EZ-Rhizo software

Abstract

Plants are sessile organisms that have to cope with the available nutritional resources and environmental constraints in the place where they germinate. To fully exploit their nearby resources, they have evolved a highly plastic and responsive root system. Adaptations to limited nutrients include a wide range of specific root responses, e.g., the emergence of new root types, root branching or specific growth of lateral roots. These root system architecture (RSA) features are of utmost importance when investigating the underlying mechanisms by forward, reverse or quantitative genetic approaches. The EZ-Rhizo software was developed to facilitate such root measurements in a fast, simple and accurate way. The performances of EZ-Rhizo in providing about 20 primary and derived RSA parameters were illustrated by looking at natural variability across 23 Arabidopsis accessions. The different RSA profiles obtained from plants grown in favorable condition illustrated the wide reservoir of natural genetic resources underlying specific features of root growth. This diversity was used here to correlate the RSA genetic variability with growth, development and environmental properties of accession origins.

Uptake of water and mineral nutrients from the soil is essential for plant life. The root system achieves these fundamental functions through its highly responsive and plastic morphology, which allows plants to exploit fully the soil physico-chemical resources. The geometry of different parts of the root system defines the so-called root system architecture. RSA is broadly determined by the genetic make-up of the plant and is subject to the abiotic and biotic environment of the root, as well as the physiological status of the plant.^1,2 Among environmental factors, the availability of nutrients such as nitrogen (N), phosphorus (P) and potassium (K) best illustrates the influence of the soil environment on RSA.^3–6 Specific root responses to nutrient and other abiotic stresses are routinely used to characterise mutants⁷ but the lack of an efficient means to capture RSA in a fast and effective way hampers forward genetic screens based on RSA other than the main root length. RSA has been used in quantitative trait locus (QTL) approaches to identify the molecular basis of intrinsic or environmental factors of RSA.^8–11 Again, only a small number of quantitative studies¹² have taken into account other RSA parameters than the main root length, mainly because of the lack of efficient tools to capture them comprehensively. To overcome this technical difficulty, the computer program EZ-Rhizo was developed and now allows fast and accurate measurement of RSA features from Arabidopsis plants growing on a vertical support.¹³ The software performs RSA analysis in a few user-supervised steps from a scanned Petri dish. The software measures 10 or more primary RSA parameters (Fig. 1) from which several other parameters are derived (e.g., lateral root density, branched zone length). The results are stored in a user-friendly database which can be queried to find specific data or exported for use in other programs such as Microsoft® Excel®.

Figure 1.

Schematic representation of RSA parameters measured by EZ-Rhizo. Main root (MR) primary features: path length (dashed line), vector length (grey arrow), vector angle (α), number of lateral root (LR, black dots). MR derived features: straightness (path:vector lengths), MR root depth (d), basal zone (path length between the root collar and the position of the first LR, white box on the left side), branched zone (path length between the first and the last LR position, grey box on the left side) and apical zone (path length from the position of the last LR to the root tip, black box on the left side), LR density over the MR, LR density over the branched zone. Lateral root (1^st or n order) primary features: position on the MR (or on the LRn-1), path length, vector length, vector angle, number of LRn+1 (open dots). Derived LR features: straightness (path:vector lengths). Overall root feature: total root size (sum of all root path length). LR (or LRn) are numbered according to their position on the MR (or LRn-1). LR parameters are illustrated here only for LR1.

Providing the EZ-Rhizo program as a freeware (http://EZ-Rhizo.psrg.org.uk) should therefore open the gate to fast and accurate RSA measurements and contribute to better progress in understanding RSA behavior. As an example to illustrate EZ-Rhizo performances, we used Arabidopsis natural variation to measure multiple RSA parameters across 23 Arabidopsis accessions grown on vertical Petri dishes in half-strength MS medium.¹³ The high natural RSA variation observed across these accessions was used in an attempt to find correlations between RSA and in vitro growth, development as well as the environment of accession origins (geography, climatology).

Natural Variation of Arabidopsis Root System Architecture

One potential application of EZ-Rhizo is the measurement of RSA variation between ecotypes for QTL analysis. An astonishing high natural variability for most RSA parameters was observed across the 23 accessions tested. The strongest variation was observed for lateral root density (number of lateral root within the branched zone) with a variation coefficient of 130%. Unexpectedly, the angle of the main root vector (defined as a straight line between the most basal and apical points of the root, Fig. 1) showed a variation of 116%. The weakest variations were about 20% and concerned the length of the apical part of the main root and the main root growth rate. To identify the main determinants of RSA variability, a principal component analysis was conducted. The results showed that lateral root number, main root path length, lateral root density (within the branched zone), main root vector angle and average lateral root length represented 39, 22, 17, 10 and 6% of the accession variability respectively. This illustrates the wide range of genetic polymorphism underlying each individual RSA parameter. Such detailed information is now available and can be used to identify parental lines for QTL analysis to investigate the molecular basis underling specific root trait (e.g., lateral root number).

Access to Specific Growth Rates

EZ-Rhizo allows RSA parameters to be captured in a non-invasive way so that growth rate and growth acceleration of specific roots can be calculated. Direct applications can be found in characterizing plants switching root growth from the main root to all or specific lateral roots.^7,14 In our study of 23 Arabidopsis accessions grown in favorable condition, important variability of growth allocations between primary and secondary root systems was observed.¹³ Furthermore, measurements uncovered strong differences of growth rate between lateral roots based on their position on the main root. The stereotypical inverted pyramid pattern of lateral root reflects better the differences in growth rate rather than the timing of lateral root emergence. Mechanisms underlying growth allocation between primary versus lateral root systems and upper branching versus lower branching remain to be elucidated.

Relations between Root Architecture and Accession Origins

Arabidopsis accessions represent a wide source of variability about all aspects of plant physiology and particularly growth and flowering time.^15,16 A comparative analysis of accession shoot and root fresh weight (FW) at the end of the vegetative cycle showed an important variability (60% for both) whereas shoot:root FW ratios were quite consistent (5 ± 0.35; 34% variability across accessions). Surprisingly, only one weak but significant correlation between the shoot:root ratio and the apical root path length (R = −0.46, p < 0.05) was observed. In our conditions (seeds were vernalised), flowering time varied from 21 to 66 days after germination, representing a variability of 33% across the 23 accessions. A weak but significant correlation of the flowering time was observed with the main root path length (R = −0.53, p < 0.05) and with the main root growth rate (R = −0.49, p < 0.05).

In an attempt to relate RSA with accession geographical origins, the average lateral root vector angle was the only RSA parameter to be significantly correlated with altitude (R=−0.67, p<0.01), latitude (R = 0.48, p < 0.05) and longitude (R = −0.54, p < 0.05). Other environmental features of accession origins available in public database such as VNAT (http://dbsgap.versailles.inra.fr/VNAT) including illumination period (hours of sunshine), water availability (rainfall) and temperature (absolute and amplitude) were tested. No correlation was found between RSA and illumination period. In contrast, rainfall amount showed significant correlations with the main root branched zone path length, its growth rate and its acceleration (R = −0.48, p < 0.05 for all three as they are correlated to each other). Absolute temperature showed a significant correlation with the average lateral root path length (R = −0.50, p < 0.05). Both absolute temperature and temperature amplitude were correlated with the average lateral root vector angle (R = 0.47, p < 0.05 and R = −0.53, p < 0.05 respectively). Taken together, these attempts to correlate RSA features with growth, development and accession original environment provided no evidence of a strong relationship. In other words, the morphology of the root system seems to play a minor role for growth and development when plants are placed in favorable condition. Further investigations are needed to answer the same question when accessions are challenged with nutritional and other abiotic stresses.

Conclusions

The EZ-Rhizo software combines non-invasive image acquisition with a newly developed program for root detection, measurement of multiple RSA parameters, data storage and data analysis. It is therefore well suited for the phenotypic description of individual plant species, accessions and mutants grown under varying nutritional and environmental conditions. In its first version, the main limitation of EZ-Rhizo is the complexity of the root system itself. Root intersections or roots growing too close to each other can not be correctly and automatically assigned to a specific root segment. To overcome this matter, future development of the software will be based on machine-learning algorithms integrating spatial and temporal ‘history’ of the root features. For now, the availability of such a program (http://EZ-Rhizo.psrg.org.uk) opens new research perspectives in the areas of functional genomics, breeding and QTL, and predictive plant performance.

Addendum to: Armengaud P, Zambaux K, Hills A, Sulpice R, Pattison RJ, Blatt MR, Amtmann A. EZ-Rhizo: Integrated software for fast and accurate measurement of root system architecture. Plant J. 2008 doi: 10.1111/j.1365313X.2008.03739.x. In press.