Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions

Frioux, Clémence; Dittami, Simon M.; Siegel, Anne

doi:10.1042/bst20190667

Cited by 3 publications

(3 citation statements)

References 106 publications

(122 reference statements)

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“…Indeed, it has been demonstrated that many GEMs are limited to well-conserved, primary metabolic pathways rather than secondary metabolic pathways, thus limiting the representation of the organisms they model (Monk et al, 2014). Such problems can be overcome with additional steps like gap-filling and manual curation (Prigent et al, 2017), but these are subject to false positives when working with unknown organisms (Henry et al, 2010;Frioux et al, 2020).…”

Section: Metabolic Network Reconstructionmentioning

confidence: 99%

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

et al. 2021

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Understanding how microorganism-microorganism interactions shape microbial assemblages is a key to deciphering the evolution of dependencies and co-existence in complex microbiomes. Metabolic dependencies in cross-feeding exist in microbial communities and can at least partially determine microbial community composition. To parry the complexity and experimental limitations caused by the large number of possible interactions, new concepts from systems biology aim to decipher how the components of a system interact with each other. The idea that cross-feeding does impact microbiome assemblages has developed both theoretically and empirically, following a systems biology framework applied to microbial communities, formalized as microbial systems ecology (MSE) and relying on integrated-omics data. This framework merges cellular and community scales and offers new avenues to untangle microbial coexistence primarily by metabolic modeling, one of the main approaches used for mechanistic studies. In this mini-review, we first give a concise explanation of microbial cross-feeding. We then discuss how MSE can enable progress in microbial research. Finally, we provide an overview of a MSE framework mostly based on genome-scale metabolic-network reconstruction that combines top-down and bottom-up approaches to assess the molecular mechanisms of deterministic processes of microbial community assembly that is particularly suitable for use in synthetic biology and microbiome engineering.

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Section: Metabolic Network Reconstructionmentioning

confidence: 99%

Microbial Systems Ecology to Understand Cross-Feeding in Microbiomes

et al. 2021

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“…Indeed, some pathways may be shared among several genomes, suggesting potential cross-feeding between microbes in the community [ 82 ]. Screening genomes for specific genes associated with particular metabolic profiles can be a powerful tool in discerning (i) evolutionary acquisition of genes and (ii) putative biochemical transformations within the ecosystem.…”

Section: Meta-omics Tools To Unravel Microbial Community Diversity An...mentioning

confidence: 99%

Ecosystem-specific microbiota and microbiome databases in the era of big data

Lobanov

Gobet

Joyce

2022

Environmental Microbiome

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The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB’s), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB’s, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB’s.

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“…Several dedicated databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), are used as knowledgebases for metabolic pathway inference and reconstruction [5] , while tools such as KEGG Mapper [6] and eggNOG-Mapper [7] can assign open reading frames to their function and predict metabolic capabilities at the genome level. However, newly generated metagenomes contain a large number of poorly characterized species, which can be hardly annotated exhaustively with traditional tools [8] .…”

Section: Introductionmentioning

confidence: 99%