Unraveling the Complexity of Chikungunya Virus Infection Immunological and Genetic Insights in Acute and Chronic Patients

Abstract

Background: The chikungunya virus (CHIKV), transmitted by infected Aedes mosquitoes, has caused a significant number of infections worldwide. In Brazil, the emergence of the CHIKV-ECSA genotype in 2014 posed a major public health challenge due to its association with more severe symptoms. Objectives/Methods: This study aimed to shed new light on the host immune response by examining the whole-blood transcriptomic profile of both CHIKV-acute and chronically infected individuals from Feira de Santana, Bahia, Brazil, a region heavily affected by CHIKV, Dengue, and Zika virus epidemics. Results: Our data reveal complex symptomatology characterized by arthralgia and post-chikungunya neuropathy in individuals with chronic sequelae, particularly affecting women living in socially vulnerable situations. Analysis of gene modules suggests heightened metabolic processes, represented by an increase in NADH, COX5A, COA3, CYC1, and cap methylation in patients with acute disease. In contrast, individuals with chronic manifestations exhibit a distinct pattern of histone methylation, probably mediated by NCOA3 in the coactivation of different nuclear receptors, KMT2 genes, KDM3B and TET2, and with alterations in the immunological response, majorly led by IL-17RA, IL-6R, and STAT3 Th17 genes. Conclusion: Our results emphasize the complexity of CHIKV disease progression, demonstrating the heterogeneous gene expression and symptomatologic scenario across both acute and chronic phases. Moreover, the identification of specific gene modules associated with viral pathogenesis provides critical insights into the molecular mechanisms underlying these distinct clinical manifestations.

Keywords: Chikungunya chronicity; Chikungunya virus; RNA-sequencing; acute infection; immune response; immunological characterization.

Figure 1

Symptoms and comorbidities panel reported by patients manifesting chronic and acute signs of Chikungunya virus infection. (A) Compromised joints in individuals with chronic and acute Chikungunya virus infection. For 10 acute individuals, it was not possible to retrieve data regarding the arthralgia sites. (B) Points of myalgia reported by chronic and acute individuals for Chikungunya virus infection. For 10 acute individuals, it was not possible to retrieve data about the myalgia points. (C) Pre-existing comorbidities described by individuals chronically affected by Chikungunya virus and individuals in the acute phase; (D) Secondary manifestations indicated by chronic and acute individuals for Chikungunya virus reported by clinical examination and medical record.

Figure 2

Weighted Gene Coexpression Network Analysis (WGCNA) for distinct Chikungunya virus infection stages and control group. (A) Composition of gene modules found differentially coexpressed in acute and chronic individuals for Chikungunya virus. In red are modules with a size of less than 50 genes; in olive modules comprising 50–100 genes; in green modules containing up to 500 genes; in blue gene size modules between 500 and 1000; and, in magenta, modules larger than 1000 genes. (B) Coexpression trait heatmap of the 31 modules was found to differentially coexpress in patients in the acute stage of infection and individuals in the chronic stage. The rows represent each of the 31 modules found, while the columns represent the different clinical stages of Chikungunya virus infection. Positive correlations are indicated in shades of red, while negative correlations are in shades of blue. *: p-values < 0.05; **: p-values < 0.01; ***: p-values < 0.001. (C) Coexpression module M30 positively correlated with CHIKV acute patients and negatively correlated with chronic individuals. Image obtained using the STRING software. Genes are colored based on subclustering identification using the MCL clustering algorithm (Table S6). (D) Coexpression module M17 positively correlated with CHIKV acute patients and negatively correlated with chronic individuals. Image obtained using the STRING software. Genes are colored based on subclustering identification using the MCL clustering algorithm (Table S7). (E) Coexpression module M28 negatively correlated with acute patients and positively correlated with CHIKV chronic individuals. Image obtained using the STRING software. Genes are colored based on subclustering identification using the MCL clustering algorithm (Table S8).