Lipidomics joins the omics evolution

Dennis, Edward A.

doi:10.1073/pnas.0812636106

Cited by 144 publications

(109 citation statements)

References 16 publications

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“…Thus, the model can accommodate differential kinetic fl ux present at the sn -1 and sn -1' positions versus sn -2 and sn -2' positions in cardiolipin that mimic the phospholipase activities that contribute to the generation of monolysocardiolipin and cardiolipin remodeling. For the analogous PE terms, the PE-specifi c values ( 1) sn PE k and ( 2) sn PE k replace the corresponding PC rate parameters and likewise for the acyl CoA terms. For simplicity, our model assumes that the relative reactivity of the sn -1 and sn -2 positions is a property of the cardiolipin molecule, i.e., ( 2) ( 1) …”

Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

“…For the analogous PE terms, the PE-specifi c values ( 1) sn PE k and ( 2) sn PE k replace the corresponding PC rate parameters and likewise for the acyl CoA terms. For simplicity, our model assumes that the relative reactivity of the sn -1 and sn -2 positions is a property of the cardiolipin molecule, i.e., ( 2) ( 1) …”

Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

“…We fi rst describe the rate of change of a given cardiolipin species with the chains ␣ 1 ␣ 2 ␣ 3 ␣ 4 at the sn -1, sn -2, sn -1', and sn -2' positions, respectively. The process of chain substitution from PC, PE, and acyl CoA into cardiolipin corresponds to a dynamic equation : Here ( 1) sn PC k is the reaction rate constant between PC and one of the sn -1 positions of cardiolipin, and ( 2) sn PC k is the reaction rate constant between PC and one of the sn -2 positions. Thus, the model can accommodate differential kinetic fl ux present at the sn -1 and sn -1' positions versus sn -2 and sn -2' positions in cardiolipin that mimic the phospholipase activities that contribute to the generation of monolysocardiolipin and cardiolipin remodeling.…”

Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

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Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics

Kiebish

Bell

Yang

et al. 2010

Journal of Lipid Research

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Lipids are critical for the regulation and control of cellular function, membrane dynamics, and signal transduction. The systematic identifi cation and analysis of all cellular lipids in a biological sample is embodied by the rapidly developing fi eld of lipidomics, which has been enabled by recent advances in mass spectrometry ( 1-3 ). A multidimensional MS-based shotgun lipidomics (MDMS-SL) approach for analysis of cellular lipidomes has been demonstrated as one of the most systematic and quantitative high-throughput platforms for global lipidomics capable of analyzing thousands of lipid molecular species directly from the lipid extracts of biological tissues ( 4, 5 ). Analysis of lipidomic data requires extensive use of bioinformatics and computational algorithms for the accurate identifi cation and quantifi cation of lipid molecular species and classes ( 5-9 ). However, most advances in bioinformatics in lipidomics have largely focused on lipid identifi cation rather than interpretation of alterations in biological function resulting in adaptive or pathologic changes in lipid metabolism ( 10 ). Thus, development of bioinformatic and systems biology approaches for the interpretation of lipidomic networks would signifi cantly advance the understanding of the roles of lipids in biological systems ( 3 ).Cardiolipin is a complex polyglycerophospholipid found almost exclusively in mitochondrial membranes of eukaryotic organisms ( 11 ). Cardiolipin is intricately involved in the effi cient production of ATP through utilizing the proton gradient as well as maintaining mitochondrial functionality ( 12-14 ). The diversity of cardiolipin molecular species varies markedly between tissues as well as among Abstract Cardiolipin is a class of mitochondrial specifi c phospholipid, which is intricately involved in mitochondrial functionality. Differences in cardiolipin species exist in a variety of tissues and diseases. It has been demonstrated that the cardiolipin profi le is a key modulator of the functions of many mitochondrial proteins. However, the chemical mechanism(s) leading to normal and/or pathological distribution of cardiolipin species remain elusive. Herein, we describe a novel approach for investigating the molecular mechanism of cardiolipin remodeling through a dynamic simulation. This approach applied data from shotgun lipidomic analyses of the heart, liver, brain, and lung mitochondrial lipidomes to model cardiolipin remodeling, including relative content, regiospecifi city, and isomeric composition of cardiolipin species. Generated cardiolipin profi les were nearly identical to those determined by shotgun lipidomics. Importantly, the simulated isomeric compositions of cardiolipin species were further substantiated through product ion analysis. Finally, unique enzymatic activities involved in cardiolipin remodeling were assessed from the parameters used in the dynamic simulation of cardiolipin profi les. Collectively, we described, verifi ed, and demonstrated a novel approach by integrating both lipidomic ana...

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Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

Section: Mathematical Computational Strategy For Simulating Cardiolipmentioning

confidence: 99%

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Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics

Kiebish

Bell

Yang

et al. 2010

Journal of Lipid Research

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“…Therefore, an increasing number of studies have been published over the last few years that aim at the identification of lipids in biological samples using different mass spectrometry techniques [3][4][5][6]. The matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) technique, introduced by Caprioli et al [7,8], permits the scanning of tissue slices directly in the mass spectrometer, and therefore takes the precise anatomical localization of different types of lipids a step further.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Distribution of Lipids in Human Brain Cortex by Imaging Mass Spectrometry

Veloso

Astigarraga

Barreda-Gómez

et al. 2011

J. Am. Soc. Mass Spectrom.

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Molecular mass images of tissues will be biased if differences in the physicochemical properties of the microenvironment affect the intensity of the spectra. To address this issue, we have performedby means of MALDI-TOF mass spectrometry-imaging on slices and lipidomic analysis in extracts of frontal cortex, both from the same postmortem tissue samples of human brain. An external calibration was used to achieve a mass accuracy of 10 ppm (1σ) in the spectra of the extracts, although the final assignment was based on a comparison with previously reported species. The spectra recorded directly from tissue slices (imaging) show excellent s/n ratios, almost comparable to those obtained from the extracts. In addition, they retain the information about the anatomical distribution of the molecular species present in autopsied frozen tissue. Further comparison between the spectra from lipid extracts devoid of proteins and those recorded directly from the tissue unambiguously show that the differences in lipid composition between gray and white matter observed in the mass images are not an artifact due to microenvironmental influences of each anatomical area on the signal intensity, but real variations in the lipid composition.

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“…Lipidomics, a type of focused metabolomics, is the comprehensive measurement of a variety of lipid classes: free fatty acids (FFA), triglycerides (TAGs), cholesterol esters (CEs), lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), lysophosphatidyl ethanolamines (LPEs), diacylglycerols (DAGs), and sphingomyelins (SMs) and ceramides, generally using LC-MS/MS (Bou Khalil et al, 2010;Bucci, 2011;Dennis, 2009). Several studies have reported the potency of lipidomic analyses for biomarker discoveries in humans in diabetes, non-alcoholic fatty liver disease (NAFLD) (Puri et al, 2009), Alzheimer's disease (Han et al, 2011;Valdes-Gonzalez et al, 2011) and cancers (Hilvo et al, 2011;Min et al, 2011).…”

Section: Lipidomics: a Review On The Use Of Lipid Metabolomics For CLmentioning

confidence: 99%

Clinical Implementation of Metabolomics

Imaizumi¹,

Nishikata²,

Yoshida³

et al. 2012

Metabolomics

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Lipidomics joins the omics evolution

Cited by 144 publications

References 16 publications

Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics

Dynamic simulation of cardiolipin remodeling: greasing the wheels for an interpretative approach to lipidomics

Anatomical Distribution of Lipids in Human Brain Cortex by Imaging Mass Spectrometry

Clinical Implementation of Metabolomics

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