Presentation is loading. Please wait.

Presentation is loading. Please wait.

Genetical Genomics to Identify Gene

Similar presentations


Presentation on theme: "Genetical Genomics to Identify Gene"— Presentation transcript:

1 Genetical Genomics to Identify Gene
Pathways Regulating Hematopoietic Stem Cells Leonid Bystrykh Ellen Weersing Bert Dontje Edo Vellenga Gerald de Haan University of Groningen, the Netherlands.

2 Stem cells (HSC)

3 Genetically regulated stem cell traits
C57BL/6 stem cells: Low frequency Low proliferation No loss during aging Low mobilization High in vivo self renewal High in vitro expansion DBA/2 stem cells: High frequency High proliferation Strong loss during aging High mobilization Low in vivo self renewal Low in vitro expansion

4 Correlating phenotypes of mouse HSC (p<0.05)
HSC number in Bm - -0.42 HSC expansion in vitro (CAFC) -0.49 - HSC in cell cycle (S-phase), HU assay (Scp2) FGF induced HSC expansion in culture Bm DNA damage/repair radiation + 0.44 RAD Bm DNA damage/repair HU - -0.49 Bm in vitro expansion Correlations were revealed by applying three methods: Spearman, Kendall, Pearson. All tests showed equivalent results, Spearman’s test values are indicated.

5 PCR, SH, SAGE, Microarrays
Correlative candidates Phenotype Genes PCR, SH, SAGE, Microarrays no genetic analysis Genome maps BXD Positional candidates Controlling locus Physical locus No or insufficient arrays Complex QTL: -quantitative change of a single gene -qualitative change of a set of genes

6 More than 1 controlling loci in case of each HSC phenotype
1. The program always counts till 2 loci. 2. The program corrects wrong choice. Best fit: Scp2 (S-phase) HSC expansion D7Mit178---D14Mit D14Mit160-+-D19Mit19 (0.5 cM cM) (40cM cM) D11Mit86---D11Mit D18Mit D19Mit40 (28cM cM) (11cM cM) Human 5q

7 Physical candidates, Scp2, ch11
10 cM 20 cM 30 cM 40 cM 50 cM 60 cM 70 cM cluster 1 cluster 2 cluster 3 Scp2 Ikaros, 6cM Lcp2, 17cM EST, w.s.AnkyrinB, 36 cM EST, Mm.24328 EST, Mm.27282 Rad50, 28.9cM Ppp2ca, Ubb D27,mCG56518,Mm.27768, 42 cM Sperm acrosomal protein Psmb6 Pafah1b1 Gemin4 EST,Mm.29347 RPS6KB1 EPX MPO Zfp147 syn Trim25 T-box2, 49 cM Hexim1, 56 cM Stat5A Stat5B Ramp2 FLJ22561 Cltc EST,Mm EST,Mm.23672 EST, Mm RecQ5b, 78 cM Blood Sep 15;100(6): A genetic and genomic analysis identifies a cluster of genes associated with hematopoietic cell turnover. de Haan G, Bystrykh LV, Weersing E, Dontje B, Geiger H, Ivanova N, Lemischka IR, Vellenga E, Van Zant G.

8 Gene expression Phenotype Controlling locus Gene locus
Correlative candidates Gene expression Phenotype Controlling locus Gene locus Positional candidates

9 Search for loci that regulate pool size Search for loci that regulate
BXD bone marrow cells BXD stem cell RNA c-kit sca-1 Variation in stem cell frequency Variation in gene expression Search for loci that regulate pool size Search for loci that regulate gene expression Candidate QTLs Candidate QTLs Compare and find overlapping QTLs Differentially expressed gene does not map to QTL of interest Differentially expressed gene does map to QTL of interest Passagers (but potentially biologically relevant Top candidates

10 Clustering of Transcriptional Networks Affecting
Hematopoietic Stem Cell Function. Leonid Bystrykh 1 , Ellen Weersing , Sue Sutton 2 , Bert Dontje , Edo Vellenga 3 , Jintao Wang 4 Kenneth F. Manly , Lu Lu 5 , Elissa Chesler , Robert W. Williams , Michael Cooke , Gerald de Haan 1* Department of Stem Cell Biology, University of Groningen, Groningen, the Netherlands Genome Institute of the Novartis Research Foundation, La Jolla, CA, USA Department of Hematology, Academic Hospital Groningen, Groningen, the Netherlands Roswell Park Cancer Institute, Buffalo, NY, USA University of Tennessee Health Science Center, Memphis, TN 38163, USA

11

12 Expected average Number of controlled transcripts QTL position (Mb)
D11Mit360: 1622 Mb D6Mit170: 866Mb D12Mit280: 1750 Mb D2Mit340: 346 Mb D12Mit203: 1720 Mb DXMit10: Mb DXMit89: Mb D2Mit293: 222Mb DXMit22: Mb D11Mit320: 1592 Mb D18Mit83: Mb D9Mit310: 1364 Mb Number of controlled transcripts D9Mit310: 1364 Mb D9Mit297: 1292 Mb D1Mit45: 93Mb D4Mit80: 612 Mb Expected average QTL position (Mb)

13 Cis Trans Trans-band MPO Runx1 syn AMLCR1 AI462102
AML1, Cbfa2, Pebp2a2, Pebpa2b AMLCR1 AI462102

14

15 Common Cis-acting transcripts in Brain and HSC QTLs
Randomly distributed according to a genes Nr per Ch

16 There is a very small number of cis acting transcripts
(88 for Brain and HSC data sets). A great number of these transcripts are small (autonomously) expressing units, ESTs and (pro)viral elements. Many of these transcrips are not purely cis acting (low LRS score). True candidates: 14

17 More: Rad50, fgf1, Brca1, Rad17, Rad51, cd44, psmb6,runx1, ppp2ca....

18 Cdc25c, a quasi-cis-acting gene
cdc25c (NM_009860) 2124 bp 102935_at 102934_s_at CDS

19

20 DDBDBDDD B BBB B B BDDD BDB B

21 Allele cdc25c B B D D D D B B D18Mit83 B B D D B B B B BXD12 BXD13
Primary fibroblasts BXD12 BXD13 BXD40 BXD6 m.w. B6 C DBA E 2.00 1.65 + - cdc25c X 14 Mb D18Mit83 Mb Mb

22 At the End Combining Microarrays and genetic models improve our search strategies for candidate genes. Cis acting genes are very rare but very important for studies of the expression networks. Combined effort is needed to store and access considerable number of microarrays data. Life will be always more complex than any of our models.

23 Genome-wide screen for candidate genes against HSC expansion
Best HSC28 like 0.0 20.0 40.0 60.0 80.0 100.0 120.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 X Chromosomes cM

24 Not all probe sets show perfect match with genome sequences.
0.0kb 2.0kb 4.0kb variable exons 103005_s_at CDS Only 1 out of 3 probe sets show match. cd44 ( ) 4093 bp cdc25c (NM_009860) 2124 bp 102935_at 102934_s_at CDS 2 overlapping highly correlating probe sets fgf1 (AK035330) 3404 bp 100494_at Only 1probe set CDS Not all probe sets show perfect match with genome sequences. Differences in array data can be due to - gene expression - splicing variations - sequence polymorphism

25 Trends Genet Jul;17(7): Genetical genomics: the added value from segregation. Jansen RC, Nap JP. Expression profiling in combination with molecular marker analysis of a segregating population makes it possible to use quantitative trail loci (QTL) analysis for identification of influential genes and gene products.


Download ppt "Genetical Genomics to Identify Gene"

Similar presentations


Ads by Google