The centromere has a conserved function across eukaryotes; however, the associated DNA sequences exhibit remarkable diversity in both size and structure. In plants, some species possess well-defined centromeres dominated by tandem satellite repeats and centromeric retrotransposons, while others have centromeric regions composed almost entirely of retrotransposons. Using a combination of bioinformatic, molecular, and cytogenetic approaches, we analyzed the centromeric landscape of Humulus lupulus. We identified novel centromeric repeats and characterized two types of centromeric organization. Cytogenetic localization on metaphase chromosomes confirmed the genomic distribution of the major repeats and revealed unique centromeric organization specifically on chromosomes 2, 8, and Y. Two centromeric types are composed of the major repeats SaazCEN and SaazCRM1 (Ty3/Gypsy) which are further accompanied by chromosome-specific centromeric satellites, Saaz40, Saaz293, Saaz85, and HuluTR120. Chromosome 2 displays unbalanced segregation during mitosis and meiosis, implicating an important role for its centromere structure in segregation patterns. Moreover, chromosome 2-specific centromeric repeat Saaz293 is a new marker for studying aneuploidy in hops. Our findings provide new insights into chromosome segregation in hops and highlight the diversity and complexity of the centromere organization in H. lupulus.

• Keywords
• Cannabaceae, asymmetric cell division, centromere, retrotransposons, sex chromosomes,
• MeSH
• Centromere * genetics   MeSH
• Chromosomes, Plant genetics   MeSH
• Humulus * genetics   MeSH
• Meiosis genetics   MeSH
• Repetitive Sequences, Nucleic Acid * genetics   MeSH
• Retroelements * genetics   MeSH
• Chromosome Segregation genetics   MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Retroelements * MeSH

BACKGROUND: Long terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. RESULTS: We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges. CONCLUSIONS: Our results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5'- and 3'- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

• Keywords
• CNN-LSTM, DNABERT, Deep learning, Eukaryote, Regulatory mechanisms, Repeat, SHAP score, Sequence analysis, TFBS, Transcription factor binding sites, Transposable elements,
• Publication type
• Journal Article MeSH