Prediction of intracellular metabolic states from extracellular metabolomic data

Aurich, Maike Kathrin; Paglia, Giuseppe; Rolfsson, Óttar; Hrafnsdottir, Sigrun; Magnusdottir, Manuela; Stefaniak, Madgalena K.; Palsson, Bernhard Ø.; Fleming, Ronan M. T.; Thiele, Ines

doi:10.1007/s11306-014-0721-3

Cited by 62 publications

(72 citation statements)

References 55 publications

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“…Metabolic models can provide a framework for analyzing the information‐rich omics datasets and are increasingly being used to investigate metabolic alternations in human diseases . In the current work, a GC‐MS‐based metabolic profiling method was used to find the metabolic signature of CML patients.…”

Section: Discussionmentioning

confidence: 99%

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

et al. 2018

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The aim of this study is to investigate the potential biomarkers associated with chronic myeloid leukemia (CML), reveal the metabolite changes related to the continuous phases of tyrosine kinase inhibitors (TKIs), and find the potential biomarkers associated with treatment effects. Fifty‐two patients with CML and 26 matched healthy people were enrolled as the discovery set. Another 194 randomly selected CML patients treated with TKI were chosen as the external validation set. Plasma samples from the patients and controls were profiled using the gas chromatography‐mass spectrometry‐based metabonomic approach. Multivariate and univariate statistical analyses were combined to select the differential metabolic features. The gas chromatography‐mass spectrometry‐based metabolomics showed a clear clustering and separation of metabolic patterns from healthy controls and pre‐ and post‐TKI treatment CML patients in the discovery set. We identified 9 metabolites that differentiated CML patients from healthy controls, including lactic acid, isoleucine, glycerol, glycine, myristic acid, d‐sorbitol, d‐galactose, d‐glucose, and myo‐inositol. Among the 9 markers, glycerol and myristic acid had the most significant association with TKI treatment effects in both discovery and external validation sets. In the receiver operating characteristic analysis, the combination of glycerol and myristic acid showed a better discrimination performance compared to a single biomarker. The results indicated that metabolic profiling has the potential for diagnosis of CML and the panel of biomarkers including myristic acid and glycerol could be useful in monitoring TKI therapeutic responses.

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Section: Discussionmentioning

confidence: 99%

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

et al. 2018

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“…For example, metabolomic measurements from physiologically normal and Leigh's syndrome fibroblasts were overlaid with a fibroblast reconstruction to study the metabolic differences between healthy and disease states . Additionally, metabolomic and transcriptional data were integrated with Recon1 to construct condition‐specific models for two cancer cell lines, which elucidated the distinct metabolism of the two cell types . The integration of metabolomic data into the metabolic network of Escherichia coli resulted in accurate prediction of aerobic and anaerobic growth .…”

Section: Systems Biology Approaches For Studying Host–microbe Co‐metamentioning

confidence: 99%

“…The integration of metabolomic data into the metabolic network of Escherichia coli resulted in accurate prediction of aerobic and anaerobic growth . Moreover, tools have been developed to allow the simultaneous contextualization of quantitative metabolomic and proteomic measurements or of transcriptomic and metabolomic datasets, yielding condition‐specific models. Another established method for the contextualization of metabolomic data is 13 C flux analysis, in which labeled carbon substrates are measured .…”

Section: Systems Biology Approaches For Studying Host–microbe Co‐metamentioning

confidence: 99%

Systems biology of host–microbe metabolomics

Heinken

Thiele

2015

WIREs Mechanisms of Disease

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The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.

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“…The analysis of extracellular metabolites from this model may provide a clearer picture of the state of degraded cells that have released their internal metabolites into the surrounding medium. This could further uncover a living cell's metabolic state (Aurich et al, ; Nicolae, Wahrheit, Bahnemann, Zeng, & Heinzle, ) in the moments immediately preceding cell death, and comparison of this to intracellular metabolites of remaining viable cells may help to interpret where the ‘points of no return’ might be for the metabolic state of a cell in response to toxins.…”

Section: Discussionmentioning

confidence: 99%

Untargeted metabolomics of neuronal cell culture: A model system for the toxicity testing of insecticide chemical exposure

Hayton

Mullaney

Trengove

2017

J of Applied Toxicology

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Toxicity testing is essential for the protection of human health from exposure to toxic environmental chemicals. As traditional toxicity testing is carried out using animal models, mammalian cell culture models are becoming an increasingly attractive alternative to animal testing. Combining the use of mammalian cell culture models with screening-style molecular profiling technologies, such as metabolomics, can uncover previously unknown biochemical bases of toxicity. We have used a mass spectrometry-based untargeted metabolomics approach to characterize for the first time the changes in the metabolome of the B50 cell line, an immortalised rat neuronal cell line, following acute exposure to two known neurotoxic chemicals that are common environmental contaminants; the pyrethroid insecticide permethrin and the organophosphate insecticide malathion. B50 cells were exposed to either the dosing vehicle (methanol) or an acute dose of either permethrin or malathion for 6 and 24 hours. Intracellular metabolites were profiled by gas chromatography-mass spectrometry. Using principal components analysis, we selected the key metabolites whose abundance was altered by chemical exposure. By considering the major fold changes in abundance (>2.0 or <0.5 from control) across these metabolites, we were able to elucidate important cellular events associated with toxic exposure including disrupted energy metabolism and attempted protective mechanisms from excitotoxicity. Our findings illustrate the ability of mammalian cell culture metabolomics to detect finer metabolic effects of acute exposure to known toxic chemicals, and validate the need for further development of this process in the application of trace-level dose and chronic toxicity studies, and toxicity testing of unknown chemicals.

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Prediction of intracellular metabolic states from extracellular metabolomic data

Cited by 62 publications

References 55 publications

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

Systems biology of host–microbe metabolomics

Untargeted metabolomics of neuronal cell culture: A model system for the toxicity testing of insecticide chemical exposure

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