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Serological testing remains a passive component of the public health response to the COVID-19 pandemic. Using a transmission model, we examine how serological testing could have enabled seropositive individuals to increase their relative levels of social interaction while offsetting transmission risks. We simulate widespread serological testing in New York City, South Florida, and Washington Puget Sound and assume seropositive individuals partially restore their social contacts. Compared to no intervention, our model suggests that widespread serological testing starting in late 2020 would have averted approximately 3300 deaths in New York City, 1400 deaths in South Florida and 11,000 deaths in Washington State by June 2021. In all sites, serological testing blunted subsequent waves of transmission. Findings demonstrate the potential benefit of widespread serological testing, had it been implemented in the pre-vaccine era, and remain relevant now amid the potential for emergence of new variants. Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a mathematical model, the authors examine how testing for antibodies could have enabled policies in which seropositive individuals increased their relative levels of social interaction while offsetting transmission risks

We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies. IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 2020 1 – 4 . Mitigation and suppression of new infections have emerged as the two predominant public health control strategies 5 . Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests 6 , 7 to identify and deploy recovered individuals 8 as focal points for sustaining safer interactions via interaction substitution, developing what we term ‘shield immunity’ at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services 9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence 10 , 11 and in the potential development of plasma-based therapies 12 – 15 . A new study models the potential effects of preferentially deploying recovered individuals, who are seropositive for anti-SARS-CoV-2 antibodies, into the community to reduce the number of interactions between susceptible and infected people, thereby limiting transmission of the virus.

Abstract Pseudomonas aeruginosa uses quorum sensing (QS) to coordinate the expression of multiple genes necessary for establishing and maintaining infection. lasR QS mutations have been shown to frequently arise in cystic fibrosis (CF) lung infections, however, there has been far less emphasis on determining whether QS system mutations arise across other environments. To test this, we utilized 852 publicly available sequenced P. aeruginosa genomes from the Pseudomonas International Consortium Database (IPCD) to study P. aeruginosa QS mutational signatures. We found that across all isolates, LasR is the most variable protein sequence compared to other QS proteins. In order to study isolates by source, we focused on a subset of 654 isolates collected from CF, wounds, and non-infection environmental isolates, where we could clearly identify their source. Using this sub-set analysis, we found that LasR mutations are not specific to CF lungs, but are common across all environments. We then used amino acid length as a proxy for observing loss of function in LasR proteins among the strains. We found that truncated LasR proteins are more abundant in P. aeruginosa strains isolated from human infection than the environment. Overall, our findings suggest that the evolution of lasR QS mutations in P. aeruginosa are common and not limited to infection environments. Importance Pseudomonas aeruginosa is an opportunistic pathogen which is often isolated from infection and environmental sources. P. aeruginosa uses quorum sensing (QS) to establish and adapt to infection environments. QS in P. aeruginosa is controlled by a complex hierarchical gene network in which the transcriptional regulator LasR has traditionally been thought to play a major controlling role. Despite this, lasR mutants are frequently isolated from chronic infection sites including the cystic fibrosis lung. Using an online P. aeruginosa strain database, we determined the frequency of mutation in key QS genes in multiple infection and non-infection environments and found that mutations and truncations in the lasR gene is more common than in other QS genes. Further, we found that lasR mutants are common in both infection and environmental strains. These findings further our understanding of QS in P. aeruginosa and have implications for the development of future therapies designed to inhibit QS. Competing Interest Statement The authors have declared no competing interest.

Abstract Background Microbiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but non-pathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). Methods We collected health information (lung function, age, BMI) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use Elastic Net regularization to train linear models predicting lung function and extract the most informative features. Results Models trained on whole microbiome quantitation outperform models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retain key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF related diabetes). In addition, our models select non-pathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. Conclusions These results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice. Competing Interest Statement The authors have declared no competing interest.