A meta-analysis of differential coexpression across age Jesse Gillis

A meta-analysis of differential coexpression across age

Jesse Gillis

Expression Analysis

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)

Differential Coexpression

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

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?

Sorting Data

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

Sample Results

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

SIRT1-Longevity interest

-Gene silencing in yeast

-Unusual but repeated pattern

-Early changes determine lifelong state

Ongoing Work

• Disease groupings– Alzheimer’s– Schizophrenia

• Aging patterns– Specific genes and theories

• Finer aging gradations– Year by year

Acknowledgments

Paul Pavlidis and the Pavlidis lab

Support from NIH and the Michael Smith Foundation for Health Research