The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions

Frontiers in Plant Science, Год журнала: 2017, Номер 8

Опубликована: Сен. 19, 2017

In their natural environment plants are part of a rich ecosystem including numerous and diverse microorganisms in the soil. It has been long recognized that some these microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important roles plant performance by improving mineral nutrition. However, full range microbes associated with potential to replace synthetic agricultural inputs only recently started be uncovered. last few years great progress made knowledge on composition rhizospheric microbiomes dynamics. There is clear evidence shape microbiome structures, most probably root exudates, also bacteria have developed various adaptations thrive niche. The mechanisms interactions processes driving alterations however largely unknown. this review we focus interaction enhancing nutrition, summarizing current several research fields can converge improve our understanding molecular underpinning phenomenon.

Язык: Английский

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Research priorities for harnessing plant microbiomes in sustainable agriculture

PLoS Biology, Год журнала: 2017, Номер 15(3), С. e2001793 - e2001793

Опубликована: Март 28, 2017

Feeding a growing world population amidst climate change requires optimizing the reliability, resource use, and environmental impacts of food production. One way to assist in achieving these goals is integrate beneficial plant microbiomes—i.e., those enhancing growth, nutrient use efficiency, abiotic stress tolerance, disease resistance—into agricultural This integration will require large-scale effort among academic researchers, industry farmers understand manage plant-microbiome interactions context modern systems. Here, we identify priorities for research this area: (1) develop model host–microbiome systems crop plants non-crop with associated microbial culture collections reference genomes, (2) define core microbiomes metagenomes systems, (3) elucidate rules synthetic, functionally programmable microbiome assembly, (4) determine functional mechanisms interactions, (5) characterize refine genotype-by-environment-by-microbiome-by-management interactions. Meeting should accelerate our ability design implement effective manipulations management strategies, which, turn, pay dividends both consumers producers supply.

Язык: Английский

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Community structure follows simple assembly rules in microbial microcosms

Nature Ecology & Evolution, Год журнала: 2017, Номер 1(5)

Опубликована: Март 27, 2017

Язык: Английский

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Establishing Causality: Opportunities of Synthetic Communities for Plant Microbiome Research

Cell Host & Microbe, Год журнала: 2017, Номер 22(2), С. 142 - 155

Опубликована: Авг. 1, 2017

Язык: Английский

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Deciphering microbial interactions in synthetic human gut microbiome communities

Molecular Systems Biology, Год журнала: 2018, Номер 14(6)

Опубликована: Июнь 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

Язык: Английский

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Biophysical processes supporting the diversity of microbial life in soil

FEMS Microbiology Reviews, Год журнала: 2017, Номер 41(5), С. 599 - 623

Опубликована: Июль 10, 2017

Soil, the living terrestrial skin of Earth, plays a central role in supporting life and is home to an unimaginable diversity microorganisms. This review explores key drivers for microbial soils under different climates land-use practices at scales ranging from soil pores landscapes. We delineate special features as habitat (focusing on bacteria) consequences communities. covers recent modeling advances that link physical processes with (termed biophysical processes). Readers are introduced concepts governing water organization associated transport properties dispersion ranges often determined by spatial highly dynamic aqueous phase. The narrow hydrological windows wetting phase connectedness crucial resource distribution longer range Feedbacks between activity their immediate environment responsible emergence stabilization structure-the scaffolding ecological functioning. synthesize insights historical contemporary studies provide outlook challenges opportunities developing quantitative framework predict component

Язык: Английский

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Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance

Environment International, Год журнала: 2018, Номер 117, С. 132 - 138

Опубликована: Май 7, 2018

There is growing understanding that the environment plays an important role both in transmission of antibiotic resistant pathogens and their evolution. Accordingly, researchers stakeholders world-wide seek to further explore mechanisms drivers involved, quantify risks identify suitable interventions. a clear value establishing research needs coordinating efforts within across nations order best tackle this global challenge. At international workshop late September 2017, scientists from 14 countries with expertise on environmental dimensions resistance gathered define critical knowledge gaps. Four key areas were identified where urgently needed: 1) relative contributions different sources antibiotics bacteria into environment; 2) environment, particularly anthropogenic inputs, evolution resistance; 3) overall human animal health impacts caused by exposure bacteria; 4) efficacy feasibility technological, social, economic behavioral interventions mitigate resistance.1.

Язык: Английский

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Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

The ISME Journal, Год журнала: 2017, Номер 11(10), С. 2181 - 2194

Опубликована: Май 16, 2017

Язык: Английский

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Absolute quantification of microbial taxon abundances

The ISME Journal, Год журнала: 2016, Номер 11(2), С. 584 - 587

Опубликована: Сен. 9, 2016

Язык: Английский

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The microbiome beyond the horizon of ecological and evolutionary theory

Nature Ecology & Evolution, Год журнала: 2017, Номер 1(11), С. 1606 - 1615

Опубликована: Окт. 13, 2017

Язык: Английский

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