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      Human epidermal stem cell differentiation is modulated by specific lipid subspecies

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          Significance

          Cells generate a vast repertoire of lipid molecules whose functions are poorly understood. To investigate whether lipids can regulate cell fate decisions, we carried out a systematic lipidomic analysis and perturbation of lipid metabolism in cultured human epidermal keratinocytes, determining associations with the onset of differentiation. We identified individual lipid species that induced exit from the epidermal stem cell compartment. Our observations suggest that more research is warranted on the regulation of biological processes via lipid species, moving beyond the more conventional contribution of proteins and nucleic acids.

          Abstract

          While the lipids of the outer layers of mammalian epidermis and their contribution to barrier formation have been extensively described, the role of individual lipid species in the onset of keratinocyte differentiation remains unknown. A lipidomic analysis of primary human keratinocytes revealed accumulation of numerous lipid species during suspension-induced differentiation. A small interfering RNA screen of 258 lipid-modifying enzymes identified two genes that on knockdown induced epidermal differentiation: ELOVL1, encoding elongation of very long-chain fatty acids protein 1, and SLC27A1, encoding fatty acid transport protein 1. By intersecting lipidomic datasets from suspension-induced differentiation and knockdown keratinocytes, we pinpointed candidate bioactive lipid subspecies as differentiation regulators. Several of these—ceramides and glucosylceramides—induced differentiation when added to primary keratinocytes in culture. Our results reveal the potential of lipid subspecies to regulate exit from the epidermal stem cell compartment.

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          Most cited references47

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity

            Summary Trained innate immunity fosters a sustained favorable response of myeloid cells to a secondary challenge, despite their short lifespan in circulation. We thus hypothesized that trained immunity acts via modulation of hematopoietic stem and progenitor cells (HSPCs). Administration of β-glucan (prototypical trained-immunity-inducing agonist) to mice induced expansion of progenitors of the myeloid lineage, which was associated with elevated signaling by innate immune mediators, such as IL-1β and granulocyte-macrophage colony-stimulating factor (GM-CSF), and with adaptations in glucose metabolism and cholesterol biosynthesis. The trained-immunity-related increase in myelopoiesis resulted in a beneficial response to secondary LPS challenge and protection from chemotherapy-induced myelosuppression in mice. Therefore, modulation of myeloid progenitors in the bone marrow is an integral component of trained immunity, which to date, was considered to involve functional changes of mature myeloid cells in the periphery.
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              Three clonal types of keratinocyte with different capacities for multiplication.

              Colony-forming human epidermal cells are heterogeneous in their capacity for sustained growth. Once a clone has been derived from a single cell, its growth potential can be estimated from the colony types resulting from a single plating, and the clone can be assigned to one of three classes. The holoclone has the greatest reproductive capacity: under standard conditions, fewer than 5% of the colonies formed by the cells of a holoclone abort and terminally differentiate. The paraclone contains exclusively cells with a short replicative lifespan (not more than 15 cell generations), after which they uniformly abort and terminally differentiate. The third type of clone, the meroclone, contains a mixture of cells of different growth potential and is a transitional stage between the holoclone and the paraclone. The incidence of the different clonal types is affected by aging, since cells originating from the epidermis of older donors give rise to a lower proportion of holoclones and a higher proportion of paraclones.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 September 2020
                25 August 2020
                25 August 2020
                : 117
                : 36
                : 22173-22182
                Affiliations
                [1] aCentre for Stem Cells and Regenerative Medicine, King’s College London , SE1 9RT London, United Kingdom;
                [2] bEuropean Bioinformatics Institute , CB10 1SD Hinxton, United Kingdom;
                [3] cLipotype GmbH , 01307 Dresden, Germany;
                [4] dRandall Centre for Cell and Molecular Biophysics, King’s College London , SE1 1UL London, United Kingdom
                Author notes
                1To whom correspondence may be addressed. Email: fiona.watt@ 123456kcl.ac.uk .

                Contributed by Fiona M. Watt, July 16, 2020 (sent for review June 4, 2020; reviewed by Nils Joakim Faergeman and Kathleen J. Green)

                Author contributions: M.V.R., A.M., U.S.E., and F.M.W. designed research; M.V.R., A.M., and C.K. performed research; U.S.E. contributed new reagents/analytic tools; and M.V.R., U.S.E., and F.M.W. wrote the paper.

                Reviewers: N.J.F., University of Southern Denmark; and K.J.G., Northwestern University.

                Author information
                https://orcid.org/0000-0002-1798-8241
                https://orcid.org/0000-0001-8201-4067
                https://orcid.org/0000-0001-9151-5154
                Article
                202011310
                10.1073/pnas.2011310117
                7486749
                32843345
                c69d8a05-c047-4d33-b9f8-c67aee7dd6b4
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 10
                Funding
                Funded by: RCUK | Medical Research Council (MRC) 501100000265
                Award ID: G1100073
                Award Recipient : Fiona M. Watt
                Funded by: Wellcome 100010269
                Award ID: 096540/Z/11/Z
                Award Recipient : Fiona M. Watt
                Categories
                Biological Sciences
                Cell Biology

                lipids,epidermis,differentiation,keratinocytes,lipidomics
                lipids, epidermis, differentiation, keratinocytes, lipidomics

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