Standardized mapping of nodulation patterns in legume roots

Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean

The symbiotic relationship between soybean [Glycine max L. (Merr.)] roots and bacteria (Bradyrhizobium japonicum) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant-Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Recent advances in methods for in situ root phenotyping

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

In situ Phenotyping of Grapevine Root System Architecture by 2D or 3D Imaging: Advantages and Limits of Three Cultivation Methods

The root system plays an essential role in the development and physiology of the plant, as well as in its response to various stresses. However, it is often insufficiently studied, mainly because it is difficult to visualize. For grapevine, a plant of major economic interest, there is a growing need to study the root system, in particular to assess its resistance to biotic and abiotic stresses, understand the decline that may affect it, and identify new ecofriendly production systems. In this context, we have evaluated and compared three distinct growing methods (hydroponics, plane, and cylindric rhizotrons) in order to describe relevant architectural root traits of grapevine cuttings (mode of grapevine propagation), and also two 2D- (hydroponics and rhizotron) and one 3D- (neutron tomography) imaging techniques for visualization and quantification of roots. We observed that hydroponics tubes are a system easy to implement but do not allow the direct quantification of root traits over time, conversely to 2D imaging in rhizotron. We demonstrated that neutron tomography is relevant to quantify the root volume. We have also produced a new automated analysis method of digital photographs, adapted for identifying adventitious roots as a feature of root architecture in rhizotrons. This method integrates image segmentation, skeletonization, detection of adventitious root skeleton, and adventitious root reconstruction. Although this study was targeted to grapevine, most of the results obtained could be extended to other plants propagated by cuttings. Image analysis methods could also be adapted to characterization of the root system from seedlings.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N₂) is fixed into bioavailable ammonia (NH₃) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit (R ² = 0.99). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

Ethylene, a Hormone at the Center-Stage of Nodulation

Nodulation is the result of a beneficial interaction between legumes and rhizobia. It is a sophisticated process leading to nutrient exchange between the two types of symbionts. In this association, within a nodule, the rhizobia, using energy provided as photosynthates, fix atmospheric nitrogen and convert it to ammonium which is available to the plant. Nodulation is recognized as an essential process in nitrogen cycling and legume crops are known to enrich agricultural soils in nitrogenous compounds. Furthermore, as they are rich in nitrogen, legumes are considered important as staple foods for humans and fodder for animals. To tightly control this association and keep it mutualistic, the plant uses several means, including hormones. The hormone ethylene has been known as a negative regulator of nodulation for almost four decades. Since then, much progress has been made in the understanding of both the ethylene signaling pathway and the nodulation process. Here I have taken a large view, using recently obtained knowledge, to describe in some detail the major stages of the process. I have not only reviewed the steps most commonly covered (the common signaling transduction pathway, and the epidermal and cortical programs), but I have also looked into steps less understood (the pre-infection step with the plant defense response, the bacterial release and the formation of the symbiosome, and nodule functioning and senescence). After a succinct review of the ethylene signaling pathway, I have used the knowledge obtained from nodulation- and ethylene-related mutants to paint a more complete picture of the role played by the hormone in nodule organogenesis, functioning, and senescence. It transpires that ethylene is at the center of this effective symbiosis. It has not only been involved in most of the steps leading to a mature nodule, but it has also been implicated in host immunity and nodule senescence. It is likely responsible for the activation of other hormonal signaling pathways. I have completed the review by citing three studies which makes one wonder whether knowledge gained on nodulation in the last decades is ready to be transferred to agricultural fields.

Root phenotyping: from component trait in the lab to breeding

In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential.

E151 (sym15), a pleiotropic mutant of pea (Pisum sativum L.), displays low nodule number, enhanced mycorrhizae, delayed lateral root emergence, and high root cytokinin levels

In legumes, the formation of rhizobial and mycorrhizal root symbioses is a highly regulated process which requires close communication between plant and microorganism. Plant mutants that have difficulties establishing symbioses are valuable tools for unravelling the mechanisms by which these symbioses are formed and regulated. Here E151, a mutant of Pisum sativum cv. Sparkle, was examined to characterize its root growth and symbiotic defects. The symbioses in terms of colonization intensity, functionality of micro-symbionts, and organ dominance were compared between the mutant and wild type. The endogenous cytokinin (CK) and abscisic acid (ABA) levels and the effect of the exogenous application of these two hormones were determined. E151 was found to be a low and delayed nodulator, exhibiting defects in both the epidermal and cortical programmes though a few mature and functional nodules develop. Mycorrhizal colonization of E151 was intensified, although the fungal functionality was impaired. Furthermore, E151 displayed an altered lateral root (LR) phenotype compared with that of the wild type whereby LR emergence is initially delayed but eventually overcome. No differences in ABA levels were found between the mutant and the wild type, but non-inoculated E151 exhibited significantly high CK levels. It is hypothesized that CK plays an essential role in differentially mediating the entry of the two micro-symbionts into the cortex; whereas it would inhibit the entry of the rhizobia in that tissue, it would promote that of the fungus. E151 is a developmental mutant which may prove to be a useful tool in further understanding the role of hormones in the regulation of beneficial root symbioses.