Transcript VERTEBRATE GENOME EVOLUTION AND FUNCTION …

Using vertebrate genome
comparisons to find gene regulatory
regions
Ross Hardison and James Taylor
Cold Spring Harbor course on Computational Genomics
Nov. 10, 2007
Major goals of comparative genomics
• Identify all DNA sequences in a genome that are
functional
– Selection to preserve function
– Adaptive selection
• Determine the biological role of each functional sequence
• Elucidate the evolutionary history of each type of
sequence
• Provide bioinformatic tools so that anyone can easily
incorporate insights from comparative genomics into their
research
Types of sequences in mammalian genomes
• About 1.5-2% codes for protein
– Almost all shows a sign for purifying selection since the primaterodent divergence
– Does not preclude positive selection acting on smaller regions or in
specific lineages
• About 45% is interspersed repeats
– 22% in ancestral repeats
– Good model for neutral DNA
– 23% in lineage-specific repeats
• About 53% is noncoding, nonrepetitive
– Minimum of 4% of genome is under purifying selection for a function
common to mammals, but does NOT code for protein
• Regulatory sequences
• Non-protein coding genes
• Other important sequences
– About 49% under no obvious selection: no conserved function?
Impact of wholegenome
alignments
Guide to functional sequences
in the human genome.
Conserved sequences
Sequences under
purifying selection
Better gene predictions
Sequences that look like
elements that regulate
gene expression
Three modes of evolution
Negative and positive selection observed at
different phylogenetic distances
:
Genome-wide local alignment chains
Human: 2.9 Gb assembly. Mask interspersed repeats, break into 300 segments of 10 Mb.
Human
Mouse
blastZ: Each segment of human is given the opportunity to align with all mouse sequences.
Run blastZ in parallel for all human segments. Collect all local alignments above threshold.
Organize local alignments into a set of chains based on position in assembly and orientation.
Level 1 chain
Level 2 chain
Net
Comparative genomics to find functional sequences
Genome
size
2,900
Find
common
sequences
blastZ,
multiZ
2,400
Human
Identify
functional
sequences: ~ 145
Mbp
All mammals
1000 Mbp
2,500
Mouse
Rat
1,200
million base pairs
(Mbp)
Also birds: 72Mb
Papers in Nature from mouse and rat and chicken genome consortia, 2002, 2004
Regional variation in divergence rates
Implications of co-variation in divergence
• Large regions (megabase sized) are changing relatively fast or slow
for (almost) all types of divergence
– Neutral substitution, insertions (except SINEs), deletion, recombination
• This is a consistent property of each region of genomic DNA
– See similar patterns for orthologous regions on independent lineages to
mouse, rat and human
• An aligned segment with a given similarity score in a fast-changing
region is MORE significant than an aligned segments with the same
similarity score in a slow-changing region.
• Must take the differential rate into account in searching for functional
DNA = DNA under selection.
Use measures of alignment quality to discriminate
functional from nonfunctional DNA
• Compute a conservation score adjusted for the local
neutral rate
• Score S for a 50 bp region R is the normalized fraction of
aligned bases that are identical
– Subtract mean for aligned ancestral repeats in the
surrounding region
– Divide by standard deviation
p = fraction of aligned sites in R that are
identical between human and mouse
m = average fraction of aligned sites that
are identical in aligned ancestral repeats in
the surrounding region
Waterston et al., Nature
n = number of aligned sites in R
Decomposition of conservation score into
neutral and likely-selected portions
Neutral DNA (ARs)
All DNA
Likely selected DNA
At least 5-6%
S is the conservation score adjusted for variation in the local substitution rate.
The frequency of the S score for all 50bp windows in the human genome is shown.
From the distribution of S scores in ancestral repeats (mostly neutral DNA), can
compute a probability that a given alignment could result from locally adjusted
neutral rate.
Waterston et al., Nature
Conservation
score S in
different types
of regions
Red: Ancestral repeats
(mostly neutral)
Blue: First class in label
Green: Second class in
label
phastCons: Likelihood of being constrained
• Phylogenetic Hidden
Markov Model
• Posterior probability that
a site is among the 10%
most highly conserved
sites
• Allows for variation in
rates along lineages
c is “conserved” (constrained)
n is “nonconserved” (aligns but
is not clearly subject to
purifying selection)
Siepel et al. (2005) Genome
Research 15:1034-1050
Larger genomes
have more of the
constrained DNA
in noncoding
regions
Expected value if coverage by
conserved elements is uniform
Siepel et al. 2005,
Genome Research
Some constrained introns are editing complementary
regions:GRIA2
Siepel et al. 2005,
Genome Research
3’UTRs can be highly constrained over large
distances
Siepel et al. 2005,
Genome Research
3’ UTRs contain RNA processing signals, miRNA targets,
other regions subject to constraints
Ultraconserved elements = UCEs
• At least 200 bp with no interspecies differences
–
–
–
–
Bejerano et al. (2004) Science 304:1321-1325
481 UCEs with no changes among human, mouse and rat
Also conserved between out to dog and chicken
More highly conserved than vast majority of coding regions
• Most do not code for protein
– Only 111 out of 481overlap with protein-coding exons
– Some are developmental enhancers.
– Nonexonic UCEs tend to cluster in introns or in vicinity of genes encoding
transcription factors regulating development
– 88 are more than 100 kb away from an annotated gene; may be distal
enhancers
GO category analysis of UCE-associated genes
• Genes in which a
coding exon overlaps a
UCE
– 91 Type I genes
– RNA binding and
modification
– Transcriptional
regulation
• Genes in the vicinity of
a UCE (no overlap of
coding exons)
– 211 Type II genes
– Transcriptional
regulation
– Developmental
regulators
Bejerano et al. (2004) Science
Intronic UCE in SOX6 enhances expression
in melanocytes in transgenic mice
UCEs
Tested UCEs
Pennacchio et al.,
http://enhancer.lbl.gov/
The most stringently conserved
sequences in eukaryotes are mysteries
• Yeast MATa2 locus
– Most conserved region in 4 species of yeast
– 100% identity over 357 bp
– Role is not clear
• Vertebrate UCEs
– More constrained than exons in vertebrates
– Noncoding UCEs are not detectable outside chordates, whereas coding
regions are
• Were they fast-evolving prior to vertebrate/invertebrate divergence?
• Are they chordate innovations? Where did they come from?
– Role of many is not clear; need for 100% identity over 200 bp is not
obvious for any
• What molecular process requires strict invariance for at least 200 nucleotides?
• One possibility: Multiple, overlapping functions
Going beyond stringent selection in
noncoding sequence to find cis-regulatory
modules
Constraint in noncoding sequences
•
•
•
Tree from Margulies et al. (2007) Genome Res.
Used to predict gene regulatory
regions with some success
Some sequences conserved
between humans and mouse show
no apparent function
– Is constraint revealing many
false positives?
Sequences regulating gene
expression in restricted lineages
are not constrained across
mammals
– Is pan-mammalian
constraint missing many
functional sequences?
phastCons can find some but not all gene
regulatory regions
HS1
HS2
LCR
HS3
HS4
HS5
phastCons
Locus control region, or LCR, is the major distal enhancer fo HBB and related,
linked genes. It has 5 DNase hypersensitive sites covering about 20 kb.
Two
extremes
of
constraint
in CRMs
CRMs= cisregulatory modules.
DNA sequences
needed in cis for
regulation of
expression, usually
transcription
E.g. promoters,
enhancers, silencers
Coverage of human by alignments with other
vertebrates ranges from 1% to 91%
5.4
Millions of
years
Human
91
92
173
220
310
360
450
5%
Distinctive divergence rates for different types of
functional DNA sequences
pTRRs: putative
transcriptional regulatory
region; likely CRMs
Sites identified as occupied
by sequence-specific
transcription factors based on
high-throughput chromatin
immunoprecipitation assayed
by hybridization to high
density tiling arrays of
genomic DNA= ChIP-chip
cis-Regulatory modules conserved beyond
mammals
•
Millions of
years
91
173
310
450
•
•
Human-chicken alignment capture
about 6% of pTRRs (likely CRMs)
Human-fish alignments capture
about 3% of pTRRs.
The pan-vertebrate CRMs tend to
regulate genes whose products
control transcription and
development
cis-Regulatory modules conserved in eutherian
mammals and marsupials
Millions of
years
91
173
310
450
• Human-marsupial alignments
capture about 32% of CRMs
(pTRRs)
– Tend to occur close to genes
involved in aminoglycan synthesis,
organelle biosynthesis
• Human-mouse alignments capture
about 75% of CRMs (pTRRs)
– Tend to occur close to genes
involved in apoptosis, steroid
hormone receptors, etc.
• Within aligned noncoding DNA of
eutherians, need to distinguish
constrained DNA (purifying
selection) from neutral DNA.
Interferon beta Enhancer-Promoter
Expected properties of gene regulatory regions
•
Can be almost anywhere
– 5’ or 3’ to gene
– Within introns
– Close or far away
•
Conserved between species (sometimes)
– Examine interspecies alignments, noncoding regions
– Evaluate likelihood of being under purifying selection, e.g. phastCons score
– Some regulatory regions are deeply conserved, others are lineage-specific
•
Enhancers and promoters: clusters of binding sites for transcription factors
(TFBSs)
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–
–
–
–
–
Resources and servers for finding TFBSs
TRANSFAC http://www.gene-regulation.com/
JASPAR http://jaspar.cgb.ki.se/cgi-bin/jaspar_db.pl
TESS
http://www.cbil.upenn.edu/cgi-bin/tess/tess
MOTIF (GenomeNet) http://motif.genome.jp/
MatInspector http://www.genomatix.de/
Finding known motifs in a query sequence
MatInspector at http://www.genomatix.de/
K. Cartharius et al. (2006) MatInspector and beyond: promoter analysis based on transcription factor
binding sites. Bioinformatics 21:2933-2942. Genomatix Software GmbH, Munchen, Germany
Query: a UCE
in SOX6
1356 bp
About 1 in 4
bp is the start
of a TFBS
match!
Conservation of TFBSs 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
Conserved TFBSs are available for some assemblies of human genome at UCSC
Genome Browser
Binding site for GATA-1
Clusters of conserved TFBSs: PReMods
http://genomequebec.
mcgill.ca/PReMod/
Blanchette et al.
(2006) Genome
Research
ESPERR
Evolutionary and Sequence Pattern
Extraction through Reduced
Representation
ESPERR: a different approach
• Don’t assume a database of known binding
motifs
• Don’t assume strict conservation of the important
sequence signals
• Instead, use alignments of validated examples to
learn sequence and evolutionary patterns that
characterize a class of elements
Objective of ESPERR
ESPERR overview
Represent columns with ancestral distributions
Group columns using evolutionary similarity
and frequency distribution
An agglomerative algorithm
Searching for encodings
Evaluate “merit” of candidate mappings
Iterate until convergence
Search convergence behavior
Regulatory potential (RP) to distinguish
functional classes
Variable order Markov models for discrimination
Use ESPERR to compute Regulatory Potential
Good performance of ESPERR for gene
regulatory regions (RP)
-1
Experimental tests of predicted cisregulatory modules
GATA-1 is required for erythroid maturation
Common
myeloid
progenitor
MEP
Hematopoietic
stem cell
G1E cells
GATA-1
Myeloblast
Common
lymphoid
progenitor
G1E-ER4 cells
Basophil
Eosinophil
Neutrophil
Aria Rad, 2007 http://commons.wikimedia.org/wiki/Image:Hematopoiesis_(human)_diagram.png
Monocyte,
macrophage
Genes Co-expressed in Late Erythroid Maturation
G1E-ER cells: proerythroblast line lacking the transcription factor GATA-1.
Can rescue by expressing an estrogen-responsive form of GATA-1
Rylski et al., Mol Cell Biol. 2003
Predicted cis-Regulatory Modules (preCRMs)
Around Erythroid Genes
preCRMs with conserved consensus GATA-1 BS
tend to be active on transfected plasmids
preCRMs with conserved consensus GATA-1
BS tend to be active after integration into a
chromosome
Examples of validated preCRMs
Correlation of Enhancer Activity with RP Score
Validation status for 99 tested fragments
preCRMs with High RP and Conserved
Consensus GATA-1 Tend To Be Validated
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).
• Patterns in alignments and conservation of some TFBSs
can be used to predict some cis-regulatory elements.
• The predictions of cis-regulatory elements for erythroid
genes are validated at a good rate.
• Databases and servers such as the UCSC Table Browser,
Galaxy, and others provide access to these data.
– http://genome.ucsc.edu/
– http://www.bx.psu.edu/
Many thanks …
Wet Lab: Yuepin Zhou, Hao Wang, Ying
Zhang, Yong Cheng, David King
Alignments, chains, nets, browsers, ideas, …
Webb Miller, Jim Kent, David Haussler
PSU Database crew: Belinda Giardine,
Cathy Riemer, Yi Zhang, Anton Nekrutenko
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