How plant roots respond to waterlogging.

Plant submergence is a major abiotic stress that impairs plant performance. Under water, reduced gas diffusion exposes submerged plant cells to an environment that is enriched in gaseous ethylene and is limited in oxygen (O2) availability (hypoxia). The capacity for plant roots to avoid and/or sustain critical hypoxia damage is essential for plants to survive waterlogging. Plants use spatiotemporal ethylene and O2 dynamics as instrumental flooding signals to modulate potential adaptive root growth and hypoxia stress acclimation responses. However, how non-adapted plant species modulate root growth behaviour during actual waterlogged conditions to overcome flooding stress has hardly been investigated. Here we discuss how changes in the root growth rate, lateral root formation, density, and growth angle of non-flood adapted plant species (mainly Arabidopsis) could contribute to avoiding and enduring critical hypoxic conditions. In addition, we discuss current molecular understanding of how ethylene and hypoxia signalling control these adaptive root growth responses. We propose that future research would benefit from less artificial experimental designs to better understand how plant roots respond to and survive waterlogging. This acquired knowledge would be instrumental to guide targeted breeding of flood-tolerant crops with more resilient root systems.

Location: root architecture structures rhizosphere microbial associations.

Root architectural phenotypes are promising targets for crop breeding, but root architectural effects on microbial associations in agricultural fields are not well understood. Architecture determines the location of microbial associations within root systems, which, when integrated with soil vertical gradients, determines the functions and the metabolic capability of rhizosphere microbial communities. We argue that variation in root architecture in crops has important implications for root exudation, microbial recruitment and function, and the decomposition and fate of root tissues and exudates. Recent research has shown that the root microbiome changes along root axes and among root classes, that root tips have a unique microbiome, and that root exudates change within the root system depending on soil physicochemical conditions. Although fresh exudates are produced in larger amounts in root tips, the rhizosphere of mature root segments also plays a role in influencing soil vertical gradients. We argue that more research is needed to understand specific root phenotypes that structure microbial associations and discuss candidate root phenotypes that may determine the location of microbial hotspots within root systems with relevance to agricultural systems.

RSAtrace3D: robust vectorization software for measuring monocot root system architecture.

BACKGROUND: The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. RESULTS: We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. CONCLUSIONS: RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

Allele mining for a drought responsive gene DRO1 determining root growth angle in donors of drought tolerance in rice (Oryza sativa L.).

Deeper Rooting 1 (DRO1) gene identified from a major QTL on chromosome 9 increases the root growth angle (RGA) and thus facilitates survival under drought and hence is an excellent candidate for rice improvement. Twenty-four major Indian upland and lowland genotypes including the 'yield under drought' (DTY) QTL donors were subjected to allele mining of DRO1 (3058 bp) using four pairs of overlapping primers. A total of 216 and 52 SNPs were identified across all genotypes in the gene and coding region (756 bp) respectively with transversions 3.6 fold more common than transitions in the gene and 2.5 times in the CDS. In 251 amino acid long protein, substitutions were found in 19 positions, wherein change in position 92 was the most frequent. Based on allele mining, the 24 genotypes can be classified into 16 primary structure variants ranging from complete functional allele (Satti, IR36 and DTY 3.1 donor, IR81896-B-B-195) to truncated non-functional alleles in PMK2, IR64, IR20 and Swarna. All the DTY donors, other than IR81896-B-B-195, and most of the upland drought tolerant cultivars (Nagina 22, Vandana and Dhagaddeshi) had accumulated 6-19 SNPs and 4-8 amino acid substitutions resulting in substantial differences in their protein structure. The expression analysis revealed that all the genotypes showed upregulation under drought stress though the degree of upregulation varied among genotypes. The information on structural variations in DRO1 gene will be very useful for the breeders, especially in the light of recent breeding programmes on improving drought tolerance using several DTY donors and upland accessions. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s12298-021-00950-2).

LARGE ROOT ANGLE1, encoding OsPIN2, is involved in root system architecture in rice.

Root system architecture is very important for plant growth and crop yield. It is essential for nutrient and water uptake, anchoring, and mechanical support. Root growth angle (RGA) is a vital constituent of root system architecture and is used as a parameter for variety evaluation in plant breeding. However, little is known about the underlying molecular mechanisms that determine root growth angle in rice (Oryza sativa). In this study, a rice mutant large root angle1 (lra1) was isolated and shown to exhibit a large RGA and reduced sensitivity to gravity. Genome resequencing and complementation assays identified OsPIN2 as the gene responsible for the mutant phenotypes. OsPIN2 was mainly expressed in roots and the base of shoots, and showed polar localization in the plasma membrane of root epidermal and cortex cells. OsPIN2 was shown to play an important role in mediating root gravitropic responses in rice and was essential for plants to produce normal RGAs. Taken together, our findings suggest that OsPIN2 plays an important role in root gravitropic responses and determining the root system architecture in rice by affecting polar auxin transport in the root tip.

Prioritizing quantitative trait loci for root system architecture in tetraploid wheat.

Optimization of root system architecture (RSA) traits is an important objective for modern wheat breeding. Linkage and association mapping for RSA in two recombinant inbred line populations and one association mapping panel of 183 elite durum wheat (Triticum turgidum L. var. durum Desf.) accessions evaluated as seedlings grown on filter paper/polycarbonate screening plates revealed 20 clusters of quantitative trait loci (QTLs) for root length and number, as well as 30 QTLs for root growth angle (RGA). Divergent RGA phenotypes observed by seminal root screening were validated by root phenotyping of field-grown adult plants. QTLs were mapped on a high-density tetraploid consensus map based on transcript-associated Illumina 90K single nucleotide polymorphisms (SNPs) developed for bread and durum wheat, thus allowing for an accurate cross-referencing of RSA QTLs between durum and bread wheat. Among the main QTL clusters for root length and number highlighted in this study, 15 overlapped with QTLs for multiple RSA traits reported in bread wheat, while out of 30 QTLs for RGA, only six showed co-location with previously reported QTLs in wheat. Based on their relative additive effects/significance, allelic distribution in the association mapping panel, and co-location with QTLs for grain weight and grain yield, the RSA QTLs have been prioritized in terms of breeding value. Three major QTL clusters for root length and number (RSA_QTL_cluster_5#, RSA_QTL_cluster_6#, and RSA_QTL_cluster_12#) and nine RGA QTL clusters (QRGA.ubo-2A.1, QRGA.ubo-2A.3, QRGA.ubo-2B.2/2B.3, QRGA.ubo-4B.4, QRGA.ubo-6A.1, QRGA.ubo-6A.2, QRGA.ubo-7A.1, QRGA.ubo-7A.2, and QRGA.ubo-7B) appear particularly valuable for further characterization towards a possible implementation of breeding applications in marker-assisted selection and/or cloning of the causal genes underlying the QTLs.

Analysis of the Small Auxin-Up RNA (SAUR) Genes Regulating Root Growth Angle (RGA) in Apple.

Small auxin upregulated RNAs (SAURs) are primary auxin response genes; the function of regulating root growth angle (RGA) is unclear in the apple rootstock. We firstly identified 96 MdSAUR genes families from new apple genome GDDH13 using the resequence database of 'Baleng Crab (BC)' and 'M9'. A total of 25 MdSAUR genes, regulating the formation of RGA, were screened for the expression profiles in stems and roots and the allelic variants of quantitative trait loci (QTL). Finally, through the joint analysis of network and protein-protein interaction, MdSAUR2, MdSAUR29, MdSAUR60, MdSAUR62, MdSAUR69, MdSAUR71, and MdSAUR84 were screened as the main candidate genes for regulating RGA. This study provides a new insight for further revealing the regulatory mechanism of RGA in apple dwarf rootstocks.

Phenotypic Diversity of Seminal Root Traits in Bread Wheat Germplasm from Different Origins.

Bread wheat (Triticum aestivum L.) is a major staple crop, and more adapted varieties are needed to ensure productivity under unpredictable stress scenarios resulting from climate changes. In the development of new genotypes, root system traits are essential since roots have a key function in water and nutrient uptake, and root architecture determines the plant's ability to spatially explore the soil resources. Genetic variation in wheat root system may be assessed at the early stages of development. This study evaluates in vitro and at the seedling stage, the genetic diversity of root growth angle (RGA), seminal root number (SRN), and radicle length (RadL) in 30 bread wheat genotypes from different origins and belonging to distinct evolutive or breeding groups. SRN and RadL were analyzed at 1, 2, 3 and 6 days after sowing (DAS) and RGA was measured through the angle between the first pair of seminal roots. A large variability was found in RGA values that ranged from 63° to 122°. Although differences were found between genotypes within the same groups, the narrower angles tended to occur among landraces, while the higher RGA values were observed in advanced lines and Australian varieties. Differences were also observed as regards the SRN (1.0-3.0, 2.7-4.7, 3.2-5.0 and 4.4-6.3 at 1, 2, 3 and 6 DAS, respectively) and RadL (0.1-1.5, 2.1-5.0, 4.0-7.5 and 5.1-13.7 cm at 1, 2, 3 and 6 DAS, respectively). Genetic variability in root traits at seedling stage allows more rapid selection of genotypes better adapted to environmental and soil constraints, necessary to Portuguese Wheat Breeding Program. It will also contribute to the definition of wheat ideotypes with improved performance under Mediterranean climate conditions.

The effect of impedance to root growth on plant architecture in wheat.

BACKGROUND AND AIMS: We were interested in the effect of impedance to root growth on root and shoot architecture of wheat. It is known that Rht-1 semi-dwarfing alleles decrease the degree of leaf stunting due to root impedance. We compared commercial wheat cultivars containing different Rht-1 alleles to determine whether leaf stunting caused by root impedance differed between cultivars. We investigated effects of impedance to root growth on the angular spread of roots. METHODS: The wheat cultivars Avalon, Robigus and Battalion, carrying semi-dwarfing alleles of Rht-1, and cv. Cadenza, carrying the tall, wild-type allele, were grown under two levels of soil strength in a sand culture system designed to allow the mechanical impedance of the root growth environment to be adjusted independently of water and nutrient availability. RESULTS: Impeded roots grew more steeply than non-impeded roots: the angular spread of roots decreased from 55° to 43° from the vertical, but the genotypic effects were weak. Root impedance reduced leaf elongation and the number of tillers. Leaf area and total root length provided a common relationship across all genotype x treatment combinations. Leaf stunting in Cadenza was more severe. CONCLUSION: Our data support the hypothesis that the severity of leaf stunting due to root impedance is related to the Rht allele. Impeded roots had a smaller angular spread.

QTLs underlying natural variation of root growth angle among rice cultivars with the same functional allele of DEEPER ROOTING 1.

BACKGROUND: The functional allele of the rice gene DEEPER ROOTING 1 (DRO1) increases the root growth angle (RGA). However, wide natural variation in RGA is observed among rice cultivars with the functional DRO1 allele. To elucidate genetic factors related to such variation, we quantitatively measured RGA using the basket method and analyzed quantitative trait loci (QTLs) for RGA in three F2 mapping populations derived from crosses between the large RGA-type cultivar Kinandang Patong and each of three accessions with varying RGA: Momiroman has small RGA and was used to produce the MoK-F2 population; Yumeaoba has intermediate RGA (YuK-F2 population); Tachisugata has large RGA (TaK-F2 population). All four accessions belong to the same haplotype group of functional DRO1 allele. RESULTS: We detected the following statistically significant QTLs: one QTL on chromosome 4 in MoK-F2, three QTLs on chromosomes 2, 4, and 6 in YuK-F2, and one QTL on chromosome 2 in TaK-F2. Among them, the two QTLs on chromosome 4 were located near DRO2, which has been previously reported as a major QTL for RGA, whereas the two major QTLs for RGA on chromosomes 2 (DRO4) and 6 (DRO5) were novel. With the LOD threshold reduced to 3.0, several minor QTLs for RGA were also detected in each population. CONCLUSION: Natural variation in RGA in rice cultivars carrying functional DRO1 alleles may be controlled by a few major QTLs and by several additional minor QTLs.

A QTL for root growth angle on rice chromosome 7 is involved in the genetic pathway of DEEPER ROOTING 1.

BACKGROUND: Root growth angle (RGA) is an important trait that influences the ability of rice to avoid drought stress. DEEPER ROOTING 1 (DRO1), which is a major quantitative trait locus (QTL) for RGA, is responsible for the difference in RGA between the shallow-rooting cultivar IR64 and the deep-rooting cultivar Kinandang Patong. However, the RGA differences between these cultivars cannot be fully explained by DRO1. The objective of this study was to identify new QTLs for RGA explaining the difference in RGA between these cultivars. RESULTS: By crossing IR64 (which has a non-functional allele of DRO1) with Kinandang Patong (which has a functional allele of DRO1), we developed 26 chromosome segment substitution lines (CSSLs) that carried a particular chromosome segment from Kinandang Patong in the IR64 genetic background. Using these CSSLs, we found only one chromosomal region that was related to RGA: on chromosome 9, which includes DRO1. Using an F2 population derived from a cross between Kinandang Patong and the Dro1-NIL (near isogenic line), which had a functional DRO1 allele in the IR64 genetic background, we identified a new QTL for RGA (DRO3) on the long arm of chromosome 7. CONCLUSIONS: DRO3 may only affect RGA in plants with a functional DRO1 allele, suggesting that DRO3 is involved in the DRO1 genetic pathway.