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Artificial selection is a promising approach to manipulate microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both used “propagule” selection strategies, whereby the best-performing communities are used as the inocula to form a new generation of communities. Both experiments were contrasted to a random selection control. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used multiple soil communities as the starting inocula, and the function under selection was the communities’ cross-feeding potential. In both experiments, the selected communities reached a higher mean function than the control. In the first experiment this was caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, this response was fueled by the large initial variance in function across communities, and stopped when the top-performing community “fixed” in the metacommunity. Our results are in agreement with basic expectations from breeding theory, pointing to some of the limitations of community-level selection experiments which can inform the design of future studies.

Microbial communities are highly diverse, and understanding the factors that promote and modulate this diversity is a major area of research in microbial ecology. Recent work has proposed a reductionist perspective to microbial coexistence, where pairwise coexistence between strains in isolation is required for their coexistence in a more complex community. In this view, species exclusion in pairwise co-culture would preclude their coexistence in a more complex community too. An alternative view is that coexistence is a more complex property of the entire community, requiring the presence of additional community members. If this view were correct, competitive exclusion in pairwise co-culture would not necessarily preclude species coexistence in more complex community contexts. Empirically testing these alternative hypotheses is complicated by the intractably high microbial diversity of most natural communities, and the challenges of reconstituting every pair of coexisting species under the exact same habitat where their community of origin was assembled. To address this challenge, we have experimentally reconstituted all possible pairwise co-cultures between stably coexisting species from 13 different, low-diversity microbial enrichment communities, which had previously been assembled in a well-controlled synthetic habitat. We find that, when isolated from the rest of their community members, most species pairs fail to coexist. This result highlights the importance of community context for microbial coexistence and indicates that pairwise exclusion may not reflect the ability of species to coexist in more complex, multispecies ecosystems., This work was partially funded by Young Investigator Award RGY0077/2016 from the Human Frontier Science Program (to A.S.). C, C.-Y. was supported by the Graduate Student Fellowship for Studying Abroad from the Ministry of Education, Taiwan.

Microbial communities frequently invade one another as a whole, a phenomenon known as community coalescence. Despite its potential importance for the assembly, dynamics, and stability of microbial consortia, as well as its prospective utility for microbiome engineering, our understanding of the processes that govern it is still very limited. Theory has suggested that microbial communities may exhibit cohesiveness in the face of invasions emerging from collective metabolic interactions across microbes and their environment. This cohesiveness may lead to correlated invasional outcomes, where the fate of a given taxon is determined by that of other members of its community-a hypothesis known as ecological coselection. Here, we have performed over 100 invasion and coalescence experiments with microbial communities of various origins assembled in two different synthetic environments. We show that the dominant members of the primary communities can recruit their rarer partners during coalescence (top-down coselection) and also be recruited by them (bottom-up coselection). With the aid of a consumer-resource model, we found that the emergence of top-down or bottom-up cohesiveness is modulated by the structure of the underlying cross-feeding networks that sustain the coalesced communities. The model also predicts that these two forms of ecological coselection cannot co-occur under our conditions, and we have experimentally confirmed that one can be strong only when the other is weak. Our results provide direct evidence that collective invasions can be expected to produce ecological coselection as a result of cross-feeding interactions at the community level.

Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities to reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically resilient communities with desired quantitative attributes.

Code and data for Changetal 2021

Purpose Multiple chronic illnesses, such as those associated with advanced age, are leading causes of poor health, disability, death, and high healthcare expenditures. Tele-homecare is a novel method for providing home care to patients with chronic illnesses. The purpose of this study was to evaluate the effectiveness of an integrated nurse-led tele-homecare program for patients with multiple chronic illnesses and a high risk for readmission. Design A randomized controlled trial. Methods Two hundred patients from a regional hospital who were scheduled to receive home care after discharge were randomly assigned to the intervention group (n = 100) or the control group (n = 100). The patients in the intervention group participated in an integrated tele-homecare program. For outcome evaluation, primary outcomes included the number of emergency department (ED) visits as well as readmittance and mortality. Secondary outcomes included patients' medication adherence, activities of daily living, health status, and quality of life (QOL). Data were collected at three time points: pretest baseline (T0), 3 months after intervention (T3), and 6 months after intervention (T6). A generalized estimating equation model was used to compare changes and evaluate the effect of differences between the two groups over time. Findings For primary outcome evaluation, we found that the tele-homecare program significantly reduced mortality and ED visits, whereas no significant effect on readmission was observed. For secondary outcome evaluation, patients' QOL indicated significant improvement. Conclusions and clinical relevance The nurse-led tele-homecare program involves daily 24-hr remote monitoring and surveillance. In this study, the system detected patients' physical changes early and provided timely and appropriate management, consequently reducing ED visits and mortality. Additionally, it improved patients' QOL. On the basis of our findings, nurses' independent roles and functions revealed that the effectiveness of this nurse-led tele-homecare program strengthened the care of patients with multiple chronic illnesses.

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype–phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure–function landscape and propose practical guidelines for navigating these ecological landscapes.

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds ofin-silicomicrobial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

Multidrug resistance protein 1 (MDR1) is one of the most important phase-3 proteins for detoxification. We hypothesized that the allele frequencies of polymorphisms in the MDRI gene in Taiwanese are different from those in other ethnic groups. The polymorphisms of A-41G, C-145G, T307C, G1199A, C1236T, G2677T, G2995A, and C3435T for the MDRI gene were determined for 110 healthy Taiwanese adults using restriction fragment length polymorphism of PCR product and the enzymes TspR Ⅰ. Stu Ⅰ, Hae Ⅱ, Pst Ⅰ Hae Ⅲ, Bse Y Ⅰ, BstZ17 Ⅰ, and Mbo Ⅰ. The results showed that all the study subjects carried the wild type at nucleotides 307, 1199, and 2995. The frequencies of the heterozygous and homozygous variations were 17.3% and 0.0%, 15.5% and 2.7%, 50.0% and 39.1%, 56.4% and 28.2%, and 45.5% and 14.5% at nucleotides -41, -145, 1236, 2677, and 3435, respectively. Compared with previous reports, for Taiwanese, the allele frequencies of polymorphisms in the MDRI gene were significantly different from Japanese, Caucasians, Africans, Chinese living in Singapore, and Chinese living in Mainland China, except for those of -41G (identical to Japanese), 1236T, and 2677T (identical to Chinese living in Singapore). In conclusion, differences in allele frequencies for polymorphisms in the MDRI gene were observed for Taiwanese in comparison with other ethnic groups and Chinese living in Singapore and Mainland China.

Artificial selection is a promising approach to manipulate the function of microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both experiments used “propagule” strategies, in which a set of the best-performing communities are used as the inocula to form a new generation of communities. In both cases, the selected communities are compared to a control treatment where communities are randomly selected. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used a diverse set of natural communities as the inoculum, and the function under selection was the cross-feeding potential of the resulting communities towards a reference bacterial strain. In both experiments, the selected communities reached a higher mean and a higher maximum function than the control. In the first experiment this is caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, the strong response of the mean is caused by the large initial variance in function across communities, and is the immediate consequence of the spread of the top-performing community in the starting group, whose function does not increase. Our results are in agreement with basic expectations of artificial selection theory, pointing out some of the limitations of community-level selection experiments which can inform the design of future studies.

To develop a quantitative theory that can predict how microbiomes assemble, and how they respond to perturbations, we must identify which descriptive features of microbial communities are reproducible and predictable, which are unpredictable, and why. The emergent metagenomic structure of communities is often quantitatively convergent in similar habitats, with highly similar fractions of the metagenome being devoted to the same metabolic pathways. By contrast, the species-level taxonomic composition is often highly variable even in replicate environments. The mechanisms behind these patterns are not yet understood. By studying the self-assembly of hundreds of communities in replicate, synthetic habitats, we show that the reproducibility of microbial community assembly reflects an emergent metabolic structure, which is quantitatively predictable from first-principles, genome-scale metabolic models. Taxonomic variability within functional groups arises through multistability in population dynamics, and the species-level community composition is predictably governed by the mutual competitive exclusion of two sub-dominant strains. Our findings provide a mechanistic bridge between microbial community structure at different levels of organization, and show that the evolutionary conservation of metabolic traits, both in terms of growth responses and niches constructed, can be leveraged to quantitatively predict the taxonomic and metabolic structure of microbial communities.

Summary For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.