The evolution of expression patterns in the Arabidopsis genome
Todd VisionDepartment of Biology
University of North Carolina at Chapel Hill
Driving forces in genome evolution
• Proximate vs. ultimate explanations• Deleterious mutations are frequent and
selection cannot effectively act on all of them– Substitutions– Insertions and deletions– Duplications– Transpositions
• Cellular processes will be affected by this rain of mutations
• At the molecular level, we must entertain ultimate explanations that do not invoke adaption
An example: Codon bias• Genes differ in the frequency that they use
the preferred codon for a given amino acid, thereby affecting– Translational efficiency– Translational accuracy
• The strongest codon bias is typically seen in short, highly expressed genes under strong purifying selection
• Realized codon bias is a balance between selection for preferred codons and a continual rain of mutations toward unpreferred codons
What are the consequences of mutational rain on the
regulatory networks that modulate gene expression?
Outline
• Arabidopsis gene expression (MPSS)
• Two evolutionary issues in the evolution of expression profiles:– Physical clustering of co-expressed genes – Divergence of duplicated genes
Digital expression profiling
• “Bar-code” counting raises fewer concerns about cross-hybridization, probe selection, background hybridization, etc.
• Serial Analysis of Gene Expression (SAGE) – Count occurrence of 10-12 bp mRNA signatures– Long SAGE: 21-22 bp signatures– Uses conventional sequencing technology
• Massively Parallel Signature Sequencing (MPSS)– Count occurrence of 17-20 bp mRNA signatures– Cloning and sequencing is done on microbeads– Commercialized by Lynx Therapeutics
MPSS library constructionAAAAAAA
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extract mRNA from tissue
AAAAAAATTTTTTT
5’ - Add standard
primer(added by cloning)
3’ - Add unique 32 bp
tag and standard
primer
AAAAAAAmRNA
Cut w/ Sau3A AAAAAAA
TTTTTTT
AAAAAAA
Convert to cDNA
TTTTTTT Add linker
Brenner et al., PNAS 97:1665-70.
Remove 3’ primer and expose single stranded unique tag
(digest, 3' 5' exonuclease)
Anneal to beads coated with unique anti-tag(32 bp, complementary to tag on mRNA) PCR
AAAAAAATTTTTTT
GATC
MPSS library construction
The result of the library construction is a set of microbeads. Each bead contains many DNA molecules, all derived from the 3’ end of a single transcript.
Beads are loaded in a monolayer on a microscope slide for the sequencing of 17 – 20 bp from the 5’ end.
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Brenner et al., PNAS 97:1665-70.
Sort by FACS to remove ‘empty’ beads
MPSS Sequencing
Repeat Cycle
8 7 6 5
Steps of four bases; overhang is shifted by four
bases in each round
NNNN
Digest with Type IIS enzyme to
uncover next 4 bases
9 bp
13 bp
CNNN 4 3 2 1
^ ^GNNN CODEC4RS DECODERED
Sequence by hybridization
16 cyclesfor 4 bp
NNXN CODEX2
XNNN CODEX4
NXNN CODEX3
NNNX CODEX1RS
RS
RS
RS
4 3 2 1NNNN
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Add adaptors
Brenner et al., Nat. Biotech. 18:630-4.
MPSS Sequencing
GATCAATCGGACTTGTCGATCGTGCATCAGCAGTGATCCGATACAGCTTTGGATCTATGGGTATAGTCGATCCATCGTTTGGTGCGATCCCAGCAAGATAACGATCCTCCGTCTTCACAGATCACTTCTCTCATTAGATCTACCAGAACTCGG..GATCGGACCGATCGACT
253212349417561672702814..2,935
123456789..30,285
Each bead provides a signature of 17-20 bp
Tag #SignatureSequence
# of Beads (Frequency)
Two sets of signatures are generated from each sample in different reading frames staggered
by two bases
Total # of tags: >1,000,000
ATG TGA
A catalog of signatures in the Arabidopsis genome
All potential signatures (GATC + 13 bp) are identified on both strands of the genomic sequence.
There is one potential signature appx. every 293 bp on each strand of genome
A signature is classified according to its position relative to the 29,084 genes & pseudogenes in the TIGR annotation
Signatures may not be unique. The number of ‘hits’ in the genome is recorded
“Hits” At genome % of total Random 1 748204 87.407% 8450572 88392 10.326% 61343 11019 1.287% 214 3512 0.410% 05 1452 0.170% 06 874 0.102% 07 470 0.055% 08 326 0.038% 09 237 0.028% 010 192 0.022% 011 158 0.018% 012-20 707 0.083% 021-30 247 0.029% 031-50 124 0.014% 0> 50 86 0.010% 0 Total 851,212 851,212
Classifying signatures
Potential alternative splicing or nested
gene
Potential alternative termination
Potential un-annotated
ORF
Potential anti-sensetranscript
Anti-sense transcript or nested
gene?
Duplicated: expression may
be from other site in genome
Triangles refer to colors used on our web page:Class 1 - in an exon, same strand as ORF.Class 2 - within 500 bp after stop codon, same strand as ORF.Class 3 - anti-sense of ORF (like Class 1, but on opposite strand).Class 4 - in genome but NOT class 1, 2, 3, 5 or 6.Class 5 - entirely within intron, same strand.Class 6 - entirely within intron, anti-sense.
Grey = potential signature NOT expressedClass 0 - signatures found in the expression libraries but not the genome.
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Typicalsignatures
Arabidopsis signatures
Class # in genome % of total1 sense exonic 203,174 24.02 3’UTR, <500 bp 44,202 5.23 anti-sense exonic 197,065 23.34 inter-genic 288,109 34.05 intronic 60,817 7.2 6 anti-sense intronic 57,845 6.8TOTAL 851,212 100.5
Based on TIGR annotation (release 3.0, July 2002)
355 genes lack potential Class 1 or 2 signatures (undetectable)
On average, there are 8.5 class 1 & 2 signatures per gene
8422 genomic signatures have secondary classes due to overlap or near overlap of two genes in the TIGR annotation.
Core Arabidopsis MPSS librariessequenced by Lynx for Blake Meyers, U. of Delaware
Signatures Distinct
Library sequenced signatures
Root 3,645,414 48,102
Shoot 2,885,229 53,396
Flower 1,791,460 37,754
Callus 1,963,474 40,903
Silique 2,018,785 38,503
TOTAL 12,304,362 133,377
Genome-wide expression profiling Arabidopsis
Of the 29,084 gene models, 14,674 match unique, expressed signatures
Chr. I
Chr. II
Chr. III
Chr. IV
Chr. V
http://www.dbi.udel.edu/mpss
Query by• Sequence• Arabidopsis gene identifier• chromosomal position• BAC clone ID• MPSS signature• Library comparison
Site includes• Library and tissue information• FAQs and help pages
Outline
• Arabidopsis gene expression (MPSS)
• Two evolutionary issues in the evolution of expression profiles:– Physical clustering of co-expressed genes – Divergence of duplicated genes
Physical clustering of co-expressionCaenorhabditis elegans Roy et al., (2002) Nature 418, 975
Lercher et al (2003) Genome Research 13, 238Drosophila melanogaster Boutanaev et al (2002) Nature 420, 666
Spellman and Rubin (2002) J Biology 1, 5Homo sapiens Caron et al (2001) Science 291, 1289
Lercher et al (2002) Nature Genetics 31, 180Saccharomyces cerevisiae Cohen et al (2000) Nature Genetics 26, 183
Hurst et al (2002) Trends in Genetics 18, 604Mannila et al (2002) Bioinformatics 18, 482
‘
• What are the proximate explanations?– shared cis-regulatory elements– chromatin packaging, etc.
• What are the ultimate explanations? – Adaptive: greater transcriptional efficiency/accuracy?– Maladaptive: mutational rain chipping away at insulators and
other mechanisms that over-ride regional controllers of gene expression?
Measuring expression distance
library 1
library 2
library 3
Clustering of tissue-specific expression
Flower (red)Silique (violet)Leaf (green)Root (blue)
Callus (white)
Chromosome 1
Statistical tests of coexpression clustering
• Measured median pairwise expression distance (MPED) in non-overlapping windows of 20 genes– Summed unique class 1 and 2 signatures for each
gene– Only one gene within each tandemly arrayed
family was counted
• Out of 100 shuffles of gene order– Zero shuffles had as many windows with small
MPED (less than 1.5) as the unshuffled data– Zero shuffles had as large a variance in MPED
among windows as the unshuffled data
Coexpression in Arabidopsis
Coexpression in Arabidopsis
Coexpression in Arabidopsis
Selection and recombination
• In regions of low recombination– deleterious mutations can hitch-hike to high
frequency along with favorable ones– favorable mutations are kept at low frequency by
linkage to deleterious ones
• Therefore, the effectiveness of natural selection is causally related to recombination rate
• Are clusters more concentrated in regions of – high recombination (i.e. are they adaptive)– low (i.e. are they maladaptive)?
Measuring recombination rate
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physical distance (Mb)
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Co-expression is greater in low recombination regions
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recombination rate (cm/Mb)
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Co-expression clusters
• MPSS data provides evidence for clusters of co-expression among non-related genes in Arabidopsis
• Co-expression is greater in regions of low recombination
• Thus, co-expression clusters may be maladapative, at least on average
Outline
• Arabidopsis gene expression (MPSS)
• Two evolutionary issues in the evolution of expression profiles:– Physical clustering of co-expressed genes – Divergence of duplicated genes
Divergence of duplicated genes
Age of duplication
Exp
ress
ion
dist
ance
Duplicated genes in Arabidopsis
Modes of gene duplication
• Tandem (unequal crossing-over)
• Dispersed (transposition)
• Segmental (polyploidy)
Divergence of duplicated genes
• All gene families of size 2 in Arabidopsis were classified as ‘dispersed’, ‘segmental’ or ‘tandem’
• Expression distance was calculated for each• The number of silent (i.e. synonymous)
substitutions per site was calculated for each (as a proxy for age since duplication)
Divergence and mode of duplication
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0 2 4 6 8silent substitutions (per site) x 10
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dispersedsegmentaltandem
Divergence of duplicated genes
• Almost all expression divergence occurs during (or immediately following) duplication
• Initial expression divergence is more extreme for tandem than dispersed duplicates
• Tandem and dispersed duplicates with the most divergent expression profiles are quickly lost
• Segmental duplicates plateau at a lower level of expression divergence than dispersed duplicates
• The average divergence in relative expression level in each tissue is about 8-fold.
Lessons learned
• Clusters of co-expression in Arabidopsis may be largely the result of a rain of weakly deleterious mutations that homogenize the expression profiles of neighboring genes
• Divergence in expression profile between duplicated genes is dependent on the nature of the mutation that gave rise to the duplication
Thanks!
• UNC Chapel Hill– Jianhua Hu
• University of Delaware – Blake Meyers
• NSF Plant Genome Research Program
– DBI-01103267 (TJV)– DBI-0110528 (BCM)