Common Fatty Markers in Diseases with Dysregulated Lipogenesis

Balgoma, David; Pettersson, Curt; Hedeland, Mikael

doi:10.1016/j.tem.2019.02.008

Cited by 20 publications

(18 citation statements)

References 14 publications

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“…Furthermore, we find a cluster of correlated lyso-lipids and of TAG species (Fig 2). TAGs with positive β -coefficients are largely consistent with common fatty markers [47]. A more detailed discussion of this observation and the association of product-to-precursor ratios of lipid metabolism enzymes to obesity measures is provided in the S1 Text.…”

Section: Resultsmentioning

confidence: 72%

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Gerl¹,

Klose²,

Surma

et al. 2019

PLoS Biol

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Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

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Section: Resultsmentioning

confidence: 72%

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Gerl¹,

Klose²,

Surma

et al. 2019

PLoS Biol

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“…The ceramide transfer protein (CERT) and the four-phosphate adaptor protein 2 (FAPP2) are known to undergo a similar fate in HCV infection [82]. An important protein involved in cellular lipid homeostasis is the sterol regulatory element binding protein (SREBP), a bHLH-zip transcription factor with three isoforms; SREBP1c regulates the expression of fatty acid (FA) biosynthesis genes [105,106], whereas SREBP2 transactivates genes implied in cholesterol biosynthesis, intracellular lipid transport, and lipoprotein import [107]. A recent study shows that the inhibition of SREBP with the retinoid derivative and RAR-α agonist AM580 prevents MERS-CoV infection by avoiding the formation of functional DMVs [105].…”

Section: Lipid-related Host Factors Associated To the Rcs' Buildupmentioning

confidence: 99%

Lipidomics Issues on Human Positive ssRNA Virus Infection: An Update

Balgoma

Montero

2020

Metabolites

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The pathogenic mechanisms underlying the Biology and Biochemistry of viral infections are known to depend on the lipid metabolism of infected cells. From a lipidomics viewpoint, there are a variety of mechanisms involving virus infection that encompass virus entry, the disturbance of host cell lipid metabolism, and the role played by diverse lipids in regard to the infection effectiveness. All these aspects have currently been tackled separately as independent issues and focused on the function of proteins. Here, we review the role of cholesterol and other lipids in ssRNA+ infection.

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“…In contrast with these saturated and monounsaturated fatty acids, polyunsaturated acids in the liver have their origin in the diet, either as essential fatty acids or by elongation of the essential fatty acids (Mohrhauer and Holman 1963). While saturated and monounsaturated fatty acids in the liver also originate from the diet, it seems that the main factor that rule their levels during fasting is their de novo synthesis (Balgoma et al 2019b). Subsequently in the metabolic cascade, the downregulation of LXR in the liver would decrease the de novo synthesis of glycerolipids with palmitic, palmitoleic, stearic, and oleic acids.…”

Section: Introductionmentioning

confidence: 99%

“…A priori, quantitative lipidomics would lead to neglect changes in minor species [TG(50:0) in our example]. However, in contrast to TG(54:5), minor TG(50:0) is associated with dysregulated lipid metabolism in the liver (Balgoma et al 2019b). Hence, relative quantification fitted the purpose of this study.…”

Section: Introductionmentioning

confidence: 99%

Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver

et al. 2020

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Introduction The abuse of anabolic androgenic steroids (AASs) is a source of public concern because of their adverse effects. Supratherapeutic doses of AASs are known to be hepatotoxic and regulate the lipoproteins in plasma by modifying the metabolism of lipids in the liver, which is associated with metabolic diseases. However, the effect of AASs on the profile of lipids in plasma is unknown. Objectives To describe the changes in the plasma lipidome exerted by AASs and to discuss these changes in the light of previous research about AASs and de novo lipogenesis in the liver. Methods We treated male Wistar rats with supratherapeutic doses of nandrolone decanoate and testosterone undecanoate. Subsequently, we isolated the blood plasma and performed lipidomics analysis by liquid chromatography-high resolution mass spectrometry. Results Lipid profiling revealed a decrease of sphingolipids and glycerolipids with palmitic, palmitoleic, stearic, and oleic acids. In addition, lipid profiling revealed an increase in free fatty acids and glycerophospholipids with odd-numbered chain fatty acids and/or arachidonic acid. Conclusion The lipid profile presented herein reports the imprint of AASs on the plasma lipidome, which mirrors the downregulation of de novo lipogenesis in the liver. In a broader perspective, this profile will help to understand the influence of androgens on the lipid metabolism in future studies of diseases with dysregulated lipogenesis (e.g. type 2 diabetes, fatty liver disease, and hepatocellular carcinoma).

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Common Fatty Markers in Diseases with Dysregulated Lipogenesis

Cited by 20 publications

References 14 publications

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Lipidomics Issues on Human Positive ssRNA Virus Infection: An Update

Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver

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