SIGBIO25

1. Search of miRNAs critical for medulloblastoma
formation using MiRaGE method
○Y-h. Taguchi（Dept. Phys., Chuo Univ.）
Jun Yasuda（Tohoku Univ.)
Present address:
Cancer Research Inst.
Ariake, Tokyo

2. Experiments
microRNA vs tumor
Computer oriented prediction
(uncertain)
Target genes genom
e
microRNA mRN
A
microRNA mRNA 2

3. 3

4. miRNA target gene list
Gene8
Gene7
Gene6
Gene5
Gene4
Gene3
Gene2
Gene1
(計算機による予測）
simple seed match
miRNA1 ○ × ○ ○ ○ ○ × ×
miRNA2 ○ × ○ ○ × × ○ ○
miRNA3 × ○ ○ × ○ ○ × ×
予 miRNA4 ○ ○ ○ × ○ ○ × ×
測
VS
miRNA1
gene1 Murine Medulloblastoma(MB)
(Dr. Tetsuo Noda’ group
gene2 (The JFCR-Cancer Institute)).
gene3
4

5. Materials
P6＝6 days after birth, normal but growing
P6
P30＝30 days after birth, normal and not
P30
growing
MB＝a few month after birth, malignant
MB
neoplasm
30% of the Ptc1 +/- mice suffers from MB.
5

6. mRNA/miRNA expression by
Array: Agilent
at
P6, P30 and MB
log(xg[mRNA/miRNA:MB or P6])
vs
log(xg[mRNA/miRNA:P30])
xg: mRNA/miRNA expression
Target gene list: simple seed match 6

7. t te s t fo r m iRNA e x p re s s io n
log(xg[miRNA:P6/MB]) vs
log(miRNA:xg[P30])
of considered miRNA(*)
(*) each miRNA is measured by multiple
probe
7

8. t test for miRNA target genes (MiRaGE method)
log(xg[mRNA:P6/MB]) – log(xg[mRNA:P30])
in target genes of considered miRNA
VS
log(xg[mRNA:P6/MB]) – log(xg[mRNA:P30])
in target genes of
any of other miRNA
8

9. P30 P6/MB
Gether the information of miRNA targets
Compare the expressions of targets
for each miRNAs
Calculate False Discovery Rate
Generate ranking
9

10. MiRaGE
miRNA Targets Dow n P-value FDR
miR-a 54 3 0.5 0.4
miR-b 120 54 0.0001 0.005
miR-c 36 1 0.5 0.7
... ... ... ... ...
miR-X 60 18 0.001 0.007
Reject miR-a & c because the FDR > 0.05
Filtrate with miRNA expression profiles
Ranking 10

11. selected by
miRNA miRNA expression / MiRaGE
miRNA P30< MB
1 mmu-miR-25 1 1 target gene P30> MB
2 m m u-m iR-466i-5p 1 1
3 mmu-miR-92a 0.75 1
4 mmu-miR-19a 1 0.69 miR-17~92 cluster family
5 mmu-miR-19b 1 0.69 members are ranked in top 5
6 m m u-m iR-3082-5p 1 0.56 by combination of MiRaGE
7 m m u-m iR-130a 1 0.5 methods and miRNA
8 m m u-m iR-130b 1 0.5
expression profiling.
9 m m u-m iR-15b 1 0.5
10 m m u-m iR-2861 1 0.5
11 m m u-m iR-3096-5p 1 0.5
12 m m u-m iR-32 0.5 1
13 m m u-m iR-322 1 0.5
14 m m u-m iR-721 1 0.5
15 m m u-m iR-149* 0.5 0.88
16 m m u-m iR-3081* 1 0.38
17 m m u-m iR-574-5p 1 0.31
18 m m u-m iR-669n 0.5 0.81 suggested contribution 11
19 m m u-m iR-1187 1 0.25 to cancer formation

12. selected by
miRNA P30> MB
miRNA miRNA expression / MiRaGE
target gene P30< MB
mmu-miR-100 1 1
mmu-miR-126-3p 1 1
mmu-miR-29c 1 1 Some of the neuron-
mmu-miR-376a 1 1 specific miRNAs and
miRNA
mmu-miR-451 1 1 tumor-suppressive
mmu-miR-99b 1 1 miRNAs seem to contribute
mmu-miR-136* 1 0.9375 to the gene expression
mmu-miR-299* 0.75 1 profiles of P30.
mmu-miR-26a 1 0.5
mmu-miR-26b 1 0.5
mmu-miR-29a 0.5 1
mmu-miR-7a-1* 1 0.5
mmu-miR-3107 1 0.4375
mmu-miR-340-5p 1 0.3125
mmu-miR-369-5p 1 0.3125
mmu-let-7a 1 0.25
mmu-let-7e 1 0.25
tumor-suppressive miRNAs
mmu-let-7g 1 0.25 neuron-specific miRNAs
12
mmu-let-7i 1 0.25

13. selected by
miRNA miRNA expression / MiRaGE
miRNA P30< P6
1 mmu-miR-106b 1.00 1.00
target gene P30> P6
2 m m u-m iR-130a 1.00 1.00
3 m m u-m iR-130b 1.00 1.00
4 m m u-m iR-15b 1.00 1.00 miR-17~92, mir-106b-25 ,
5 mmu-miR-17 1.00 1.00 mir-106a-363
6 mmu-miR-20a 1.00 1.00
cluster family members are
ranked in top 5 by combination of
7 mmu-miR-20b 1.00 1.00
MiRaGE methods and miRNA
8 m m u-m iR-301b 1.00 1.00 expression profiling.
9 m m u-m iR-322 1.00 1.00
10 m m u-m iR-721 1.00 1.00
11 mmu-miR-93 1.00 1.00
12 m m u-m iR-542-3p 1.00 0.94
13 m m u-m iR-3081* 1.00 0.88
14 m m u-m iR-335-3p 1.00 0.88
15 m m u-m iR-199a-5p 1.00 0.81
16 m m u-m iR-199b* 1.00 0.81
17 mmu-miR-19a 1.00 0.81
13
18 mmu-miR-1 9 b 1.00 0.81

14. selected by
miRNA miRNA expression / MiRaGE miRNA P30> P6
m m u-m iR-29c 1.00 1.00 target gene P30< P6
mmu-miR-376a 1.00 1.00
m m u-m iR-451 1.00 1.00
Some of the neuron-
mmu-let-7b 1.00 0.94
specific miRNAs and
miRNA
mmu-let-7e 1.00 0.94
tumor-suppressive
mmu-let-7g 1.00 0.94 miRNAs seem to contribute
mmu-let-7i 1.00 0.94 to the gene expression
m m u-m iR-98 1.00 0.94 profiles of P30.
m m u-m iR-126-3p 0.75 1.00
m m u-m iR-299* 0.75 1.00
m m u-m iR-29a 0.75 1.00
mmu-let-7a 0.75 0.94
m m u-m iR-3070b-3p 1.00 0.69
m m u-m iR-138 1.00 0.63
m m u-m iR-3107 1.00 0.56
m m u-m iR-181a-1* 0.50 1.00 tumor-suppressive miRNAs
mmu-let-7d 0.50 0.94
neuron-specific miRNAs
14
m m u-m iR-1937b 0.25 1.00

15. MiRaGE method + miRNA expression
successfully pick up biologically important
miRNAs. Further (wet) experiments which
supress miRNA expression with tiny LNA is
now planed.
If it is successful, our method can find miRNAs
which control tumor formation.
15

16. Significance of reciprocal relationship
between miRNA and its target genes.
〈 log 1 0 P〉
t.test of P-values between
top n miRNAs and others:
P30 → MB
P(mRNA:down|miRNA:up)
P=0.05 P(mRNA:up|miRNA:down)
P(miRNA:down|mRNA:up)
P(miRNA:up|mRNA:down)
16
n

17. Significance of reciprocal relationship
between miRNA and its target genes.
〈 log 1 0 P〉
t.test of P-values between
P=0.05 top n miRNAs and others:
P30 → P6
P(mRNA:down|miRNA:up)
P(mRNA:up|miRNA:down)
P(miRNA:down|mRNA:up)
P(miRNA:up|mRNA:down)
17
n

18. MiRaGE method + miRNA expression satisfy
reciprocal relationship very well. In our
knowledge, this is for the first time to do this for
such a large number of miRNAs
18

19. Discussion:
What causes successful achievement?
Point 1:
Usage of “good” maicroarry
Affymetric ☓
Agilent ○
Point 2:
Negative set = genes not targeted by
considered miRNA but done by other miRNAs
19

20. let-7a transfection (Taguchi & Yasuda, 2010)
As for Points:
Although we do
not know the
reason, off-
target genes
targeted by
other miRNAs
are more
expressive.
off target target
20

21. Conclusion:
MiRaGE (MiRNA Ranking by Gene Expression)
method is very simple, but
1) can successfully pickup biologically important
genes
and
2) can detect reciprocal relationship between
miRNAs and their target genes (mRNA).
21

22. Acknowledgements:
We thank Drs. Tetsuo Noda
and Katsuyuki Yaginuma for
providing reagents.
These works were supported
by KAKENHI (23300357) .
22

23. Related Talk:
Tomorrow
(38)/SIG-BIO 15:30 - 15:45
Gene expression regulation during
differentiation from murine ES cells due to
microRNA
○Masato Yoshizawa,Y-h. Taguchi(Chuo Univ.)
23