Explore the vast range of titles available.

Keystone taxa in ecological communities are native taxa that play an especially important role in the stability of their ecosystem. However, we still lack an effective framework for identifying these taxa from the available high-throughput sequencing without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. In addition, while most microbial interaction models assume pair-wise relationships, it is yet unclear whether pair-wise interactions dominate the system, or whether higher-order interactions are relevant. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the taxa. Our method does not assume a priori knowledge of pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real high-throughput sequencing of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module – multiple candidate keystone species with correlated occurrence. The keystone analysis of single-time-point cross-sectional data is also later verified by the evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities. Keystone taxa in ecological communities are native taxa that have an especially important role in the stability of their ecosystem. This study introduces a novel method for detecting keystones in microbial communities by comparing data with and without specific species.

Microbial communities often exhibit incredible taxonomic diversity, raising questions regarding the mechanisms enabling species coexistence and the role of this diversity in community functioning. On the one hand, many coexisting but taxonomically distinct microorganisms can encode the same energy-yielding metabolic functions, and this functional redundancy contrasts with the expectation that species should occupy distinct metabolic niches. On the other hand, the identity of taxa encoding each function can vary substantially across space or time with little effect on the function, and this taxonomic variability is frequently thought to result from ecological drift between equivalent organisms. Here, we synthesize the powerful paradigm emerging from these two patterns, connecting the roles of function, functional redundancy and taxonomy in microbial systems. We conclude that both patterns are unlikely to be the result of ecological drift, but are inevitable emergent properties of open microbial systems resulting mainly from biotic interactions and environmental and spatial processes. Microbial communities may often be composed of a wide diversity of taxa that perform similar functions. Here, the authors discuss the roles of function, functional redundancy and taxonomy in microbial community assembly and coexistence.

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities. Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. Here, in a community of two competing E. coli strains, the authors show that the relative abundances of the strains can be stabilized and steered dynamically with remarkable precision by coupling the cells to an automated computer-controlled feedback-loop.

Bacterial chemotaxis, the directed movement of cells along gradients of chemoattractants, is among the best-characterized subjects in molecular biology 1 – 10 , but much less is known about its physiological roles 11 . It is commonly seen as a starvation response when nutrients run out, or as an escape response from harmful situations 12 – 16 . Here we identify an alternative role of chemotaxis by systematically examining the spatiotemporal dynamics of Escherichia coli in soft agar 12 , 17 , 18 . Chemotaxis in nutrient-replete conditions promotes the expansion of bacterial populations into unoccupied territories well before nutrients run out in the current environment. Low levels of chemoattractants act as aroma-like cues in this process, establishing the direction and enhancing the speed of population movement along the self-generated attractant gradients. This process of navigated range expansion spreads faster and yields larger population gains than unguided expansion following the canonical Fisher–Kolmogorov dynamics 19 , 20 and is therefore a general strategy to promote population growth in spatially extended, nutrient-replete environments. Pioneering bacterial cells use chemotaxis along self-generated attractant gradients to facilitate rapid colonization of nutrient-replete environments.

Cells in dense bacterial suspensions can self-organize into highly robust collective oscillatory motion, while individual cells move in an erratic manner; their interaction is modelled to reveal a weak synchronization mechanism. Collective oscillation in bacteria In recent years, various effects of collective behaviour have been reported in 'active matter'—systems that contain large numbers of self-propelled particles. Such studies offer clues to understanding organization in biological systems at various scales and to strategies for designing smart materials. Yilin Wu and colleagues studied dense suspensions of Escherichia coli bacteria and observed a striking effect of collective oscillatory motion. Individual bacteria move in an erratic manner, but when they are averaged over tens or hundreds of micrometres, steady, synchronized oscillations become apparent. The authors present a model of noisy self-propelled particles with strictly local interactions that can account for the observations. Such oscillatory behaviour could point to a new direction for studying self-organization in active matter. Collective oscillatory behaviour is ubiquitous in nature 1 , having a vital role in many biological processes from embryogenesis 2 and organ development 3 to pace-making in neuron networks 4 . Elucidating the mechanisms that give rise to synchronization is essential to the understanding of biological self-organization. Collective oscillations in biological multicellular systems often arise from long-range coupling mediated by diffusive chemicals 2 , 5 , 6 , 7 , 8 , 9 , by electrochemical mechanisms 4 , 10 , or by biomechanical interaction between cells and their physical environment 11 . In these examples, the phase of some oscillatory intracellular degree of freedom is synchronized. Here, in contrast, we report the discovery of a weak synchronization mechanism that does not require long-range coupling or inherent oscillation of individual cells. We find that millions of motile cells in dense bacterial suspensions can self-organize into highly robust collective oscillatory motion, while individual cells move in an erratic manner, without obvious periodic motion but with frequent, abrupt and random directional changes. So erratic are individual trajectories that uncovering the collective oscillations of our micrometre-sized cells requires individual velocities to be averaged over tens or hundreds of micrometres. On such large scales, the oscillations appear to be in phase and the mean position of cells typically describes a regular elliptic trajectory. We found that the phase of the oscillations is organized into a centimetre-scale travelling wave. We present a model of noisy self-propelled particles with strictly local interactions that accounts faithfully for our observations, suggesting that self-organized collective oscillatory motion results from spontaneous chiral and rotational symmetry breaking. These findings reveal a previously unseen type of long-range order in active matter systems (those in which energy is spent locally to produce non-random motion) 12 , 13 . This mechanism of collective oscillation may inspire new strategies to control the self-organization of active matter 14 , 15 , 16 , 17 , 18 and swarming robots 19 .

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype–Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine. The FarmGTEx Project aims to understand genetic control of gene activity under diverse biological and environmental contexts in domestic animals, providing a foundation for improving animal precision breeding, adaptation and human health.

Clinically relevant bacteria have been engineered to lyse synchronously at a threshold population density and release genetically encoded therapeutics; treatment of mice with these bacteria slowed the growth of tumours. Engineering self-control into anti-tumour bacteria There is growing interest in using bacteria as living therapeutics, although the complications due to host responses and long-term effectiveness remain to be established. Omar Din et al . have now engineered a quorum-sensing clock into a strain of Salmonella known to target solid tumours by releasing a tumour-targeting toxin. The clock drives periodic lyses of the bacterial colony, thereby controlling the bacterial population and ensuring sustained anti-tumour toxin delivery in a mouse cancer model. Although the system as it stands does not represent an effective cure, this work indicates that synthetic biology can be harnessed to achieve dynamic and sustained delivery of therapeutics in vivo . The widespread view of bacteria as strictly pathogenic has given way to an appreciation of the prevalence of some beneficial microbes within the human body 1 , 2 , 3 . It is perhaps inevitable that some bacteria would evolve to preferentially grow in environments that harbour disease and thus provide a natural platform for the development of engineered therapies 4 , 5 , 6 . Such therapies could benefit from bacteria that are programmed to limit bacterial growth while continually producing and releasing cytotoxic agents in situ 7 , 8 , 9 , 10 . Here we engineer a clinically relevant bacterium to lyse synchronously at a threshold population density and to release genetically encoded cargo. Following quorum lysis, a small number of surviving bacteria reseed the growing population, thus leading to pulsatile delivery cycles. We used microfluidic devices to characterize the engineered lysis strain and we demonstrate its potential as a drug delivery platform via co-culture with human cancer cells in vitro . As a proof of principle, we tracked the bacterial population dynamics in ectopic syngeneic colorectal tumours in mice via a luminescent reporter. The lysis strain exhibits pulsatile population dynamics in vivo , with mean bacterial luminescence that remained two orders of magnitude lower than an unmodified strain. Finally, guided by previous findings that certain bacteria can enhance the efficacy of standard therapies 11 , we orally administered the lysis strain alone or in combination with a clinical chemotherapeutic to a syngeneic mouse transplantation model of hepatic colorectal metastases. We found that the combination of both circuit-engineered bacteria and chemotherapy leads to a notable reduction of tumour activity along with a marked survival benefit over either therapy alone. Our approach establishes a methodology for leveraging the tools of synthetic biology to exploit the natural propensity for certain bacteria to colonize disease sites.

Microbial species rarely exist in isolation. In naturally occurring microbial systems there is strong evidence for a positive relationship between species diversity and productivity of communities. The pervasiveness of these communities in nature highlights possible advantages for genetically engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially as functional complexity increases. Here, we demonstrate a methodology for designing robust synthetic communities that include competition for nutrients, and use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We computationally explore all two- and three- strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and further tuning the community composition. In naturally occurring microbial systems, there is a positive relationship between species diversity and productivity of the community. Here the authors perform model selection to find potential amensal interactions that yield robust stable synthetic microbial consortia.

Recent work has suggested that the prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. In this study, we addressed that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that the PFC has a multidimensional code for context, decisions and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases and show that they do not arise from distinct populations but from a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in the PFC preserve sensory choice information for the duration of the stimulus integration period.Aoi et al. used a new dimensionality-reduction method to disentangle the contributions of different task variables to neural population activity, which revealed rotational dynamics in monkey PFC during context-dependent decision-making.

Neuroblastoma, the most frequent solid tumor in infants, shows very diverse outcomes from spontaneous regression to fatal disease. When these different tumors originate and how they evolve are not known. Here we quantify the somatic evolution of neuroblastoma by deep whole-genome sequencing, molecular clock analysis and population-genetic modeling in a comprehensive cohort covering all subtypes. We find that tumors across the entire clinical spectrum begin to develop via aberrant mitoses as early as the first trimester of pregnancy. Neuroblastomas with favorable prognosis expand clonally after short evolution, whereas aggressive neuroblastomas show prolonged evolution during which they acquire telomere maintenance mechanisms. The initial aneuploidization events condition subsequent evolution, with aggressive neuroblastoma exhibiting early genomic instability. We find in the discovery cohort ( n  = 100), and validate in an independent cohort ( n  = 86), that the duration of evolution is an accurate predictor of outcome. Thus, insight into neuroblastoma evolution may prospectively guide treatment decisions. Somatic evolutionary analysis of neuroblastoma, a pediatric tumor, proposes a common fetal time of origin. Notably, high-risk tumors exhibit early genomic instability and prolonged evolution, and this evolutionary duration predicts clinical outcomes.