Production of a vast antibody repertoire is essential for protection against pathogens. Variable region germline complexity contributes to repertoire diversity and is a standard feature of mammalian immunoglobulin loci, but functional V region genes are limited in swine. For example, the porcine lambda light chain locus is composed of 23 variable (V) genes and 4 joining (J) genes, but only 10 or 11 V and 2 J genes are functional. Allelic variation in V and J may increase overall diversity within a population, yet lead to repertoire holes in individuals lacking key alleles. Previous studies focused on heavy chain genetic variation, thus light chain allelic diversity is not known. We characterized allelic variation of the porcine immunoglobulin lambda variable (IGLV) region genes. All intact IGLV genes in 81 pigs were amplified, sequenced, and analyzed to determine their allelic variation and functionality. We observed mutational variation across the entire length of the IGLV genes, in both framework and complementarity determining regions (CDRs). Three recombination hotspots were also identified, suggesting that non-allelic homologous recombination is an evolutionarily alternative mechanism for generating germline antibody diversity. Functional alleles were greatest in the most highly expressed families, IGLV3 and IGLV8. At the population level, allelic variation appears to help maintain the potential for broad antibody repertoire diversity in spite of reduced gene segment choices and limited germline sequence modification. The trade-off may be a reduction in repertoire diversity within individuals that could result in increased variation in immunity to infectious disease and response to vaccination.

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model’s validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

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