MIA: non-targeted mass isotopolome analysis

Weindl, Daniel; Wegner, André; Hiller, Karsten

doi:10.1093/bioinformatics/btw317

Cited by 22 publications

(23 citation statements)

References 14 publications

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“…Identifications were manually confirmed with AMDIS and MASSHUNTER. Unidentified labelled compounds were analysed for pairwise similarity to known compounds using the mass isotopolome analyser (MIA) software [36] (electronic supplementary material, methods S3). The technical replicate data files of each 13 C-labelled and nonlabelled 12 C samples were then loaded into NON-TARGETED TRACER FATE DETECTOR (NTFD) software for 13 C-enrichment analysis [37].…”

Section: (G) Data Analysis and Validationmentioning

confidence: 99%

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

et al. 2018

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Metabolite exchange is fundamental to the viability of the cnidarian-Symbiodiniaceae symbiosis and survival of coral reefs. Coral holobiont tolerance to environmental change might be achieved through changes in Symbiodiniaceae species composition, but differences in the metabolites supplied by different Symbiodiniaceae species could influence holobiont fitness. Using 13 C stable-isotope labelling coupled to gas chromatography-mass spectrometry, we characterized newly fixed carbon fate in the model cnidarian Exaiptasia pallida (Aiptasia) when experimentally colonized with either native Breviolum minutum or non-native Durusdinium trenchii. Relative to anemones containing B. minutum, D. trenchii-colonized hosts exhibited a 4.5-fold reduction in 13 C-labelled glucose and reduced abundance and diversity of 13 C-labelled carbohydrates and lipogenesis precursors, indicating symbiont species-specific modifications to carbohydrate availability and lipid storage. Mapping carbon fate also revealed significant alterations to host molecular signalling pathways. In particular, D. trenchii-colonized hosts exhibited a 40-fold reduction in 13 C-labelled scyllo-inositol, a potential interpartner signalling molecule in symbiosis specificity. 13 C-labelling also highlighted differential antioxidant-and ammoniumproducing pathway activities, suggesting physiological responses to different symbiont species. Such differences in symbiont metabolite contribution and host utilization may limit the proliferation of stress-driven symbioses; this contributes valuable information towards future scenarios that select in favour of less-competent symbionts in response to environmental change.

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Section: (G) Data Analysis and Validationmentioning

confidence: 99%

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

et al. 2018

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“…Metabolites in all samples were identified using Automated Mass Spectral Deconvolution and Identification System software (AMDIS) with the NIST special database 14 (National Institute of Standards and Technology, USA). The mass isotopomer distributions (MIDs) of all compounds were detected and their 13 C-labelling enrichment in symbiotic Aiptasia were investigated using MIA 43 . Pathways associated with these 13 C-enriched metabolites were explored using MetaboAnalyst v3.0 44 .…”

Section: Methodsmentioning

confidence: 99%

Meta-analysis reveals host-dependent nitrogen recycling as a mechanism of symbiont control inAiptasia

Cui

Liew

et al. 2018

Preprint

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17The metabolic symbiosis with photosynthetic algae of the genus Symbiodinium allows corals to 18 thrive in the oligotrophic environments of tropical seas. Many aspects of this relationship have 19 been investigated using transcriptomic analyses in the emerging model organism Aiptasia. 20However, previous studies identified thousands of putatively symbiosis-related genes, making it 21 difficult to disentangle symbiosis-induced responses from undesired experimental parameters. 22Using a meta-analysis approach, we identified a core set of 731 high-confidence symbiosis-23 associated genes that reveal host-dependent recycling of waste ammonium and amino acid 24 synthesis as central processes in this relationship. Combining transcriptomic and metabolomic 25 analyses, we show that symbiont-derived carbon enables host recycling of ammonium into 26 nonessential amino acids. We propose that this provides a regulatory mechanism to control 27 symbiont growth through a carbon-dependent negative feedback of nitrogen availability to the 28 symbiont. The dependence of this mechanism on symbiont-derived carbon highlights the 29 susceptibility of this symbiosis to changes in carbon translocation, as imposed by environmental 30 stress. 31 32 Introduction 33

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“…Great progress has been made in the development of induced specific cell types starting from patient fibroblasts (Xu et al 2015 ). New methods in tracer-based metabolomics allows for fingerprinting of metabolism with unsupervised non-targeted tracer fate detection (Hiller et al 2013 ; Weindl et al 2016 ). Mathematical models are continuously being improved to convert the observed MIDs of metabolites into flux pattern (for reviews see (Buescher et al 2015 ; Vasilakou et al 2016 )).…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Flux analysis of inborn errors of metabolism

Reijngoud

2018

J of Inher Metab Disea

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Patients with an inborn error of metabolism (IEM) are deficient of an enzyme involved in metabolism, and as a consequence metabolism reprograms itself to reach a new steady state. This new steady state underlies the clinical phenotype associated with the deficiency. Hence, we need to know the flux of metabolites through the different metabolic pathways in this new steady state of the reprogrammed metabolism. Stable isotope technology is best suited to study this. In this review the progress made in characterizing the altered metabolism will be presented. Studies done in patients to estimate the residual flux through the metabolic pathway affected by enzyme deficiencies will be discussed. After this, studies done in model systems will be reviewed. The focus will be on glycogen storage disease type I, medium-chain acyl-CoA dehydrogenase deficiency, propionic and methylmalonic aciduria, urea cycle defects, phenylketonuria, and combined D,L-2-hydroxyglutaric aciduria. Finally, new developments are discussed, which allow the tracing of metabolic reprogramming in IEM on a genome-wide scale. In conclusion, the outlook for flux analysis of metabolic derangement in IEMs looks promising.

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MIA: non-targeted mass isotopolome analysis

Cited by 22 publications

References 14 publications

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis

Meta-analysis reveals host-dependent nitrogen recycling as a mechanism of symbiont control inAiptasia

Flux analysis of inborn errors of metabolism

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