Publications by authors named "Marina Wright-Muelas"

6 Publications

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An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines.

Metabolomics 2020 10 7;16(10):107. Epub 2020 Oct 7.

Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.

Introduction: It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.

Objectives: Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.

Methods: We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint).

Results: Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.

Conclusion: Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.
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http://dx.doi.org/10.1007/s11306-020-01725-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541387PMC
October 2020

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Authors:
Giulio Superti-Furga Daniel Lackner Tabea Wiedmer Alvaro Ingles-Prieto Barbara Barbosa Enrico Girardi Ulrich Goldmann Bettina Gürtl Kristaps Klavins Christoph Klimek Sabrina Lindinger Eva Liñeiro-Retes André C Müller Svenja Onstein Gregor Redinger Daniela Reil Vitaly Sedlyarov Gernot Wolf Matthew Crawford Robert Everley David Hepworth Shenping Liu Stephen Noell Mary Piotrowski Robert Stanton Hui Zhang Salvatore Corallino Andrea Faedo Maria Insidioso Giovanna Maresca Loredana Redaelli Francesca Sassone Lia Scarabottolo Michela Stucchi Paola Tarroni Sara Tremolada Helena Batoulis Andreas Becker Eckhard Bender Yung-Ning Chang Alexander Ehrmann Anke Müller-Fahrnow Vera Pütter Diana Zindel Bradford Hamilton Martin Lenter Diana Santacruz Coralie Viollet Charles Whitehurst Kai Johnsson Philipp Leippe Birgit Baumgarten Lena Chang Yvonne Ibig Martin Pfeifer Jürgen Reinhardt Julian Schönbett Paul Selzer Klaus Seuwen Charles Bettembourg Bruno Biton Jörg Czech Hélène de Foucauld Michel Didier Thomas Licher Vincent Mikol Antje Pommereau Frédéric Puech Veeranagouda Yaligara Aled Edwards Brandon J Bongers Laura H Heitman Ad P IJzerman Huub J Sijben Gerard J P van Westen Justine Grixti Douglas B Kell Farah Mughal Neil Swainston Marina Wright-Muelas Tina Bohstedt Nicola Burgess-Brown Liz Carpenter Katharina Dürr Jesper Hansen Andreea Scacioc Giulia Banci Claire Colas Daniela Digles Gerhard Ecker Barbara Füzi Viktoria Gamsjäger Melanie Grandits Riccardo Martini Florentina Troger Patrick Altermatt Cédric Doucerain Franz Dürrenberger Vania Manolova Anna-Lena Steck Hanna Sundström Maria Wilhelm Claire M Steppan

Nat Rev Drug Discov 2020 07;19(7):429-430

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http://dx.doi.org/10.1038/d41573-020-00056-6DOI Listing
July 2020

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data.

Sci Rep 2019 11 29;9(1):17960. Epub 2019 Nov 29.

Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK.

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.
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http://dx.doi.org/10.1038/s41598-019-54288-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884504PMC
November 2019

A brain-permeable inhibitor of the neurodegenerative disease target kynurenine 3-monooxygenase prevents accumulation of neurotoxic metabolites.

Commun Biol 2019 24;2:271. Epub 2019 Jul 24.

1Manchester Institute of Biotechnology and School of Chemistry, The University of Manchester, Manchester, M1 7DN UK.

Dysregulation of the kynurenine pathway (KP) leads to imbalances in neuroactive metabolites associated with the pathogenesis of several neurodegenerative disorders, including Huntington's disease (HD). Inhibition of the enzyme kynurenine 3-monooxygenase (KMO) in the KP normalises these metabolic imbalances and ameliorates neurodegeneration and related phenotypes in several neurodegenerative disease models. KMO is thus a promising candidate drug target for these disorders, but known inhibitors are not brain permeable. Here, 19 new KMO inhibitors have been identified. One of these () is neuroprotective in a HD model but is minimally brain penetrant in mice. The prodrug variant () crosses the blood-brain barrier, releases in the brain, thereby lowering levels of 3-hydroxykynurenine, a toxic KP metabolite linked to neurodegeneration. Prodrug will advance development of targeted therapies against multiple neurodegenerative and neuroinflammatory diseases in which KP likely plays a role, including HD, Alzheimer's disease, and Parkinson's disease.
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http://dx.doi.org/10.1038/s42003-019-0520-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656724PMC
May 2020

Rational cell culture optimization enhances experimental reproducibility in cancer cells.

Sci Rep 2018 02 14;8(1):3029. Epub 2018 Feb 14.

Manchester Centre for Integrative Systems Biology and Doctoral Training Centre, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data. Baseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce. We discuss the implications of the cells' metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We then devised 'metabolically rationalized standard' assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.
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http://dx.doi.org/10.1038/s41598-018-21050-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813001PMC
February 2018

GeneGini: Assessment via the Gini Coefficient of Reference "Housekeeping" Genes and Diverse Human Transporter Expression Profiles.

Cell Syst 2018 Feb 7;6(2):230-244.e1. Epub 2018 Feb 7.

School of Chemistry, 131, Princess Street, Manchester M1 7DN, UK; The Manchester Institute of Biotechnology, 131, Princess Street, Manchester M1 7DN, UK. Electronic address:

The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the "housekeeping" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.
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http://dx.doi.org/10.1016/j.cels.2018.01.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840522PMC
February 2018