http://orcid.org/0000-0002-7134-8990
Figures
Abstract
Introduction
Circulating microRNAs (miRNA) are promising biomarkers for human diseases. Our study hypothesizes that circulating miRNA would reveal candidate biomarkers related to airway hyperresponsiveness (AHR) and provide biologic insights into asthma epigenetic influences.
Methods
Serum samples obtained at randomization for 160 children in the Childhood Asthma Management Program were profiled using a TaqMan miRNA array set. The association of the isolated miRNA with methacholine PC20 was assessed. Network and pathway analyses were performed. Functional validation of two significant miRNAs was performed in human airway smooth muscle cells (HASMs).
Results
Of 155 well-detected circulating miRNAs, eight were significantly associated with PC20 with the strongest association with miR-296-5p. Pathway analysis revealed miR-16-5p as a network hub, and involvement of multiple miRNAs interacting with genes in the FoxO and Hippo signaling pathways by KEGG analysis. Functional validation of two miRNA in HASM showed effects on cell growth and diameter.
Conclusion
Reduced circulatory miRNA expression at baseline is associated with an increase in PC20. These miRNA provide biologic insights into, and may serve as biomarkers of, asthma severity. miR-16-5p and -30d-5p regulate airway smooth muscle phenotypes critically involved in asthma pathogenesis, supporting a mechanistic link to these findings. Functional ASM phenotypes may be directly relevant to AHR.
Citation: Davis JS, Sun M, Kho AT, Moore KG, Sylvia JM, Weiss ST, et al. (2017) Circulating microRNAs and association with methacholine PC20 in the Childhood Asthma Management Program (CAMP) cohort. PLoS ONE 12(7): e0180329. https://doi.org/10.1371/journal.pone.0180329
Editor: Christophe Leroyer, Universite de Bretagne Occidentale, FRANCE
Received: January 11, 2017; Accepted: June 8, 2017; Published: July 27, 2017
Copyright: © 2017 Davis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The miRNA dataset is available at the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nih.gov/geo/) GSE74770.
Funding: This work is supported by the following National Institute of Health (US) grants: R01 HL127332, R01 HL129935, U01 HL65899, R01 NR013391. ATK is supported via K25 HL091124. JSD is supported by T32 HL007427. MS is supported by T32 HL007118. QL is supported by R01 HL114769. QL is also supported by the Scholar Award from the American Asthma Foundation. National Institutes of Health (US): https://www.nih.gov/. American Asthma Foundation (US): http://www.americanasthmafoundation.org/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
I. Introduction
Asthma is a chronic inflammatory respiratory disease that affects greater than 300 million people worldwide [1]. It is characterized by airway obstruction due to a combination of smooth muscle hyperresponsiveness and inflammation [2]. The economic costs for asthma including drug therapy and hospitalizations is significant [3]. It remains challenging to generate risk assessment, predict prognosis, and determine optimal treatment response in asthmatics.
Circulating microRNAs (miRNAs) are promising biomarkers for human diseases [4] and may be helpful in a variety of clinical scenarios from risk assessment to monitoring response to treatment [5]. miRNA characteristics and function have been well described in the literature [6]. In brief, miRNAs are a class of small RNAs that inhibit gene expression by binding to the 3’-untranslated region (UTR) of messenger RNAs to degrade or suppress the translation of the mRNA. Given the availability of miRNA mimics and antagonists, these small RNAs have been proposed as therapeutic targets. Circulating miRNAs are highly stable in the serum [7]. miRNA plasma biomarkers have been proposed for neurological conditions [8], cancer detection/prognosis [9], cardiovascular disease [10], and other conditions including an emerging role in respiratory diseases [11]. Translational methods have been applied in order to generate screening tests [12].
Prior studies of circulating miRNA in asthma have been performed. One study explored serum miRNA expression and detected three miRNAs in childhood asthma patients with significantly higher expression than healthy controls [13]. Other studies have shown differential expression of miRNA in epithelial and airway cells between asthma and healthy controls [14]. A recent study explored differential expression of circulating miRNA in asthmatics, nonasthmatic patients with allergic rhinitis, and normal patients and was able to identify a subset of circulating miRNA expressed in asthmatic and allergic rhinitis patients [15]. Studies are lacking regarding quantitative severity measures, which may be more revealing of specific asthma pathobiology and resistant to misclassification bias.
Methacholine PC20 is a quantitative marker of airways responsiveness, which is a cardinal feature of asthma and has been tightly linked to exacerbations and other asthma outcomes. Our study investigated the association of circulating miRNA with methacholine PC20 at time of randomization in the Childhood Asthma Management Program (CAMP) [16]. Airway hyperresponsiveness (AHR) in CAMP was an inclusion criterion for the trial; the degree of airway responsiveness has been linked to disease severity [17]. Our hypothesis is that specific miRNAs may be mediating AHR thereby providing unique biologic insights into asthma pathogenesis. We detected AHR related miRNAs previously associated with asthma, but not PC20, in addition to a novel association of miR-296, that may have an immunomodulatory effect. Pathway analysis of the PC20 associated miRNAs resulted in identification of two pathways known to be biologically significant for AHR. Functional validation of miR-16-5p and miR-30d-5p in human airway smooth muscle cells (HASM) demonstrated effects on cell growth and average cell diameter, respectively, supporting a mechanistic link to these findings.
II. Materials and methods
CAMP (Clinicaltrials.gov register: NCT00000575) was a multi-center, randomized, double-blinded clinical trial evaluating safety and efficacy of inhaled budesonide vs. nedocromil vs. placebo in 1041 pediatric patients over a mean 4.3 years. Trial design and methodology have been detailed [18]. Inclusion criteria were notable for children aged 5–12 years, chronic asthma symptoms, and PC20 < 12.5 mg/mL. Children were excluded if their asthma was severe, for a confounding or complicating condition, or if the child could not perform acceptable spirometry or methacholine challenge. Methacholine challenge was performed 2 weeks prior to randomization [16].
Blood serum samples from 160 CAMP subjects obtained at randomization were profiled for miRNA as described [19]. Technical replicates were assessed in ~10% of the population cohort demonstrating high miRNA-miRNA correlations. To limit the effect of race on miRNA expression (20), all subjects were self-identified non-Hispanic Caucasians. miRNA were annotated with usage of miRBase [20] release 21 (www.mirbase.org/). Analysis was limited to miRNAs detected in ≥50% of samples. The CAMP Genetics Ancillary Study was approved by each individual study center’s Internal Review Board (IRB). Informed consent and assent was obtained from parents and participants, respectively.
For data analysis, quantile normalization on the detected miRNAs was performed sample-wise to the mean of the data matrix using MatLab (MathWorks Inc., Natick, MA) function quantilenorm. Least squares linear regression (both univariate and multivariate) was performed using R [21] to identify miRNA (miR cycle threshold or CT value) associated with the pulmonary function phenotype of interest, log2 PC20. A least squares multivariate linear regression model including miR CT value, age, sex, and height was also calculated for each miRNA. A sensitivity analysis to assess outlier influence, and non-parametric models was also performed. The p-values were corrected using the Benjamini and Hochberg false discovery rate (FDR).
The miRNA dataset is available at the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nih.gov/geo/) GSE74770.
A regulatory network between miRNA and genes was created with usage of Cytoscape (http://www.cytoscape.org/) [22] and CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker/) [23] with Regulatory Interaction in Network Analysis (RegIN) miRTarBase release 6.1 (http://projects.bigcat.unimaas.nl/cytargetlinker/regins/regins-mirtarbase/) [24]. The Database for Annotation, Visualization and Integrated Discovery (DAVID, Version 6.8 (10/2016), https://david.ncifcrf.gov/home.jsp [25] was used for KEGG [26, 27] pathway analysis and gene ontology.
Functional validation of two significant miRNA was performed in human airway smooth muscle (HASM) cells as previously detailed [28]. The cells were transfected with 10nM of either scramble control (AllStars Negative Control siRNA, Qiagen) or miR mimic (Qiagen) using RNAiMax (Life Technology) according to manufacturer’s protocol. Seventy-two hours after transfection, cells were trypsinized for 8 minutes and then measured for both cell number and cell size by Moxi Z Cell Analyzer (Orflo). Cell growth was presented as the percentage of cell number relative to scramble control. Average cell diameter (um) was compared in mimic-transfected versus scramble-transfected HASM cells. Data (mean±SE) were obtained from three independent experiments.
III. Results
Study population
Population characteristics of the 160 CAMP subjects are shown in Table 1. The cohort was limited to self-identified non-Hispanic whites due to the significant effects of race on miRNA expression [29]. For the selected individuals, the global characteristics at randomization are representative of the larger CAMP non-Hispanic white cohort.
Circulatory miRNA association with PC20
There were a total of 754 non-housekeeping miRNA mapping to mirBase release 21 on the array, and 155 (20.6%) miRNA were detected in at least 50% of the samples. Eight microRNAs were significantly associated with PC20 (Table 2), based on a nominal p-value < 0.05 at a FDR p-value < 0.20. The latter was chosen as a higher cut-off given the nature of this hypothesis generating experiment. Based on prior literature, five of these eight miRNA (63%) had prior evidence of differential expression in human asthma. All associations had a positive slope such that as miR cycle threshold increased so did the PC20; this corresponds to a relationship of increasing miR CT (decreasing miRNA expression) with increasing PC20 (decreasing AHR). The strongest association was found with PC20 and hsa-miR-296-5p, as shown in Fig 1. Sensitivity analysis (S1 Table) revealed no significant changes in parameters for the models with the exception of non-significance of hsa-miR-30d. Subsequent multivariate analysis including miR CT, age, sex, and height was consistent with the univariate model (S2 Table). Nonparametric analysis including both rank-order univariate and multivariate models were also performed and were consistent with the parametric models except for the significance of hsa-miR-451a in the nonparametric model (S3 and S4 Tables). Further investigation of miR-30d demonstrated significance in the parametric and non-parametric models with miR-30d cycle threshold characterized by principally having high and low CT values (bimodality) rather than unimodality. This bimodality likely explains non-significance in the sensitivity analysis, while suggesting that miR-30d may still have functional relationship with AHR.
Pathway and ontology analysis
Pathway analysis of the significant miRNAs (S5 Table) was performed with usage of Cytoscape and CyTargetLinker. The miRNA based on both nominal and FDR p-values were used to generate and create a network with Cytoscape and CyTargetLinker (Fig 2) containing multiple genes. The resultant genes were analyzed with DAVID for KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis with the FoxO and Hippo signaling pathways being the most relevant to asthma (Table 3, Figs 3 and 4).
Gene ontology (GOTERM_BP_DIRECT) analysis also revealed functionality of the network related to translation, RNA processing, post-transcriptional regulation of gene expression, ncRNA metabolic process, and other processes (S6 Table). These functions are consistent with the known actions of miRNA targeting.
Functional validation
Based on our prior miRNA sequencing of human airway smooth muscle cells, [28] of the miRNAs in the PC20 network (Fig 2), two, miR-16-5p and miR-30d-5p, are significantly expressed. We therefore evaluated the effect of these miRNA on HASM phenotypes using miR-mimics. Mimics of miR-16-5p decreased and miR-30d-5p increased cell growth and average cell diameter, respectively, compared to scramble control (Fig 5).
HASM cells were transfected with 10 nM of either scramble control or miR-16-5p mimic (left panel; or miR-30d-5p in right panel). Seventy-two hours after transfection, cells were trypsinized and measured for both cell number and cell size by Moxi Z Cell Analyzer. Cell growth was presented as the percentage of cell number relative to the scramble control. Average cell size (um in diameter) was compared in mimic-transfected versus scramble-transfected HASM cells. Data were obtained from three independent experiments. For the miR-16 and miR-30d experiments, the p-value is 0.0009 and 0.03, respectively.
IV. Discussion
In this study, we examined serum samples from 160 CAMP asthmatics and found 8 miRNA significantly associated with PC20, a defining measure of airways hyperresponsiveness. Based on prior literature, five of the eight miRNA (63%) had evidence of differential expression related to human asthma, but not PC20, with a good portion of these in case-control studies of human bronchial epithelial cells. Three novel miRNAs were identified, including our strongest association, miR-296-5p. Pathway analysis of the miRNA targets implicates effects of both the Hippo and FoxO signaling pathways with both pathways implicated in airways hyperresponsiveness [30, 31]. Lastly, functional validation demonstrated that miR-16-5p resulted in decreased airway smooth muscle cell growth and miR-30d-5p increased airway smooth muscle cell size compared to scramble controls.
Our most significant association was found with hsa-miR-296-5p (Table 2). There are no previous reports in the literature regarding this miRNA in association with asthma. miR-296 targets IKBKE, which is involved in signaling pathways including Toll-like receptor signaling and signal transduction prompting apoptosis [32]. IKBKE is highly expressed in immune cells and is a known target of the NFκB gene [33]. The NFκB pathway’s involvement in asthma and inflammation has been well described in the literature [34], and includes modulation of AHR in allergen challenged mice [35]. Moreover, IKBKE itself is a known therapeutic target for asthma, with IKBKE targeting demonstrating significant attenuation of airways responsiveness and inflammation in a murine model of asthma [36]. Therefore, miR-296 may attenuate immune response and could modulate AHR via the NFκB pathway.
miR-16-5p was also significant in our study and differential expression of this miRNA in asthmatic airway cells has been reported [37]. Expression profiling of human airway biopsies has showed miR-16 to be highly expressed, leading to the hypothesis that miR-16 along with other miRNAs may have a significant influence on gene expression in the airways [38]. Our network analysis demonstrated that miR-16 plays a key role as the central hub, both interacting with other miRNAs and mediating expression of dozens of genes (Fig 2). Thus, miR-16 appears to play a notable role in the modulation of genes influencing airways hyperresponsiveness in asthma. In addition to its central effect on downstream gene expression, miR-16 mimics result in decreased airway smooth muscle growth. While the exact significance of this finding is unknown, prior work focused on small airway cell layers suggests that differential growth between layers may mediate different effects on airway buckling [39].
As mentioned, several of our other AHR associated miRNA, including hsa-miR-30d, -128, -138, and -203a, have been detected in studies involving human airway cells of asthmatics [14]. The association of hsa-miR-203 has been validated in epithelial cells from a small number of asthmatics and healthy subjects with identification of the top-ranked predicted target, aquaporin gene (AQp4). In turn, the expression of AQp4 was subsequently noted to be significantly higher in asthmatic cells [14]. Other studies have shown up-regulation of miR-203 in serum of children with atopic dermatitis and increased IgE level [40] in addition to airway epithelial cell apoptosis [41]. Thus miR-203 may indirectly affect airways responsiveness via an inflammatory mechanism. In contrast, our work demonstrates that miR-30d-5p increases average HASM cell diameter compared to scramble controls. Increased airway smooth muscle cell size can result in both further mechanical airway narrowing in addition to increased contribution of inflammatory mediators [42]. Increase in airway smooth muscle tissue mass related to both hypertrophy and hyperplasia has been noted a major driver of airway narrowing and thus AHR in asthmatics [43]. It is very likely that miRNA act via increases in ASM cell size/diameter and thus, mechanistically may directly cause increased AHR (decreased PC20).
Focusing on validated miRNA targets, pathway analysis from our associated miRNAs was notable for multiple genes in both the FoxO and Hippo signaling pathways (Figs 3 and 4). For the former pathway, a mouse experiment showed alternative activation of alveolar macrophages with resultant type 2 allergic airway inflammation with subsequent airway remodeling [30]. For the latter pathway, it has been shown that it is a notable regulatory pathway with versatile function including a key gene (Yes-associated protein or YAP) implicated in airway smooth muscle hyperplasia [31]. Both of these pathways have a plausible link to the phenotype of airways hyperresponsiveness. As noted above, miR-16 also appears to be a central hub in our serum microRNA network and may work in concert with other miRNA to modulate immune pathways and subsequently AHR. Functional validation would be needed for further elucidation of possible molecular mechanisms between miRNAs and asthma related to this pathway. Lastly, gene ontology analysis (S6 Table) demonstrated processes such as RNA processing, post-transcriptional regulation of gene expression, and other likely putative effects of miRNAs.
This study has several strengths including a large sample size of pediatric asthma patients from the CAMP cohort, a large number of interrogated miRNAs, validation of prior associations in the literature with our reported miRNA findings, and subsequent functional validation of miRNA in HASM. The large sample size and number of interrogated miRNAs provides a good breadth of characterization and power to detect associations in light of lower starting concentrations of miRNA in the circulation. Additionally, the CAMP cohort was clinically well characterized with standard methodologies including methacholine challenge testing, which should minimize potential for measurement error. Analysis of biological replicates as discussed in the methods section also showed high miRNA-miRNA correlations. Although the CAMP serums were stored for years prior to this interrogation, prior studies have shown the stored samples can result in reliable miRNA concentrations months to years later [44]. Lastly, miRNA targeting is an imprecise science with new associations being discovered on a regular basis. However, our study used miRTarBase (validated miRNA-target interaction), which assesses only functionally annotated miRNAs, lending functional credence to our network and pathway analyses; this was enhanced by our functional studies in HASM cells.
In summary, this study detected eight circulating miRNAs associated with PC20 in a pediatric asthma population with mild-moderate persistent asthma. These miRNA appear to be associated with individual and pathway evidence of immune modulation that could affect AHR; complementary functional validation of miR-16-5p and miR-30d-5p in HASM demonstrate effects on cell growth and diameter, respectively. The majority of these miRNAs had been associated with asthma in prior studies. Nonetheless, the most significant association was a novel association with miR-296, and this miRNA may be a viable serum biomarker for altered immunity and AHR in pediatric asthmatic patients.
Further study of our PC20 associated miRNAs, both in terms of external validation and additional functional mechanisms, may provide insight into epigenetic influences in asthma pathobiology and have clinical implications such as risk assessment and treatment responses. Given that miRNA can therapeutically decrease airways responsiveness in murine models of asthma [45–47], future work may also yield novel therapeutic approaches to targeting asthma via miRNA modulation of AHR.
Supporting information
S1 Table. Sensitivity analysis for circulatory miRNA association by least squares linear regression with methacholine PC20 (univariate model, unranked) with outlier values removed and with detection of miRNA in at least 50% of samples.
https://doi.org/10.1371/journal.pone.0180329.s001
(DOCX)
S2 Table. Circulatory miRNA association by least squares linear regression with methacholine PC20 (multivariate model adjusting for age, sex, and height, unranked) with detection of miRNA in at least 50% of samples.
https://doi.org/10.1371/journal.pone.0180329.s002
(DOCX)
S3 Table. Circulatory miRNA association by least squares linear regression with methacholine PC20 (univariate model, ranked) with detection of miRNA in at least 50% of samples.
https://doi.org/10.1371/journal.pone.0180329.s003
(DOCX)
S4 Table. Circulatory miRNA association by least squares linear regression with methacholine PC20 (multivariate model adjusting for age, sex, and height, ranked) with detection of miRNA in at least 50% of samples.
https://doi.org/10.1371/journal.pone.0180329.s004
(DOCX)
S5 Table. miRBase Accession numbers for cytoscape (univariate model, unranked).
https://doi.org/10.1371/journal.pone.0180329.s005
(DOCX)
S6 Table. DAVID gene ontology (GO) analysis (GOTERM_BP_DIRECT).
https://doi.org/10.1371/journal.pone.0180329.s006
(DOCX)
References
- 1. To T, Stanojevic S, Moores G, Gershon AS, Bateman ED, Cruz AA, et al. Global asthma prevalence in adults: findings from the cross-sectional world health survey. BMC public health. 2012;12:204. pmid:22429515;
- 2. Fanta CH. Asthma. The New England journal of medicine. 2009;360(10):1002–14. pmid:19264689.
- 3. Bahadori K, Doyle-Waters MM, Marra C, Lynd L, Alasaly K, Swiston J, et al. Economic burden of asthma: a systematic review. BMC pulmonary medicine. 2009;9:24. pmid:19454036;
- 4. Weiland M, Gao XH, Zhou L, Mi QS. Small RNAs have a large impact: circulating microRNAs as biomarkers for human diseases. RNA biology. 2012;9(6):850–9. pmid:22699556.
- 5. Witwer KW. Circulating microRNA biomarker studies: pitfalls and potential solutions. Clinical chemistry. 2015;61(1):56–63. pmid:25391989.
- 6. Hoy AM, Buck AH. Extracellular small RNAs: what, where, why? Biochemical Society transactions. 2012;40(4):886–90. pmid:22817753;
- 7. Russo F, Di Bella S, Nigita G, Macca V, Lagana A, Giugno R, et al. miRandola: extracellular circulating microRNAs database. PloS one. 2012;7(10):e47786. pmid:23094086;
- 8. Sheinerman KS, Tsivinsky VG, Crawford F, Mullan MJ, Abdullah L, Umansky SR. Plasma microRNA biomarkers for detection of mild cognitive impairment. Aging. 2012;4(9):590–605. pmid:23001356;
- 9. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proceedings of the National Academy of Sciences of the United States of America. 2008;105(30):10513–8. pmid:18663219;
- 10. Tijsen AJ, Pinto YM, Creemers EE. Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases. American journal of physiology Heart and circulatory physiology. 2012;303(9):H1085–95. pmid:22942181.
- 11. Booton R, Lindsay MA. Emerging role of MicroRNAs and long noncoding RNAs in respiratory disease. Chest. 2014;146(1):193–204. pmid:25010962.
- 12. Sheinerman KS, Tsivinsky VG, Umansky SR. Analysis of organ-enriched microRNAs in plasma as an approach to development of Universal Screening Test: feasibility study. Journal of translational medicine. 2013;11:304. pmid:24330742;
- 13. Wang Y, Yang L, Li P, Huang H, Liu T, He H, et al. Circulating microRNA Signatures Associated with Childhood Asthma. Clinical laboratory. 2015;61(5–6):467–74. pmid:26118177.
- 14. Jardim MJ, Dailey L, Silbajoris R, Diaz-Sanchez D. Distinct microRNA expression in human airway cells of asthmatic donors identifies a novel asthma-associated gene. American journal of respiratory cell and molecular biology. 2012;47(4):536–42. pmid:22679274.
- 15. Panganiban RP, Wang Y, Howrylak J, Chinchilli VM, Craig TJ, August A, et al. Circulating microRNAs as biomarkers in patients with allergic rhinitis and asthma. The Journal of allergy and clinical immunology. 2016;137(5):1423–32. pmid:27025347.
- 16. The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Controlled clinical trials. 1999;20(1):91–120. pmid:10027502.
- 17. Weiss ST, Van Natta ML, Zeiger RS. Relationship between increased airway responsiveness and asthma severity in the childhood asthma management program. American journal of respiratory and critical care medicine. 2000;162(1):50–6. pmid:10903219.
- 18. Recruitment of participants in the childhood Asthma Management Program (CAMP). I. Description of methods: Childhood Asthma Management Program Research Group. The Journal of asthma: official journal of the Association for the Care of Asthma. 1999;36(3):217–37. pmid:10350219.
- 19. Kho AT, Sharma S, Davis JS, Spina J, Howard D, McEnroy K, et al. Circulating MicroRNAs: Association with Lung Function in Asthma. PloS one. 2016;11(6):e0157998. pmid:27362794;
- 20. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic acids research. 2008;36(Database issue):D154–8. pmid:17991681;
- 21.
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015.
- 22. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498–504. pmid:14597658;
- 23. Kutmon M, Kelder T, Mandaviya P, Evelo CT, Coort SL. CyTargetLinker: a cytoscape app to integrate regulatory interactions in network analysis. PloS one. 2013;8(12):e82160. pmid:24340000;
- 24. Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic acids research. 2011;39(Database issue):D163–9. pmid:21071411;
- 25. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research. 2009;37(1):1–13. pmid:19033363;
- 26. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic acids research. 2016;44(D1):D457–62. pmid:26476454;
- 27. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000;28(1):27–30. pmid:10592173;
- 28. Hu R, Pan W, Fedulov AV, Jester W, Jones MR, Weiss ST, et al. MicroRNA-10a controls airway smooth muscle cell proliferation via direct targeting of the PI3 kinase pathway. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2014;28(5):2347–57. pmid:24522205;
- 29. Huang RS, Gamazon ER, Ziliak D, Wen Y, Im HK, Zhang W, et al. Population differences in microRNA expression and biological implications. RNA biology. 2011;8(4):692–701. pmid:21691150;
- 30. Chung S, Lee TJ, Reader BF, Kim JY, Lee YG, Park GY, et al. FoxO1 regulates allergic asthmatic inflammation through regulating polarization of the macrophage inflammatory phenotype. Oncotarget. 2016;7(14):17532–46. pmid:27007158;
- 31. Zhou J, Xu F, Yu JJ, Zhang W. YAP is up-regulated in the bronchial airway smooth muscle of the chronic asthma mouse model. International journal of clinical and experimental pathology. 2015;8(9):11132–9. pmid:26617833;
- 32. Robson JE, Eaton SA, Underhill P, Williams D, Peters J. MicroRNAs 296 and 298 are imprinted and part of the GNAS/Gnas cluster and miR-296 targets IKBKE and Tmed9. Rna. 2012;18(1):135–44. pmid:22114321;
- 33. Patel MN, Bernard WG, Milev NB, Cawthorn WP, Figg N, Hart D, et al. Hematopoietic IKBKE limits the chronicity of inflammasome priming and metaflammation. Proceedings of the National Academy of Sciences of the United States of America. 2015;112(2):506–11. pmid:25540417;
- 34. Edwards MR, Bartlett NW, Clarke D, Birrell M, Belvisi M, Johnston SL. Targeting the NF-kappaB pathway in asthma and chronic obstructive pulmonary disease. Pharmacology & therapeutics. 2009;121(1):1–13. pmid:18950657.
- 35. Sheller JR, Polosukhin VV, Mitchell D, Cheng DS, Peebles RS, Blackwell TS. Nuclear factor kappa B induction in airway epithelium increases lung inflammation in allergen-challenged mice. Experimental lung research. 2009;35(10):883–95. pmid:19995280;
- 36. Ziegelbauer K, Gantner F, Lukacs NW, Berlin A, Fuchikami K, Niki T, et al. A selective novel low-molecular-weight inhibitor of IkappaB kinase-beta (IKK-beta) prevents pulmonary inflammation and shows broad anti-inflammatory activity. British journal of pharmacology. 2005;145(2):178–92. pmid:15753951;
- 37. Solberg OD, Ostrin EJ, Love MI, Peng JC, Bhakta NR, Hou L, et al. Airway epithelial miRNA expression is altered in asthma. American journal of respiratory and critical care medicine. 2012;186(10):965–74. pmid:22955319;
- 38. Williams AE, Larner-Svensson H, Perry MM, Campbell GA, Herrick SE, Adcock IM, et al. MicroRNA expression profiling in mild asthmatic human airways and effect of corticosteroid therapy. PloS one. 2009;4(6):e5889. pmid:19521514;
- 39. Moulton DE, Goriely A. Possible role of differential growth in airway wall remodeling in asthma. Journal of applied physiology. 2011;110(4):1003–12. pmid:21252217.
- 40. Lv Y, Qi R, Xu J, Di Z, Zheng H, Huo W, et al. Profiling of serum and urinary microRNAs in children with atopic dermatitis. PloS one. 2014;9(12):e115448. pmid:25531302;
- 41. Ke XF, Fang J, Wu XN, Yu CH. MicroRNA-203 accelerates apoptosis in LPS-stimulated alveolar epithelial cells by targeting PIK3CA. Biochemical and biophysical research communications. 2014;450(4):1297–303. pmid:24996183.
- 42. Dekkers BG, Maarsingh H, Meurs H, Gosens R. Airway structural components drive airway smooth muscle remodeling in asthma. Proceedings of the American Thoracic Society. 2009;6(8):683–92. pmid:20008876.
- 43. Martin JG, Ramos-Barbon D. Airway smooth muscle growth from the perspective of animal models. Respir Physiol Neurobiol. 2003;137(2–3):251–61. pmid:14516730.
- 44. Grasedieck S, Scholer N, Bommer M, Niess JH, Tumani H, Rouhi A, et al. Impact of serum storage conditions on microRNA stability. Leukemia. 2012;26(11):2414–6. pmid:22504138.
- 45. Mattes J, Collison A, Plank M, Phipps S, Foster PS. Antagonism of microRNA-126 suppresses the effector function of TH2 cells and the development of allergic airways disease. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(44):18704–9. pmid:19843690;
- 46. Collison A, Mattes J, Plank M, Foster PS. Inhibition of house dust mite-induced allergic airways disease by antagonism of microRNA-145 is comparable to glucocorticoid treatment. The Journal of allergy and clinical immunology. 2011;128(1):160–7.e4. pmid:21571357.
- 47. Kumar M, Ahmad T, Sharma A, Mabalirajan U, Kulshreshtha A, Agrawal A, et al. Let-7 microRNA-mediated regulation of IL-13 and allergic airway inflammation. The Journal of allergy and clinical immunology. 2011;128(5):1077–85 e1–10. pmid:21616524.