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Whole-Genome Sequencing Analysis from the Chikungunya Virus Caribbean Outbreak Reveals Novel Evolutionary Genomic Elements.

ABSTRACT: BACKGROUND:Chikungunya virus (CHIKV), an alphavirus and member of the Togaviridae family, is capable of causing severe febrile disease in humans. In December of 2013 the Asian Lineage of CHIKV spread from the Old World to the Americas, spreading rapidly throughout the New World. Given this new emergence in naïve populations we studied the viral genetic diversity present in infected individuals to understand how CHIKV may have evolved during this continuing outbreak. METHODOLOGY/PRINCIPLE FINDINGS:We used deep-sequencing technologies coupled with well-established bioinformatics pipelines to characterize the minority variants and diversity present in CHIKV infected individuals from Guadeloupe and Martinique, two islands in the center of the epidemic. We observed changes in the consensus sequence as well as a diverse range of minority variants present at various levels in the population. Furthermore, we found that overall diversity was dramatically reduced after single passages in cell lines. Finally, we constructed an infectious clone from this outbreak and identified a novel 3' untranslated region (UTR) structure, not previously found in nature, that led to increased replication in insect cells. CONCLUSIONS/SIGNIFICANCE:Here we preformed an intrahost quasispecies analysis of the new CHIKV outbreak in the Caribbean. We identified novel variants present in infected individuals, as well as a new 3'UTR structure, suggesting that CHIKV has rapidly evolved in a short period of time once it entered this naïve population. These studies highlight the need to continue viral diversity surveillance over time as this epidemic evolves in order to understand the evolutionary potential of CHIKV.

SUBMITTER: Stapleford KA

PROVIDER: S-EPMC4726740 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Whole-Genome Sequencing Analysis from the Chikungunya Virus Caribbean Outbreak Reveals Novel Evolutionary Genomic Elements.

Stapleford Kenneth A KA Moratorio Gonzalo G Henningsson Rasmus R Chen Rubing R Matheus Séverine S Enfissi Antoine A Weissglas-Volkov Daphna D Isakov Ofer O Blanc Hervé H Mounce Bryan C BC Dupont-Rouzeyrol Myrielle M Shomron Noam N Weaver Scott S Fontes Magnus M Rousset Dominique D Vignuzzi Marco M

PLoS neglected tropical diseases 20160125 1

<h4>Background</h4>Chikungunya virus (CHIKV), an alphavirus and member of the Togaviridae family, is capable of causing severe febrile disease in humans. In December of 2013 the Asian Lineage of CHIKV spread from the Old World to the Americas, spreading rapidly throughout the New World. Given this new emergence in naïve populations we studied the viral genetic diversity present in infected individuals to understand how CHIKV may have evolved during this continuing outbreak.<h4>Methodology/princi ...[more]

PMID: 26807575

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Project description:Chikungunya virus (CHIKV), a mosquito-borne alphavirus of the Togaviridae family, is part of a group of emergent diseases, including arbovirus, constituting an increasing public health problem in tropical areas worldwide. CHIKV causes a severe and debilitating disease with high morbidity. The first Colombian autochthonous case was reported in the Colombian Caribbean region in September 2014. Within the next two to three months, the CHIKV outbreak reached its peak. Although the CHIKV pattern of clinical symptomatology has been documented in different epidemiological studies, understanding of the relationship between clinical symptomatology and variation in phenotypic response to CHIKV infection in humans remains limited. We performed a cross sectional study following 1160 individuals clinically diagnosed with CHIKV at the peak of the Chikungunya outbreak in the Colombian Caribbean region. We examined the relationship between symptomatology and diverse phenotypic responses. Latent Class Cluster Analysis (LCCA) models were used to characterize patients' symptomatology and further identify subgroups of individuals with differential phenotypic response. We found that most individuals presented fever (94.4%), headache (73.28%) and general discomfort (59.4%), which are distinct clinical symptoms of a viral infection. Furthermore, 11/26 (43.2%) of the categorized symptoms were more frequent in women than in men. LCCA disclosed seven distinctive phenotypic response profiles in this population of CHIKV infected individuals. Interestingly, 282 (24.3%) individuals exhibited a lower symptomatic "extreme" phenotype and 74 (6.4%) patients were within the severe complex "extreme" phenotype. Although clinical symptomatology may be diverse, there are distinct symptoms or group of symptoms that can be correlated with differential phenotypic response and perhaps susceptibility to CHIKV infection, especially in the female population. This suggests that, comparatively to men, women are a CHIKV at-risk population. Further study is needed to validate these results and determine whether the distinct LCCA profiles are a result of the immune response or a mixture of genetic, lifestyle and environmental factors. Our findings could contribute to the development of machine learning approaches to characterizing CHIKV infection in other populations. Preliminary results have shown prediction models achieving up to 92% accuracy overall, with substantial sensitivity, specificity and accuracy values per LCCA-derived cluster.

Project description:The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions.IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children's hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions.

Dataset Information

Whole-Genome Sequencing Analysis from the Chikungunya Virus Caribbean Outbreak Reveals Novel Evolutionary Genomic Elements.

Publications

Whole-Genome Sequencing Analysis from the Chikungunya Virus Caribbean Outbreak Reveals Novel Evolutionary Genomic Elements.

Similar Datasets

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