A flexible geometric model for leaf shape descriptions with high accuracy

Accurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs

LEAF-E: a tool to analyze grass leaf growth using function fitting

In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile

Population genomic structure of the gelatinous zooplankton species Mnemiopsis leidyi in its nonindigenous range in the North Sea

Nonindigenous species pose a major threat for coastal and estuarine ecosystems. Risk management requires genetic information to establish appropriate management units and infer introduction and dispersal routes. We investigated one of the most successful marine invaders, the ctenophore Mnemiopsis leidyi, and used genotyping-by-sequencing (GBS) to explore the spatial population structure in its nonindigenous range in the North Sea. We analyzed 140 specimens collected in different environments, including coastal and estuarine areas, and ports along the coast. Single nucleotide polymorphisms (SNPs) were called in approximately 40 k GBS loci. Population structure based on the neutral SNP panel was significant (F-ST .02; p < .01), and a distinct genetic cluster was identified in a port along the Belgian coast (Ostend port; pairwise F-ST .02-.04; p < .01). Remarkably, no population structure was detected between geographically distant regions in the North Sea (the Southern part of the North Sea vs. the Kattegat/Skagerrak region), which indicates substantial gene flow at this geographical scale and recent population expansion of nonindigenous M. leidyi. Additionally, seven specimens collected at one location in the indigenous range (Chesapeake Bay, USA) were highly differentiated from the North Sea populations (pairwise F-ST .36-.39; p < .01). This study demonstrates the utility of GBS to investigate fine-scale population structure of gelatinous zooplankton species and shows high population connectivity among nonindigenous populations of this recently introduced species in the North Sea. OPEN RESEARCH BADGES This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at: The DNA sequences generated for this study are deposited in the NCBI sequence read archive under SRA accession numbers -, and will be made publically available upon publication of this manuscript

Overexpression of GA20-OXIDASE1 impacts plant height, biomass allocation and saccharification efficiency in maize

Increased biomass yield and quality are of great importance for the improvement of feedstock for the biorefinery. For the production of bioethanol, both stem biomass yield and the conversion efficiency of the polysaccharides in the cell wall to fermentable sugars are of relevance. Increasing the endogenous levels of gibberellic acid (GA) by ectopic expression of GA20-OXIDASE1 (GA20-OX1), the rate-limiting step in GA biosynthesis, is known to affect cell division and cell expansion, resulting in larger plants and organs in several plant species. In this study, we examined biomass yield and quality traits of maize plants overexpressing GA20-OX1 (GA20-OX1). GA20-OX1 plants accumulated more vegetative biomass than control plants in greenhouse experiments, but not consistently over two years of field trials. The stems of these plants were longer but also more slender. Investigation of GA20-OX1 biomass quality using biochemical analyses showed the presence of more cellulose, lignin and cell wall residue. Cell wall analysis as well as expression analysis of lignin biosynthetic genes in developing stems revealed that cellulose and lignin were deposited earlier in development. Pretreatment of GA20-OX1 biomass with NaOH resulted in a higher saccharification efficiency per unit of dry weight, in agreement with the higher cellulose content. On the other hand, the cellulose-to-glucose conversion was slower upon HCl or hot-water pretreatment, presumably due to the higher lignin content. This study showed that biomass yield and quality traits can be interconnected, which is important for the development of future breeding strategies to improve lignocellulosic feedstock for bioethanol production

Gloxinia—an open-source sensing platform to monitor the dynamic responses of plants

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations

Physiological basis of chilling tolerance and early-season growth in miscanthus

Background and Aims: The high productivity of Miscanthus x giganteus has been at least partly ascribed to its high chilling tolerance compared with related C-4 crops, allowing for a longer productive growing season in temperate climates. However, the chilling tolerance of M. x giganteus has been predominantly studied under controlled environmental conditions. The understanding of the underlying mechanisms contributing to chilling tolerance in the field and their variation in different miscanthus genotypes is largely unexplored. Methods: Five miscanthus genotypes with different sensitivities to chilling were grown in the field and scored for a comprehensive set of physiological traits throughout the spring season. Chlorophyll fluorescence was measured as an indication of photosynthesis, and leaf samples were analysed for biochemical traits related to photosynthetic activity (chlorophyll content and pyruvate, Pi dikinase activity), redox homeostasis (malondialdehyde, glutathione and ascorbate contents, and catalase activity) and water-soluble carbohydrate content. Key Results: Chilling-tolerant genotypes were characterized by higher levels of malondialdehyde, raffinose and sucrose, and higher catalase activity, while the chilling-sensitive genotypes were characterized by higher concentrations of glucose and fructose, and higher pyruvate, Pi dikinase activity later in the growing season. On the early sampling dates, the biochemical responses of M. x giganteus were similar to those of the chilling-tolerant genotypes, but later in the season they became more similar to those of the chilling-sensitive genotypes. Conclusions: The overall physiological response of chilling-tolerant genotypes was distinguishable from that of chilling-sensitive genotypes, while M. x giganteus was intermediate between the two. There appears to be a trade-off between high and efficient photosynthesis and chilling stress tolerance. Miscanthus x giganteus is able to overcome this trade-off and, while it is more similar to the chilling-sensitive genotypes in early spring, its photosynthetic capacity is similar to that of the chilling-tolerant genotypes later on

Overcoming challenges in variant calling : exploring sequence diversity in candidate genes for plant development in perennial ryegrass (Lolium perenne)

Revealing DNA sequence variation within the Lolium perenne genepool is important for genetic analysis and development of breeding applications. We reviewed current literature on plant development to select candidate genes in pathways that control agronomic traits, and identified 503 orthologues in L. perenne. Using targeted resequencing, we constructed a comprehensive catalogue of genomic variation for a L. perenne germplasm collection of 736 genotypes derived from current cultivars, breeding material and wild accessions. To overcome challenges of variant calling in heterogeneous outbreeding species, we used two complementary strategies to explore sequence diversity. First, four variant calling pipelines were integrated with the VariantMetaCaller to reach maximal sensitivity. Additional multiplex amplicon sequencing was used to empirically estimate an appropriate precision threshold. Second, a de novo assembly strategy was used to reconstruct divergent alleles for each gene. The advantage of this approach was illustrated by discovery of 28 novel alleles of LpSDUF247, a polymorphic gene co-segregating with the S-locus of the grass self-incompatibility system. Our approach is applicable to other genetically diverse outbreeding species. The resulting collection of functionally annotated variants can be mined for variants causing phenotypic variation, either through genetic association studies, or by selecting carriers of rare defective alleles for physiological analyses