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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Current Sampling Methods for Gut Microbiota: A Call for More Precise Devices

Frontiers in Cellular and Infection Microbiology, Journal Year: 2020, Volume and Issue: 10

Published: April 9, 2020

The development of next-generation sequencing technology enables researchers to explore and understand the gut microbiome from a broader deeper perspective. However, results different studies on microbiota are highly variable even in same disease, which make it difficult guide clinical diagnosis treatment. ideal sampling method should be less invasive, no cross-contamination, bowel preparation, can collect at sites. In current status, technologies usually based samples collected feces, mucosal biopsy intestinal fluid, etc. parts gastrointestinal tract possess various physiological characteristics essential for particular species living. What's more, methods more or defective. For example, fecal just proxy microbiota, while biopsies invasive patients not suitable healthy controls. this review, we summarized their advantages shortcomings. New technologies, such as Brisbane Aseptic Biopsy Device intelligent capsule, were also mentioned order call inspiration developing future precise description microbiome.

Language: Английский

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Developing a new class of engineered live bacterial therapeutics to treat human diseases

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: April 8, 2020

Abstract A complex interplay of metabolic and immunological mechanisms underlies many diseases that represent a substantial unmet medical need. There is an increasing appreciation the role microbes play in human health disease, evidence accumulating new class live biotherapeutics comprised engineered could address specific disease. Using tools synthetic biology, nonpathogenic bacteria can be designed to sense respond environmental signals order consume harmful compounds deliver therapeutic effectors. In this perspective, we describe considerations for design development achieve regulatory patient acceptance.

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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Gastrointestinal biofilms in health and disease

Nature Reviews Gastroenterology & Hepatology, Journal Year: 2021, Volume and Issue: 18(5), P. 314 - 334

Published: Jan. 28, 2021

Language: Английский

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Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies

Chemical Reviews, Journal Year: 2022, Volume and Issue: 123(1), P. 31 - 72

Published: Nov. 1, 2022

The human microbiome is composed of a collection dynamic microbial communities that inhabit various anatomical locations in the body. Accordingly, coevolution with host has resulted these playing profound role promoting health. Consequently, perturbations can cause or exacerbate several diseases. In this Review, we present our current understanding relationship between health and disease development, focusing on microbiomes found across digestive, respiratory, urinary, reproductive systems as well skin. We further discuss strategies by which composition function be modulated to exert therapeutic effect host. Finally, examine technologies such multiomics approaches cellular reprogramming microbes enable significant advancements research engineering.

Language: Английский

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The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View

Trends in cancer, Journal Year: 2020, Volume and Issue: 6(3), P. 192 - 204

Published: Feb. 7, 2020

The collection of microbes that live in and on the human body - microbiome can impact cancer initiation, progression, response to therapy, including immunotherapy. mechanisms by which microbiomes cancers yield new diagnostics treatments, but much remains unknown. interactions between microbes, diet, host factors, drugs, cell-cell within itself likely involve intricate feedbacks, no single component explain all behavior system. Understanding role host-associated microbial communities systems will require a multidisciplinary approach combining ecology, immunology, cell biology, computational biology approach.

Language: Английский

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A microbial sea of possibilities: current knowledge and prospects for an improved understanding of the fish microbiome

Reviews in Aquaculture, Journal Year: 2019, Volume and Issue: 12(2), P. 1101 - 1134

Published: Aug. 13, 2019

Abstract The mucosal surfaces of fish represent an important barrier that supports and regulates a diverse array microbial assemblages contributes to the overall health fitness host. For farmed species, knowledge how these host–microbial systems adapt respond various stressors is pivotal for managing health, nutrition optimizing productivity in aquaculture. While our understanding communities factors shape them now suggest balanced microbiota critical healthy functioning fish, mechanisms behind interactions are still poorly understood. Much existing research has focused on characterizing taxonomic diversity different across body (e.g. skin, gills gastrointestinal tract), response changing nutrition, environmental conditions. However, specific functional contributions (or members) remain elusive, especially or diseased fish. Here, we review current their interplay likely involvement with We also seek address identify gaps explore future prospects improving

Language: Английский

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A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007-2016

Microbiome, Journal Year: 2019, Volume and Issue: 7(1)

Published: Feb. 26, 2019

The National Institutes of Health (NIH) is the primary federal government agency for biomedical research in USA. NIH provides extensive support human microbiome with 21 27 and Centers (ICs) currently funding this area through their extramural programs. This analysis portfolio briefly reviews early history field at NIH, summarizes program objectives resources developed recently completed 10-year (fiscal years 2007–2016) $215 M Human Microbiome Project (HMP) program, evaluates scope range $728 investment activities outside HMP over fiscal 2012–2016, highlights some specific areas which emerged from investment. closes a few comments on technical needs knowledge gaps remain to be able advance next decade outcomes progress microbiome-based interventions treating disease supporting health.

Language: Английский

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Spatial metabolomics of in situ host–microbe interactions at the micrometre scale

Nature Microbiology, Journal Year: 2020, Volume and Issue: 5(3), P. 498 - 510

Published: Feb. 3, 2020

Language: Английский

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