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Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology. Studies of microbial community assembly mechanisms typically use metrics for turnover within the whole community. Here, the authors develop an alternative approach based on turnover within lineages and dissect mechanistic change in grassland bacterial assembly under experimental warming.

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind’s predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server ( http://papers.genomics.lbl.gov/carbon ) and takes just 30 seconds for the typical genome.

We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the \"CAT\" approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100-1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.

Bacteriophages (phages) are critical players in the dynamics and function of microbial communities and drive processes as diverse as global biogeochemical cycles and human health. Phages tend to be predators finely tuned to attack specific hosts, even down to the strain level, which in turn defend themselves using an array of mechanisms. However, to date, efforts to rapidly and comprehensively identify bacterial host factors important in phage infection and resistance have yet to be fully realized. Here, we globally map the host genetic determinants involved in resistance to 14 phylogenetically diverse double-stranded DNA phages using two model Escherichia coli strains (K-12 and BL21) with known sequence divergence to demonstrate strain-specific differences. Using genome-wide loss-of-function and gain-of-function genetic technologies, we are able to confirm previously described phage receptors as well as uncover a number of previously unknown host factors that confer resistance to one or more of these phages. We uncover differences in resistance factors that strongly align with the susceptibility of K-12 and BL21 to specific phage. We also identify both phage-specific mechanisms, such as the unexpected role of cyclic-di-GMP in host sensitivity to phage N4, and more generic defenses, such as the overproduction of colanic acid capsular polysaccharide that defends against a wide array of phages. Our results indicate that host responses to phages can occur via diverse cellular mechanisms. Our systematic and high-throughput genetic workflow to characterize phage-host interaction determinants can be extended to diverse bacteria to generate datasets that allow predictive models of how phage-mediated selection will shape bacterial phenotype and evolution. The results of this study and future efforts to map the phage resistance landscape will lead to new insights into the coevolution of hosts and their phage, which can ultimately be used to design better phage therapeutic treatments and tools for precision microbiome engineering.

The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities. Synopsis Analysis of microbial interactions in a synthetic human gut microbiome community shows that pairwise microbial interactions are major drivers of multi‐species community dynamics. The study reveals ecological drivers, metabolite hub species and ecologically sensitive organisms in the network. A data‐driven pipeline is used to construct a predictive dynamic model of a diverse anaerobic human gut microbiome community. Design principles of stable coexistence and history‐dependence are elucidated. Ecological roles and metabolite profiles are analyzed for each organism. The study highlights challenges in using phylogenetic and exo‐metabolomic “signals” to predict microbial interactions and community functions. Graphical Abstract Analysis of microbial interactions in a synthetic human gut microbiome community shows that pairwise microbial interactions are major drivers of multi‐species community dynamics. The study reveals ecological drivers, metabolite hub species and ecologically sensitive organisms in the network.

Genome sequencing has revealed an incredible diversity of bacteria and archaea, but there are no fast and convenient tools for browsing across these genomes. It is cumbersome to view the prevalence of homologs for a protein of interest, or the gene neighborhoods of those homologs, across the diversity of the prokaryotes. We developed a web-based tool, fast . genomics , that uses two strategies to support fast browsing across the diversity of prokaryotes. First, the database of genomes is split up. The main database contains one representative from each of the 6,377 genera that have a high-quality genome, and additional databases for each taxonomic order contain up to 10 representatives of each species. Second, homologs of proteins of interest are identified quickly by using accelerated searches, usually in a few seconds. Once homologs are identified, fast . genomics can quickly show their prevalence across taxa, view their neighboring genes, or compare the prevalence of two different proteins. Fast . genomics is available at https://fast.genomics.lbl.gov .

An inability to reliably predict quantitative behaviors for novel combinations of genetic elements limits the rational engineering of biological systems. We developed an expression cassette architecture for genetic elements controlling transcription and translation initiation in Escherichia coli: transcription elements encode a common mrnA start, and translation elements use an overlapping genetic motif found in many natural systems. We engineered libraries of constitutive and repressor-regulated promoters along with translation initiation elements following these definitions. We measured activity distributions for each library and selected elements that collectively resulted in expression across a 1,000-fold observed dynamic range. We studied all combinations of curated elements, demonstrating that arbitrary genes are reliably expressed to within twofold relative target expression windows with ~93% reliability. We expect the genetic element definitions validated here can be collectively expanded to create collections of public-domain standard biological parts that support reliable forward engineering of gene expression at genome scales. One main goal of synthetic biology is to make the engineering of biology easier 1,2. DNA synthesis and assembly has progressed to the point where entire metabolic pathways, chromosomes and genomes can now be synthesized and transplanted 3-5. However, our capacity to rationally design increasingly complicated genetic systems as enabled by improvements in DNA construction methods has not kept pace 2,6. One of the greatest claimed barriers to efficient and scalable genetic design is the lack of standard parts that can be reused reliably in novel combinations 6,7. Many examples instead highlight, even within well-studied organisms such as E. coli, how seemingly simple genetic functions behave differently in different settings 8,9. For example, a prokaryotic ribosome-binding site (RBS) element that initiates translation for one coding sequence might not function at all with another coding sequence 10. If the genetic elements that encode control of central cellular processes such as transcription and translation cannot be reliably reused, then there is little chance that higher-order objects encoded from such basic elements will be reliable in larger-scale systems 6,11. Standard biological parts could, in theory, enable hierarchical abstraction of biological functions 1,2,12,13. The behavior of integrated genetic systems could then be represented via simpler models of individual elements and ultimately mapped to underlying genetic sequences whose encoded functions are dependent on a limited number of measurable or calculable intrinsic variables. Such abstraction of function seems necessary to manage biological complexity and to allow the engineering of increasingly sophisticated genetic systems 6,12,14. We engineered ~500 transcription and translation initiation elements that are compatible within a standardized genetic context, or expression operating unit (EOU), that enables predictable forward engineering of gene expression over a wide dynamic range. We characterized representative parts for each type by testing more than 1,200 part-part combinations to establish and validate functional composition rules while quantifying scores for part activity. From this data we also estimated the 'quality' of each part, a second-order statistic that represents the extent to which the activity of a part varies across changes in context 15. Our results demonstrate how, when combined with standardized transcription control elements, a more physically complex design for the control of translation initiation creates simply modeled parts enabling reliable forward engineering of gene expression. results Prioritizing part composition puzzles In related work, we systematically assembled and tested all combinations of frequently used prokaryotic transcription and translation control elements to quantify average part activities and also variation in activities as parts are reused in novel combinations 15. Here we focus on developing rules for a genetic layout architecture underlying gene expression cassettes that eliminate

Unraveling the drivers of community structure and succession in response to environmental change is a central goal in ecology. Although the mechanisms shaping community structure have been intensively examined, those controlling ecological succession remain elusive. To understand the relative importance of stochastic and deterministic processes in mediating microbial community succession, a unique framework composed of four different cases was developed for fluidic and nonfluidic ecosystems. The framework was then tested for one fluidic ecosystem: a groundwater system perturbed by adding emulsified vegetable oil (EVO) for uranium immobilization. Our results revealed that groundwater microbial community diverged substantially away from the initial community after EVO amendment and eventually converged to a new community state, which was closely clustered with its initial state. However, their composition and structure were significantly different from each other. Null model analysis indicated that both deterministic and stochastic processes played important roles in controlling the assembly and succession of the groundwater microbial community, but their relative importance was time dependent. Additionally, consistent with the proposed conceptual framework but contradictory to conventional wisdom, the community succession responding to EVO amendment was primarily controlled by stochastic rather than deterministic processes. During the middle phase of the succession, the roles of stochastic processes in controlling community composition increased substantially, ranging from 81.3% to 92.0%. Finally, there are limited successional studies available to support different cases in the conceptual framework, but further well-replicated explicit time-series experiments are needed to understand the relative importance of deterministic and stochastic processes in controlling community succession.

The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes (“L” and “S”) shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community. Fitness landscapes largely shape the dynamics of evolution, but it is unclear how they shift upon ecological diversification. By engineering genome-wide knockout libraries of a nascent bacterial community, Ascensao et al. show how ecological and epistatic patterns combine to shape adaptive landscapes.

Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.