Bioinformatic Identification and Expression Analyses of the MAPK-MAP4K Gene Family Reveal a Putative Functional MAP4K10-MAP3K7/8-MAP2K1/11-MAPK3/6 Cascade in Wheat (Triticum aestivum L.).

The mitogen-activated protein kinase (MAPK) cascades act as crucial signaling modules that regulate plant growth and development, response to biotic/abiotic stresses, and plant immunity. MAP3Ks can be activated through MAP4K phosphorylation in non-plant systems, but this has not been reported in plants to date. Here, we identified a total of 234 putative TaMAPK family members in wheat (Triticum aestivum L.). They included 48 MAPKs, 17 MAP2Ks, 144 MAP3Ks, and 25 MAP4Ks. We conducted systematic analyses of the evolution, domain conservation, interaction networks, and expression profiles of these TaMAPK-TaMAP4K (representing TaMAPK, TaMAP2K, TaMAP3K, and TaMAP4K) kinase family members. The 234 TaMAPK-TaMAP4Ks are distributed on 21 chromosomes and one unknown linkage group (Un). Notably, 25 of these TaMAP4K family members possessed the conserved motifs of MAP4K genes, including glycine-rich motif, invariant lysine (K) motif, HRD motif, DFG motif, and signature motif. TaMAPK3 and 6, and TaMAP4K10/24 were shown to be strongly expressed not only throughout the growth and development stages but also in response to drought or heat stress. The bioinformatics analyses and qRT-PCR results suggested that wheat may activate the MAP4K10-MEKK7-MAP2K11-MAPK6 pathway to increase drought resistance in wheat, and the MAP4K10-MAP3K8-MAP2K1/11-MAPK3 pathway may be involved in plant growth. In general, our work identified members of the MAPK-MAP4K cascade in wheat and profiled their potential roles during their response to abiotic stresses and plant growth based on their expression pattern. The characterized cascades might be good candidates for future crop improvement and molecular breeding.

MantaID: a machine learning-based tool to automate the identification of biological database IDs.

The number of biological databases is growing rapidly, but different databases use different identifiers (IDs) to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID, a data-driven, machine learning-based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within 2 min. MantaID supports the discovery and exploitation of ID from large quantities of databases (e.g. up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application and application programming interfaces were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate and comprehensive identification of large quantities of IDs and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.