First, a spontaneously expectorated sputum was sampled. Then a second sputum sample was collected after induction via the nebulization of saline solution 0. Clinical microbiology data were obtained from the cultivation of sputum samples collected from the same patients during a previous within-week consultation.

The suspensions were transferred into 0. The peptides were analyzed with a Q Exactive HF tandem mass spectrometer ThermoFisher Scientific coupled to an Ultimate Nano LC System ThermoFisher Scientific. The tandem mass spectrometer was operated in data-dependent mode.

Briefly, a scan cycle was initiated with a full scan of peptide ions in the ultra-high-field Orbitrap analyzer, followed by the selection of a single precursor, its dissociation in high-energy collisional mode, and scan of its fragments.

Eventually, a list of excluded masses was activated. Each sample was analyzed once under these MS conditions to generate a mass exclusion list and three additional times under these conditions but using a mass exclusion list Supplementary Table S2 to guide spectra acquisition.

gz , with filtering to retain one representative taxon per species belonging to the superkingdoms Bacteria, Archaea and Eukaryota. The NCBInrS database includes 50,, protein sequences from , taxa, totaling 20,,, amino acid residues. At each taxonomical rank, the Taxon-Spectrum Matches TSMs as first defined in Pible et al.

Families were validated in the first-round search if identified with i more than a given family-specific peptides or ii more than a given number of additional TSMs explaining the presence of this lineage. The thresholds for these two parameters were established depending on the amount of signals in the dataset and the superkingdom considered.

The thresholds are indicated for each dataset Table 1. Together with all their descendants, all the representatives of these families were extracted from the full NCBInr database downloaded on Genera were validated in the second-round search if identified with i more than a given genus-specific peptides or ii more than a given number of additional TSMs explaining the presence of this lineage.

These signal-dependent thresholds are indicated in Table 1. All descendants of these genera were extracted from NCBInr for a third Mascot search performed with the same settings.

The FDR was estimated with a search against a decoy database with the Mascot engine. Proteins were grouped into protein families, and their abundance was reported based on parsimony. Table 1. Taxonomical differential analysis of the microbial composition of spontaneous and induced sputum was assessed in terms of taxa abundance by using the R package metacoder Foster et al.

Reactome Gene Sets RHSA -enrichment was done via Metascape Zhou et al. The sum of the normalized spectral abundance factor NSAF of each human protein contributing to the functional Reactome terms was used as their relative abundance.

The NSAF values have been calculated as described previously Christie-Oleza et al. Within each phylum, the sum of spectral counts of the proteins for a taxon were used to assign abundance values to protein taxon. The microbial proteins were KEGG-annotated using the online tool GhostKOALA 1 Kanehisa et al.

To assess the impact of the sample type on the investigation of the respiratory microbiota, pairs of spontaneous SS and induced sputum IS samples collected from three CF adult patients during a follow-up consultation Table 2 were analyzed following the approach described in Figure 1.

To retrieve the taxonomic and functional protein annotations, the acquired mass spectrometry data were interpreted in a three-step cascaded search. Specifically, the first two rounds of the search were used to optimize the identification of taxa in the sample. The third round performed a more classical protein search on the most adequate database comprising only the identified genera.

An average of Figure 1. Experimental workflow of the study. The proteins extracted were submitted to a short in-gel migration and digested into peptides using trypsin. A total of 1, human proteins were identified, of which were common to the SS samples while proteins were shared in the IS ones Supplementary Table S1.

The most abundant among the latter were the alpha-amylase and trypsinogen precursors, SA9 protein, serum albumin, and the polymeric immunoglobulin receptor. The functional annotation of the identified human proteins revealed that key processes and pathways linked to the CF -affected airways were commonly identified in both IS and SS samples Figure 3.

Figure 2. Relative protein abundance according to their human, fungal or bacterial origin. Figure 3. Enrichment functional analysis of the human proteins identified in both induced sputum IS and spontaneou sputum SS samples from the three patients.

The Representative Reactome Gene Sets terms identified by annotating human proteins from SS and IS samples are indicated in blue and orange, respectively.

Their relative abundance corresponds to the sum of the normalized spectral abundance factor of each human proteins contributing to the functional reactome terms.

A Patient 1, B Patient 2 and C Patient 3. Results from the analysis of the three technical replicates per sample are described in Table 1. The relative abundance of each microbial genera was estimated by normalizing their assigned TSMs by the sum of TSMs attributed to all microbial genera.

A total of 38 different microbial genera were confidently identified in the 6 samples. Among them, 9 bacterial genera were found systematically in the three pairs of samples: Streptomyces , Bacillus , Clostridium , Pseudomonas, Nocardia , Flavobacterium , Paenibacillus , Labrenzia , and Paraburkholderia Table 4 ; Figure 4.

The four first cited genera were the most abundant and collectively represented on average To evaluate the impact of the sampling method on the abundance of the microbial components in sputum samples, a comparison analysis was performed between the microbial genera found in pairs of SS and IS samples.

No significant difference was observed between the abundances of these microbial genera in SS and IS from Patients 1 and 3. Taxa that were differentially identified according to the sample were Micromonospora identified in the spontaneous condition only SS1 , Neisseria and Cupriavidus observed in induced sputa IS1 and IS3, respectively , Burkholderia uniquely found in SS1 and SS2, and Corynebacterium found only in IS3.

Interestingly, the methodology is able to detect not only bacteria but also fungi. Among the fungi, both Aspergillus and Penicillium were identified in sputum samples from Patient 1. These two fungal genera were found in the IS of Patient 2, but only Penicillium was observed in the SS2. Aspergillus was the only fungal genus found in samples from Patient 3.

Figure 4. Relative abundance of microbial taxa at the genus level in spontaneous sputum SS versus induced sputum IS samples determined following the mass-exclusion list strategy. The inner circle indicates the relative abundance of the microbial taxa found in SS sample whereas the outer circle corresponds to the results for IS sample.

Each value corresponds to the mean of the analytical triplicates per sample. CF clinical management is routinely performed by the cultivation of sputum samples on a panel of agar media, including selective and enriched media, followed by identification of targeted pathogens via morphological analysis, Gram stain, MALDI-TOF or, less frequently, PCR on the positive cultures.

Culturing the sputum samples of the 3 CF patients allowed identifying a total of 14 strains belonging to 9 microbial genera Table 2.

Among them, the metaproteomics approach allowed the detection of known CF pathogens such as Pseudomonas , Mycobacterium, and Aspergillus but not Staphylococcus and Candida. Concomitantly, 28 and 12 additional microbial genera were identified in the IS and SS samples, respectively, following sample profiling by tandem mass spectrometry Figure 4.

To describe the functional composition of the microbiota, microbial proteins identified in each sample were annotated and assigned to KO KEGG Orthology terms. These were grouped into 49 KO parent-terms. The most important categories of annotated microbial proteins, based on the abundance and number of KO-terms, are involved in pathways relevant to airway infection such as evasion from the host immune system or microbial pathogenicity.

KO-terms responsible for multidrug resistance K, K were identified in SS from Patient 1 SS1 and quorum sensing K in IS from Patient 1 IS1.

KO-terms involved in lipopolysaccharide biosynthesis and transport K , lipoarabinomannan biosynthesis K and chemotaxis K were both found in the sputa, whether SS or IS, from patients 2 and 3. Additionally, KO-terms for Type IV secretion system K and metallic and iron transport system K were, respectively, found in SS and IS from Patient 3 SS3 and IS3.

The annotated microbial proteins of interest highlighted the role of specific microbiota components in the colonization of the CF respiratory tract Figure 5 ; Table 5.

In addition, proteins involved in cationic antimicrobial peptide repulsion SecA, cheY, degP and multidrug resistance ABCG2. PDR, efrB were detected in both SS and IS samples from Patients 1 and 2.

Biofilm formation and host colonization are increased with the production of chemotaxis and flagellar motility proteins found in all the samples. The molecular chaperones DnaK SS2 and IS2 and GroEL SS2 and IS2, SS3 and IS3 are responsible for protein protection or degradation due to the oxidative stress mainly established by the host immune system.

Additionally, metallic and iron transport systems, which are greatly involved in bacterial virulence, were detected AfuA, FhuB and EcfA2 both in SS3 and IS3. Figure 5. Functional annotation of the microbial proteins found in spontaneous and induced sputum samples.

The relative abundance of each category corresponds to the sum of the relative abundance of each KEGG Orthology KO term of interest of the related category, which is indicated by the size of the dot.

The color scale corresponds to the number of KO terms of interest involved in each category. To evaluate the effect of the sampling method on the ability to recover functional information, a comparative analysis of the abundance of the microbial protein functions in SS and IS samples was performed.

The relative abundance of KO terms was compared in paired SS and IS samples. 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. 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.

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.

This approach is well adapted for interpreting metaproteomics data without a priori Bassignani et al. 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.

sapiens 6 compared to microorganisms 1. 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.

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. Because of the development of new quantitation strategies, such as the ones based on data-independent acquisition Aakko et al.

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.

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.

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, ; Menetrey et al. 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.

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.

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.

Such operations could be further optimized with specific efforts of industrial development to obtain the results more quickly and automatize the operations. 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. 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.

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. 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.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Aakko, J. Data-independent acquisition mass spectrometry in Metaproteomics of gut microbiota-implementation and computational analysis.

Proteome Res. doi: PubMed Abstract CrossRef Full Text Google Scholar. Acosta, N. Sputum microbiota is predictive of long-term clinical outcomes in young adults with cystic fibrosis. Thorax 73, — Bassignani, A. Benefits of iterative searches of large databases to interpret large human gut Metaproteomic data sets.

Bevivino, A. Deciphering the ecology of cystic fibrosis bacterial communities: towards systems-level integration. Trends Mol. Blau, H. Induced sputum compared to bronchoalveolar lavage in young, non-expectorating cystic fibrosis children.

Bodas, M. Adapting Proteostasis and autophagy for controlling the pathogenesis of cystic fibrosis lung disease. Christie-Oleza, J. Synonyms: far-reaching , sweeping , broad , widespread More Synonyms of wide-ranging. Copyright © HarperCollins Publishers. Video: pronunciation of wide-ranging.

You may also like. English Quiz. adjective dealing with, including or affecting a great many different things. The newspaper had carried out a wide-ranging survey giving a unique insight into the minds of the public today. This attack carried wide-ranging implications for the government.

Collins English Dictionary. adjective extending over a large area; extensive or diversified in scope. Most material © , , by Penguin Random House LLC.

Modified entries © by Penguin Random House LLC and HarperCollins Publishers Ltd. Examples of 'wide-ranging' in a sentence wide-ranging. These examples have been automatically selected and may contain sensitive content that does not reflect the opinions or policies of Collins, or its parent company HarperCollins.

We welcome feedback: report an example sentence to the Collins team. Read more…. The Guardian Times, Sunday Times The Sun COBUILD Collocations wide-ranging. wide-ranging discussion.

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