Development of crops with improved nitrogen use efficiency (NUE) is essential for sustainable agriculture. However, achieving this goal has proven difficult since NUE is a complex trait encompassing physiological and developmental processes. We thought to tackle this problem by taking a systems biology approach to identify candidate target genes. First, we used a supervised machine-learning algorithm to predict a NUE gene network in Arabidopsis (Arabidopsis thaliana). Second, we identified BT2, a member of the Bric-a-Brac/Tramtrack/Broad gene family, as the most central and connected gene in the NUE network. Third, we experimentally tested BT2 for a role in NUE. We found NUE decreased in plants overexpressing BT2 gene compared to wild-type plants under limiting nitrate conditions. In addition, NUE increased compared to wild-type plants under low nitrate conditions in double mutant plants in bt2 and its closely related homolog bt1, indicating a functional redundancy of BT1 and BT2 for NUE. Expression of the nitrate transporter genes NRT2.1 and NRT2.4 increased in the bt1/bt2 double mutant compared to wild-type plants, with a concomitant 65% increase in nitrate uptake under low nitrate conditions. Similar to Arabidopsis, we found that mutation of the BT1/BT2 ortholog gene in rice (Oryza sativa) OsBT increased NUE by 20% compared to wild-type rice plants under low nitrogen conditions. These results indicate BT gene family members act as conserved negative regulators of nitrate uptake genes and NUE in plants and highlight them as prime targets for future strategies to improve NUE in crops.

Motivation: Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning methods, such as Support Vector Machines (SVMs), have shown superior prediction accuracy. However, these methods lack the simple biological intuition provided by co-expression networks (CNs), limiting their practical usefulness.Results: In this work, we present Discriminative Local Subspaces (DLS), a novel method that combines supervised machine learning and co-expression techniques with the goal of systematically predict genes involved in specific biological processes of interest. Unlike traditional CNs, DLS uses the knowledge available in Gene Ontology (GO) to generate informative training sets that guide the discovery of expression signatures: expression patterns that are discriminative for genes involved in the biological process of interest. By linking genes co-expressed with these signatures, DLS is able to construct a discriminative CN that links both, known and previously uncharacterized genes, for the selected biological process. This article focuses on the algorithm behind DLS and shows its predictive power using an Arabidopsis thaliana dataset and a representative set of 101 GO terms from the Biological Process Ontology. Our results show that DLS has a superior average accuracy than both SVMs and CNs. Thus, DLS is able to provide the prediction accuracy of supervised learning methods while maintaining the intuitive understanding of CNs.Availability: A MATLAB® implementation of DLS is available at http://virtualplant.bio.puc.cl/cgi-bin/Lab/tools.cgiContact: tfpuelma@uc.clSupplementary Information: Supplementary data are available at http://bioinformatics.mpimp-golm.mpg.de/.

SummaryGENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.Availability and ImplementationGENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius.Supplementary information Supplementary data are available at Bioinformatics online.