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Metasystem to Study Emergence of Infectious Diseases

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Part of my work that uses model organisms to study emergence of infectious diseases. (* previous presentation got deleted by accident). This has relevance to nosocomial infections, immune-compromised state, necrotizing fasciitis and systems approach to study such emergence of new infectious disease and manipulate host responses for many immune-modulated diseases.

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Metasystem to Study Emergence of Infectious Diseases

  1. 1. A High-Throughput Amenable Metasystem to study Emergence of Host-Microbe Maladaptations Department of Molecular Biology, Massachusetts General Hospital Department of Genetics, Harvard Medical School Suresh Gopalan, Ph.D Work done late 2006 – mid 2010 Based on presentations at: 1. Broad Institute of Harvard & MIT, Infectious Disease Initiative – Sep 24, 2010 2. Sigma XI Invited Lecture Series, U.S. Army Natick Soldier RD&E Center (NSRDEC) – May 25, 2010
  2. 2. A ‘metasystem’ of ‘framework model organisms’ to study ‘emergence’ of host-microbe ‘maldaptations’ ‘organismal’
  3. 3. 1. Why is it important? – i.e., practical significance 2. What is needed to study? 3. How this simplified model system satisfies that goal? 4. Where can we go from this? System development point of view and provide experiments supporting conjectures when possible
  4. 4. Need and rationale Societal induced mingling of new hostmicrobe combinations (including zoonotic) Nosocomial (hospital acquired infections)
  5. 5. Human Microbiome niche/composition alteration http://nihroadmap.nih.gov/hmp/index.asp
  6. 6. Transmission of maladapted microbes
  7. 7. THE PROPOSITION 1. Change in ‘system status’ of host and microbe under appropriate environments favors adaptation to microbe related diseases. System status Changes (rewiring and cross-talk) in existing signaling modules & gene regulatory networks etc. (e.g., biofilm forming microbe and immuno-compromised host) 2. The changes are characteristic and predictive of types of interaction 3. Continued opportunity to interact would lead to permanent fixation of this adapted state through genetic changes. And… 1. Such emergence of adaptation is difficult to study in natural settings. 2. Design of an appropriate model(s) can facilitate study of emergence of such adaptations under controlled environment.
  8. 8. PLANTS WORMS MAMMALS OTHER PATHWAYS SENSORS LRR TIR Variable: Kinase / PEST/ nothing kinase NBS NBS LRR Pto PBS1 LRR NLRs TLRs CC Viral RNA/ dsDNA LRR MAPK cascade TIR NBS TIR NPR1 nucleus nucleus Immunity (including anti-pathogenics, inflammation, cell death)
  9. 9. A model for discussion today: Host: Arabidopsis seedlings in a submerged environment Microbes: Human opportunistic pathogens Plant pathogens Commonly ‘innocuous’ laboratory microbes That recapitulates some features of the proposed need………….
  10. 10. Visual phenotype of Arabidopsis seedlings interacting with different microbes Ctrl P. aeruginosa – PA14 B. subtilis E. coli – Dh5a P. syringae – DC3000 Does it involve some known virulence components…….?
  11. 11. lasR: a key regulator of quorum sensing and a subset of virulence factor expression GacA/GacS Ctrl P. aeruginosa – PA14 RsmY/RsmZ PA14::lasR LasI/LasR RhlI/RhlR B. subtilis E. coli – Dh5a HCN, pyocyanin, biofilm, virulence P. syringae – DC3000 DC3000::hrcC DC3000/AvrB
  12. 12. hrcC host PLANTS Simple microbial growth……..? pcd Avr R AvrB Defense Ctrl SENSORS P. aeruginosa – PA14 TIR Variable: Kinase / PEST/ nothing CC NBS NBS LRR kinase LRR Pto PBS1 MAPK cascade PA14::lasR B. subtilis NPR1 E. coli – Dh5a nucleus P. syringae – DC3000 DC3000::hrcC Immunity DC3000/AvrB
  13. 13. Bacteria do not grow well in the plant growth medium & Bacterial load does not correlate with visual host damage day 0 microbe PA14 PA14::lasR B.subtilis E. coli DC3000 DC3000/AvrB DC3000::hrcC 5.38 5.30 4.00 5.62 4.84 4.70 4.84 day 3 conditioned medium medium whole well 6.9 SD 0.27 7.7 SD 0.39 > 9.5 ND ND > 9.5 4.4 SD 0.5 5.6 SD 0.16 6.6 SD 0.18 6.3 SD 0.15 5.35 SD 0.13 7.7 SD 0.3 6.9 SD 0.02 5.04 SD 0.35 > 9.5 ND ND > 9.5 ND ND > 9.5 Can we get past visual symptoms?
  14. 14. Readout RMP: Relative metabolic potential Host growth Virulence effectors RMP Host immunity Pathogen growth rate & pathogen load ONE measure of RMP: Use a reporter (luciferase for e.g.,) under a constitutive promoter e.g., 35S as a readout? Seed source: Albrecht von Arnim, UTennessee
  15. 15. Luciferase activity as a measure of host damage A day 0 7 day 3 day 5 Log10 RLU 6 5 4 3 2 1 hrcC DC/AvrB DC Ec Bs lasR PA14 Ctrl 0 Luciferase expressed under a CaMV 35S constitutive promoter hrcC host AvrB Avr R pcd Defense TopCountNXT: Brian Seed’s lab - CCIB
  16. 16. Would every microbe cause similar damage to varying extents…..?
  17. 17. Additional evidence for relevance and variety Ctrl B. subtilis S. aureus Day 4 1. Not every microbe will cause host damage in this system (i.e., not non-specific) 2. Even laboratory E.coli causes damage through active host-microbe interaction Ctrl Ctrl Dh5a Dh5a Dh5a --GFP Dh5a GFP 3 dpi
  18. 18. Newer virulence factors to be discovered in P. aeruginosa Bs PA14/GFP PAO1 hcnC_2 hcnC_1 exoTUY toxA pscD lasR PA14 4 3 2 1 0 Ctrl Log10RLU 6 5 PA14 mutants: Rahme, Tan, Miyata, Drenkard, Liberati, Urbach, Ausubel AMENABILITY TO HIGH-THROUGHPUT AUTOMATION ASSISTED SCREENS A day 0 7 day 3 day 5 5 4 3 2 1 hrcC DC/AvrB DC Ec Bs lasR PA14 0 Ctrl Log10 RLU 6
  19. 19. A POWERFUL SYSTEM TO IDENTIFY POTENT ANTI-INFECTIVES BY COMPOUND & OTHER SCREENS 7 6 day 0 day 3 day 5 4 3 2 1 4 24 t/R L2 RL 2 an / K G en 24 4 44 L2 2 R A1 4 t/P G en K an / PA 14 PA 14 trl 0 C log10RLU 5 No evidence for biofilm formation on leaves
  20. 20. One of the many evidences for importance of using an organismal model host
  21. 21. Do the different microbes cause similar damage? SYTOX GREEN PROBE
  22. 22. Sytox green staining of membrane permeabilized cells
  23. 23. Fluorescence based assay is also quantitative - Isocyte trial 1 Laser: 488 nm; Dichroic: 560 DLRP Red: Em 610 LP Visible light Expected fluorescence pattern Green: Em 510-540
  24. 24. Luminescence and Fluorescence (two color) serve as two complementary read-outs for different aspects of ‘system status’ RMP vs. host membrane damage Remarkably simple workflow!
  25. 25. Do the different microbes cause similar damage? SYTOX GREEN PROBE
  26. 26. Some characteristic damages revealed by Sytox green staining DC DC/AB PA14 Syto59 Akin to necrotizing fasciitis ? Plan to test in mice with Mike Wessels & Laurence Rahme Scale bar: 50 mm
  27. 27. ctrl
  28. 28. ctrl
  29. 29. 50 µm 50 µm 50 µm DC3000
  30. 30. 5 µm 5 µm 5 µm PA14
  31. 31. lasR - pervasive
  32. 32. Characteristic stomatal staining pattern during infection with PA14 Scale bar: 10 mm PA14 lasR Does this mean bacteria invade guard cells…?
  33. 33. Despite characteristic stating pattern, no evidence of intact bacteria in stomatal guard cells during interaction with P. aeruginosa Scale bar: 2 mm Scale bar: 500 nm EM: Mary McKee – Program in Membrane Biology/CSB
  34. 34. SUMMARY (so far..) 1. Under appropriate conditions even innocuous microbes can adapt to cause significant host damage
  35. 35. SUMMARY (so far..) 1. Under appropriate conditions even innocuous microbes can adapt to cause significant host damage 2. A model system utilizing and highlighting such potential (genetics, biology) to study such adaptations 3. Not general or non-specific 4. Known virulence factors and mechanisms are operative 5. High-throughput automation assisted screens – read-outs for.. 6. These interactions represent different modes of adaptation 7. Note, we haven’t given an opportunity for genetic change yet! 8. Predictive ‘System status’ changes of preexisting components and signaling machinery in host and microbe????
  36. 36. Evidence for bacterial ‘system status’ change GacA/GacS RsmY/RsmZ LasI/LasR RhlI/RhlR HCN, pyocyanin, biofilm, virulence
  37. 37. PA14 = gacA gacA 20 µm PA14 vs. gacA 7 6 5 day0/1 day3/1 4 day5/1 3 day0/3 day5/3 2 1 0 ctrl pa14 gaca lasR RL2244 GacA, LasR role in worms, plants and other pathosystems…. Dh5a
  38. 38. Evidence for bacterial ‘system status’ change 10 µm 10 µm lasR = gacA/lasR PA14 or gacA LasR replacement cassette through Eliana Drenkard
  39. 39. Identifying Novel Rewired Signaling Modules GacA/GacS RsmY/RsmZ LasI/LasR RhlI/RhlR GacA/GacS X ? LasI/LasR RhlI/RhlR ? virulence virulence
  40. 40. Evidence for host ‘system status’ alteration in this system Observed………. Stomatal guard cell patterning defect……. PA14 Uninfected PA14::lasR Expected………..?
  41. 41. Expected… 1. Single cell spacing rule! 2. Set of LRR containing RLKs, a peptide ligand, a specific MAP kinase cascade Myb related transcription factors IMPLY: Host ‘system status’ (hormone, inter-cellular signals etc.) altered in this system – probably affecting the execution of immune response e.g., as in the case of DC3000/AvrB seemingly clustered cell death, but no defense. Submerged seedlings do show induction of defense related genes – earlier work with bacterial and host derived defense elicitors Denoux…… Gopalan ..Ausubel, Dewdney and microarray data (not shown)
  42. 42. IMPAIRED HORMONAL SIGNALING INTERACTIONS Pieterse et. al., volume 5 number 5 MAY 2009 nature chemical biology review
  43. 43. IMPAIRED HORMONAL SIGNALING INTERACTIONS ‘System status change’ PR1::GUS PDF1.2::GUS B.s 8000 7000 PA14 6000 5000 PR1 4000 PDF1.2 3000 lasR 2000 1000 Xcr D DC C /A B hr cC PA -4 8 Bs h DC - 4 /A 8h B48 h Ec Bs Xcc Ct rl PA 14 ga cA la sR 0
  44. 44. AOS SID2 ET SA JA JAR1 CTR1 Pst Cor C JA-Ile EIN2 NPR1 N SCF/COI1 EIN3 JAZs ERF1 PR1 = crosses with PDF1.2::GUS MYC2/JIN1 PDF1.2
  45. 45. High-throughput measurement technologies DNA, RNA, Protein measurements Protein, metabolite measurements Next Generation Sequencing
  46. 46. Computational and Integration tools and Knowledge bases Klipp & Leibermeister, 2006 MetaCyc
  47. 47. ROLE OF A CONSERVED MODULE?? Fig. 13 A core network of two modules negatively correlated to each other (top left, red edges); all genes in the two modules are positively correlated to each other (bottom left, blue edges). Upstream elements (overlapping modules) are represented as green nodes with black edges.
  48. 48. Data Source: Arabidopsis MPSS Plus: miRNA targets - Solexa, Blake Meyers, Pam green etc., 147 miRNA, 74 unique members, 208 unique target genes
  49. 49. laccase family protein / diphenol oxidase family protein PA14 Ratio gacA lasR B. subtilis E. coli DC3000 19.97 16.19 12.09 6.45 3.51 7.93 Signal Value range: untreated: 80 PA14: 1600 Tempting to speculate…….. A possible miRNA regulated gene, or a regulated miRNA DC3000/AvrB DC3000::hrcC 8.51 1.47
  50. 50. Organism Every gene Arabidopsis Transposon insertions in most known coding genes and other parts of genome P.aeruginosa Nearly ever gene (Ausubel lab) B. subtilis E.coli P. syringae Special Knowledge Framework Already evident alteration in crossregulation of known dominant innate immune responses Highly antibiotic resistant Already evident novel regulatory mechanisms Resemble Under construction necrotizing fasciitis? (David Rudner et. Knowledge to B. al, Broad) anthracis (for e.g.)? Available Not available X. campaestris Not available Currently the serendipitous strain mutation(s) How microbe keeps host alive (metabolically active?) Can be used to confirm some hypotheses Y Y Y Y Y N
  51. 51. SYSTEM AMENABLE TO CHEMICAL & GENETIC SCREENs AND OVERLAY WITH OTHER GENETIC, METABOLIC, SIGNALING, AND NEW ‘TO BE INFERRED’ INFORMATION FROM SYSTEM-WIDE DATA
  52. 52. A ‘metasystem’ of ‘framework model organisms’ to study host-microbe ‘maldaptations’ Metasystem: Each microbe (representing different modes of interaction) interacting with the host Arabidopsis seedling (organismal). Framework model organisms: Each organism used here are extensively studied models with large resources, and are considered benchmark for building new theories, technologies etc. Maladaptations: Commonly considered ‘innocuous’ microbes acquiring capability to inflict host damage under appropriate conditions through ‘system status’ change. Thus the system positioned well for integrative approach to building a ‘knowledge framework’ on environments that lead to new host-microbe ‘maladaptations’ and extent of adaptations to guide appropriate action. The system and concept also paves way for complementary models to be built!
  53. 53. http://nihroadmap.nih.gov/hmp/index.asp Nosocomial (hospital acquired infections) niche/composition alteration
  54. 54. Societal induced and artificial intermingling
  55. 55. Transmission of maladapted microbes
  56. 56. Summary advantages: System and Approach 1. Genetics, readily available tools 2. Many well known dominant pathways 3. High throughput and automation – genetic (host and microbe) and compound screens 4. Long history of reference and knowledge 5. Continually emerging measurement and computational tools 6. Direct homologous components, structural similarity, modular similarity with human health and agricultural relevant organisms
  57. 57. ACKNOWLEDGEMENTS FRED AUSUBEL Department of Molecular Biology, Massachusetts General Hospital & Department of Genetics, Harvard Medical School Current and former members of the Ausubel Lab Albrecht von Arnim, University of Tennessee Brian Seed’s Lab Center for Computational and Integrative Biology, MGH Su Chiang, Sean Johnston, ICCB/NERC, HMS - Longwood Supporters (potential collaborators) on unfunded NIH and other grant Apps. Fred Ausubel, George Church, Gary Ruvkun, David Rudner, Laurence Rahme, Michael Wessels YOU!!!

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