Recognition of regulatory signals

1
Recognition of regulatory signals

• Mikhail S. Gelfand
• IntegratedGenomics-Moscow
• NATO ASI School, October 2001

2
Why?

• Additional annotation tool (e.g. specificity of
  transporters and enzymes from large families)
• Important for practice (in addition to metabolic
  reconstruction)
• Interesting from the evolutionary point of view

3
Overview

• 0. Biological introduction
• 1. Algorithms
• Representation of signals
• Deriving the signal
• Site recognition
• 2. Comparative genomics
• Phylogenetic footprinting
• Consistency filtering

4
Some biology

• Transcription (DNA ? RNA)
• Splicing (pre-mRNA ? mRNA)
• Translation (mRNA ? protein)
• Regulation of transcription in prokaryotes
• and eukaryotes
• Initiation of translation

5
Transcription and translation in prokaryotes
6
Initiation of transcription (bacteria)
7
Translation in prokaryotes
8
Translation (details)
9
Splicing (eukaryotes)
10
Regulation of transcriptionin prokaryotes
11
Structure of DNA-binding domain. Example 1
12
Structure of DNA-binding domain. Example 2
13
Protein-DNA interactions
14
Regulation of transcriptionin eukaryotes
15
Representation of signals

• Consensus
• Pattern (consensus with degenerate positions)
• Positional weight matrix (PWM, or profile)
• Logical rules
• RNA signals

16
Consensus

• codB CCCACGAAAACGATTGCTTTTT
• purE GCCACGCAACCGTTTTCCTTGC
• pyrD GTTCGGAAAACGTTTGCGTTTT
• purT CACACGCAAACGTTTTCGTTTA
• cvpA CCTACGCAAACGTTTTCTTTTT
• purC GATACGCAAACGTGTGCGTCTG
• purM GTCTCGCAAACGTTTGCTTTCC
• purH GTTGCGCAAACGTTTTCGTTAC
• purL TCTACGCAAACGGTTTCGTCGG
• consensus ACGCAAACGTTTTCGT

17
Pattern

• codB CCCACGAAAACGATTGCTTTTT
• purE GCCACGCAACCGTTTTCCTTGC
• pyrD GTTCGGAAAACGTTTGCGTTTT
• purT CACACGCAAACGTTTTCGTTTA
• cvpA CCTACGCAAACGTTTTCTTTTT
• purC GATACGCAAACGTGTGCGTCTG
• purM GTCTCGCAAACGTTTGCTTTCC
• purH GTTGCGCAAACGTTTTCGTTAC
• purL TCTACGCAAACGGTTTCGTCGG
• consensus ACGCAAACGTTTTCGT
• pattern aCGmAAACGtTTkCkT

18
Frequency matrix
I ?j ?b f(b,j)log f(b,j) / p(b)
Information content
19
Sequence logo
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Positional weight matrix (PWM)
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• Probabilistic motivation log-likelihood (up to a
  linear transformation)
• More probabilistic motivation z-score (with the
  suitable base of the logarithm)
• Thermodynamical motivation free energy (assuming
  independence of positions, up to a linear
  transformation)
• Pseudocounts

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Logical rules, trees etc.
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Compilation of samples

• Initial sample
• GenBank
• specialized databases
• literature (reviews)
• literature (original papers)
• Correction of GenBank errors
• Checking the literature
• removal of predicted sites
• Removal of duplicates

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Re-alignment approaches

• Initial alignment by a biological landmark
• start of transcription for promoters
• start codon for ribosome binding sites
• exon-intron boundary for splicing sites
• Deriving the signal within a sliding window
• Re-alignment
• etc. etc. until convergence

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Gene starts of Bacillus subtilis

• dnaN ACATTATCCGTTAGGAGGATAAAAATG
• gyrA GTGATACTTCAGGGAGGTTTTTTAATG
• serS TCAATAAAAAAAGGAGTGTTTCGCATG
• bofA CAAGCGAAGGAGATGAGAAGATTCATG
• csfB GCTAACTGTACGGAGGTGGAGAAGATG
• xpaC ATAGACACAGGAGTCGATTATCTCATG
• metS ACATTCTGATTAGGAGGTTTCAAGATG
• gcaD AAAAGGGATATTGGAGGCCAATAAATG
• spoVC TATGTGACTAAGGGAGGATTCGCCATG
• ftsH GCTTACTGTGGGAGGAGGTAAGGAATG
• pabB AAAGAAAATAGAGGAATGATACAAATG
• rplJ CAAGAATCTACAGGAGGTGTAACCATG
• tufA AAAGCTCTTAAGGAGGATTTTAGAATG
• rpsJ TGTAGGCGAAAAGGAGGGAAAATAATG
• rpoA CGTTTTGAAGGAGGGTTTTAAGTAATG
• rplM AGATCATTTAGGAGGGGAAATTCAATG

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• dnaN ACATTATCCGTTAGGAGGATAAAAATG
• gyrA GTGATACTTCAGGGAGGTTTTTTAATG
• serS TCAATAAAAAAAGGAGTGTTTCGCATG
• bofA CAAGCGAAGGAGATGAGAAGATTCATG
• csfB GCTAACTGTACGGAGGTGGAGAAGATG
• xpaC ATAGACACAGGAGTCGATTATCTCATG
• metS ACATTCTGATTAGGAGGTTTCAAGATG
• gcaD AAAAGGGATATTGGAGGCCAATAAATG
• spoVC TATGTGACTAAGGGAGGATTCGCCATG
• ftsH GCTTACTGTGGGAGGAGGTAAGGAATG
• pabB AAAGAAAATAGAGGAATGATACAAATG
• rplJ CAAGAATCTACAGGAGGTGTAACCATG
• tufA AAAGCTCTTAAGGAGGATTTTAGAATG
• rpsJ TGTAGGCGAAAAGGAGGGAAAATAATG
• rpoA CGTTTTGAAGGAGGGTTTTAAGTAATG
• rplM AGATCATTTAGGAGGGGAAATTCAATG
• cons. aaagtatataagggagggttaataATG
• num. 001000000000110110000000111
• 760666658967228106888659666

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• dnaN ACATTATCCGTTAGGAGGATAAAAATG
• gyrA GTGATACTTCAGGGAGGTTTTTTAATG
• serS TCAATAAAAAAAGGAGTGTTTCGCATG
• bofA CAAGCGAAGGAGATGAGAAGATTCATG
• csfB GCTAACTGTACGGAGGTGGAGAAGATG
• xpaC ATAGACACAGGAGTCGATTATCTCATG
• metS ACATTCTGATTAGGAGGTTTCAAGATG
• gcaD AAAAGGGATATTGGAGGCCAATAAATG
• spoVC TATGTGACTAAGGGAGGATTCGCCATG
• ftsH GCTTACTGTGGGAGGAGGTAAGGAATG
• pabB AAAGAAAATAGAGGAATGATACAAATG
• rplJ CAAGAATCTACAGGAGGTGTAACCATG
• tufA AAAGCTCTTAAGGAGGATTTTAGAATG
• rpsJ TGTAGGCGAAAAGGAGGGAAAATAATG
• rpoA CGTTTTGAAGGAGGGTTTTAAGTAATG
• rplM AGATCATTTAGGAGGGGAAATTCAATG
• cons. tacataaaggaggtttaaaaat
• num. 0000000111111000000001
• 5755779156663678679890

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Positional information content before and after
re-alignment
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Positional nucleotide frequencies after
re-alignment (aGGAGG pattern)
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Enhancement of a weak signal
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Deriving the signal ab initio

• Discrete (pattern-driven) approaches word
  counting
• Continuous (profile-driven) approaches
  optimization

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Word counting. Short words

• Consider all k-mers
• For each k-mer compute the number of sequences
  containing this k-mer
• (maybe with some mismatches)
• Select the most frequent k-mer

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• Problem Complete search is possible only for
  short words
• Assumption if a long word is over-represented,
  its subwords also are overrepresented
• Solution select a set of over-represented words
  and combine them into longer words

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Word counting. Long words

• Consider some k-mers
• For each k-mer compute the number of sequences
  containing this k-mer
• (maybe with some mismatches)
• Select the most frequent k-mer

35

• Problem what k-tuples to start with?
• 1st attempt those actually occurring in the
  sample.
• But the correct signal (the consensus word) may
  not be among them.

36

• 2nd attempt those actually occurring in the
  sample and some neighborhood.
• But
• again, the correct signal (the consensus word)
  may not be among them
• the size of the neighborhood grows exponentially

37
Graph approach

• Each k-mer in each sequence corresponds to a
  vertex. Two k-mers are linked by an arc, if they
  differ in at most h positions (hltltk).
• Thus we obtain an n-partite graph (n is the
  number of sequences).
• A signal corresponds to a clique (a complete
  subgraph) or at least a dense subgraph with
  vertices in each part.

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A simple algorithm

• Remove vertices that cannot be extended to
  complete subgraphs
• that is, do not have arcs to all parts of the
  graph
• Remove pairs that cannot be extended
• that is, do not form triangles with the third
  vertex in all parts of the graph
• Etc.
• (will not work as is for dense subgraphs)

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Optimization. EM algorithms

• Generate an initial set of profiles (e.g. seed
  with all k-mers)
• For each profile
• find the best (highest scoring) representative in
  each sequence
• update the profile
• Iterate until convergence

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• This algorithm converges.
• However, it cannot leave the basin of attraction.
• Thus, if the initial approximation is bad, it
  will converge to nonsense.
• Solution stochastic optimization.

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Simulated annealing

• Goal maximize the information content I
• I ?j ?b f(b,j)log f(b,j) / p(b)
• or any other measure of homogeneity of the sites

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• Let A be the current signal (set of candidate
  sites), and let I(A) be the corresponding
  information content.
• Let B be a set of sites obtained by randomly
  choosing a different site in one sequence, and
  let I(B) be its information content.
• if I(B) ? I(A), B is accepted
• if I(B) lt I(A), B is accepted with probability
• P exp (I(B) I(A)) / T
• The temperature T decreases exponentially, but
  slowly the initial temperature is chosen such
  that almost all changes are accepted.

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Gibbs sampler

• Again, A is a signal (set of sites), and I(A) is
  its information content.
• At each step a new site is selected in one
  sequence with probability
• P exp (I(Anew)
• For each candidate site the total time of
  occupation is computed.
• (Note that the signal changes all the time)

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Use of symmetry

• DNA-binding factors and their signals
• Co-operative homogeneous
• Palindromes
• Repeats
• Co-operative non-homogeneous
• Cassetes
• Others
• RNA signals

45
Recognition PWM/profiles

• The simplest technique positional nucleotide
  weights are
• W(b,j)ln(N(b,j)0.5) 0.25?iln(N(i,j)0.5)
• Score of a candidate site b1bk is the sum of the
  corresponding positional nucleotide weights
• S(b1bk ) ?j1,,kW(bj,j)

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Distribution of RBS profile scores on sites
(green) and non-sites (red)
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Pattern recognition

• Linear discriminant analysis
• Logical rules
• Syntactic analysis
• Context-sensitive grammars
• Perceptron
• Neural networks

48
Neural networks architecture

• 4?k input neurons (sensors), each responsible for
  observing a particular nucleotide at particular
  position
• OR 2?k neurons (one discriminates between purines
  and pyrimidines, the other, between AT and GC)
• One or more layers of hidden neurons
• One output neuron

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• Each neuron is connected to all neurons of the
  next layer
• Each connection is ascribed a numerical weight
• A neuron
• Sums the signals at incoming connections
• Compares the total with the threshold (or
  transforms it according to a fixed function)
• If the threshold is passed, excites the outcoming
  connections (resp. sends the modified value)

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Training

• Sites and non-sites from the training sample are
  presented one by one.
• The output neuron produces the prediction.
• The connection weights and thresholds are
  modified if the prediction is incorrect.
• Networks differ by architecture, particulars of
  the signal processing, the training schedule

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Use of sequence context

• Presence of multiple co-operative sites
• ArgR (E. coli), purine regulator (Pyrococcus)
• XylRCRP CytRCRP (E. coli)
• MEFMyoD in muscle-specific promoters (mammals)
• Location relative to promoters
• repressors vs. activators

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Benchmarking

• Difficult, because
• Different algorithms are optimized for different
  performance parameters
• Incompatible training sets
• Difficult to construct a homogeneous and
  unambiguous testing set
• Unobserved sites
• Competition between closely located sites
• Activation in specific conditions
• non-specific binding (52 out of 54 candidate
  HNF-1 binding sites do bind the factor)

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Promoters of E. coli

• PWM at false positive rate 1 per 2000 bp
• 25 of all promoters,
• 60 of constitutive (non-activated) promoters
• PWM perform as well as neural networks

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Eukaryotic promoters
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Ribosome binding sites

• Information content of the profile predicts the
  average reliability of predictions

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CRP (E. coli)
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Comparative approach to the analysis of regulation

• Making good predictions
• with bad rules

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Regulation of transcription in prokaryotes

• Difficult
• Small sample size
• Weak signals (or we do not know what features are
  relevant, maybe the DNA structure)

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CRP (E. coli)
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GenBank entry for the E. coli genome
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• Many genomes are available gt
• gt comparative approach
• Basic assumption
• Regulons (sets of co-regulated genes) are
  conserved
• well in some cases
• in fact, in many cases

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Corollary The consistency check

• True sutes occur upstream of orthologous genes
• False sites are scattered at random

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Orthologs

• Orthologous genes
• diverged by specitation
• retain cellular role
• Paralogous genes
• diverged by duplication
• retain biochemical function only

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Orthology (definition)
 
 
 

duplication

 
 

• Genomes are shown as black pipes
• 1st event duplication
• 2nd event specitation
• Genes of the same color are orthologous
• Genes of different color are paralogous

 

 
 
 
 
 
 
A1 B1
A2 B2
Genome 1 Genome
2 A1 and A2 are orthologs, B1 and B2 are
orthologs, all other pairs are paralogs
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Search for orthologs (fast and dirty)
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The basic procedure
Genome 1
Genome 2
Genome N
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Accounting for the operon structure
68
Checklist

• Presence of orthologous transcription factors
• Really orthologous (BETs, COGs etc. are not
  sufficient)
• Conservation of the DNA-binding domain
• Conservation of the core pathway

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Purine regulons of E. coli and H. influenzae
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Predicted purine transporters
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Changes in the operon structure more examples

• glnK-amtB loci of methanogenic acrhaebacteria

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Tryptophan operons
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Heat chock (HrcA) regulons / CIRCE elements
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Closely related genomes Phylogenetic footprinting

• Regulatory sites are more conserved than
  non-coding regions in general and are often seen
  as conserved islands in alignments of gene
  upstream regions.

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High conservation
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Low conservation
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Degeneration of sites
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Problems and solutions

• Unique members of regulons may be lost use of
  additional genomes decreases the number of
  orphan regulon members.
• Closely related factors may have similar sites
  careful analysis of function and analysis of
  particular sites is usually sufficient to resolve
  ambiguities.
• Too many genomes and regulons apply preliminary
  automated screening.

79
Modification ubiquitous regulators

• Present in many genomes
• Only core regulon is conserved
• Mode of regulation may vary
• Signals may be slightly different

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Arginine repressor ArgR/AhrC
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ABC transporters (periplasmic components)
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Modification horizontal transfer

• Impossible to resolve the orthology
  relationships a homologous regulated gene is
  sufficient for corroboration
• Often rgulate large loci (several adjacent
  operons)
• Signals are mainly conserved

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New signals

• Select a group of related genomes
• In each genome select metabolically related genes
• Add possibly co-transcribed genes
• Compare upstream regions for each genome
  independently
• Construct profiles
• Compare constructed profiles if similar, then
  relevant

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The purine regulon of Pyrococcus spp.

• Use functional annotation and COGs to select
  genes encoding enzymes from purine pathway purA,
  purB, purC, purF, purD, purE, purL-I, purL-II,
  purT, guaA.
• Construct profiles for each genome. The quality
  of profiles is weak (lt 1 bit/position).
• However, the profiles are almost identical.
• There is no significant similarity of upstream
  regions (outside sites). Thus the profiles are
  probably correct.
• Low specificity of profiles, thus gt300 candidate
  genes in each genome.
• Observation in upstream regions of all genes
  from the initial sample the candidate sites occur
  twice with 22 bp spacer.
• The new rule is absolutely specific only one
  additional gene in each genome.

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2895752_Ef
PyrP_Bc
UraA_Ec
PyrP_Bs
996
UAPA_En
714
UraA_Hi
940
UAPC_En
997
YcdG_Ec
758
YgfO
997
746
965
YicE
YtiP_Bs
PH
981
980
997
YgfU
2239289_Bs
969
PA A
778
2635740_Bs
PF
965
999
2314333_Hp
749
979
2635741_Bs
2689889_Bb
998
YjcD_Hi
994
1000
Y326_Mj
PbuX_Bs
2689890_Bb
YjcD
YgfQ
YieG
YicO
86
Sources

• G. Stormo
• J. Fickett
• W. Miller
• I. Dubchak
• Yuh et al. (1998)
• Tronche et al. (1997)
• textbooks

87
Discussions and collaboration

• Farid Chetouani (Institute Pasteur)
• Eugene Koonin (NCBI)
• Yuri Kozlov (Aginomoto)
• Leonid Mirny (Harvard - MIT)
• Alexander Mironov (GosNIIGenetika)
• Vasily Lybetsky (Inst. Probl. Inform. Trans.)
• Andrey Osterman (IntegratedGenomics)
• Danila Perumov (Inst. Nucl. Phys.)
• Pavel Pevzner (UC San Diego)
• Michael Roytberg (Inst. Math. Probl. Biol.)

88
Collaborators

• Andrey A. Mironov
• A. B. Rakhmaninova
• Vadim Brodyansky
• Lyudmila Danilova
• Anna Gerasimova
• Alexey Kazakov
• Ekaterina Kotelnikova

• Olga Laikova
• Pavel Novichkov
• Ekaterina Panina
• Elya Permina
• Dmitry Ravcheev
• Dmitry Rodionov
• Natalya Sadovskaya
• Alexey Vitreschak