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Nutrient-Rich Innovation: The Benefits of Hydroponic Crop Production

Nanoceria was found to be non toxic for Danio rerio embryos exposed up to 200 mg l−1 nanoceria during 72 h.Table S1† illustrates the diversity in the measured effect concentration of nanoceria. Even for a given species, the results varied widely between studies. For example, Lee et al. showed significant mortality of D. magna after 96 h of expo-sure to 1 mg l−1 of 15 and 30 nm nanoceria103 while no toxicity was measure in D. magma after the same duration at 10 mg l−1 or a 48 h exposure at 1000 mg l−1 nanoceria.Van Hoecke et al. exposed D. magna to higher concentrations of 14, 20, and 29 nm nanoceria for 21 days, and found an LC50 of approximately 40 mg l−1 for the two smaller particles and 71 mg l−1 for the 29 nm particles.When combining all aquatic toxicity data, including C. elegans , no trends were observed between the nanoparticle size and the toxicity. We observed one extreme value, which is a report of reduction in life span of C. elegans at a concentration of 0.172 μg L−1 . 92 Some have suggested that the toxicity at low concentration can be explained by differences in the aggregation state as a function of concentration. NPs may be less aggregated at lower concentration.105 However, the nanoceria used in this study were positively charged, coated with hexa-methyleneteramine . It is possible that this coating rendered nanoceria much more toxic. Another Fig. 1 depicts the median of the lowest observed effect concentration and the EC10 or LC10 toward different species. This figure illustrates the high variability of the observed LOEC/EC10 between studies for a same organism . Based on the LOEC/EC10, the more sensitive species is the cyanobacte-rium Anabaena, while the least sensitive is Daphnia magna. No toxicity was observed up to 5000 mg Ce/L for the zebrafish Danio rerio and Thamnocephalus platyurus Fig. 1. It is noteworthy that exposure models predict concentrations significantly lower than those for which ecotoxicity investigations have encountered toxic effects. Therefore, nanoceria might not have any impact at environmental concentrations,growing pot despite the fact that some results are more worrying. More-over, most of the nano-ecotoxicology performed on aquatic organisms used a single species or a short trophic links and do not take into account important parameters such as the colloidal destabilization of the nanoceria, their interactions with organic molecules/ particles naturally occurring or bio-excreted, or the flux between compartments of the ecosystems .

To work under more realistic scenario of exposure, few nano-ecotoxicological studies are now performed in aquatic mesocosms with low doses of nanoceria, chronic and long-term exposure. Such studies will allow obtaining reliable exposure and impact data and their integration into an environmental risk assessment model that is currently missing.Although the data on environmental effects are far from complete, it is useful to consider case studies in order to gain knowledge about key data gaps and to give a first impression of relative risks based on current knowledge. While this case study is useful to point out areas where research is most needed, it is critical to point out the limitations of this case study. First, nanoceria have not yet been detected or measured in environmental media, and the actual environmental concentrations are not known. Second, very little is known about the fate and transport of nanoceria in the environment. Third, the toxicity data base is still very limited. Only a select few ecological receptor species have been tested to date and few if any sub-chronic or chronic exposures have been performed in longer lived organisms or in environmentally realistic exposure scenarios. The following case study characterizes the likely exposure concentrations and compares them to toxicity values for soil and water based on emissions due to combustion of fuels containing nanoceria additives and for discharge of chemical mechanical planarization media into sanitary sewers.Based on Table S1,† with the exception of HMT coated nanoceria, which do not apply to this case study and for which coating controls are lacking, the lowest EC10 value measured so far is 8000 ng l−1 for luminescence inhibition in cyano-bacteria.Previous estimates have been made for nanoceria used as a fuel catalyst and arriving in soil and water following atmospheric discharge106 in the UK based on known market size for this product. Clearly there is a wide disparity between concentrations likely to occur due to fuel catalyst combustion106 and the lowest toxicity values observed so far . However, there remains concern that nanoceria may enter water courses through its uses in specialized industrial polishing or chemical/mechanical planarization.Without specialized local knowledge on where these industrial concerns are located, the quantities of nanoceria used, that are disposed of from the premises, and the capacity of the associated sewage treatment plant, the local receiving water concentrations cannot be predicted.

Unfortunately, knowing global or national consumption of nanoceria in the polishing industry would not allow us to predict water concentrations. This is because the use of the product would not be evenly geographically spaced, or linked directly to human population density. However, it is possible to ask: what discharge would be needed to exceed the 8000 ng L−1 toxicity threshold for aquatic exposures? The dilution factor for sewage effluent recommended by EU risk assessment is 10. So effluent would need to contain 80 μg L−1 nanoceria. However, it is estimated that on entering an WWTP 95% of the nanoceria would enter sludge and only 5% pass through into the effluent.In that case the influent concentration would need to be 1.6 mg l−1 nanoceria. WWTPs are designed around population equivalents which tend to be around 160–200 L per PE per day in the UK so a PE unit would need to discharge 256–320 mg Ce per day to receiving waters. Given the current uses of nanoceria, this only seems likely to occur if a large industrial facility is directly discharging wastewater containing high concentrations of nanoceria directly into a sanitary sewer. Note that a population equivalent is a unit describing a given biodegradable load as measured by its biological oxygen demand.We have comprehensively reviewed what is known for nano-ceria about the environmental releases, methods for detection and characterization, fate and transport, toxicity and likelihood of toxicity in soil and water from acute exposures. Initial estimates of releases suggest that the majority of nanoceria will ultimately end up in landfills, with lesser amounts emitted to air, soil and water in that order. Once nanoceria enters the environment, it has been shown that NOM will have a major impact on their fate, transport and toxicity. As with other nanomaterials, aggregation is a key consideration and this has been shown to be influenced by water chemistry and interactions with natural coatings such as NOM. An important feature of nanoceria with respect to its behavior and toxicity is its valence state. There are several techniques that can characterize this property in environmental and bio-logical media, such as XAS, but most require relatively high concentrations. While we didn’t identify studies that detected nanoceria in natural environments or environmental media, a suite of techniques have been used to detect and character-ize them in complex toxicity testing media and in controlled laboratory studies. Thus, a major data gap and area for future research is the prediction and measurement of actual nano-ceria concentrations in the environment, either from point sources or non-point sources.

As a whole nanoceria appears to exhibit similar aquatic toxicity values other commonly studied manufactured nano-materials. For example,square pot a recent review found that species average LC50 values for Ag nanoparticles ranged from 0.01 mg L−1 to 40 mg L−1 while species mean LC50 values for ZnO ranged from 0.1–500 mg L−1 . 116 The range of EC50 values reported for Ce are similar to those for ZnO. Although reported toxicity data here uses LC10 and LOEC values, the range of species means 0.05–25.9 mg L−1 and many of the reported LC50 values are within the range of 0.1–100 mg L−1 , suggesting similar acute toxicity to ZnO NPs in aquatic expo-sures. This is of course based on the available data, which are predominantly on the toxicity of nanoceria to aquatic organisms, with sediment and terrestrial organism data severely lacking. For example, few if any studies have investi-gated toxicity in sediment dwelling organisms, which are likely to be exposed to nanoceria in the aquatic environment due to aggregation, settling and accumulation of nanoceria in sediment. Given the persistence of nanoceria, chronic studies are lacking as we are aware of only the C. elegans study.Equally important, very few species from few taxonomic groups have been tested. Large taxonomic groups such as insects and gastropods have not been tested and only one non-mammalian vertebrate spe-cies has been tested . Another difficulty is that most of the studies were performed with different nano-particles, doses, duration, organisms, exposure media, and their results are not directly comparable. Perhaps due to these differences, there are no apparent patterns to suggest that, as a whole, particle size has a major impact on toxicity. A problem in conducting realistic toxicity studies is the likely transformation of the free particles into homo or hetero-aggregates or even organic complexes in the real environment. There have been few studies that investigated the impact of size across a wide range of systematically varied particle sizes within a single study. Such studies are needed to definitively establish weather size is important. On the other hand coating may be an important variable given the extreme sensitivity seen with HMT coated particles in C. elegans. Coating was demonstrated to be a major determinant of toxicity in a more well controlled study that systematically varied coating properties and used coating controls.2 Of all of the taxonomic groups, toxicity is most well studied in vascular terrestrial plants. Overt phytoxicity of nano-ceria seems minimal and, while root to shoot translocation of these particles is often measurable it is generally quite low. In summary, although the literature on nanoceria impacts on terrestrial plants is not extensive, it is clear that overt phytotoxicity is minimal, even at excessive exposure concentrations. The data do suggest accumulation of nano-ceria within plant tissues, although the precise form of the element that crosses into the plant and the mechanism driving that process remains unknown. The potential trans-generational effects noted in the literature,as well as the complete lack of information on trophic transfer, are areas of concern. In addition, studies investigating environmentally relevant concentrations, potentially secondary effects from nanoceria exposure, including impacts on symbiotic micro-organisms or on edible tissue nutritional quality, certainly warrant further investigation. As a whole, the aquatic and terrestrial toxicity testing data for animals and microorganisms spans multiple orders of magnitude for acute toxicity values . This large variation can be exhibited within a single species exposed to similar nanoceria. For example, toxicity values for D. magna range from around 1–100 mg l−1 for fairly similar particles. Based on the overall toxicity database, it appears that C. elegans is the most sensitive animal and Anabaena is the most sensitive microorganism tested to date, although an important caveat is that the same endpoints were not com-pared across all species and that exposure systems varied. Interestingly no toxicity was observed in the fish species that has been tested even at extremely high exposure concentrations . Unfortunately, only two fish studies have been reported in the literature. There is a complete lack of toxicity testing data for sediment dwelling organisms, and extremely limited data for soil invertebrates. As a whole the data suggest that acute toxicity is possible at low μg L−1 concentrations in the water column. Data are lacking on soils and sediments, but toxicity values are likely to be far lower. One study indicated toxicity at lower concentrations than these values when 8 nm nanoceria were coated with HMT. Since no coating controls were used, it is critical that the influence of this coating and other similar positively charged coatings be studied using a similar end-point and suitable controls. The use and disposal of any nanoceria containing products with this coating should also be evaluated. It is not clear whether the chronic nature of this exposure or the influence of the coating on uptake and toxicity explain why this toxicity threshold is so low.

Productivity was reported in amount per area with most crops reporting tons per acre

As a pixel is made up of the sum of its fractional surface components, we assume that the temperature of a pixel can be modeled by a linear mixture of its thermal components, that is, the sum of the LST for each of those components multiplied by their fractional portion of the pixel. To capture thermal variability within surface covers, each of the three components is broken down into sub-classes that are expected to share similar thermal properties, referred to as thermal classes going forward. These thermal classes resulted in each of the three surface covers having more than one thermal endmember, one for each thermal class. The endmembers that were used to model the expected temperature of each pixel were determined by the classes that were contained in that given pixel. To further evaluate crop-specific patterns of LST, we tested two hypotheses: 1) Crops with higher LST residuals, on average, will show declining yields over the study period, as would be indicative of stress; and 2) Crops with higher ET rates will shed more energy through latent heat flux and therefore have lower average LST values than crops with lower ET rates. To test the first hypothesis, yield data were obtained at the county level from the four counties that were part of the study area using annual agricultural statistics reports .The overall productivity for each crop type was calculated using an average of the county statistics, weighed by the relative area of that crop in each county. Because yield data are not available at the field-scale, county-level statistics were the closest proxy of productivity in the study area that could be obtained. Therefore, while the yield data and crop LST residuals are not directly relatable since the residuals only refer to a spatial subset of what is reported by the yield data,growing blackberries in containers the yield data is expected to give a general sense of which crops were faring well and which were most stressed within the study area.

To test the second hypothesis, we evaluated the correlation between average crop LST and the daily ET rate of each crop. ET rates were calculated as the product of the daily reference ET, as reported by the Belridge CIMIS station for each of the three dates, and the crop coefficient for each of the studied species, as calculated for June in the Southern San Joaquin Valley of California in a dry year . An evaluation of mean crop temperatures of pure pixels of each species by year showed that the temperature of each species relative to one another did not deviate greatly from year to year . The almonds had one of the top two coolest mean temperatures in each of the three years. The three citrus species, orange, lemons and tangerines, consistently had the three highest temperatures in each year. Cherries always had the highest average temperature of any crop except citrus. Every crop showed its highest mean temperature in 2014, likely attributable to the later flight time. The consistency suggests that thermal patterns are indicative of core biophysical properties, physiological properties, or irrigation practices that stay constant and allow for detailed analysis between species across time.Crops with higher residuals showed warmer measured temperatures than would be expected while crops with low residuals showed cooler temperatures than expected. High residuals are assumed indicative of stress. On average, crop residuals increased from 2013 to 2015 with average residuals of 0.14, 0.97, and 1.1 °C respectively. This positive year-to year trend of residuals indicates an increase in relative stress from the 2013 scene to the 2015 scene. This trend may be indicative of larger environmental and political consequences of the progressing drought with increased stress due to reduced irrigation and increased water restrictions. Alternately, the increase in relative stress could be resultant from more local scene and date-specific factors such as irrigation timing, differences in radiation load, or vapor pressure deficit.Fig 3.11 illustrates that the species-level trends in crop productivity from 2013-2015, as measured by yield per unit area, were captured well by the LST residual data. The percentage change in yield per unit area from 2013 to 2015 was compared with the average residual for each crop over all three years. We expected crops with higher LST residuals to have greater declines in yields, as would be the result of stressed vegetation.

Cherries and pistachios both showed the highest residuals and the largest declines in yields, a result that supported our hypothesis that high temperature residuals indicate unhealthy crops. Crops with the lowest residuals were hypothesized to be the least-stressed and therefore expected to have a relatively stable yield or an increase in yield. The crops with the lowest residuals did not have the largest increases in yield, however there was general agreement between the two trends overall with an inverse relationship apparent. While between-crop residual and yield data from 2013-2015 showed agreement, within-crop changes in residuals from year to year did not correlate with within-crop changes in yields. For example, both the average residual and the average yield of pistachio trees declined from 2013 to 2014, changes in stress that are opposite in implication. This suggests that this method is more suitable for comparing relative stress between crops than comparing stress of one crop over time .We calculated an expected LST for each pixel as a function of its fractional cover of soil, NPV, and GV and the expected temperature for the thermal classes contained within it. Although deviations from this relationship were presumed to indicate relative levels of plant stress, there may have been other factors that contributed to the deviations from the expected GV/LST pattern. For full interpretation of the residual results, the effect of various factors on the modeled, expected LST will be discussed: a) non-linearity of GV fraction estimation, b) shade effects, c) plant stress, d) error in fractional cover estimates, e) timing of flights, f) spatial variability in environmental variables and g) choice of thermal groups. First, expected LST is estimated using pixel fractions derived from MESMA, a linear spectral mixture model. However, in actuality spectral mixing is nonlinear due to multiple scattering of photons . This effect is expected to be prominent in agricultural orchards due to the vertical structure of the canopy, density of trees, and transmittance of radiation through the leaves . As shown in Somers et al., , tree-soil mixtures within a citrus orchard canopy as modeled by a linear mixture analysis will lead to an underestimation of GV for < 50% GV cover and an overestimation of green vegetation when GV cover is >50%.

These errors will likely be smaller with dark soils than bright soils because there are fewer photons reflected by darker objects . Nonlinearity can result in RMSE values of between 4 and 10% in citrus orchards for cover fractions . This error in GV fraction will lead the LST model to overestimate temperatures when pixels contain less than 50% GV and underestimate temperatures when the GV fraction exceeds 50% . Subsequently, pixels with low GV fraction will overestimate temperature, reducing the residual, while pixels with a high GV fraction will underestimate temperature,square pot increasing the negative residual. However, the errors due to multiple scattering in this study are expected to be low because canopy endmembers were used in the linear unmixing and these endmember already capture multiple scattering. Second, just as the linear spectral mixture does not account for photon interactions when estimating fractional cover, the linear thermal model used to model LST is also subject to nonlinear effects. Shade will cause error in soil temperature estimation that can lead to an overestimation of soil temperatures in mixed pixels. Thermal soil endmembers for the model were calculated based on the average temperature for pure soil pixels. A pure soil pixel is unlikely to be influenced by shadows, and its temperature will be a function of full solar radiation. However, as vegetation cover increases in a pixel, a larger percentage of the present soil will be shaded, up until the vegetation fraction reaches 100% and the effect cancels out . Shaded soil would be expected to be cooler than non-shaded soil, therefore the soil endmembers that are being used to model the soil temperature will be warmer than the actual shaded soil in mixed pixels. This will lead the temperatures of mixed pixels to be modeled as too warm, and the corresponding residuals to be too low. Similarly, vegetation is subject to shading effects as well as differences in structure and orientation that influence LST. Jones et al. found that leaf temperatures vary by as much as 15°C between full sun and deep shade. Therefore, factors such as the orientation of the leaves, canopy structure, and row spacing are all important controls on plant temperatures as they influence the amount of vegetation in a field that is shaded. These factors also affect the surface aerodynamic roughness, which governs how readily vegetation can transfer heat and moisture to the atmosphere. The height and structure of a crop canopy determines its aerodynamic roughness, with rougher vegetation being more tightly coupled to the atmospheric moisture deficit, which increases plant ET and decreases canopy temperature . In an aerodynamically rougher crop canopy, heat is also more readily transferred to the atmosphere by sensible heat flux. For these reasons, the remotely sensed surface temperature depends not only on the fractional cover of a pixel, but also on the composition of vegetation within a pixel. Two pixels with the same fractional cover of vegetation can have different thermal behaviors due to differences in the distribution of that vegetation, its height, and structure . The model aims to account for these influences by using canopy-level image endmembers and creating multiple thermal classes for different groups of perennial crops, so the overall error attributable to canopy shading is assumed to be small. Third, plant stress will alter the GV/LST relationship in a way that, while not introducing error, will lead LST residuals to vary by GV fraction. If a plant is stressed, its actual temperature will be warmer than expected, leading to a positive residual. While the model is designed for such a result, the side effect is that pixels with larger fractions of stressed vegetation will have higher residuals than pixels that have small fractions of stressed vegetation, as indicated by the increasing LST residuals with GV fractions in Fig 3.13C. Therefore, if plants are stressed, we expect that GV fraction and LST residual will have a positive correlation. We examined the relationship between LST residual and GV fraction for each of the studied crops in Figure 3.13 and found a trend of increasing residuals with increasing fractional cover, a result that we believe is indicative of crop stress. The relationship between residuals and GV fraction is shown by the positive linear trend lines in Figure 3.14 and the growing shaded area with fractional cover between the modeled and observed lines in Figure 3.13C. Fourth, an under or over estimation of fractional cover will propagate into LST residual errors; however, we do not believe that the distribution of errors will change the robustness of the results. Given mean LST values of 306.3 K, 321.3 K and 326.6 K for GV, NPV and soil respectively over all years and within the fields studied, the largest LST residual errors would result from a fraction error between soil and GV. MESMA has proven high fractional estimation accuracy for green vegetation. When looking at spectral separability between turfgrass, tree, paved, roof, soil, and NPV, Wetherley et al. found that mixtures of tree/soil were the second most separable pair after turfgrass/soil. Using synthetic mixtures, this study observed that soil, when mixed with tree, had a fractional accuracy of 0.976 while tree, when mixed with soil, had a fractional accuracy of 0.896. Therefore, we believe that fraction errors between GV and soil will be less than 10%. Furthermore, partitioning the landscape into soil and green vegetation is a necessary step in estimating crop stress and water use, and is therefore included in comparable models such as the VHI and WDI.

Cell identity in the SAM is thus largely an issue of location rather than its developmental history

The astute student however, will note that this interpretation is not quite universal, as some researchers further postulate the existence of a fourth “Organizing Center” inserted between the CZ and RM tissues. Although not shown in Figure 2.0, the OC is equivalent to the rounded apex of L3, pushing the remaining part of the RM somewhat deeper into the stem. Until better genetic evidence is available though, only CZ, PZ, and RM will be used for the remainder of this dissertation. While the numbering system shown in Figure 12 does provide a useful set of spatial coordinates, it is also somewhat misleading as it implies that the SAM is static structure, unchanging over time. This could not be further from the truth. Instead, it must be remembered that the SAM is a site of plant growth, and as a result its cells are in a state of constant flux as they divide, grow, and differentiate. For example, the repeated perpendicular divisions that occur in L1 and L2 actually cause these layers to expand sideways, where the displaced cells eventually bend around the curve of the apical dome and become part of the cylindrical stem surface. The motion is reminiscent of the path taken by water droplets in an umbrella-shaped fountain, though the individual plant cells move considerably slower. If growth by lateral displacement is followed to its logical extremes, it is important to note that all of the founding cells will be pushed off to the sides over time, while new ones take their place in the middle. No single cell in the SAM is a permanent resident. The overall shape and size of an SAM is perhaps more analogous to a standing wave,growing pot where stability is the illusion caused by a dynamic equilibrium. Maintaining that wave is of course a difficult challenge, as the inputs to that equilibrium must be precisely matched to its outputs at all times.

Failure todo so would quickly rob the plant of its ability to grow, with obvious consequences for survival. Exactly how this balance is maintained is not fully understood, but the motion of the cells makes at least one part of the process perfectly clear: the cells must change their identity as they are moved from one place to another. Those that start in the CZ for example, switch to PZ gene expression patterns as they move further away from the middle, and may later adopt leaf and flower identities as they are incorporated into mature organs.The ability of a cell to determine its location within the SAM structure is thus of paramount importance, yet it must do so in the absence of any stationary reference point. So far as currently understood, each cell solves this problem in exactly the same way a person would do so: it talks to its neighbors. Based on what the individual cell sees and what its neighbors report seeing, it is possible to work out exactly where the cell is located in the overall plant structure. Of course in actual plant tissues such communication occurs largely through to the exchange of proteins, hormones, and RNA molecules, though increasingly evidence suggests that mechanical forces in the cell wall may also contribute some information [2]. Some molecules can travel further distances than others, some are modified en-route in order to become functional, and still others move from cell to cell in precise patterns, much like the knight in a game of chess. When these molecules are produced in different areas of the plant, the surrounding cells can estimate their relative locations to each other simply by reading the chemical bar-code in their local milieu, and then develop accordingly. At the present time, only a few such routes of chemical communication have been identified, two of which are plant hormones: auxin and cytokinin. Auxin is best known for increasing the volume of cells, though it also has roles in apical dominance and tropism growth patterns. Cytokinin meanwhile is known for stimulating cell division, in addition to other roles in senescence and pathogen responses.

Together the function of the two hormones would appear to complement each other very well in terms of overall growth, yet within the SAM they appear to mix about as well as oil and water. Cells that respond to auxin often don’t respond to cytokinin, and vice versa. Why this should be so is not well understood, but studies of root vasculature development suggest that their mutual exclusion is actually used to generate spontaneous patterns that help guide plant development. In callus tissue, the two hormones are often found to have response patterns arranged in a polka-dot like arrays, where each hormone “dot” is surrounded by a circular field belonging to the other. The SAM is organized around a single such dot, where cytokinin responses occur in the RM, and auxin responses occur in the PZ which often occur in discrete foci corresponding to new lateral organs. The CZ cells in contrast, do not appear to be sensitive to either hormone, but instead express both auxin and cytokinin biosynthesis genes . The production of cytokinin in the L1 and L2 is also consistent with the distribution of bioactive cytokinin concentrations observed with immunological techniques and with GFP reporter systems. This suggests a stable arrangement of three mutual exclusion zones within the SAM, which closely correspond to the known CZ, PZ, and RM tissues. Root apical meristems in contrast, appear to be based on the reciprocal arrangement, as roots have an auxin response dot in the middle surrounded by cytokinin responses in the overarching root cap, concentrated in the root cap columella cells.Another potential communication system that has been extensively studied involves a potential feedback loop between the CZ and RM cells, thought to be carried out by WUS and CLV3. WUS is a homeodomain transcription factor produced exclusively within the RM, but is capable of moving 2-5 cell diameters away from its center of origin.

WUS has also been shown to activate transcription of CLAVATA3 in the overlying CZ cells by directly binding to the CLV3 promoter. CLV3 in turn, is thought to be a small secreted oligopeptide that is modified with a few arabinose sugars. The mature glycoprotein then travels through the apoplast to reach leucine-rich receptor kinases in the RM, such as CLV1 or BARELY ANY MERISTEM1, thereby triggering a signaling cascade that ultimately suppresses WUS transcription. Many of the intermediate biochemical steps however, have not yet been fully identified, which makes it difficult to fully reject the feedback loop null hypothesis. There is also evidence of a more complex set of feedback loops, as WUS has been found to regulate components of the cytokinin signal transduction pathway , and exogenous cytokinin are able to stimulate WUS transcription. Altered cytokinin signalling pathways have also been shown to affect CLV3 expression patterns. WUSCHEL-LIKE HOMEOBOX5 , which is closely related to WUS, is known to participate in auxin pathways within the root, while the generation of SAMs from callus or root tissue has repeatedly been shown to require a pre-incubation on auxin rich media, where it may actually stimulate auxin transport . Micro RNA molecules may also be involved, as a variant of AUXIN RESPONSE FACTOR 10 that was resistant to miR160a was able to increase WUS and CLV3 expression patterns. Clearly, there is a lot going on. To help clarify how such cross-talk contributes to SAM structure, the research presented in this dissertation explores two closely related subjects. The first is the regulation of CLV3, which was studied by resolving the promoter structure of this gene in chapter 3. The results suggest that CLV3 is regulated in part by auxin responses,square pot while activation and/or repression is likely to be controlled complicated set of cis-motifs in the 3’ enhancer region. The presence of these 3’ motifs in a known transposon also suggests a novel origin of the WUS/CLV3 feedback loop. Chapter 4 meanwhile, explores the possibility that WUS and cytokinin responses form a second feedback loop necessary for SAM structure. This was done by narrowing down the possible cellular and biochemical routes by which cytokinin could affect WUS transcription, translation, and protein movement. The results however, suggest that the two pathways are atlargely independent of each other, though cytokinin responses may increase WUS stability in the RM. Unexpectedly, the data also found that the absence of cytokinin responses in the CZ is a critical part of SAM structure. The cytokinin response-free cells were also found to have an enhanced protein degradation mechanism, which may help shape the WUS protein gradient. Interestingly, WUS proteins were found to be rapidly degraded following auxin treatments, suggesting a model in which the SAM structure is defined by cytokinin-induced stability in the RM, and auxin-induced protein degradation in the surrounding CZ and PZ cells.The WUS-CLV3 feedback loop has long been an attractive model to explain how SAM structure is maintained in a dynamically changing cellular environment. Simply by combining activation of CLV3 with the repression of WUS, computer simulations have repeatedly shown that this is sufficient to maintain constant population of cells with CZ and RM identity. However, despite the simplicity of this model, the molecular mechanisms that carry out the feedback loop have instead revealed a number of potential complications. On the forward path for example, WUS is known to be a bi-functional transcription factor, activating and repressing several hundred different target genes.

Currently it is not currently known exactly how WUS switches from activator to repressor, but it has been shown to directly bind to DNA motifs in AGAMOUS and CLV3 regulatory regions, where it activates their transcription. Additional binding sites on repressed targets such as KANADI1, YABBY3, ASYMMETRIC LEAVES2 have also been identifie. Complicating this model of is the observation that CLV3 activation requires both WUS and SHOOTMERISTEMLESS in leaf tissues, suggesting that the presence of WUS alone is not sufficient. In addition WUS has also found to directly interact with the GRAS domain transcription factor HAMl, as well as the potent transcriptional repressor TOPLESS. TPL itself further has been shown to assemble a protein complex with Sin3 ASSOCIATED PROTEIN and HISTONE DEACETYLASE 19 [49, 50], suggesting a potential link between WUS and chromatin modification. In order to discriminate between the two models, this study began by attempting to identify the cis-regulatory environment around the CLV3 locus. The CLV3 expression pattern was firstcarefully recorded with a GFP reporter, which in contrast to previously published RNA in-situ’s, found layer-specific differences in CLV3 transcriptional output. The regulatory regions of CLV3 were then annotated by mapping predicted transcription factor binding sites and computationally significant cis-motifs, which were further resolved with phylogenetic footprinting. This analysis found that CLV3 has a very simple 5’ promoter, containing an auxin responsive element, suggestive of ubiquitous expression. The 3’ enhancer in contrast, contained at least 3 large cis-regulatory modules, two of which were found within a naturally occurring transposon, while the 3rd included several known WUS binding sites. On the basis of promoter deletion experiments, all three cis-regulatory modules were found to be required for CLV3 activation. The existence of the transposon in turn, has several implications for the evolution of the WUS-CLV3 feedback loop and Brassicaceae plant anatomy. Previous reports of the CLV3 expression pattern have consistently found it localized to the apex of the SAM, where it is often used as an indicator of CZ cell identity. Within this region, the expression pattern is somewhat variable, as previous RNA in-situ revealed a narrow inverted cone-shape, while GFP and GUS reporters often produce more indistinct rounded shape 3-4 cell layers deep. In contrast, the present study found a slightly more complex pattern when viewed as a longitudinal section. In perfectly centered sections, the pCLV3:mGFP5-ER reporter often appears in an inverted cone shape, but the expression zone is noticeably broader than the previous RNA in-situ results . As the section plane is displaced from the central axis and becomes more tangential, a conspicuous gap is frequently visible, where the L2 cells have less fluorescence than those immediately above and below. This suggests a bi-partite expression pattern where a flat, circular domain occurs specifically in the L1, and a second spherical domain occurs underneath in the L3 cells . In order to identify the CLV3 regulatory structure, this work began by annotating all known regulatory motifs on an 8kb genomic sequence centered on At2g27250.