Functional SARS-CoV-2 cross-reactive CD4+ T cells established in early childhood decline with age.

Pre-existing SARS-CoV-2-reactive T cells have been identified in SARS-CoV-2-unexposed individuals, potentially modulating COVID-19 and vaccination outcomes. Here, we provide evidence that functional cross-reactive memory CD4+ T cell immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is established in early childhood, mirroring early seroconversion with seasonal human coronavirus OC43. Humoral and cellular immune responses against OC43 and SARS-CoV-2 were assessed in SARS-CoV-2-unexposed children (paired samples at age two and six) and adults (age 26 to 83). Pre-existing SARS-CoV-2-reactive CD4+ T cell responses targeting spike, nucleocapsid, and membrane were closely linked to the frequency of OC43-specific memory CD4+ T cells in childhood. The functional quality of the cross-reactive memory CD4+ T cell responses targeting SARS-CoV-2 spike, but not nucleocapsid, paralleled OC43-specific T cell responses. OC43-specific antibodies were prevalent already at age two. However, they did not increase further with age, contrasting with the antibody magnitudes against HKU1 (ß-coronavirus), 229E and NL63 (α-coronaviruses), rhinovirus, Epstein-Barr virus (EBV), and influenza virus, which increased after age two. The quality of the memory CD4+ T cell responses peaked at age six and subsequently declined with age, with diminished expression of interferon (IFN)-γ, interleukin (IL)-2, tumor necrosis factor (TNF), and CD38 in late adulthood. Age-dependent qualitative differences in the pre-existing SARS-CoV-2-reactive T cell responses may reflect the ability of the host to control coronavirus infections and respond to vaccination.

Identification of resident memory CD8+ T cells with functional specificity for SARS-CoV-2 in unexposed oropharyngeal lymphoid tissue.

Cross-reactive CD4+ T cells that recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are more commonly detected in the peripheral blood of unexposed individuals compared with SARS-CoV-2­reactive CD8+ T cells. However, large numbers of memory CD8+ T cells reside in tissues, feasibly harboring localized SARS-CoV-2­specific immune responses. To test this idea, we performed a comprehensive functional and phenotypic analysis of virus-specific T cells in tonsils, a major lymphoid tissue site in the upper respiratory tract, and matched peripheral blood samples obtained from children and adults before the emergence of COVID-19 (coronavirus disease 2019). We found that SARS-CoV-2­specific memory CD4+ T cells could be found at similar frequencies in the tonsils and peripheral blood in unexposed individuals, whereas functional SARS-CoV-2­specific memory CD8+ T cells were almost only detectable in the tonsils. Tonsillar SARS-CoV-2­specific memory CD8+ T cells displayed a follicular homing and tissue-resident memory phenotype, similar to tonsillar Epstein-Barr virus­specific memory CD8+ T cells, but were functionally less potent than other virus-specific memory CD8+ T cell responses. The presence of preexisting tissue-resident memory CD8+ T cells in unexposed individuals could potentially enable rapid sentinel immune responses against SARS-CoV-2.

Multiplex qPCR discriminates variants of concern to enhance global surveillance of SARS-CoV-2.

With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69-70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1.

Understanding disclosed and cryptic HIV transmission risk via genetic analysis: what are we missing and when does it matter?

PURPOSE OF REVIEW: To discuss the recent HIV phylogenetic analyses examining HIV transmission patterns among and within risk groups. RECENT FINDINGS: Phylodynamic analysis has recently been applied to multiple HIV outbreaks among people who inject drugs to determine whether HIV transmission is ongoing. Large-scale analyses of datasets of HIV sequences collected for drug-resistance testing provide population-level insights into transmission patterns. One focus across world regions has been to investigate whether age-disparity is a driver of HIV transmission. In sub-Saharan Africa, researchers have examined transmission between heterosexuals and MSM and between high prevalence fishing communities and inland communities. In the US and the UK, cryptic risk groups such as nondisclosed MSM and the partners of transgender women are increasingly being uncovered based on their position in densely sampled molecular transmission networks. SUMMARY: Analysis of HIV genetic sequence can resolve viral transmission patterns between risk groups at unprecedented scales and levels of detail. Future research should focus on understanding the effect of missing data on inferences and the biases of different methods. Uncovering groups and patterns obscured from traditional epidemiolocal analyses is exciting but should not compromise the privacy of the groups in question.

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE.

We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

Using nearly full-genome HIV sequence data improves phylogeny reconstruction in a simulated epidemic.

HIV molecular epidemiology studies analyse viral pol gene sequences due to their availability, but whole genome sequencing allows to use other genes. We aimed to determine what gene(s) provide(s) the best approximation to the real phylogeny by analysing a simulated epidemic (created as part of the PANGEA_HIV project) with a known transmission tree. We sub-sampled a simulated dataset of 4662 sequences into different combinations of genes (gag-pol-env, gag-pol, gag, pol, env and partial pol) and sampling depths (100%, 60%, 20% and 5%), generating 100 replicates for each case. We built maximum-likelihood trees for each combination using RAxML (GTR + Γ), and compared their topologies to the corresponding true tree's using CompareTree. The accuracy of the trees was significantly proportional to the length of the sequences used, with the gag-pol-env datasets showing the best performance and gag and partial pol sequences showing the worst. The lowest sampling depths (20% and 5%) greatly reduced the accuracy of tree reconstruction and showed high variability among replicates, especially when using the shortest gene datasets. In conclusion, using longer sequences derived from nearly whole genomes will improve the reliability of phylogenetic reconstruction. With low sample coverage, results can be highly variable, particularly when based on short sequences.