Genomic and epidemiologic characteristics of SARS-CoV-2 persistent infections in California, January 2021 - July 2023

TL;DR

This study identifies and characterizes 69 persistent SARS-CoV-2 infections in California, revealing their genomic and epidemiological traits to better understand virus evolution and public health implications.

Contribution

A framework for detecting persistent SARS-CoV-2 infections using genomic and epidemiologic data, revealing patterns of intra-host evolution and risk factors.

Findings

Persistent infections lasted up to 400 days and showed significant demographic and clinical differences compared to general SARS-CoV-2 cases.

Mutations in the Spike receptor binding domain suggest immune evasion and convergent evolution across persistent infections.

Two notable persistent infections revealed substantial intra-host evolution and viral subpopulation competition.

Abstract

Novel SARS-CoV-2 variants demonstrating considerable intra-host evolution emerged throughout the pandemic. The persistent infections thought to give rise to these variants, however, have been difficult to identify at scale. This study sought to detect and characterize persistent infection cases in California using routine epidemiologic and genomic surveillance data. We identified 69 persistent infection cases with collection dates between January 2021 and July 2023 ranging from 21 to 400 days in duration, with an average of 44 days. Significant differences were identified in age distribution, sex, hospitalizations, and deaths between persistent infection cases and all sequenced California SARS-CoV-2 cases. Underlying health conditions were identified for the majority of cases with available medical records. In these cases, the Spike receptor binding domain was enriched for nonsynonymous mutations, and these mutations demonstrated convergent evolution indicative of immune evasion and were observed in previous persistent infections. We describe a 400-day B.1.429 infection that demonstrates substantial intra-host evolution, and a BA.5.11 persistent infection revealing apparent competition between two intra-host viral subpopulations. By establishing a framework for detecting persistent infections, this study lays the groundwork for other public health organizations to monitor and investigate highly divergent SARS-CoV-2 viruses.

Genomic surveillance has been used to monitor the evolution and spread of SARS-CoV-2 variants throughout the pandemic. When a new variant emerges, it is often due to the accumulation of mutations during a persistent infection, i.e., in an individual who was unable to clear the virus after an infection. Using genomic and epidemiologic surveillance data, we identify 69 of these persistent infections in California and provide demographic and clinical characteristics of these infections compared to the broader population of SARS-CoV-2 infections. The identification of risk factors for persistent infections provides important insight into the epidemiology of SARS-CoV-2, while the identification of shared mutations between these infections enhances our understanding of SARS-CoV-2 evolution that may result in new variants. Ultimately, our work may help public health practitioners to better monitor persistent infections in the future, prior to the potential emergence and spread of novel variants into the community.

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