PeptideManager: a peptide selection tool for targeted proteomic studies involving mixed samples from different species

The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.

[1]  Philip Brownridge,et al.  The importance of the digest: proteolysis and absolute quantification in proteomics. , 2011, Methods.

[2]  Ruedi Aebersold,et al.  Options and considerations when selecting a quantitative proteomics strategy , 2010, Nature Biotechnology.

[3]  Daniel B. Martin,et al.  Computational prediction of proteotypic peptides for quantitative proteomics , 2007, Nature Biotechnology.

[4]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[5]  Bruno Domon,et al.  Targeted proteomics strategy applied to biomarker evaluation , 2013, Proteomics. Clinical applications.

[6]  Richard D. Smith Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application. , 2012, Clinical chemistry.

[7]  B. Chait Mass Spectrometry: Bottom-Up or Top-Down? , 2006, Science.

[8]  S. Hubbard,et al.  Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics. , 2012, Omics : a journal of integrative biology.

[9]  H. Schlüter,et al.  Amino acids: chemistry, functionality and selected non-enzymatic post-translational modifications. , 2012, Journal of proteomics.

[10]  E. Kohn,et al.  Proteomics and biomarkers in clinical trials for drug development. , 2011, Journal of proteomics.

[11]  María Martín,et al.  Activities at the Universal Protein Resource (UniProt) , 2013, Nucleic Acids Res..

[12]  Vassilios Ioannidis,et al.  ExPASy: SIB bioinformatics resource portal , 2012, Nucleic Acids Res..

[13]  Henry H. N. Lam,et al.  A database of mass spectrometric assays for the yeast proteome , 2008, Nature Methods.

[14]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[15]  Tatiana Tatusova,et al.  NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins , 2004, Nucleic Acids Res..

[16]  Tatiana A. Tatusova,et al.  NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy , 2011, Nucleic Acids Res..

[17]  Connie R. Jimenez,et al.  iTRAQ-based Proteomics Profiling Reveals Increased Metabolic Activity and Cellular Cross-talk in Angiogenic Compared with Invasive Glioblastoma Phenotype* , 2009, Molecular & Cellular Proteomics.

[18]  Michele Magrane,et al.  UniProt Knowledgebase: a hub of integrated protein data , 2011, Database J. Biol. Databases Curation.

[19]  S. Niclou,et al.  A novel eGFP-expressing immunodeficient mouse model to study tumor-host interactions , 2008, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[20]  E. Schröck,et al.  Side population in human glioblastoma is non-tumorigenic and characterizes brain endothelial cells , 2013, Brain : a journal of neurology.

[21]  J. Mesirov,et al.  Prediction of high-responding peptides for targeted protein assays by mass spectrometry , 2009, Nature Biotechnology.

[22]  Craig Lawless,et al.  CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches* , 2011, Molecular & Cellular Proteomics.

[23]  E. Birney,et al.  The International Protein Index: An integrated database for proteomics experiments , 2004, Proteomics.

[24]  S. Carr,et al.  Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry , 2013, Nature Methods.

[25]  Heidi Zhang,et al.  Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer. , 2007, Journal of proteome research.

[26]  The UniProt Consortium,et al.  The Universal Protein Resource (UniProt) 2009 , 2008, Nucleic Acids Res..

[27]  Z. Tezak,et al.  Regulatory perspective on translating proteomic biomarkers to clinical diagnostics. , 2011, Journal of proteomics.

[28]  H. Christofk,et al.  A label‐free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen , 2008, Proteomics.

[29]  Darryl B. Hardie,et al.  Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. , 2014, Biochimica et biophysica acta.

[30]  Pei Wang,et al.  A targeted proteomics–based pipeline for verification of biomarkers in plasma , 2011, Nature Biotechnology.

[31]  David R Goodlett,et al.  Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study. , 2012, Journal of proteome research.

[32]  V. Marx Targeted proteomics , 2013, Nature Methods.

[33]  Eric W. Deutsch,et al.  A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis , 2013, Nature.

[34]  Brian T Chait,et al.  Chemistry. Mass spectrometry: bottom-up or top-down? , 2006, Science.

[35]  B. Domon,et al.  Selected reaction monitoring applied to proteomics. , 2011, Journal of mass spectrometry : JMS.

[36]  E. Schröck,et al.  A Novel, Diffusely Infiltrative Xenograft Model of Human Anaplastic Oligodendroglioma with Mutations in FUBP1, CIC, and IDH1 , 2013, PloS one.

[37]  P. Gimotty,et al.  A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. , 2012, Journal of proteome research.

[38]  Zhaojing Meng,et al.  Targeted mass spectrometry approaches for protein biomarker verification. , 2011, Journal of proteomics.

[39]  R. Aebersold,et al.  Selected reaction monitoring for quantitative proteomics: a tutorial , 2008, Molecular systems biology.

[40]  R. Aebersold,et al.  Scoring proteomes with proteotypic peptide probes , 2005, Nature Reviews Molecular Cell Biology.

[41]  Jens M. Rick,et al.  Quantitative mass spectrometry in proteomics: a critical review , 2007, Analytical and bioanalytical chemistry.

[42]  Robertson Craig,et al.  Open source system for analyzing, validating, and storing protein identification data. , 2004, Journal of proteome research.

[43]  T. Taxt,et al.  Anti-VEGF treatment reduces blood supply and increases tumor cell invasion in glioblastoma , 2011, Proceedings of the National Academy of Sciences.

[44]  Johannes Griss,et al.  The Proteomics Identifications (PRIDE) database and associated tools: status in 2013 , 2012, Nucleic Acids Res..

[45]  Ruedi Aebersold,et al.  N-Glycoprotein SRMAtlas , 2013, Molecular & Cellular Proteomics.

[46]  T. Brüning,et al.  Biomarker research with prospective study designs for the early detection of cancer. , 2014, Biochimica et biophysica acta.

[47]  R. Bjerkvig,et al.  A reproducible brain tumour model established from human glioblastoma biopsies , 2009, BMC Cancer.

[48]  Ruedi Aebersold,et al.  Protein expression changes in ovarian cancer during the transition from benign to malignant. , 2012, Journal of proteome research.

[49]  Eric W. Deutsch,et al.  The PeptideAtlas project , 2005, Nucleic Acids Res..

[50]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[51]  Ermir Qeli,et al.  Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data. , 2014, Journal of proteomics.

[52]  Alexey I Nesvizhskii,et al.  Protein identification by tandem mass spectrometry and sequence database searching. , 2007, Methods in molecular biology.

[53]  Rolf Bjerkvig,et al.  In vivo models of primary brain tumors: pitfalls and perspectives , 2012, Neuro-oncology.

[54]  Robertson Craig,et al.  The use of proteotypic peptide libraries for protein identification. , 2005, Rapid communications in mass spectrometry : RCM.

[55]  S. Rappaport,et al.  Selected Reaction Monitoring , 2020, Definitions.

[56]  Melissa J. Landrum,et al.  RefSeq: an update on mammalian reference sequences , 2013, Nucleic Acids Res..

[57]  R. Sederoff,et al.  Understanding the role of proteolytic digestion on discovery and targeted proteomic measurements using liquid chromatography tandem mass spectrometry and design of experiments. , 2013, Journal of proteome research.

[58]  Mehdi Mirzaei,et al.  Less label, more free: Approaches in label‐free quantitative mass spectrometry , 2011, Proteomics.

[59]  Cathy H. Wu,et al.  The Universal Protein Resource (UniProt) , 2004, Nucleic Acids Res..

[60]  Y. Karamanos,et al.  Comparative and Quantitative Global Proteomics Approaches: An Overview , 2013, Proteomes.

[61]  Yassene Mohammed,et al.  PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. , 2014, Journal of proteomics.