Multi-omic Characterization of Pediatric ARDS via Nasal Brushings

doi:10.21203/rs.3.rs-1455115/v1

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Research Article

Multi-omic Characterization of Pediatric ARDS via Nasal Brushings

https://doi.org/10.21203/rs.3.rs-1455115/v1

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Rationale

While nasal brushing transcriptomics can identify disease subtypes in chronic pulmonary diseases, it is unknown whether this is true in pediatric ARDS (PARDS).

Objectives

Determine whether nasal transcriptomics and methylomics can identify clinically meaningful PARDS subgroups that reflect important pathobiological processes.

Methods

Nasal brushings and serum were collected on days 1, 3, 7, and 14 from control and PARDS subjects from two centers. PARDS duration was the primary endpoint.

Measurements and Main Results

Twenty-four control and 39 PARDS subjects were enrolled. Two nasal methylation patterns were identified. Methyl Subgroup 1 had hypomethylation of cell adhesion genes and had only PARDS subjects. Methyl Subgroup 2 contained PARDS and control subjects with hypomethylation of cell metabolism genes. Neither methylation pattern had longer PARDS duration. Four transcriptomic patterns were identified with temporal patterns indicating injury, repair, and regeneration. Over time, both inflammatory (Subgroup B) and cell injury (Subgroup D) patterns transitioned to repair (Subgroup A) and eventually homeostasis (Subgroup C). Control specimens were largely Subgroup C. In comparison with 17 serum biomarkers, the nasal transcriptome was more predictive of prolonged PARDS. Initial Transcriptomic Subgroup B or D subjects had median PARDS duration of 8 days compared to 2 in A or C (p=0.02). For predicting PARDS duration ≥ 3 days, nasal transcriptomics was more sensitive and serum biomarkers more specific.

Conclusions

PARDS nasal transcriptome may reflect distal lung injury, repair, and regeneration. A combined nasal PCR and serum biomarker assay could be useful for predictive and diagnostic enrichment.

Registration

Clinicaltrials.gov NCT#03539783 May 29, 2018

Transcriptomics

Subclassification

Acute Lung Injury

Pediatric Acute Respiratory Distress Syndrome

In the pediatric intensive care unit (PICU), pediatric acute respiratory distress syndrome (PARDS) is a leading source of morbidity and mortality [1]. Despite decades of research and many large, multicenter, randomized clinical trials in ARDS, the only consensus therapies are supportive: the use of low tidal volume ventilation and employing a restrictive fluid strategy [2, 3]. A criticism of many studies in ARDS has been their failure to account for etiologic, biologic, and physiologic differences [4]. Recent work has suggested the presence of a hyperinflammatory ARDS subgroup. In latent group analysis of a clinical trial of the FACTT trial [3] subjects with greater inflammation were more likely to survive with a fluid liberal strategy, than subjects without this subphenotype [5]. Similarly, hyperinflammatory ARDS patients had lower mortality with higher positive end expiatory pressure (PEEP) levels [6]. Thus, inflammatory serum biomarkers may differentiate ARDS subphenotypes that confer greater mortality risk and might be helpful in directing therapy.

However, there is likely a limit to the extent to which serum assays accurately reflect lung pathology. While peripheral blood gene expression profiling in pediatric sepsis had identified important subgroups that correlate with outcome [7, 8], there is ~ 40% concordance and ~ 20% discordance of gene expression between lung and peripheral leukocytes [9]. RAGE, ANG2 and inflammatory cytokines are increased in multiple non-lung conditions and likely lack specificity. Bronchial and nasal gene expression profiling was highly diagnostic for the presence of lung cancer in smokers [10], and for corticosteroid sensitivity in asthma [11], and nasal transcriptomic profiling can differentiate between COPD and non-COPD in smokers [12]. Differences in nasal DNA methylation have been established in pediatric and adult asthma [13, 14]. Recent functional data demonstrated increased nasal potential difference in at-risk adults who go onto develop ARDS [15]. This, the nasal epithelium may provide a window into distal lung epithelial processes.

Given these studies, we hypothesized that the nasal transcriptome could be used to define and provide pathobiological insights into different PARDS subgroups. We found four patterns of gene expression that corresponded to injury (two patterns), repair/regeneration, and homeostasis and that PARDS duration was greater in subjects with the first two compared to the latter two patterns. Complimentary studies of the epigenome suggested the presence of risk loci related to certain genes important in epithelial cell function and transcriptomic and methylomic profiling of concomitant nasal and bronchial brushings showed similarities in the upper and lower conducting airways. Lastly, predictive modeling suggested that serum biomarkers and nasal transcriptomics provided different and complementary information.

This article has an online materials and methods and data supplement, which is accessible from this issue's table of content online at www.atsjournals.org.

Human Subjects

Research was conducted under approval from the Cincinnati Children’ Hospital and Children’s Hospital of Philadelphia Institutional Review Boards (2017 − 1345 and 20-017205 respectively) and registered with clinicaltrials.gov (NCT#03539783).

Subject Eligibility

Patients admitted to the PICU < 18 years of age who were invasively mechanically ventilated and meeting consensus PARDS criteria [1] or who were admitted to the PICU for a non-pulmonary reason and with expected hospitalization ≥ 7 days were eligible for enrollment. Patients with limited resuscitation orders, that required chronic mechanical ventilation, with a baseline oxygen requirement (≥ 2 LPM PARDS, any for Control) or with high-risk of nasal bleeding were excluded. Informed written consent was obtained from the patient’s parent or guardian with patient assent as appropriate.

Study Design

Consented patients had nasal brushings and serum collected on the day of enrollment (day 1), and on study days 3, 7, and 14 if they were still admitted. No blood was collected if it could not be drawn from a vascular access device or from a clinically indicated venipuncture, and a patient, family, or provider could refuse brushing without removal from the study.

Nasal Brushing

Nasal inferior turbinate brushings were collected, and brush heads were cut and preserved in RNAProtect buffer (Qiagen) at -80˚C.

Bronchial Brushing

In subjects undergoing clinically indicated bronchoscopy in the CCHMC PICU bronchial brushing was performed in a healthy-appearing distal bronchus with concomitant nasal brushing.

RNA & DNA Extraction and Sequencing

Brushes were thawed on ice with 1:100 β-mercaptoethanol to reduce mucus disulfide bond. The extract was passed through a QIAShredder and AllPrep Columns (Qiagen) with extraction of first DNA and then RNA (after DNAse treatment). Barcoded libraries were created using New England Biosystems Single Cell/Low Input RNA kit per manufacturer instructions. For each specimen, ten million, 150 base pair, paired-end read were obtained using a NovaSeq 6000. Eluted DNA was sheared using a Covaris M220 device and methylated cytosines converted to thymidines using the NEBNext Enzymatic MethylSeq kit with barcoding. Each methylated DNA library was sequenced at 20X genomic coverage using a NovaSeq 6000.

Serum Protein Quantification and Predictive Statistic Comparisons

Serum samples were analyzed in duplicate for Angiopoietin2 (ANG2), Granzyme B (GrB), Intercellular Adhesion Molecule1 (ICAM1), Interferon-γ (IFNγ), Interleukin-6 (IL6), IL8, IL10, IL17, IL18, Surfactant Protein D, Tumor Necrosis Factorα (TNF-α), TNF Receptor Soluble Factor 1A (TNFSF1A), and RAGE using R&D Systems reagents and a Luminex 200 instrument.

Bioinformatics

RNA reads were aligned to GRCh38 and count matrices generated using STAR. Human differential gene expression was calculated using DESeq2 and other packages described in the supplement. For metagenomic analysis, bacterial and viral RNA sequences were aligned using Kraken2 and normalized reads assessed using vegan. DNA sequences were aligned to GRCh38 and methylated and non-methylated coverage matrices generated using Bismark. Coverage files were analyzed using methylKit and other packages described in the supplement. ToppGene was used for gene set enrichment analysis (GSEA). For genes and microbial species, a false discovery rate of < 0.1 was considered significant.

Statistical Analysis

Non-parametric continuous variables were compared using Wilcoxon rank sum test using a Dunn test with Bonferroni correction for post hoc comparisons. Categorical variables were compared by fisher exact test using the R statistical package and rstatix. Finalfit was used for logistic regression, and receiver operator characteristic curves were generated and analyzed using pROC. p-values < 0.05 were considered significant.

Data Availability

Datasets are publicly available as GSE192364 and GSE192926. Other data is freely available by contacting the corresponding author.

Subject Enrollment and Cohort Characteristics

We enrolled 24 control and 39 PARDS subjects from April 1, 2018 to June 30, 2021. Subject characteristics are presented in Table 1, and Supplemental Table 1 provides patient-specific data and describes assays conducted on each specimen, and Supplemental Fig. 1 summarizes the analyzed specimens.

Our overall approach was (1) identify differences in the epigenetic state of PARDS vs. Control and between PARDS subjects, (2) characterize transcriptional differences, (3) compare the ability of nasal transcriptome vs. serum biomarkers to predict PARDS duration, and (4) identify potential regulatory nodes by reconciling transcriptomic and methylomic data.

The Methylation Patterns of Nasal and Bronchial Epithelial Cells Do Not Differ by Collection Site

We first compared DNA methylation of paired nasal and bronchial specimens. We analyzed 8 paired brushings from 3 PARDS and 1 control subjects. By site, 0.2% percent of regions were differentially methylated (DMRs) between the upper and lower airways with 48% corresponding to annotated CgP islands or shores, and 9% promoters (24 genes, Supplemental Table 2, Supplemental Fig. 2A&B). By k-means clustering, specimens were more similar by subject than by collection site (Supplemental Fig. 2C) indicating similarity of the nasal and bronchial methylomes.

PARDS Patients Have Hypomethylation of Promoters Related to Inflammation

We sought to identify patterns of DNA methylation in the nasal epithelium of PARDS and control subjects. Methylation analysis of day 1 specimens from 13 control and 20 PARDS patients showed segregation into two groups which we termed Methyl Subgroup 1 and Methyl Subgroup 2 (Fig. 1A&B). Methyl Subgroup 1 was comprised of 12 PARDS subjects and Methyl Subgroup 2 was composed of 8 PARDS and all control subjects (Table 2). There were no clear differences in age or sex between groups. Since exposure to of the nasal epithelium to oxygen might impact DNA methylation, we assessed which subjects had been treated with at least 24 hours of supplemental oxygen in the 7 days prior. Three Subgroup 1 and zero Subgroup 2 subjects had been exposed to > 24 hours of nasopharyngeal supplemental oxygen (i.e. most were intubated shortly after presentation). Among PARDS subjects, there were no substantial differences in PARDS severity, PARDS duration, or illness severity between Methyl Subgroups.

Methyl Subgroup 1 subjects had hypomethylation of genes potentially important in PARDS. Using a threshold of 25% differential methylation and an adjusted p-value threshold of 0.1, 0.8% of loci were differentially methylated. Compared to Methyl Subgroup 2, in Methyl Subgroup 1, 85 transcription start sites were hypo- and 24 were hypermethylated (Supplemental Table 3). Subgroup 1 hypomethylated genes were related to cell adhesion, and hypermethylated genes were related to cell metabolism (Fig. 1C). Thus, a subset of PARDS subjects have a localized increased chromatin accessibility for genes that might be important in inflammatory cell recruitment and epithelial cell function.

Methylation Changes Over Time

DNA methylation is considered a relatively stable epigenetic feature. We performed methylomic analysis on nasal brushings from control and PARDS subjects in which we had at least two specimens to assess Methylomic Subgroup stability. There were 9 control and 21 PARDS subjects with two or more evaluable specimens (70 total). Methyl Subgroup was stable in 15 of 21 PARDS and 9 of 9 control subjects with all control subjects remaining in Methyl Subgroup 2 (Fig. 2A). There was no consistent pattern in the six PARDS subjects with changes in Methylomic Subgroup (Fig. 2B). The nasal epigenome is stable in control subjects but changes in PARDS with respect to genes that might be important in PARDS pathobiology.

mRNA-Seq Data Filtering, Normalization and Batch Correction

One hundred forty-two nasal specimens from 56 subjects were sequenced, aligned, and normalized with removal of mitochondrial and ribosomal transcripts. Transcripts without at least five reads in half of specimens, and specimens without at least 100,000 reads and 5,000 unique transcripts were excluded from further analysis. Expression values of 10,846 transcripts in 129 specimens from 37 PARDS and 15 control subjects were normalized by batch prior to analysis (Supplemental Fig. 3A-C). Analyzing PARDS specimens suggested that six or seven principal component best characterized the data, and covariate analysis for PARDS severity, comorbidity, infectious etiology, direct lung injury, race, sex, and age only identified age as being significantly correlated with any component and this only explaining 0.6% of dataset variance (Supplemental Fig. 3D-E).

Nasal and Bronchial Transcriptomes are Dynamic

In comparing the transcriptomes of the nasal and bronchial epithelium, twelve specimens from five PARDS and one control subject were evaluable. By k-means clustering, principal component analysis, and Euclidean distance, specimens tended to cluster more closely by subject than by collection site (Supplemental Fig. 4). As in healthy adults [16], upper and lower conducting airway gene expression is similar in PARDS.

Four Different Transcriptional Patterns in the Nasal Epithelium of PARDS Subjects

We performed k-means clustering of each evaluable specimen and identified four subgroups within our dataset which we termed A, B, C, and D (Fig. 3A&B). Principal component 1 genes were largely related to ciliary cell function and Interleukin-4 (IL-4) signaling, and principal component 2 genes were largely related to inflammation and chemokine signaling (Supplemental Tables 4&5). We defined a differentially expressed gene (DEG) as one with 2-fold changed expression with an adjusted p-value of less than 0.1. Subgroups A, B, C, and D contained 1,375; 834; 841 and 2,038 DEGs and had substantial DEG overlap (Fig. 3C, Supplemental Table 6, Supplemental Fig. 5). GSEA showed Subgroup A enriched for IL-4, IL-10, and IL-13 signaling. Subgroup B had downregulation of ciliary function-related processes and upregulation of innate immune ones. Subgroup C had upregulation of these same ciliary processes and downregulation of IL-4, IL-10, and IL-13 signaling. Subgroup D had downregulation of both microtubule and innate immune processes and upregulation of processes related to epithelial integrity (Fig. 3D-F). In evaluating the abundance of cell-specific mRNAs, Subgroup A and B both had reduced abundance of ciliated cell mRNAs and Subgroup B had increased myeloid cell mRNAs. Subgroup C had a greater abundance of ciliated cell mRNAs compared to all others, and Subgroup D had reduction epithelial stem cell mRNAs (secretory and basal cells, Table 3). The only notable difference in clinical characteristics between the subgroups was a trend towards fewer ventilator free days in Subgroup B (Table 4).

In evaluating how PARDS Subgroups changed over time, Subgroups B and D were restricted to earlier time points with the proportion of specimens classified as Subgroup C increasing over time. Although the time intervals between specimens was not consistent, Subgroups B and D tended to transition to Subgroups A and C, and Subgroup C specimens remained consistent (Fig. 4A). Taken together, these data support a model in which Subgroups B and D represent two different modes of injury. Subgroup B is characterized by innate immune activation and ciliary dysfunction, and Subgroup D is characterized by epithelial dysfunction without inflammation. Both Subgroups transition to Subgroup A which is anti-inflammatory with increased mRNA levels of cytokines important in epithelial repair and differentiation (IL-4, IL-10, and IL-13). Subgroup C is homeostatic with restored epithelial function (Fig. 4B).

Comparison of PARDS and Control Nasal Transcriptomes

We reanalyzed the PARDS specimens and 27 control specimens from sixteen control subjects. Three control subjects developed lung injury (a new oxygen requirement) and one developed PARDS over the course of the study. This last subject clustered with Subgroup B, and 1, 3, and 3 specimens of the lung injury control subjects clustered with A, B, and C respectively. Among control subjects who did not develop lung injury, 3, 4, 11, and 1 clustered with A, B, C, or D respectively (Supplemental Fig. 6). These data suggest that the nasal transcriptome reflects lung injury in non-PARDS subjects and that the homeostatic state of the nasal epithelium is Subgroup C.

Microbiome Differences between Control and Two PARDS Subgroups

Although we did not identify any differences in viral infection between Transcriptomic Subgroups (Table 4) the presence of respiratory viruses might influence the nasal transcriptome. We first visualized day 1 and all PARDS nasal specimens by infectious agent (Supplemental Fig. 7A&B). For this analysis, we used a shotgun metagenomic approach in which read sequences are aligned to viral and bacterial genomes. In day 1 Subgroup A, B, C, and D specimens, 67%, 42%, 24%, and 40% had a diagnosed viral infection respectively; however, in analyzing all time points, 47%, 36%, 27%, and 33% had a diagnosed viral infection—consistent with dataset analysis showing that infectious agent did not explain a significant portion of data set variation (Supplemental Fig. 3). For more comprehensive assessment of the microbiome, we performed shotgun metagenomics with comparison by Transcriptomic Subgroup and PARDS severity. Microbial diversity was increased in nasal brushings of PARDS subjects, not different between Transcriptomic Subgroups, and greater in moderate PARDS compared to severe or no PARDS (Supplemental Fig. 7C&D). There were no differences in the fraction of bacterial or viral reads between Transcriptomic Subgroups (Supplemental Fig. 7E&F). The dissimilarity of control vs. Transcriptomic Subgroups was significant (p < 0.001) but limited to seven bacterial an no viral species (Supplemental Tables 7&8), and there were no significant microbiome differences by PARDS Severity. Neither bacteria nor specific viruses influence PARDS Transcriptomic Subgroup.

Nasal Transcriptomic Subgroup Predicts Prolonged PARDS Better than Serum Biomarkers

If our inflammation and epithelial cell injury model were correct, then we would expect longer PARDS duration in Subgroups B and D. Limiting our analysis to the first available specimen (Fig. 5A), we used days meeting PARDS criteria as a primary endpoint because a sizable number of subjects remained intubated after PARDS resolution (Fig. 5B) making ventilator free days a less-reliable measure of lung injury resolution. We found that the number of days meeting PARDS criteria was greater in Nasal Transcriptomic Subgroups B&D compared to Subgroups A & C (Fig. 5C&D, median 8 vs 2 days, p = 0.02).

There is much interest in serum biomarker panels for ARDS and PARDS subclassification. We quantified serum levels of seventeen serum biomarkers obtained at the time of nasal brushing. None of the seventeen assayed biomarkers had a significant association with Nasal Transcriptomic Subgroup (Supplemental Fig. 8). To compare the ability of serum biomarkers and nasal transcriptomic subgroups to predict continued PARDS, the optimal predictive threshold for each at each time point was determined by receiver operator characteristic, and sensitivity, specificity, positive predictive value, and negative predictive value were determined for each biomarker and Nasal Transcriptomic Subgroup B or D (Fig. 5E, Supplemental Fig. 9). The sensitivity of Nasal Transcriptomic Subgroup B or D to predict continued PARDS at up to 7 days was as good or better than any of the assayed serum biomarkers, although there was some diminishment over time. The specificity of Subgroup B or D with regards to continued PARDS was poor. The positive predictive value of Transcriptomic Subgroup was poor for continued PARDS at day 3 (60%), but gradually improved to 80% by day 10 while the positive predictive value of all serum biomarkers diminished over time. In contrast, the negative predictive value of Transcriptomic Subgroup at 3 days was very good at 92% but diminished to 38% in predicting continued PARDS at 10 days while biomarker negative predictive value improved for longer-duration PARDS. These data indicate that serum biomarkers and nasal transcriptomics yield different information with low serum biomarkers being specific for the rapid resolution of PARDS but high serum biomarkers lacking sensitivity for continued PARDS up to one week later. In contrast, transcriptomics lacks specificity but has good sensitivity for continued PARDS up to seven days and is more predictive of prolonged PARDS than serum biomarkers.

Functional Epigenetic Modules in the Nasal Epithelium of PARDS Subjects

To identify genes and functions that were coordinately controlled at the epigenetic and transcriptional levels, we performed functional enrichment for genes with increased mRNA and reduced methylated DNA or reduced mRNA and increased methylated DNA by Nasal Transcriptomic and Methylomic Subgroups. Although Methyl Subgroup 1 was composed entirely of PARDS subjects and Transcriptomic Subgroup B had longer PARDS duration, there was coordinate hypomethylation and increased mRNA levels of inflammatory genes in the combined B2 subgroup suggesting that epithelial inflammation is not regulated at the epigenetic level. In contrast, combined subgroup C2 had hypermethylation and reduced mRNA levels of immune-associated genes suggesting that this may be important for a return to homeostasis (Fig. 6A). This is consistent with hypomethylation and increased gene expression of cilia-associated genes in combined subgroup C2 (Fig. 6B). The genes for each combined subgroup analysis are listed in Supplemental Table 9 and additional GSEA analyses in Supplemental Fig. 10.

Nasal transcriptomics and methylomics have been shown to reflect lung disease subtypes in asthma and lung cancer, but this study is the first to demonstrate similar capability in an acute condition like PARDS. We found that a subset of PARDS patients had hypomethylation of genes that might be important in impaired epithelial cell function, and that the nasal transcriptome changed in ways that would be expected during epithelial cell injury, repair, and regeneration. Subjects with either inflammation and epithelial cell dysfunction (Subgroup B) or loss of epithelial stem cell genes (Subgroup D) had longer PARDS duration.

The nasal transcriptome provides different information than is available from serum biomarkers. There was a subset of PARDS patients identified by serum biomarkers but not nasal transcriptomics. These patients had relatively short PARDS duration. Nasal transcriptomics was a better predictor of longer PARDS course than serum biomarkers consistent with the time needed for epithelial repair and regeneration. Our findings mirror the hyperinflammatory, compensatory hypoinflammatory, and resolution phases of sepsis. Further interrogation of the individual roles of inflammatory cell subtypes should be undertaken. If our findings are validated in follow-up studies, then perhaps a limited PCR panel could be developed to identify subjects at risk for prolonged PARDS for either clinical purposes or for study screening.

Our findings regarding DNA methylation risk loci that were associated with PARDS could explain the spectrum of lung injury severity among individuals experiencing a similar insult. In comparing the expected transcriptional changes with these differentially methylated regions, genes related to epithelial function were the only ones that may be epigenetically regulated. A prospective study of at-risk patients will be needed to test whether these regions represent true PARDS risk loci.

We found no association of different viral or bacterial species with different PARDS subgroups. This could be due to a relatively small number of specimens compared to the large number of pathogenic viruses and bacteria, but it could also be due to common injury-response pathways that any pathogen-specific signal was diluted by other samples at similar points in the progression through injury, repair, and regeneration. If validated prospectively, these genes might be targeted to prevent lung injury progression.

We note several limitations of this study. First, the overall number of subjects is small, but the longitudinal nature of the analysis somewhat mitigates this limitation. Second, our injury-repair-regeneration model is based solely on mRNA and not procedures such as flow cytometry. Third, although we performed correlative analysis with a small number of concurrent bronchial specimens, we did not have specimens from the alveolar compartment and can only report associations of nasal transcriptomic patterns with clinical measures of lung injury severity. Performing studies that directly compare gene expression of alveolar and conducing airway epithelial cells will be needed to quantify the degree of similarity between these two compartments. Fourth, most patients had longitudinal brushings in the same nares due to presence of a nasogastric tube. It is possible that some element of repair/regeneration signals in PARDS subjects was from the procedure itself. However, this was absent in control subjects who also had longitudinal brushings. Fifth, while control subjects were critically ill, there were substantial differences in terms of underlying genetic disorders or developmental delay and immunocompromised state. Sixth, the transcriptional processes that define our subgroups exist in a continuum. While specimens on the extremes are easily classified, several intermediate specimens could justifiably be classified differently. Lastly and perhaps most importantly, these findings need to be validated in a second cohort of PARDS patients and should also be tested in adult with ARDS.

Nasal transcriptomic profiling in PARDS likely reflects injury, repair, and regenerative processes of the distal lung and can identify patients likely to experience a prolonged PARDS course.

Ethics Approval and Consent to Participate

Research was performed under Cincinnati Children’s Hospital and Children’s Hospital of Philadelphia Institutional Review Board approvals 2017-1345 and 20-017205 respectively and with written informed consents of parents or guardians.

Consent for Publication

Not applicable

Availability of Data and Material

Datasets are available on Geodatasets as GSE192364 and GSE192926. Other data are available by contacting the corresponding author.

Competing Interests

The authors declare no conflicts of interest.

Funding

2020 SCCM Discovery Award, Society of Critical Care Medicine’s Research Discovery, the Critical Care Research Network (Varisco)

2019 Center for Pediatric Genomics Pilot Grant (Varisco)

R01HL141229 (Varisco)

Author Contributions

JGW and BMV conceptualized the project and wrote the manuscript.

JGW, RJ, RLJ, and PML each developed critical assays or developed protocols necessary for the project.

DH, NY, AP, MP, KMR, and HRW each provided critical insights or performed critical bioinformatic analyses.

All authors aided in the writing and revising of the final manuscript.

ACKNOWLEDGMENTS

We would like to thank current and former members of the CCHMC clinical research team, Toni Yunger, Erin Stoneman, Kelli Krallman, and Abby Gibson and the CHOP clinical research team, Stephen Famularo, and Jill Thompson for their contributions. We also thank the CCHMC DNA core and Paul Colbert with CCHMC Information Services for his computational assistance.

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Tables 1 to 4 are available in the Supplementary Files section.

No competing interests reported.

MethylomicandTranscriptomicPhenotypingofPediatricAcuteRespiratoryDistressSyndromeUsingNasalBrushingsSupplement20220315.docx
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TableS1.pdf
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Table1.pdf
Table 1: Subject Demographics
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Table 2: Methyl Subgroup Comparisons
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Table 3: PARDS Transcriptomic Subgroup Cell-Specific mRNAs
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Table 4: Initial Nasal Transcriptomic Subgroup Comparisons

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Submission checks completed at journal
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Multi-omic Characterization of Pediatric ARDS via Nasal Brushings

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