School of Computer and Information Science
University of South Australia
Thesis Proposal
Discovery of interaction modules between hypoxia regulated mRNA
and miRNA by Splitting Averaging-Bayesian Network Learning
By
Jeya Karthika Pandian Umapathy
Supervisor
Dr. Lin Liu
13th June 2010
INFT 4017
CIS Research Methods
Master of Science
(Computer and Information Science)
1
Abstract
Biologists suspect that the miRNA mediates the hypoxia induced suppression of mRNA
expression post-transcriptionally, in breast cancer affected cells. Manual discovery of interaction
modules between the miRNA and mRNA proves to be impossible owing to the large data set.
Hence, we use a computational method using Splitting Averaging – Bayesian Networks,
proposed by Liu et al., for learning any connections between miRNA and mRNA datasets
derived from breast cancer affected cells. The research helps the biologists in confirming their
assumptions. We also propose to validate this computational method proposed by Liu et al., in
terms of usefulness and effectiveness, in discovering the miRNA-mRNA interactions.
Keywords
miRNA, mRNA, Hypoxia, HIF, gene expression, post-transcriptional, down-regulation, Upregulation, mixed-regulation, Splitting Averaging Bayesian network strategy.
2
Contents
1. Introduction
1.1.
Background
...4
…4
1.1.1. Ribonucleic Acids (RNAs)
…4
1.1.2. Messenger RNA (mRNA)
…5
1.1.3. MicroRNA (miRNA)
…5
1.1.4. miRNA functions
…6
1.2.
Research questions
…6
1.3.
Scope and limitations
…7
2. Literature Review
…8
2.1.
Why to learn miRNA-mRNA interactions?
…8
2.2.
Effect of Hypoxia on mRNA
…8
2.3.
Computational Methods – Why?
…9
2.4.
Related Works
…9
3. Research Methodology
…10
3.1.
SA – BN Strategy
…11
3.2.
Process flow
…12
3.3.
Tools Used
…12
3.4.
Data Sources
…13
4. Expected Outcomes
…13
5. Conclusion
…14
Appendix A – Extended Abstract
…15
Appendix B – Process flow of the SA-BN strategy
…16
Appendix C – miRNA interactions inside the cell
…17
Appendix D – Time Line for the research
…18
Appendix E – Tentative thesis chapters outline
…21
References
…23
3
1. Introduction
The advancement in the computing field marked the beginning of Bioinformatics during
1960's. With the advent of internet, the field was blotted with remarkable findings which would
have otherwise been difficult. The alliance between genomics and bioinformatics aided the quest
to reveal the mechanisms going on inside the minuscule cell of the human body at molecular
level.
1.1.
Background
In an attempt to understand the events at molecular level, it was discovered that
ribosomes were the hub of protein synthesis and that the messages were transmitted from DNA
to the ribosomes through RNA (Paustian & Roberts 2006). This discovery by Sydney Brenner,
Francois Jacob and Matthew Meselson in 1961 accelerated the interests in the field and raised
numerous questions in the minds of biologists (Paustian & Roberts 2006).
1.1.1. Ribonucleic Acids (RNAs)
RNA became the source of attraction to biologists and was investigated frequently. The
RNA's were found to constitute about 60% of the ribosome’s weight and it plays an
indispensable role in catalyzing the protein synthesis (Moore & Steitz 2002).
RNA or the Ribonucleic Acid is a nucleotide polymer, which is 21 to 25 nucleotides in
length (Lagos-Quintana et al. 2001). Each nucleotide has a nitrogenous base, a ribose sugar, and
a phosphate (Barciszewski & Clark 1999). The nitrogenous bases found in RNA are Cytosine,
Guanine, Adenine and, Uracil, which are abbreviated respectively as C, G, A and U.
4
As a part of ribosome, RNA has a biological significance owing to its role in DNARibosome communication and protein synthesis (Clancy 2008). RNA has been classified into
numerous types based on its function as snRNA or Small Nuclear RNA, siRNA or Small
Interfering RNA, snoRNA or Small Nucleolar RNA, miRNA or microRNA, tRNA or transfer
RNA, mRNA or messenger RNA and rRNA or the ribosomal RNA (Clancy 2008).
1.1.2. Messenger RNA (mRNA)
The messenger RNA is a single stranded RNA, which plays the role of the
communicator. The mRNA is formed inside the nucleus of the cell by the transcription of DNA
molecule. After the mRNA matures, it transports the “protein-blue prints” from the DNA to the
ribosomes in the cytoplasm, for the purpose of protein synthesis (Clancy 2008 & Johnston &
Bose 1972). The interactions of the mRNa inside the cell can be understood from the figure in
Appendix C.
1.1.3. MicroRNA (miRNA)
The first miRNA, lin-4 was initially found to exist in C. elegans (Bentwichet al. 2005,
Berezikov et al. 2006, Lee et al. 1993 & Wightman et al. 1993). From then on it was tracked
through numerous species including humans and its functions were elucidated. So far many
hundreds of miRNAs have been found and this figure is speculated to increase tremendously
with further investigation of cells at molecular level (He & Hannon 2004). It has been found that
they are mostly single stranded and non-coding nucleotide polymers (Lagos-Quintana et al.
2001).
5
1.1.4. miRNA functions
The miRNAs play a prominent role in cellular activities ranging from cell differentiation
to development (Ambros 2004, Bushati & Cohen 2007 & Du & Zamore 2007). The
identification of the regulatory targets of the miRNAs aids in determining the miRNA functions
based on the functions of the target (Bartel 2004, Enright et al. 2003 & Lewis et al. 2003).
In the words of Barciszewski & Clark (1999, pp. 10), miRNA performs a ‘feat of
gymnastics’ in the cellular level. Changes in miRNA mediates changes in the neural system (De
Pietri Tonelli et al. 2008), immune system (Schickel 2008), cardiac system (Divakaran et al.
2008), etc.. The most significant being its ability to control the translation of mRNA either by
accelerating or degrading its expression post-transcriptionally (Shyu et al. 2008). It can upregulate, down-regulate and mix-regulate mRNA gene expression.
1.2.
Research questions
The messenger RNA which is affected by Hypoxia (Hypoxia is actually a condition of
human body during which the oxygen supply to the tissues is well below the normal
physiological requirement. This is prevalent in case of an occurrence of certain abnormal
changes in the tissue or cell) has been observed to result in the repression or down-regulation of
mRNA in the MCF7 breast cancer derived cells. The biologists suspect that this may have been
mediated by the changes occurring in miRNA. We propose to test this hypothesis by applying a
computational method proposed by Liu et al., (2009b) and finding whether miRNA has any
interesting interactions with the hypoxia affected mRNA.
6
?
mRNA affected by
Hypoxia
Down-regulation
of mRNA
According to the work of Mole et al. (2009), it is found that Hypoxia Inducible Factor
(HIF) plays an indirect role in the gene repression of hypoxia affected mRNA. Hence this raises
a new suspicion that the gene repression by hypoxia could have been mediated by the miRNA.
So, the construction of the network of miRNA-mRNA interactions will enable the inference of
any interesting connections between them leading to the confirmation of the hypothesis.
mRNA affected by
Hypoxia
1.3.
Mediated by
changes in miRNA
Down-regulation
of mRNA
Scope and limitations
The thesis focuses only on the breast cancer affected cells. It focuses on finding the
interactions between the miRNA and hypoxia regulated mRNA, which may prove that the gene
repression of hypoxia regulated mRNA was mediated by the changes in miRNA. The research
may or may not find new interactions between miRNA and mRNA. However it does not
concentrate on finding any new connections between mRNA and miRNA.
The thesis also focuses on validating the computational method proposed by Liu et al.
(2009b) in terms of the usefulness and effectiveness in this particular application area.
7
2. Literature Review
2.1.
Why to learn miRNA-mRNA interactions?
The changes in miRNA gene regulation are known to cause abnormalities in human body
leading to diseases like cancer (Wienholds & Plasterk 2005). Up-regulation, down-regulation
and mixed-regulations were observed to be caused by miRNA but mostly, down-regulation
dominated in tumors (Lu et al. 2005). The disruption of its rheostatic function (Baek et al. 2008)
causes tumor development and also accelerates the carcinogenic tumor growth to malignant
state. The study of miRNA regulation networks and targets, will henceforth aid us in
understanding the cause for the abnormal physiological conditions and enlighten the otherwise
unknown biological procedures of human body (Liu et al. 2009a).
2.2.
Effect of Hypoxia on mRNA
Hypoxia can be described as a condition in human body during which the whole human
body or merely a part of it is affected by a decrease in the oxygen supply. This condition helps in
the process of proper metabolism in the cellular level and also sometimes the regulation of the
numerous genes in the human body (Mole et al. 2009). The blood flowing through the arteries
delivers the oxygen to the cells by diffusion. During this diffusion, the partial pressure is usually
100mmHg. But, if this pressure falls below 40mmHg, it becomes lethal. When this insufficiency
occurs, lactic acid is formed from the hydrogen for producing little energy by temporary
anaerobic metabolism. The increase in lactic acid inside the cell may cause inadequate blood
flow, hypoxemia, etc.., often leading to death (Roach et al. 2001).
8
Recent research on the MCF7 cells (breast cancer affected cells) reveals that hypoxia
suppresses the mRNAs from expessing their genes. The research by Mole et al. (2009) reveals
that the Hypoxia Inducible Factor (HIF) indirectly suppresses the gene regulation. Hence, it
raises the possibility that miRNAs could have been mediated the down-regulation of mRNA
suppressed by hypoxia.
2.3.
Computational methods - Why?
The biologists obtained the mRNA (Elvidge et al. 2009) and miRNA (Camps et al. 2009)
data from the breast cancer derived cells. On the precinct of verifying the hypothesis, the large
number of possible combinations proves manual analysis and verification as impossible. Hence,
it is difficult for the biologists to test every possible connection between miRNA and mRNA
pairs to verify the hypothesis. So, we utilize computational approaches and methods to identify
any connections between the miRNA and mRNA data.
2.4.
Related Works
The past few years has seen many computational methods for the purpose of validating
the possible hypotheses regarding the miRNA targeting information. In 2005, Yoon et al. came
up with a new computational method based on prediction, depending on the idea that the binding
structure between the miRNA and the mRNA will be normal and it’s the same even if many
binding sites exists on the mRNA. He used weighted bipartite graphs to form the binding
structures between the micro and messenger RNA. But, this resulted in a higher rate of false
discovery since it relied only on the sequence data.
9
Bayesian parameter learning was used by Huang et al. (2006) to learn the interactions
between miRNA and mRNA using both sequence data and expression data. The bi-clustering
approach (Joung et al. 2007) utilizing the expression profile data and sequence information
ventured to find the miRNA regulatory modules (MRMs). A rule-based approach was proposed
by Tran et al., to study miRNA-mRNA interactions assuming that expression profiles of miRNA
and mRNA will be quite similar in a given module. The above mentioned methods have minimal
false discovery rate but, fail to use a sample category which is a critical factor since many of the
biological experiment data measure up to different phenotypic or conditional groups.
The functional miRNA-mRNA regulatory modules (FMRMs) were learnt for the miRNA
and target mRNA special conditions by Liu et al. (2009a). But, this work focused only on downregulation. The next paper by Liu et al., (2009b) used Splitting Averaging - Bayesian Network
strategy to discover the miRNA-mRNA interactions utilizing the target sequencing information,
expression profiles of miRNA and mRNA. This work covers all the down & up regulation and
also provided a method to analyze miRNA-mRNA interactions in various physiological
disorders. This strategy posed minimal false discovery rate by using the sample category
information. Hence, we propose to use this strategy for learning the miRNA-mRNA interactions.
Additionally, due to the fact that the author of the paper works in the same university, we are
able to get a good amount of guidance and information.
3. Research Methodology
Of the various Computational methods, Construction of the network structure has gained
importance in building diagnostic models for diseases like cancer (Sebastiani et al. 2004). And,
10
Bayesian network learning has been found to best serve the purpose. Variations in the normal
Bayesian network structure learning yields better results in our application area.
3.1.
SA-BN Strategy
The methodology employed to discover the regulatory interaction modules between
hypoxia regulated mRNA and miRNA is the Splitting Averaging-Bayesian Network Learning
strategy.
“Bayesian network is actually a probabilistic graphical model which signifies a set of
arbitrary variables and their conditional dependencies using directed acyclic graph”
(Heckermann 1995). It can be used to construct the probabilistic relationship network for
pathological conditions. A Bayesian network consists of n nodes (representing a random
variable, attribute or a hypothesis) connected via edges (representing the existence and direction
of relationship). It can be used to learn unobserved variables in the network, for learning about
the parameters, or to learn about the structure. There are numerous algorithms for learning
Bayesian networks.
In Splitting Averaging – Bayesian network learning, we actually split the dataset based
on the sample category information. This is followed by Bayesian network learning for the split
data. Now, The Bayesian networks learnt from split data are merged together in to a single
network using the averaging strategy. This reduces the false discovery rate.
The process of learning Bayesian networks computationally proves to be impossible,
owing to the large data set and the fact that exponentially increasing number of network
structures is possible. We utilize the miRNA targeting information to limit such possibilities (Liu
11
et al. 2009b). Hence, we utilize the target sequencing information to minimize the false
discovery rate (Liu et al., 2009b).
3.2.
Process Flow
In this method, we use the expression profiles of miRNA and mRNA along with the
target sequencing information to learn a Bayesian network of miRNA-mRNA interactions. The
normalized differentially expressed profile datasets of miRNA and mRNA are split based on
sample category information. We discretize the expression profile data as a measure to
standardize them, since it is obtained from different platforms. We now use the Bayesian
network learning to obtain the interaction dependencies between the discretized data of miRNA
and target mRNA. The two structures learnt from the split data are now merged using the
averaging strategy of Bayesian Networks. This process is illustrated in the process flow diagram
in the Appendix B.
3.3.
Tools Used
The free open source data mining tool “R package” written initially by Robert Gentleman
and Ross Ihaka in 1997 and improved till date, is used for the purpose of pre-processing the
datasets. The R package tool is continuously being used for the statistical purposes and graphs. It
has nearly become the de-facto for data-mining. We use the current version, R 2.11. released on
31st May 2010 for our research.
Further to R, we also use the Bio-conductor package, “BioC” for the purpose of analysis
of genomic data. This is an open source add-on for R package especially to analyse DNA and
12
RNA microarray experiment datasets. We use the current developer version, BioC 2.6 for our
research.
3.4.
Data Sources
We use heterogeneous data in our computational approach. This includes miRNA target
information, expression profiles of miRNA and expression profiles of mRNA.
Numerous databases are available for the miRNA targeting information, for example,
miR-200 can be used for our research (Griffths-Jones, 2008).
We use the differentially expressed profile data of miRNA obtained from Array Express
(http://www.ebi.ac.uk/microarray-as/ae/) using the accession number E-MEXP-1111 (Camps et
al. 2009). The differentially expressed profile data of mRNA is obtained from GEO
(http://www.ncbi.nlm.nih.gov/geo/) using the GEO accession number GSE3188 (Elvidge et al.
2009). These are samples from the breast cancer affected MCF7 cells.
4. Expected Outcomes
We expect to obtain the possible connections between the hypoxia regulated mRNA and
changes in miRNA after the final merging of the Bayesian network. We expect to find some of
the targets for miRNAs from the mRNA repressed by Hypoxia. The results will confirm the
already proved literature and may enlighten us with new possibilities. The use of heterogeneous
data and sample categories is expected to minimize the false discovery rate. We also expect to
validate the usefulness and effectiveness of this computational method proposed by Liu et al.,
(2009 b).
13
5. Conclusion
In this research, we utilize an existing computational approach of Splitting Averaging –
Bayesian Network learning to infer the complex miRNA-mRNA interactions in breast cancer
affected cells. The research helps in validating the hypothesis of the effect of hypoxia on the
breast cancer derived cells as well as in determining the usefulness of the SA-BN strategy in
inferring the miRNA-mRNA interactions.
14
Appendix A
Extended Abstract
The Ribonucleic Acid (RNA), which is a nucleotide polymer, plays a prominent role in
protein synthesis and the regulation of gene expression. Of the various types of RNAs, the
microRNA (miRNA) controls messenger RNA (mRNA) translation by accelerating or degrading
its expression. This has been found to be the cause for various abnormalities in human bodies.
Current research reveals that miRNAs are responsible for gene regulations by posttranscriptional control of their mRNAs and, it may result in the development of tumor or even
inducing the carcinogenic tumor growth to malignant state. Hence it proves vital to know the
miRNA-mRNA interactions to aid the prevention and treatment of various physiological
conditions.
Recent research on the breast cancer affected cells reveals that the expression of mRNAs
is suppressed by hypoxia. Biologists have discovered that HIF (Hypoxia Inducible Factor)
indirectly regulates the gene expression. This has led them to assume that miRNAs could be the
indirect reason for suppression of mRNA expression by hypoxia. However, given the large
number of mRNAs and miRNAs, it is impossible for biologists to test each and every miRNAmRNA pair, to verify their assumption.
In this research, we apply a computational method proposed by Liu et al., on the miRNA
and mRNA expression data sets obtained from breast cancer derived cells, to discover possible
connections between the miRNAs and mRNAs and, help biologists to verify their assumption on
the role of miRNAs mediating the suppression of mRNA expressions by hypoxia. At the same
time, we want to validate the usefulness and effectiveness of the method proposed by Liu et al.,
in discovering meaningful miRNA-mRNA interactions in this application area.
15
Appendix B
Process flow of the SA-BN Strategy
(Source: Liu, B, Li, J, Tsykin, A, Liu, L, Gaur, A & Goodall, G 2009b, 'Exploring complex
miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy', BMC
Bioinformatics, vol. 10, no. 1, pp. 408)
16
Appendix C
miRNA interactions inside the cell
(Source: National Institutes of Health, “Talking Glossary of Genetic Terms”, National Human
Genome
Research
Institute,
Viewed
on
June
12
2010,
<http://www.genome.gov/glossary/?id=123>)
17
Appendix D
Time Line for Study Period 2, 2010
SP 2, 2010
Tasks
Week 1
Supervisor Search Finalization.
Meeting the Supervisor.
Week 2
Decide on the Research Area.
Go through the base papers.
Week 3
Start Background Study on Bio-informatics.
Learn about Bayesian Networks.
Week 4
Decide on meeting schedule and working space.
Complete the Induction program.
Continue background study.
Week 5
Deeper discussion about the research area.
Determine the exact objectives and requirements of the
research.
Continue Background study.
Week 6
Start working on the thesis abstract.
Submit a document to test the understanding about the
research.
Discussion of the document and base paper.
Teaching Break
Start working on literature review.
Learn how to use R package.
Teaching Break
Submit an annotated list of bibliography to be used in the
literature review.
18
Discussion of the annotated bibliography.
Completion and review of the thesis abstract.
Week 7
Submission of thesis abstract.
Start working on thesis proposal.
Week 8
Work on the literature review.
Meeting with Bing Liu to understand the concepts and
methodology to be used.
Week 9
Complete the literature review.
Start on the methodology and remaining parts of proposal.
Week 10
First draft submission to supervisor.
Prepare Extended Abstract and send to supervisor.
Week 11
Feedback discussion for the first draft and extended
abstract.
Submit Extended Abstract
Week 12
Second draft submission to supervisor and feedback.
Third draft submission to Supervisor and feedback.
Presentation slides preparation and feedback.
Week 13
Presentation and Thesis Proposal.
19
Time Line for Study Period 5, 2010
SP 5, 2010
Tasks
Week 1
Start Data Preparation.
Week 2
Complete data preparation.
Week 3
Input the data to the Software for learning the miRNA and
mRNA connections and obtain results.
Week 4
Analyze the results.
Work on the thesis Literature review.
Week 5
Work on the Thesis Methodology.
Week 6
Finalize the results of analysis.
Complete the Literature review and Methodology.
Week 7
Validate the methodology used.
Determine its usefulness and efficiency.
Teaching Break
Work on the results for the thesis.
Teaching Break
Work on recommendations and conclusion of the thesis.
Week 8
First draft submission to supervisor.
Week 9
First draft feedback and discussion.
Prepare abstract for the thesis.
Week 10
Second draft submission to supervisor.
Week 11
Second draft feedback and discussion.
Prepare presentation slides.
Week 12
Final draft of thesis and discussion.
Presentation slides feedback and discussion
Week 13
Presentation and Final Thesis submission.
20
Appendix E
Tentative Thesis Chapters Outline
6. Introduction
6.1.
Background
6.2.
Motivation
6.3.
Research Purpose
6.4.
Research Objectives
6.5.
Scope and Limitations
6.6.
Thesis Organization
7. Literature Review
7.1.
Background
7.2.
miRNA and mRNA Interaction
7.3.
Effect of Hypoxia
7.4.
Methods to discover miRNA-mRNA interaction modules
7.5.
Computational discovery of miRNA-mRNA interaction modules
7.6.
Various Computational methods
7.7.
Use of Bayesian Networks in Bio-informatics
7.8.
Splitting Averaging – Bayesian Network Learning
7.9.
Related Works
8. Research Methodology
8.1.
Data sources
8.2.
Data mining tools
8.3.
Data Pre-processing
8.3.1. Splitting the data
8.3.2. Results and observations
8.4.
Learning the interaction Bayesian network
21
8.4.1. Learning network from the Split dataset
8.4.2. Combining the network by Averaging strategy
9. Results and Proceedings
9.1.
Identifying the connections between miRNA and mRNA
9.2.
Inference from the SA-BN learning
9.3.
Observations
9.4.
Validation of SA-BN Strategy
9.4.1. Usefulness
9.4.2. Effectiveness
9.4.3. Results and observations
10. Discussion
11. Recommendations
12. Conclusion
13. References
22
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