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Hequan Sun

Publications and source records attributed to Hequan Sun.

2 recordsLinked to original sources

Sex without crossovers mimics clonal reproduction in Rhynchospora tenuis.

Meiotic recombination ensures accurate chromosome segregation and promotes genetic diversity by generating crossovers between homologous chromosomes1. Although essential in most sexually reproducing organisms, recombination is variably regulated and can be absent in some lineages, a condition known as achiasmy2. However, obligate achiasmy in both sexes of a sexual species has not been documented. Here we investigate Rhynchospora tenuis, a flowering plant with the lowest known chromosome number and inverted meiosis3. Combining genomics with molecular experiments, we show that R. tenuis undergoes obligate, genome-wide achiasmy in both male and female meiosis. Despite normal early meiotic axis formation, synapsis fails, crossovers are undetectable cytologically and genetically, and univalents persist at metaphase I. Haplotype-specific accumulation of transposable elements generates segregation distortion favouring the transmission of larger, repeat-rich chromosomes. Sexual reproduction is nevertheless retained: fertilization yields viable seeds only when translocation-compatible gametes meet, indicating strong post-meiotic selection against incompatible homozygous combinations. As a result, all surviving offspring are genetically identical, effectively maintaining heterozygosity by sexual reproduction with parental genotype restitution mimicking clonal reproduction. We propose that recombination loss, a low chromosome number, inverted meiosis and selection for compatible gamete combinations together enable faithful segregation and clonal-like inheritance despite sexual reproduction. These findings blur the boundary between sex and clonality, linking genome architecture, recombination loss and transmission bias.

Journal Article

UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.

RNA secondary structure is essential for understanding the functions of non-coding RNAs, ribosomal RNAs, and viral genomes. However, accurate prediction of long RNA structures remains challenging due to complex long-range interactions and the limited availability of long-RNA training data. We present UFold-X, a dual-branch deep learning framework that combines a convolutional encoder for local structure modeling with a Mamba-based Visual State Space Module for capturing long-range dependencies. A dynamic gating mechanism adaptively integrates the two branches according to sequence length. UFold-X was evaluated on multiple benchmark datasets containing RNAs up to 5000 nucleotides. To rigorously assess generalization, we introduced a cross-clan benchmark for long RNAs. Under this stringent setting, UFold-X achieved performance comparable to state-of-the-art classical approaches while achieving the best performance among deep learning-based methods. Additional cross-family and within-family evaluations further demonstrated robust transferability and competitive predictive performance. UFold-X also maintained excellent computational efficiency, requiring only 0.08 s per sequence on average. To assess biological consistency, we developed a SHAPE-based reactivity prediction variant (UFold-X-R) and an integrated metric, the Hybrid Reactivity-Pairing Score (HRPS). UFold-X-R showed strong agreement with experimental icSHAPE data and achieved the highest HRPS among all evaluated methods. A user-friendly web server is available at https://ufold-x.ai4bread.com.

Nucleic Acid Conformation