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?