Neutralizing antibody responses over time in demographically and clinically diverse individuals recovered from SARS-CoV-2 infection in the United States and Peru: A cohort study.

BACKGROUND: People infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) experience a wide range of clinical manifestations, from asymptomatic and mild illness to severe illness and death, influenced by age and a variety of comorbidities. Neutralizing antibodies (nAbs) are thought to be a primary immune defense against the virus. Large, diverse, well-characterized cohorts of convalescent individuals provide standardized values to benchmark nAb responses to past SARS-CoV-2 infection and define potentially protective levels of immunity. METHODS AND FINDINGS: This analysis comprises an observational cohort of 329 HIV-seronegative adults in the United States (n = 167) and Peru (n = 162) convalescing from SARS-CoV-2 infection from May through October 2020. The mean age was 48 years (range 18 to 86), 54% of the cohort overall was Hispanic, and 34% identified as White. nAb titers were measured in serum by SARS-CoV-2.D614G Spike-pseudotyped virus infection of 293T/ACE2 cells. Multiple linear regression was applied to define associations between nAb titers and demographic variables, disease severity and time from infection or disease onset, and comorbidities within and across US and Peruvian cohorts over time. nAb titers peaked 28 to 42 days post-diagnosis and were higher in participants with a history of severe Coronavirus Disease 2019 (COVID-19) (p < 0.001). Diabetes, age >55 years, male sex assigned at birth, and, in some cases, body mass index were also independently associated with higher nAb titers, whereas hypertension was independently associated with lower nAb titers. nAb titers did not differ by race, underlying pulmonary disease or smoking. Two months post-enrollment, nAb ID50 (ID80) titers declined 3.5 (2.8)-fold overall. Study limitations in this observational, convalescent cohort include survivorship bias and missing early viral loads and acute immune responses to correlate with the convalescent responses we observed. CONCLUSIONS: In summary, in our cohort, nAb titers after SARS-CoV-2 infection peaked approximately 1 month post-diagnosis and varied by age, sex assigned at birth, disease severity, and underlying comorbidities. Our data show great heterogeneity in nAb responses among people with recent COVID-19, highlighting the challenges of interpreting natural history studies and gauging responses to vaccines and therapeutics among people with recent infection. Our observations illuminate potential correlations of demographic and clinical characteristics with nAb responses, a key element for protection from COVID-19, thus informing development and implementation of preventative and therapeutic strategies globally. TRIAL REGISTRATION: ClinicalTrials.gov NCT04403880.

Post-COVID symptom profiles and duration in a global convalescent COVID-19 observational cohort: Correlations with demographics, medical history, acute COVID-19 severity and global region.

Background: Post-COVID conditions are characterised by persistent symptoms that negatively impact quality of life after SARS-CoV-2 diagnosis. While post-COVID risk factors and symptoms have been extensively described in localised regions, especially in the global north, post-COVID conditions remain poorly understood globally. The global, observational cohort study HVTN 405/HPTN 1901 characterises the convalescent course of SARS-CoV-2 infection among adults in North and South America and Africa. Methods: We categorised the cohort by infection severity (asymptomatic, symptomatic, no oxygen requirement (NOR), non-invasive oxygen requirement (NIOR), invasive oxygen requirement (IOR)). We applied a regression model to assess correlations of demographics, co-morbidities, disease severity, and concomitant medications with COVID-19 symptom persistence and duration across global regions. Results: We enrolled 759 participants from Botswana, Malawi, South Africa, Zambia, Zimbabwe, Peru, and the USA a median of 51 (interquartile range (IQR) = 35-66) days post-diagnosis, from May 2020 to March 2021. 53.8% were female, 69.8% were 18-55 years old (median (md) = 44 years old, IQR = 33-58). Comorbidities included obesity (42.8%), hypertension (24%), diabetes (14%), human immunodeficiency virus (HIV) infection (11.6%) and lung disease (7.5%). 76.2% were symptomatic (NOR = 47.4%; NIOR = 22.9%; IOR = 5.8%). Median COVID-19 duration among symptomatic participants was 20 days (IQR = 11-35); 43.4% reported symptoms after COVID-19 resolution, 33.6% reported symptoms ≥30 days, 9.9% reported symptoms ≥60 days. Symptom duration correlated with disease severity (P < 0.001, NIOR vs NOR; P = 0.003, IOR vs NOR), lung disease (P = 0.001), race (P < 0.05, non-Hispanic Black vs White), and global region (P < 0.001). Prolonged viral shedding correlated with persistent abdominal pain (odds ratio (OR) = 5.51, P < 0.05) and persistent diarrhoea (OR = 6.64, P < 0.01). Conclusions: Post-COVID duration varied with infection severity, race, lung disease, and region. Better understanding post-COVID conditions, including regionally-diverse symptom profiles, may improve clinical assessment and management globally. Registration: Clinicaltrials.gov (#NCT04403880).

Cellular and humoral responses to an HIV DNA prime by electroporation boosted with recombinant vesicular stomatitis virus expressing HIV subtype C Env in a randomized controlled clinical trial.

BACKGROUND: HIV subtypes B and C together account for around 60% of HIV-1 cases worldwide. We evaluated the safety and immunogenicity of a subtype B DNA vaccine prime followed by a subtype C viral vector boost. METHODS: Fourteen healthy adults received DNA plasmid encoding HIV-1 subtype B nef/tat/vif and env (n = 11) or placebo (n = 3) intramuscularly (IM) via electroporation (EP) at 0, 1, and 3 months, followed by IM injection of recombinant vesicular stomatitis virus encoding subtype C Env or placebo at 6 and 9 months. Participants were assessed for safety, tolerability of EP, and Env-specific T-cell and antibody responses. RESULTS: EP was generally well tolerated, although some device-related adverse events did occur, and vaccine reactogenicity was mild to moderate. The vaccine stimulated Env-specific CD4 + T-cell responses in greater than 80% of recipients, and CD8 + T-cell responses in 30%. Subtype C Env-specific IgG binding antibodies (bAb) were elicited in all vaccine recipients, and antibody-dependent cell-mediated cytotoxicity (ADCC) responses to vaccine-matched subtype C targets in 80%. Negligible V1/V2 and neutralizing antibody (nAb) responses were detected. CONCLUSIONS: This prime/boost regimen was safe and tolerable, with some device-related events, and immunogenic. Although immunogenicity missed targets for an HIV vaccine, the DNA/rVSV platform may be useful for other applications. CLINICALTRIALS: gov: NCT02654080.

Sequencing genes in silico using single nucleotide polymorphisms.

BACKGROUND: The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive. RESULTS: To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes. CONCLUSIONS: Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.

Associations of human leukocyte antigen with neutralizing antibody titers in a tetravalent dengue vaccine phase 2 efficacy trial in Thailand.

The recombinant, live, attenuated, tetravalent dengue vaccine CYD-TDV has shown efficacy against all four dengue serotypes. In this exploratory study (CYD59, NCT02827162), we evaluated potential associations of host human leukocyte antigen (HLA) alleles with dengue antibody responses, CYD-TDV vaccine efficacy, and virologically-confirmed dengue (VCD) cases. Children 4-11 years old, who previously completed a phase 2b efficacy study of CYD-TDV in a single center in Thailand, were included in the study. Genotyping of HLA class I and II loci was performed by next-generation sequencing from DNA obtained from 335 saliva samples. Dengue neutralizing antibody titers (NAb) were assessed as a correlate of risk and protection. Regression analyses were used to assess associations between HLA alleles and NAb responses, vaccine efficacy, and dengue outcomes. Month 13 NAb log geometric mean titers (GMTs) were associated with decreased risk of VCD. In the vaccine group, HLA-DRB1*11 was significantly associated with higher NAb log GMT levels (beta: 0.76; p = 0.002, q = 0.13). Additionally, in the absence of vaccination, HLA associations were observed between the presence of DPB1*03:01 and increased NAb log GMT levels (beta: 1.24; p = 0.005, q = 0.17), and between DPB1*05:01 and reduced NAb log GMT levels (beta: -1.1; p = 0.001, q = 0.07). This study suggests associations of HLA alleles with NAb titers in the context of dengue outcomes. This study was registered with clinicaltrials.gov: NCT02827162.

Empirical evaluations of analytical issues arising from predicting HLA alleles using multiple SNPs.

BACKGROUND: Numerous immune-mediated diseases have been associated with the class I and II HLA genes located within the major histocompatibility complex (MHC) consisting of highly polymorphic alleles encoded by the HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1 loci. Genotyping for HLA alleles is complex and relatively expensive. Recent studies have demonstrated the feasibility of predicting HLA alleles, using MHC SNPs inside and outside of HLA that are typically included in SNP arrays and are commonly available in genome-wide association studies (GWAS). We have recently described a novel method that is complementary to the previous methods, for accurately predicting HLA alleles using unphased flanking SNPs genotypes. In this manuscript, we address several practical issues relevant to the application of this methodology. RESULTS: Applying this new methodology to three large independent study cohorts, we have evaluated the performance of the predictive models in ethnically diverse populations. Specifically, we have found that utilizing imputed in addition to genotyped SNPs generally yields comparable if not better performance in prediction accuracies. Our evaluation also supports the idea that predictive models trained on one population are transferable to other populations of the same ethnicity. Further, when the training set includes multi-ethnic populations, the resulting models are reliable and perform well for the same subpopulations across all HLA genes. In contrast, the predictive models built from single ethnic populations have superior performance within the same ethnic population, but are not likely to perform well in other ethnic populations. CONCLUSIONS: The empirical explorations reported here provide further evidence in support of the application of this approach for predicting HLA alleles with GWAS-derived SNP data. Utilizing all available samples, we have built "state of the art" predictive models for HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1. The HLA allele predictive models, along with the program used to carry out the prediction, are available on our website.

A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires' disease.

Although Toll-like receptors (TLRs) are critical mediators of the immune response to pathogens, the influence of polymorphisms in this gene family on human susceptibility to infection is poorly understood. We demonstrated recently that TLR5 recognizes flagellin, a potent inflammatory stimulus present in the flagellar structure of many bacteria. Here, we show that a common stop codon polymorphism in the ligand-binding domain of TLR5 (TLR5392STOP) is unable to mediate flagellin signaling, acts in a dominant fashion, and is associated with susceptibility to pneumonia caused by Legionella pneumophila, a flagellated bacterium. We also show that flagellin is a principal stimulant of proinflammatory cytokine production in lung epithelial cells. Together, these observations suggest that TLR5392STOP increases human susceptibility to infection through an unusual dominant mechanism that compromises TLR5's essential role as a regulator of the lung epithelial innate immune response.

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise procedure.

BACKGROUND: Genotyping technologies enable us to genotype multiple Single Nucleotide Polymorphisms (SNPs) within selected genes/regions, providing data for haplotype association analysis. While haplotype-based association analysis is powerful for detecting untyped causal alleles in linkage-disequilibrium (LD) with neighboring SNPs/haplotypes, the inclusion of extraneous SNPs could reduce its power by increasing the number of haplotypes with each additional SNP. METHODS: Here, we propose a haplotype-based stepwise procedure (HBSP) to eliminate extraneous SNPs. To evaluate its properties, we applied HBSP to both simulated and real data, generated from a study of genetic associations of the bactericidal/permeability-increasing (BPI) gene with pulmonary function in a cohort of patients following bone marrow transplantation. RESULTS: Under the null hypothesis, use of the HBSP gave results that retained the desired false positive error rates when multiple comparisons were considered. Under various alternative hypotheses, HBSP had adequate power to detect modest genetic associations in case-control studies with 500, 1,000 or 2,000 subjects. In the current application, HBSP led to the identification of two specific SNPs with a positive validation. CONCLUSION: These results demonstrate that HBSP retains the essence of haplotype-based association analysis while improving analytic power by excluding extraneous SNPs. Minimizing the number of SNPs also enables simpler interpretation and more cost-effective applications.

Empirical vs Bayesian approach for estimating haplotypes from genotypes of unrelated individuals.

BACKGROUND: The completion of the HapMap project has stimulated further development of haplotype-based methodologies for disease associations. A key aspect of such development is the statistical inference of individual diplotypes from unphased genotypes. Several methodologies for inferring haplotypes have been developed, but they have not been evaluated extensively to determine which method not only performs well, but also can be easily incorporated in downstream haplotype-based association analyses. In this paper, we attempt to do so. Our evaluation was carried out by comparing the two leading Bayesian methods, implemented in PHASE and HAPLOTYPER, and the two leading empirical methods, implemented in PL-EM and HPlus. We used these methods to analyze real data, namely the dense genotypes on X-chromosome of 30 European and 30 African trios provided by the International HapMap Project, and simulated genotype data. Our conclusions are based on these analyses. RESULTS: All programs performed very well on X-chromosome data, with an average similarity index of 0.99 and an average prediction rate of 0.99 for both European and African trios. On simulated data with approximation of coalescence, PHASE implementing the Bayesian method based on the coalescence approximation outperformed other programs on small sample sizes. When the sample size increased, other programs performed as well as PHASE. PL-EM and HPlus implementing empirical methods required much less running time than the programs implementing the Bayesian methods. They required only one hundredth or thousandth of the running time required by PHASE, particularly when analyzing large sample sizes and large umber of SNPs. CONCLUSION: For large sample sizes (hundreds or more), which most association studies require, the two empirical methods might be used since they infer the haplotypes as accurately as any Bayesian methods and can be incorporated easily into downstream haplotype-based analyses such as haplotype-association analyses.

A haplotype-linkage analysis method for estimating recombination rates using dense SNP trio data.

Meiotic recombination is an important evolution force in shaping human genomes and creating human diversity. Recent explorations suggest that meiotic recombination events tend to happen in certain regions of the genome, leading to the hypothesis of recombination hot spots. To identify such hot spots, we describe an empirical method for estimating the recombination rate, which reflects both historical and current meiotic events, using unphased genotypes from nuclear families, in particular, parents-child trios. The key idea is that use of the haplotypic polymorphisms with multiple adjacent SNPs will increase the number of informative meioses and hence would improve the power of linkage analysis. Since haplotypes of individuals are not directly observed, e.g., in HapMap data, we infer the haplotypes simultaneously while estimating recombination rate. We refer this described method as haplotype-linkage (HALIN) method. Our simulation results show that HALIN gives unbiased estimates of recombination rate. We apply HALIN to analyze the genotype data of chromosome 20 from HapMap data and compare the results to the results using two existing methods, Bayesian coalescent method described by McVean et al. [2004] science 304:581-584 and empirical method by Clarke and Cardon ([2005] Genetics). Results suggest that HALIN identified 75 hot spots on chromosome 20, and 85% of them are also identified by the method by McVean et al. ([2004] science 304:581-584), in addition to a few new hot spots. In comparison with Clarke and Cardon's result, estimated recombination rates (RRs) under these 75 hot spots are significantly greater than those outside of hot spots, which support the general consistency in discovering recombination hot spots between two methods.

Toward understanding MHC disease associations: partial resequencing of 46 distinct HLA haplotypes.

We carried out a resequencing project that examined 552 kb of sequence from each of 46 individual HLA haplotypes representing a diversity of HLA allele types, generating nearly 27 Mb of fully phased genomic sequence. Haplotype blocks were defined extending from telomeric of HLA-F to centromeric of HLA-DP including in total 5186 MHC SNPs. To investigate basic questions about the evolutionary origin of common HLA haplotypes, and to obtain an estimate of rare variation in the MHC, we similarly examined two additional sets of samples. In 19 independent HLA-A1, B8, DR3 chromosomes, the most common HLA haplotype in Northern European Caucasians, variation was found at 11 SNP positions in the 3600-kb region from HLA-A to DR. Partial resequencing of 282 individuals in the gene-dense class III region identified significant variability beyond what could have been detected by linkage to common SNPs.

HLA-E, HLA-F, and HLA-G polymorphism: genomic sequence defines haplotype structure and variation spanning the nonclassical class I genes.

Despite several studies that defined the polymorphism of the nonclassical human leukocyte antigen-E (HLA-E), HLA-F, and HLA-G genes, most polymorphisms thus far examined in correlative studies were derived from the coding sequences of these genes. In addition, some discrepancies and ambiguities in the available data have persisted in current databases. To expand the data available and to resolve some of the discrepant data, we have defined protocols that allow for the amplification of 6 to 7 kb of contiguous genomic sequence for each gene, including all of the coding and intron sequences, approximately 2 kb of 5' flanking promoter sequence, and 1 kb of 3' flanking sequence. Using long-range polymerase chain reaction (PCR) protocols, generating either one or two PCR products depending on the locus, amplified genomic DNA was directly sequenced to completion using a set of about 30 primers over each locus to yield contiguous sequence data from both strands. Using this approach, we sequenced 33 genomic DNAs, from Asian, African American, and Caucasian samples. The results of this analysis confirmed several previously reported coding sequence variants, identified several new allelic variants, and also defined extensive variation in intron and flanking sequences. It was possible to construct haplotype maps and to identify tagging single nucleotide polymorphisms that can be used to detect the composite variation spanning all three genes.

Genetic association of the antiviral restriction factor TRIM5alpha with human immunodeficiency virus type 1 infection.

The innate antiviral factor TRIM5alpha restricts the replication of some retroviruses through its interaction with the viral capsid protein, leading to abortive infection. While overexpression of human TRIM5alpha results in modest restriction of human immunodeficiency virus type 1 (HIV-1), this inhibition is insufficient to block productive infection of human cells. We hypothesized that polymorphisms within TRIM5 may result in increased restriction of HIV-1 infection. We sequenced the TRIM5 gene (excluding exon 5) and the 4.8-kb 5' putative regulatory region in genomic DNA from 110 HIV-1-infected subjects and 96 exposed seronegative persons, along with targeted gene sequencing in a further 30 HIV-1-infected individuals. Forty-eight single nucleotide polymorphisms (SNPs), including 20 with allele frequencies of >1.0%, were identified. Among these were two synonymous and eight nonsynonymous coding polymorphisms. We observed no association between TRIM5 polymorphism in HIV-1-infected subjects and their set-point viral load after acute infection, although one TRIM5 haplotype was weakly associated with more rapid CD4(+) T-cell loss. Importantly, a TRIM5 haplotype containing the nonsynonymous SNP R136Q showed increased frequency among HIV-1-infected subjects relative to exposed seronegative persons, with an odds ratio of 5.49 (95% confidence interval = 1.83 to 16.45; P = 0.002). Nonetheless, we observed no effect of individual TRIM5alpha nonsynonymous mutations on the in vitro HIV-1 susceptibility of CD4(+) T cells. Therefore, any effect of TRIM5alpha polymorphism on HIV-1 infection in primary lymphocytes may depend on combinations of SNPs or on DNA sequences in linkage disequilibrium with the TRIM5alpha coding sequence.

A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies.

The rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.

Estimating haplotype frequencies and standard errors for multiple single nucleotide polymorphisms.

Estimating haplotype frequencies becomes increasingly important in the mapping of complex disease genes, as millions of single nucleotide polymorphisms (SNPs) are being identified and genotyped. When genotypes at multiple SNP loci are gathered from unrelated individuals, haplotype frequencies can be accurately estimated using expectation-maximization (EM) algorithms (Excoffier and Slatkin, 1995; Hawley and Kidd, 1995; Long et al., 1995), with standard errors estimated using bootstraps. However, because the number of possible haplotypes increases exponentially with the number of SNPs, handling data with a large number of SNPs poses a computational challenge for the EM methods and for other haplotype inference methods. To solve this problem, Niu and colleagues, in their Bayesian haplotype inference paper (Niu et al., 2002), introduced a computational algorithm called progressive ligation (PL). But their Bayesian method has a limitation on the number of subjects (no more than 100 subjects in the current implementation of the method). In this paper, we propose a new method in which we use the same likelihood formulation as in Excoffier and Slatkin's EM algorithm and apply the estimating equation idea and the PL computational algorithm with some modifications. Our proposed method can handle data sets with large number of SNPs as well as large numbers of subjects. Simultaneously, our method estimates standard errors efficiently, using the sandwich-estimate from the estimating equation, rather than the bootstrap method. Additionally, our method admits missing data and produces valid estimates of parameters and their standard errors under the assumption that the missing genotypes are missing at random in the sense defined by Rubin (1976).