Computational Biology and Modeling of Gene Regulationin Bacteria

Julio Collado-Vides
Universidad Nacional Autónoma de México
Centro de Ciencias Genómicas
Cuernavaca, Mexico
GIW, Brisbane, Australia, Dec 1-3, 2008

UNA

M

Campus Morelos

The outline:

Collado-Vides J and Salgado H. J. Bacteriol. rejected1

Escherichia coli: Model with knowledge
As of July 2008
Escherichia coli

Regulons
TF BSsPromoters
TFs
TUs
and more..

361
2342
1754
163
3356

Bacillus subtilis

Textpresso access toliterature on gene
regulation

2472 full papers
3125 abstracts
4200 curation notes2View Matches Page

Rodríguez-Penagos, C., Salgado, H., Martínez-Flores, I., and Collado-Vides, J. (2007) “Automaticreconstruction of a bacterial regulatory network using Natural Language Processing” BMC Bioinformatics 8:
293.

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Proximal sites in σ70 promoters:
enable direct contact with RNAP

-65 to +20 (1991)-95 to +20 (2008)

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1991:
2008:

119 σ70 promoters421 σ70 promoters with 1382 TF-BSs
Only 26 promoters (6%) lack a proximal site

Collado-Vides J., Magasanik B. and Gralla J.D. (1991) "Control site location and transcriptional regulation in
Escherichia coli" Microbiol. Reviews. 55:371-394
Collado-Vides et al., (2009) “Bioinformatics resources for the study of gene regulation in bacteria” J. of Bacteriology(inaugural issue in Computational Biology) -in press.

Origins of RegulonDB: 1991 and 1998 -ten years ago

Collado-Vides J., Magasanik B. and Gralla J.D. (1991)"Control site location and transcriptional regulation in
Escherichia coli" Microbiol. Reviews. 55:371-394

Huerta A.M.; Salgado H.; Thieffry D., and ColladoVides J. (1998) “RegulonDB: A Database on
Transcription Regulation in Escherichia coli”
Nucleic Acids Res. 26: 55-60

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Different sigma-types of promoter
predictions
CutoffAccuracy(µ - X σ)(sensitivity,precision)

39-1.9 (0.14 – 2 * 0.98)

0.85 (0.75,0.95)

121

7.32 (10.8 – 1 * 3.6)

0.9 (0.9,0.9)412

6.23 (9.56 – 1 * 3.2)

0.6 (0.5,0.6)

1767

1243

4.48 (5.6 – 1 * 2.2)

0.6 (0.6,0.5)

Sigma1944

NA-Sigma54

152

147

7.10 (9.7 – 0.5 * 3.2)

0.8 (0.7,1.0)

Total

3,316

2,609

--

Sigma
FactorNo. of Promoters

Promoters w-

Sigma24

6160

Sigma24a39

Sigma28

205

Sigma28

123

Sigma32

3122

Sigma38

81

38

Sigma32

455

Sigma19

11

Sigma54

32

16

Sigma38

Total

226

142

Strong Evidence*

Sigma#Predictios#Regions

Araceli Huerta
aRhodius et al. 2006 PLoS

Position Weight matrices

 f' 
w i, j = ln i, j  pi 

Olivier Sand, Jean Valéry Turatsinze and Jacques van Helden

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Theoretical ditribution of scores•matrix-distrib (RSAT) computa la distribución teórica de scores completa.
P(W=w|M): probability offinding a score equal tow.
P(W >= w|M):
Probability of finding by
chance a score larger oequal to w. We use thisas a p-value.

We expect byrandom a scoreequal to 10
every 270kbs

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Empirical score distribution• 10 permutations

Shuffled Matrices7

A comprehensive
repertoire of PWMs
for 54 TFs- P-values 1.60E-05 or smaller

Medina-Rivera A., Abreu-Goodger C., Salgado H.,
Collado-Vides J., and van Helden J. “The good, the badand the ugly: evaluating transcription factor binding
motifs in a genome repertoire (submitted)

Factor
DgsA
FhlA
GcvA
HipB
MelR
MetR
UlaR
XylR
ArgR
GntR
Rob
NtrC
OxyR
GadE
NsrR
MetJ
ModE
MalT
TyrR
CysB
Fur
LexA
NagC
GlpR
CytR
TorR
TrpR
AgaR
IscR
PhoB
PurR
FNR
IclR
OmpR
ArgP
Nac
NanR
SoxS
RcsAB
MarA
AraC
CRP
DnaA
GalR
ArcA
IHF
FadR
Fis
NarL
CsgD
DeoR
FlhDC
FruR
GalS
Lrp
NarP
PhoP
RhaS

AnnotatedSites
74541064430
13617115525
720
19976
2914231081011
1020
1979
10
22
412
918
1016
18
241
8
889
100
12219
80
4720
12756
1820
4

PredictedSites
1010741
42
17
12
2370
37
2392
42
51073
1220
50
13
48
77
20
63
15
911
1225
69
92
109
1840
3
12534
1221
19176
13
1937
69
15134
24
9725
28
14
18
36
65
35

TP
7
4
54
10
644
28
12
514
94
4
18
5
14
13
650
19914
64
5548
726
3
6132423340
11910151000000000PPV0,70,40,70,10,20,40,30,20,40,30,20,20,20,80,40,20,40,70,30,51,00,20,50,20,40,40,50,40,20,10,10,20,20,20,30,30,40,10,20,10,20,20,10,10,20,10,10,00,00,00,00,00,00,00,00,00,00,0

Sensitivity
1,01,01,01,01,01,01,01,00,90,90,80,80,80,80,80,70,70,70,70,70,70,70,60,60,60,50,50,50,40,40,40,30,30,30,30,30,20,20,20,20,20,20,10,10,10,10,10,00,00,00,00,00,00,00,00,00,00,0

Recov. Percent
100,0100,0100,0100,0100,0100,0100,0100,093,392,383,382,481,880,080,072,071,470,068,466,765,865,564,360,960,050,050,045,540,040,036,832,930,027,325,025,022,222,2
20,0
18,8
16,7
16,612,512,510,1
10,0
8,3
2,3
1,3
0,00,00,00,00,00,00,00,00,08

OperonBiopaxCrppdhR
Crp Crp Crp
Crp FnrPdhR

aceE

aceF

lpd
ArcA Fis ArcA

Irma Martínez
Transcriptional Regulatory Interaction

Transcriptional Regulation

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TFs and sigma specific transcription initiation

Topology of this updated network ?
Figure 1ACabcdefghiBabcdefghi

Dabcdefghi

abcdefghi

Freyre-González J., Alonso-Pavón J.A., Treviño-Quintana L., and Collado-Vides J. “Functional architecture ofEscherichia coli: new insights provided by a natural decomposition approach” (2008) Genome Biology 9: R154

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Identifying global TFs by network decomposition (Julio Freyre)11

Feedback loops in the regulatory network: several positive ones

Para ver esta película, debe
disponer de QuickTime™ y de
un descompresor .

Freyre-González J., Alonso-Pavón J.A., Treviño-Quintana L., and Collado-Vides J. “Functional architecture ofEscherichia coli: new insights provided by a natural decomposition approach” (2008) Genome Biology 9: R154

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Phenotypic space: Discretization of expression -dialogue withRegulonDB.

Discretization-1: An Absolute Reference
To discretize the expression of a gene across many experiments,
we used the complete dynamic range of expression as the reference.
This will allow us to explore the plasticity of a gene’s expression.

arsR

araB

fliY

Histograms of the expression level of different genes in different conditions.
The dynamic range and the typical values are easily observed.
1) 380 experiments, 730 chips. Faith JJ et al., Many Microbe Microarrays Database: uniformly normalized
Affymetrix compendia with structured experimental metadata. Nucleic Acids Research.

Discretization-2: The Algorithm

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Discretization-3: Measuring Congruence to Validate

Error bars cover 95% of thevariability in the shuffled data

Discretization-4: Discretized data will enable..
•Infere discrete regulatory functions.
Towards a discrete dynamical model of E. coli’s transcriptional regulatory network•Estimate the number of functional states (cell types).
How many essentially different gene expression patterns does E. coli’s network encode?

In the simulated E.coli network,
We expect 3-5 steps to reach an
attractor vs a larger number in achaotic network .
Santiago Sandoval and Maximino Aldana

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Functional domains of CRP:
DNA binding and allosteric, cAMP binding

DNA binding

5’-ATGTCACATTAATTGCGTTGCGC-3’
DNA binding C-terminal
domaincAMP N-terminal binding domain

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LewinI

Inducible systems

Repressible systems

I
Symbols
Effector

II

Activator
Repressor

II16

Future:1. We need to integrate signals and effectors
with regulatory mechanisms

Presence of lactose & absence of glucose

Condition

SignalP
ATP <=> cyclic‐AMP + diphosphate

EIIA

cyclic‐AMP‐CRP+lacZYA

lacZ

Lactose

LacY

LactoseAllolactose

Allolactose‐LacI

Signal17

2. integrating signal sensing, regulation, affected metabolic
capabilities and feedback.

Signaling
Pathways

MolecularInteraction
Networks

Metabolicconsequences

Hierarchy vs circular causality

3. Regulation: a variety of mechanisms, interactions and effects

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Extending the trilogy?

GENETICREGULATION

Mechanism

Executors Target site

EffectTranscription
Transcription Factors: ProteinDNA

ProteinDNA

TF: transcription
factor

TFBS: transcription factor
binding sites. Operator sites

Regulates promoter
activity.

Structural: DNA-Protein

ProteinDNA

Supercoiling, Methylation of DNA

NAS: nucleoid
associatedproteins (IHF,HNS, HU)

DNA can be altered as aresult of interaction of skilledproteins

Modifies accessibilityof promoter to DNA
regions.

Sigma Factors: Protein-Protein

ProteinProtein

Example: Sigma Factors

Providesspecificity ofRNAP-promoter

RNAP core have toassociates with different
sigma factors to be activatedunder different environmental
conditions.

REPRESSION
Example:ACTIVATION
Example:Confers functionality
and specificity toRNAP fpr promoter
recognition

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GENETIC
Mechanism

REGULATION

Executors Target Interaction EffectTranscription
Transcriptional Attenuation:
Protein-DNA
Example: Attenuation in amino acid biosynthetic trpoperonRNAProtein

RNA structure
formed as stabletranscript hairpin
followed by aseries of U’s.

The RNA polymerase getsstuck in the termination fork
and finally it is released,stopping the transcription.

RNARNAIt acts by pairingwith their targetmRNAs.

The target is the mRNA withcomplementary sequence

It affects terminationof transcription
readthrough at asingle discrete sitethat precedes aregulated gene.

Posstranscriptional Regulation
Antisense RNA: RNA-RNA
REPRESSION
Example:ACTIVATION
Example:Regulation ofsynthesis ofproteins, byaffecting mRNAtranslation

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High throughput transcription start sites(TSSs) mapping. Active annotation
Aim: To enrich our understanding of transcriptional
regulation by mapping as many TSS as possible toidentify:
Promoters
Transcription Factors Binding sites
Operon structure
In collaboration with Enrique Morett, IBT, UNAM

Our limited knowledge of promoters in E. coli21

Magallanes Project:
Global Transcription Initiation Mapping and Active Promoter Annotation

Transcriptional
units database

Microarraydatabase

Primerdesign

Experimental mapping procedure:
Directed Mapping of Transcriptional Start Sites (DMTSS) by 5’ RACE (rapidamplification cDNA ends) modified for high-throughput

Results
anddata analysis

Active annotation inRegulon DB*Transcription start site
*Promoter assignation*Sigma factor assignation
Etc.

Strategy to directed mapping of transcription start sites (DMTSS)

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A

B

D

A. ycbBp

B. serSp

B. ychHp

48 Control TSSs mappedtdk

KnownTSS

hnsgalU

CTSS*

Examples of the 273 new TSSs mapped

TGTTGTCTATACTTTACACATAAGGAAGAGGGGTATTCCCTGTTACAACCCAGAAAGTTCCGGAGGTGACATATGAAACGCAATTG CGGTAATGTTGTTACTGTATCCCTGTGGTCGCAGGCTGTGGCCACATCTCCCATTTAATTCGATAAGCACAGGATAAGTTTATATACTGAAGATAAGCCTGATGAGTAACAGGCTTGCTCGTCATACTTTCGTGAGTATTGGCGTTGTACAGGCAAGT23

Promoter scores of genes with known or unknown function
About half of the new TSS are for genes with unknown function
Are they the product of real promoters?

Total promoters by gene
Distance of the TSS from the initial ATG codon
10080% of genes604020012% of genes mapped

34567

total promoters% of genes in RegulonDB

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Sequencing with 454 GS20 instrument
Clonal and fast!
DNA library construction

DNA breaking

A & B adapters
incorporation

single strand purification

ssDNA libraryclonal PCRs
emulsion
microreactor
sbinding ssDNA librarywith capture beads

Clonal amplification
for PCR

breaking emulsions

Pyrosequencing

load capture
beads inmicroplates
pyrosequencing reaction

dataExamples of the pyrosequencing results:
Multiple sequences of known TSS.

Color of the arrows represents growth condition

Confirmation of the recently mapped dps TSS by S. Busby’s lab

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Transcription not always starts
at an exact nucleotide!
cspA**TSS, P1

TSS, P2

Results of Pyrosequencing
More than 1000 TSSs mapped. Only the highquality ones will be incorporated in RegulonDB
New information about relative expression indifferent conditions.
New information about mechanism of transcription
initiation.

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Computational Genomics Program

Funding: NIGMS, UNAM and Conacyt

Undergraduate program in genomics

COLD SPRING
HARBORLABORATORY
Ph.D. fellowship27

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