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.

Emergence of community behaviors in the gut microbiota upon drug treatment.

Pharmaceuticals can directly inhibit the growth of gut bacteria, but the degree to which such interactions manifest in complex community settings is an open question. Here, we compared the effects of 30 drugs on a 32-species synthetic community with their effects on each community member in isolation. While most individual drug-species interactions remained the same in the community context, communal behaviors emerged in 26% of all tested cases. Cross-protection during which drug-sensitive species were protected in community was 6 times more frequent than cross-sensitization, the converse phenomenon. Cross-protection decreased and cross-sensitization increased at higher drug concentrations, suggesting that the resilience of microbial communities can collapse when perturbations get stronger. By metabolically profiling drug-treated communities, we showed that both drug biotransformation and bioaccumulation contribute mechanistically to communal protection. As a proof of principle, we molecularly dissected a prominent case: species expressing specific nitroreductases degraded niclosamide, thereby protecting both themselves and sensitive community members.

Microbial transformation of dietary xenobiotics shapes gut microbiome composition.

Diet is a major determinant of gut microbiome composition, and variation in diet-microbiome interactions may contribute to variation in their health consequences. To mechanistically understand these relationships, here we map interactions between ∼150 small-molecule dietary xenobiotics and the gut microbiome, including the impacts of these compounds on community composition, the metabolic activities of human gut microbes on dietary xenobiotics, and interindividual variation in these traits. Microbial metabolism can toxify and detoxify these compounds, producing emergent interactions that explain community-specific remodeling by dietary xenobiotics. We identify the gene and enzyme responsible for detoxification of one such dietary xenobiotic, resveratrol, and demonstrate that this enzyme contributes to interindividual variation in community remodeling by resveratrol. Together, these results systematically map interactions between dietary xenobiotics and the gut microbiome and connect toxification and detoxification to interpersonal differences in microbiome response to diet.

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.

Understanding the relationship between intergenerational mobility and community violence.

Violence is a key mechanism in the reproduction of community disadvantage. The existing evidence indicates that violence in a community impacts the intergenerational mobility of its residents. The current study explores the possibility of a reverse relationship. This study provisionally tests the hypothesis that depressed intergenerational mobility in a community may also spark subsequent community violence. We deploy a county measure of intergenerational mobility captured during early adulthood for a cohort of youth born between 1980 and 1986 and raised in low-income families [R. Chetty, N. Hendren, Quart. J. Econom. 133, 1163-1228 (2018)]. We model the relationship between county mobility scores and two county-level outcomes: violent crime and homicide. We find that a county's level of intergenerational mobility as measured by the Chetty-Hendren data is a major predictor of its rate of violent crime and homicide in 2008, when the youth in Chetty's mobility cohort were young adults (the same age the mobility measure was captured). In fact, mobility is a significantly stronger and more consistent predictor of community violent crime and homicide rates than more commonly used factors like poverty, inequality, unemployment, and law enforcement presence.

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.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology.

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

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.

Naturalized species drive functional trait shifts in plant communities.

Despite decades of research documenting the consequences of naturalized and invasive plant species on ecosystem functions, our understanding of the functional underpinnings of these changes remains rudimentary. This is partially due to ineffective scaling of trait differences between native and naturalized species to whole plant communities. Working with data from over 75,000 plots and over 5,500 species from across the United States, we show that changes in the functional composition of communities associated with increasing abundance of naturalized species mirror the differences in traits between native and naturalized plants. We find that communities with greater abundance of naturalized species are more resource acquisitive aboveground and belowground, shorter, more shallowly rooted, and increasingly aligned with an independent strategy for belowground resource acquisition via thin fine roots with high specific root length. We observe shifts toward herbaceous-dominated communities but shifts within both woody and herbaceous functional groups follow community-level patterns for most traits. Patterns are remarkably similar across desert, grassland, and forest ecosystems. Our results demonstrate that the establishment and spread of naturalized species, likely in combination with underlying environmental shifts, leads to predictable and consistent changes in community-level traits that can alter ecosystem functions.

Hypernetwork modeling and topology of high-order interactions for complex systems.

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

Persistent species relationships characterize migrating bird communities across stopover sites and seasons.

Global migrations of diverse animal species often converge along the same routes, bringing together seasonal assemblages of animals that may compete, prey on each other, and share information or pathogens. These interspecific interactions, when energetic demands are high and the time to complete journeys is short, may influence survival, migratory success, stopover ecology, and migratory routes. Numerous accounts suggest that interspecific co-migrations are globally distributed in aerial, aquatic, and terrestrial systems, although the study of migration to date has rarely investigated species interactions among migrating animals. Here, we test the hypothesis that migrating animals are communities engaged in networks of ecological interactions. We leverage over half a million records of 50 bird species from five bird banding sites collected over 8 to 23 y to test for species associations using social network analyses. We find strong support for persistent species relationships across sites and between spring and fall migration. These relationships may be ecologically meaningful: They are often stronger among phylogenetically related species with similar foraging behaviors and nonbreeding ranges even after accounting for the nonsocial contributions to associations, including overlap in migration timing and habitat use. While interspecific interactions could result in costly competition or beneficial information exchange, we find that relationships are largely positive, suggesting limited competitive exclusion at the scale of a banding station during migratory stopovers. Our findings support an understanding of animal migrations that consist of networked communities rather than random assemblages of independently migrating species, encouraging future studies of the nature and consequences of co-migrant interactions.