Linking volatile metabolites from bacterial pathogens to exhaled breath condensate of people with cystic fibrosis

Abstract

Obtaining sputum samples from people with cystic fibrosis (pwCF) for microbiology has become challenging due to the positive clinical effects of the cystic fibrosis transmembrane conductance regulator modulator therapy, elexacaftor-tezacaftor-ivacaftor (ETI). Although ETI improves lung function and reduces sputum production, recent data shows that bacterial pathogens persist, making continued monitoring of infection important. As an alternative to sputum sampling, this study developed a non-invasive technique called 'Cough Breath' (CB) to identify volatile organic compounds (VOCs) in exhaled breath condensate (EBC) and link them to cystic fibrosis (CF) bacterial pathogens using purge and trap GC-MS. The CB culturing approach was able to isolate pathogens from expectorated particulates simultaneously with EBC collection; however, culturing positivity was low, with 6% of samples collected (n=47) positive for either Pseudomonas aeruginosa or Staphylococcus aureus. From EBC, we identified VOCs matching those uniquely produced by P. aeruginosa (7), S. aureus (12), Achromobacter xylosoxidans (8) and Granulicatella adiacens (2); however, the overall detection rate was also low. Expanding to VOCs produced across multiple pathogens identified 30 frequently detected in the EBC of pwCF, including 2,3-pentanedione, propyl pyruvate, oxalic acid diallyl ester, methyl isobutyl ketone, methyl nitrate, 2-propenal, acetonitrile, acetoin and 2,3-butanedione. Comparing isolate volatilomes and EBC samples from the same pwCF enhanced detection rates with key VOCs, such as 2,3-pentanedione (86%) and propyl pyruvate (83%), in P. aeruginosa isolates. Further investigation showed that VOC production differed across strains and at different growth phases, creating variability that may explain the overall low EBC detection rate. Although this study successfully cultured CF pathogens from cough particulates and matched their unique VOCs in EBC samples, our results indicate that microbial volatiles more generally indicative of infection, such as 2,3-pentanedione, may have the most utility in aiding diagnostics in pwCF on ETI who have reduced sputum production in the clinic.

Keywords: cough breath; cystic fibrosis; exhaled breath condensate; pathogens; volatile organic compounds.

Fig. 1.. Schematic diagram demonstrating the novel CB dual sampling approach. Each subject was asked to intermittently cough into the sampling unit for a total of ten coughs whilst breathing for 5–7 min to collect EBC. If available, sputum samples (n=25) were also collected, and the CB filter units (n=47) were cultured for bacterial pathogens using rich media, then isolated on selective agar. Simultaneously, EBC samples (n=98) were collected and frozen for VOC analysis. The sampling approach was opportunistic, where subjects provided a CB or sputum sample, if possible, but all sampling events included an EBC collection.

Fig. 2.. The unique VOCs produced by each CF pathogen detected in the EBC of pwCF. (a) Network visualization of the associations between pathogen-specific VOCs and EBC samples from pwCF, generated using Cytoscape v. 3.9.1. The network shows interactions between VOCs detected in EBC that are uniquely produced by CF pathogens including PA, SA, AX, SM, RM, GA and Streptococcus spp. The innermost nodes represent VOCs that were emitted by multiple pathogens, whilst the outermost nodes represent VOCs that were exclusively produced by each pathogen and were detected in EBC samples. Each line in the network represents an association between a pathogen, a VOC and its presence in EBC. (b) Pathogen-specific VOC detection rates per isolate and EBC samples. Unique VOCs for each pathogen are displayed, with detection rates based on the percentage of isolates or EBC samples in which they were present. Detection rates were calculated as a percentage of total isolates for each species: PA (n=21), SA (n=24), AX (n=10) and EBC samples (n=98). VOCs detected in at least two isolates or EBC samples are shown.

Fig. 3.. Overview of VOCs detected in EBC and their associations with CF pathogens. (a) Heatmap illustrating the relative frequency of VOCs across various sample types highlighted with their corresponding Z-scores accounting for different sample sizes of each group. The colour gradient in the heatmap represents Z-scores, indicating how the relative frequency of each VOC in a sample type deviates from the overall mean. VOCs are arranged based on their detection frequency in EBC, with the most frequently identified VOCs at the top and less frequent ones at the bottom. Asterisks indicate the detection frequency of each VOC within sample types as follows: *25–49.99%, **50–74.99% and ***>75%. VOCs with a detection frequency below 25% are not marked with an asterisk, and samples without any VOC detection are not coloured. ‘Other’ CF pathogens include SM, RM, GA and Streptococcus spp. (b) Alluvial plot illustrating the connections between VOCs detected in isolates and the EBC of the same pwCF. Isolates are colour-coded based on the pathogens: yellow for SA, blue for PA and maroon for AX, showing the specific associations between pathogens and VOCs. (c) The percentage of EBC samples that shared volatiles with isolates from the same pwCF, calculated based on the number of EBC samples containing VOCs shared with isolates of key CF pathogens, including PA, SA and AX (n=19), relative to the total number of EBC samples matched to an isolate from the same pwCF (n=35). (d) Confusion matrix heatmap of VOC detection accuracy in EBC and corresponding isolates from the same pwCF. The heatmap displays detection accuracy for key VOCs, including 2,3-pentanedione and propyl pyruvate across different conditions. Rows indicate compound presence or absence in isolates, whilst columns represent presence or absence in EBC samples, with colour gradients for PA (blue) and SA (yellow). Detection rates are shown as the percentage of samples with matching VOCs in both isolates and EBC, with CIs included to address variability due to sample size differences.

Fig. 4.. Variation in VOC production by different strains of CF isolates over 24 h across various growth phases. Growth curves are shown for (a) PA, (b) AX, (c) SA and (d) SG, alongside the VOCs consistently detected across all three replicates for each isolate at specific time points during the exponential and stationary growth phases.