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      Lipidomics of facial sebum in the comparison between acne and non-acne adolescents with dark skin

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          Abstract

          Lipidomics is advantageous in the study of sebum perturbations occurring in acne. An extended evaluation of the sebum lipid profiles in acne-prone sebaceous areas is lacking in dark skin. Yet, there is a void space in understanding how the building blocks of sebum lipids, i.e. individual fatty acids (FAs), are intertwined with acne-prone skin. We aimed to determine the sebum lipidome in facial areas of adolescents with and without acne in Nigeria. A cross-sectional analytical study was conducted in 60 adolescents/young adults divided in 30 acne patients (15F, 15M) and 30 age and sex-matched controls. Sebum samples obtained from foreheads and cheeks were analysed separately by gas chromatography–mass spectrometry (GCMS) and thin layer chromatography (HPTLC). Distributions of sebum components were investigated with multivariate ANOVA-simultaneous component analysis (ASCA). Sebum incretion in acne was paralleled by significantly higher abundance of triglycerides, wax esters, and squalene together with monounsaturated FAs (MUFAs), and straight chain saturated FAs (SFAs), especially those with odd-carbon chain, i.e. C13:0, C15:0, and C17:0. Profiling weight/weight percentage of individual components revealed that, in acne, the free FAs (FFAs) array was shifted towards higher relative abundance of the SFAs C15:0, C16:0, and C17:0 and lower percentage of the anteiso-branched FFAs with 12, 14, 16, and 18 carbons. In acne patients, MUFAs and PUFAs were quantitatively increased and decreased on foreheads and cheeks, respectively. Relative abundance of fatty alcohols was decreased in acne independent on the site. The results indicated that acne associates with site-specific derangement of the pathways regulating the balance among odd straight-chain and branched-chain SFAs, MUFAs, which included sapienate (C16:1n-10), PUFAs, and squalene.

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          Most cited references41

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          ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.

          Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.
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            Enzyme promiscuity drives branched-chain fatty acid synthesis in adipose tissues

            Summary Fatty acid synthase (FASN) predominantly generates straight-chain fatty acids using acetyl-CoA as the initiating substrate. However, monomethyl branched-chain fatty acids (mmBCFAs) are also present in mammals but thought to be primarily diet-derived. Here we demonstrate that mmBCFAs are de novo synthesized via mitochondrial BCAA catabolism, exported to the cytosol by adipose-specific expression of carnitine acetyltransferase (CrAT), and elongated by FASN. Brown fat exhibits the highest BCAA catabolic and mmBCFA synthesis fluxes, whereas these lipids are largely absent from liver and brain. mmBCFA synthesis is also sustained in the absence of microbiota. We identify hypoxia as a potent suppressor of BCAA catabolism that decreases mmBCFA synthesis in obese adipose tissue, such that mmBCFAs are significantly decreased in obese animals. These results identify adipose tissue mmBCFA synthesis as a novel link between BCAA metabolism and lipogenesis, highlighting roles for CrAT and FASN promiscuity that influence acyl-chain diversity in the lipidome.
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              Prevalence, severity, and severity risk factors of acne in high school pupils: a community-based study.

              A cross-sectional, community-based study was performed to determine the prevalence and severity of acne vulgaris in adolescents and of factors influencing the acne severity risk. The presence of acne was clinically determined and the secondary outcome measures of family acne history and the relation of acne to nutrition habits, emotional stress, menstruation, and smoking were recorded in a questionnaire. A representative sample of 1,002 pupils aged 16+/-0.9 years was enrolled. The overall acne prevalence was 93.3, 94.4% for boys and 92.0% for girls. Moderate to severe acne was observed in 14%. The prevalence of moderate to severe acne was 19.9% in pupils with and 9.8% in those without a family history of acne (P<0.0005; OR: 2.3). Acne severity risk increased with the number of family members with acne history. A mother with acne history influenced the severity of acne the most. Increasing pubertal age, seborrhea, the premenstrual phase, mental stress, and sweet and oily foods were recognized as risk factors for moderate to severe acne. In contrast, gender, spicy foods, and smoking were not associated with acne severity. In conclusion, acne is a common disorder in Iranian adolescents, with a low rate of moderate to severe acne. A genetic background is suggested, with mother's acne history being the most important prognostic factor. Skin quality and certain nutrition habits may affect acne severity.
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                Author and article information

                Contributors
                emanuela.camera@ifo.gov.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 August 2021
                16 August 2021
                2021
                : 11
                : 16591
                Affiliations
                [1 ]Dermatology Unit, Federal Medical Centre, Keffi, Nasarawa Nigeria
                [2 ]GRID grid.419467.9, ISNI 0000 0004 1757 4473, Laboratory of Cutaneous Physiopathology, , San Gallicano Dermatological Institute-IRCCS, ; Rome, Italy
                [3 ]GRID grid.7841.a, Department of Chemistry, , University of Rome ‘La Sapienza’, ; Rome, Italy
                Article
                96043
                10.1038/s41598-021-96043-x
                8367971
                34400712
                33ce85f6-d880-4932-9d16-1e56d53a3252
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 May 2021
                : 4 August 2021
                Funding
                Funded by: Fondation René Touraine
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                data mining,data processing,lipidomics,biomarkers
                Uncategorized
                data mining, data processing, lipidomics, biomarkers

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