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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., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Understanding the molecular pathogenesis of MLL fusion oncoprotein (MLL-FP) leukaemia has spawned epigenetic therapies that have improved clinical outcomes in this often-incurable disease. Using genetic and pharmacological approaches, we define the individual and combined contribution of KAT6A, KAT6B and KAT7, in MLL-FP leukaemia. Whilst inhibition of KAT6A/B is efficacious in some pre-clinical models, simultaneous targeting of KAT7, with the novel inhibitor PF-9363, increases the therapeutic efficacy. KAT7 interacts with Menin and the MLL complex and is co-localised at chromatin to co-regulate the MLL-FP transcriptional program. Inhibition of KAT6/KAT7 provides an orthogonal route to targeting Menin to disable the transcriptional activity of MLL-FP. Consequently, combined inhibition rapidly evicts the MLL-FP from chromatin, potently represses oncogenic transcription and overcomes primary resistance to Menin inhibitors. Moreover, PF-9363 or genetic depletion of KAT7 can also overcome acquired genetic/non-genetic resistance to Menin inhibition. These data provide the molecular rationale for rapid clinical translation of combination therapy in MLL-FP leukaemia.

Sheets of coarse-grained S304H austenitic steel were processed by high-pressure sliding (HPS) at room temperature and a ultrafine-grained microstructure with a mean grain size of about 0.14 µm was prepared. The microstructure changes and creep behavior of coarse-grained and HPS-processed steel were investigated at 500-700 °C under the application of different loads. It was found that the processing of S304H steel led to a significant improvement in creep strength at 500 °C. However, a further increase in creep temperature to 600 °C and 700 °C led to the deterioration of creep behavior of HPS-processed steel. The microstructure results suggest that the creep behavior of HPS-processed steel is associated with the thermal stability of the SPD-processed microstructure. The recrystallization, grain growth, the coarsening of precipitates led to a reduction in creep strength of the HPS-processed state. It was also observed that in the HPS-processed microstructure the fast formation of σ-phase occurs. The σ-phase was already formed during slight grain coarsening at 600 °C and its formation was enhanced after recrystallization at 700 °C.