1. A meta-analysis of differential coexpression across age Jesse Gillis

2. Expression Analysis

3. Interpretations Differential Expression – Functional: Tissue differences – Dysfunctional: Disease expression profiling Coexpression – GO groups Differential Coexpression – If functional association indicates coexpression then change in functional association would indicate change in coexpression – Tumor network vs normal network – Ageing involves many coordinated changes (functional and dysfunctional)

4. Differential Coexpression

5. Theories of aging Antagonistic pleiotropy – Early and late effects – P53 suppresses tumors and stem cells Mutation accumulation – Decreased selection, extrinsic mortality predicting lifespan – cancer Fetal programming – Maternal stress, cardiovascular risks Senescence versus development – Often both

6. Background Differential coexpression across age Human microarray studies from Gemma’s database were categorized by their subject’s ages into the four groups – “prenatal” – “child/young adult” (0-18 years) – “adult” (19-54) – “older adult” (55+) 8 to 13 studies for each age group 2803 individual microarrays (repurposed) Problem: How to generalize taking a difference to multiple conditions?

7. Sorting Data

8. Wavelets and Differential Coexpression Separates Data into: Lifelong coexpression Lifelong change in coexpression Early life change in coexpression Late life change in coexpression Basis Set

9. Sample Results

10. GO group Validation Differential coexpression AUC 0.77 Coexpression values AUC 0.65 Random gene sets AUC 0.49 GO groups 25-30 genes leave-one-out-validation Take home message: Patterns of differential coexpression predict related fuction

11. SIRT1 -Longevity interest -Gene silencing in yeast -Unusual but repeated pattern -Early changes determine lifelong state

12. Ongoing Work Disease groupings – Alzheimer’s – Schizophrenia Aging patterns – Specific genes and theories Finer aging gradations – Year by year

13. Acknowledgments Paul Pavlidis and the Pavlidis lab Support from NIH and the Michael Smith Foundation for Health Research