Cancer cell binding to E-selectin transfected human endothelia.

Human endothelial cells were transiently transfected with E-Selectin which enabled us to study tumor cell/endothelial interactions following engagement of E-Selectin without the added complications of metabolic stimulation, morphological changes, and/or up regulation of other adhesion molecules due to cytokine induction. Similar results were received from in vitro binding studies and FACS analyses on both Tumor Necrosis Factor-alpha activated and E-Selectin transfected endothelial cells. These data suggest that this methodology is appropriate for dissecting the individual activities of E-selectin while minimizing the participation of other adhesion molecules, thereby allowing us to develop a better understanding of the role of E-Selectin and endothelia in metastatic disease.     

Plant growth-inhibitory pseudomonads colonizing winter wheat (Triticum aestivum L.) roots

Summary Pseudomonads, which inhibit root extension, can be present in the winter wheat rhizosphere in large numbers, but they are not detectable until late winter or early spring. Their presence was not related to the presence of wheat straw residues or type of tillage, although they were present on the wheat residues when they appeared in the rhizosphere. Wheat seedlings were more sensitive to the bacteria at 15° C than at 20° C during bioassays. The type of agar used in the bioassay can affect the results obtained. The inhibitory factor expressed by the pseudomonads is quite variable and is radically affected by transfer of isolates.Contribution from Agric. Res. Serv. U.S. Dept. of Agric., in cooperation with the College of Agric. Res. Center. Washington State Univ., Pullman, WA 99164 (Scientific Paper No. 6743); and Agricultural Research Council, Letcombe Laboratory, Wantage, Oxon OX12 9JT, England.     

Cancer induction by methylcholanthrene and metastatic spread of transplantable tumor in Chediak Higashi (beige) mice

Summary We compared the beige mouse (bg/bg) model of Chediak-Higashi syndrome to the phenotypically normal counterpart (bg/+) in their sensivity to tumor induction and growth. No differences were observed in the incidence or time of appearance of tumors nor in the time of death, in bg/bg or bg/+ injected with 0.1 mg 3 MCA. When grafted with the transplantable 3LL tumor, the tumor grew comparably at the graft site in both groups.Bg/bg mice did however, have significantly more lung metastases than bg/+ in some experiments. The intravenous injection of non-metastasizing B16 melanoma cells demonstrated that this difference was not due to variation in trapping of circulating tumor cells by the lung.     

Focus Issue on Roots: Image-Based High-Throughput Field Phenotyping of Crop Roots

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.Crop root systems represent an underexplored target for improvements as part of community efforts to ensure that global crop yields and productivity keep pace with population growth (Godfray et al., 2010; Gregory and George, 2011; Nelson et al., 2012). The challenge in improving crop root systems is that yield and productivity also depend on soil fertility, which is also a major constraint to global food production (Lynch, 2007). Hence, desired improvements to crop root systems include enhanced water use efficiency and water acquisition given the increased likelihood of drought in future climates (Intergovernmental Panel on Climate Change, 2014). Over the long term, the development of crop genotypes with improved root phenotypes requires advances in the characterization of root system architecture (RSA) and in the relationship between RSA and function.The emerging discipline of plant phenomics aims to expand the scope, throughput, and accuracy of plant trait estimates (Furbank, 2009). In the case of plant roots, structural traits may describe RSA as geometric or topological measures of the root shape at various scales (e.g. diameters and width of the whole root system or a single branch; Lynch, 1995; Den Herder et al., 2010). These traits can be used to predict yield under specific conditions such as drought or low fertility. Understanding the diversity and development of root architectural traits is crucial, because spatial and temporal root deployment affects plant fitness, especially water and nutrient acquisition (Rich and Watt, 2013). Thus, improving plant performance may benefit from improvements in the characterization of root architecture, including understanding how trait variation arises as a function of genotype and environmental conditions (Band et al., 2012; Shi et al., 2013).Current efforts to understand the structure of crop root systems have already led to a number of imaging solutions (Lobet et al., 2013) that are able to extract root architecture traits under various conditions (Fiorani et al., 2012), including laboratory conditions (de Dorlodot et al., 2007) in which plants are often grown in pots or glass containers (Zeng et al., 2008; Armengaud et al., 2009; Le Bot et al., 2010; Clark et al., 2011; Lobet et al., 2011; Naeem et al., 2011; Galkovskyi et al., 2012). In the case of pots, expensive magnetic resonance imaging technologies represent one noninvasive approach to capture high-resolution details of root architecture (Schulz et al., 2013), similar to the capabilities of x-ray microcomputed tomography (μCT) systems. X-ray systems allow capturing of the root architecture at a fine scale in containers with a wide variety of soil types (Mairhofer et al., 2012; Mooney et al., 2012). It has been shown that x-ray μCT paired with specifically designed algorithms has sufficient resolution to recover the root structure in many cases (Mairhofer et al., 2013). Nevertheless, x-ray μCT systems are currently unable to image mature root systems because of technical restrictions in container size.As an alternative, root systems can be imaged directly with a digital camera when grown in glass containers with transparent media such as gellan gum or transparent soil replacements (Downie et al., 2012). Such in situ imaging benefits from controlled lightning conditions during image acquisition, even more so when focusing on less complex root structures of younger plants that allow three-dimensional reconstruction (Clark et al., 2011; Topp et al., 2013). Under such controlled conditions, it is expected that imaging would enable the study of growth of roots over time (Spalding and Miller, 2013; Sozzani et al., 2014).However, all of the above-listed solutions have been used primarily to assess root structures in the early seedling stage (French et al., 2009; Brooks et al., 2010; Sozzani et al., 2014) to approximately 10 d after germination (Clark et al., 2011), which makes it all but impossible to directly observe mature root systems. For example, primary and seminal roots make up the major portion of the seedling root system in maize (Zea mays) during the first weeks after germination. Later in development, postembryonic shoot-borne roots become the major component of the maize root system (Hochholdinger, 2009), not yet accessible to laboratory phenotyping platforms. In addition to phenological limitations, current phenotyping approaches for root architecture require specialized growth conditions with aerial and soil environments that differ from field conditions; the effects of such differences on RSA are only sparsely reported in literature (Hargreaves et al., 2009; Wojciechowski et al., 2009).Indeed, high-throughput field phenotyping can be seen as a new frontier for crop improvement (Araus and Cairns, 2014) because imaging a mature root system under realistic field conditions poses unique challenges and opportunities (Gregory et al., 2009; Zhu et al., 2011; Pieruschka and Poorter, 2012). Challenges are intrinsic to roots grown in the field because the in situ belowground imaging systems to date are unable to capture fine root systems. As a consequence, initial attempts to characterize root systems in the field focused on the manual extraction of structural properties. Manual approaches analyzed the root system’s branching hierarchy in relation to root length and rooting depth (Fitter, 1991). In the late 1980s, imaging techniques were first used (Tatsumi et al., 1989) to estimate the space-filling behavior of roots, an estimation process that was recently automated (Zhong et al., 2009). A weakness of such approaches is that exact space-filling properties, such as the fractal dimension, are sensitive to the incompleteness of the excavated root network (Nielsen et al., 1997, 1999). In particular, the box counting method was criticized for above-ground branching networks of tree crowns (Da Silva et al., 2006). The same critiques apply to root systems, because fine secondary or tertiary roots can be lost or cutoff or can adhere to each other during the cleaning process, making it impossible to analyze the entire network.As an alternative, the shovelomics field protocol has been proposed to characterize the root architecture of maize under field conditions (Trachsel et al., 2011). In shovelomics, the researcher excavates the root at a radius of 20 cm around the hypocotyl and 20 cm below the soil surface. This standardized process captures the majority of the root system biomass within the excavation area. After excavation, the shoot is separated from the root 20 cm above the soil level and washed in water containing mild detergent to remove soil. The current procedure places the washed root on a phenotyping board consisting of a large protractor to measure dominant root angles with the soil level at depth intervals and marks to score length and density classes of lateral roots. A digital caliper is used to measure root stem diameters (Fig. 1). Observed traits vary slightly from crop to crop but generally fit into the following categories by depth or root class: angle, number, density, and diameter. In this way, field-based shovelomics allows the researcher to visually quantify the excavated structure of the root crown and compare genotypes via a common set of traits that do not depend on knowledge or observation of the entire root system network. Of note, shovelomics is of particular use in developing countries, which have limited access to molecular breeding platforms (Delannay et al., 2012), and for which direct phenotypic selection is an attractive option.Open in a separate windowFigure 1.A, Classic shovelomics scoring board to score the angle of maize roots with the soil tissue. B, An example to score rooting depth and angle in common bean.Nonetheless, direct phenotypic selection for root traits of field-grown root systems comes with a number of caveats and drawbacks. To date, the quantification of mature root systems is highly dependent on the researcher, reducing the repeatability of measured quantities (Gil et al., 2007). In addition, manual approaches impose limitations on both the number of accessible traits and the number of samples collected. For example, a typical shovelomics phenotyper can gather 10 to 12 traits from a sample in 2 min or approximately 200 samples in a typical workday (Trachsel et al., 2011). This means that evaluating a statistically more significant trial of 1,000 samples may take 5 d or more. Such a time span introduces variation owing to plant growth and phenology. On the other hand, shovelomics is adaptable to both monocot and dicot roots; therefore, its procedure can be scaled over the huge variety of root morphologies in dicots and monocots. For these reasons, we contend that shovelomics represents a valued target for an automated high-throughput phenotyping approach in the field.We introduce an imaging approach for high-throughput phenotyping of mature root systems under realistic soil conditions to address the limitations of manual data collection (Fig. 2, I and II). Our new algorithms directly extract root architecture traits from excavated root images (Fig. 2, III and IV). Rapid imaging of the roots and decoupled analysis of the collected data at a later time improve both the throughput and synchronization of experiments. In addition to traits that are developed from shovelomics, our algorithms extract previously inaccessible traits such as root tip diameter, spatial distribution, and root tissue angle (RTA). Overall, our approach allows imaging directly at field sites with an easily reproducible imaging setup and involves no transportation cost or expensive hardware. We demonstrate the utility of our approach in the field-based analysis of maize and cowpea (Vigna unguiculata) architecture.Open in a separate windowFigure 2.I, Imaging board on the example of a maize root. The experiment tag is used to capture an experiment number, and the scale marker allows the correction of camera tilting and transforming image coordinates into metric units. II, Camera mounted on a tripod placed on top of the imaging board coated with blackboard paint. Note that images were taken with protection against direct sunlight not shown in the image. III, Example of the segmentation of the original image into a binary image and then into a series of image masks that serve as input to estimate traits for monocot and dicot roots. The sample is that of a maize root, 40 d after planting at the URBC. IV, The imaging pipeline for dicot roots and sparse monocot roots: Original image on the imaging board (a), derived distance map where the lighter gray level represents a larger diameter of the imaged object (b), medial axis includes loops (c), and loop RTP with a sample of the root branching structure (d). Colors are randomly assigned to each path. The sample is that of a cowpea root, approximately 30 d after planting at the URBC.     

Focus Issue on Roots: The Optimal Lateral Root Branching Density for Maize Depends on Nitrogen and Phosphorus Availability

Observed phenotypic variation in the lateral root branching density (LRBD) in maize (Zea mays) is large (1–41 cm⁻¹ major axis [i.e. brace, crown, seminal, and primary roots]), suggesting that LRBD has varying utility and tradeoffs in specific environments. Using the functional-structural plant model SimRoot, we simulated the three-dimensional development of maize root architectures with varying LRBD and quantified nitrate and phosphorus uptake, root competition, and whole-plant carbon balances in soils varying in the availability of these nutrients. Sparsely spaced (less than 7 branches cm⁻¹), long laterals were optimal for nitrate acquisition, while densely spaced (more than 9 branches cm⁻¹), short laterals were optimal for phosphorus acquisition. The nitrate results are mostly explained by the strong competition between lateral roots for nitrate, which causes increasing LRBD to decrease the uptake per unit root length, while the carbon budgets of the plant do not permit greater total root length (i.e. individual roots in the high-LRBD plants stay shorter). Competition and carbon limitations for growth play less of a role for phosphorus uptake, and consequently increasing LRBD results in greater root length and uptake. We conclude that the optimal LRBD depends on the relative availability of nitrate (a mobile soil resource) and phosphorus (an immobile soil resource) and is greater in environments with greater carbon fixation. The median LRBD reported in several field screens was 6 branches cm⁻¹, suggesting that most genotypes have an LRBD that balances the acquisition of both nutrients. LRBD merits additional investigation as a potential breeding target for greater nutrient acquisition.At least four major classes of plant roots can be distinguished based on the organ from which they originate: namely the seed, the shoot, the hypocotyl/mesocotyl, and other roots (Zobel and Waisel, 2010). The last class is lateral roots, which form in most plants the majority of the root length, but not necessarily of the root weight, as lateral roots have smaller diameter. Lateral roots start with the formation of lateral root primordia, closely behind the root tip of the parent root. These primordia undergo nine distinguishable steps, of which the last step is the emergence from the cortex of the parent root just behind the zone of elongation, usually only a few days after the first cell divisions that lead to their formation (Malamy and Benfey, 1997). However, not all primordia develop into lateral roots; some stay dormant (Dubrovsky et al., 2006), although dormancy of primordia may not occur in maize (Zea mays; Jordan et al., 1993; Ploshchinskaia et al., 2002). The final number of lateral roots is thereby dependent on the rate of primordia formation as well as the percentage of primordia that develop into lateral roots. This process of primordia formation and lateral root emergence is being studied intensively, including the genes that are activated during the different steps and the hormones regulating the process (López-Bucio et al., 2003; Dubrovsky et al., 2006; Osmont et al., 2007; Péret et al., 2009; Lavenus et al., 2013). Significant genotypic variation in the density of lateral roots has been observed, ranging from no lateral roots to 41 roots cm⁻¹ in maize (Trachsel et al., 2010; Lynch, 2013). This suggests that clear tradeoffs exist for the development of lateral roots and that these genotypes have preprogrammed growth patterns that are adaptive to specific environments. While some of the variation for lateral root branching density (LRBD) that has been observed across environments, for example by Trachsel et al. (2010), is constitutive, many genotypes have strong plasticity responses of LRBD to variations in soil fertility (Zhu et al., 2005a; Osmont et al., 2007). Both the nutrient and carbon status of the plant and the local nutrient environment of the (parent) root tip influence LRBD. Many studies have documented these plasticity responses, and others have tried to unravel parts of the sensing and signaling pathways that regulate LRBD. The utility of root proliferation into a nutrient patch has been studied and debated (Robinson et al., 1999; Hodge, 2004), but much less so the utility of having fewer or more branches across the whole root system. Our understanding of the adaptive significance of variation in LRBD among genotypes is thereby limited, with many studies not accounting for relevant tradeoffs. In this study, we integrate several functional aspects of LRBD with respect to nutrient acquisition, root competition, and internal resource costs and quantify these functional aspects using the functional structural plant model SimRoot. SimRoot simulates plant growth with explicit representation of root architecture in three dimensions (Fig. 1; Supplemental Movie S1). The model focuses on the resource acquisition by the root system and carbon fixation by the shoot while estimating the resource utilization and requirements by all the different organs.

Table I.

Minimum, maximum, and median LRBD in different populations phenotyped by various researchers at several locations in the worldLocations are as follows: D, Juelich, Germany; PA, State College, PA; and SA, Alma, South Africa. Some of the experiments included nutrient treatments: LN, low nitrogen availability; and LP, low phosphorus availability. Data were collected by counting the number of roots along a nodal root segment. Data were supplied by the person indicated under source: H.S., H. Schneider; L.Y., L. York; A.Z., A. Zhan; and J.P., J.A. Postma. WiDiv, Wisconsin Diversity panel; IBM, intermated B73 × Mo17; NAM, nested association mapping.

Hydrogen Photoproduction by Immobilized N2-Fixing Cyanobacteria: Understanding the Role of the Uptake Hydrogenase in the Long-Term Process

We have investigated two approaches to enhance and extend H₂ photoproduction yields in heterocystous, N₂-fixing cyanobacteria entrapped in thin alginate films. In the first approach, periodic CO₂ supplementation was provided to alginate-entrapped, N-deprived cells. N deprivation led to the inhibition of photosynthetic activity in vegetative cells and the attenuation of H₂ production over time. Our results demonstrated that alginate-entrapped ΔhupL cells were considerably more sensitive to high light intensity, N deficiency, and imbalances in C/N ratios than wild-type cells. In the second approach, Anabaena strain PCC 7120, its ΔhupL mutant, and Calothrix strain 336/3 films were supplemented with N₂ by periodic treatments of air, or air plus CO₂. These treatments restored the photosynthetic activity of the cells and led to a high level of H₂ production in Calothrix 336/3 and ΔhupL cells (except for the treatment air plus CO₂) but not in the Anabaena PCC 7120 strain (for which H₂ yields did not change after air treatments). The highest H₂ yield was obtained by the air treatment of ΔhupL cells. Notably, the supplementation of CO₂ under an air atmosphere led to prominent symptoms of N deficiency in the ΔhupL strain but not in the wild-type strain. We propose that uptake hydrogenase activity in heterocystous cyanobacteria not only supports nitrogenase activity by removing excess O₂ from heterocysts but also indirectly protects the photosynthetic apparatus of vegetative cells from photoinhibition, especially under stressful conditions that cause an imbalance in the C/N ratio in cells.     

Evaluating Bias of Illumina-Based Bacterial 16S rRNA Gene Profiles

Massively parallel sequencing of 16S rRNA genes enables the comparison of terrestrial, aquatic, and host-associated microbial communities with sufficient sequencing depth for robust assessments of both alpha and beta diversity. Establishing standardized protocols for the analysis of microbial communities is dependent on increasing the reproducibility of PCR-based molecular surveys by minimizing sources of methodological bias. In this study, we tested the effects of template concentration, pooling of PCR amplicons, and sample preparation/interlane sequencing on the reproducibility associated with paired-end Illumina sequencing of bacterial 16S rRNA genes. Using DNA extracts from soil and fecal samples as templates, we sequenced pooled amplicons and individual reactions for both high (5- to 10-ng) and low (0.1-ng) template concentrations. In addition, all experimental manipulations were repeated on two separate days and sequenced on two different Illumina MiSeq lanes. Although within-sample sequence profiles were highly consistent, template concentration had a significant impact on sample profile variability for most samples. Pooling of multiple PCR amplicons, sample preparation, and interlane variability did not influence sample sequence data significantly. This systematic analysis underlines the importance of optimizing template concentration in order to minimize variability in microbial-community surveys and indicates that the practice of pooling multiple PCR amplicons prior to sequencing contributes proportionally less to reducing bias in 16S rRNA gene surveys with next-generation sequencing.