Search

The spillovers of betacoronaviruses in humans and the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlight the need for broad coronavirus countermeasures. We describe five monoclonal antibodies (mAbs) cross-reacting with the stem helix of multiple betacoronavirus spike glycoproteins isolated from COVID-19 convalescent individuals. Using structural and functional studies, we show that the mAb with the greatest breadth (S2P6) neutralizes pseudotyped viruses from three different subgenera through the inhibition of membrane fusion, and we delineate the molecular basis for its cross-reactivity. S2P6 reduces viral burden in hamsters challenged with SARS-CoV-2 through viral neutralization and Fc-mediated effector functions. Stem helix antibodies are rare, oftentimes of narrow specificity, and can acquire neutralization breadth through somatic mutations. These data provide a framework for structure-guided design of pan-betacoronavirus vaccines eliciting broad protection.

Background: Hospital healthcare workers (HCW), in particular those involved in the clinical care of COVID-19 cases, are presumably exposed to a higher risk of acquiring the disease than the general population. Methods: Between April 16 and 30, 2020 we conducted a prospective, SARS-CoV-2 seroprevalence study in HCWs in Southern Switzerland. Participants were hospital personnel with varying COVID-19 exposure risk depending on job function and working site. They provided personal information (including age, sex, occupation, and medical history) and self-reported COVID-19 symptoms. Odds ratio (OR) of seropositivity to IgG antibodies was estimated by univariate and multivariate logistic regressions. Findings: Among 4726 participants, IgG antibodies to SARS-CoV-2 were detected in 9.6% of the HCWs. Seropositivity was higher among HCWs working on COVID-19 wards (14.1% (11.9–16.5)) compared to other hospital areas at medium (10.7% (7.6–14.6)) or low risk exposure (7.3% (6.4–8.3)). OR for high vs. medium wards risk exposure was 1.42 (0.91–2.22), P = 0.119, and 1.98 (1.55–2.53), P<0.001 for high vs. low wards risk exposure. The same was for true for doctors and nurses (10.1% (9.0–11.3)) compared to other employees at medium (7.1% (4.8–10.0)) or low risk exposure (6.6% (5.0–8.4)). OR for high vs. medium profession risk exposure was 1.37 (0.89–2.11), P = 0.149, and 1.75 (1.28–2.40), P = 0.001 for high vs. low profession risk exposure. Moreover, seropositivity was higher among HCWs who had household exposure to COVID-19 cases compared to those without (18.7% (15.3–22.5) vs. 7.7% (6.9–8.6), OR 2.80 (2.14–3.67), P<0.001). Interpretation: SARS-CoV-2 antibodies are detectable in up to 10% of HCWs from acute care hospitals in a region with high incidence of COVID-19 in the weeks preceding the study. HCWs with exposure to COVID-19 patients have only a slightly higher absolute risk of seropositivity compared to those without, suggesting that the use of PPE and other measures aiming at r

Previous genome-wide association studies (GWAS) of HIV-1–infected populations have been underpowered to detect common variants with moderate impact on disease outcome and have not assessed the phenotypic variance explained by genome-wide additive effects. By combining the majority of available genome-wide genotyping data in HIV-infected populations, we tested for association between ∼8 million variants and viral load (HIV RNA copies per milliliter of plasma) in 6,315 individuals of European ancestry. The strongest signal of association was observed in the HLA class I region that was fully explained by independent effects mapping to five variable amino acid positions in the peptide binding grooves of the HLA-B and HLA-A proteins. We observed a second genome-wide significant association signal in the chemokine (C-C motif) receptor (CCR) gene cluster on chromosome 3. Conditional analysis showed that this signal could not be fully attributed to the known protective CCR5Δ32 allele and the risk P1 haplotype, suggesting further causal variants in this region. Heritability analysis demonstrated that common human genetic variation—mostly in the HLA and CCR5 regions—explains 25% of the variability in viral load. This study suggests that analyses in non-European populations and of variant classes not assessed by GWAS should be priorities for the field going forward.

Background: Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients. Methods: We used sequence data from routine genotypic tests spanning the protease and partial reverse transcriptase regions in the Virolab and EuResist databases that collated data from five European countries. Superinfection was indicated when sequences of a patient failed to cluster together in phylogenetic trees constructed with selected sets of control sequences. A subset of the indicated cases was validated by re-sequencing pol and env regions from the original samples. Results: 4425 patients had at least two sequences in the database, with a total of 13816 distinct sequence entries (of which 86% belonged to subtype B). We identified 107 patients with phylogenetic evidence for superinfection. In 14 of these cases, we analyzed newly amplified sequences from the original samples for validation purposes: only 2 cases were verified as superinfections in the repeated analyses, the other 12 cases turned out to involve sample or sequence misidentification. Resistance to drugs used at the time of strain replacement did not change in these two patients. A third case could not be validated by re-sequencing, but was supported as superinfection by an intermediate sequence with high degenerate base pair count within the t

HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10−12). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the ‘intermediate phenotype’ nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host–pathogen interaction.

HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10−12). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the ‘intermediate phenotype’ nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host–pathogen interaction.

Background: Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients. Methods: We used sequence data from routine genotypic tests spanning the protease and partial reverse transcriptase regions in the Virolab and EuResist databases that collated data from five European countries. Superinfection was indicated when sequences of a patient failed to cluster together in phylogenetic trees constructed with selected sets of control sequences. A subset of the indicated cases was validated by re-sequencing pol and env regions from the original samples. Results: 4425 patients had at least two sequences in the database, with a total of 13816 distinct sequence entries (of which 86% belonged to subtype B). We identified 107 patients with phylogenetic evidence for superinfection. In 14 of these cases, we analyzed newly amplified sequences from the original samples for validation purposes: only 2 cases were verified as superinfections in the repeated analyses, the other 12 cases turned out to involve sample or sequence misidentification. Resistance Conclusions: Routine genotyping data are informative for the detection of HIV superinfection; however, most cases of non-monophyletic clustering in patient phylogenies arise from sample or sequence mix-up rather than from superinfection, which emphasizes the importance of validation. Non-transient superinfection was rare in our mainly treatment experienced cohort, and we found a single case of possible transmitted drug resistance by this route. We therefore conclude that in our large cohort

Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients.