1. Background: Epigenetic changes like non-
coding RNA-associated gene silencing, histone
modification, and DNA and RNA methylation can
alter patterns of gene expression and ultimately
become a causal force in the development and
progression of human disease. N6-methyl-
Adenosine (m6A) methylation is an epigenetic
modification that occurs both in DNA and RNA
and has recently been discovered to play
important roles in RNA metabolism, mRNA
translation, post-transcriptional protein
expression, miRNA expression, and other
regulatory processes. Specifically, the m6A
methylation sites in genes’ 3’UTRs are speculated
to be involved in functional variability.
M6A mRNA methylation is essentially catalyzed by
the multicomponent METTL3-METTL14 complex.
Previous studies reveal that METTL3 knockdown
influences total m6A levels, and some METTL14
knockdown experiments have been performed in
HeLa and 293FT cells, but their role in breast
cancer development, especially the role of
METTL14, is largely unknown. To examine the
effect, we first performed a METTL14
knockdown on a triple negative breast cancer
cell line and performed differential analysis to
elucidate the effect of this knockdown on gene
expression and m6A levels. A corresponding
genome-wide small RNA expression is planned
for the same study.
Methods: Gene wide gene expression profiling
was completed of the METTL14 knockdown on
cell-line MDA-MB-231 using RNA-seq protocol.
Short sequence reads were aligned to human
genome hg19 build with TopHat aligner, followed
by HTSeq for gene expression quantification and
DESeq for normalization and differential gene
expression analysis. We identified 15 differentially
expressed genes with p-value < 0.01, fold-change
> 2, and minimal expression level > 1 (RPKM
unit). MiRNA expression analysis will be
evaluated using the FLICKER pipeline, followed
by DESeq algorithm.
Results: As expected, METTL14 had been
identified among 9 down-regulated genes, along
with 6 up-regulated genes. In addition,
hierarchical clustering algorithm (heat map)
revealed a distinct pattern of gene expression. We
also examined miRNAs that potentially targeted
these differentially expressed genes, and a list of
miRNAs was obtained.
Conclusion: To obtain the most comprehensive
characterization of the METTL14 knockdown's
impact, additional experiments will be combined
to examine the effect of miRNA expression, with
or without m6A methylation, in relation to miRNA.
Our study is the first step to reveal the complex
relationship between m6A methylation and gene,
miRNA, and post-transcription protein expression.
Abstract
A Characterization of the Effect of Methyltransferase Knockdown on Gene and
microRNA Expression
Janaya Lee Shelly3, Subbarayalu Panneerdoss1,3, Santosh Timilsina1, Harry Chen1, Yufei Huang2, Manjeet K Rao1,3, Yidong Chen4
1Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
2Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
3Department of Cellular and Structural Biology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
4Department of Epidemiology & Biostatistics, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
Results
Acknowledgements
Supported by a 2015 Summer
Undergraduate Research Fellowship to
Janaya Shelly from the Cancer Prevention
& Research Institute of Texas (CPRIT)
Training Grant (RP140105)
Janaya Shelly would also like to
acknowledge Dr. Yidong Chen for his
superior mentorship during the completion
of her project..
References
 Fu, Ye, et al. "Gene expression regulation
mediated through reversible m6A RNA
methylation." Nature Review Genetics 15:
293-306. Print.
 Liu, Jianzhao, et al. "A METTL3-METTl14
complex mediates mammalian nuclear
RNA N6-adenosine methylation." Nature
Chemical Biology 10 (2014): 93-95. Print.
Future Directions
 Use FLICKER and DESeq to perform
miRNA expression analysis
 Obtain and expand list of miRNAs that
target differentially expressed genes
 Study further the relationship between
miRNA expression, m6A methylation
status, and miRNA location in the 3’UTR.
Methods
RNA-seq TopHat HTSeq DESeq
Figure 1: Methods Flowchart | RNA-seq protocol was performed, followed by TopHat read alignment, HTSeq read/gene counting, and DESeq differential expression analysis
Figure 2: Excel Table | shows top 15 differentially
expressed (p-value < 0.01, 2FC > 2) genes
Figure 3: Volcano Plot | shows differentially expressed
genes, where red is overexpression and green is
underexpression
Figure 4: Scatter Plot | shows variation and expression
levels of genes in the sample
Figure 5: Heat Map | shows distinct pattern of gene
expression