Immune biomarkers predicting bronchiolitis disease severity: A systematic review

Accepted Manuscript Review Immune Biomarkers Predicting Bronchiolitis Disease Severity: A Systematic Review David G. Hancock, Elena C. Cavallaro, Elizabeth Doecke, Molly Reynolds, Billie Charles-Britton, Dani-Louise Dixon, Kevin D. Forsyth PII: DOI: Reference:

S1526-0542(18)30129-5 https://doi.org/10.1016/j.prrv.2018.11.004 YPRRV 1298

To appear in:

Paediatric Respiratory Reviews

Please cite this article as: D.G. Hancock, E.C. Cavallaro, E. Doecke, M. Reynolds, B. Charles-Britton, D-L. Dixon, K.D. Forsyth, Immune Biomarkers Predicting Bronchiolitis Disease Severity: A Systematic Review, Paediatric Respiratory Reviews (2018), doi: https://doi.org/10.1016/j.prrv.2018.11.004

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1 IMMUNE

BIOMARKERS

PREDICTING

BRONCHIOLITIS

DISEASE

SEVERITY: A SYSTEMATIC REVIEW

David G Hancock1 [email protected] Elena C Cavallaro2 [email protected] Elizabeth Doecke1 [email protected] Molly Reynolds1 [email protected] Billie Charles-Britton1 [email protected] Dani-Louise Dixon2 [email protected] Kevin D Forsyth1 [email protected]

Affiliations: 1

Department of Paediatrics and Child Health, Flinders University, Bedford Park,

Australia 2

Intensive and Critical Care Unit, Flinders University and Flinders Medical Centre,

Bedford Park, Australia

Corresponding Author: David G Hancock Department of Paediatrics and Child Health, Flinders University, Bedford Park, Australia [email protected] +61 4 1469 5171

2 ABSTRACT Bronchiolitis is one of the leading causes of hospitalisation in infancy, with highly variable clinical presentations ranging from mild disease safely managed at home to severe disease requiring invasive respiratory support. Identifying immune biomarkers that can predict and stratify this variable disease severity has important implications for clinical prognostication/disposition. A systematic literation search of the databases Embase, PubMed, ScienceDirect, Web of Science, and Wiley Online Library was performed. English language studies that assessed the association between an immune biomarker and bronchiolitis disease severity among children aged less than 24 months were included. 252 distinct biomarkers were identified across 90 studies. A substantial degree of heterogeneity was observed in the bronchiolitis definitions, measures of disease severity, and study designs. 99 biomarkers showed some significant association with disease severity, but only 18 were significant in multiple studies. However, all of these candidate biomarkers had comparable studies that reported conflicting results. Conclusion: The heterogeneity among included studies and the lack of a consistently significant biomarker highlight the need for consensus on bronchiolitis definitions and severity measures, as well as further studies assessing their clinical utility both in isolation and in combination.

Abbreviations: None used

Keywords: Bronchiolitis; Biomarkers; Respiratory Syncytial Virus; Disease Severity; Systematic Review

3 INTRODUCTION Viral bronchiolitis is one of the leading causes of infant morbidity worldwide [1]. Approximately 20% of infants will develop acute bronchiolitis in their first year of life [2], although only 2-3% will require hospitalisation [3,4]. Morbidity can extend beyond the acute phase of infection, with various studies identifying a link between bronchiolitis and the development of recurrent wheezing and asthma during early childhood [5,6]. Despite bronchiolitis being one of the most common causes of hospitalisation in infancy, with significant acute and chronic disease morbidity, there is a lack of targeted interventions/therapies, and understanding into the immune mechanisms underlying disease severity and clinical outcomes remains limited.

One of the primary challenges in bronchiolitis management are the highly heterogeneous disease presentations, ranging from mild disease not requiring intervention or hospitalization, to severe disease requiring intensive care admission and mechanical ventilation. While there are a number of predisposing risk factors that have been associated with the development of more severe disease, including specific viruses, prematurity, lower age at time of infection, pre-existing cardiorespiratory disorders, and immunodeficiency [7-9], these risk factors fail to cleanly predict or stratify disease severity. Thus, determination of prospective severity is generally limited to highly subjective and variable clinical interpretation [10]. The identification of clinical biomarkers to accurately predict disease severity in bronchiolitis would enable enhanced decision-making thus ensuring appropriate disposition, therapeutic intervention, and follow-up. Additionally, biomarkers have the potential to serve as novel therapeutic targets for immunomodulatory treatments.

4 Numerous studies have assessed immunological biomarker concentrations against disease severity in blood and airway samples in infants with bronchiolitis. However, the integration and interpretation of these studies has been limited by significant differences in study design, bronchiolitis definition, and disease severity measures. As such, a systematic approach was used to review studies characterizing immune biomarkers implicated in the prediction of disease severity, and explore the inconsistencies present in the definitions and bronchiolitis severity measures across these studies.

MATERIALS AND METHODS Search Strategy The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to conduct the systematic review. A systematic search of the databases Embase (from 1974), PubMed (from 1946), ScienceDirect (from ~1856), Web of Science (from 1990), and Wiley Online Library (from ~1989) through to January 2018 was completed. The search terms used were adapted for each database: (1) bronchiolitis OR "Respiratory Syncytial Virus Infections"[Mesh] OR ((viral OR virus) AND (lower respiratory tract infection OR ((wheeze OR wheezing) AND (hospital OR hospitalisation)))) AND (2)

"Bronchiolitis/complications"[MeSH

Index"[Mesh] OR severity OR severe AND

Terms]

OR

"Severity

of

Illness

5 (3) child OR children OR childhood OR infant OR infantile OR infancy OR pediatric OR neonate OR newborn AND (4)

"Intercellular

Signaling

Peptides

and

Proteins"[Mesh]

OR

"Immunoproteins"[Mesh] OR biomarker OR immune marker OR inflammatory marker OR atopy OR atopic OR gene OR polymorphism OR cytokine OR chemokine OR interleukin OR interferon OR immunoglobulin OR IgE OR IgA OR IgG OR surfactant OR leukotriene OR prostaglandin OR peptide OR eosinophil OR neutrophil OR monocyte OR macrophage OR dendritic cell OR mast cell OR basophil OR lymphocyte OR T cell OR B cell OR white blood cell OR NK cell OR epithelial OR granulocyte OR T helper OR leukocyte NOT (5)"bronchiolitis obliterans" OR "obliterative bronchiolitis"

The reference lists of identified papers were also reviewed for additional relevant studies. The databases were restricted to English-language studies.

Eligibility Criteria This review included studies that assessed the utility of immune biomarkers for differentiating mild bronchiolitis from severe bronchiolitis, rather than biomarkers for differentiating bronchiolitis from healthy controls. Studies were included that met the following criteria: (1) Contained an assessment of viral bronchiolitis as defined by the individual study in children less than 24 months of age. (2) Contained a measure of disease severity as defined by the individual study.

6 (3) Contained an immune biomarker measured before or during the index admission (4) Contained an assessment of the association between the immune biomarker and different bronchiolitis severity groups (i.e. mild versus severe) amongst infants with bronchiolitis. (5) English language. (6) Full publications.

Studies that classified their population as bronchiolitis, but that included infants older than 24 months, were excluded unless they performed a subgroup analysis in infants less than 24 months. Studies with populations that would meet some bronchiolitis definitions, but which were not specifically defined as bronchiolitis in that individual study, and studies that included mixed bronchiolitis and pneumonia/upper respiratory infections were also excluded. Studies that only compared bronchiolitis severity groups to healthy controls (i.e. severe bronchiolitis versus control) were excluded.

Study Selection and Data Extraction Two authors independently screened all articles for eligibility; initially based on titles/abstracts, followed by a full text review. Disagreements were resolved by consensus among all screening authors. Data was extracted manually by one author (Supplementary Tables 1, 2) and reviewed by a second independent author.

All associations between immune biomarkers and disease severity were extracted. When

multiple

different

statistical

tests

were

performed

on

the

same

biomarker/severity measure comparison, the one adjusted for the most variables (i.e. the multivariate versus univariate analysis) was extracted. Given the potential many-

7 to-many associations between severity measures and biomarkers measured in any given study (e.g. both serum and bronchoalveolar lavage interleukin 6 with both oxygen requirement and severity score), the results from individual studies were merged in the summary of results Tables 1-4 so that each paper could only contribute a single value for each of the 3 possible associations (i.e. a significant positive association, a significant negative association, and/or no significant association). This was done for simplicity of presentation and so that a single study with multiple measured associations did not overly skew the summary of results tables. The full list of measured associations is presented in Supplementary Table 1.

Risk of Bias in Individual Studies The risk of bias in each study was determined using a modified Newcastle-Ottawa scale [11], which assesses participant selection ("Was the bronchiolitis definition adequate?", "Is the cohort representative of the average bronchiolitis population?", "Are the severity groups from the same community of patients?") , comparability ("Does the study control for age?", "Does the study control for 1 other factor"), and outcome/exposure assessment ("Is the Biomaker assessment blinded to case status?", "Is the biomaker assessment the same for the severity groups?", "Is the non-response rate described?") to a maximum of 8 stars/points.

RESULTS Study Characteristics Supplementary Figure 1 details the study selection process. A total of 90 studies were included in the final systematic review [12-101]. The included studies are described in Supplementary Table 2.

8

The bronchiolitis age range was defined as less than 3 months in 1 study, 6 months in 10 studies, 9 months in 3 studies, 12 months in 36 studies, 15 months in 4 studies, 18 months in 6 studies, and 24 months in 30 studies. 50 studies restricted their analysis to RSV bronchiolitis, while the remaining 40 studies investigated mixed-virus bronchiolitis. 39 studies restricted their bronchiolitis definition to those infants presenting with their first wheezing presentation. 24 studies excluded ex-preterm infants, while 48 studies excluded infants with other cardio-respiratory and immune comorbidities. Only 22 studies controlled for age in multivariate models, while 22 studies controlled for a variable other than age in multivariate models.

The sample sizes in the included studies ranged from 12 to 1356 infants, with 32 studies having a sample size less than 50, 26 studies less than 100, and 32 studies greater than 100. The Newcastle-Ottawa scale quality scores ranged from 1 to 7 out of a maximum of 8.

The severity outcome measures varied significantly between studies. These included oxygen saturation, respiratory rate, respiratory distress, need for and/or duration of oxygen therapy, need for and/or duration of intravenous fluids/feeding support, need for and/or duration of mechanical ventilation, need for and/or duration of hospitalisation, hospital disposition (i.e. normal ward versus paediatric/neonatal intensive care unit), and the duration of wheezing, as well as various clinical severity scores/composites (Supplementary Table 1). 26 unique clinical scores/composites were used among the included studies, the majority of which were only used by a single study/author group (Supplementary Table 1).

9

Cytokines and Chemokines 43 distinct cytokines, chemokines, and cytokine/chemokine receptors were assessed for their association with disease severity among 45 studies that met the inclusion criteria.

23 biomarkers showed a significant association with disease severity in at least one study, 10 of which showed a consistent association in at least 2 studies (Table 1). Chemokine (C-C motif) ligand-2, chemokine (C-C motif) ligand-3, interleukin-4, interleukin-6, interleukin-8, interleukin-15, and the interleukin-4/interferon-gamma ratio showed a significant positive association with disease severity in multiple studies. Chemokine (C-C motif) ligand-4, interferon-gamma, and interleukin-10 showed a significant negative association with disease severity in multiple studies. However, no biomarker showed a consistent association in at least 2 studies without at least one other study reporting either an opposite association or no significant association. Furthermore, the reason for these contrasting results could not be easily rationalised by differences in study design (i.e. population age range, restriction to RSV bronchiolitis, exclusion of ex-preterm infants/other comorbidities), differences in biomarker type (i.e. RNA versus protein), or differences in sample location (i.e. nasopharyngeal aspirate versus bronchoalveolar lavage versus serum/plasma/whole blood).

No significant association with disease severity was observed for chemokine (C-C motif) ligand-17, chemokine (C-X-C motif) ligand 9, interferon-alpha, interferonbeta, interferon-lambda2/3, interleukin-1 receptor alpha, interleukin-2, soluble

10 interleukin-2 receptor alpha, interleukin-3, interleukin-9, interleukin-11, interleukin12, interleukin-12p40, interleukin-12p70, interleukin-13, interleukin-25, interleukin33, tumour necrosis factor beta/Lymphotoxin, and soluble tumour necrosis factor receptor 2 in any study (Supplementary Table 1).

Immune Cell Subsets 41 immune cell subsets and 44 immune cell subset-expressed biomarkers were assessed for their association with disease severity among 23 studies that met the inclusion criteria.

24 immune cell subsets or their expressed biomarkers showed a significant association with disease severity in at least 1 study (Table 2). Of these, only CD4 T cell, CD8 T cell, total lymphocyte, and natural killer cell numbers showed a significant negative association in at least 2 studies (Table 2). However, none of these cell subsets showed a consistent association without at least one other study reporting either an association in the opposite direction or no significant association. Once again, the reason for these contrasting results could not be easily rationalised by differences in study design, differences in biomarker type, or differences in sample location.

No significant association with disease severity was observed for subpopulations of B cells (total), CD4 T cells (central memory, effector memory, naive, and terminally differentiated), CD8 T cells (central memory, effector memory, naive, and terminally differentiated), dendritic cells (double negative (CD11c-CD123-) and myeloid (CD11c+CD123-)), V delta 1 and V delta 2 gamma-delta T cells (total, central

11 memory, effector memory, naive, and terminally differentiated), granulocytes (total), leukocytes, invariant natural killer T cells (total), macrophages (total), monocytes (total, intermediate, and non-classical), and neutrophils (total and segmented) in any study.

No significant association with disease severity was also observed in the expression of the following biomarkers among CD4 T cells (expression of BCL-2, BCL-XL, interferon-gamma, interleukin-4, and the interleukin-4/interferon-gamma expression ratio), CD8 T cells (expression of BCL-2, BCL-XL, interferon-gamma, interleukin-4, and perforin, as well as the interleukin-4/ interferon-gamma expression ratio), double negative and myeloid dendritic cells (expression of interleukin-15), monocytes (expression of CD14, HLA-ABC, toll-like receptor 4), and natural killer cells (expression of BCL2 and BCLXL protein, and AKT1, AKT2, BAX, CXCR4, FOXO3, IRAK1, MAPK1, MAPK8, NFATC2, STAT1, TGFB1, and TGFB2 mRNA) in any study (Supplementary Table 1).

Gene Variants 50 gene variants were assessed for their association with disease severity among 17 studies that met the inclusion criteria.

15 gene variants were reported to have an association with disease severity, although only interleukin-4 rs2243250 (-590CT) and toll-like receptor 4 rs4986790 (+896AG; Asp299Gly) were significantly associated with disease severity in multiple studies (Table 3). However, both gene variants were reported as non-significant or showed an opposite association in other studies.

12

No significant association with disease severity was observed for the following variants in genes: chemokine (C-C motif) ligand-5 (rs2280788, rs2107538, rs2280789, and rs1065341), CD14 (-159C/T and -550C/T), interferon alpha 5 (rs10757212), interleukin-1 receptor-like 1 (rs11685480 and rs1420101), interleukin4 receptor alpha (I50V and Q551R), interleukin-9 (-345A/G), interleukin-10 (592A/C), interleukin-13 (-445A/G), interleukin-28B (rs8099917 and rs12979860), JUN (rs11688), mannose-binding lectin 2 (multiple variants), ORMDL3 (rs7216389, rs11650680, and rs12603332), toll-like receptor 1 (rs5743618), toll-like receptor 2 (rs1898830, rs5743708, rs7656411), toll-like receptor 3 (rs3775291), toll-like receptor 4 (rs1927911), toll-like receptor 6 (rs5743810), toll-like receptor 7 (rs179008), tolllike receptor 8 (rs2407992), toll-like receptor 9 (rs187084), toll-like receptor 10 (rs4129009), tumour necrosis factor alpha

(-308A/G), vitamin D-receptor

(rs10735810) in any study (Supplementary Table 1).

Other Immune Biomarkers

74 other immune biomarkers were assessed for their association with disease severity among 42 studies that met the inclusion criteria.

37 biomarkers showed a significant association with disease severity, although the majority of these were only assessed in a single study (Table 4). Lactate dehydrogenase was significantly negatively associated with disease severity in 2 studies, but 2 additional studies reported a non-significant association and a significant positive association, respectively. LL37 (cathelicidin) was significantly

13 negatively associated with disease severity in 2 studies, both published by the same group.

No significant association with disease severity was observed for: albumin, basic fibroblast growth factor, caspase, CD14, cyclooxygenase 1 activity, cyclooxygenase 2 activity, creola bodies,

exhaled nitric oxide, glucocorticoid receptor alpha,

granulocyte macrophage colony stimulating factor; histamine, human neutrophil elastase, leukotriene C4, leukotriene D4 and pooled leukotrienes C4/D4/E4, lymphocyte antigen 96 (MD-2), melanoma differentiation-associated protein 5, matrix metalloproteinase-1, matrix metalloproteinase-2, matrix metalloproteinase-9, NFκB, platelet-derived growth factor-BB, prostaglandin metabolite 13,14-dihydro-15-ketoprostaglandin F2 alpha, prostaglandin E2, procalcitonin, retinoic acid-inducible gene I, soluble intercellular adhesion molecule 1, superoxide dismutase 2, tissue inhibitor of metalloproteinases 1, toll-like receptor 2, toll-like receptor 3, toll-like receptor 4, toll-like receptor 7, toll-like receptor 8, toll-like receptor 9, thymic stromal lymphopoietin, and vascular endothelial growth factor in any study (Supplementary Table 1).

14 DISCUSSION Viral bronchiolitis is characterised by a highly variable disease severity spectrum. The identification of immune biomarkers that can predict and stratify disease severity has important consequences for clinical management and the development of novel targeted interventions. With the advances in rapid, point-of-care testing technologies for RNA, DNA, and protein, biomarker-based decision rules are becoming more and more feasible in clinical practice.

This systematic review failed to identify any biomarker that was consistently associated with disease severity across multiple studies without other studies showing contrasting results. However, the results of this review were undoubtedly influenced by the significant heterogeneity in study design among included studies. It may also suggest that no single biomarker is likely to be sufficient, and that panels of biomarkers may be needed to provide sufficient discriminatory power. There were a number of identified biomarkers that were significantly associated with disease severity in multiple studies that represent candidates for further investigation, including chemokine (C-C motif) ligand-2, chemokine (C-C motif) ligand-3, chemokine (C-C motif) ligand-4, CD4 T cell counts, CD8 T cell counts, interferongamma, interleukin-4, interleukin-6, interleukin-8, interleukin-15, the interleukin4/interferon-gamma ratio, lactate dehydrogenase, LL37 (cathelicidin), natural killer cell counts, the interleukin-4 rs2243250 gene variant, the toll-like receptor 4 rs4986790 gene variant, and total lymphocyte counts (Tables 1-4).

Perhaps the most important observation in this systematic review was the clear heterogeneity among definitions [102] and selection criteria among the included

15 studies. The included studies varied substantially in; bronchiolitis age range, included viruses, restriction to first wheezing presentation, exclusion/inclusion of ex-preterm infants or infants with other cardio-respiratory/immune comorbidities, severity outcome measures, sample size, and quality scores (Supplementary Tables 1, 2). Importantly, many of the included studies also showed that these same factors were independently associated with disease severity and/or biomarker expression. For example, restricting analyses to children with bronchiolitis aged less than versus greater than 6 months was shown to significantly influence the association between interleukin 4, interleukin 10, and interferon gamma levels and disease severity [74]. Another, source of heterogeneity that was shown to critically influence the results was the biomarker material, with one study showing a significant positive association with interleukin 15 protein in serum, but a significant negative association with interleukin 15 mRNA from isolated peripheral blood mononuclear cells [63]. These and similar results suggest that the heterogeneity between studies is not merely cosmetic, but is critically influencing the reported associations. This becomes even more relevant when it is considered that the majority of included studies performed univariate analyses that did not account for these confounders (Supplementary Table 2).

One interesting observation was the significant association between classical T helper cell 2 and T helper cell 1 cytokines [103]; increased interleukin-4 and decreased interferon-gamma (and the associated increased interleukin-4/interferon-gamma ratio), with disease severity in multiple studies (Table 1). Indeed, it was initially hypothesized that it was the unique ability of respiratory syncytial virus to polarise the immune response towards a T helper cell 2 response (prototypically driving immunity against extracellular parasites/helminths) [103], away from a T helper cell 1 response

16 (prototypically driving immunity against intracellular pathogens) [103], which underlies its association with more severe disease and the subsequent development of recurrent wheezing/asthma [3]. Consistent with this, the association was identified mainly in studies that assessed respiratory syncytial virus-specific bronchiolitis, although there were still studies of this type that showed a non-significant or opposite association (Supplementary Table 1). However, this association was not identified for the other so-called T helper cell 2 cytokines (interleukin-5, interleukin-9, and interleukin-13) and T helper cell 1 cytokines (interleukin-2, tumour necrosis factoralpha, tumour necrosis factor-beta/Lymphotoxin) in multiple studies (Supplementary Table 1). Furthermore, while increased interleukin-4 and decreased interferon-gamma may represent useful candidate biomarkers in respiratory syncytial virus disease, the concept of T helper cell polarization likely represents an oversimplification of true in vivo immune phenotypes [103,104].

Increased interleukin-6 and interleukin-8 levels and decreased interleukin-10 levels were also significantly associated with disease severity in multiple studies (Table 1). Interleukin-6 and interleukin-8 are the prototypical "pro-inflammatory" cytokines, while interleukin-10 is the prototypical "anti-inflammatory" cytokine, and these cytokines are all generally differentially regulated in the majority of pathogen responses. Given the diverse roles for each of these cytokines in the inflammatory response, together the changing patterns of these cytokines likely reflect the increased magnitude of the pro-inflammatory response in more severe bronchiolitis, rather than a unique polarisation of the immune response. Similarly, we also observed an association between increased chemokine (C-C motif) ligand-2 and chemokine (C-C motif) ligand-3 levels and decreased chemokine (C-C motif) ligand-4 levels with

17 disease severity (Table 1). These chemokines play a number of overlapping roles in the recruitment of various immune cell populations and may again be more reflective of the magnitude of immune response rather than a distinct immune phenotype.

Lymphopenia (and associated decreased CD4 T cell counts, CD8 T cell counts, and natural killer cell counts) was also identified as a severity biomarker across multiple studies (Table 2). Lymphopenia is a well-established marker of disease severity in diseases such as sepsis, where it had been previously established as one of the systemic inflammatory response syndrome (SIRS) criteria [105]. However, for all the identified measures of lymphopenia, there were a greater number of studies showing no significant association versus a significant association, and thus the utility of this marker is unclear. Similarly, lactate dehydrogenase (Table 4) has also been suggested as a biomarker of disease severity in other contexts [106]. However, most significant results for this biomarker in our review were from a single research group, who also identified opposite significant associations in their other studies [60,68,70], which again makes the utility of this biomarker unclear.

Finally, a significant association with disease severity was also identified for the gene variants, interleukin-4 rs2243250 and toll-like receptor 4 rs4986790 (Table 3). While the mechanisms underlying these associations appear logical, given the association between severity and interleukin-4 protein (Table 1) and the known association between toll-like receptor 4 and respiratory syncytial virus infection [107], the clinical utility of rare gene variants for useful decision rules/prognostication tools is more difficult to establish.

18 The primary limitation of this systematic review is due to the heterogeneity of the underlying studies, making meta-analysis impossible and synthesis extremely challenging. However, this heterogeneity also reinforces the need for systematic review to limit the influence of selection bias on the presented results. The presented data focused on biomarkers identified in multiple studies to limit the effect of study design on the reported results. Finally, to further address this limitation, the complete data from each paper is presented in Supplementary Table 1 as a resource for other authors to analyse trends in the data.

CONCLUSIONS In summary, significant heterogeneity was identified between studies making synthesis extremely challenging. While we identified a number of candidate biomarkers that were significantly associated with disease severity in multiple studies that warrant further investigation, no single biomarker showed a consistent association across all studies investigating its use. Without the development of consensus bronchiolitis definitions and universal disease severity measures [102], future research into bronchiolitis and synthesis of the existing literature will continue to be restricted by this heterogeneity.

19 Conflict of interest: None

Funding: None

Educational Aims: 

To review immune biomarkers to differentiate mild bronchiolitis from severe bronchiolitis



To highlight the variability in bronchiolitis definitions and disease severity scores

Directions for Future Research:  

Validate the candidate biomarkers in studies with consensus bronchiolitis definitions Investigate the utility of combination biomarker panels, rather than single biomarkers in isolation

20 REFERENCES 1. Nair H, Simoes EA, Rudan I, Gessner BD, Azziz-Baumgartner E, Zhang JS, Feikin DR, Mackenzie GA, Moisi JC, Roca A, Baggett HC, Zaman SM, Singleton RJ, Lucero MG, Chandran A, Gentile A, Cohen C, Krishnan A, Bhutta ZA, Arguedas A, Clara AW, Andrade AL, Ope M, Ruvinsky RO, Hortal M, McCracken JP, Madhi SA, Bruce N, Qazi SA, Morris SS, El Arifeen S, Weber MW, Scott JA, Brooks WA, Breiman RF, Campbell H, Severe Acute Lower Respiratory Infections Working G (2013) Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis. Lancet 381 (9875):1380-1390. doi:10.1016/S0140-6736(12)61901-1 2. Hall CB, Weinberg GA, Iwane MK, Blumkin AK, Edwards KM, Staat MA, Auinger P, Griffin MR, Poehling KA, Erdman D, Grijalva CG, Zhu Y, Szilagyi P (2009) The burden of respiratory syncytial virus infection in young children. The New England journal of medicine 360 (6):588-598. doi:10.1056/NEJMoa0804877 3. Meissner HC (2016) Viral Bronchiolitis in Children. The New England journal of medicine 374 (1):62-72. doi:10.1056/NEJMra1413456 4. Shay DK, Holman RC, Newman RD, Liu LL, Stout JW, Anderson LJ (1999) Bronchiolitis-associated hospitalizations among US children, 1980-1996. Jama 282 (15):1440-1446 5. Bacharier LB, Cohen R, Schweiger T, Yin-Declue H, Christie C, Zheng J, Schechtman KB, Strunk RC, Castro M (2012) Determinants of asthma after severe respiratory syncytial virus bronchiolitis. J Allergy Clin Immunol 130 (1):91-100 e103. doi:10.1016/j.jaci.2012.02.010 6. Sigurs N, Gustafsson PM, Bjarnason R, Lundberg F, Schmidt S, Sigurbergsson F, Kjellman B (2005) Severe respiratory syncytial virus bronchiolitis in infancy and asthma and allergy at age 13. American journal of respiratory and critical care medicine 171 (2):137-141. doi:10.1164/rccm.200406-730OC 7. Murray J, Bottle A, Sharland M, Modi N, Aylin P, Majeed A, Saxena S, Medicines for Neonates Investigator G (2014) Risk factors for hospital admission with RSV bronchiolitis in England: a population-based birth cohort study. PLoS One 9 (2):e89186. doi:10.1371/journal.pone.0089186 8. Marguet C, Lubrano M, Gueudin M, Le Roux P, Deschildre A, Forget C, Couderc L, Siret D, Donnou MD, Bubenheim M, Vabret A, Freymuth F (2009) In very young infants severity of acute bronchiolitis depends on carried viruses. PLoS One 4 (2):e4596. doi:10.1371/journal.pone.0004596 9. Purcell K, Fergie J (2004) Driscoll Children's Hospital respiratory syncytial virus database: risk factors, treatment and hospital course in 3308 infants and young children, 1991 to 2002. The Pediatric infectious disease journal 23 (5):418-423 10. Fernandes RM, Andrade MG, Constant C, Malveiro D, Magalhaes M, Abreu D, Azevedo I, Sousa E, Salgado R, Bandeira T (2016) Acute viral bronchiolitis: Physician perspectives on definition and clinically important outcomes. Pediatric pulmonology 51 (7):724-732. doi:10.1002/ppul.23346 11. Wells G, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P (2014) The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Non Randomised Studies in Meta-Analyses. http://www.ohri.ca/programs/clinicalepidemiology/oxford.asp. 12. Ahout IM, Jans J, Haroutiounian L, Simonetti ER, van der Gaast-de Jongh C, Diavatopoulos DA, de Jonge MI, de Groot R, Ferwerda G (2016) Reduced Expression of HLA-DR on Monocytes During Severe Respiratory Syncytial Virus Infections. The

21 Pediatric infectious disease journal 35 (3):e89-96. doi:10.1097/inf.0000000000001007 13. Alonso Fernandez J, Roine I, Vasquez A, Caneo M (2005) Soluble interleukin-2 receptor (sCD25) and interleukin-10 plasma concentrations are associated with severity of primary respiratory syncytial virus (RSV) infection. Eur Cytokine Netw 16 (1):81-90 14. Alvarez AE, Marson FAL, Bertuzzo CS, Bastos JCS, Baracat ECE, Brandao MB, Tresoldi AT, das Neves Romaneli MT, Almeida CCB, de Oliveira T, Schlodtmann PG, Correa E, de Miranda MLF, Dos Reis MC, De Pieri JV, Arns CW, Ribeiro JD (2018) Association between single nucleotide polymorphisms in TLR4, TLR2, TLR9, VDR, NOS2 and CCL5 genes with acute viral bronchiolitis. Gene 645:7-17. doi:10.1016/j.gene.2017.12.022 15. Amanatidou V, Sourvinos G, Apostolakis S, Neonaki P, Tsilimigaki A, Krambovitis E, Spandidos DA (2008) RANTES promoter gene polymorphisms and susceptibility to severe respiratory syncytial virus-induced bronchiolitis. The Pediatric infectious disease journal 27 (1):38-42. doi:10.1097/INF.0b013e31814d4e42 16. Amat F, Henquell C, Verdan M, Roszyk L, Mulliez A, Labbe A (2014) Predicting the severity of acute bronchiolitis in infants: should we use a clinical score or a biomarker? Journal of medical virology 86 (11):1944-1952. doi:10.1002/jmv.23850 17. Amirav I, Luder AS, Kruger N, Borovitch Y, Babai I, Miron D, Zuker M, Tal G, Mandelberg A (2008) A double-blind, placebo-controlled, randomized trial of montelukast for acute bronchiolitis. Pediatrics 122 (6):e1249-1255. doi:10.1542/peds.2008-1744 18. Assefa D, Amin N, Dozor AJ, Parton LA (2011) Attenuated interleukin8/leukocyte immunoresponse in preterm infants compared with term infants hospitalized with respiratory syncytial virus bronchiolitis: a pilot study. Human immunology 72 (9):708-711. doi:10.1016/j.humimm.2011.05.016 19. Bennett BL, Garofalo RP, Cron SG, Hosakote YM, Atmar RL, Macias CG, Piedra PA (2007) Immunopathogenesis of respiratory syncytial virus bronchiolitis. The Journal of infectious diseases 195 (10):1532-1540. doi:10.1086/515575 20. Bertrand P, Lay MK, Piedimonte G, Brockmann PE, Palavecino CE, Hernandez J, Leon MA, Kalergis AM, Bueno SM (2015) Elevated IL-3 and IL-12p40 levels in the lower airway of infants with RSV-induced bronchiolitis correlate with recurrent wheezing. Cytokine 76 (2):417-423. doi:10.1016/j.cyto.2015.07.017 21. Bont L, Heijnen CJ, Kavelaars A, van Aalderen WM, Brus F, Draaisma JM, Pekelharing-Berghuis M, van Diemen-Steenvoorde RA, Kimpen JL (2001) Local interferon-gamma levels during respiratory syncytial virus lower respiratory tract infection are associated with disease severity. The Journal of infectious diseases 184 (3):355-358. doi:10.1086/322035 22. Bont L, Heijnen CJ, Kavelaars A, van Aalderen WM, Brus F, Draaisma JT, Geelen SM, van Vught HJ, Kimpen JL (1999) Peripheral blood cytokine responses and disease severity in respiratory syncytial virus bronchiolitis. The European respiratory journal 14 (1):144-149 23. Brand HK, Ahout IM, de Ridder D, van Diepen A, Li Y, Zaalberg M, Andeweg A, Roeleveld N, de Groot R, Warris A, Hermans PW, Ferwerda G, Staal FJ (2015) Olfactomedin 4 Serves as a Marker for Disease Severity in Pediatric Respiratory Syncytial Virus (RSV) Infection. PLoS One 10 (7):e0131927. doi:10.1371/journal.pone.0131927 24. Brand HK, Ferwerda G, Preijers F, de Groot R, Neeleman C, Staal FJ, Warris A, Hermans PW (2013) CD4+ T-cell counts and interleukin-8 and CCL-5 plasma

22 concentrations discriminate disease severity in children with RSV infection. Pediatric research 73 (2):187-193. doi:10.1038/pr.2012.163 25. Caballero MT, Serra ME, Acosta PL, Marzec J, Gibbons L, Salim M, Rodriguez A, Reynaldi A, Garcia A, Bado D, Buchholz UJ, Hijano DR, Coviello S, Newcomb D, Bellabarba M, Ferolla FM, Libster R, Berenstein A, Siniawaski S, Blumetti V, Echavarria M, Pinto L, Lawrence A, Ossorio MF, Grosman A, Mateu CG, Bayle C, Dericco A, Pellegrini M, Igarza I, Repetto HA, Grimaldi LA, Gudapati P, Polack NR, Althabe F, Shi M, Ferrero F, Bergel E, Stein RT, Peebles RS, Boothby M, Kleeberger SR, Polack FP (2015) TLR4 genotype and environmental LPS mediate RSV bronchiolitis through Th2 polarization. The Journal of clinical investigation 125 (2):571-582. doi:10.1172/jci75183 26. Cepika AM, Gagro A, Bace A, Tjesic-Drinkovic D, Kelecic J, BaricicVoskresensky T, Matic M, Drazenovic V, Marinic I, Mlinaric-Galinovic G, TjesicDrinkovic D, Vrtar Z, Rabatic S (2008) Expression of chemokine receptor CX3CR1 in infants with respiratory syncytial virus bronchiolitis. Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology 19 (2):148-156. doi:10.1111/j.1399-3038.2007.00611.x 27. Chung HL, Kim WT, Kim JK, Choi EJ, Lee JH, Lee GH, Kim SG (2005) Relationship between atopic status and nasal interleukin 10 and 11 levels in infants with respiratory syncytial virus bronchiolitis. Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology 94 (2):267-272. doi:10.1016/s1081-1206(10)61307-5 28. Da Dalt L, Callegaro S, Carraro S, Andreola B, Corradi M, Baraldi E (2007) Nasal lavage leukotrienes in infants with RSV bronchiolitis. Pediatric Allergy and Immunology 18 (2):100-104. doi:10.1111/j.1399-3038.2006.00500.x 29. De Weerd W, Twilhaar WN, Kimpen JLL (1998) T cell subset analysis in peripheral blood of children with RSV bronchiolitis. Scandinavian Journal of Infectious Diseases 30 (1):77-80. doi:10.1080/003655498750002349 30. Diaz PV, Pinto RA, Mamani R, Uasapud PA, Bono MR, Gaggero AA, Guerrero J, Goecke A (2012) Increased expression of the glucocorticoid receptor beta in infants with RSV bronchiolitis. Pediatrics 130 (4):e804-811. doi:10.1542/peds.2012-0160 31. Diaz PV, Valdivia G, Gaggero AA, Bono MR, Zepeda G, Rivas M, Uasapud P, Pinto RA, Boza ML, Guerrero J (2015) Pro-Inflammatory Cytokines in Nasopharyngeal Aspirate From Hospitalized Children With Respiratory Syncytial Virus Infection With or Without Rhinovirus Bronchiolitis, and Use of the Cytokines as Predictors of Illness Severity. Medicine 94 (39):e1512. doi:10.1097/MD.0000000000001512 32. Elliott MB, Welliver RC, Sr., Laughlin TS, Pryharski KS, LaPierre NA, Chen T, Souza V, Terio NB, Hancock GE (2007) Matrix metalloproteinase-9 and tissue inhibitor of matrix metalloproteinase-1 in the respiratory tracts of human infants following paramyxovirus infection. Journal of medical virology 79 (4):447-456. doi:10.1002/jmv.20830 33. Emboriadou M, Hatzistilianou M, Magnisali C, Sakelaropoulou A, Exintari M, Conti P, Aivazis V (2007) Human neutrophil elastase in RSV bronchiolitis. Annals of clinical and laboratory science 37 (1):79-84 34. Faber TE, Groen H, Welfing M, Jansen KJG, Bont LJ (2012) Specific increase in local IL-17 production during recovery from primary RSV bronchiolitis. Journal of Medical Virology 84 (7):1084-1088. doi:10.1002/jmv.23291 35. Faber TE, Schuurhof A, Vonk A, Koppelman GH, Hennus MP, Kimpen JL, Janssen R, Bont LJ (2012) IL1RL1 gene variants and nasopharyngeal IL1RL-a levels

23 are associated with severe RSV bronchiolitis: a multicenter cohort study. PLoS One 7 (5):e34364. doi:10.1371/journal.pone.0034364 36. Flores P, Guimaraes J, Videira Amaral JM (2011) Th1 and th2 cytokine expression in nasopharyngeal secretions during acute bronchiolitis in children younger than two years old. Allergologia et immunopathologia 39 (1):3-9. doi:10.1016/j.aller.2010.03.008 37. Gadish T, Soferman R, Merimovitch T, Fireman E, Sivan Y (2010) Exhaled Nitric Oxide in Acute Respiratory Syncytial Virus Bronchiolitis. Archives of Pediatrics & Adolescent Medicine 164 (8):727-731 38. Gagro A, Tominac M, KrŠUloviĆ-HreŠIĆ V, BaĆE A, MatiĆ M, DraŽEnoviĆ V, MlinariĆ-GalinoviĆ G, Kosor E, Gotovac K, BolanČA I, Batinica S, RabatiĆ S (2004) Increased Toll-like receptor 4 expression in infants with respiratory syncytial virus bronchiolitis. Clinical & Experimental Immunology 135 (2):267-272. doi:10.1111/j.1365-2249.2004.02364.x 39. Garcia-Garcia ML, Calvo C, Moreira A, Canas JA, Pozo F, Sastre B, Quevedo S, Casas I, Del Pozo V (2017) Thymic stromal lymphopoietin, IL-33, and periostin in hospitalized infants with viral bronchiolitis. Medicine 96 (18):e6787. doi:10.1097/md.0000000000006787 40. Garcia-Salido A, Onoro G, Melen GJ, Gomez-Pina V, Serrano-Gonzalez A, Ramirez-Orellana M, Casado-Flores J (2015) Serum sRAGE as a potential biomarker for pediatric bronchiolitis: a pilot study. Lung 193 (1):19-23. doi:10.1007/s00408014-9663-9 41. Garofalo R, Dorris A, Ahlstedt S, Welliver RC (1994) Peripheral blood eosinophil counts and eosinophil cationic protein content of respiratory secretions in bronchiolitis: relationship to severity of disease. Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology 5 (2):111-117 42. Garofalo R, Welliver RC, Ogra PL, Welliver DR (1991) Concentrations of LTB4, LTC4, LTD4 and LTE4 in bronchiolitis due to respiratory syncytial virus. Pediatric Allergy and Immunology 2 (1):30-37. doi:10.1111/j.1399-3038.1991.tb00177.x 43. Garofalo RP, Hintz KH, Hill V, Ogra PL, Welliver Sr RC (2004) Production of Interferon Gamma in Respiratory Syncytial Virus Infection of Humans Is Not Associated with Interleukins 12 and 18. Journal of Medical Virology 73 (2):289-294 44. Garofalo RP, Hintz KH, Hill V, Patti J, Ogra PL, Welliver RC, Sr. (2004) A comparison of epidemiologic and immunologic features of bronchiolitis caused by influenza virus and respiratory syncytial virus. Journal of medical virology 75 (2):282-289. doi:10.1002/jmv.20268 45. Garofalo RP, Patti J, Hintz KA, Hill V, Ogra PL, Welliver RC (2001) Macrophage inflammatory protein-1alpha (not T helper type 2 cytokines) is associated with severe forms of respiratory syncytial virus bronchiolitis. The Journal of infectious diseases 184 (4):393-399. doi:10.1086/322788 46. Gentile DA, Doyle WJ, Zeevi A, Howe-Adams J, Kapadia S, Trecki J, Skoner DP (2003) Cytokine gene polymorphisms moderate illness severity in infants with respiratory syncytial virus infection. Human immunology 64 (3):338-344 47. Giugno KM, Machado DC, Amantea SL, Menna Barreto SS (2004) Concentrations of interleukin-2 in the nasopharyngeal secretion of children with acute respiratory syncytial virus bronchiolitis. Jornal de Pediatria 80 (4):315-320 48. Griggs KM, Forsyth KD, Dixon DL (2011) Nasopharyngeal prostaglandin E(2) in infant bronchiolitis. Experimental lung research 37 (10):600-605. doi:10.3109/01902148.2011.622427

24 49. Hassan MA, Eldin AM, Ahmed MM (2008) T - helper2 /T - helper1 imbalance in respiratory syncytial virus bronchiolitis in relation to disease severity and outcome. The Egyptian journal of immunology / Egyptian Association of Immunologists 15 (2):153-160 50. Heidema J, Lukens MV, van Maren WW, van Dijk ME, Otten HG, van Vught AJ, van der Werff DB, van Gestel SJ, Semple MG, Smyth RL, Kimpen JL, van Bleek GM (2007) CD8+ T cell responses in bronchoalveolar lavage fluid and peripheral blood mononuclear cells of infants with severe primary respiratory syncytial virus infections. Journal of immunology (Baltimore, Md : 1950) 179 (12):8410-8417 51. Hennus MP, van Vught AJ, Brabander M, Brus F, Jansen NJ, Bont LJ (2013) Mechanical ventilation drives inflammation in severe viral bronchiolitis. PLoS One 8 (12):e83035. doi:10.1371/journal.pone.0083035 52. Hoebee B, Bont L, Rietveld E, van Oosten M, Hodemaekers HM, Nagelkerke NJ, Neijens HJ, Kimpen JL, Kimman TG (2004) Influence of promoter variants of interleukin-10, interleukin-9, and tumor necrosis factor-alpha genes on respiratory syncytial virus bronchiolitis. The Journal of infectious diseases 189 (2):239-247. doi:10.1086/380908 53. Hoebee B, Rietveld E, Bont L, Oosten M, Hodemaekers HM, Nagelkerke NJ, Neijens HJ, Kimpen JL, Kimman TG (2003) Association of severe respiratory syncytial virus bronchiolitis with interleukin-4 and interleukin-4 receptor alpha polymorphisms. The Journal of infectious diseases 187 (1):2-11. doi:10.1086/345859 54. Hosakote YM, Jantzi PD, Esham DL, Spratt H, Kurosky A, Casola A, Garofalo RP (2011) Viral-mediated inhibition of antioxidant enzymes contributes to the pathogenesis of severe respiratory syncytial virus bronchiolitis. American journal of respiratory and critical care medicine 183 (11):1550-1560. doi:10.1164/rccm.2010101755OC 10.1164/rccm.201010-1755OC. Epub 2011 Mar 4. 55. Huang J, Zhang M, Zhang X, Lu A, Wang L, Chen C (2014) IFN-gamma CA microsatellite polymorphism is associated with susceptibility to respiratory syncytial virus Infection and severity. Acta paediatrica (Oslo, Norway : 1992) 103 (12):e544547. doi:10.1111/apa.12767 56. Kawamata R, Gunji Y, Ozaki A, Wakabayashi K, Miyajimaa Y, Monden Y, Numazaki K, Takahashi K (2017) Relationships Between Adipokine Profiles, Physique Index, and Severity of Bronchiolitis in Infancy. Fetal and pediatric pathology 36 (5):347-356. doi:10.1080/15513815.2017.1332121 57. Kawasaki Y, Aoyagi Y, Abe Y, Go H, Imamura T, Kaneko M, Ito M, Katayose M, Hashimoto K, Hosoya M (2009) Serum KL-6 levels as a biomarker of lung injury in respiratory syncytial virus bronchiolitis. Journal of medical virology 81 (12):21042108. doi:10.1002/jmv.21634 58. Kawasaki Y, Endo K, Suyama K, Sato M, Ito M, Hashimoto K, Hosoya M (2011) Serum SP-D levels as a biomarker of lung injury in respiratory syncytial virus bronchiolitis. Pediatric pulmonology 46 (1):18-22. doi:10.1002/ppul.21270 59. Kott KS, Salt BH, McDonald RJ, Jhawar S, Bric JM, Joad JP (2008) Effect of secondhand cigarette smoke, RSV bronchiolitis and parental asthma on urinary cysteinyl LTE4. Pediatric pulmonology 43 (8):760-766. doi:10.1002/ppul.20853 60. Laham FR, Trott AA, Bennett BL, Kozinetz CA, Jewell AM, Garofalo RP, Piedra PA (2010) LDH concentration in nasal-wash fluid as a biochemical predictor of bronchiolitis severity. Pediatrics 125 (2):e225-233. doi:10.1542/peds.2009-0411 61. Lai CC, Tai HY, Shen HD, Chung WT, Chung RL, Tang RB (2004) Elevated levels of soluble adhesion molecules in sera of patients with acute bronchiolitis.

25 Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 37 (3):153-156 62. Leahy TR, McManus R, Doherty DG, Grealy R, Carr MJ, Slattery D, Ryan T (2016) Viral Bronchiolitis is Associated with Altered Cytokine Gene Expression and Lymphocyte Activation Status. The Pediatric infectious disease journal. doi:10.1097/INF.0000000000001299 63. Leahy TR, McManus R, Doherty DG, Grealy R, Coulter T, Smyth P, Blackshields G, Sheils O, Carr MJ, Purandare N, Geary M, Hodemaekers HM, Janssen R, Bont L, Slattery D, Ryan T (2016) Interleukin-15 is associated with disease severity in viral bronchiolitis. The European respiratory journal 47 (1):212-222. doi:10.1183/13993003.00642-2015 64. Liu XT, Ren L, Zhou LL, Xiao QY, Deng Y, Liu EM (2015) ORMDL3 variants associated with bronchiolitis susceptibility in a Chinese population. Genetics and molecular research : GMR 14 (4):19155-19162. doi:10.4238/2015.December.29.25 65. Mandelberg A, Tal G, Naugolny L, Cesar K, Oron A, Houri S, Gilad E, Somekh E (2006) Lipopolysaccharide hyporesponsiveness as a risk factor for intensive care unit hospitalization in infants with respiratory syncitial virus bronchiolitis. Clinical and experimental immunology 144 (1):48-52. doi:10.1111/j.1365-2249.2006.03030.x 66. Mansbach JM, Hasegawa K, Ajami NJ, Petrosino JF, Piedra PA, Tierney CN, Espinola JA, Camargo CA (2017) Serum LL-37 Levels Associated With Severity of Bronchiolitis and Viral Etiology. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 65 (6):967-975. doi:10.1093/cid/cix483 67. Mansbach JM, Piedra PA, Borregaard N, Martineau AR, Neuman MI, Espinola JA, Camargo CA, Jr. (2012) Serum cathelicidin level is associated with viral etiology and severity of bronchiolitis. J Allergy Clin Immunol 130 (4):1007-1008.e1001. doi:10.1016/j.jaci.2012.07.044 68. Mansbach JM, Piedra PA, Laham FR, McAdam AJ, Clark S, Sullivan AF, Camargo CA, Jr. (2012) Nasopharyngeal lactate dehydrogenase concentrations predict bronchiolitis severity in a prospective multicenter emergency department study. The Pediatric infectious disease journal 31 (7):767-769. doi:10.1097/INF.0b013e3182565eae 69. Marguet C, Bocquel N, Benichou J, Basuyau JP, Hellot MF, Couderc L, Mallet E, Mace B (2008) Neutrophil but not eosinophil inflammation is related to the severity of a first acute epidemic bronchiolitis in young infants. Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology 19 (2):157-165. doi:10.1111/j.1399-3038.2007.00600.x 70. Mehta R, Scheffler M, Tapia L, Aideyan L, Patel KD, Jewell AM, Avadhanula V, Mei M, Garofalo RP, Piedra PA (2014) Lactate dehydrogenase and caspase activity in nasopharyngeal secretions are predictors of bronchiolitis severity. Influenza and other respiratory viruses 8 (6):617-625. doi:10.1111/irv.12276 71. Mella C, Suarez-Arrabal MC, Lopez S, Stephens J, Fernandez S, Hall MW, Ramilo O, Mejias A (2013) Innate immune dysfunction is associated with enhanced disease severity in infants with severe respiratory syncytial virus bronchiolitis. The Journal of infectious diseases 207 (4):564-573. doi:10.1093/infdis/jis721 72. Moreno-Solis G, Fernandez-Gutierrez F, Torres - Borrego J, Torcello-Gaspar R, Gomez-Chaparro Moreno JL, Perez - Navero JL (2015) Low serum 25hydroxyvitamin D levels and bronchiolitis severity in Spanish infants. European Journal of Pediatrics 174 (3):365-372 73. Moreno-Solis G, Torres-Borrego J, de la Torre-Aguilar MJ, Fernandez-Gutierrez F, Llorente-Cantarero FJ, Perez-Navero JL (2015) Analysis of the local and systemic

26 inflammatory response in hospitalized infants with respiratory syncitial virus bronchiolitis. Allergologia et immunopathologia 43 (3):264-271. doi:10.1016/j.aller.2014.02.002 74. Nicholson EG, Schlegel C, Garofalo RP, Mehta R, Scheffler M, Mei M, Piedra PA (2016) Robust Cytokine and Chemokine Response in Nasopharyngeal Secretions: Association With Decreased Severity in Children With Physician Diagnosed Bronchiolitis. The Journal of infectious diseases. doi:10.1093/infdis/jiw191 75. Nuolivirta K, He Q, Grondahl-Yli-Hannuksela K, Koponen P, Korppi M, Helminen M (2012) Mannose-binding lectin gene polymorphisms in infants with bronchiolitis and post-bronchiolitis wheezing. Allergology international : official journal of the Japanese Society of Allergology 61 (2):305-309. doi:10.2332/allergolint.11-OA-0385 76. Nuolivirta K, He Q, Vuononvirta J, Koponen P, Helminen M, Korppi M (2012) Toll-like receptor 3 L412F polymorphisms in infants with bronchiolitis and postbronchiolitis wheezing. The Pediatric infectious disease journal 31 (9):920-923. doi:10.1097/INF.0b013e31825aff25 77. Nuolivirta K, Tormanen S, Terasjarvi J, Vuononvirta J, Koponen P, Korppi M, Helminen M, Peltola V, He Q (2016) Post-bronchiolitis wheezing is associated with toll-like receptor 9 rs187084 gene polymorphism. Sci Rep 6:31165. doi:10.1038/srep31165 78. Nuolivirta K, Vuononvirta J, Peltola V, Koponen P, Helminen M, He Q, Korppi M (2013) Toll-like receptor 2 subfamily genotypes are not associated with severity of bronchiolitis or postbronchiolitis wheezing in infants. Acta paediatrica (Oslo, Norway : 1992) 102 (12):1160-1164. doi:10.1111/apa.12425 79. Papoff P, Moretti C, Cangiano G, Bonci E, Roggini M, Pierangeli A, Scagnolari C, Antonelli G, Midulla F (2011) Incidence and predisposing factors for severe disease in previously healthy term infants experiencing their first episode of bronchiolitis. Acta paediatrica (Oslo, Norway : 1992) 100 (7):e17-23. doi:10.1111/j.1651-2227.2011.02181.x 80. Piedra FA, Mei M, Avadhanula V, Mehta R, Aideyan L, Garofalo RP, Piedra PA (2017) The interdependencies of viral load, the innate immune response, and clinical outcome in children presenting to the emergency department with respiratory syncytial virus-associated bronchiolitis. PLoS One 12 (3):e0172953. doi:10.1371/journal.pone.0172953 81. Pitrez PM, Brennan S, Sly PD (2005) Inflammatory profile in nasal secretions of infants hospitalized with acute lower airway tract infections. Respirology 10 (3):365370. doi:10.1111/j.1440-1843.2005.00721.x 82. Raes M, Peeters V, Alliet P, Gillis P, Kortleven J, Magerman K, Rummens JL (1997) Peripheral blood T and B lymphocyte subpopulations in infants with acute respiratory syncytial virus brochiolitis. Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology 8 (2):97102 83. Raiden S, Sananez I, Remes-Lenicov F, Pandolfi J, Romero C, De Lillo L, Ceballos A, Geffner J, Arruvito L (2017) Respiratory Syncytial Virus (RSV) Infects CD4+ T Cells: Frequency of Circulating CD4+ RSV+ T Cells as a Marker of Disease Severity in Young Children. The Journal of infectious diseases 215 (7):1049-1058. doi:10.1093/infdis/jix070 84. Resch B, Gusenleitner W, Muller W (2003) Procalcitonin, interleukin-6, Creactive protein and leukocyte counts in infants with bronchiolitis. The Pediatric infectious disease journal 22 (5):475-476. doi:10.1097/01.inf.0000066196.23839.b0

27 85. Scagnolari C, Midulla F, Pierangeli A, Moretti C, Bonci E, Berardi R, De Angelis D, Selvaggi C, Di Marco P, Girardi E, Antonelli G (2009) Gene expression of nucleic acid-sensing pattern recognition receptors in children hospitalized for respiratory syncytial virus-associated acute bronchiolitis. Clinical and vaccine immunology : CVI 16 (6):816-823. doi:10.1128/cvi.00445-08 86. Scagnolari C, Midulla F, Riva E, Monteleone K, Solimini A, Bonci E, Cangiano G, Papoff P, Moretti C, Pierangeli A, Antonelli G (2012) Evaluation of interleukin 28B single nucleotide polymorphisms in infants suffering from bronchiolitis. Virus research 165 (2):236-240. doi:10.1016/j.virusres.2012.02.018 87. Scagnolari C, Midulla F, Trombetti S, Pierangeli A, Tromba V, Grossi R, Di Marco P, Dianzani C, Girardi E, Antonelli G (2007) Upregulation of interferoninduced genes in infants with virus-associated acute bronchiolitis. Experimental biology and medicine (Maywood, NJ) 232 (10):1355-1359. doi:10.3181/0705-bc-124 88. Selvaggi C, Pierangeli A, Fabiani M, Spano L, Nicolai A, Papoff P, Moretti C, Midulla F, Antonelli G, Scagnolari C (2014) Interferon lambda 1-3 expression in infants hospitalized for RSV or HRV associated bronchiolitis. The Journal of infection 68 (5):467-477. doi:10.1016/j.jinf.2013.12.010 89. Semple MG, Dankert HM, Ebrahimi B, Correia JB, Booth JA, Stewart JP, Smyth RL, Hart CA (2007) Severe respiratory syncytial virus bronchiolitis in infants is associated with reduced airway interferon gamma and substance P. PLoS One 2 (10):e1038. doi:10.1371/journal.pone.0001038 90. Shein SL, Li H, Gaston B (2016) Blood eosinophilia is associated with unfavorable hospitalization outcomes in children with bronchiolitis. Pediatric pulmonology 51 (1):77-83. doi:10.1002/ppul.23219 91. Skoner DP, Fireman P, Caliguiri L, Davis H (1990) Plasma elevations of histamine and a prostaglandin metabolite in acute bronchiolitis. The American review of respiratory disease 142 (2):359-364. doi:10.1164/ajrccm/142.2.359 92. Smyth RL, Fletcher JN, Thomas HM, Hart CA (1997) Immunological responses to respiratory syncytial virus infection in infancy. Archives of disease in childhood 76 (3):210-214 93. Smyth RL, Mobbs KJ, O'Hea U, Ashby D, Hart CA (2002) Respiratory syncytial virus bronchiolitis: disease severity, interleukin-8, and virus genotype. Pediatric pulmonology 33 (5):339-346 94. Suarez-Arrabal MC, Mella C, Lopez SM, Brown NV, Hall MW, Hammond S, Shiels W, Groner J, Marcon M, Ramilo O, Mejias A (2015) Nasopharyngeal bacterial burden and antibiotics: Influence on inflammatory markers and disease severity in infants with respiratory syncytial virus bronchiolitis. The Journal of infection 71 (4):458-469. doi:10.1016/j.jinf.2015.06.010 95. Tal G, Mandelberg A, Dalal I, Cesar K, Somekh E, Tal A, Oron A, Itskovich S, Ballin A, Houri S, Beigelman A, Lider O, Rechavi G, Amariglio N (2004) Association between common Toll-like receptor 4 mutations and severe respiratory syncytial virus disease. The Journal of infectious diseases 189 (11):2057-2063. doi:10.1086/420830 96. Tasker RC, Roe MF, Bloxham DM, White DK, Ross-Russell RI, O'Donnell DR (2004) The neuroendocrine stress response and severity of acute respiratory syncytial virus bronchiolitis in infancy. Intensive care medicine 30 (12):2257-2262. doi:10.1007/s00134-004-2470-7 97. Taylor CE, Webb MSC, Milner AD, Milner PD, Morgan LA, Scott R, Stokes GM, Swarbrick AS, Toms GL (1989) Interferon alfa-infectious virus, and virus

28 antigen secretion in respiratory syncytial virus infections of graded severity. Archives of Disease in Childhood 64 (12):1656-1660 98. Welliver RC, Garofalo RP, Ogra PL (2002) Beta-chemokines, but neither T helper type 1 nor T helper type 2 cytokines, correlate with severity of illness during respiratory syncytial virus infection. The Pediatric infectious disease journal 21 (5):457-461 99. Yamada Y, Yoshihara S (2010) Creola bodies in infancy with respiratory syncytial virus bronchiolitis predict the development of asthma. Allergology international : official journal of the Japanese Society of Allergology 59 (4):375-380. doi:10.2332/allergolint.09-OA-0165 100. Zhang M, Lu Y, Zhang X, Lu A, Wang L, Chen C (2016) Interleukin-4 polymorphism is associated with severity of respiratory syncytial virus infection. Journal of paediatrics and child health 52 (1):25-29. doi:10.1111/jpc.12985 101. Zhou L, Xiao Q, Zhao Y, Huang A, Ren L, Liu E (2015) The impact of viral dynamics on the clinical severity of infants with respiratory syncytial virus bronchiolitis. Journal of Medical Virology 87 (8):1276-1284. doi:10.1002/jmv.24111 102. Hancock DG, Charles-Britton B, Dixon DL, Forsyth KD (2017) The heterogeneity of viral bronchiolitis: A lack of universal consensus definitions. Pediatric pulmonology. doi:10.1002/ppul.23750 103. Raphael I, Nalawade S, Eagar TN, Forsthuber TG (2015) T cell subsets and their signature cytokines in autoimmune and inflammatory diseases. Cytokine 74 (1):5-17. doi:10.1016/j.cyto.2014.09.011 104. Zhu J, Yamane H, Paul WE (2010) Differentiation of effector CD4 T cell populations (*). Annu Rev Immunol 28:445-489. doi:10.1146/annurev-immunol030409-101212 105. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama 315 (8):801-810. doi:10.1001/jama.2016.0287 106. Thijs J, Krastev T, Weidinger S, Buckens CF, de Bruin-Weller M, BruijnzeelKoomen C, Flohr C, Hijnen D (2015) Biomarkers for atopic dermatitis: a systematic review and meta-analysis. Current opinion in allergy and clinical immunology 15 (5):453-460. doi:10.1097/ACI.0000000000000198 107. Haynes LM, Moore DD, Kurt-Jones EA, Finberg RW, Anderson LJ, Tripp RA (2001) Involvement of toll-like receptor 4 in innate immunity to respiratory syncytial virus. Journal of virology 75 (22):10730-10737. doi:10.1128/JVI.75.22.1073010737.2001

29 Table 1: Significant Chemokines, Cytokines, and Receptors Association with Increased Severity Biomarker Positive No Association Negative Chemokine (C-C motif) [98,44] [19,20,24,32,45,51,74,80] ligand 2 Chemokine (C-C motif) [45,98,44] [20,32,34,51,73,74,80] ligand 3 Chemokine (C-C motif) [73] [19,20,32,73,74,80] [19,73] ligand 4 Chemokine (C-C motif) [24,32,45,44,74,98,80] [24] ligand 5 Chemokine (C-C motif) [34] [20,32,69,74,98,80] ligand 11 Chemokine (C-X-C [80] [20,24,32,34,80] [74] motif) ligand 10 [13,17[19,21,25,45, Interferon gamma [80] 20,22,30,32,34,36,43,62,7 49,74,89,98] 3,81,39,80] Interferon lambda 1 [88] Interleukin 1 alpha [34] [20,51] [17,20,24,25,30,32,34,51,6 Interleukin 1 beta [31] 2,74,80] Interleukin 1 receptor[35] like 1 alpha [17Interleukin 4 [20,25,49] 20,22,21,24,30,32,36,45,7 [74] 3,92,98,80] Interleukin 4/Interferon [25,45,98,4 [17,73] gamma Ratio 9] [17,19,20,30,32,45,74,98,8 Interleukin 5 [25] 0] [24,30,31,3 [17,19,20,25,32,51,62,71,7 Interleukin 6 4] [19] 3,74,84,94,80] [73] Interleukin 7 [19,20,32,74,80] [62] [17,18,22,2 [19Interleukin 8 4] 21,24,25,30,32,62,69,71,7 [19] [31,69,93] 3,74,81,94,18,80] [13,19-21,24,27,30Interleukin 10 [17] 32,48,62,71,73,74,81,39,8 [19,20,62] 0] Interleukin 10/Interferon [13] gamma Ratio Interleukin 15 [63,80] [20,32,74,80] [63] Interleukin 17 [19,20,24,25,30,32,34,80] [74] Interleukin 18 [43] Tumour Necrosis Factor [19,20,24,25,30[17] alpha 32,62,71,73,74,94,56,80]

30 Table 2: Significant Immune Cell Subsets Association with Increased Severity Biomarker Positive No Association Negative CD4 T cells [50,62,63,82] [24,29,83] CD4 T cells: CD25+ [29] CD4 T cells: RSV+ [83] CD8 T cells [50,62,63,82] [24,29] CD8 T cells: Chemokine (C-X-C [26] [26] motif) Receptor 1 Eosinophils [90] [33,41,69,71,79] [49] Gamma delta T cells [62] [29] Lymphocytes [22,30,62,63,69,71,101] [29,79,96] Monocytes: Classical Subset [12] Monocytes: HLA-DR [12] Neutrophils: Band [71] [71] NK cells [82] [50,62,63] [24,29] NK cells: FAS (mRNA) [63] NK cells: FLT3 (mRNA) [63] NK cells: GAPDH (mRNA) [63] NK cells: JAK3 (mRNA) [63] NK cells: NFKB1 (mRNA) [63] NK cells: STAT5A (mRNA) [63] NK cells: TNFRSF10A (mRNA) [63] Plasmacytoid DCs (CD11c[63] CD123+) Plasmacytoid DCs (CD11c[63] CD123+): Interleukin 15 (%) T cells [50,62,63,82] [29] T cells: CD45RA+ [29] T cells: CD45RO+ [29]

31 Table 3: Significant Genetic Variants Biomarker Chemokine (C-C motif) ligand-5 rs2107538 Interferon gamma rs2430561 Interferon gamma rs3138557 Interleukin 10 variants Interleukin 1 receptor-like 1 rs1921622 Interleukin 4 rs2070874 Interleukin 4 rs2243250 Interleukin 6 -174 G/C Transforming growth factor beta 1 variants (codon10C/T, codon25C/G) Toll-like receptor 4 rs4986790 Toll-like receptor 4 rs4986791 Toll-like receptor 4 variants (rs4986790, rs4986791) Toll-like receptor 9 rs352162 Toll-like receptor 9 rs187084 Vitamin D-receptor rs2228570

Association with Severity No Significant Association [14] [14] [46] [46] [55] [46] [46] [35] [100] [25,53,100] [25] [46] [46] [46]

[46]

[25,95,14] [95]

[14] [14]

[65] [14] [14] [14]

[14] [14] [14]

32 Table 4: Significant Other Biomarkers Biomarker 2'-5'-oligoadenylate synthase 25-hydroxyvitamin D Caspase 3/7 Catalase Cortisol C Reactive Protein Eosinophilic cationic protein F2-isoprostane GATA3/TBX21 Ratio Granulocyte colony stimulating Factor Growth Hormone Glutothione S-Transferase-mu Glucocorticoid Receptor alpha/beta ratio Glucocorticoid Receptor beta Insulin-like growth factor 1 Krebs von den Lungen-6 Lactate Dehydrogenase Lactate Dehydrogenase/Caspase 3/7 Ratio Leptin Leptin/Adiponectin Ratio LL37 Leukotriene B4 Leukotriene E4 Malondialdehyde Myeloperoxidase Olfactomedin 4 P56 (LCK) Periostin Peroxiredoxin1 Protein kinase R Prolactin Superoxide dismutase 1 Superoxide dismutase Soluble RAGE Surfactant protein D Substance P Thymic stromal lymphopoietin

Association with Increased Severity Positive No Association Negative [87] [67,72] [72] [70] [54] [30] [96] [79] [16,57,84] [41] [69,92] [54] [25] [24]

[19,20,24,32,74,80]

[96] [54] [30] [30] [96] [57] [70]

[16] [60,80]

[60,68,80]

[56]

[96]

[70] [56] [67,66] [42] [59] [54] [27] [23]

[28] [42] [92,80] [23] [87]

[39]

[39] [54] [87]

[96]

[40]

[54] [54] [40]

[58] [89] [39]

[20,39]