Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.
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ABSTRACT:
SUBMITTER: Hu XP
PROVIDER: S-EPMC4460870 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:Amino acid (AA) metabolism is severely disturbed in critically ill ICU patients. To be able to make a more scientifically based decision on the type of protein or AA nutrition to deliver in ICU patients, comprehensive AA phenotyping with measurements of plasma concentrations and whole body production (WBP) is needed. Therefore, we studied ICU patients and matched control subjects using a novel pulse isotope method to obtain in-depth metabolic analysis. In 51 critically ill ICU patients (SOFA~6.6) and 49 healthy controls, we measured REE and body composition/phase-angle using BIA. In the postabsorptive state, we collected arterial (ized) blood for CRP and AA. Then, we administered an 8 mL solution containing 18 stable AA tracers as a pulse and calculated WBP. Enrichments: LC-MS/MS and statistics: t-test, ANCOVA. Compared to healthy, critically ill ICU patients had lower phase-angle (p < 0.00001), and higher CRP (p < 0.0001). Most AA concentrations were lower in ICU patients (p < 0.0001), except tau-methylhistidine and phenylalanine. WBP of most AA were significantly (p < 0.0001) higher with increases in glutamate (160%), glutamine (46%), and essential AA. Remarkably, net protein breakdown was lower. There were only weak relationships between AA concentrations and WBP. Critically ill ICU patients (SOFA 8-16) had lower values for phase angle (p = 0.0005) and small reductions of most plasma AA concentrations, but higher tau-methylhistidine (p = 0.0223) and hydroxyproline (p = 0.0028). Remarkably, the WBP of glutamate and glutamine were lower (p < 0.05), as was their clearance, but WBP of tau-methylhistidine (p = 0.0215) and hydroxyproline (p = 0.0028) were higher. Our study in critically ill ICU patients shows that comprehensive metabolic phenotyping was able to reveal severe disturbances in specific AA pathways, in a disease severity dependent way. This information may guide improving nutritional compositions to improve the health of the critically ill patient. CLINICAL TRIAL REGISTRY: Data are from the baseline measurements of study NCT02770092 (URL: https://clinicaltrials.gov/ct2/show/NCT02770092) and NCT03628365 (URL: https://clinicaltrials.gov/ct2/show/NCT03628365).
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Project description:Aquatic microorganisms are typically identified as either oligotrophic or copiotrophic, representing trophic strategies adapted to low or high nutrient concentrations, respectively. Here, we sought to take steps towards identifying these and additional adaptations to nutrient availability with a quantitative analysis of microbial resource use in mixed communities. We incubated an estuarine microbial community with stable isotope labeled amino acids (AAs) at concentrations spanning three orders of magnitude, followed by taxon-specific quantitation of isotopic incorporation using NanoSIMS analysis of high-density microarrays. The resulting data revealed that trophic response to AA availability falls along a continuum between copiotrophy and oligotrophy, and high and low activity. To illustrate strategies along this continuum more simply, we statistically categorized microbial taxa among three trophic types, based on their incorporation responses to increasing resource concentration. The data indicated that taxa with copiotrophic-like resource use were not necessarily the most active, and taxa with oligotrophic-like resource use were not always the least active. Two of the trophic strategies were not randomly distributed throughout a 16S rDNA phylogeny, suggesting they are under selective pressure in this ecosystem and that a link exists between evolutionary relatedness and substrate affinity. The diversity of strategies to adapt to differences in resource availability highlights the need to expand our understanding of microbial interactions with organic matter in order to better predict microbial responses to a changing environment. manuscript accepted by PLoS ONE 4 datasets: 1) fluorescence data for 3 treatments combined, 2) isotopic data for treatment = LOW, 3) isotopic data for treatment = MEDIUM, 4) isotopic data for treatment = HIGH
2014-03-26 | E-GEOD-56119 | biostudies-arrayexpress
Project description:Aquatic microorganisms are typically identified as either oligotrophic or copiotrophic, representing trophic strategies adapted to low or high nutrient concentrations, respectively. Here, we sought to take steps towards identifying these and additional adaptations to nutrient availability with a quantitative analysis of microbial resource use in mixed communities. We incubated an estuarine microbial community with stable isotope labeled amino acids (AAs) at concentrations spanning three orders of magnitude, followed by taxon-specific quantitation of isotopic incorporation using NanoSIMS analysis of high-density microarrays. The resulting data revealed that trophic response to AA availability falls along a continuum between copiotrophy and oligotrophy, and high and low activity. To illustrate strategies along this continuum more simply, we statistically categorized microbial taxa among three trophic types, based on their incorporation responses to increasing resource concentration. The data indicated that taxa with copiotrophic-like resource use were not necessarily the most active, and taxa with oligotrophic-like resource use were not always the least active. Two of the trophic strategies were not randomly distributed throughout a 16S rDNA phylogeny, suggesting they are under selective pressure in this ecosystem and that a link exists between evolutionary relatedness and substrate affinity. The diversity of strategies to adapt to differences in resource availability highlights the need to expand our understanding of microbial interactions with organic matter in order to better predict microbial responses to a changing environment. manuscript accepted by PLoS ONE
2014-03-26 | GSE56119 | GEO
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Project description:Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.
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Project description:Aquatic microorganisms are typically identified as either oligotrophic or copiotrophic, representing trophic strategies adapted to low or high nutrient concentrations, respectively. Here, we sought to take steps towards identifying these and additional adaptations to nutrient availability with a quantitative analysis of microbial resource use in mixed communities. We incubated an estuarine microbial community with stable isotope labeled amino acids (AAs) at concentrations spanning three orders of magnitude, followed by taxon-specific quantitation of isotopic incorporation using NanoSIMS analysis of high-density microarrays. The resulting data revealed that trophic response to AA availability falls along a continuum between copiotrophy and oligotrophy, and high and low activity. To illustrate strategies along this continuum more simply, we statistically categorized microbial taxa among three trophic types, based on their incorporation responses to increasing resource concentration. The data indicated that taxa with copiotrophic-like resource use were not necessarily the most active, and taxa with oligotrophic-like resource use were not always the least active. Two of the trophic strategies were not randomly distributed throughout a 16S rDNA phylogeny, suggesting they are under selective pressure in this ecosystem and that a link exists between evolutionary relatedness and substrate affinity. The diversity of strategies to adapt to differences in resource availability highlights the need to expand our understanding of microbial interactions with organic matter in order to better predict microbial responses to a changing environment.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-02-14 | E-GEOD-59821 | biostudies-arrayexpress
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