Figures
Abstract
Background
MicroRNAs are key transcriptional and network regulators previously associated with asthma susceptibility. However, their role in relation to asthma severity has not been delineated.
Objective
We hypothesized that circulating microRNAs could serve as biomarkers of changes in lung function in asthma patients.
Methods
We isolated microRNAs from serum samples obtained at randomization for 160 participants of the Childhood Asthma Management Program. Using a TaqMan microRNA array containing 754 microRNA primers, we tested for the presence of known asthma microRNAs, and assessed the association of the individual microRNAs with lung function as measured by FEV1/FVC, FEV1% and FVC%. We further tested the subset of FEV1/FVC microRNAs for sex-specific and lung developmental associations.
Results
Of the 108 well-detected circulating microRNAs, 74 (68.5%) had previously been linked to asthma susceptibility. We found 22 (20.3%), 4 (3.7%) and 8 (7.4%) microRNAs to be associated with FEV1/FVC, FEV1% and FVC%, respectively. 8 (of 22) FEV1/FVC, 3 (of 4) FEV1% and 1 (of 8) FVC% microRNAs had functionally validated target genes that have been linked via genome wide association studies to asthma and FEV1 change. Among the 22 FEV1/FVC microRNAs, 9 (40.9%) remain associated with FEV1/FVC in boys alone in a sex-stratified analysis (compared with 3 FEV1/FVC microRNAs in girls alone), 7 (31.8%) were associated with fetal lung development, and 3 (13.6%) in both. Ontology analyses revealed enrichment for pathways integral to asthma, including PPAR signaling, G-protein coupled signaling, actin and myosin binding, and respiratory system development.
Citation: Kho AT, Sharma S, Davis JS, Spina J, Howard D, McEnroy K, et al. (2016) Circulating MicroRNAs: Association with Lung Function in Asthma. PLoS ONE 11(6): e0157998. https://doi.org/10.1371/journal.pone.0157998
Editor: Tesfaye B. Mersha, Cincinnati Children's Hospital Medical center, UNITED STATES
Received: February 8, 2016; Accepted: June 8, 2016; Published: June 30, 2016
Copyright: © 2016 Kho 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 complete data is available on NCBI GEO under the accession number GSE74770.
Funding: This work is supported by the National Institutes of Health grants R01 HL127332, R01 HL129935, U01 HL65899, R01 HL092197, R01 NR013391, K25 HL091124 (ATK) and T32 HL007427 (JSD).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Asthma affects ~23 million individuals in the United States and ~300 million individuals worldwide[1] with rising prevalence. Over US$50 billion were spent on asthma in the U.S.A. in 2011. Despite this, progress toward novel diagnostic markers and therapies for asthma has been slow. MicroRNAs are single-stranded RNA molecules of 19–24 nucleotides in length. MicroRNAs are post-transcriptional regulators that bind to complementary sequences (often imperfectly) on target messenger RNA transcripts (mRNAs), usually resulting in translational repression or gene silencing[2]. In addition to regulating gene expression, microRNAs control a wide range of cell processes, including cell differentiation and growth, development, metabolism, signaling and apoptosis, as well as disease processes linked with cancer and inflammation[3]. Although human studies exploring microRNA differences between asthmatic patients and healthy controls have been performed, comprehensive assessment of microRNAs in inflammatory diseases, including asthma, remains largely unexplored.
With proper storage, circulating microRNAs are stable over many years[4–8]. The stability of circulating microRNA is characterized by its resistance to RNase activity, extreme pH and temperature[9–11], largely because they are bound to proteins, lipoproteins or are within exosomes[4, 12–16]. The combination of stability and measurability supports circulating microRNAs as noninvasive, sensitive biomarkers of disease[9, 10, 17]. In inflammatory diseases, circulating microRNAs likely arise from 2 sources: activated immune cells and tissues damaged by the immune attack[17]. To date, 2 asthma studies of limited sample sizes have analyzed circulating microRNAs[18, 19] and identified 30 microRNAs of which 4 been linked to asthma in previous studies of airway cells.
MicroRNA studies in asthma have mainly focused on asthma diagnosis. Quantitative severity measures may be more specific to asthma pathobiology, as they are not subject to potential diagnostic bias or misclassification. In this study, we investigated the association of circulating microRNAs in 160 asthmatic children with lung function measures as quantifiers of asthma severity at the time of randomization of the Childhood Asthma Management Program (CAMP) clinical trial. We focused on measures of lung function due to their easy measurement, reproducibility and role as part of current asthma treatment guidelines[20]. Given that this is the first large scale analysis of circulating microRNAs in asthma severity, we note that a significant proportion of microRNAs detected in the serum of childhood asthmatics have previously been associated with asthma in tissue studies, providing direct evidence of biological significance. Below, we describe the association of microRNAs with the degree of lung function impairment and the biochemical pathways predicted to be affected by these microRNAs, which together may yield novel insights into asthma pathogenesis.
Materials and Methods
Study population and samples
CAMP was a multicenter, randomized, double-blinded clinical trial testing the safety and efficacy of inhaled budesonide, nedocromil and placebo in 1041 children with mild to moderately severe asthma over a 4.3-year average. The trial design and methodology have been published[21, 22]. Entry criteria included asthma symptoms and/or medication use for ≥6 months in the previous year and airway responsiveness with PC20 ≤ 12.5 mg/ml. Spirometry was performed on a Collins Stead-Wells dry-seal Survey III spirometer[21]. At least 3 acceptable maneuvers meeting American Thoracic Society (ATS) standards were required, with at least 2 reproducible (forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) within 5% of best) maneuvers required for each test. Outcome measures of lung function included FEV1 and FVC as a percent of predicted (FEV1% and FVC%) and FEV1/FVC.
Blood serum from 160 CAMP subjects obtained at randomization were microRNA profiled with 13 subjects profiled twice to model technical replicability. In order to limit the known effects of race on microRNA expression[23, 24], the subjects were limited to self-identified non-Hispanic Caucasians. The subject characteristics are listed in Table 1. The CAMP Genetics Ancillary Study was approved by the Brigham and Women's Hospital Internal Review Board, and informed written consent/assent was obtained from all participants and their guardians.
Values shown as mean ± one standard deviation.
Human fetal lung tissue from 15 subjects aged 67 to 115 days post conception with non-smoking mothers were acquired through the tissue retrieval program of the National Institute of Child Health and Development at the University of Maryland Brain and Tissue Bank for Developmental Disorders (Baltimore, MD), and the University of Washington Center for Birth Defects Research (Seattle, WA) as previously described[25]. This tissue collection has been designated an institutional review board (IRB)-exempt protocol by the University of Missouri–Kansas City Pediatric IRB. This ancillary data is not shown in full in this report.
MicroRNA isolation, primers and annotations
Total RNA from 1 mL of serum from each CAMP subject was isolated using Norgen Biotek RNA isolation kit (Thorold, ON, Canada). We averaged 4 ng/ul of total RNA in 20 ul measured by RiboGreen. Isolated RNA was reverse-transcribed and the product was pre-amplified using Megaplex PreAmp Primers and TaqMan PreAmp Master Mix (Applied Biosystems, Grand Island, NY). Total RNA was isolated from human fetal lung tissue samples using the RNeasy mini kit (Qiagen, Valencia, CA).
TaqMan microRNA quantitative PCR primers were from Life Technologies Megaplex RT Primers, Human Pool Set v3.0 (Omaha, NE) which contains 754 primers representing 738 unique human microRNAs (miRBase release 21, https://tools.lifetechnologies.com/content/sfs/brochures/megaplex-pools-array-card-content.xlsx) and 4 housekeeping primers (RNU44, RNU48, U6, ath-MIR159). Samples were run on the QuantStudio 12K Flex Real-Time PCR System with OpenArray Block (Life Technologies, Carlsbad, CA). Initial quality control was performed per manufacturer protocol, using predefined thresholds for amplification scores (>1.24) and Cq (>0.80) confidence intervals. We excluded microRNAs from further analysis if they were detected in fewer than 75% of the 160 subjects (173 samples).
All microRNAs in this study were annotated by their miRBase release 21 (June 2014, ftp://mirbase.org/pub/mirbase/21/)[26] symbol and accession number. The complete dataset is accessible at the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nih.gov/geo/) as GSE74770.
Data analysis: Normalization and linear regression model
We quantile normalized (sample-wise) the data matrix of 758 microRNA primers x 173 samples of detected microRNA cycle threshold values (miR_Ct) to the matrix mean using the Matlab (MathWorks Inc, Natick, MA) function quantilenorm.
We used miR_Ct values that pass initial quality controls: amplification scores (>1.24) and Cq (>0.80) confidence intervals. We limited our analysis to 108 of the 738 non-housekeeping microRNA primers that were detected in at least 75% of the study samples for their miR_Ct linear associations with the following lung function measures/phenotypes separately: pre-bronchodilator FEV1 and FVC as percentages of predicted (FEV1% and FVC%) and FEV1/FVC, separately [27]. We used least squares linear regression models (Matlab function regress) to identify microRNAs with miR_Ct values that are associated with the each phenotype of interest.
For the sample-wise vector FEV1/FVC, we used the linear model FEV1/FVC = I × B0 + miR_Ct × B1 (**) for each microRNA, where miR_Ct represented the vector of sample-wise quantile-normalized quality-controlled cycle threshold values, I the vector of 1's with the same length as miR_Ct and B1 the scalar regression coefficient of interest. We considered the corresponding microRNA to be significantly associated with FEV1/FVC if the 95% confidence interval of B1 did not contain zero. We found 22 microRNAs significantly associated with FEV1/FVC. To assess the multiple comparisons error (i.e., false positive rate) in this strategy, we performed a permutation analysis[28] of the miR_Ct data matrix of 108 microRNAs x 173 samples. In each iteration of the permutation analysis, we shuffled the sample FEV1/FVC labels once and counted the number of microRNAs that were significant, i.e., whose B1 95% confidence interval excluded zero in (**). We iterated this process up to 10,000 times, each time noting the number of significant microRNAs or false positives whose distribution is shown in Figure A in S1 File. The median and mean number of false positives were 4 and 5.6, corresponding to false discovery rates of 18% (~4/22) and 25% (~5.6/22) respectively. Even though the p-value for the linear regression was not used in our strategy to determine significant association, we show it and the multiple testing adjusted p-value [29] computed using Matlab function mafdr.
Besides quantile normalized miR_Ct values, we also considered microRNA-/primer-wise rank normalized miR_Ct and corresponding phenotype variables as model inputs to assess the effect of outlier variables. In order to evaluate the association of the microRNA identified via the initial percent predicted analyses with raw lung function measures, multivariable analyses of FEV1 and FVC (in liters), and FEV1/FVC were performed, adjusted for age, sex and height, cf. Table B in S1 File.
Ontology analysis and genome wide associations
Ontological pathways analysis was performed using ClueGO and CluePedia[30, 31] plug-ins in Cytoscape (http://www.cytoscape.org/) focusing on lung function microRNAs that were either previously reported to be associated with asthma or significantly associated with gestational age in our human fetal lung data, i.e., age = I × B0 + miR_Ct × B1. Functionally validated target genes for microRNAs of interest were obtained from miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/ version 15 September 2015) [32]. Genome wide association study (GWAS) linked asthma and lung function mapped ontology traits were obtained from GWAS Catalog, the NHGRI-EBI catalog of published GWAS (https://www.ebi.ac.uk/gwas/ version 18 April 2016).
Results
Study population
The population characteristics of the 160 CAMP subjects are shown in Table 1. Due to known effects of race on microRNA expression [23, 24], we limited this study to self-identified non-Hispanic Caucasians. For these subjects, the global characteristics at randomization are representative of the larger CAMP non-Hispanic Caucasian cohort[22] (data not shown).
Detected circulating microRNAs are involved in asthma susceptibility
Of the 738 non-housekeeping microRNAs annotated with miRBase release 21 on the array, 108 (14.6%) were detected in at least 75% of these pediatric asthma samples, cf. Table A in S1 File. We investigated the tissue specificity and asthma susceptibility of these 108 microRNAs by noting their detection in asthma relative to non-asthma conditions in 15 human tissues studies, Table 2. 74 (68.5%) had evidence of at least 1 reported prior differential expression in relation to human asthma susceptibility, and 34 (31.5%) had been reported in 2 or more studies.
The source tissue and sizes of asthma and control populations are shown.
MicroRNA expression associated with lung function
We investigated the linear association of the microRNA with 3 measures of lung function, FEV1/FVC, FEV1% and FVC%, cf. Tables 3–5. FEV1/FVC had the greatest number of microRNA associations with 22 of the 108 (20.4%) circulating microRNA nominally associated with FEV1/FVC with a 18–25% false discovery rate. As indicated in the Materials and Methods section, even though the p-value for the linear regression was not used in our strategy to determine significance, we noted that the p-value and multiple testing adjusted p-value [29] for these genes were <0.05 and <0.25 respectively. 4 microRNAs were differentially expressed in association with FEV1%, while 8 were associated with FVC%. Most FEV1% and FEV1/FVC microRNAs were positively associated with lung function outcomes, while most FVC% microRNAs were associated with decrements in this measure of lung function.
The regression slope mean (beta) and its 95% confidence interval are shown. In the second column "Asthma Lit (Fetal)", "Y" indicates a report in at least 1 study listed in Table 2, "Y2" indicates a report in 2 or more of these studies, "(f)" indicates significant correlation of the microRNA in fetal lung tissue with gestational age. The third column "Target genes with GWAS asthma/FEV1 change" lists functionally validated target genes from miRTarBase that have GWAS linked asthma and lung function mapped ontology traits in GWAS Catalog.
From an effect estimate perspective, the strongest association with FEV1/FVC was miR-15b-5p, which was associated with a 1.98% increase (Beta) in FEV1/FVC for every 1 unit Ct change (2-fold change in microRNA expression), likely mediated in part by the strong negative effect of this microRNA on FVC. Similarly, each doubling in miR-27b-3p was associated with an average 2.68% decrease (Beta) in FEV1%, and each doubling of miR-320a was associated with an average 3.30% increase (Beta) in FVC%. Three examples of these associations are illustrated in Fig 1. For FEV1/FVC, FEV1% and FVC% respectively, 12 (55%), 3 (75%) and 6 (75%) of their associated microRNAs had prior evidence of associations with asthma susceptibility in 15 other studies, Table 2. Furthermore, 8 (of 22) FEV1/FVC and 3 (of 4) FEV1%, and 1 (of 8) FVC% microRNAs had functionally validated target genes that have been linked via GWAS to asthma and FEV1 change, Tables 3–6. All functionally validated target genes of all our FEV1/FVC, FEV1% and FVC% microRNAs from miRTarBase are listed in Table C in S1 File.
(A) Association of serum miR-331-3p with FEV1/FVC in childhood asthma. (B) Association of serum miR-27b-3p with FEV1% in childhood asthma. (C) Sex-specific association of miR-409-3p with FEV1/FVC in asthmatic boys.
Sex specific microRNA associations with FEV1/FVC
Given the preponderance of lung function associations with FEV1/FVC, and the known differences in the ratio by sex, we hypothesized that sex-specific differences in lung development may play a role. We investigate this by analyzing the 22 FEV1/FVC associated microRNAs above via sex stratified analyses. 9 (40.9%) of the 22 microRNA were associated in males alone, with 3 (13.6%) associated in females alone, Table 7. miR-409-3p is an example of a male associated FEV1/FVC microRNA, Fig 1C.
In the first column, "(f)" indicates significant correlation of the microRNA in fetal lung tissue with gestational age.
Lung function microRNAs in fetal lung development
In order to elucidate the role that developmental alterations might play in the microRNA-lung function associations, we analyzed the 22 FEV1/FVC microRNAs for association with developmental gestational age in a sample of 30 human fetal lung samples; we have previously described the utility of these samples in a transcriptomic study of human lung development[46]. 7 (31.8%) of the 22 microRNAs were associated with gestational age: miR-126-3p, miR-203a-3p, miR-26a-5p, miR-30b-5p, miR-342-3p, miR-409-3p and miR-942-5p, Table 3. This included 3 (miR-342-3p, miR-409-3p and miR-942-5p) associated with the male sex-specific FEV1/FVC analysis, Table 7.
Ontology analyses of lung function microRNAs
Pathway analyses focusing two sets of overlapping microRNA associations were performed using ClueGO and CluePedia[30, 31] plug-ins in Cytoscape. The first analysis focused on the FEV1/FVC microRNAs that were previously reported as differentially expressed in at least 2 case-control asthma studies, Table 3: miR-203a-3p, miR-26a-5p, miR-30b-5p and miR-454-3p. These 4 microRNAs were assessed for common target connectivity and associated pathways, Fig 2A. Multiple pathways are represented by these targets including PPAR signaling, G-protein coupled signaling, actin and myosin binding, fat cell differentiation, and SMAD protein signaling.
(A) FEV1/FVC microRNAs (miR-203a-3p, miR-26a-5p, miR-30b-5p and miR-454-3p) that were reported in at least 2 of 15 other asthma case-control studies listed in Table 2. (B) FEV1/FVC (miR-342-3p, miR-409-3p and miR-942-5p) microRNAs that were associated with boys alone in the sex stratified analysis and also correlated with gestational age in our fetal lung samples.
Our second pathway analysis focused on the 3 FEV1/FVC microRNAs that were associated with boys alone in the sex stratified analysis and also correlated with gestational age in our fetal lung samples: miR-342-3p, miR-409-3p and miR-942-5p. The significant pathways here included chromatin and histone modification, respiratory system development and the β-catenin pathway, Fig 2B.
Discussion
Circulating microRNAs offer the potential to capture the inflammatory milieu in large-scale population studies without the expense and invasiveness required for direct studies of asthmatic airway cells. Since serum is easily measured, microRNAs may serve as disease biomarkers. In this study, we interrogated serum samples from 160 childhood asthmatics upon entry into a large clinical trial. Following quality control, 108 microRNAs were expressed in at least 75% of the samples. Of these, 74 (68.5%) had previously been associated with asthma susceptibility in at least one prior study of airway cells, supporting the easy detection of disease specific microRNAs in circulation. As measures of lung function, we interrogated the role of circulating microRNAs in relation to FEV1%, FVC% and FEV1/FVC; noting that a significant proportion of the detected circulating microRNAs were associated with FEV1/FVC and a smaller number with FEV1% and FVC% (Tables 3–5). These associations included microRNAs both previously associated with asthma, as well as a significant proportion of novel associations. 8 (of 22) FEV1/FVC and 3 (of 4) FEV1%, and 1 (of 8) FVC% microRNAs had functionally validated target genes that have been linked via GWAS to asthma and FEV1 change, Together, these data support the biological role of microRNAs in asthma severity, and the easily measurable microRNAs as biomarkers of lung function within the circulation of asthmatics.
Of the 22 microRNAs associated with FEV1/FVC, 12 had previously been associated with asthma, Table 3: miR-126-3p, miR-1290, miR-15b-5p, miR-16-5p, miR-203a-3p, miR-26a-5p, miR-30b-5p, miR-301a-3p, miR-342-3p, miR-374a-5p, miR-454-3p and miR-660-5p. Of these, 4 microRNAs (miR-203a-3p, miR-26a-5p, miR-30b-5p and miR-454-3p) were previously reported as differentially expressed in at least 2 case-control asthma studies, with each associated in at least one study of bronchoscopically sampled airway cells. This supports the hypothesis that microRNAs may influence asthma susceptibility directly via targeting genes and cellular processes that may be involved in augmenting airflow obstruction. For instance, miR-203 has been associated with asthma in two airway epithelial cell studies[33, 41] and is upregulated in the serum of subjects with atopic dermatitis[47]. Several studies have highlighted the potential of miR-203 to influence asthma via inflammatory mechanisms, which may result in the abnormalities in FEV1/FVC noted in the current study. These include the association of increases in serum miR-203 with increased IgE level[47] and with airway epithelial cell apoptosis[48]. miR-26a has previously been associated with asthma in studies of bronchial epithelial cells, bronchoalveolar lavage fluid and serum[18, 34, 41]. In contrast to the inflammatory effects of miR-203, we have reported that miR-26a is strongly expressed in airway smooth muscle (ASM) cells [49]. Prior work has indicated that miR-26a is induced in human ASM cells following physical stretch and that increased levels of miR-26a causes ASM cellular hypertrophy by directly targeting GSK3B (glycogen synthase kinase-3b)[50]. This effect is reversible following miR-26a inhibition[50]. While direct molecular mechanistic functional experiments are beyond the scope of the current study, we emphasize that these associations are consistent with divergent biologic effects on differing airway cells affecting lung function via microRNAs and that these airway phenomena are easily detectable via serum sampling.
While known asthma microRNA associations with lung function in asthma support direct biologic modulation resulting from disease susceptibility, novel microRNAs were also associated with FEV1/FVC. For instance miR-186-5p, while not previously associated with asthma, may play a crucial role in the regulation of acetylcholine packaging and degradation [51]. In turn, the addition of anticholinergic therapy in patients with moderate to severe asthma has been shown to significantly improve lung function [52]. Thus, miR-186-5p may influence lung function via the modulation of airway tone via the cholinergic pathway.
Of the 4 microRNAs associated with FEV1%, 3 (miR-142-3p, miR-374-5p and miR-454-3p) were also associated with FEV1/FVC, supporting a common underlying mechanism. Both miR-374 and miR-454 have previously been associated with asthma in a case-control study of bronchial epithelial cells[33]. While little is known about the effect of these microRNAs on the airway, miR-374 target genes are enriched in alveolar epithelial cells during hyperoxic stress and recovery supporting a role in structural repair of the lung[53]. Only one microRNA, miR-15b-5p was noted in common with both FVC% and FEV1/FVC. miR-15b is down-regulated in association with asthma[36]. While the mechanistic basis for this association is unknown, lower lung miR-15b also differentiates smokers with chronic obstructive pulmonary disease from smokers without airflow obstruction via altered TGFβ signaling, supporting this as a potential role for this microRNA in lung function[54].
While the reported circulating microRNAs may directly influence lung function in asthma via active targeting of inflammatory or structural biology relevant to asthma, microRNAs are also key drivers of normal human embryonic and fetal development [55, 56], including that of the lung[57, 58]. Disruptions in the FEV1/FVC may arise as a result of dysanapsis [59], nonisotropic growth of lung airways and parenchyma is a feature of asthma and airway hyperresponsiveness which is greater in early childhood in boys than girls. We therefore tested for potential dysanapsis via analyses stratified by sex. Of the 22 circulating microRNAs associated with FEV1/FVC, over half of the microRNAs (9 in boys and 3 in girls) were significantly associated in one sex only, Table 7. To further explore whether these microRNAs might be mediators of lung developmental processes, we then examined these microRNAs for association with developmental age in a collection of human fetal lungs[46]. Globally, 7 of the 22 FEV1/FVC microRNAs were also associated with gestational age, with 3 (miR-342-3p, miR-409-3p and miR-942-5p) associated with both human fetal lung development and with FEV1/FVC in asthmatic boys alone. The developmental role of miR-409-3p has previously been postulated in non-asthmatic lung disease; miR-409 is differentially expressed in fetal lungs and in patients with idiopathic pulmonary fibrosis[60]. The developmental association of miR-342-3p is also intriguing, as it encodes for adipocyte differentiation from mesenchymal stem cells[61, 62]. While this may not directly affect FEV1/FVC in asthma, we have previously reported that increases in body mass index (BMI) are associated with decrements in FEV1/FVC in the CAMP cohort; these associations are significantly stronger in boys than girls[63]. Thus, developmental alterations regulated via microRNAs may directly influence airway biology through influences on the developing lung or indirectly alter lung function via other mechanisms.
Pathway analyses of the predicted microRNA targets from our analyses are consistent with the mediation of important asthma and developmental pathways via microRNAs resulting in altered lung function. For instance, in the evaluation of the 3 microRNAs associated with both lung function and consistently aligned with asthma susceptibility (Fig 2), multiple pathways were over-represented, including PPAR signaling, G-protein coupled signaling, and myosin binding. The β2-adrenergic receptor is a prime example of a G-protein coupled receptor and its role in airway tone and asthma has been widely espoused[64]; we have previously reported the genetic association of haplotypes within the β2AR gene with FEV1/FVC[65]. Peroxisome proliferator-activated receptor gamma (PPARG) is augmented in the bronchial submucosa, the airway epithelium, and the smooth muscle of asthmatics, as compared with control subjects. The intensity of PPARG expression in bronchial submucosa, as well as airway epithelium and smooth muscle, is negatively related to FEV1 [66]. Therapeutic targeting of the PPAR pathway has been postulated for treatment of a variety of inflammatory lung diseases, including asthma[67]. Finally, myosin-binding mediates airway smooth muscle tone and has long been implicated in dynamic airway luminal narrowing and airways responsiveness in asthma[68, 69].
Similarly, the microRNA targets of resulting from the overlap analysis of microRNAs associated with both fetal lung development and lung function represented pathways carefully aligned with significant biology, including respiratory system development and the β-catenin pathway. β-catenin not only regulates cell to cell adhesion as a protein interacting with cadherin, but also functions as a component of the Wnt signaling pathway[70]. In turn, the Wnt/β-catenin pathway is crucial for the patterning of the early lung morphogenesis in mice and humans[71]. We have previously implicated genotypic variation within the Wnt signaling pathway with alterations in lung function in childhood asthmatics[25]. Recently, it has been shown that modulation of the β-catenin pathway can abrogate experimental models of allergic airways disease[72], lending further potential significance to our findings.
Our study has a number of unique strengths. Among these are a large sample size combined with a large number of interrogated microRNAs. Our sample size provides us with the power to detect associations despite lower starting concentrations of microRNAs within the circulation. The CAMP cohort was well characterized using standardized lung function methodologies and blood sampling procedures across the CAMP clinical sites, thereby minimizing bias related to measurement error. Additionally, we performed analysis of biologic replicates in about 10% of the population cohort that showed high microRNA-microRNA correlations (rank correlations of >0.90 for the replicate samples—data not shown). Among the weaknesses to this study include the fact that the reported associations have yet to be functionally validated. We note that the CAMP clinical trial randomization occurred approximately 20 years ago[21, 22]. Despite this, multiple studies have shown that carefully stored samples can yield reliable microRNA concentrations months to years later[7, 8, 73]. That we are able to detect a substantial number of asthma associated microRNAs within the serum of the CAMP asthmatics 20 years following randomization enhances the validity of our approach. Finally, we note that 74 microRNAs previously associated with asthma were well detected in our serum samples; the majority of these were not associated in this study of lung function in asthmatic children. It is likely that these microRNAs function to influence asthma in ways independent of structural airways biology, such as via the immune or inflammatory response. Additional studies of these circulating microRNAs in conjunction with other phenotypes are warranted.
In conclusion, serum microRNAs are associated with lung function measures in the asthmatic child and appear to reflect in vivo airway cell biology. These findings support the likelihood that microRNAs can serve as easily measurable, circulating biomarkers of asthma severity. Given that microRNAs have been shown to have prognostic value in both cancer[9, 74–77] and other inflammatory processes[78–82], futher studies on the predictive value of microRNAs in relation to asthma outcomes are warranted.
Supporting Information
S1 File. This file contains Figure A, and Tables A-C described below.
The distribution of the number of false positives or significant microRNAs from 10,000 permutations of the sample FEV1/FVC labels of the 108 microRNAs x 173 samples data matrix (Figure A). 108 microRNAs identified by their miRBase release 21 symbol measured in our Childhood Asthma Management Program (CAMP) serum samples: 160 subjects, 173 samples. Asthma Literature (Column 2) is based on detection in 15 asthma microRNA studies in Table 2. Yes indicates report in at least 1 study. Yes2 indicates report in 2 or more studies (Table A). Multivariable microRNA-lung function associations adjusted for age, sex and height. FEV1 and FVC values in liters (Table B). All functionally validated target genes for our FEV1/FVC, FEV1% and FVC% microRNAs from miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/ version 15 September 2015) (Table C).
https://doi.org/10.1371/journal.pone.0157998.s001
(DOC)
Acknowledgments
This work is supported by the NIH grants R01 HL127332, R01 HL129935, U01 HL65899, R01 HL092197, R01 NR013391, K25 HL091124 (ATK) and T32 HL007427 (JSD). This manuscript is subject to the NIH public access policy.
Author Contributions
Conceived and designed the experiments: ATK KGT. Performed the experiments: ATK JSD SS. Analyzed the data: DH J. Spina K. McEnroy K. Moore J. Sylvia WQ. Contributed reagents/materials/analysis tools: ATK JSD STW KGT. Wrote the paper: ATK STW KGT.
References
- 1. Masoli M, Fabian D, Holt S, Beasley R. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy. 2004;59(5):469–78. pmid:15080825.
- 2. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33. Epub 2009/01/27. pmid:19167326.
- 3. Plank M, Maltby S, Mattes J, Foster PS. Targeting translational control as a novel way to treat inflammatory disease: the emerging role of microRNAs. Clinical and experimental allergy: journal of the British Society for Allergy and Clinical Immunology. 2013;43(9):981–99. Epub 2013/08/21. pmid:23957346.
- 4. Ge Q, Zhou Y, Lu J, Bai Y, Xie X, Lu Z. miRNA in plasma exosome is stable under different storage conditions. Molecules. 2014;19(2):1568–75. Epub 2014/01/30. pmid:24473213.
- 5. Callari M, Tiberio P, De Cecco L, Cavadini E, Dugo M, Ghimenti C, et al. Feasibility of circulating miRNA microarray analysis from archival plasma samples. Analytical biochemistry. 2013;437(2):123–5. Epub 2013/03/19. pmid:23499963.
- 6. Mraz M, Malinova K, Mayer J, Pospisilova S. MicroRNA isolation and stability in stored RNA samples. Biochemical and biophysical research communications. 2009;390(1):1–4. Epub 2009/09/23. pmid:19769940.
- 7. 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. Epub 2012/04/17. pmid:22504138.
- 8. Grasedieck S, Sorrentino A, Langer C, Buske C, Dohner H, Mertens D, et al. Circulating microRNAs in hematological diseases: principles, challenges, and perspectives. Blood. 2013;121(25):4977–84. Epub 2013/04/04. pmid:23550041.
- 9. Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell research. 2008;18(10):997–1006. Epub 2008/09/04. pmid:18766170.
- 10. 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. Epub 2008/07/30. pmid:18663219; PubMed Central PMCID: PMC2492472.
- 11. Zen K, Zhang CY. Circulating microRNAs: a novel class of biomarkers to diagnose and monitor human cancers. Med Res Rev. 2012;32(2):326–48. Epub 2012/03/03. pmid:22383180.
- 12. Rayner KJ, Hennessy EJ. Extracellular communication via microRNA: lipid particles have a new message. J Lipid Res. 2013;54(5):1174–81. Epub 2013/03/19. pmid:23505318; PubMed Central PMCID: PMC3622315.
- 13. Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011;13(4):423–33. Epub 2011/03/23. pmid:21423178; PubMed Central PMCID: PMC3074610.
- 14. Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proceedings of the National Academy of Sciences of the United States of America. 2011;108(12):5003–8. Epub 2011/03/09. pmid:21383194; PubMed Central PMCID: PMC3064324.
- 15. Li L, Zhu D, Huang L, Zhang J, Bian Z, Chen X, et al. Argonaute 2 complexes selectively protect the circulating microRNAs in cell-secreted microvesicles. PloS one. 2012;7(10):e46957. Epub 2012/10/19. pmid:23077538; PubMed Central PMCID: PMC3471944.
- 16. Lasser C. Identification and analysis of circulating exosomal microRNA in human body fluids. Methods Mol Biol. 2013;1024:109–28. Epub 2013/05/31. pmid:23719946.
- 17. Mi S, Zhang J, Zhang W, Huang RS. Circulating MicroRNAs as Biomarkers for Inflammatory Diseases. MicroRNA. 2013;2(1):64–72.
- 18. Panganiban RP, Pinkerton MH, Maru SY, Jefferson SJ, Roff AN, Ishmael FT. Differential microRNA expression in asthma and the role of miR-1248 in regulation of IL-5. American journal of clinical and experimental immunology. 2012;1(2):154–65. Epub 2012/01/01. pmid:23885321; PubMed Central PMCID: PMC3714196.
- 19. 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. Epub 2016/03/31. pmid:27025347.
- 20. Expert Panel Report 3 (EPR-3): Guidelines for the Diagnosis and Management of Asthma-Summary Report 2007. The Journal of allergy and clinical immunology. 2007;120(5 Suppl):S94–138. Epub 2007/12/06. pmid:17983880.
- 21. The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Control Clin Trials. 1999;20(1):91–120. pmid:10027502.
- 22. Long-term effects of budesonide or nedocromil in children with asthma. The Childhood Asthma Management Program Research Group. N Engl J Med. 2000;343(15):1054–63. pmid:11027739.
- 23. Bovell LC, Shanmugam C, Putcha BD, Katkoori VR, Zhang B, Bae S, et al. The prognostic value of microRNAs varies with patient race/ethnicity and stage of colorectal cancer. Clinical cancer research: an official journal of the American Association for Cancer Research. 2013;19(14):3955–65. Epub 2013/05/31. pmid:23719259; PubMed Central PMCID: PMC3746330.
- 24. 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. Epub 2011/06/22. pmid:21691150; PubMed Central PMCID: PMC3225983.
- 25. Sharma S, Tantisira K, Carey V, Murphy AJ, Lasky-Su J, Celedon JC, et al. A role for Wnt signaling genes in the pathogenesis of impaired lung function in asthma. American journal of respiratory and critical care medicine. 2010;181(4):328–36. Epub 2009/11/21. pmid:19926868; PubMed Central PMCID: PMC2822972.
- 26. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic acids research. 2008;36(Database issue):D154–8. Epub 2007/11/10. pmid:17991681; PubMed Central PMCID: PMC2238936.
- 27.
Johnson RA, Wichern DW. Applied multivariate statistical analysis. 5th ed. Upper Saddle River, N.J.: Prentice Hall; 2002. xviii, 767 p. p.
- 28.
Good PI. Permutation, parametric and bootstrap tests of hypotheses. 3rd ed. New York: Springer; 2005. xix, 315 p. p.
- 29. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995;57(1):289–300.
- 30. Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661–3. Epub 2013/01/18. pmid:23325622; PubMed Central PMCID: PMC3582273.
- 31. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3. Epub 2009/02/25. pmid:19237447; PubMed Central PMCID: PMC2666812.
- 32. Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic acids research. 2016;44(D1):D239–47. Epub 2015/11/22. pmid:26590260; PubMed Central PMCID: PMC4702890.
- 33. Jardim MJ, Dailey L, Silbajoris R, Diaz-Sanchez D. Distinct microRNA Expression in Human Airway Cells of AsthmaticDonors Identifies a Novel Asthma-associated Gene. American journal of respiratory cell and molecular biology. 2012. Epub 2012/06/09. pmid:22679274.
- 34. Levanen B, Bhakta NR, Torregrosa Paredes P, Barbeau R, Hiltbrunner S, Pollack JL, et al. Altered microRNA profiles in bronchoalveolar lavage fluid exosomes in asthmatic patients. The Journal of allergy and clinical immunology. 2013;131(3):894–903. Epub 2013/01/22. pmid:23333113; PubMed Central PMCID: PMC4013392.
- 35. Liu F, Qin HB, Xu B, Zhou H, Zhao DY. Profiling of miRNAs in pediatric asthma: upregulation of miRNA-221 and miRNA-485-3p. Molecular medicine reports. 2012;6(5):1178–82. Epub 2012/08/17. pmid:22895815.
- 36. Nakano T, Inoue Y, Shimojo N, Yamaide F, Morita Y, Arima T, et al. Lower levels of hsa-mir-15a, which decreases VEGFA, in the CD4+ T cells of pediatric patients with asthma. The Journal of allergy and clinical immunology. 2013;132(5):1224–7 e12. Epub 2013/08/21. pmid:23954351.
- 37. Nicodemus-Johnson J, Laxman B, Stern RK, Sudi J, Tierney CN, Norwick L, et al. Maternal asthma and microRNA regulation of soluble HLA-G in the airway. The Journal of allergy and clinical immunology. 2013;131(6):1496–503. Epub 2013/03/29. pmid:23534973; PubMed Central PMCID: PMC3779062.
- 38. Perry MM, Baker JE, Gibeon DS, Adcock IM, Chung KF. Airway smooth muscle hyperproliferation is regulated by microRNA-221 in severe asthma. American journal of respiratory cell and molecular biology. 2014;50(1):7–17. Epub 2013/08/16. pmid:23944957; PubMed Central PMCID: PMC3930931.
- 39. Pinkerton M, Chinchilli V, Banta E, Craig T, August A, Bascom R, et al. Differential expression of microRNAs in exhaled breath condensates of patients with asthma, patients with chronic obstructive pulmonary disease, and healthy adults. The Journal of allergy and clinical immunology. 2013;132(1):217–9. Epub 2013/05/01. pmid:23628339.
- 40. Seumois G, Vijayanand P, Eisley CJ, Omran N, Kalinke L, North M, et al. An integrated nano-scale approach to profile miRNAs in limited clinical samples. American journal of clinical and experimental immunology. 2012;1(2):70–89. Epub 2013/01/11. pmid:23304658; PubMed Central PMCID: PMC3538381.
- 41. 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. Epub 2012/09/08. pmid:22955319; PubMed Central PMCID: PMC3530212.
- 42. Suojalehto H, Lindstrom I, Majuri ML, Mitts C, Karjalainen J, Wolff H, et al. Altered microRNA expression of nasal mucosa in long-term asthma and allergic rhinitis. International archives of allergy and immunology. 2014;163(3):168–78. Epub 2014/02/12. pmid:24513959.
- 43. Tsitsiou E, Williams AE, Moschos SA, Patel K, Rossios C, Jiang X, et al. Transcriptome analysis shows activation of circulating CD8+ T cells in patients with severe asthma. The Journal of allergy and clinical immunology. 2012;129(1):95–103. Epub 2011/09/16. pmid:21917308.
- 44. 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. Epub 2009/06/13. pmid:19521514; PubMed Central PMCID: PMC2690402.
- 45. Yamamoto M, Singh A, Ruan J, Gauvreau GM, O'Byrne PM, Carlsten CR, et al. Decreased miR-192 expression in peripheral blood of asthmatic individuals undergoing an allergen inhalation challenge. BMC genomics. 2012;13:655. Epub 2012/11/23. pmid:23170939; PubMed Central PMCID: PMC3598672.
- 46. Kho AT, Bhattacharya S, Tantisira KG, Carey VJ, Gaedigk R, Leeder JS, et al. Transcriptomic analysis of human lung development. American journal of respiratory and critical care medicine. 2010;181(1):54–63. Epub 2009/10/10. pmid:19815808; PubMed Central PMCID: PMC2797628.
- 47. 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. Epub 2014/12/23. pmid:25531302; PubMed Central PMCID: PMC4274001.
- 48. 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. Epub 2014/07/06. pmid:24996183.
- 49. 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. Epub 2014/02/14. pmid:24522205; PubMed Central PMCID: PMC3986841.
- 50. Mohamed JS, Lopez MA, Boriek AM. Mechanical stretch up-regulates microRNA-26a and induces human airway smooth muscle hypertrophy by suppressing glycogen synthase kinase-3beta. The Journal of biological chemistry. 2010;285(38):29336–47. Epub 2010/06/08. pmid:20525681; PubMed Central PMCID: PMC2937966.
- 51. Nadorp B, Soreq H. Predicted overlapping microRNA regulators of acetylcholine packaging and degradation in neuroinflammation-related disorders. Frontiers in molecular neuroscience. 2014;7:9. Epub 2014/02/28. pmid:24574962; PubMed Central PMCID: PMC3918661.
- 52. Rodrigo GJ, Castro-Rodriguez JA. What is the role of tiotropium in asthma?: a systematic review with meta-analysis. Chest. 2015;147(2):388–96. Epub 2014/10/17. pmid:25322075.
- 53. Bhargava M, Dey S, Becker T, Steinbach M, Wu B, Lee SM, et al. Protein expression profile of rat type two alveolar epithelial cells during hyperoxic stress and recovery. American journal of physiology Lung cellular and molecular physiology. 2013;305(9):L604–14. Epub 2013/09/10. pmid:24014686; PubMed Central PMCID: PMC3840279.
- 54. Ezzie ME, Crawford M, Cho JH, Orellana R, Zhang S, Gelinas R, et al. Gene expression networks in COPD: microRNA and mRNA regulation. Thorax. 2012;67(2):122–31. Epub 2011/09/24. pmid:21940491.
- 55. Foshay KM, Gallicano GI. Small RNAs, big potential: the role of MicroRNAs in stem cell function. Current stem cell research & therapy. 2007;2(4):264–71. Epub 2008/01/29. pmid:18220910.
- 56. Zhao Y, Srivastava D. A developmental view of microRNA function. Trends in biochemical sciences. 2007;32(4):189–97. Epub 2007/03/14. pmid:17350266.
- 57. Khoshgoo N, Kholdebarin R, Iwasiow BM, Keijzer R. MicroRNAs and lung development. Pediatric pulmonology. 2013;48(4):317–23. Epub 2013/01/03. pmid:23281163.
- 58. Mendell JT. miRiad roles for the miR-17-92 cluster in development and disease. Cell. 2008;133(2):217–22. Epub 2008/04/22. pmid:18423194; PubMed Central PMCID: PMC2732113.
- 59.
Gappa M, Stocks J, Frey U. Chapter 3: Assessing lung growth and function in infants and young children. In: Frey U, Gerritsen J, editors. Respiratory Diseases in Infants and Children. 11. Sheffield: European Respiratory Society Journals, Ltd.; 2006. p. 22–40.
- 60. Milosevic J, Pandit K, Magister M, Rabinovich E, Ellwanger DC, Yu G, et al. Profibrotic role of miR-154 in pulmonary fibrosis. American journal of respiratory cell and molecular biology. 2012;47(6):879–87. Epub 2012/10/09. pmid:23043088; PubMed Central PMCID: PMC3547095.
- 61. Wang L, Xu L, Xu M, Liu G, Xing J, Sun C, et al. Obesity-Associated MiR-342-3p Promotes Adipogenesis of Mesenchymal Stem Cells by Suppressing CtBP2 and Releasing C/EBPalpha from CtBP2 Binding. Cellular physiology and biochemistry: international journal of experimental cellular physiology, biochemistry, and pharmacology. 2015;35(6):2285–98. Epub 2015/04/22. pmid:25895816.
- 62. Chartoumpekis DV, Zaravinos A, Ziros PG, Iskrenova RP, Psyrogiannis AI, Kyriazopoulou VE, et al. Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice. PloS one. 2012;7(4):e34872. Epub 2012/04/13. pmid:22496873; PubMed Central PMCID: PMC3319598.
- 63. Tantisira KG, Litonjua AA, Weiss ST, Fuhlbrigge AL. Association of body mass with pulmonary function in the Childhood Asthma Management Program (CAMP). Thorax. 2003;58(12):1036–41. Epub 2003/12/04. pmid:14645968; PubMed Central PMCID: PMC1746552.
- 64. Penn RB, Panettieri RA Jr., Benovic JL. Mechanisms of acute desensitization of the beta2AR-adenylyl cyclase pathway in human airway smooth muscle. American journal of respiratory cell and molecular biology. 1998;19(2):338–48. Epub 1998/08/12. pmid:9698608.
- 65. Hawkins GA, Tantisira K, Meyers DA, Ampleford EJ, Moore WC, Klanderman B, et al. Sequence, haplotype, and association analysis of ADRbeta2 in a multiethnic asthma case-control study. American journal of respiratory and critical care medicine. 2006;174(10):1101–9. Epub 2006/08/26. pmid:16931635; PubMed Central PMCID: PMC2648111.
- 66. Benayoun L, Letuve S, Druilhe A, Boczkowski J, Dombret MC, Mechighel P, et al. Regulation of peroxisome proliferator-activated receptor gamma expression in human asthmatic airways: relationship with proliferation, apoptosis, and airway remodeling. American journal of respiratory and critical care medicine. 2001;164(8 Pt 1):1487–94. Epub 2001/11/13. pmid:11704601.
- 67. Belvisi MG, Mitchell JA. Targeting PPAR receptors in the airway for the treatment of inflammatory lung disease. British journal of pharmacology. 2009;158(4):994–1003. Epub 2009/08/26. pmid:19703165; PubMed Central PMCID: PMC2785522.
- 68. Fredberg JJ, Inouye DS, Mijailovich SM, Butler JP. Perturbed equilibrium of myosin binding in airway smooth muscle and its implications in bronchospasm. American journal of respiratory and critical care medicine. 1999;159(3):959–67. Epub 1999/03/02. pmid:10051279.
- 69. Fredberg JJ, Inouye D, Miller B, Nathan M, Jafari S, Raboudi SH, et al. Airway smooth muscle, tidal stretches, and dynamically determined contractile states. American journal of respiratory and critical care medicine. 1997;156(6):1752–9. Epub 1997/12/31. pmid:9412551.
- 70. Kikuchi A. Regulation of beta-catenin signaling in the Wnt pathway. Biochemical and biophysical research communications. 2000;268(2):243–8. Epub 2000/02/19. pmid:10679188.
- 71. Zhang M, Shi J, Huang Y, Lai L. Expression of canonical WNT/beta-CATENIN signaling components in the developing human lung. BMC developmental biology. 2012;12:21. Epub 2012/08/01. pmid:22846383; PubMed Central PMCID: PMC3480893.
- 72. Reuter S, Martin H, Beckert H, Bros M, Montermann E, Belz C, et al. The Wnt/beta-catenin pathway attenuates experimental allergic airway disease. Journal of immunology. 2014;193(2):485–95. Epub 2014/06/15. pmid:24929002.
- 73. De Guire V, Robitaille R, Tetreault N, Guerin R, Menard C, Bambace N, et al. Circulating miRNAs as sensitive and specific biomarkers for the diagnosis and monitoring of human diseases: promises and challenges. Clinical biochemistry. 2013;46(10–11):846–60. Epub 2013/04/09. pmid:23562576.
- 74. Bianchi F, Nicassio F, Marzi M, Belloni E, Dall'olio V, Bernard L, et al. A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. EMBO Mol Med. 2011;3(8):495–503. Epub 2011/07/12. pmid:21744498; PubMed Central PMCID: PMC3377091.
- 75. Cho WC. Promises and challenges in developing miRNA as a molecular diagnostic tool for lung cancer. Expert Rev Mol Diagn. 2011;11(8):763–6. Epub 2011/10/26. pmid:22022936.
- 76. Heegaard NH, Schetter AJ, Welsh JA, Yoneda M, Bowman ED, Harris CC. Circulating micro-RNA expression profiles in early stage nonsmall cell lung cancer. Int J Cancer. 2012;130(6):1378–86. Epub 2011/05/06. pmid:21544802; PubMed Central PMCID: PMC3259258.
- 77. Shen Y, Tang D, Yao R, Wang M, Wang Y, Yao Y, et al. microRNA expression profiles associated with survival, disease progression, and response to gefitinib in completely resected non-small-cell lung cancer with EGFR mutation. Med Oncol. 2013;30(4):750. Epub 2013/11/08. pmid:24198203.
- 78. Zampetaki A, Kiechl S, Drozdov I, Willeit P, Mayr U, Prokopi M, et al. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circulation research. 2010;107(6):810–7. Epub 2010/07/24. pmid:20651284.
- 79. Hromadnikova I, Kotlabova K, Hympanova L, Doucha J, Krofta L. First Trimester Screening of Circulating C19MC microRNAs Can Predict Subsequent Onset of Gestational Hypertension. PloS one. 2014;9(12):e113735. Epub 2014/12/17. pmid:25502889.
- 80. Xu P, Zhao Y, Liu M, Wang Y, Wang H, Li YX, et al. Variations of microRNAs in human placentas and plasma from preeclamptic pregnancy. Hypertension. 2014;63(6):1276–84. Epub 2014/03/26. pmid:24664294.
- 81. Prats-Puig A, Ortega FJ, Mercader JM, Moreno-Navarrete JM, Moreno M, Bonet N, et al. Changes in circulating microRNAs are associated with childhood obesity. J Clin Endocrinol Metab. 2013;98(10):E1655–60. Epub 2013/08/10. pmid:23928666.
- 82. Shrivastava S, Petrone J, Steele R, Lauer GM, Di Bisceglie AM, Ray RB. Up-regulation of circulating miR-20a is correlated with hepatitis C virus-mediated liver disease progression. Hepatology. 2013;58(3):863–71. Epub 2013/02/08. pmid:23390075; PubMed Central PMCID: PMC3664107.