Metabolic dysregulation of the lysophospholipid/autotaxin axis in the chromosome 9p21 gene SNP rs10757274

Meckelmann, Sven W.; Hawksworth, Jade; White, Daniel A.; Andrews, Robert; Rodrigues, Patrícia; O’Connor, Anne E; Álvarez-Jarreta, Jorge; Tyrrell, Victoria J.; Hinz, Christine; Zhou, You; Williams, Julie; Aldrovandi, Maceler; Watkins, William J.; Engler, Adam J.; Allen, Stuart M.; Acharya, Jay; Mitchell, J. A.; Aoki, Junken; Kano, Kuniyuki; Humphries, Steve E.; O'Donnell, Valerie Bridget

doi:10.1101/789768

Cited by 4 publications

(5 citation statements)

References 66 publications

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“…However, the underlying mechanism for the involvement of rs10757274 in cardiovascular disease is still not clear [32]. Recent studies have shown that rs10757274 risk allele can cause metabolic dysregulation of the lysophospholipid/autotaxin axis and increase the risk of CAD, which may be helpful for the identification of novel CAD risk markers that are independent of TCRFs [38].…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease

Liu¹,

Fang²,

Xiong³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background: Premature coronary artery disease (PCAD) has a poor prognosis and a high mortality and disability rate. Accurate prediction of the risk of PCAD is very important for the prevention and early diagnosis of this disease. Machine learning (ML) has been proven a reliable method used for disease diagnosis and for building risk prediction models based on complex factors. The aim of the present study was to develop an accurate prediction model of PCAD risk that allows early intervention. Methods: We performed retrospective analysis of single nucleotide polymorphisms (SNPs) and traditional cardiovascular risk factors (TCRFs) for 131 PCAD patients and 187 controls. The data was used to construct classifiers for the prediction of PCAD risk with the machine learning (ML) algorithms LogisticRegression (LRC), RandomForestClassifier (RFC) and GradientBoostingClassifier (GBC) in scikit-learn. Three quarters of the participants were randomly grouped into a training dataset and the rest into a test dataset. The performance of classifiers was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and concordance index. R packages were used to construct nomograms. Results: Three optimized feature combinations (FCs) were identified: RS-DT-FC1 (rs2259816, rs1378577, rs10757274, rs4961, smoking, hyperlipidemia, glucose, triglycerides), RS-DT-FC2 (rs1378577, rs10757274, smoking, diabetes, hyperlipidemia, glucose, triglycerides) and RS-DT-FC3 (rs1169313, rs5082, rs9340799, rs10757274, rs1152002, smoking, hyperlipidemia, high-density lipoprotein cholesterol). These were able to build the classifiers with an AUC >0.90 and sensitivity >0.90. The nomograms built with RS-DT-FC1, RS-DT-FC2 and RS-DT-FC3 had a concordance index of 0.94, 0.94 and 0.90, respectively, when validated with the test dataset, and 0.79, 0.82 and 0.79 when validated with the training dataset. Manual prediction of the test data with the three nomograms resulted in an AUC of 0.89, 0.92 and 0.83, respectively, and a sensitivity of 0.92, 0.96 and 0.86, respectively. Conclusions: The selection of suitable features determines the performance of ML models. RS-DT-FC2 may be a suitable FC for building a high-performance prediction model of PCAD with good sensitivity and accuracy. The nomograms allow practical scoring and interpretation of each predictor and may be useful for clinicians in determining the risk of PCAD.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease

Liu¹,

Fang²,

Xiong³

et al. 2022

Front. Biosci. (Landmark Ed)

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“…In individuals harboring the SNP rs10757274 on chromosome 9p21, which increases the risk for coronary heart disease by 30% independent of traditional risk factors, the activity of plasma ENPP2 was uncoupled from the production of LPAs. 37 Thus, the inversion of the ENPP2/LPA ratio in the circulation might be representative of increased atherogenesis due to enhanced production of LPAs at the vessel wall.…”

Section: Discussionmentioning

confidence: 99%

Endothelial ENPP2 (Ectonucleotide Pyrophosphatase/Phosphodiesterase 2) Increases Atherosclerosis in Female and Male Mice

Karshovska¹,

Mohibullah²,

Zhu

et al. 2022

ATVB

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BACKGROUND: Maladapted endothelial cells (ECs) secrete ENPP2 (ectonucleotide pyrophosphatase/phosphodiesterase 2; autotaxin)—a lysophospholipase D that generates lysophosphatidic acids (LPAs). ENPP2 derived from the arterial wall promotes atherogenic monocyte adhesion induced by generating LPAs, such as arachidonoyl-LPA (LPA20:4), from oxidized lipoproteins. Here, we aimed to determine the role of endothelial ENPP2 in the production of LPAs and atherosclerosis. METHODS: We quantified atherosclerosis in mice harboring loxP-flanked Enpp2 alleles crossed with Apoe –/– mice expressing tamoxifen-inducible Cre recombinase under the control of the EC-specific bone marrow X kinase promoter after 12 weeks of high-fat diet feeding. RESULTS: A tamoxifen-induced EC-specific Enpp2 knockout decreased atherosclerosis, accumulation of lesional macrophages, monocyte adhesion, and expression of endothelial CXCL (C-X-C motif chemokine ligand) 1 in male and female Apoe –/– mice. In vitro, ENPP2 mediated the mildly oxidized LDL (low-density lipoprotein)-induced expression of CXCL1 in aortic ECs by generating LPA20:4, palmitoyl-LPA (LPA16:0), and oleoyl-LPA (LPA18:1). ENPP2 and its activity were detected on the endothelial surface by confocal imaging. The expression of endothelial Enpp2 established a strong correlation between the plasma levels of LPA16:0, stearoyl-LPA (LPA18:0), and LPA18:1 and plaque size and a strong negative correlation between the LPA levels and ENPP2 activity in the plasma. Moreover, endothelial Enpp2 knockout increased the weight of high-fat diet–fed male Apoe –/– mice. CONCLUSIONS: We demonstrated that the expression of ENPP2 in ECs promotes atherosclerosis and endothelial inflammation in a sex-independent manner. This might be due to the generation of LPA20:4, LPA16:0, and LPA18:1 from mildly oxidized lipoproteins on the endothelial surface.

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“…Cell pellets of 10 6 cells were stored at −80 °C until extraction. Following a protocol already described in the literature, 23 lipids were extracted by a two-step liquid−liquid extraction using 2-propanol-hexane followed by a modified Bligh and Dyer protocol. 27 Further information can be found in the Supporting Information.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…21,22 In lipidomics or metabolomics, the detection of differences between samples is important to discover the influence of environmental changes, genetic differences, or biological markers for a certain disease pattern. 23 Therefore, the quality of the detected features is important so that they can actually be assigned to substances contained in the samples and reliably detected across all samples. Often, high background noise leads to the detection of many "features" which do not have a clear connection to a measurable chemical signal which we refer to as false-positive features.…”

Section: ■ Introductionmentioning

confidence: 99%

Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry

Tötsch

Fjeldsted

Stow

et al. 2021

J. Am. Soc. Mass Spectrom.

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Ion mobility as an additional separation dimension can help to resolve and annotate metabolite and lipid biomarkers and provides important information about the components in a sample. Identifying relevant information in the resulting data is challenging because of the complexity of the data and data evaluation strategies for both targeted or nontargeted workflows. Frequently, feature analysis is used as a first step to search for differences between samples in discovery workflows. However, follow-up experimentation often leads to more targeted data extraction methods. In both cases, optimizing data sets for data extraction can make an important contribution to the overall results. In this work, we evaluate the effect of experimental conditions including acquisition sampling rate and data pretreatment on lipid standards and lipid extracts as examples of complex biological samples analyzed by liquid chromatography coupled to drift time ion mobility quadrupole time-of-flight mass spectrometry. The results show that a reduction of both peak variation and background noise can be achieved by optimizing the sampling rate. The use of data pretreatment including data smoothing, intensity thresholding, and spike removal also play an important role in improving detection and annotation of analytes from complex biological samples, whereas nonoptimal data sampling rates and preprocessing can lead to adverse effects including the loss or alternation of small, or closely eluting, low-abundant peaks.

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Metabolic dysregulation of the lysophospholipid/autotaxin axis in the chromosome 9p21 gene SNP rs10757274

Cited by 4 publications

References 66 publications

A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease

A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease

Endothelial ENPP2 (Ectonucleotide Pyrophosphatase/Phosphodiesterase 2) Increases Atherosclerosis in Female and Male Mice

Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry

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