Literature DB >> 21931151

Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach.

Kathleen M Carroll1, Deborah M Simpson, Claire E Eyers, Christopher G Knight, Philip Brownridge, Warwick B Dunn, Catherine L Winder, Karin Lanthaler, Pinar Pir, Naglis Malys, Douglas B Kell, Stephen G Oliver, Simon J Gaskell, Robert J Beynon.   

Abstract

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

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Year:  2011        PMID: 21931151      PMCID: PMC3237070          DOI: 10.1074/mcp.M111.007633

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  42 in total

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Authors:  E Boles; F Schulte; T Miosga; K Freidel; E Schlüter; F K Zimmermann; C P Hollenberg; J J Heinisch
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2.  Added value for tandem mass spectrometry shotgun proteomics data validation through isoelectric focusing of peptides.

Authors:  Manfred Heller; Mingliang Ye; Philippe E Michel; Patrick Morier; Daniel Stalder; Martin A Jünger; Ruedi Aebersold; Frédéric Reymond; Joël S Rossier
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3.  Search of sequence databases with uninterpreted high-energy collision-induced dissociation spectra of peptides.

Authors:  J R Yates; J K Eng; K R Clauser; A L Burlingame
Journal:  J Am Soc Mass Spectrom       Date:  1996-11       Impact factor: 3.109

4.  Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.

Authors:  Paola Picotti; Bernd Bodenmiller; Lukas N Mueller; Bruno Domon; Ruedi Aebersold
Journal:  Cell       Date:  2009-08-06       Impact factor: 41.582

5.  Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

Authors:  John R S Newman; Sina Ghaemmaghami; Jan Ihmels; David K Breslow; Matthew Noble; Joseph L DeRisi; Jonathan S Weissman
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Review 6.  The top genes: on the distance from transcript to function in yeast glycolysis.

Authors:  Dan G Fraenkel
Journal:  Curr Opin Microbiol       Date:  2003-04       Impact factor: 7.934

7.  High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Authors:  Jess Allen; Hazel M Davey; David Broadhurst; Jim K Heald; Jem J Rowland; Stephen G Oliver; Douglas B Kell
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8.  Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics.

Authors:  Jennifer A Siepen; Emma-Jayne Keevil; David Knight; Simon J Hubbard
Journal:  J Proteome Res       Date:  2007-01       Impact factor: 4.466

9.  Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas.

Authors:  Nichole L King; Eric W Deutsch; Jeffrey A Ranish; Alexey I Nesvizhskii; James S Eddes; Parag Mallick; Jimmy Eng; Frank Desiere; Mark Flory; Daniel B Martin; Bong Kim; Hookeun Lee; Brian Raught; Ruedi Aebersold
Journal:  Genome Biol       Date:  2006       Impact factor: 13.583

10.  Absolute multiplexed quantitative analysis of protein expression during muscle development using QconCAT.

Authors:  Jenny Rivers; Deborah M Simpson; Duncan H L Robertson; Simon J Gaskell; Robert J Beynon
Journal:  Mol Cell Proteomics       Date:  2007-05-17       Impact factor: 5.911

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  29 in total

1.  Choice of LC-MS methods for the absolute quantification of drug-metabolizing enzymes and transporters in human tissue: a comparative cost analysis.

Authors:  Hajar Al Feteisi; Brahim Achour; Jill Barber; Amin Rostami-Hodjegan
Journal:  AAPS J       Date:  2015-02-06       Impact factor: 4.009

Review 2.  Proteome dynamics: revisiting turnover with a global perspective.

Authors:  Amy J Claydon; Robert Beynon
Journal:  Mol Cell Proteomics       Date:  2012-11-02       Impact factor: 5.911

3.  IQcat: multiplexed protein quantification by isoelectric QconCAT.

Authors:  Ryan J Austin; Deborah K Chang; Carly A Holstein; Lik W Lee; Jenni Risler; Jonathan H Wang; Lee Adams; Nicolle B Krusberski; Daniel B Martin
Journal:  Proteomics       Date:  2012-06-19       Impact factor: 3.984

4.  Mass spectrometry-based workflow for accurate quantification of Escherichia coli enzymes: how proteomics can play a key role in metabolic engineering.

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Journal:  Mol Cell Proteomics       Date:  2014-01-29       Impact factor: 5.911

5.  Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them.

Authors:  Jingyi Jessica Li; Guo-Liang Chew; Mark D Biggin
Journal:  Nucleic Acids Res       Date:  2017-11-16       Impact factor: 16.971

6.  Mass spectrometry based proteomics for absolute quantification of proteins from tumor cells.

Authors:  Hong Wang; Sam Hanash
Journal:  Methods       Date:  2015-03-17       Impact factor: 3.608

Review 7.  Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.

Authors:  Nathan P Manes; Aleksandra Nita-Lazar
Journal:  J Proteomics       Date:  2018-02-13       Impact factor: 4.044

8.  Improving metabolic flux predictions using absolute gene expression data.

Authors:  Dave Lee; Kieran Smallbone; Warwick B Dunn; Ettore Murabito; Catherine L Winder; Douglas B Kell; Pedro Mendes; Neil Swainston
Journal:  BMC Syst Biol       Date:  2012-06-19

9.  A software toolkit and interface for performing stable isotope labeling and top3 quantification using Progenesis LC-MS.

Authors:  Da Qi; Philip Brownridge; Dong Xia; Katherine Mackay; Faviel F Gonzalez-Galarza; Jenna Kenyani; Victoria Harman; Robert J Beynon; Andrew R Jones
Journal:  OMICS       Date:  2012-08-13

10.  Label-free protein quantification for plant Golgi protein localization and abundance.

Authors:  Nino Nikolovski; Pavel V Shliaha; Laurent Gatto; Paul Dupree; Kathryn S Lilley
Journal:  Plant Physiol       Date:  2014-08-13       Impact factor: 8.340

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