Purifying selection and adaptive evolution proximate to the zoonosis of SARS-CoV-1 and SARS-CoV-2 (preprint)

Over the past two decades the pace of spillovers from animal viruses to humans has accelerated, with COVID-19 becoming the most deadly zoonotic disease in living memory. Prior to zoonosis, it is conceivable that the virus might largely be subjected to purifying selection, requiring no additional selective changes for successful zoonotic transmission. Alternatively, selective changes occurring in the reservoir species may coincidentally preadapt the virus for human-to-human transmission, facilitating spread upon cross-species exposure. Here we quantify changes in the genomes of SARS-CoV-2 and SARS-CoV-1 proximate to zoonosis to evaluate the selection pressures acting on the viruses. Application of molecular-evolutionary and population-genetic approaches to quantify site-specific selection within both SARS-CoV genomes revealed strong purifying selection across many genes at the time of zoonosis. Even in the viral surface-protein Spike that has been fast-evolving in humans, there is little evidence of positive selection proximate to zoonosis. Nevertheless, in SARS-CoV-2, NSP12, a core protein for viral replication, exhibited a region under adaptive selection proximate to zoonosis. Furthermore, in both SARS-CoV-1 and SARS-CoV-2, regions of adaptive selection proximate to zoonosis were found in ORF7a, a putative Major Histocompatibility Complex modulatory gene. These findings suggest that these replication and immunomodulatory proteins have played a previously underappreciated role in the adaptation of SARS coronaviruses to human hosts.

Infection by SARS-CoV-2 with alternate frequencies of mRNA vaccine boosting for patients undergoing antineoplastic treatment for cancer (preprint)

Patients undergoing antineoplastic therapies often exhibit reduced immune response to COVID-19 vaccination, necessitating assessment of alternate boosting frequencies for these patients. However, data on reinfection risks to guide clinical decision-making is limited. We quantified reinfection risks of SARS-CoV-2 at different mRNA boosting frequencies of patients on antineoplastic therapies. Antibody levels following Pfizer-BioNTech BNT162b2 vaccination were analyzed for patients without cancer, with cancer undergoing various treatments, and treated with different antineoplastic therapeutics. Using long-term antibody data from other coronaviruses in an evolutionary framework, we estimated infection probabilities based on antibody levels and projected waning. We calculated cumulative probabilities of breakthrough infection for alternate booster schedules over two years. Annual boosting reduced risks for targeted or hormonal treatments, immunotherapy, and chemotherapy-immunotherapy combinations similarly to the general population. Patients receiving no treatment or chemotherapy exhibited higher risks, suggesting that accelerated vaccination schedules should be considered. Patients treated with rituximab therapy posed the highest infection risk, suggesting that a combination of frequent boosting and additional interventions may be warranted for mitigating SARS-CoV-2 infection in these patients.

Wastewater as a back door to serology? (preprint)

Wastewater surveillance is a powerful tool for monitoring the prevalence of infectious disease in urban populations, with predictive value for upcoming increases in cases and hospitalizations. The approach primarily used in disease surveillance has been to measure the number of viral copies detected in wastewater for a study area of known population, and systems for wastewater monitoring have been put in place worldwide during the SARS-CoV-2 pandemic. However, the potential to measure other biomarkers such as proteins and metabolites in wastewater has not been fully explored. Here we develop an approach to determine antibody optical density and titer measurements from wastewater. We measured abundance of anti-SARS-CoV-2 spike IgG and IgA from typical fresh samples of community wastewater, and from a collection of frozen samples dating from 2020-22. The assay described can be performed with readily available commercial reagents, at a moderate per-sample cost, facilitating non-invasive population level immune surveillance. Our findings demonstrate the feasibility of indirect serological surveillance through wastewater for population level monitoring, and the protocol described will enable the collection of larger data sets and development of models that can be leveraged to anticipate public health needs.

Projecting the seasonality of endemic COVID-19 (preprint)

Importance Successive waves of infection by SARS-CoV-2 have left little doubt that COVID-19 will transition to an endemic disease, yet the future seasonality of COVID-19 remains one of its most consequential unknowns. Foreknowledge of spatiotemporal surges would have immediate and long-term consequences for medical and public health decision-making. Objective To estimate the impending endemic seasonality of COVID-19 in temperate population centers via a phylogenetic ancestral and descendent states approach that leverages long-term data on the incidence of circulating coronaviruses. Design We performed a comparative evolutionary analysis on literature-based monthly verified cases of HCoV-NL63, HCoV-229E, HCoV-HKU1, and HCoV-OC43 infection within populations across the Northern Hemisphere. Ancestral and descendent states analyses on human-infecting coronaviruses provided projections of the impending seasonality of endemic COVID-19. Setting Quantitative projections of the endemic seasonality of COVID-19 were based on human endemic coronavirus infection incidence data from New York City (USA); Denver (USA); Tampere (Finland); Trøndelag (Norway); Gothenburg (Sweden); Stockholm (Sweden); Amsterdam (Netherlands); Beijing (China); South Korea (Nationwide); Yamagata (Japan); Hong Kong; Nakon Si Thammarat (Thailand); Guangzhou (China); and Sarlahi (Nepal). Main Outcome(s) and Measure(s) The primary projection was the monthly relative frequency of SARS-CoV-2 infections in each geographic locale. Four secondary outcomes consisted of empirical monthly relative frequencies of the endemic human-infecting coronaviruses HCoV-NL63, -229E, -HKU1, and -OC43. Results We project asynchronous surges of SARS-CoV-2 across locales in the Northern Hemisphere. In New York City, SARS-CoV-2 incidence is projected in late fall and winter months (Nov.–Jan.), In Tampere, Finland; Yamagata, Japan; and Sarlahi, Nepal incidence peaks in February. Gothenburg and Stockholm in Sweden reach peak incidence between November and February. Guangzhou, China; and South Korea. In Denver, incidence peaks in early Spring (Mar.). In Amsterdam, incidence rises in late fall (Dec.), and declines in late spring (Apr.). In Hong Kong, the projected apex of infection is in late fall (Nov.–Dec.), yet variation in incidence is muted across other seasons. Seasonal projections for Nakhon Si Thammarat, Thailand and for Beijing, China are muted compared to other locations. Conclusions and Relevance This knowledge of likely spatiotemporal surges of COVID-19 is fundamental to medical preparedness and expansions of public health interventions that anticipate the impending endemicity of this disease and mitigate COVID-19 transmission. These results provide crucial guidance for adaptive public health responses to this disease, and are vital to the long-term mitigation of COVID-19 transmission. Key Points Question Under endemic conditions, what are the projected spatiotemporal seasonal surges of COVID-19? Findings We applied a phylogenetic ancestral and descendent states approach, leveraging long-term data on the incidence of circulating coronaviruses. We found that seasonal surges are expected in or near the winter months; dependent on the specific population center, infections are forecasted to surge in the late fall, winter, or early spring. Meaning Globally, endemic COVID-19 surges should be expected to occur asynchronously, often coincident with local expected surges of other human-infecting respiratory viruses.