Next Article in Journal
Hybrid Composite Membrane of Phosphorylated Chitosan/Poly (Vinyl Alcohol)/Silica as a Proton Exchange Membrane
Next Article in Special Issue
Specifically Targeted Transport of Plasma Membrane Transporters: From Potential Mechanisms for Regulating Cell Health or Disease to Applications
Previous Article in Journal
Traditional Chinese Medicine Extract Properties Incorporated Energy Analysis for Membrane Concentration Processes
Previous Article in Special Issue
Quantitative Analysis of the Membrane Affinity of Local Anesthetics Using a Model Cell Membrane
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Membranes
Volume 11
Issue 9
10.3390/membranes11090674
membranes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease

by
Stanislav Kotlyarov
1,* and
Anna Kotlyarova
2
1
Department of Nursing, Ryazan State Medical University, 390026 Ryazan, Russia
2
Department of Pharmacology and Pharmacy, Ryazan State Medical University, 390026 Ryazan, Russia
*
Author to whom correspondence should be addressed.
Membranes 2021, 11(9), 674; https://doi.org/10.3390/membranes11090674
Submission received: 23 July 2021 / Revised: 26 August 2021 / Accepted: 30 August 2021 / Published: 31 August 2021
(This article belongs to the Special Issue Membrane Transport in Health and Disease: From Basic Science to Therapeutic Applications)
Browse Figures
Versions Notes

Abstract

:
Smoking is a key modifiable risk factor for developing the chronic obstructive pulmonary disease (COPD). When smoking, many processes, including the reverse transport of cholesterol mediated by the ATP binding cassette transporter A1 (ABCA1) protein are disrupted in the lungs. Changes in the cholesterol content in the lipid rafts of plasma membranes can modulate the function of transmembrane proteins localized in them. It is believed that this mechanism participates in increasing the inflammation in COPD. Methods: Bioinformatic analysis of datasets from Gene Expression Omnibus (GEO) was carried out. Gene expression data from datasets of alveolar macrophages and the epithelium of the respiratory tract in smokers and COPD patients compared with non-smokers were used for the analysis. To evaluate differentially expressed genes, bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2), and the GEO2R and Phantasus tools (v. 1.11.0). Results: The conducted bioinformatic analysis showed changes in the expression of the ABCA1 gene associated with smoking. In the alveolar macrophages of smokers, the expression levels of ABCA1 were lower than in non-smokers. At the same time, in most of the airway epithelial datasets, gene expression did not show any difference between the groups of smokers and non-smokers. In addition, it was shown that the expression of ABCA1 in the epithelial cells of the trachea and large bronchi is higher than in small bronchi. Conclusions: The conducted bioinformatic analysis showed that smoking can influence the expression of the ABCA1 gene, thereby modulating lipid transport processes in macrophages, which are part of the mechanisms of inflammation development.

1. Introduction

According to rough estimates, more than a billion people smoke in the world [1,2,3]. Smoking is the main cause of the chronic obstructive pulmonary disease (COPD), the medical and social significance of which is steadily increasing [4,5]. High levels of prevalence, morbidity, and mortality of COPD carry a heavy economic burden for patients, their families, society, and the state [6,7,8,9].
According to modern concepts, the pathogenesis of COPD is based on inflammation in the bronchi, in which many cells are involved. Macrophages play an important role in the development and progression of COPD [10,11,12]. These cells are heterogeneous in their functions and demonstrate both pro- and anti-inflammatory activity, participate in the production of many humoral factors, recruit other cells. It is believed that the heterogeneity of macrophages is based on the peculiarities of their carbohydrate and lipid metabolism [13,14,15,16,17,18,19].
It is known that smoking disrupts the transport of lipids and lipid-like molecules, including cholesterol in lung cells, which may be one of the links in a complex chain of processes underlying the development and progression of COPD [20,21,22,23,24]. Cholesterol is the most important component of the plasma membranes of cells and determines their structure and function through the regulation of some transmembrane proteins. A number of recent studies indicate that reverse cholesterol transport (RCT) participates not only in ensuring the homeostasis of cellular cholesterol but also in the innate immune response [25,26]. The participation of cholesterol in the innate immune response is mediated by the ATP binding cassette transporter A1 (ABCA1) transporter that regulates RCT.
ABCA1 belongs to a large group of ATP-binding (ABC) transporters that facilitate the movement of a wide range of substrates through cell membranes. There are 48 ABC transporters in humans, which are divided into 7 subfamilies (ABCA-ABCG) based on structural characteristics. At the moment, the role of only a few ABC transporters in lung function and the development of their diseases is well known. For example, ABCA3 is involved in the formation of a surfactant, and mutations of the ABCC7 gene (also known as cystic fibrosis transmembrane conductance regulator (CFTR)) are the cause of cystic fibrosis.
The ABCA subfamily in humans includes 12 proteins that are well known for their participation in lipid transport. ABCA1 is one of the most well-studied representatives of the ABCA subfamily. ABCA1 is expressed in various cells of many organs and it participates in the export of cholesterol and phospholipids from the cell to extracellular acceptors, thereby regulating the lipid homeostasis of cells [27,28]. Due to its role in the reverse transport of cholesterol, ABCA1 is considered an important participant in the pathogenesis of atherosclerosis. However, this is not the only known biological function of the transporter. Changing the cholesterol content in macrophages participates in the regulation of inflammation, phagocytosis, and apoptosis. ABCA1 is expressed at high levels in lung tissues and as it is believed it plays an important role in the development of COPD [29,30]. Lipid metabolism plays an important role in lung function. Moreover, the lungs are an organ with unique lipid biology. In this regard, it is interesting how smoking affects the expression of ABCA1 in various lung cells.
The purpose of this study is to analyze the patterns of expression of the human ABCA1 gene—transporter in smoking and COPD using bioinformatics analysis methods. To do this, we use the available capabilities of modern developments that ensure the availability of biological data for repeated analysis. Similar approaches are widely used in research, including for the analysis of the differential expression of ABC transporter gene profiles in the epithelium of the respiratory tract [31].

2. Materials and Methods

2.1. Data Collection

As a data source for the analysis, publicly available sets containing information on gene expression in the airway epithelium and alveolar macrophages were used. The analysis was carried out on data sets (gene sets) obtained from The Gene Expression Omnibus (GEO), The National Center for Biotechnology Information (NCBI). The Gene Expression Omnibus (GEO) is a web database containing gene expression data and hybridization arrays, chips, microarrays (https://www.ncbi.nlm.nih.gov/geo). The search for data sets for analysis was carried out using the keywords “alveolar macrophages” “smoking”, “airway epithelium/airway epithelial” (Figure 1).
Criteria for including sets in the analysis: (1) sets containing data on gene expression in the airway epithelium and alveolar macrophages obtained from both relatively healthy non-smokers and smokers or COPD patients; (2) comparable biomaterial sampling conditions and the presence of pre-processed gene expression data. The analysis did not include sets obtained from patients with lung cancer and other respiratory diseases, in addition to COPD, sets of experimental data obtained in animal models, as well as datasets that do not allow forming comparison groups of smokers and non-smokers. The availability of data on gene expression in COPD patients in the sets was not a prerequisite for inclusion.
According to the search criteria, the following sets were selected for analysis: GSE13896, GSE130928, GSE4498, GSE76324, GSE18385, GSE64614, GSE11906, GSE11784 (Table 1).
GSE13896 contained data on gene expression in alveolar macrophages obtained during bronchoalveolar lavage in 24 healthy non-smokers, 34 smokers, and 12 smokers with COPD [32]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
GSE130928 contained data on gene expression in alveolar macrophages obtained during bronchoalveolar lavage in 24 healthy non-smokers, 42 smokers, and 22 smokers with COPD [33]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
GSE4498 contained data on gene expression in the bronchial epithelium in 10 phenotypically normal smokers compared with 12 non-smokers [34,35]. The data were obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
Data on gene expression in the 3rd–4th-order bronchial epithelium obtained in 20 healthy non-smokers and 31 healthy smokers, and in the 10th–12th-order bronchial epithelium obtained in 57 healthy non-smokers and 52 healthy smokers were included in the analysis from the GSE76324 set [36,37]. The data were obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
GSE18385 contained data on gene expression in the epithelium of the bronchi of 3-4 orders, obtained in 21 healthy non-smokers, 31 healthy smokers, and small respiratory tract (10th–12th orders of the bronchi) in 51 healthy non-smokers, 58 healthy smokers, obtained by bronchoscopy [38]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
Data on gene expression in the bronchial epithelium, including the trachea [n = 27] and bronchi of the 4–6th order [n = 20] obtained in healthy non-smokers and the epithelium of the distal respiratory tract (bronchi of the 10th–12th order) obtained in 44 healthy non-smokers and 36 healthy smokers were included in the analysis from the GSE64614 set [39]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
Data on gene expression in samples of the tracheal epithelium, bronchi of the 2nd–3rd order, and bronchi of the 10–12th order, selected by fiber-optic bronchoscopy in 124 people (42 healthy non-smokers, 49 healthy smokers and 33 smokers with chronic respiratory symptoms and smokers with COPD) were included in the analysis from the GSE11906 set [40]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
Data on gene expression in the bronchial epithelium of the 10th–12th order obtained in 63 healthy non-smokers, 72 healthy smokers, and 22 patients with COPD were included in the analysis from the GSE11784 set [41,42]. The data was obtained using the GPL570 platform [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
The datasets selected for analysis were obtained from different studies and differed in data normalization methods. GSE13896, GSE4498, GSE76324, GSE18385, GSE64614, GSE11906, GSE11784, used the MAS5 normalization method, where GSE130928 used the Robust Multi-array Average (RMA) method. The sets were analyzed in accordance with the methods of obtaining and normalizing data that were used in the original study. Data from different sets were analyzed independently of each other and were not combined for analysis.

2.2. Data Extraction

For each dataset, the following information was extracted: the platform, the number of smokers, COPD patients and healthy non-smokers, smoking experience (pack-years index), the location in the respiratory tract from which samples were obtained (bronchial generation, trachea) and pre-processed gene expression data. To analyze the data on the expression of the ABCA1 gene, comparison groups were formed: smokers, healthy individuals, and patients with COPD (Table 2). Table 2 shows the demographic and clinical characteristics of the patients whose data make up the sets selected for analysis sets (GSE13896, GSE130928, GSE4498, GSE18385, GSE11906, GSE11784), except for the sets GSE76324, GSE64614, which do not contain information about patients.
In addition to the smoking status and the presence of COPD, data on smoking intensity (the pack-years index) were also taken into account. Other available categorical data from the sets were not analyzed in this study.

2.3. Differential Expression Analysis

In this study, the analysis of the differential expression of the ABCA1 gene in each of the sets in the comparison groups was carried out using GEO2R, Phantasus (v. 1.11.0), and the limma package in R (v. 4.0.2).
GEO2 (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is an interactive web tool for the analysis to compare gene expression levels in groups in a dataset of GEO. Phantasus (https://artyomovlab.wustl.edu/phantasus/)—a web application for visual and interactive gene expression analysis.
Using these tools, data on the differential expression of the ABCA1 gene in the comparison groups for each set, including p-value, logFC, were obtained. If necessary, log2 transformation and quantile normalization was performed. To adjust the level of statistical significance during multiple comparisons the algorithm of Benjamini& Hochberg (FDR-false discovery rate) was used, implemented in GEO2R, and using the limma package and the p. adjust function in R (v. 4.0.2) [43]. All p values satisfying the condition < 0.05 at FDR ≤ 0.1 were taken as statistically significant.
Visualization of the ABCA1 gene expression levels in the comparison groups in each of the data sets was carried out using the Phantasus tool (v. 1.11.0) [44]. The data is visualized as box diagrams. The diagrams visualize the minimum value (lower part of the vertical line), the first-third quartile (box), the median (horizontal line inside the box), and the maximum value (upper part of the vertical line) of the data distribution.

3. Results

The conducted bioinformatic analysis of the data sets of alveolar macrophages (GSE13896 and GSE130928) showed that smoking alters the expression of the ABCA1 gene. A statistically significant decrease in the expression levels of ABCA1 in the alveolar macrophages of smokers compared with non-smokers is determined (Figure 2). No differences in the expression of ABCA1 were found in smokers and patients with COPD. At the same time, a downregulated expression of the gene in COPD patients was also marked compared with non-smokers, which was found in the GSE130928 set (Figure 2b).
The obtained results correspond to the available data that cigarette smoke suppresses the RCT in alveolar macrophages, mediated by the ABCA1 transporter. Alveolar macrophages are important participants in inflammation in COPD [45,46]. These unique cells are in constant contact with inhaled microorganisms and exogenous particles and provide participation as the first line of the body defense. Macrophages are not homogeneous in their origin and participation in the pathogenesis of COPD. They play an important role not only in the implementation of the innate immune response but also perform a number of regulatory functions, participate in apoptosis [45,46].
The analysis of ABCA1 gene expression in the epithelium of the respiratory tract in smokers showed contradictory results. No statistically significant changes in the expression of ABCA1 were found in the sets GSE4498, GSE11906, GSE64614, GSE76324, and GSE18385, whereas in the set GSE11784 the expression of the ABCA1 gene was upregulated in smokers (more than 10 pack-years) (Figure 3).
It was also found that both smokers and non-smokers have upregulated expression of ABCA1 in the epithelial cells of the large bronchi (generation 2nd–4th) than small (generation 10th–12th) ones (Figure 4).
In general, the obtained results indicate that cigarette smoking, which is a modifiable risk factor for the development of COPD, is associated with differentiated patterns of the ABCA1 gene expression.

4. Discussion

We conducted a bioinformatic analysis of the ABCA1 gene expression in alveolar macrophages and airway epithelium in smokers, non-smokers, and COPD patients from GEO datasets and showed that the levels of gene expression in the alveolar macrophages of smokers are lower than in non-smokers. These data may indicate the effect of smoking on the expression of ABCA1.
Our experimental project included the analysis of publicly available databases obtained from alveolar macrophages (GSE13896, GSE130928) and respiratory tract epithelium (sets GSE4498, GSE76324, GSE18385, GSE64614, GSE11906, GSE11784) to determine the differential expression of the ABCA1 gene associated with smoking. Bioinformatic analysis of gene expression data placed in publicly available databases is widely used in research both for evaluating differentially expressed genes and for their functional analysis.
Using tools for online analysis, we formed comparison groups: smokers, healthy non-smokers, and patients with COPD. Statistically significant differences in gene expression levels were taken into account, which was corrected in accordance with the algorithm of Benjamini & Hochberg.
In a previous study using similar tools, the authors analyzed the differential expression of ABC transporter genes in the epithelium of the respiratory tract during smoking, in patients with COPD and bronchial asthma [31]. We analyzed the differential expression of one representative of a large family of ABC transporters—ABCA1 to confirm the information about the effect of smoking on it.
Literature data suggest that smoking has a significant effect on the expression and function of ABCA1 in the respiratory tract. The data obtained in recent years have expanded our understanding of the function of the ABCA1 protein [30]. This representative of a large family of ABC transporters is a key participant in the formation of high-density lipoprotein (HDL) due to its ability to export cholesterol and phospholipids from the cell to the extracellular acceptor. In this regard, the role of ABCA1 is well known in the pathogenesis of atherosclerosis [47]. However, taking into account the high levels of ABCA1 expression in lung tissues, it becomes obvious that the function of the transporter is much more extensive than it was thought previously [48,49,50]. The significance of Abca1 for lung function is well demonstrated by experimental data with gene knockout in mice that develop pronounced morphological changes in the lungs that increase with age and are characterized by the accumulation of foamy macrophages, destruction of alveolar septa, and epithelization of the alveoli due to severe hypertrophy and hyperplasia of type II pneumocytes [51]. The described morphological changes were accompanied by a decrease in tidal volume and hyperventilation [52].
The expression of ABCA1 is influenced by many factors. The transporter has a complex transcriptional and post-transcriptional regulation, which involves both cholesterol metabolites and humoral inflammatory factors [53,54,55,56,57,58].
The choice of alveolar macrophages for analysis is due to the multifaceted role of ABCA1 in the function of these cells. It is known that lung macrophages act as the first line of immune defense of the lungs. These cells are heterogeneous in their origin and functions. They play an important role in the pathogenesis of COPD and their number increases significantly in the lungs with COPD. ABCA1 is involved in providing several functions of macrophages associated with inflammation [59,60]. The activity of phagocytosis by macrophages may be associated with the levels of expression and functional activity of ABCA1, which ensures the removal of excess cholesterol engulfed during phagocytosis. Conversely, there is a decrease in the phagocytic activity of ABCA1 deficient macrophages. A decrease in the expression and functional activity of the transporter leads to a decrease in the RCT and its excessive accumulation in macrophages, which has great consequences for their inflammatory activation [26,61]. Cholesterol can directly act as a trigger for the cellular inflammatory response and affect some signaling pathways.
An important mechanism that ensures the participation of ABCA1 in inflammation is the transporter-mediated regulation of the cholesterol content in the lipid rafts of plasma membranes [62,63]. Lipid rafts are specialized membrane microdomains of the plasma membrane of cells, enriched with cholesterol and sphingolipids. The structure of lipid rafts is dynamic, which is associated with the constantly changing content of both lipids and proteins. Cholesterol is the most important component of lipid rafts, as it is necessary for their formation and configuration [64]. Moreover, cholesterol performs not only a structural role, due to the rigid sterane backbone. It is believed that it is able to interact directly with the transmembrane domains of proteins and influence their activity [65]. ABCA1, by changing the cholesterol level, ensures the stability of lipid rafts and leads to the activation or deactivation of related proteins, for example, Toll-like receptor TLR4, which regulates the inflammatory response to lipopolysaccharide (LPS) of Gram-negative bacteria and plays an important role in the pathogenesis of COPD. Upon activation, TLR4 is localized in lipid rafts and their destruction disrupts the signal transduction of the receptor [66].
A decrease in the expression of ABCA1 and its functional activity during smoking leads to a decrease in RCT and intracellular accumulation of cholesterol, which contributes to the activation of inflammation through several mechanisms [26,61,67]. Conversely, an increase in the functional activity of ABCA1 can have an anti-inflammatory effect by removing excess cholesterol [48,68,69].
Thus, a decrease in the expression of ABCA1 caused by smoking can lead to the inflammatory activation of macrophages. The data obtained in this study indicate that smoking is associated with a decrease in the expression of ABCA1 in alveolar macrophages.
Cholesterol transport is not the only function of ABCA1, since it is involved in the movement of other lipids that are, for example, part of a surfactant [70]. This role is well demonstrated by a study with the accumulation of vacuoles in type II pneumocytes in mice with a knockout of the Abca1 gene, which indicates insufficient surfactant secretion [52]. Thus, the role of ABCA1 in macrophages and alveolar epithelial cells may be different.
The analysis of the expression of ABCA1 in the epithelium of large and small airways showed that there are variations in the results depending on which data set is being studied. In most datasets (GSE4498, GSE11906, GSE64614, GSE76324, and GSE18385), there were no statistically significant differences in the expression of ABCA1 in the comparison groups, whereas, in the GSE11784 set, the expression of the ABCA1 gene was increased in smokers. When interpreting these data, differences in the expression of ABCA1 in different types of bronchial epithelial cells should be taken into account [71]. However, the data sets did not differentiate cell types. In addition, in some sets, differences were found in the expression of ABCA1 in the epithelial cells of the trachea and large bronchi, compared with small airways. In a previous study, similar expression dynamics were demonstrated for another representative of the ABCA subfamily, ABCA13, whose function is not completely clear, but it is also believed to be involved in lipid transport [31]. These data may indicate that there are differences in lipid transport in different parts of the respiratory tract.
Previous studies have already shown a violation of ABCA1 expression in smoking and COPD [59,60,72]. The data obtained in this study confirm the available data. It should be noted that COPD is a heterogeneous disease with various clinical manifestations, which are based on the features of pathophysiological mechanisms, many of which are not clear today. Taking into account the large heterogeneity of COPD patients and the fact that different lung cells with differences in lipid metabolism may be involved in the pathogenesis of COPD in different ways, more data are needed to interpret and understand violations of the expression and functional activity of ABCA1 in smoking and COPD. It is also important that many other factors besides smoking, including bacterial colonization of the bronchi, can affect lipid metabolism and lipid transport processes in COPD [73]. However, in general, the data accumulated to date indicate the important role of lipids located at the intersection of many signaling pathways in providing immune protection of the lungs.
It should be noted that the present study has some limitations due to the fact that the data sets contain a small number of patients; there is not enough information about the patients taking medications that can affect lipid metabolism. However, these limitations, typical for bioinformatic analysis, may be useful for planning further experimental research.
In this regard, it is interesting to further study the role of the ABCA1 transporter in different cells of the respiratory tract, as well as in cells with different functional activity. Bioinformatic analysis is a useful tool that can be used to analyze data to obtain new information on gene expression, as well as when planning experimental studies.

5. Conclusions

Thus, the conducted bioinformatic analysis showed that smoking can influence the expression of the ABCA1 gene, thereby modulating lipid transport processes in macrophages and epithelium of the respiratory tract, which are part of the mechanisms of inflammation development.

Author Contributions

Conceptualization, S.K.; methodology, S.K.; software, S.K.; validation, S.K. and A.K.; formal analysis, S.K. and A.K.; investigation, S.K.; resources, S.K.; data curation, S.K. and A.K.; writing—original draft preparation, S.K.; writing—review and editing, S.K. and A.K.; visualization, S.K.; supervision, S.K.; project administration, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Proctor, R.N. Tobacco and the global lung cancer epidemic. Nat. Rev. Cancer 2001, 1, 82–86. [Google Scholar] [CrossRef]
  2. World Health Organization. WHO Report on the Global Tobacco Epidemic, 2019: Offer Help to quit Tobacco Use; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
  3. Jafari, A.; Rajabi, A.; Gholian-Aval, M.; Peyman, N.; Mahdizadeh, M.; Tehrani, H. National, regional, and global prevalence of cigarette smoking among women/females in the general population: A systematic review and meta-analysis. Environ. Health Prev. Med. 2021, 26, 1–13. [Google Scholar] [CrossRef]
  4. Spira, A.; Beane-Ebel, J.; Shah, V.; Liu, G.; Schembri, F.; Yang, X.; Palma, J.; Brody, J.S. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc. Natl. Acad. Sci. USA 2004, 101, 10143–10148. [Google Scholar] [CrossRef] [Green Version]
  5. Vogelmeier, C.F.; Criner, G.J.; Martínez, F.J.; Anzueto, A.; Barnes, P.J.; Bourbeau, J.; Celli, B.R.; Chen, R.; Decramer, M.; Fabbri, L.M.; et al. Informe 2017 de la iniciativa global para el diagnóstico, tratamiento y prevención de la enfermedad pulmonar obstructiva crónica: Resumen ejecutivo de gold. Am. J. Respir. Crit. Care Med. 2017, 53, 128–149. [Google Scholar] [CrossRef]
  6. Soriano, J.B.; Kendrick, P.J.; Paulson, K.R.; Gupta, V.; Abrams, E.M.; Adedoyin, R.A.; Adhikari, T.B.; Advani, S.M.; Agrawal, A.; Ahmadian, E.; et al. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: A systematic analysis for the global burden of disease study 2017. Lancet Respir. Med. 2020, 8, 585–596. [Google Scholar] [CrossRef]
  7. Quaderi, S.; Hurst, J. The unmet global burden of COPD. Glob. Health Epidemiol. Genom. 2018, 3, e4. [Google Scholar] [CrossRef] [Green Version]
  8. Blanco, I.; Diego, I.; Bueno, P.; Casas-Maldonado, F.; Miravitlles, M. Geographic distribution of COPD prevalence in the world displayed by geographic information system maps. Eur. Respir. J. 2019, 54, 1900610. [Google Scholar] [CrossRef]
  9. Løkke, A.; Lange, P.; Lykkegaard, J.; Ibsen, R.; Andersson, M.; Licht, S.D.F.; Hilberg, O. Economic Burden of COPD by disease severity—A nationwide cohort study in Denmark. Int. J. Chronic Obstr. Pulm. Dis. 2021, 16, 603–613. [Google Scholar] [CrossRef] [PubMed]
  10. Yamasaki, K.; Van Eeden, S.F. Lung macrophage phenotypes and functional responses: Role in the pathogenesis of COPD. Int. J. Mol. Sci. 2018, 19, 582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Barnes, P.J. Inflammatory mechanisms in patients with chronic obstructive pulmonary disease. J. Allergy Clin. Immunol. 2016, 138, 16–27. [Google Scholar] [CrossRef] [Green Version]
  12. Russell, R.; Culpitt, S.V.; DeMatos, C.; Donnelly, L.; Smith, M.; Wiggins, J.; Barnes, P.J. Release and activity of matrix metalloproteinase-9 and tissue inhibitor of metalloproteinase-1 by alveolar macrophages from patients with chronic obstructive pulmonary disease. Am. J. Respir. Cell Mol. Biol. 2002, 26, 602–609. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Batista-Gonzalez, A.; Vidal, R.; Criollo, A.; Carreño, L.J. New insights on the role of lipid metabolism in the metabolic reprogramming of macrophages. Front. Immunol. 2020, 10, 2993. [Google Scholar] [CrossRef] [PubMed]
  14. Bossche, J.V.D.; O’Neill, L.; Menon, D. Macrophage immunometabolism: Where are we (going)? Trends Immunol. 2017, 38, 395–406. [Google Scholar] [CrossRef]
  15. Remmerie, A.; Scott, C.L. Macrophages and lipid metabolism. Cell. Immunol. 2018, 330, 27–42. [Google Scholar] [CrossRef]
  16. Izquierdo, E.; Cuevas, V.D.; Fernández-Arroyo, S.; Riera-Borrull, M.; Orta-Zavalza, E.; Joven, J.; Rial, E.; Corbi, A.L.; Escribese, M.M. Reshaping of human macrophage polarization through modulation of glucose catabolic pathways. J. Immunol. 2015, 195, 2442–2451. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Agarwal, A.R.; Kadam, S.; Brahme, A.; Agrawal, M.; Apte, K.; Narke, G.; Kekan, K.; Madas, S.; Salvi, S. Systemic immuno-metabolic alterations in chronic obstructive pulmonary disease (COPD). Respir. Res. 2019, 20, 171. [Google Scholar] [CrossRef]
  18. Angela, M.; Endo, Y.; Asou, H.K.; Yamamoto, T.; Tumes, D.J.; Tokuyama, H.; Yokote, K.; Nakayama, T. Fatty acid metabolic reprogramming via mTOR-mediated inductions of PPARγ directs early activation of T cells. Nat. Commun. 2016, 7, 13683. [Google Scholar] [CrossRef]
  19. Viola, A.; Munari, F.; Sánchez-Rodríguez, R.; Scolaro, T.; Castegna, A. The metabolic signature of macrophage responses. Front. Immunol. 2019, 10, 1462. [Google Scholar] [CrossRef] [Green Version]
  20. Rahman, I.; Van Schadewijk, A.A.M.; Crowther, A.J.L.; Hiemstra, P.S.; Stolk, J.; MacNee, W.; de Boer, W. 4-Hydroxy-2-nonenal, a specific lipid peroxidation product, is elevated in lungs of patients with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2002, 166, 490–495. [Google Scholar] [CrossRef]
  21. Aoshiba, K.; Koinuma, M.; Yokohori, N.; Nagai, A. Immunohistochemical evaluation of oxidative stress in murine lungs after cigarette smoke exposure. Inhal. Toxicol. 2003, 15, 1029–1038. [Google Scholar] [CrossRef]
  22. Malhotra, D.; Thimmulappa, R.; Navas-Acien, A.; Sandford, A.; Elliott, M.; Singh, A.; Chen, L.; Zhuang, X.; Hogg, J.; Pare, P.; et al. Decline in NRF2-regulated antioxidants in chronic obstructive pulmonary disease lungs due to loss of its positive regulator, DJ-1. Am. J. Respir. Crit. Care Med. 2008, 178, 592–604. [Google Scholar] [CrossRef] [Green Version]
  23. Davies, P.; Sornberger, G.C.; Huber, G.L. The stereology of pulmonary alveolar macrophages after prolonged experimental exposure to tobacco smoke. Lab. Investig. 1977, 37, 297–306. [Google Scholar]
  24. Hannan, S.E.; Harris, J.O.; Sheridan, N.P.; Patel, J.M. Cigarette smoke alters plasma membrane fluidity of rat alveolar macrophages. Am. Rev. Respir. Dis. 1989, 140, 1668–1673. [Google Scholar] [CrossRef]
  25. Azzam, K.M.; Fessler, M.B. Crosstalk between reverse cholesterol transport and innate immunity. Trends Endocrinol. Metab. 2012, 23, 169–178. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Tall, A.R.; Yvan-Charvet, L. Cholesterol, inflammation and innate immunity. Nat. Rev. Immunol. 2015, 15, 104–116. [Google Scholar] [CrossRef] [Green Version]
  27. Ouimet, M.; Barrett, T.; Fisher, E.A. HDL and reverse cholesterol transport. Circ. Res. 2019, 124, 1505–1518. [Google Scholar] [CrossRef]
  28. Jessup, W.; Gelissen, I.C.; Gaus, K.; Kritharides, L. Roles of ATP binding cassette transporters A1 and G1, scavenger receptor BI and membrane lipid domains in cholesterol export from macrophages. Curr. Opin. Lipidol. 2006, 17, 247–257. [Google Scholar] [CrossRef] [PubMed]
  29. Kotlyarov, S.; Kotlyarova, A. The role of ABC transporters in lipid metabolism and the comorbid course of chronic obstructive pulmonary disease and atherosclerosis. Int. J. Mol. Sci. 2021, 22, 6711. [Google Scholar] [CrossRef] [PubMed]
  30. Kotlyarov, S. Participation of ABCA1 transporter in pathogenesis of chronic obstructive pulmonary disease. Int. J. Mol. Sci. 2021, 22, 3334. [Google Scholar] [CrossRef]
  31. Aguiar, J.A.; Tamminga, A.; Lobb, B.; Huff, R.D.; Nguyen, J.P.; Kim, Y.; Dvorkin-Gheva, A.; Stampfli, M.R.; Doxey, A.C.; Hirota, J.A. The impact of cigarette smoke exposure, COPD, or asthma status on ABC transporter gene expression in human airway epithelial cells. Sci. Rep. 2019, 9, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Shaykhiev, R.; Krause, A.; Salit, J.; Strulovici-Barel, Y.; Harvey, B.-G.; O’Connor, T.P.; Crystal, R.G. Smoking-dependent reprogramming of alveolar macrophage polarization: Implication for pathogenesis of chronic obstructive pulmonary disease. J. Immunol. 2009, 183, 2867–2883. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. O’Beirne, S.L.; Kikkers, S.A.; Oromendia, C.; Salit, J.; Rostmai, M.R.; Ballman, K.V.; Kaner, R.J.; Crystal, R.G.; Cloonan, S.M. Alveolar macrophage immunometabolism and lung function impairment in smoking and chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2020, 201, 735–739. [Google Scholar] [CrossRef] [PubMed]
  34. Harvey, B.-G.; Heguy, A.; Leopold, P.L.; Carolan, B.J.; Ferris, B.; Crystal, R.G. Modification of gene expression of the small airway epithelium in response to cigarette smoking. J. Mol. Med. 2006, 85, 39–53. [Google Scholar] [CrossRef] [PubMed]
  35. Tilley, A.E.; Harvey, B.-G.; Heguy, A.; Hackett, N.R.; Wang, R.; O’Connor, T.P.; Crystal, R.G. Down-regulation of the notch pathway in human airway epithelium in association with smoking and chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2009, 179, 457–466. [Google Scholar] [CrossRef] [Green Version]
  36. Wang, G.; Zhou, H.; Strulovici-Barel, Y.; Al-Hijji, M.; Ou, X.; Salit, J.; Walters, M.S.; Staudt, M.; Kaner, R.J.; Crystal, R.G. Role of OSGIN1 in mediating smoking-induced autophagy in the human airway epithelium. Autophagy 2017, 13, 1205–1220. [Google Scholar] [CrossRef] [Green Version]
  37. Wang, G.; Wang, R.; Ferris, B.; Salit, J.; Strulovici-Barel, Y.; Hackett, N.R.; Crystal, R.G. Smoking-mediated up-regulation of GAD67 expression in the human airway epithelium. Respir. Res. 2010, 11, 150. [Google Scholar] [CrossRef] [Green Version]
  38. Wang, R.; Wang, G.; Ricard, M.J.; Ferris, B.; Strulovici-Barel, Y.; Salit, J.; Hackett, N.R.; Gudas, L.J.; Crystal, R.G. Smoking-induced upregulation of AKR1B10 expression in the airway epithelium of healthy individuals. Chest 2010, 138, 1402–1410. [Google Scholar] [CrossRef] [Green Version]
  39. Yang, J.; Zuo, W.-L.; Fukui, T.; Chao, I.; Gomi, K.; Lee, B.; Staudt, M.; Kaner, R.J.; Strulovici-Barel, Y.; Salit, J.; et al. Smoking-dependent distal-to-proximal repatterning of the adult human small airway epithelium. Am. J. Respir. Crit. Care Med. 2017, 196, 340–352. [Google Scholar] [CrossRef]
  40. Raman, T.; O’Connor, T.P.; Hackett, N.R.; Wang, W.; Harvey, B.-G.; Attiyeh, M.; Dang, D.T.; Teater, M.; Crystal, R.G. Quality control in microarray assessment of gene expression in human airway epithelium. BMC Genom. 2009, 10, 493. [Google Scholar] [CrossRef] [Green Version]
  41. Tilley, A.E.; O’Connor, T.P.; Hackett, N.R.; Strulovici-Barel, Y.; Salit, J.; Amoroso, N.; Zhou, X.K.; Raman, T.; Omberg, L.; Clark, A.; et al. Biologic phenotyping of the human small airway epithelial response to cigarette smoking. PLoS ONE 2011, 6, e22798. [Google Scholar] [CrossRef]
  42. Gindele, J.A.; Kiechle, T.; Benediktus, K.; Birk, G.; Brendel, M.; Heinemann, F.; Wohnhaas, C.T.; Leblanc, M.; Zhang, H.; Strulovici-Barel, Y.; et al. Intermittent exposure to whole cigarette smoke alters the differentiation of primary small airway epithelial cells in the air-liquid interface culture. Sci. Rep. 2020, 10, 1–17. [Google Scholar] [CrossRef] [Green Version]
  43. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57, 289–300. [Google Scholar] [CrossRef]
  44. Zenkova, D.K.V.; Sablina, R.; Artyomov, M.; Sergushichev, A. Phantasus: Visual and Interactive Gene Expression Analysis. Available online: https://genome.ifmo.ru/phantasus (accessed on 30 July 2021).
  45. Barnes, P.J. Alveolar macrophages in Chronic Obstructive Pulmonary Disease (COPD). Cell. Mol. Boil. 2004, 50. [Google Scholar]
  46. Vlahos, R. Role of alveolar macrophages in chronic obstructive pulmonary disease. Front. Immunol. 2014, 5, 435. [Google Scholar] [CrossRef] [Green Version]
  47. Soumian, S.; Albrecht, C.; Davies, A.H.; Gibbs, R.G.J. ABCA1 and atherosclerosis. Vasc. Med. 2005, 10, 109–119. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Chai, A.B.; Ammit, A.J.; Gelissen, I.C. Examining the role of ABC lipid transporters in pulmonary lipid homeostasis and inflammation. Respir. Res. 2017, 18, 1–9. [Google Scholar] [CrossRef] [Green Version]
  49. He, P.; Smith, A.; Gelissen, I.C.; Ammit, A.J. The effect of statins and the synthetic LXR agonist T0901317 on expression of ABCA1 transporter protein in human lung epithelial cell lines in vitro. Pharmacol. Rep. 2019, 71, 1219–1226. [Google Scholar] [CrossRef]
  50. Jacobo-Albavera, L.; Domínguez-Pérez, M.; Medina-Leyte, D.; González-Garrido, A.; Villarreal-Molina, T. The role of the ATP-binding cassette A1 (ABCA1) in human disease. Int. J. Mol. Sci. 2021, 22, 1593. [Google Scholar] [CrossRef]
  51. McNeish, J.; Aiello, R.J.; Guyot, D.; Turi, T.; Gabel, C.; Aldinger, C.; Hoppe, K.; Roach, M.L.; Royer, L.J.; de Wet, J.; et al. High density lipoprotein deficiency and foam cell accumulation in mice with targeted disruption of ATP-binding cassette transporter-1. Proc. Natl. Acad. Sci. USA 2000, 97, 4245–4250. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Bates, S.R.; Tao, J.-Q.; Collins, H.L.; Francone, O.L.; Rothblat, G.H. Pulmonary abnormalities due to ABCA1 deficiency in mice. Am. J. Physiol. Cell. Mol. Physiol. 2005, 289, L980–L989. [Google Scholar] [CrossRef] [PubMed]
  53. Yin, K.; Liao, D.-F.; Tang, C.-K. ATP-binding membrane cassette transporter A1 (ABCA1): A possible link between inflammation and reverse cholesterol transport. Mol. Med. 2010, 16, 438–449. [Google Scholar] [CrossRef] [Green Version]
  54. Francone, O.L.; Aiello, R.J. ABCA1: Regulation, function and relationship to atherosclerosis. Curr. Opin. Investig. Drugs 2002, 3. [Google Scholar]
  55. Zanotti, I.; Potì, F.; Pedrelli, M.; Favari, E.; Moleri, E.; Franceschini, G.; Calabresi, L.; Bernini, F. The LXR agonist T0901317 promotes the reverse cholesterol transport from macrophages by increasing plasma efflux potential. J. Lipid Res. 2008, 49, 954–960. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Hussein, M.A.; Shrestha, E.; Ouimet, M.; Barrett, T.; Leone, S.; Moore, K.J.; Herault, Y.; Fisher, E.A.; Garabedian, M.J. LXR-mediated ABCA1 expression and function are modulated by high glucose and PRMT2. PLoS ONE 2015, 10, e0135218. [Google Scholar] [CrossRef] [Green Version]
  57. Chawla, A.; Boisvert, W.A.; Lee, C.-H.; Laffitte, B.A.; Barak, Y.; Joseph, S.; Liao, D.; Nagy, L.; Edwards, P.A.; Curtiss, L.K.; et al. A PPARγ-LXR-ABCA1 pathway in macrophages is involved in cholesterol efflux and atherogenesis. Mol. Cell 2001, 7, 161–171. [Google Scholar] [CrossRef]
  58. Park, Y.; Pham, T.X.; Lee, J. Lipopolysaccharide represses the expression of ATP-binding cassette transporter G1 and scavenger receptor class B, type I in murine macrophages. Inflamm. Res. 2012, 61, 465–472. [Google Scholar] [CrossRef] [PubMed]
  59. He, P.; Gelissen, I.C.; Ammit, A.J. Regulation of ATP binding cassette transporter A1 (ABCA1) expression: Cholesterol-dependent and independent signaling pathways with relevance to inflammatory lung disease. Respir. Res. 2020, 21, 1–11. [Google Scholar] [CrossRef]
  60. Sonett, J.; Goldklang, M.; Sklepkiewicz, P.; Gerber, A.; Trischler, J.; Zelonina, T.; Westerterp, M.; Lemaître, V.; Okada, Y.; Armiento, J.D. A critical role for ABC transporters in persistent lung inflammation in the development of emphysema after smoke exposure. FASEB J. 2018, 32, 6724–6736. [Google Scholar] [CrossRef]
  61. Landry, Y.D.; Denis, M.; Nandi, S.; Bell, S.; Vaughan, A.M.; Zha, X. ATP-binding cassette transporter A1 expression disrupts raft membrane microdomains through its ATPase-related functions. J. Biol. Chem. 2006, 281, 36091–36101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Zhu, X.; Owen, J.S.; Wilson, M.D.; Li, H.; Griffiths, G.L.; Thomas, M.J.; Hiltbold, E.M.; Fessler, M.; Parks, J.S. Macrophage ABCA1 reduces MyD88-dependent toll-like receptor trafficking to lipid rafts by reduction of lipid raft cholesterol. J. Lipid Res. 2010, 51, 3196–3206. [Google Scholar] [CrossRef] [Green Version]
  63. Bi, X.; Vitali, C.; Cuchel, M. ABCA1 and inflammation. Arter. Thromb. Vasc. Biol. 2015, 35, 1551–1553. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Simons, K.; Ehehalt, R. Cholesterol, lipid rafts, and disease. J. Clin. Investig. 2002, 110, 597–603. [Google Scholar] [CrossRef] [PubMed]
  65. Fantini, J.; Epand, R.M.; Barrantes, F.J. Cholesterol-recognition motifs in membrane proteins. Adv. Exp. Med. Biol. 2019, 1135, 3–25. [Google Scholar] [CrossRef]
  66. Ruysschaert, J.-M.; Lonez, C. Role of lipid microdomains in TLR-mediated signalling. Biochim. Biophys. Acta Biomembr. 2015, 1848, 1860–1867. [Google Scholar] [CrossRef]
  67. Francone, O.L.; Royer, L.; Boucher, G.; Haghpassand, M.; Freeman, A.; Brees, D.; Aiello, R.J. Increased cholesterol deposition, expression of scavenger receptors, and response to chemotactic factors in abca1 deficient macrophages. Arter. Thromb. Vasc. Biol. 2005, 25, 1198–1205. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Tang, C.; Houston, B.A.; Storey, C.; LeBoeuf, R.C. Both STAT3 activation and cholesterol efflux contribute to the anti-inflammatory effect of apoA-I/ABCA1 interaction in macrophages. J. Lipid Res. 2016, 57, 848–857. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Sun, Y.; Ishibashi, M.; Seimon, T.; Lee, M.; Sharma, S.; Fitzgerald, K.; Samokhin, A.O.; Wang, Y.; Sayers, S.; Aikawa, M.; et al. Free cholesterol accumulation in macrophage membranes activates toll-like receptors and p38 mitogen-activated protein kinase and induces Cathepsin, K. Circ. Res. 2009, 104, 455–465. [Google Scholar] [CrossRef]
  70. Zhou, J.; You, Y.; Ryan, A.J.; Mallampalli, R.K. Upregulation of surfactant synthesis triggers ABCA1-mediated basolateral phospholipid efflux. J. Lipid Res. 2004, 45, 1758–1767. [Google Scholar] [CrossRef] [Green Version]
  71. Bates, S.R.; Tao, J.-Q.; Yu, K.J.; Borok, Z.; Crandall, E.D.; Collins, H.L.; Rothblat, G.H. Expression and biological activity of ABCA1 in alveolar epithelial cells. Am. J. Respir. Cell Mol. Biol. 2008, 38, 283–292. [Google Scholar] [CrossRef] [Green Version]
  72. Jubinville, E.; Talbot, M.; Bérubé, J.-C.; Hamel-Auger, M.; Maranda-Robitaille, M.; Beaulieu, M.-J.; Aubin, S.; Paré, M.; Kallend, D.G.; Arsenault, B.; et al. Interplay between cigarette smoking and pulmonary reverse lipid transport. Eur. Respir. J. 2017, 50, 1700681. [Google Scholar] [CrossRef] [Green Version]
  73. Kotlyarov, S.; Kotlyarova, A. Molecular mechanisms of lipid metabolism disorders in infectious exacerbations of chronic obstructive pulmonary disease. Int. J. Mol. Sci. 2021, 22, 7634. [Google Scholar] [CrossRef] [PubMed]
Membranes 11 00674 g001 550
Figure 1. Flowcharts for microarray datasets selection.
Figure 1. Flowcharts for microarray datasets selection.
Membranes 11 00674 g001
Membranes 11 00674 g002 550
Figure 2. Patterns of expression of the ABCA1 gene in alveolar macrophages (a—GSE13896; b—GSE130928) associated with smoking status: between healthy non-smokers (NS, dark blue color) and smokers without a COPD diagnosis (S, light blue color) and with a COPD diagnosis (COPD, orange color). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform. GSE13896 used the Mas 5.0 Normalization method and GSE130928 used the RMA Normalization method. The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Figure 2. Patterns of expression of the ABCA1 gene in alveolar macrophages (a—GSE13896; b—GSE130928) associated with smoking status: between healthy non-smokers (NS, dark blue color) and smokers without a COPD diagnosis (S, light blue color) and with a COPD diagnosis (COPD, orange color). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform. GSE13896 used the Mas 5.0 Normalization method and GSE130928 used the RMA Normalization method. The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Membranes 11 00674 g002
Membranes 11 00674 g003 550
Figure 3. Patterns of ABCA1 expression in the respiratory tract epithelium (a—GSE4498; b—GSE11784; c—GSE64614; d—GSE76324; e—GSE11906) associated with smoking status: non-smokers (NS) non-smokers–small airways (NS–S); non-smokers–large airways (NS–L); smokers without a diagnosis of COPD (S); smokers–large airways (S–L); smokers–small airways (S–S) and with a diagnosis of COPD (COPD). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform (using the Mas 5.0 Normalization method). The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Figure 3. Patterns of ABCA1 expression in the respiratory tract epithelium (a—GSE4498; b—GSE11784; c—GSE64614; d—GSE76324; e—GSE11906) associated with smoking status: non-smokers (NS) non-smokers–small airways (NS–S); non-smokers–large airways (NS–L); smokers without a diagnosis of COPD (S); smokers–large airways (S–L); smokers–small airways (S–S) and with a diagnosis of COPD (COPD). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform (using the Mas 5.0 Normalization method). The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Membranes 11 00674 g003
Membranes 11 00674 g004 550
Figure 4. Patterns of ABCA1 expression in the epithelium of the upper and lower respiratory tract (a—GSE18385; b—GSE11906) associated with smoking status: non-smokers–large airways (NS–L), non-smokers–small airways (NS–S), smokers–large airways (S–L), smokers–small airways (S–S), non-smokers–trachea (NS–T), non-smokers–trachea (NS–T), diagnosis of COPD–small airways (COPD). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform (using the Mas 5.0 Normalization method). The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Figure 4. Patterns of ABCA1 expression in the epithelium of the upper and lower respiratory tract (a—GSE18385; b—GSE11906) associated with smoking status: non-smokers–large airways (NS–L), non-smokers–small airways (NS–S), smokers–large airways (S–L), smokers–small airways (S–S), non-smokers–trachea (NS–T), non-smokers–trachea (NS–T), diagnosis of COPD–small airways (COPD). All datasets were generated from the Affymetrix Human Genome U133 Plus 2 microarray platform (using the Mas 5.0 Normalization method). The data distribution is visualized in the form of box diagrams. Statistically significant differences (p values and p values adjusted using the algorithm of Benjamini & Hochberg) are shown with asterisks: * p < 0.01, FDR ≤ 5%.
Membranes 11 00674 g004
Table
Table 1. Characteristics of the analyzed GEO datasets.
Table 1. Characteristics of the analyzed GEO datasets.
DataSetCharacteristicMethod of Obtaining the MaterialMicroarray PlatformReferences
GSE13896Alveolar macrophages obtained from healthy non-smokers, smokers, and smokers with COPDBronchoalveolar lavageGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[32]
GSE130928Alveolar macrophages obtained from healthy non-smokers, smokers, and smokers with COPDBronchoalveolar lavageGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[33]
GSE4498The bronchial epithelium of the 10th–12th order obtained in healthy non-smokers and phenotypically normal smokers BronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[34,35]
GSE76324Bronchial epithelium of the 3rd–4th and 10th–12th order, obtained in healthy non-smokers and smokersBronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[36,37]
GSE18385Bronchial epithelium of the 3rd–4th and 10th–12th order, obtained in healthy non-smokers and smokersBronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[38]
GSE64614Bronchial epithelium, including trachea and bronchi of the 4th–6th order obtained from healthy non-smokers and epithelium of the distal respiratory tract (bronchi of the 10th–12th order) of healthy non-smokers and healthy smokers BronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[39]
GSE11906Bronchial epithelium of trachea, bronchi of the 2nd–3rd order, and bronchi of the 10th–12th order obtained from healthy non-smokers, smokers, and smokers with COPDBronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[40]
GSE11784Bronchial epithelium of the 10th–12th order obtained in healthy non-smokers, smokers, and patients with COPDBronchoscopyGPL570 Affymetrix Human Genome U133 Plus 2.0 Array[41,42]
Table
Table 2. Demographic and clinical characteristics of patients in GEO datasets.
Table 2. Demographic and clinical characteristics of patients in GEO datasets.
DataSetCharacteristicComparison Groups
Non-SmokersSmokersCOPD
GSE13896Age (year)
Pack-years index
Sex (M/F)		40.2 ± 8.3
-
18/6		42.1 ± 7.8
27.5 ± 18.1
25/9		54.2 ± 9.3
51.5 ± 29.2
10/2
GSE130928Age (year)
Pack-years index
Sex (M/F)		40.3 ± 8.2
-
18/6		42.6 ± 7.8
27.1 ± 17.0
29/13		53.8 ± 7.9
43.4 ± 28.0
18/4
GSE4498Age (year)
Pack-years index
Sex (M/F)		42.3 ± 7.7
-
10/2		44.0 ± 3.9
25.8 ± 9.1
7/3		-
GSE18385Age (year)
Pack-years index
Sex (M/F)		41.1 ± 10.5
-
51/21		43.1 ± 7.0
28.1 ± 17.2
59 / 30		-
GSE11906Age (year)
Pack-years index
Sex (M/F)		42.6 ± 9.7
-
32/10		42.9 ± 6.3
26.9 ± 15.9
35/14		55.6 ± 7.7
36.4 ± 21.9
26/7
GSE11784Age (year)
Pack-years index
Sex (M/F)		40.3 ± 12
-
40/23		42.5 ± 7.6
27.2 ± 15.9
52/20		51.6 ± 8.6
40.9 ± 28.2
18/4
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kotlyarov, S.; Kotlyarova, A. Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease. Membranes 2021, 11, 674. https://doi.org/10.3390/membranes11090674

AMA Style

Kotlyarov S, Kotlyarova A. Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease. Membranes. 2021; 11(9):674. https://doi.org/10.3390/membranes11090674

Chicago/Turabian Style

Kotlyarov, Stanislav, and Anna Kotlyarova. 2021. "Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease" Membranes 11, no. 9: 674. https://doi.org/10.3390/membranes11090674

APA Style

Kotlyarov, S., & Kotlyarova, A. (2021). Bioinformatic Analysis of ABCA1 Gene Expression in Smoking and Chronic Obstructive Pulmonary Disease. Membranes, 11(9), 674. https://doi.org/10.3390/membranes11090674

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop