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The Effect of Statins on Blood Gene Expression in COPD

  • Ma’en Obeidat,

    Affiliation The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada

  • Nick Fishbane,

    Affiliation The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada

  • Yunlong Nie,

    Affiliation The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada

  • Virginia Chen,

    Affiliation Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, Canada

  • Zsuzsanna Hollander,

    Affiliation Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, Canada

  • Scott J. Tebbutt,

    Affiliations The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada, Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, Canada, Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada

  • Yohan Bossé,

    Affiliation Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Québec, Canada

  • Raymond T. Ng,

    Affiliation Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, Canada

  • Bruce E. Miller,

    Affiliation Respiratory Therapy Area Unit, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, United States of America

  • Bruce McManus,

    Affiliations The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada, Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, BC, Canada

  • Stephen Rennard,

    Affiliation Division of Pulmonary and Critical Care Medicine, University of Nebraska Medical Center, Omaha, Nebraska, United States of America

  • Peter D. Paré,

    Affiliations The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada, Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada

  • Don D. Sin

    don.sin@hli.ubc.ca

    Affiliations The University of British Columbia Centre for Heart Lung Innovation, St Paul’s Hospital, Vancouver, BC, Canada, Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, BC, Canada

The Effect of Statins on Blood Gene Expression in COPD

  • Ma’en Obeidat, 
  • Nick Fishbane, 
  • Yunlong Nie, 
  • Virginia Chen, 
  • Zsuzsanna Hollander, 
  • Scott J. Tebbutt, 
  • Yohan Bossé, 
  • Raymond T. Ng, 
  • Bruce E. Miller, 
  • Bruce McManus
PLOS
x

Abstract

Background

COPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown.

Objective

Identify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD.

Methods

Whole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser.

Results

25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington’s disease, Parkinson’s disease and acute myeloid leukemia gene signatures.

Conclusion

The blood gene signature of statins’ use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology.

Introduction

Chronic obstructive pulmonary disease (COPD) is characterized by chronic irreversible airflow limitation that is often accompanied by systemic inflammation and comorbidities [1,2]. COPD currently ranks as the fourth leading cause of death, and is predicted to become the third by 2020 [3]. Statins are widely used lipid-lowering agents that act through inhibiting 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, the rate-limiting step of hepatocyte cholesterol synthesis. Statins reduce the risk of cardiovascular morbidities and mortality [4]. Statins are also reported to have pleiotropic effects including anti-inflammatory, anti-oxidant, anti-thrombogenic and neuroprotective properties [5,6].

A number of retrospective studies have shown that statins play a beneficial role in COPD. The endpoints in these studies included reductions in the rate of hospitalization [7], lung function decline[8], and death [9]. Meta-analysis approaches have further supported the beneficial role of statins on outcomes among COPD patients [1012]. A number of epidemiological studies reported significant correlations between serum lipids and lung function measures. In the Third National Health and Nutrition Examination Survey (NHANES), forced expiratory volume in one second (FEV1) was positively related to LDL and negatively related to HDL [13]. Additionally, oxidized LDL levels were increased in COPD patients and were associated with lung function, inflammation, and oxidative stress [14]. Conversely, associations in the opposite direction were reported in the Multi- Ethnic Study of Atherosclerosis (MESA) study, in which HDL levels were negatively associated with FEV1/FVC ratio and percent emphysema [15]. Increased HDL levels have also been reported in patients with severe COPD [16,17].

At the molecular level, many genes involved in cholesterol homeostasis and transport are expressed in the lungs [18,19], and in vitro and in vivo studies have demonstrated the importance of cholesterol homeostasis to lung physiology (reviewed in [20,21]). Additionally, in animal models that mimic smoke-induced lung injury, statins were shown to inhibit airway epithelial injury [22] and to reverse smoke-induced pulmonary hypertension and prevent emphysema [23].

Recently, the largest clinical trial to date showed that simvastain 40mg/day did not affect exacerbation rates or the time to a first exacerbation in patients with COPD who were at high risk for exacerbations[24]. However, because this study excluded all patients with indications for statins such as those with cardiovascular comorbidities, diabetes, and even preclinical diabetes (i.e. elevated hemoglobin A1C), the results may lack generalizability to a “real-world” setting in which 20 to 40% of COPD patients are taking statins for cardiovascular indications. The impact of statins, beyond those on the cardiovascular system, in this setting is largely unknown.

The objective of the current study was to investigate the effects of statins on blood gene expression in a cohort of well-phenotyped COPD patients and to determine whether the changes in blood gene expression could suggest potential benefits in COPD.

Methods

Study participants

Study subjects were a subset of those in the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) cohort[25]. ECLIPSE is a non-interventional, multicentre, longitudinal prospective three-year study of individuals with COPD and control subjects. Blood was collected in PAXGene tubes and frozen at -80°C. A total of 619 subjects were selected for studying prognostic signatures of frequent COPD exacerbations. Out of the 619 subjects, 168 were statin users. The ECLIPSE study was conducted in accordance with the Declaration of Helsinki and good clinical practice guidelines, and has been approved by the relevant ethics and review boards at the participating centres. At the University of British Columbia (UBC), the Research Ethics Board (REB) number is H11-00786. Study participants received written informed consent. Patient information was anonymized and de-identified prior to the analysis. ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960.

Microarray data processing

Total RNA was extracted on QIAcube (Qiagen Inc) from the 64 PAXgene blood using the PAXgene Blood miRNA kit from PreAnalytix (Cat. #763134) according to manufacturer’s instructions. RNA was amplified and hybridized overnight to the Affymetrix Human Gene 1.1 ST array plates at the TSRI DNA Array Core Facility, Scripps Research Institute (La Jolla, CA). Array plates were scanned using the Affymetrix GeneTitan MC Scanner (Affymetrix Inc.) with default settings. Quality control of the microarray data was performed using oligo (oligo Bioconductor package)[26] and RMA Express (http://rmaexpress.bmbolstad.com/)[27]. Factor Analysis for Robust Microarray Summarization (FARMS Bioconductor package) [28] was used to background correct, normalize, and summarize the data and filter out non-informative probe sets. The raw expression data is available on the NCBI Gene Expression Omnibus (GEO) under accession number GSE71220, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71220.

Differential gene expression analysis

Differential gene expression analysis was performed using the Linear Models for Microarray Data (Limma) Bioconductor package[29]. To adjust the differential expression analysis for potential confounders, we created a propensity score and used the score as a covariate in Limma. The propensity score assigns a probability for being a statin user based on observed baseline characteristics [30]. Thus, for each patient, the propensity score is the probability that that patient would have been assigned to the statin group rather than control, given their measured characteristics. Using the StepAIC() function from the R stats package, ten covariates were identified from a collection of available phenotypes using a bidirectional stepwise regression with Akaike information criterion (AIC); these were age, asthma, smoking, BMI, FEV1, FEV1% predicted, inhaled corticosteroid use, hypertension, diabetes and cardiovascular history. Next, propensity score was computed for each individual using a logistic regression model with those ten covariates using the glm() function from the R stats package. Finally, the resulting propensity score was used as a covariate when modeling the statin effect on gene expressions using the R package limma.

Testing for differential gene expression using the propensity score produced a distribution of inflated test statistics. To correct for this, Surrogate Variable Analysis (SVA) [31] was used to identify components of expression heterogeneity and unknown sources of variation. SVA analysis was performed in the sva Bioconductor package [32]. The differential expression analysis in limma was then adjusted for propensity score, neutrophil percentage, monocyte percentage and for the 39 significant surrogate variables found in SVA.

Pathway enrichment analysis

Genes differentially expressed with respect to statin use were tested for enrichment in biological processes and pathways using the web-based gene set anaLysis toolkit (WebGestalt) [33]. Gene symbols were used as input for WebGestalt and tested for enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [34] and Gene Ontology (GO) processes [35]. Enrichment was undertaken using a hypergeometric model [36] and adjusting for multiple testing using the Benjamini Hochberg (BH) method [37].

ENCODE transcription factor enrichment analysis

To gain insight into what transcription factors are enriched in the promoters and 5`regions of genes within modules differentially expressed with statin use, we used the encyclopedia of the DNA elements (ENCODE) Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Significance Tool [38]. Gene symbols were used as input, and “all IDs” was selected as the background region for the hypergeometric test. A window of 500bp to 5000bp length from the transcription start sites was used for enrichment as provided by the software, and used all cell lines as input.

Similarity with other gene expression profiles

To gain insights into the biological significance of the statins-associated gene expression profile, the ProfileChaser online tool [39] was used to identify gene expression datasets from the Gene Expression Omnibus with similar expression profiles. ProfileChaser scores the similarity between two differentially expressed profiles (one supplied by the user and the comparison profile) using a weighted Pearson’s correlation. A null distribution for this measure is computed from the correlation coefficients of all experimental comparisons available in the ProfileChaser database, allowing for the calculation of a conservative false discovery rate (FDR) for each retrieved result. The expression values of statins-associated genes at nominal P values (P< 0.05) were used as input for ProfileChaser.

ProfileChaser output was manually filtered to keep only experiments that have more than 20 samples, and experiments performed in whole blood or blood-derived cell types.

Statistical analysis software

All statistical analyses were performed using R version 3.0.1 (http://www.r-project.org/). The following Bioconductor packages were used for microarray data processing: oligo[26], RMA Express [27], and FARMS [28]. The SVA package [32] was used for the surrogate variable analysis, and the differential expression was performed using the Limma package [29].

Results

In this study, 168 statin users and 452 non- users were included. The demographics of study participants are presented in Table 1. The two groups were different with respect to their age, gender, pack-years of smoking and as expected, diabetes and cardiovascular history. Applying FARMS filtering; the resulting blood gene expression dataset included 11,523 probesets that map to 7185 unique genes.

The results of differential gene expression analysis with respect to statin use adjusted for propensity score and surrogate variables are shown in Table 2. The table shows 25 and 8 genes were differentially expressed at Benjamini Hochberg FDR less than 10 and 5%, respectively. The most significant differentially expressed gene was the low-density lipoprotein receptor (LDLR) gene, which was up-regulated among COPD patients using statins (log2 fold-change of 0.097). The second gene was the the chemokine (C-X-C- motif) receptor 2 (CXCR2, log2 fold-change of -0.04), which was down-regulated among statin users. This was followed by sterol-C4-methyl oxidase-like (SC4MOL, log2 fold-change of 0.07) and family with sequence similarity 108, member A1 (FAM108A, log2 fold-change of 0.07), both up-regulated among statin users. Fig 1 shows a volcano plot of the results, annotating genes differentially expressed at FDR <5%.

thumbnail
Fig 1. Volcano plot of gene differentially expressed between statin users and non-users.

The plot shows the log fold-change on the X-axis versus the unadjusted P values (on the–log10 scale) on the Y-axis. The blue and red dots represent genes that showed fold change in either direction that is greater than 0.05 and have unadjusted P value <0.01. Genes that had an FDR-adjusted P value less than 0.05 for differential expression are annotated on the graph.

https://doi.org/10.1371/journal.pone.0140022.g001

At the pathway enrichment level, the 25 genes with FDR <10% were significantly enriched in a number of GO processes summarized in Table 3. Most of these processes were related to cholesterol homeostasis and metabolism. The most significant GO processes included cholesterol homeostasis, sterol homeostasis, regulation of plasma lipoprotein particle levels, lipid homeostasis, and sterol metabolic process. There were no significant KEGG pathways enriched among the 25 genes.

thumbnail
Table 3. GO biological processes’ enrichment among statin differentially expressed genes.

https://doi.org/10.1371/journal.pone.0140022.t003

The 25 genes that met the 10% FDR for differential expression were tested for enrichment in certain transcription factors binding sites as determined from ChIP-Seq experiments from the ENCODE project. The 18 genes were enriched for the transcription factor sterol regulatory element binding protein 2 (SREBP-2), at FDR = 1.12x10-4.

Finally, the expression profiles of 707 differentially expressed genes identified in this study at nominal P values (P<0.05) were compared with publically available experiments in Gene Expression Omnibus (GEO) to gain better insights and generate hypotheses with respect to the potential benefits of statins. The results of ProfileChaser are shown in Table 4. The tested expression profiles were compared to the “statin users vs. non users” profile. The most significantly correlated profile was from Huntington’s disease patients, and the direction of effect was that “normal vs. presymptomatic/ symptomatic Huntington’s” correlated with statin users vs. non-users. This was followed by the profile of acute myeloid leukemia; control vs. acute myeloid leukemia profile correlated with statin vs. non-statin users profile. The third correlated profile was that of Parkinson’s disease, where the disease vs. control profile correlated with statin vs. non-statin users profile.

Discussion

In the current study, a gene signature which correlated with statin use in the blood of COPD patients was identified. 25 genes were differentially expressed at 10% FDR. These genes were significantly enriched in pathways and biological processes related to cholesterol homeostasis and metabolism, consistent with statins’ mechanisms of action.

In hepatocytes, statins inhibit the 3-hydroxymethyl glutaryl coenzyme A (HMG-CoA) reductase enzyme; the rate-limiting step in cholesterol synthesis that converts HMG-CoA to mevalonic acid, a cholesterol precursor[40]. When cells are deprived of cholesterol, proteolytic cleavage generates the transcription factor SREBP-2 that translocates to the nucleus, binds and activates the promoters of SREBP-2-regulated genes involved in cholesterol biosynthesis such as HMG-CoA-synthase, HMG-CoA reductase, and cholesterol uptake genes including the low-density lipoprotein receptor (LDLR) [41]. In turn, the increased LDLR expression leads to more cellular uptake of LDL cholesterol, depleting it from the circulation. The other important transcription factor involved in cholesterol homeostasis is liver X receptor (LXR), which responds to elevated levels of cholesterol by up-regulating cholesterol efflux genes such as the ATP-binding cassette, sub-family A, member 1 (ABCA1), and ATP-binding cassette sub-family G member 1 (ABCG1) [4244].

The statins’ blood gene signature identified in this study is reflective of this mechanism. Indeed, the most significantly associated gene was the LDLR, which was up-regulated among patients using statin. Of the strongly correlated genes, ABCG1 was down-regulated with statin use, in line with previous reports [45]. Furthermore, sterol-C4-methyl oxidase-like (SC4MOL) and 24-dehydrocholesterol reductase (DHCR24, also known as seladin 1) were significantly up-regulated among statin users. DHCR24 up-regulation has been previously reported with simvastatin treatment [46] and mediates the anti-inflammatory effects of reconstituted HDL [47]. Myosin regulatory light chain interacting protein (MYLIP) gene, also known as the inducible degrader of the LDLR (IDOL) was significantly down-regulated with statin use. Loss of function mutation in MYLIP has been identified in individuals with low LDL [48]. Furthermore, the ENCODE data which indicated SREBP-2 as the only transcription factor enriched among statins differentially expressed genes suggest that this pathway is orchestrated through this transcription factor.

The most significantly enriched pathways in the statin gene signature were cholesterol homeostasis and lipoprotein levels. Significant associations between serum lipid levels and lung function have been reported, albeit not always in the same direction [1317]. Taken together, the differential gene expression results at the level of individual genes, pathways, and transcription factor enrichment strongly support the biological plausibility of the identified gene signature.

Comparing the statin-use signature with GEO profiles is an unbiased approach to identify shared expression profiles for different diseases, drugs, or perturbations. Concordant expression profiles leads to novel insights into the molecular mechanisms underlying a disease/drug of interest and generates hypotheses. Using ProfileChaser to find correlated profiles, a number of diseases were identified as having an expression profile that is similar to the statin use signature. The strongest correlation was with a Huntington’s disease (HD) expression profile[49]. The direction of effect was that HD controls vs. symptomatic or presymptomatic patient’s profiles correlate with statin user vs. non-users, suggesting a potential beneficial role for statins in HD patients. One study in rats have shown that statins, together with rofecoxib, exert a synergistic neuroprotective effect against malonic acid induced Huntington's disease and related cognitive dysfunction [50].

The second correlated expression profile with statin use was a signature of improved prognosis among pediatric acute myeloid leukemia patients (AML). In tumor cell lines, statins were shown to inhibit the growth of acute myeloid leukemia cells [51], to synergistically increase the killing of the human erythroleukemia cell line K562 by the chemotherapeutic agent cytosine arabinoside [52], and to inhibit the proliferation and cytotoxicity of a human leukemic natural killer cell line [53]. The third correlated signature with statin use was a signature of Parkinson’s disease (PD) patients when compared with healthy or neurodegenerative disease controls[54]. This suggests that statin may have an unfavorable effect on PD. A number of epidemiological studies have investigated the risk of PD in statin users with conflicting results (reviewed in [55,56]).

While there were no direct expression profile similarities or pathway enrichment in known respiratory disease or related phenotypes, the most significantly differentially expressed genes among statin users have been shown to play a role in lung pathophysiology (reviewed in [20]). In animal models, Ldlr-/- male mice (but not females) showed impaired alveologenesis [57]. In contrast, other studies have shown that Ldlr-/- mice fed with an atherogenic diet did not exhibit lung destruction or inflammatory changes, while airspace enlargement developed in the lungs of apolipoprotein E-deficient (Apoe(-/-)) mice exposed to the same diet[58]. Abcga1-/- mice have a pulmonary phenotype characterized by excess cholesterol in alveolar macrophages and type II cells [5961]. Abcga1-/- mice also demonstrated an exaggerated inflammatory response to LPS and K. pneumoniae [62]. CXCR2 is a chemokine receptor that is expressed by leukocytes, endothelial and epithelial cells, with IL-8 being the major ligand in humans[63]. CXCR2 plays an important role in neutrophil homestasis [64,65], and increased expression of CXCR2 has been reported in bronchial biopsies from COPD patients with severe exacerbations[66].

The current study has a number of limitations. First, information on the type and dose of statin was not captured in the ECLIPSE cohort. Studies have shown that different statins produce divergent gene expression profiles [67,68]. Second, gene expression is tissue specific, and hence it is possible that a similar approach in respiratory-relevant tissues such as lung tissue, or airway epithelial cells could lead to different results. Furthermore, in vitro and in vivo studies are needed to delineate the effect of statins in these tissues. Third, the results of this study are context specific, and as such different COPD cohorts may produce different results. For example, in COPD patients with evidence of existing systemic inflammation (such as elevated C reactive protein), statin use may produce a different expression profile. The same applies to the studies that showed correlated expression profiles with statins signature. Results from these studies need to be interpreted in the context of their respective cohort. In Parkinson’s disease, for instance, the low LDL cholesterol correlation with the disease may be a consequence rather than a cause of the disease. Future studies are warranted to investigate statins’ effects on these diseases. Finally, serum lipid levels were not available on ECLIPSE subjects, and it was not possible to correlate serum lipids with lung function and explore the impact of statin use on lipid levels and gene expression.

In conclusion, the current study has shown that statins have a significant effect on blood gene expression from COPD patients. Statins altered cholesterol homeostasis and metabolism pathways with no direct effect on known pathways related to respiratory diseases. The results suggest that reported benefits of statin use in COPD patients may mainly be driven by their effect on cholesterol homeostasis and the resulting cardiovascular protection. Further studies on the relevance of cholesterol homeostasis on lung pathophysiology in respiratory tissues such as lung or airway epithelium are warranted. The study highlights the use of gene expression profiling as a tool to inform prospective epidemiological studies and clinical trials.

Author Contributions

Conceived and designed the experiments: MO PDP DDS. Performed the experiments: MO NF YN VC PDP DDS. Analyzed the data: MO NF YN VC. Contributed reagents/materials/analysis tools: ZH SJT YB RTN BEM BM SR. Wrote the paper: MO NF PDP DDS.

References

  1. 1. GOLD. Global Initiative for Chronic Obstructive Lung disease 2011.
  2. 2. Decramer M, Janssens W. Chronic obstructive pulmonary disease and comorbidities. The Lancet Respiratory Medicine. 2013;1(1):73–83. pmid:24321806
  3. 3. Murray CJL, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. The Lancet. 1997;349(9064):1498–504.
  4. 4. Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20?536 high-risk individuals: a randomised placebocontrolled trial. The Lancet. 2002;360(9326):7–22.
  5. 5. Arnaud C, Burger F, Steffens S, Veillard NR, Nguyen TH, Trono D, et al. Statins Reduce Interleukin-6–Induced C-Reactive Protein in Human Hepatocytes: New Evidence for Direct Antiinflammatory Effects of Statins. Arteriosclerosis, Thrombosis, and Vascular Biology. 2005;25(6):1231–6. pmid:15790934
  6. 6. Young RP, Hopkins R, Eaton TE. Pharmacological actions of statins: potential utility in COPD. European Respiratory Review. 2009;18(114):222–32. pmid:20956147
  7. 7. Blamoun AI, Batty GN, DeBari VA, Rashid AO, Sheikh M, Khan MA. Statins may reduce episodes of exacerbation and the requirement for intubation in patients with COPD: evidence from a retrospective cohort study. International Journal of Clinical Practice. 2008;62(9):1373–8. pmid:18422598
  8. 8. Alexeeff SE, Litonjua AA, Sparrow D, Vokonas PS, Schwartz J. Statin Use Reduces Decline in Lung Function. American Journal of Respiratory and Critical Care Medicine. 2007;176(8):742–7. pmid:17673694
  9. 9. Frost FJ, Petersen H, Tollestrup K, Skipper B. INfluenza and copd mortality protection as pleiotropic, dose-dependent effects of statins*. Chest. 2007;131(4):1006–12. pmid:17426203
  10. 10. Horita N, Miyazawa N, Kojima R, Inoue M, Ishigatsubo Y, Ueda A, et al. Statins reduce all-cause mortality in chronic obstructive pulmonary disease: a systematic review and meta-analysis of observational studies. Respiratory Research. 2014;15(1):80.
  11. 11. Janda S, Park K, FitzGerald JM, Etminan M, Swiston J. Statins in copd: A systematic review. Chest. 2009;136(3):734–43. pmid:19376844
  12. 12. Dobler C, Wong K, Marks G. Associations between statins and COPD: a systematic review. BMC Pulmonary Medicine. 2009;9(1):32.
  13. 13. Cirillo DJ, Agrawal Y, Cassano PA. Lipids and Pulmonary Function in the Third National Health and Nutrition Examination Survey. American Journal of Epidemiology. 2002;155(9):842–8. pmid:11978588
  14. 14. Shen Y, Yang T, Guo S, Li X, Chen L, Wang T, et al. Increased serum ox-LDL levels correlated with lung function, inflammation, and oxidative stress in COPD. Mediators of inflammation. 2013;2013:972347. Epub 2013/10/01. pmid:24078777
  15. 15. Burkart KM, Manichaikul A, Wilk JB, Ahmed FS, Burke GL, Enright P, et al. APOM and high-density lipoprotein cholesterol are associated with lung function and per cent emphysema. European Respiratory Journal. 2014;43(4):1003–17. pmid:23900982
  16. 16. Tisi GM, Conrique A, Barrett-Connor E, Grundy SM. Increased high density lipoprotein cholesterol in obstructive pulmonary disease (predominant emphysematous type). Metabolism. 1981;30(4):340–6. pmid:7207205
  17. 17. Reed RM, Iacono A, DeFilippis A, Eberlein M, Girgis RE, Jones S. Advanced chronic obstructive pulmonary disease is associated with high levels of high-density lipoprotein cholesterol. The Journal of heart and lung transplantation: the official publication of the International Society for Heart Transplantation. 2011;30(6):674–8. Epub 2011/02/11.
  18. 18. Nistor A, Simionescu M. Uptake of low density lipoproteins by the hamster lung. Interactions with capillary endothelium. The American review of respiratory disease. 1986;134(6):1266–72. Epub 1986/12/01. pmid:3024539
  19. 19. Hass MA, Longmore WJ. Regulation of lung surfactant cholesterol metabolism by serum lipopoteins. Lipids. 1980;15(6):401–6. Epub 1980/06/01. pmid:7401938
  20. 20. Gowdy KM, Fessler MB. Emerging roles for cholesterol and lipoproteins in lung disease. Pulmonary pharmacology & therapeutics. 2013;26(4):430–7. Epub 2012/06/19. pmid:22706330
  21. 21. Azzam KM, Fessler MB. Crosstalk between reverse cholesterol transport and innate immunity. Trends in Endocrinology & Metabolism. 2012;23(4):169–78. pmid:22406271
  22. 22. Davis BB, Zeki AA, Bratt JM, Wang L, Filosto S, Walby WF, et al. Simvastatin inhibits smoke-induced airway epithelial injury: implications for COPD therapy. The European respiratory journal. 2013;42(2):350–61. Epub 2012/11/28. pmid:23180589
  23. 23. Wright JL, Zhou S, Preobrazhenska O, Marshall C, Sin DD, Laher I, et al. Statin reverses smoke-induced pulmonary hypertension and prevents emphysema but not airway remodeling. Am J Respir Crit Care Med. 2011;183(1):50–8. Epub 2010/08/17. pmid:20709821
  24. 24. Criner GJ, Connett JE, Aaron SD, Albert RK, Bailey WC, Casaburi R, et al. Simvastatin for the Prevention of Exacerbations in Moderate-to-Severe COPD. New England Journal of Medicine. 2014;370(23):2201–10. pmid:24836125
  25. 25. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, Edwards L, et al. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). The European respiratory journal. 2008;31(4):869–73. Epub 2008/01/25. pmid:18216052
  26. 26. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26(19):2363–7. pmid:20688976
  27. 27. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64. Epub 2003/08/20. pmid:12925520
  28. 28. Hochreiter S, Clevert D-A, Obermayer K. A new summarization method for affymetrix probe level data. Bioinformatics (Oxford, England). 2006;22(8):943–9.
  29. 29. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology. 2004;3:Article3. Epub 2006/05/02.
  30. 30. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate behavioral research. 2011;46(3):399–424. pmid:21818162
  31. 31. Leek JT, Storey JD. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet. 2007;3(9):e161.
  32. 32. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics (Oxford, England). 2012;28(6):882–3. Epub 2012/01/20.
  33. 33. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Research. 2013;41(W1):W77–W83.
  34. 34. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32(Database issue):D277–80. Epub 2003/12/19. pmid:14681412
  35. 35. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. Epub 2000/05/10. pmid:10802651
  36. 36. Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23(2):257–8. pmid:17098774
  37. 37. 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.
  38. 38. Auerbach RK, Chen B, Butte AJ. Relating Genes to Function: Identifying Enriched Transcription Factors using the ENCODE ChIP-Seq Significance Tool. Bioinformatics. 2013.
  39. 39. Engreitz JM, Chen R, Morgan AA, Dudley JT, Mallelwar R, Butte AJ. ProfileChaser: searching microarray repositories based on genome-wide patterns of differential expression. Bioinformatics (Oxford, England). 2011;27(23):3317–8. Epub 2011/10/05.
  40. 40. Stancu C, Sima A. Statins: mechanism of action and effects. Journal of cellular and molecular medicine. 2001;5(4):378–87. Epub 2002/06/18. pmid:12067471
  41. 41. Miserez AR, Muller PY, Barella L, Barella S, Staehelin HB, Leitersdorf E, et al. Sterol-regulatory element-binding protein (SREBP)-2 contributes to polygenic hypercholesterolaemia. Atherosclerosis. 2002;164(1):15–26. Epub 2002/07/18. pmid:12119189
  42. 42. Wong J, Quinn CM, Brown AJ. SREBP-2 positively regulates transcription of the cholesterol efflux gene, ABCA1, by generating oxysterol ligands for LXR. Biochem J. 2006;400(3):485–91. pmid:16901265
  43. 43. Chawla A, Repa JJ, Evans RM, Mangelsdorf DJ. Nuclear Receptors and Lipid Physiology: Opening the X-Files. Science. 2001;294(5548):1866–70. pmid:11729302
  44. 44. Yvan-Charvet L, Wang N, Tall AR. Role of HDL, ABCA1, and ABCG1 Transporters in Cholesterol Efflux and Immune Responses. Arteriosclerosis, Thrombosis, and Vascular Biology. 2010;30(2):139–43. pmid:19797709
  45. 45. Genvigir FD, Rodrigues AC, Cerda A, Hirata MH, Curi R, Hirata RD. ABCA1 and ABCG1 expressions are regulated by statins and ezetimibe in Caco-2 cells. Drug metabolism and drug interactions. 2011;26(1):33–6. Epub 2011/03/23. pmid:21417795
  46. 46. Ramos MC, Sierra S, Ramirez C, Velasco J, Burgos JS. Simvastatin Modulates the Alzheimer's Disease-Related Gene seladin-1. Journal of Alzheimer's Disease. 2012;28(2):297–301. pmid:21987590
  47. 47. McGrath KC, Li XH, Puranik R, Liong EC, Tan JT, Dy VM, et al. Role of 3beta-hydroxysteroid-delta 24 reductase in mediating antiinflammatory effects of high-density lipoproteins in endothelial cells. Arterioscler Thromb Vasc Biol. 2009;29(6):877–82. Epub 2009/03/28. pmid:19325144
  48. 48. Sorrentino V, Fouchier SW, Motazacker MM, Nelson JK, Defesche JC, Dallinga-Thie GM, et al. Identification of a loss-of-function inducible degrader of the low-density lipoprotein receptor variant in individuals with low circulating low-density lipoprotein. European heart journal. 2013;34(17):1292–7. Epub 2013/01/18. pmid:23324548
  49. 49. Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, et al. Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(31):11023–8. pmid:16043692
  50. 50. Kumar A, Sharma N, Mishra J, Kalonia H. Synergistical neuroprotection of rofecoxib and statins against malonic acid induced Huntington's disease like symptoms and related cognitive dysfunction in rats. European journal of pharmacology. 2013;709(1–3):1–12. Epub 2013/04/09. pmid:23562615
  51. 51. Burke LP, Lewis LD, Perez RP. Ubiquinone does not rescue acute myeloid leukemia cells from growth inhibition by statins. Leukemia. 2003;17(1):267–8. Epub 2003/01/17. pmid:12529692
  52. 52. Holstein SA, Hohl RJ. Interaction of cytosine arabinoside and lovastatin in human leukemia cells. Leukemia research. 2001;25(8):651–60. Epub 2001/06/09. pmid:11397469
  53. 53. Crosbie J, Magnussen M, Dornbier R, Iannone A, Steele T. Statins inhibit proliferation and cytotoxicity of a human leukemic natural killer cell line. Biomarker Research. 2013;1(1):33. pmid:24359683
  54. 54. Scherzer CR, Eklund AC, Morse LJ, Liao Z, Locascio JJ, Fefer D, et al. Molecular markers of early Parkinson's disease based on gene expression in blood. Proceedings of the National Academy of Sciences. 2007;104(3):955–60.
  55. 55. Dolga AM, Culmsee C, de Lau L, Winter Y, Oertel WH, Luiten PG, et al. Statins—increasing or reducing the risk of Parkinson's disease? Experimental neurology. 2011;228(1):1–4. Epub 2010/11/30. pmid:21111736
  56. 56. Roy A, Pahan K. Prospects of statins in Parkinson disease. The Neuroscientist: a review journal bringing neurobiology, neurology and psychiatry. 2011;17(3):244–55. Epub 2011/01/22.
  57. 57. Massaro D, Massaro GD. Developmental Alveologenesis: New Roles for ApoE and LDL Receptor. Pediatr Res. 2011;70(5):458–61. pmid:21796016
  58. 58. Goldklang M, Golovatch P, Zelonina T, Trischler J, Rabinowitz D, Lemaitre V, et al. Activation of the TLR4 signaling pathway and abnormal cholesterol efflux lead to emphysema in ApoE-deficient mice. American journal of physiology Lung cellular and molecular physiology. 2012;302(11):L1200–8. Epub 2012/03/27. pmid:22447954
  59. 59. Baldan A, Gomes AV, Ping P, Edwards PA. Loss of ABCG1 results in chronic pulmonary inflammation. Journal of immunology (Baltimore, Md: 1950). 2008;180(5):3560–8. Epub 2008/02/23.
  60. 60. Wojcik AJ, Skaflen MD, Srinivasan S, Hedrick CC. A critical role for ABCG1 in macrophage inflammation and lung homeostasis. Journal of immunology (Baltimore, Md: 1950). 2008;180(6):4273–82. Epub 2008/03/07.
  61. 61. Bates SR, Tao JQ, Collins HL, Francone OL, Rothblat GH. Pulmonary abnormalities due to ABCA1 deficiency in mice. American journal of physiology Lung cellular and molecular physiology. 2005;289(6):L980–9. Epub 2005/08/02. pmid:16055479
  62. 62. Draper DW, Madenspacher JH, Dixon D, King DH, Remaley AT, Fessler MB. ATP-binding cassette transporter G1 deficiency dysregulates host defense in the lung. Am J Respir Crit Care Med. 2010;182(3):404–12. Epub 2010/04/17. pmid:20395559
  63. 63. Zarbock A, Stadtmann A. CXCR2: From Bench to Bedside. Frontiers in Immunology. 2012;3.
  64. 64. Eash KJ, Greenbaum AM, Gopalan PK, Link DC. CXCR2 and CXCR4 antagonistically regulate neutrophil trafficking from murine bone marrow. J Clin Invest. 2010;120(7):2423–31. Epub 2010/06/03. pmid:20516641
  65. 65. Mei J, Liu Y, Dai N, Hoffmann C, Hudock KM, Zhang P, et al. Cxcr2 and Cxcl5 regulate the IL-17/G-CSF axis and neutrophil homeostasis in mice. J Clin Invest. 2012;122(3):974–86. Epub 2012/02/14. pmid:22326959
  66. 66. Qiu Y, Zhu J, Bandi V, Atmar RL, Hattotuwa K, Guntupalli KK, et al. Biopsy Neutrophilia, Neutrophil Chemokine and Receptor Gene Expression in Severe Exacerbations of Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine. 2003;168(8):968–75. pmid:12857718
  67. 67. Leszczynska A, Gora M, Plochocka D, Hoser G, Szkopinska A, Koblowska M, et al. Different statins produce highly divergent changes in gene expression profiles of human hepatoma cells: a pilot study. Acta biochimica Polonica. 2011;58(4):635–9. Epub 2011/12/02. pmid:22132374
  68. 68. Andreux PA, Mouchiroud L, Wang X, Jovaisaite V, Mottis A, Bichet S, et al. A method to identify and validate mitochondrial modulators using mammalian cells and the worm C. elegans. Sci Rep. 2014;4.