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DNA methylation arrays are the most used methods to access DNA methylation due to their cost-benefit ratio14. The present study described a detailed protocol using a commercially available microarray platform to evaluate DNA methylation in a pilot study performed in a Brazilian cohort. The obtained results from the pilot study confirmed the effectiveness of the protocol. Figure 3 shows the sample comparability and the complete bisulfite conversion32.
As a quality control step, the ChAMP algorithm recommended the exclusion of CpGs sites during the filtering process. The aim of excluding probes is to improve data analysis and eliminate bias. The low-quality CpGs (p-values lower than 0.05) were removed to eliminate experimental noise in the dataset. The targets remained passed in the density plot analysis. Zhou33 described the importance of filtering CpGs near SNPs to avoid mismatches, misinterpretation of polymorphic cytosines' methylation, and causing switch color of type I probe design34. Also, as XY chromosomes are differentially impacted by imprinting, Heiss and Just35 reinforced the importance of filtering those probes because, in females, the problems with hybridization may be confounding factors35.
The DMAPs expiration date, the formamide opening date, the analytical quality of the absolute ethanol, and total leucocyte counts are considered critical steps in the protocol.
Furthermore, according to our observations, the cell-type estimation is essential in performing the bioinformatics analysis. The Houseman method performs the cell type estimation as described in Tian's study30. This method is based on 473 specific CpG sites that can predict the percentages of the most important cell types, such as granulocytes, monocytes, B cells, and T cells36. We used the recommended function "myRefbase" from the ChAMP package. After the estimation, the ChAMP algorithm adjusts the beta values and eliminates this bias from the dataset. This step is crucial in studies focused on obesity because this population has a considerable difference in white blood cells due to their chronic inflammatory state.
We only changed the original cap map for the common PCR seal regarding method modification and troubleshooting. After each centrifugation process, the seal was changed for a new one. We could not use the standard heat sealing and adapted it using aluminum foil around the plate.
Although commercial assays have been considered a gold standard for epigenetic studies, one limitation of the protocol could be the specificity of the reagents and equipment from a unique brand37,38,39,40. Another limitation is the lack of indicators that allow identification of the correct progress of the experiment41.
The standardization of the present protocol represents a great guide for epigenetic research, reducing human errors during the process and allowing successful data analysis and comparability between different studies.
According to our results, DNA methylation experiments are suitable for studies comparing individuals with and without obesity43. Also, the proposed bioinformatics analysis provided high-quality data and could be considered in large-scale studies.
Using the SVD analysis, we identified that the obesity-related traits (BMI, WC, and FM) influenced the variability in DNA methylation data. As a significant result, the cell-type estimation indicates that both natural killer cells (NK) and B cells were higher in women with obesity than in women without obesity (Figure 5). The higher counts of those cells could be explained by the low-grade inflammatory state of these individuals44. We observed that patients with obesity have hypo- and hypermethylated CpGs in promoter regions of genes associated with fat mass. Most of the sites were hypomethylated, which could be related to the natural increase in reactive oxygen species (ROS) levels in these individuals. This oxidative stress condition may promote guanine perturbance at the dinucleotide site, forming 8-hydroxy-2'-deoxyguanosine (8-OHdG), resulting in a 5mCp-8-OHdG dinucleotide site, and causing TET enzymes recruitment. All these events could be responsible for promoting DNA hypomethylation and hypermethylation by different mechanisms of action45.
In addition, the rate of adipogenesis seems to increase in individuals with obesity, with approximately 10% of new cells to old cells46,47. Epigenetic contributions, emphasizing the obesogenic environment, can alter the cells' proliferation and differentiation rates, favoring the development of fat mass48. Epigenetic changes can also affect adipogenic programs, facilitating or restricting their development. Primary transcription factors (PPARγ or C / EBPα) or the assembly of multiprotein complexes, positioned in downstream promoter regions operated by including or excluding epigenetic modifying enzymes, regulate gene expression through hyper- or hypomethylation45. The PPARγ pathway has been previously described to alter the WNT pathway, which had genes enriched in this study. Although it is still unknown how WNT signaling occurs during adipogenesis, recent studies have reported that it might have essential roles in adipocyte metabolism, particularly under obesogenic conditions49.