## 3 Results and Discussion
### Analysis of Mutation-Associated Motifs in Z-DNA-Intersected Enhancer Elements  
The next stage of our investigation into the mutations identified within the loci of functional regions and Z-DNA-forming sequences involved annotating their clinical significance using snpEff, based on data from the dbSNP and ClinVar databases. Our primary focus was on pathogenic and unknown mutations. It was found that all known pathogenic mutations (the list of keywords is provided in Section 2.5 of the Materials and Methods) were located within TEs. Consequently, further analysis focused on unknown mutations located within SEs. We categorized the dataset containing unknown mutations into five subsets, classifying them as follows: SNPs, long insertions, short insertions, long deletions, and short deletions. Since our primary interest lay in inherited mutations, we focused on identifying motifs in sequences where mutations were present in at least 10% of the analyzed genomes. This filtering step allowed us to exclude potential somatic mutations  
Figure 3: Violin plots depicting mutation concentration distributions (mutations normalized by region length) within the top quartile (25% most variable regions) across background regions, super-enhancer spacers, enhancers within super-enhancers, and typical enhancers. Mutation data were derived from 100 individuals of European ancestry (1000 Genomes Project). and artifacts arising from sequencing data processing. The data table was structured to include mutation occurrences, with unique identifiers summarized in columns. To assess the conservation of mutation loci, we predicted transcription factor binding motifs. First, nucleotide sequences were generated for each mutation. These sequences were defined by the mutation itself, as well as by 100-nucleotide flanks from the start and end of the mutation region, according to the coordinates in the hg38 genome. The corresponding region from hg38 was used as the reference sequence. Subsequently, for both the mutation-containing sequences and their corresponding reference sequences, transcription factor binding motifs were predicted using HOMER2. Thus, we obtained sets of motifs identified in the reference sequences and in the sequences generated at the positions of the studied mutations. To understand the functional implications of these motifs and their alterations, it is essential to analyze how they influence transcriptional regulation and potentially other cellular processes. Changes in motifs can influence the binding affinity of transcription factors. Binding motifs of transcription factors are mainly located in gene promoters and enhancers. Additionally, these motifs exhibit additive properties. Consequently, alterations in the number of TF binding motifs can directly impact the efficiency of gene expression [55]. To determine whether a mutation leads to the gain or loss of a specific motif at its locus, we mapped the motifs generated from the mutated sequences onto the reference genome using MEME FIMO. Similarly, we scanned the mutated sequences for occurrences of motifs derived from the reference sequences. Four distinct motifs emerged within the super-enhancer region on chromosome 10, with three motifs identified in spacer elements (motif 1: RGKGGGCGCKGGC, motif 2: GGGGGTGTGTCC, motif 4: GCCACRCCCACCY) and one motif located within the active enhancer itself (motif 3: DGGGYGKGGC) (Figure 4). Notably, motifs 1, 2, and 4 were found in the spacer region of the super-enhancer, while motif 3 was located within the enhancer region itself. This super-enhancer region is characterized by low sequence complexity, predominantly consisting of (GT) \(n\) repeats (Match Percentage: 67%). Within the spacer, a tandem repeat sequence, (GT)9G3(TG)2 TATCT(GT)3G5 CACGTGATG(T)3(G)3T, was also identified (Match Percentage: 80%) [56]. Tandem repeats are characterized by an increased rate of mutagenesis, as well as elevated frequencies of aberrant recombinations and structural rearrangements [57].