Deciphering the Microbiome: Integrating Theory, New Technologies, and Inclusive Science

Milligan-McClellan, Kathryn; Dundore‐Arias, Jose Pablo; Klassen, Jonathan L.; Shade, Ashley; Kinkel, Linda L.; Wolfe, Benjamin E.

doi:10.1128/msystems.00583-22

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…The resulting information can be used to initialize and parameterize mechanistic trait-based models spanning a hierarchy of structural complexity to explore the drivers of variation in the distribution and co-occurrence of microbial traits 17 . Moreover, incorporating emerging concepts and theory outside of traditional microbiome science, such as thermodynamic and biophysical theory, proves valuable for understanding traits of microorganisms 18 . This integration facilitates the search for generalizable principles or ‘rules’ that are applicable across diverse microbiome systems 19 , furthering theory development, and the elaboration of large-scale predictive models 15 .…”

Section: Mainmentioning

confidence: 99%

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Marschmann,

Tang,

Zhalnina

et al. 2024

Nat Microbiol

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Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

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Section: Mainmentioning

confidence: 99%

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Marschmann,

Tang,

Zhalnina

et al. 2024

Nat Microbiol

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“…Microbiome science has contributed greatly to our understanding of microbial life and provided crucial insights on the pivotal roles and capabilities of microbial communities on our planet, from global elements cycling to human health [9][10][11] . However, we still lack a comprehensive understanding of how these communities are assembled, maintained, and function as a system [6][7][8] . In particular, the underlying mechanism of microbe-microbe interactions and how microbial communities respond to perturbations remains poorly understood.…”

Section: Mainmentioning

confidence: 99%

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Moyne,

Al-Bassam,

Lieng

et al. 2023

Preprint

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Microbiome science has greatly contributed to our understanding of microbial life and its essential roles for the environment and human health. However, the nature of microbial interactions and how microbial communities respond to perturbations remains poorly understood, resulting in an often descriptive and correlation-based approach to microbiome research. Achieving causal and predictive microbiome science would require direct functional measurements in complex communities to better understand the metabolic role of each member and its interactions with others. In this study we present a new approach that integrates transcription and translation measurements to predict competition and substrate preferences within microbial communities, consequently enabling the selective manipulation of the microbiome. By performing metatranscriptomic (metaRNA-Seq) and metatranslatomic (metaRibo-Seq) analysis in complex samples, we classified microbes into functional groups (i.e. guilds) and demonstrated that members of the same guild are competitors. Furthermore, we predicted preferred substrates based on importer proteins, which specifically benefited selected microbes in the community (i.e. their niche) and simultaneously impaired their competitors. We demonstrated the scalability of microbial guild and niche determination to natural samples and its ability to successfully manipulate microorganisms in complex microbiomes. Thus, the approach enhances the design of pre- and probiotic interventions to selectively alter members within microbial communities, advances our understanding of microbial interactions, and paves the way for establishing causality in microbiome science.

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“…However, we need to develop robust methodologies for genomic integration in order to determine the minimum complexity required to mechanistically represent the ecology and function of soil microbiomes in numerical models [15,16]. In particular, the integration of emerging concepts and theory outside of traditional microbiome science (e.g., thermodynamic and biophysical theory) may be useful for understanding traits of microorganisms [17], facilitating the search for generalizable principles or "rules" that are applicable across diverse microbiome systems, furthering theory development, and the elaboration of large-scale predictive models [18].…”

Section: Introductionmentioning

confidence: 99%

Life history strategies and niches of soil bacteria emerge from interacting thermodynamic, biophysical, and metabolic traits

Brodie

Marschmann

Tang

et al. 2023

Preprint

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To comprehensively represent the diverse traits found in soil microbiomes in biogeochemical models, it is crucial to leverage information from microbial genomes. This can be achieved by incorporating microbial traits into a theory-based hierarchical framework, allowing for emergent behavior to occur through trait interactions. Here, we combined theory-based predictions of substrate uptake kinetics with a new genome-informed trait-based dynamic energy budget model to predict life history traits and trade-offs of soil bacteria. These bacteria play a critical role in determining the long-term fate of soil carbon through the biochemical transformation of plant root-derived carbon. Without calibration, and using only genome-derived microbial parameters, the model successfully predicted substrate preferences during plant growth and captured resource-dependent trade-offs between growth rate (power) and growth efficiency (yield) that are fundamental to microbial fitness and carbon retention in soil. This approach provides a generalizable data-driven foundation for the trait-based representation of microorganisms in biogeochemical models.

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Deciphering the Microbiome: Integrating Theory, New Technologies, and Inclusive Science

Cited by 8 publications

References 41 publications

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Guild and Niche Determination Enable Targeted Alteration of the Microbiome

Life history strategies and niches of soil bacteria emerge from interacting thermodynamic, biophysical, and metabolic traits

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