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Eukaryotic protein kinases belong to a large superfamily with hundreds to thousands of copies and are components of essentially all cellular functions. The goals of this study are to classify protein kinases from 25 plant species and to assess their evolutionary history in conjunction with consideration of their molecular functions. The protein kinase superfamily has expanded in the flowering plant lineage, in part through recent duplications. As a result, the flowering plant protein kinase repertoire, or kinome, is in general significantly larger than other eukaryotes, ranging in size from 600 to 2500 members. This large variation in kinome size is mainly due to the expansion and contraction of a few families, particularly the receptor-like kinase/Pelle family. A number of protein kinases reside in highly conserved, low copy number families and often play broadly conserved regulatory roles in metabolism and cell division, although functions of plant homologues have often diverged from their metazoan counterparts. Members of expanded plant kinase families often have roles in plant-specific processes and some may have contributed to adaptive evolution. Nonetheless, non-adaptive explanations, such as kinase duplicate subfunctionalization and insufficient time for pseudogenization, may also contribute to the large number of seemingly functional protein kinases in plants.

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration. Translating genotype to phenotype is a grand challenge in biology. Here, the authors investigate the utility of genome, transcriptome, and methylome data and their combinations in predicting six plant complex traits and uncovering key genes and genetic interactions in Arabidopsis.

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

• Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. • An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. • The segmentation-based method was error-prone (correlation between true and predicted seed counts, r² = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r² = 0.9996) and highly accurate fruit counts (r² = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. • Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

Plants balance their competing requirements for growth and stress tolerance via a sophisticated regulatory circuitry that controls responses to the external environments. We have identified a plant-specific gene, COST1 (constitutively stressed 1), that is required for normal plant growth but negatively regulates drought resistance by influencing the autophagy pathway. An Arabidopsis thaliana cost1 mutant has decreased growth and increased drought tolerance, together with constitutive autophagy and increased expression of drought-response genes, while overexpression of COST1 confers drought hypersensitivity and reduced autophagy. The COST1 protein is degraded upon plant dehydration, and this degradation is reduced upon treatment with inhibitors of the 26S proteasome or autophagy pathways. The drought resistance of a cost1 mutant is dependent on an active autophagy pathway, but independent of other known drought signaling pathways, indicating that COST1 acts through regulation of autophagy. In addition, COST1 colocalizes to autophagosomes with the autophagosome marker ATG8e and the autophagy adaptor NBR1, and affects the level of ATG8e protein through physical interaction with ATG8e, indicating a pivotal role in direct regulation of autophagy. We propose a model in which COST1 represses autophagy under optimal conditions, thus allowing plant growth. Under drought, COST1 is degraded, enabling activation of autophagy and suppression of growth to enhance drought tolerance. Our research places COST1 as an important regulator controlling the balance between growth and stress responses via the direct regulation of autophagy.

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

Plant iron deficiency (−Fe) activates a complex regulatory network that coordinates root Fe uptake and distribution to sink tissues. In Arabidopsis (Arabidopsis thaliana), FER-LIKE FE DEFICIENCY–INDUCED TRANSCRIPTION FACTOR (FIT), a basic helix-loop-helix (bHLH) transcription factor (TF), regulates root Fe acquisition genes. Many other −Fe-induced genes are FIT independent, and instead regulated by other bHLH TFs and by yet unknown TFs. The cis-regulatory code, that is, the cis-regulatory elements (CREs) and their combinations that regulate plant −Fe-responses, remains largely elusive. Using Arabidopsis root transcriptome data and coexpression clustering, we identified over 100 putative CREs (pCREs) that predicted −Fe-induced gene expression in computational models. To assess pCRE properties and possible functions, we used large-scale in vitro TF binding data, positional bias, and evolutionary conservation. As one example, our approach uncovered pCREs resembling IDE1 (iron deficiency-responsive element 1), a known grass −Fe response CRE. Arabidopsis IDE1-likes were associated with FIT-dependent gene expression, more specifically with biosynthesis of Fe-chelating compounds. Thus, IDE1 seems to be conserved in grass and nongrass species. Our pCREs matched among others in vitro binding sites of B3, NAC, bZIP, and TCP TFs, which might be regulators of −Fe responses. Altogether, our findings provide a comprehensive source of cis-regulatory information for −Fe-responsive genes that advance our mechanistic understanding and inform future efforts in engineering plants with more efficient Fe uptake or transport systems.

The National Science Foundation’s (NSF) Research Traineeship (NRT) Program fosters interdisciplinarity. It is designed to train scholars with the agility to move beyond their strict disciplinary boundaries. The goal of the Program is to create innovative graduate educational opportunities for broad workforce development. We assessed the professional development skills and activities documented in the annual reports of 20 NRT projects to assess which technical and transferable skills were commonly highlighted, how these skills were integrated into their educational programming, which stakeholders were targeted, and how much time was allocated to the associated activities. We found communication (43%), job readiness (42%), and team science (26%) to be the most common professional development skills provided by the 20 NRT projects. We go into greater depth about three of these NRT projects to more deeply characterize programmatic challenges and successes. Then, we highlight strategies to manage potential points of friction and to recommend approaches that could be adopted as part of other graduate professional development projects. These innovations have the potential to promote transformative change in graduate STEAM education, nationwide.