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Mario Cáceres

TRANSCRIPTOMICS(or the analysis of the transcriptome)

• Identify and annotate the complete set of genes encoded within a genome

• Determine the entire DNA sequence of an organism

• Characterize the gene-expression profiles in different tissues and cell types on a genome-wide scale

• Ascertain the function of each and every gene product

• Understand the genetic basis of the phenotypic differences between individuals and species

Main objectives of genomics

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Central dogma of molecular biology

Central dogma of molecular biology

Genome

Proteome Transcriptome

Complete DNA content of an organism with all its genesand regulatory sequences

Complete set of transcripts andrelative levels of expression ina particular cell or tissue under

defined conditions at a given time

Complete collection of proteinsand their relative levels

in each cell

Transcription

Translation

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RNA profiling provides clues to:

- Functional differences between tissues and cell types

- Function and interaction between genes

- Gene regulation and regulatory sequences

- Identification of candidate genes for any givenprocess or disease

- Expressed sequences and genes of a genome

Why study of RNA is so important?

Beyond the genome

Gene Number ≠ Complexity

Co

mp

lexity

Gene regulation

Gene

Transcriptome

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1. Methods for transcriptome analysis

2. Genome transcription landscape

3. Types of RNAs and functions

4. Analysis of gene structure

5. Transcriptome profiling (tissues, individuals)

6. Gene expression and evolution

Overview

Methods for transcriptome analysis

Gene expression arrays- Quantification of transcript abundance- Single/multiple 3’ probes

Genome tiling arrays- Identification of transcribed sequences- Multiple probes along the genome

Alternative splicing arrays- Quantification of different RNA isoforms- Probes in exons and exon-exon junctions

End of CDS or 3’ UTR(100-500 bp)

Inclusion isoform

Exclusion isoform

+

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Methods for transcriptome analysis

• RNA-tag sequencing- Quantification of transcript abundance- Single end reads of each RNA species

• Whole RNA sequencing- Identification of transcribed sequences- Multiple reads along each RNA species

The ENCODE project

• Pilot phase (2003-2007)Identification of all functional elements in 1% of the human genome

• Production phase (2007-?)Identification of all functional elements in the whole human genome

ENCyclopedia of DNA Elements

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Main results from ENCODE project

Pervasive transcription of the human genome(14.7% of the analyzed bases are transcribed in at least one tissue sample, but as much as 93% could be present in primary transcripts)

Identification of many novel non-protein transcripts (ncRNAs either overlapping protein-coding loci or in regions thought to be transcriptionally silent)

Numerous unrecognized transcription start sites

Great complexity and overlap of transcribed regions(multiple overlapping transcripts of different sizes from the same or opposite DNA strands)

The human transcription landscape

(Kapranov et al.,Science 2002)

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The human transcription landscape

(Kapranov et al.,Science 2002)

Most transcribed nucleotides are outside annotated exons

The human transcription landscape

(Kapranov et al., Science 2007)

Genomic distribution of long RNAs

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Genome transcription relevance?

1. Transcriptional noise

2. Functional

Types of RNAs

Figure 1.12 Genomes 3 (© Garland Science 2007)

(Non‐coding)

piRNA

PASR PALR PASR

Structural RNAs Regulatory RNAs

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Types of RNAs and characteristics

Regulation, imprinting~35000>200 bpLong non-codingRNAs

lncRNAs

Gametogenesis, transposon defense~3300026-30 ntpiwi RNAspiRNAs

(rasi RNAs)

Gene expressionregulation~100021-23 ntmicro RNAsmiRNAs

RNA modification~37560-300 ntsmall nucleolarRNAs

snoRNAs

Splicing?100-300 ntsmall nuclear RNAs

snRNAs

Translation~100074-95 nttransfer RNAstRNAs

Component ofribosome~300-400114-5000 ntribosomal RNAsrRNAs

Coding for proteins~30,000Several kbmessenger RNAmRNA

FunctionNumberSizeNameType

Messenger RNAs

Typical mRNA

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Micro RNAs

Small non-coding RNAs (21-23 nt) involved in post-transcriptional regulation of gene expression by binding to the 3’ UTR of target mRNAs

Identified in the early 1990s, but not recognized as a distinct class of regulators until the early 2000s

Abundant in many cell types and may be involved in different developmental processes (complex organisms)

Target around 60% of mammalian genes and each miRNA can repress hundreds of genes

Micro RNAs pathway

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Other new non-coding RNAS

Transcriptional complexity

(61%-72% genes)

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Long range transcription

Long range transcription could be more frequent thanpreviously thought and involve both exons located very faraway (up to Mb) and exons in different chromosomes

(Gingeras et al., Nat. Rev. Genet. 2007)

Mapping of CAGE-tags to the human genome

Identification of new TSS

Carninci et al., Nat. Genet. 2006

(all) (>2)

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Alternative splicing affords extensive proteomic andregulatory diversity from a limited repertoire of genes

Cassette exons: Exons that are spliced out of certain mRNAs to alter the amino acid sequence of the expressed protein.

Alternative splicing (AS)

Large-scale analysis of alt. splicing

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Alternative splicing facts

92-98% of multi exon genes are subjected to alternative splicing (almost 86% of human genes)

~100,000 AS events of different frequency detected in human tissues and many show variation between them

Less than 20% of alternative splicing events appear to be conserved between humans and mice

Alternative splicing could be a key mechanism in generating proteomic diversity

Alternative splicing in human tissues

Wang et al.

Pan et al.

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Mouse

Rat

Su et al. 2004

Transcript profiling across tissues

Human

http://biogps.gnf.org/

Transcript profiling in the brain

Relationship between tissues

Functional differences between tissues

Identification of tissue specific promoters

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Transcriptome

reflects Biology

ALL – acute lymphoblastic leukemia

AML – acute myeloid leukemia

Transcript profiling in cancer

Molecular classificationof cancer types

- Predictive

- Treatment

Transcript profiling across individuals

Characterization of expression levels in lymphoblastoidcell lines from individuals of hapMap populations

Many genes with variation accross individuals

Gene expression phenotypes are highly heritable

Higher proportion of expression differences related tocis-variants (although they are also easier to detect)

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Transcript profiling across individuals

Mapping gene expression variants

(moderate effects, more genes)

(bigger effects, few genes)

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Mapping gene expression variants

Mapping gene expression variants

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Persistence of lactase expression

Lactase production in adults shows large variability in human populations and seems related with pastoralism:

Persistence of lactase expression

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Persistence of lactase expression

Coding vs. regulatory evolution

Regulatory changes have unique properties that could make them especially important in phenotypic evolution:

• Reduced pleiotropical effects

• Fine-tuning of gene function

• Co-dominance and more efficient selection

Expression analysis are an important tool in evolution study

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Multiple levels of regulatory evolution

- Basal promoter- Tissue-specific element- Distant enhancer

- Gene duplication or deletion

- Chromosomal inversion- TE insertion

- Transcription factor- microRNA

- Alternative splicing- Alternative 5’ or 3’ UTRs- RNA editing

Epigeneticchromatin

modifications

GeneGeneexpressionexpression

levelslevelsRegulatory change in cis

Structuralchanges Changes in

trans-actingregulator

Changes inthe mRNAsequence

- DNA methylation- Histone modifications

Comparison of gene-expression levels in the brain during human evolution

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Microarrays and brain evolution

Table 1 | Comparative studies of human and non-human primate gene expression in the brain by oligonucleotide arrays Study Platform Coverage Tissue

compared Species compareda

Sex Age (years)

Tissue origin

Follow-up validation

3 humans all males adults 3 chimps all males adults

Enard et al. (2002)

HG_U95A arrays

12533 probe sets (~9100 genes)

Brain frontal cortex (area 9), plus liver

1 orangutan male adult

autopsy (PMI = 2-6 h)

Northern

5 humans 3 mal./2 fem. 30-71 4 chimps 1 mal./3 fem. 1-24

Cáceres et al. (2003)

HG_U95Av2 arrays

12558 probe sets (~9100 genes)

Brain frontal, and temporal cortex (several regions), plus heart

4 rhesus 2 mal./1 fem. 1-9

surgery and autopsy (PMI = 2-14 h)

cDNA arrays, real-time PCR and in-situ hybridization

3 humans all males. 45-70 3 chimps all males. 12-40

Khaitovich et al. (2004)

HG_U95Av2 arrays

12558 probe sets (~9100 genes)

Brain frontal, temporal and occipital cortex (several regions)

autopsy -

Uddin et al. (2004)

HG_U133A and B arrays

44792 probe sets (~16400 genes)

Brain anterior cingulate cortex

3 humans 1 chimp 1 gorilla 3 rhesus

all females female male 2 mal./1 fem.

14-28 34 41 4-11

autopsy (PMI < 12 h.)

-

Khaitovich et al. (2005)

HG_U133 Plus2 arrays

54613 probe sets (~20700 genes)

Brain frontal cortex (area 9), plus liver, heart, kidney and testis

6 humans 5 chimps

all males all males

45-70 7-40

autopsy -

Khaitovich et al. (2006)

ENCODE 0.1 Fwd. and Rev. arrays

30 Mb of DNA

Brain frontal cortex (area 9), plus heart and testis

6 humans 5 chimps

all males all males

45-70 7-40

autopsy -

Several studies have compared gene-expression levelsin the adult brain of humans and non-human primates:

(Modified from Preuss, Cáceres et al., Nat. Rev. Genet. 2004)

2. Increased expression levels in human brain compared to non-human primates

1. Acceleration of gene-expression changes in the brain during human evolution

(Preuss, Cáceres et al., Nat. Rev. Genet. 2004)

(Enard, Khaitovich et al., Science 2002)

Gene-expression evolution patterns

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Gene-expression changes in cortex

Bayes t-test(PPDE > 0.99%)

612 genes

SAM (t –test FDR < 0.1%)

577 genes

Metanalysis identified 533 differentially expressed genes in human-chimp cortex with good confirmation by RT-PCR:

89%Sensitivity (TP/TP+FN)

64%Specificity (TP/TP+FP)

78 (61)Total probe sets (genes)

All PCRsRT-PCR validation

y = 0.4775x

R2 = 0.5023

-6

-4

-2

0

2

4

6

-4 -3 -2 -1 0 1 2 3 4 5

Diff. exp. ps.

Array log2 (Hs/Pt ratio)

PC

R lo

g2

(Hs

/Pt

rati

o)

533 4479

40.2% Dec. / 59.8% Inc.(Cáceres, Oldham et al., in prep.)

Functional classification of genes

Genes differentially expressed in human and chimp cortexbelong to a wide diversity of functional categories:

Cell adhesion and signaling

Signal transduction

Cell cycle and proliferation

Cell organization and biogenesis

Nucleic acid metabolism

Carbohydrate metabolism

Lipid metabolism

Protein metabolism

Response to stress

Transport

Development

Unknown

Biological process categories 38

71

45

24

18 33

82

86

46

54

45

76

(Cáceres, Oldham et al., in prep.)

**

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Follow-up of expression differences

mRNA levels

Protein levels

Cellulardistribution

Phenotypicchanges

Westernblots

cDNAmicroarrays

Northernblots

Real-timeRT-PCR

Immunohistochemistry

In situhybridization

Regulatorystudies

Molecularevolution

Functionalanalysis

Why thrombospondins?

Thrombospondins are extracellular matrix proteins thatregulate cell interactions in multiple tissues

- THBS1 and THBS2(Subfamily A)

- THBS3, THBS4 and COMP(Subfamily B)

(Christopherson et al., Cell 2005)

Synapse formation

THBS domains

In rodents, THBSs have beenimplicated in the induction ofsynapse formation by astrocytesboth in vitro and in vivo

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THBSs expression patterns

Real-time RT-PCR

mRNA and protein levels of THBS4 and THBS2 are specifically upregulated in human cortex:

~6 foldincrease

Western blot analysis

~2 foldincrease

******

*** ***

(Cáceres et al., Cerebral Cortex 2006)

Cortical distribution of THBS4

Chimp RhesusHuman

Human

Chimp

(Cáceres et al., Cerebral Cortex 2006)

In situ hybridization(THBS4 mRNA)

Immunohistochemistry(THBS4 protein)

Chimp RhesusHuman

Humans sections exhibit denser THBS4labeling of the neuropil, consistent with a

potential role in synaptic activity and plasticity

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– Greater density of synapses

– Higher synapses turnover

– Increased neurite growth

Increased levels of thrombospondins could be related to changes in synaptic organization and plasticity in the cortex:

Hu

man

Mac

aqu

e

(Duan et al., Cereb.Cortex 2003)

Possible functional consequences?