Positive And Negative Regulation Of Defense Responses Against Pseudomonas Syringae In Arabidopsis

University of Minnesota Ph.D. dissertation. March 2014. Major: Plant Biological Sciences. Advisor: Jane Glazebrook. 1 computer file (PDF); vi, 147 pages.Immune signaling in plants involves both positive and negative regulators. Maintaining a balance between growth and defense responses is important because there is a fitness cost to the plants if immune responses are left unchecked. Suppression of immune responses in the absence of pathogens as well as after the threat has passed is critical in maintaining such a balance between growth and defense responses. Upon pathogen perception, the positive regulators counter the immune repression to induce defense responses. We investigated the roles of two genes, CBP60a and PCRK1 in the regulation of defense responses against Pseudomonas syringae pathogen in the model system Arabidopsis thaliana . CBP60a is a negative regulator of immune responses. We showed that CBP60a is a CaM binding protein and that CaM binding is important for its function in transducing defense signals. Mutants of CBP60a were more resistant to Pseudomonas syringae infection suggesting that CBP60a was a negative regulator of defense responses. We found that CBP60a functions in repressing immune signaling under conditions where the plants are not challenged by a pathogen. We also investigated the role of a putative kinase, PCRK1, in immune signaling. We showed that pcrk1 mutants are more susceptible to Pseudomonas syringae than wild type plants suggesting that PCRK1 has a positive role in immune responses. We also showed that PCRK1 is important for immunity triggered by some of the conserved Microbe Associated Molecular Patterns (MAMP) as well endogenous signals generated as a result of pathogen activity called Damage Associated Molecular Patterns (DAMP)

Branch angle and leaflet shape are associated with canopy coverage in soybean

Abstract Early canopy coverage is a desirable trait that is a major determinant of yield in soybean (Glycine max). Variation in traits comprising shoot architecture can influence canopy coverage, canopy light interception, canopy‐level photosynthesis, and source‐sink partitioning efficiency. However, little is known about the extent of phenotypic diversity of shoot architecture traits and their genetic control in soybean. Thus, we sought to understand the contribution of shoot architecture traits to canopy coverage and to determine the genetic control of these traits. We examined the natural variation for shoot architecture traits in a set of 399 diverse maturity group I soybean (SoyMGI) accessions to identify relationships between traits, and to identify loci that are associated with canopy coverage and shoot architecture traits. Canopy coverage was correlated with branch angle, number of branches, plant height, and leaf shape. Using previously collected 50K single nucleotide polymorphism data, we identified quantitative trait locus (QTL) associated with branch angle, number of branches, branch density, leaflet shape, days to flowering, maturity, plant height, number of nodes, and stem termination. In many cases, QTL intervals overlapped with previously described genes or QTL. We also found QTL associated with branch angle and leaflet shape located on chromosomes 19 and 4, respectively, and these QTL overlapped with QTL associated with canopy coverage, suggesting the importance of branch angle and leaflet shape in determining canopy coverage. Our results highlight the role individual architecture traits play in canopy coverage and contribute information on their genetic control that could help facilitate future efforts in their genetic manipulation

Additional file 1 of Variation in shoot architecture traits and their relationship to canopy coverage and light interception in soybean (Glycine max)

Supplementary Material 1: Fig S1: Canopy coverage of all the accessions in the study over the planting season in 2018 and 2019 modeled by logistic regression. All the accessions included in the study are represented as individual logistic regression lines. Fig S2: Variation between different genotypes in all traits measured in the study in years 2018 and 2019. Averages for each trait was calculated at plot level in 2018 and 2019 and plotted as bar graph. Traits shown are canopy coverage (CC) traits: average canopy coverage (ACC), days to 50% canopy coverage (CC50), canopy coverage at R2 (CCR2), max growth rate (%/week) (MCC_w) and max growth rate (%/day) (MCC_d); light interception (LI) traits: photosynthetically active radiation at 50 % plant height (PAR50H); plant height at 50% photosynthetically active radiation (H50PAR) and photosynthetically active radiation at Ground (PARG); plant shape parameters: maximum height normalized (Sh_H) , maximum width relative to height (Sh_W) and area under the curve (Sh_A); shoot architecture traits: node number (NO), branch number (BN), branching zone (BZ), branching ratio (BR), branch angle (BA), branching density (BD), branching orientation (BO), leaf length (LL), leaf width (LW), leaf area (LA), petiole length at node 4 (PL4), petiole slope at node 4 (PS4), petiole angle node 4 (PA4), internode length at node 4 (IL4) and internode slope at node 4 (IS4). Error bars are standard deviations from mean. Fig S3: Logistic function was used to model the light interception in different accessions: (a) The PAR values shown as relative light intensity measured along every 10 cm increment from bottom to top the plants in a row (Y axis) was plotted against the height of plant, normalized to 1 m height to account for variation in height between accessions (X axis). Logistic fit for each rep (different colored lines) as well as the mean fit (black solid line) are shown for each accession. 95% confidence interval for the fit is indicated (black dashed line). (b) The PAR data was converted to absorbance and plotted against normalized height for each accession. A logistic function was fit to the data. The mean values for the fit for each accession is shown as black solid line with the black dotted lines showing the 95% confidence intervals for the fit. Fig S4: Beta distribution function was used to express the shape of soybean plants: Width of the plant along the length of the plant was measured at 0, 12.5 25 37.5, 50, 65.5, 75, 87.5 and 100 % height of the plant. A beta distribution function was used to fit the width data to approximate the shape of the plant. Outcome of a beta distribution fit for each accession in the study is shown. The mean values for the fit for each plant in an accession is shown as black solid line with the black dotted lines showing the 95% confidence intervals for the fit. From the fit, the overall shape could be described in terms of three parameters: peak height (Sh_H), area under the curve (Sh_A), width scaling factor (Sh_W). Fig S5: An illustration depicting the assumptions made to simplify the CO2 assimilation rate estimation. (i) Plant row is approximated by a prism-like shape with the cross section outline of the outline shape model based on beta distribution with the three parameters peak height (Sh_H), area under the curve (Sh_A) and width scaling factor (Sh_W); (ii) the top-facing surface of the prism represents the photosynthetically active tissue shown in green (iii) the light comes from strait up shown as yellow arrows. Fig S6: Length of internodes and petioles from the top 4 nodes on the main stem of select accessions: a) Length of internodes were measured from the top four nodes of the accessions 5R22C11D, 5R14C48D, Bert and PI612732 and plotted on the y-axis. A linear regression was used to model the internode lengths for each accession and a slope was fitted based on the regression. The slopes for each accession are shown as a bar graph. Upper panel shows results from 2018 and the lower panel from 2019. On the right, pictures indicate the shape of two selected accessions showing most differences in their internode slope (IS4) values displaying variation for the shape at the top of the plant (indicated as yellow curves). b) Length of petioles were measured from the top four nodes of the accessions 5R22C11D, 5R14C48D, Bert and PI612732 and plotted on the y-axis. A linear regression was used to model the petiole lengths for each accession and a slope was fitted based on the regression. The slopes for each accession are shown as a bar graph. The upper panel shows results from 2018 and the lower panel from 2019. Pictures on the right show the petiole and internode images from the top of the plant. The petiole slope was derived from the top four petioles (PS4) and is indicated with a yellow arrow. Fig S7: Imaging for branch angle measurements. Junction of every primary branch on the main stem was imaged using a blue strip background to show the angle of branching. Each image was captured with the camera placed at a 90 degree angle to the blue background to avoid parallax error. Image J was used to measure the angle and average branch angle per plant was calculate