Structure and evolution of prokaryotic transcriptional regulatory networks Group Leader MRC Laboratory of Molecular Biology Cambridge M. Madan Babu

Networks in Biology Nodes Links Interaction A B Network Proteins Physical Interaction Protein-Protein A B Protein Interaction Metabolites Enzymatic conversion Protein-Metabolite A B Metabolic Transcription factor Target genes Transcriptional Interaction Protein-DNA A B Transcriptional

Overview of research Evolution of biological systems Evolution of transcriptional networks Evolution of networks within and across genomes Nature Genetics (2004) J Mol Biol (2006a) Evolution of transcription factors Nuc. Acids. Res (2003) Structure and dynamics of transcriptional networks Structure and function of biological systems Uncovering a distributed architecture in networks Methods to study network dynamics J Mol Biol (2006b)J Mol Biol (2006c)Nature (2004) Discovery of novel DNA binding proteins Data integration, function prediction and classification Nuc. Acids. Res (2005)Cell Cycle (2006) C C H H Discovery of transcription factors in Plasmodium Evolution of global regulatory hubs

Evolution of the regulatory network across organisms Evolution of local network structure (motifs) Structure of the transcriptional regulatory network Components, local & global structure Outline Evolution of components in the network (genes and interactions) Evolution of global network structure (scale-free structure)

Evolution of the regulatory network across organisms Evolution of local network structure (motifs) Structure of the transcriptional regulatory network Components, local & global structure Outline Evolution of components in the network (genes and interactions) Evolution of global network structure (scale-free structure)

Structure of the transcriptional regulatory network Scale free network (Global level) all transcriptional interactions in a cell Albert & Barabasi Madan Babu M, Luscombe N, Aravind L, Gerstein M & Teichmann SA Current Opinion in Structural Biology (2004) Motifs (Local level) patterns of Interconnections Uri Alon & Rick Young Basic unit (Components) transcriptional interaction Transcription factor Target gene

Properties of transcriptional networks Local level: Transcriptional networks are made up of motifs which perform information processing task Global level: Transcriptional networks are scale-free conferring robustness to the system

Transcriptional networks are made up of motifs Single input Motif - Co-ordinates expression - Enforces order in expression - Quicker response ArgR ArgD ArgEArgF Multiple input Motif - Integrates different signals - Quicker response TrpRTyrR AroM AroL Network Motif “Patterns of interconnections that recur at different parts and with specific information processing task” Feed Forward Motif - Responds to persistent signal - Filters noise Crp AraC AraBAD Function Shen-Orr et. al. Nature Genetics (2002) & Lee et. al. Science (2002)

N (k)  k  1 Scale-free structure Presence of few nodes with many links and many nodes with few links Transcriptional networks are scale-free Scale free structure provides robustness to the system Albert & Barabasi, Rev Mod Phys (2002)

Scale-free networks exhibit robustness Robustness – The ability of complex systems to maintain their function even when the structure of the system changes significantly Tolerant to random removal of nodes (mutations) Vulnerable to targeted attack of hubs (mutations) – Drug targets? Hubs are crucial components in such networks

Summary I - Structure Transcriptional networks are made up of motifs that have specific information processing task Transcriptional networks are scale-free which confers robustness to such systems, with hubs assuming importance Madan Babu M, Luscombe N et. al Current Opinion in Structural Biology (2004)

Evolution of the regulatory network across organisms Evolution of local network structure (motifs) Structure of the transcriptional regulatory network Components, local & global structure Outline Evolution of components in the network (genes and interactions) Evolution of global network structure (scale-free structure)

Dataset 112 TFs 711 TGs 1295 Interactions E. coli transcriptional regulatory network Shen-orr et al (2002) Nature Genetics Madan Babu & Teichmann (2003) Nucleic acids Research Salgado et al (2002) Nucleic Acids Research

Step 1 E. coli Procedure to reconstruct regulatory network Define TFs and TGs Step 2 Genome of interest Identify orthologs in the genome of interest Step 3 Reconstruct interactions if orthologous TFs and TGs exist in the genome of interest and are known to interact in E. coli Genome of interest Similar to Yu H et. al, Genome Research (2004) Verified with COGS, Tatusov, Koonin, Lipman, Science (1998)

Bacillus anthracis A2012 (5544 genes)Streptomyces coelicolor (7769 genes) Reconstructed transcriptional networks completely sequenced prokaryotic genomes 20 Archaeal, 156 Bacterial Genomes

175 completely sequenced prokaryotic genomes 20 Archaeal 156 Bacterial Genomes

Evolution of networks across organisms How do regulatory interactions change during the course of organismal evolution ? Evolving interactions Change in environment Evolving interactions Change in environment Ancestral network In a particular environment

Selection can operate at three levels of organization Network (all transcriptional interactions in a cell) Motifs (patterns of interconnections) Interactions (transcriptional interaction) Transcription factor Target gene Madan Babu M, Luscombe N et. al Current Opinion in Structural Biology (2004)

Evolution of the basic unit Network (all transcriptional interactions in a cell) Motifs (patterns of interconnections) Interactions (transcriptional interaction) Transcription factor Target gene Madan Babu M, Luscombe N et. al Current Opinion in Structural Biology (2004)

Transcription factors and target genes may co-evolve or evolve independently of each other Co-evolution Independent evolution Work on protein interaction network has shown that interacting proteins tend to co-evolve

Target genes present (%) Transcription factors present (%) Transcription factors evolve rapidly and independently of their target genes Does not mean they lose transcription factors Instead they evolve their own set of regulators

Predicted Transcription Factors from the different genomes Nimwegen, TIGS (2003); R enea et. al, JMB (2004) ; Aravind et. al, FEMs letters (2005) Transcription factors Proteome size B. pertussis B. parapertussis B. bronchiseptica M. magnetotacticum Pirellula_sp D. hafniense N. punctiforme B. japonicum Nostoc Sp L. interrogans S. coelicolor Winged HTH Classical prokaryotic HTH C-terminal effector domain Cro/C1 type HTH FIS like Winged HTH Classical prokaryotic HTH C-terminal effector domain Cro/C1 type HTH FIS like Winged HTH Classical prokaryotic HTH C-terminal effector domain Cro/C1 type HTH FIS like Escherichia coli K12 (4311 genes) Bacillus anthracis A2012 (5544 genes) Streptomyces coelicolor (7769 genes)

organism A interaction 0001: yes interaction 0002: yes interaction 0003: yes interaction 0004: no interaction 0005: yes interaction 0006: no. interaction 1295: yes organism B interaction 0001: yes interaction 0002: no interaction 0003: yes interaction 0004: yes interaction 0005: yes interaction 0006: no. interaction 1295: yes..... organism Z interaction 0001: no interaction 0002: no interaction 0003: no interaction 0004: yes interaction 0005: yes interaction 0006: no. interaction 1295: no Interaction conservation profile interaction organism A organism B organism Z A B C D E F G H Do organisms with similar lifestyle conserve similar interactions ? Procedure to construct tree based on similarity of conserved networks

Distance tree based on interactions present Tree based on network similarity Closely related organisms with similar lifestyle cluster together Organisms with similar lifestyle but belonging to different phylogenetic groups cluster together

Lifestyle similarity index Define a lifestyle class for each of the 176 organism based on 4 attributes Oxygen requirement Optimal growth temperature Habitat Pathogen or not e.g: E. coli would belong to the class: Facultative:Mesophilic:Host-associated:No Each cell represents Average similarity in interaction content between organisms

Lifestyle similarity index LSI = 1.42 p-value < Organisms with similar lifestyle conserve similar interactions

Transcription factors tend to evolve rapidly than their target genes. This coupled with the observation that different genomes evolve their own transcription factors means that they sense and respond to different signals in their environment. Summary I - Evolution of the basic unit At the level of regulatory interactions, organisms with similar lifestyle conserve similar regulatory interactions indicating the influence of environment on gene regulation.

Evolution of network motifs across organisms Network (all transcriptional interactions in a cell) Motifs (patterns of interconnections) Interactions (transcriptional interaction) Transcription factor Target gene Madan Babu M, Luscombe N et. al Current Opinion in Structural Biology (2004)

Interactions in motifs may be conserved as a unit or may evolve like any other interaction in the network Complete conservation or absence Partial conservation Work on protein interaction network has shown that motifs tend to be completely conserved

Generation of motif conservation profiles

Motifs are only partially conserved in many genomes 0% 100% Motifs Genomes E. coli Partially conserved motifs

Are interactions in motifs more conserved than other interactions in the network? Simulation of network evolution Negative selection for interactions in motifs Interactions in motifs are selected against Positive selection for interactions in motifs Interactions in motifs are selectively conserved Neutral selection for interactions in motifs Interactions in motifs are neutrally conserved

Interactions in motifs evolve like any other interaction in the network Selection for motifs Observed trend in genomes Neutral conservation of motifs Selection against motifs Extent of conservation of interactions in motif

Orthologous genes can be embedded in different motifs according to requirements dictated by lifestyle Interactions in motifs evolve like any other interaction in the network Feed forward motif Single input motif Responds to persistent signalQuick response E. coli stable environment – requires persistent signal H. influenzae unstable environment – requires quick response

Evolutionarily closely related organisms that have dissimilar lifestyle do not conserve network motifs Salmonella typhi (  proteobacteria) Fnr NarL NuoN Vibrio cholerae (  proteobacteria) Haemophilus somnus (  proteobacteria) Xylella fastidiosa (  proteobacteria) Blochmannia floridanus (  proteobacteria) Evolutionarily distantly related organisms that have similar lifestyle conserve network motifs R. palustris (  proteobacteria) B. pertussis (  proteobacteria) N. punctiforme (Cyanobacteria) S. avermitilis (Actinobacteria) D. hafniense (Firmicute) Fnr NarL

Lifestyle similarity index Define a lifestyle class for each of the 176 organism based on 4 attributes Oxygen requirement Optimal growth temperature Habitat Pathogen or not e.g: E. coli would belong to the class: Facultative:Mesophilic:Host-associated:No Each cell represents Average similarity in motif content between organisms

Organisms with similar lifestyle conserve network motifs and hence may regulate target genes in a similar manner Lifestyle similarity index LSI = 1.34 p-value < 3x10 -3 Organisms with similar lifestyle conserve network motifs and hence may regulate target genes in a similar manner

Even though motifs are not conserved as whole units, organisms with similar lifestyle tend to conserve similar motifs Summary II - Evolution of network motifs Losing or gaining interactions can result in embedding orthologous genes in different motif contexts

Evolution of global structure Network (all transcriptional interactions in a cell) Motifs (patterns of interconnections) Interactions (transcriptional interaction) Transcription factor Target gene Madan Babu M, Luscombe N et. al Current Opinion in Structural Biology (2004)

Regulatory hubs may be conserved or lost and replaced Conservation of hubs Replacement of hubs Work on protein interaction network has shown that hubs tend to be conserved

Are hubs more conserved than other nodes in the network? Simulation of network evolution Negative selection for hubs Hubs in networks are are selected against Positive selection for hubs Hubs in networks are selectively conserved Neutral selection for nodes Nodes in networks are neutrally conserved

Regulatory hubs are lost as rapidly as other transcription factors in the network

Crp NarL Crp NarL E. coli H. influenzae B. pertussis NarL Crp Regulatory hubs which are condition specific can be either lost or replaced The same protein in organisms living in different lifestyles may confer different adaptive value. Hence it may emerge as a regulatory hub in the organism to which it confers high adaptive value and not in the others Different proteins should emerge as hubs in organisms with different lifestyle

CcpA (85) ComK (48) AbrB (41) Fur (37) PhoP (33) CodY (30) Known transcriptional regulatory network of B. subtilis Crp (188) Fnr (109) Ihf (95) ArcA (69) NarL (65) Lrp (52) Known transcriptional regulatory network of E. coli Different proteins emerge as regulatory hubs Scale-free structure emerged independently in evolution Hubs evolve according to requirements dictated by life style

Even though hubs can be lost or replaced, organisms with different lifestyle evolve a similar structure where different proteins emerge as hubs as dictated by their lifestyle Summary III - Evolution of global structure

Conclusion Transcription factors evolve independently of their target genes Organisms with similar lifestyle conserve similar interactions Interactions in motifs are not conserved as whole units Organisms with similar lifestyle conserve similar motifs Hubs are not completely conserved and can be lost or replaced Different proteins emerge as hubs in organisms as dictated by lifestyle Transcriptional networks in prokaryotes are flexible and adapt to their environment by tinkering individual interactions

Sarah Teichmann MRC-LMB, Cambridge, U.K Acknowledgements MRC - Laboratory of Molecular Biology National Institutes of Health L Aravind NCBI, NIH, Bethesda, USA Evolutionary dynamics of prokaryotic transcriptional regulatory networks Madan Babu M, Teichmann, SA & Aravind L, JMB (2006) 358:

Madan Babu M & Teichmann SA Nucleic Acids Research (2003) Trends in Genetics (2003) Evolution of transcription factors duplication of TF duplication of TG duplication of TG + TF Teichmann SA & Madan Babu M Nature Genetics (2004) Growth of transcriptional regulatory networks Luscombe N, Madan Babu M et. al Nature (2004) Condition specific usage of transcriptional regulatory networks Past work from our lab

Co-regulated pairs of TGs TF – TG pairs Random pair of genes E. coli a Co-regulated pairs of TGs TF – TG pairs Random pair of genes V. cholerae b

Transcription Factor conservation profile Can be used to predict presence/absence of specific response regulatory pathways