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Digital methylation-specific PCR: New applications for liquid biopsy

  • Gabriela Casani Cardoso , Fernando Augusto de Oliveira Ganzella , Guilherme Miniskiskosky , Regiane Stafim da Cunha and Edneia Amancio de Souza Ramos EMAIL logo
Published/Copyright: February 12, 2024
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Biomolecular Concepts
From the journal Biomolecular Concepts Volume 15 Issue 1

Abstract

Epigenetic analysis is a fundamental part of understanding pathophysiological processes with potential applications in diagnosis, prognosis, and assessment of disease susceptibility. Epigenetic changes have been widely studied in chronic obstructive pulmonary disease (COPD), but currently, there is no molecular marker used to improve the treatment of patients. Furthermore, this progressive disease is a risk factor for the development of more severe COVID-19. Methylation-specific polymerase chain reaction (MSP-PCR) plays an important role in the analysis of DNA methylation profiles, and it is one of the most widely used techniques. In this context, the combination of MSP-PCR with emerging PCR technologies, such as digital PCR (dPCR), results in more accurate analyses of the DNA methylation profile of the genes under study. In this study, we propose the application of the MSP-dPCR technique to evaluate the methylation profile of the ADAM33 gene from saliva samples and lung tissue biopsies of patients with COPD and COVID-19. MSP-dPCR generated a measurable prediction of gene methylation rate, with the potential application of this combined technology for diagnostic and prognostic purposes. It has also proven to be a powerful tool for liquid biopsy applications.

Graphical Abstract

Keywords: DNA methylation; polymerase chain reaction; epigenetic; COVID-19; chronic obstructive pulmonary disease

Introduction

Studies of the epigenetic mechanisms of DNA modification reveal information about the gene expression profile that is directly linked to the phenotype of the organism [1]. DNA methylation events have been extensively studied and associated with several biological processes, including aging, pathologies, or susceptibility to such conditions [2–4]. DNA methylation occurs at cytosines flanked by guanines, the so-called CpG islands. Methylation of these islands has been implicated as a mechanism that prevents gene transcription. Specifically studying the methylation of a gene or its promoter can provide insights into the gene’s activation or inactivation across various contexts [1,5]. To access DNA methylation, various techniques are used to differentiate methylated or unmethylated cytosines by treating DNA with sodium bisulfite [6]. These techniques include DNA sequencing, microarrays, and for specific regions, methylation-specific polymerase chain reaction (MSP-PCR) [7]. The MSP-PCR technique was first described by Herman et al. (1996) [8] and predicts that sodium bisulfite-treated DNA will amplify under two conditions: either as methylated (M) or unmethylated (U) sequence. This determination of methylation status occurs after DNA sequencing determination of methylation status in specific samples [9,10]. To achieve this, primers are designed to amplify regions rich in CpG dinucleotides (CpG islands) and contain as many downstream CpGs as possible in the 3′ region of the sequence. The two primer pairs are designed to amplify exactly the same region. The PCR products from this amplification are visualized by electrophoretic separation, making it possible to identify the products of the unmethylated, methylated, or both conditions [8,11]. More recently, this has been done through quantitative PCR (qPCR). Although simple, inexpensive, and highly sensitive, the classical MSP-PCR technique [8] is not quantitative and does not allow the measurement of methylation levels on DNA samples [12,13].

In recent years, PCR techniques have been improved and expanded with the development of various platforms for qPCR and, more recently, for digital PCR (dPCR) [14]. dPCR provides researchers with an attractive solution for absolute DNA quantification to aid in detecting genetic variants, including those found in liquid biopsies, where it demonstrates high sensitivity [15–17]. Copy number variation analysis or gene expression studies are also common applications of this technology.

dPCR is based on the individualization of PCR into multiple partitions, which function as independent reactions through Poisson’s random distribution. It allows absolute quantification of the original copy number of the amplified gene without interference from parameters such as amplification efficiency, by endpoint detection of amplified products and the binarization signal: (0) absence or (1) presence [18,19]. The dPCR technique includes high sensitivity and specificity analysis for low-copy number genes, following the standards established by the Digital PCR MiQE guideline (2020) [18]. The high detection sensitivity of this method has led to increased awareness and use of this technique for diagnostic purposes, particularly in the context of liquid biopsies [16,20]. Liquid biopsies have gained considerable attention over the past decade as an attractive method for noninvasively detecting, screening, and monitoring disease in various health conditions. Blood, body fluids such as urine or stool, saliva, sputum, or various secretions offer numerous advantages over traditional biopsies, such as comprehensive tissue sampling, repeatable sample collection during the disease’s progression, and accessibility in vulnerable individuals [21–23]. However, a crucial question in personalized medicine revolves around determining the most suitable biomarkers for health monitoring.

A biomarker is defined as a measurable, objective indicator of the normal and abnormal physiological state of an individual, indicating any change in that state. If these biomarkers are properly applied and patients are properly educated on how to respond to altered biomarker levels, this could immensely benefit rapid and personalized healthcare [24]. Disease detection by liquid biopsy is generally achieved by detecting molecular markers of specific genetic or epigenetic changes in tissues throughout the body [25]. Accordingly, the use of saliva in the study of respiratory diseases could assist in pathology monitoring. Saliva is a non-invasive biofluid that is easy to collect, transport, and store [26]. Because of its accessibility and association with systemic diseases, saliva stands as a prime candidate for easy health status monitoring [24,27]. The term asthma–COPD overlap (ACO) has been used to identify a heterogeneous condition where patients exhibit airflow limitation that is not entirely reversible, along with clinical and inflammatory characteristics present in both asthma and COPD. However, the diagnosis of ACO can be difficult in clinical practice, since there is still controversy regarding its definition, pathophysiology, and impact [28]. The discovery of molecular markers might redefine the boundaries of COPD diagnosis in clinical settings. Furthermore, COPD was often associated with COVID-19 severity during the pandemic year, due to the high mortality rate within this patient population [29,30]. As epigenetic factors influence both diseases, it is important to define how the group could be better diagnosed or need a different treatment, for example.

The ADAM33 protein, part of the ADAM protein family (A Disintegrin And Metalloproteinase), is encoded by the ADAM33 gene located on chromosome 20p13 and plays a vital biological role by activating Th2 cytokines and growth factors [31]. Soluble ADAM33 protein can increase angiogenesis, impacting lung functions and causing airflow obstruction independent of inflammation [32]. As ADAM33 expression is restricted to mesenchymal cells, including airway fibroblasts, myofibroblasts, and smooth muscle cells [33], it is proposed that dysregulated ADAM33 activity is associated with airway remodeling and bronchial hyperresponsiveness, considered a risk factor for asthma [32,34], due to increased expression in this disease [34]. In the context of COPD, it is well established that ADAM33 is involved in the pathophysiology and inflammatory response, including alterations in pro- and pre-inflammatory cytokines [35–37]. Several studies investigate the role of soluble ADAM33 and the upregulation of its mRNA, crucial in extracellular matrix damage and as part of the pathogenic mechanism of COPD [38,39].

It is known in the literature that the ADAM33 gene has well-defined epigenetic mechanisms in other non-communicable diseases, such as in breast cancer, where ADAM33 is regulated by DNA methylation, and its differential expression can influence the disease severity [40]. Although there is no scientific evidence showing the epigenetic profile of this gene in patients affected by COVID-19, a recent study found that SARS-CoV-2 infection, evolving into severe COVID-19, was associated with DNA methylation changes throughout the genome of peripheral blood mononuclear cells. When comparing severe COVID-19 with uninfected controls, 40,904 differentially methylated loci were identified [41]. Therefore, ADAM33 could be an excellent candidate as both a biomarker and a treatment target for COPD.

The selection of validated biomarkers, combined with high-quality detection tools like dPCR, could make saliva an important biopsy fluid for the detection of respiratory diseases. Combining this knowledge, this study aims to adapt the MSP-PCR protocol developed by Herman et al. [8] to the dPCR platform (MSP-dPCR) using QIAGEN technology. The objective is to employ this technique to assess the methylation profile of the promoter region of the ADAM33 gene in saliva samples from patients with chronic obstructive pulmonary disease (COPD), both with and without COVID-19. This highly sensitive and specific MSP-dPCR bisulfite conversion assay promises to be an essential tool for further understanding the mechanistic role of DNA methylation of genes that may lead to human disease, providing broad population-based results through an affordable platform capable of detecting specific DNA methylation patterns.

Materials and methods

Saliva and lung samples

Saliva samples (n = 20) were collected between February and April 2022 from COPD patients, with or without COVID-19 diagnosed by PCR for the SARS-CoV-2 virus. Patients were seen at the COPD outpatient clinic and/or the post-COVID outpatient clinic of the Pulmonology Department of the Clinical Hospital Complex – UFPR/EBSERH – Curitiba, Paraná, Brazil. During the appointment, patients collected approximately 1 mL of raw saliva in sterile 2 mL tubes, which were then stored at −20°C until further processing. For the COVID-19 positive and negative controls, samples were obtained from the Laboratory of Human Cytogenetics and Oncogenetics (LabCHO) during the screening of COVID-19-contaminated individuals performed by the Department of Biological Sciences of the Federal University of Paraná. Lung samples (n = 29) were collected from patients who died due to COVID-19, while they were bedridden and connected to mechanical ventilation systems. The collections were performed by surgical biopsy by the medical team from the Pontifical Catholic University of Paraná at a tertiary hospital in Curitiba, Paraná, Brazil. The collections were conducted between June and August 2020 and March and August 2021. All samples were stored in an ultra-freezer at −80°C until processing.

DNA isolation and sodium bisulfite treatment

To obtain DNA from saliva samples, the entire 1 mL of saliva underwent the QIAamp Circulating Nucleic Acid protocol (Qiagen, Hilden, Germany) following the manufacturer’s specifications. To obtain DNA from lung samples, approximately 1 mm3 of tissue was transferred to 2 mL tubes containing 1 mL of TES buffer (Tris-HCl, pH 8.0, at 10 mM, EDTA at 50 mM, SDS at 0.5%, RNase at 0.1 mg/mL, and Proteinase K at 0.05 mg/mL). The samples were homogenized in a TissueLyser LT device (Qiagen, Hilden, Germany) at 40 Hz cycles for 40 s and processed according to the specified protocol [42]. The purified DNA was quantified using a NanoDropLite spectrophotometer (ThermoFisher Scientific, Waltman, MA, USA) and subjected to agarose gel electrophoresis (0.8%). Approximately 2 µg of purified DNA was treated with sodium bisulfite using the Epitec Plus Bisulfite Kit (Qiagen, Hilden, Germany) according to the manufacturer’s recommendations.

Primer design

The primers were selected according to established methods in the referenced study [40] and were presumably chosen for their specificity and effectiveness in amplifying the target regions of interest in these genes. The primer sequences are described in Table 1.

Table 1

Sequence of oligos used as primers for the MSP-dPCR

Gene Forward 5′–3′ Reverse 5′–3′
ADAM33 M GTTTGAGGTTGTATCGGGTA ACTCGCAACTCCGACTCCG
ADAM33 U GTTTGAGGTTGTATTGGGTA ACTCACAACTCCAACTCCA

M, methylated; U, unmethylated.

MSP-dPCR

Approximately 50 ng bisulfite-treated DNA was added to a 40 μL reaction containing 0.4 μM oligos and 1× EvaGreen (Qiagen, Hilden, Germany). Methylated (M) and unmethylated (U) reactions were performed simultaneously under the same conditions and a master mix was prepared separately using the specific primers for the U/M conditions. Since COVID-19-positive and -negative samples were used as controls, no 100% methylated or 100% unmethylated controls were needed for this assay. The QIAcuity 24-well nanoplates with 26,400 partitions were used on the QIAcuity 5 plex Digital PCR System (Qiagen, Hilden, Germany), where the nucleic acid template was randomly distributed across all available partitions and individually amplified. PCR cycling conditions for ADAM33 are as follows: 95°C for 2 min; 40 cycles of denaturation at 95°C for 15 s, annealing at 56°C for 20 s, and extension at 72°C for 15 s, followed by 5 min at 40°C. The amplification target was detected at the end of the PCR process by assessing the presence or absence of fluorescence resulting from intercalating dyes. Since the PCR is randomly partitioned, it is possible for a positive reaction to contain more than one target molecule, so the Poisson model is utilized to determine the probability of a microreaction receiving zero, one, two, three, four, or five copies of the target molecule. The total number of copies of the target molecule in all valid partitions of a well was calculated by multiplying the average number of target molecules per partition by the number of valid partitions. Copies per microliter can also be calculated based on the known number of target copies per partition and the partition volume. All analyses were performed using the QIAcuity Suite software (Qiagen, Hilden, Germany).

Statistical analysis

The absolute quantification data (copies/µL) obtained from MSP-dPCR were analyzed using the Student’s T-test. A P-value of <0.05 was considered statistically significant.

The data for unmethylated ADAM33 and methylated ADAM33 from the same DNA input concentration were processed using the formula below [43]. The resulting values are expressed as percentages of methylated and unmethylated copies.

Percentage of methylation = M ( M + U ) × 100 ,

Percentage of unmethylation = U ( M + U ) × 100 ,

M is the methylated copies/µL and U is the unmethylated copies/µL.

  1. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee. This research has been approved by the Ethics and Research Committees of CONEP (National Commission for Ethics in Research) under the protocol codes CAAE 30188020.7.1001 and 31687620.2.0000.0096.

  2. Informed consent: Informed consent has been obtained from all individuals included in this study.

Results

The saliva and lung samples were initially categorized into three groups to investigate the range of hypothesized molecular markers. The first group comprised patients without COVID-19 or COPD (control group). The second group included patients who tested positive for SARS-CoV-2 via qPCR at the time of sample collection (COVID-19-positive group). The third group encompassed saliva and lung samples from COPD patients diagnosed with COVID-19 by qPCR at any time between 2021 and 2022 (COPD + COVID-19 group). Additionally, a fourth group of patients diagnosed solely with COPD was included in the saliva analysis. The samples then underwent the procedure outlined in the flowchart below (Figure 1).

Figure 1 Flowchart of the methodology used to evaluate DNA methylation of the ADAM33 gene. M, methylated copies; U, unmethylated copies.
Figure 1

Flowchart of the methodology used to evaluate DNA methylation of the ADAM33 gene. M, methylated copies; U, unmethylated copies.

After DNA extraction and sodium bisulfite treatment, all the samples within the same group were diluted to a similar concentration. For saliva samples, the range was between 44.6 and 69.2 ng/μL (median = 58.9 ng/μL; Q 1 = 53.95 ng/μL; Q 3 = 62.95 ng/μL) in the COPD + COVID-19 group. The COVID-19-positive control had a concentration of 30.1 ng/μL, while the COVID-19-negative control group had 24 ng/μL. In the case of lung samples, the range was from 228.5 to 229.8 ng/μL in the COPD + COVID-19 group. The concentration of the COVID-19 group ranged from 63.0 to 256 ng/μL (median = 156.75 ng/μL). Solely within the COPD group, the range was between 11 and 47.8 ng/μL (median = 12.8 ng/μL; Q 1 = 11.75 ng/μL; Q 3 = 17.45 ng/μL). After sample standardization by the guidelines (dMIQE, Table Supplementary material), MSP-dPCR was performed for the ADAM33 gene [20].

In saliva samples, the range of ADAM33 methylated copies was between 19.25 and 53.08 copies/μL in the COVID-19 group. In the COVID-19 + COPD group, the range of ADAM33 methylated copies was between 16.29 and 632.8 copies/μL. In the negative control group, the range of methylated copies was between 15.32 and 329.1 copies/μL. The ADAM33 unmethylated copies in the COVID-19 group ranged from 25.64 to 31.48 copies/μL. In the COVID-19 + COPD group, the range was from 7.721 to 138.9 copies/μL. In the COPD group, the range was from 15.97 to 605.8 copies/μL. In the negative control group, the unmethylated copies ranged from 16.57 to 31.61 copies/μL (Figure 2). We observed a higher number of methylated copies by μL of DNA in the control group and a higher number of unmethylated copies in the COPD group (p < 0.005). For the COPD + COVID-19 group, we observed a significant number of unmethylated copies by μL of DNA. In our analysis by copies/ng, we found a significant value to the unmethylated copies in the COPD group (Figure S1). The confidence interval of MSP-dPCR data for ADAM33 in saliva samples is presented in Table 2.

Figure 2 Fluorescence intensity of the analyzed partitions in (a) methylated and (b) unmethylated ADAM33 assays in saliva samples. The red line indicates the threshold between positive and negative partitions (gray dots). *dot plot obtained from the QIAcuity 5 plex (QIAGEN) equipment showing the rainfall on the samples analyzed.
Figure 2

Fluorescence intensity of the analyzed partitions in (a) methylated and (b) unmethylated ADAM33 assays in saliva samples. The red line indicates the threshold between positive and negative partitions (gray dots). *dot plot obtained from the QIAcuity 5 plex (QIAGEN) equipment showing the rainfall on the samples analyzed.

Table 2

Confidence interval of MSP-dPCR data for ADAM33 in saliva samples

Control U Control M COVID-19 U COVID-19 M COPD + COVID-19 U COPD + COVID-19 M COPD U COPD M
Lower 95% CI of mean −361.4 −333.5 −79.19 −73.53 24.32 31.91 38.44 11.30
Upper 95% CI of mean 433.5 461.4 173.5 179.2 68.09 75.68 88.70 61.56

For lung samples, the COVID-19 group showed a range of methylated ADAM33 copies between 0.209 and 0.532 copies/μL, while the range of unmethylated copies was 0.16–350 copies/μL. In the COVID-19 + COPD group, the range of methylated ADAM33 copies was between 1.30 and 1.84 copies/μL, with unmethylated copies ranging from 6.11 to 7.90 copies/μL (Figure 3). We observed an increase in unmethylated copies in both conditions, with a more pronounced increase when associated with COPD by copies/uL (p = 0.0385), but not copies/ng of DNA (Figure S2).

Figure 3 Fluorescence intensity of the analyzed partitions in (a) methylated and (b) unmethylated ADAM33 assays in lung samples. The red line indicates the threshold between positive and negative partitions (gray dots). *dot plot obtained from the QIAcuity 5 plex (QIAGEN) equipment showing the rainfall on the samples analyzed.
Figure 3

Fluorescence intensity of the analyzed partitions in (a) methylated and (b) unmethylated ADAM33 assays in lung samples. The red line indicates the threshold between positive and negative partitions (gray dots). *dot plot obtained from the QIAcuity 5 plex (QIAGEN) equipment showing the rainfall on the samples analyzed.

Analyzing the number of copies or the methylation rate in lung tissue of patients exclusively with COPD was not feasible, as it is not common to biopsy this tissue in those patients. In addition, several studies have described changes in the methylation profile preceding the appearance of many diseases, which could influence the results of our analysis since all available samples in this study were from individuals who died from COVID-19 and had no previous biopsy to compare. Graphical and statistical analyses of the copy numbers are detailed in Supplementary material 3. To compare the sample groups, the average methylation rates were calculated and plotted on bar graphs. Figure 4 shows the analysis of the methylation profile of ADAM33 in saliva (Figure 4a) and in lung tissue (Figure 4b). It was observed that the methylation profile was very similar between the groups. In saliva, 63.9% of the ADAM33 gene copies were methylated in the control group, 52.8% in the COVID-19 positive group, 53.8% in the COPD + COVID-19 group, and 36.4% in the COPD group. The difference between the COPD group and the control group was statistically significant (p = 0.0001). Otherwise, in the lung samples, the control group exhibited 14% of the ADAM33 gene copies methylated, in the COVID-19 positive group, ADAM33 displayed a 26.7% rate of methylation, while in the COPD + COVID-19 group, ADAM33 showed 18.7% of methylation.

Figure 4 ADAM33 methylation rate in the control group, COVID-19 group, and/or COPD + COVID-19 group in saliva (a) and lung tissue (b). *p = 0.0001.
Figure 4

ADAM33 methylation rate in the control group, COVID-19 group, and/or COPD + COVID-19 group in saliva (a) and lung tissue (b). *p = 0.0001.

The confidence interval of MSP-dPCR data for ADAM33 in lung samples is described in Table 3. A negative 95% CI for some groups was observed, which may be related to the sample size, the standard deviation, and the binomial nature of the data in which high quantities of one condition result in low quantities of the other condition.

Table 3

Confidence interval of MSP-dPCR data for ADAM33 in lung samples

COVID-19 U COVID-19 M COPD + COVID-19 U COPD + COVID-19 M
Lower 95% CI of mean 39.60 −6.947 24.43 −38.24
Upper 95% CI of mean 106.9 60.40 138.2 75.57

U, unmethylated; M, methylated.

Discussion

The MSP-PCR technique is an important tool in molecular research and clinical diagnosis within the field of epigenetics. It can be used as a diagnostic tool to determine disease stage, predict treatment response, and assess the risk of recurrence [44–46]. In addition, it can be used to monitor the efficacy of epigenetic treatments, such as demethylating agent therapies, designed to reverse abnormal DNA methylation [47,48]. The identification of epigenetic biomarkers through MSP-dPCR holds potential value for disease screening, patient stratification, and personalized treatment approaches [49,50].

Asthma and COPD share similarities and significant differences. They share genetic and environmental risk factors including allergens, infections, and smoking. Notably, ADAM33 mRNA and protein expression are restricted to mesenchymal cells, including airway fibroblasts, myofibroblasts, and smooth muscle [51,52]. In bronchial biopsies from normal and asthmatic airways, ADAM33 colocalizes with smooth muscle and fibroblast-like cells in the submucosa, supporting its association with bronchial hyperresponsiveness and airway remodeling [53]. However, no disease-related differences in overall expression levels were identified. A study revealed that the increase of TGF-β contributed to the soluble form ADAM33 (sADAM33), leading to epithelial damage. This finding suggests a potential synergism with ADAM33 in the pathogenesis of asthma and COPD, emphasizing the intricate interplay between genetic and environmental factors in these complex diseases [53,54].

In COPD, the ADAM33 gene has been associated with heightened lung epithelial cell responsiveness, contributing to a poorer prognosis of the disease, this association has been linked to the methylation status of this gene [55], leading to the hypothesis that varying methylation levels might correlate with the severity of allergic asthma, rendering it a potential target for future therapeutic and diagnostic approaches [56]. However, although ADAM33 has been identified as a factor underlying asthma exacerbation [57], studies have shown no significant difference in CpG methylation of peripheral blood leukocytes between asthmatic adults and controls, suggesting that treatment response differs in mild and severe asthma as well as in different tissue types [58,59]. Regarding the conclusions of several studies, polymorphonuclear cells are not a well-recommended tissue type for studying the epigenetic process in asthma, unlike airway epithelial cells and nasal epithelial cells. The methylation mechanism of the ADAM33 gene might elucidate why lung cell injury is more severe in COPD or ACO with SARS-CoV-2 infection. Our results revealed a higher percentage of methylation in saliva samples in the control group compared to other groups. When comparing individuals without respiratory disease to those in the COPD group, the ADAM33 hypermethylation rate was 63.9% compared to 36.4%, respectively. When comparing individuals without COPD who had COVID-19 or those with COPD who had COVID-19, the hypermethylation rates remained almost unchanged, at 52.8 and 53.8%, respectively. Collectively, these findings suggest that COPD individuals tend to have higher rates of unmethylated ADAM33. It is consistent with the literature that these individuals have higher expression of ADAM33, which is associated with worsened prognosis in COPD. Moreover, within the same group, we observed a decrease in the rate of unmethylation of ADAM33, from 63.6 to 46.2%, which suggests that SARS-CoV-2 infection might influence the methylation profile. However, further experiments and analyses are needed to better understand the effects of COVID-19 on DNA methylation as numerous factors could potentially alter methylation levels in these patients. These methylation rates are further supported by analyzing the number of copies between the groups, where the number of methylated copies of ADAM33 is higher in controls than in COPD patients, and conversely (p < 0.0001). Additional data comparing different important markers for both diseases need to be analyzed, yet our findings indicate that the percentage of the ADAM33 methylation profile in saliva could be an important diagnostic and prognostic factor for COPD severity, particularly in the context of COVID-19. Further investigation is necessary to ascertain whether this alteration might extend to other viral infections. However, this necessitates new experiments to elucidate the effects of ADAM33 methylation. It is noteworthy that despite the diversity of cell-originated DNA, methylation assessment emerges as a compelling avenue, considering the potential differentiation in methylation patterns between cells in both healthy and diseased states. However, it appears that saliva might be considered a more promising biomarker for COPD, since there were no differences between the methylation rates of ADAM33 in the lung tissue among individuals with COVID-19, regardless of COPD status. Nonetheless, additional studies are warranted to substantiate this proposition, encompassing diverse cohorts of respiratory diseases and varying biological specimens.

Considering the valuable applications of MSP-PCR in identifying diverse methylation profiles, its adaptation to the most modern available techniques is intriguing, potentially enhancing its performance. However, the MSP-dPCR technique displayed the ‘rain’ phenomenon in fluorescence intensity graphs. This occurrence might stem from the exceedingly small size of the products, approximately 50 base pairs, potentially leading to a greater influx of DNA copies within each partition compared to traditional gene expression dPCR. Another possible situation is that bisulfite conversion causes fragmentation of DNA due to harsh conditions [60]. It was described that bisulfite-converted DNA may produce a “rain” profile in dPCR, resulting in poor resolution [61]. Then, this is another possible reason why there are some poor resolutions in our results. Consequently, this could elevate the probability of equipment misinterpretation between negative and positive partitions, owing to the heightened likelihood of both methylated and non-methylated copies cohabiting within the same partition. Nonetheless, dPCR remains an excellent application option for MSP-PCR due to its advanced features that offer several advantages compared to previous generations of PCR. Apart from offering high sensitivity and precision, dPCR is less sensitive to inhibitors that can affect the results, as observed in qPCR, making it suitable for complex or poor-quality samples [50].

In summary, the MSP-dPCR technique offers specificity and precision in analyzing DNA methylation profiles. To achieve this, it is crucial to emphasize that the dPCR and the primers for the unmethylated and methylated conditions must be carefully standardized and validated, following the guidelines for dPCR [2,51,52]. Moreover, the utilization of normalizing genes might not be necessary in the specific case of analyzing percentages of unmethylated and methylated conditions of the same sample by MSP-dPCR, which facilitates data analysis. Using the MSP-dPCR technique, this study evaluated for the first time the methylation profile of ADAM33 from saliva and lung tissue samples, establishing connections to COPD and COVID-19 diseases. Therefore, the combination of MSP-PCR and dPCR presents an opportunity for even more robust and accurate results in DNA methylation studies in across several pathologies.

# Equality contribution.

Acknowledgements

We would like to thank Ph.D. Giseli Klassen (LabEpi-UFPR) for primer support, Ph.D. Jaqueline de Oliveira (LabCHO-UFPR) for the sample controls, and Dr. Josiane Marchioro (HC-EBSERH/UFPR) for the clinical supervision. We would also like to thank Dr. Felipe Tuon for collecting and storing the lung samples until they were prepared for the molecular assays and Ph.D. Marco Antonio Ribas Cavalieri by statistical assistance.

  1. Funding information: This research was funded by Fundação Araucária and Ministério da Saúde do Brasil, grant number SUS2020131000104 and CP11/2020, CAPES foundation, and CNPq Code 001.

  2. Author contributions: All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Mattei AL, Bailly N, Meissner A. DNA methylation: A historical perspective. Trends Genet. 2022 Jul;38(7):676–707.10.1016/j.tig.2022.03.010Search in Google Scholar PubMed

[2] Heyn H, Esteller M. DNA methylation profiling in the clinic: Applications and challenges. Nat Rev Genet. 2012;13(10):679–92.10.1038/nrg3270Search in Google Scholar PubMed

[3] Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68.10.1038/nbt.1685Search in Google Scholar PubMed

[4] Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6(8):597–610.10.1038/nrg1655Search in Google Scholar PubMed

[5] Coffee B. Methylation-specific PCR. Curr Protoc Hum Genet. 2009;10(6):10.6.1–10.6.14.Search in Google Scholar

[6] Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, et al. A genomic sequencing protocol that yields a positive display of 5- methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89(5):1827–31.10.1073/pnas.89.5.1827Search in Google Scholar PubMed PubMed Central

[7] Hattori N, Liu YY, Ushijima T. DNA methylation analysis. Methods Mol Biol. 2023;2691:165–83.10.1007/978-1-0716-3331-1_13Search in Google Scholar PubMed

[8] Herman JG, Graff JR, Myöhänen S, Nelkin BD, Baylin SB, Methylation-specific PCR. A novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A. 1996;93(18):9821–6.10.1073/pnas.93.18.9821Search in Google Scholar PubMed PubMed Central

[9] Li LC, Dahiya R. MethPrimer: Designing primers for methylation PCRs. Bioinformatics. 2002;18(11):1427–31.10.1093/bioinformatics/18.11.1427Search in Google Scholar PubMed

[10] Li LC. Designing PCR primer for DNA methylation mapping. Methods Mol Biol. 2007;402:371–84.10.1007/978-1-59745-528-2_19Search in Google Scholar PubMed

[11] Hernández HG, Tse MY, Pang SC, Arboleda H, Forero DA. Optimizing methodologies for PCR-based DNA methylation analysis. Biotechniques. 2013;55(4):181–97.10.2144/000114087Search in Google Scholar PubMed

[12] Derks S, Lentjes MHFM, Hellebrekers DMEI, De Bruïne AP, Herman JG, Van Engeland M. Methylation-specific PCR unraveled. Cell Oncol. 2004;26(5–6):291–9.10.1155/2004/370301Search in Google Scholar PubMed PubMed Central

[13] Licchesi JDF, Herman JG. Methylation-specific PCR. Methods Mol Biol. 2009;507:305–23.10.1007/978-1-59745-522-0_22Search in Google Scholar PubMed

[14] Zhu H, Zhang H, Xu Y, Laššáková S, Korabečná M, Neužil P. PCR past, present and future. Biotechniques. 2020;69(4):317–25.10.2144/btn-2020-0057Search in Google Scholar PubMed PubMed Central

[15] Palacín‐aliana I, García‐romero N, Asensi‐puig A, Carrión‐navarro J, González‐rumayor V, Ayuso‐sacido Á. Clinical utility of liquid biopsy‐based actionable mutations detected via ddpcr. Biomedicines. 2021;9(8):906–26.10.3390/biomedicines9080906Search in Google Scholar PubMed PubMed Central

[16] Moreno-Manuel A, Calabuig-Fariñas S, Obrador-Hevia A, Blasco A, Fernández-Díaz A, Sirera R, et al. dPCR application in liquid biopsies: divide and conquer. Expert Rev Mol Diagn. 2021;21(1):3–15.10.1080/14737159.2021.1860759Search in Google Scholar PubMed

[17] Crucitta S, Ruglioni M, Novi C, Manganiello M, Arici R, Petrini I, et al. Comparison of digital PCR systems for the analysis of liquid biopsy samples of patients affected by lung and colorectal cancer. Clin Chim Acta. 2023;541:117239.10.1016/j.cca.2023.117239Search in Google Scholar PubMed

[18] Whale AS, De Spiegelaere W, Trypsteen W, Nour AA, Bae Y-K, Benes V, et al. The digital MIQE guidelines update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clin Chem. 2020;66(8):1012–29.10.1093/clinchem/hvaa219Search in Google Scholar PubMed

[19] Quan PL, Sauzade M, Brouzes E. DPCR: A technology review. Sens (Switz). 2018;18(4):1271.10.3390/s18041271Search in Google Scholar PubMed PubMed Central

[20] Zhang L, Parvin R, Fan Q, Ye F. Emerging digital PCR technology in precision medicine. Biosens Bioelectron. 2022;211:114344.10.1016/j.bios.2022.114344Search in Google Scholar PubMed

[21] Santos V, Freitas C, Fernandes MGO, Sousa C, Reboredo C, Cruz-Martins N, et al. Liquid biopsy: The value of different bodily fluids. Biomark Med. 2022;16(2):127–45.10.2217/bmm-2021-0370Search in Google Scholar PubMed

[22] Ponti G, Manfredini M, Tomasi A. Non-blood sources of cell-free DNA for cancer molecular profiling in clinical pathology and oncology. Crit Rev Oncol Hematol. 2019;141:36–42.10.1016/j.critrevonc.2019.06.005Search in Google Scholar PubMed

[23] Garzarelli V, Ferrara F, Primiceri E, Chiriacò MS. Biofluids manipulation methods for liquid biopsy in minimally-invasive assays. MethodsX. 2022;9:101759.10.1016/j.mex.2022.101759Search in Google Scholar PubMed PubMed Central

[24] Aro K, Wei F, Wong DT, Tu M. Saliva liquid biopsy for point-of-care applications. Front Public Heal. 2017;5:77.10.3389/fpubh.2017.00077Search in Google Scholar PubMed PubMed Central

[25] Nikanjam M, Kato S, Kurzrock R. Liquid biopsy: Current technology and clinical applications. J Hematol Oncol. 2022;15:131.10.1186/s13045-022-01351-ySearch in Google Scholar PubMed PubMed Central

[26] Henson BS, Wong DT. Collection, storage, and processing of saliva samples for downstream molecular applications. Methods Mol Biol. 2010;666:21–30.10.1007/978-1-60761-820-1_2Search in Google Scholar PubMed

[27] Javaid MA, Ahmed AS, Durand R, Tran SD. Saliva as a diagnostic tool for oral and systemic diseases. J Oral Biol Craniofacial Res. 2016;6(1):67–76.10.1016/j.jobcr.2015.08.006Search in Google Scholar PubMed PubMed Central

[28] Fouka E, Papaioannou AI, Hillas G, Steiropoulos P. Asthma-COPD overlap syndrome: Recent insights and unanswered questions. J Pers Med. 2022;12(5).10.3390/jpm12050708Search in Google Scholar PubMed PubMed Central

[29] Singh D, Mathioudakis AG, Higham A. Chronic obstructive pulmonary disease and COVID-19: Interrelationships. Curr Opin Pulm Med. 2022;28(2):76–83.10.1097/MCP.0000000000000834Search in Google Scholar PubMed PubMed Central

[30] Leung JM, Niikura M, Yang CWT, Sin DD. COVID-19 and COPD. Eur Respir J. 2020;56(2):2002108.10.1183/13993003.02108-2020Search in Google Scholar PubMed PubMed Central

[31] Shapiro SD, Owen CA. ADAM-33 surfaces as an asthma gene. N Engl J Med. 2002 Sep 19;347(12):936–8.10.1056/NEJMcibr022144Search in Google Scholar PubMed

[32] Davies ER, Kelly JFC, Howarth PH, Wilson DI, Holgate ST, Davies DE, et al. Soluble ADAM33 initiates airway remodeling to promote susceptibility for allergic asthma in early life. JCI Insight. 2016;1(11):e87632.10.1172/jci.insight.87632Search in Google Scholar PubMed PubMed Central

[33] Van Eerdewegh P, Little RD, Dupuis JE, Del Mastro RG, Falls K, Simon J, et al. Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature. 2002;418(6896):426–30.Search in Google Scholar

[34] Tripathi P, Awasthi S, Gao P. ADAM metallopeptidase domain 33 (ADAM33): a promising target for asthma. Mediators Inflamm. 2014;2014:572025.10.1155/2014/572025Search in Google Scholar PubMed PubMed Central

[35] Wang X, Li L, Xiao J, Jin C, Huang K, Kang X, et al. Association of ADAM33 gene polymorphisms with COPD in a northeastern Chinese population. BMC Med Genet. 2009;10(1):1–7.10.1186/1471-2350-10-132Search in Google Scholar PubMed PubMed Central

[36] Wang X, Li W, Huang K, Kang X, Li Z, Yang C, et al. Genetic variants in ADAM33 are associated with airway inflammation and lung function in COPD. BMC Pulm Med. 2014;14(1):173.10.1186/1471-2466-14-173Search in Google Scholar PubMed PubMed Central

[37] Zhou DC, Zhou CF, Toloo S, Shen T, Tong SL, Zhu QX. Association of a disintegrin and metalloprotease 33 (ADAM33) gene polymorphisms with the risk of COPD: an updated meta-analysis of 2,644 cases and 4,804 controls. Mol Biol Rep. 2015;42(2):409–22.10.1007/s11033-014-3782-5Search in Google Scholar PubMed

[38] Holgate ST. ADAM metallopeptidase domain 33 (ADAM33): identification and role in airways disease. Drug N Perspect. 2010;23(6):381–7.10.1358/dnp.2010.23.6.1437242Search in Google Scholar PubMed

[39] Fachri M, Hatta M, Massi MN, Santoso A, Wikanningtyas TA, Dwiyanti R, et al. The strong correlation between ADAM33 expression and airway inflammation in chronic obstructive pulmonary disease and candidate for biomarker and treatment of COPD. Sci Rep. 2021;11(1):23162.Search in Google Scholar

[40] Seniski GG, Camargo AA, Ierardi DF, Ramos EAS, Grochoski M, Ribeiro ESF, et al. ADAM33 gene silencing by promoter hypermethylation as a molecular marker in breast invasive lobular carcinoma. BMC Cancer. 2009;9:80.10.1186/1471-2407-9-80Search in Google Scholar PubMed PubMed Central

[41] Corley MJ, Pang APS, Dody K, Mudd PA, Patterson BK, Seethamraju H, et al. Genome-wide DNA methylation profiling of peripheral blood reveals an epigenetic signature associated with severe COVID-19. J Leukoc Biol. 2021;110(1):1–6.10.1002/JLB.5HI0720-466RSearch in Google Scholar PubMed PubMed Central

[42] Sambrook J, Russell DW. Purification of nucleic acids by extraction with phenol: chloroform. Cold Spring Harb Protoc. 2006 Jun;2006(1):pdb.prot4455.10.1101/pdb.prot4455Search in Google Scholar PubMed

[43] Arroyo K, Nargizyan A, Andrade FG, Myint SS, Lu S, Pandey P, et al. Development of a droplet digitalTM PCR DNA methylation detection and quantification assay of prenatal tobacco exposure. Biotechniques. 2022;72(4):121–33.10.2144/btn-2021-0099Search in Google Scholar PubMed

[44] Ghadiri Moghaddam F, Farajnia S, Karbalaei-Mahdi M, Monir L. Epigenetic insights in the diagnosis, prognosis, and treatment selection in CRC, an updated review. Mol Biol Rep. 2022 Oct;49(10):10013–22.10.1007/s11033-022-07569-wSearch in Google Scholar PubMed

[45] Chen S, Su J, Zhao Z, Shao Y, Dou Y, Li F, et al. DNA framework-supported electrochemical analysis of DNA methylation for prostate cancers. Nano Lett. 2020;20(10):7028–35.10.1021/acs.nanolett.0c01898Search in Google Scholar PubMed

[46] Yamashita K, Hosoda K, Nishizawa N, Katoh H, Watanabe M. Epigenetic biomarkers of promoter DNA methylation in the new era of cancer treatment. Cancer Sci. 2018;109(12):3695.10.1111/cas.13812Search in Google Scholar PubMed PubMed Central

[47] Follo MY, Finelli C, Mongiorgi S, Clissa C, Bosi C, Testoni N, et al. Reduction of phosphoinositide-phospholipase C beta1 methylation predicts the responsiveness to azacitidine in high-risk MDS. Proc Natl Acad Sci. 2009;106(39):16811–6.10.1073/pnas.0907109106Search in Google Scholar PubMed PubMed Central

[48] Zhu Z, Lin S, Wu X, Xu J, Li L, Ye W, et al. Decitabine and cisplatin are synergistic to exert anti-tumor effect on gastric cancer via inducing SOX2 dna demethylation. Onco Targets Ther. 2021;14:623–36.10.2147/OTT.S276168Search in Google Scholar PubMed PubMed Central

[49] How Kit A, Nielsen HM, Tost J. DNA methylation based biomarkers: Practical considerations and applications. Biochimie. 2012;94(11):2314–37.10.1016/j.biochi.2012.07.014Search in Google Scholar PubMed

[50] Shahraki K, Shahraki K, Ghasemi Boroumand P, Sheervalilou R. Promotor methylation in ocular surface squamous neoplasia development: epigenetics implications in molecular diagnosis. Expert Rev Mol Diagn. 2023;23(9):753–69.10.1080/14737159.2023.2240238Search in Google Scholar PubMed

[51] Sahu A, Swaroop S, Kant S, Banerjee M. Signatures for chronic obstructive pulmonary disease (COPD) and asthma: A comparative genetic analysis. Br J Biomed Sci. 2021;78(4):177–83.10.1080/09674845.2021.1905988Search in Google Scholar PubMed

[52] Van Eerdewegh P, Little RD, Dupuis J, Del Mastro RG, Falls K, Simon J, et al. Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature. 2002;418(6896):426–30.10.1038/nature00878Search in Google Scholar PubMed

[53] Fachri M, Hatta M, Massi MN, Santoso A, Wikanningtyas TA, Dwiyanti R, et al. The strong correlation between ADAM33 expression and airway inflammation in chronic obstructive pulmonary disease and candidate for biomarker and treatment of COPD. Sci Rep. 2021 Nov;11(1):23162.10.1038/s41598-021-02615-2Search in Google Scholar PubMed PubMed Central

[54] Puxeddu I, Pang YY, Harvey A, Haitchi HM, Nicholas B, Yoshisue H, et al. The soluble form of a disintegrin and metalloprotease 33 promotes angiogenesis: Implications for airway remodeling in asthma. J Allergy Clin Immunol. 2008;121(6):1400–6.10.1016/j.jaci.2008.03.003Search in Google Scholar PubMed

[55] Yang Y, Haitchi HM, Cakebread J, Sammut D, Harvey A, Powell RM, et al. Epigenetic mechanisms silence a disintegrin and metalloprotease 33 expression in bronchial epithelial cells. J Allergy Clin Immunol. 2008;121(6):1393–9.10.1016/j.jaci.2008.02.031Search in Google Scholar PubMed

[56] Postma DS, Kerkhof M, Boezen HM, Koppelman GH. Asthma and chronic obstructive pulmonary disease: Common genes, common environments? Am J Respir Crit Care Med. 2011;183(12):1588–94.10.1164/rccm.201011-1796PPSearch in Google Scholar PubMed

[57] Yang PJ, Li RN, Huang CC, Wang TH, Ko YC, Huang MS, et al. The methylation patterns of a disintegrin and metalloproteinase 33 gene (ADAM33) in adult asthma. Int Arch Allergy Immunol. 2013;161(1):74–80.10.1159/000343280Search in Google Scholar PubMed

[58] Lin PI, Shu H, Mersha TB. Comparing DNA methylation profiles across different tissues associated with the diagnosis of pediatric asthma. Sci Rep. 2020;10(1):151.10.1038/s41598-019-56310-4Search in Google Scholar PubMed PubMed Central

[59] Sheikhpour M, Maleki M, Ebrahimi Vargoorani M, Amiri V. A review of epigenetic changes in asthma: methylation and acetylation. Clin Epigenetics. 2021;13(1):65.10.1186/s13148-021-01049-xSearch in Google Scholar PubMed PubMed Central

[60] Kresse SH, Brandt-Winge S, Pharo H, Flatin BTB, Jeanmougin M, Vedeld HM, et al. Evaluation of commercial kits for isolation and bisulfite conversion of circulating cell-free tumor DNA from blood. Clin Epigenet. 2023;15(1):151.10.1186/s13148-023-01563-0Search in Google Scholar PubMed PubMed Central

[61] Yu M, Heinzerling TJ, Grady WM. DNA methylation analysis using droplet digital pcr. In: Karlin-Neumann G, Bizouarn F, editors. Digital PCR. Methods in molecular biology. Vol 1768, New York, NY: Humana Press; 2018.10.1007/978-1-4939-7778-9_21Search in Google Scholar PubMed

Received: 2023-10-31
Revised: 2024-01-22
Accepted: 2024-01-23
Published Online: 2024-02-12

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/bmc-2022-0041
Keywords for this article
DNA methylation; polymerase chain reaction; epigenetic; COVID-19; chronic obstructive pulmonary disease
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. Antitumor activity of 5-hydroxy-3′,4′,6,7-tetramethoxyflavone in glioblastoma cell lines and its antagonism with radiotherapy
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  5. β-Amyloid peptide modulates peripheral immune responses and neuroinflammation in rats
  6. A novel approach for protein secondary structure prediction using encoder–decoder with attention mechanism model
  7. Diurnal and circadian regulation of opsin-like transcripts in the eyeless cnidarian Hydra
  8. Withaferin A alters the expression of microRNAs 146a-5p and 34a-5p and associated hub genes in MDA-MB-231 cells
  9. Toxicity of bisphenol A and p-nitrophenol on tomato plants: Morpho-physiological, ionomic profile, and antioxidants/defense-related gene expression studies
  10. Review Articles
  11. Polycystic ovary syndrome and its management: In view of oxidative stress
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