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BackgroundHost genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample.ResultsWe derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates.ConclusionsWe discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract.

The absence of microbial exposure early in life leaves individuals vulnerable to immune overreaction later in life, manifesting as immunopathology, autoimmunity, or allergies. A key factor is thought to be a "critical window" during which the host's immune system can "learn" tolerance, and beyond which learning is no longer possible. Animal models indicate that many mechanisms have evolved to enable critical windows, and that their time limits are distinct and consistent. Such a variety of mechanisms, and precision in their manifestation suggest the outcome of strong evolutionary selection. To strengthen our understanding of critical windows, we explore their underlying evolutionary ecology using models encompassing demographic and epidemiological transitions, identifying the length of the critical window that would maximize fitness in different environments. We characterize how direct effects of microbes on host mortality, but also indirect effects via microbial ecology, will drive the optimal length of the critical window. We find that indirect effects such as magnitude of transmission, duration of infection, rates of reinfection, vertical transmission, host demography, and seasonality in transmission all have the effect of redistributing the timing and/or likelihood of encounters with microbial taxa across age, and thus increasing or decreasing the optimal length of the critical window. Declining microbial population abundance and diversity are predicted to result in increases in immune dysfunction later in life. We also make predictions for the length of the critical window across different taxa and environments. Overall, our modeling efforts demonstrate how critical windows will be impacted over evolution as a function of both host-microbiome/pathogen interactions and dispersal, raising central questions about potential mismatches between these evolved systems and the current loss of microbial diversity and/or increases in infectious disease.

Microbial communities associated with plant leaf surfaces (i.e., the phyllosphere) are increasingly recognized for their role in plant health. While accumulating evidence suggests a role for host filtering of its microbiota, far less is known about how community composition is shaped by dispersal, including from neighboring plants. We experimentally manipulated the local plant neighborhood within which tomato, pepper, or bean plants were grown in a 3-month field trial. Focal plants were grown in the presence of con- or hetero-specific neighbors (or no neighbors) in a fully factorial combination. At 30-day intervals, focal plants were harvested and replaced with a new age- and species-matched cohort while allowing neighborhood plants to continue growing. Bacterial community profiling revealed that the strength of host filtering effects (i.e., interspecific differences in composition) decreased over time. In contrast, the strength of neighborhood effects increased over time, suggesting dispersal from neighboring plants becomes more important as neighboring plant biomass increases. We next implemented a cross-inoculation study in the greenhouse using inoculum generated from the field plants to directly test host filtering of microbiomes while controlling for directionality and source of dispersal. This experiment further demonstrated that focal host species, the host from which the microbiome came, and in one case the donor hosts' neighbors, contribute to variation in phyllosphere bacterial composition. Overall, our results suggest that local dispersal is a key factor in phyllosphere assembly, and that demographic factors such as nearby neighbor identity and biomass or age are important determinants of phyllosphere microbiome diversity.

Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serologic studies (e.g., during influenza pandemics) has made relevant contributions, but specific conditions of the current situation require adaptation. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies actually means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studied, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine global transmission of severe acute respiratory syndrome coronavirus 2.

There is increasing interest in the plant microbiome as it relates to both plant health and agricultural sustainability. One key unanswered question is whether we can select for a plant microbiome that is robust after colonization of target hosts. We used a successive passaging experiment to address this question by selecting upon the tomato phyllosphere microbiome. Beginning with a diverse microbial community generated from field-grown tomato plants, we inoculated replicate plants across 5 plant genotypes for 4 45-d passages, sequencing the microbial community at each passage. We observed consistent shifts in both the bacterial (16S amplicon sequencing) and fungal (internal transcribed spacer region amplicon sequencing) communities across replicate lines over time, as well as a general loss of diversity over the course of the experiment, suggesting that much of the naturally observed microbial community in the phyllosphere is likely transient or poorly adapted within the experimental setting. We found that both host genotype and environment shape microbial composition, but the relative importance of genotype declines through time. Furthermore, using a community coalescence experiment, we found that the bacterial community from the end of the experiment was robust to invasion by the starting bacterial community. These results highlight that selecting for a stable microbiome that is well adapted to a particular host environment is indeed possible, emphasizing the great potential of this approach in agriculture and beyond. In light of the consistent response of the microbiome to selection in the absence of reciprocal host evolution (coevolution) described here, future studies should address how such adaptation influences host health.

A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.

The timing of life history events has important fitness consequences. Since the 1950s, researchers have combined first principles and data to predict the optimal timing of life history transitions. Recently, a striking mystery has emerged. Such transitions can be shaped by a completely different branch of the tree of life: species in the microbiome. Probing these interactions using testable predictions from evolutionary theory could illuminate whether and how host-microbiome integrated life histories can evolve and be maintained. Beyond advancing fundamental science, this research program could yield important applications. In an age of microbiome engineering, understanding the contexts that lead to microbiota signaling shaping ontogeny could offer novel mechanisms for manipulations to increase yield in agriculture by manipulating plant responses to stressful environments, or to reduce pathogen transmission by affecting vector efficiency. We combine theory and evidence to illuminate the essential questions underlying the existence of microbiome-dependent ontogenetic timing (MiDOT) to fuel research on this emerging topic.

The evolution of host immunity occurs in the context of the microbiome, but little theory exists to predict how resistance against pathogens might be influenced by the need to tolerate and regulate commensal microbiota. We present a general model to explore the optimal investment in host immunity under conditions in which the host can, versus cannot easily distinguish among commensal versus pathogenic bacteria, and when commensal microbiota can, versus cannot protect the host against the impacts of pathogen infection. We find that a loss of immune vigilance associated with innate immunity over evolutionary time can occur due to the challenge of discriminating between pathogenic and other microbe species. Further, we find the greater the protective effect of microbiome species, acting either directly or via competition with a pathogen, or the higher the costs of immunity, the more likely the loss of immune vigilance is. Conversely, this effect can be reversed when pathogens increase host mortality. Generally, the magnitude of costs of immunity required to allow evolution of decreased immune vigilance are predicted to be lowest when microbiome and pathogen species most resemble each other (in terms of host recognition), and when immune effects on the pathogen are weak. Our model framework makes explicit the core trade-offs likely to shape the evolution of immunity in the context of microbiome/pathogen discrimination. We discuss how this informs interpretation of patterns and process in natural systems, including vulnerability to pathogen emergence.

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment. Author summary: While the SARS-CoV-2 outbreak continues, the deployment of vaccines in many regions has blunted the severity of SARS-CoV-2 infections and decreased hospitalizations. However, the medium-term impacts of the duration of severity-blocking immunity, and its potential interactions with heterogeneous vaccine uptake (e.g. from vaccine hesitancy) or more robust vaccines, remain unknown. To titrate these effects, we use immuno-epidemiological models to examine potential future scenarios. We find that sufficient vaccine hesitancy (and correspondingly higher vaccination rates among adopters) can rapidly increase the fraction of individuals infected after waned severity-blocking immunity even when robust vaccines are deployed. This result underlines that pharmaceutical developments for broadly protective vaccines should be combined with campaigns to increase vaccine uptake globally. We also show that this fraction is highly dependent on underlying immune uncertainties, which illustrates the importance of accurately measuring immune parameters for proper prediction based on hospitalization data. [ABSTRACT FROM AUTHOR]

First discovered in 1947, Zika virus (ZIKV) infection remained a little-known tropical disease until 2015, when its apparent association with a considerable increase in the incidence of microcephaly in Brazil raised alarms worldwide. There is limited information on the key factors that determine the extent of the global threat from ZIKV infection and resulting complications. Here, we review what is known about the epidemiology, natural history, and public health effects of ZIKV infection, the empirical basis for this knowledge, and the critical knowledge gaps that need to be filled.

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

Serologic studies are crucial for understanding current and future dynamics of the coronavirus disease (COVID-19) pandemic. In the past few months, much discussion about serologic studies and key issues with [...]

As of 24 April 2020, the SARS-CoV-2 epidemic has resulted in over 830,000 confirmed infections in the United States.sup.1. The incidence of COVID-19, the disease associated with this new coronavirus, continues to rise. The epidemic threatens to overwhelm healthcare systems, and identifying those regions where the disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency medical care and public health resources. Globally, the risk of severe outcomes associated with COVID-19 has consistently been observed to increase with age.sup.2,3. We used age-specific mortality patterns in tandem with demographic data to map projections of the cumulative case burden of COVID-19 and the subsequent burden on healthcare resources. The analysis was performed at the county level across the United States, assuming a scenario in which 20% of the population of each county acquires infection. We identified counties that will probably be consistently, heavily affected relative to the rest of the country across a range of assumptions about transmission patterns, such as the basic reproductive rate, contact patterns and the efficacy of quarantine. We observed a general pattern that per capita disease burden and relative healthcare system demand may be highest away from major population centers. These findings highlight the importance of ensuring equitable and adequate allocation of medical care and public health resources to communities outside of major urban areas. Projection of the number of COVID-19 cases and the associated burden on healthcare resources using a modified SEIR model reveals that rural regions in the United States are at risk of higher per capita case burdens, which could lead to health systems being overwhelmed in these areas., Author(s): Ian F. Miller [sup.1] , Alexander D. Becker [sup.1] , Bryan T. Grenfell [sup.1] [sup.2] , C. Jessica E. Metcalf [sup.1] [sup.2] Author Affiliations: (1) Department of Ecology and [...]

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution.