Temporal Metabolic Profiling of Plasma in Response to Endotoxemia in Humans

Elective administration of bacterial endotoxin (LPS) to healthy human subjects has been used as a reproducible experimental procedure providing mechanistic insights into how cells, tissues and organs respond to systemic inflammation. Low doses of LPS transiently alter many physiologic and metabolic processes in a qualitatively similar manner to those observed after acute injury and systemic inflammation (1, 2); thus allow the analysis of body's responses to infectious stress at many physiological levels for building better understanding of the relevant pathology. Analysis of the metabolic response, in this regard, is of special interest since metabolic composition of a tissue is uniquely altered in response to stimuli due to collective effects of the regulations at transcriptional and translational levels. Therefore, concentrations of metabolites of a sample at a given time, i.e. the “metabolome” (3), can be thought of as the metabolic fingerprint representative of the state of the body at that time and give information about the dominant regulatory mechanisms. Although the changes in the major metabolites, such as lipids, amino acids and glucose, has been previously documented for human endotoxemia (4); current study constitutes the first attempt of a complete metabonomic analysis that describes the coherent temporal patterns of the metabolic landscape in plasma following exposure to LPS.

Metabonomic analysis is comprised of global biochemical profiles determined in human plasma samples from healthy subjects. Nineteen subjects were administered either placebo (saline, n=4) or LPS (n=15) by a bolus injection and blood samples were collected at 5 time points within a 24 hour post-treatment period. Global biochemical profiles obtained by GC/MS and LC/MS/MS platforms represented information on 366 biochemicals including amino acids, short peptides, carbohydrates, lipids, nucleotides, cofactors and vitamins, xenobiotics and intermediate products of major energy production pathways. Within this dataset, we first identified those metabolites which have differential temporal profiles between control and LPS groups. The most significant difference between the two groups was at 6hr time point, which was also an inflection point separating development and recovery phases of the LPS induced metabolic changes. Then, through consensus clustering (5), we identified subsets of the metabolites with common coherent profiles. This analysis yielded two clusters with opposing directionality as shown in Figure 1. The first cluster (16 metabolites) was up-regulated within the first 6hr and down-regulated by the 24^th hr and was mostly composed of metabolites from pathways related to lipid metabolism. The second cluster (21 metabolites), in contrast, was down-regulated within the first 6hr post-LPS, and then up-regulated by the 24^th hr. Strikingly 15 out of 21 metabolites in this cluster were amino acids or derivatives and an additional 2 were dipeptides.

Figure 1: Differential patterns of metabolic response to LPS. Two clusters of plasma metabolites reflect two distinct patterns with opposing temporal directionality.

Preferential early clearance of amino acids can be attributed to hepatic uptake for the synthesis of acute phase proteins, while build-up of fatty acids in the plasma can be due to extensive lipolysis as a result of cytokine-mediated changes in lipid metabolism. Also, early build-up of some precursor molecules in sterol/steroid related pathways may indicate the propensity for hormone biosynthesis to suppress inflammation and promote recovery. These results highlight the changes in lipid and amino acid metabolism in response to infectious stress while also reflecting the most-closely associated plasma metabolites pointing out to co-regulatory schemes governing that response.

References

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4.       Fong YM, Marano MA, Moldawer LL, Wei H, Calvano SE, Kenney JS, et al. The acute splanchnic and peripheral tissue metabolic response to endotoxin in humans. J Clin Invest. 1990;85(6):1896-904.

5.       Nguyen TT, Nowakowski RS, Androulakis IP. Unsupervised selection of highly coexpressed and noncoexpressed genes using a consensus clustering approach. OMICS A Journal of Integrative Biology. 2009;13(3):219-37.