## 4 Discussion  
Organ-on-chip models have become increasingly relevant research instruments. Crucial for their application is their proper characterization, including ADME pharmacogenomic profiling, in order for one to fully understand their response upon challenge. While targeted sequencing achieved significantly greater sequencing depth for the ADME genes than WGS, the relative coverage variability among these target genes, as indicated by comparable enrichment ratio ranges, suggests that intrinsic factors associated with specific gene regions influence coverage uniformity irrespective of the sequencing methodology. Both methods  
Figure 10: Genome-wide distribution of non-CpG 5 mC/5 hmC and read depth in the AdMSC mitochondrial genome. (**A**) Adaptive sampling (AS) library. The mitochondrial chromosome is displayed linearly (10 bp windows). The blue shaded trace (left \(y\)-axis) shows mean read coverage; the red trace (right \(y\)-axis) shows the percentage of non-CpG cytosines (CHH + CHG) called as modified after strict filtering of Dorado output—only 5 mC or 5 hmC events recorded in the MM tag whose corresponding ML probability \(\geq\) 0.80 were retained. (**B**) Whole genome sequencing (WGS) library processed with the same pipeline. Both datasets reveal a uniformly low (<1%) non-CpG signal that is independent of depth. (**C**) Scatter plot of per-window coverage versus non-CpG methylation for the AS library (1 bp windows; \(n\) = 16,569). Spearman \(\rho\) = 0.028, \(p\) = 3.6 \(\times\) 10\({}^{-4}\). The dashed line is an ordinary least squares trend (slope = \(-\)0.0005). (**D**) Equivalent plot for the WGS library (1 bp windows; \(n\) = 16,569). Spearman \(\rho\) = \(-\)0.027, \(p\) = 5.8 \(\times\) 10\({}^{-4}\); dashed line slope = \(-\)0.0000. The absence of any correlation confirms that the residual methylation signal is not an artefact of local sequencing depth and supports the conclusion that AdMSC mtDNA is essentially unmethylated at non-CpG sites. showed there were some trade-offs. AS provides deeper and more uniform coverage across the targets (reflected in higher, more consistent normalized coverage). WGS coverage across these specific genes is inherently more variable. However, both methods appear capable of detecting gene-to-gene variations in methylation levels, and the relative methylation patterns look broadly similar, despite the differences in coverage characteristics. These findings have important implications for pharmacogenomic research, where the accurate characterization of ADME gene methylation may influence drug metabolism phenotypes. The consistent methylation patterns detected across both sequencing approaches confirm that the observed epigenetic modifications in these genes represent genuine biological signals rather than technical artifacts. Notably, we identified differential methylation patterns in key drug-metabolizing enzyme genes that correlated with specific genetic variants, suggesting potential epigenetic regulation of drug response. The detection of these methylation patterns was consistent across both AS and WGS methodologies, though AS provided greater resolution for detecting subtle methylation differences at critical regulatory regions. These epigenetic variations may explain some of the inter-individual variability in drug metabolism that cannot be attributed to genetic polymorphisms alone. Future studies should investigate the functional consequences of these methylation patterns on gene expression and enzymatic activity, potentially offering new insights into previously unexplained pharmacokinetic variability and adverse drug reactions. Previously, Sanger sequencing, Southern blot, and quantitative PCR have been the methods of choice for the mtDNA analysis [13; 23; 24; 25; 26]. However, these techniques are expensive, with low sensitivity and throughput. Recently, short-read next-generation sequencing techniques have been more and more explored for mtDNA sequencing and single point mutations detection.