Proteomic Characterization of Alternative Splicing and Coding Polymorphism

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Proteomic Characterization of Alternative
Splicing and Coding Polymorphism

• Nathan Edwards
• Center for Bioinformatics and Computational
  Biology
• University of Maryland, College Park

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Proteomics

• Proteins are the machines that drive much of
  biology
• Genes are merely the recipe
• The direct characterization of a samples
  proteins en masse.
• What proteins are present?
• How much of each protein is present?

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Systems Biology

• Establish relationships by
• Choosing related samples,
• Global characterization, and
• Comparison.

Gene / Transcript / Protein Gene / Transcript / Protein
Measurement Predetermined Unknown
Discrete (DNA) Genotyping Sequencing
Continuous Gene Expression Proteomics
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Samples

• Healthy / Diseased
• Cancerous / Benign
• Drug resistant / Drug susceptible
• Progression or Prognosis
• Bound / Unbound
• Tissue specific
• Cellular location specific
• Mitochondria, Membrane

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2D Gel-Electrophoresis

• Protein separation
• Molecular weight (MW)
• Isoelectric point (pI)
• Staining
• Birds-eye view of protein abundance

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2D Gel-Electrophoresis
Bécamel et al., Biol. Proced. Online 2002494-104
.
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Paradigm Shift

• Traditional protein chemistry assay methods
  struggle to establish identity.
• Identity requires
• Specificity of measurement (Precision)
• A reference for comparison

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Mass Spectrometry for Proteomics

• Measure mass of many (bio)molecules
  simultaneously
• High bandwidth
• Mass is an intrinsic property of all
  (bio)molecules
• No prior knowledge required

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Mass Spectrometer

• ElectronMultiplier(EM)

• Time-Of-Flight (TOF)
• Quadrapole
• Ion-Trap

• MALDI
• Electro-SprayIonization (ESI)

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High Bandwidth
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Mass is fundamental!
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Mass Spectrometry for Proteomics

• Measure mass of many molecules simultaneously
• ...but not too many, abundance bias
• Mass is an intrinsic property of all
  (bio)molecules
• ...but need a reference to compare to

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Mass Spectrometry for Proteomics

• Mass spectrometry has been around since the turn
  of the century...
• ...why is MS based Proteomics so new?
• Ionization methods
• MALDI, Electrospray
• Protein chemistry automation
• Chromatography, Gels, Computers
• Protein sequence databases
• A reference for comparison

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Sample Preparation for Peptide Identification
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Single Stage MS
MS
m/z
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Tandem Mass Spectrometry(MS/MS)
m/z
Precursor selection
m/z
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Tandem Mass Spectrometry(MS/MS)
Precursor selection collision induced
dissociation (CID)
m/z
MS/MS
m/z
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Peptide Identification

• For each (likely) peptide sequence
• 1. Compute fragment masses
• 2. Compare with spectrum
• 3. Retain those that match well
• Peptide sequences from protein sequence databases
• Swiss-Prot, IPI, NCBIs nr, ...
• Automated, high-throughput peptide identification
  in complex mixtures

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Why dont we see more novel peptides?

• Tandem mass spectrometry doesnt discriminate
  against novel peptides......but protein
  sequence databases do!
• Searching traditional protein sequence databases
  biases the results towards well-understood
  protein isoforms!

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What goes missing?

• Known coding SNPs
• Novel coding mutations
• Alternative splicing isoforms
• Alternative translation start-sites
• Microexons
• Alternative translation frames

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Why should we care?

• Alternative splicing is the norm!
• Only 20-25K human genes
• Each gene makes many proteins
• Proteins have clinical implications
• Biomarker discovery
• Evidence for SNPs and alternative splicing stops
  with transcription
• Genomic assays, ESTs, mRNA sequence.
• Little hard evidence for translation start site

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Novel Splice Isoform

• Human Jurkat leukemia cell-line
• Lipid-raft extraction protocol, targeting T cells
• von Haller, et al. MCP 2003.
• LIME1 gene
• LCK interacting transmembrane adaptor 1
• LCK gene
• Leukocyte-specific protein tyrosine kinase
• Proto-oncogene
• Chromosomal aberration involving LCK in
  leukemias.
• Multiple significant peptide identifications

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Novel Splice Isoform
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Novel Splice Isoform
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Novel Mutation

• HUPO Plasma Proteome Project
• Pooled samples from 10 male 10 female healthy
  Chinese subjects
• Plasma/EDTA sample protocol
• Li, et al. Proteomics 2005. (Lab 29)
• TTR gene
• Transthyretin (pre-albumin)
• Defects in TTR are a cause of amyloidosis.
• Familial amyloidotic polyneuropathy
• late-onset, dominant inheritance

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Novel Mutation
Ala2?Pro associated with familial amyloid
polyneuropathy
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Novel Mutation
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Expressed Sequence Tags (ESTs)

• Cheap, fast, coding
• Single sequencing reads of mRNA
• Sequence from 5 or 3 end
• No assembly

http//www.ncbi.nlm.nih.gov/About/primer/est.html
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Searching ESTs

• Proposed long ago
• Yates, Eng, and McCormack Anal Chem, 95.
• Now
• Protein sequences are sufficient for protein
  identification
• Computationally expensive/infeasible
• Difficult to interpret
• Make EST searching feasible for routine searching
  to discover novel peptides.

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Searching Expressed Sequence Tags (ESTs)

• Pros
• No introns!
• Primary splicing evidence for annotation
  pipelines
• Evidence for dbSNP
• Often derived from clinical cancer samples

• Cons
• No frame
• Large (8Gb)
• Untrusted by annotation pipelines
• Highly redundant
• Nucleotide error rate 1

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Compressed EST Peptide Sequence Database

• For all ESTs mapped to a UniGene gene
• Six-frame translation
• Eliminate ORFs lt 30 amino-acids
• Eliminate amino-acid 30-mers observed once
• Compress to C2 FASTA database
• Complete, Correct for amino-acid 30-mers

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Compressed EST Peptide Sequence Database

• For all ESTs mapped to a UniGene gene
• Six-frame translation
• Eliminate ORFs lt 30 amino-acids
• Eliminate amino-acid 30-mers observed once
• Compress to C2 FASTA database
• Complete, Correct for amino-acid 30-mers

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Compressed EST Database

• Gene centric compressed EST peptide sequence
  database
• 20,774 sequence entries
• 8Gb vs 223 Mb
• 35 fold compression
• 22 hours becomes 15 minutes
• E-values improve by similar factor!
• Makes routine EST searching feasible
• Search ESTs instead of IPI?

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Back to the lab...

• Current LC/MS/MS workflows identify a few
  peptides per protein
• ...not sufficient for protein isoforms
• Need to raise the sequence coverage to (say) 80
• ...protein separation prior to LC/MS/MS analysis
• Potential for database of splice sites of
  (functional) proteins!

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Microorganism Identification by MALDI Mass
Spectrometry

• Direct observation of microorganism biomarkers in
  the field.
• Peaks represent masses of abundant proteins.
• Statistical models assess identification
  significance.

B.anthracisspores
MALDI Mass Spectrometry
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Key Principles

• Protein mass from protein sequence
• No introns, few PTMs
• Specificity of single mass is very weak
• Statistical significance from many peaks
• Not all proteins are equally likely to be
  observed
• Ribosomal proteins, SASPs

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Rapid Microorganism Identification Database
(www.RMIDb.org)

• Protein Sequences
• 8.1M (2.9M)
• Species
• 18K
• Genbank,
• Microbial, Virus, Plasmid
• RefSeq
• CMR,
• Swiss-Prot
• TrEMBL

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Rapid Microorganism Identification Database
(www.RMIDb.org)
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Informatics Issues

• Need good species / strain annotation
• B.anthracis vs B.thuringiensis 
• Need correct protein sequence
• B.anthracis Sterne a/ß SASP
• RefSeq/Gb MVMARN... (7442 Da)
• CMR MARN... (7211 Da)
• Need chemistry based protein classification

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Spectral Matching

• Detection vs. identification
• Increased sensitivity
• No novel peptides
• NIST GC/MS Spectral Library
• Identifies small molecules,
• 100,000s of (consensus) spectra
• Bundled/Sold with many instruments
• Dot-product spectral comparison
• Current project Peptide MS/MS

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Peptide DLATVYVDVLK
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Peptide DLATVYVDVLK
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Hidden Markov Models for Spectral Matching

• Capture statistical variation and consensus in
  peak intensity
• Capture semantics of peaks
• Extrapolate model to other peptides
• Good specificity with superior sensitivity for
  peptide detection

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Conclusions

• Molecular biology bioinformatics provide a
  reference for biotechnologies
• Foundation of systems biology
• Peptides identify more than just proteins
• Untapped source of disease biomarkers
• Compressed peptide sequence databases make
  routine EST searching feasible

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Future Research Directions

• Identification of protein isoforms
• Optimize proteomics workflow for isoform
  detection
• Identify splice variants in cancer cell-lines
  (MCF-7) and clinical brain tumor samples
• dbPep for genomic annotation

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Future Research Directions

• Proteomics for Microorganism Identification
• Specificity of tandem mass spectra
• Revamp RMIDb prototype
• Incorporate spectral matching

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Acknowledgements

• Catherine Fenselau, Steve Swatkoski
• UMCP Biochemistry
• Chau-Wen Tseng, Xue Wu
• UMCP Computer Science
• Cheng Lee
• Calibrant Biosystems
• PeptideAtlas, HUPO PPP, X!Tandem
• Funding NIH/NCI, USDA/ARS