Cross-species complementation reveals conserved functions for EARLY FLOWERING 3 between monocots and dicots

Plant responses to the environment are shaped by external stimuli and internal signaling pathways. In both the model plant Arabidopsis thaliana (Arabidopsis) and crop species, circadian clock factors are critical for growth, flowering, and circadian rhythms. Outside of Arabidopsis, however, little is known about the molecular function of clock gene products. Therefore, we sought to compare the function of Brachypodium distachyon (Brachypodium) and Setaria viridis (Setaria) orthologs of EARLY FLOWERING 3, a key clock gene in Arabidopsis. To identify both cycling genes and putative ELF3 functional orthologs in Setaria, a circadian RNA-seq dataset and online query tool (Diel Explorer) were generated to explore expression profiles of Setaria genes under circadian conditions. The function of ELF3 orthologs from Arabidopsis, Brachypodium, and Setaria was tested for complementation of an elf3 mutation in Arabidopsis. We find that both monocot orthologs were capable of rescuing hypocotyl elongation, flowering time, and arrhythmic clock phenotypes. Using affinity purification and mass spectrometry, our data indicate that BdELF3 and SvELF3 could be integrated into similar complexes in vivo as AtELF3. Thus, we find that, despite 180 million years of separation, BdELF3 and SvELF3 can functionally complement loss of ELF3 at the molecular and physiological level

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

The next generation of training for arabidopsis researchers: Bioinformatics and Quantitative Biology

It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges

Components of Water Use Efficiency Have Unique Genetic Signatures in the Model C 4 Grass Setaria

Plant growth and water use are interrelated processes influenced by genetically controlled morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific × recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use are correlated strongly in this system; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use, and WUE all were heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions can be utilized to predictably modulate trait values given a specified precipitation regime in the model C genus

NAPPN: Who We Are, Where We Are Going, and Why You Should Join Us!

The area of high-throughput plant phenomics focuses on developing and deploying hardware and software, including sensors, analysis techniques, and computational methods, with the requisite expertise to rapidly discover and measure a wide array of plant traits. From there, scientists in many disciplines are employed to generate the knowledge to develop plants and plant products that support humanity with reduced environmental impacts. To advance in an area that requires expertise from fields such as biology, computer science, engineering, mathematics, physics, and statistics, reaching out across disciplines is a must and training in new areas can help. Here we report on how the North American Phenotyping Network (NAPPN) was founded, describe its mission to bridge disciplinary and national boundaries, and outline future plans for its expansion. To get involved with NAPPN and join as a member at no cost, visit the organization’s website at

Replaying the evolutionary tape to investigate subgenome dominance in allopolyploid Brassica napus.

Allopolyploidisation merges evolutionarily distinct parental genomes (subgenomes) into a single nucleus. A frequent observation is that one subgenome is 'dominant' over the other subgenome, often being more highly expressed. Here, we 'replayed the evolutionary tape' with six isogenic resynthesised Brassica napus allopolyploid lines and investigated subgenome dominance patterns over the first 10 generations postpolyploidisation. We found that the same subgenome was consistently more dominantly expressed in all lines and generations and that >70% of biased gene pairs showed the same dominance patterns across all lines and an in silico hybrid of the parents. Gene network analyses indicated an enrichment for network interactions and several biological functions for the Brassica oleracea subgenome biased pairs, but no enrichment was identified for Brassica rapa subgenome biased pairs. Furthermore, DNA methylation differences between subgenomes mirrored the observed gene expression bias towards the dominant subgenome in all lines and generations. Many of these differences in gene expression and methylation were also found when comparing the progenitor genomes, suggesting that subgenome dominance is partly related to parental genome differences rather than just a byproduct of allopolyploidisation. These findings demonstrate that 'replaying the evolutionary tape' in an allopolyploid results in largely repeatable and predictable subgenome expression dominance patterns

Replaying the evolutionary tape to investigate subgenome dominance in allopolyploid Brassica napus.

Allopolyploidisation merges evolutionarily distinct parental genomes (subgenomes) into a single nucleus. A frequent observation is that one subgenome is 'dominant' over the other subgenome, often being more highly expressed. Here, we 'replayed the evolutionary tape' with six isogenic resynthesised Brassica napus allopolyploid lines and investigated subgenome dominance patterns over the first 10 generations postpolyploidisation. We found that the same subgenome was consistently more dominantly expressed in all lines and generations and that >70% of biased gene pairs showed the same dominance patterns across all lines and an in silico hybrid of the parents. Gene network analyses indicated an enrichment for network interactions and several biological functions for the Brassica oleracea subgenome biased pairs, but no enrichment was identified for Brassica rapa subgenome biased pairs. Furthermore, DNA methylation differences between subgenomes mirrored the observed gene expression bias towards the dominant subgenome in all lines and generations. Many of these differences in gene expression and methylation were also found when comparing the progenitor genomes, suggesting that subgenome dominance is partly related to parental genome differences rather than just a byproduct of allopolyploidisation. These findings demonstrate that 'replaying the evolutionary tape' in an allopolyploid results in largely repeatable and predictable subgenome expression dominance patterns

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning