Global epistasis and the emergence of function in microbial consortia.

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Genomic structure predicts metabolite dynamics in microbial communities.

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Personalized Mapping of Drug Metabolism by the Human Gut Microbiome.

The human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities. Dozens of drugs have been shown to be metabolized by single isolates from the gut microbiome, but the extent of this phenomenon is rarely explored in the context of microbial communities. Here, we develop a quantitative experimental framework for mapping the ability of the human gut microbiome to metabolize small molecule drugs: Microbiome-Derived Metabolism (MDM)-Screen. Included are a batch culturing system for sustained growth of subject-specific gut microbial communities, an ex vivo drug metabolism screen, and targeted and untargeted functional metagenomic screens to identify microbiome-encoded genes responsible for specific metabolic events. Our framework identifies novel drug-microbiome interactions that vary between individuals and demonstrates how the gut microbiome might be used in drug development and personalized medicine.

Interspecies Competition Impacts Targeted Manipulation of Human Gut Bacteria by Fiber-Derived Glycans.

Development of microbiota-directed foods (MDFs) that selectively increase the abundance of beneficial human gut microbes, and their expressed functions, requires knowledge of both the bioactive components of MDFs and the mechanisms underlying microbe-microbe interactions. Here, gnotobiotic mice were colonized with a defined consortium of human-gut-derived bacterial strains and fed different combinations of 34 food-grade fibers added to a representative low-fiber diet consumed in the United States. Bioactive carbohydrates in fiber preparations targeting particular Bacteroides species were identified using community-wide quantitative proteomic analyses of bacterial gene expression coupled with forward genetic screens. Deliberate manipulation of community membership combined with administration of retrievable artificial food particles, consisting of paramagnetic microscopic beads coated with dietary polysaccharides, disclosed the contributions of targeted species to fiber degradation. Our approach, including the use of bead-based biosensors, defines nutrient-harvesting strategies that underlie, as well as alleviate, competition between Bacteroides and control the selectivity of MDF components.

Marine DNA Viral Macro- and Microdiversity from Pole to Pole.

Microbes drive most ecosystems and are modulated by viruses that impact their lifespan, gene flow, and metabolic outputs. However, ecosystem-level impacts of viral community diversity remain difficult to assess due to classification issues and few reference genomes. Here, we establish an ∼12-fold expanded global ocean DNA virome dataset of 195,728 viral populations, now including the Arctic Ocean, and validate that these populations form discrete genotypic clusters. Meta-community analyses revealed five ecological zones throughout the global ocean, including two distinct Arctic regions. Across the zones, local and global patterns and drivers in viral community diversity were established for both macrodiversity (inter-population diversity) and microdiversity (intra-population genetic variation). These patterns sometimes, but not always, paralleled those from macro-organisms and revealed temperate and tropical surface waters and the Arctic as biodiversity hotspots and mechanistic hypotheses to explain them. Such further understanding of ocean viruses is critical for broader inclusion in ecosystem models.

Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome.

Ocean microbial communities strongly influence the biogeochemistry, food webs, and climate of our planet. Despite recent advances in understanding their taxonomic and genomic compositions, little is known about how their transcriptomes vary globally. Here, we present a dataset of 187 metatranscriptomes and 370 metagenomes from 126 globally distributed sampling stations and establish a resource of 47 million genes to study community-level transcriptomes across depth layers from pole-to-pole. We examine gene expression changes and community turnover as the underlying mechanisms shaping community transcriptomes along these axes of environmental variation and show how their individual contributions differ for multiple biogeochemically relevant processes. Furthermore, we find the relative contribution of gene expression changes to be significantly lower in polar than in non-polar waters and hypothesize that in polar regions, alterations in community activity in response to ocean warming will be driven more strongly by changes in organismal composition than by gene regulatory mechanisms. VIDEO ABSTRACT.

Evolution of community outreach and engagement at National Cancer Institute-Designated Cancer Centers, an evolving journey.

Cancer mortality rates have declined during the last 28 years, but that process is not equitably shared. Disparities in cancer outcomes by race, ethnicity, socioeconomic status, sexual orientation and gender identity, and geographic location persist across the cancer care continuum. Consequently, community outreach and engagement (COE) efforts within National Cancer Institute-Designated Cancer Center (NCI-DCC) catchment areas have intensified during the last 10 years as has the emphasis on COE and catchment areas in NCI's Cancer Center Support Grant applications. This review article attempts to provide a historic perspective of COE within NCI-DCCs. Improving COE has long been an important initiative for the NCI, but it was not until 2012 and 2016 that NCI-DCCs were required to define their catchment areas rigorously and to provide specific descriptions of COE interventions, respectively. NCI-DCCs had previously lacked adequate focus on the inclusion of historically marginalized patients in cancer innovation efforts. Integrating COE efforts throughout the research and operational aspects of the cancer centers, at both the patient and community levels, will expand the footprint of COE efforts within NCI-DCCs. Achieving this change requires sustained commitment by the centers to adjust their activities and improve access and outcomes for historically marginalized communities.

Microbiome Assembly in Fermented Foods.

For thousands of years, humans have enjoyed the novel flavors, increased shelf-life, and nutritional benefits that microbes provide in fermented foods and beverages. Recent sequencing surveys of ferments have mapped patterns of microbial diversity across space, time, and production practices. But a mechanistic understanding of how fermented food microbiomes assemble has only recently begun to emerge. Using three foods as case studies (surface-ripened cheese, sourdough starters, and fermented vegetables), we use an ecological and evolutionary framework to identify how microbial communities assemble in ferments. By combining in situ sequencing surveys with in vitro models, we are beginning to understand how dispersal, selection, diversification, and drift generate the diversity of fermented food communities. Most food producers are unaware of the ecological processes occurring in their production environments, but the theory and models of ecology and evolution can provide new approaches for managing fermented food microbiomes, from farm to ferment.

Charting a landmark-driven path forward for population genetics and ancient DNA research in Africa.

Population history-focused DNA and ancient DNA (aDNA) research in Africa has dramatically increased in the past decade, enabling increasingly fine-scale investigations into the continent's past. However, while international interest in human genomics research in Africa grows, major structural barriers limit the ability of African scholars to lead and engage in such research and impede local communities from partnering with researchers and benefitting from research outcomes. Because conversations about research on African people and their past are often held outside Africa and exclude African voices, an important step for African DNA and aDNA research is moving these conversations to the continent. In May 2023 we held the DNAirobi workshop in Nairobi, Kenya and here we synthesize what emerged most prominently in our discussions. We propose an ideal vision for population history-focused DNA and aDNA research in Africa in ten years' time and acknowledge that to realize this future, we need to chart a path connecting a series of "landmarks" that represent points of consensus in our discussions. These include effective communication across multiple audiences, reframed relationships and capacity building, and action toward structural changes that support science and beyond. We concluded there is no single path to creating an equitable and self-sustaining research ecosystem, but rather many possible routes linking these landmarks. Here we share our diverse perspectives as geneticists, anthropologists, archaeologists, museum curators, and educators to articulate challenges and opportunities for African DNA and aDNA research and share an initial map toward a more inclusive and equitable future.

Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure.

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

Structured community transitions explain the switching capacity of microbial systems.

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Coexistence of many species under a random competition-colonization trade-off.

The competition-colonization (CC) trade-off is a well-studied coexistence mechanism for metacommunities. In this setting, it is believed that the coexistence of all species requires their traits to satisfy restrictive conditions limiting their similarity. To investigate whether diverse metacommunities can assemble in a CC trade-off model, we study their assembly from a probabilistic perspective. From a pool of species with parameters (corresponding to traits) sampled at random, we compute the probability that any number of species coexist and characterize the set of species that emerges through assembly. Remarkably, almost exactly half of the species in a large pool typically coexist, with no saturation as the size of the pool grows, and with little dependence on the underlying distribution of traits. Through a mix of analytical results and simulations, we show that this unlimited niche packing emerges as assembly actively moves communities toward overdispersed configurations in niche space. Our findings also apply to a realistic assembly scenario where species invade one at a time from a fixed regional pool. When diversity arises de novo in the metacommunity, richness still grows without bound, but more slowly. Together, our results suggest that the CC trade-off can support the robust emergence of diverse communities, even when coexistence of the full species pool is exceedingly unlikely.

The need for an intersectionality framework in precision medicine research.

Precision medicine research has seen growing efforts to increase participation of communities that have been historically underrepresented in biomedical research. Marginalized racial and ethnic communities have received particular attention, toward the goal of improving the generalizability of scientific knowledge and promoting health equity. Against this backdrop, research has highlighted three key issues that could impede the promise of precision medicine research: issues surrounding (dis)trust and representation, challenges in translational efforts to improve health outcomes, and the need for responsive community engagement. Existing efforts to address these challenges have predominantly centered on single-dimensional demographic criteria such as race, ethnicity, or sex, while overlooking how these and additional variables, such as disability, gender identity, and socioeconomic factors, can confound and jointly impact research participation. We argue that increasing cohort diversity and the responsiveness of precision medicine research studies to community needs requires an approach that transcends conventional boundaries and embraces a more nuanced, multi-layered, and intersectional framework for data collection, analyses, and implementation. We draw attention to gaps in existing work, highlight how overlapping layers of marginalization might shape and substantiate one another and affect the precision-medicine research cycle, and put forth strategies to facilitate equitable advantages from precision-medicine research to diverse participants and internally heterogeneous communities.

scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

Changing climate and reorganized species interactions modify community responses to climate variability.

While an array of ecological mechanisms has been shown to stabilize natural community dynamics, how the effectiveness of these mechanisms-including both their direction (stabilizing vs. destabilizing) and strength-shifts under a changing climate remains unknown. Using a 35-y dataset (1985 to 2019) from a desert stream in central Arizona (USA), we found that as annual mean air temperature rose 1°C and annual mean precipitation reduced by 40% over the last two decades, macroinvertebrate communities experienced dramatic changes, from relatively stable states during the first 15 y of this study to wildly fluctuating states highly sensitive to climate variability in the last 10 y. Asynchronous species responses to climatic variability, the primary mechanism historically undergirding community stability, greatly weakened. The emerging climate regime-specifically, concurrent warming and prolonged multiyear drought-resulted in community-wide synchronous responses and reduced taxa richness. Diversity loss and new establishment of competitors reorganized species interactions. Unlike manipulative experiments that often suggest stabilizing roles of species interactions, we found that reorganized species interactions switched from stabilizing to destabilizing influences, further amplifying community fluctuations. Our study provides evidence of climate change-induced modifications of mechanisms underpinning long-term community stability, resulting in an overall destabilizing effect.

Ecological barriers mediate spatiotemporal shifts of bird communities at a continental scale.

Species' range shifts and local extinctions caused by climate change lead to community composition changes. At large spatial scales, ecological barriers, such as biome boundaries, coastlines, and elevation, can influence a community's ability to shift in response to climate change. Yet, ecological barriers are rarely considered in climate change studies, potentially hindering predictions of biodiversity shifts. We used data from two consecutive European breeding bird atlases to calculate the geographic distance and direction between communities in the 1980s and their compositional best match in the 2010s and modeled their response to barriers. The ecological barriers affected both the distance and direction of bird community composition shifts, with coastlines and elevation having the strongest influence. Our results underscore the relevance of combining ecological barriers and community shift projections for identifying the forces hindering community adjustments under global change. Notably, due to (macro)ecological barriers, communities are not able to track their climatic niches, which may lead to drastic changes, and potential losses, in community compositions in the future.

A mutant fitness assay identifies bacterial interactions in a model ocean hot spot.

Bacteria that assemble in phycospheres surrounding living phytoplankton cells metabolize a substantial proportion of ocean primary productivity. Yet the type and extent of interactions occurring among species that colonize these micron-scale "hot spot" environments are challenging to study. We identified genes that mediate bacterial interactions in phycosphere communities by culturing a transposon mutant library of copiotrophic bacterium Ruegeria pomeroyi DSS-3 with the diatom Thalassiosira pseudonana CCMP1335 as the sole source of organic matter in the presence or absence of other heterotrophic bacterial species. The function of genes having significant effects on R. pomeroyi fitness indicated explicit cell-cell interactions initiated in the multibacterial phycospheres. We found that R. pomeroyi simultaneously competed for shared substrates while increasing reliance on substrates that did not support the other species' growth. Fitness outcomes also indicated that the bacterium competed for nitrogen in the forms of ammonium and amino acids; obtained purines, pyrimidines, and cofactors via crossfeeding; both initiated and defended antagonistic interactions; and sensed an environment with altered oxygen and superoxide levels. The large genomes characteristic of copiotrophic marine bacteria are hypothesized to enable responses to dynamic ecological challenges occurring at the scale of microns. Here, we discover >200 nonessential genes implicated in the management of fitness costs and benefits of membership in a globally significant bacterial community.

Maximum temperatures determine the habitat affiliations of North American mammals.

Addressing the ongoing biodiversity crisis requires identifying the winners and losers of global change. Species are often categorized based on how they respond to habitat loss; for example, species restricted to natural environments, those that most often occur in anthropogenic habitats, and generalists that do well in both. However, species might switch habitat affiliations across time and space: an organism may venture into human-modified areas in benign regions but retreat into thermally buffered forested habitats in areas with high temperatures. Here, we apply community occupancy models to a large-scale camera trapping dataset with 29 mammal species distributed over 2,485 sites across the continental United States, to ask three questions. First, are species' responses to forest and anthropogenic habitats consistent across continental scales? Second, do macroclimatic conditions explain spatial variation in species responses to land use? Third, can species traits elucidate which taxa are most likely to show climate-dependent habitat associations? We found that all species exhibited significant spatial variation in how they respond to land-use, tending to avoid anthropogenic areas and increasingly use forests in hotter regions. In the hottest regions, species occupancy was 50% higher in forested compared to open habitats, whereas in the coldest regions, the trend reversed. Larger species with larger ranges, herbivores, and primary predators were more likely to change their habitat affiliations than top predators, which consistently affiliated with high forest cover. Our findings suggest that climatic conditions influence species' space-use and that maintaining forest cover can help protect mammals from warming climates.

Interspecies interactions determine growth dynamics of biopolymer-degrading populations in microbial communities.

Microbial communities perform essential ecosystem functions such as the remineralization of organic carbon that exists as biopolymers. The first step in mineralization is performed by biopolymer degraders, which harbor enzymes that can break down polymers into constituent oligo- or monomeric forms. The released nutrients not only allow degraders to grow, but also promote growth of cells that either consume the degradation products, i.e., exploiters, or consume metabolites released by the degraders or exploiters, i.e., scavengers. It is currently not clear how such remineralizing communities assemble at the microscale-how interactions between the different guilds influence their growth and spatial distribution, and hence the development and dynamics of the community. Here, we address this knowledge gap by studying marine microbial communities that grow on the abundant marine biopolymer alginate. We used batch growth assays and microfluidics coupled to time-lapse microscopy to quantitatively investigate growth and spatial distribution of single cells. We found that the presence of exploiters or scavengers alters the spatial distribution of degrader cells. In general, exploiters and scavengers-which we collectively refer to as cross-feeder cells-slowed down the growth of degrader cells. In addition, coexistence with cross-feeders altered the production of the extracellular enzymes that break down polymers by degrader cells. Our findings reveal that ecological interactions by nondegrading community members have a profound impact on the functions of microbial communities that remineralize carbon biopolymers in nature.