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Tiantian Ye

Publications and source records attributed to Tiantian Ye.

2 recordsLinked to original sources

Dual Roles of RAD23b and RAD4 on the Desiccation Tolerance of Germinated Seeds.

Desiccation tolerance (DT) is a survival trait enabling orthodox seeds to withstand extremely low water content. While some protective factors are characterised, it remains mechanistically obscure. Here, based on PEG-induced DT re-establishment in germinated Brassica napus L. seeds, we investigated the dual functions of nucleotide excision repair (NER) components RAD23b and RAD4 in DNA repair and transcriptional regulation of root development. PEG pre-treatment alleviated dehydration-induced DNA damage and activated NER genes, suggesting the involvement of NER in seed DT. Unexpectedly, Arabidopsis atrad23b mutant and BnRAD23b/BnRAD4 over-expressing seeds all exhibited significantly decreased DT after dry back, which evoked a hypothesis that BnRAD23b-BnRAD4 functions beyond NER. Normally, BnRAD4 interacted with BnRAD23b and repressed the expression of root development genes NAC103, EMB1444, RRA1 by directly binding to STRE elements within their promoters. Dehydration stress alleviated this repression, drove transcriptional reprogramming and might redirect the complex to execute DNA repair. Genetic analyses revealed that germinated seeds of atnac103, atemb1444, and atrra1 single mutants all exhibited reduced DT, and double mutants under atrad23b background almost abolished DT. This study suggests that RAD23b and RAD4 may regulate DT re-establishment of germinated seeds through balancing genome integrity and radicle development, with implications for DT study broadly.

Brassica napus L.

SHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing.

MOTIVATION: Single-cell Hi-C (scHi-C) technologies have significantly advanced our understanding of the 3D genome organization. However, scHi-C data are often sparse and noisy, leading to substantial computational challenges in downstream analyses. RESULTS: In this study, we introduce SHICEDO, a novel deep-learning model specifically designed to enhance scHi-C contact matrices by imputing missing or sparsely captured chromatin contacts through a generative adversarial framework. SHICEDO leverages the unique structural characteristics of scHi-C matrices to derive customized features that enable effective data enhancement. Additionally, the model incorporates a channel-wise attention mechanism to mitigate the over-smoothing issue commonly associated with scHi-C enhancement methods. Through simulations and real-data applications, we demonstrate that SHICEDO outperforms the state-of-the-art methods, achieving superior quantitative and qualitative results. Moreover, SHICEDO enhances key structural features in scHi-C data, thus enabling more precise delineation of chromatin structures such as A/B compartments, TAD-like domains, and chromatin loops. AVAILABILITY AND IMPLEMENTATION: SHICEDO is publicly available at https://github.com/wmalab/SHICEDO.

Single-Cell Analysis