Dataset Information

A systemic workflow for profiling metabolome and lipidome in tissue.

ABSTRACT: Simple metabolome and lipidome sample preparation procedures involving two successive extractions using small pieces of tissue, and a subsequent metabolite identification (MetID) strategy were developed. The sample preparation can significantly circumvent incomplete analysis due to insufficient amounts of tissue as a result of splitting into several aliquots for multiple measurements, with advantages over the similar previously reported methods in metabolite coverage, extraction efficiency, method robustness and friendly experimental operation. A MetID strategy, based on the integration of MS information mining (including adduct ions, in-source CID, MS information from both ESI (+) and ESI (-), characteristic fragmentation ions (CFIs), constant neutral losses (CNLs) and multimers) and in silico MS simulation, was demonstrated. A large number of adduct ions (83 features), in-source CID (123 features), ESI (+/-) ionization (20 features), CFIs& CNLs (more than 120 features) and multimers (17 features) were mined by manually or in silico recognition/filtering, which provide the most suspicious structures for subsequent in silico MS simulation. The unknown features presented the same score distribution as the known (83 features) features with scores ?25% (geomean score: 52%) and with satisfactory match for the main ions of interest. The MS/MS noise and fragment ions of coeluted quasi-molecular ions of interest are the main reason for the low score in the simulation. Manual check/evaluation is always suggested for the simulation with a score less than 50%. This strategy presents satisfactory performance with 2.5 times more metabolites structurally characterized compared with that of the traditional method based on accurate-mass-based MS and MS/MS library matching. This strategy would be useful for potentially identifying metabolites without available MS/MS information in the library.

SUBMITTER: Lu D

PROVIDER: S-EPMC6628932 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A systemic workflow for profiling metabolome and lipidome in tissue.

Lu Dasheng D Xue Liming L Feng Chao C Jin Yu'e Y Wu Chunhua C Xie Cen C Gonzalez Frank J FJ Wang Guoquan G Zhou Zhijun Z

Journal of chromatography. A 20181230

Simple metabolome and lipidome sample preparation procedures involving two successive extractions using small pieces of tissue, and a subsequent metabolite identification (MetID) strategy were developed. The sample preparation can significantly circumvent incomplete analysis due to insufficient amounts of tissue as a result of splitting into several aliquots for multiple measurements, with advantages over the similar previously reported methods in metabolite coverage, extraction efficiency, meth ...[more]

PMID: 30638710

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Project description:BackgroundDengue virus (DENV) is the most widespread arbovirus with an estimated 100 million infections occurring every year. Endemic in the tropical and subtropical areas of the world, dengue fever/dengue hemorrhagic fever (DF/DHF) is emerging as a major public health concern. The complex array of concurrent host physiologic changes has hampered a complete understanding of underlying molecular mechanisms of dengue pathogenesis.Methodology/principle findingsSystems level characterization of serum metabolome and lipidome of adult DF patients at early febrile, defervescence, and convalescent stages of DENV infection was performed using liquid chromatography- and gas chromatography-mass spectrometry. The tractability of following metabolite and lipid changes in a relatively large sample size (n = 44) across three prominent infection stages allowed the identification of critical physiologic changes that coincided with the different stages. Sixty differential metabolites were identified in our metabolomics analysis and the main metabolite classes were free fatty acids, acylcarnitines, phospholipids, and amino acids. Major perturbed metabolic pathways included fatty acid biosynthesis and ?-oxidation, phospholipid catabolism, steroid hormone pathway, etc., suggesting the multifactorial nature of human host responses. Analysis of phospholipids and sphingolipids verified the temporal trends and revealed association with lymphocytes and platelets numbers. These metabolites were significantly perturbed during the early stages, and normalized to control levels at convalescent stage, suggesting their potential utility as prognostic markers.Conclusions/significanceDENV infection causes temporally distinct serum metabolome and lipidome changes, and many of the differential metabolites are involved in acute inflammatory responses. Our global analyses revealed early anti-inflammatory responses working in concert to modulate early pro-inflammatory processes, thus preventing the host from development of pathologies by excessive or prolonged inflammation. This study is the first example of how an omic- approach can divulge the extensive, concurrent, and dynamic host responses elicited by DENV and offers plausible physiological insights to why DF is self limiting.

Dataset Information

A systemic workflow for profiling metabolome and lipidome in tissue.

Publications

A systemic workflow for profiling metabolome and lipidome in tissue.

Similar Datasets

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