https://orcid.org/0000-0003-3841-1676
Figures
Abstract
Pseudomonas aeruginosa is a hallmark pathogen of cystic fibrosis (CF) airway infections, capable of reaching high cell densities despite its limited ability to directly utilize mucin glycoproteins as a nutrient source. In the CF lung, however, P. aeruginosa may access preferred carbon sources (e.g., amino acids and short-chain fatty acids) through metabolic cross-feeding with co-colonizing mucin-degrading microbes. Although host-derived enzymes such as neutrophil elastase can also degrade mucins, the extent to which host-mediated mucin breakdown supports P. aeruginosa growth remains unclear. Thus, here we compared the nutritional impact of microbial versus host mucolytic activity on P. aeruginosa physiology. Analyses of CF sputum revealed patient-specific variability in mucin integrity that is shaped by both host and microbial factors. We demonstrate that mucin degradation by anaerobic bacteria through proteolysis, glycolysis, and fermentation, promotes robust P. aeruginosa growth, unlike mucin processed by neutrophil elastase alone. Targeted metabolomics identified acetate and propionate as key metabolites driving this cross-feeding, while transcriptomic and phenotypic analyses revealed that P. aeruginosa engages in diauxic growth on a broader set of mucin-derived substrates. Unexpectedly, cross-feeding with anaerobes triggered the induction of P. aeruginosa denitrification and fermentation pathways, suggesting redox remodeling despite being cultured under oxygen-replete conditions. Finally, the transcriptional profile of P. aeruginosa grown on anaerobe-conditioned mucins more closely resembled its in vivo gene expression, more so than when grown on intact or neutrophil-degraded mucins. Together, these findings provide new insight into the potential role of interspecies metabolic interactions in shaping pathogen physiology in the inflammatory, polymicrobial, and mucus-rich environment of the CF airways.
Author summary
Cystic fibrosis (CF) airways contain viscous mucus that traps both pathogens and commensals. The major pathogen, P. aeruginosa thrives in these mucus-rich, inflamed environments, but how it acquires nutrients to sustain growth is poorly understood. We demonstrate that while host neutrophil enzymes degrade mucin polymers, this degradation alone does not provide substantial nutrients to support P. aeruginosa proliferation. In contrast, co-colonizing anaerobic microbiota extensively degrade mucins and generate short-chain fatty acids and other metabolites that strongly promote P. aeruginosa growth. We show that anaerobe-degraded mucins not only support faster growth but also trigger redox remodeling and gene expression changes that closely resemble P. aeruginosa behavior in CF patient sputum. This work highlights the important role of interspecies metabolic interactions in shaping CF airway infections and suggests new consideration for therapeutic strategies targeting airway microbiomes.
Citation: Arif SJ, Hoffman KM, Flynn JM, Wiggen TD, Lucas SK, Villarreal AR, et al. (2025) Host- and microbial-mediated mucin degradation differentially shape Pseudomonas aeruginosa physiology and gene expression. PLoS Pathog 21(10): e1013568. https://doi.org/10.1371/journal.ppat.1013568
Editor: Rohit Ruhal, VIT University: Vellore Institute of Technology, INDIA
Received: August 6, 2025; Accepted: September 25, 2025; Published: October 3, 2025
Copyright: © 2025 Arif et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Code and data are shared at https://github.com/Hunter-Lab-UMN/Arif_SJ_2025. Raw RNA-seq files are deposited under NCBI bioproject PRJNA1279252.
Funding: This work was supported by National Institute for Allergy and Infectious Disease (NIAID) and National Heart, Lung, Blood Institute (NHLBI) Research Project Grants (AI177613, HL136919) to RCH. JMF was supported by a Cystic Fibrosis Foundation fellowship (FLYNN16F0). The funders played no role in study design, data collection and analysis, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The gel forming mucins MUC5AC and MUC5B comprise approximately 97% of secreted airway mucins and serve as the structural backbone of the viscoelastic mucus barrier [1,2]. In healthy airways, mucins are integral components of innate immunity, contributing to mucus rheology, sequestration of antimicrobials, and defense against inhaled pathogens [1,3–8]. Conversely, mucin hypersecretion, aberrant glycosylation, proteolytic degradation, and changes in mucus viscosity and elasticity are common features of chronic lung disease and are associated with increased susceptibility to persistent infection [9–14].
In the cystic fibrosis (CF) airways, mucins exhibit lower molecular weights than those from non-CF individuals, a phenotype historically attributed to proteolytic degradation by neutrophil-derived enzymes [4,9,14–16]. More recent studies, however, reveal a more complex pattern in which mucins are both degraded and hyperconcentrated. Proteolytic cleavage occurs at antibody recognition sites, with MUC5AC being particularly susceptible [9,15,17], while concentrations of both MUC5AC and MUC5B are markedly elevated in sputum relative to healthy controls [17]. This apparent paradox likely reflects a compensatory upregulation of mucin synthesis and secretion in response to chronic inflammation [18,19], which occurs alongside oxidative stress-induced cross-linking that further alters mucin polymer structure [20]. As a result, CF sputum contains a heterogeneous mixture of intact polymers, degraded fragments, and abnormally cross-linked networks rather than being uniformly degraded.
Beyond host processes, members of the airway microbiota also contribute to mucin degradation [21–23]. Pseudomonas aeruginosa, for example, produces several secreted metalloproteases, including LasA, elastase B, and alkaline protease, which are abundant in CF sputum [24–26]. Elastase B, in particular, can degrade mucins as effectively as human neutrophil elastase (NE) [16,25], and clinical isolates deficient in elastase production lack this capacity [27]. Mucins in sputum from chronically infected people with CF (pwCF) degrade more rapidly ex vivo than those from healthy controls, likely reflecting microbial mucolytic activity [14,16]. Despite this, P. aeruginosa exhibits poor growth on intact mucin as a sole carbon source, suggesting that degradation alone is insufficient to support robust proliferation [28,29].
In addition to canonical pathogens, the airways harbor a diverse community of strict and facultative anaerobic bacteria that are often overlooked in clinical diagnostics [28,30,31]. Many of these anaerobes possess potent mucin-degrading capabilities, similar to those found in the oral cavity and gastrointestinal tract. We previously showed that such activity is retained in CF sputum, where anaerobes contribute to mucin breakdown through proteolytic and glycolytic processes, liberating amino acids and glycans while generating short-chain fatty acids (SCFAs) such as acetate and propionate via mixed acid fermentation [28,32]. These metabolites can enhance pathogen growth and virulence, and direct transcriptomic and metabolomic profiling of CF sputum supports the presence of in vivo cross-feeding interactions between anaerobes and P. aeruginosa [28].
Despite this evidence, it remains unclear whether host-derived mucin degradation, such as that mediated by NE, similarly expands the pool of bioavailable nutrients for P. aeruginosa. In this study, we compared the effects of host and microbial mucolytic processes on mucin integrity, nutrient availability, and P. aeruginosa physiology. We show that mucin degradation by anaerobes, but not NE, enhances P. aeruginosa growth, supporting a metabolic transition from glycolytic substrates to SCFAs (e.g., acetate), and promoting changes in cell morphology, redox state (NADH/NAD+ ratio), and core energy metabolism. Notably, anaerobe-conditioned mucins triggered the unexpected induction of denitrification and fermentation pathways, consistent with adaptation to a hypoxic, nutrient-rich environment. Finally, using a transcriptional accuracy scoring approach [33,34], we demonstrate that the gene expression profile of P. aeruginosa grown on anaerobe-degraded mucins more closely resembles its transcriptome in CF sputum, relative to growth on intact or NE-degraded mucins. These findings underscore the potential importance of interspecies metabolic interactions in shaping pathogen behavior and provide new insight into how P. aeruginosa may sustain infection within the mucus-rich, polymicrobial, inflammatory environment of the CF airways.
Results
Mucin integrity is variable in CF sputum and bronchoalveolar lavage
Given prior evidence of mucin degradation in the CF airways [17,26–29], we first assessed mucin integrity in expectorated sputum samples from 10 individuals with CF. Because no single method fully resolves mucin size, structure, and integrity [35], we applied complementary approaches to evaluate mucin degradation relative to intact controls; size exclusion chromatography (SEC) via fast protein liquid chromatography and ELISA-based detection of MUC5B.
Representative FPLC traces revealed two discrete peaks: (i) high molecular weight (HMW) mucins (>1000 kDa; peak 1), and (ii) lower molecular weight species (peak 2) (Fig 1a). Based on previously published studies [9] and our own analysis of FPLC molecular standards (S1 Fig), the peak 1:peak 2 area-under-the-curve (AUC) ratio was used as a proxy for mucin integrity, with lower ratios indicating greater degradation. Purified salivary mucin (PSM), which is predominantly composed of MUC5B, displayed high peak 1 AUCs, whereas CF sputum samples exhibited variable peak 1:peak 2 ratios (Figs 1b and S2), suggesting substantial inter-individual variability in mucin integrity.
(a) Representative size exclusion chromatography (SEC) profiles of mucins isolated from CF sputum relative to purified salivary mucin (PSM, control). (b) Peak 1:Peak 2 area-under-curve (AUC) ratios from individual CF sputum samples (subjects A-J). (c) ELISA quantification of MUC5B immunoreactivity across SEC fractions. (d) Peak 1: Peak 2 ratios for MUC5B ELISA data. Error bars represent mean + /- standard deviation of triplicate measurements.
To evaluate whether this heterogeneity extended to the mucin polypeptide backbone, SEC fractions were analyzed by ELISA for MUC5B immunoreactivity (Fig 1c and 1d). As expected, intact mucins displayed MUC5B epitope reactivity concentrated in HMW fractions (fractions 2–5). In contrast, CF sputum exhibited lower immunoreactivity in peak 1 and detectable signal in peak 2 (fractions 9–15), consistent with proteolytic cleavage of the mucin backbone. Altogether, these data confirm substantial heterogeneity in mucin integrity across samples and are consistent with recent reports describing a mixed mucin phenotype in CF sputum [9,15,17–20].
Both host and microbial enzymes contribute to mucin degradation
We next evaluated the relative contributions of host and microbial enzymes to mucin degradation. To do so, high-molecular weight porcine gastric mucin (PGM), chosen for its commercial availability and ease of use, was purified and dialyzed to create a minimal mucin medium (MMM) as described previously [32]. MMM was then treated with either a physiologically relevant concentration (5 μg/mL) [36] of neutrophil elastase (‘NE’), an anaerobic mucin-degrading community (‘AMDC’, composed of Veillonella, Fusobacterium, Prevotella spp., and other less abundant taxa (S3 Fig)), or both together (AMDC+NE). AMDC was originally enriched on mucin medium from a healthy volunteer [28], and was used here as a reproducible model of mucin-degrading airway anaerobes, rather than a direct representation of patient-specific community structures, which vary widely. Samples were incubated for 48h under anoxia and stored for further analyses.
Mucin integrity was then evaluated by FPLC as before (Fig 2a and 2b). We note that due to preparation by the manufacturer, PGM integrity is comparatively low relative to purified human mucin. However, when compared to untreated MMM, NE treatment alone shifted mucins from HMW to lower molecular weight fractions, as reflected by a reduction in peak 1 AUC and increase in peak 2 (Fig 2a and 2b). AMDC treatment similarly reduced peak 1 AUC, though to a lesser extent. Combined AMDC+NE treatment yielded additive degradation, producing the greatest loss of HMW material.
(a,b) FPLC-derived chromatograms showing a) peak 1 (high-molecular weight) and b) peak 2 (lower molecular-weight) fractions after treatment of minimal mucin medium (MMM) with neutrophil elastase (NE), the anaerobic mucin degrading community (AMDC), or both (AMDC+NE). (c,d) MUC5AC ELISA quantification of c) peak 1 (pooled fractions 2-5) and d) peak 2 (pooled fractions 9-16) fractions. Error bars represent the mean and standard deviation of n = 3 biological replicates with n = 3 technical replicates each. ***, p < 0.001.
Because MMM was generated from PGM, which is primarily composed of MUC5AC and contains little MUC5B, we used MUC5AC as the ELISA readout. Surprisingly, ELISA analyses showed that despite reductions in HMW material, MUC5AC immunoreactivity remained largely stable across treatments in peak 1 (Fig 2c). In contrast, peak 2 immunoreactivity was markedly increased following anaerobic degradation (Fig 2d), suggesting that anaerobes extensively process mucin polypeptides beyond the sites targeted by NE. These data demonstrate that anaerobic CF microbiota produce qualitatively distinct mucolytic effects compared to neutrophil elastase.
Anaerobe-mediated mucin degradation enhances P. aeruginosa growth
We next tested whether mucin degradation products generated by host or microbial processes differentially supported P. aeruginosa growth. Growth media were prepared by normalizing mucin degradation supernatants (NE, AMDC, AMDC+NE) to equivalent total organic carbon (TOC) content relative to untreated minimal mucin medium (MMM). Under all conditions, P. aeruginosa exhibited similar early exponential growth (OD ~ 0.2 at 10h) (Fig 3a). However, only AMDC- and AMDC+NE treated mucins yielded significantly higher final cell densities after 36h (p < 0.001). In contrast, NE-treated mucin provided minimal additional growth benefit relative to untreated controls. Similar growth enhancement was observed across multiple clinical isolates, indicating that cross-feeding is not strain-specific (Figs 3b and S4). These results indicate that anaerobe-mediated mucin degradation liberates nutrients not generated by NE alone and more effectively supports P. aeruginosa growth.
(a) Growth of P. aeruginosa PA14 on untreated, NE-treated, AMDC-treated, or AMDC+NE-treated mucins normalized by total organic carbon (TOC). (b) Final culture densities at 36h for PA14 and three CF clinical isolates. Full growth curves are shown in S4 Fig. Error bars represent mean + /- standard deviation and data shown are representative of three biological replicates for each experiment. Data were compared using a Kruskal-Wallis test with Dunn’s multiple comparisons.
Short-chain fatty acids contribute to cross-feeding but do not fully explain enhanced growth
Given the abundance of SCFAs in CF sputum and BALF [28,37,38], we hypothesized that SCFAs generated by anaerobic fermentation drive the observed cross-feeding. HPLC analysis of mucin degradation supernatants confirmed that AMDC and AMDC+NE treatments produced substantial acetate and propionate, while NE treatment did not (as expected)(Fig 4a and 4b). All other metabolites tested (butyrate, lactate, succinate) fell below detection limits. During P. aeruginosa growth on AMDC-treated mucin, both acetate and propionate were progressively consumed, coinciding with increased cell density (Fig 4c and 4d). Supplementing MMM with acetate and propionate reproduced the AMDC growth phenotype (Fig 4e, compare black lines). However, P. aeruginosa mutants deficient in SCFA catabolic pathways (ΔaceA, ΔprpB, ΔacsA, ΔacsAΔprpB) retained the ability to grow on AMDC-conditioned mucins, suggesting that SCFAs alone do not fully account for the growth advantage. Other anaerobe-generated metabolites likely also contribute to nutrient availability.
(a,b) SCFA concentrations in mucin degradation supernatants. (c,d) Acetate and propionate depletion during P. aeruginosa growth. (e) Growth of PA14 and isogenic mutant strains (ΔaceA, ΔacsA, ΔprpB, and ΔacsAΔprpB) on MMM alone, MMM supplemented with acetate, propionate, or both, and on AMDC-treated mucin. Maximal OD of wild-type P. aeruginosa PA14 grown on untreated MMM is denoted by the dashed brown line (OD=~0.4).
Anaerobe-degraded mucins elicit distinct transcriptional responses in P. aeruginosa
To gain a deeper understanding of metabolites exchanged and the impact of anaerobe- and NE-mediated mucin degradation on P. aeruginosa, we performed RNAseq on P. aeruginosa grown on untreated, NE-treated, and AMDC-treated MMM. RNA was extracted after 4h and 16h, representing early to mid-log phase and the secondary mid-log phase observed in the AMDC condition, respectively. As anticipated, principal component analysis reflected growth phase (4h v. 16h), but at each time point, AMDC-treated cultures displayed distinct transcriptional profiles compared to NE-treated or untreated MMM (Fig 5a and 5b), underscoring the discrete effect of anaerobe-derived metabolites on P. aeruginosa gene expression.
(a) Principal component analysis of RNAseq data at 4h and 16h. (b) Volcano plots of differential gene expression relative to untreated MMM. padj <0.01, log2fc>1. (c,d,e) Relative mean expression of genes involved in (c) central carbon metabolism, (d) aromatic metabolism, and (e) electron transport/ redox pathways. Relative mean expression calculated as average gene expression among all conditions and timepoints. Significance: padj < 0.05, *; < 0.01, **; or <0.001, ***.
Comparing untreated and NE conditions revealed that at 4h, only 53 genes (30 upregulated, 23 downregulated) were differentially expressed (log2fold >1, padj<0.01) (Fig 5b and S1 File). Upregulated genes were primarily associated with branched-chain amino acid degradation and Entner-Doudoroff/Embden-Meyerhof-Parnas pathways implicated in glucose metabolism. Downregulated genes included lipC (lipase), exaA (alcohol dehydrogenase), and the quorum sensing signal receptor gene, qscR (Fig 5c). No genes were differentially expressed between untreated and NE culture conditions at 16h (Fig 5b and S1 File), further supporting that NE-degraded mucins have limited effect on P. aeruginosa physiology.
Comparison of P. aeruginosa transcriptomes when grown on AMDC-degraded mucin relative to the untreated control revealed far greater differences in gene expression. At 4h and 16h, 530 genes (262 up, 268 down) and 195 genes (135 up, 60 down) were differentially expressed, respectively (Fig 5b and S1 File). Many were associated with central metabolic pathways (Fig 5c), including cbrB, encoding a transcriptional regulator that increases expression of transporters and catabolic pathways in response to altered intracellular metabolite pools. Interestingly, despite depletion of acetate and propionate in the growth medium (see Fig 4b), genes associated with acetate metabolism (acsA) and the glyoxylate shunt (aceA, prpBC) were unexpectedly downregulated during growth on AMDC-degraded mucin. We note, however, that this transcriptional pattern has been shown to occur in acetate-rich environments when other preferred carbon sources are also present [39]. Indeed, genes involved in glucose uptake (oprB) and its metabolism to glucose-6-phosphate (glk) were upregulated in AMDC-degraded medium at 4h, as were those involved in Entner-Doudoroff and pentose phosphate pathways (zwf, pgl, edd, and eda). Surprisingly, some genes (zwf, edd) were also upregulated in P. aeruginosa grown on NE-degraded mucins at 4h, which may suggest the catabolism of glucose or other mucin-derived monosaccharides during the early to mid-log phase of growth. Additionally, genes associated with the homogentisate pathway and other peripheral pathways were similarly upregulated at 4h, while downregulated pathways during early stages of growth included basic amino acid metabolism and transport (hut, oprD), polyamine utilization (spu), and pyrroloquinolone biosynthesis (pqq) (Fig 5c).
By 16h, genes involved in EDEMP pathways showed reduced expression in the AMDC condition, indicating a decreased reliance on glycolytic metabolism. Conversely, expression of genes responsible for the transport and catabolism of aromatic compounds, including the β-ketoapidate pathway (ben, cat, pca, pob), significantly increased. Interestingly, this pathway was previously shown to be upregulated in Acinetobacter baumanii due to mucin degradation [40]. Additionally, expression of antA (encoding anthranilate dioxygenase) increased, suggesting the metabolism of anthranilate, a precursor of tryptophan metabolism and the Pseudomonas quionolone signal (PQS). It is also worth noting that ascA, aceA, and prpBC showed increased expression at both time points. This suggests that while P. aeruginosa likely favors cleaved mucin glycans during early exponential growth, it may also use SCFAs as secondary carbon sources. This observation is consistent with their depletion from the growth medium (Fig 4b and 4d) and suggests the likely exchange of additional metabolites.
Anaerobe-derived metabolites drive redox remodeling despite oxygen-replete conditions
Despite oxygen-replete culture conditions, anaerobe-degraded mucin unexpectedly induced early activation of denitrification and fermentative pathways, including the nar and nir operons, encoding nitrate and nitrite reductases, respectively, in addition to arginine deiminase (arcABCD) and pyruvate fermentation (lldH) genes (Fig 5e). These pathways, regulated by Dnr (an fnr-type transcription factor that was also highly expressed), are typically induced during hypoxia or redox imbalance. With the exception of nirM, upregulation of denitrification and fermentative pathways was not observed in the NE condition, suggesting that anaerobe-derived metabolites drive a remodeling of electron transport, among other pathways, in P. aeruginosa.
We next asked what advantage, if any, would be gained by P. aeruginosa in re-wiring redox pathways and electron transport machinery. One hypothesis is that increased expression of denitrification and fermentative pathways would enable P. aeruginosa to rapidly adapt to the dynamic oxygen conditions of the CF airways [41]. To test this possibility, P. aeruginosa was grown aerobically in MMM and ADMC-treated MMM for 4h, followed by a rapid shift to anoxic MMM containing 50mM nitrate. Contrary to our prediction, AMDC-“primed” P. aeruginosa did not gain any fitness benefit during the shift to anoxia (Fig 6a), suggesting that anaerobic gene induction reflects redox rather than oxygen limitation per se.
(a) P. aeruginosa growth following oxic-to-anoxic shift (t = 0) in minimal mucin medium (MMM) or AMDC-treated MMM (both media contained 50mM KNO3). Data shown are the mean + / S.D. of three biological replicates. (b) Cell size measurements and representative images after 4h aerobic growth. Violin plots represent n = 338 to 1533 bacterial cells captured across three images derived from three independent biological replicates. (c) Intracellular NADH/NAD+ ratio after 4h growth. Data reflect the mean of three biological replicates. Data in (b) and (c) were compared using an ordinary one-way ANOVA with Tukey’s correction.
Consistent with this, P. aeruginosa grown on AMDC-treated mucin displayed larger cell sizes compared to cells grown on NE-degraded or untreated MMM (Fig 6b). An increase in cell size is a recognized phenotypic response to excess electron donor availability [42,43]. Likewise, cells grown on AMDC-treated mucin exhibited a more reduced intracellular redox state, reflected by higher NADH/NAD+ ratios (Fig 6c) relative to cells grown on NE-degraded or untreated MMM. Taken together, these results support a model in which rapid growth, fueled by anaerobe-derived metabolites, outpaces the respiratory capacity of the cell, triggering compensatory activation of less efficient denitrification and fermentation pathways to restore redox balance.
P. aeruginosa gene expression on AMDC-degraded mucin is a better recapitulation of its in vivo transcriptional profile
Finally, to assess the in vivo relevance of the transcriptional changes observed, we applied an established accuracy score approach [34,35] that compares P. aeruginosa gene expression in vitro to transcriptomes derived from CF sputum. The AS2 metric calculates the proportion of genes whose expression in vitro falls within two standard deviations of their mean expression across 24 metatranscriptomes derived from CF sputum [34] (S2 File). At 4h, P. aeruginosa grown on anaerobe-degraded mucin exhibited the highest median AS2 score (84.2%), compared to NE-treated mucin (81.5%) and untreated MMM (79.4%) (Fig 7a and S2 File). These findings indicate that anaerobe-degraded mucins more closely approximate the in vivo transcriptional state of P. aeruginosa in CF airways. As expected, AS2 scores converged across conditions at 16h, consistent with earlier RNAseq data (Fig 5b) showing reduced transcriptional divergence between conditions during later growth (Fig 7a).
(a) Accuracy score (AS2) comparison across conditions at 4h and 16h. Each data point represents a biological replicate (n=3 for each growth condition and time point). Dashed line represents the highest AS2 score obtained to date (Lewin et al., 2023). (b) Functional category accuracy (TIGRFAM meta, main, and sub roles). AS2 scores for all TIGRFAM categories are shown in S3 File. (c) Venn diagram of unique accurate genes across conditions. 633 genes were not accurate in any condition tested. Panel c was created in BioRender. Hunter, R (2025) https://Biorender.com/ydkj34v.
We next evaluated which specific functional pathways contributed to these differences. Using TIGRFAM functional annotations, we compared AS2 scores across metabolic, regulatory, and virulence-related gene categories, as previously described [34,35]. In nearly every major TIGRFAM functional role, AMDC-conditioned mucin either matched or exceeded untreated MMM in transcriptional accuracy (Fig 7b and S3 File), with particularly strong improvements in metabolism, electron transport, protein secretion, iron homeostasis, and amino acid biosynthesis. NE-treated mucin modestly improved accuracy in a subset of categories but was consistently outperformed by AMDC across most functional pathways.
We also compared individual genes that were accurately expressed under each condition. AMDC-degraded mucin uniquely recapitulated the in vivo expression pattern for 219 genes, compared to 57 and 29 uniquely accurate genes in untreated and NE-treated mucins, respectively (Fig 7c and S4 File). Many AMDC-specific accurate genes included those involved in redox metabolism (e.g. narI, napC, nirC, cyoABDE), acetate utilization (acsA), and denitrification pathways previously identified in our RNA-seq analyses (Fig 5c). In contrast, some genes accurately expressed in untreated and NE conditions – such as multidrug efflux pumps (mexB-oprM), type VI secretion components (vgrG4, tsi4), and glyoxylate shunt enzymes (aceA, glcB) were less accurate in the AMDC condition, reflecting distinct metabolic adaptations. An additional 179 genes were accurate in both AMDC-treated and NE-treated mucin, but not in untreated MMM, suggesting that mucin degradation, regardless of the mucolytic source, can broadly influence P. aeruginosa gene expression.
Discussion
P. aeruginosa is highly successful in establishing chronic infections in CF airways, yet the nutritional mechanisms that support its growth in vivo remain incompletely defined. While previous studies demonstrated that P. aeruginosa can degrade mucins via secreted proteases (e.g., LasB) and stimulate mucin production through host pathways such as EGFR signaling [27,30], its capacity to directly grow on intact mucins as a sole nutrient source is limited. Conversely, co-colonizing anaerobic bacteria, which are often enriched in CF airways, possess a broad repertoire of proteolytic and glycosidic enzymes that generate fermentation byproducts, particularly amino acids and SCFAs, that can serve as preferred carbon sources for P. aeruginosa [29,39]. However, the extent to which host versus microbial mucolytic activity shapes nutrient availability for P. aeruginosa has remained poorly defined.
Our analyses confirm that mucin integrity in CF sputum is highly heterogeneous, consistent with recent work showing that airway mucins exist in a mixed state of intact polymers, degraded fragments, and abnormally cross-linked networks [9,15–20]. Proteolytic activity preferentially targets MUC5AC, while chronic inflammatory signaling increases mucin synthesis and release, producing hyperconcentration of both MUC5AC and MUC5B. These mucin degradation and compensatory production processes create a mucus environment that is simultaneously enriched in intact mucins yet structurally compromised, a condition that exacerbates impaired mucociliary clearance.
Within this pathological context, we systematically compared the effects of neutrophil elastase (NE), the dominant host protease in CF sputum, and mucin-degrading anaerobes on mucin integrity and P. aeruginosa physiology. Both NE and anaerobic bacteria degraded mucin polymers, yet only anaerobe-driven mucolysis significantly enhanced P. aeruginosa growth, even after normalization for total carbon. NE yielded smaller mucin fragments, but provided little additional nutritional value for the pathogen, as reflected by the CFU counts remaining significantly lower in the NE-only condition compared to AMDC+NE. These findings indicate that NE-mediated proteolysis alone does not generate sufficient growth substrates, whereas anaerobe-mediated degradation produces a more diverse nutrient pool capable of sustaining larger viable populations.
Anaerobic degradation released abundant acetate and propionate that fueled robust P. aeruginosa growth. Interestingly, deletion of SCFA catabolism genes only partially impaired growth, indicating that additional byproducts of anaerobic mucin degradation (e.g., amino acids, sugars, or other fermentation intermediates) also contribute to cross-feeding. This metabolic complexity helps explain why glyoxylate shunt genes (aceA, glcB) were not significantly induced in the AMDC condition. Although acetate was liberated, shunt expression is strongest when acetate is the predominant carbon source, and our transcriptomic data likely reflect a metabolic hierarchy. Importantly, acetate depletion from the medium and the growth phenotypes of SCFA catabolism mutants confirm functional utilization of the substrate. Together, these findings support a model in which anaerobic communities act as mucolytic “gatekeepers”, unlocking mucin-derived nutrients otherwise inaccessible to P. aeruginosa.
An unexpected outcome was the induction of denitrification and fermentative pathways in P. aeruginosa grown on anaerobe-degraded mucin, despite oxygen-replete conditions. This metabolic remodeling is unlikely to reflect classical oxygen sensing, as anaerobe-primed cells did not exhibit enhanced growth following a shift to anoxia. Instead, we propose that rapid growth fueled by anaerobe-derived electron donors imposes redox pressure on P. aeruginosa, exceeding the capacity of aerobic respiration alone to reoxidize NADH. Under these conditions, cells increase in size, membrane space for respiratory complexes becomes limiting, and alternative electron acceptors (e.g., nitrate, pyruvate, arginine) are utilized to maintain intracellular redox balance [42,44]. Consistent with this model, P. aeruginosa grown on AMDC-degraded mucin displayed both increased cell size and elevated NADH/NAD+ ratios.
This form of metabolic flexibility may be highly advantageous in the CF lung, where steep oxygen gradients, fluctuating nutrient availability, and dense microbial communities create dynamic and spatially heterogeneous environments. Indeed, prior studies have documented denitrification pathway activation in CF sputum and airway mucus plugs, even when bulk oxygen is detectable [45,46]. Our data suggest that cross-feeding interactions with mucin-degrading anaerobes may contribute to this metabolic state by creating an electron donor-rich environment that functionally mimics aspects of hypoxia.
To quantitatively assess the in vivo relevance of these findings, we benchmarked P. aeruginosa transcriptomes from mucin degradation conditions to CF sputum using the AS2 framework [33,34]. AMDC-conditioned mucin yielded the highest transcriptional similarity to sputum samples, particularly during early growth, outperforming both NE-treated and untreated mucins. Functional improvements were observed across multiple metabolic pathways, including iron acquisition, electron transport, amino acid metabolism, and secretion systems – each a hallmark of P. aeruginosa adaptation during chronic CF infection. While synthetic sputum models remain superior in recapitulating the full complexity of chronic CF conditions [33,34,47], anaerobe-degraded mucin narrowed this gap and may ultimately better approximate the state of P. aeruginosa encountered during early colonization, when anaerobes are relatively abundant.
While neutrophil processes are well-established contributors to mucin degradation in the CF airway [16], our data suggest that their role in shaping nutrient pools for P. aeruginosa is limited compared to that of mucin-degrading anaerobes. This distinction likely reflects fundamental differences in enzymatic activity. Host proteases efficiently cleave polypeptide backbones but lack the broad glycosidase activities required to fully liberate sugars, SCFAs, and other fermentation intermediates. In contrast, anaerobic bacteria contribute a more expansive mucolytic repertoire, yielding a wider array of substrates that can support P. aeruginosa growth.
A limitation of our study is the use of porcine gastric mucin (PGM) as the substrate for generating minimal mucin medium. PGM does not fully recapitulate the biochemical features of airway mucins and is predominantly composed of MUC5AC, whereas CF airway secretions are also enriched in MUC5B. These different isoforms may influence bacterial gene expression and remain an important avenue for future investigation. In addition, RNA-seq analyses were performed in strain PA14 rather than in CF-adapted isolates. PA14 provides robust annotation and genetic tractability but lacks chronic infection-associated adaptations. Nevertheless, the cross-feeding phenotype was reproduced in multiple CF strains, and AMDC-conditioned mucins best captured in vivo transcriptional patterns, supporting the broader relevance of our findings.
In summary, these findings highlight the critical role of interspecies metabolic interactions in shaping P. aeruginosa physiology within the mucus-rich, polymicrobial, and inflammatory environment of the CF airways. By elucidating how mucin degradation products generated by anaerobes fuel P. aeruginosa growth and alter its metabolic programs, data presented here provide new insight into the nutritional ecology of chronic CF infections.
Methods
Ethics statement
This study was approved by the University of Minnesota Institutional Review Board (IRB# 1511M80532). All participants provided informed formal written consent.
Patient cohort and sample collection
Adult participants were recruited at the University of Minnesota Cystic Fibrosis Center. Spontaneously expectorated sputum was collected, placed on ice, and transported to the UMN Department of Microbiology & Immunology for processing. Salivary mucins for control experiments were collected from healthy volunteers.
Mucin purification and isolation
High molecular weight mucins were purified from saliva for use as MUC5B and MUC5AC standards. To isolate mucin, saliva (~50mL) was mixed with cOmplete protease inhibitor cocktail (Roche) and solubilized with 6M guanidine hydrochloride (GuHCl), 100mM Tris-HCl, 50mM dithiothreitol (DTT), and 4mM EDTA. Samples were incubated at 37°C for 1h, centrifuged at 15,000 rpm for 1hr, and dialyzed against MilliQ water at room temperature (2 x 1h) and 4°C (overnight). Lyophilized mucins were resuspended in 3M GuHCl, clarified by centrifugation (7000 rpm, 3 min), filtered (0.2μm), and purified by fast protein liquid chromatography (FPLC; ÄKTA Pure, Cytiva) using a 10/200mm Tricorn column packed with Sepharose CL-2B. Aliquots (500 μL) were loaded and eluted isocratically at 0.4mL/min in 50mM phosphate buffer (pH 7.2) containing 150mM NaCl. Fractions were frozen at -80°C for storage.
Mucin analysis by size exclusion chromatography
For sputum samples, 6M GuHCl was added at a 6:1 (v/v) ratio, followed by incubation at 80°C for 2h with intermittent mixing. Samples were clarified using 0.45μm centrifugal filters (Thermo) and analyzed via FPLC as above. Eighteen 1-mL fractions were collected and stored at -80°C for downstream ELISA. Mucins derived from bacterial cultures were similarly processed after debris removal by centrifugation and filtration. Area-under-the-curve (AUC) calculations were performed using UNICORN 7.0 software (Cytiva).
Enzyme linked immunosorbent assays (ELISA)
For each sample, fractions from peak 1 (fractions 2–5) and peak 2 (fractions 9–16) were pooled. Standards were generated using serial dilutions of purified salivary mucin. Samples and standards (50μL) were applied in triplicate to block MaxiSorp (Thermo), dried at 45°C (~2.5 h) until liquid had evaporated, washed three times with 300μL TBS-T (1X TBS + 0.1% Tween 20) and blocked with 300μL Protein-Free Blocking Buffer (Thermo) for 1h at RT. For MUC5B detection, plates were incubated with anti-MUC5B antibody (8C11) (Sigma) diluted 1:1000 in TBS-T + 2% BSA for 1h at RT. Plates were washed three times TBS-T and incubated with goat-anti-mouse HRP (Thermo) diluted 1:2000 in TBS-T + 2% BSA for 1h. For MUC5AC, anti-MUC5AC (45M1)(Thermo) was used, followed by HRP-conjugated secondary (1:1000). SuperSignal ELISA Femto Substrate (Thermo) was used for luminescence detection on a Synergy H1 plate reader (BioTek).
Bacterial strains and growth conditions
P. aeruginosa strain PA14, three clinical isolates (LC107, LC115, LC120), and isogenic mutants of PA14 (ΔaceA, ΔacsA, ΔprpB, and ΔacsΔprpB) generated previously [28] were maintained on Luria Bertani (LB) broth or agar. The anaerobic mucin-degrading bacterial community (‘AMDC’, S3 Fig) originally described in (28), was revived from freezer stock and passaged twice (48h) on a defined minimal mucin medium (see below) in an anaerobic chamber (95% N2, 5% H2, 5% CO2) (Coy Laboratory Products).
In vitro mucin degradation
A minimal mucin medium (MMM) was prepared by dissolving porcine gastric mucin (PGM) (Sigma) in Milli-Q water (30g/L), autoclaved, and clarified as described previously [32]. The resulting MMM contained 50mM KH2PO4, 150mM NaCl, 1mM MgSO4, and a trace mineral mix [28]. MMM was incubated with (i) no treatment, (ii) AMDC (OD600 = 0.3), (iii) 5 µg/mL neutrophil elastase (Abcam), and (iv) both AMDC and NE together. All cultures were incubated anaerobically at 37°C for 48h. Degradation was assessed by FPLC and ELISA as described above.
Total organic carbon (TOC) analysis
7mL volumes of in vitro mucin degradation samples were frozen and lyophilized as described above. 150mg of dehydrated sample were analyzed for TOC using a Vario Max Cube elemental analyzer (Elementar). TOC-normalized supernatants (normalized to 14% TOC) were used for subsequent P. aeruginosa growth assays.
P. aeruginosa growth assays
P. aeruginosa was cultured overnight in LB and washed three times with phosphate buffered saline (PBS). 1 x 105 CFU/mL were inoculated into 200µL of TOC-normalized mucin degradation supernatants in 96-well plates, sealed with Breathe Easy membranes (Diversified Biotech) and incubated aerobically at 37°C with orbital shaking in a Synergy H1 microplate reader (BioTek). OD600 was measured hourly for 36h. All conditions were performed with three biological and three technical replicates. ΔaceA, ΔacsA, ΔprpB, and ΔacsΔprpB mutant growth assays were similarly performed using MMM or AMDC-supernatants supplemented with 15mM acetate, 15mM propionate, or both. For oxic-to-anoxic shift experiments, PA14 was grown in MMM or AMDC-MMM aerobically for 4h, then transferred to an anaerobic chamber and inoculated 1:10 into MMM + 50mM KNO3. Anaerobic growth was monitored for 16h in microplate readers housed within the chamber.
High performance liquid chromatography (HPLC)
Mucin degradation supernatants were clarified by centrifugation, filtered through a 3,000 MWCO PES concentrator (Thermo) and analyzed using a Dionex UltiMate 3000 HPLC (Thermo) equipped with an Acclaim organic acid column (4.0 x 250 mm, 5 μm). Samples (10μL) were eluted isocratically (100mM Na2SO4, pH 2.6 with CH3SO3H) at 1mL/min for 24h following an 8 min incubation step [48]. SCFAs were quantified relative to standard curves.
RNA extraction and sequencing
PA14 cultures were grown for 4h or 16h in TOC-normalized mucin degradation supernatants (60mL each), harvested by centrifugation, and snap-frozen. RNA was extracted using the RNeasy Mini Kit (Qiagen) with β-mercaptoethanol lysis and DNaseI treatment, followed by cleanup with the RNA Clean and Concentrator kit (Zymo). rRNA depletion was performed using Ribo-Zero Plus [Illumina], and libraries were prepared using TruSeq RNA Library Prep kit (Illumina) per manufacturer’s instructions. Sequencing was performed on the Illumina NovaSeq6000 platform (50 bp paired-end reads) at the University of Minnesota Genomics Center.
RNA-seq analysis
Raw fastq files were quality-checked (FastQC), aligned to the P. aeruginosa PA14 genome (NCBI RefSeq 008463.1) using the R ‘Subread’ package [49], and annotated via Pseudomonas.com. Features with low read counts (<10) or non-protein coding genes were filtered. DESeq2 [50] was used for normalization, principal component analysis, and differential gene expression testing (log2fold-change > 1, padj <0.001). Code and data are shared at https://github.com/Hunter-Lab-UMN/Arif_SJ_2025. Raw RNA-seq files are deposited under NCBI bioproject PRJNA1279252.
Accuracy score analysis
Accuracy scores (AS2) were calculated as previously described [34,35]. RNAseq data were realigned to the P. aeruginosa PA01 genome to enable comparison to published CF sputum metatranscriptomes. For each gene, z-scores were calculated relative to the mean expression across 24 CF sputum samples [33]. AS2 scores represent the percentage of genes falling within +/- 2 standard deviations of in vivo expression. TIGRFAM category-level AS2 values were similarly computed. Sunburst plots were generated using the ggsunburst R package [51]. Statistical comparisons were performed using Kruskal-Wallis tests with Dunn’s post-hoc correction in Prism 10.
Supporting information
S1 Fig. FPLC of high-molecular weight standards.
HMW standards in the GE Gel Filtration Calibration Kit as analyzed by FPLC. HMW compounds (i.e., intact mucin) elute earlier than those of lower molecular weight (i.e., degraded mucin).
https://doi.org/10.1371/journal.ppat.1013568.s001
(TIF)
S2 Fig. Mucin integrity in CF sputum.
(a) Size exclusion chromatography profiles of mucins isolated from CF sputum relative to purified sinus mucin (PSM, dashed line). (b) Area-under-curve (AUC) data for Peak 1 (solid color) and Peak 2 (hatched color) of individual sputum samples compared to PSM.
https://doi.org/10.1371/journal.ppat.1013568.s002
(TIF)
S3 Fig. AMDC Composition.
Composition of the anaerobic mucin degrading community (AMDC) as determined by 16S rRNA gene sequencing.
https://doi.org/10.1371/journal.ppat.1013568.s003
(TIF)
S4 Fig. Clinical isolate growth on mucin.
Growth curves of P. aeruginosa clinical isolates on untreated MMM, and NE-, AMDC-, and AMDC+NE-treated MMM. Each isolate exhibited increased growth in the cell-free supernatants of AMDC- and AMDC+NE-treated mucin.
https://doi.org/10.1371/journal.ppat.1013568.s004
(TIF)
S1 File. RNASeq of PA14 grown on intact and degraded mucins.
P. aeruginosa differential gene expression in untreated (MMM), NE-treated, and AMDC-treated mucin.
https://doi.org/10.1371/journal.ppat.1013568.s005
(S1_File.XLSX)
S2 File. Accuracy scores of PA14 genes.
Raw counts, normalized sequence counts, and accuracy scores for the core P. aeruginosa gene set (5147 genes).
https://doi.org/10.1371/journal.ppat.1013568.s006
(S2_File.XLSX)
S3 File. TIGRFAM accuracy scores.
AS2 and z-scores for TIGRFAM-annotated P. aeruginosa genes and their expression during growth on intact and degraded mucins.
https://doi.org/10.1371/journal.ppat.1013568.s007
(S3_File.XLSX)
S4 File. Individual PA14 gene expression accuracy.
https://doi.org/10.1371/journal.ppat.1013568.s008
(S4_File.XLSX)
Acknowledgments
We thank current and former members of the Hunter lab for discussions and manuscript feedback, as well as the attendees of the Atlanta CF Models Workshop (Georgia Institute of Technology, 2023, supported by the Cystic Fibrosis Foundation, #WHITEL20A0), particularly Gina Lewin, Rebecca Duncan, and Marvin Whiteley for assistance with the accuracy score framework. We also thank Kristin Jesse and the University of Minnesota Cystic Fibrosis center for patient recruitment and sample acquisition.
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