Dataset Information

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.

ABSTRACT:

Introduction

We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.

Objectives

We aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.

Methods

Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis.

Results

We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples.

Conclusions

Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.

SUBMITTER: Lee HJ

PROVIDER: S-EPMC6616221 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.

Lee Ho-Joon HJ Kremer Daniel M DM Sajjakulnukit Peter P Zhang Li L Lyssiotis Costas A CA

Metabolomics : Official journal of the Metabolomic Society 20190709 7

<h4>Introduction</h4>We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.<h4>Objectives</h4>We aim to analyze a large-scale heterogeneous data compendium gene ...[more]

PMID: 31289941

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Project description:Intrinsically disordered proteins are predicted to be highly abundant and play broad biological roles in eukaryotic cells. In particular, by virtue of their structural malleability and propensity to interact with multiple binding partners, disordered proteins are thought to be specialized for roles in signaling and regulation. However, these concepts are based on in silico analyses of translated whole genome sequences, not on large-scale analyses of proteins expressed in living cells. Therefore, whether these concepts broadly apply to expressed proteins is currently unknown. Previous studies have shown that heat-treatment of cell extracts lead to partial enrichment of soluble, disordered proteins. On the basis of this observation, we sought to address the current dearth of knowledge about expressed, disordered proteins by performing a large-scale proteomics study of thermostable proteins isolated from mouse fibroblast cells. With the use of novel multidimensional chromatography methods and mass spectrometry, we identified a total of 1320 thermostable proteins from these cells. Further, we used a variety of bioinformatics methods to analyze the structural and biological properties of these proteins. Interestingly, more than 900 of these expressed proteins were predicted to be substantially disordered. These were divided into two categories, with 514 predicted to be predominantly disordered and 395 predicted to exhibit both disordered and ordered/folded features. In addition, 411 of the thermostable proteins were predicted to be folded. Despite the use of heat treatment (60 min at 98 degrees C) to partially enrich for disordered proteins, which might have been expected to select for small proteins, the sequences of these proteins exhibited a wide range of lengths (622 +/- 555 residues (average length +/- standard deviation) for disordered proteins and 569 +/- 598 residues for folded proteins). Computational structural analyses revealed several unexpected features of the thermostable proteins: (1) disordered domains and coiled-coil domains occurred together in a large number of disordered proteins, suggesting functional interplay between these domains; and (2) more than 170 proteins contained lengthy domains (>300 residues) known to be folded. Reference to Gene Ontology Consortium functional annotations revealed that, while disordered proteins play diverse biological roles in mouse fibroblasts, they do exhibit heightened involvement in several functional categories, including, cytoskeletal structure and cell movement, metabolic and biosynthetic processes, organelle structure, cell division, gene transcription, and ribonucleoprotein complexes. We believe that these results reflect the general properties of the mouse intrinsically disordered proteome (IDP-ome) although they also reflect the specialized physiology of fibroblast cells. Large-scale identification of expressed, thermostable proteins from other cell types in the future, grown under varied physiological conditions, will dramatically expand our understanding of the structural and biological properties of disordered eukaryotic proteins.

Dataset Information

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.

Introduction

Objectives

Methods

Results

Conclusions

Publications

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.

Similar Datasets

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