Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection.
Journal
Nature microbiology
ISSN: 2058-5276
Titre abrégé: Nat Microbiol
Pays: England
ID NLM: 101674869
Informations de publication
Date de publication:
25 Oct 2024
25 Oct 2024
Historique:
received:
16
01
2024
accepted:
27
09
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
aheadofprint
Résumé
Pseudomonas aeruginosa frequently causes antibiotic-recalcitrant pneumonia, but the mechanisms driving its adaptation during human infections remain unclear. To reveal the selective pressures and adaptation strategies at the mucosal surface, here we investigated P. aeruginosa growth and antibiotic tolerance in tissue-engineered airways by transposon insertion sequencing (Tn-seq). Metabolic modelling based on Tn-seq data revealed the nutritional requirements for P. aeruginosa growth, highlighting reliance on glucose and lactate and varying requirements for amino acid biosynthesis. Tn-seq also revealed selection against biofilm formation during mucosal growth in the absence of antibiotics. Live imaging in engineered organoids showed that biofilm-dwelling cells remained sessile while colonizing the mucosal surface, limiting nutrient foraging and reduced growth. Conversely, biofilm formation increased antibiotic tolerance at the mucosal surface. Moreover, mutants with exacerbated biofilm phenotypes protected less tolerant but more cytotoxic strains, contributing to phenotypic heterogeneity. P. aeruginosa must therefore navigate conflicting physical and biological selective pressures to establish chronic infections.
Identifiants
pubmed: 39455898
doi: 10.1038/s41564-024-01842-3
pii: 10.1038/s41564-024-01842-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 310030_189084
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 200021_188623
Organisme : European Molecular Biology Organization (EMBO)
ID : ALTF 12-2022
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Breidenstein, E. B. M., de la Fuente-Núñez, C. & Hancock, R. E. W. Pseudomonas aeruginosa: all roads lead to resistance. Trends Microbiol. 19, 419–426 (2011).
pubmed: 21664819
doi: 10.1016/j.tim.2011.04.005
Horcajada, J. P. et al. Epidemiology and treatment of multidrug-resistant and extensively drug-resistant Pseudomonas aeruginosa infections. Clin. Microbiol. Rev. 32, e00031–19 (2019).
pubmed: 31462403
pmcid: 6730496
doi: 10.1128/CMR.00031-19
Valderrey, A. D. et al. Chronic colonization by Pseudomonas aeruginosa of patients with obstructive lung diseases: cystic fibrosis, bronchiectasis, and chronic obstructive pulmonary disease. Diagn. Microbiol. Infect. Dis. 68, 20–27 (2010).
pubmed: 20727465
doi: 10.1016/j.diagmicrobio.2010.04.008
Fahy, J. V. & Dickey, B. F. Airway mucus function and dysfunction. N. Engl. J. Med. 363, 2233–2247 (2010).
pubmed: 21121836
pmcid: 4048736
doi: 10.1056/NEJMra0910061
Dolan, S. K. Current knowledge and future directions in developing strategies to combat Pseudomonas aeruginosa infection. J. Mol. Biol. 432, 5509–5528 (2020).
pubmed: 32750389
doi: 10.1016/j.jmb.2020.07.021
Hibbert, T. M., Whiteley, M., Renshaw, S. A., Neill, D. R. & Fothergill, J. L. Emerging strategies to target virulence in Pseudomonas aeruginosa respiratory infections. Crit. Rev. Microbiol. https://doi.org/10.1080/1040841X.2023.2285995 (2023).
doi: 10.1080/1040841X.2023.2285995
pubmed: 37999716
Rossi, E. et al. Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-00477-5 (2020).
doi: 10.1038/s41579-020-00477-5
pubmed: 33214718
Martínez-Solano, L., Macia, M. D., Fajardo, A., Oliver, A. & Martinez, J. L. Chronic Pseudomonas aeruginosa infection in chronic obstructive pulmonary disease. Clin. Infect. Dis. 47, 1526–1533 (2008).
pubmed: 18990062
doi: 10.1086/593186
Cao, P. et al. A Pseudomonas aeruginosa small RNA regulates chronic and acute infection. Nature 618, 358–364 (2023).
pubmed: 37225987
pmcid: 10247376
doi: 10.1038/s41586-023-06111-7
Jorth, P. et al. Regional isolation drives bacterial diversification within cystic fibrosis lungs. Cell Host Microbe 18, 307–319 (2015).
pubmed: 26299432
pmcid: 4589543
doi: 10.1016/j.chom.2015.07.006
Friedman, L. & Kolter, R. Two genetic loci produce distinct carbohydrate-rich structural components of the Pseudomonas aeruginosa biofilm matrix. J. Bacteriol. 186, 4457–4465 (2004).
pubmed: 15231777
pmcid: 438632
doi: 10.1128/JB.186.14.4457-4465.2004
Jenal, U. & Malone, J. Mechanisms of cyclic-di-GMP signaling in bacteria. Annu. Rev. Genet. 40, 385–407 (2006).
pubmed: 16895465
doi: 10.1146/annurev.genet.40.110405.090423
Hickman, J. W., Tifrea, D. F. & Harwood, C. S. A chemosensory system that regulates biofilm formation through modulation of cyclic diguanylate levels. Proc. Natl Acad. Sci. USA 102, 14422–14427 (2005).
pubmed: 16186483
pmcid: 1234902
doi: 10.1073/pnas.0507170102
Ceri, H. et al. The Calgary Biofilm Device: new technology for rapid determination of antibiotic susceptibilities of bacterial biofilms. J. Clin. Microbiol. 37, 1771–1776 (1999).
pubmed: 10325322
pmcid: 84946
doi: 10.1128/JCM.37.6.1771-1776.1999
Ciofu, O. & Tolker-Nielsen, T. Tolerance and resistance of Pseudomonas aeruginosa biofilms to antimicrobial agents—how P. aeruginosa can escape antibiotics. Front. Microbiol. 10, 913 (2019).
pubmed: 31130925
pmcid: 6509751
doi: 10.3389/fmicb.2019.00913
de la Fuente-Nunez, C., Cesaro, A. & Hancock, R. E. W. Antibiotic failure: beyond antimicrobial resistance. Drug Resist. Updat. 71, 101012 (2023).
pubmed: 37924726
doi: 10.1016/j.drup.2023.101012
Walters, M. C., Roe, F., Bugnicourt, A., Franklin, M. J. & Stewart, P. S. Contributions of antibiotic penetration, oxygen limitation, and low metabolic activity to tolerance of Pseudomonas aeruginosa biofilms to ciprofloxacin and tobramycin. Antimicrob. Agents Chemother. 47, 317–323 (2003).
pubmed: 12499208
pmcid: 148957
doi: 10.1128/AAC.47.1.317-323.2003
Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. Science 355, 826–830 (2017).
pubmed: 28183996
doi: 10.1126/science.aaj2191
Santi, I., Manfredi, P., Maffei, E., Egli, A. & Jenal, U. Evolution of antibiotic tolerance shapes resistance development in chronic Pseudomonas aeruginosa infections. MBio 12, e03482–20 (2021).
pubmed: 33563834
pmcid: 7885114
doi: 10.1128/mBio.03482-20
Harrison, J. J. et al. Elevated exopolysaccharide levels in Pseudomonas aeruginosa flagellar mutants have implications for biofilm growth and chronic infections. PLoS Genet. 16, e1008848 (2020).
pubmed: 32530919
pmcid: 7314104
doi: 10.1371/journal.pgen.1008848
Jennings, L. K. et al. Pseudomonas aeruginosa aggregates in cystic fibrosis sputum produce exopolysaccharides that likely impede current therapies. Cell Rep. 34, 108782 (2021).
pubmed: 33626358
pmcid: 7958924
doi: 10.1016/j.celrep.2021.108782
Marvig, R. L., Sommer, L. M., Molin, S. & Johansen, H. K. Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat. Genet. 47, 57–64 (2015).
pubmed: 25401299
doi: 10.1038/ng.3148
López-Jiménez, A. T. & Mostowy, S. Emerging technologies and infection models in cellular microbiology. Nat. Commun. 12, 6764 (2021).
pubmed: 34799563
pmcid: 8604907
doi: 10.1038/s41467-021-26641-w
Shi, D., Mi, G., Wang, M. & Webster, T. J. In vitro and ex vivo systems at the forefront of infection modeling and drug discovery. Biomaterials 198, 228–249 (2019).
pubmed: 30384974
doi: 10.1016/j.biomaterials.2018.10.030
Wagner, C. E., Wheeler, K. M. & Ribbeck, K. Mucins and their role in shaping the functions of mucus barriers. Annu. Rev. Cell Dev. Biol. 34, 189–215 (2018).
pubmed: 30296390
doi: 10.1146/annurev-cellbio-100617-062818
Roy, M. G. et al. Muc5b is required for airway defence. Nature 505, 412–416 (2014).
pubmed: 24317696
doi: 10.1038/nature12807
Rossy, T. et al. Pseudomonas aeruginosa type IV pili actively induce mucus contraction to form biofilms in tissue-engineered human airways. PLoS Biol. 21, e3002209 (2023).
pubmed: 37527210
pmcid: 10393179
doi: 10.1371/journal.pbio.3002209
Lewin, G. R. et al. Application of a quantitative framework to improve the accuracy of a bacterial infection model. Proc. Natl Acad. Sci. USA 120, e2221542120 (2023).
pubmed: 37126703
pmcid: 10175807
doi: 10.1073/pnas.2221542120
Turner, K. H., Wessel, A. K., Palmer, G. C., Murray, J. L. & Whiteley, M. Essential genome of Pseudomonas aeruginosa in cystic fibrosis sputum. Proc. Natl Acad. Sci. USA 112, 4110–4115 (2015).
pubmed: 25775563
pmcid: 4386324
doi: 10.1073/pnas.1419677112
Palmer, K. L., Mashburn, L. M., Singh, P. K. & Whiteley, M. Cystic fibrosis sputum supports growth and cues key aspects of Pseudomonas aeruginosa physiology. J. Bacteriol. 187, 5267–5277 (2005).
pubmed: 16030221
pmcid: 1196007
doi: 10.1128/JB.187.15.5267-5277.2005
Palmer, K. L., Aye, L. M. & Whiteley, M. Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J. Bacteriol. 189, 8079–8087 (2007).
pubmed: 17873029
pmcid: 2168676
doi: 10.1128/JB.01138-07
Leoni Swart, A. et al. Pseudomonas aeruginosa breaches respiratory epithelia through goblet cell invasion in a microtissue model. Nat. Microbiol. https://doi.org/10.1038/s41564-024-01718-6 (2024).
doi: 10.1038/s41564-024-01718-6
pubmed: 38858595
Hasan, S., Sebo, P. & Osicka, R. A guide to polarized airway epithelial models for studies of host–pathogen interactions. FEBS J. 285, 4343–4358 (2018).
pubmed: 29896776
doi: 10.1111/febs.14582
Cain, A. K. et al. A decade of advances in transposon-insertion sequencing. Nat. Rev. Genet. 21, 526–540 (2020).
pubmed: 32533119
pmcid: 7291929
doi: 10.1038/s41576-020-0244-x
Winstanley, C., O’Brien, S. & Brockhurst, M. A. Pseudomonas aeruginosa evolutionary adaptation and diversification in cystic fibrosis chronic lung infections. Trends Microbiol. 24, 327–337 (2016).
pubmed: 26946977
pmcid: 4854172
doi: 10.1016/j.tim.2016.01.008
Gao, C. et al. Lactate utilization is regulated by the FadR-type regulator LldR in Pseudomonas aeruginosa. J. Bacteriol. 194, 2687–2692 (2012).
pubmed: 22408166
pmcid: 3347178
doi: 10.1128/JB.06579-11
El Husseini, N. et al. Characterization of the Entner–Doudoroff pathway in Pseudomonas aeruginosa catheter-associated urinary tract infections. J. Bacteriol. https://doi.org/10.1128/jb.00361-23 (2023).
McMorran, B. J., Merriman, M. E., Rombel, I. T. & Lamont, I. L. Characterisation of the pvdE gene which is required for pyoverdine synthesis in Pseudomonas aeruginosa. Gene 176, 55–59 (1996).
pubmed: 8918232
doi: 10.1016/0378-1119(96)00209-0
Watson, A. R. et al. Metabolic independence drives gut microbial colonization and resilience in health and disease. Genome Biol. 24, 78 (2023).
pubmed: 37069665
pmcid: 10108530
doi: 10.1186/s13059-023-02924-x
Bartell, J. A. et al. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat. Commun. 8, 14631 (2017).
pubmed: 28266498
pmcid: 5344303
doi: 10.1038/ncomms14631
McShane, A. et al. Mucus. Curr. Biol. 31, R938–R945 (2021).
pubmed: 34375594
pmcid: 8759706
doi: 10.1016/j.cub.2021.06.093
Maurice, N. M., Bedi, B. & Sadikot, R. T. Pseudomonas aeruginosa biofilms: host response and clinical implications in lung infections. Am. J. Respir. Cell Mol. Biol. 58, 428–439 (2018).
pubmed: 29372812
pmcid: 5894500
doi: 10.1165/rcmb.2017-0321TR
Kong, W. et al. ChIP-seq reveals the global regulator AlgR mediating cyclic di-GMP synthesis in Pseudomonas aeruginosa. Nucleic Acids Res. 43, 8268–8282 (2015).
pubmed: 26206672
pmcid: 4787818
doi: 10.1093/nar/gkv747
Petrova, O. E., Cherny, K. E. & Sauer, K. The Pseudomonas aeruginosa diguanylate cyclase GcbA, a homolog of P. fluorescens GcbA, promotes initial attachment to surfaces, but not biofilm formation, via regulation of motility. J. Bacteriol. 196, 2827–2841 (2014).
pubmed: 24891445
pmcid: 4135668
doi: 10.1128/JB.01628-14
Kuchma, S. L. et al. BifA, a cyclic-Di-GMP phosphodiesterase, inversely regulates biofilm formation and swarming motility by Pseudomonas aeruginosa PA14. J. Bacteriol. 189, 8165–8178 (2007).
pubmed: 17586641
pmcid: 2168662
doi: 10.1128/JB.00586-07
Manner, C. et al. A genetic switch controls Pseudomonas aeruginosa surface colonization. Nat. Microbiol. 8, 1520–1533 (2023).
pubmed: 37291227
doi: 10.1038/s41564-023-01403-0
Roy, A. B., Petrova, O. E. & Sauer, K. The phosphodiesterase DipA (PA5017) is essential for Pseudomonas aeruginosa biofilm dispersion. J. Bacteriol. 194, 2904–2915 (2012).
pubmed: 22493016
pmcid: 3370607
doi: 10.1128/JB.05346-11
Muggeo, A., Coraux, C. & Guillard, T. Current concepts on Pseudomonas aeruginosa interaction with human airway epithelium. PLoS Pathog. 19, e1011221 (2023).
pubmed: 36996043
pmcid: 10062669
doi: 10.1371/journal.ppat.1011221
Cont, A., Rossy, T., Al-Mayyah, Z. & Persat, A. Biofilms deform soft surfaces and disrupt epithelia. Elife 9, e56533 (2020).
pubmed: 33025904
pmcid: 7556879
doi: 10.7554/eLife.56533
Meirelles, L. A., Perry, E. K., Bergkessel, M. & Newman, D. K. Bacterial defenses against a natural antibiotic promote collateral resilience to clinical antibiotics. PLoS Biol. 19, e3001093 (2021).
pubmed: 33690640
pmcid: 7946323
doi: 10.1371/journal.pbio.3001093
Purssell, A. & Poole, K. Functional characterization of the NfxB repressor of the mexCD-oprJ multidrug efflux operon of Pseudomonas aeruginosa. Microbiology 159, 2058–2073 (2013).
pubmed: 23924707
doi: 10.1099/mic.0.069286-0
Matsuo, Y., Eda, S., Gotoh, N., Yoshihara, E. & Nakae, T. MexZ-mediated regulation of mexXY multidrug efflux pump expression in Pseudomonas aeruginosa by binding on the mexZ-mexX intergenic DNA. FEMS Microbiol. Lett. 238, 23–28 (2004).
pubmed: 15336398
Laborda, P. et al. Mutations in the efflux pump regulator MexZ shift tissue colonization by Pseudomonas aeruginosa to a state of antibiotic tolerance. Nat. Commun. 15, 2584 (2024).
pubmed: 38519499
pmcid: 10959964
doi: 10.1038/s41467-024-46938-w
Lister, P. D., Wolter, D. J. & Hanson, N. D. Antibacterial-resistant Pseudomonas aeruginosa: clinical impact and complex regulation of chromosomally encoded resistance mechanisms. Clin. Microbiol. Rev. 22, 582–610 (2009).
pubmed: 19822890
pmcid: 2772362
doi: 10.1128/CMR.00040-09
Fair, R. J. & Tor, Y. Antibiotics and bacterial resistance in the 21st century. Perspect. Med. Chem. 6, 25–64 (2014).
Bulitta, J. B. et al. Two mechanisms of killing of Pseudomonas aeruginosa by tobramycin assessed at multiple inocula via mechanism-based modeling. Antimicrob. Agents Chemother. 59, 2315–2327 (2015).
pubmed: 25645838
pmcid: 4356757
doi: 10.1128/AAC.04099-14
Clark, S. T. et al. Phenotypic diversity within a Pseudomonas aeruginosa population infecting an adult with cystic fibrosis. Sci. Rep. 5, 10932 (2015).
pubmed: 26047320
pmcid: 4456944
doi: 10.1038/srep10932
Mowat, E. et al. Pseudomonas aeruginosa population diversity and turnover in cystic fibrosis chronic infections. Am. J. Respir. Crit. Care Med. 183, 1674–1679 (2011).
pubmed: 21297072
doi: 10.1164/rccm.201009-1430OC
O’Brien, S. et al. High virulence sub-populations in Pseudomonas aeruginosa long-term cystic fibrosis airway infections. BMC Microbiol. 17, 30 (2017).
pubmed: 28158967
pmcid: 5291983
doi: 10.1186/s12866-017-0941-6
Bartell, J. A. et al. Evolutionary highways to persistent bacterial infection. Nat. Commun. 10, 629 (2019).
pubmed: 30733448
pmcid: 6367392
doi: 10.1038/s41467-019-08504-7
Armbruster, C. R. et al. Adaptation and genomic erosion in fragmented Pseudomonas aeruginosa populations in the sinuses of people with cystic fibrosis. Cell Rep. 37, 109829 (2021).
pubmed: 34686349
pmcid: 8667756
doi: 10.1016/j.celrep.2021.109829
Bensel, T. et al. Lactate in cystic fibrosis sputum. J. Cyst. Fibros. 10, 37–44 (2011).
pubmed: 20947455
doi: 10.1016/j.jcf.2010.09.004
Davey, L. E. et al. A genetic system for Akkermansia muciniphila reveals a role for mucin foraging in gut colonization and host sterol biosynthesis gene expression. Nat. Microbiol. 8, 1450–1467 (2023).
pubmed: 37337046
doi: 10.1038/s41564-023-01407-w
Flynn, J. M., Phan, C. & Hunter, R. C. Genome-wide survey of Pseudomonas aeruginosa PA14 reveals a role for the glyoxylate pathway and extracellular proteases in the utilization of mucin. Infect. Immun. 85, e00182–17 (2017).
pubmed: 28507068
pmcid: 5520445
doi: 10.1128/IAI.00182-17
Hoet, P. H. & Nemery, B. Polyamines in the lung: polyamine uptake and polyamine-linked pathological or toxicological conditions. Am. J. Physiol. Lung Cell. Mol. Physiol. 278, L417–L433 (2000).
pubmed: 10710513
doi: 10.1152/ajplung.2000.278.3.L417
Liu, Z., Hossain, S. S., Morales Moreira, Z. & Haney, C. H. Putrescine and its metabolic precursor arginine promote biofilm and c-di-GMP synthesis in Pseudomonas aeruginosa. J. Bacteriol. 204, e0029721 (2022).
pubmed: 34723645
doi: 10.1128/JB.00297-21
Barth, A. L. & Pitt, T. L. The high amino-acid content of sputum from cystic fibrosis patients promotes growth of auxotrophic Pseudomonas aeruginosa. J. Med. Microbiol. 45, 110–119 (1996).
pubmed: 8683546
doi: 10.1099/00222615-45-2-110
Thomas, S. R., Ray, A., Hodson, M. E. & Pitt, T. L. Increased sputum amino acid concentrations and auxotrophy of Pseudomonas aeruginosa in severe cystic fibrosis lung disease. Thorax 55, 795–797 (2000).
pubmed: 10950901
pmcid: 1745865
doi: 10.1136/thorax.55.9.795
DePas, W. H. et al. Exposing the three-dimensional biogeography and metabolic states of pathogens in cystic fibrosis sputum via hydrogel embedding, clearing, and rRNA labeling. MBio 7, e00796–16 (2016).
pubmed: 27677788
pmcid: 5040109
doi: 10.1128/mBio.00796-16
Racanelli, A. C., Kikkers, S. A., Choi, A. M. K. & Cloonan, S. M. Autophagy and inflammation in chronic respiratory disease. Autophagy 14, 221–232 (2018).
pubmed: 29130366
pmcid: 5902194
doi: 10.1080/15548627.2017.1389823
Cantin, A. M., Hartl, D., Konstan, M. W. & Chmiel, J. F. Inflammation in cystic fibrosis lung disease: pathogenesis and therapy. J. Cyst. Fibros. 14, 419–430 (2015).
pubmed: 25814049
doi: 10.1016/j.jcf.2015.03.003
Marvig, R. L. et al. Within-host microevolution of Pseudomonas aeruginosa in Italian cystic fibrosis patients. BMC Microbiol. 15, 218 (2015).
pubmed: 26482905
pmcid: 4612410
doi: 10.1186/s12866-015-0563-9
Stanford, G. E., Dave, K. & Simmonds, N. J. Pulmonary exacerbations in adults with cystic fibrosis: a grown-up issue in a changing cystic fibrosis landscape. Chest 159, 93–102 (2021).
pubmed: 32966813
doi: 10.1016/j.chest.2020.09.084
Woo, J. K. K., Webb, J. S., Kirov, S. M., Kjelleberg, S. & Rice, S. A. Biofilm dispersal cells of a cystic fibrosis Pseudomonas aeruginosa isolate exhibit variability in functional traits likely to contribute to persistent infection. FEMS Immunol. Med. Microbiol. 66, 251–264 (2012).
pubmed: 22765766
doi: 10.1111/j.1574-695X.2012.01006.x
Sousa, A. M. & Pereira, M. O. Pseudomonas aeruginosa diversification during infection development in cystic fibrosis lungs—a review. Pathogens 3, 680–703 (2014).
pubmed: 25438018
pmcid: 4243435
doi: 10.3390/pathogens3030680
Stewart, P. S. et al. Conceptual model of biofilm antibiotic tolerance that integrates phenomena of diffusion, metabolism, gene expression, and physiology. J. Bacteriol. 201, e00307–e00319 (2019).
pubmed: 31501280
pmcid: 6805107
doi: 10.1128/JB.00307-19
Tseng, B. S. et al. The extracellular matrix protects Pseudomonas aeruginosa biofilms by limiting the penetration of tobramycin. Environ. Microbiol. 15, 2865–2878 (2013).
pubmed: 23751003
pmcid: 4045617
doi: 10.1111/1462-2920.12155
Winans, J. B., Wucher, B. R. & Nadell, C. D. Multispecies biofilm architecture determines bacterial exposure to phages. PLoS Biol. 20, e3001913 (2022).
pubmed: 36548227
pmcid: 9778933
doi: 10.1371/journal.pbio.3001913
Thurlow, L. R. et al. Staphylococcus aureus biofilms prevent macrophage phagocytosis and attenuate inflammation in vivo. J. Immunol. 186, 6585–6596 (2011).
pubmed: 21525381
doi: 10.4049/jimmunol.1002794
Rowe, W. J., Lebman, D. A. & Ohman, D. E. Mechanism of resistance to phagocytosis and pulmonary persistence in mucoid Pseudomonas aeruginosa. Front. Cell Infect. Microbiol. 13, 1125901 (2023).
pubmed: 37009499
pmcid: 10050686
doi: 10.3389/fcimb.2023.1125901
Mishra, M. et al. Pseudomonas aeruginosa Psl polysaccharide reduces neutrophil phagocytosis and the oxidative response by limiting complement-mediated opsonization. Cell Microbiol. 14, 95–106 (2012).
pubmed: 21951860
doi: 10.1111/j.1462-5822.2011.01704.x
Malhotra, S., Limoli, D. H., English, A. E., Parsek, M. R. & Wozniak, D. J. Mixed communities of mucoid and nonmucoid Pseudomonas aeruginosa exhibit enhanced resistance to host antimicrobials. MBio 9, e00275–18 (2018).
pubmed: 29588399
pmcid: 5874919
doi: 10.1128/mBio.00275-18
Köhler, T. et al. Personalized aerosolised bacteriophage treatment of a chronic lung infection due to multidrug-resistant Pseudomonas aeruginosa. Nat. Commun. 14, 3629 (2023).
pubmed: 37369702
pmcid: 10300124
doi: 10.1038/s41467-023-39370-z
Strathdee, S. A., Hatfull, G. F., Mutalik, V. K. & Schooley, R. T. Phage therapy: from biological mechanisms to future directions. Cell 186, 17–31 (2023).
pubmed: 36608652
pmcid: 9827498
doi: 10.1016/j.cell.2022.11.017
Mishra, R. et al. Mechanopathology of biofilm-like Mycobacterium tuberculosis cords. Cell 186, 5135–5150.e28 (2023).
pubmed: 37865090
pmcid: 10642369
doi: 10.1016/j.cell.2023.09.016
Luckett, K. A. & Ganesh, K. Engineering the immune microenvironment into organoid models. Annu. Rev. Cancer Biol. 7, 1972–1988.e16 (2023).
doi: 10.1146/annurev-cancerbio-061421-040659
Holloway, B. W. & Morgan, A. F. Genome organization in Pseudomonas. Annu. Rev. Microbiol. 40, 79–105 (1986).
pubmed: 3535656
doi: 10.1146/annurev.mi.40.100186.000455
Hoang, T. T., Karkhoff-Schweizer, R. R., Kutchma, A. J. & Schweizer, H. P. A broad-host-range Flp-FRT recombination system for site-specific excision of chromosomally-located DNA sequences: application for isolation of unmarked Pseudomonas aeruginosa mutants. Gene 212, 77–86 (1998).
pubmed: 9661666
doi: 10.1016/S0378-1119(98)00130-9
Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
pubmed: 19363495
doi: 10.1038/nmeth.1318
Simon, R., Priefer, U. & Pühler, A. A broad host range mobilization system for in vivo genetic engineering: transposon mutagenesis in gram negative bacteria. Nat. Biotechnol. 1, 784–791 (1983).
doi: 10.1038/nbt1183-784
Choi, K.-H. & Schweizer, H. P. mini-Tn7 insertion in bacteria with single attTn7 sites: example Pseudomonas aeruginosa. Nat. Protoc. 1, 153–161 (2006).
pubmed: 17406227
doi: 10.1038/nprot.2006.24
Rybtke, M. T. et al. Fluorescence-based reporter for gauging cyclic di-GMP levels in Pseudomonas aeruginosa. Appl. Environ. Microbiol. 78, 5060–5069 (2012).
pubmed: 22582064
pmcid: 3416407
doi: 10.1128/AEM.00414-12
Jacobs, M. A. et al. Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 100, 14339–14344 (2003).
pubmed: 14617778
pmcid: 283593
doi: 10.1073/pnas.2036282100
Basta, D. W., Bergkessel, M. & Newman, D. K. Identification of fitness determinants during energy-limited growth arrest in Pseudomonas aeruginosa. MBio 8, e01170–17 (2017).
pubmed: 29184024
pmcid: 5705914
doi: 10.1128/mBio.01170-17
Fulcher, M. L. & Randell, S. H. Human nasal and tracheo-bronchial respiratory epithelial cell culture. Methods Mol. Biol. 945, 109–121 (2013).
pubmed: 23097104
doi: 10.1007/978-1-62703-125-7_8
Jiménez-Torres, J. A., Peery, S. L., Sung, K. E. & Beebe, D. J. Lumenext: a practical method to pattern luminal structures in ECM gels. Adv. Health. Mater. 5, 198–204 (2016).
doi: 10.1002/adhm.201500608
Chen, Y. et al. Validation of human small airway measurements using endobronchial optical coherence tomography. Respir. Med. 109, 1446–1453 (2015).
pubmed: 26427628
doi: 10.1016/j.rmed.2015.09.006
Horsfield, K. & Cumming, G. Morphology of the bronchial tree in man. J. Appl. Physiol. 24, 373–383 (1968).
pubmed: 5640724
doi: 10.1152/jappl.1968.24.3.373
DeJesus, M. A., Ambadipudi, C., Baker, R., Sassetti, C. & Ioerger, T. R. TRANSIT—a software tool for Himar1 TnSeq analysis. PLoS Comput. Biol. 11, e1004401 (2015).
pubmed: 26447887
pmcid: 4598096
doi: 10.1371/journal.pcbi.1004401
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
DeJesus, M. A. & Ioerger, T. R. Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes. J. Bioinform. Comput. Biol. 14, 1642004 (2016).
pubmed: 26932272
pmcid: 5654600
doi: 10.1142/S021972001642004X
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
pubmed: 19131956
doi: 10.1038/nprot.2008.211
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
pubmed: 35325185
pmcid: 9252805
doi: 10.1093/nar/gkac194
The pandas development team. pandas-dev/pandas: Pandas 1.0.3. Zenodo https://doi.org/10.5281/zenodo.3509134 (2020).
McKinney, W. Data structures for statistical computing in python. in Proceedings of the 9th Python in Science Conference 56–61. https://doi.org/10.25080/Majora-92bf1922-00a (SciPy, 2010).
Pedregosa, F. et al. AnchorScikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
EUCAST Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin. Microbiol. Infect. 9, ix–xv (2003).
doi: 10.1046/j.1469-0691.2003.00790.x
Meirelles, L. A. & Newman, D. K. Phenazines and toxoflavin act as interspecies modulators of resilience to diverse antibiotics. Mol. Microbiol. 117, 1384–1404 (2022).
pubmed: 35510686
pmcid: 10249331
doi: 10.1111/mmi.14915
Masid, M., Ataman, M. & Hatzimanikatis, V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat. Commun. 11, 2821 (2020).
pubmed: 32499584
pmcid: 7272419
doi: 10.1038/s41467-020-16549-2
Jankowski, M. D., Henry, C. S., Broadbelt, L. J. & Hatzimanikatis, V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 95, 1487–1499 (2008).
pubmed: 18645197
pmcid: 2479599
doi: 10.1529/biophysj.107.124784
Salvy, P. et al. pyTFA and matTFA: a Python package and a Matlab toolbox for thermodynamics-based flux analysis. Bioinformatics 35, 167–169 (2019).
pubmed: 30561545
doi: 10.1093/bioinformatics/bty499
Soh, K. C. & Hatzimanikatis, V. Network thermodynamics in the post-genomic era. Curr. Opin. Microbiol. 13, 350–357 (2010).
pubmed: 20378394
doi: 10.1016/j.mib.2010.03.001
Soh, K. C. & Hatzimanikatis, V. Constraining the flux space using thermodynamics and integration of metabolomics data. Methods Mol. Biol. 1191, 49–63 (2014).
pubmed: 25178783
doi: 10.1007/978-1-4939-1170-7_3
Thiele, I. et al. A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst. Biol. 5, 8 (2011).
pubmed: 21244678
pmcid: 3032673
doi: 10.1186/1752-0509-5-8
Dulbecco, R. & Freeman, G. Plaque production by the polyoma virus. Virology 8, 396–397 (1959).
pubmed: 13669362
doi: 10.1016/0042-6822(59)90043-1
Wheeler, K. M. et al. Mucin glycans attenuate the virulence of Pseudomonas aeruginosa in infection. Nat. Microbiol. 4, 2146–2154 (2019).
pubmed: 31611643
pmcid: 7157942
doi: 10.1038/s41564-019-0581-8
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
pubmed: 22743772
doi: 10.1038/nmeth.2019
Krull, A., Buchholz, T.-O. & Jug, F. Noise2Void - Learning denoising from single noisy images. in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2124–2132 (IEEE, 2019).
Ho, T. K. Random decision forests. in Proceedings of 3rd International Conference on Document Analysis and Recognition 278–282 (IEEE Comput. Soc. Press, 1995).
van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014).
pubmed: 25024921
pmcid: 4081273
doi: 10.7717/peerj.453
Fiorio, C. & Gustedt, J. Two linear time Union-Find strategies for image processing. Theor. Comput. Sci. 154, 165–181 (1996).
doi: 10.1016/0304-3975(94)00262-2
Wu, K., Otoo, E. & Shoshani, A. Optimizing connected component labeling algorithms. in Medical Imaging 2005: Image Processing (eds Fitzpatrick, J. M. & Reinhardt, J. M.) 5747, 1965 (SPIE, 2005).
Burger, W. & Burge, M. J. Principles of Digital Image Processing (Springer, 2009); https://doi.org/10.1007/978-1-84800-195-4
Reiss, T. H. Recognizing Planar Objects Using Invariant Image Features (Springer, 1993); https://doi.org/10.1007/BFb0017553
Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).
pubmed: 32939066
pmcid: 7759461
doi: 10.1038/s41586-020-2649-2
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
doi: 10.1109/MCSE.2007.55
Waskom, M. seaborn: statistical data visualization. JOSS 6, 3021 (2021).
doi: 10.21105/joss.03021
Meirelles, L. A. et al. Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection. Zenodo https://doi.org/10.5281/zenodo.13629466 (2024).