Abstract

   The specific cytokines that regulate pediatric acute respiratory
   distress syndrome (PARDS) pathophysiology remains unclear. Here, we
   evaluated the respiratory cytokine profile in PARDS to identify the
   molecular signatures associated with severe disease. A multiplex
   suspension immunoassay was used to profile 45 cytokines, chemokines and
   growth factors. Cytokine concentrations were compared between severe
   and non-severe PARDS, and correlated with oxygenation index (OI).
   Partial least squares regression modelling and regression coefficient
   plots were used to identify a composite of key mediators that
   differentially segregated severe from non-severe disease. The mean
   (standard deviation) age and OI of this cohort was 5.2 (4.9) years and
   17.8 (11.3), respectively. Early PARDS patients with severe disease
   exhibited a cytokine signature that was up-regulated for IL-12p70,
   IL-17A, MCP-1, IL-4, IL-1β, IL-6, MIP-1β, SCF, EGF and HGF. In
   particular, pro-inflammatory cytokines (IL-6, MCP-1, IP-10, IL-17A,
   IL-12p70) positively correlated with OI early in the disease. Whereas
   late PARDS was characterized by a differential lung cytokine signature
   consisting of both up-regulated (IL-8, IL-12p70, VEGF-D, IL-4, GM-CSF)
   and down-regulated (IL-1β, EGF, Eotaxin, IL-1RA, and PDGF-BB) profiles
   segregating non-severe and severe groups. This cytokine signature was
   associated with increased transcription, T cell activation and
   proliferation as well as activation of mitogen-activated protein kinase
   pathway that underpin PARDS severity.

   Subject terms: Cytokines, Infectious diseases, Biomarkers, Paediatric
   research

Introduction

   Pediatric acute respiratory distress syndrome (PARDS) accounts for less
   than 5% of pediatric intensive care unit (PICU) admissions but is
   disproportionately associated with high mortality and
   morbidity^[36]1,[37]2. PARDS is clinically diagnosed^[38]3 and arises
   mainly from respiratory infections^[39]2. Development of PARDS is
   unpredictable and the mainstay of treatment has thus far, been
   supportive^[40]2,[41]4. Comprehensive translational studies in PARDS to
   identify effective therapies have been limited, specifically, the
   interrogation of biological processes occurring in the pulmonary
   microenvironment has been constrained by (a) the relatively low
   incidence, (b) under recognition and (c) difficulty of obtaining
   bronchoalveolar lavage in children^[42]5.

   PARDS, like its adult counterpart, acute respiratory distress syndrome
   (ARDS), is associated with intense lung inflammation, edema and
   proteinaceous alveolar exudation resulting in impaired
   oxygenation^[43]6. Release of proinflammatory cytokines from injured
   lung architecture (alveolar and bronchial cells) and immune cells
   weakens the capillary and alveolar endothelium^[44]7,[45]8. As damage
   progresses, proteins leak into the alveolar space, further stimulating
   the influx of neutrophils and macrophages into the area, propagating
   the inflammatory response that ultimately leads to diffuse alveolar
   damage^[46]7,[47]9. The resultant hypoxemia is secondary to severe
   ventilation–perfusion mismatch.

   Cytokines in ARDS have been extensively studied in the adult population
   in the hope of identifying potential biomarkers and therapeutic
   targets^[48]10–[49]12. However, extrapolation of data gleaned from
   adults to the pediatric population is inappropriate because of
   differing clinical definitions^[50]3,[51]13, epidemiology^[52]2,[53]14
   developmental pulmonary mechanical, microstructural and immunological
   factors^[54]15. Few studies have been conducted to describe plasma
   levels of cytokines in children with PARDS. Plasma IL-6 levels were
   up-regulated in children with severe PARDS in the first week of
   disease^[55]16. Elevations of plasma pro-inflammatory (IL-6, IL-8,
   IL-18) and anti-inflammatory cytokines (IL-10 and TNF-R2) in
   non-survivors compared to survivors was demonstrated in a large PARDS
   multicentre study involving 194 children with heterogenous case
   histories^[56]17. These cytokines were found to correlate with both the
   oxygenation index (OI) and Pediatric Risk of Mortality 3 (PRISM-3)
   score^[57]17. One smaller study examined lower respiratory tract
   cytokines in children intubated for respiratory failure secondary to
   respiratory infection and demonstrated that CCL7 was found to correlate
   with severity of illness^[58]18. In this study, only three patients
   fulfilled criteria for moderate/severe PARDS.

   We postulate that evaluating the cytokine response in the pulmonary
   microenvironment of patients with PARDS will be more informative than
   the systemic compartment, especially in the context of direct lung
   injury. As such, our study aimed to evaluate the cytokine profile in
   the deep tracheal lavage fluid (DTL) (surrogate for bronchoalveolar
   lavage fluid)^[59]19 and plasma in children with PARDS to determine the
   profile associated with severe disease. We hypothesized that the
   pulmonary cytokine profile is more closely associated with an
   inflammatory signature that correlates with OI, a clinical marker of
   pulmonary disease severity, as opposed to clinical markers of systemic
   disease severity. We will also explored the trend in cytokine
   concentrations at early and late phases of PARDS to determine if the
   mediators for continual severe disease changed temporally with disease
   course.

Results

   Over the 1-year study period, there were 45 patients who fulfilled the
   Pediatric Acute Lung Injury Consensus Conference (PALICC) criteria for
   PARDS^[60]3. Of these, 16 patients were selected who had evidence of
   direct lung injury and sufficient blood and DTL fluid samples for
   analysis (Table [61]1). All patients had pneumonia, and 12 patients
   also fulfilled criteria for sepsis. The mean (standard deviation) age
   and OI of this cohort was 5.2 (4.9) years and 17.8 (11.3),
   respectively. Notably, all patients in the non-severe group had a
   positive polymerase chain reaction (PCR) test for common respiratory
   viruses compared to the severe group which only had 3/8 (37.5%)
   positive viral PCR. Severe PARDS was expectedly associated with greater
   use of transfusions, oscillatory ventilation, pulmonary vasodilators,
   systemic corticosteroids, neuromuscular blockers and extracorporeal
   membrane oxygenation – though this was only statistically significant
   for transfusions. The overall PICU mortality in this cohort was 2/16
   (12.5%), and both non-survivors had severe PARDS.

Table 1.

   Clinical characteristics of patients in the non-severe and severe
   groups.
   Characteristics Non-severe
   (n = 8) Severe
   (n = 8) P value
   Age 3.0 (2.7) 7.4 (5.7) 0.069
   Male gender 5 (62.5) 5 (62.5) 1.000
   Chronic comorbidities 7 (87.5) 6 (75) 1.000
   Oxygenation index 10.6 (3.6) 23.1 (12.2) 0.033
   PIM 2 score 10.5 (12.1) 25.5 (30.0) 0.210
   PELOD score 6.4 (7.3) 15.8 (14.2) 0.154
   Infective etiology
   Viral 8 (100) 3 (37.5) 0.026
   Bacterial 5 (52.5) 3 (37.5) 0.620
   Co-infection 5 (62.5) 1 (12.5) 0.119
   No organism 0 (0) 3 (37.5) 0.200
   Inotrope 5 (62.5) 7 (75) 0.569
   Transfusion 1 (12.5) 7 (87.5) 0.010
   HFOV 2 (25.0) 4 (50.0) 0.608
   Pulmonary vasodilators 0 (0) 3 (37.5) 0.200
   Prone 4 (50) 2 (25) 0.608
   Steroids 5 (62.5) 7 (75) 0.569
   NMB 1 (12.5) 5 (62.5) 0.119
   Diuretics 8 (100) 7 (87.5) 1.000
   ECMO 0 (0) 2 (25) 0.467
   Multiorgan dysfunction 5 (62.5) 8 (100) 0.200
   Mortality 0 (0) 2 (25) 0.467
   VFD 17.3 (10.4) 10.5 (11.0) 0.228
   IFD 14.1 (8.2) 8.8 (9.8) 0.279
   [62]Open in a new tab

   Bold values signify p < 0.05.

   Categorical and continuous data are presented as counts (percentages)
   and mean (standard deviation) respectively.

   PIM 2 Pediatric Index of Mortality, PELOD Pediatric Logistic Organ
   Dysfunction, HFOV high frequency oscillatory ventilation, NMB
   neuromuscular blockade, ECMO extracorporeal membrane oxygenation, VFD
   ventilator free days, IFD intensive care unit free days.

Deep tracheal lavage fluid cytokines in early PARDS (timepoint 1)

   The concentration levels of 45 DTL cytokines quantified using multiplex
   suspension immunoassays comparing early and late timepoints were
   similar (p > 0.05; Supplementary Table [63]1A) regardless of severity.
   When comparing the DTL cytokines of severe vs. non-severe cases in the
   early timepoint, multivariate analysis using partial least squares
   regression (PLSR) demonstrated clear segregation of clusters
   corresponding to the comparison groups based on their cytokine profiles
   (principal component 1: 47%, principal component 2: 54%) (Fig. [64]1A).
   A 10-cytokine signature comprising of up-regulated IL-12p70, IL-17A,
   MCP-1, IL-4, IL-1β, IL-6, MIP-1β, SCF, EGF and HGF distinguished severe
   from non-severe PARDS. Pathway enrichment analysis of the 10-cytokine
   signature revealed positive regulation of transcription, T cell
   proliferation/activation and activation of mitogen-activated protein
   kinase (MAPK) as enriched Gene Ontology (GO) biological processes
   (Supplementary Table [65]2). Using the upregulated cytokine data,
   unbiased bioinformatic analysis also identified several associated
   respiratory diseases from the Gene Association Disease (GAD) database.

Figure 1.

   [66]Figure 1
   [67]Open in a new tab

   Respiratory cytokines showed distinct segregation between non-severe
   and severe PARDS in the partial least squares regression model (left)
   in early (A) and late (B) timepoints of disease. Regression coefficient
   plots identifying the top ten respiratory cytokines associated with
   severe PARDS (right). *Raised on both univariate and multivariate
   analysis.

   Involvement of pro-inflammatory cytokines IL-17A and IL-12p70
   (p = 0.0455 and p = 0.0188 respectively) in the early timepoint was
   identified by univariate analysis (Fig. [68]2), consistent with the
   PLSR analysis. IL-17A [8.6 (2.1) vs. 3.6 (3.0)] and IL-12p70 [7.9 (3.2)
   vs. 4.2 (1.3)] were higher in the severe compared to the non-severe
   group (Supplementary Table [69]3A). DTL cytokines which correlated
   significantly with the degree of lung disease (OI) included IL-1RA
   (r = 0.54), IL-6 (r = 0.53), IP-10 (r = 0.67), LIF (r = 0.52), MCP-1
   (r = 0.66) and SDF-1α (r = 0.52) (Fig. [70]3). None of the DTL
   cytokines correlated with the systemic severity of illness scores
   [Pediatric Index of Mortality 2 (PIM 2)^[71]20 and Pediatric Logistic
   Organ Dysfunction (PELOD)^[72]21 scores.) (Supplementary Table [73]4).

Figure 2.

   [74]Figure 2
   [75]Open in a new tab

   Respiratory and plasma cytokine concentration in severe and non-severe
   PARDS at timepoint 1 and 2. p-value indicates the difference between
   the two severity groups using t-test.

Figure 3.

   [76]Figure 3
   [77]Open in a new tab

   Correlation plots between early timepoint respiratory cytokines and the
   oxygenation index. r = Pearson’s correlation coefficient.

Deep tracheal lavage fluid cytokines in late PARDS (timepoint 2)

   In the late timepoint, widespread changes in DTL cytokines segregated
   severe from non-severe cases to a greater extent (principal component
   1: 49%, principal component 2: 83%) (Fig. [78]1B). Comparing severe to
   non-severe PARDS at this second timepoint, a cytokine signature
   comprising of up-regulated and down-regulated cytokines (IL-8,
   IL-12p70, VEGF-D, IL-4, GM-CSF, IL-1β, EGF, Eotaxin, IL-1RA, and
   PDGF-BB) was uncovered. Notably, IL-12p70 remains elevated in severe
   cases in the late timepoint.

   In the univariate analysis, GM-CSF [8.8 (3.3) vs. 4.6 (1.5);
   p = 0.0182], IFN-α [4.8 (1.7) vs. 1.6 (1.0); p = 0.0135], IL-17A [7.1
   (1.7) vs. 2.9 (0.4); p = 0.0202], IL-27 [12.6 (2.0) vs. 9.8 (2.1);
   p = 0.0435], IL-12p70 [7.3 (3.3) vs. 3.7 (1.7); p = 0.0303] and VEGF-D
   [9.9 (1.9) vs. 6.3 (1.7); p = 0.0294] were higher in the severe
   compared to non-severe group (Fig. [79]2 and Supplementary Table
   [80]5A). None of the DTL cytokines correlated with the OI, nor systemic
   severity of illness scores (PIM 2 and PELOD scores) (Supplementary
   Table [81]4). Collectively, the data suggested an early
   pro-inflammatory cytokine response that underpins PARDS severity, of
   which IL-12p70 and IL-17A remains elevated in the late phase of severe
   cases.

Plasma cytokines in early PARDS (timepoint 1)

   The PLSR result for plasma cytokines is presented in Fig. [82]4A
   showing cytokine profiles which clearly segregated non-severe and
   severe groups into two clusters with up-regulation of IL-6, IFN-alpha,
   HGF, LIF and IP-10, and down-regulation of IL-1β, IL-7, bNGF, PIGF-1
   and IL-8.

Figure 4.

   [83]Figure 4
   [84]Open in a new tab

   Plasma cytokines showed distinct segregation between non-severe and
   severe PARDS in the partial least squares regression model (left) in
   early (A) and late (B) timepoints of disease. Regression coefficient
   plots identifying the top ten plasma cytokines associated with severe
   PARDS (right).

   In the univariate analysis, only HGF [11.2 (0.9) vs. 8.6 (1.3);
   p = 0.0257] was higher in the severe compared to the non-severe group
   (Fig. [85]2 and Supplementary Table [86]3B). Whereas, PIGF-1 [2.9 (0.7)
   vs. 4.0 (0.8); p = 0.0220] was lower in the severe compared to the
   non-severe group (Fig. [87]2). Several plasma cytokines correlated
   significantly with the OI, PIM and PELOD scores. IL-6 (r = 0.66), IL-10
   (r = 0.58) and VEGF-A (r = 0.60) were positively correlated with the OI
   (Supplementary Fig. [88]1). MCP-1 (r = − 0.58) and SCF (r = 0.54)
   correlated with the PIM 2 score, and IL-27 (r = 0.60), IL-15
   (r = − 0.63) and HGF (r = 0.77) with the PELOD score.

Plasma cytokines in late PARDS (timepoint 2)

   The PLSR plots comparing non-severe and severe disease during the
   course of PARDS also achieved good separation (Fig. [89]4B) and
   identified a plasma cytokine signature associated with up-regulation of
   IL-4, TNF-α, IL-1RA, SCF and LIF, and down-regulation of PIGF-1, bNGF,
   RANTES, IL-8 and BDNF.

   In the univariate analysis, only BDNF [4.5 (1.9) vs. 7.2 (1.3);
   p = 0.0099] was lower in the severe compared to non-severe group at
   resolution (Fig. [90]2 and Supplementary Table [91]5B). Only HGF
   (r = 0.76) correlated with PELOD score (Supplementary Table [92]4).
   Similar to DTL fluid, plasma cytokine concentrations were indifferent
   between the early and late timepoints, except IL-8 which showed a
   significant decrease between at the second timepoint [8.8 (0.0) vs. 5.2
   (1.3); p = 0.0185] (supplementary Table [93]1B).

Discussion

   In this cohort of pneumonia induced PARDS, several respiratory
   cytokines were found to be distinctively associated with lung disease.
   Some inflammatory drivers (IL-17A, IL-12p70, IL-6, MCP-1, IP-10) were
   found to be higher in the lungs or correlated with severe PARDS early
   in the disease, whereas, others were found to peak later in the disease
   (GM-CSF, IFN-α). Both univariate and multivariate analysis indicated
   IL-12p70 and IL-17A as an early duplex cytokine signature. Varying
   immunoregulatory, homeostatic and repair factors (IL-1RA, LIF, SDF-1α,
   IL-27, IL-10, HGF, VEGF-A, VEGF-D) in the lungs and plasma were also
   observed to be higher or correlated with severe lung disease during the
   course of illness, in line with the knowledge that cytokines form
   biological networks that manifests biochemically and
   physiologically^[94]8,[95]22. These are summarised in Fig. [96]5.

Figure 5.

   [97]Figure 5
   [98]Open in a new tab

   Cytokine profile in severe pediatric acute respiratory distress
   syndrome. PARDS pediatric acute respiratory distress syndrome.

   Our study reports a higher production of IL-12p70 in severe compared
   with non-severe PARDS at both early [7.9 (3.2) vs. 4.2 (1.3);
   p = 0.0188] and late [7.3 (3.3) vs. 3.7 (1.7); p = 0.0303] phases of
   disease. Respiratory concentrations of IL-12p70 were also evidently
   higher than plasma concentrations [5.5 (2.9) vs. 3.4 (1.5);
   p = 0.0015). IL-12p70 is commonly expressed by antigen presenting
   cells^[99]23. Part of the IL-12 cytokine family, IL-12p70 consists of
   subunits p35 and p40, and its main function includes induction of
   IFNγ-production from T cells, enhancement of cytotoxic T cells (CTL),
   and differentiation of naïve T cells into Th-1 effectors, suggesting a
   key role for IL-12 in the development of cell-mediated immunity
   (CMI)^[100]23,[101]24. IL-27 also being part of the IL-12 family, plays
   a dual pro-inflammatory and immunoregulatory role^[102]25. IL-27 is
   involved in initial Th-1 progression via the STAT1-mediated T-box
   expressed in T cells (T-bet) pathway, but later induction of IL-10
   suppresses Th-1, Th-2 and Th-17 responses^[103]26. It showed a higher
   expression in severe PARDS which was apparent at later stages of the
   disease, likely related to the need to attenuate uncontrolled pulmonary
   inflammation.

   Another cytokine found to be associated with severe PARDS, IL-17, is
   secreted by Th-17, γδ T cells, invariant natural killer cells (iNKT),
   and type three innate lymphoid cells (ILC3) which drives neutrophil
   infiltration and pathogen clearance^[104]27,[105]28. It binds to IL-17
   receptors which consist of heterodimeric complexes^[106]29 and are
   expressed on a variety of cells, including epithelial cells and
   neutrophils. Its binding results in activation of downstream signalling
   for neutrophil chemotaxis, including IL-8, MCP-1 and IP-10, and
   activating factors IL-6 and GM-CSF, which were also consistently found
   to be raised in our study^[107]30,[108]31. Enhanced production of MCP-1
   and IP-10 in the lungs of patients with PARDS likely resulted in
   increased recruitment of circulating neutrophils, monocytes and
   macrophages in keeping with Th-1 responses^[109]32,[110]33. Whereas,
   GM-CSF exert anti-apoptotic properties on neutrophils causing
   accumulation of neutrophils in the lungs^[111]34.

   Early expression of IL-6 showed an increase corresponding to the degree
   of lung injury within 24 h of PARDS in the lung microenvironment
   (r = 0.53), as well as, systemically (r = 0.66). IL-6 concentrations
   were significantly higher in the respiratory samples [13.4 (2.8) vs.
   6.4 (2.6); p < 0.001] and is consistent with clinical evidence of
   direct lung injury. IL-6 is produced by the innate immune system in
   response to activation of pathogen associated molecular patterns
   (PAMPs) or damage associated molecular patterns (DAMPs)^[112]8.The lack
   of reduction in IL-6 with disease resolution indicates that potential
   involvement of other mediators in disease resolution and the need to
   evaluate the lung inflammatory microenvironment in a holistic and
   relational manner. Leukemia inhibitory factor (LIF) is also a member of
   the IL-6 family of cytokines, and has been shown to be induced by IL-1β
   in pneumonia^[113]35. Lung epithelium including type 2 alveolar cells,
   and to a lesser extent, alveolar macrophages, are the main source of
   LIF^[114]36. LIF activates STAT3 signalling and upregulates
   phospholipase A2^[115]37. Its presence was shown to be important in
   controlling lung injury, achieving tissue homeostasis after pneumonia
   and correlated with severity of lung injury in patients with
   ARDS^[116]38,[117]39. Our study identified the presence of LIF in
   pneumonia induced PARDS and showed a weak correlation with severity of
   lung disease (r = 0.52).

   Our study has several limitations. Firstly, the sample size is small
   and a larger cohort will be necessary to validate its findings.
   Moreover, future inclusion of healthy controls will provide useful
   normative data. Secondly, though deep tracheal lavage was used as a
   surrogate for bronchoalveolar lavage specimens, there is no data which
   confirms they are directly comparable and representative of alveolar
   epithelial lining fluid. Lastly, confirmation of mechanistic and
   functional consequences of the identified signatures will require
   functional assays and animal testing. These limitations will be the
   basis for further experimental steps in identifying key
   biomarkers/signatures in the pathogenesis of PARDS.

Conclusion

   The immunological processes occurring in the lungs and systemic
   circulation of children with PARDS are poorly studied. Our study
   identifies a complex mixture of pro-inflammatory cytokines (IL-6,
   MCP-1, IP-10, IL-17A, IL-12p70, GM-CSF, IFN-α), immunoregulatory
   cytokines (IL-12p70, IL-1RA, IL-27, IL-10) and homeostatic/repair
   factors (PIGF-1, HGF, VEGF-A, VEGF-D) which were associated with
   disease severity and time course of PARDS. An early duplex cytokine
   signature involving IL-12p70 and IL-17A was identified to underpin
   PARDS severity. Further targeted studies to identify key pathways and
   druggable targets will be necessary to prevent or treat PARDS.

Methods

Study design and ethics

   This study was conducted in a 16-bedded, multidisciplinary PICU at KK
   Women’s and Children’s Hospital, Singapore from November 2018 to
   October 2019 (1 year). The study protocol has been approved by the
   institutional review board (Singhealth Centralised Institutional Review
   Board reference number: 2017-3076), informed consent was obtained from
   a parent and/or legal guardian of all subjects before entrance into the
   study and all methods were carried out in accordance with relevant
   guidelines and regulations. Reporting was conducted in compliance to
   the STROBE (strengthening the reporting of observational studies in
   epidemiology) guidelines^[118]40.

Patients

   We prospectively studied 16 patients with direct PARDS (8 non-severe
   and 8 severe) who fulfilled the Pediatric Acute Lung Injury Consensus
   Conference (PALICC) criteria on two consecutive arterial blood gases
   four hours apart^[119]3. In brief, the PALICC criteria include (1) the
   presence of a known clinical insult within 7 days of PARDS, (2)
   respiratory failure not fully explained by cardiac failure or fluid
   overload, (3) new infiltrates on chest imaging consistent with acute
   pulmonary parenchymal disease, and (4) OI > / = 4. If an arterial blood
   gas was not available for calculation of OI, the oxygenation saturation
   index (OSI > / = 5) was used instead. Patients entered the study within
   24 h of developing PARDS. There were no patients who had
   “do-not-resuscitate” orders.

Definitions and severity scoring

   Sepsis and organ dysfunction were defined according to the
   International Pediatric Sepsis Consensus Conference^[120]41. Overall,
   the disease severity on admission was estimated by the Pediatric Index
   of Mortality 2 (PIM 2)^[121]20 and Pediatric Logistic Organ Dysfunction
   (PELOD)^[122]21 scores, where a higher score indicates greater
   severity.. Patients were followed up until PICU discharge or 28 days,
   whichever earlier. Those who survived to PICU discharge or 28 days were
   classified as survivors. Ventilator free days (VFD) were defined as
   days alive and free from invasive mechanical ventilation up to
   28 days^[123]42. Intensive care unit free days (IFD) were defined as
   days alive and discharged from intensive care up to 28 days.

Blood sampling and processing

   The first blood sample (timepoint 1—early PARDS) was obtained within
   24 h of developing PARDS^[124]43. A second sample (timepoint 2—late
   PARDS) was obtained prior to extubation. If a patient remained
   intubated for a non-pulmonary indication, the second sample was
   obtained when the managing physician deemed that the patient was ready
   for extubation from the pulmonary standpoint. In non-survivors, the
   second sample were taken prior to death.

   Blood was obtained from indwelling arterial cannulas or central venous
   lines, and collected into EDTA vacutainer tubes. Blood samples were
   transported to the laboratory within 2 h and centrifuged at 596×g for
   15 min. The supernatant was collected and centrifuged again at 2095×g
   for 10 min. The plasma was then aspirated and stored at − 80 °C.

Deep tracheal lavage fluid sampling and processing

   Deep tracheal lavage (DTL) fluid was obtained concurrently with the
   blood samples. Open suction through the endotracheal tube with a
   sterile appropriately sized suction catheter was performed. Here, the
   patient was transiently disconnected from the ventilator at the
   endotracheal tube end, the suction catheter was advanced as far as
   possible and wedged. 1–3 ml of normal saline was instilled through the
   catheter and subsequently suctioned at 80–100 cmH[2]O into a sterile
   trap bottle. Collected fluid was then transported to the laboratory
   within 2 h and centrifuged at 300×g for 10 min. The cell free
   supernatant was then collected and stored at − 80 °C. For the DTL fluid
   samples, the urea dilution method was used to standardise lavage fluid
   concentration^[125]44.

Cytokine profiling

   The levels of 45 cytokines [eotaxin (CCL11), IL-8, IP-10 (CXCL10),
   MCP-1 (CCL2), MIP1α (CCL3), MIP-1β (CCL4), SDF-1α (CXCL12), RANTES
   (CCL5), IFNγ, IL-12p70, IL-13, IL-1β, TNF-α, IL-4, IL-6, GM-CSF, IL-18,
   IL-10, IL-17A, Il-27, IFN-α, IL-15, IL-1α, IL-1RA, IL-7, brain-derived
   neurotrophic factor (BDNF), basic nerve growth factor (bNGF), epidermal
   growth factor (EGF), hepatic growth factor (HGF), leukemia inhibitory
   factor (LIF), platelet-derived growth factor BB (PDGF-BB), placental
   growth factor 1 (PIGF-1), stem cell factor (SCF), vascular endothelial
   growth factor A (VEGF-A), VEGF-D, GRO-alpha (CXCL1), IL-2, IL-5, IL-21,
   IL-22, IL-23, IL-9, IL-31, TNF-β, fibroblast growth factor (FGF-2)]
   were measured in the plasma and cell-free DTL fluid using a
   Procartaplex 45-plex Human Cytokine and Chemokine Panel (Luminex) as
   described earlier^[126]22. Briefly, 10 μL of DTL or serum was added to
   10 μL of primary antibody-conjugated, magnetic beads on a 96
   DropArray plate (Curiox Biosystems, Singapore) and mixed on a plate
   shaker at 450 rpm for 120 min at 25 °C while being protected from
   light. Subsequently, the plate was washed three times with wash buffer
   using the LT210 Washing Station (Curiox) before 5 μL of the secondary
   antibody was added to each well and incubation done on a plate shaker
   set at 450 rpm for 30 min at 25 °C with protection of the plate from
   light. The plate was washed three times with the wash buffer, and 10 μL
   of streptavidin–phycoerythrin was then added to each well and mixed at
   450 rpm for 30 min at 25 °C with protection of the plate from light.
   The plate was washed with wash buffer three times prior to its transfer
   to a 96 conical well microtiter plate with 60 μL of reading buffer per
   well for data acquisition using the Bio-Plex Luminex 200 (BioRad). The
   beads are classified by the red classification laser (635 nm) into its
   distinct sets, while a green reporter laser (532 nm) excites the
   phycoerythrin, a fluorescent reporter tag bound to the detection
   antibody. Quantitation of the 45 cytokines in each sample was then
   determined by extrapolation to a six-or seven-point standard curve
   using five-parameter logistic regression modelling^[127]45.
   Calibrations and validations were performed prior to runs and on a
   monthly basis respectively. DTL and serum cytokine levels are captured
   as pg/mL for both timepoints of PARDS. All samples were run in
   duplicates and the average was reported. Cytokines with more than 50%
   missing data were not analysed. The cytokine concentrations had an
   intra-assay coefficient of variation within 15%.

   10 out of 45 cytokines which had greater than 50% of data points out of
   dynamic range were not analysed (FGF-2, GRO-alpha, IL-2, IL-21, IL-2,
   IL-23, IL-31, IL-5, IL-9, TNF-β). Concentrations of all analysed
   cytokines were higher in DTL compared to plasma, except IFN-γ, IL-18
   and PDGFBB (Supplementary Table [128]6).

Statistical analysis

   Clinical characteristics of patients in the severe and non-severe PARDS
   groups were first compared using the Fisher-exact and Student’s t-test
   for categorical and continuous variables, respectively. These were
   reported as counts (percentages) and mean (standard deviation).
   Subsequently, to find out which of the cytokines were most related to
   disease severity, we compared DTL cytokine concentration in the
   following groups; (i) severe and non-severe PARDS, and (ii) between
   longitudinal samples taken during the course of PARDS. All data were
   Log2 transformed prior to analysis and normality was assumed. Pearson’s
   correlation coefficient was used to quantify the relationship between
   DTL cytokine concentration and pulmonary severity of illness (OI), and
   repeated for systemic severity of illness (PIM2 and PELOD).
   Statistically significant comparison of cytokines and correlation were
   graphically represented by box and whisker plots and scatterplots with
   an interposed linear regression line, respectively.

   Cytokine signatures were further analysed by partial least squares
   regression (PLSR) modelling (Unscrambler X version 10.1) PLSR is a form
   of supervised regression method performed to visualize the global
   cytokine changes in PARDS. Full cross-validation was applied in PLSR to
   increase model performance and for the calculation of coefficient
   regression values. Regression coefficient plots were then created to
   identify the top ten cytokines that are associated with severity and
   the early timepoint. Statistical analysis and graphical representation
   were performed using GraphPad Prism 8 (GraphPad Software, USA) and
   STATA version 15.0 statistical software (StataCorp, College Station,
   Texas). All t-tests were two-tailed and statistical significance was
   taken as p < 0.05.

Pathway enrichment analysis

   PLSR coefficient-derived cytokines were then imputed into the Database
   for Annotation, Visualization and Integrated Discovery database (DAVID
   Bioinformatics Resources 6.8), and cross-referenced against Gene
   Ontology (GO) and Gene Association Disease (GAD) databases for
   biological process enrichment analysis as previously described^[129]22.
   p < 0.05 based on Fisher Exact analysis and fold change > 10 were
   imposed as enriched pathways.

Supplementary Information

   [130]Supplementary Information.^ (245KB, docx)

Author contributions

   J.J.M.W., J.H.L. and Y.H.L. made substantial contributions to the
   conception and design of the work. J.J.M.W. and H.L.T. were responsible
   for acquisition of samples. J.J.M.W., J.L.Z., J.Y.L., J.G.Y. and Y.H.L.
   were responsible for the analysis and interpretation of data. J.J.M.W.
   drafted the main manuscript. All authors revised the manuscript
   substantially for scientific content. All authors have read and approve
   the submitted version.

Funding

   This study was funded by the KKH Academic Medicine Research Start-up
   grant, project number KKH-AM/2019/03 (KRDUK19AR100) (Principal
   Investigator: JJMW).

Competing interests

   The authors declare no competing interests.

Footnotes

   Publisher's note

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Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-021-93705-8.

References