Controlling the human microbiome

Liu, Yang‐Yu

doi:10.1016/j.cels.2022.12.010

Cited by 6 publications

(53 citation statements)

References 154 publications

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“…Project Consortium, 2012). These conditions can exhibit their own dynamics, influenced by factors such as meal intake or the menstrual cycle (Liu, 2023). Environmental conditions do not only vary macroscopically but also microscopically due to the spatial organization of cells.…”

Section: What Are Microbiomes?mentioning

confidence: 99%

“…Population models based on di erential equations ODE-based population models of microbiomes focus on the dynamics of species abundances. In the review by Liu (2023), dynamic population models were categorized into species-only models and mediator-explicit models. Species-only models account for direct interactions among species but do not consider the mode of action (e.g., interactions via metabolites or signaling molecules).…”

Section: Erential Equations-quantitative and Dynamic Models Of Biolog...mentioning

confidence: 99%

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Microbiome modeling: a beginner's guide

Lange,

Kranert,

Krüger

et al. 2024

Front. Microbiol.

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Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

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Section: What Are Microbiomes?mentioning

confidence: 99%

Section: Erential Equations-quantitative and Dynamic Models Of Biolog...mentioning

confidence: 99%

Microbiome modeling: a beginner's guide

Lange,

Kranert,

Krüger

et al. 2024

Front. Microbiol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, the identification of heritable microbes can depend on the statistical method used. For example, the subset of microbes identified as being under host control may depend on how the interactions within the microbiome are accounted for during statistical modeling [ 35 ]. Developing appropriate quantitative genetic models for host–microbiome interactions is an active area of research [ 3 , 5 , 31 , 36 , 37 ].…”

Section: Delineating Host Genetic Control From Microbiome Heritabilitymentioning

confidence: 99%

Evolutionary implications of host genetic control for engineering beneficial microbiomes

Henry

Bergelson

2023

Current Opinion in Systems Biology

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“…Human microbiomes have been empirically revealed to undergo nonrandom and repeatable community assembly and succession, such as the trait-based community assembly revealed in infant gut [ 1 , 2 ]; as well as the quick recoverable assembly of human oral biofilm microbiome (OBM) after clinical scaling [ 3 ]. Mechanisms underlying microbiota assembly are one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities [ 4 , 5 ]. Until now, several theories, such as priority effects [ 6 ], metabolic cooperation [ 7 – 9 ], interspecies bacterial competition [ 10 ], and hydrodynamic disturbance [ 11 ], have been proposed to uncover rules underlying community assembly from the perspective of metabolic interaction.…”

Section: Introductionmentioning

confidence: 99%

“…Until now, several theories, such as priority effects [ 6 ], metabolic cooperation [ 7 – 9 ], interspecies bacterial competition [ 10 ], and hydrodynamic disturbance [ 11 ], have been proposed to uncover rules underlying community assembly from the perspective of metabolic interaction. However, metabolism in bacterial flora is extremely complex and redundant, so it is a big challenge to map the metabolic network for precise manipulation on both community structure and function [ 4 ]. In this case, excavating an applicable ecological network with more universality, specificity, and regulatory power is of great significance for understanding assembly rules and realizing precise manipulation of bacterial flora.…”

Section: Introductionmentioning

confidence: 99%

Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly

Su,

Xu,

Cui

et al. 2023

Microbiome

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Background The mechanism of microbiota assembly is one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities. Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interactions, is characterized by universality, specificity, and regulatory power, which therefore may influence the assembly processes of human microbiota. However, the regulating role of QS in microbiota assembly is rarely reported. In this study, we developed an optimized in vitro oral biofilm microbiota assembling (OBMA) model to simulate the time-series assembly of oral biofilm microbiota (OBM), by which to excavate the QS network and its regulating power in the process. Results By using the optimized OBMA model, we were able to restore the assembly process of OBM and generate time-series OBM metagenomes of each day. We discovered a total of 2291 QS protein homologues related to 21 QS pathways. Most of these pathways were newly reported and sequentially enriched during OBM assembling. These QS pathways formed a comprehensive longitudinal QS network that included successively enriched QS hubs, such as Streptococcus, Veillonella-Megasphaera group, and Prevotella-Fusobacteria group, for information delivery. Bidirectional cross-talk among the QS hubs was found to play critical role in the directional turnover of microbiota structure, which in turn, influenced the assembly process. Subsequent QS-interfering experiments accurately predicted and experimentally verified the directional shaping power of the longitudinal QS network in the assembly process. As a result, the QS-interfered OBM exhibited delayed and fragile maturity with prolonged membership of Streptococcus and impeded membership of Prevotella and Fusobacterium. Conclusion Our results revealed an unprecedented longitudinal QS network during OBM assembly and experimentally verified its power in predicting and manipulating the assembling process. Our work provides a new perspective to uncover underlying mechanism in natural complex microbiota assembling and a theoretical basis for ultimately precisely manipulating human microbiota through intervention in the QS network.

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Controlling the human microbiome

Cited by 6 publications

References 154 publications

Microbiome modeling: a beginner's guide

Microbiome modeling: a beginner's guide

Evolutionary implications of host genetic control for engineering beneficial microbiomes

Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly

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