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serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Menezes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Many questions remain about the prevalence and effects of SARS-CoV-2 infection in malaria-endemic African countries like Uganda, particularly in vulnerable groups such as pregnant women. We describe SARS-CoV-2 immunoglobulin (Ig)G and IgM antibody responses and clinical outcomes in mother-infant dyads enrolled in malaria chemoprevention trials in Uganda. From December 2020-February 2022, among 400 unvaccinated pregnant women enrolled at 12-20 weeks gestation and followed through delivery, 128 (32%) were seronegative for anti-SARS-CoV-2 IgG and IgM at enrollment and delivery, 80 (20%) were infected prior to or early in pregnancy, and 192 (48%) were infected or re-infected with SARS-CoV-2 during pregnancy. We observed preferential binding of plasma IgG to Wuhan-Hu-1-like antigens in individuals seroconverting up to early 2021, and to Delta variant antigens in a subset of individuals in mid-2021. Breadth of IgG binding to all variants improved over time, consistent with affinity maturation of the antibody response in the cohort. No women experienced severe respiratory illness during the study. SARS-CoV-2 infection in early pregnancy was associated with lower median length-for-age Z-score at age 3 months compared with no infection or late pregnancy infect (-1.54 versus -0.37 and -0.51, P = 0.009). These findings suggest that pregnant Ugandan women experienced high levels of SARS-CoV-2 infection without severe respiratory illness. Variant-specific serology testing demonstrated evidence of antibody affinity maturation at the population level. Early gestational SARS-CoV-2 infection was associated with transient shorter stature in early infancy. Further research should explore the significance of this finding and define targeted measures to prevent infection in pregnancy.

The associations between longitudinal dynamics and the breadth of SARS-CoV-2 neutralizing antibody (nAb) response with various Long COVID phenotypes before vaccination are not known. The capacity of antibodies to cross-neutralize a variety of viral variants may be associated with ongoing pathology and persistent symptoms. We measured longitudinal neutralizing and cross-neutralizing antibody responses to pre- and post-SARS-CoV-2 Omicron variants in participants infected early in the COVID-19 pandemic, before widespread rollout of SARS-CoV-2 vaccines. Cross-sectional regression models adjusted for clinical covariates and longitudinal mixed-effects models were used to determine the impact of the breadth and rate of decay of neutralizing responses on the development of Long COVID symptoms, as well as Long COVID phenotypes. We identified several novel relationships between SARS-CoV-2 antibody neutralization and the presence of Long COVID symptoms. Specifically, we show that, although nAb responses to the original, infecting strain of SARS-CoV-2 were not associated with Long COVID in cross-sectional analyses, cross-neutralization ID50 levels to the Omicron BA.5 variant approximately 4 months following acute infection was independently and significantly associated with greater odds of Long COVID and with persistent gastrointestinal and neurological symptoms. Longitudinal modeling demonstrated significant associations in the overall levels and rates of decay of neutralization capacity with Long COVID phenotypes. A higher proportion of participants had antibodies capable of neutralizing Omicron BA.5 compared with BA.1 or XBB.1.5 variants. Our findings suggest that relationships between various immune responses and Long COVID are likely complex but may involve the breadth of antibody neutralization responses.

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Protection against Plasmodium falciparum, which is primarily antibody-mediated, requires recurrent exposure to develop. The study of both naturally acquired limited immunity and vaccine induced protection against malaria remains critical for ongoing eradication efforts. Towards this goal, we deployed a customized P. falciparum PhIP-seq T7 phage display library containing 238,068 tiled 62-amino acid peptides, covering all known coding regions, including antigenic variants, to systematically profile antibody targets in 198 Ugandan children and adults from high and moderate transmission settings. Repeat elements - short amino acid sequences repeated within a protein - were significantly enriched in antibody targets. While breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, no such differences were observed for peptides without repeats, suggesting that antibody responses to repeat-containing regions may be more exposure dependent and/or less durable in children than responses to regions without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross-reactive epitopes. PfEMP1 shared motifs with the greatest number of other antigens, partly driven by the diversity of PfEMP1 sequences. These data suggest that the large number of repeat elements and potential cross-reactive epitopes found within antigenic regions of P. falciparum could contribute to the inefficient nature of malaria immunity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific CD4+ T cells are likely important in immunity against coronavirus 2019 (COVID-19), but our understanding of CD4+ longitudinal dynamics following infection and of specific features that correlate with the maintenance of neutralizing antibodies remains limited. Here, we characterize SARS-CoV-2-specific CD4+ T cells in a longitudinal cohort of 109 COVID-19 outpatients enrolled during acute infection. The quality of the SARS-CoV-2-specific CD4+ response shifts from cells producing interferon gamma (IFNγ) to tumor necrosis factor alpha (TNF-α) from 5 days to 4 months post-enrollment, with IFNγ-IL-21-TNF-α+ CD4+ T cells the predominant population detected at later time points. Greater percentages of IFNγ-IL-21-TNF-α+ CD4+ T cells on day 28 correlate with SARS-CoV-2-neutralizing antibodies measured 7 months post-infection (⍴ = 0.4, p = 0.01). mRNA vaccination following SARS-CoV-2 infection boosts both IFNγ- and TNF-α-producing, spike-protein-specific CD4+ T cells. These data suggest that SARS-CoV-2-specific, TNF-α-producing CD4+ T cells may play an important role in antibody maintenance following COVID-19.

BackgroundThe great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.MethodsLeveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.ResultsWe identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.ConclusionsEarly immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.FundingSupport for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

We describe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell responses, soluble markers of inflammation, and antibody levels and neutralization capacity longitudinally in 70 individuals with PCR-confirmed SARS-CoV-2 infection. Participants represent a spectrum of illness and recovery, including some with persistent viral shedding in saliva and many experiencing post-acute sequelae of SARS-CoV-2 infection (PASC). T cell responses remain stable for up to 9 months. Whereas the magnitude of early CD4+ T cell immune responses correlates with severity of initial infection, pre-existing lung disease is independently associated with higher long-term SARS-CoV-2-specific CD8+ T cell responses. Among participants with PASC 4 months following coronavirus disease 2019 (COVID-19) symptom onset, we observe a lower frequency of CD8+ T cells expressing CD107a, a marker of degranulation, in response to Nucleocapsid (N) peptide pool stimulation, and a more rapid decline in the frequency of N-specific interferon-γ-producing CD8+ T cells. Neutralizing antibody levels strongly correlate with SARS-CoV-2-specific CD4+ T cell responses.

Biased seroprevalence estimates can occur using serological assays optimized with validation sets unrepresentative of disease spectrum in the general population. Correct interpretation of serosurveys for severe acute respiratory syndrome coronavirus 2 requires quantifying variations in sensitivity with disease severity and over time.

Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serologic studies (e.g., during influenza pandemics) has made relevant contributions, but specific conditions of the current situation require adaptation. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies actually means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studied, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine global transmission of severe acute respiratory syndrome coronavirus 2.