Integrated Genomic and Proteomic Analyses of Gene Expression in Mammalian Cells Tian et. al....
-
Upload
alannah-betty-todd -
Category
Documents
-
view
219 -
download
0
Transcript of Integrated Genomic and Proteomic Analyses of Gene Expression in Mammalian Cells Tian et. al....
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?