Interpretable Prediction of SARS-CoV-2 Epitope-Specific TCR Recognition Using a Pre-Trained Protein Language Model.

The emergence of the novel coronavirus, designated as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a significant threat to public health worldwide. There has been progress in reducing hospitalizations and deaths due to SARS-CoV-2. However, challenges stem from the emergence of SARS-CoV-2 variants, which exhibit high transmission rates, increased disease severity, and the ability to evade humoral immunity. Epitope-specific T-cell receptor (TCR) recognition is key in determining the T-cell immunogenicity for SARS-CoV-2 epitopes. Although several data-driven methods for predicting epitope-specific TCR recognition have been proposed, they remain challenging due to the enormous diversity of TCRs and the lack of available training data. Self-supervised transfer learning has recently been proven useful for extracting information from unlabeled protein sequences, increasing the predictive performance of fine-tuned models, and using a relatively small amount of training data. This study presents a deep-learning model generated by fine-tuning pre-trained protein embeddings from a large corpus of protein sequences. The fine-tuned model showed markedly high predictive performance and outperformed the recent Gaussian process-based prediction model. The output attentions captured by the deep-learning model suggested critical amino acid positions in the SARS-CoV-2 epitope-specific TCRß sequences that are highly associated with the viral escape of T-cell immune response.

Division of cortical cells is regulated by auxin in Arabidopsis roots.

The root cortex transports water and nutrients absorbed by the root epidermis into the vasculature and stores substances such as starch, resins, and essential oils. The cortical cells are also deeply involved in determining epidermal cell fate. In Arabidopsis thaliana roots, the cortex is composed of a single cell layer generated by a single round of periclinal division of the cortex/endodermis initials. To further explore cortex development, we traced the development of the cortex by counting cortical cells. Unlike vascular cells, whose number increased during the development of root apical meristem (RAM), the number of cortical cells did not change, indicating that cortical cells do not divide during RAM development. However, auxin-induced cortical cell division, and this finding was confirmed by treatment with the auxin transport inhibitor N-1-naphthylphthalamic acid (NPA) and examining transgenic plants harboring CO2::ΔARF5, in which cortical expression of truncated AUXIN RESPONSE FACTOR5 (ΔARF5) induces auxin responses. NPA-induced cortical auxin accumulation and CO2::ΔARF5-mediated cortical auxin response induced anticlinal and periclinal cell divisions, thus increasing the number of cortical cells. These findings reveal a tight link between auxin and cortical cell division, suggesting that auxin is a key player in determining root cortical cell division.

Root Development and Stress Tolerance in rice: The Key to Improving Stress Tolerance without Yield Penalties.

Roots anchor plants and take up water and nutrients from the soil; therefore, root development strongly affects plant growth and productivity. Moreover, increasing evidence indicates that root development is deeply involved in plant tolerance to abiotic stresses such as drought and salinity. These findings suggest that modulating root growth and development provides a potentially useful approach to improve plant abiotic stress tolerance. Such targeted approaches may avoid the yield penalties that result from growth-defense trade-offs produced by global induction of defenses against abiotic stresses. This review summarizes the developmental mechanisms underlying root development and discusses recent studies about modulation of root growth and stress tolerance in rice.