Effects of contagious respiratory infections on breath biomarkers

doi:10.21203/rs.3.rs-2858447/v1

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Effects of contagious respiratory infections on breath biomarkers

https://doi.org/10.21203/rs.3.rs-2858447/v1

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published 28 Jan, 2024

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Background

Due to their immediate exhalation after generation at the cellular/microbiome levels, exhaled volatile organic compounds (VOCs) may provide real-time information on pathophysiological mechanisms and host response to infections. In recent years, metabolic profiling of most frequent respiratory infection gained interest as it holds potential for early non-invasive detection of pathogens and monitoring of disease progression and response to therapy.

Methods

In contrast to previous studies with pre-selected patient groups, we conducted a real-time mass-spectrometry based breath profiling in hundreds of consecutive subjects under an actual respiratory infection screening scenario. Recruited subjects were grouped for further comparisons, based on multiplex-PCR confirmed infection (infected by common respiratory pathogen(s) and healthy) and presence or absence of flu like symptoms.

Results

Amongst recruitments, we obtained 256 healthy cases and 223 infected/coinfected (171 mono-infections, 52 coinfections) with Haemophilus influenza, Streptococcus pneumoniae and Rhinovirus. We observed multiple effects of these mono-infections and co-infections onto the exhaled VOC profiles and variations, especially on endogenous ketone, short-chain fatty acid, organosulfur, aldehyde and terpene concentrations. Based on VOCs origins, we encountered changes in patient’s energy metabolism, systemic microbial immune homeostasis, inflammation, oxidative stress and antioxidative defense. Presence of bacterial pathogens depicted more complex metabolic effects and cross-talk – most likely due to their own metabolism.

Conclusion

Alike our recent reports on COVID-19 and in line with other recent multi-omics and clinical microbiological reports, these results offered unique insight into common respiratory infections, pathogenesis, ‘host-microbiome-pathogen’ interactions. Breathomics depicted the non-invasive potential for ‘monitoring’ respiratory mono-infections and coinfections.

Pulmonary bacteria

Respiratory virus

Infections

Coinfection

Volatile organic compound (VOC)

PTR-MS

Breathomics

Since the dawn of the present pandemic, public health resources, clinical focus and translational research efforts have been employed globally on respiratory infections and coinfections, more than ever before. Certain long-existing pathogens e.g. Streptococcus pneumoniae, Haemophilus influenzae and Rhinovirus are largely associated with most frequent respiratory infections(DeMuri et al., 2018; Torres et al., 2021), where disease symptoms are similar to SARS-CoV-2 infections. Despite the state-of-the-art medical knowledge, biomedical facilities and technological advancements – differential diagnosis, monitoring and management of highly contagious respiratory pathogens face daily obstacles due to the rising complexity in disease mechanisms, manifestations and host response(Zaas et al., 2009; Chow et al., 2022; Natalini et al., 2022). While rapid point-of-care diagnostics are largely developed and optimized for population screening, continuous/repeated monitoring of disease progression and host’s response to pathogen and/or therapy still remains challenging(Message et al., 2008; Zaas et al., 2014; Hou et al., 2020).

Besides in vitro and/or in vivo pre-clinical (animal model) studies(Traxler et al., 2018, 2019; Gonzalez-Juarbe et al., 2020), clinical investigations are extremely important to mechanistically address complex disease mechanisms and host-(microbiome)-pathogen(s) interactions(Tsalik et al., 2016; Remy et al., 2022; Di Simone et al., 2023). Thus, profiling of pathobiology driven in vivo metabolic/biochemical changes at host’s cellular and systemic microbial levels may address those striving frontiers in human medicine and pave avenues towards new prophylactic and/or therapeutic targets. Volatile organic compounds (VOCs) of various endogenous and/or exogenous origins and chemical classes are continuously exhaled in trace concentrations (ppbV – pptV range)(Miekisch et al., 2023). Apart from physiological(Sukul et al., 2015, 2016, 2020, 2022a; Sukul and Trefz, n.d.), metabolic(Sukul et al., 2022b, 2018; Trefz et al., 2019; Sukul et al., 2021; Pugliese et al., 2022) and therapeutic monitoring(Brock et al., 2017; Löser et al., 2020), real-time mass-spectrometry based profiling of quantified differences/changes in alveolar concentrations of breath VOCs could provide unique insights into disease mechanisms of respiratory infections and host response(Grassin-Delyle et al., 2021; Remy et al., 2022). Recently, we have developed an advanced setup for safe breath sampling and VOCs based patient monitoring under highly contagious respiratory infections(Sukul et al., 2022c) and have successfully applied that for breath screening in hundreds of subjects at the COVID-19 test center as well as to monitor critically ill (mechanically ventilated) patients at the intensive care unit.

Most of recent cross-sectional breathomics studies have reported differences in VOC profiles between pre-selected SARS-CoV-2 patient groups vs. healthy volunteers and proposed unique/specific diagnostic markers/patterns/signatures for disease detection(Ruszkiewicz et al., 2020; Grassin-Delyle et al., 2021; Ibrahim et al., 2021). Our previous study tried to apply a more realistic setup within a screening scenario in a COVID-19 test center(Remy et al., 2022) and could not confirm previous postulated specific VOC marker sets but found differences most probably related to the host response in the COVID-19 patients. As the host response may not be highly specific for the causative pathogen, the effects of other most common respiratory pathogens on breath biomarker profiles have to be explored in more detail.

Here, we have analyzed exhaled VOC profiles of patients with the most common pulmonary mono-infections (viral and bacterial) and coinfections (viral-bacterial and bacterial-bacterial) within the screened cohort.

Experimental setup and ethics:

Advanced experimental setup for breath sampling and patient monitoring were developed in compliance with unavoidable infection safety mandates of the present global pandemic(Sukul et al., 2022c). Setups and associated methods were successfully applied at our universities’ COVID-19 test centre without interfering the routine screening activities(Remy et al., 2022).

Upon the approval by our institutional ethics committee (IEC) of the University Medicine Rostock (approval No: A 2020 0085), the single-centred prospective observational study was conducted as per the amended ‘Declaration of Helsinki’ guidelines. It was primarily intended for real-time mass-spectrometry based breath biomarker profiling in spontaneously breathing human subjects in parallel to conventional RT-qPCR (for SARS-CoV-2) and multiplex PCR (for other respiratory pathogens) tests.

In order to avoid preselection bias, we conducted all measurements in a prospective screening scenario – without considering extrinsic factors e.g. subject’s lifestyle, habits, habitats, chronic comorbidities and diet/therapy.

Human subjects:

After obtaining signed informed consents, 708 non-preselected subjects were consecutively enrolled (on randomly selected working days between 09.00 – 13.00 hrs) from the COVID-19 test centre. Besides encountering SARS-CoV-2 infected (only 5.08 % as per the regional infection occurrence rate between October 2020 – January 2021) and healthy subjects, we obtained a large number of asymptomatic and symptomatic (mild and moderate) cases infected and/or coinfected with other pathogens.

While we have recently published the physio-metabolic and pathophysiological effects of SARS-CoV-2 infection(Remy et al., 2022) on breath biomarkers, yet undisclosed host’s and systemic microbial (e.g. pulmonary and gut) response to most common respiratory pathogens viz. Haemophilus influenza, Streptococcus pneumoniae and Rhinovirus are addressed here. The demographic data from subjects analysed in the present study are listed in Table 1.

Table 1. Demographic data.

Subject’s age, gender and infection status is listed here.

Infection safety measures and breath sampling protocol:

Customized mouthpiece attachments (incorporating mainstream and side-stream filters) and detailed analytical methods are described explicitly in our previous study(Sukul et al., 2022c). In brief, sampling area (for participants/patients) was separated from that of the investigator(s). Only the silico-steel transfer (i.e. 6 m long and heated at 100 °C) line of the PTR-ToF-MS (proton transfer reaction – time of flight – mass spectrometer) entered the sampling area and was attached to the mouthpiece via a side-stream filter. Sampling area was disinfected and well vented after each participation and all contaminated materials were disposed after single use. Please see the detailed methods of our recent clinical study(Remy et al., 2022).

Subjects rested for 5 min at the sitting posture(Sukul et al., 2015) and then removed their face mask to perform normal oral breathing through the sterile mouthpiece for 3 min by following our state-of-the-art breathing protocol(Sukul et al., 2020).

PTR-ToF-MS measurements of breath composition and VOC data processing:

A PTR-ToF-MS 1000 (Ionicon Analytik GmbH, Innsbruck, Austria) with mass resolution of 1000–2000 m/Δm was applied for continuous side-stream sampling and breath-resolved analysis of VOCs under pre-optimized conditions(Sukul et al., 2022c). The PTR sampling flow (100 ml/min), time-resolution (200 ms), drift-tube temperature (75 °C), voltage (610 V) and pressure (2.3 mbar) were adjusted to attain the desired E/N ratio (139 Td). After automatically recording a data file/min the mass scale was recalibrated upon defined masses viz. 21.0226 (H₃O⁺-isotope), 29.998 (NO⁺) and 59.049 (protonated C₃H₆O).

PTR-MS viewer software (version 3.228) was used for raw data processing. Continuously measured (in counts per seconds) VOC data were normalized to primary ion (H₃O⁺ counts) and were assigned to expiratory (end-tidal) and inspiratory (room air) phases via custom-made ‘breath tracker’ algorithm (applying endogenous acetone as a tracker mass)(Schwoebel et al., 2011; Sukul et al., 2022c). In every 15 min, room air was measured. Please see the detailed methods of our recent clinical study(Remy et al., 2022).

Quantification and Statistical analysis

VOCs from potential exogenous origin were excluded. Quantification of VOCs was partly performed via reaction rate coefficients (k-rates) between VOC and H₃O⁺ (at the E/N ratio of 140 Th)(Cappellin et al., 2012; Remy et al., 2022) and partly via multi-component mixture of matrix (under breath humidity) adapted standard reference substances by using a liquid calibration unit (LCU, Ionicon Analytik GmbH, Innsbruck, Austria)(Trefz et al., 2018; Remy et al., 2022). VOCs with concentrations below LOD (limit of detection) were excluded from statistical analysis. Please see the detailed methods of our recent clinical study(Remy et al., 2022).

In each participant, VOC concentrations were averaged over one minute of measurement. Statistical analysis was performed on VOC data from the 2^nd minute of all measurements. Statistically significant differences between exhaled VOC concentrations in different groups and subgroups were compared by Kruskal- Wallis ANOVA on ranks (pairwise multiple comparisons, p-value ≤ 0.05) test in SigmaPlot (v. 14). As per Table 2, the following statistical queries (Q1 – Q13) were conducted:

Q1 – Q6 (All mono-infections):

Four groups viz. H. influenzae positive (n = 97), S. pneumonia positive (n = 40), Rhinovirus positive (n = 34) and healthy (n = 256) were compared to each other.

Q7 – Q8 (Asymptomatic mono-infections):

Asymptomatic positive subgroups of H. influenzae (n = 55), S. pneumonia (n = 25) and healthy cohort (n = 256) were compared to each other.

Q9 – Q10 (Symptomatic mono-infections):

Symptomatic positive subgroups of H. influenzae (n = 42), S. pneumonia (n = 15) and healthy cohort (n = 256) were compared to each other.

Q11 – Q13 (Coinfections):

Coinfections (bacterial-bacterial and viral-bacterial) of H. influenzae + S. pneumonia (n=24), H. influenzae positive + Rhinovirus (n = 16), S. pneumonia positive + Rhinovirus (n = 12) were compared with healthy cohort (n=256).

In order to observe the inter-individual physio-metabolic variations under the presence of pathogens and symptoms, coefficients of variations (RSD in %) in different study groups were calculated by rating sample standard deviation over sample mean group-wise.

In order to understand the correlations between differentially expressed VOCs (from different endogenous origins), a dimension reduction factor analysis (Factor extraction via principal components method, factor scores via regression method and 1-tailed significance at p value ≤ 0.05, regression values R ≥ 0.500) was performed within each study group and subgroup in SPSS (v. 27).

Amongst 708 recruited subjects, we obtained 223 infected (171 mono-infections, 52 coinfections) and 256 noninfected cases. Out of mono-infections, 97 tested positive for H. influenza, 40 for S. pneumoniae and 34 for Rhinovirus. Amongst coinfections, 24 tested positive for H. influenza + S. pneumoniae, 16 for H. influenza + Rhinovirus and 12 for S. pneumoniae + Rhinovirus. Demographic data from all subjects are shown in Table 1.

Heat map of relative differences in exhaled alveolar VOC profiles between study groups and subgroups are presented in Figure 1. Non-quantitative expression of normalized (onto maximum) mean values of VOC concentrations in seven groups with subgroups based on symptoms. The groups are healthy subjects without any infection and/or symptoms, all H. influenzae positive mono-infection, all S. pneumoniae positive mono-infection, all Rhinovirus positive mono-infection and all coinfections with H. influenza + S. pneumoniae, H. influenza + Rhinovirus and S. pneumoniae + Rhinovirus are presented here. Selection criteria of VOCs are described in the methods section. H. influenzae and S. pneumoniae cases are sub-grouped into symptomatic and asymptomatic mono-infections.

Distribution of non-quantitative expression of VOCs (measured parameters/features) from the Figure 1 is presented in as violin plots within the supplement (Figure S1).

Statistical comparisons (based on absolute concentrations of VOCs) between study groups and subgroups are presented (as queries Q1 – Q13) in Table 2. Six VOCs viz. acetone, acetic acid, dimethyl sulphide, crotonaldehyde, pentanal and limonene turned out to express differentially in pair-wise multiple comparisons. Statistically significant differences in substance concentrations (p-value ≤ 0.05) are marked in bold.

In accordance with Table 2, box-plots are presented in Figure 2 that demonstrate the distributions of these six VOCs in different study groups and subgroups. Statistically significant differences (p-value ≤ 0.05) are marked with ‘#’.

Coefficients of variation or relative standard deviations (RSDs in %) of these VOCs within study groups and subgroups are presented in Table 3. Pair-wise correlations (along with statistical significances) between these six VOCs within all study groups and subgroups are presented in Table 4. Statistically significant (1-tailed significance at p-value ≤ 0.05) regressions (R value ≥ 0.500) are marked in bold.

Table 2. Results from inter-group statistical comparisons.

Kruskal-Wallis-Test ANOVA on Ranks (Bonferroni correction for pairwise multiple comparisons) for independent samples was performed between defined groups and subgroups. Statistically significant differences in substance concentrations (p-value ≤ 0.05) are marked in bold.

Table 3. Coefficient of variations in different study groups.

Relative standard deviations (RSDs) are calculated by rating sample standard deviation over sample mean group-wise and are presented in %.

Table 4. Inter-VOC correlations in different study groups.

Dimension reduction factor analysis (factor extraction via principal components method, factor score via regression method) was performed in each study group and subgroup to determine correlations between differentially expressed VOCs (from different endogenous origins). Statistically significant (1-tailed significance at p-value ≤ 0.05) regressions (R value ≥ 0.500) are marked in bold.

Changes in compositions of exhaled VOCs potentially produced via host’s cellular and microbial processes may elucidate effects of and/or response to respiratory infection (viral and bacterial) and coinfections (viral-bacterial and bacterial-bacterial). Primarily, we conducted real-time mass-spectrometry based prospective breath profiling in 708 non-preselected and consecutive subjects under the actual screening scenario of respiratory infections(Remy et al., 2022). Amongst the screened subjects, 36 were SARS-CoV-2 positive (RT-qPCR confirmed), 256 were healthy (without any pathogen and/or symptom) and 416 cases were either infected/coinfected by other respiratory pathogens (Multiplex-PCR confirmed) or were only with flu like symptoms (without any pathogen). While the effects of SARS-CoV-2 infection were addressed earlier(Remy et al., 2022), here, we explored 223 cases infected/coinfected with most common respiratory pathogens (viz. H. influenza, S. pneumoniae and Rhinovirus) with respect to 256 healthy subjects.

Thus, 479 subjects were grouped further – based on infection status (infected or healthy) and presence or absence of flu like symptoms. Depending on disease status and symptoms, we observed significant differences in exhaled alveolar concentrations of ketones, organosulfurs, short-chain fatty acids (SCFA), saturated- and unsaturated aldehydes and terpenes. These VOCs are largely associated with host’s energy metabolism, gut-microbial biochemistry, inflammation and immune response and, host’s antioxidative defense and oxidative stress. While aligned with recent multi-omics, clinical microbiology and biochemical reports, our findings indicate unique insight into diverse effects of respiratory infections on various host-microbial metabolic processes. Translation of such knowledge on ‘host-microbiome-pathogen’ interactions may pave the paths towards non-invasive monitoring of pathobiological events, disease manifestation and novel therapeutic targets.

Effects on host’s energy metabolism

Volatile ketone bodies are potentially produced from energy metabolism at the cellular and organ levels. The most abundant breath VOC, acetone is mainly originating from glycolysis and lipolysis(Kalapos, 2003). Human cells, microbiome as well as gram-negative and gram-positive bacterial pathogens like H. influenza and S. pneumoniae primarily use carbohydrates continuously for energy production(Othman et al., 2014; Fan et al., 2021). As the energy demand is high for colonization and pathogenesis, acetone exhalation increased significantly (in comparison to healthy cohort) in these bacterial mono-infections – especially during the early pathogenesis (for bacterial proliferation)(Chen, 1964; Bacterial growth laws reflect the evolutionary importance of energy efficiency - PMC, n.d.) that corresponds to the asymptomatic phase. In contrast to that, Rhinovirus does not have own metabolism and it replicates via lytic cycle (by releasing its RNA genome from the viral capsid into polyprotein translational site of host’s cytoplasm)(Blaas and Fuchs, 2016; Bochkov and Gern, 2016) and induces anabolic reprogramming of host cellular energy expenditure for viral replication(Gualdoni et al., 2018). Thus, Rhinovirus mono-infection employs glucose uptake and glycogenolysis and doesn’t increase acetone production.

Consequently, acetone exhalation remained unaffected by H. influenza or S. pneumoniae while coinfected with Rhinovirus due to viral inhibitory effects on cellular glycolysis. Nevertheless, in case of coinfection between H. influenza and S. pneumoniae one may assume that acetone exhalation would increase due to cumulative effects. Here, breath acetone did not change which, indicate immune-mediated competition between these two bacteria(Tikhomirova and Kidd, 2013). Studies have shown that hydrogen peroxide produced by S. pneumoniae kills H. influenza and other pathogens in vitro(Pericone et al., 2000) as well as neuraminidase produced by S. pneumoniae desialylates sialic acid – decorated lipo-oligosaccharide structures of H. influenzae that may reduce the bacterial viability in vivo (Shakhnovich et al., 2002; Swords et al., 2004).

Effects on systemic microbial metabolism, immunomodulatory and inflammatory response

SCFA like acetic acid and organosulfur such as dimethyl sulphide are largely produced in the lower gut by dietary fibers/starch fermentating(Silva et al., 2020; Wang et al., 2020; Sukul et al., 2022b) and methylating bacteria(Tangerman, 2009; Ramos-Molina et al., 2019). These bacteria maintain the anaerobic condition to regulate intestinal permeability, nutrient absorption and adaptive immune response(Singhal and Shah, 2020; Cai et al., 2022; Li et al., 2022). Most importantly, the bio-chemical interplay between intestinal and pulmonary microbiota regulates upper and lower respiratory tract health and infections via the ‘gut-lung axis’(Piters et al., 2020; Sencio et al., 2020; Moroishi et al., 2022).

Pre-clinical and clinical models demonstrated that SCFA-acetate is produced by systemic-microbiome (pulmonary and gut) as an immunomodulatory response against respiratory pathogens(López-López et al., 2020; Antunes et al., 2022, 2023; Machado et al., 2022). Thus, increased breath acetic acid in the cohorts of all three mono-infections indicates host-microbial response against pathogens. In contrast to that, dimethyl sulphide exhalation differed only in case of bacterial mono-infections. There are evidences that gut microbiota contribute to host’s methionine (i.e. precursor of S-adenosylmethionine) metabolism and thereby, regulate macrophage mediated inflammatory response(Schuijt et al., 2016; Ji et al., 2019; Wu et al., 2022). Thus, high dimethyl sulphide concentrations at the asymptomatic cases (significant in S. pneumoniae) and decreased concentrations in presence of disease symptoms (significant in H. influenzae) indicate a change in corresponding microbial activity (inflammatory) between early bacterial pathogenesis and infection progression. As human Rhinovirus infection remains restricted to respiratory tract epithelium, nose and nasopharynx and does not manifest elsewhere (including gut), no effects are seen on organosulfur exhalation.

Surprisingly, none of the three coinfections altered exhaled SCFA or organosulfur profiles. This is most likely due to a cumulative dysbiosis of systemic microbiome(Hanada et al., 2018; Sencio et al., 2021) – as observed in case of viral-bacterial superinfection and secondary bacterial pneumonia.

Effects on anti-oxidative defense and oxidative stress

Infections primarily down-regulate host’s antioxidative defense in order to facilitate pathogenesis(Birben et al., 2012; Veskoukis, 2020). This further elevates reactive oxygen species (e.g. hydroxyl radical) production for oxidative modification of nucleic acids, proteins, lipids and metabolites and thereby, leads to oxidative stress. Reactive aliphatic aldehydes (e.g. α, β-unsaturated and saturated) are thus, produced due to lipid peroxidation(Grimsrud et al., 2008) and are subject to Michael addition reaction with essential amino acids (i.e. also known as protein carbonylation).

Significantly increased pentanal exhalation at the asymptomatic phase of S. pneumoniae mono-infection, therefore, indicates increased redox reactions at the cellular level. S. pneumoniae is anaerobic in nature. As primary pathogenesis of S. pneumoniae takes place under the aerobic condition of upper respiratory tract, the pathogen must adapt to high oxygen environment(Bortoni et al., 2009). S. pneumoniae produces hydrogen peroxide and pneumococcal autolysin(Zahlten et al., 2015) as metabolic byproducts, which contribute to its virulence, to host’s oxidative stress response by inducing airway inflammation and oxidative resistance (modulating oxidative burst) in broncho-pulmonary epithelial cells(Yesilkaya et al., 2013). Thus, increased host’s airway-lung epithelial oxidative stress (to promote oxidative killing of S. pneumoniae by human neutrophils) elevates pentanal production significantly in S. pneumoniae mono-infection, especially during early pathogenesis. Being aerobic, H. influenzae does not need to induce oxidative defense mechanism against oxygen rich environment. On the other hand, Rhinovirus is known to down-regulate mitochondrial respiration within human primary bronchial epithelial cells and increase proton leak, in vitro(Wark et al., 2016). Consecutive in vivo host’s response increases pro-inflammatory cytokine (e.g. interleukin-8) release(Biagioli et al., 1999), which regulates oxidative stress (mitochondrial/endoplasmic reticular) exposure in order to stabilize mitochondrial functions(Kaul et al., 2000).

In case of coinfections involving S. pneumoniae, exhaled pentanal did not increase due to immune-mediated competitions between pathogens. Unlike pentanal, hepatic metabolism of endogenous ethanol gives rise to highly-reactive acetaldehyde molecules(Wilson and Matschinsky, 2020) which are condensed together within the moist environment of the airways and form unsaturated crotonaldehyde(Dick et al., 2016; Sukul et al., 2022a). Therefore, this VOC remained independent of the oxidative stress-pathogen interplay.

Limonene and its metabolites act as cellular antioxidant by removing free radicles(de Souza et al., 2019). Hepatic microsomal metabolism of limonene boosts our anti-oxidative defense and anti-inflammatory response(Santana et al., 2020; Limonene and ursolic acid in the treatment of diabetes: Citrus phenolic limonene, triterpenoid ursolic acid, antioxidants and diabetes, 2020). Increased limonene exhalations in Rhinovirus mono-infection indicate host’s anti-inflammatory response against the viral oxidative stress and injuries in the respiratory tract. Due to the above-discussed host’s oxidative response mechanism (to promote neutrophil aggressiveness) against S. pneumoniae, limonene exhalation remained unaffected.

Noteworthy, that all these are single point measurements and therefore, will have considerable intra-individual variations if measured repeatedly/longitudinally. Although by applying differential features (e.g., from the violin plots or box-plots) we may certainly generate ROC curves and calculate attractive AUC values (representing test sensitivity and specificity) to claim differential diagnostic markers for these respiratory pathogens, we restrained ourselves from such a trending scientific fallacy. Refitting of pre-sorted/pre-processed data to the present model will undoubtedly results in high sensitivity and specificity but will have no clinical/real-life relevance without considering enough independent measures.

While looking at the limitations, the present study is aimed to enhance our basic, translational and clinical understanding of respiratory mono-infections and coinfections and therefore, we did not validate our present findings in another large independent cohort within this study. While considering another population/ethnicity, our present observations may vary based on genetic predispositions, infection rate, population immunity regime, bacterial strains, viral variants, comorbidity and respective pathogenesis models/mechanisms.

In conclusion, no unique or disease/pathogen specific breath biomarker was found. Differences in exhaled concentrations of VOCs due to the presence or absence of infection, coinfection and disease symptoms employed unconventional views onto varying metabolic effects of these respiratory pathogens and corresponding host-microbial response. Interestingly, endogenous VOCs did not differ significantly between respiratory mono-infections (Q4 – Q6) but they differed while compared to the healthy cohort (Q1 – Q3). This indicates alike host-microbial responses against these pathogens which, are also reflected within the group-wise distribution of inter-individual variations (RSDs). Homeostatic interplay and differences between host’s energy metabolism, inflammation, oxidative stress and systemic microbial immunity reflected unique metabolic cross-talk on VOC profiles (and corresponding variations) under respiratory infection. Relatively high variations in symptomatic cohorts indicate broad inter-individual range of host-microbial responses. Such large variations were also observed within the previously examined SARS-CoV-2 cohort(Remy et al., 2022). Similar to the symptomatic cases of COVID-19, the overall expressions of VOC concentrations were suppressed in symptomatic mono-infections of S. pneumoniae and in coinfections between S. pneumoniae and Rhinovirus. Therefore, it is unlikely to employ punctually measured VOCs for primary diagnosis of respiratory infections or to detect a specific pathogen. As viruses do not have own metabolism but bacteria do, more complex metabolic interactions and effects were observed in bacterial infections. VOC expressions under coinfections indicated immune mediated competitions between different pathogens. Correlations between VOCs from different endogenous origins have touched upon unknown frontiers of pathogenesis that summons further longitudinal investigations. As VOC expressions can non-invasively denominate both promotion and/or suppression of certain metabolic process in relation to another, those could be translated to monitor disease progression and to elucidate new antibiotic/antiviral targets.

Acknowledgements

We thank all physicians and medical staff from the COVID-19 test center and all members from the ROMBAT group for their valuable support during the sampling procedures.

The study was supported by European Union’s Regional Development Fund (EFRE) (J.K. Schubert and W. Miekisch), H2020-EU-ITN-IMPACT Marie-Skłodowska-Curie grant (IMPACT. 674911) (J.K. Schubert and W. Miekisch) and the Inno-INDIGO-NCDs-CAPomics Project (BMBF 01DQ16010) (J.K. Schubert and W. Miekisch). Funders had no role in the design, data collection, analysis, interpretation, manuscript preparations or decision to submit the manuscript for publication.

Authorship

W.M., J.K.S. and P.S. designed this analysis and optimized methods with P.T. for breathomics under high safety condition. P.F. contributed to the analysis design. N.K., R.R. and J.B. recruited subjects and carried out measurements. P.T., N.K., R.R., J.B., A.C.K., L.R., P.F., J.K.S. and P.S. executed data analysis. N.K., P.F. and P.S. prepared the results. P.F. and P.S. executed visualization. P.F., W.M., J.K.S. and P.S. performed statistical evaluations. P.S., W.M. and J.K.S. did analytical and clinical interpretations. J.K.S., W.M., P.F. and P.S. supervised the entire project. J.K.S. and W.M. provided all resources. P.F., P.T. and A.C.K. executed resource management. P.S. and P.F. prepared the original draft. All authors have read the journal's authorship agreement, reviewed and approved the manuscript.

Data availability

All data are presented within the manuscript and in the supplement. Any additional data or information of the study will be made available to others after reasonable request made to the corresponding author.

Conflict of interest

The authors have nothing to disclose.

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FigureS1.pdf
Supplementary Figure S1: Violin plots based on measured variables presented in the heat map. Group and sub-group wise cumulative distribution of normalised variables from Figure 1 is presented here. Plots are depicting overall expressions of measured independent variables (VOCs) in each group and between multiple groups.

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Effects of contagious respiratory infections on breath biomarkers

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