Integrating metabolomics and phenomics with systems models of cardiac hypoxia.
ABSTRACT Hypoxia is the major cause of necrotic cell death in myocardial infarction. Cellular energy supply and demand under hypoxic conditions is regulated by many interacting signaling and transcriptional networks, which complicates studies on individual proteins and pathways. We apply an integrated systems approach to understand the metabolic and functional response to hypoxia in muscle cells of the fruit fly Drosophila melanogaster. In addition to its utility as a hypoxia-tolerant model organism, Drosophila also offers advantages due to its small size, fecundity, and short life cycle. These traits, along with a large library of single-gene mutations, motivated us to develop new, computer-automated technology for gathering in vivo measurements of heart function under hypoxia for a large number of mutant strains. Phenotype data can be integrated with in silico cellular networks, metabolomic data, and microarrays to form qualitative and quantitative network models for prediction and hypothesis generation. Here we present a framework for a systems approach to hypoxia in the cardiac myocyte, starting from nuclear magnetic resonance (NMR) metabolomics, a constraint-based metabolic model, and phenotypic profiles.
SourceAvailable from: Eiichiro Fukusaki[Show abstract] [Hide abstract]
ABSTRACT: The Drosophila melanogaster embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of Drosophila embryogenesis. Therefore, the aim of this study is to assess the changes in embryos' metabolome that occur at different stages of the Drosophila embryonic development. Time course samples of Drosophila embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of Drosophila embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of Drosophila embryo.PLoS ONE 08/2014; 9(8):e99519. DOI:10.1371/journal.pone.0099519 · 3.53 Impact Factor
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ABSTRACT: Abstract Lou, Bih-Show, Pei-Shan Wu, Yitong Liu, and Jong-Shyan Wang. Effects of acute systematic hypoxia on human urinary metabolites using LC-MS-based metabolomics. High Alt Med Biol 15:00-00, 2014.- Aims: The metabolic variability and response to acute systematic hypoxia have been characterized by the high resolution of liquid chromatography/time-of-flight/mass spectrometry (LC-TOF/MS) in this study. Specifically, we compared the urinary metabolic profiles of six healthy sedentary men under normoxia (21% O2) with acute systematic hypoxic conditions of 12% (equivalent to about 4500 m in altitude) and 15% O2 (equivalent to about 3000 m in altitude) for 2 h in a normobaric hypoxia chamber. Results: A clear separation of dose-dependent responses was visualized by Partial Least Squares Discriminant Analysis (PLS-DA) between normoxic and hypoxic conditions. Over one thousand features were found in this study, about 10% of which showed significant change from hypoxia treatment and 26 metabolites were identified; however, there is great variability in metabolite concentrations among the 6 subjects, which reflects the diversity of human systems. Within the variability, we found that 1-methyladenosine and 5-methylthioadenosine are conspicuously upregulated; on the other hand, 3-inodoleacetic acid and L-glutamic acid were downregulated. Conclusion: The increase in purine metabolic products (uric acid, xanthine, and hypoxathine) results from hypoxia; this increase can be used as a marker for the hypoxic condition. 1-Methyladenosine was also highly upregulated from MH to SH and may be a very sensitive biomarker that reflects cellular hypoxia, due to its potential connection to HIF-1. The increase of free carnitine and acetyl carnitines, on the other hand, signals a change in the pathway of energy, or lipid, metabolism.High altitude medicine & biology 03/2014; 15(2). DOI:10.1089/ham.2013.1130 · 1.82 Impact Factor
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ABSTRACT: Metabolomic analyses can reveal associations between an organism's metabolome and further aspects of its phenotypic state, an attractive prospect for many life-sciences researchers. The metabolomic approach has been employed in some, but not many, insect study systems, starting in 1990 with the evaluation of the metabolic effects of parasitism on moth larvae. Metabolomics has now been applied to a variety of aspects of insect biology, including behaviour, infection, temperature stress responses, CO2 sedation, and bacteria–insect symbiosis. From a technical and reporting standpoint, these studies have adopted a range of approaches utilising established experimental methodologies. Here, we review current literature and evaluate the metabolomic approaches typically utilised by entomologists. We suggest that improvements can be made in several areas, including sampling procedures, the reduction in sampling and equipment variation, the use of sample extracts, statistical analyses, confirmation, and metabolite identification. Overall, it is clear that metabolomics can identify correlations between phenotypic states and underlying cellular metabolism that previous, more targeted, approaches are incapable of measuring. The unique combination of untargeted global analyses with high-resolution quantitative analyses results in a tool with great potential for future entomological investigations.Entomologia Experimentalis et Applicata 02/2015; 155(1). DOI:10.1111/eea.12281 · 1.71 Impact Factor