Metabolic and genetic basis for auxotrophies in Gram-negative species

Seif, Yara; Choudhary, Kumari Sonal; Hefner, Ying; Anand, Amitesh; Yang, Laurence T.; Kim, Jaehyung

doi:10.1073/pnas.1910499117

Cited by 47 publications

(54 citation statements)

References 76 publications

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“…Apparently, based on the results of this model, these selective pressures might have led LAB to lose the ability to synthesise amino acids from inorganic compounds as it gives them a competitive advantage, which is in agreement with a previous study regarding auxotrophies prediction. Several auxotrophies for amino acids were predicted in Gram‐negative bacteria using genome‐scale metabolic reconstruction and some of them were postulated to confer a fitness in in vivo experiments depending on the environmental conditions (Seif et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Resource allocation explains lactic acid production in mixed‐culture anaerobic fermentations

Regueira

Rombouts

Wahl

et al. 2020

Biotech & Bioengineering

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Lactate production in anaerobic carbohydrate fermentations with mixed cultures of microorganisms is generally observed only in very specific conditions: the reactor should be run discontinuously and peptides and B vitamins must be present in the culture medium as lactic acid bacteria (LAB) are typically auxotrophic for amino acids. State‐of‐the‐art anaerobic fermentation models assume that microorganisms optimise the adenosine triphosphate (ATP) yield on substrate and therefore they do not predict the less ATP efficient lactate production, which limits their application for designing lactate production in mixed‐culture fermentations. In this study, a metabolic model taking into account cellular resource allocation and limitation is proposed to predict and analyse under which conditions lactate production from glucose can be beneficial for microorganisms. The model uses a flux balances analysis approach incorporating additional constraints from the resource allocation theory and simulates glucose fermentation in a continuous reactor. This approach predicts lactate production is predicted at high dilution rates, provided that amino acids are in the culture medium. In minimal medium and lower dilution rates, mostly butyrate and no lactate is predicted. Auxotrophy for amino acids of LAB is identified to provide a competitive advantage in rich media because less resources need to be allocated for anabolic machinery and higher specific growth rates can be achieved. The Matlab™ codes required for performing the simulations presented in this study are available at https://doi.org/10.5281/zenodo.4031144.

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Section: Resultsmentioning

confidence: 99%

Resource allocation explains lactic acid production in mixed‐culture anaerobic fermentations

Regueira

Rombouts

Wahl

et al. 2020

Biotech & Bioengineering

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“…any of a selection of metabolites can be used for growth) (Seif et al . 2020 ), perhaps explaining why amino acid auxotrophy has often been documented. In nature, auxotrophic interactions may be prevalent: an analysis of 979 metabolic networks predicted that 76% of bacterial genomes were auxotrophic for at least one metabolite.…”

Section: Auxotrophy In Aquatic Environmentsmentioning

confidence: 99%

Auxotrophic interactions: a stabilizing attribute of aquatic microbial communities?

Johnson

Alexander

Bier

et al. 2020

FEMS Microbiology Ecology

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Auxotrophy, or an organism's requirement for an exogenous source of an organic molecule, is widespread throughout species and ecosystems. Auxotrophy can result in obligate interactions between organisms, influencing ecosystem structure and community composition. We explore how auxotrophy-induced interactions between aquatic microorganisms affect microbial community structure and stability. While some studies have documented auxotrophy in aquatic microorganisms, these studies are not widespread, and we therefore do not know the full extent of auxotrophic interactions in aquatic environments. Current theoretical and experimental work suggests that auxotrophy links microbial community members through a complex web of metabolic dependencies. We discuss the proposed ways in which auxotrophy may enhance or undermine the stability of aquatic microbial communities, highlighting areas where our limited understanding of these interactions prevents us from being able to predict the ecological implications of auxotrophy. Finally, we examine an example of auxotrophy in harmful algal blooms to place this often theoretical discussion in a field context where auxotrophy may have implications for the development and robustness of algal bloom communities. We seek to draw attention to the relationship between auxotrophy and community stability in an effort to encourage further field and theoretical work that explores the underlying principles of microbial interactions.

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“…Despite the importance of these biomass components for a cell's metabolic sustainability, many strains of E. coli have lost the ability to synthesize some of these cofactors and amino acids throughout their evolutionary history [27] . The evolution of auxotrophy is commonly observed in clinical strains of E. coli , and thus understanding the metabolic consequences of auxotrophy can lead to a better understanding of the interactions between pathogenic microbes and their host environment [28,29] . The ME-model was applied to study E. coli auxotrophs under in silico conditions where availability of the essential metabolite is limited.…”

Section: Clustering Growth Conditions By Predicted Biomass Compositionmentioning

confidence: 99%

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Lloyd

Monk

Yang

et al. 2020

Preprint

Self Cite

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Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli . Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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Metabolic and genetic basis for auxotrophies in Gram-negative species

Cited by 47 publications

References 76 publications

Resource allocation explains lactic acid production in mixed‐culture anaerobic fermentations

Resource allocation explains lactic acid production in mixed‐culture anaerobic fermentations

Auxotrophic interactions: a stabilizing attribute of aquatic microbial communities?

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

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