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

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

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

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.