A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin

3. A Systems Biology Perspec2ve on Plant-‐Pathogen Interac2ons Leighton Pritchard

4. A Con2nuum l Pathogenicity is a loaded term: l  o4en reﬂects human interest in the system l  disease on crop plants could be coincidental to ‘wild type’ interac<ons l A con<nuum of interac<on modes, including symbiosis and pathogenicity l The loca<on of the system on this con<nuum may depend on context l  e.g. Pectobacterium atrosep/cum:potato no impact host death

5. A basic observa2on Pathogen Host Biological cells (and organisms) can be represented as networks

6. Biological networks l Common way to represent structure l  Several biological subsystems are networks l Universal representa<on l  All biological systems have parts that can be represented as networks l Networks (a.k.a. graphs) are mathema<cally well-‐ understood: Graph Theory l  Many tools exist, relevant to biology

8. Biological networks l Metabolic networks (e.g. KEGG) (generic) Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

9. Biological networks l Regulatory/signalling networks (mouse) (Drosophila)

10. Biological networks l Protein-‐protein interac<on networks (Arabidopsis/H.arabidopsidis/P.syringae) (yeast)

12. What is a network? l Networks have nodes (a.k.a. ver<ces) l  Nodes typically represent ‘things’: „ proteins, chemical compounds, people, towns, junc<ons… l Nodes are connected by edges (a.k.a. arcs) l  Edges typically indicate some rela<onship between nodes „ physical interac<on, substrate:product, friends on Facebook l  Edges may be directed (from one node to another) or undirected (no or ambiguous direc<on) „ chemical conversion: directed; interac<on: undirected n1 n2

13. What is a network? l Networks have nodes (a.k.a. ver<ces) l  Nodes typically represent ‘things’: „ proteins, chemical compounds, people, towns, junc<ons… l Nodes are connected by edges (a.k.a. arcs) l  Edges typically indicate some rela<onship between nodes „ physical interac<on, substrate:product, friends on Facebook l  Edges may be directed (from one node to another) or undirected (no or ambiguous direc<on) „ chemical conversion: directed; interac<on: undirected n1 n2

14. What is a network? l Networks have nodes (a.k.a. ver<ces) l  Nodes typically represent ‘things’: „ proteins, chemical compounds, people, towns, junc<ons… l Nodes are connected by edges (a.k.a. arcs) l  Edges typically indicate some rela<onship between nodes „ physical interac<on, substrate:product, friends on Facebook l  Edges may be directed (from one node to another) or undirected (no or ambiguous direc<on) „ chemical conversion: directed; interac<on: undirected n1 n2 n1 n2 n1 n2 n1 n2

15. Many things are networks l My Facebook friends network: l  Nodes: people l  Edges: friendships between people l Useful concepts for biology: l  ‘friend of a friend’; ‘six degrees of separa<on’; clusters of friends Solange Mateo Montalcini Maeve Price Peter Cock Catherine Tackley Gavin Cowie Steffi Keir Yvonne McAvoy Jennifer White Rachel Clewes Juan Morales Karen Faulds David Ian Ellis Laura Banasiak Andrea Semião Daniel Tackley Andrew Lipscombe Bleddyn Hughes Sue Stovell Laura Didymus Hywel Griffiths Charles Twist Christian Payne Helen Johnson Phil Parsonage Colin McGill Allan N. Gunn Will Allwood Katherine Hollywood Judith Robertson Andrew Murdoch David Broadhurst Lydia Castelli Miles Armstrong Paul Keir Fiona White Gagg Lizzie Wilberforce Joanne Fitchet Laura Baxter Alison Gilhespie Jorunn Bos James Gagg Andy Smith Clare Baxter Susan Somerville Neil Bhaduri Joanna Jones Colleen Gagg Susan Quinn McGhee Al Macmillan Norman StewartKevin Knox Susan BreenMichael Barrow Phil Dennison Andrew McKenzie Matthew Blackburn Christelle Robert Tim Arrowsmith Emma Robertson Jane Ballany Chris Thorpe Andrew Dalke Sonia Humphris Juan Morales Eleanor Gilroy Chris McDonald Natalie Homer Anna Åsman Ruth Polwart Tim Morley Kenny Duncan Iddo Friedberg Remco Stam Ramesh Vetukuri Louise Matheson Simon Easterman Philip Law Craig Shaddy Shadbolt Simon Garrett Agata Kaczmarek Simon Pendlebury Rays Jiang Christiane AusJena Pedro Mendes Iris Stone Ingo Hein Adriana Ravagnani Eduard Venter Charles Gordon David Cooke Jonathan Gagg Roger Jarvis Ross McMahon Stefan Engelhardt Edgar Huitema Thomas Pritchard Tracy Canham Sophien Kamoun Florietta Jupe Ambreen Owen Hazel McLellan

16. Many things are networks l My Facebook friends network: l  Nodes: people l  Edges: friendships between people l Useful concepts for biology: l  ‘friend of a friend’; ‘six degrees of separa<on’; clusters of friends Solange Mateo Montalcini Maeve Price Peter Cock Catherine Tackley Gavin Cowie Steffi Keir Yvonne McAvoy Jennifer White Rachel Clewes Juan Morales Karen Faulds David Ian Ellis Laura Banasiak Andrea Semião Daniel Tackley Andrew Lipscombe Bleddyn Hughes Sue Stovell Laura Didymus Hywel Griffiths Charles Twist Christian Payne Helen Johnson Phil Parsonage Colin McGill Allan N. Gunn Will Allwood Katherine Hollywood Judith Robertson Andrew Murdoch David Broadhurst Lydia Castelli Miles Armstrong Paul Keir Fiona White Gagg Lizzie Wilberforce Joanne Fitchet Laura Baxter Alison Gilhespie Jorunn Bos James Gagg Andy Smith Clare Baxter Susan Somerville Neil Bhaduri Joanna Jones Colleen Gagg Susan Quinn McGhee Al Macmillan Norman StewartKevin Knox Susan BreenMichael Barrow Phil Dennison Andrew McKenzie Matthew Blackburn Christelle Robert Tim Arrowsmith Emma Robertson Jane Ballany Chris Thorpe Andrew Dalke Sonia Humphris Juan Morales Eleanor Gilroy Chris McDonald Natalie Homer Anna Åsman Ruth Polwart Tim Morley Kenny Duncan Iddo Friedberg Remco Stam Ramesh Vetukuri Louise Matheson Simon Easterman Philip Law Craig Shaddy Shadbolt Simon Garrett Agata Kaczmarek Simon Pendlebury Rays Jiang Christiane AusJena Pedro Mendes Iris Stone Ingo Hein Adriana Ravagnani Eduard Venter Charles Gordon David Cooke Jonathan Gagg Roger Jarvis Ross McMahon Stefan Engelhardt Edgar Huitema Thomas Pritchard Tracy Canham Sophien Kamoun Florietta Jupe Ambreen Owen Hazel McLellan

17. Many things are networks l  Google Maps l  Nodes: road junc<ons (and end points in culs de sacs) l  Edges: roads l  Structure view l  Flow/traffic view l  Useful concepts for biology: l  Network ‘flow’ or ‘flux’; distance on a network; shortest path

20. Networks are abstract l Networks are collec<ons of nodes and edges l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important l If a network describes biology well… l  …what is true about the network will be true about the biology l  (some networks describe biology be`er than others) l Abstract truths about networks can be true about biology l  If a network of type X is robust to random damage, and a biological network is of type X, we can say that the biological network is robust to random damage.

22. Networks are abstract

23. Networks are abstract

25. Networks are abstract l Networks are collec<ons of nodes and edges l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important l If a network describes biology well… l  …what is true about the network will be true about the biology l  (some networks describe biology be`er than others)

26. Networks are abstract l Networks are collec<ons of nodes and edges l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important l If a network describes biology well… l  …what is true about the network will be true about the biology l  (some networks describe biology be`er than others) n1 n2 n3 n4 n5

27. Networks are abstract l Networks are collec<ons of nodes and edges l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important l If a network describes biology well… l  …what is true about the network will be true about the biology l  (some networks describe biology be`er than others) l Any network with this structure has the same behaviour l  Behaviour of speciﬁc regulatory network is dictated by its structure: l  Behaviour dependent on structure of system as a whole: need to understand this at a systems level MacLean and Studholme. A Boolean model of the Pseudomonas syringae hrp regulon predicts a <ghtly regulated system. PLoS ONE (2010) vol. 5 (2) pp. e9101 doi:10.1371/journal.pone.0009101

28. Networks are abstract l Networks are collec<ons of nodes and edges l Proper<es of the network are the proper<es of that collec<on l  What a node or edge represents is not important l If a network describes biology well… l  …what is true about the network will be true about the biology l  (some networks describe biology be`er than others) l Abstract truths about networks can be true about the biology they represent l  If a network of type X is robust to random damage, and a biological network is of type X, we can say that the biological network is robust to random damage.

29. Choosing a representa2on l Network should be an adequate representa<on of biology l  Choice of representa<on should suit biological ques<on l  e.g. do we represent chemical compounds, or moie<es?

32. Choosing a representa2on l What does this diagram mean? l  Are all enzymes expressed at same <me? l  Are all enzymes expressed in all <ssues? l  Are all metabolites always available? l  30-‐40% of metabolic ac<vity has no known gene associated with it (Chen and Vitkup. Distribu<on of orphan metabolic ac<vi<es. Trends Biotechnol (2007) vol. 25 (8) pp. 343-‐348 doi: 10.1016/j.<btech.2007.06.001) Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

33. Choosing a representa2on l What does this diagram mean? l  Are all enzymes expressed at same <me? l  Are all enzymes expressed in all <ssues? l  Are all metabolites always available? l  30-‐40% of metabolic ac<vity has no known gene associated with it (Chen and Vitkup. Distribu<on of orphan metabolic ac<vi<es. Trends Biotechnol (2007) vol. 25 (8) pp. 343-‐348 doi: 10.1016/j.<btech.2007.06.001) Michal (Ed.), Biochemical Pathways, John Wiley and Sons, New York, 1999.

34. Choosing a representa2on l Biological networks are dynamic l  There may be homeostasis, but it’s dynamic homeostasis l  “The only steady-‐state is death” l What kind of dynamics? l  Kine<c equa<ons l  ODE/Stochas<c representa<on of processes „ e.g. enzyme kine<cs E + S ⌦ ES ⌦ EP ! E + P v = [S]Vmax [S] + [Km]

35. Choosing a representa2on l Biological networks are dynamic l  There may be homeostasis, but it’s dynamic homeostasis l  “The only steady-‐state is death” l What kind of dynamics? l  Kine<c equa<ons l  ODE/Stochas<c representa<on of processes „ e.g. enzyme kine<cs E + S ⌦ ES ⌦ EP ! E + P v = [S]Vmax [S] + [Km]

36. Choosing a representa2on l Biological networks are dynamic l  There may be homeostasis, but it’s dynamic homeostasis l  “The only steady-‐state is death” l What kind of dynamics? l  Boolean (on/oﬀ, 0/1) „ e.g. regula<on/signalling nodes <me

37. Host-‐pathogen interac2on Pathogen Host A representa<on of host and pathogen as two networks

38. Host-‐pathogen interac2on Pathogen Host PAMP/MAMP detec<on: host immune receptor detects (interacts with) non-‐self chemical species derived from microbe/pathogen

39. Host-‐pathogen interac2on Pathogen Host Eﬀector ac<on I: pathogen-‐derived species (probably protein) interacts with host network component

40. Host-‐pathogen interac2on Pathogen Host Eﬀector ac<on II: pathogen-‐derived species (probably protein) manipulates (interacts with) host network process

41. Host-‐pathogen interac2on Pathogen Host Eﬀector-‐triggered resistance I: host immune receptor interacts with pathogen-‐derived eﬀector

42. Host-‐pathogen interac2on Pathogen Host Effector-‐triggered resistance II: host immune receptor detects self-‐ modifica<on (induced by pathogen effector)

43. Host-‐pathogen interac2on Pathogen Host Host-‐pathogen interac2on is the coming together of two networks into a single network: diﬀerent proper2es than either network alone

44. Host-‐pathogen interac2on Pathogen Host How does this aﬀect culturability? Tight connec2on correlates with obligate biotrophy, hence diﬃcult to culture?

45. Host-‐pathogen interac2on

46. Host-‐pathogen interac2on Pathogen Host l How does host/pathogen network respond to interac<on? l What is best way to aàck a network? l What is best way to defend against mul<ple aàck strategies? l Are some parts of a network predictably more influen<al than others?

50. Influence in networks l Efficient aàckers: l  cause greatest favourable host disrup<on for least effort l  should target influen<al points in host network l Efficient defenders: l  protect against greatest amount of poten<al change for least effort l  protect against most commonly-‐targeted points in network l  should target influen<al points in host network l Greatest benefit for least cost l What are the most influen<al points in a network?

53. Influence in networks l Efficient aàckers: l  cause greatest favourable host disrup<on for least effort l  should target influen<al points in host network l Efficient defenders: l  protect against greatest amount of poten<al change for least effort l  protect against most commonly-‐targeted points in network l  should target influen<al points in host network l Greatest benefit for least cost l What are the most influen2al points in a network? l  can we predict/iden<fy them?

54. Robustness in biological networks l Biological networks are typically robust and error-‐tolerant l  (necessary for descent with modiﬁca<on) l  e.g. only 17% of yeast genes essen<al to cell viability in rich media Winzeler et al. Func<onal characteriza<on of the S. cerevisiae genome by gene dele<on and parallel analysis. Science (1999) vol. 285 (5429) pp. 901-‐906

55. Robustness in biological networks l Biological networks are typically robust and error-‐tolerant l  (necessary for descent with modiﬁca<on) l  e.g. only 17% of yeast genes essen<al to cell viability in rich media Winzeler et al. Func<onal characteriza<on of the S. cerevisiae genome by gene dele<on and parallel analysis. Science (1999) vol. 285 (5429) pp. 901-‐906

56. Structural robustness in biological networks l Some network structures enhance robustness l  Many biological networks have converged to same network structures Barabási and Oltvai. Network biology: understanding the cell's func<onal organiza<on. Nat Rev Genet (2004) vol. 5 (2) pp. 101-‐13 doi: 10.1038/nrg1272 Kitano. Biological robustness. Nat Rev Genet (2004) vol. 5 (11) pp. 826-‐37 doi:10.1038/nrg1471 •  Aa: random Erdös-‐Renyi graph: not robust to random aàck (not common in biology) •  Ba: random ‘scale-‐free’ network: robust to random aàck (most biological networks) •  Ca: hierarchical network: robust to random aàck (many signalling networks)

57. l Some network structures enhance robustness l  Many biological networks have converged to same network structures Barabási and Oltvai. Network biology: understanding the cell's func<onal organiza<on. Nat Rev Genet (2004) vol. 5 (2) pp. 101-‐13 doi: 10.1038/nrg1272 Kitano. Biological robustness. Nat Rev Genet (2004) vol. 5 (11) pp. 826-‐37 doi:10.1038/nrg1471 •  Aa: random Erdös-‐Renyi graph: not robust to random aàck (not common in biology) •  Ba: random ‘scale-‐free’ network: robust to random aàck (most biological networks) •  Ca: hierarchical network: robust to random aàck (many signalling networks) Structural robustness in biological networks

58. l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks •  Pathways from detec<on (e.g. immune recep<on) to host response •  Signalling pathways •  E.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’

59. l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks MAMP detec2on •  Pathways from detec<on (e.g. immune recep<on) to host response •  Signalling pathways •  e.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’

60. l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks MAMP detec2on •  Pathways from detec<on (e.g. immune recep<on) to host response •  Signalling pathways •  E.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’ chi<n chi<nase

61. l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks MAMP detec2on •  Pathways from detec<on (e.g. immune recep<on) to host response •  Signalling pathways •  E.g. Cladosporum fulvum Avr4 suppresses produc<on of chi<n, a ‘bridge’ chi<n chi<nase Avr4

62. l Network bridges/bo`lenecks l  essen<al intermediate nodes in a network l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks MAMP detec2on •  Redundancy and cross-‐talk in signalling pathways protects against this fragility •  e.g. PTI/ETI cross-‐talk

63. l Network hubs l  highly-‐connected nodes l  characteris<c of ‘scale-‐free’ (and similar) networks l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks •  Why do hubs occur? •  How many hubs do we expect? •  How are they related to biology?

64. l Network hubs l  highly-‐connected nodes l  characteris<c of ‘scale-‐free’ (and similar) networks l  dele<on or disrup<on dissociates (breaks) the network Structural robustness in biological networks •  Why do hubs occur? •  How many hubs do we expect? •  How are they related to biology?

65. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Robust because of node degree distribu<on l  Very few ‘hubs’; most nodes make few connec<ons l  Random dele<on more likely to remove node with few connec<ons Structural robustness in biological networks Albert et al. Error and a`ack tolerance of complex networks. Nature (2000) vol. 406 (6794) pp. 378-‐82 doi: 10.1038/35019019

66. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Robust because of node degree distribu<on l  Very few ‘hubs’; most nodes make few connec<ons l  Random dele<on more likely to remove node with few connec<ons Structural robustness in biological networks Albert et al. Error and a`ack tolerance of complex networks. Nature (2000) vol. 406 (6794) pp. 378-‐82 doi: 10.1038/35019019

67. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Diagnos<c ‘degree distribu<on’ (count of connec<ons to each node) l  Yeast protein interac<on network has power-‐law distribu<on l  Essen<al 17% of genes correlated with highly-‐connected nodes (hubs) Structural robustness in biological networks

68. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Diagnos<c ‘degree distribu<on’ (count of connec<ons to each node) l  Yeast protein interac<on network has power-‐law distribu<on l  Essen<al 17% of genes correlated with highly-‐connected nodes (hubs) Structural robustness in biological networks

69. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Most studied biological networks are ‘scale-‐free’ l  ‘Scale-‐free’ property proposed to arise from network evolu<on l  ‘older’ nodes more likely to be hubs l  ‘older’ nodes more likely to be func<onally-‐conserved, sequence constrained? l  Hubs are good targets for network disrup<on: what role do they play in pathogen/host evolu<on? Structural robustness in biological networks

70. l Power-‐law (a.k.a. ‘scale-‐free’) networks l  Most studied biological networks are ‘scale-‐free’ l  ‘Scale-‐free’ property proposed to arise from network evolu<on l  ‘older’ nodes more likely to be hubs l  ‘older’ nodes more likely to be func<onally-‐conserved, sequence constrained? l  Hubs are good targets for network disrup<on: what role do they play in pathogen/host evolu<on? Structural robustness in biological networks

71. l Bacterial Type III eﬀectors engage a limited set of host processes across host kingdoms e.g.: l  turnover by modula<on of ubiqui<na<on l  altera<on of transcrip<on l  altera<on of phosphoryla<on l Strategies such as the targe<ng of ubiqui<na<on are used by bacterial fungal and oomycete pathogens across a range of hosts Structural robustness in biological networks

72. l Bacterial Type III eﬀectors engage a limited set of host processes across host kingdoms e.g.: l  turnover by modula<on of ubiqui<na<on l  altera<on of transcrip<on l  altera<on of phosphoryla<on l Strategies such as the targe<ng of ubiqui<na<on are used by bacterial fungal and oomycete pathogens across a range of hosts Structural robustness in biological networks

73. The Guard Hypothesis l The Guard Hypothesis describes indirect R gene:effector interac<on l  Direct R gene:effector interac<on could lead to overwhelming R gene load l  A. thaliana has ≈200 R genes (1% of gene complement) l If ‘hubs’ are common targets for pathogens… l  …guarding the hub with one R gene is more efficient than gene-‐for-‐gene interac<ons l  …network topology implies the Guard Hypothesis l If ‘hubs’ are universal targets… l  …network topology determines which nodes are likely to be involved in host-‐pathogen interac<on Dangl and Jones. Plant pathogens and integrated defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161

74. The Guard Hypothesis l The Guard Hypothesis describes indirect R gene:effector interac<on l  Direct R gene:effector interac<on could lead to overwhelming R gene load l  A. thaliana has ≈200 R genes (1% of gene complement) l If ‘hubs’ are common targets for pathogens… l  …guarding the hub with one R gene is more efficient than gene-‐for-‐gene interac<ons l  …network topology implies the Guard Hypothesis l If ‘hubs’ are universal targets… l  …network topology determines which nodes are likely to be involved in host-‐pathogen interac<on Dangl and Jones. Plant pathogens and integrated defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161

75. Dangl and Jones. Plant pathogens and integrated defence responses to infec<on. Nature (2001) vol. 411 (6839) pp. 826-‐33 doi:10.1038/35081161 The Guard Hypothesis l The Guard Hypothesis describes indirect R gene:effector interac<on l  Direct R gene:effector interac<on could lead to overwhelming R gene load l  A. thaliana has ≈200 R genes (1% of gene complement) l If ‘hubs’ are common targets for pathogens… l  …guarding the hub with one R gene is more efficient than gene-‐for-‐gene interac<ons l  …network topology implies the Guard Hypothesis l If ‘hubs’ are universal targets… l  …network topology determines which nodes are likely to be involved in host-‐pathogen interac<on

76. Interac2ons with hubs l Host: Arabidopsis thaliana l Pathogens: Pseudomonas syringae, Hyaloperonospora arabidopsidis l  Independent effector evolu<on l  Matrix-‐2-‐hybrid (yeast-‐2-‐hybrid) l  Pathogen effectors share more common targets than expected (if random) l  Common targets more highly connected (i.e. are ‘hubs’) than expected (if random) Mukhtar MS, et al. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333: 596–601. doi:10.1126/science.1203659.

79. Modules in networks l Mo<fs are small subnetworks l  Many have speciﬁc dynamic and logic behaviour: „ Accelerate/slow response „ Enforce sequen<al responses „ Lock signal on or oﬀ „ Filter out noise in signals „ Generate pulse in response to signal „ Generate oscilla<ons „ Integrate and process mul<ple signals Shoval and Alon. SnapShot: network mo<fs. Cell (2010) vol. 143 (2) pp. 326-‐e1 doi:10.1016/j.cell.2010.09.050

80. Modules in networks l Mo<fs are small subnetworks l  Many have speciﬁc dynamic and logic behaviour: „ Accelerate/slow response „ Enforce sequen<al responses „ Lock signal on or oﬀ „ Filter out noise in signals „ Generate pulse in response to signal „ Generate oscilla<ons „ Integrate and process mul<ple signals Shoval and Alon. SnapShot: network mo<fs. Cell (2010) vol. 143 (2) pp. 326-‐e1 doi:10.1016/j.cell.2010.09.050

81. Modules in networks l Mo<fs are small subnetworks l  Many have speciﬁc dynamic and logic behaviour: „ Generate pulse in response to signal „ Generate oscilla<ons Shoval and Alon. SnapShot: network mo<fs. Cell (2010) vol. 143 (2) pp. 326-‐e1 doi:10.1016/j.cell.2010.09.050

82. Modules in networks l Bow-‐<e structure l Many inputs → restricted set of intermediates → many outputs

83. Modules in networks l Bow-‐<e structure l Many inputs → restricted set of intermediates → many outputs l  e.g. complex nutrients → metabolic intermediates → complex compounds

84. Modules in networks l Open ques<ons: l  Do a`ackers preferen<ally target (or introduce) par<cular mo<fs? l  Do a`ackers preferen<ally target the ‘knots’ of bow-‐<e structures?

85. Influence in networks l Network structure (topology) is not everything l Network topology is determined by dynamic processes n1 n2 n3 n4 n5 n1 n2 n3 n4 n5 n1 n2 n3 n4 n5 Idealised topology Expression paèrn 1 Expression paèrn 2

86. Inﬂuence in networks l Network structure (topology) is not everything l Dynamic processes are overlaid on topology n1 n2 n3 n4 n5 n1 n2 n3 n4 n5 Idealised topology Reac<on kine<cs dictate rela<ve ﬂux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]

87. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Some processes more influen<al because of dynamic (kine<c) considera<ons l  ODE representa<on of biochemical network l  Used to understand biochemical pathways l  Used in ra<onal drug design: target/priori<se elements with large control coefficients n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Kacser and Burns. The molecular basis of dominance. Gene/cs (1981) vol. 97 (3-‐4) pp. 639-‐66 Kacser and Burns. The control of flux. Biochem Soc Trans (1995) vol. 23 (2) pp. 341-‐66 Westerhoff and Kell. What biotechnologists knew all along ...?. J Theor Biol (1996) vol. 182 (3) pp. 411-‐420 Sato et al. Network Modeling Reveals Prevalent Nega<ve Regulatory Rela<onships between Signaling Sectors in Arabidopsis Immune Signaling. PLoS Pathog (2010) vol. 6 (7) pp. E1001011 doi:10.1371/journal.ppat.1001011

88. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Some processes more influen<al because of dynamic (kine<c) considera<ons l  ODE representa<on of biochemical network l  Used to understand biochemical pathways l  Used in ra<onal drug design: target/priori<se elements with large control coefficients n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Kacser and Burns. The molecular basis of dominance. Gene/cs (1981) vol. 97 (3-‐4) pp. 639-‐66 Kacser and Burns. The control of flux. Biochem Soc Trans (1995) vol. 23 (2) pp. 341-‐66 Westerhoff and Kell. What biotechnologists knew all along ...?. J Theor Biol (1996) vol. 182 (3) pp. 411-‐420 Sato et al. Network Modeling Reveals Prevalent Nega<ve Regulatory Rela<onships between Signaling Sectors in Arabidopsis Immune Signaling. PLoS Pathog (2010) vol. 6 (7) pp. E1001011 doi:10.1371/journal.ppat.1001011

89. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Key points: l  Rela<ve change in pathway flux in response to a change in [enzyme] is the flux control coefficient l  Rela<ve change in [metabolite] in response to a change in [enzyme] is the concentra2on control coefficient l  Control coefficient = 0 ⇒ no influence l  Control coefficient = 1 ⇒ strong posi<ve influence l  Control coefficient = -‐1 ⇒ strong nega<ve influence n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]

90. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Key points: l  Rela<ve change in pathway flux in response to a change in [enzyme] is the flux control coefficient l  Rela<ve change in [metabolite] in response to a change in [enzyme] is the concentra2on control coefficient l  Control coefficient = 0 ⇒ no influence l  Control coefficient = 1 ⇒ strong posi<ve influence l  Control coefficient = -‐1 ⇒ strong nega<ve influence l We might expect aWackers to target network elements with large control coefficients n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]

91. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Key points: l  Control coefficients dependent on rest of network: calculated at same <me l  Control coefficients are a system-‐level property (can’t be determined in isola<on) l  It is unusual for any single element to have complete control over any part of the network l  (Nearly) no rate-‐limi<ng steps l  Any part of the network is typically under control of mul<ple other network elements l  Distributed/democra<c control is the norm n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904 D. Fell, Understanding the Control of Metabolism, first ed., Portland Press, 1997.

92. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Key points: l  Control coefficients dependent on rest of network: calculated at same <me l  Control coefficients are a system-‐level property (can’t be determined in isola<on) l  It is unusual for any single element to have complete control over any part of the network l  (Nearly) no rate-‐limi<ng steps l  Any part of the network is typically under control of mul<ple other network elements l  Distributed/democra<c control is the norm n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904 D. Fell, Understanding the Control of Metabolism, first ed., Portland Press, 1997.

93. Metabolic Control Analysis (MCA) l Metabolic Control Analysis (MCA) l Key points: l  Control coefficients dependent on rest of network: calculated at same <me l  Control coefficients are a system-‐level property (can’t be determined in isola<on) l  It is unusual for any single element to have complete control over any part of the network l  (Nearly) no rate-‐limi<ng steps l  Any part of the network is typically under control of mul<ple other network elements l  Distributed/democra2c control is the norm n1 n2 n3 n4 n5 Reac<on kine<cs dictate rela<ve flux v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904 D. Fell, Understanding the Control of Metabolism, first ed., Portland Press, 1997.

94. Metabolic Control Analysis (MCA) l Yeast glycolysis l Most enzyme kine<c parameters known l Fit to known fluxes, then parameter-‐scan (>8000 dis<nct simula<ons) l Three regimes of control found: l  Main regime: only significant control by hexose transport (HXT) and hexokinase (HK) l  Minor regime: HXT, HK and alcohol dehydrogenase (ADH) l  Biologically inaccessible regime: [GLCi] ≈ 300mM phosphofructokinase (PFK) control Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904

95. Metabolic Control Analysis (MCA) l Yeast glycolysis l Most enzyme kine<c parameters known l Fit to known fluxes, then parameter-‐scan (>8000 dis<nct simula<ons) l Three regimes of control found: l  Main regime: only significant control by hexose transport (HXT) and hexokinase (HK) l  Minor regime: HXT, HK and alcohol dehydrogenase (ADH) l  Biologically inaccessible regime: [GLCi] ≈ 300mM phosphofructokinase (PFK) control Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904

96. Metabolic Control Analysis (MCA) l Yeast glycolysis l Most enzyme kine<c parameters known l Fit to known fluxes, then parameter-‐scan (>8000 dis<nct simula<ons) l Three regimes of control found: l  Main regime: only significant control by hexose transport (HXT) and hexokinase (HK) l  Minor regime: HXT, HK and alcohol dehydrogenase (ADH) l  Biologically inaccessible regime: [GLCi] ≈ 300mM under phosphofructokinase (PFK) control Pritchard and Kell. Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904

97. Metabolic Control Analysis (MCA) l Yeast glycolysis l HXT dominates pathway control l External [hexose] is a signal, as HXT is sensi<ve to it. Pritchard and Kell. Schemes of ﬂux control in a model of Saccharomyces cerevisiae glycolysis. Eur J Biochem (2002) vol. 269 (16) pp. 3894-‐904

98. Distributed Control l MCA implies distributed control of networks l Network topology also implies distributed control (minimal interven<on sets: MIS) l What does this imply for host-‐pathogen interac<ons? l  Several points in network are influen<al „ Can be predicted with sufficient informa<on about system l  A pathway/network element may be under distributed control „ May need to hit several parts of the network to produce change „ Single effectors unlikely to be sufficient

101. Distributed Control l MCA implies distributed control of networks l Network topology also implies distributed control l What does this imply for host-‐pathogen interac<ons? l  Several points in network are influen<al l  A pathway/network element may be under distributed control „ Pathogens may require ‘sets’ of effectors „ Implies ‘Redundant Effector Groups’ and func2onal redundancy? Kvitko et al. Dele<ons in the repertoire of Pseudomonas syringae pv. tomato DC3000 type III secre<on effector genes reveal func<onal overlap among effectors. PLoS Pathog (2009) vol. 5 (4) pp. E1000388 doi:10.1371/journal.ppat.1000388

102. Distributed Control l MCA implies distributed control of networks l Network topology also implies distributed control l What does this imply for host-‐pathogen interac<ons? l  Context-‐dependence of eﬀector func<on: „ H.arabidopsidis ATR13 suppresses callose deposi<on „ P. syringae HopM1 suppresses callose deposi<on „ ATR13 complements callose deposi<on, but does not fully restore virulence in HopM1 mutant (EDV) K.H. Sohn, R. Lei, A. Nemri, J.D.G. Jones, The downy mildew eﬀector proteins ATR1 and ATR13 promote disease suscep<bility in Arabidopsis thaliana, Plant Cell 19 (2007) 4077–4090.

103. Distributed Control l We can consider ‘system’ as deﬁning a landscape, permi~ng types of control l Autocra<c control: l  Flat landscape l  Can move any network element to any ‘state’ l Democra<c control: l  Rugged landscape (constrained by rest of network) l  Network elements restricted to ‘valleys’ in the landscape Bar-‐Yam et al. Systems biology. A`ractors and democra<c dynamics. Science (2009) vol. 323 (5917) pp. 1016-‐7 doi:10.1126/science.1163225

106. Distributed Control l We can consider ‘system’ as deﬁning a landscape, permi~ng types of control l Autocra<c control: l  Flat landscape l  Can move any network element to any ‘state’ l Democra<c control: l  Rugged landscape (constrained by rest of network) l  Network elements restricted to ‘valleys’ in the landscape l Pathogens introduce new elements that change the landscape: eﬀectors Bar-‐Yam et al. Systems biology. A`ractors and democra<c dynamics. Science (2009) vol. 323 (5917) pp. 1016-‐7 doi:10.1126/science.1163225

107. A state-‐based model of interac2on l Prevailing model: zig-‐zag(-‐zig…) Hein et al. The zig-‐zag-‐zig in oomycete-‐plant interac<ons. Mol Plant Pathol (2009) vol. 10 (4) pp. 547-‐62 doi:10.1111/j. 1364-‐3703.2009.00547.x Jones and Dangl. The plant immune system. Nature (2006) vol. 444 (7117) pp. 323-‐9 doi:10.1038/nature05286

108. A state-‐based model of interac2on l Prevailing model: zig-‐zag(-‐zig…) l Has some problems: l  scope (only host immune system, not rest of interac<on with pathogen) l  ordering of events (are PTI/ETI etc. dis<nct and well-‐ordered?) l  <mescale (evolu<onary, or during interac<on?) l  size scale (organism or cell level) l  Quan<ta<ve or qualita<ve (what is the ‘amplitude’ of defence?)

113. A state-‐based model of interac2on l Prevailing model: zig-‐zag(-‐zig…) l Has some problems: l  scope (only host immune system, not rest of interac<on with pathogen) l  ordering of events (are PTI/ETI etc. dis<nct and well-‐ordered?) l  <mescale (evolu<onary, or during interac<on?) l  size scale (organism or cell level) l  Quan<ta<ve or qualita<ve (what is the ‘amplitude’ of defence?) l Is there a more general framework for host-‐pathogen interac2ons? Pritchard L, Birch P (2011) A systems biology perspec<ve on plant-‐microbe interac<ons: Biochemical and structural targets of pathogen eﬀectors. Plant Science 180: 584–603. doi:10.1016/j.plantsci.2010.12.008.

114. A state-‐based model of interac2on l Biological cells can be represented as networks l Each element in the network can be quan<ﬁed: l  enzyme concentra<on (or expression level) l  metabolite concentra<on l  phosphoryla<on/ubiqui<na<on/charge states as dis<nct en<<es l  etc. l We represent lists of values as vectors [v1, v2, v3, . . . , vk]

115. A state-‐based model of interac2on l Biological cells can be represented as networks l Each element in the network can be quan<ﬁed: l  enzyme concentra<on (or expression level) l  metabolite concentra<on l  phosphoryla<on/ubiqui<na<on/charge states as dis<nct en<<es l  etc. l We represent ordered lists of values as vectors [v1, v2, v3, . . . , vk]

116. A state-‐based model of interac2on l Biological cells can be represented as networks l Each element in the network can be quan<ﬁed: l  enzyme concentra<on (or expression level) l  metabolite concentra<on l  phosphoryla<on/ubiqui<na<on/charge states as dis<nct en<<es l  etc. l We represent ordered lists of values as vectors [v1, v2, v3, . . . , vk]

117. A state-‐based model of interac2on l Vectors are co-‐ordinates in space l  vectors of length two: points on a surface (2D space) l  vectors of length three: points in 3D space l  vectors of length k: points in k-‐dimensional space l Points that are close together are ‘similar’

118. A state-‐based model of interac2on l Vectors are co-‐ordinates in space l  vectors of length two: points on a surface (2D space) l  vectors of length three: points in 3D space l  vectors of length k: points in k-‐dimensional space l Points that are close together are ‘similar’

119. A state-‐based model of interac2on l Let our vector represent the measured state of the cell (e.g. host-‐pathogen) system l  enzyme/metabolite concentra<ons, etc. l Each point in k-‐space represents a diﬀerent state of the system l  similar states are close together in k-‐space [v1, v2, v3, . . . , vk]

120. A state-‐based model of interac2on l Let our vector represent the measured state of the cell (e.g. host-‐pathogen) system l  enzyme/metabolite concentra<ons, etc. l Each point in k-‐space represents a diﬀerent state of the system l  similar states are close together in k-‐space [v1, v2, v3, . . . , vk]

121. A state-‐based model of interac2on l States that lead to similar phenotypes can be grouped in phases: l  regions of space where cell state corresponds to named behaviour l Temporal evolu<on of a cell can be viewed as a transi<on through states v1 v2 apoptosis ROS produc<on seed leaf root HR

122. A state-‐based model of interac2on l States that lead to similar phenotypes can be grouped in phases: l  regions of space where cell state corresponds to named behaviour l Temporal evolu<on of a cell can be viewed as a transi<on through states v1 v2 apoptosis ROS produc<on seed leaf root HR

123. A state-‐based model of interac2on l Complex systems can behave in complex ways l A common feature of complex systems is aJractors l  A`ractors are ‘endpoints’: states, or sets of states, to which the system is ‘a`racted’ l  Analogous to stable equilibria: when the system is perturbed, it returns to its a`ractor. l  Do cell phenotypes correspond to a`ractors?

126. A state-‐based model of interac2on l Complex systems can behave in complex ways l A common feature of complex systems is aJractors l  A`ractors are ‘endpoints’: states, or sets of states, to which the system is ‘a`racted’ l  Analogous to stable equilibria: when the system is perturbed, it returns to its a`ractor. l  Do cell phenotypes correspond to a`ractors? apoptosis ROS produc<on seed leaf root HR

127. A state-‐based model of interac2on l A`ractors are associated with the regions of space that lead to them: ‘basins’ l A`ractors can be: l  Single points l  Cycles l  Complex ‘regions’

128. A state-‐based model of interac2on l A`ractors are associated with the regions of space that lead to them: ‘basins’ l A`ractors can be: l  Single points l  Cycles l  Complex ‘regions’

129. A state-‐based model of interac2on l Interac<on of a pathogen with the host can push the system from one basin of aJrac/on to another l There may be mul<ple routes between basins of a`rac<on, depending on the direc<on or <ming of perturba<on l  There may be more than one way to provoke a speciﬁc outcome from the host (or from the pathogen)

130. A state-‐based model of interac2on l Interac<on of a pathogen with the host can push the system from one basin of aJrac/on to another l There may be mul<ple routes between basins of a`rac<on, depending on the direc<on or <ming of perturba<on l  There may be more than one way to provoke a speciﬁc outcome from the host (or from the pathogen)

131. A state-‐based model of interac2on l Effectors may divert the expected WT system trajectory: l ‘Pushing’ the host cell state towards a different aJractor/state l ‘State’ may be a developmental checkpoint l Diversion of the trajectory may also be beneficial to the host l The pathogen may detect the host state and respond accordingly (e.g. <ssue-‐specific effector produc<on in Us/lago maydis; stage-‐ and <ssue-‐specific oomycete effectors) v1 v2 nutrient produc<on PTI seed Epidermal cell root HR

132. A state-‐based model of interac2on l The Jones-‐Dangl Zig-‐Zag(-‐Zig) model is encapsulated within a state-‐based model PTI No challenge ETI ETS

138. A state-‐based model of interac2on l The state-‐based model has advantages: l  Scope: can include host and pathogen, and extend beyond host immunity l  Ordering: explicit ordering of events represented by paths in the model (determined by model) l  Timescale: explicit (determined by model) l  Size scale: can include mul<cellular systems l  Quan2ta2ve or qualita2ve: explicit (dependent on model) PTI No challenge ETI ETS

143. Summary l Biological systems have natural network representa<ons l  But representa<on must be reasonable and suit the ques<on being asked l Interac<on of host and pathogen makes a new single network from two ini<al networks l Network topology affects l  Network behaviour l  Suscep<bility to aàck (hubs, bridges) l Network dynamics affect l  Network behaviour l  Suscep<bility to aàck (distributed control) l A state-‐based framework may be useful for understanding host-‐ pathogen interac<ons

144. Acknowledgements l Systems Biology at Aberystwyth/Manchester l  Doug Kell, David Broadhurst, Pedro Mendes, Roy Goodacre, Andy Woodward, Simon Garre`, l Computa<onal biology at JHI l  Peter Cock l Phytophthora research at JHI l  Paul Birch, Steve Whisson, Miles Armstrong l Bacteriology research at JHI l  Ian Toth, Sonia Humphris, Nicola Holden l Many, many discussions with colleagues

145. Danger Theory l Proposed by computer scien<sts in machine learning: avoids detec<on ‘bloat’ of one ‘recogni<on gene’ per threat. l Popular in (animal) immunology; Analogous to Guard Hypothesis and Dense Overlapping Regions (DORs) l Integra<on of mul<ple signals and contextual cues Aickelin et al. Danger theory: The link between AIS and IDS?. Lect Notes Comput Sc (2003) vol. 2787 pp. 147-‐155

146. Danger Theory l Some signals ‘cri<cal’ and require immediate response (e.g. avirulence gene products?) l Other signals contextual – require ‘processing’ (e.g. MAMPs) Boller and Felix. A renaissance of elicitors: percep<on of microbe-‐associated molecular pa`erns and danger signals by pa`ern-‐recogni<on receptors. Annu. Rev. Plant. Biol. (2009) vol. 60 pp. 379-‐406 doi:10.1146/annurev.arplant. 57.032905.105346

147. Danger Theory l Context dependence and non-‐linear signal may lead to problems of interpreta<on in experiments. l Danger R when signal ≥ 5 l  a+b+c+d = 6 ⇒ R l  a+b+c = 4 ⇒ no R l  a+b+d = 4 ⇒ no R l  a+c+d = 5 ⇒ R l  b+c+d = 5 ⇒ R l  a+b = 3 ⇒ no R l {c and d} required for R?

150. Danger Theory l Context dependence and non-‐linear signal may lead to problems of interpreta<on in experiments. l Danger R when signal ≥ 5 l  a+b+c+d = 6 ⇒ R l  a+b+c = 4 ⇒ no R l  a+b+d = 4 ⇒ no R l  a+c+d = 5 ⇒ R l  b+c+d = 5 ⇒ R l  a+b = 3 ⇒ no R l {c and d} required for R? l No: a+b+c+e, a+b+d+e ⇒ R

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