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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Studying Emergence of Host-Microbe Maladaptations Using a High-Throughput Metasystem
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. A ‘metasystem’ of ‘framework model organisms’ to study
‘emergence’ of host-microbe ‘maldaptations’
‘organismal’
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. Need and rationale
Societal induced
mingling of new hostmicrobe combinations
(including zoonotic)
Nosocomial
(hospital acquired infections)
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.
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. 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. 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. 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. 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. 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. 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
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. 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. 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. One of the many evidences for importance of using an organismal model host
21. Do the different microbes cause similar damage?
SYTOX GREEN PROBE
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. 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. Do the different microbes cause similar damage?
SYTOX GREEN PROBE
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
33. Characteristic stomatal staining pattern during infection with PA14
Scale bar: 10 mm
PA14
lasR
Does this mean bacteria invade guard cells…?
34. 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
35. SUMMARY (so far..)
1. Under appropriate conditions even innocuous microbes can adapt to cause
significant host damage
36. 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????
41. Evidence for host ‘system status’ alteration in this system
Observed……….
Stomatal guard cell patterning defect…….
PA14
Uninfected
PA14::lasR
Expected………..?
42. 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)
43. IMPAIRED HORMONAL SIGNALING INTERACTIONS
Pieterse et. al., volume 5 number 5 MAY 2009 nature chemical biology review
44. 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
48. 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.
50. 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
51. 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
52. SYSTEM AMENABLE TO CHEMICAL & GENETIC SCREENs AND
OVERLAY WITH OTHER GENETIC, METABOLIC, SIGNALING, AND
NEW ‘TO BE INFERRED’ INFORMATION FROM SYSTEM-WIDE DATA
53. 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!
57. 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
58. 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!!!