Publications by authors named "Farah Mughal"

4 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

Microfluidic channel-assisted screening of hematopoietic malignancies.

Genes Chromosomes Cancer 2014 Mar 16;53(3):255-63. Epub 2013 Dec 16.

Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, M1, 7ND, UK.

A simple microfluidic fluorescence in situ hybridization (FISH) device allowing accurate analysis of interphase nuclei in 1 hr in narrow channels is presented. Photolithography and fluorosilicic acid etching were used to fabricate microfluidic channels (referred to as FISHing lines) that allowed analysis of 10 samples on a glass microscope slide 0.2 µl of sample volume was used to fill a micro-channel, which resembled a 250-fold reduction compared to conventional FISH. FISH signals were comparable to conventional FISH, with 50-fold less probe consumption and 10-fold less time. Cells were immobilized in single file in channels just exceeding the diameter of the cells, and were used for minimal residual disease (MRD) analysis. To test the micro-channels for application in FISH, MRD was simulated by mixing K562 cells (an established chronic myeloid leukemia cell line) carrying the BCR/ABL fusion gene across 1:1 to 1:1,000 Jurkat cells (an established acute lymphoblastic leukemia cell line). The limit of detection was seen to be 1:100 cells and 1:1,000 cells for FISHing lines and conventional FISH, respectively; however, the conventional method seemed to over-score the presence of K562 cells. This may in part be attributed to FISHing lines practically eliminating the chance of duplicate screening of cells and hastened the time of screening, enhancing scoring of all cells within the channels. This was compared to 1 in 500 cells on the slide being analyzed with the conventional FISH.
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http://dx.doi.org/10.1002/gcc.22137DOI Listing
March 2014