TeaTFactor: a prediction tool for tea plant transcription factors based on BERT.

A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at http://teatfactor.tlds.cc, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.

Different Valence States of Copper Ion Delivery against Triple-Negative Breast Cancer.

Different valence states of copper (Cu) ions are involved in complicated redox reactions in vivo, which are closely related to tumor proliferation and death pathways, such as cuproptosis and chemodynamic therapy (CDT). Cu ion mediated Fenton-like reagents induced tumor cell death which presents compelling attention for the CDT of tumors. However, the superiority of different valence states of Cu ions in the antitumor effect is unknown. In this study, we investigated different valence states of Cu ions in modulating tumor cell death by Cu-chelated cyanine dye against triple-negative breast cancer. The cuprous ion (Cu+) and copper ion (Cu2+) were chelated with four nitrogen atoms of dipicolylethylenediamine-modified cyanine for the construction of Cu+ and Cu2+ chelated cyanine dyes (denoted as CC1 and CC2, respectively). Upon 660 nm laser irradiation, the CC1 or CC2 can generate reactive oxygen species, which could disrupt the cyanine structure, achieving the rapid release of Cu ions and initiating the Fenton-like reaction for CDT. Compared with Cu2+-based Fenton-like reagent, the CC1 with Cu+ exhibited a better therapeutic outcome for the tumor due to there being no need for a reduction by glutathione and a shorter route to generate more hydroxyl radicals. Our findings suggest the precision delivery of Cu+ could achieve highly efficient antitumor therapy.

Biomimetic Nanoemulsion for Synergistic Photodynamic-Immunotherapy Against Hypoxic Breast Tumor.

Photodynamic therapy (PDT) is commonly used as an "in situ vaccine" to enhance the response rate of PD-1/PD-L1 antibodies. Unfortunately, the high cost and adverse effects of these antibodies, and the hypoxic state of solid tumors limits the efficacy of synergistic photodynamic-immunotherapy. Here, we developed a biomimetic nanoemulsion camouflaged with a PD-1-expressing cell membrane for synergistic photodynamic-immunotherapy against hypoxic breast tumors. The perfluorocarbon of the nanoemulsion could provide oxygen as the source of PDT against hypoxic tumors. Moreover, co-delivering a photosensitizer and the PD-1 protein (substituting for a PD-L1 antibody) achieves the synergy effect of PDT and immunotherapy. Synergistic photodynamic-immunotherapy completely inhibited primary and distant subcutaneous 4T1 tumors, mechanistically by boosting the maturation of dendritic cells and tumor infiltration of cytotoxic T lymphocytes.