1. Relating Protein Abundance & mRNA Expression Mark B Gerstein Yale (Comp. Bio. & Bioinformatics) NIDA site visit at Yale 2007.12.19

2. [Greenbaum et al. Bioinformatics 2002, 18, 587.] Why relate amounts of protein & mRNA Gene expression - major place for regulation (easy to measure) vs. Concentration of protein - major determinant of activity where k s,i and k d,i are the protein synthesis and degradation rate constants At steady state: P i = k s;i [mRNA i ] k d,i = k s,i [ mRNA i ] – k d,i P i dPi dt Expectations from simple kinetic models: Outliers from trend interesting

3. [Graphic: Jeong et al, Nature, 41:411] Protein interaction networks [Graphic: http://proton.chem.yale.edu] Protein complexes In protein complexes, one expects stoichiometric abundance of component proteins and that mRNA expression levels should be correlated with protein abundance …Among pathways, this is expected to a lesser degree between interacting proteins Relationship of Protein Abundance to Complexes and Pathways

4. mRNA expression levels Microarrays Affymetrix PCR SAGE Protein abundance 2D Gel ElectrophoresisMultiple staining options Small dynamic range DIGECy3 vs. Cy5 labeling Large dynamic range ICAT, iTRAQMS-based Relative abundance (ratio of isotopically labeled species) Large dynamic range MudPITLC-MS/MS SILACStable isotope labeling with amino acids in cell culture - for MS analysis TAP-TagWeissman and O’Shea (Oct. 2003) Sources of experimental data [http://www.biology.ucsc.edu/mcd/images/microarray.gif]

5. PARE: proteomics.gersteinlab.org Upload or use pre-loaded mRNA, protein datasets Analyze all or analyze MIPS or GO subset [Yu et al., BMC Bioinfo. '07] Open-source code Downloadable

6. PARE: a web-based tool for correlating mRNA expression and protein abundance PARE main page (1) Select mRNA, protein datasets: -use pre-loaded datasets -upload datasets (2) Choose categorization method: -correlate all -MIPS complexes -GO biological processes -GO molecular function -GO cellular component Select MIPS, GO subsets (opt.) Display results (3) Display -Linear or log-log correlation for selected subset(s) -Tabulate data, correlation values for selected subset(s) -Label (on plot) and tabulate outlying datapoints [Yu et al., BMC Bioinfo. '07]

7. Correlated data Log-log plot of correlation -linear fit -outliers labeled Calculation of mutual information PARE output [Yu et al., BMC Bioinfo. '07]

8. Correlation of subsets (GO, MIPS) Yeast ref. datasets: “Correlate all” vs. GO cellular component subsets for particular cellular locations [Yu et al., BMC Bioinfo. '07]

9. PARE: pre-loaded datasets [Yu et al., BMC Bioinfo. '07]

10. Connecting PARE with datasets from NIDA investigators Protein abundance (iTRAQ datasets) Mouse (Nairn lab) - samples from 3 brain regions: cortex, striatum, hippocampus Green monkey (Taylor lab) - several brain regions - caudate, dlPFC, mPFC, NacC, NacS, PFC11, PFC13, PrCO, putamen each treated with saline, PCP, and cocaine mRNA datasets obtained from expression database Mouse - Sandberg et al. PNAS 2000, 97, 11038.

11. mRNA expression ratio (hippocampus/cortex) protein abundance ratio (hippocampus/cortex) Mouse brain: correlation of mRNA & protein expression For 96 genes with differential expression in hippocampus vs. cortex Plan to correlate abundance for individual pathways and complexes with C. Bruce ("John", talking later)

12. Acknowledgements iTRAQ datasets: Angus Nairn, Erika Andrade, Dilja Krueger Jane Taylor Chris Colangelo, Mark Shifman (YPED) PARE: Anne Burba, Eric Yu