Metabolomics annotates ABHD3 as a physiologic regulator of medium-chain phospholipids

Long, Jonathan Z.; Cisar, Justin S.; Milliken, David; Niessen, Sherry; Wang, Chu; Trauger, Sunia A.; Siuzdak, Gary; Cravatt, Benjamin F.

doi:10.1038/nchembio.659

Cited by 60 publications

(66 citation statements)

References 24 publications

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“…This latter result is remarkably consistent with the recent finding that adipocyte gene repression by rosi is unrelated to nearby PPARγ binding (Step et al, 2014). As an example, the Abhd3 gene, encoding a phospholipid lipase (Long et al, 2011), was rosi-up by 2-fold only in 129 mice (Figure 4C), consistent with an upstream 129-selective PPARγ site (Figure 4D) with a 129-stronger DR1 motif-altering SNP (Figure 4E). Other examples of strain-selective rosi-induced genes are shown in Figure S6C.…”

Section: Resultsmentioning

confidence: 63%

Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo

Soccio

Chen

Rajapurkar

et al. 2015

Cell

110

113

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SUMMARY SNPs affecting disease risk often reside in non-coding genomic regions. Here we show that SNPs are highly enriched at mouse strain-selective adipose tissue binding sites for PPARγ, a nuclear receptor for antidiabetic drugs. Many such SNPs alter binding motifs for PPARγ or cooperating factors, and functionally regulate nearby genes whose expression is strain-selective and imbalanced in heterozygous F1 mice. Moreover, genetically-determined binding of PPARγ accounts for mouse strain-specific transcriptional effects of TZD drugs, providing proof-of-concept for personalized medicine related to nuclear receptor genomic occupancy. In human fat, motif-altering SNPs cause differential PPARγ binding, provide a molecular mechanism for some expression quantitative trait loci, and are risk factors for dysmetabolic traits in genome-wide association studies. One PPARγ motif-altering SNP is associated with HDL levels and other metabolic syndrome parameters. Thus, natural genetic variation in PPARγ genomic occupancy determines individual disease risk and drug response.

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Section: Resultsmentioning

confidence: 63%

Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo

Soccio

Chen

Rajapurkar

et al. 2015

Cell

110

113

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“…Mass spectrometry-based untargeted metabolomics has enabled simultaneous quantitative measurements of thousands of metabolites using minimal amounts of biological samples, providing functional readouts of physiological and pathological states of biological individuals at the systems level (Patti et al 2012b). Although relatively new compared to genomics and proteomics, metabolomics has revealed new metabolic pathways in cell biology and improved our understanding of disease pathogenesis (Weiss and Kim 2012;Griffin et al 2011;Patti et al 2012a;Long et al 2011). To enable a better understanding of physiological and pathological Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-1026-5) contains supplementary material, which is available to authorized users.…”

Section: Introductionmentioning

confidence: 99%

Normalization and integration of large-scale metabolomics data using support vector regression

et al. 2016

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Introduction Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra-and inter-batch) is unavoidable and is a major confounding factor for largescale metabolomics studies. Objectives We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses. Methods We developed a machine learning algorithmbased method, support vector regression (SVR), for largescale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.Results After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected. Conclusion SVR normalization can effectively remove the unwanted intra-and inter-batch variations, and is much better than other common normalization methods.

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“…Untargeted metabolomics analysis was performed on organic-soluble brain extracts from 2-to 6-mo-old ABHD12 +/+ and ABHD12 −/− mice (n = 3-5) as described previously (11). The molecular assignment of ABHD12-regulated metabolites was achieved by high mass-accuracy measurements, MS/MS fragmentation analysis, and coelution studies with synthetic lipid standards (37). Tissue lipid levels in ABHD12 −/− , ABHD12 +/− , and ABHD12 +/+ littermates (6 mo old; n = 5) were quantified by targeted MRM methods (35).…”

Section: Methodsmentioning

confidence: 99%

ABHD12 controls brain lysophosphatidylserine pathways that are deregulated in a murine model of the neurodegenerative disease PHARC

Blankman

Long

Trauger

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

167

279

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Advances in human genetics are leading to the discovery of new disease-causing mutations at a remarkable rate. Many such mutations, however, occur in genes that encode for proteins of unknown function, which limits our molecular understanding of, and ability to devise treatments for, human disease. Here, we use untargeted metabolomics combined with a genetic mouse model to determine that the poorly characterized serine hydrolase α/β-hydrolase domain-containing (ABHD)12, mutations in which cause the human neurodegenerative disorder PHARC (polyneuropathy, hearing loss, ataxia, retinosis pigmentosa, and cataract), is a principal lysophosphatidylserine (LPS) lipase in the mammalian brain. ABHD12 −/− mice display massive increases in a rare set of very long chain LPS lipids that have been previously reported as Toll-like receptor 2 activators. We confirm that recombinant ABHD12 protein exhibits robust LPS lipase activity, which is also substantially reduced in ABHD12 −/− brain tissue. Notably, elevations in brain LPS lipids in ABHD12 −/− mice occur early in life (2-6 mo) and are followed by age-dependent increases in microglial activation and auditory and motor defects that resemble the behavioral phenotypes of human PHARC patients. Taken together, our data provide a molecular model for PHARC, where disruption of ABHD12 causes deregulated LPS metabolism and the accumulation of proinflammatory lipids that promote microglial and neurobehavioral abnormalities.lipidomics | neuroinflammation G enome mapping and sequencing have facilitated determination of the genetic basis for over 3,500 inherited diseases in humans (1), with additional pathogenic mutations still being discovered. For many Mendelian disorders, however, the molecular and cellular mechanisms that link genotype to disease phenotype remain poorly understood. This problem is perhaps most apparent for diseases where the causative mutations occur in genes that encode for proteins with unannotated or poorly characterized functions. Here, we focus on one such disease -the neurodegenerative disorder PHARC (polyneuropathy, hearing loss, ataxia, retinosis pigmentosa, and cataract).PHARC [Mendelian Inheritance in Man (MIM) no. 612674; Online Mendelian Inheritance in Man database, http://omim.org] is a rare, autosomal recessive disorder that causes polymodal sensory and motor defects associated with demyelination of sensomotor neurons, retinal dystrophy, and cerebellar atrophy (2). PHARC symptoms are slowly progressive and begin in the childhood or teenage years; heterozygous carriers are unaffected (3). In 2010, PHARC was reported to be caused by homozygous mutations in ABHD12, which codes for the poorly characterized serine hydrolase enzyme α/β-hydrolase domain-containing (ABHD)12 (3). To date, five distinct ABHD12 mutations have been identified in patients with PHARC, all of which are expected to lead to complete loss of ABHD12 expression (3, 4). PHARC, therefore, likely represents a human ABHD12 null model.We demonstrated previously that ABHD12 is a membranebound e...

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Metabolomics annotates ABHD3 as a physiologic regulator of medium-chain phospholipids

Cited by 60 publications

References 24 publications

Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo

Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo

Normalization and integration of large-scale metabolomics data using support vector regression

ABHD12 controls brain lysophosphatidylserine pathways that are deregulated in a murine model of the neurodegenerative disease PHARC

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