Genome Comparisons and Gene Regulation

Penn State University, Center for Comparative Genomics and Bioinformatics: Webb Miller, Francesca Chiaromonte, Anton Nekrutenko, Ross Hardison; James Taylor, David King, Hao Wang

University of California at Santa Cruz: David Haussler, Jim Kent

National Human Genome Research Institute: Laura Elnitski

Children’s Hospital of Philadelphia: Mitch WeissLawrence Livermore National Laboratory: Ivan

Ovcharenko

CSH Nov. 6, 2005

DNA sequences of mammalian genomes

• Human: 2.9 billion bp, “finished”– High quality, comprehensive sequence, very few gaps

• Mouse, rat, dog, oppossum, chicken, frog etc. etc etc.• About 40% of the human genome aligns with mouse

– This is conserved, but not all is under selection.

• About 5-6% of the human genome is under purifying selection since the rodent-primate divergence

• About 1.5% codes for protein• The 4.5% of the human genome that is under selection

but does not code for protein should have:– Regulatory sequences– Non-protein coding genes– Other important sequences

Silent and repressed chromatin

Transcription initiation and pausing

General transcription initiation factors, GTIFs

Assemble on promoter

Repressors bindto negative controlelements

Basal and activated transcription

Activators bind to enhancers

Contact for activation

Pol IIaIIDPolII

Enhancer

PromoterCoactivators

Coactivators and/or activators sometimes recruit enzymes that modify chromatin structure to facilitate transcription.

Histone acetylationNucleosome remodeling

Promoter for RNA Polymerase II

Regulate efficiency at which minimal promoter is used

Minimal promoter: binding of GTIFs and RNA Pol II

DPE

Bad news for prediction:TATA box is moderately well-defined, but in large

datasets of mammalian promoters, only about 11% have TATA boxes !

Inr (YANWYY) and DPE are not well-defined sequences.Good news for prediction of promoters:

About 70% are in CpG islandsAlmost all encompass the 5’ end of genes

Enhancers: Specific DNA sequences that cause an increase

in transcription• Can act in a variety of positions:

– 5’ to gene (similar to an upstream activation sequence)

– Internal to a gene (e.g. in an intron)– 3’ to a gene

• Can act at a considerable distance from the gene– Current studies implicate enhancers as far as 200kb to 500kb away from genes.

– Other genes can be between an enhancer and its target gene.

• Contain a set of binding sites for transcriptional activators.– Sequence-specific binding sites– Short: roughly 6-8bp

Interferon beta Enhancer-Promoter

Many regulatory DNA sequences in SV40 control region

Sequence-specific

Domainopening

is associated with movement to non-hetero-chromatic regions

Expected properties of regulatory elements

• Conserved between species– Examine interspecies alignments

• Enhancers and promoters: clusters of binding sites for transcription factors– Use TRANSFAC, TESS, MOTIF (GenomeNet), etc to find matches to binding

sites for transcription factors

• Binding sites conserved between species– Servers to find conserved matches to factor binding sites

• Comparative genomics at Lawrence Livermore http://www.dcode.org/– zPicture and rVista– Mulan and multiTF– ECR browser

• Consite http://mordor.cgb.ki.se/cgi-bin/CONSITE/consite– The database GALA records conserved (and nonconserved) matches to factor

binding sites (http://www.bx.psu.edu/)

• Can be almost anywhere– 5’ or 3’ to gene– Within introns– Close or far away

Conservation score S

in different types of regions

Red: Ancestral repeats (mostly neutral)Blue: First class in labelGreen: Second class in label

Waterston et al., Nature

Use measures of alignment texture to discriminate functional classes of

DNA• Mouse Cons track (L-scores) and phastCons are

measures of alignment quality.– Match > Mismatch > Gap

• Alternatively, can analyze the patterns within alignments (texture) to try to distinguish among functional classes– Regulatory regions vs bulk DNA– Patterns are short strings of matches, mismatches,

gaps– Find frequencies for each string using training

sets• 93 known regulatory regions• 200 ancestral repeats (neutral)

• Regulatory potential genome-wide– Elnitski et al. (2003) Genome Research 13: 64-72.

What types of regulatory sequences may we hope to find?

• Sequence signature: specific binding sites– Promoters– Enhancers– Repressor binding sites– But these “signatures” are short and occur frequently in any long sequence

• Sequence signature unknown, maybe none– Compact, silent chromatin– Insulators, boundaries– Release from pausing– Movement from inactive to active compartments

Coverage of human by alignments with other vertebrates ranges from 1% to 91%

Human

0 20 40 60 80 100

Fugu

Tetraodon

Zebrafish

Frog

Chicken

Platypus

Opossum

Cow

Dog

Rat

Mouse

Chimp

Percent of human aligning with second species

5.4

9192

310

360

450

173

Millions ofyears

220

5%

Neutral DNA “cleared out” over 200Myr

Most human DNA is not alignable to species separated by more than 200 yr. Divergence dates from Kumar and Hedges (Nature 1998) and Hedges (Nature Rev Genet 2002)

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500

Divergence from common ancestor to human, Myr ago

Percent of human not aligned

Chimp

Mouse, Rat

Cow

Dog

Opossum

Chick Frog FishPlatypus

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

Distinctive divergence rates for different types of functional DNA

sequences

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40

50

60

70

80

90

100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

GenomeCoding exonsUltraconserved (HM)Log. (Genome)

Large divergence in cis-regulatory modules from opossum to platypus

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100

0 100 200 300 400 500

Time of divergence from common ancestor to human, Myr ago

GenomeKnown regulatory regionsCpG islandsFunctional promotersCoding exonsUltraconserved (HM)

Marsupial genome adds substantially to the conserved fraction of

regulatory regionsAdditive contribution of each 2nd species to conservation

0

20

40

60

80

100

Ultra conservedCoding exons

miRNAsCpG islands

Known regulatory regionsFunctional promoters

cTFBSs

Whole genome

Percent

PrimateEutherianMarsupialMonotremeAvianAmphibianFish

The distal Major regulatory element of the human HBA gene complex is conserved in

opossum but not beyond

cis-Regulatory modules conserved from human to fish

310

450

91

173

Millions ofyears

• About 20% of CRMs• Tend to regulate genes

whose products control transcription and development

• Recent reports:– Sandelin, A. et al.

(2004). BMC Genomics 5: 99.

– Woolfe, A. et al. (2005). PLoS Biol 3: e7

– Plessy, C., Dickmeis, T., Chalme,l F., Strahle, U. (2005) Trends Genet. 21: 207-10.

cis-Regulatory modules conserved from human to chicken

310

450

91

173

Millions ofyears

• About 40% of CRMs• Noncoding sequences

conserved from human to chicken tend to clusters in gene-poor regions– Conservation jungles– Hillier et al. (2004) Nature

• Stable gene deserts are conserved from human to chicken– Ovcharenko et al., (2005)

Genome Res. 15: 137-145.

• Conserved noncoding sequences in stable gene deserts tend to be long-range enhancers– Nobrega, M.A., Ovcharenko,

I., Afzal, V., Rubin, E.M. (2003) Science 302: 413.

cis-Regulatory modules conserved in eutherian mammals (and marsupials?)

310

450

91

173

Millions ofyears

• About 80-90% of CRMs• Within aligned noncoding

DNA of eutherians, need to distinguish constrained DNA (purifying selection) from neutral DNA.

Score multi-species alignments for features associated with function

• Multiple alignment scores – Binomial, parsimony (Margulies et al., 2003)

• PhastCons – Siepel and Haussler, 2003; Siepel et al. 2005– Phylogenetic Hidden Markov Model– Posterior probability that a site is among the 10% most highly conserved sites

– Allows for variation in rates and autocorrelation in rates

• Factor binding sites conserved in human, mouse and rat – Tffind (from M. Weirauch, Schwartz et al., 2003)

• Score alignments by frequency of matches to patterns distinctive for CRMs– Regulatory potential (Elnitski et al., 2003; Kolbe et al., 2004)

Binding sites conserved between species

• tffind: Identify high-quality matches to a weight matrix in one sequence (e.g. human) that also aligns with other sequences (e.g. mouse and rat)

• Look for matches to weight matrix in 2nd and 3rd sequences, in the part of the alignment that aligns to match to weight matrix in first species

• GALA records these matchesHMR

Program does not find this, but some studies show that it can happen.

Matt Weirach

Conserved transcription factor binding sites

• Track on UCSC Genome Browser (human)• GALA (www.bx.psu.edu)• rVista

– Can export alignments from zPicture and Mulan

– ECR browser– All at dcode.org

• ConSite

Use measures of alignment texture to discriminate

functional classes of DNA• Compute the probability of matching a pattern characteristic of regulatory regions– Analyze alignments as short strings of matches, mismatches, gaps

– Find probabilities for each string using as training sets• 93 known regulatory regions• 200 ancestral repeats (neutral)

– Construct Markov models that give good separation of regulatory regions from neutral DNA

– Regulatory potential of all 100 bp windows in the genome

Computing Regulatory Potential (RP)

Alignment seq1 G T A C C T A C T A C G C A seq2 G T G T C G - - A G C C C A seq3 A T G T C A - - A A T G T ACollapsed alphabet 1 2 1 3 4 5 7 7 6 8 3 6 3 9

• A 3-way alignment has 124 types of columns. Collapse these to a smaller alphabet with characters s (for example, 1-9).

•Train two order t Markov models for the probability that t alignment columns are followed by a particular column in training sets:

–positive (alignments in known regulatory regions)–negative (alignments in ancestral repeats, a model for neutral DNA)–E.g. Frequency that 3 4 is followed by 5:

0.001 in regulatory regions0.0001 in ancestral repeats•RP of any 3-way alignment is the sum of the log likelihood ratios of

finding the strings of alignment characters in known regulatory regions vs. ancestral repeats.

∑ ⎟⎟⎠

⎞⎜⎜⎝

⎛=

−−

−−

segment in 1

1

)|(

)|(log

a taaaAR

taaaREG

sssp

ssspRP

KK

RP and phastCons in HBB locus control region

- Both RP and phastCons are high in exons- RP peaks in many cis-regulatory modules- phastCons peaks in more regions

http://genome.ucsc.edu/

LCRHBB HBD HBG2 HBG1 HBE

More species and better models improve discriminatory power of RP

scores

ROC curves for different RP scores, tested on a set of known regulatory regions from the HBB gene complex

RP and phastCons can discriminate most known functional elements from

neutral DNA

Leveraging genome evolution to discover function

• Overall goals and core concepts• All-vs-all whole-genome comparisons

– Comparison of no two species is ideal for finding all functional sequences

• Alignment scores:– Aid in finding functional elements– Discriminate between functional classes

• Example of experimental tests of the bioinformatic predictions

Genes co-expressed in late erythroid maturation

• G1E-ER cells: proerythroblast line from mice lacking the transcription factor GATA-1. – Can restore the activity of GATA-1 by expressing an estrogen-

responsive form of GATA-1– Allows cells to mature further to erythroblasts

• Use microarray analysis of each to find genes that increase or decrease expression upon induction. – Walsh et al., (2004) BLOOD; Image from k-means cluster, GEO:

repressed induced

genes

time after restoration of GATA-1

Predicting cis-regulatory modules (preCRMs)

Identify a genomic region with a regulated gene.

Find all intervals whose RP score exceeds an empirical threshold.

Subtract exons

Find all matches to GATA-1 binding sites that are conserved (cGATA-1_BS)

Intervals with RP scores above the threshold and with a cGATA-1_BS within 50bp are preCRMs.

Predicted cis-regulatory modules (preCRMs) around erythroid genes

+

-

Test predicted cis-regulatory modules (preCRMs)

• Enhancement in transient transfections of erythroid cells

• Activation and induction of reporter genes after site-directed, stable integration in erythroid cells

• Chromatin immunoprecipitation (ChIP) for GATA-1

Dual luciferase assay

FF luciferaseHBGtest

Ren luciferasetk

K562 cells

prom

prom

Validation of preCRM in Alas2

Negative controls do not enhance transient expression

0

1

2

3

4

5

6

7

parentLucFog1N1Fog1N2Hipk2N2Gata2N2Alas2N1HS2N1HS2N2Alas2N2Vav2N1Vav2N2CdmN1

Coro2aN1Gata2r.2N1

Fold change

Negative controls are segments of mouse DNA that align with rat and human but have low RP scores and do not have a match to a GATA-1 binding site. They have almost no effect on the level of expression of the reporter gene in erythroid cells.

7 of 24 Zfpm1 preCRMs enhance transient expression

9 of 24 Zfpm1 preCRMs enhance after stable integration at RL5

All preCRMs in Gata2 are functional in at least one assay

ChIP data are from publications from E. Bresnick’s lab.

Assay Number Number %tested positive validated

Transient 62 21 34 transfectionsSite-directed 62 21 34 integrantsEither expressionassay 62 33 53

About half of the preCRMs are validated as functional

GATA-1 ChIPs 17 11 65

Positive correlation between enhancer activity and regulatory potential

0

1

2

3

4

5

6

7

-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

RP score, mean

Maximum fold increase, transient or stable

Max, validated

Max, nonvalidated

Max, NC

Developmental regulation of the HBB gene complex

transcription, in erythroid cells

embryonicfetaladult

locus control region

High throughput DNase I hypersensitive sites find known

regulatory regions

R

Long transcripts run through OR genes into globin genes

Conclusions

• Particular types of functional DNA sequences are conserved over distinctive evolutionary distances.

• Multispecies alignments can be used to predict whether a sequence is functional (signature of purifying selection).

• Alignments can be used to predict certain functional regions, including some cis-regulatory elements.

• The predictions of cis-regulatory elements for erythroid genes are validated at a good rate.

• Databases such as the UCSC Table Browser, GALA and Galaxy provide access to these data.

• Expect improvements at all steps.

Many thanks …

Wet Lab: Yuepin Zhou, Hao Wang, Ying Zhang, Yong Cheng, David King

PSU Database crew: Belinda Giardine, Cathy Riemer, Yi Zhang, Anton Nekrutenko

Alignments, chains, nets, browsers, ideas, …Webb Miller, Jim Kent, David Haussler

RP scores and other bioinformatic input:Francesca Chiaromonte, James Taylor, Shan Yang, Diana Kolbe, Laura Elnitski

Funding from NIDDK, NHGRI, Huck Institutes of Life Sciences at PSU