Integrated Genomic and Proteomic Analyses of
Gene Expression in Mammalian Cells
Tian et. al.
Molecular & Cellular Proteomics 3:960-969, 2004
MEDG 505, 13/01/04, Anca Petrescu
Genomics vs Proteomics
Honore et. Al., BioEssays 26:901-15
Technologies
ICAT-MS
Honore et. Al., BioEssays 26:901-15
2D-DIGE
2 experiments
Steady state mRNA-protein levels 2 cell lines representing distinct hematopoietic stages
Stage-specific developmental markers
Dynamic state mRNA-protein levels Liver response to treatment with 3 drugs
Drug response genes
Steady-state experiment
Early myeloid differentiation process Stage-specific developmental markers
EML kit-ligand dependent multipotent progenitors
MPRO lineage-committed GM-CSF dependent
Oligo-arrays + ICAT-MS
Microarray data
1,199 mRNA’s differentially expressed
5% of Agilent 22,788 gene-array 23,574 60-mer probes
568
631 MPRO
EML
ICAT-MS data
672 unique proteins
2,919 cysteine-containing peptides 253 [1], 419 [>1]
425 have corresponding mRNA 157 [1], 268 [>1]
Honore et. Al., BioEssays 26:901-15
mRNA vs Protein 150 signature genes
29 - correlated
67 - mRNA
52 - protein
2 - anticorrelated
Correlated genes
GENE UPREGULATED mRNA PROTEIN
c-kit EML 9 7
c-kit ligand
EML - 5
RACK1 MPRO 2 -
[mitochondrial]
Anti-correlated genes
Atp5o: H+-ATP synthase O subunit
↑mRNA ↓protein translation-inhibitory protein binds to 3’-UTR of β-subunit mRNA
8 other mitochondrial genes
Commonly shared post-transcriptional regulatory mechanism
[RNA processing]
Anti-correlated genes
HNRNP AO
↓mRNA ↑protein 5/6 RNA processing genes 12 yeast RNA processing genes
Post-transcriptional regulatory mechanisms likely for this class of genes
Correlation significance
Observed correlation: r = 0.64
Null hypothesis: r = 0 Rejection → not related by chance Significance: p~10-20 (Fisher’s z-transform)
Null hypothesis: r = 1 Rejection → rule out noise
Correlation significance Null hypothesis: perfect correlation
Computer-simulate noise r=1 data mRNA/protein error models 1,000 Montecarlo runs
Simulated r=0.9 vs observed r=0.64 biological significance
Dynamic-state experiment
dynamic process of drug response
PPARα and –γ agonists WY-14653, TZD, BRL-49653 C57BL/6J mice: 1,2,3,7 daily doses [n=3]
liver mRNA vs. protein levels Oligoarrays vs 2D-DIGE Experimental vs control (vehicle)
2D-DIGE
calculate p-value of variance/spot/treatments
top 70 variant spotsQuality/brightness
30 candidate spots → MSDiscard + consolidate spots
12 candidate genes (r=0.54) 144 data points: 12 genes x 3 drugs x 4 time points
Honore et. Al., BioEssays 26:901-15
mRNA vs Protein
8 genes: r>0.65 (p<0.01) 1 gene: r=-0.70
mRNA vs Protein
Signature mRNA’s/proteins (same proportion)Abs(logRatio) > 0.2, p < 0.05
mRNA vs Protein
Pearson correlation ~0.7 Similar info of drug treatments with both data
Axis: expression ratios
Genomics vs ProteomicsHonore et. Al., BioEssays 26:901-15
40%
Protein expression more variable?
Why anti-correlated? Does this make evolutionary sense?What kinds of genes might we expect this for?
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