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Guard SE, Chapnick DA, Poss ZC, Ebmeier CC, Jacobsen J, Nemkov T, Ball KA, Webb KJ, Simpson HL, Coleman S, Bunker E, Ramirez A, Reisz JA, Sievers R, Stowell MHB, D'Alessandro A, Liu X, Old WM. Multiomic Analysis Reveals Disruption of Cholesterol Homeostasis by Cannabidiol in Human Cell Lines. Mol Cell Proteomics 2022; 21:100262. [PMID: 35753663 PMCID: PMC9525918 DOI: 10.1016/j.mcpro.2022.100262] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/12/2022] [Accepted: 06/21/2022] [Indexed: 01/18/2023] Open
Abstract
The nonpsychoactive cannabinoid, cannabidiol (CBD), is Food and Dug Administration approved for treatment of two drug-resistant epileptic disorders and is seeing increased use among the general public, yet the mechanisms that underlie its therapeutic effects and side-effect profiles remain unclear. Here, we report a systems-level analysis of CBD action in human cell lines using temporal multiomic profiling. FRET-based biosensor screening revealed that CBD elicits a sharp rise in cytosolic calcium, and activation of AMP-activated protein kinase in human keratinocyte and neuroblastoma cell lines. CBD treatment leads to alterations in the abundance of metabolites, mRNA transcripts, and proteins associated with activation of cholesterol biosynthesis, transport, and storage. We found that CBD rapidly incorporates into cellular membranes, alters cholesterol accessibility, and disrupts cholesterol-dependent membrane properties. Sustained treatment with high concentrations of CBD induces apoptosis in a dose-dependent manner. CBD-induced apoptosis is rescued by inhibition of cholesterol synthesis and potentiated by compounds that disrupt cholesterol trafficking and storage. Our data point to a pharmacological interaction of CBD with cholesterol homeostasis pathways, with potential implications in its therapeutic use.
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Affiliation(s)
- Steven E Guard
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Douglas A Chapnick
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
| | - Zachary C Poss
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Christopher C Ebmeier
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Jeremy Jacobsen
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Travis Nemkov
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, Colorado, USA
| | - Kerri A Ball
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Kristofor J Webb
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Helen L Simpson
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Stephen Coleman
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Eric Bunker
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
| | - Adrian Ramirez
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
| | - Julie A Reisz
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, Colorado, USA
| | - Robert Sievers
- Department of Chemistry and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
| | - Michael H B Stowell
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, Colorado, USA
| | - Xuedong Liu
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, USA
| | - William M Old
- Department of Molecular, Cellular & Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA.
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Crook OM, Chung CW, Deane CM. Challenges and Opportunities for Bayesian Statistics in Proteomics. J Proteome Res 2022; 21:849-864. [PMID: 35258980 PMCID: PMC8982455 DOI: 10.1021/acs.jproteome.1c00859] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Indexed: 12/27/2022]
Abstract
Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.
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Affiliation(s)
- Oliver M. Crook
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - Chun-wa Chung
- Structural
and Biophysical Sciences, GlaxoSmithKline
R&D, Stevenage SG1 2NY, United Kingdom
| | - Charlotte M. Deane
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
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Davies V, Harvey WT, Reeve R, Husmeier D. Improving the identification of antigenic sites in the H1N1 influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. J R Stat Soc Ser C Appl Stat 2019; 68:859-885. [PMID: 31598013 PMCID: PMC6774336 DOI: 10.1111/rssc.12338] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise measures of the antigenic similarity of virus strains. We propose a sparse hierarchical Bayesian model that can deal with the pairwise structure and inherent experimental variability in the H1N1 data through the introduction of latent variables. The latent variables represent the underlying HI titre measurement of any given pair of virus strains and help to account for the fact that, for any HI titre measurement between the same pair of virus strains, the difference in the viral sequence remains the same. Through accurately representing the structure of the H1N1 data, the model can select virus sites which are antigenic, while its latent structure achieves the computational efficiency that is required to deal with large virus sequence data, as typically available for the influenza virus. In addition to the latent variable model, we also propose a new method, the block‐integrated widely applicable information criterion biWAIC, for selecting between competing models. We show how this enables us to select the random effects effectively when used with the model proposed and we apply both methods to an A(H1N1) data set.
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Heydari J, Lawless C, Lydall DA, Wilkinson DJ. Bayesian hierarchical modelling for inferring genetic interactions in yeast. J R Stat Soc Ser C Appl Stat 2015; 65:367-393. [PMID: 27134314 PMCID: PMC4843957 DOI: 10.1111/rssc.12126] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Quantitative fitness analysis (QFA) is a high throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene to infer genetic interaction strengths genomewide, examining thousands of separate genotypes. We introduce Bayesian hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches. Our new approach models population dynamics and genetic interaction simultaneously, thereby avoiding passing information between models via a univariate fitness summary. Matching experimental structure more closely, Bayesian hierarchical approaches use data more efficiently and find new evidence for genes which interact with yeast telomeres within a published data set.
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Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice. Nat Commun 2014; 5:3837. [PMID: 24815183 PMCID: PMC4024759 DOI: 10.1038/ncomms4837] [Citation(s) in RCA: 138] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 04/09/2014] [Indexed: 01/19/2023] Open
Abstract
Mitochondrial function is an important determinant of the ageing process; however, the mitochondrial properties that enable longevity are not well understood. Here we show that optimal assembly of mitochondrial complex I predicts longevity in mice. Using an unbiased high-coverage high-confidence approach, we demonstrate that electron transport chain proteins, especially the matrix arm subunits of complex I, are decreased in young long-living mice, which is associated with improved complex I assembly, higher complex I-linked state 3 oxygen consumption rates and decreased superoxide production, whereas the opposite is seen in old mice. Disruption of complex I assembly reduces oxidative metabolism with concomitant increase in mitochondrial superoxide production. This is rescued by knockdown of the mitochondrial chaperone, prohibitin. Disrupted complex I assembly causes premature senescence in primary cells. We propose that lower abundance of free catalytic complex I components supports complex I assembly, efficacy of substrate utilization and minimal ROS production, enabling enhanced longevity.
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