Abstract
Biofilms are complex microbial communities that have a critical function in many natural ecosystems, industrial settings as well as in recurrent and chronic infections. Biofilms are highly heterogeneous and dynamic assemblages that display complex responses to varying environmental factors, and those properties present substantial challenges for their study and control. In recent years, there has been a growing interest in developing improved biofilm models to offer more precise and comprehensive representations of these intricate systems. However, an objective assessment for ascertaining the ability of biofilms in model systems to recapitulate those in natural environments has been lacking. In this Perspective, we focus on medical biofilms to delve into the current state-of-the-art in biofilm modelling, emphasizing the advantages and limitations of different approaches and addressing the key challenges and opportunities for future research. We outline a framework for quantitatively assessing model accuracy. Ultimately, this Perspective aims to provide a comprehensive and critical overview of medically focused biofilm models, with the intent of inspiring future research aimed at enhancing the biological relevance of biofilm models.
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References
Slavkin, H. C. Biofilms, microbial ecology and Antoni van Leeuwenhoek. J. Am. Dent. Assoc. 128, 492–495 (1997).
Bourgeois, J. F. & Barja, F. The history of vinegar and of its acetification systems. Arch. Sci. 62, 147–160 (2009).
Mack, W. N., Mack, J. P. & Ackerson, A. O. Microbial film development in a trickling filter. Microb. Ecol. 2, 215–226 (1975).
Vert, M. et al. Terminology for biorelated polymers and applications (IUPAC Recommendations 2012). Pure Appl. Chem. 84, 377–410 (2012).
Bamford, N. C., MacPhee, C. E. & Stanley-Wall, N. R. Microbial primer: an introduction to biofilms — what they are, why they form and their impact on built and natural environments. Microbiology 169, 001338 (2023).
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563 (2016).
Crivello, G., Fracchia, L., Ciardelli, G., Boffito, M. & Mattu, C. In vitro models of bacterial biofilms: innovative tools to improve understanding and treatment of infections. Nanomaterials 13, 904 (2023).
Guzmán-Soto, I. et al. Mimicking biofilm formation and development: recent progress in in vitro and in vivo biofilm models. iScience 24, 102443 (2021).
Merritt, J. H., Kadouri, D. E. & O’Toole, G. A. Growing and analyzing static biofilms. Curr. Protoc. Microbiol. https://doi.org/10.1002/9780471729259.mc01b01s00 (2005).
Ciofu, O., Moser, C., Jensen, P. O. & Hoiby, N. Tolerance and resistance of microbial biofilms. Nat. Rev. Microbiol. 18, 621–635 (2022).
Dufrene, Y. F. & Persat, A. Mechanomicrobiology: how bacteria sense and respond to forces. Nat. Rev. Microbiol. 18, 227–240 (2020).
Yuan, L., Straub, H., Shishaeva, L. & Ren, Q. Microfluidics for biofilm studies. Annu. Rev. Anal. Chem. 16, 139–159 (2023).
Vyas, H. K. N., Xia, B. & Mai-Prochnow, A. Clinically relevant in vitro biofilm models: a need to mimic and recapitulate the host environment. Biofilm 4, 100069 (2022).
Nowakowska, J., Landmann, R. & Khanna, N. Foreign body infection models to study host–pathogen response and antimicrobial tolerance of bacterial biofilm. Antibiotics 3, 378–397 (2014).
Diban, F. et al. Biofilms in chronic wound infections: innovative antimicrobial approaches using the in vitro Lubbock chronic wound biofilm model. Int. J. Mol. Sci. 24, 1004 (2023).
Jiang, Y. et al. Manipulation of saliva-derived microcosm biofilms to resemble dysbiotic subgingival microbiota. Appl. Environ. Microbiol. 87, e02371–e02420 (2021).
Tang, M. et al. Evaluating bacterial pathogenesis using a model of human airway organoids infected with Pseudomonas aeruginosa biofilms. Microbiol. Spectr. 10, e0240822 (2022).
Wu, B. et al. Human organoid biofilm model for assessing antibiofilm activity of novel agents. npj Biofilms Microbiomes 7, 8 (2021).
Horvath, T. D. et al. Interrogation of the mammalian gut–brain axis using LC-MS/MS-based targeted metabolomics with in vitro bacterial and organoid cultures and in vivo gnotobiotic mouse models. Nat. Protoc. 18, 490–529 (2023).
Dalton, T. et al. An in vivo polymicrobial biofilm wound infection model to study interspecies interactions. PLoS ONE 6, e27317 (2011).
DeLeon, S. et al. Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model. Infect. Immun. 82, 4718–4728 (2014).
Ehrlich, G. D. et al. Mucosal biofilm formation on middle-ear mucosa in the chinchilla model of otitis media. JAMA 287, 1710–1715 (2002).
Jensen, L. K., Johansen, A. S. B. & Jensen, H. E. Porcine models of biofilm infections with focus on pathomorphology. Front. Microbiol. 8, 1961 (2017).
Christensen, L. D. et al. Impact of Pseudomonas aeruginosa quorum sensing on biofilm persistence in an in vivo intraperitoneal foreign-body infection model. Microbiology 153, 2312–2320 (2007).
Bottagisio, M., Coman, C. & Lovati, A. B. Animal models of orthopaedic infections. A review of rabbit models used to induce long bone bacterial infections. J. Med. Microbiol. 68, 506–537 (2019).
Tan, M. L. L., Chin, J. S., Madden, L. & Becker, D. L. Challenges faced in developing an ideal chronic wound model. Expert Opin. Drug. Discov. 18, 99–114 (2023).
Thomsen, K. et al. Animal models of chronic and recurrent Pseudomonas aeruginosa lung infection: significance of macrolide treatment. APMIS 130, 458–476 (2022).
Vanderpool, E. J. & Rumbaugh, K. P. Host–microbe interactions in chronic rhinosinusitis biofilms and models for investigation. Biofilm 6, 100160 (2023).
Carey, A. J. et al. Urinary tract infection of mice to model human disease: practicalities, implications and limitations. Crit. Rev. Microbiol. 42, 780–799 (2016).
Kolpen, M. et al. Bacterial biofilms predominate in both acute and chronic human lung infections. Thorax 77, 1015–1022 (2022).
Phalak, P., Chen, J., Carlson, R. P. & Henson, M. A. Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC Syst. Biol. 10, 90 (2016).
Head, D., Marsh, P. D., Devine, D. A. & Tenuta, L. M. A. In silico modeling of hyposalivation and biofilm dysbiosis in root caries. J. Dent. Res. 100, 977–982 (2021).
Roberts, M. E. & Stewart, P. S. Modeling antibiotic tolerance in biofilms by accounting for nutrient limitation. Antimicrob. Agents Chemother. 48, 48–52 (2004).
Duddu, R., Chopp, D. L. & Moran, B. A two-dimensional continuum model of biofilm growth incorporating fluid flow and shear stress based detachment. Biotechnol. Bioeng. 103, 92–104 (2009).
Sauer, K. et al. The biofilm life cycle: expanding the conceptual model of biofilm formation. Nat. Rev. Microbiol. 20, 608–620 (2022).
Sharma, S. et al. Microbial biofilm: a review on formation, infection, antibiotic resistance, control measures, and innovative treatment. Microorganisms 11, 1614 (2023).
O’Toole, G. A. et al. Model systems to study the chronic, polymicrobial infections in cystic fibrosis: current approaches and exploring future directions. mBio 12, e0176321 (2021).
Barraza, J. P. & Whiteley, M. A Pseudomonas aeruginosa antimicrobial affects the biogeography but not fitness of Staphylococcus aureus during coculture. mBio https://doi.org/10.1128/mBio.00047-21 (2021).
Ibberson, C. B., Barraza, J. P., Holmes, A. L., Cao, P. & Whiteley, M. Precise spatial structure impacts antimicrobial susceptibility of S. aureus in polymicrobial wound infections. Proc. Natl Acad. Sci. USA 119, e2212340119 (2022).
Cornforth, D. M., Diggle, F. L., Melvin, J. A., Bomberger, J. M. & Whiteley, M. Quantitative framework for model evaluation in microbiology research using Pseudomonas aeruginosa and cystic fibrosis infection as a test case. mBio 11, e03042–e03119 (2020).
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).
Lewin, G. R., Stocke, K. S., Lamont, R. J. & Whiteley, M. A quantitative framework reveals traditional laboratory growth is a highly accurate model of human oral infection. Proc. Natl Acad. Sci. USA 119, e2116637119 (2022).
Duran-Pinedo, A. et al. Long-term dynamics of the human oral microbiome during clinical disease progression. BMC Biol. 19, 240 (2021).
Duran-Pinedo, A. E. et al. Community-wide transcriptome of the oral microbiome in subjects with and without periodontitis. ISME J. 8, 1659–1672 (2014).
Duran-Pinedo, A. E., Solbiati, J., Teles, F. & Frias-Lopez, J. Subgingival host–microbiome metatranscriptomic changes following scaling and root planing in grade II/III periodontitis. J. Clin. Periodontol. 50, 316–330 (2023).
Jorth, P. et al. Metatranscriptomics of the human oral microbiome during health and disease. mBio 5, e01012–e01014 (2014).
Nowicki, E. M. et al. Microbiota and metatranscriptome changes accompanying the onset of gingivitis. mBio 9, e00575–e00618 (2018).
Haft, D. H. et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
Bang-Andreasen, T. et al. Total RNA sequencing reveals multilevel microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol. Ecol. 96, fiaa016 (2020).
Cornforth, D. et al. Pseudomonas aeruginosa transcriptome during human infection. Proc. Natl Acad. Sci. USA 115, E5125–E5134 (2018).
Crabbe, A. et al. Transcriptional and proteomic responses of Pseudomonas aeruginosa PAO1 to spaceflight conditions involve Hfq regulation and reveal a role for oxygen. Appl. Environ. Microbiol. 77, 1221–1230 (2011).
Frias-Lopez, J. et al. Microbial community gene expression in ocean surface waters. Proc. Natl Acad. Sci. USA 105, 3805–3810 (2008).
Nuccio, E. E. et al. Community RNA-seq: multi-kingdom responses to living versus decaying roots in soil. ISME Commun. 1, 72 (2021).
Ott, E. et al. Molecular repertoire of Deinococcus radiodurans after 1 year of exposure outside the International Space Station within the Tanpopo mission. Microbiome 8, 150 (2020).
Shi, Y., Tyson, G. W. & DeLong, E. F. Metatranscriptomics reveals unique microbial small RNAs in the ocean’s water column. Nature 459, 266–269 (2009).
Zhao, X. et al. Phenotypic, genomic, and transcriptomic changes in an Acinetobacter baumannii strain after spaceflight in China’s Tiangong-2 space laboratory. Braz. J. Microbiol. 53, 1447–1464 (2022).
Caglar, M. U. et al. The E. coli molecular phenotype under different growth conditions. Sci. Rep. 7, 45303 (2017).
Chen, W. H. et al. Integration of multi-omics data of a genome-reduced bacterium: prevalence of post-transcriptional regulation and its correlation with protein abundances. Nucleic Acids Res. 44, 1192–1202 (2016).
Choi, Y. W., Park, S. A., Lee, H. W., Kim, D. S. & Lee, N. G. Analysis of growth phase-dependent proteome profiles reveals differential regulation of mRNA and protein in Helicobacter pylori. Proteomics 8, 2665–2675 (2008).
Corbin, R. W. et al. Toward a protein profile of Escherichia coli: comparison to its transcription profile. Proc. Natl Acad. Sci. USA 100, 9232–9237 (2003).
Jayapal, K. P. et al. Uncovering genes with divergent mRNA–protein dynamics in Streptomyces coelicolor. PLoS ONE 3, e2097 (2008).
Kwon, T., Huse, H. K., Vogel, C., Whiteley, M. & Marcotte, E. M. Protein-to-mRNA ratios are conserved between Pseudomonas aeruginosa strains. J. Proteome Res. 13, 2370–2380 (2014).
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).
Maier, T. et al. Quantification of mRNA and protein and integration with protein turnover in a bacterium. Mol. Syst. Biol. 7, 511 (2011).
Zhang, M. et al. Impact of growth rate on the protein–mRNA ratio in Pseudomonas aeruginosa. mBio 14, e0306722 (2023).
Ohayon, S., Girsault, A., Nasser, M., Shen-Orr, S. & Meller, A. Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification. PLoS Comput. Biol. 15, e1007067 (2019).
Palmblad, M. Theoretical considerations for next-generation proteomics. J. Proteome Res. 20, 3395–3399 (2021).
Swaminathan, J. et al. Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat. Biotechnol. https://doi.org/10.1038/nbt.4278 (2018).
Azimi, S., Klementiev, A. D., Whiteley, M. & Diggle, S. P. Bacterial quorum sensing during infection. Annu. Rev. Microbiol. 74, 201–219 (2020).
Azimi, S., Lewin, G. R. & Whiteley, M. The biogeography of infection revisited. Nat. Rev. Microbiol. 20, 579–592 (2022).
Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).
Hall-Stoodley, L., Costerton, J. W. & Stoodley, P. Bacterial biofilms: from the natural environment to infectious diseases. Nat. Rev. Microbiol. 2, 95–108 (2004).
Ibberson, C. B. & Whiteley, M. The social life of microbes in chronic infection. Curr. Opin. Microbiol. 53, 44–50 (2020).
Connell, J. L., Kim, J., Shear, J. B., Bard, A. J. & Whiteley, M. Real-time monitoring of quorum sensing in 3D-printed bacterial aggregates using scanning electrochemical microscopy. Proc. Natl Acad. Sci. USA 111, 18255–18260 (2014).
Connell, J. L., Ritschdorff, E. T., Whiteley, M. & Shear, J. B. 3D printing of microscopic bacterial communities. Proc. Natl Acad. Sci. USA 110, 18380–18385 (2013).
Kim, D. et al. Spatial mapping of polymicrobial communities reveals a precise biogeography associated with human dental caries. Proc. Natl Acad. Sci. USA 117, 12375–12386 (2020).
Stacy, A. et al. Bacterial fight-and-flight responses enhance virulence in a polymicrobial infection. Proc. Natl Acad. Sci. USA 111, 7819–7824 (2014).
Stacy, A., McNally, L., Darch, S. E., Brown, S. P. & Whiteley, M. The biogeography of polymicrobial infection. Nat. Rev. Microbiol. 14, 93–105 (2016).
Lidstrom, M. E. & Konopka, M. C. The role of physiological heterogeneity in microbial population behavior. Nat. Chem. Biol. 6, 705–712 (2010).
Blattman, S. B., Jiang, W., Oikonomou, P. & Tavazoie, S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201 (2020).
Homberger, C., Barquist, L. & Vogel, J. Ushering in a new era of single-cell transcriptomics in bacteria. Microlife 3, uqac020 (2022).
Ma, P. et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell 186, 877–891.e14 (2023).
McNulty, R. et al. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nat. Microbiol. 8, 934–945 (2023).
Wang, B. et al. Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection. Nat. Microbiol. 8, 1846–1862 (2023).
Avila Cobos, F., Vandesompele, J., Mestdagh, P. & De Preter, K. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 34, 1969–1979 (2018).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Harrison, J. J. et al. Microtiter susceptibility testing of microbes growing on peg lids: a miniaturized biofilm model for high-throughput screening. Nat. Protoc. 5, 1236–1254 (2010).
Millar, M. R., Linton, C. J. & Sherriff, A. Use of a continuous culture system linked to a modified Robbins device or flow cell to study attachment of bacteria to surfaces. Methods Enzymol. 337, 43–62 (2001).
Goeres, D. M. et al. A method for growing a biofilm under low shear at the air–liquid interface using the drip flow biofilm reactor. Nat. Protoc. 4, 783–788 (2009).
Perrin, A., Herbelin, P., Jorand, F. P. A., Skali-Lami, S. & Mathieu, L. Design of a rotating disk reactor to assess the colonization of biofilms by free-living amoebae under high shear rates. Biofouling 34, 368–377 (2018).
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).
Edwards, S. & Kjellerup, B. V. Exploring the applications of invertebrate host–pathogen models for in vivo biofilm infections. FEMS Immunol. Med. Microbiol. 65, 205–214 (2012).
Schoenborn, A. A., Clapper, H., Eckshtain-Levi, N. & Shank, E. A. Rhizobacteria impact colonization of Listeria monocytogenes on Arabidopsis thaliana roots. Appl. Environ. Microbiol. 87, e0141121 (2021).
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Rumbaugh, K.P., Whiteley, M. Towards improved biofilm models. Nat Rev Microbiol (2024). https://doi.org/10.1038/s41579-024-01086-2
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DOI: https://doi.org/10.1038/s41579-024-01086-2