Fenn et al. Critical Care (2022) 26:203 https://doi.org/10.1186/s13054-022-04068-z RESEARCH Open Access Composition and diversity analysis of the lung microbiome in patients with suspected ventilator‑associated pneumonia Dominic Fenn1,3*, Mahmoud I. Abdel‑Aziz1,3, Pouline M. P. van Oort2,3, Paul Brinkman1,3, Waqar M. Ahmed3,4, Timothy Felton3,4, Antonio Artigas3,4, Pedro Póvoa3,5, Ignacio Martin‑Loeches3,6, Marcus J. Schultz3,7, Paul Dark3,4, Stephen J. Fowler3,4 and Lieuwe D. J. Bos3,7 on behalf of The BreathDx Consortium Abstract Background: Ventilator-associated pneumonia (VAP) is associated with high morbidity and health care costs, yet diagnosis remains a challenge. Analysis of airway microbiota by amplicon sequencing provides a possible solution, as pneumonia is characterised by a disruption of the microbiome. However, studies evaluating the diagnostic capabili‑ ties of microbiome analysis are limited, with a lack of alignment on possible biomarkers. Using bronchoalveolar lavage fluid (BALF) from ventilated adult patients suspected of VAP, we aimed to explore how key characteristics of the micro‑ biome differ between patients with positive and negative BALF cultures and whether any differences could have a clinically relevant role. Methods: BALF from patients suspected of VAP was analysed using 16s rRNA sequencing in order to: (1) differentiate between patients with and without a positive culture; (2) determine if there was any association between microbi‑ ome diversity and local inflammatory response; and (3) correctly identify pathogens detected by conventional culture. Results: Thirty-seven of 90 ICU patients with suspected VAP had positive cultures. Patients with a positive culture had significant microbiome dysbiosis with reduced alpha diversity. However, gross compositional variance was not strongly associated with culture positivity (AUROCC range 0.66–0.71). Patients with a positive culture had a signifi‑ cantly higher relative abundance of pathogenic bacteria compared to those without [0.45 (IQR 0.10–0.84), 0.02 (IQR 0.004–0.09), respectively], and an increased interleukin (IL)-1β was associated with reduced species evenness (rs = − 0.33, p < 0.01) and increased pathogenic bacteria presence (rs = 0.28, p = 0.013). Untargeted 16s rRNA pathogen detection was limited by false positives, while the use of pathogen-specific relative abundance thresholds showed better diagnostic accuracy (AUROCC range 0.89–0.998). Conclusion: Patients with positive BALF culture had increased dysbiosis and genus dominance. An increased caspase-1-dependent IL-1b expression was associated with a reduced species evenness and increased pathogenic bacterial presence, providing a possible causal link between microbiome dysbiosis and lung injury development *Correspondence: d.w.fenn@amsterdamumc.nl 1 Department of Respiratory Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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However, measures of diversity were an unreliable predictor of culture positivity and 16s sequencing used agnostically could not usefully identify pathogens; this could be overcome if pathogen-specific relative abundance thresholds are used. Keywords: Ventilator-associated pneumonia, Microbiome, Next-generation sequencing Introduction with conventional cultures of commonly associated VAP Ventilator-associated pneumonia (VAP) develops in bacteria. patients who have been mechanically ventilated for more than 48 h [1, 2] and is associated with high morbidity and Methods health care costs [1, 3, 4]. Early diagnosis and treatment Design, subjects and setting is critical [5], but the clinical diagnosis of VAP remains This is a post hoc analysis of the ‘Molecular Analysis of a challenge. Non-specific symptomology, clinical scor- Exhaled Breath as Diagnostic Test for Ventilator–Associ- ing systems and radiological features susceptible to ated Pneumonia’—study (BreathDx), as described by van inter-rater variability [6, 7] can lead to overdiagnosis and Oort et al. [19, 20]. BreathDx was an international mul- inappropriate use of antibiotics. ticentre, prospective observational cohort study of intu- 16s rRNA gene amplicon analysis is widely used for bated and ventilated patients suspected of VAP. Patients studying the lung microbiome by profiling bacterial com- were recruited between February 2016 and February position and diversity in a sample [8, 9]. Named after the 2018 from four ICUs: the Amsterdam University Medi- target gene, it relies on conserved and highly variable cal Centers (UMC)—location Academic Medical Center regions of the 16s rRNA gene found in all prokaryotes to (AMC), Amsterdam, the Netherlands; Manchester Uni- identify bacteria [10, 11]. Using this technique, the rela- versity NHS Foundation Trust—Wythenshawe Hospital tive abundance of gut bacteria in the lung of critically ill (WH), Manchester University NHS Foundation Trust— patients has consistently been shown to be increased Manchester Royal Infirmary (MRI) and Salford Royal in patients with acute respiratory distress syndrome NHS Foundation Trust (SRFT), Manchester, UK. Inclu- (ARDS) [9]. Furthermore, changes in lung microbiome sion criteria were (1) 18 years and older and (2) intuba- composition are linked to an altered host response and tion and mechanical ventilation for > 48 h and (3) clinical might explain the development of lung injury [12, 13]. suspicion of VAP. Suspected VAP was defined by (1) sys- From an ecological perspective, pneumonia has been temic signs of infection [temperature > 38 or < 36.5 °C; described as an abrupt and emergent disruption in the white blood cell count < 4000 or > 12,000/mm3, purulent complex homeostasis of such microbiota [14]. However, tracheal secretions], and (2) new infiltrates on chest X-ray data from patients with VAP have been compared only [19]. Patients were excluded if they: (1) were deemed to that from uninfected control patients on the intensive clinically inappropriate to collect samples from (e.g. end- care unit (ICU) and not to patients in whom clinical sus- of-life care); or (2) were in strict isolation (e.g. Middle picion of VAP had arisen. A better understanding of the East respiratory syndrome, Ebola or resistant tubercu- composition of the lung microbiome in this clinically rel- losis). For this current analysis, samples were selected if evant population could further extend our understand- data from both BALF semi-quantitative cultures and 16s ing of VAP development and facilitate identification of rRNA analysis were available. Patient assent at the time patients who have bacterial pneumonia. When further of inclusion was obtained from a designated consultee developed, genomic techniques could help determine with deferred written consent taken from patients who which patients should receive broad spectrum antibiot- regained capacity, as previously outlined in the study pro- ics while awaiting microbiological confirmation [1, 6]. tocol [20]. The study was approved by respective institu- However, studies evaluating its diagnostic capabilities are tional review boards and registered by the UK Clinical limited [15–17] with a lack of alignment on etiological Research Network (ID no. 19086). diagnostic markers [16, 18]. Using bronchoalveolar lavage fluid (BALF) from ven- Study procedure and sample collection tilated adult patients suspected of VAP, we hypothe- Patients were included, and BALF samples were col- sised that the lung microbiome differs between patients lected within 24 hours of the clinical suspicion of VAP with positive and negative cultures. We aimed to link using either a directed or non-directed broncho-lavage these changes in composition and diversity of the lung approach. Directed BAL was performed following BTS microbiome to alveolar inflammatory response. Last, guidelines [21]. Non-directed BAL was performed by we hypothesised that microbial composition is aligned connecting a syringe to a 50-cm suction catheter before Fenn et al. Critical Care (2022) 26:203 Page 3 of 12 20 ml 0.9% saline was injected into the patient’s airway. sequencing-derived relative abundance for each genus An aspirate of at least 4 ml was collected and aliquoted was then summed to give a total relative abundance of for: (1) routine culturing and (2) storage at − 80 °C pathogens per sample. for 16s rRNA sequencing once the study had finished. While BALF recovery was to be performed prior to the Additional biomarker analysis initiation of antibiotics, delaying treatment for the pur- Interleukin (IL)-1b and IL-8 concentrations in BALF pose of the study would have not been ethically permis- were estimated using enzyme-linked immunosorbent sible. Consequently, BALF may have been collected while assay (ELISA). Commercial ELSIA kits from R&D Sys- patients were just started on antibiotics. tems Inc. (Bio-Techne, Minneapolis, USA) were used according to manufacturer’s protocols. Reference standard and pathogen selection A positive (non-directed) BAL culture with a cut-off of Study endpoints ≥ ­104 CFU/ml was used as the primary reference test, as The primary endpoint of this study is the diagnostic described previously [19]. Pathogen identification accu- accuracy of microbial composition and diversity analysis racy was only tested in the following pathogens: Pseu- for patients suspected of VAP with and without a posi- domonas aeruginosa, Staphylococcus aureus, Klebsiella tive BALF culture. Secondary endpoints are: (I) the asso- pneumoniae and Haemophilus influenzae. Other causa- ciation between microbial diversity and composition and tive pathogens were also found but in lower frequency local inflammatory response, (II) the pathogen concord- limiting the assessment of diagnostic accuracy. However, ance between 16s sequencing and conventional culture, the selected pathogens represented the majority of clini- and (III) the diagnostic accuracy of 16s sequencing for cally relevant organisms associated with VAP [6, 22, 23] the presence of the selected bacteria in culture. that are commonly encountered in patients suspected of VAP in North-Western Europe, where the study was Sample size and calculation conducted. The sample size calculation for the BreathDx study has previously been described and predetermined based on Sample processing and 16s rRNA gene sequencing. the development of a novel diagnostic test to exclude Duplicate PCRs of the 16s rRNA gene region V4–V5 VAP and allow clinicians to withhold antibiotic treatment were performed as described before [24, 25]. In brief, the [19, 20]. For this post hoc analysis, no formal sample size PowerFecal DNA Kit (Qiagen, Venlo, Netherlands) was calculation was performed—instead, the number of avail- used for DNA extraction of BALF before PCR ampli- able patients served as the sample size. fication using the Illumina MiSeq platform (CGEB- Integrated Microbiome Resource, Halifax, Canada) and Statistical analysis primer pair 515F/926R was performed [26, 27]. The Statistical analysis was performed in R (version 3.6.1) sequencing facility was kept unaware of patient clinical through the R studio interface. Downstream analysis of status or diagnosis. Further details on RNA extraction 16s rRNA was performed using the vegan package within and amplicon library preparation are provided in the R to assess community composition and ASV level dif- online supplement (see Additional file 1). The DADA2 ferences [29]. Diversity and composition data were com- pipeline and amplicon sequence variant (ASV) table gen- pared between patient groups using the Mann–Whitney eration was selected over the more conventional opera- U test. To determine whether the differences in BALF tional taxonomic unit (OTU) approach due to: (1) the microbiota were driven by culture positivity, beta diver- increased downstream resolution, sensitivity and speci- sity testing was performed using a permutational mul- ficity [27, 28]; (2) data set independency of ASVs enables tivariate analysis of variance (PERMANOVA). The easier extrapolation to other studies and theoretical for- PERMANOVA was performed using Bray–Curtis dis- mulation of a clinical cut-off value [28]. The EzBioCloud similarity matrix and BALF culture result as the depend- database (version May 2018) was used to assess taxo- ent variable. The following co-variates as identified by nomic classification of the identified ASVs. Carney et al. were included: age, gender, study site and Compositional variance is described by alpha and disease severity [Clinical Pulmonary Infection Score beta diversity indices, while taxonomic profiling pro- (CPIS) and (Acute Physiology and Chronic Health Eval- vides details on microbial community and pathogen uation II score (APACHEII)] [30] and days of mechani- presence. The average relative abundance of pathogenic cal ventilation (MV) before clinical suspicion of VAP. bacteria was estimated per patient sample by firstly To assess diagnostic potential of diversity measures, the selecting for genera that were only identified by both area under the receiver operating characteristics curve 16s rRNA analysis and conventional BALF culture. 16s (AUROCC) was used. Spearman’s rank correlation was Fenn et al. Critical Care (2022) 26:203 Page 4 of 12 used to assess (I) the correlation between alpha diversity rRNA analysis detected Haemophilus, Enterococcus and measures and inflammatory biomarkers (IL-1b and IL-8) Prevotella as the most abundant genera (Additional file 1: and (II) the presence of pathogenic bacteria and inflam- Fig. S3). mation. For the pathogen identification and specific anal- yses, AUROCC was calculated and used to identify an Diversity analysis optimal relative abundance threshold with a predefined Comparing the alpha diversity measures at the ASV sensitivity of at least 99% [20]. Using this cut-off, the level between patients with and without positive BALF BALF relative abundance was then dichotomised before culture, there was a significantly decreased diversity two-by-two contingency tables were constructed for each for patients with a positive culture compared to those pathogen. Diagnostic test characteristics are reported without (p < 0.01, Fig. 1, panels A–C). There was, how- for this cut-off [sensitivity, specificity, positive predictive ever, no difference in richness (p = 0.48, Fig. 1, panel D). value (PPV) and negative predictive value (NPV)]. Speci- PERMANOVA testing showed a significant difference in ficity and predictive values of 16s rRNA bacterial analysis beta diversity between patients with and without a posi- were calculated with 95% binomial confidence intervals. tive culture (p = 0.001, adjusted p = 0.04, Fig. 1 panel E) after correction for possible confounders (age, gender Results and study site). Despite the observed diversity differences Sample and patient characteristics gross compositional variance using alpha diversity could One hundred and eight patients suspected of VAP were not reliably discriminate culture positivity (AUROCC recruited over the study period. Sufficient BALF for both range 0.66–0.71, Fig. 1, panel F). semi-quantitative and 16s rRNA culture was available There was a significant negative correlation between from 91 patients (83.3%). 16s rRNA sample depth was IL-1b and evenness (R = − 0.33, p < 0.01, Fig. 2 panel assessed, and one sample was removed due to inadequate A). However, no additional significant correlation was bacterial count. Of the remaining 90 patients, 37 (41%) observed between the remaining diversity indices (Fig. 2, had positive BALF cultures. Patient characteristics are panel B–H). summarised in Table 1. The remaining patients were not included in the analysis (Additional file 1: Table S1 and Microbiota compositional and relative abundance Fig. S1). A similar time frame of mechanical ventilation of pathogens and the clinical suspicion of VAP was observed in both The majority of patients with positive culture showed patient groups. At the time of inclusion, BALF was to increased genus dominance contributing to the be collected prior to the initiation of antibiotics; how- decreased diversity observed when compared to patients ever, for some patients antibiotics may have been started with a negative culture (Fig. 1, panel G). For these before BALF recovery due to clinical need. Data regard- patients, genus dominance was particularly evident for ing this were unavailable for the analysis. After qual- Pseudomonas and Klebsiella. ity control and processing, 953,975 reads were obtained Patients with positive BALF cultures had a significantly from the 90 samples, resulting in over 5000 individual higher relative abundance of pathogenic bacteria com- ASVs with an average of 4743 reads per sample (IQR pared to patients with negative cultures [culture positive: 1244–12,182 reads). Negative control samples (N = 2) of 0.45 (IQR 0.10–0.84), culture negative: 0.02 (IQR 0.004– saline solution were included in the sequencing to iden- 0.09), p < 0.01, Fig. 3, panel A]. A positive correlation was tify potential contaminants. Bioinformatic processing found between the relative abundance of pathogenic bac- showed minimal contamination with little to no reads teria and IL-1b (rs = 0.28, p = 0.013, Fig. 3, panel B), but detectable (Additional file 1: Analytical pipeline, Fig. S2 there was no significant correlation with IL-8 (rs = 0.06, and Table S2). Prior to downstream analysis, ASVs were p = 0.61, Fig. 3, panel C). filtered using a threshold of 0.001% of the total reads before being assigned to taxonomic rank. Concordance in identification of pathogens Eight individual genera were isolated using cultures Of the 37 patients with positive BALF cultures, 16s (Table 1), whereas 16s rRNA analysis was able to detect sequencing was able to correctly identify the causative over 80 using a predefined sensitivity threshold of 95%. pathogen in 33 (89.2%) samples while using a minimal The top three identified genera by conventional culture relative abundance threshold of > 1% to define patho- were Staphylococcus (35%) Pseudomonas (27%) and gen presence over potential background contamination. Haemophilus (11%). Similarly, for patients with a posi- However, it failed to correctly identify the pathogen tive BALF culture, the three most abundant genera found in 4 (10.8%) positive cultures and could not detect using 16s sequencing were identical (see Additional file 1: Escherichia Coli (Fig. 1 panel G). Conversely, of the Fig. S3). In patients with a negative BALF culture, 16s patients with negative BALF cultures, using the same Fenn et al. Critical Care (2022) 26:203 Page 5 of 12 Table 1 Patient characteristics Culture negative (N = 53) Culture positive (N = 37) p Age, median (IQR) yrs 59.0 (48–68) 56.5 (38.0–66.5) 0.275 Male, n (%) 35 (66.0) 23 (62.2) 0.877 Days on ­MV*, median (IQR) 8 (4.5–12.5) 7 (5–10) 0.697 Admission type, n (%) 0.218 Medical 29 (54.7) 13 (35.1) Planned surgical 10 (18.9) 11 (29.7) Emergency surgical 13 (24.5) 13 (35.1) Unrecorded 1 (1.9) 0 Trauma, n (%) 12 (22.6) 18 (48.6) 0.019 Neurosurgery, n (%) 11 (20.8) 12 (32.4) 0.315 COPD, n (%) 8 (15.1) 5 (13.5) 1.000 ARDS, n (%) 3 (5.7) 0 (0.0) 0.381 APACHEII score, median (IQR) 21 (15–24) 14 (10–20) 0.009 CPIS score, median (IQR) 5 (4–6) 7 (6–7) < 0.001 Tmax, °C, median (IQR) 38 (37–39) 38 (37–38) 0.551 WCC, median (IQR) ­109/ml 15.5 (11.3–21.0) 13 (11–17) 0.074 PaO2/FiO2 max, median (IQR) mmHg 240 (180–283.5) 232.5 (168.8–283.1) 0.85 Pmax, median (IQR) ­cmH2O 20 (16–25) 21 (16–24) 0.874 PEEP, median (IQR) ­cmH2O 8 (5–10) 8 (5–10) 0.983 Tidal volume, median (IQR) ml 479 (429.5–580) 538 (449–620) 0.298 Genus culture results**, n (%) Enterobacter 2 (5.4) Escherichia 2 (5.4) Haemophilus 4 (10.8) Klebsiella 4 (10.8) Pseudomonas 10 (27.0) Serratia 1 (2.7) Staphylococcus 13 (35.1) Stenotrophomonas 1 (2.7) ICU LOS, median (IQR) days 18 (14.0–26.8) 22 (16–35) 0.154 Hospital LOS, median (IQR) days 26.5 (15.5–42.8) 34 (21.8–64.5) 0.284 ICU mortality, n (%) 12 (22.6) 4 (10.8) 0.244 ARDS acute respiratory distress syndrome, APACHE acute physiology and chronic health evaluation, CPIS clinical pulmonary infection score, Tmax maximum temperature, WCC​ white cell count, FiO2 max maximum inspired fraction of oxygen ratio, Pmax maximum airway pressure, PEEP positive end-expiratory pressure, LOS length of stay * Days on mechanical ventilation (MV) until VAP suspicion ** Potentially > 1 cultured pathogen per patient minimal relative abundance threshold (> 1%), one or application with high negative predictive value, the more pathogenic bacteria were detected by 16s analy- following relative abundance cut-offs were identified: sis in 35 (66%) of the 53 samples, albeit with a reduced Pseudomonas = 0.3; Klebsiella = 0.2, Haemophilus = 0.1 relative abundance (Fig. 3, panel A). To further explore and Staphylococcus = 2.5 × ­10−5. The sensitivity, speci- the diagnostic potential of 16s rRNA analysis, it was ficity and predictive values of BALF 16s rRNA analysis compared to the culture results for each of the selected are shown in Fig. 4—Table 2. Sensitivity and PPV were pathogens. Pathogen concordance and identification relatively good for Pseudomonas and Klebsiella but performed better when considering each pathogen were much lower for Staphylococcus (PPV 30%; 95% individually (AUROCC range 0.89–0.998, Fig. 4). Using CI 17–47%) and for Haemophilus (PPV 36%; 95% CI the pre-specified high sensitivity needed for clinical 11–69%). Fenn et al. Critical Care (2022) 26:203 Page 6 of 12 Fig. 1 Lung microbiota is altered in patients with VAP (A–C) compared with (N = 37) patients without (N = 53) but showed no difference in compositional richness (D). Principal coordinate analysis showed significant differences between the microbial composition of patients with and without VAP (E), the X-axis indicating principal coordinate (PCoA) 1 and the Y-axis PCoA 2 on Bray–Curtis dissimilarity measure of 16S microbiome data. Despite significant dysbiosis in patients with VAP, compositional variance could not reliably diagnose VAP (F) when comparing evenness, Simpson’s diversity index and Shannon diversity index as predictor variables and in BALF culture as the outcome for all patients (N = 90). Individual patient bar plots split by VAP diagnosis compared with BALF culture-dependent result (G). The relative abundance of the top 10 genera along with all pathogens identified by semi-quantitative culture for each sample is shown. “Other” genus combines remaining genera in each sample Fenn et al. Critical Care (2022) 26:203 Page 7 of 12 Fig. 2 Spearman’s rank correlation analysis between alpha diversity measures (evenness, Simpson’s diversity index, Shannon diversity index and richness) and interleukin (IL)-1b (panel A–D) or IL-8 (panel E–H) expression Discussion were suspected of having VAP [18, 31–34]. It has been In this study, patients with suspected VAP and a positive suggested that Shannon diversity index of the microbi- culture had increased dysbiosis of lower airway microbi- ome in tracheal aspirates was a good diagnostic marker ome and an increased prevalence of pathogenic bacteria for lower respiratory tract infections [35], but we could compared to patients who were suspected of VAP but not replicate this diagnostic accuracy. Furthermore, the had negative cultures. Furthermore, based on the stud- hereby presented evidence demonstrated that measures ied culture-independent bacterial analysis, a decreased of microbial diversity were insufficiently accurate to pre- species evenness and an increased presence of patho- dict culture positivity. Importantly, our study differs from genic bacteria were associated with an increased local all previous reports in that only patients with a clinical inflammatory response indicated by a rise in IL-1b con- suspicion of VAP were included, resulting in a control centration. However, measures of microbial diversity group more representative of the real-life scenario where were insufficiently accurate to discriminate culture posi- clinical decisions must be made. tivity, and agnostic pathogen detection using 16s rRNA Patients with more dysbiosis and loss of diversity also sequencing was limited by high number of false positives. had higher levels of IL-1b in BAL fluid. This positive An alternative approach based upon pathogen-specific association could be related to the increased activation relative abundance diagnostic thresholds obtained from of the inflammasome complexes as demonstrated by 16s sequencing showed an improved diagnostic accuracy. Trachalaki et al. [36]. Critical to host immunity, inflam- We observed that the lung microbiome of patients sus- masomes mediate the caspase-1 inflammatory pathway pected of VAP with a positive culture had more patho- responsible for the maturation and release of IL-1b, a genic genus dominance and decreased diversity. This is potent proinflammatory cytokine [37–39]. This relation in line with the description of pneumonia from an eco- was confirmed in the current study and might elucidate logical perspective, in which loss of eubiosis and diver- a treatable association of microbiome dysbiosis, elevated sity are ecological indications for pathogenic bacterial caspase-1 activity and lung injury [9, 38]. Similar correla- overgrowth and central events in the development of tions were not observed for IL-8. The proximity of BALF infection [14]. This finding extends the results of several sampling to the disease onset (within 24 hours) may pro- previous studies into a population in which all patients vide an explanation for this, in that IL-1b is released to Fenn et al. Critical Care (2022) 26:203 Page 8 of 12 Fig. 3 Pathogenic bacteria relative abundance per patient sample (N = 90) is altered in patients suspected of VAP with positive BALF cultures compared to those with negative cultures (A). Spearman’s rank correlation analysis showed a positive correlation between pathogenic bacteria relative abundance and IL-1b expression (B) but not for IL-8 release (C) stimulate leucocyte activation immediately after pulmo- detection of individual genus, even if non-dominant, was nary insult, whereas IL-8 is expressed later to aid leuco- associated with a positive BALF culture. Consequently, cyte reprogramming as the disease state progresses [40, this method could lead to an increased number of false 41]. negatives in our cohort. A good concordance was observed for identification of In light of this, an alternative approach was evalu- the genus of the causative pathogen between 16s rRNA ated using pathogen-specific relative abundance thresh- analysis and conventional culture. This is in line with olds. We found that 16s sequencing was better suited to studies by Emonet et al. [16] and Miao et al. [42], which exclude Pseudomonas and Klebsiella presence. However, demonstrated that molecular analysis accurately identi- for Staphylococcus and Haemophilus the cut-offs were fied bacteria grown by conventional culture. However, less reliable. This is in line with results from a similar despite this, the diagnostic performance of untargeted comparison in invasively ventilated COVID-19 patients 16s sequencing in the present study was limited by a high [43]. This could be a result of reduced taxonomic reso- number of false positives, making clinical application of lution of 16s sequencing: limited to genus rather than an unbiased approach unlikely. To overcome this, previ- species level. Consequently, all species with a shared ous studies have suggested that pathogen dominance, as genus, including those other than Staphylococcus aureus represented by an individual species’ relative abundance and Haemophilus influenzae, were incorporated, poten- tenfold greater than any other microbes, could be used tially over representing pathogen presence. This is likely [42]. However, for this study some patients 16s rRNA to limit the role 16s sequencing might have to pathogen Fenn et al. Critical Care (2022) 26:203 Page 9 of 12 Fig. 4 Relative abundance (RA) is depicted, comparing BALF culture positivity for selected pathogens with their identified diagnostic threshold. For each pathogen, sensitivity, specificity, positive predictive value (PPV), negative predictive value and area under the curve (AUC) are given with BALF conventional culture as the primary reference point and the identified 16s sequencing diagnostic relative abundance cut-off as the predictor. Values given with 95% binomial confidence intervals exclusion in VAP. In addition, selecting a satisfactory selected pathogens. Although the BreathDx study pro- diagnostic cut-off for Staphylococcus proved to be the tocol and methodology were predefined and published most challenging. Unlike other pathogens, patients with a [20], this was a post hoc analysis with no formal sample very low, even undetectable relative abundance had posi- size calculation. The comparisons between culture posi- tive a BALF culture. This is likely due to the rigid cell wall tive and negative patients were sufficiently powered, but of Staphylococcus aureus that makes DNA extraction dif- the pathogen-specific analyses were hampered by the ficult [44]. Alternative sequencing methods have been limited sample size. The choice of respiratory specimen developed to maximise its detection [44]; however, such retrieval (BAL or non-directed-BAL) was led by the treat- optimisation was not performed in the current study. ing team and based on patient stability. Unfortunately, A strength of this study was the international multi- these data were not recorded for all study sites. While centred design and inclusion of patients only with clinical we recognise concerns regarding differing pathogen yield suspicion of VAP. We performed a comprehensive cul- exist and could introduce bias, an increasing number of ture-independent analysis and conventional culturing for studies have shown non-directed BAL to be an effective Fenn et al. Critical Care (2022) 26:203 Page 10 of 12 surrogate [45, 46]. Another important consideration is the lungs. These platforms have shown promising patho- the lack of quantitative PCR (qPCR). This would have gen identification with increasing accuracy and reduced helped determine the interference of pathogen absolute processing times that could be performed at the bedside. abundance and effect of contaminates in samples with a More recently, the advent of rapid full-length 16s rRNA lower biomass. Saline negative controls were included to gene nanopore sequencing using the MinION sequencer better understand the impact of background contamina- (Nanopore Technologies, Oxford) has shown that genus tion; however, while these samples showed minimal con- identification using 16s rRNA is possible within as lit- tamination, we recognise that the low number of controls tle as 2 h [54]. However, while encouraging, caution is and read count achieved makes determination of the needed if 16s sequencing is to be used as a truly agnostic source difficult. As such, a degree of uncertainty remains tool. Instead, an approach reliant on pathogen-specific regarding the origin of contamination and caution is relative abundance thresholds could provide a better advised in the interpretation of the negative controls. platform for clinical integration if used in a “rule out” An approach that utilises both qPCR and the inclusion fashion. Further prospective studies and replication in of controls from a variety of sources is better suited for larger cohorts are needed first. However, the continued future 16s sequencing studies [30, 47]. decreasing cost of metagenomics, reduced taxonomic Data regarding antibiotic initiation were not avail- resolution of 16s rRNA sequencing and lack of antibiotic able for this analysis; consequently, patients may have susceptibility are likely to limit this role, with truly agnos- received antibiotics before BALF was collected. How- tic and unbiased metagenomic sequencing at the beside ever, given that BALF recovery was to be done within the superseding it. first 24 h, even if the patients received a dose of antibiot- ics just prior to sampling, it is less likely to impact bac- Conclusion terial yield [48]. However, we were unable to confirm In conclusion, patients who were suspected of VAP with this and the bacterial yield from conventional culturing a positive culture had increased dysbiosis and genus might be negatively biased. The inclusion of such patients dominance compared to patients with negative cultures. was a pragmatic approach and more reflective of clini- An increased inflammasome caspase-1-dependent IL-1b cal practice. Nonetheless, caution should be taken in release was associated with a reduced species evenness relation to the interpretation of this data. Consideration and increased pathogenic bacterial presence, providing should also be given to our reference standard (BALF a possible causal link between microbiome dysbiosis and culture ≥ ­104 CFU/ml) to dichotomise patients. We rec- lung injury development. However, measures of diversity ognise that the use of microbiological diagnosis in isola- were an unreliable predictor of culture positivity and 16s tion is imperfect and that true VAP diagnosis in practice sequencing used in an unbiased capacity showed limita- often relies upon more, namely clinical suspicion in com- tions for pathogen identification, that may be overcome bination with a thorough clinical examination and simul- if pathogen-specific relative abundance thresholds are taneous microbiological culture. However, capturing and used. standardising clinical decisions such as these is inherently difficulty, particularly across multiple study sites. Conse- Supplementary Information quently, quantitative culture while not perfect remains The online version contains supplementary material available at https://​doi.​ the most commonly used reference standard to evalu- org/​10.​1186/​s13054-​022-​04068-z. ate novel index tests in VAP research [49]. Additionally, Additional file 1. Table S1. Patient characteristics of all patients sus‑ the inclusion of only patients with a clinical suspicion pected of VAP during study period. Table S2. Absolute counts shown for strengthens our standard and better reflects clinical prac- the top genera identified from control samples. Figure S1. Study flow tice; however, we recognise that a negative BALF culture chart. Figure S2. Comparative absolute count analysis showing genera identified in BALF samples compared to negative control samples. Figure might mis-represent patients and not definitively exclude S3. The top 10 identified genera enriched in BALF negative specimens, VAP, especially if they have received antibiotics. shown for both negative and positive BALF cultures. Microbiota analysis. The results of this study imply that while bacterial com- Library Preparation & 16s rRNA sequencing analytical pipeline. position and diversity derived from 16s rRNA analysis differ between patients suspected of VAP with and with- Acknowledgements The authors would like to acknowledge the contribution from the BreathDx out positive BALF culture, its place in clinical practice Consortium: Waqar M. Ahmed, Antonio Artigas Raventos, Jonathan Bannard- is not guaranteed. Targeted multiplex PCRs that offer Smith, Lieuwe D. J. Bos, Marta Camprubi, Luis Coelho, Paul Dark, Alan Davie, rapid and early pathogen detection [50–52] and real- Emili Diaz, Gemma Goma, Timothy Felton, Stephen J. Fowler, Royston Gooda‑ cre, Craig Johnson, Hugo Knobel, Oluwasola Lawal, Jan-Hendrik Leopold, Igna‑ time metagenomic sequencing of respiratory specimens cio Martin-Loeches, Tamara M. E. Nijsen, Pouline M. P. van Oort, Pedro Povoa, [53] challenge it and may further advance the role of Nicholas J. W. Rattray, Guus Rijnders, Marcus J. Schultz, Ruud Steenwelle, Peter culture-independent assessment of pathogen presence in Fenn et al. Critical Care (2022) 26:203 Page 11 of 12 J. Sterk, Jordi Valles, Fred Verhoeckx, Anton Vink, Hans Weda, Iain R. White, Immunity to Infection and Respiratory Medicine, Faculty of Biology, Medicine Tineke Winters, Tetyana Zakharkina. and Health, University of Manchester, Manchester, UK. 4 Critical Care Depart‑ ment, CIBER Enfermedades Respiratorias, Corporación Sanitaria Universitaria Author contributions Parc Taulí, Universitat Autonoma de Barcelona, Sabadell, Spain. 5 Hospital de DF, PvO, WA, TF, IM- L, PP, AA, MJS, SJF and LDB provided substantial contribu‑ São Francisco Xavier, Centro Hospitalar Lisboa Ocidental, CHRC, NOVA Medical tions to the conception or design of the work. DF, MIA, WA, PB, PvO, WA and School, New University of Lisbon, Estr. Forte do Alto Duque, 1449‑005 Lisbon, LDB performed the acquisition and analysis. DF, LDB, PB and MIA helped in Portugal. 6 Department of Intensive Care Medicine, Multidisciplinary Intensive interpretation of data for the work. All the authors contributed to drafting the Care Research Organization (MICRO), St. James’s Hospital, James’ St, Dublin, work or revising it critically for important intellectual content, final approval Ireland. 7 Department of Intensive Care, Amsterdam UMC Location University of the version to be published, and agreement to be accountable. All authors of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands. read and approved the final manuscript. Received: 14 February 2022 Accepted: 22 June 2022 Funding The project was funded by the European Union FP7 Marie Curie Actions, under the Industry-Academia Partnerships and Pathways (IAPP) programme (MC-IAPP BreathDx 611951). WA, TF, PD and SJF were supported by the NIHR Manchester Biochemical Research Centre. References 1. 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