Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data

Basu, Sumanta; Duren, William L.; Evans, Charles R.; Burant, Charles F.; Michailidis, George; Karnovsky, Alla

doi:10.1093/bioinformatics/btx012

Cited by 175 publications

(141 citation statements)

References 44 publications

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“…To investigate and visualsze the correlations between metabolites and gut bacterial composition (order level), we used debiased sparse partial correlation (DSPC) algorithm option of the Metscape v3.1.3 which is a plug‐in for Cytoscape . DSPC is an extension of Gaussian graphical model.…”

Section: Methodsmentioning

confidence: 99%

Comparison of the metabolomic profiles of irritable bowel syndrome patients with ulcerative colitis patients and healthy controls: new insights into pathophysiology and potential biomarkers

Keshteli

Madsen

Mandal

et al. 2019

Aliment Pharmacol Ther

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Background: Evaluation of the metabolomic profile of patients with irritable bowel syndrome offers an opportunity to identify novel pathophysiological targets and biomarkers that could discriminate this disorder from related conditions. Aim: To identify potential urinary biomarkers that discriminate irritable bowel syndrome patients from ulcerative colitis patients in remission and healthy controls and to explore the pathophysiology of irritable bowel syndrome using a metabolomic approach.Methods: Urine samples were collected from 39 irritable bowel syndrome patients, 53 ulcerative colitis patients in clinical remission and 21 healthy controls. Urinary metabolites were identified and quantified using direct infusion/liquid chromatography tandem mass spectrometry and gas-chromatography mass spectrometry. Results:Patients with irritable bowel syndrome had a unique urinary metabolome that could separate them from ulcerative colitis patients with an area under the curve = 0.99 (95% confidence interval 0.95-1.00). The most important metabolites for this separation were a group of amino acids and organic acids. In addition, subjects with irritable bowel syndrome could be discriminated from healthy controls using their metabolic fingerprints. Irritable bowel syndrome patients had lower urinary Phosphatidyl choline acyl-alkyl C38:6, dopamine and p-hydroxybenzoic acid than healthy controls. Levels of some urinary metabolites including histamine correlated significantly with irritable bowel syndrome symptom severity scores. Conclusions:Irritable bowel syndrome patients have a unique urinary metabolomic profile compared to ulcerative colitis patients in clinical remission or healthy subjects. These data suggest that metabolomic profiling may provide important insights into pathophysiology and testable biomarkers to discriminate irritable bowel syndrome from other disorders that can mimic this condition and can be used to assess its severity and identify potential novel pathophysiological pathways.Stephen Vanner and L. Dieleman contributed equally for this article.

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Section: Methodsmentioning

confidence: 99%

Comparison of the metabolomic profiles of irritable bowel syndrome patients with ulcerative colitis patients and healthy controls: new insights into pathophysiology and potential biomarkers

Keshteli

Madsen

Mandal

et al. 2019

Aliment Pharmacol Ther

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“…Crude extracts of yam presented a range of triterpenoids, including cholesterol, reflecting the production of glycosylated steroidal saponins within this crop (Sautour et al, 2007). Similarly, cassava leaves showed an accumulation of amyrins and (Basu et al, 2017) in Cytoscape (Shannon, 2003), and an in-house pathway mapper, Biosynlab (Royal Holloway University of London, UK).…”

Section: Current Progress In Defining the Metabolome Of Rtb Cropsmentioning

confidence: 99%

“…Untargeted metabolite profiling takes c. 60 min per sample per analytical platform and data analysis plus manual curation takes c. 10 h per 100 samples. Example statistical visualizations created using SIMCA‐P (Umetrics), Metscape (Basu et al , ) in Cytoscape (Shannon, ), and an in‐house pathway mapper, Biosynlab (Royal Holloway University of London, UK).…”

Section: Introductionmentioning

confidence: 99%

Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops

et al. 2020

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Summary Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large‐scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics‐based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on‐going genomic and phenotypic studies will enhance ’omics‐wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.

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“…Most current metabolic flux analysis methods heavily depend on a predetermined metabolic network and are mostly focused on the analysis of 13 C tracer experiments 4,5,6,7 . However, large numbers of 'unknown' metabolites in the metabolomics datasets strongly indicate that currents metabolic network are far from complete, especially for secondary metabolism and central metabolism of non-model organisms 8,9,10 .…”

Section: Introductionmentioning

confidence: 99%

Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

Huan

Moseley

2019

Preprint

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Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 175 publications

References 44 publications

Comparison of the metabolomic profiles of irritable bowel syndrome patients with ulcerative colitis patients and healthy controls: new insights into pathophysiology and potential biomarkers

Comparison of the metabolomic profiles of irritable bowel syndrome patients with ulcerative colitis patients and healthy controls: new insights into pathophysiology and potential biomarkers

Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops

Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

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