Published in http://www.mdpi.com/2073-4409/7/6/54 Presentation at GIW2017 31th Oct - 3rd Nov, Seoul,Korea http://www.kobic.kr/giw-bioinfo2017/

1. Tensor decomposition­based unsupervised
feature extraction identified the universal
nature of sequence­non­specific off­target
regulation of mRNA mediated by microRNA
transfection
Y­h. Taguchi
Department of Physics, Chuo University
Tokyo, Japan

2. Bian and Sun, “Noncoding RNAs in Neural Stem Cell
Development” in “ Neural Stem Cells and Therapy”
What is miRNAs?
DICERDICER

3. Many miRNA transfection experiments
Principal Purpose
Identification of mRNAs targeted by transfected
miRNAs
On the other hands, there are many mRNAs
associated with gene expression alteration caused
by miRNA transfection, but not targeted by but not targeted by
transfected miRNA.transfected miRNA. sequence­non­specific →
off­target regulation of mRNA mediated by
microRNA

4. mRNA
Mechanism of sequence­non­specific off­target regulation
Protein
Transfected
miRNAs
Dis­function
miRNAs

5. Method = identification of commonly altered mRNAs
miRNA 1
Off­target
Transfected
miRNA 2
miRNA 4
miRNA 5
miRNA 6
miRNA 3
mRNA
target
commonly altered mRNAs

6. How to identify commonly altered mRNAsHow to identify commonly altered mRNAs
Tensor decomposition (TD) unsupervised feature extraction
(FE)
Q:What is TD?
A: Extension of matrix factorization to tensor.
xijk = ∑l1,l2,l3
G(l1,l2,l3)xl1i xl2j xl3k
i
k j
xijk
Tensor
l2
l1
l3
GCore tensor i
xl1i
j
xl2j
k xl3k
Singular
value matrix

7. miRNA (M)
Control
Treated
N0
---
2
N0
---
2
miRNA non­specific regulation
miRNA specific regulation
Expression
tensor
 R N  M  2
TD
Control
Treated
miRNA
independent
expression
m
RN
A (N
)
1­P
P­values computed with
assuming Gaussian for
mRNA singular value vector
Synthetic dataSynthetic data
Successful identification of
commonly regulated mRNAs

8. What is PCA based unsupervised FE?
N features
Categorical
multiclasses
In contrast to usual usage of PCA, not samples but
features are embedded into Q dimensional space.
PCA
PC1
samplesPC Loadings
M samples
N × M Matrix X (numerical values)
PC2
PC1
PC Score
+
+ +
+ +
++
+
+
+
++ +
+
+
No distinction
between classes

9. Synthetic example
10 samples
10 samples
90 features 10 features
N(0)
N()
[N()+N(0)]/2
+:Top 10 outliers

Thus, extracting outliers
selects features distinct
between two classes in an
unsupervised way.
Accuracy:(100 trials)Accuracy:(100 trials)
89.5% (
52.6% (
PC1
PC2
Normal μ：mean
Distribution ½ :SD

10. Real data (from gene expression omnibus)Real data (from gene expression omnibus)
Transfected miRNAcell lines (cancer)

11. Results for exp 4Results for exp 4
Control
Treated
miRNA
independent
expression

12. Results for exp 1Results for exp 1
Gens singular value vectors
first
second
miRNA non­specific regulation
miRNA specific regulation

13. Genes (~102
) selected in each of eleven experiments are
significantly overlappedP­value
Odds ratio

14. Genes (~102
) selected in each of eleven experiments
share significantly the TF target genes.
Genes selected in each of eleven experiments
TFs whose target genes are enriched in selected genes
TF

15. Genes (~102
) selected in each of eleven experiments
are enriched in common KEGG [(i)~(x)] pathways

16. 10 KEGG pathways 10 KEGG pathways
(i) Ribosome:hsa03010,
(ii) Alzheimer’s disease:hsa05010,
(iii) Parkinson’s disease:hsa05012,
(iv) Oxidative phosphorylation:hsa00190,
(v) Pathogenic Escherichia Coli infection:hsa05130,
(vi) Huntington’s disease:hsa05016,
(vii) Cardiac muscle contraction:hsa04260,
(viii) Non­alcoholic fatty liver disease (NAFLD):hsa04932,
(ix) Protein processing in endoplasmic reticulum:hsa04141,
(x) Proteoglycans in cancer:hsa05205.
Although more biological validations were performed,
they are omitted here because of lack of time.
Anyway, selected genes are obviously biologically similar.

17. Number of experiments →
Number of miRNAs Rank
Although there are no direct evidences that selected genes
are really regulated by sequence­non­specific off­target
effects, there are some indirect evidences.
Genes selected more
frequently among eleven
experiments are targeted
by more miRNAs
Genes targeted by more
miRNAs are more likely
affected by sequence­non­
specific off­target effect

18. Genes selected in eleven experiments are significantly
overlapped with …..
Upper : genes whose expression is reported to be altered
by 16 DICER KO experiments in Enrichr.
Lower: genes that bind to DICER (IP experiments).

19. Conclusions
We have identified mRNAs regulated by sequence­
non­specific off­target effect mediated by microRNA
transfection, over eleven experiments.
They are significantly overlapped with one another
over eleven experiments.
They are enriched in various biological terms and
concepts.
They are overlapped with genes associated with
altered expression by DICER KO and genes that
bind to DICER.