Abstract
SummaryBackgroundComprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library.MethodsHere we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus.FindingsBy comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively.InterpretationAVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.FundingThis work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).
Highlights
Unbiased profiling of antiviral antibody binding specificities has broad utility for epidemiological investigations, surveillance for emerging viruses, and the diagnosis of infections.1À4 Phage ImmunoPrecipitation Sequencing (“PhIP-Seq”)[5] with a peptide library spanning the human virome (“VirScan”)[6] provides a platform for comprehensive, high-throughput, low-cost analysis of antiviral antibodies
The Singapore Neurologic Infections Program (SNIP) study aims to describe the epidemiology of CNS infections in Singapore; improve the diagnosis of etiologies of CNS infections through a systematic clinical, laboratory and neuroradiological evaluation and extensive diagnostic testing; evaluate the prognosis, long-term outcomes and socio-economic costs of CNS infections; and establish an archive of biological tissues from patients with encephalitis and CNS infections that can be utilized for future testing for emerging pathogens or non-infectious etiologies
Acute versus convalescent VirScan profiles The quality of the VirScan library was assessed via sequencing to a depth of 360-fold coverage
Summary
Unbiased profiling of antiviral antibody binding specificities has broad utility for epidemiological investigations, surveillance for emerging viruses, and the diagnosis of infections.1À4 Phage ImmunoPrecipitation Sequencing (“PhIP-Seq”)[5] with a peptide library spanning the human virome (“VirScan”)[6] provides a platform for comprehensive, high-throughput, low-cost analysis of antiviral antibodies. A significantly reactive peptide was considered only in the context of the specific virus it was designed to represent. This ignored sequence homology between related viruses, and any potential for antibody cross-reactivity. Relying solely upon the intended viral representations of reactive peptides to diagnose infections will result in both false negative results (“missing” proteins from highly similar organisms) and false positive results (reactivity due to unappreciated cross-reactive antibodies). We previously relied on each virus's proteome “size” to establish virus-specific thresholds for seropositivity. This approach ignored the proportional representation of each virus within the reactive set of peptides and the overall representation of each virus in the library. Using a binary threshold for seropositivity is far less informative than a probabilistic assessment of a viral infection
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