Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

Novel Role of AaMYBC1 in Regulating Actinidia arguta Vine Architecture by Elongating Internode Based on Multi-Omics Analysis of Transgenic Tobacco

The internode length affects the status of fruiting branches and shapes the vine architecture. MYB TFs (transcription factors) have been widely studied and reported to control many biological processes including secondary metabolism, abiotic stresses, growth and development, etc. However, the roles of MYB TFs in regulating internode length remain poorly understood. Here, we demonstrated that a secondary metabolism-related R2R3-MYB TF AaMYBC1 from Actinidia arguta was involved in the regulation of internode length by combined analysis of transcriptome and metabolome of transgenic tobacco plants. The metabolome analysis of OE (over-expressed tobacco) and WT (wild-typed tobacco) showed that there were a total of 1000 metabolites, 176 of which had significant differences. A key metabolite pme1651 annotated as indole 3-acetic acid belonged to phytohormone that was involved in internode length regulation. The RNA-seq analysis presented 446 differentially expressed genes (DEGs) between OE and WT, 14 of which were common DEGs in KEGG and GO enrichment. Through the combined analysis of metabolome and transcriptome in transgenic and wild-type tobacco, three key genes including two SAUR and a GH3 gene were possibly involved in internode elongation. Finally, a regulatory module was deduced to show the role of AaMYBC1 in internode elongation. Our results proposed a molecular mechanism of AaMYBC1 regulating internode length by mediated auxin signaling, implying the potential role in regulating the vine architecture.

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO2 conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Phenotyping Almond Orchards for Architectural Traits Influenced by Rootstock Choice

The cropping potential of almond (Prunus amygdalus (L.) Batsch, syn P. dulcis (Mill.)) cultivars is determined by their adaptation to edaphoclimatic and environmental conditions. The effects of scion–rootstock interactions on vigor have a decisive impact on this cropping success. Intensively planted orchards with smaller less vigorous trees present several potential benefits for increasing orchard profitability. While several studies have examined rootstock effects on tree vigor, it is less clear how rootstocks influence more specific aspects of tree architecture. The objective of this current study was to identify which architectural traits of commercially important scion cultivars are influenced by rootstock and which of these traits can be useful as descriptors of rootstock performance in breeding evaluations. To do this, 6 almond cultivars of commercial significance were grafted onto 5 hybrid rootstocks, resulting in 30 combinations that were measured after their second year of growth. We observed that rootstock choice mainly influenced branch production, but the effects were not consistent across the different scion–rootstock combinations evaluated. This lack of consistency in response highlights the importance of the unique interaction between each rootstock and its respective scion genotype.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology

BACKGROUND

Functional-structural plant models (FSPMs) explore and integrate relationships between a plant's structure and processes that underlie its growth and development. In the last 20 years, scientists interested in functional-structural plant modelling have expanded greatly the range of topics covered and now handle dynamical models of growth and development occurring from the microscopic scale, and involving cell division in plant meristems, to the macroscopic scales of whole plants and plant communities.

SCOPE

The FSPM approach occupies a central position in plant science; it is at the crossroads of fundamental questions in systems biology and predictive ecology. This special issue of Annals of Botany features selected papers on critical areas covered by FSPMs and examples of comprehensive models that are used to solve theoretical and applied questions, ranging from developmental biology to plant phenotyping and management of plants for agronomic purposes. Altogether, they offer an opportunity to assess the progress, gaps and bottlenecks along the research path originally foreseen for FSPMs two decades ago. This review also allows discussion of current challenges of FSPMs regarding (1) integration of multidisciplinary knowledge, (2) methods for handling complex models, (3) standards to achieve interoperability and greater genericity and (4) understanding of plant functioning across scales.

CONCLUSIONS

This approach has demonstrated considerable progress, but has yet to reach its full potential in terms of integration and heuristic knowledge production. The research agenda of functional-structural plant modellers in the coming years should place a greater emphasis on explaining robust emergent patterns, and on the causes of possible deviation from it. Modelling such patterns could indeed fuel both generic integration across scales and transdisciplinary transfer. In particular, it could be beneficial to emergent fields of research such as model-assisted phenotyping and predictive ecology in managed ecosystems.

Modelling transport of inhibiting and activating signals and their combined effects on floral induction: application to apple tree

Floral induction (FI) in shoot apical meristems (SAM) is assumed to be triggered by antagonistic endogenous signals. In fruit trees, FI occurs in some SAM only and is determined by activating and inhibiting signals originating from leaves and fruit, respectively. We developed a model (SigFlow) to quantify on 3D structures the combined impact of such signals and distances at which they act on SAM. Signal transport was simulated considering a signal 'attenuation' parameter, whereas SAM fate was determined by probability functions depending on signal quantities. Model behaviour was assessed on simple structures before being calibrated and validated on a unique experimental dataset of 3D digitized apple trees with contrasted crop loads and subjected to leaf and fruit removal at different scales of tree organization. Model parameter estimations and comparisons of two signal combination functions led us to formulate new assumptions on the mechanisms involved: (i) the activating signal could be transported at shorter distances than the inhibiting one (roughly 50 cm vs 1 m) (ii) both signals jointly act to determine FI with SAM being more sensitive to inhibiting signal than activating one. Finally, the genericity of the model is promising to further understand the physiological and architectural determinisms of FI in plants.

Apple whole genome sequences: recent advances and new prospects

In 2010, a major scientific milestone was achieved for tree fruit crops: publication of the first draft whole genome sequence (WGS) for apple (Malus domestica). This WGS, v1.0, was valuable as the initial reference for sequence information, fine mapping, gene discovery, variant discovery, and tool development. A new, high quality apple WGS, GDDH13 v1.1, was released in 2017 and now serves as the reference genome for apple. Over the past decade, these apple WGSs have had an enormous impact on our understanding of apple biological functioning, trait physiology and inheritance, leading to practical applications for improving this highly valued crop. Causal gene identities for phenotypes of fundamental and practical interest can today be discovered much more rapidly. Genome-wide polymorphisms at high genetic resolution are screened efficiently over hundreds to thousands of individuals with new insights into genetic relationships and pedigrees. High-density genetic maps are constructed efficiently and quantitative trait loci for valuable traits are readily associated with positional candidate genes and/or converted into diagnostic tests for breeders. We understand the species, geographical, and genomic origins of domesticated apple more precisely, as well as its relationship to wild relatives. The WGS has turbo-charged application of these classical research steps to crop improvement and drives innovative methods to achieve more durable, environmentally sound, productive, and consumer-desirable apple production. This review includes examples of basic and practical breakthroughs and challenges in using the apple WGSs. Recommendations for "what's next" focus on necessary upgrades to the genome sequence data pool, as well as for use of the data, to reach new frontiers in genomics-based scientific understanding of apple.

Stochastic modelling of tree architecture and biomass allocation: application to teak (Tectona grandis L. f.), a tree species with polycyclic growth and leaf neoformation

Background and aims

For a given genotype, the observed variability of tree forms results from the stochasticity of meristem functioning and from changing and heterogeneous environmental factors affecting biomass formation and allocation. In response to climate change, trees adapt their architecture by adjusting growth processes such as pre- and neoformation, as well as polycyclic growth. This is the case for the teak tree. The aim of this work was to adapt the plant model, GreenLab, in order to take into consideration both these processes using existing data on this tree species.

Methods

This work adopted GreenLab formalism based on source-sink relationships at organ level that drive biomass production and partitioning within the whole plant over time. The stochastic aspect of phytomer production can be modelled by a Bernoulli process. The teak model was designed, parameterized and analysed using the architectural data from 2- to 5-year-old teak trees in open field stands.

Key results

Growth and development parameters were identified, fitting the observed compound organic series with the theoretical series, using generalized least squares methods. Phytomer distributions of growth units and branching pattern varied depending on their axis category, i.e. their physiological age. These emerging properties were in accordance with the observed growth patterns and biomass allocation dynamics during a growing season marked by a short dry season.

Conclusions

Annual growth patterns observed on teak, including shoot pre- and neoformation and polycyclism, were reproduced by the new version of the GreenLab model. However, further updating is discussed in order to ensure better consideration of radial variation in basic specific gravity of wood. Such upgrading of the model will enable teak ideotypes to be defined for improving wood production in terms of both volume and quality.