## 3 Results and Discussion  
### Analysis of Z-DNA Distribution Across Enhancer Elements  
The first stage of this study focused on examining the intersection between predicted Z-DNA-forming sequences and different classes of enhancer elements. Z-DNA loci were identified using the Z-DNA-BERT model, a neural network architecture specifically optimized for Z-DNA prediction, as described by [PERSON] et al. [40]. This model, trained with sequence-optimized parameters, demonstrated superior predictive performance compared to alternative machine learning approaches. The predicted Z-DNA coordinates, derived from the human genome assembly hg38, were intersected with datasets representing typical enhancers, super-enhancer enhancers, super-enhancer spacers, and background regions. To ensure balanced comparisons, background regions were defined as genomic segments without regulatory elements, matching the length distribution of typical enhancers (see details in Section 2.1 of Materials and Methods). In this part of the study, analyses were conducted for six cell lines: HeLa, HCT116, MDA-MB-231, MCF7, BT-549, and LNCaP. Given the known association of Z-DNA with chromatin activity regulation and its role in epigenetic regulation processes, such as modulating chromatin accessibility through the recruitment of chromatin-remodeling proteins [31], the goal was to explore its potential functional relevance in the genome. To this end, the investigation began by examining the co-occurrence patterns of Z-DNA with functional genomic regions, including super-enhancers, enhancers within super-enhancers, and typical enhancers. To visualize the distribution patterns of Z-DNA across different enhancer classes, heat maps were generated, capturing the density of intersections across chromosomal regions (Figure 1). For illustration, Figure 1 presents the heat maps corresponding to the HCT116 cell line; similar distribution patterns were observed in the other cell lines, with detailed results provided in Figure S1 in the Supplementary Materials. Notably, Figure 1 reveals that at the proximal ends of chromosomes 13, 14, 15, 21, and 22, there is a visibly lower distribution of Z-DNA-forming sequences. This can be explained by the acrocentric nature of these chromosomes, which are known to exhibit a reduced density of enhancer elements in these regions. The heat maps revealed a marked decrease in signal intensity for background regions, suggesting a lower prevalence of Z-DNA-forming sequences in non-regulatory genomic segments. This aligns with previous reports indicating that non-canonical DNA structures constitute approximately 13% of the human genome [51], with around 0.8% specifically attributed to Z-DNA [31]. Importantly, previous studies employing knock-in and CRISPR interference (CRISPRi) strategies have shown that targeted disruption of Z-DNA-forming regions within enhancer elements leads to a measurable reduction in enhancer activity and chromatin accessibility, predominantly through compromised transcription factor recruitment and disruption of chromatin phase-separation dynamics [9; 52; 53]. Consistent with these observations, our computational identification of enriched Z-DNA sequences within super-enhancers highlights their potential functional relevance, providing robust genomic targets for future experimental validation. Subsequent experimental work using targeted CRISPR-based perturbations at these Z-DNA loci will be essential to conclusively elucidate their precise regulatory contributions. The observed 93-fold reduction in background regions (Table S2) suggests that these overlaps are likely due to random occurrences. Specifically, typical enhancers (TE) showed a moderate reduction (mean fold-change across six cell lines = 2.79 \(\pm\) 0.38) and enhancers within super-enhancers (SE-E) demonstrated a similar moderate reduction (mean = 3.72 \(\pm\) 1.05), whereas super-enhancer regions (SE) exhibited only slight reductions (mean = 1.13 \(\pm\) 0.04) (Table S2).