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Mitochondrial oxidative phosphorylation (OXPHOS) is under the control of both mitochondrial (mtDNA) and nuclear genomes and is central to energy homeostasis. To investigate how its function and regulation are integrated within cells, we systematically combined four cell-based assays of OXPHOS physiology with multiplexed measurements of nuclear and mtDNA gene expression across 2,490 small-molecule perturbations in cultured muscle. Mining the resulting compendium revealed, first, that protein synthesis inhibitors can decouple coordination of nuclear and mtDNA transcription; second, that a subset of HMG-CoA reductase inhibitors, combined with propranolol, can cause mitochondrial toxicity, yielding potential clues about the etiology of statin myopathy; and, third, that structurally diverse microtubule inhibitors stimulate OXPHOS transcription while suppressing reactive oxygen species, via a transcriptional mechanism involving PGC-1α and ERRα, and thus may be useful in treating age-associated degenerative disorders. Our screening compendium can be used as a discovery tool both for understanding mitochondrial biology and toxicity and for identifying novel therapeutics.

Facioscapulohumeral muscular dystrophy (FSHD) is caused by misexpression of the DUX4 transcription factor in skeletal muscle that results in transcriptional alterations, abnormal phenotypes and cell death. To gain insight into the kinetics of DUX4-induced stresses, we activated DUX4 expression in myoblasts and performed longitudinal RNA sequencing paired with proteomics and phosphoproteomics. This analysis revealed changes in cellular physiology upon DUX4 activation, including DNA damage and altered mRNA splicing. Phosphoproteomic analysis uncovered rapid widespread changes in protein phosphorylation following DUX4 induction, indicating that alterations in kinase signaling might play a role in DUX4-mediated stress and cell death. Indeed, we demonstrate that two stress-responsive MAP kinase pathways, JNK and p38, are activated in response to DUX4 expression. Inhibition of each of these pathways ameliorated DUX4-mediated cell death in myoblasts. These findings uncover that the JNK pathway is involved in DUX4-mediated cell death and provide additional insights into the role of the p38 pathway, a clinical target for the treatment of FSHD.

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years’ experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay’s ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors’ dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1–2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1–2 weeks. This protocol is an update to Nat. Protoc . 11, 1757–1774 (2016): https://doi.org/10.1038/nprot.2016.105 We provide an updated protocol for image-based profiling with Cell Painting. A detailed procedure, with standardized conditions for the assay, is presented, along with a comprehensive description of parameters to be considered when optimizing the assay.

Glycerol-3-phosphate acyltransferase (GPAT)1 is a mitochondrial outer membrane protein that catalyzes the first step of de novo glycerolipid biosynthesis. Hepatic expression of GPAT1 is linked to liver fat accumulation and the severity of nonalcoholic fatty liver diseases. Here we present the cryo-EM structures of human GPAT1 in substrate analog-bound and product-bound states. The structures reveal an N-terminal acyltransferase domain that harbors important catalytic motifs and a tightly associated C-terminal domain that is critical for proper protein folding. Unexpectedly, GPAT1 has no transmembrane regions as previously proposed but instead associates with the membrane via an amphipathic surface patch and an N-terminal loop–helix region that contains a mitochondrial-targeting signal. Combined structural, computational and functional studies uncover a hydrophobic pathway within GPAT1 for lipid trafficking. The results presented herein lay a framework for rational inhibitor development for GPAT1. GPAT1 is a mitochondrial outer membrane protein that catalyzes the first step of glycerolipid biosynthesis. Cryo-EM structures and functional studies of human GPAT1 uncover the molecular architecture and mechanism of this important acyltransferase.

Nat. Biotechnol. 26, 343–351 (2008); published online 24 February 2008; corrected after print 8 July 2008 In the version of this article initially published, on p.348, column 2, paragraph 2, line 7, the following sentence was incorrect: “Statins block the synthesis of cholesterol—a precursor to ubiquinone.

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years' experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1 to 2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1 to 2 weeks.Competing Interest StatementS.S. and A.E.C. serve as scientific advisors for companies that use image-based profiling and Cell Painting (AEC: Recursion, SS: Waypoint Bio, Dewpoint Therapeutics) and receive honoraria for occasional talks at pharmaceutical and biotechnology companies. D.G. is an employee of Bayer, AG, Pharmaceuticals. S.G., B.Z., G.H. are employees of Merck Healthcare KGaA, Darmstadt, Germany J.D.B. and T.G. were employed at Pfizer for the duration of this work. S.Y. was employed at Takeda for the duration of this work. S.E.S. was employed at Biogen for the duration of her contributions to this work. C.-H.L. was employed at Janssen Pharmaceutica at the time of writing. J.B.C & P.J.A. are employees of the Novartis Institutes for Biomedical Research, Cambridge MA, USA and declare no competing financial interests E.M., G.W., T.M, L.M. & J.P. are employees of AstraZeneca, Cambridge, UK K.J. was employed at AstraZeneca for the duration of this work. D.J.L. and S.H. are employees of Pfizer, Inc.Footnotes* Author name fixed* https://github.com/carpenterlab/2022_Cimini_NatureProtocols