genome biology and biotechnology

Genome Biology and Genome Biology and BiotechnologyBiotechnology

8. The transcriptome 8. The transcriptome

Prof. M. ZabeauProf. M. ZabeauDepartment of Plant Systems Biology Department of Plant Systems Biology

Flanders Interuniversity Institute for Biotechnology (VIB)Flanders Interuniversity Institute for Biotechnology (VIB)University of GentUniversity of Gent

International course 2005International course 2005

Functional Functional MapsMaps

or “-omes”or “-omes”

proteins

ORFeome

Localizome

Phenome

Transcriptome

Interactome

Proteome

Genes or proteins

Genes

Mutational phenotypes

Expression profiles

Protein interactions

1 2 3 4 5 n

DNA Interactome Protein-DNA interactions

“Conditions”

After: Vidal M., Cell, 104, 333 (2001)

Cellular, tissue location

SummarySummary

¤ Transcriptome mapping– Identification of transcribed regions in the genome

• Experimental confirmation of predicted gene models• Discovery of non-coding RNA genes

– The “evolving” transcriptome map shows that• The genome contains many more “genes” than simply genes

coding for proteins

¤ Transcriptome profiling– Functional characterization of genes based on expression

patterns• Cluster analysis of expression patterns• Identification of co-regulated gene clusters• Classification of tumors

Transcriptome mapping platformsTranscriptome mapping platforms

¤ Large scale EST sequencing– Primarily used to identify protein coding genes– Noisy data sets that have been difficult to interpret

¤ Large scale full-length cDNA sequencing– Technically very difficult and laborious– Limited to a few model organisms: mouse and human

¤ Microarray technologies– Become increasingly powerful as the density of the

microarrays has increased tremendously– Providing the most detailed view of the transcribed regions

in the genome

EST Sequencing EST Sequencing

¤ 3’ or 5’ ESTs sequences of individual cDNA clones– cDNAs are often truncated at the 5’ end (not full length)– Typically done on 5.000 to 10.000 clones per library

• Identifies the 1000 to 2000 most abundantly expressed genes

¤ Identifying ~70% of the protein coding genes requires– Sequencing several 10s or even 100s of libraries– Typically EST data bases contain >200.000 to 500.000 ESTs

¤ EST sequence assemblies yield unigene collections– Clusters of overlapping sequence reads from the same gene

5’EST

3’EST

poly A

Cloned cDNAvector vector

Full length cDNA SequencingFull length cDNA Sequencing

¤ Technically very challenging– Special techniques for selecting full length cDNA clones

• 5’ end (Capped end) selection• Aggressive subtraction/normalization required to cover “all” genes

¤ Mouse and human “FANTOM” full length cDNA libraries– Large scale sequencing of >> million 5' end and 3'-end sequences – Complete sequencing of >100.000 full length cDNA clones

¤ Full length cDNAs define transcriptional units (TU)– segments of the genome from which transcripts are generated– TUs are DNA strand-specific, and are typically bounded by

promoters at one end and termination sequences at the other

Reprinted from: The FANTOM consortium, Nature 420, 563 - 573 (2002)

Transcriptional UnitsTranscriptional Units

¤ Transcriptional units (TUs) comprise – Protein coding transcripts (genes) and non-coding transcripts

(genes?)

– Alternatively spliced transcripts– Transcripts with alternative 5' start– Transcripts with alternative 3' ends

¤ Frequently transcripts are made from both strands– Sense and antisense transcripts

• are considered to be made from separate TUs

¤ The transcriptome is much more complex than we have always thought!

The complexity of the transcriptomeThe complexity of the transcriptome

Sense transcriptsProtein coding transcripts

Anti-sense transcriptsNon-protein coding transcripts

Reprinted from: The FANTOM consortium, Nature 420, 563 - 573 (2002)

Mouse transcriptomeMouse transcriptome

¤ The FANTOM 2 transcriptome – 60,770 completely sequenced clones– comprises ~37.000 TUs– ~60% coding transcripts (~20.500 genes) – ~40% non coding transcripts (~16.500 new genes)

• 29% are spliced• Typical polyadenylation sites: RNA Pol II-mediated transcription• Many are antisense transcripts to coding transcripts

¤ Estimate of the complete mouse transcriptome– 70.000 transcriptional units

• 40.000 coding transcriptional units (>23.000 protein coding genes?)

• 30.000 non-coding transcriptional units

Experimental annotation of the human Experimental annotation of the human genome using microarray technologygenome using microarray technology

¤ Microarrays with 2 probes for each predicted exon¤ Hybridized with a total of 69 cDNA samples

– Gene validation based on correlated exon expression

Reprinted from: Shoemaker et. al., Nature 409, 922 (2001)

Analysis of Chromosome 22 genesAnalysis of Chromosome 22 genes

Reprinted from: Shoemaker et. al., Nature 409, 922 (2001)

correct

correct Ab initioMerged genesIncorrect exon

The transcriptional activity of human The transcriptional activity of human Chromosome 22 Chromosome 22

¤ Paper describes– Global transcriptional activity in placental RNA using

• DNA microarrays of 19,525 PCR fragments (300 bp to 1.4 kb) representing nearly all of the unique (nonrepetitive) sequences of human Chromosome 22

Rinn et al., Genes & Dev. 17: 529-540 (2003)

Array design2.000 bp1.0000

probes

Average exon

Reprinted from: Rinn et al., Genes & Dev. 17: 529-540 (2003)

The human Chr The human Chr 22 22

placental placental transcriptometranscriptome

PCR probes

Annotated genes

Transcription

Annotatedgene

Novelgene

Reprinted from: Rinn et al., Genes & Dev. 17: 529-540 (2003)

The human Chr 22 placental transcriptomeThe human Chr 22 placental transcriptome

¤ Twice as many sequences are transcribed than previously reported– Equal number of transcribed sequences in unannotated

regions as in annotated regions

¤ Transcripts from unannotated regions comprise– transcripts internal to annotated introns – transcripts that are antisense to annotated genes– a large portion of the novel transcripts is evolutionarily

conserved in the mouse

Novel RNAs Identified From an In-Depth Analysis Novel RNAs Identified From an In-Depth Analysis of the Transcriptome of Human Chromosomes 21 of the Transcriptome of Human Chromosomes 21

and 22 and 22

¤ Paper describes– Transcriptome analysis of nonrepetitive regions of

chromosomes 21 and 22 in 11 different cell lines using• High density oligonucleotide arrays with a 35 bp resolution

– uniformly spaced 25-mers oligonucleotide probes

Kampa et. al., Genome Res. 13: 331-342 (2003)

Array design1.000 bp5000

probes

Average exon

Reprinted from: Kampa et. al., Genome Res. 13: 331-342 (2003)

Transcription maps based on adjacent Transcription maps based on adjacent probesprobes intensitiesintensities

¤ Transfrags– adjacent probes detecting transcripts

¤ Well-annotated genes– 80% to 90% of the known genes show alternative splicing

Reprinted from: Kampa et. al., Genome Res. 13: 331-342 (2003)

Transcriptome maps Transcriptome maps of Chr 21 and 22of Chr 21 and 22

¤ 50% of the transcription falls outside known genes– 75% contain no ORFs and are thus non-coding– ~10% is antisense to known genes

¤ Transcriptome is greater than previously estimated– the total number of transcripts is much larger than the present

estimates of 25,000 genes

Global Identification of Human Global Identification of Human Transcribed Sequences with Genome Transcribed Sequences with Genome

Tiling Arrays Tiling Arrays

¤ Paper presents– Transcriptome analysis of the nonrepetitive regions of the human

genome in human liver tissue RNA using• High density oligonucleotide arrays with a 46 bp resolution

– uniformly spaced 36-mer oligonucleotide probes• A total of 51,874,388 36-mer probes

– representing 1.5 Gb of nonrepetitive human genomic DNA

Bertone et. al., Science 306, 2242-2246 (2004)


probes

Average exon

senseanti-sense

Annotated genes aligned with microarray Annotated genes aligned with microarray fluorescence intensities fluorescence intensities

Reprinted from: Bertone et. al., Science 306, 2242-2246 (2004)

probes

Exon/intron

probes

Exon/intron

Identification of Novel Transcription Identification of Novel Transcription Units Units

¤ Novel transcription units – Transcribed regions outside of previously annotated exons

¤ Identified 8958 novel transcription units – Over half were distal to annotated genes – Many transcription units are homologous to mouse genome

sequences


Transcriptional Maps of 10 Human Transcriptional Maps of 10 Human Chromosomes at 5-Nucleotide ResolutionChromosomes at 5-Nucleotide Resolution

¤ Paper presents– Transcriptome analysis of the nonrepetitive regions of the 10 human

chromosomes (30% of the genome) in 8 cell lines RNA using• Ultra high density oligonucleotide arrays with a 5 bp resolution

– Tiling array of 25-mer oligonucleotide probes with a 20 bp overlap

Cheng et. al., Science. 308: 1149-1154 (2005)


probes

Average exon

Reprinted from: Cheng et. al., Science. 308: 1149-1154 (2005)

Correlation of poly A+ transcripts to Correlation of poly A+ transcripts to annotationsannotations

¤ Larger amount of transcripts – 57% novel transcripts in

unannotated regions• Intergenic and intronic

¤ Novel transcripts frequently– overlap with other transcripts– spliced


Poly A+ and poly A– transcription in the nucleus Poly A+ and poly A– transcription in the nucleus and cytosoland cytosol

¤ Analysis of poly A+ and poly A– transcripts– poly A– transcripts are twice as abundant as poly A+– A large proportion of the transcripts is found exclusively in

the nucleus or the cytoplasm

Poly A- Poly A+

nucleus

cytoplasm


ConclusionsConclusions

¤ Transcriptome mapping experiments show that – a larger percentage of the genome is transcribed than can

be accounted for by the current state of genome annotations

– The human transcriptome is composed of • a network of overlapping transcripts (> 50% of the transcripts)• Poly A– RNAs potentially comprise almost half of the human

transcriptome

¤ Our understanding of the human transcriptome is still evolving…– What are the functions of the non-coding transcripts?

Reprinted from: Mattick, Science. 309: 1527-1528 (2005)

The complexity of the transcriptomeThe complexity of the transcriptome

A Gene Expression Map for the A Gene Expression Map for the Euchromatic Genome of Euchromatic Genome of Drosophila Drosophila

melanogastermelanogaster

¤ Paper presents– Transcriptome map of the Drosophila genome

• using microarrays with 179,972 unique 36-nucleotide probes– 61,371 exon probes for the 13,197 predicted genes– 30,787 splice junction probes– 87,814 nonexon probes from intronic and intergenic

regions• Using RNA from six developmental stages during the

Drosophila life cycle

Stolc et. al., Science, 306, 655-660 (2004)

Genomic expression patterns Genomic expression patterns

¤ 93% of all annotated gene were significantly expressed– confirmed 2426 annotated

genes not yet validated through an EST sequence

¤ The majority of the genes are developmentally regulated

Reprinted from: Stolc et. al., Science, 306, 655-660 (2004)

Transcriptome map of Drosophila Transcriptome map of Drosophila

¤ 41% of intergenic and intronic probes are expressed – One fraction does not correspond to exons and may

represent putative noncoding transcription units– 15% of the intergenic and intronic probes are

developmentally regulated

¤ Alternative splicing– 53% of expressed Drosophila genes exhibit exon skipping– 46% of genes showed multiple patterns of exon expression

suggesting alternative splicing or alternative promoter usage

¤ Alternative splicing in Drosophila– Much higher than previously estimated


Transcriptome or Gene Expression Transcriptome or Gene Expression ProfilesProfiles

¤ The transcriptome is dynamic– Changes rapidly and dramatically in response to perturbations,

environmental stimuli or during normal cellular events– Changes in the patterns of gene expression provide clues

about • cellular functions • biochemical pathways• regulatory mechanisms

¤ Transcriptome or gene expression profiling aims to– Monitor the expression levels of “all” genes– Correlate expression profiles with biological activity

• Identifying genetic networks and pathways• Identifying the function of unknown genes• Diagnose physiological (disease) states

Reprinted from: Lockhart and Winzeler, Nature 405, 827 (2000)

Eukaryotic TranscriptomeEukaryotic Transcriptome

Abundance Copies Number of Number of

class per cell genes transcripts

abundant > 1,000 4 50.000

intermediate 100 - 1,000 500 100.000

scarce 1 - 100 11.000 150.000

Total 11.500 300.000

Reprinted from: “The Cell ”

Transcriptome Profiling Transcriptome Profiling PlatformsPlatforms

¤ DNA sequencing based methods– DNA sequencing of individual cDNA clones to count the number of

times a cDNA clone is present in a cDNA library– Limited resolution but measures absolute RNA levels

¤ DNA fragment analysis based methods– PCR-based amplification of DNA fragments derived from mRNA or

cDNA whereby• Each DNA fragment represents a different mRNA

– Currently primarily used for not (yet) sequenced species

¤ Array-based hybridization methods– Hybridization to microarrays with gene-specific DNA probes– Has become the most performant and most widely used platform

• High resolution exon microarrays allow quantitative analysis of alternatively spliced transcripts

Cluster Analysis and Display of Genome-Cluster Analysis and Display of Genome-wide Expression Patterns wide Expression Patterns

¤ Paper presents– Method for analyzing and representing genome-wide expression

data• Cluster analysis of data using standard statistical algorithms to

arrange genes according to similarity in pattern of gene expression

• The output is displayed graphically, conveying the clustering and the expression data simultaneously in a form intuitive for biologists

Eisen et. Al., PNAS 95, 14863 (1998)

Cluster Analysis of Expression Cluster Analysis of Expression PatternsPatterns

¤ A logical basis for organizing gene expression data is to group genes with similar patterns of expression – using a mathematical description of similarity that captures

• similarity in "shape" of expression profiles

¤ Since there is no a priori knowledge of gene expression patterns, unsupervised methods are favored– Pair wise average-linkage cluster analysis - a form of

hierarchical clustering - similar to that used in sequence and phylogenetic analysis

– Yields a similarity tree: branch lengths reflect the degree

of similarity between the objects

Reprinted from: Eisen et. Al., PNAS 95, 14863 (1998)

Example: Similarity Tree of CDK GenesExample: Similarity Tree of CDK Genes0.1

Ms_CDKC_1_CAA65979.1

CAK1AT_BAA28775.1

Le_CDKb2_1_CAC15504.1

Le_CDKB1_1_CAC15503.1

At_CDKA_2_AAA32831.1

Ms_cdc2F_CAA65982.1

put4CAK_AT1_4_3436-5676_prot

At_CDKB1_1_BAA01624.1

Ms_CDKB1_1_MsD

CDC2b-like_VERO

CDC2FbAt_VERO

CDC2FaAt_VERO

Ms_CDKA_2_CAA50038.1

Ms_CDKA_1_AAB41817.1

Ms_CDKE_1_CAA65981.1

put35prot_AT5_5_4281-5693_prot

putCDKC2_T42526

At_CDKC_2

At_CDKC_1

put10Cprot.tfa

Os_CDKD_1_CAKR2_CAA4117

put5CAK_OK

GraphicalGraphical RepresentationRepresentation

¤ Combines clustering with a graphical

representation of the primary data – By representing each data point with a color that is a

quantitative reflection of the experimental observations• Green: down regulated• Red: up regulated

¤ Images show contiguous patches of color – Representing groups of genes that share similar expression

patterns over multiple conditions

¤ Analysis of clustered genes shows that– The clustered genes share common functions in cellular

processes



Cluster 1

Cluster 2

Different experimental observations

Differentgenes

GraphicalGraphical RepresentationRepresentation


Cluster Analysis Cluster Analysis of Combined of Combined

Yeast Data SetsYeast Data Sets

•Synchronized cell division•Sporulation•Heath shock•Reducing agents•Low temperature

Genes of Similar Function Cluster Genes of Similar Function Cluster TogetherTogether


Histones

Ribosomal proteins

Global Analysis of the Genetic Network Global Analysis of the Genetic Network Controlling a Bacterial Cell Cycle Controlling a Bacterial Cell Cycle

¤ Paper presents – full-genome evidence that bacterial cells use discrete

transcription patterns to control cell division• Demonstrating that genes involved in a given cell function are

activated at the time of execution of that function

Laub et. Al., Science, 290, 5499 (2000)

Cell division in the bacterium Cell division in the bacterium Caulobacter Caulobacter crescentuscrescentus

¤ A complex genetic network controls cell division – DNA replication and the ordered biogenesis of cell structures

Reprinted from: Laub et. Al., Science, 290, 5499 (2000)

Microarray Analysis of the Control of cell Microarray Analysis of the Control of cell divisiondivision

¤ Experimental set up– Constructed DNA microarrays containing 2966 predicted

ORFs– Isolated swarmer cells which were allowed to proceed

synchronously through the 150-min cell cycle• RNA was harvested from samples taken at 15-min intervals

– identified RNAs which varied in function of the cell cycle• Using an algorithm to identify expression profiles that varied in

a cyclical manner – identified 553 cell cycle-regulated transcripts including the

72 genes with previously characterized cell cycle-regulated

promoters


Clustered Expression Clustered Expression Profiles for the 553 Cell Profiles for the 553 Cell

Cycle-regulated Cycle-regulated TranscriptsTranscripts


¤ Temporally regulated genes are – maximally expressed at specific

times throughout the entire cell cycle

– Genes were induced immediately before or coincident with each cell cycle-regulated event

Profiles Profiles Profiles of Genes Associated With DNA Profiles of Genes Associated With DNA Replication and Cell Division Replication and Cell Division


Expression Profiles of Genes Involved in Flagellar Expression Profiles of Genes Involved in Flagellar BiogenesisBiogenesis

¤ Genes for flagellar biogenesis are – organized in a 4-level

transcriptional hierarchy– The expression of each class of

genes is required for expression of all subsequent classes

– Pili and flagellar biogenesis are apparently organized as a temporal transcriptional

cascades



¤ The global analysis of bacterial cell cycle regulation – has established the outline of the complex genetic circuitry

that controls bacterial cell cycle progression – identified 553 genes whose mRNA levels varied as a

function of the cell cycle, demonstrating that• (i) genes involved in a given cell function are activated at the

time of execution of that function• (ii) genes encoding proteins that function in complexes are

coexpressed• (iii) temporal cascades of gene expression control in

multiprotein structure biogenesis


Gene expression profiling predicts clinical Gene expression profiling predicts clinical outcome of breast cancer outcome of breast cancer

¤ Paper presents– The application of gene expression profiling to diagnose

breast cancer patients• that are likely to develop metastases and should receive

chemotherapy

– Exemplifies the clinical applications of microarray technology

Van 'T Veer et. al., Nature 415, 530 (2002)

Experimental designExperimental design

¤ Microarray hybridizations– Oligonucleotide microarrays for 25.000 human genes– Selected 98 primary breast cancers from

• 44 patients with good prognosis (disease-free for >5 years)• 34 patients with poor prognosis (developed metastases within 5

years)– 20 patients with BRCA1 and BRCA2 mutations

– Hybridized RNA isolated from frozen tumor material

¤ Data analysis– Two-dimensional unsupervised hierarchical clustering of

• The 98 tumor samples• the 5000 genes that were significantly regulated

Reprinted from: Van 'T Veer et. al., Nature 415, 530 (2002)


Cluster Analysis of 98 Breast TumoursCluster Analysis of 98 Breast Tumours

Good prognosis

Poor prognosis


Prognostic expression markersPrognostic expression markers

¤ Identification of predictive genes– 3-step supervised classification method selected

1. From 5000 significantly regulated genes 231 genes were selected as significantly associated with the disease outcome

2. The 231 genes were rank ordered on the correlation3. an optimal set was selected iteratively that showed the

strongest power to classify the tumors

¤ Selected 70 genes that – correctly predict 85% of the patients– Can be used to diagnose patients for chemotherapy


Expression profiles of the 70 predictive Expression profiles of the 70 predictive genesgenes

sensitivityaccuracy


¤ Microarray-based expression profiling is – Currently the most powerful tool for functional gene

analysis– Comprehensive approach to investigate the response of

genes • under a broad spectrum of conditions such as

– Genetic backgrounds– Perturbations– Environmental stimuli

¤ Continued increases in probe density– Provide more detailed analyses of the different transcripts

• Alternative promoter usage• Alternative splicing• Non-coding transcripts

Recommended readingRecommended reading

¤ Transcriptome maps– Human transcription map

• Cheng et. al., Science. 308: 1149-1154 (2005)

¤ Expression profiling– Clustering methods

• Eisen et. Al., PNAS 95, 14863 (1998)

– Gene expression profiling and breast cancer prediction• Van 'T Veer et. al., Nature 415, 530 (2002)

– Global analysis of the bacterial cell cycle• Laub et. Al., Science, 290, 5499 (2000)

Further reading Further reading ¤ Transcriptome maps

– Human transcriptome map• Shoemaker et. al., Nature 409, 922 (2001)• Rinn et al., Genes & Dev. 17: 529-540 (2003)• Kampa et. al., Genome Res. 13: 331-342 (2003)• Bertone et. al., Science 306, 2242-2246 (2004)• Ota et. al., Nature Genet. 36: 40 - 45 (2004)

– Mouse transcriptome• The FANTOM consortium, Nature 420, 563 - 573 (2002)

– Drosophila transcription map• Stolc et. al., Science, 306, 655-660 (2004)

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