Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning

Abstract

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein–protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

System biology approach to identify the novel biomarkers in glioblastoma multiforme tumors by using computational analysis

“…To find the potential biomarkers for GBM, an integrated approach was used that included the proteomic and transcriptomic modeling of molecular networks of microarray data ( Noor et al, 2023a ) that have not been established previously. We used gene expression data for defining the prospective gene/protein biomarkers that can be targeted for the treatment of glioblastoma multiforme utilizing the system biology approach based on microarray datasets.…”

Identification and characterization of codon usage pattern and influencing factors in HFRS-causing hantaviruses

“…The nucleoprotein, encoded by the S segment, of hantaviruses consist of 429 to 433 amino acids ( 22 ). This nucleoprotein interacts with the host proteins and limits the activation of the major antiviral signaling pathways in affected cells ( 23 ).…”

“…Compared to non-pathogenic strains, pathogenic hantaviruses significantly alter the transcriptional activity of many cellular genes (18,19). Recent studies provide solid evidence that hantaviruses' nucleocapsid proteins have a key role in virus transcription, replication, and assembly (20,21). The nucleoprotein, encoded by the S segment, of hantaviruses consist of 429 to 433 amino acids (22).…”