Title: Similar gene expression profiles define leptospirosis clinical outcomes

Authors: Nivison Ruy Rocha Nery Júnior; Daniela Barreiro Claro; Janet C. Lindow

Addresses: Semantic Formalisms and Applications Research Group (FORMAS), Computer Science Department, Federal University of Bahia (UFBA), Salvador, Brazil ' Semantic Formalisms and Applications Research Group (FORMAS), Computer Science Department, Federal University of Bahia (UFBA), Salvador, Brazil ' Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06520, USA

Abstract: Leptospirosis, an acute, febrile disease with high case fatality, is prevalent in many tropical, urban regions. The mechanisms leading to death from leptospirosis are not fully understood. However, recent studies indicate that differences in the immune response during acute infection are associated with fatality. To identify transcriptional signatures that could differentiate survivors and case fatalities, we analysed data obtained from full human genome transcriptome profiling of whole blood from patients with different disease outcomes. Using clustering algorithms, we identified unique groups, demonstrating that surviving patients and fatal cases have significant differences in their transcriptional profiles. We also confirmed our prior findings, which showed expression differences in genes involved in the immune response.

Keywords: clustering analysis; leptospirosis; gene expression.

DOI: 10.1504/IJBRA.2020.113021

International Journal of Bioinformatics Research and Applications, 2020 Vol.16 No.4, pp.373 - 386

Received: 06 Nov 2017
Accepted: 14 Apr 2018
Published online: 16 Feb 2021 *

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