Lung gene expression study identifying Zscan2 and Bag6 as novel tissue repair players in an experimental model of COPD

doi:10.21203/rs.3.rs-3221303/v1

Download PDF

Article

Lung gene expression study identifying Zscan2 and Bag6 as novel tissue repair players in an experimental model of COPD

https://doi.org/10.21203/rs.3.rs-3221303/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Chronic obstructive pulmonary disease (COPD) is a common chronic lung disease with an ever-increasing incidence. Despite years of drug research and approvals, we are still not able to halt progress or restore normal lung function. Our previous studies have demonstrated that liver growth factor (LGF) has an effect on the repair of the affected tissue in a mouse model of cigarette smoke exposure (CSE), but by what pathways it achieves this is unknown. The present study aimed to identify differentially expressed genes (DEGs) between emphysematous mice treated with LGF to identify potential biomarkers for the treatment of pulmonary emphysema.

Methods

The emphysema mouse model was induced by prolonged exposure to cigarette smoke. To determine the gene expression profile of the lung in smokers treated or not with LGF, lung messenger RNA (mRNA) gene expression was assessed with the Agilent Array platform and validated in mouse lung samples.

Results

The treated group significantly improved lung function (~ 35%) and emphysema level (~ 20%). Microarray analysis demonstrated a total of 290 DEGs (1.5-fold up- or downregulated). The expression trends of 2 genes (Zscan2 and Bag6) were different in emphysematous lungs treated with LGF compared to untreated lungs.

Conclusions

Therefore, Zscan2 and Bag6 could play a role in regulating inflammation and the immune response in the lung that undergoes partial lung regeneration.

Biological sciences/Molecular biology

Health sciences/Biomarkers

differentially expressed genes (DEGs)

liver growth factor (LGF)

tissue repair

murine model

cigarette smoke exposure (CSE)

Chronic obstructive pulmonary disease (COPD) affects over 300 million people around the world and is currently the third most common cause of mortality [1]. COPD is a progressive lung disease characterized by persistent airflow limitation in chronic bronchitis and tissue damage with substantial reduction in solid lung texture and airspace enlargement in emphysema, eventually leading to irreversible airflow limitation and persistent respiratory symptoms [2]. While the exact mechanisms underlying the pathophysiology are not known, it is generally accepted that COPD is subject to genetic and environmental control. Cigarette smoke exposure (CSE) remains the greatest modifiable risk factor in the Western world [3].

The current treatment for COPD involves the administration of inhaled bronchodilators and corticosteroids. However, there is no treatment capable of stopping the disease from progressing or repairing the lung [4]. Cell‒cell interactions between alveolar epithelial cells and other cell types are promoted through the release of growth factors, including liver, keratinocyte, epithelial or vascular endothelial growth factor [5] [6]. Repair mechanisms to restore normal airway architecture are inefficient in COPD patients. Consequently, the use of growth factors as promoters of cellular proliferation and differentiation mechanisms appeared to be a promising strategy for stimulating tissue repair under injury conditions [7]. Identifying new players involved in tissue repair could provide valuable information on possible therapeutic targets for COPD.

Liver growth factor (LGF) is an albumin-bilirubin complex of 64 kDa with mitogenic properties that was purified in 1986 in rat liver by the group of Dr. Díaz-Gil [8]. The antifibrotic and antioxidant properties of LGF, as well as regenerative effects, have been described in various rodent models [9] [10]. Furthermore, we have already described the beneficial effects of LGF in a rat model by cadmium chloride (ClCd₂)-induced pulmonary fibrosis [11] and more recently in AKR/J [12] or C57BL/6J [13] [14] mice with CSE-induced emphysema.

Recent advances in genomics have enabled genome-wide messenger RNA (mRNA) profiling to provide new insights into the pathogenesis of COPD [15] [16] [17] [18] [19] [20] [21]. While these studies identify genes associated with disease phenotypes or smoking, additional in vivo studies are needed to clarify the mechanism and determine causality. Analysis of microarray data demonstrated a large number of small airway epithelial genes that were significantly up- or downregulated in response to smoking. While by no means complete, this subset of genes modulated by smoking provides a working list of potential targets for therapeutic intervention to prevent the development of COPD and to evaluate the efficacy of COPD-related therapies.

The availability of whole-genome transcriptomic signatures in lung tissue allows comparisons between treated and untreated emphysematous mice. The objectives of this study were to compare and contrast the molecular changes in mouse models after LGF treatment with the molecular changes in the lungs caused by cigarette smoke.

Mouse model

LGF treatment of the chronic CSE-induced emphysema model was assessed as previously described [22]. A total of 24 adult inbred male, 10-week-old C57BL/6J mice (Charles River Laboratories, France) susceptible to smoking-induced emphysema were used. Mice were divided into air-exposed (control CTL, n = 8) and cigarette smoke-exposed (CSE, n = 16) groups, of which half were treated with the growth factor LGF (CSE + LGF, n = 8) two weeks before the sacrifice of the animals once the lung was already damaged (Fig. 1).

Smoking animals were exposed to a mainstream CSE of 4 unfiltered cigarettes (3R4F, University of Kentucky, Lexington, KY) per day, twice daily (5 days/week) over a period of 9.5 months to follow the previously published methodology [23]. Non-smoking mice were exposed to room air. The efficiency of smoking exposure was evaluated spectrophotometrically by randomly measuring the blood levels of carboxyhemoglobin (COHb) in the animals with an IL 682 CO-Oximeter (Instrumentation Laboratory) following previously described methodology [23]. The mean percent COHb of control mice was 0.2 ± 0.05%, and that of mice exposed to smoke chamber was within the nontoxic 13.9 ± 0.02% value, confirming the correct exposure to tobacco smoke. Nine months after CSE, when emphysema was once established, half of the smoker group animals were treated with four intraperitoneal injections (twice a week for 2 wk) of 150 µl of a solution containing 1.7 µg LGF per mouse (CSE + LGF group) or saline solution (CSE group). This optimal dose of LGF has been used in different model systems using an identical or similar schedule [14].

Lung function was studied by measuring changes in quasistatic lung compliance (C_L, ml/cmH₂O) and maximum expiratory volume (V_max, ml) at a pressure of 25 mbar after inspiratory pressure-controlled ventilation, following previously described methodology [23]. After 9.5 months, mice were sacrificed by an intraperitoneal injection with an overdose of pentobarbital. Lung tissue was obtained from the cardiopulmonary block after tying the right bronchus. Thus, the right lung lobes were frozen, and the left lung lobe was fixed by filling it with 4% buffered formalin solution at a constant airway pressure of 25 cmH₂O for 24 h. Fixed lungs were kept in formalin solution until they were paraffin embedded, cut into 5-µm-thick sections using a rotary microtome HM325 (Microm International GmbH) for morphometric analysis following the previously published methodology [23] to confirm emphysema by measuring the mean linear intercept (L_M, µm) and the alveolar internal area (AIA, µm²) in the alveolus.

RNA extraction and RNA quality control of lung tissue samples

Total RNA of the right lung lobe tissue from 6 mice per experimental group was extracted using TRIzol (Invitrogen, Carlsbad, CA, USA) and an RNeasy® Plus Mini Kit (Qiagen GmbH, Germany), yielding 6 µg from each tissue. RNA quantity and purity tests from each sample were measured by a NanoDrop spectrophotometer (ND-1000), including an OD_260/280 ratio of 1.7 to 2.3. RNA integrity and genome DNA contamination tests were assessed by standard denaturing formaldehyde gel electrophoresis and Agilent 2100 Bioanalyzer (measure RNA integrity number). The quality of the RNA labelling was verified by hybridization to a test chip, and only test chips with a 3′ to 5′ ratio of < 3 were deemed satisfactory. RNA from each cell sample was sent to Arraystar Inc. (Rockville, MD, USA) for microarray analysis.

Lung Gene Expression Microarrays

To determine the gene expression profile of the lung in smokers, RNA was assessed for gene expression with the Agilent Array platform (Agilent Technologies, Inc., Santa Clara, CA, USA) by Arraystar, Inc. (Rockville, MD, USA). Sample preparation and microarray hybridization were performed based on the Agilent One-Colour Microarray-Based Gene Expression Analysis protocol. Briefly, total RNA from each sample was linearly amplified, transcribed into fluorescent complementary RNA (cRNA) and labelled with Cy3-UTP using the manufacturer’s Agilent’s Quick Amp Labelling protocol (version 5.7). The labelled cRNAs were purified by an RNeasy Mini Kit (Qiagen GmbH, Germany). The concentration and specific activity of the labelled cRNAs (pmol Cy3/µg cRNA) were measured by a NanoDrop ND-1000 spectrophotometer. One microgram of each labelled cRNA was fragmented by adding 11 µl of 10 × blocking agent and 2.2 µl of 25 × fragmentation buffer and then heated at 60°C for 30 min. Finally, 55 µl of 2 × GE hybridization buffer was added to dilute the labelled cRNA. The labelled cRNAs were hybridized onto the Whole Mouse Genome Oligo Microarray (4 x 44K) in Agilent´s SureHyb hybridization chambers, printed using Agilent´s 60-mer SurePrint technology. The array includes probes representing ∼39,000 full-length mouse genes and transcripts, all with public domain annotations. One hundred microliters of hybridization solution was dispensed into the gasket slide and assembled into the gene expression microarray slide. The slides were incubated for 17 hours at 65°C in a hybridization oven. After washing, slides were scanned with the Microarray Scanner G2505C.

Microarray Data Analysis

Image processing and data extraction and analysis also were performed by Arraystar Inc. (Rockville, MD, USA) using their established protocols. Agilent Feature Extraction software (v11.0.1.1) was used to analyse acquired array images. Quantile normalization and subsequent data processing were performed using Agilent GeneSpring GX (v12.1) software as follows: (1) per array, by dividing the raw data by the 50th percentile of all measurements; and (2) per gene, by dividing the raw data by the median of the expression level for the gene in all samples. The distribution of log2 ratios among all samples was nearly the same. After quantile normalization of the raw data, genes in at least 12 out of 18 samples were chosen for differentially expressed gene (DEG) screening. Further data analysis was also performed using Agilent GeneSpring GX software, and hierarchical clustering analysis was performed to show the distinguishable gene expression profiling among samples. DEGs with statistical significance between two groups were identified through volcano plot filtering. The threshold was fold change (FC) ≥ 2.0 and p value ≤ 0.05. The p values were further adjusted using the false discovery rate (FDR) approach, according to the Benjamini ‑ Hochberg (BH) method. Genes with an FDR < 0.1 and FC > 1.5 or < 0.67 were considered DEGs between the groups.

GO functional and pathway analysis

GO functional analysis (Gene Ontology Biological Processes, www.geneontology.org) and pathway analysis were applied to determine the role of these DEGs in these GO terms or biological pathways. Based on the latest KEGG (Kyoto Encyclopedia of Genes and Genomes, www.genome.jp/kegg) database, pathway enrichment analysis of the DEGs was performed. To identify the pathways associated with the groups, tools in DAVID (Database for Annotation, Visualization and Integrated Discovery, https://david.ncifcrf.gov) were used to screen the pathways enriched in the DEGs from the samples using the Expression Analysis Systematic Explorer score (a modified Fisher's exact t ‑test) with BH multiple testing correction. A KEGG pathway with a BH‑ corrected p < 0.05 was considered to be significantly enriched.

Quantitative real-time PCR for validation of the top genes

The results of the 10 hub genes selected among those that demonstrate a high discriminatory value will be confirmed by the quantitative technique of polymerase chain reaction with reverse transcriptase (qRT‒PCR) following the standard protocol described in previous articles from our group [13] and using the same RNA samples that had been used for the microarray analysis. First strand cDNA was synthesized from 2 µg of RNA in a 100 µl reaction volume using the TaqMan Reverse Transcriptase Reaction Kit (Applied Biosystems) with random hexamers as primers. The cDNA was diluted 1:100 or 1:50, and each dilution was run in triplicate wells. Five microliters was used for each TaqMan PCR in a 25 µl final reaction volume using premade kits from Applied Biosystems. The relative quantity (ΔΔCt) was determined using the algorithm provided by Applied Biosystems using the beta-actin (Actb) gene as the internal control to normalize the expression of the target genes mentioned above and the average value for control non-smokers as the calibrator. The Actb probe was labelled with VIC, and the probes for the genes of interest were labelled with FAM. The PCRs were run in an Applied Biosystems Sequence Detection System 7500 by 40 cycles of 15 seconds at 94°C, 30 seconds at 55°C, and 30 seconds at 72°C. For comparison purposes, the data for each individual were normalized to the median across all control non-smokers and smoker samples, as was done with microarray data (see above).

Statistical analysis

Data are presented as the mean ± standard deviation (SD). Normally distributed data were assessed for significance by Student’s t test or ANOVA as appropriate. Data that were not normally distributed were assessed for significance using the Mann–Whitney U test or the Kruskal–Wallis test with Dunn’s post-test for multiple comparisons as appropriate. Prism version 8 (GraphPad) was used for data analysis. A two-sided p value less than 0.05 was considered to be statistically significant.

LGF reverts emphysema in the CSE model: functional and morphometric studies

Functionally, pulmonary emphysema induced by chronic CSE was reversed by LGF treatment, as measured by lung compliance (C_L, ml/cmH₂O) and maximum expiratory volume (V_max, ml) at 25 mbar (Fig. 2A): mean ± SD, C_L= 0.158 ± 0.023 ml/cmH₂O in the CTL group vs. 0.23 ± 0.012 ml/cmH₂O in the CSE group, p = 0.029 vs. 0.15 ± 0.003 ml/cmH₂O in the CSE + LGF group, p = 0.010; V_max= 1.084 ± 0.01 ml in the CTL group vs. 1.345 ± 0.02 ml in the CSE group, p = 0.005 vs. 1.140 ± 0.015 ml in the CSE + LGF group.

Morphometrically, as measured by the mean linear intercept (L_M, µm) and alveolar internal area (AIA, µm²), LGF was able to revert emphysema in CSE + LGF mice compared to CSE mice, which presented enlargement of the alveolar space, and CTL mice, which presented normal alveolar architecture: mean ± SD, L_M= 48.2 ± 0.54 µm in the CTL group vs. 55.6 ± 0.32 µm in the CSE group, p = 0.016 vs. 52.2 ± 0.58 µm in the CSE + LGF group, p = 0.013; AIA = 1745.2 ± 22.3 µm² in the CTL group vs. 2279.8 ± 28.4 µm² in the CSE group, p = 0.017 vs. 1920.6 ± 15.6 µm² in the CSE + LGF group, p = 0.016 (Fig. 2B). Histological sections of lungs in CTL, CSE and CSE + LGF mice are shown in Fig. 2C.

Lung tissue transcriptome signature of LGF treatment

To identify the possible biomarker of COPD treatment, we utilized transcriptome sequencing to measure the gene expression variation in control, emphysema and LGF-treated emphysema. Cluster analysis arranges samples into groups based on their expression levels, which allows us to hypothesize about the relationships among samples. A heat map and supervised hierarchical clustering analysis were performed on DEGs representing mRNA expression profiles of most changed genes (Fig. 3). The dendrogram shows the relationships among the expression levels of samples. “Red” indicates high relative expression, and “green” indicates low relative expression. Compared with the CSE group, the LGF-treated emphysema group was the closest, and the emphysema group was the farthest.

Volcano plots are useful tools for visualizing the differential expression between two different conditions. They are constructed using FC values and p-values, allowing you to visualize the relationship between FC (magnitude of change) and statistical significance (which accounts for both magnitude of change and variability). They also allow the isolation of subsets of genes based on these values (Fig. 4). The vertical lines correspond to 2.0-fold up and down, and the horizontal line represents a p-value of 0.05. Therefore, the red dot in the plot represents the differentially expressed genes with statistical significance.

They are constructed using FC values and p-values, and thus allow you to visualize the relationship between FC (magnitude of change) and statistical significance (which takes both magnitude of change and variability into consideration). They also allow subsets of genes to be isolated, based on those values. The vertical lines correspond to 2.0-fold up and down and the horizontal line represents a p-value of 0.05. So the red point in the plot represents the differentially expressed genes with statistical significance.

Venn diagram showing the overlap of the LGF treatment signature with murine smoking signature genes differentially expressed in smokers (CSE) vs. non-smokers (CTL); CSE + LGF vs. CSE and CSE + LGF vs. CTL (Fig. 5).

Gene expression analysis of the lung tissue revealed that emphysematous mice treated with LGF showed significant overexpression of 90 genes as well as a subexpression of 200 more genes (where the most significant, FDR < 10%). Data on the FC, p value and FDR of these 10 genes are shown in Table 1.

Functional analysis associated with the DEGs with the different most significant categories of GO for up- (Fig. 6) and downregulated genes (Fig. 7) of the CSE + LGF group vs. the CSE group. Significant pathways of DEGs up- and downregulated (Fig. 8) between the CSE + LGF and CSE groups.

Gene ontology processes and gene sets enriched among treated or untreated murine smoking overlapping gene-associated mRNAs (Table 2).

Gene ontology processes (GO) and gene sets enriched among treated or untreated murine smoking overlapping gene-associated mRNAs.

Identification of potential biomarkers: qRT‒PCR to verify the RNA-seq results

Instructed by the RNA-seq results, we further studied the 10 most significantly changed genes in different groups of study. qRT‒PCR was applied to investigate the immune response of different groups. Changes in mRNA levels were measured for the following genes: m1ap, Zscan2, bmp2, march1, and Bag6 (upregulated in the LGF-treated group vs. the CSE group, Fig. 9) and ighg, oas1d, myog, dclre1c and map3k8 (downregulated, Fig. 10). The gene expression of Zscan2 and Bag6 was sharply increased in the CSE + LGF group compared to the CSE group. Representing mean ± SD, for Zscan2, we found values of 0.903 ± 0.242 in the CSE group vs. 1.524 ± 0.323 in the CSE + LGF group, p = 0.0284. For Bag6, 1.061 ± 0.138 in the CSE group vs. 1.390 ± 0.12 in the CSE + LGF group, p = 0.0438 (Fig. 10). LGF treatment upregulated the mRNA of these specific markers.

COPD is a heterogeneous disease in which chronic bronchiolitis and emphysema are the most important phenotypes and continue to be the leading causes of mortality worldwide [1]. Bronchodilators and anti-inflammatory agents provide the main treatment for patients with COPD; however, they have poor treatment results. A better understanding of the pathophysiology of disease development is recognized as increasingly clinically important, and the identification of critical therapeutic targets specifically for COPD is critically important. Liver growth factor (LGF) has demonstrated utility in an experimental emphysema model that is capable of reversing functional and morphometric changes induced by tobacco smoke [12] [13]. What remains unclear is the mechanism through which it exerts its therapeutic effect on the lung.

With the development of sequencing technology and bioinformatic analysis, the mRNA network hypothesis may partially illustrate disease onset and progression. Despite an increasing number of studies on mRNA networks, the molecular mechanisms of COPD are not yet fully understood [24]. RNA therapeutics offer high specificity and represent safer and reversible alternatives to DNA-based gene therapies. RNA drugs may offer unique opportunities to expand the spectrum of therapeutic targets, several of which are approved or currently in clinical trials. In the present study, we utilized transcriptome array analysis of lung tissue from three groups (eight control animals, eight animals with emphysema treated with a growth factor (LGF) and eight untreated animals with emphysema) and identified 1,700 DEmRNAs (90 upregulated and 200 downregulated). We hypothesized that certain dysregulated RNAs could exert their functions either individually or cooperatively, and some of them could be exploited as potential biomarkers. Zscan2 (zinc finger and SCAN domain-containing protein 2) and Bag6 BCL2-associated athanogene 6 appear to have been identified as potential tissue repair players in an experimental model of COPD treated with growth factors based on lung gene expression.

Zinc finger proteins (ZFPs) are a superfamily of transcription factors encoded by over 2% of the human genome sequence. It contains at least one zinc finger domain that could bind specific DNA sequences and thus regulate DNA expression levels. In addition, ZFPs can also interact with RNA, lipids, membranes, and proteins via zinc finger domains or specific structural domains such as the SCAN domain [25]. Recent research shows that transcription factors containing the SCAN domain (SCAND-TFs) contribute to suppressing the stress response in cancer [26]. Meanwhile, the outlier expression of ZFPs causes various pathological processes, such as tumorigenesis and progression, diabetes, skin diseases and neurodegeneration. Particularly in tumours, ZFPs have important effects on cell proliferation, epithelial mesenchymal transition, invasion and metastasis, inflammation and apoptosis. In addition to the cell cycle, drug resistance, cancer stem cells and DNA methylation have been observed in a wide range of cancers [27] [28], including colon [29], breast, lung and gastric cancers and hepatocellular carcinoma [30]. In addition, several studies have demonstrated that myeloid zinc finger 1 (MZF1) has two functions in colon cancer, which may both promote cancer proliferation and inhibit cancer progression by apoptosis [31].

Zscan2 (also called ZFP29 or SCAND2P) is a transcription factor that plays a role in embryonic stem cell maintenance, telomere elongation in a small population of cells during embryonic development and genomic stability. This process helps maintain the integrity of the genome and allows for the preservation of stem cell populations [32]. Because of complex regulatory mechanisms, Zscan transcription factors may exhibit promoting or prohibitive effects on angiogenesis and cellular apoptosis. In addition to cell differentiation and proliferation, cell migration and invasion, the properties of stem cells and susceptibility to chemotherapy [25]. However, the role of Zscan2 in lung tissue repair and COPD is relatively less explored. This is the first study that might have discovered an association between Zscan2 expression and tissue repair processes in the lung affected by COPD. The exact mechanisms through which Zscan2 may contribute to tissue repair in COPD are not clear and require further investigation.

Bag6 (also known as HLA (human leukocyte antigen)-B-associated transcript 3, BAT3 or Scythe) is a chaperone nucleocytoplasmic shuttling protein with several structural patterns. Include an N-terminal ubiquitin homology region, a polyproline region, and a zinc finger-like domain. It is localized in the mitochondria and involved in controlling protein quality and regulating protein degradation pathways [33]. It interacts with various proteins, including misfolded or damaged proteins, to facilitate their proper folding, assembly, or degradation. Bag6 has been implicated in various cellular processes, such as apoptosis (from the formation of a caspase-3- cleaved Scythe C-terminal fragment), autophagy (mitophagy after mitochondrial depolarization), antigen presentation and T-cell response and NK cell activity in immune responses identified as a ligand for NKp30 involved in antitumor immunity of NK cells [34]. Scythe is critical for normal development and viability, probably through regulation of programmed cell death and cell proliferation [35]. The exact role of Bag6 in the lung is not yet fully understood, but there is evidence to suggest that it may play a role in regulating inflammation and immune responses in the lung. Bag6 appears to be a negative regulator in the innate immune system of non-small cell lung cancer patients [36] [37] and a risk factor for lung cancer [38] [39], even in early non-smoker lung adenocarcinoma patients [40]. Germline variants of genes involved in nuclear factor-kappa B (NF-κB) activation are associated with the risk of developing COPD and lung cancer [41].

In summary, in our study, the lung genes Zscan2 and Bag6 seem to have been identified as potential tissue repair players in an experimental model of COPD treated with growth factors. Both genes appear to play a role in regulating inflammation and immune responses in the lung and targeting may be a potential therapeutic strategy for inflammation and injury-associated lung diseases.

Actb Beta-actin

AIA Alveolar internal area

BH Benjamini ‑Hochberg

C_L Lung compliance

ClCd₂ cadmium chloride

COHb Carboxyhemoglobin

COPD Chronic obstructive pulmonary disease

cRNA Complementary RNA

CSE Cigarette smoke exposure

CTL Control

DAVID Database for Annotation, Visualization and Integrated Discovery

DEGs Differentially expressed genes

FC Fold change

FDR False discovery rate

GO Gene ontology

KEEG Kyoto Encyclopedia of Genes and Genomes

LGF Liver growth factor

L_M Mean linear intercept

MZF1 Myeloid zinc finger 1

mRNA messenger RNA

NF-κB nuclear factor-kappa B

qRT‒PCR Quantitative real-time reverse transcription polymerase chain reaction

SCAND-TFs SCAN domain-containing transcription factors

SD Standard deviation

V_max Maximum expiratory volume

ZFPs Zinc finger proteins

Ethics approval

Animals were housed in the Core Facility at the Biomedical Research Institute - Fundación Jiménez Díaz (IIS-FJD), Madrid (animal facilities ES280790000089). The protocols were approved by the Animal Welfare and Ethics Committee from IIS-FJD (PROEX 322/14) in agreement with Spanish legislation (RD 53/2013) based on the European Union Council Directive (2010/63/UE) concerning animal protection for research purposes. The study was reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). All surgery was performed under anaesthesia (ketamine:xylazine, 1:3), and all efforts were made to minimize animal suffering. Euthanasia method by an intraperitoneal injection with an overdose of sodium pentobarbital.

Consent for publication

Not applicable

Availability of data and materials

The datasets supporting the conclusions of this article are available in the Figshare database repository, with the public DOI: 10.6084/m9.figshare.23735637.

Competing interests

The authors declare that they have no competing interests.

Funding

This work is supported in part by the Health Research Program from Carlos III Health Institute (ISCII, PI13/01909 and PI20/01416, Spain).

Authors' contributions

SPR and GPB conceived and designed the study. SPR generated the animal model. LSC and ACCP performed the RNA studies. SPR and GPB analysed and interpreted the data. SPR was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank Rosa Faner for helping us to understand the data; Ignacio Mahíllo Fernández for his help with the statistics analysis; Pablo Mínguez Paniagua for their help in the functional analysis of selected genes; and to the Library Service of the Fundación Jiménez Díaz for their help in the corrections of the text.

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Lond Engl. 2018;392:1789–858.
Hogg JC, Timens W. The Pathology of Chronic Obstructive Pulmonary Disease. Annu Rev Pathol Mech Dis. 2009;4:435–59.
Hikichi M, Mizumura K, Maruoka S, Gon Y. Pathogenesis of chronic obstructive pulmonary disease (COPD) induced by cigarette smoke. J Thorac Dis. 2019;11:S2129–40.
Halpin DMG, Criner GJ, Papi A, Singh D, Anzueto A, Martinez FJ, et al. Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee Report on COVID-19 and Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2021;203:24–36.
Ware LB, Matthay MA. Keratinocyte and hepatocyte growth factors in the lung: roles in lung development, inflammation, and repair. Am J Physiol Lung Cell Mol Physiol. 2002;282:L924-940.
Crosby LM, Waters CM. Epithelial repair mechanisms in the lung. Am J Physiol Lung Cell Mol Physiol. 2010;298:L715-731.
Muyal JP, Muyal V, Kotnala S, Kumar D, Bhardwaj H. Therapeutic potential of growth factors in pulmonary emphysematous condition. Lung. 2013;191:147–63.
Díaz-Gil JJ, Escartín P, García-Cañero R, Trilla C, Veloso JJ, Sánchez G, et al. Purification of a liver DNA-synthesis promoter from plasma of partially hepatectomized rats. Biochem J. 1986;235:49–55.
Gonzalo-Gobernado R, Perucho J, Vallejo-Muñoz M, Casarejos MJ, Reimers D, Jiménez-Escrig A, et al. Liver Growth Factor “LGF” as a Therapeutic Agent for Alzheimer’s Disease. Int J Mol Sci. 2020;21:9201.
Condezo-Hoyos L, Arribas SM, Abderrahim F, Somoza B, Gil-Ortega M, Díaz-Gil JJ, et al. Liver growth factor treatment reverses vascular and plasmatic oxidative stress in spontaneously hypertensive rats. J Hypertens. 2012;30:1185–94.
Martínez-Galán L, del Puerto-Nevado L, Pérez-Rial S, Díaz-Gil JJ, González-Mangado N, Peces-Barba G. [Liver growth factor improves pulmonary fibrosis secondary to cadmium administration in rats]. Arch Bronconeumol. 2010;46:20–6.
Pérez-Rial S, Del Puerto-Nevado L, Girón-Martínez A, Terrón-Expósito R, Díaz-Gil JJ, González-Mangado N, et al. Liver growth factor treatment reverses emphysema previously established in a cigarette smoke exposure mouse model. Am J Physiol Lung Cell Mol Physiol. 2014;307:L718-726.
Girón-Martínez Á, Pérez-Rial S, Terrón-Expósito R, Díaz-Gil JJ, González-Mangado N, Peces-Barba G. Proliferative activity of liver growth factor is associated with an improvement of cigarette smoke-induced emphysema in mice. PloS One. 2014;9:e112995.
Navarrete A, Rupérez FJ, Mendes TO, Pérez-Rial S, Girón-Martínez A, Terrón-Expósito R, et al. A metabolomic approach shows sphingosine 1-phosphate and lysophospholipids as mediators of the therapeutic effect of liver growth factor in emphysema. J Pharm Biomed Anal. 2017;139:238–46.
Bossé Y, Postma DS, Sin DD, Lamontagne M, Couture C, Gaudreault N, et al. Molecular signature of smoking in human lung tissues. Cancer Res. 2012;72:3753–63.
Morissette MC, Lamontagne M, Bérubé J-C, Gaschler G, Williams A, Yauk C, et al. Impact of cigarette smoke on the human and mouse lungs: a gene-expression comparison study. PloS One. 2014;9:e92498.
Faner R, Cruz T, Casserras T, López-Giraldo A, Noell G, Coca I, et al. Network Analysis of Lung Transcriptomics Reveals a Distinct B-Cell Signature in Emphysema. Am J Respir Crit Care Med. 2016;193:1242–53.
Obeidat M, Nie Y, Fishbane N, Li X, Bossé Y, Joubert P, et al. Integrative Genomics of Emphysema-Associated Genes Reveals Potential Disease Biomarkers. Am J Respir Cell Mol Biol. 2017;57:411–8.
Obeidat M, Dvorkin-Gheva A, Li X, Bossé Y, Brandsma C-A, Nickle DC, et al. The Overlap of Lung Tissue Transcriptome of Smoke Exposed Mice with Human Smoking and COPD. Sci Rep. 2018;8:11881.
Pengcheng, Wang Y, Zhang N, Zhao X, Li R, Wang Y, et al. Comprehensive identification of RNA transcripts and construction of RNA network in chronic obstructive pulmonary disease. Respir Res. 2022;23:154.
Feng X, Dong H, Li B, Yu L, Zhu J, Lou C, et al. Integrative analysis of the expression profiles of whole coding and non-coding RNA transcriptomes and construction of the competing endogenous RNA networks for chronic obstructive pulmonary disease. Front Genet. 2023;14:1050783.
Sevilla-Montero J, Labrousse-Arias D, Fernández-Pérez C, Fernández-Blanco L, Barreira B, Mondéjar-Parreño G, et al. Cigarette Smoke Directly Promotes Pulmonary Arterial Remodeling and Kv7.4 Channel Dysfunction. Am J Respir Crit Care Med. 2021;203:1290–305.
Pérez-Rial S, Barreiro E, Fernández-Aceñero MJ, Fernández-Valle ME, González-Mangado N, Peces-Barba G. Early detection of skeletal muscle bioenergetic deficit by magnetic resonance spectroscopy in cigarette smoke-exposed mice. PloS One. 2020;15:e0234606.
Qiao X, Hou G, He Y-L, Song D-F, An Y, Altawil A, et al. The Novel Regulatory Role of the lncRNA-miRNA-mRNA Axis in Chronic Inflammatory Airway Diseases. Front Mol Biosci. 2022;9:927549.
Huang M, Chen Y, Han D, Lei Z, Chu X. Role of the zinc finger and SCAN domain-containing transcription factors in cancer. Am J Cancer Res. 2019;9:816–36.
Sheta M, Yoshida K, Kanemoto H, Calderwood SK, Eguchi T. Stress-Inducible SCAND Factors Suppress the Stress Response and Are Biomarkers for Enhanced Prognosis in Cancers. Int J Mol Sci. 2023;24:5168.
Rhie SK, Yao L, Luo Z, Witt H, Schreiner S, Guo Y, et al. ZFX acts as a transcriptional activator in multiple types of human tumors by binding downstream from transcription start sites at the majority of CpG island promoters. Genome Res. 2018;28:310–20.
Jen J, Wang Y-C. Zinc finger proteins in cancer progression. J Biomed Sci. 2016;23:53.
Shujie L, Sima X, Liu X, Chen H. Zinc Finger Proteins: Functions and Mechanisms in Colon Cancer. Cancers. 2022;14:5242.
Xinxin L, Han M, Zhang H, Liu F, Pan Y, Zhu J, et al. Structures and biological functions of zinc finger proteins and their roles in hepatocellular carcinoma. Biomark Res. 2022;10:2.
Brix DM, Bundgaard Clemmensen KK, Kallunki T. Zinc Finger Transcription Factor MZF1-A Specific Regulator of Cancer Invasion. Cells. 2020;9:223.
Lord T, Law NC, Oatley MJ, Miao D, Du G, Oatley JM. A novel high throughput screen to identify candidate molecular networks that regulate spermatogenic stem cell functions†. Biol Reprod. 2022;106:1175–90.
Jin-Gu L, Ye Y. Bag6/Bat3/Scythe: A novel chaperone activity with diverse regulatory functions in protein biogenesis and degradation. BioEssays. 2013;35:377–85.
Reiners KS, Topolar D, Henke A, Simhadri VR, Kessler J, Sauer M, et al. Soluble ligands for NK cell receptors promote evasion of chronic lymphocytic leukemia cells from NK cell anti-tumor activity. Blood. 2013;121:3658–65.
Desmots F, Russell HR, Lee Y, Boyd K, McKinnon PJ. The reaper-binding protein scythe modulates apoptosis and proliferation during mammalian development. Mol Cell Biol. 2005;25:10329–37.
Charrier M, Mezquita L, Lueza B, Dupraz L, Planchard D, Remon J, et al. Circulating innate immune markers and outcomes in treatment-naïve advanced non–small cell lung cancer patients. Eur J Cancer. 2019;108:88–96.
Huang J-P, Li J, Xiao Y-P, Xu L-G. BAG6 negatively regulates the RLR signaling pathway by targeting VISA/MAVS. Front Immunol [Internet]. 2022 [cited 2023 Jun 23];13. Available from: https://www.frontiersin.org/articles/10.3389/fimmu.2022.972184
Zhao J, Wang H, Hu W, Jin Y. Effect of HLA-B-associated Transcript 3 Polymorphisms on Lung Cancer Risk: A Meta-Analysis. Med Sci Monit. 2014;20:2461–5.
Chen J, Zang Y-S, Xiu Q. BAT3 rs1052486 and rs3117582 polymorphisms are associated with lung cancer risk: a meta-analysis. Tumour Biol J Int Soc Oncodevelopmental Biol Med. 2014;35:9855–8.
Renieri A, Mencarelli MA, Cetta F, Baldassarri M, Mari F, Furini S, et al. Oligogenic germline mutations identified in early non-smokers lung adenocarcinoma patients. Lung Cancer Amst Neth. 2014;85:168–74.
Baranasic J, Niazi Y, Chattopadhyay S, Rumora L, Ćorak L, Dugac AV, et al. Germline variants of the genes involved in NF-kB activation are associated with the risk of COPD and lung cancer development. Acta Pharm Zagreb Croat. 2023;73:243–56.

Tables 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Lung gene expression study identifying Zscan2 and Bag6 as novel tissue repair players in an experimental model of COPD

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Mouse model

RNA extraction and RNA quality control of lung tissue samples

Lung Gene Expression Microarrays

Microarray Data Analysis

GO functional and pathway analysis

Quantitative real-time PCR for validation of the top genes

Statistical analysis

Results

LGF reverts emphysema in the CSE model: functional and morphometric studies

Lung tissue transcriptome signature of LGF treatment

Identification of potential biomarkers: qRT‒PCR to verify the RNA-seq results

Discussion

Conclusions

List of abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1