Cystic fibrosis (CF) is a recessive genetic disease in which mutations of the CF transmembrane conductance regulator (CFTR) gene result in a multi-systemic disorder. Despite a characteristic impairment of different organs, the primary cause of morbidity and early mortality in this disease is attributable to progressive airway and lung parenchymal damage through a cycle of infection, inflammation, and tissue damage (Cuthbertson et al., 2020).

Increased knowledge of airway infections in CF and their trajectories represents a keystone for a better understanding of the underlying CF pathophysiology. Their deep characterization could open new perspectives to improve their clinical management. Our understanding of the polymicrobial characteristics of CF airway infections and, more generally, of the CF respiratory microbiome is driven by pioneering studies applying next-generation sequencing (NGS)-based strategies (van der Gast et al., 2011; Quinn et al., 2014; Coburn et al., 2015; Feigelman et al., 2017). These works established the connections between significant changes in the airway microbiome composition and increased age, antibiotic use, decreased lung function, environment, and CF disease progression (Héry-Arnaud et al., 2019; Cuthbertson et al., 2020). However, host-pathogen interactions, microbiota dynamics, and their impact on the disease are only partially unveiled.

Besides the powerful metagenomics technology, deep shotgun proteomics represents a promising toolbox to get quicker unbiased information on protein samples, whatever their complexity. Metaproteomics, combining high-resolution tandem mass spectrometry with liquid reverse phase chromatography, has different facets: this methodology can precisely identify taxa present, estimate the respective biomasses of the components of the microbiota (Pible et al., 2020), describe how the microorganisms function by means of the detected proteins (Van Den Bossche et al., 2021a) and deliver unique functional characterization of the host molecular response (Van Den Bossche et al., 2021a; Grenga et al., 2022). Its potential to assess the lung microbiota using as starting material sputum, bronchoalveolar lavage (BAL), or epithelial brush has been recently evoked (Grenga et al., 2019; Wang et al., 2020; Hardouin et al., 2021).

Traditionally, BAL has been used to define airway microbiology and inflammation in young CF children. This sampling method presents an invasive character, the requirement of anesthesia, and is time- and resources- consuming. Compared to this gold standard for diagnosing lower airway microbiota, induced sputum samples showed a good bacteriologic correlation with BAL (Blau et al., 2014; Dubourg et al., 2015). Because it is a non-invasive method of sampling, the relevance of sputum for the study of the pulmonary microbiome is demonstrated (Lim et al., 2013; Hahn et al., 2016; Hogan et al., 2016; Feigelman et al., 2017; Pattison et al., 2017); (Acosta et al., 2018; Bevivino et al., 2019). Sputum is the recommended specimen for routine microbiological monitoring of CF patients and can be sampled either spontaneously for expectorating patients or induced in non-expectorating patients (Hogan et al., 2016; Cuthbertson et al., 2020). Thus, sputum has the advantage of possible iterative sampling for fine-tuned monitoring (Françoise and Héry-Arnaud, 2020). CF sputum samples are among the most challenging samples because of their heterogeneity and viscosity and the low ratio of microbial protein load (Graf et al., 2021). The latest published protocol for proteomics comprises a significant number of steps for the preprocessing of sputum, microbial enrichment, protein extraction, and proteolysis (Graf et al., 2021). Taxonomical identification of pathogens by mass spectrometry proteotyping, i.e., Matrix Assisted Laser Desorption Ionisation - Time of Flight (MALDI-ToF)-based identification, is rapidly obtainable following isolate cultivation and, over the last decade, has become a standard diagnostic tool in the microbiological laboratory (Suarez et al., 2013). A more complete and informative description of the taxon diversity can be obtained directly from the biological sample through tandem mass spectrometry-based proteotyping, avoiding culture bias (Grenga et al., 2019). Here, we assessed this methodology on sputum samples to establish the composition of the CF microbiota and the associated functionalities. However, as a general rule in medical microbiology, results may depend highly on the analyzed sample type and quality. Therefore, in this proof-of-concept study, we compared the respective diagnosis value of spontaneous and induced sputum samples to determine the sample providing the more accurate results using tandem mass spectrometry.

A comprehensive understanding of the taxonomy and functionality of the CF airway polymicrobial microbiota could improve infection diagnosis, management, and prediction. We applied an innovative tandem mass-spectrometry-based proteotyping approach to analyze paired spontaneous and induced sputum samples collected from three CF patients, revealing the structure of the microbial communities but also uncovering the microbial functions encoded by the most abundant proteins. These data illustrate how a quick analysis of sputum samples can be performed to identify and quantify the main taxa and obtain host-related information. Induced or spontaneous sputum samples were shown to be equivalent for this methodology, paving the road for its wide application.

Although a protocol for metaproteomic analysis of CF sputum samples has been previously proposed (Graf et al., 2021), the need to create dedicated databases obtained after 16S RNA gene amplicon sequencing on the sputum sample and the multiple steps for enrichment in microorganisms make the approach incompatible with routine microbiological diagnosis. The protocol described in the present study does not require time-consuming steps of mechanical and enzymatic homogenization, differential centrifugations, and filtrations. Instead, the protocol is easy to perform and delivers a higher percentage of assigned spectra. Furthermore, compared to DNA extraction and sequencing, either 16S RNA gene amplicons or metagenomics, the tandem mass spectrometry-based proteotyping proposed herein does not introduce amplification bias and delivers the whole panorama of microorganisms present in the sample. Last, our approach performs more quickly than time-consuming culture-dependent methods that are stochastics per nature and is more comprehensive.

Characterizing low biomass clinical microbiome is challenging because of the high abundance of the host signal and the lack of comprehensiveness of the database used for the interpretation. This last item is not anymore prominent as almost all pathogens and clinically relevant opportunistic microorganisms have publicly available whole-genome sequences. The remaining difficulty, i.e., the dynamic range of microbial and host proteins in sputum samples, has been tackled by removing the most abundant host proteins in bronchoalveolar lavage fluid (BALF) samples via two sequential analytical and bioinformatical workflows based on a custom database comprising human and microbial protein sequences expected in BALF (Jagtap et al., 2018). Here, we have developed another option based on a list of excluded mass/charge signals to avoid the tandem mass spectrometer spending too much time on these uninformative signals. While most of these host signals arise from conserved human proteins, whether the list established here could be used for other studies or should be revisited to be more specific of the new samples should be carefully evaluated. We also increased the ratio of interpreted MS/MS spectra by applying a smart strategy to reduce the search space and adjust the database for each sample to the microorganisms present in the sample. This approach is well adapted for interpreting metaproteomics data without a priori (Bassignani et al., 2021; Jouffret et al., 2021; Grenga et al., 2022). Here, we obtained the list of taxa present in the samples at the genus level in most cases, but the precision of the identification can be theoretically increased at the species level as soon as more signals are recorded on the same samples. Because of the higher abundance of the host proteins compared to the microbial ones and of the mass spectrometry sampling effect, as expected, the peptides/protein ratio is higher for H. sapiens (6) compared to microorganisms (1.4) and other Eukaryota (2). The best current practice in metaproteomics recommends informing the protein sequences database with data obtained by metagenomics or 16S rRNA amplicon sequencing performed on the same samples (Van Den Bossche et al., 2021b). However, this would require (i) more biological material, while the sputum samples could be difficult to obtain, and (ii) more time, while quick analysis of clinical samples is of utmost interest for the medical diagnostic. Our approach involves a sample-specific database construction taxonomically-informed, with the difference that the taxonomical information is from the dataset itself instead of relying on additional metataxonomics or metagenomics data with their own shortcomings. The most striking characteristic of the methodology is the possibility to estimate the relative abundance of the taxa identified, gaining further insight into the microbial community structure and allowing monitoring its dynamics longitudinally through the comparison of sample profiles. For this, the low microbial signal quantitation is based on the TSM concept, first described by Pible et al. (2020). Because of the development of new quantitation strategies, such as the ones based on data-independent acquisition (Aakko et al., 2020), the accuracy of this quantitative methodology could be even further improved.

When interpreting the recorded signals, host proteins are identified. Several assigned functions to this host signal were previously and directly linked to the CF-affected airways, such as complement cascade, signaling by interleukins and defective CFTR, apoptotic execution phase and programmed cell death (Soleti et al., 2013), scavenging of heme (Tyrrell and Callaghan, 2016), autophagy (Bodas and Vij, 2019; Zhao et al., 2020), cellular response to stress or antimicrobial peptides (Laube et al., 2006; Lecaille et al., 2016). Among these functionalities, apoptosis and programmed cell death greatly participate to the onset of the disease via multiple mechanisms that could be defective for the neutrophils or increased in epithelial cells, and then constantly contribute to the inflammatory state. In this context of inflammation and infection, scavenging of heme is important in heme management for heme-iron recycling but also in response to oxidative stress and in vesicle-mediated transport for the reticulum endoplasmic trafficking that are greatly imbalanced. With these conditions and stresses due to CFTR default that facilitates the colonization of the lung by microbial pathogens and the subsequent potential establishment of chronic infections leading to tissue damage, the airways provide a myriad of mechanisms of defense, notably lung antimicrobial peptides present at the surface of the epithelial cells which are markedly increased to prevent or in response to microbial colonization and infections. The insights of these functionalities highlighted by the proposed approach could be more deeply analyzed.

Clinical CF management is currently based on classical culture-based approaches to identify whether the most common CF pathogens are present in the sputum sample, and quantify them. However, this a priori approach suffers several limitations despite being a reference method allowing strain recovery and further antimicrobial susceptibility testing. Here, we compared the identified pathogens detected via morphological analysis after a cultivation step of sputum samples in the framework of the microbiological monitoring of CF patients and the tandem mass spectrometry proteotyping. Some of the cultured microorganisms, such as Escherichia coli in Patient 1, Staphylococcus aureus and Candida albicans in the three patients, were not detected by the described metaproteomics approach, probably due to the 8 days delay and the antibiotic therapies administered between classic microbiological assessment and sputum sample collection for the metaproteomics analysis. Above all, our metaproteomics-based approach without a priori allows enlarging the vision of the microbiota representatives including potential pathogenic microorganisms for CF patients and opportunistic ones. With the emergence of new CF pathogens and the identification of unusual species (Marchandin, 2012; Menetrey et al., 2021), depending on the specific environment and exposition of patients, the approach presented here appears highly complementary to the more classic microbiological approaches and could help clinical management of airway infections with its additional valuable information. The clinical relevance of the taxa detected by our mass spectrometry-based approach remains to be clarified by more extensive and longitudinal studies of the CF lung microbiota. On the other hand, the lack of detection of some taxa identified by culture in the previous week at various loads also has to be clarified to optimize the approach, as corresponding peptides/proteins might have been excluded from analysis due to too stringent analysis criteria (step 3 in Figure 1) after mass-exclusion list implementation.

Last but not least, the lung microbiota is a complex microbial ecosystem in which each microorganism may play a key role or become a major player in the course of the infection. Identifying specific functionalities, such as virulence factors in specific microorganisms may lead to a better understanding of the lung microbiota’s relation to disease evolution and outcome and could be used as prognostic markers of the onset of infection or exacerbations in CF patients. Here, we have specifically pointed out proteins known to be involved in microbial pathogenicity and virulence. Proteins for iron sequestration, flagellar motility and antibiotic resistance, and biofilm growth are all virulence factors that play a major role during bacterial infection to counteract the host defense and clearance mechanisms.

With the perspective of the application of the methodology for diagnostic in the clinic, we evaluated to 5 h the time to perform all the experimental operations and interpretation: sample preparation (2 h), mass spectrometry (1 h), and automatized interpretation (2 h). Such operations could be further optimized with specific efforts of industrial development to obtain the results more quickly and automatize the operations. Due to the known high variability between CF patients and the unicity of each patient’s microbiota (e.g., specific mutations in the CFTR gene and the individual patient treatment regimens), the analysis of a larger sample size may have revealed additional microorganisms/functions. Still, it would not have impacted the comparative analysis of each paired sputum sample. Conversely, a much larger sample number will be required to investigate and validate the biological significance of the metaproteomics results.

In summary, the sputum microbiota of three CF patients presents a personal signature with no significant difference in microbial features and activity in terms of abundance between paired spontaneous and induced sputum samples allowing a comprehensive characterization of the CF microbiota, including bacteria and fungi components, whatever the sampling method. By applying our innovative metaproteomics-based approach, we can detect the abundant microbial genera, including specific CF pathogens and the less explored and emerging ones. This additional information is a valuable complement to the classical microbiological diagnosis of CF airway infection and can be provided quickly. This is a major achievement as an unbiased view of the microbiome could be highly complementary and relevant for the clinical management of CF patients and for further personalized and predictive medicine by improving knowledge about the host-pathogen dynamics and CF pathophysiology.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD034628 and 10.6019/PXD034628.

The studies involving human participants were reviewed and approved by Centre de Ressources et de Compétences de la Mucoviscidose in the University Hospital of Montpellier (France). The patients/participants provided their written informed consent to participate in this study.

JA, RC, and LG designed the study. RC collected the samples and clinical data. PH performed sample preparation for metaproteomic analysis. PH, OP, HM, KC, and LG conceived and performed data and statistical analyses. PH, JA, and LG drafted the manuscript. HM, JA, RC, and LG critically revised the manuscript. All authors contributed to the article and approved the submitted version.

This work was funded in part by the ANR program “Phylopeptidomics” (grant number ANR-17-CE18-0023-01).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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