Mario Vaneechoutte & Pieter Deschaght
Current developments in anti-biofilm strategies
and
(assessing their efficacy with an ex vivo sputum)
biofilm models
ECFS Basic Sciences
Session Microbiology
Saturday 31 March 2012
St. Maxime, France
1
Many novel antibacterial/anti-biofilm
treatment opportunities for chronic infections
are being developed
Chronic infection/Biofilm models to test/predict activity
Clinical trial with patients: cumbersome
I. Novel anti- (Pseudomonas) biofilm strategies
1. quorum sensing inhibitors: e.g., furanones, garlick (allicine)
2. antisense RNA strategies to block bacterial transcription and translation
3. antiserum against DNA-binding protein IHF to degrade matrix structure
4. D-amino acids to replace D-ala to degrade matrix structure
5. bacteriophages, with polysaccharide depolymerases to degrade matrix structure
6. iron chelators: e.g., desferoxamine, lactoferrine, conalbumin
7. nitric oxide (NO), toxic to mucoid strains
8. itaconate to block the glyoxylate shunt: waken up persister cells
9. antibiotics combined with the above strategies/compounds
Activity testing/predicting models
II. Models for predicting the in vivo activity
of anti-biofilm treatments
Which model has the highest predictive power
regarding the biofilm eradication succes in the patient?
1. Diffusion antibiogram, starting from planktonic cells
2. Microtiter plate (peg) / glass biofilm susceptibility testing
3. Rotating wall vessel biofilms - Flow cell biofilms
4. Artificial sputum culture with porcine/bovine mucus and herring DNA
5. Co-culture models of ∆F508 cell lines and P. aeruginosa
6. Animal infection models
7. Ex vivo biofilm sputum model
Patient
I. Novel anti- (Pseudomonas) biofilm strategies
1. quorum sensing inhibitors: e.g., furanones, garlick (allicine)
JAC 53: 1054-1061
2004
Res. Microbiol. 160: 144-151.
2009
5
I. Novel anti- (Pseudomonas) biofilm strategies
2. antisense strategies to block bacterial transcription and translation
Hu et al. 2011. World J Microbiol Biotechnol. DOI 10.1007/s11274-011-0658-x
MotA, a cytoplasmic membrane protein
generates the force necessary to drive the flagellum
is one of the key regulation factors in the initial period of biofilm formation.
Inhibition of P. aeruginosa biofilm formation by
the cell-penetrating peptide (KFF)3K + anti-motA-Peptide Nucleic Acid (PNA)
No treatment
Biofilm formation
1 µM (KFF)3K-PNA
5 µM (KFF)3K-PNA
10 µM (KFF)3K-PNA
6
I. Novel anti- (Pseudomonas) biofilm strategies
3. antiserum against DNA-binding protein IHF
Extracellular DNA (eDNA) is a key component of EPS in many pathogenic biofilms.
Whitchurch et al. 2002. Extracellular DNA required for bacterial biofilm formation.
Science 295: 1487  pulmozyme (rh DNAse)
Goodman et al. 2011. Mucosal Immunol 4: 625-637.
DNABII family of proteins have strong structural influence on intracellular DNA.
DNABII is also critical for the integrity of the EPS matrix of biofilms that contain eDNA.
In vitro:
DNABII rapidly disrupts the biofilm EPS formed by multiple human pathogens in vitro.
Synergism with otherwise ineffective traditional antimicrobial approaches in vitro.
7
I. Novel anti- (Pseudomonas) biofilm strategies
3. antiserum against DNA-binding protein IHF
Goodman et al. 2011. Mucosal Immunol 4: 625-637.
Viable planktonic bacteria released from
a nontypeable Haemophilus influenzae (NTHI) biofilm
after treatment with anti-DNAIIB (= anti-IHF)
8
I. Novel anti- (Pseudomonas) biofilm strategies
4. D-amino acids (D-AAs)
Kolodkin-Gal et al. 2010. Science 328: 627-629.
Bacillus subtilis
D-AAs: D-tyrosine, D-leucine, D-tryptophan, and D-methionine
inhibit biofilm formation + degrade biofilm.
In contrast, the corresponding L-isomers were inert in the biofilm-inhibition assay.
Individual D-AAs varied in their activity:
D-tyrosine was more effective (at 3 µM) than D-methionine (at 2 mM)
Mixture of the 4 D-AAs was most potent: 10 nM
Bacteria produce D-amino acids (D-AAs) in stationary phase/mature biofilm
 D-AAs replace D-ala in cell wall, anchor for TasA fibers (Bacillus subtilis)
 TasA can no longer bind to cell wall
[Biofilm matrix = EPS + amyloid fibers composed of the protein TasA]
 Biofilm disruption (see also our results with the EVSM)
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I. Novel anti- (Pseudomonas) biofilm strategies
5. bacteriophages
Hughes et al. 1998a. J Appl Microbiol 85: 583-590.
Hughes et al. 1998.b. Microbiol 144: 3039–3047
Lytic zone
EPS degradation zone
10
I. Novel anti- (Pseudomonas) biofilm strategies
5. bacteriophages
Sutherland et al. 2004. FEMS Microbiol. 232: 1-6.
11
I. Novel anti- (Pseudomonas) biofilm strategies
5. bacteriophages
Glonti et al. 2010. J Appl Microbiol 108: 695-702.
Khawaldeh et al. 2011. J Med Microbiol 60: 1697-1700.
12
I. Novel anti- (Pseudomonas) biofilm strategies
6. iron chelators: desferoxamine, lactoferrin, conalbumin, EDTA, EGTA
Moreau-Marquis et al. 2008. Am J Physiol Lung Cell Mol Physiol 295: L25–L37
Iron in
CF lung bronchoalveolar lavage (BAL) fluid, CF sputum: 8 µM
BAL isolated from healthy patients:
0.018 µM
due to intrinsic iron sequestration problem of ∆F508 CFTR cells
O'May et al. 2009. J Med Microbiol 58:765-773.
"In addition, clinical strains responded differently to different chelators."
Musk & Hergenrother. 2008. J Appl Microbiol 105: 380-388.
13
I. Novel anti- (Pseudomonas) biofilm strategies
6. iron chelators: e.g., desferoxamine, lactoferrine, conalbumin
Moreau-Marquis et al. 2009. Am J Respir Cell Mol Biol 41: 305-313.
Culture of P. aeruginosa biofilm, during 6 hours, on ∆F508 airway cells
Live/Dead staining + CLSM.
No treatment
Tobramycine
1000 µg/ml
Desferoxamine
400 µg/ml (DFO)
Tobramycine
+ DFO
See also our results with EVSM
14
I. Novel anti- (Pseudomonas) biofilm strategies
7. nitric oxide
Yoon et al. 2006. J Clin Invest 116: 436-446.
at pH 6.5, 15 mM NO2– kills mucA mutant P. aeruginosa in CF airway conditions after 16 days
has no adverse effects on cultured human airway epithelia in vitro.
In this study, we believe that we have discovered the Achilles’ heel
of the formidable mucoid form of P. aeruginosa,
which could lead to improved treatment for CF airway disease.
15
I. Novel anti- (Pseudomonas) biofilm strategies
7. nitric oxide
P. aeruginosa is capable of robust anaerobic growth by respiration
using nitrate (NO3–) or nitrite (NO2–) as terminal electron acceptors.
NAR
NO3-
NIR
NO2-
NOR
NO
NOS
N 2O
N2
CF ASL and sputum concentrations of NO3– and NO2–: up to 600 μM
final electron acceptors for anaerobic respiration and growth by P. aeruginosa
P. aeruginosa uses NAR and NIR to reduce NO3– to NO2– to NO
increased levels of NO, a toxic intermediate of NO3– and NO2– reduction
synthesis of protective NO reductase (NOR) by P. aeruginosa.
Leukocyte attacks + leukocyte killing by P. aeruginosa rhamnolipids
ROS
mucA mutations
alginate production
Mucoid strains are
mucoid conversion
most sensitive to HNO2
NOR
sensitivity to HNO2
16
I. Novel anti- (Pseudomonas) biofilm strategies
8. blocking the glyoxylate shunt with itaconate
Persister cells use the glyoxylate shunt instead of the Krebs cycle
Krebs cycle
 reducing agents: NADH, FADH2
 18 ATP
 rapid growth
 ROS
 Oxydative stress
 Bactericidal
17
I. Novel anti- (Pseudomonas) biofilm strategies
8. blocking the glyoxylate shunt with itaconate
Lindsey et al. 2008. Virulence determinants from a cystic fibrosis isolate of
Pseudomonas aeruginosa include isocitrate lyase. Microbiol 154: 1616-1627.
Persister cells
switch off Krebs cycle
switch to glyoxylate shunt
 low NADH/FADH2 production
 low ATP production
 slow growth (dormancy)
 intrinsic AB resistance
 low ROS production
 high resistance to killing
itaconate
Isocitrate lyase is absent in man
 good antimicrobial target
inhibition by itaconate:
18
I. Novel anti- (Pseudomonas) biofilm strategies
9. novel antibiotic formulations and combinations
AB + AB: Tré-Hardy et al. 2009. Int J Antimicrob Agents 34: 370-374.
AB + AMP: Nagant et al. 2010. Appl Microbiol Biotechnol 88: 251-263
AB+ Phage: Comeau et al. 2008. PLoS ONE 2(8): e799.
19
II. Models for predicting the in vivo activity
of anti-biofilm treatments
Which model has the highest predictive power
regarding the biofilm eradication succes in the patient?
1. Diffusion antibiogram, starting from planktonic cells
2. Microtiter plate (peg) biofilm susceptibility testing
3. Rotating wall vessel biofilms - Flow cell biofilms
4. Artificial sputum culture with bovine mucus
5. Co-culture models of CF cell lines and P. aeruginosa
6. Animal infection models
7. Ex vivo biofilm sputum model
Patient
II. II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
1. Diffusion antibiogram for P. aeruginosa, cultured from sputum of CF patients:
= starting from planktonic cells: Foweraker et al. (2005)
irreproducible within and between labs
even same colony morphology yields different susceptibility patterns
limited correlation between susceptibility and clinical outcome
Foweraker et al. 2005. JAC 55: 921-927.
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
2. Microtiter biofilm-based susceptibility testing:
Tré-Hardy et al. 2009. Int J Antimicrob Agents 33: 40-45.
Observations:
strong differences between planktonic cells and biofilm grown cells
strong differences between young and mature biofilms
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
2. Microtiter biofilm-based susceptibility testing
23
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
2. Microtiter biofilm-based susceptibility testing
Moskowitz et al. 2011. Ped Pulmonol 46: 184-192.
Set up: 39 participants.
Treated with 14-day courses of two antibiotics,
that were selected on basis of diffusion antibiogram (planktonic cells)
or on basis of microtiter biofilm susceptibility testing results
Conclusions: In this pilot study,
antibiotic regimens based on biofilm testing
did not differ significantly from
regimens based on conventional testing
in terms of microbiological and clinical responses.
24
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
3. Rotating wall vessel technology: low shear
Crabbé et al. 2009. Environm Microbiol
25
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
4. Articial sputum medium
10 mg/ml porcine stomach mucin
1.4 mg/ml herring sperm DNA
26
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
5. Co-cell culture with Pseudomonas aeruginosa
Observations:
biofilm formation on lung epithelial cell culture vs biofilm on abiotic surfaces (glass):
1500-fold more production of biofilm
25-fold increase of resistance to tobramycin
Limitations:
Long term infection difficult: cells rapidly killed by P. aeruginosa
No mucus compound
No human immunity compound
Limited complexity of microflora: 1 species, 1 strain (PAO1)
Many parameters, such as coating, cell line, cell maturity, buffer, ... influence outcome
Moreau-Marquis et al. 2008. Am J Physiol Lung Cell Mol Physiol 295: L25-L37.
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
6. Animal infection models: CFTR knockouts of mouse, rat, pig
Limitations:
Expensive, cumbersome, ethical considerations
And still: Limited chronic colonization (artificial: sea weed alginate beads)
No human cells, mucus, immune compounds
No original biofilm
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
In summary: Whatever modification to the susceptibility testing biofilm model:
planktonic growth - biofilm
young biofilm - mature biofilm
plastic biofilm - cell line associated biofilm
young cell lines - mature cell lines
(nonchronic) animal infection models
 very different predictions about biofilm formation and
biofilm susceptibility
Which one predicts most reliably the susceptibility
of the P. aeruginosa biofilm in the patient?
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Limitations of current biofilm models:
Each model or variation of parameters leads to strongly different predictions
about biofilm formation and biofilm susceptibility
Original biofilm structure (mucus associated microcolonies) as in patient is absent
Multiple genotypes and phenotypes of P. aeruginosa are absent (usually PAO1)
Extracellular human DNA is absent
Mucus from patient is absent
Leukocytes, cytokines of patient are absent
30
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
Time for a different approach?
1. Add the anti-Pseudomonas - anti-biofilm treatment (at break point concentration)
directly to P. aeruginosa colonized sputum of CF patients
= address the original biofilm in the original patient environment
2. Monitor the effect of the treatment on the P. aeruginosa load
in comparison with the P. aeruginosa load of untreated sputum
7. the Ex Vivo Sputum Biofilm Model
(EVSM)
31
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Is using sputum a valid approach
for testing susceptibility
of P. aeruginosa biofilms in the CF airways?
This depends on the localisation of the chronic biofilm colonisation/infection
1. At the epithelium of lungs?
group of Gerald Pier
Foweraker. 2009. Recent advances in cystic fibrosis. Brit Med Bull 89: 93-110.
or?
2. In the lumen of the conductive airways, within the mucus layer?  sputum
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Where is the chronic biofilm colonisation/infection located?
MAMs: mucus associated microcolonies
Percentage of bacteria
at distances of 5-17 and 2-5 µm
from epithelial surface
of lungs from 9 CF patients
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Bjarnsholt et al. 2009. Ped Pulmonol 44: 547-558.
Expectorated sputum contains the persistent biofilm fraction from CF airways
34
Results with ex vivo biofilm model
(culture based analysis)
Patient 7
1.00E+09
1.00E+09
1.00E+08
1.00E+08
1.00E+07
1.00E+07
1.00E+06
1.00E+05
1.00E+04
1.00E+03
Cells/ml (log)
Cells/ml (log)
Patient 14
1.00E+06
1.00E+05
1.00E+04
1.00E+03
1.00E+02
1.00E+02
1.00E+01
1.00E+01
1.00E+00
1.00E+00
Results are very patient dependent  Need for personalized approach
Strains isolated from sputum and recultured (red) are rapidly killed
Same strains in original sputum associated biofilm (purple) are not
DFO is not very effective in original sputum biofilm
Airway model of Moreau-Marquis et al. (2009)
Tobra + EDTA can eradicate all cultivable biofilm cells in some patients
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Results with ex vivo biofilm model
(culture based analysis)
Effect
van(400
Tobra
1000 µg/ml)
en D-Metacids
en D-Tyr
op
Effects of
Tobra
vs (400
1000enµg/ml)
and D-amino
(3 µM)
added to patient sputum colonized
with P. aeruginosa
groei P. aeruginosa
Bacteriën/ml
1.00E+09
1.00E+08
400 µg/ml Tobra
1000 µg/ml Tobra
1.00E+07
1.00E+06
levend Tobra Tobra Tobra D-Tyr D-Met Tobra D-Tyr
+ D- + D+ D- + DTyr Met
Tyr + Met
D-Met
Behandeling
36
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Methods to assess treatment efficacy
A. Troublesome approaches:
1. Culture: only cultivable cells are assessed, not dormant biofilm part. Workload high.
2. DNA-qPCR: also dead cells are assessed  Treatment effects are not observable.
3. Reverse transcription qPCR: cumbersome:
- RNA instability
- different transcription levels of different genes
in biofilm-associated dormant and planktonic cells.
4. Life/Dead staining: too much interference of free (leukocyte) DNA in sputum
B. Possible approaches:
1. FISH  biofilm structure is assessed. Quantification troublesome?
2. PMA + DNA-qPCR: All living cells but no dead cells are quantified.
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
PMA + DNA-qPCR: All living cells and no dead cells are quantified
Nocker A, Cheung CY, Kamper AK. 2006. Comparison of propidium monoazide with
ethidium monoazide for differentiation of live vs. dead bacteria by selective removal of DNA
from dead cells. J Microbiol Meth 67: 310-320.
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Advantages of using colonized sputum
Readily available in large quantities
Personalized information
Other treatments of patient present (mucolytics, potentiators, correctors)
Differences in genetic CFTR background of CF patients present
Differences in modifier genes & immune respons of CF patients present
Differences in status of colonisation (recent, long-term) present
Most probably: highest predictive power regarding treatment success in patient.
II. Models for predicting the in vivo efficacy
of anti-biofilm treatments
7. Ex vivo sputum biofilm model
Limitations of the ex vivo sputum model
Unequal distribution of colonisation?
When obtained after physiotherapy, MAM distribution turns out to be fairly even
The EVSM is not suited
to find out how reduced P. aeruginosa /bacterial colonisation affects patient health.
to assess the side effects of antibacterial treatments on the airway epithelium:
 ex vivo primary cell lines might be most informative and most personalized.
to assess CFTR corrector and potentiator effects
 ex vivo primary cell lines might be most informative and most personalized.
40
Special thanks to
Pieter Deschaght & Leen Van Simaey (LBR)
the sputum donors
the nursing staff of MucoGent, University Hospital Gent, Belgium
Linda Mahieu, Marleen Vanderkerken and Ann Raman
MucoVereniging België
The ex vivo sputum model
Slides available at: http://users.ugent.be/~mvaneech/LBR.htm
Mario.Vaneechoutte@UGent.be