, the sequence reads of samples were trimmed and classified into operational taxonomic units (OTUs) with 97% similarity and then assigned to the Greengenes reference database (v138). Differential abundance analysis was used to determine statistically significant differences in OTUs between COVID-19 and healthy groups. Next, biodiversity analyses including the alpha diversity (intragroup diversity) and beta diversity (intergroup diversity) using defined indexes were estimated. Then, the co-occurrence network at the species level was constructed using the Pearson correlation coefficient calculation between pairs of OTUs in R software and visualized using Cytoscape software. Ultimately, the hub OTUs at the species level were identified using the cytoHubba plugin of Cytoscape based on Maximal Clique Centrality (MCC) algorithm. Results: The results of the metagenomic analysis revealed that the intestinal microbiome in healthy individuals has a higher biodiversity compared to COVID-19 patients. Indeed, healthy people also have a higher percentage of beneficial bacteria such as bifidobacteria adolescentis compared to COVID-19 patients; in contrast, COVID-19 patients have higher levels of opportunistic and pathogenic bacteria such as Streptococcus anginosus than healthy people. Also, by constructing a co-occurrence network at the species level, Bifidobacterium longum in the healthy group and Veillonella parvulain the COVID-19 group were found as hub species. Conclusion: The results of this study shed light on the relationship between the gut microbiome and COVID-19. These results could be helpful for understanding the pathogenesis, clinical features, and treatment of COVID-9.

Evaluation of Genes and Molecular Pathways Involved in the Progression of Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma: A Systems Biology Approach.

Today, Monoclonal Gammopathy of Undetermined Significance (MGUS) is known as a plasma cell malignancy susceptible to evolving into the life-threatening stage, multiple myeloma (MM), without prominent clinical manifestations. Despite the discovery of advanced therapies and multiple pathogenic markers, the complexity of MM development has made it an incurable malignancy. In this study, the microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database and analyzed using the LIMMA package of R-software to determine differentially expressed genes (DEGs) in MGUS and MM compared to the control samples. Enrichment analysis of DEGs was evaluated using the GeneCodis4 software. Protein-protein interaction (PPI) networks were constructed via the GeneMANIA database, and Cytoscape visualized them. The Molecular Complex Detection (MCODE) plugin from Cytoscape was used to identify the key modules from the PPI network. Afterward, the hub genes were recognized using the cytoHubba plug-in in Cytoscape. Eventually, the correlation between hub-DEGs and MM-specific survival was evaluated via the PrognoScan database. A total of 138 (MM-normal) and 136 (MGUS-normal) DEGs were obtained from the datasets, and 62 common DEGs between MGUS and MM diseases (26 up-regulated and 36 down-regulated genes) were screened out for subsequent analyses. Following enrichment analyses and the PPI network's evaluation, FOS, FOSB, JUN, MAFF, and PPP1R15A involved in the progression of MGUS to MM were detected as the hub genes. The survival analysis revealed that FOS, FOSB, and JUN among hub genes were significantly associated with disease-specific survival (DSS) in MM. Identifying the genes involved in the progression of MGUS to MM can help in the design of preventive strategies as well as the treatment of patients. In addition, their evaluation can be effective in the survival of patients.

Bioinformatics analysis of microarray data to identify hub genes, as diagnostic biomarker of HELLP syndrome: System biology approach.

BACKGROUND: HELLP syndrome is one of the disorders characterized by hemolysis, increased liver enzymes and decreased platelet count. So far, many molecular pathways and genes have been identified in relation to the pathogenesis of this syndrome; however, the main cause of the incidence and progression of the disease has not been identified. Using the biological system approach is a way to target patients by identifying genes and molecular pathways. In this study, we investigated genes and important molecular factors in the pathogenesis of HELLP syndrome. MATERIAL AND METHODS: In this study, the microarray dataset was downloaded from Gene Expression Omnibus (GEO) database and analyzed using the GEO2R online tool for identifying differentially expressed genes (DEGs). Enrichment analysis of DEGs was evaluated using the Enrichr database. Then, protein-protein interaction (PPI) networks were constructed via the STRING database; they were visualized by Cytoscape. Then the STRING database was used to construct PPI networks. The hub genes were recognized using the cytoHubba. Ultimately, the interaction of the miRNA-hub genes and drug-hub genes were also evaluated. RESULT: After analysis, it was found that some genes with the highest degree of connectivity are involved in the pathogenesis of HELLP syndrome, which are known as the hub genes. These genes are as follows: KIT, JAK2, LEP, EP300, HIST1H4L, HIST1H4F, HIST1H4H, MMP9, THBS2, and ADAMTS3. Has-miR-34a-5p was also most associated with hub genes. CONCLUSION: Finally, it can be said, that the identification of genes and molecular pathways in HELLP syndrome can be helpful in identifying the pathogenesis pathways of the disease, and designing therapeutic targets.