Common principles and best practices for engineering microbiomes

Nature Reviews Microbiology, Journal Year: 2019, Volume and Issue: 17(12), P. 725 - 741

Published: Sept. 23, 2019

Language: Английский

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Deciphering microbial interactions in synthetic human gut microbiome communities

Molecular Systems Biology, Journal Year: 2018, Volume and Issue: 14(6)

Published: June 1, 2018

Article21 June 2018Open Access Transparent process Deciphering microbial interactions in synthetic human gut microbiome communities Ophelia S Venturelli Corresponding Author [email protected] orcid.org/0000-0003-2200-1963 Department of Biochemistry, University Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Alex V Carr Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, Garth Fisher Ryan H Hsu orcid.org/0000-0001-8221-224X California Institute Quantitative Biosciences, Rebecca Lau Benjamin P Bowen Susan Hromada Trent Northen Adam Arkin Bioengineering, Energy Biosciences Institute, Information *,1, Carr2,‡,‡, Fisher2,‡, Hsu3, Lau2, Bowen2, Hromada1, Northen2 Arkin2,3,4,5 1Department 2Environmental 3California 4Department 5Energy ‡These authors contributed equally to work ‡Correction added on 27 2018 after first online publication: C was corrected *Corresponding author. Tel: +1 608 263 7017; E-mail: Molecular Biology (2018)14:e8157https://doi.org/10.15252/msb.20178157 See also: Abreu et al (June 2018) PDFDownload PDF article text main figures. Peer ReviewDownload a summary the editorial decision including letters, reviewer comments responses feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The ecological forces that govern assembly stability microbiota remain unresolved. We developed generalizable model-guided framework predict higher-dimensional consortia from time-resolved measurements lower-order assemblages. This method employed decipher diverse community. show pairwise are major drivers multi-species community dynamics, as opposed higher-order interactions. inferred network exhibits high proportion negative frequent positive Ecological responsive recipient species were discovered network. Our model demonstrated prevalent interaction topology enables robust coexistence implementing feedback loop balances disparities monospecies fitness levels. could generate history-dependent initial proportions frequently do not originate bistability. Measurements extracellular metabolites illuminated metabolic capabilities potential molecular basis In sum, these methods defined roles human-associated intestinal design principles communities. Synopsis Analysis shows dynamics. study reveals drivers, metabolite hub ecologically sensitive organisms A data-driven pipeline is used construct predictive dynamic anaerobic Design stable history-dependence elucidated. profiles analyzed each organism. highlights challenges using phylogenetic exo-metabolomic "signals" functions. Introduction Microbes have evolved occupy nearly every environment Earth, spanning extreme environments such acid mine drains hot springs multicellular organisms. dense collection microorganisms inhabits gastrointestinal tract (Lozupone al, 2012; Earle 2015; Tropini 2017) performs numerous functions impact physiology, nutrition, behavior, development (Ley 2005; Fischbach Sonnenburg, 2011; Foster McVey Neufeld, 2013; Louis 2014; Sharon Rooks Garrett, 2016). Functions partitioned among genetically distinct populations interact perform complex chemical transformations exhibit emergent properties colonization resistance at level. Such collective realized combined operating multiple time spatial scales be achieved single population. degree structuring varies across length scales: At macroscale hundreds micrometers, bacteria cluster into habitats, whereas scale intermixing members has been observed (Donaldson Mark Welch 2017). composed bacterial species, majority which span Firmicutes, Bacteroidetes, Actinobacteria phyla 2006). Constituent strains shown persist an individual over long periods time, demonstrating (Faith 2013). Perturbations system dietary shifts or antibiotic administration can shift point alternative state (Relman, 2012). While identities co-occurrence relationships individuals elucidated (Faust 2012), we lack quantitative understanding how shape assembly, stability, response perturbations. For example, enable dominant Bacteroidetes well understood (Fischbach 2011). resilience microbiomes, capacity recover perturbations, strongly linked diversity. Indeed, reduction diversity associated with diseases, suggesting high-dimensional functionally heterogeneous ecosystem promotes health (Sommer Understanding factors influencing implications targeted interventions modulate states. Central problem inferring unknown developing tools temporal changes behaviors environmental stimuli. Cooperation competition feedbacks influence functional activities stability. Negative dominate inter-relationships aquatic microcosms (Foster Bell, However, prevalence cooperation occupying other remains elusive. Direct resources space, biomolecular warfare, production toxic waste products (Hibbing 2010). Positive stem secreted utilized member detoxification environment. Pairwise modified third organism, leading effects (Bairey driver large structure function, represent key nodes manipulated control states (Gibson Predicting dynamics step toward organizational Computational models different resolutions analyze Raes, Dynamic computational investigate structure, dynamical systems theory parameter sensitivity (Astrom Murray, Generalized Lotka–Volterra (gLV) ordinary differential equation represents limited number parameters deduced time-series data. Here, develop systematic modeling experimental interrogate mediating assembly. Time-resolved assemblages train revealed Specific context identified exhibited overall community, specific Firmicutes displayed outgoing sub-network variations parameters. showed due slow convergence steady composition networks enriched exo-metabolomics profiling, data pinpointed set predicted mediate failed forecast influential modulating Together, results combinations couplings realize repertoire behaviors. Results Probing aimed dissect reduced complexity Actinobacteria, Proteobacteria. To end, ecology encompassing Bacteroides thetaiotaomicron (BT), ovatus (BO), uniformis (BU), vulgatus (BV), Blautia hydrogenotrophica (BH), Collinsella aerofaciens (CA), Clostridium hiranonis (CH), Desulfovibrio piger (DP), Eggerthella lenta (EL), Eubacterium rectale (ER), Faecalibacterium prausnitzii (FP), Prevotella copri (PC) designed mirror natural (Fig 1A; Qin These contribute significantly implicated diseases (Watterlot 2008; Larsen 2010; Thota Fujimoto Haiser Scher Table 1). Figure 1. Experimental high-throughput characterization Phylogenetic tree 12-member analysis performed concatenated alignment single-copy marker genes obtained via PhyloSift (preprint: Darling 2014). Maximum likelihood trees generated default options. bar substitutions per site alignment. Schematic study. Species approximately 1:1 19:1 based absorbance 600 nm (OD600) microtiter plates liquid-handling robotic manipulation. Approximately 12 h, samples collected multiplexed 16S rRNA gene sequencing (black circles). Relative abundance measured V3–V4 region dual-indexed primers compatible Illumina platform (stacked plot, bottom right). Serial transfers 24 intervals (purple bars, top) transferring aliquot fresh media 1:20 dilution. parallel, OD600 performed. Download figure PowerPoint associations previous literature. Arrows pointing up down denote associations, respectively Association(s) Inflammatory autoimmune disease (↑) (Scher 2013), autism (↓) (Kang 2013) (BV) Ulcerative colitis (Bamba 1995) (BU) Metabolic/immunological dysfunction (Gauffin Cano 2012) (BO) Type I diabetes (Giongo 2011) (BT) (↑)(Bloom (FP) Crohn's 2008), inflammatory bowel (Segain 2000), Celiac (De Palma 2010) (BH) Healthy colon (Nava (ER) II (Larsen (CA) Colon cancer (Moore Moore, 1995), rheumatoid arthritis (Chen 2016) (EL) Cardiac drug (Haiser (Thota 2011), (DP) Regressive (Finegold (CH) None reported Synthetic arrayed chamber automated procedure (see Materials Methods). rich Methods) selected support growth all monospecies. serially transferred 24-h prevent lag phases being eliminated allow approach monitoring many cell generations. Further, serial also reflect recurrent perturbations diet colonic transit 1B). Multiplexed 12-h elucidate stages. relative computed sum read counts organism divided total reads condition Since construction aided absolute information (Bucci 2016; Widder 2016), biomass monitored 30 min (OD600). Cellular traits adhesion, size, (Stevenson addition, counting colony-forming units (CFU) biased dormant sub-populations, selection solid vs. liquid media, stage (Jansson Prosser, 1997; Volkmer Heinemann, Ou trained estimated thus automatically accounts any biases. infer interactions, (66 combinations) ratio values (PW1 dataset, Appendix Fig S1, Dataset EV1). Monospecies sequencing, broad range rates, carrying capacities (M S2). single-species dominance (Appendix S1). distribution PW1 provided insight variability presence second S3A). Absolute normalized maximum value evaluate baseline species. CH lowest coefficient variation (CV), indicating levels S3B). remaining bimodal (FP, DP, PC, BH, CA), long-tail (ER EL), and/or CV (ER, PC), altered further probe consortia, 15 2B, EV2) inoculated (95% A, 5% B, wherein percentages reversed) characterized our workflow (PW2 S4). classified following categories threshold 72 h: (i) dominance; (ii) both persisted above threshold; (iii) history dependence whereby mapped structures; (iv) did quantitatively satisfy thresholds cases 1–3. subset category weak potentially attributed biological replicates. qualitative 51 (t = 0) final h) PW2 reciprocal percentages, S5A). 50, 24, 12% dominance, coexistence, dependence, S5B). 2. Model training generalized function fits T3 represented points lines, respectively. subplot, x- y-axis, Stars datasets mean squared errors greater than 0.15. Error bars 1 s.d. least three Temporal 95% B values. Time Data lines T3, Inferred inter-species coefficients gLV T3. Gray green edges (αij < > coefficients. edge width node size magnitude (xe −μiαii−1), highlight significant less 1e-5 displayed. Construction growth, intra-species sensitivity. equations given by: where n, μ, αii, αij intra-species, coefficients, minimize overfitting data, regularized estimation implemented penalized Three sets evaluated capability: (T1) M; (T2) M, PW1; (T3) PW1, PW2. regularization (λ) scanned balance goodness fit sparsity S6). parameterized captured 2A B). accurately EL; CA; BO, CH; ER, BH; BH error between Thresholding yielded densely connected 77% pairs interaction. connectivity varied 75 79% ranging 1e-6 1e-3. Interaction 1e-3 expected change rate Of 56 21% positive, 2C). arise resource competition, by-products. secretion (BO, BV, BU, BT) net network, EL, positively stimulated S7A). unidirectional (−/0, 36%), bidirectional (−/−, 32%), (+/−, 26%) S7B). contribution full dictated coupled incoming Therefore, prediction about role requires simulation model. FP five determine abundance, examined 6-member FP, CH, DP. required alter twofold, dual moderately increased h S7C). enhanced conditions. Corroborating notion, Figs S3 Strong deciphered enhancement productivity compared null representing productivities integral S8). 38% twofold predictions models, consistent BU; BO; BT, BV; BO lower model, mutual inhibitory topologies BT; ER comparison (EL, ER) mutualism, augment productivity. Hierarchical clustering similar patterns S9A pattern relationships. distantly related display (e.g., DP PC CA) closely exhi

Language: Английский

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Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere

Nature Ecology & Evolution, Journal Year: 2019, Volume and Issue: 3(10), P. 1445 - 1454

Published: Sept. 26, 2019

Language: Английский

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Microbiome-based therapeutics

Nature Reviews Microbiology, Journal Year: 2022, Volume and Issue: 20(6), P. 365 - 380

Published: Jan. 6, 2022

Language: Английский

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Trophic Interactions and the Drivers of Microbial Community Assembly

Current Biology, Journal Year: 2020, Volume and Issue: 30(19), P. R1176 - R1188

Published: Oct. 1, 2020

Despite numerous surveys of gene and species content in heterotrophic microbial communities, such as those found animal guts, oceans, or soils, it is still unclear whether there are generalizable biological ecological processes that control their dynamics function. Here, we review experimental theoretical advances to argue networks trophic interactions, which the metabolic excretions one primary resource for another, constitute central drivers community assembly. Trophic interactions emerge from deconstruction complex forms organic matter into a wealth smaller intermediates, some released environment serve nutritional buffet community. The structure emergent network rate at resources supplied many features assembly, including relative contributions competition cooperation emergence alternative states. Viewing assembly through lens also has important implications spatial communities well functional redundancy taxonomic groups. Given ubiquity across environments, they impart common logic can enable development more quantitative predictive ecology.

Language: Английский

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Understanding Competition and Cooperation within the Mammalian Gut Microbiome

Current Biology, Journal Year: 2019, Volume and Issue: 29(11), P. R538 - R544

Published: June 1, 2019

The mammalian gut harbors a vast community of microorganisms — termed the microbiota whose composition and dynamics are considered to be critical drivers host health. These factors depend, in part, upon manner which microbes interact with one another. Microbes known engage myriad different ways, ranging from unprovoked aggression actively feeding each other. However, relative extent these interactions occur between within is unclear. In this minireview we assess our current knowledge microbe–microbe microbiota, array methods used uncover them. particular, highlight discrepancies methodologies: some studies have revealed rich networks cross-feeding microbes, whereas others suggest that more typically locked conflict cooperate only rarely. We argue reconcile contradictions must recognize members can vary across condition, space, time through embracing dynamism will able comprehensively understand ecology communities.

Language: Английский

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Revisit gut microbiota and its impact on human health and disease

Journal of Food and Drug Analysis, Journal Year: 2019, Volume and Issue: 27(3), P. 623 - 631

Published: Feb. 1, 2019

Trillions of microbes have evolved with and continue to live on human beings. With the rapid advances in tools technology recent years, new knowledge insight cross-talk between their hosts gained. It is aim this work critically review summarize literature reports role microbiota mechanisms involved progress development major diseases, which include obesity, hypertension, cardiovascular disease, diabetes, cancer, Inflammatory Bowel Disease (IBD), gout, depression arthritis, as well infant health longevity.

Language: Английский

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Use and abuse of correlation analyses in microbial ecology

The ISME Journal, Journal Year: 2019, Volume and Issue: 13(11), P. 2647 - 2655

Published: June 28, 2019

Language: Английский

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Massively parallel screening of synthetic microbial communities

Proceedings of the National Academy of Sciences, Journal Year: 2019, Volume and Issue: 116(26), P. 12804 - 12809

Published: June 11, 2019

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic are combinatorially constructed assayed high throughput. However, assembling many combinations of microbes logistically complex difficult achieve on timescale commensurate microbial growth. Here, introduce kChip, droplets-based platform that performs rapid, massively parallel, bottom-up construction communities. We first show kChip enables phenotypic characterization across conditions. Next, screen ∼100,000 multispecies comprising up 19 soil isolates, identified sets promote growth model plant symbiont

Language: Английский

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Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community

Nature Microbiology, Journal Year: 2021, Volume and Issue: 6(2), P. 196 - 208

Published: Jan. 4, 2021

Language: Английский

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