Transcriptomic, metabolomic, physiological, and computational modeling approaches were integrated to gain insight into the mechanisms of antibiotic tolerance in an in vitro biofilm system. Pseudomonas aeruginosa biofilms were grown in drip flow reactors on a medium composed to mimic the exudate from a chronic wound. After 4 days, the biofilm was 114 μm thick with 9.45 log10 CFU cm−2. These biofilms exhibited tolerance, relative to exponential-phase planktonic cells, to subsequent treatment with ciprofloxacin. The specific growth rate of the biofilm was estimated via elemental balances to be approximately 0.37 h−1 and with a reaction-diffusion model to be 0.32 h−1, or one-third of the maximum specific growth rate for planktonic cells. Global analysis of gene expression indicated lower transcription of ribosomal genes and genes for other anabolic functions in biofilms than in exponential-phase planktonic cells and revealed the induction of multiple stress responses in biofilm cells, including those associated with growth arrest, zinc limitation, hypoxia, and acyl-homoserine lactone quorum sensing. Metabolic pathways for phenazine biosynthesis and denitrification were transcriptionally activated in biofilms. A customized reaction-diffusion model predicted that steep oxygen concentration gradients will form when these biofilms are thicker than about 40 μm. Mutant strains that were deficient in Psl polysaccharide synthesis, the stringent response, the stationary-phase response, and the membrane stress response exhibited increased ciprofloxacin susceptibility when cultured in biofilms. These results support a sequence of phenomena leading to biofilm antibiotic tolerance, involving oxygen limitation, electron acceptor starvation and growth arrest, induction of associated stress responses, and differentiation into protected cell states. IMPORTANCE Bacteria in biofilms are protected from killing by antibiotics, and this reduced susceptibility contributes to the persistence of infections such as those in the cystic fibrosis lung and chronic wounds. A generalized conceptual model of biofilm antimicrobial tolerance with the following mechanistic steps is proposed: (i) establishment of concentration gradients in metabolic substrates and products; (ii) active biological responses to these changes in the local chemical microenvironment; (iii) entry of biofilm cells into a spectrum of states involving alternative metabolisms, stress responses, slow growth, cessation of growth, or dormancy (all prior to antibiotic treatment); (iv) adaptive responses to antibiotic exposure; and (v) reduced susceptibility of microbial cells to antimicrobial challenges in some of the physiological states accessed through these changes.
A hallmark of Pseudomonas aeruginosa is its ability to establish biofilm-based infections that are difficult to eradicate. Biofilms are less susceptible to host inflammatory and immune responses and have higher antibiotic tolerance than free-living planktonic cells. Developing treatments against biofilms requires an understanding of bacterial biofilm-specific physiological traits. Research efforts have started to elucidate the intricate mechanisms underlying biofilm development. However, many aspects of these mechanisms are still poorly understood. Here, we addressed questions regarding biofilm metabolism using a genome-scale kinetic model of the P. aeruginosa metabolic network and gene expression profiles. Specifically, we computed metabolite concentration differences between known mutants with altered biofilm formation and the wild-type strain to predict drug targets against P. aeruginosa biofilms. We also simulated the altered metabolism driven by gene expression changes between biofilm and stationary growth-phase planktonic cultures. Our analysis suggests that the synthesis of important biofilm-related molecules, such as the quorum-sensing molecule Pseudomonas quinolone signal and the exopolysaccharide Psl, is regulated not only through the expression of genes in their own synthesis pathway, but also through the biofilm-specific expression of genes in pathways competing for precursors to these molecules. Finally, we investigated why mutants defective in anthranilate degradation have an impaired ability to form biofilms. Alternative to a previous hypothesis that this biofilm reduction is caused by a decrease in energy production, we proposed that the dysregulation of the synthesis of secondary metabolites derived from anthranilate and chorismate is what impaired the biofilms of these mutants. Notably, these insights generated through our kinetic model-based approach are not accessible from previous constraint-based model analyses of P. aeruginosa biofilm metabolism. Our simulation results showed that plausible, non-intuitive explanations of difficult-to-interpret experimental observations could be generated by integrating genome-scale kinetic models with gene expression profiles.
BackgroundDespite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.ResultsWe developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.ConclusionsOur simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.
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