Dr. Jia Meng I would also like to thank the funding support from the National Natural Science Foundation of China (Grant No. 31871315).

1. Speaker: Jiangyuan Liu
Supervisor: Dr Jia Meng
The Analysis of FTO-targeted mRNA
m6A Methylation in Mouse
Transcriptome

2. Outline
• Background information
• Methods
• Results and analysis
• Conclusion
• Acknowledgements

3. Background information
1. Fat mass and obesity-associated (FTO) protein
An enzyme energy utilization and metabolism
FTO gene body mass index and risk for obesity
FTO, a demethylase N6-methyladenosine (m6A) (most
abundant methylation on mRNA)

4. Background information
2. m6A-specific methylated RNA immunoprecipitation with
next generation sequencing (MeRIP-Seq)
Mapping the m6A RNA methylomes with
a ~100-nucleotide resolution
Steps:
① Fragmentation
② Immunoprecipitation
③ PCR amplification and high-
throughput sequencing
④ m6A peak identification by
comparison between IP and
INPUT signals

5. Background information
Why MeRIP-Seq must require input control samples?
Transcriptome-wide RNA methylation
Transcriptional regulation Enzymatic regulation
It is possible that some peaks exist in both IP and input signal, which is attributed to
transcriptional up-regulation.
Comparison between
the immunoprecipitated
sample and the input
sample
Distinguish m6A peaks from those
peaks caused by transcriptional up-
regulation
m6A RNA immunoprecipitation signal
Signal
Signal
Input signal
Locus Locus
m6A peak
Not m6A peak

6. Background information
A gene set (e.g. differential expression)Different conditions
Influence
Predefined bins called Terms
Classified into
Application:
(e.g. FasR—receptor, apoptosis, plasma membrane)
Compose
GO system of classification
Underlying biological
processes
How it works: (hypergeometric test)
All genes in a Term
All genes in GO system
Input genes that belong to a Term
All input genes ÷Fold enrichment=
Output: GO terms, p-values and fold enrichments.
3. Gene Ontology (GO) Term Enrichment Analysis

7. Methods
1
2
3
4
5
mm10
Wild type: WT IP 1 WT INPUT 1
WT IP 2 WT INPUT 2
WT IP 3 WT INPUT 3
FTO K/O IP 1 FTO K/O INPUT 1
FTO K/O IP 2 FTO K/O INPUT 2
FTO K/O IP 3 FTO K/O INPUT 3
FTO
Knockout:
Mouse midbrain
6

8. Results and analysis
1. Differential mRNA methylation between wild type and FTO
knockout cell lines in mouse midbrain (From exomePeak)
The number of consistent significant differential methylation peaks for three
replicates: 1,129 (hypermethylation peaks) on the mRNAs of 912 genes
The number of transcriptome-wide m6A peaks: 37,968 on the mRNAs of
15,731 genes
912 15,731 ≈ 5.8%
FTO-targeted genes / All genes whose mRNA contain m6A sites:

9. Results and analysis
2. Differential expression between wild type and FTO knockout
cell lines in mouse midbrain (from Cuffdiff and CummeRbund)
The values in x- and y-axis: FPKM, a normalization of gene expression level
No statistically significant
changes of gene expression
level
Why?
FTO can demethylate
m3T in ssDNA, but low
activity
Gene expression

10. Results and analysis
3. Functional enrichment analysis of FTO-targeted genes
(from DAVID)
Neuronal function

11. Results and analysis
4. A web application developed by Shiny
(accessed by http://180.208.58.19:3838/sample-apps/m6A_v2/)
m6A peak search:

12. Results and analysis
Term search:
Fold enrichment = (Count / List Total) / (Pop Hits / Pop Total)

13. Conclusion
Results
• Identification of FTO-targeted genes and the positions of m6A sites on their
mRNAs
• FTO might not participate in gene expression.
• FTO is involved in many biological processes (particularly for neuronal function)
Future plans
Two new functions for Term search of the web application:
• Category search (e.g. diseases)
• Upload a gene list

14. Acknowledgements
I appreciate the supervision and guidance from my
supervisor Dr. Jia Meng and his PhD student Zhen Wei.