Systems Biology and Genomics of Microbial Pathogens
From virulence gene discovery to vaccine development and therapeutic intervention
Ramy Karam Aziz, PhDDepartment of Microbiology and ImmunologyFaculty of Pharmacy, Cairo University, Egypt
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Acknowledgement
Rick StevensArgonne National Laboratory
Ross OverbeekThe Fellowship for
Interpretation of Genomes FIG
Malak KotbUniversity of North Dakota Bernhard Palsson
UC San Diego
Victor NizetUC San Diego
Pep CharusantiUC San Diego and Novo Nordisk
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Acknowledgement
Wollongong/ Queensland:Mark Walker and his lab
UCSDVictor Nizet LabJohn BuchananJason ColeAndrew HollandsBernhard Palsson lab
UTHSC Memphis• Rita Kansal• Sarah Rowe• Bill Taylor
Argonne & U Chicago• Rick Stevens• Ross Overbeek• Veronika Vonstein
Cairo University
Helmholtz Ctr.G.S. Chhatwaal
SDSURob Edwards U North Dakota
• Malak Kotb• Kotb Lab
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Main goal
• Introducing the basics of the emerging fields of
systems biology and genomics using examples
from my research on microbial pathogens in the past
15 years
2 Feb 2016
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Outline• Introduction
– The re-emerging danger of infectious diseases– What are systems biology and genomics?
• Pathogen #1: Group A Streptococcus– Virulence gene discovery– The quest for vaccine targets
• Pathogen #2: Streptococcus iniae– Reconstructing the virulome of S. iniae
• Pathogen #3: Shiga toxin-producing EHEC– Predicting novel drug targets against pathogenic E. coli
• Conclusion• Post scriptum…
2 Feb 2016
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INFECTIOUS DISEASES AGAIN?Introduction
2 Feb 2016
The killers return!
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Alarming WHO and CDC reports (2014)
2 Feb 2016
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Alarming WHO and CDC reports (2014)
2 Feb 2016
Superbugs vs. antibiotic bottleneck!
2 Feb 2016 SCITA BIOFANS 2016 Courtesy: Nina Haste, UCSD
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SYSTEMS BIOLOGY & GENOMICSIntroduction
2 Feb 2016
Defining the concepts
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Systems biology? New?
• Holistic (genome-wide)– The whole is not necessarily equal to the sum of the parts
• Unbiased/ hypothesis-free hypothesis-generating– Often times you don’t know what you’re looking for when
you start• Integrative (multi-omics)
– Integrating comprehensive data into overlaid networks
2 Feb 2016
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Systems biology? New?
2 Feb 2016
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Systems biology? New?
2 Feb 2016
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Systems biologyWeather maps:Systems, subsystems, and individual components
Significant vs. marginaleffectors vs. noise
2 Feb 2016
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Systems biology• Holistic… but including all details!
– The case of traffic maps
• Integrative (multi-omics)2 Feb 2016
http://cdn.theatlanticcities.com/img/upload/2011/12/01/20111130-road/largest.jpg
http://vector1media.com/spatialsustain/wp-content/uploads/2008/04/air-traffic.gif
Systems biology
Metabolism“traffic map”
2 Feb 2016 SCITA BIOFANS 2016
Genomics
FOOTBALLOMICS(Find it on SlidShare.net)
What can football teach us about genomics & systems biology?
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Lessons to learn from football…
2 Feb 2016
Lesson #1:
18-25 players (genes) are listed, but only 11 are "transcribed" into the pitch and those are the ones that are likely to be "expressed"…
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Lessons to learn from football…
2 Feb 2016
Lesson #2: The "game" is highly regulated:
Some players may be induced or repressed in the middle of the game
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Lessons to learn from football…
2 Feb 2016
Lesson #3: systems biology and “–omics”
Genome vs. sum of its genes/ systems vs. reductionist view:
a team's outcome is not necessarily equal to the
sum of its players efforts
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Lessons to learn from football…
2 Feb 2016
Lesson #4a: A players "function" is context dependent: player-player interactions are key to interpret the overall outcome
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Lessons to learn from football…• Lesson #4b:
– A players "function" is context dependent. A protein expressed in another bacterial host may behave very differently.
Salah with Chelsea vs. Salah with Fiorentina & now Rome
2 Feb 2016
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APPLICATION ON 3 PATHOGENS
2 Feb 2016
Applying systems biology to better understand microbial pathogenesis and neutralize it when necessary
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GROUP A STREPTOCOCCUSPathogen #1
2 Feb 2016
Integration of multi-omic data to understand microbial genotypic and phenotypic variation
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Background: invasive GAS infections• Invasive group A streptococcal (GAS) infections have
reemerged in the 1980s
Sore throat
ImpetigoE
rysi
pela
sNecrotizing Fasciitis
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Experimental model
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Experimental model
Parent in vitro Animal-Passaged5-14 days
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Proteomics of phenotypic variants
2 Feb 2016
4 5 6 7 8 9 10pI
105
75
50
35
30
25
15
MWt(kDa)
AP (+protease inhibitor)
Aziz et al., 2004. Mol Microbiol, 51(1):123
WT (+protease inhibitor)
4 5 6 7 8 9 10pI
105
75
50
35
30
25
15
MWt(kDa)
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Lessons to learn from football…
2 Feb 2016
Lesson #2 (remember): The "game" is highly regulated: Most players are “differentially expressed” at different time points…
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Discovery of Streptodornase1 (Sda1)
2 Feb 2016
105
75
50
35
30
25
4 5 6 7 8 9pI
MWt(kDa)
SpeB
SICGAPDH
MF3
MF/SpeF
SibA
AmyASLO
Ska
M
Sda
NADGH
B5
CAMP
Spy0136SpeA
EndoS
Tr: Trypsin peaks.S: StreptodornaseD peaks.
Tr
S
SS
S
S
S
Tr
S
S
S
C IEF-Gel Overlay
MALDI-TOF MS
Aziz et al., 2004. Mol Microbiol, 54(1):184
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Discovery of Streptodornase1 (Sda1)• At UCSD, Buchanan and Nizet et al. knocked out the sda1 gene, and
found out that it helps releasing the bacteria from NETs.
rSda1-329 rSda1-3900
20
40
60
80
100
Recombinant Proteins
% R
educ
tion
in D
NA
conc
entr
atio
n
rSda1-329
rSda1-390
Aziz et al., 2004. Mol. Microbiol., 54(1):184 Buchanan et al., 2006, Current Biology 16 (4): 396
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The perfect storm
http://stke.sciencemag.org/content/vol2007/issue379/cover.dtl
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Emergence of flesh-eating streptococci
M1T1
Aziz & Kotb. Emerg Infect Dis. 2008 Oct;14(10):1511-7
M1 SF370
2 Feb 2016
M1T1Chromosome
M1T1.Z(Sda1)
M1T1.Y(SpeA)
M1T1.X
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A model for virulence gene exchange
hylP hol lys tox prx attR
hylP’ Lys’ tox’ prx’ attR’hol’
Phage 1
Phage 2
2 Feb 2016
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Group A hyaluronic acid capsule
2 Feb 2016
J Bacteriol. 2012 Nov;194(22):6154-61
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Group A carbohydrate (Lancefield) antigen
2 Feb 2016
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Conclusion: GAS genomics• Genomics + integration of multiple data (genomic,
transcriptomic, proteomic, immuno-proteomic, and phenotypic) allowed us to:– discover the genes responsible for some of classical virulence
factors (streptodornase, carbohydrate antigen, and capsule)– identify prophages that largely determine the differences
between different streptococcal strains. Those prophages encode toxins (e.g., streptodornases and superantigens), which play a major role in virulence.
• “Genome mining” provides a gold trove for:– novel drug discovery: through targeting virulence genes– reverse vaccinology: through finding novel vaccine targets from
sequence data
2 Feb 2016
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Lessons to learn from football…• Lesson #5:
– Newly acquired players have an initial advantage, but “defense/immunity” starts building up herd immunity
M. Salah with Fiorentina stopped scoring after a while!
2 Feb 2016
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Humans; bacteria; phages; mobile toxins
2 Feb 2016
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STREPTOCOCCUS INIAEPathogen #2
2 Feb 2016
Reconstructing pathogenesis from the genome/ Fishing for virulence genes in the fish pathogen Streptococcus iniae
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Streptococcus iniae, 5 years ago…
2 Feb 2016
Possible routes of S. iniae infection:Nares/ Gills/ G.I.T.
Signs/ Symptoms:Lethargy, anorexia, loss of orientation, ulcers, exopthalmia or “popeye”, organ damage and meningoencephalitis
S. iniae threatens the worldwide aquaculture industry (loss: $200 M/year) and poses an emerging risk to humans who handle raw fish (25 invasive human cases until 2007).
Courtesy: Carlo Milani
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Reconstruction…
2 Feb 2016
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Can we reconstruct virulence?
2 Feb 2016
Genome Dynamics (Krager). 2009;6:21-34.
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Genome annotation and reconstruction
Credit: V. Fischetti
2 Feb 2016
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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but
interpretation of genomes is the bottleneck• The SEED database
http://www.theseed.org• RAST: rapid annotation using subsystems
technology
2 Feb 2016
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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but
interpretation of genomes is the bottleneck• The SEED database
http://www.theseed.org• RAST: rapid annotation using subsystems
technology
2 Feb 2016
April 2014
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Genome annotation and reconstruction• Genome sequencing is now cheap and rapid, but
interpretation of genomes is the bottleneck• The SEED database
http://www.theseed.org• RAST: rapid annotation using subsystems
technology
2 Feb 2016
October 2014
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Automated (metabolic) reconstruction
2 Feb 2016
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Automated (metabolic) reconstruction
2 Feb 2016
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How to reconstruct the “virulome”?• Bottom-up approach
(reconstruction)– Reverse genetics– Start from the genome
• Tools used:– RAST subsystems
analysis: 60 candidate virulence genes
– Text mining BLAST results
– In silico hybridization
• Top-down approach (genetics)– Forward genetics– Start from phenotypes
• Methodology:– Transposon mutagenesis
followed by screening mutants in a hybrid striped bass model
2 Feb 2016
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Reconstructed “virulome”
2 Feb 2016
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Verified virulence factors• M-like proteins and C5a peptidase
2 Feb 2016
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Verified virulence factors• Polysaccharide deacetylase (Pdi): a potential
peptidoglycan deacetylase
Lysozyme resistance
2 Feb 2016
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Lessons to learn from football…
2 Feb 2016
Lesson #6a: Not all players are equally dangerousTargeting key players =
• Virulence gene inhibition
• Reverse vaccinology
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SHIGA TOXIN-PRODUCING E. COLIPathogen #3
2 Feb 2016
Using genome-wide metabolic reconstruction for drug target prediction
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E. coli O157:H7• Enterohemorrhagic
(hemorrhagic colitis or HUS)
• Shiga toxins-encoding
• Food-associated outbreaks
• Ancestor of the Jack-in-the-Boxstrain (1993 outbreak in Western USA)
• Record number of prophages (25 prophages in Sakai)
2 Feb 2016
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E. coli O157:H7 genome• Full sequence to allow high-resolution analysis of
single mutations
2 Feb 2016
Genome Announc. 2014 Aug 14;2(4).
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E. coli O157:H7 genome• Full sequence to allow high-resolution analysis of
single mutations
2 Feb 2016
Genome Announc. 2014 Aug 14;2(4).
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Computational modeling of 55 E. coli genomes
2 Feb 2016
Monk et al. PNAS 2013
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Lessons to learn from football…
2 Feb 2016
Lesson #1: (remember)
18-25 players (genes) are listed, but only 11 are "transcribed" into the pitch and those are the ones that are likely to be "expressed"…
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Lessons to learn from football…• Lesson #6b:
Not all players are equally dangerous and not all teams can survive without “essential” players
2 Feb 2016
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In silico analysis of gene essentiality1) Delete genes (pathways) in silico
2) Compute whether growth is possible on multiple substrates
3) Perform experiments to validate4) Inconsistencies indicate knowledge gaps
2 Feb 2016 Courtesy: Pep Charusanti
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In silico analysis of gene essentiality1) Delete genes (pathways) in silico
2) Compute whether growth is possible on multiple substrates
3) Perform experiments to validate4) Inconsistencies indicate knowledge gaps
2 Feb 2016 Courtesy: Pep Charusanti
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Methodology: precise gene deletion
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Datsenko KA, Wanner BL. PNAS 2000 Jun 6;97(12):6640-5.
2 Feb 2016
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Analysis of synthetic lethality
• In silicodeletegene pairs (same strategy)
2 Feb 2016 Courtesy: Pep Charusanti
ISC-FOPCU 2015
Applying the workflow
25 April 2015
Aziz et al. Sci Rep. 2015
ISC-FOPCU 2015
Applying the workflow
25 April 2015
Aziz et al. Sci Rep. 2015
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Results…
2 Feb 2016
List 197 genes75 pairs
List 262 genes52 pairs
List 336 genes31 pairs
Aziz et al. Sci Rep. 2015
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Results…
2 Feb 2016
Aziz et al. Sci Rep. 2015
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WITH SYSTEMS BIOLOGY, WE CAN:
General Conclusion
2 Feb 2016
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Using systems biology, we …• discovered novel virulence factors in well-studied
organisms (e.g., sda1, gac, hasB’1)
• understood the molecular basis for genotypic variations and phenotypic states of an organism (M1T1 GAS)
• predicted the virulence potential of a previously unsequenced, poorly studied organism (S. iniae)
• predicted drug and vaccine targets and are testing the predictions
2 Feb 2016
Post Scriptum:The Human Microbiome vs.
Pharmacotherapy
Ramy Karam Aziz, PhDDepartment of Microbiology and ImmunologyFaculty of Pharmacy, Cairo University, Egypt
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THE HUMAN MICROBIOME AND DRUGS
2 Feb 2016
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The human microbiome The human
microbiome is the summation of microorganisms that reside on the surface and in deep layers of skin, in the saliva and oral mucosa, in the conjunctiva, and in the gastrointestinal and urogenital tracts.
2 Feb 2016
The Economist. Aug 2012
1013 ~ 1014
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PharmacoMicrobiomics: Human microbiome vs. Drugs
2 Feb 2016
Mariam Rizkallah
Rama Saad
The PharmacoMicrobiomics Database
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http://pharmacomicrobiomics.org
Rizkallah et al. CPPM Med 2012
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Your questions?
Nucleic Acids Res. 2010 Jul;38(13): Cover