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Abstract Being the first plant to have its genome sequenced, Arabidopsis thaliana (Arabidopsis) is a well-established genetic model plant system. Studies on Arabidopsis have provided major insights into the physiological and biochemical nature of plants. Methods that allow us to study organisms’ metabolism computationally include using genome-scale metabolic models (GEMs). Despite its popularity, no GEM currently maps the metabolic activity in the roots of Arabidopsis, which is the organ that faces and responds to stress conditions in the soil. We have developed a comprehensive metabolic model of the Arabidopsis root system—AraRoot. The final model includes 2,682 reactions, 2,748 metabolites, and 1,310 genes. Analyzing the metabolic pathways in this model identified 158 possible bottleneck genes that impact biomass production, most of which were found to be related to phosphorous-containing- and energy-related pathways. Further insights into tissue-specific metabolic reprogramming conclude that the cortex layer in the roots is likely responsible for root growth under prolonged exposure to high salt conditions. At the same time, the endodermis and epidermis are responsible for producing metabolites responsible for increased cell wall biosynthesis. The epidermis was found to have a very poor ability to regulate its metabolism during exposure to high salt concentrations. Overall, AraRoot is the first metabolic model that comprehensively captures the biomass formation and stress responses of the tissues in the Arabidopsis root system. 

Abstract Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure = 0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one. 

Abstract Gene regulatory networks (GRNs) are defined by a cascade of transcriptional events by which signals, such as hormones or environmental cues, change development. To understand these networks, it is necessary to link specific transcription factors (TFs) to the downstream gene targets whose expression they regulate. Although multiple methods provide information on the targets of a single TF, moving from groups of co-expressed genes to the TF that controls them is more difficult. To facilitate this bottom-up approach, we have developed a web application named TF DEACoN. This application uses a publicly available Arabidopsis thaliana DNA Affinity Purification (DAP-Seq) data set to search for TFs that show enriched binding to groups of co-regulated genes. We used TF DEACoN to examine groups of transcripts regulated by treatment with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), using a transcriptional data set performed with high temporal resolution. We demonstrate the utility of this application when co-regulated genes are divided by timing of response or cell-type-specific information, which provides more information on TF/target relationships than when less defined and larger groups of co-regulated genes are used. This approach predicted TFs that may participate in ethylene-modulated root development including the TF NAM (NO APICAL MERISTEM). We used a genetic approach to show that a mutation in NAM reduces the negative regulation of lateral root development by ACC. The combination of filtering and TF DEACoN used here can be applied to any group of co-regulated genes to predict GRNs that control coordinated transcriptional responses.