Metabolic Footprinting:  A New Approach to Identify Physiological Changes in Complex Microbial Communities upon Exposure to Toxic Chemicals

Henriques, Inês; Aga, Diana S.; Mendes, Pedro; O’Connor, Seamus; Love, Nancy G.

doi:10.1021/es062796t

Cited by 28 publications

(7 citation statements)

References 23 publications

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“…Stressor DNP induces heat shock proteins [36] and is well known for its ability to uncouple oxidative phosphorylation [5,37]. Stressor PCP induces heat shock proteins [36], is an uncoupler of oxidative phosphorylation, and is also reported to upregulate genes coding for efflux pumps that transport chemicals from the cell [3].…”

Section: Theory and Hypothesismentioning

confidence: 99%

Adaptations in microbiological populations exposed to dinitrophenol and other chemical stressors

Ray

Peters

2010

Enviro Toxic and Chemistry

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Microbiological populations in natural and engineered systems may experience multiple exposures to chemical stressors, which may affect system functions. The impact of such exposures on the metabolism of a population of Pseudomonas aeruginosa was studied using respirometry. Two serial exposures to low concentrations of 2,4-dinitrophenol (DNP), pentachlorophenol (PCP), or N-ethyl maleimide (NEM) did not affect metabolism beyond that expected for a single exposure. However, at higher concentrations, three exposures to DNP led to a combination of metabolic stress and resilience in the population. At a low DNP concentration of 400 mg/L, multiple exposures led to increased stress but indicated no development of resilience. At a high DNP concentration of 1,200 mg/L, no biological activity was observed, indicating that the population did not survive the exposure. At intermediate concentrations of 800 and 900 mg/L DNP, stress was observed, but it was found to decrease after multiple exposures. This, combined with the observation that the size of the population decreased, indicated that resilience in the population had developed because of elimination of the weaker organisms in the population. In contrast, the lack of resilience at the lower DNP concentration was attributed to the survival of the strong as well as weak members, lowering the resilience of the population as a whole. The development of resilience within a window of stressor concentrations is an important finding with implications for predicting the performance of biotreatment processes and biosensor technologies and for interpreting ecotoxicity risk assessments.

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Section: Theory and Hypothesismentioning

confidence: 99%

Adaptations in microbiological populations exposed to dinitrophenol and other chemical stressors

Ray

Peters

2010

Enviro Toxic and Chemistry

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“…With the development of exometabolomics pipelines, the metabolic connections between microbes have begun to be studied at a large scale and have allowed for a more comprehensive approach to monitoring the dynamic transformations of relatively complex mixtures of substrates [5]. Some key examples include optimizing multiple steps of lignocellulose degradation [6, 7], understanding metabolic interactions between species in mixed communities [8], and determining the ecological role of individuals within a mixed community [9–11]. We have recently found exometabolite niche partitioning in two soil environments where sympatric microbes were found to target largely non-overlapping portions of the available substrates, thus minimizing substrate competition [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

et al. 2017

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BackgroundMixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species’ metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community.ResultsHere we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster.ConclusionsOur results indicate that a significant portion of a model community’s overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1478-2) contains supplementary material, which is available to authorized users.

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“…With the development of exometabolomics pipelines, the metabolic connections between microbes have begun to be studied at a large scale and have allowed for a more comprehensive approach to monitoring the dynamic transformations of relatively complex mixtures of substrates 9 . Some key examples include optimizing multiple steps of lignocellulose degradation 10 , 11 , understanding metabolic interactions between species in mixed communities 12 , and determining the ecological role of individuals within a mixed community 13 − 15 . We have recently found exometabolite niche partitioning in two soil environments where sympatric microbes were found to target largely non-overlapping portions of the available substrates, thus minimizing substrate competition 14 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic substrate preferences and predicted metabolic properties of a simple microbial consortium

Erbilgin

Bowen

Kosina

et al. 2016

Preprint

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32 33 Microorganisms are typically found as complex microbial communities that 34 altogether govern global biogeochemical cycles. Microbes have developed highly 35 regulated metabolic capabilities to efficiently use available substrates including 36 preferential substrate usage that can result in diauxic shifts. This and other 37 metabolic behaviors have been discovered in studies of microbes in mono-38 culture when grown on low-complexity (e.g. two-component) mixtures of 39 substrates, however, little is known about how species partition environmental 40 substrates through substrate competition in more complex substrate mixtures. 41 Here we use exometabolomic profiling to examine the time-varying substrate 42 depletion from a mixture of 19 amino acids and glucose by two Pseudomonads 43 and one Bacillus species isolated from ground water. We examine if the first 44 substrates depleted result in maximal growth rate, or relate to growth medium or 45 biomass composition and find surprisingly few correlations. Patterns of substrate 46 depletion are modeled, and these models are used to examine if substrate usage 47preferences and substrate depletion kinetics of three microbial isolates can be 48used to predict the metabolism of the pooled isolates in co-culture. We find that 49 most of the substrates fit the model predictions, indicating that the microbes are 50 not altering their behaviors for these substrates in the presence of competitors. 51Glucose and histidine were depleted more slowly than predicted, while proline, 52glycine, glutamate, lysine, and arginine were all consumed significantly faster; 53 these compounds highlight substrates that could be involved in species-species 54 interactions within the consortium. 55 56Introduction 57 58Microbial communities drive elemental cycling 1 , such as the carbon cycle 2 and 59 the nitrogen cycle 3 . We are also learning that they are critical for the health of 60 their host plant 4 or animal 5 . In both cases the net microbial metabolic 61 processes are of particular interest for predicting nutrient cycles 6,7 and 62 harnessing microbes to improve host health 8 .The environments in which 63 microbial communities live often contain complex mixtures of substrates, and 64understanding how microbial communities partition these substrates is central to 65predicting community metabolism and developing interventions that alter 66 community structure and/or metabolic activities. 67 68Exometabolomics, also known as metabolic footprinting, is a powerful platform 69 for studying how microbes and their consortia modify substrate pools, as analysis 70is only of the extracellular metabolites 9 . With the development of 71 exometabolomics pipelines, the metabolic connections between microbes have 72 begun to be studied at a large scale and have allowed for a more comprehensive 73 approach to monitoring the dynamic transformations of relatively complex 74 mixtures of substrates 9 . Some key examples include optimizing multiple steps of 75 lignocellulose degradation 10,11 , understan...

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Metabolic Footprinting: A New Approach to Identify Physiological Changes in Complex Microbial Communities upon Exposure to Toxic Chemicals

Cited by 28 publications

References 23 publications

Adaptations in microbiological populations exposed to dinitrophenol and other chemical stressors

Adaptations in microbiological populations exposed to dinitrophenol and other chemical stressors

Dynamic substrate preferences predict metabolic properties of a simple microbial consortium

Dynamic substrate preferences and predicted metabolic properties of a simple microbial consortium

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