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Journal club

Journal club. Wouter 10 dec 2013. Why. Interest in autism Follow-up of gene-finding Interesting: two papers in same issue Cell similar findings. Overview paper. Select hcASD-genes (9) and pASD-genes (122) Use data Kang & reduce spatial and temporal number of windows

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Journal club

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  1. Journal club Wouter 10 dec2013

  2. Why • Interest in autism • Follow-up of gene-finding • Interesting: two papers in same issue Cell similar findings

  3. Overview paper • Select hcASD-genes (9) and pASD-genes (122) • Use data Kang & reduce spatial and temporal number of windows • Find enrichment of pASD in coexpression networks in 4 areas • Test enrichment with: 1) hypergeometric test 2) hcASD permutation 3) pASD permutation 4) number of genes selected in network 5) cross validation 6) single period weighted 7) excluding TBR1 • Focus on TBR1 • TADA confirming pASD genes higher chance in midfetal period • Further improve spatial resolution to layers • Analyze temporal behavior of layer found • Find cell type • Immunostaining in midfetal CPi cortex

  4. Introduction • No common genetic variation reproducible linked to autism • However, sequencing has recently led to discovery of de novo loss of function (LoF) mutation. • De novo LoF mutations are expected to play role in 15% of patients • List of associated genes is steadily growing • Associated loci heterogeneous with respect to biological function  challenge for translation

  5. Hypothesis Goal

  6. Gene selection • Total of 1043 families (987 previously published, 56 additional exome sequenced) • LoF = premature stop codon, splice-site disruption, or frameshift insertion/ deletion • 144 LoF de novo mutations identified

  7. Chance of true ASD gene • Subset of 599 quartets: 75 LoF in 72 affected versus 34 in 32 unaffected (OR=2.21, p=5e-5) FDR of gene ≥ 2 independent cases with LoF • Permutation: p=0.1975 to find 2 LoF in same gene by chance • 9 genes with ≥ 2 LoF genes found • 45.6 more often than expected (9/0.1975) • FDR = 0.022 (1/45.6) • Chance of true ASD gene is 0.978 • Analogue chance of true ASD for 1-hit gene (0.55) and 3-hit gene (0.9998)

  8. hcASD / pASD genes hc = high confidence (m=9) • LoF in gene in two unrelated cases (FDR 0.02) • LoF in three cases (FDR 0.0002) p = probably (m=122) • LoF in one case (FDR 0.45) Use these genes to construct spatiotemporal coexpression networks

  9. Transcriptome data

  10. Transcriptome data

  11. Transcriptome data • Expression in • 16 brain regions • 57 clinically unremarkable postmortem subjects (31M 26F) • 15 periods from 5.7 PCW to 82 Y (Thus, 16*14=240 spatiotemporal units) • Partitioned in subsets • Temporal partitioning: 13 sliding windows of three consecutive time periods • Why?

  12. Coexpression network • Network = hcASD gene + max 20 top correlated genes + edges • For each gene (M = 16,947 + 9), vector of expression values, by brain-region and brain-sample • Per spatiotemporal window, correlation of expression-vectors between gene-pairs • Per hcASD, select 20 top correlated genes with abs. cor. ≥ 0.7 • Edges are are correlations between each gene-pair of network with abs. cor. ≥ 0.7

  13. Spatial partitioning – step 1 • Why? • Select period, in which networks are most enriched for pASD genes  period 3-7 (10-38 PCW)

  14. Spatial partitioning – step 2 • Select coherent subsets of brain regions based on period 3-7 • Summarize gene-expression per brain region by median expression across all samples • Compute pairwise correlation between brain regions • Subsequent, hierarchical clustering (distance is 1-corr2) • 4 clusters of brain regions Thus, 4*13 = 52 spatiotemporal windows, with coexpression networks constructed

  15. Results

  16. Results

  17. Hypergeometric test • Probability of k successes in n draws without replacement k = number of successes drawn (nr pASD-genes in network) K = total number of successes (total nr pASD = 122) n = number of draws (genes in network, ≤ 20) N = population (16,947 genes) Problem: larger genes more chance of de novo LoF mutations

  18. Permutation test 1 • Tests if true hcASD genes are crucial to enrichment with pASD found • Select 9 pseudo hcASD genes (based on the likelihood of observing 2-hit de novo LoF mutations by chance, taking gene size and GC-content into account) • Build corresponding coexpression networks in concerning spatiotemporal windows & test enrichment with pASD genes • 100,000 iterations

  19. Permutation test 1

  20. Permutation test 2 • Identical, but with true hcASD, and permutation of pASD

  21. Permutation test 3 • Permutation of hcASD, with true pASD • For varying number of genes in coexpression network

  22. Cross-validation • Remove 1 hcASD and 12 pASD (10%) • Reconstruct 52 spatiotemporal coexpression networks • Success = 1 of top three networks most enriched for pASD • top three PFC-MSC 3-5 & 4-6, MD-CBC 8-10 • Success in 100% of 200 iterations

  23. Single period weighted analyses • Before, 3 periods equally weighted • Now, middle period weight 1, periods immediately before and after weighted 0.5

  24. Questions • How does “increasing resolution” influence subsequent results? • Why take expression in subjects older than say 1 year into account? • Why not report correlation between hcASD gene-expression?

  25. About brainregions • V1C, ITC, IPC, A1C, STC: non-significant in permutation: dropped • PFC-MSC: 107 sample (period 3-5) & 140 (period 4-6) • MD-CBC: only 26 samples (period 8-10): dropped • Two PFC-MSC networks referred to as midfetal networks PFC-MSC = Pre-Frontal-Cortex & Primary-Motor-Somatosensory-Cortex

  26. TADA • = transmission and the novo association- test • Why? To test if pASD in midfetal network are more likely true ASD genes than estimated with FDR (55%) • TADA combines family and case-control data

  27. TADA • Additional, case (935) control (870) data included from ARRA (Liu) (Liu 2013. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet.)

  28. Biological interpretation

  29. T-box, brain, 1 (TBR1) • TBR1=hcASD • Known transcription factor involved in forebrain development • In mice • Postnatal day 0 ( = human midfetal) • RNA-seq of cortex • Compare expression in TBR1-/- & TBR1+/+ (n=?) • 4 of differentially expressed genes (DEX) in coex- network • TBR1 previously known to regulate these DEX- genes • (not mentioned if DEX- genes are pASD- genes)

  30. Analysis excluding TBR1

  31. Laminar-Specific Expression Data • To improve spatial resolution • PFC-MSC (Pre-Frontal-Cortex & Primary-Motor-Somatosensory-Cortex) • NB: cortex is grey matter and contains cell bodies • Test nine cortex-layers from 4 brains from www.brainspan.org • Apply original coexpression networks and estimate connectivity per layer ( = sum correlations, weighted for mean correlation in layer) • Permute rijk over mean(rk) = null distribution of connectivity

  32. Laminar-Specific Expression Data

  33. Subsequent analyses of inner cortical plate (CPi) • Why? To test if localization to CPi is specific to period 3-5. (might change over time due to neuronal migration in early brain development) • How? • Two mice brains (m&f) • Expression at six time points • Three zones of layers: select genes upregulated in 1 zone only • Test per zone, the zone-specific genes for enrichment in period 3-5 PFC-MSC network (hypergeometric test)

  34. Subsequent analyses of inner cortical plate (CPi) • NB: CPi corresponds to deep mouse layer • Thus, finding of CPi as specific layer is not driven by neurons eventually migrating to superficial layer

  35. Cell-Type-Specific Markers • Five cell-types specific marker genes from independent dataset • Enrichment for cortical glutamergic projections neurons (100,000 permutations of hcASD)

  36. Immunostaining / In situ hybridization • Staining hcASD genes: TBR1, POGZ, CHD8, DYRK1A, SCN2A (i.s.h.) • TBR1 restricted to CPi (inner cortical plate)

  37. Discussion Willsey et al • Results suggest marked locus heterogeneity point to a much smaller set of pathophysiological mechanisms • Clear evidence role synaptic proteins. Indeed, the CPi neurons of midfetal PFC-MSC are among first to form synapsis. • Findings suggest that ASD genes converge at additional time points and brain regions • Small set of hcASD genes: prioritizes specificity over sensitivity • Results important to subsequent further understanding of pathophysiology

  38. Parikshak et al. • Compares ASD to intellectual disability (ID) • Maps ASD and ID genes on coexpression networks • ASD genes enriched in superficial cortical layers & glutaminergic projections neurons • Distinct patterns of ASD and ID

  39. Journal club Wouter 10 dec2013

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