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Abstract The large variety of inflorescence architectures evolved in grasses depends on shape, longevity and determinacy of meristems directing growth of the main and lateral axes. The CLAVATA pathway is known to regulate meristem size and inflorescence architecture in grasses. However, how individual meristem activities are determined and integrated to generate specific inflorescences is not yet understood. We found that activity of distinct meristems in the barley inflorescence is controlled by a signalling pathway comprising the receptor-like kinaseHordeum vulgareCLAVATA1 (HvCLV1) and the secreted CLAVATA3/EMBRYO-SURROUNDING REGION RELATED (CLE)-family peptide FON2-LIKE CLE PROTEIN1 (HvFCP1). HvFCP1 and HvCLV1 interact to promote spikelet formation, but restrict inflorescence meristem and rachilla proliferation.Hvfcp1orHvclv1mutants generate additional rows of spikelets and supernumerary florets from extended rachilla activity.HvFCP1/HvCLV1signalling coordinates meristem activity through regulation of trehalose-6-phosphate levels. Our discoveries outline a path to engineer inflorescence architecture via specific regulation of distinct meristem activities. 

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’. 

Abstract Genomic prediction has accelerated breeding processes and provided mechanistic insights into the genetic bases of complex traits. To further optimize genomic prediction, we assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. Surprisingly, short read-based genome assembly performs comparably to or even better than long read-based assembly. Due to higher gene coverage, exome capture and multi-allelic variants outperform genotyping-by-sequencing and bi-allelic variants, respectively. Tetraploid models show higher prediction accuracy than octoploid models for most traits, likely due to the greater genetic distances among tetraploids. Depending on the trait in question, different types of variants need to be integrated for optimal predictions. Our study provides insights into the factors influencing genomic prediction outcomes, guiding best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding. 

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 pre- diction 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 dif- ferent 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. 

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 Oomycete plant pathogen,Phytophthora capsici, causes root, crown, and fruit rot of winter squash (Cucurbita moschata) and limits production. SomeC. moschatacultivars develop age-related resistance (ARR), whereby fruit develop resistance toP. capsici14 to 21 days postpollination (DPP) because of thickened exocarp; however, wounding negates ARR. We uncovered the genetic mechanisms of ARR of twoC. moschatacultivars, Chieftain and Dickenson Field, that exhibit ARR at 14 and 21 DPP, respectively, using RNA sequencing. The sequencing was conducted using RNA samples from ‘Chieftain’ and ‘Dickenson Field’ fruit at 7, 10, 14, and 21 DPP. A differential expression and subsequent gene set enrichment analysis revealed an overrepresentation of upregulated genes in functional categories relevant to cell wall structure biosynthesis, cell wall modification/organization, transcription regulation, and metabolic processes. A pathway enrichment analysis detected upregulated genes in cutin, suberin monomer, and phenylpropanoid biosynthetic pathways. A further analysis of the expression profile of genes in those pathways revealed upregulation of genes in monolignol biosynthesis and lignin polymerization in the resistant fruit peel. Our findings suggest a shift in gene expression toward the physical strengthening of the cell wall associated with ARR toP. capsici. These findings provide candidate genes for developingCucurbitacultivars with resistance toP. capsiciand improve fruit rot management inCucurbitaspecies. 

Basic helix–loop–helix (bHLH) proteins are one of the largest families of transcription factor (TF) in eukaryotes, and ~30% of all flowering plants’ bHLH TFs contain the aspartate kinase, chorismate mutase, and TyrA (ACT)-like domain at variable distances C-terminal from the bHLH. However, the evolutionary history and functional consequences of the bHLH/ACT-like domain association remain unknown. Here, we show that this domain association is unique to the plantae kingdom with green algae (chlorophytes) harboring a small number of bHLH genes with variable frequency of ACT-like domain’s presence. bHLH-associated ACT-like domains form a monophyletic group, indicating a common origin. Indeed, phylogenetic analysis results suggest that the association of ACT-like and bHLH domains occurred early in Plantae by recruitment of an ACT-like domain in a common ancestor with widely distributed ACT DOMAIN REPEAT ( ACR ) genes by an ancestral bHLH gene. We determined the functional significance of this association by showing that Chlamydomonas reinhardtii ACT-like domains mediate homodimer formation and negatively affect DNA binding of the associated bHLH domains. We show that, while ACT-like domains have experienced faster selection than the associated bHLH domain, their rates of evolution are strongly and positively correlated, suggesting that the evolution of the ACT-like domains was constrained by the bHLH domains. This study proposes an evolutionary trajectory for the association of ACT-like and bHLH domains with the experimental characterization of the functional consequence in the regulation of plant-specific processes, highlighting the impacts of functional domain coevolution. 

The plant science corpus consists of the titles and abstracts of plant science articles in PubMed published prior to 2021 with a small number of 2021 records due to modification of records. The columns are: Index: integer index serving as identifier PMID: PubMed identifier Date: Publication date Journal: journal where the article was published Title: Title of the article Abstract: Abstract of the article Corpus: Title and abstract combined Text classification score: plant science record prediction model score Preprocessed corpus: Corpus after lower-casing, stop word removal, removal of non-alphanumeric and non-white space characters, lemmitisation Topic: index of topics after topic modeling 

Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis- regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k -mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k -mers, referred to as putative cis -regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.