Complete Genomic Assembly of Mauritian Cynomolgus Macaque Killer Ig-like Receptor and Natural Killer Group 2 Haplotypes.

Mauritian-origin cynomolgus macaques (MCMs) serve as a powerful nonhuman primate model in biomedical research due to their unique genetic homogeneity, which simplifies experimental designs. Despite their extensive use, a comprehensive understanding of crucial immune-regulating gene families, particularly killer Ig-like receptors (KIR) and NK group 2 (NKG2), has been hindered by the lack of detailed genomic reference assemblies. In this study, we employ advanced long-read sequencing techniques to completely assemble eight KIR and seven NKG2 genomic haplotypes, providing an extensive insight into the structural and allelic diversity of these immunoregulatory gene clusters. Leveraging these genomic resources, we prototype a strategy for genotyping KIR and NKG2 using short-read, whole-exome capture data, illustrating the potential for cost-effective multilocus genotyping at colony scale. These results mark a significant enhancement for biomedical research in MCMs and underscore the feasibility of broad-scale genetic investigations.

Shedding of infectious SARS-CoV-2 despite vaccination.

The SARS-CoV-2 Delta Variant of Concern is highly transmissible and contains mutations that confer partial immune escape. The emergence of Delta in North America caused the first surge in COVID-19 cases after SARS-CoV-2 vaccines became widely available. To determine whether individuals infected despite vaccination might be capable of transmitting SARS-CoV-2, we compared RT-PCR cycle threshold (Ct) data from 20,431 test-positive anterior nasal swab specimens from fully vaccinated (n = 9,347) or unvaccinated (n = 11,084) individuals tested at a single commercial laboratory during the interval 28 June- 1 December 2021 when Delta variants were predominant. We observed no significant effect of vaccine status alone on Ct value, nor when controlling for vaccine product or sex. Testing a subset of low-Ct (<25) samples, we detected infectious virus at similar rates, and at similar titers, in specimens from vaccinated and unvaccinated individuals. These data indicate that vaccinated individuals infected with Delta variants are capable of shedding infectious SARS-CoV-2 and could play a role in spreading COVID-19.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings.

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater and air sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings.

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater andair sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

Evolution of a globally unique SARS-CoV-2 Spike E484T monoclonal antibody escape mutation in a persistently infected, immunocompromised individual.

Prolonged infections in immunocompromised individuals may be a source for novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, particularly when both the immune system and antiviral therapy fail to clear the infection and enable within-host evolution. Here we describe a 486-day case of SARS-CoV-2 infection in an immunocompromised individual. Following monotherapy with the monoclonal antibody Bamlanivimab, the individual's virus acquired resistance, likely via the earliest known occurrence of Spike amino acid variant E484T. Recently, E484T has arisen again as a derivative of E484A in the Omicron Variant of Concern, supporting the hypothesis that prolonged infections can give rise to novel variants long before they become prevalent in the human population.

Estimating hybridization in the wild using citizen science data: A path forward.

Genomic evidence of introgression in natural populations has reinvigorated the study of hybridization in recent years. Still, it is largely unknown how frequently individual organisms mate across species lines. Recently, Justyn et al. suggested that eBird, one of the world's largest citizen science databases, may supply adequate data for estimating hybridization rates. Here, we compare Justyn et al.'s estimates-and their conclusions that hybridization is rare-with estimates from museum and molecular data. We also estimate hybridization using eBird observations from areas and times when hybridization is possible, namely, in contact zones during the breeding season. These estimates are all considerably higher than those reported in Justyn et al., emphasizing that inferences from multiple datasets can differ radically. Finally, we demonstrate an approach for predicting the location of hybrid zones using eBird data, which can be done with high confidence and with unprecedented resolution. We show that citizen science data, far from settling the question of how frequently bird species hybridize, instead offer a promising step toward more focused study of hybrid zones.