Bassham, D. C. | Search bioRxiv

bioRxiv2021

Interplay between brassinosteroids and TORC signaling in Arabidopsis revealed by integrated multi-dimensional analysis

Brassinosteroids (BR) and Target of Rapamycin Complex (TORC) are two major processes coordinating plant growth and stress responses. BRs function through a signaling pathway to extensively regulate gene expression and TORC is known to regulate translation and autophagy. Recent studies revealed that these two pathways crosstalk, but a system-wide view of their interplay is still missing. Thus, we performed transcriptome, proteome, and phosphoproteome profiling of Arabidopsis mutants with altered levels of either BIN2 or RAPTOR1B, two key players in BR and TORC signaling, respectively. We found that perturbation of BIN2 or RAPTOR1B levels affects a common set of gene-products involved in growth and stress responses. Additionally, we performed Multiplexed Assay for Kinase Specificity (MAKS), which provided a system-wide view of direct BIN2 substrates. Furthermore, phosphoproteomic data was used to reconstruct a kinase-signaling network and to identify novel proteins dependent on BR and/or TORC signaling pathways. Loss of function mutants of many of these proteins led to an altered BR response and/or modulated autophagy activity. Altogether, these results provide genome-wide evidence for crosstalk between BR and TORC signaling and established a kinase signaling network that defines the molecular mechanisms of BR and TORC interactions in the regulation of plant growth/stress balance.

plant biology↗

bioRxiv2021

The Case for Retaining Natural Language Descriptions of Phenotypes in Plant Databases and a Web Application as Proof of Concept

Similarities in phenotypic descriptions can be indicative of shared genetics, metabolism, and stress responses, to name a few. Finding and measuring similarity across descriptions of phenotype is not straightforward, with previous successes in computation requiring a great deal of expert data curation. Natural language processing of free text descriptions of phenotype is often less resource intensive than applying expert curation. It is therefore critical to understand the performance of natural language processing techniques for organizing and analyzing biological datasets and for enabling biological discovery. For predicting similar phenotypes, a wide variety of approaches from the natural language processing domain perform as well as curation-based methods. These computational approaches also show promise both for helping curators organize and work with large datasets and for enabling researchers to explore relationships among available phenotype descriptions. Here we generate networks of phenotype similarity and share a web application for querying a dataset of associated plant genes using these text mining approaches. Example situations and species for which application of these techniques is most useful are discussed. Database URLsThe database and analytical tool called QuOATS are available at https://quoats.dill-picl.org/. Code for the web application is available at https://git.io/Jtv9J. Datasets are available for direct access via https://zenodo.org/record/7947342#.ZGwAKOzMK3I. The code for the analyses performed for the publication is available at https://github.com/Dill-PICL/Plant-data and https://github.com/Dill-PICL/NLP-Plant-Phenotypes.

bioinformatics↗