Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for highconfidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.

Diatoms are a diverse group of mainly photosynthetic algae, responsible for 20% of worldwide oxygen production, which can rapidly respond to favourable conditions and often outcompete other phytoplankton. We investigated the contribution of horizontal gene transfer (HGT) to its ecological success. A large-scale phylogeny-based prokaryotic HGT detection procedure across nine sequenced diatoms showed that 3-5% of their proteome has a horizontal origin and a large influx occurred at the ancestor of diatoms. More than 90% of HGT genes are expressed, and species-specific HGT genes in Phaeodactylum tricornutum undergo strong purifying selection. Genes derived from HGT are implicated in several processes including environmental sensing, and expand the metabolic toolbox. Cobalamin (vitamin B12) is an essential cofactor for roughly half of the diatoms and is only produced by bacteria. Five consecutive genes involved in the final synthesis of the cobalamin biosynthetic pathway, which could function as scavenging and repair genes, were detected as HGT. The full suite of these genes were detected in the cold-adapted diatom Fragilariopsis cylindrus. This might give diatoms originating from the Southern Ocean, a region typically depleted in cobalamin, a competitive advantage. Overall, we show that HGT is a prevalent mechanism that is actively used in diatoms to expand its adaptive capabilities.

No abstract