Systemic integrative analysis of the network of molecules associated with neuroimmune dysregulation induced by viral hepatitis.

• Grant number: 24/08016-8
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: February 01, 2025
• End date: December 31, 2027
• Field of knowledge: Biological Sciences - Genetics - Molecular Genetics and Genetics of Microorganisms
• Principal Investigator: Otávio Cabral Marques
• Grantee: Adriel Leal Nóbile
• Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Viral hepatitis A, B, C, D, and E (respectively, VHA, VHB, VHC, VHD, and VHE) constitute a group of viruses that present significant differences in terms of physiopathology, taxonomy, biological cycle, structure, and genetics. However, all of them result in similar pathologies, such as jaundice, liver lesions, fatigue, dizziness, nausea, vomiting, fever, abdominal pain, dark urine, and pale stools. Additionally, extra-hepatic symptoms have been observed, including neurological conditions such as cerebrovascular events, encephalomyelitis, cognitive impairment, and psychiatric disorders such as anxiety, fatigue, and depression. However, the network of molecules associated with this neuroimmunological dysregulation induced by these viruses is still poorly investigated. The aim of this study is to perform an integrative and systematic analysis of the network of molecules associated with neuroimmunological dysregulation induced by viral hepatitis. To this end, we will use public transcriptome data from the Gene Expression Omnibus (GEO) and clinical information from the Cancer Genome Atlas (TCGA). We will investigate the signaling pathways and molecular signatures associated with the dysregulation of the network of molecules overexpressed in these infections, characterizing them through genetic ontologies. We will select genes that may be related to neuroimmunological dysregulation and evaluate how these genes stratify patients using machine learning techniques, such as Linear Discriminant Analysis. Additionally, we will rank the most important genes for the development of patient phenotypes using machine learning techniques such as Random Forest. Subsequently, we will perform co-expression analysis in single-cell cohorts to identify cellular subpopulations using the Seurat package in the R language. Validation of these analyses and additional tests will be conducted at Stanford University during the Research Internship Abroad (BEPE), as described in the recommendation letter. We will use methods such as cell culture obtained from patients, validating findings through techniques such as flow cytometry or confocal microscopy, aimed at characterizing new neuroimmunopathological mechanisms in the context of these infections. Thus, we will promote a translational and multidisciplinary study that may identify useful biomarkers for the diagnosis of patients with viral hepatitis, as well as enabling the development of alternative and/or novel immunizations.