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Alternative Splicing and Disease: an overview

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Alternative Splicing and Disease: an overview Shoba Ranganathan Professor and Chair Bioinformatics Dept. of Chemistry and Biomolecular Sciences & Adjunct Professor – PowerPoint PPT presentation

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Title: Alternative Splicing and Disease: an overview


1
Alternative Splicing and Disease an overview
  • Shoba Ranganathan
  • Professor and Chair Bioinformatics
  • Dept. of Chemistry and Biomolecular Sciences
    Adjunct Professor
  • ARC CoE in Bioinformatics Dept. of Biochemistry
  • Macquarie University Yong Loo Lin School of
    Medicine
  • Sydney, Australia National University of
    Singapore, Singapore
  • (shoba.ranganathan_at_mq.edu.au) (shoba_at_bic.nus.edu.s
    g)
  • Visiting scientist _at_
  • Institute for Infocomm Research (I2R), Singapore

2
Outline of the talk
  • Background
  • Determining gene architecture
  • Graph theory in AS
  • Whole genome analysis results
  • AS and disease

3
Unexpectedly low number of genes in the human
genome
Drosophila 14,000 genes
C.elegans 19,000 genes
Human 22,000-25000 genes
  • How can the genome of Drosophila contain fewer
    genes than the undoubtedly simpler organism C.
    elegans?
  • This raises the possibility of expanded diversity
    leading to biological complexity

www.utexas.edu, www.sih.m.u-tokyo.ac.jp
http//pub.tv2.no/multimedia/na/archive/
4
Sources of Biological complexity
  • With a limited number of genes
  • Enhanced regulation of genes and pathways
  • Post-translational modifications
  • Alternative splicing

5
A Genomic View
6
Spliceosomal splicing
7
Maniatis Tasic, 2002
8
Protein Diversity
9
(No Transcript)
10
Alternative splicing
  • Splicing is a regulated process that removes the
    non-coding sequence from transcripts to produce
    mRNA (Bernot, 2004).
  • Contradicts the central dogma of molecular
    biology
  • One gene one protein

11
Why AS?
  • Protein diversity (Neverov et al., 2005).
  • Form of spatial and temporal regulation (Lopez,
    1995)
  • Errors in splicing lead to diseases (Orengo
    Cooper, 2007)
  • Drug discovery (Levanon Sorek, 2003)

12
Usual way of studying AS
  • One gene at a time tedious for
    genomes
  • Collect intron-exon structures for all isoforms
  • Try to analyze them again one isoform at a time
    and then gene by gene.
  • Unsuitable for genes with large numbers of
    transcripts.

13
Usual way of studying AS
14
Why use bioinformatics?
  • Most research into alternative splicing is
    limited to a few genes (reductionist approach)
  • Bioinformatics overcomes this by facilitating a
    systems biology approach
  • Information can be obtained for all genes in a
    genome
  • This can be done for many genomes allowing for
    comparative genomics

15
Where is the splicing?
  • Information on the intron-exon (coding/non-coding)
    arrangement of a gene is essential.
  • Aligning mRNA/EST sequence to their co-ordinate
    genomic sequences will give the arrangement of
    exons in a gene. (MGAlign, Ranganathan et al
    2003 MGAlignIt, Lee et al 2003)

16
Outline of the talk
  • Background
  • Determining gene architecture
  • Graph theory in AS
  • Whole genome analysis results
  • AS and disease

17
MGAlignIt (Lee et al., 2003)
  • Fast heuristic approach and highly accurate
  • Capitalizes on the fact that the mRNA sequence
    constitutes a very small percentage of the
    genomic sequence

15
18
MGAligns biological alignment strategy
19
MGAlignIt web service
http//origin.bic.nus.edu.sg/mgalign
20
Benchmarking
  • Dataset human Chr 22 from the Sanger Centre
    (Collins et al., 2003)
  • 936 annotated mRNA (5176 exons)
  • 48Mbp long human Chr 22 genomic sequence

21
Some successes
  • Short internal exons (exon 2 9 bp exon 9
    21bp)
  • Short terminal exons (exon 1 15 bp)

22
MGAlign performance
  • More savings in computer time with longer gDNA
    sequences
  • Based on 41 randomly chosen genomic fragments

sim4
spidey
mgalign
23
Outline of the talk
  • Background
  • Determining gene architecture
  • Graph theory in AS
  • Whole genome analysis results
  • AS and disease

24
Problem Königsberg bridges (1700s)
  • The residents of Königsberg, Germany, wondered if
    it was possible to take a walking tour of the
    town that crossed each of the seven bridges over
    the Presel river exactly once.
  • Leonhard Euler, 1736 (father of graph theory)

25
Graph theory for AS
  • First used for AS by Heber et al. (2002).
  • Each independent segment represented as a node,
    connected by arrows.
  • Node here is not necessarily based on introns
    and exons simply a common contiguous segment of
    the gene.
  • Human ADSL (adenylosuccinate lyase) gene

26
Our splicing graph approach
  • A biologists viewpoint each exon should be a
    node and each intron, an edge (connection).
  • Automatic generation of AS clusters from gene
    structure.
  • Identifying Reference distinct Exon and its
    associated variants.
  • Simple rules for classifying alternative splicing
    events and visualization system for studying all
    variants from a single gene.
  • Single-line diagram Experimentalist way of
    Alternative splicing analysis

27
Making the splicing graph
28
Usual classification of AS events(Leipzig et
al., 2004)
29
Representing splice variants of the same gene as
a splicing graph
30
Normal representation of transcripts human
hyalouronidase HYAL1 gene ENSG00000114378 (an
early version)
www.ebi.ac.uk/asd
31
Splicing Graph representation of the same gene
Intron retention
Alternative Termination site
Exon skipping
Transcripts are shown as exon numbers 5239
639 1734 1834 124 134.
32
Single-line Splice Diagram
Patterns using the above exon numbers are shown
as 5239 639 1734 1834 124
134.
  • A Digraph or DAG (Directed Acyclic Graph)
  • Graphs for which every unilateral orientation is
    traceable
  • Experimentalists way of Alternative Splicing
    analysis (for a gene of interest with all
    transcripts) for validating splive junctions
  • Intron retention is clearly visible

33
Our extended classification
Automatic rule-based classification
34
Our extended classification
35
Where to make your splicing graphs
36
Outline of the talk
  • Background
  • Determining gene architecture
  • Graph theory in AS
  • Whole genome analysis results
  • AS and disease

37
AS Databases (Of men and mice)
  • Does not provide sufficient information for
    multi-gene comparison to understand the
    phenomenon of AS.

6
38
Genome-wide AS analysisI said the fly
39
Homology
  • Similarity between biological sequences due to
    shared ancestry
  • Orthology
  • Homologous sequences are orthologous if separated
    by a speciation event
  • The divergent copies of a singe gene in the
    resulting species are orthologous genes.
  • At least 25 - 30 similarity at the protein level

13
40
Gene Ontology
  • Provides a controlled vocabulary to describe gene
    and gene product attributes in organisms.
  • Three organizing principles
  • Cellular component
  • A component of a cell, e.g. nucleus
  • Biological process
  • Series of events accomplished by one or more
    ordered assemblies, e.g. signal transduction
  • Molecular function
  • Describes activities, e.g. catalytic activity

14
41
AS genes in Bovine genome
  • Part of bovine annotation project
  • 16560 human genes, 15986 mouse genes, 4567 bovine
    genes
  • Data extracted from ASTD and Ensembl (Hubbard et
    al., 2002)
  • Orthologous genes found using Biomart from
    Ensembl
  • Gene Ontology using Blast2GO (Conesa et al.,
    2005)
  • 2458 (out of 4567) Ensembl AS genes have GO
    annotations
  • 1716 AS genes can be further annotated

16
42
Percentage of AS genes and orthologous spliced
genes in bovine, human and mouse
  • Orthologous genes were analysed in order to
    reduce bias in the data.

17
43
Gene Level AS Analysis of orthologous subset
  • Percentage of bovine genes showing AS events are
    fewer compared to human.

18
44
AS Event Analysis of the orthologous subset
  • of AS events in bovine similar to human
  • implies that more splice variants are obtained
    from fewer bovine genes.

19
45
Gene Ontology analysis
  • Gene Ontology using Blast2GO (Conesa et al.,
    2005)
  • 2458 (out of 4567) AS genes has GO annotations in
    Ensembl
  • 1716 AS genes can be further annotated

46
Outline of the talk
  • Background
  • Determining gene architecture
  • Graph theory in AS
  • Whole genome analysis results
  • AS and disease

47
Implications for disease
  • Diagnostics from early recognition of splice
    variants associated with disease, based on
    nucleotide detection
  • Treatment options using siRNA
  • Aberrant splicing in survival of motor neuron 1
    gene (SMN1) in spinal muscular atrophy (Cartegni
    and Krainer 2002)
  • Suppressing anti-apoptotic AS variant of Bcl-x
    pre-mRNA in prostate and breast cancer cells
    (Mercatante et al. 2001)
  • Correcting CFTR mis-splicing (Friedman et al.
    1999)

48
Many diseases are caused by AS
Myotonic dystropy
49
Why study farm animals?
  • Provide valuable insights into gene function and
    genetic and environmental influences on animal
    production and human diseases. (Roberts et al.,
    2009 )
  • The size and relatively long intervals between
    generations, domestic species are widely used to
    unravel the mechanisms involved in programming
    the development of an embryo and fetus, resulting
    in adult onset of diseases (King et. al., 2007 ,
    Padmanabhan et al., 2007)
  • Mapping human disease genes to bovine orthologous
    genes is an excellent mode for carrying out
    analytical work and verifying the suitability of
    cow as a model organism.

50
Mapping human disease genes to bovine genome
  • 94 human disease genes were extracted from NCBI
    Genes and Disease database to analyse which of
    these genes were alternatively spliced in human
    and bovine genomes.
  • AS analysis was conducted on 66 spliced genes.
  • 17 orthologous spliced genes were observed in
    bovine.

51
Human disease genes Conservation of cassette
exons in bovine orthologous genes
  • Cassette exons occur in 38 of human disease genes
    and 14 orthologous bovine genes.

52
Human disease genes Cassette exons present and
regulated in bovine orthologous genes
  • 3 genes with cassette exons in human were present
    and regulated in bovine.

53
Human disease genes Intron retention present and
constitutive in bovine orthologues
  • Intron retention in nine human genes out of
    which, IR in five genes was present and
    constitutive in bovine

54
Protein domain analysis of the orthologous
disease gene set
  • Carried out Pfam domain search on 8 human disease
    genes to identify the effects of alternative
    splicing on the functional protein domains.
  • Genes responsible for spinal muscular atrophy and
    colon cancer are spliced in bovine and resulted
    in probable structure and function disruption.
  • 4 disease genes (glaucoma, Tangier, spinal
    muscular atrophy and colon cancer ) had all the
    domains from their human counterparts conserved
    in bovine.

55
Conclusion
Our results provide a window of opportunity for
more in-depth analysis over a larger dataset,
where the cow can serve as a model organism for
many more human diseases.
56
Acknowledgements
  • PhD students at the
  • National University of Singapore (Bernett T.K.
    Lee)
  • Macquarie University (Durgaprasad Bollina and
    Elsa Chacko)
  • Colleagues and A/Prof. Tin Wee Tan, NUS
  • All of you

57
Invitation to attend InCoB2009International
Conference in Bioinformatics(incob.apbionet.org)
Singapore, 7-11 Sept. at Matrix,
BiopolisKeynote Nobel Laureate Robert Huber,
58
7 Sept Tutorials and Bioinformatics Education
workshop (WEBCB)8 Sept Clinical Bioinformatics
(CBAS) and SYMBIO (Students) Symposia9-11
Sept Scientific Meeting
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