Focus Issue on Roots: Perennial Roots to Immortality,

Maximum lifespan greatly varies among species, and it is not strictly determined; it can change with species evolution. Clonal growth is a major factor governing maximum lifespan. In the plant kingdom, the maximum lifespans described for clonal and nonclonal plants vary by an order of magnitude, with 43,600 and 5,062 years for Lomatia tasmanica and Pinus longaeva, respectively. Nonclonal perennial plants (those plants exclusively using sexual reproduction) also present a huge diversity in maximum lifespans (from a few to thousands of years) and even more interestingly, contrasting differences in aging patterns. Some plants show a clear physiological deterioration with aging, whereas others do not. Indeed, some plants can even improve their physiological performance as they age (a phenomenon called negative senescence). This diversity in aging patterns responds to species-specific life history traits and mechanisms evolved by each species to adapt to its habitat. Particularities of roots in perennial plants, such as meristem indeterminacy, modular growth, stress resistance, and patterns of senescence, are crucial in establishing perenniality and understanding adaptation of perennial plants to their habitats. Here, the key role of roots for perennial plant longevity will be discussed, taking into account current knowledge and highlighting additional aspects that still require investigation.There is enormous diversity among the types of perennial plants and among their patterns of aging (Jones et al., 2014). Perennial plants can be divided into herbaceous (or perennial herbs) and woody perennials (trees and shrubs), and therefore, they represent very diverse organisms in size and complexity from some herbs that weigh a few grams to huge trees like sequoias (Sequoia sempervirens). Among perennial herbs, the slowest growing species described thus far, Borderea pyrenaica (a small geophyte growing in the Central Pyrenees of northeastern Spain), is also the one with the longest maximum lifespan (350 years; Fig. 1). Interestingly, fecundity of this species increases with aging, representing a case of negative senescence (Garcia et al., 2011; Morales et al., 2013). If mortality falls as size increases and if size increases with age, then mortality will fall with age, and negative senescence occurs (Vaupel et al., 2004). Negative senescence is not common in the tree of life, but it seems to occur in not only some perennial herbs, such as B. pyrenaica (Garcia et al., 2011) and Plantago lanceolata (Roach and Gampe, 2004), but also, other phylogenetically distant organisms, such as turtles (Jones et al., 2014). Other perennial herbs with higher biomass production rates and consequently, larger sizes, such as stinging nettle (Urtica dioica), are much shorter-lived (a few years only). In this case, however, perenniality is achieved by allocating an important part of their energy to asexual reproduction (production of stolons; i.e. clonal propagation), giving rise to new entire clonal plants (Koskela, 2002). Indeed, this process happens in several other plant species with rapid growth that we commonly find in gardens, such as strawberries (Fragaria × ananassa) or raspberries (Rubus idaeus). Stolons can be produced aboveground or underground (in the latter case, forming rhizomes). Van Dijk (2009) elegantly reviewed the direct and indirect methods currently used to estimate plant age in clonal and nonclonal plants, showing several examples of plant species using clonal propagation with maximum lifespans of thousands of years, with the most notable example, King’s Lomatia (Lomatia tasmanica), being dated at 43,600 years (Lynch et al., 1998). Only one wild-living clone of this species is known. Clonal propagation is the only means for propagation, because it is a sterile ancient clone. When a branch falls, that branch produces new roots, establishing a new plant that is genetically identical to its parent (Lynch et al., 1998). Here, the production of new roots becomes essential for achieving potential immortality. Another example of extreme longevity is the bristlecone pine (Pinus longaeva), with a maximum lifespan of 5,062 years. It holds the record of longevity of a single individual within the plant kingdom, which was observed by Tom Harlan during 2012 in a living individual of this species in the White Mountains (the location has not been reported; Earle, 2013).Open in a separate windowFigure 1.Examples of extreme longevity in perennial plants. A, B. pyrenaica, the perennial herb with the longest lifespan described to date. B, A cross section of the tuber of B. pyrenaica showing the scars left by the five meristematic points in the spiral. C, P. longaeva, the species with the individual with the longest lifespan ever recorded (not using clonal propagation). D, C. nodosa meadow, with a detail of the rhizomes (E) that allow clonal propagation and potential immortality in this species. [See online article for color version of this figure.]The enormous diversity in lifespans within a species responds to specific life history traits and mechanisms evolved by each individual to adapt to its habitat. Particularities of roots in perennial plants, such as meristem indeterminacy, modular growth, stress resistance, and patterns of senescence, are crucial in understanding adaptation of perennial plants to their habitats, explaining differences in longevity. Here, the key role of roots in providing long lifespans in perennial plants will be discussed, taking into account current knowledge and highlighting additional aspects that still require investigation.     

Focus Issue on Roots: Root Nutrient Foraging

During a plant''s lifecycle, the availability of nutrients in the soil is mostly heterogeneous in space and time. Plants are able to adapt to nutrient shortage or localized nutrient availability by altering their root system architecture to efficiently explore soil zones containing the limited nutrient. It has been shown that the deficiency of different nutrients induces root architectural and morphological changes that are, at least to some extent, nutrient specific. Here, we highlight what is known about the importance of individual root system components for nutrient acquisition and how developmental and physiological responses can be coupled to increase nutrient foraging by roots. In addition, we review prominent molecular mechanisms involved in altering the root system in response to local nutrient availability or to the plant''s nutritional status.In natural and agricultural soils, the ability of plants to quickly and efficiently acquire nutrients may determine their competitive success and productivity. Because mineral elements interact differently with themselves and other soil constituents or are carried by water out of the rooted soil volume, their availability to plants may decrease and lead to nutrient deficiency. Under these conditions, plants activate foraging responses that include morphological changes, such as the modulation of root system architecture (RSA) or root hair formation, and physiological changes, such as the release of nutrient-mobilizing root exudates or the expression of nutrient transporters (Gojon et al., 2009; Hinsinger et al., 2009; Gruber et al., 2013). These responses are often spatially coupled to increase the root-soil interaction zone and improve the ability of the plant to intercept immobile nutrients. Noteworthy, although not discussed herein, symbiosis or associative rhizosphere microorganisms can also alter the RSA and enhance the foraging capacity of the plant (Gutjahr and Paszkowski, 2013). Here, we provide an update on the morphological responses induced by plants to forage sparingly available nutrients and some of the underlying molecular mechanisms known to date to be involved in RSA adaptations to nutrient availabilities.     

Focus Issue on Roots: Plant Nutrition: Root Transporters on the Move

Nutrient and water uptake from the soil is essential for plant growth and development. In the root, absorption and radial transport of nutrients and water toward the vascular tissues is achieved by a battery of specialized transporters and channels. Modulating the amount and the localization of these membrane transport proteins appears as a way to drive their activity and is essential to maintain nutrient homeostasis in plants. This control first involves the delivery of newly synthesized proteins to the plasma membrane by establishing check points along the secretory pathway, especially during the export from the endoplasmic reticulum. Plasma membrane-localized transport proteins are internalized through endocytosis followed by recycling to the cell surface or targeting to the vacuole for degradation, hence constituting another layer of control. These intricate mechanisms are often regulated by nutrient availability, stresses, and endogenous cues, allowing plants to rapidly adjust to their environment and adapt their development.Plants take up nutrients and water from the soil and transport them to the leaves to support photosynthesis and plant growth. However, most soils around the world do not provide optimal conditions for plant colonization. Consequently, plants have evolved sophisticated mechanisms to adjust to deficiency or excess of nutrients and water supply. Membrane transport proteins, including channels and transporters, play crucial roles in the uptake of nutrients and water from the soil and in their radial transport to the root vasculature. Newly synthesized membrane transport proteins have to be properly targeted to a defined compartment, usually the plasma membrane, to efficiently ensure their function. The trafficking of membrane transport proteins along the secretory pathway is tightly controlled and involves the recognition of exit signals by gatekeeper protein complexes. After reaching the plasma membrane, membrane transport proteins can be endocytosed and subsequently recycled to the cell surface or targeted to the vacuole for degradation. Because the subcellular localization of proteins directly influences their activity, modulating the localization of membrane transport proteins constitutes a powerful way to control nutrient and water uptake in plants. This review discusses the fundamental mechanisms at stake in membrane protein secretion and endocytosis, with a specific focus on membrane transport proteins, and how endogenous and exogenous cues affect their dynamics to integrate uptake of nutrients and water to plant growth conditions.     

Focus Issue on Roots: Branching Out in Roots: Uncovering Form,Function, and Regulation

Root branching is critical for plants to secure anchorage and ensure the supply of water, minerals, and nutrients. To date, research on root branching has focused on lateral root development in young seedlings. However, many other programs of postembryonic root organogenesis exist in angiosperms. In cereal crops, the majority of the mature root system is composed of several classes of adventitious roots that include crown roots and brace roots. In this Update, we initially describe the diversity of postembryonic root forms. Next, we review recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in the plant models Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa). While many common signals, regulatory components, and mechanisms have been identified that control the initiation, morphogenesis, and emergence of new lateral and adventitious root organs, much more remains to be done. We conclude by discussing the challenges and opportunities facing root branching research.Branching through lateral and adventitious root formation represents an important element of the adaptability of the root system to its environment. Both are regulated by nutrient and hormonal signals (Bellini et al., 2014; Giehl and von Wirén, 2014), which act locally to induce or inhibit root branching. The net effect of these adaptive responses is to increase the surface area of the plant root system in the most important region of the soil matrix for resource capture (e.g. surface layers for phosphorus uptake and deeper layers for nitrate uptake) or to secure anchorage. Different species use different combinations of lateral or adventitious roots to achieve this, with lateral roots dominating the root system of eudicots while adventitious (crown and brace) roots dominate the root system of monocots, including in cereal crops.Our understanding of the mechanisms controlling lateral and adventitious root development has accelerated in recent years, primarily through research on model plants. The simple anatomy and the wealth of genetic resources in Arabidopsis (Arabidopsis thaliana) have greatly aided embryonic and postembryonic root developmental studies (De Smet et al., 2007; Péret et al., 2009a; Fig. 1, A and E). Nevertheless, impressive recent progress has been made studying root branching in other crop species, notably cereals such as maize (Zea mays) and rice (Oryza sativa).Open in a separate windowFigure 1.A to D, Schematics showing diversity in root system architecture at both seedling (left) and mature (right) stages in eudicots (A and C) and monocots (B and D). A, Arabidopsis root system. B, Maize root system. C, Tomato root system (for clarity, stem-derived adventitious roots are only shown in the labeled region). D, Wheat root system. E and F, Cross sections of emerging lateral root primordia in Arabidopsis (E) and rice (F). E and F are adapted from Péret et al. (2009b).In this Update, we initially describe the diversity of postembryonic root forms in eudicots and monocots (Fig. 1). Next, we highlight recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in Arabidopsis, rice, and maize. Due to space limits, we cannot provide an exhaustive review of this subject area, focusing instead on recent research advances. However, we direct readers to several recent in-depth reviews on lateral root (Lavenus et al., 2013; Orman-Ligeza et al., 2013) and adventitious root development (Bellini et al., 2014).     

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping

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Focus Issue on Roots: Functional Soil Microbiome: Belowground Solutions to an Aboveground Problem

There is considerable evidence in the literature that beneficial rhizospheric microbes can alter plant morphology, enhance plant growth, and increase mineral content. Of late, there is a surge to understand the impact of the microbiome on plant health. Recent research shows the utilization of novel sequencing techniques to identify the microbiome in model systems such as Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). However, it is not known how the community of microbes identified may play a role to improve plant health and fitness. There are very few detailed studies with isolated beneficial microbes showing the importance of the functional microbiome in plant fitness and disease protection. Some recent work on the cultivated microbiome in rice (Oryza sativa) shows that a wide diversity of bacterial species is associated with the roots of field-grown rice plants. However, the biological significance and potential effects of the microbiome on the host plants are completely unknown. Work performed with isolated strains showed various genetic pathways that are involved in the recognition of host-specific factors that play roles in beneficial host-microbe interactions. The composition of the microbiome in plants is dynamic and controlled by multiple factors. In the case of the rhizosphere, temperature, pH, and the presence of chemical signals from bacteria, plants, and nematodes all shape the environment and influence which organisms will flourish. This provides a basis for plants and their microbiomes to selectively associate with one another. This Update addresses the importance of the functional microbiome to identify phenotypes that may provide a sustainable and effective strategy to increase crop yield and food security.In recent years, the term plant microbiome has received substantial attention, since it influences both plant health and productivity. The plant microbiome encompasses the diverse functional gene pool, originating from viruses, prokaryotes, and eukaryotes, associated with various habitats of a plant host. Such plant habitats range from the whole organism (individual plants) to specific organs (e.g. roots, leaves, shoots, flowers, and seeds, including zones of interaction between roots and the surrounding soil, the rhizosphere; Rout and Southworth, 2013). The rhizosphere is the region of the soil being continuously influenced by plant roots through the rhizodeposition of exudates, mucilages, and sloughed cells (Uren, 2001; Bais et al., 2006; Moe, 2013). Thus, plant roots can influence the surrounding soil and inhabiting organisms. Mutually, the rhizosphere organisms can influence the plant by producing regulatory compounds. Thus, the rhizospheric microbiome acts as a highly evolved external functional milieu for plants (for review, see Bais et al., 2006; Badri et al., 2009b; Pineda et al., 2010; Shi et al., 2011; Philippot et al., 2013; Spence and Bais, 2013; Turner et al., 2013a; Spence et al., 2014). In another sense, it is considered as a second genome to a plant (Berendsen et al., 2012). Plant rhizospheric microbiomes have positive or negative influence on plant growth and fitness. It is influenced directly by beneficial mutualistic microbes or pathogens and indirectly through decomposition, nutrient solubilization, nutrient cycling (Glick 1995), secretion of plant growth hormones (Narula et al., 2006; Ortíz-Castro et al., 2008; Ali et al., 2009; Mishra et al., 2009), antagonism of pathogens (Kloepper et al., 2004), and induction of the plant immune system (Pieterse et al., 2001; Ramamoorthy et al., 2001; Vessey, 2003; Rudrappa et al., 2008, 2010). The establishment of plant and rhizospheric microbiome interaction is a highly coordinated event influenced by the plant host and soil. Recent studies show that plant host and developmental stage has a significant influence on shaping the rhizospheric microbiome (Peiffer et al., 2013; Chaparro et al., 2014).There are various factors involved in the establishment of the rhizospheric and endophytic microbiome. They are greatly affected by soil and host type (Bulgarelli et al., 2012; Lundberg et al., 2012). Apart from these factors, other external factors such as biotic/abiotic stress, climatic conditions, and anthropogenic effects also can impact the microbial population dynamics in particular plant species. Plant host species differences can mainly be perceived from the secretory exudates by microbes. The root exudates act as a crucial driving force for multitrophic interactions in the rhizosphere involving microbes, neighboring plants, and nematodes (Bais et al., 2006). Thus, it is important to understand root exudate-shaped microbial community profiling in establishing phenotypes involved in plant health. Microbial components associated with plant hosts have to respond to these exudates along with utilizing them in order to grow competitively in a complex interactive root environment. Commonly, there are three groups of microbes present in the rhizosphere, commensal, beneficial, and pathogenic microbes, and their competition for plant nutrition and interactions confer the overall soil suppressiveness against pathogens and insects (Berendsen et al., 2012).Traditionally, the components of the plant microbiome were characterized by isolating and culturing microbes on different media and growth conditions. These culture-based techniques missed the vast majority of microbial diversity in an environment or in plant-associated habitats, which is now detectable by modern culture-independent molecular techniques for analyzing whole environmental metagenomes (comprising all organisms’ genomes). Over the last 5 years, these culture-independent techniques have dramatically changed our view of the microbial diversity in a particular environment, from which only less than 1% are culturable (Hugenholtz et al., 1998). After discovering the importance of the conserved 16S ribosomal RNA (rRNA) sequence (Woese and Fox, 1977) and the first use of denaturing gradient gel electrophoresis (DGGE) of the amplified 16S rRNA gene in the analysis of a microbial community (Muyzer et al., 1993), there was a sudden explosion of research toward microbial ecology using various molecular fingerprinting techniques. Apart from DGGE, thermal gradient gel electrophoresis, and fluorescence in situ hybridization, clone library construction of microbial community-amplified products and sequencing emerged as other supporting techniques for better understanding of microbial ecology (Muyzer, 1999). Furthermore, there are many newer techniques used to understand the microbiome, from metagenomics to metaproteomics (Friedrich, 2006; Mendes et al., 2011; Knief et al., 2012; Rincon-Florez et al., 2013; Schlaeppi et al., 2014; Yergeau et al., 2014). These techniques cover the whole microbiome, instead of selecting particular species, unlike conventional microbial analysis. However, their presence was not yet correlated well with the phenotypic manifestation (phenome) they establish in the host plant.As a consequence of population growth, food consumption is also increasing. On the other hand, cultivable agricultural land and productivity are significantly reduced due to global industrialization, drought, salinity, and global warming (Gamalero et al., 2009). This problem is only addressed by practicing the sustainable agriculture that protects the health of the ecosystem. The basic principle of sustainable agriculture is to significantly reduce the chemical input, such as fertilizers, insecticides, and herbicides, while reducing the emission of greenhouse gas. Manipulation of the plant microbiome has great potential in reducing the incidence of pests and diseases (van Loon et al., 1998; Kloepper et al., 2004; Van Oosten et al., 2008), promoting plant growth and plant fitness, and increasing productivity (Kloepper and Schroth 1978; Lugtenberg and Kamilova, 2009; Vessey, 2003). Single strains or mixed inoculum treatments induced resistance to multiple plant diseases (Jetiyanon and Kloepper, 2002). In recent years, several microbial biofertilizers and inoculants were formulated, produced, marketed, and successfully used by farmers worldwide (Bhardwaj et al., 2014). Although plants are being considered as a metaorganism (East, 2013), our understanding of the exact manifestation of this microbiome on plant health in terms of phenotypes is insufficient. Of late, there is a surge to understand and explore the genomic wealth of rhizosphere microbes. Hence, this Update will focus mainly on existing knowledge based on the root microbiome, its functional importance, and its potential relationship to the establishment of a host phenome, toward achieving sustainable agriculture.     

High-Throughput Phenotyping of Chlamydomonas Swimming Mutants Based on Nanoscale Video Analysis

Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.     

Focus Issue on Roots: Natural Variation of Root Traits: From Development to Nutrient Uptake

The root system has a crucial role for plant growth and productivity. Due to the challenges of heterogeneous soil environments, diverse environmental signals are integrated into root developmental decisions. While root growth and growth responses are genetically determined, there is substantial natural variation for these traits. Studying the genetic basis of the natural variation of root growth traits can not only shed light on their evolution and ecological relevance but also can be used to map the genes and their alleles responsible for the regulation of these traits. Analysis of root phenotypes has revealed growth strategies and root growth responses to a variety of environmental stimuli, as well as the extent of natural variation of a variety of root traits including ion content, cellular properties, and root system architectures. Linkage and association mapping approaches have uncovered causal genes underlying the variation of these traits.Since their advent more than 400 million years ago, vascular plants have drastically transformed the land surface of our planet and facilitated the dense colonization of its land masses (Algeo and Scheckler, 1998; Gibling and Davies, 2012). Key to this was the evolution of root systems that enable plants to forage their environment for nutrients and water and anchor themselves tightly in the soil substrate. Soils are very heterogeneous environments, and because of the constant need to optimize root distribution in the soil according to sometimes conflicting parameters, root growth and development are some of the most plastic traits in plants. This plasticity is guided by environmental information that is integrated into decisions regarding how fast and in which direction to grow and where and when to place new lateral roots (LRs; Malamy and Ryan, 2001; Malamy, 2005). The distribution and function of roots are of crucial importance for plants. In fact, they are considered the most limiting factors for plant growth in almost all natural ecosystems (Den Herder et al., 2010). Not surprisingly, the plant root system plays a major role in yield and overall plant productivity (Lynch, 1995; Den Herder et al., 2010).The extent of plasticity is determined by genetic components (Pigliucci, 2005). For instance, one ecotype of a plant species may be able to increase root growth rate on a certain stimulus, whereas another ecotype lacks this characteristic (Gifford et al., 2013). The genetic components that govern traits in different ecotypes represent the outcome of adaptation arising from the selection of those traits that allow better adapted populations to reproduce more successfully (higher fitness) than less well-adapted populations (Trontin et al., 2011; Savolainen, 2013). Although local adaptation is common in plants and animals, its genetic basis is still poorly understood (Savolainen et al., 2013). Traits that drive local adaptation are often quantitative traits shaped by multiple genes. Therefore, phenotypic differences are often caused by allelic variation at several loci, each of them making small contributions to the trait (Weigel and Nordborg, 2005; Rockman, 2012). Studying the genetic basis of the natural variation of traits cannot only shed light on the evolution of these traits and their ecological relevance but also, can be used to map the genes responsible for the regulation of these traits.Most efforts to study intraspecies genetic variation to find trait-governing genes or identify useful traits have been conducted in crop species and the model plant Arabidopsis (Arabidopsis thaliana). Whereas in crop species, traits that are used have been subjected to human-directed selection during domestication, often with the aim of increasing productivity, in Arabidopsis, it is mostly natural selection that is examined. Arabidopsis is widely distributed around the world, inhabiting diverse environments that include beaches, rocky slopes, riverbanks, roadsides, and areas surrounding agriculture fields (Horton et al., 2012). A large number of accessions has been collected over the past decades from locations all over the world and made available to the scientific community. Importantly, these accessions of Arabidopsis exhibit a striking diversity of phenotypic variation of morphology and physiology (Koornneef et al., 2004) and can be used to understand the genetic and molecular bases of traits using quantitative genetics. Variations of traits are measured in a panel of genetically distinct plant strains and then correlated with the occurrence of genetic markers in these plants. Linked or associated genome regions can eventually be identified, and additional analysis can be conducted to find the causal genes. Self-fertilizing species, such as Arabidopsis, are particularly suited for such approaches, because they can be maintained as inbred lines and therefore, need to be genotyped only one time, after which they can be phenotyped multiple times. In the past, natural variation has been used to map causal genes mainly by using recombinant inbred lines (RILs) approaches; these are very powerful but lack a high mapping resolution, and they can only capture a very small subset of the allelic diversity (Korte and Farlow, 2013). However, the advent of new and cheap large-scale genotyping and sequencing technologies has enabled large-scale, high-resolution genotyping (Horton et al., 2012) and even the complete sequencing of a large number of plant strains (http://1001genomes.org; 3,000 Rice Genomes Project, 2014). With these data, genome-wide association studies (GWASs) for identification of alleles responsible for many different quantitative traits have become feasible (Weigel, 2012). In these studies, traits of a large number of accessions are measured and subsequently associated with genotyped markers, most frequently single-nucleotide polymorphism. Although GWASs are a very powerful tool and in principle, allow for a high mapping accuracy, a notable disadvantage is that the complexity of the population structure can confound these studies. However, there has been remarkable progress addressing this issue (Atwell et al., 2010; Segura et al., 2012).In this review, we highlight recent progress in understanding the genetic bases of natural variation of growth, development, and physiology of the root system. After briefly explaining how root growth and development give rise to the root system architecture (RSA), we highlight natural variation and what has been learned from it for fundamental processes in root growth and development, root growth responses to nutrient availability, and ion uptake and homeostasis.     

Focus Issue on Roots: Beyond the Barrier: Communication in the Root through the Endodermis

The root endodermis is characterized by the Casparian strip and by the suberin lamellae, two hydrophobic barriers that restrict the free diffusion of molecules between the inner cell layers of the root and the outer environment. The presence of these barriers and the position of the endodermis between the inner and outer parts of the root require that communication between these two domains acts through the endodermis. Recent work on hormone signaling, propagation of calcium waves, and plant-fungal symbiosis has provided evidence in support of the hypothesis that the endodermis acts as a signaling center. The endodermis is also a unique mechanical barrier to organogenesis, which must be overcome through chemical and mechanical cross talk between cell layers to allow for development of new lateral organs while maintaining its barrier functions. In this review, we discuss recent findings regarding these two important aspects of the endodermis.Soil contains water and dissolved nutrients needed for plant growth, but also holds pathogens and toxic compounds that can be detrimental to the plant. The root system, which is directly in contact with soil particles, can integrate environmental cues to adjust its development in order to optimize nutrient (Péret et al., 2011; Lynch, 2013) and water uptake (Cassab et al., 2013; Lynch, 2013; Bao et al., 2014) or avoid regions of high salinity (Galvan-Ampudia et al., 2013). Once anchored in the soil, roots must deal with the constraints of their local environment and develop specific barriers to balance uptake of nutrients, water, and interactions with symbionts with protection against detrimental biotic and abiotic factors.In young roots, these barriers are mainly formed by the deposition of hydrophobic polymers such as lignin and suberin within the primary cell wall of the endodermis, which separates the pericycle from the cortex (Fig. 1), and of the exodermis, which lies between the cortex and the epidermis (Nawrath et al., 2013). Although formation of an exodermis is species dependent, the endodermis is a distinguishing figure of extant vascular plants (Raven and Edwards, 2001). Within this layer, two barriers (i.e. the Casparian strip and the suberin lamellae) are sequentially deposited and regulate water and nutrient movements between the inner and outer parts of the root. In this review, we discuss how the presence of these two major endodermal barriers affects communication between the different cell layers of the root. We focus on recent articles highlighting the importance of the endodermis in this communication during various biological and developmental processes.Open in a separate windowFigure 1.Endodermal barriers affect radial movement of water and solutes through the root. A, At the root tip, to move from the soil to the outer tissues of the root and then into the stele, water and solute molecules can use either the apoplastic (black lines), symplastic (dotted lines), or transcellular (dashed lines) pathways. B, The deposition of the Casparian strip in the endodermis prevents the free apoplastic diffusion of molecules between the outer part and the inner part of the root forcing molecules to pass through the symplast of endodermal cells. C, The deposition of suberin lamellae prevents the uptake of molecules from the apoplast directly into the endodermis forcing molecules to enter the symplast from more outer tissue layers. Suberin deposition is also likely to prevent the backflow of water and ions out of the stele. Passage cells are unsuberized and may facilitate the uptake of water and nutrients in older parts of the root. Cor, Cortex; End, endodermis; Epi, epidermis; Peri, pericycle; Vasc, vasculature. Figure redrawn and modified from Geldner et al. (2013).     

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.     

Focus Issue on Roots: Root Cortical Aerenchyma Enhances Nitrogen Acquisition from Low-Nitrogen Soils in Maize

Suboptimal nitrogen (N) availability is a primary constraint for crop production in developing nations, while in rich nations, intensive N fertilization carries substantial environmental and economic costs. Therefore, understanding root phenes that enhance N acquisition is of considerable importance. Structural-functional modeling predicts that root cortical aerenchyma (RCA) could improve N acquisition in maize (Zea mays). We evaluated the utility of RCA for N acquisition by physiological comparison of maize recombinant inbred lines contrasting in RCA grown under suboptimal and adequate N availability in greenhouse mesocosms and in the field in the United States and South Africa. N stress increased RCA formation by 200% in mesocosms and by 90% to 100% in the field. RCA formation substantially reduced root respiration and root N content. Under low-N conditions, RCA formation increased rooting depth by 15% to 31%, increased leaf N content by 28% to 81%, increased leaf chlorophyll content by 22%, increased leaf CO₂ assimilation by 22%, increased vegetative biomass by 31% to 66%, and increased grain yield by 58%. Our results are consistent with the hypothesis that RCA improves plant growth under N-limiting conditions by decreasing root metabolic costs, thereby enhancing soil exploration and N acquisition in deep soil strata. Although potential fitness tradeoffs of RCA formation are poorly understood, increased RCA formation appears be a promising breeding target for enhancing crop N acquisition.Nitrogen (N) deficiency is one of the most limiting factors in maize (Zea mays) production worldwide (Ladha et al., 2005). In developing countries such as those in sub-Saharan Africa, less than 20 kg N ha⁻¹ is applied to fields of smallholder farmers due to high fertilizer cost (Azeez et al., 2006; Worku et al., 2007). In developed countries, intensive N fertilization is used to maintain satisfactory yield (Tilman et al., 2002). In the United States, N fertilizers are the greatest economic and energy cost for maize production (Ribaudo et al., 2011). However, less than half of the N applied to crops is actually acquired, and most of the remaining N becomes a source of environmental pollution (Raun and Johnson, 1999; Smil, 1999; Tilman et al., 2002). For example, N and phosphorus (P) effluents into marine systems from agriculture cause eutrophication and hypoxic zones (Diaz and Rosenberg, 2008; Robertson and Vitousek, 2009). Nitrate contamination in surface water and groundwater systems poses serious health risks, such as methemoglobinemia and N-nitroso-induced cancers (UNEP and WHRC, 2007). Emission of nitrous oxides from agricultural activities contributes to ozone damage and global warming (Kulkarni et al., 2008; Sutton et al., 2011). Furthermore, the production of N fertilizers requires considerable energy from fossil fuels, and since energy costs have risen in recent years, farmers face economic pressure from increasing N fertilizer costs, which are linked to higher food prices. It is estimated that a 1% increase in crop N efficiency could save more than $1 billion (U.S.) annually worldwide (Kant et al., 2011). Therefore, even a small improvement in N efficiency would have significant positive impacts on the environment and the economy.Soil N is heterogenous and dynamic. The bioavailability of soil N depends on the balance between the rates of mineralization, nitrification, and denitrification. These processes are determined by several factors, including soil composition, microbial activity, soil temperature, and soil water status (Miller and Cramer, 2004). The predominant form of soil N available to plants in most agricultural systems is nitrate, which is highly soluble in water and thus mobile in the soil (Barber, 1995; Marschner, 1995). Mineralization of organic matter and/or the application of N fertilizer at the beginning of the growing season followed by precipitation and irrigation create a pulse of nitrate that may exceed the N acquisition capacity of seedlings and leach below the root zone. Therefore, it has been proposed that increasing the speed of root exploration of deep soil strata could benefit N acquisition (Lynch, 2013). However, the structural investments and metabolic expenditures of root systems are substantial and can exceed half of daily photosynthesis (Lambers et al., 2002). Therefore, full consideration of the costs and benefits of root systems is crucial for identifying root traits to improve crop production, especially in water- and nutrient-deficient environments (Lynch, 2007). Taking rhizoeconomics and the spatiotemporal availability of soil N into account, Lynch (2013) proposed a root ideotype for enhanced N acquisition in maize called Steep, Cheap, and Deep, in which Steep refers to architectural phenes and Cheap refers to phenes that reduce the metabolic cost of soil exploration. One element of this ideotype is abundant root cortical aerenchyma (RCA).RCA consists of enlarged air spaces in the root cortex (Esau, 1977). RCA is known to form in response to hypoxia, and the role of RCA in improving oxygen transport to roots of many plant species under hypoxic conditions has been well researched (Vartapetian and Jackson, 1997; Jackson and Armstrong, 1999; Mano and Omori, 2007, 2013). Interestingly, RCA can also form in response to drought and edaphic stresses such as N, P, and sulfur deficiencies (Drew et al., 1989; Bouranis et al., 2003; Fan et al., 2003; Zhu et al., 2010a), which suggests that the benefit of RCA extends beyond facilitating oxygen transport. Several lines of evidence suggest that RCA enhances root metabolic efficiency under stress. Fan et al. (2003) found that RCA formation significantly reduced root segment respiration and P content of root tissue, which allowed greater shoot growth in soils with low P availability. Under drought, maize genotypes with high RCA formation had greater root length, deeper rooting, better leaf water status, and 8 times greater yield than closely related genotypes with low RCA (Zhu et al., 2010a). Effects of RCA on root respiration were more pronounced for large-diameter roots compared with small-diameter roots (Jaramillo et al., 2013). Results from the functional-structural plant model SimRoot showed that RCA formation could be an adaptive response to deficiency of N, P, and potassium by decreasing the metabolic cost of soil exploration. By reducing root respiration, RCA decreases the carbon cost of soil exploration, and by decreasing the N and P content of root tissue, RCA permits internal reallocation of nutrients to growing root tissue, which is particularly beneficial under conditions of low N and P availability (Postma and Lynch, 2011a). Under suboptimal P availability, RCA increased the growth of a simulated 40-d-old maize plant by 70% (Postma and Lynch, 2011b). In the case of N, RCA increased the growth of simulated maize plants up to 55% in low-N conditions, and plants benefit from RCA more in high-N-leaching environments than in low-N-leaching environments (Postma and Lynch, 2011a). In addition, the formation of RCA decreases critical soil nutrient levels, defined as the soil fertility below which growth is reduced, suggesting that cultivars with high RCA may require less fertilizer under nonstressed conditions. These in silico results suggest that RCA has potential utility for improving crop nutrient acquisition in both high- and low-input agroecosystems.The overall objective of this research was to assess the utility of RCA for N acquisition in maize under N-limiting conditions. Maize near-isophenic recombinant inbred lines (RILs) sharing a common genetic background (i.e. descending from the same parents) with common root phenotypes but contrasting in RCA formation were grown under N stress to test the hypothesis that RCA formation is associated with reduced root respiration, reduced tissue nutrient content, greater rooting depth, enhanced N acquisition, and therefore greater plant growth and yield under N limitation.     

Focus Issue on Roots: Visualization of Root Water Uptake: Quantification of Deuterated Water Transport in Roots Using Neutron Radiography and Numerical Modeling

Our understanding of soil and plant water relations is limited by the lack of experimental methods to measure water fluxes in soil and plants. Here, we describe a new method to noninvasively quantify water fluxes in roots. To this end, neutron radiography was used to trace the transport of deuterated water (D₂O) into roots. The results showed that (1) the radial transport of D₂O from soil to the roots depended similarly on diffusive and convective transport and (2) the axial transport of D₂O along the root xylem was largely dominated by convection. To quantify the convective fluxes from the radiographs, we introduced a convection-diffusion model to simulate the D₂O transport in roots. The model takes into account different pathways of water across the root tissue, the endodermis as a layer with distinct transport properties, and the axial transport of D₂O in the xylem. The diffusion coefficients of the root tissues were inversely estimated by simulating the experiments at night under the assumption that the convective fluxes were negligible. Inverse modeling of the experiment at day gave the profile of water fluxes into the roots. For a 24-d-old lupine (Lupinus albus) grown in a soil with uniform water content, root water uptake was higher in the proximal parts of lateral roots and decreased toward the distal parts. The method allows the quantification of the root properties and the regions of root water uptake along the root systems.Understanding how and where plant roots extract water from soil remains an open question for both plant and soil scientists. One of the open questions concerns the locations of water uptake along the root system (Frensch and Steudle, 1989; Doussan et al., 1998; Steudle, 2000; Zwieniecki et al., 2003; Javaux et al., 2008). A motivation of these studies is that a better prediction of root water uptake may help to optimize irrigation and identify optimal traits to capture water. Despite its importance, there is little experimental information on the spatiotemporal distribution of the uptake zone along roots growing in soil. The lack of experimental data is largely due to the technical difficulties in measuring water fluxes in soils and roots.Quantitative information on the rate and location of root water uptake along roots growing in soil is needed to better understand the function of roots in extracting water from the soil and tolerating drought events. Such information may show which parts of roots are more involved in water extraction and how root hydraulic properties change during root growth and exposure to water-limiting conditions. For instance, it is not clear how root anatomy and the hydraulic conductivity of roots change as the soil becomes dry or the transpiration demand increases. Quantitative information of the location of root water uptake can be used to estimate the spatial distribution of hydraulic conductivities along roots. This information is needed to parameterize the most recent and advanced models of root water uptake, such as those of Doussan et al. (1998) and Javaux et al. (2008).Most of the experimental information on the spatial distribution of water uptake is limited to roots grown in hydroponic and aeroponic cultures (Frensch and Steudle, 1989; Varney and Canny, 1993; Zwieniecki et al., 2003; Knipfer and Fricke, 2010a). These investigations substantially improved our knowledge of the mechanism of water transport in roots. However, roots grown in hydroponic and aeroponic cultures may have different properties than those of roots grown in soils. As the soil dries, the hydraulic conductivity of roots and of the root-soil interface changes and likely affects the profile of root water uptake (Blizzard and Boyer, 1980; Nobel and Cui, 1992; Huang and Nobel, 1993; McCully, 1995; North and Nobel, 1997; Carminati et al., 2011; Knipfer et al., 2011; McLean et al., 2011; Carminati, 2012).New advances in imaging techniques are opening new avenues for noninvasively studying water uptake by roots in soils (Doussan et al., 1998; Garrigues et al., 2006; Javaux et al., 2008; Pohlmeier et al., 2008; Moradi et al., 2011). Imaging methods such as x-ray computed tomography, light transmission imaging, NMR, and computed neutron radiography allow quantifying the changes of water content in the root zone with different accuracy and spatial resolution. However, due to the concomitant soil water redistribution, the local changes in soil water content are not trivially related to root uptake. Consequently, the estimation of root water uptake requires coupling the imaging methods with the modeling of water flow in the soil, which, in turn, requires accurate information on the hydraulic properties of soil and roots. An additional complexity is represented by the peculiar and only partly understood hydraulic properties of the soil in the vicinity of the roots, the so-called rhizosphere.The hydraulic properties of the rhizosphere are influenced by root and microorganism activity, soil compaction due to root growth, and the formation of air-filled gaps between soil and roots when roots shrink (Nye, 1994; North and Nobel, 1997; Carminati et al., 2010; Aravena et al., 2011; Moradi et al., 2011; Carminati, 2013; Zarebanadkouki and Carminati, 2014). To date, it has been technically difficult to quantify the hydraulic properties of the rhizosphere. Carminati et al. (2011) showed that the hydraulic properties of the first 1 to 2 mm near the root affect the profile of water content and water potential toward the root.Recently, we introduced a novel method to noninvasively trace the flow of water in soil and roots (Zarebanadkouki et al., 2012, 2013). The method combines neutron radiography and the injection of deuterated water (D₂O). Neutron radiography is an imaging technique that allows one to quantify the water distribution in thin soil samples with high accuracy and spatial resolution (Moradi et al., 2008). D₂O is an isotope of normal water. Its chemical and physical properties are similar to those of water, but in contrast to water, it is almost transparent in neutron transmission imaging (Matsushima et al., 2012). This property makes D₂O an excellent tracer for neutron imaging of water flow.In our previous experiments (Zarebanadkouki et al., 2012, 2013), D₂O was injected next to selected roots and its transport was monitored using time-series neutron radiography with a spatial resolution of 150 μm and a temporal resolution of 10 s for a duration of 2 h. We grew lupine (Lupinus albus) in aluminum containers (width of 25 cm, height of 30 cm, and thickness of 1 cm) filled with a sandy soil. The soil was partitioned into different compartments with a 1-cm layer of coarse sand acting as a capillary barrier (three vertical and four horizontal layers placed at regular intervals). The capillary barriers limited the transport of D₂O into a given region of soil and facilitated the quantification of D₂O transport into the roots. Figure 1 shows selected neutron radiographs of D₂O injection during the day and night. This figure is modified from Zarebanadkouki et al. (2013). The radiographs show that (1) the radial transport of D₂O into the roots was faster during the day than during the night and (2) the axial transport of D₂O along the roots was visible only during the day, while it was negligible at night. The differences between nighttime and daytime measurements were caused by the net flow of water induced by transpiration.Open in a separate windowFigure 1.Neutron radiographs of two samples after injection of 4 mL of D₂O during the day (A and B) and during the night (C and D). D₂O was injected in one compartment during the nighttime and in two compartments during the daytime. The images show the differences between the actual radiographs at time t and the radiograph before injection (t = 0). Brighter colors indicate lower neutron attenuation and higher D₂O-water ratio. The images show that (1) the transport of D₂O was faster during the day than during the night and (2) D₂O moved axially beyond the capillary barrier toward the shoot only during the day. Images are closeups of the original field of view of 15.75 × 15.75 cm showing the distribution of D₂O in the soil and root after D₂O injection. Figures are extracted from Zarebanadkouki et al. (2013). (A neutron radiograph of the whole sample used for daytime measurement is given in Figure 9.) [See online article for color version of this figure.]The interpretation of tracing experiments with D₂O in which water and D₂O are mixed is not straightforward (Carminati and Zarebanadkouki, 2013; Warren et al., 2013a, 2013b). To determine the convective fluxes from the radiographs, Zarebanadkouki et al. (2012, 2013) introduced a diffusion-convection model of D₂O transport in roots. The model was solved analytically. The model described the increase of the average D₂O concentration in the root with a double-exponential equation, in which the rate constants of the first and second phases were related to the transport of D₂O into the cortex and the stele of the roots. Although the model included important details of the root structure, such as different pathways of water across the root tissue, the diffusion of D₂O across the root tissue was strongly simplified. In particular, our previous model assumed that as soon as the roots were immersed in D₂O, the apoplastic free space of the root cortex was instantaneously saturated with D₂O. In other words, we assumed that all cortical cells and the root endodermis were simultaneously immersed in an identical concentration of D₂O equal to that of the soil. Additionally, we assumed that D₂O concentration inside the cortical cell and the root stele was uniform (well-stirred compartment).Although the radiographs clearly showed a significant axial transport of D₂O beyond the capillary barrier during the daytime (Fig. 1B), the model of Zarebanadkouki et al. (2013) was not capable of simulating it appropriately. Indeed, our previous model could only simulate the changes in D₂O concentration in the root segments immersed in D₂O. Since the concentration of D₂O in the root segment beyond the capillary barrier carries additional information on the axial and radial fluxes along the roots, we decided to modify our model to include such information.Another approximation of the previous model was the assumption that the radial water flow to the root was uniform along the root segment immersed in D₂O. However, Zarebanadkouki et al. (2013) found significant variations in root water uptake along the roots and suggested that root water uptake should be measured with a better spatial resolution.The objective of this study was to provide an adequate model to interpret tracing experiments with D₂O. We developed two different models to describe the transport of D₂O into roots. (1) In the first model, we described the transport of D₂O into the roots by taking into account the different pathways of water across the root tissue (i.e. the apoplastic and the cell-to-cell pathways). Although this model captures the complexity of the root structure, it requires several parameters, such as the ratio of the water flow in the apoplast over the water flow in the cell-to-cell pathway. We refer to this model as the composite transport model. (2) In the second model, we simplified the root tissue into a homogenous flow domain comprising both pathways. The latter model is a simplification of the complex root anatomy, but it has the advantage of requiring fewer parameters. We refer to this model as the simplified model.In the next sections, we introduce the two modeling approaches and run a sensitivity analysis to test whether the transport of D₂O into roots is sensitive to the parameters of the composite transport model. The question was, do we need the composite transport model to accurately estimate the water flow into the roots based on the experiments with neutron radiography? Or alternatively, can we use the simplified model to estimate the fluxes without the need of introducing several parameters?Our final goal was to develop a numerical procedure to extract quantitative information on the water fluxes and the root hydraulic properties based on the tracing experiments with neutron radiography. Based on the results of the sensitivity analysis, we chose the simplified model to simulate the experiments. By fitting the observed D₂O transport into the roots, we calculated the profiles of water flux across the roots of a 24-d-old lupine as well as the diffusion permeability of its roots.     

High-Throughput Phenotyping of Chlamydomonas Swimming Mutants Based on Nanoscale Video Analysis

Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.