4. lecture ws 2004/05bioinformatics iii1 intro: transcriptional regulatory networks regulondb:...

4. Lecture WS 2004/05

Bioinformatics III 1

Intro: Transcriptional regulatory networks

RegulonDB: database with information on transcriptional regulation and operon

organization in E.coli; 105 regulators affecting 749 genes

7 regulatory proteins (CRP, FNR, IHF, FIS, ArcA, NarL and Lrp) are sufficient

to directly modulate the expression of more than half of all E.coli genes.

Out-going connectivity follows a power-law distribution In-coming connectivity follows

exponential distribution (Shen-Orr).

Martinez-Antonio, Collado-Vides, Curr Opin Microbiol 6, 482 (2003)



Intro: frequency of co-regulation

Regulation by multiple TFs occurs in half of genes.

In most cases, a „gobal“ regulator (with > 10 interactions) works together with a

more specific local regulator.




Intro: regulation of TFs and club co-regulation

However, in a process of decisions and

information flux, the number of controlled

or affected elements is not the only factor

to be considered.

A hierarchy of different levels of decision

is natural to our understanding of how

things get done.

In general, global regulators work

together with other global regulators.

Dynamics of decison-making is a

cooperative process of different

subsets of the network put into action

at certain moments.




Intro: response to changes in environmental conditions

The second function of TFs is to sense changes in environmental conditions or

other internal signals encoding changes.


Global environment growth conditions in which TFs are regulating.

# in brackets indicates how many additional TFs participate in the

same number of conditions.



Do we need to rely on experiments?

Determine homology between the domains and protein families

of TFs and regulated genes

and proteins of known 3D structure.

Determine uncharacterized E.coli proteins with

DNA-binding domains, thus identify large majority

of E.coli TFs.

Finding: 75% of all TFs are two-domain proteins.

Analysis of domain architecture shows that 75% of

the TFs have arisen by gene duplication.

Babu, Teichmann, Nucl. Acid Res. 31, 1234 (2003)

Sarah Teichmann

MRC LMB Cambridge

Madan Babu,

PhD student at LMB



Flow chart of method to identify TFs in E.coli

SUPERFAMILY database (C. Chothia)

contains a library of HMM models based

on the sequences of proteins in SCOP for

predicted proteins of completely

sequenced genomes.

In addition to our set of 271 transcription

factors, there are eight transcription

factors without a DBD assignment that

have known regulatory information.

Remove all DNA-binding proteins involved

in replication/repair etc.




3D structures of putative (and real) TFs in E.coli

The three-dimensional

structures of the 11 DBD

families seen in the 271

identified transcription

factors in E.coli. The

figure highlights the fact

that even though the

helix–turn–helix motif

occurs in all families

except the nucleic acid

binding family, the

scaffolds in which the

motif occurs are very

different.




Domain architectures of TFs

The 74 unique domain architectures of the 271

identified TFs. Each functional class is represented by

a different shape and each family within the functional

class is represented by a different colour.

The DBDs are represented as rectangles. The partner

domains are represented as hexagons (small

molecule-binding domain), triangles (enzyme

domains), circles (protein interaction domain),

diamonds (domains of unknown function) and the

receiver domain has a pentagonal shape.

The letters A, R, D and U denote activators,

repressors, dual regulators and TFs of unknown

function, and the number of TFs of each type is given

next to each domain architecture.

Architectures of known 3D structure are denoted by

asterisks, and ‘+’ are cases where the regulatory

function of a TF has been inferred by indirect methods,

so that the DNA-binding site is not known.




Evolution of TFs

10% 1-domain proteins




TFs have evolved by extensive recombination of domains.

Proteins with the same sequential arrangement of domains are likely to be direct

duplicates of each other.

74 distinct domain architectures have duplicated to give rise to 271 TFs.




Organisation of transcriptional regulatory network

For 121 TFs, there is information on their regulated genes.

They can be divided into 10 general functional categories.




Regulatory cascades

The TF regulatory network in E.coli.

When more than one TF regulates a gene,

the order of their binding sites is as given in

the figure. An arrowhead is used to indicate

positive regulation when the position of the

binding site is known.

Horizontal bars indicates negative regulation

when the position of the binding site is

known. In cases where only the nature of

regulation is known, without binding site

information, + and – are used to indicate

positive and negative regulation.

The DBD families are indicated by circles of

different colours as given in the key. The

names of global regulators are in bold.




Design principles of regulatory networks

Wiring diagrams of regulatory networks resemble somehow electrical circuits.

Try to break down networks into basic building blocks.

Search for „network motifs“ as patterns of interconnections that recur in many

different parts of a network at frequencies much higher than those found in

randomized networks.

Shen-Orr et al. Nature Gen. 31, 64 (2002)

Uri Alon

Weizman Institute



Detection of motifs

Represent transcriptional network as a connectivity matrix M

such that Mij = 1 if operon j encodes a TF that transcriptionally regulates operon i

and Mij = 0 otherwise.

Scan all n × n submatrices of M generated by choosing n nodes that lie in a

connected graph, for n = 3 and n = 4.

Submatrices were enumerated efficiently by recursively searching for nonzero

elements.

Compute a P value for submatrices representing each type of connected subgraph

by comparing # of times they appear in real network vs. in random network.

For n = 3, the only significant motif is the feedforward loop.

For n = 4, only the overlapping regulation motif is significant.

SIMs and multi-input modules were identified by searching for identical rows of M.




DOR detection

Consider all operons regulated by ≥ 2 TFs.

Define (nonmetric) distance measure between operons k and j, based on the # of

TFs regulating both operons:

d(k,j) = 1/ (1+n fnMk,n Mj,n)2)

Where fn = 0.5 for global TFs and fn = 1 otherwise.

Cluster operons with average-linkage algorithm.

DORs correspond to clusters with more than 10 connections

with a ratio of connections to TFs > 2.




Network motifs found in E.coli transcript-regul network

a, Feedforward loop: a TF X regulates a second TF

Y, and both jointly regulate one or more operons

Z1...Zn.b, Example of a feedforward loop (L-arabinose utilization).

c, SIM motif: a single TF, X, regulates a set of

operons Z1...Zn. X is usually autoregulatory. All

regulations are of the same sign. No other

transcription factor regulates the operons.

d, Example of a SIM system (arginine biosynthesis).

e, DOR motif: a set of operons Z1...Zm are each

regulated by a combination of a set of input

transcription factors, X1...Xn. DOR-algorithm detects

dense regions of connections, with a high ratio of

connections to transcription factors. f, Example of a DOR (stationary phase response).




Significance of motifs




Regulatory network

Each TF appears only in a single subgraph except for global TFs that can appear in

several subgraphs.




Regulatory networks




Structural organization of transcript-regul networks

Modules: observation that reg. Networks are highly interconnected, very few

modules can be entirely separated from the rest of the network.

Babu et al. Curr Opin Struct Biol. 14, 283 (2004)



Evolution of the gene regulatory network

Larger genomes tend to have more TFs per gene.




Cross-organism comparison

Many TF families are specific to

individual phylogenetic groups or

greatly expanded in some genomes.


In contrast to the high level of conservation of other regulatory and signalling

systems across the crown group eukaryotes,

some of the TF families are dramatically different in the various lineages.



Regulatory interactions across organisms

Are regulatory interactions conserved among organisms? Apparently yes.

Orthologous TFs regulate orthologous target genes.

As expected, the conservation of genes and interaction is related to the

phylogenetic difference between organisms.

Above: Many interactions of (a) can be mapped to pathogenetic Pseudomonas

aeruginosa that is related to E.coli (b).

Very few interactions can be mapped from (a) to (c).




Regulatory interactions across organisms

Observation: there is no bias towards conservation of network motifs.

Regulatory interactions in motifs are lost or retained at the same rate as the other

interactions in the network.

The transcriptional network appears to evolve in a step-wise manner, with loss

and gain of individual interactions probably playing a greater role than loss and

gain of whole motifs or modules.

Observation: TFs are less conserved than target genes, which suggests that

regulation of genes evolves faster than the genes themselves.




Integrate transcriptional regulatory information and gene-expression data for

multiple conditions in Saccharomyces cerevisae.

5 conditions cell cycle

sporulation

diauxic shift

DNA damage

stress response

Something spectacular at the end

Luscombe, Babu, … Teichmann, Gerstein, Nature 431, 308 (2004)



SANDY: topological measures + network motifs

Luscombe et al. Nature 431, 308 (2004)

+ some post-analysis



Dynamic representation of transript. regul. network

c, Standard statistics (global topological measures and local network motifs) describing network structures. These vary between endogenous and exogenous conditions; those that are high compared with other conditions are shaded. (Note, the graph for the static state displays only sections that are active in at least one condition, but the table provides statistics for the entire network including inactive regions.)

Luscombe, Babu, … Teichmann, Gerstein, Nature 431, 308 (2004)

a, Schematics and summary of properties for the endogenous and exogenous sub-networks.

b, Graphs of the static and condition-specific networks. Transcription factors and target genes are shown as nodes in the upper and lower sections of each graph respectively, and regulatory interactions are drawn as edges; they are coloured by the number of conditions in which they are active. Different conditions use distinct sections of the network.




Interpretation

Half of the targets are uniquely expressed in only one condition; in contrast, most

TFs are used across multiple processes.

The active sub-networks maintain or rewire regulatory interactions, over half of

the active interactions are completely supplanted by new ones between conditions.

Only 66 interactions are retained across ≥ 4 conditions.

They are always „on“ and mostly regulate house-keeping functions.

The calculations divide the 5 condition-specific networks into 2 categories:

endogenous and exogenous.

Endogenous processes are multi-stage, operate with an internal transcriptional

program

Exogenous processes are binary events that react to external stimuli with a

rapid turnover of expressed genes.



Figure 2 Newly derived 'follow-on' statistics for network structures. a, TF hub usage in different cellular conditions. The cluster diagram shades cells by the normalized number of genes targeted by TF hubs in each condition. One cluster represents permanent hubs and the others condition-specific transient hubs. Genes are labelled with four-letter names when they have an obvious functional role in the condition, and seven-letter open reading frame names when there is no obvious role. Of the latter, gene names are red and italicised when functions are poorly characterized. Starred hubs show extreme interchange index values, I = 1. b, Interaction interchange (I) of TF between conditions. A histogram of I for all active TFs shows a uni-modal distribution with two extremes. Pie charts show five example TFs with different proportions of interchanged interactions. We list the main functions of the distinct target genes regulated by each example transcription factor. Note how the TFs' regulatory functions change between conditions. c, Overlap in TF usage between conditions. Venn diagrams show the numbers of individual TFs (large intersection) and pair-wise TF combinations (small intersection) that overlap between the two endogenous conditions.





Interpretation

Most hubs (78%) are transient = they are influential in one condition, but less

so in others.

Exogenous conditions have fewer transient hubs (different ).

„Transient hub“: capacity to change interactions between connections.



a, The 70 TFs active in the cell cycle. The

diagram shades each cell by the normalized

number of genes targeted by each TF in a

phase. Five clusters represent phase-specific

TFs and one cluster is for ubiquitously active

TFs. Both hub and non-hub TFs are included.

b, Serial inter-regulation between phase-

specific TFs. Network diagrams show TFs

that are active in one phase regulate TFs in

subsequent phases. In the late phases, TFs

apparently regulate those in the next cycle.

c, Parallel inter-regulation between phase-

specific and ubiquitous TFs in a two-tiered

hierarchy. Serial and parallel inter-regulation

operate in tandem to drive the cell cycle while

balancing it with basic house-keeping

processes. Luscombe et al. Nature 431, 308 (2004)

TF inter-regulation during the cell cycle time-course




Summary

Integrated analysis of transcriptional regulatory information and condition-specific

gene-expression data; post-analysis, e.g. - Identification of permanent and transient hubs- interchange index- overlap in TF usage across multiple conditions.

Large changes in underlying network architecture in response to diverse stimuli, TFs alter their interactions to varying degrees,

thereby rewiring the network some TFs serve as permanent hubs, most act transiently environmental responses facilitate fast signal propagation cell cycle and sporulation proceed via multiple stages

Many of these concepts may also apply to other biological networks.

4. lecture ws 2004/05bioinformatics iii1 intro: transcriptional regulatory networks regulondb:...

Documents

coli tfs

regulation of tfs

identified tfs

real tfs

multiple tfs

function of tfs

additional tfs

domain architectures