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.

SUMMARYThe natural range of Arabidopsis thaliana (Arabidopsis) encompasses geographical regions that have greatly differing local climates, including harshness of winter temperatures. A question thus raised is whether differences in freezing tolerance might contribute to local adaptation in Arabidopsis. Consistent with this possibility is that Arabidopsis accessions differ in freezing tolerance and that those collected from colder northern latitudes are generally more tolerant to freezing than those collected from warmer southern latitudes. Moreover, recent studies with Arabidopsis genotypes collected from sites in Sweden (SW) and Italy (IT) have established that the two accessions are locally adapted, that the SW ecotype is more tolerant of freezing than the IT ecotype, and that genetic differences between the two ecotypes that condition local adaptation and freezing tolerance map to a region that includes the C-repeat binding factor (CBF) locus. The CBF locus includes three genes -CBF1, CBF2 and CBF3 -that are induced by low temperature and encode transcription factors that regulate a group of more than 100 genes, the CBF regulon, which impart freezing tolerance. Here we show that cold induction of most CBF regulon genes is lower in IT plants compared with SW plants, and that this is due to the IT CBF2 gene encoding a non-functional CBF2 protein. The non-functional IT CBF2 protein also contributes to the lower freezing tolerance of the IT plants compared with the SW plants. Taken together, studies on the SW and IT ecotypes provide evidence that natural variation in the CBF pathway has contributed to adaptive evolution in these Arabidopsis populations.

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought.

Summary 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.

SummaryStorage roots of cassava (Manihot esculenta Crantz), a major subsistence crop of sub‐Saharan Africa, are calorie rich but deficient in essential micronutrients, including provitamin A β‐carotene. In this study, β‐carotene concentrations in cassava storage roots were enhanced by co‐expression of transgenes for deoxy‐d‐xylulose‐5‐phosphate synthase (DXS) and bacterial phytoene synthase (crtB), mediated by the patatin‐type 1 promoter. Storage roots harvested from field‐grown plants accumulated carotenoids to ≤50 μg/g DW, 15‐ to 20‐fold increases relative to roots from nontransgenic plants. Approximately 85%–90% of these carotenoids accumulated as all‐trans‐β‐carotene, the most nutritionally efficacious carotenoid. β‐Carotene‐accumulating storage roots displayed delayed onset of postharvest physiological deterioration, a major constraint limiting utilization of cassava products. Large metabolite changes were detected in β‐carotene‐enhanced storage roots. Most significantly, an inverse correlation was observed between β‐carotene and dry matter content, with reductions of 50%–60% of dry matter content in the highest carotenoid‐accumulating storage roots of different cultivars. Further analysis confirmed a concomitant reduction in starch content and increased levels of total fatty acids, triacylglycerols, soluble sugars and abscisic acid. Potato engineered to co‐express DXS and crtB displayed a similar correlation between β‐carotene accumulation, reduced dry matter and starch content and elevated oil and soluble sugars in tubers. Transcriptome analyses revealed a reduced expression of genes involved in starch biosynthesis including ADP‐glucose pyrophosphorylase genes in transgenic, carotene‐accumulating cassava roots relative to nontransgenic roots. These findings highlight unintended metabolic consequences of provitamin A biofortification of starch‐rich organs and point to strategies for redirecting metabolic flux to restore starch production.

Several factors affect the yield potential and geographical range of crops including the circadian clock, water availability, and seasonal temperature changes. In order to sustain and increase plant productivity on marginal land in the face of both biotic and abiotic stresses, we need to more efficiently generate stress-resistant crops through marker-assisted breeding, genetic modification, and new genome-editing technologies. To leverage these strategies for producing the next generation of crops, future transcriptomic data acquisition should be pursued with an appropriate temporal design and analyzed with a network-centric approach. The following review focuses on recent developments in abiotic stress transcriptional networks in economically important crops and will highlight the utility of correlation-based network analysis and applications.

One of the parental diploid genomes (subgenomes) in an allopolyploid often exhibits higher gene expression levels compared to the other subgenome(s) in the nucleus. However, the genetic basis and deterministic fate of subgenome expression dominance remains poorly understood. We examined the establishment of subgenome expression dominance in six isogenic resynthesized Brassica napus (rapeseed) allopolyploid lines over the first ten generations, and uncovered consistent expression dominance patterns that were biased towards the Brassica oleracea 'C' subgenome across each of the independent lines and generations. The number and direction of gene dosage changes from homoeologous exchanges (HEs) was highly variable between lines and generations, however, we recovered HE hotspots overlapping with those in multiple natural B. napus cultivars. Additionally, we found a greater number of 'C' subgenome regions replacing 'A' subgenome regions among resynthesized lines with rapid reduction in pollen counts and viability. Furthermore, DNA methylation differences between subgenomes mirrored the observed gene expression bias towards the 'C' subgenome in all lines and generations. Gene-interaction network analysis indicated an enrichment for network interactions and several biological functions for 'C' subgenome biased pairs, but no enrichment was observed for 'A' subgenome biased pairs. These findings demonstrate that "replaying the evolutionary tape" in allopolyploids results in repeatable and predictable subgenome expression dominance patterns based on preexisting genetic differences among the parental species.

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.