Evaluation of SmartStax and SmartStax PRO maize against western corn rootworm and northern corn rootworm: efficacy and resistance management.

BACKGROUND: Cases of western corn rootworm (WCR) field-evolved resistance to Cry3Bb1 and other corn rootworm (CRW) control traits have been reported. Pyramid products expressing multiple CRW traits can delay resistance compared to single trait products. We used field studies to assess the pyramid CRW corn products, SmartStax (expressing Cry3Bb1 and Cry34Ab1/Cry35Ab1) and SmartStax PRO (expressing Cry3Bb1, Cry34Ab1/Cry35Ab1 and DvSnf7), at locations with high WCR densities and possible Cry3Bb1 resistance, and to assess the reduction in adult emergence attributable to DvSnf7 and other traits. Insect resistance models were used to assess durability of SmartStax and SmartStax PRO to WCR resistance. RESULTS: SmartStax significantly reduced root injury compared to non-CRW-trait controls at all but one location with measurable WCR pressure, while SmartStax PRO significantly reduced root injury at all locations, despite evidence of Cry3Bb1 resistance at some locations. The advantage of SmartStax PRO over SmartStax in reducing root damage was positively correlated with root damage on non-CRW-trait controls. DvSnf7 was estimated to reduce WCR emergence by approximately 80-95%, which modeling indicated will improve durability of Cry3Bb1 and Cry34Ab1/Cry35Ab1 compared to SmartStax. CONCLUSION: The addition of DvSnf7 in SmartStax PRO can reduce root damage under high WCR densities and prolong Cry3Bb1 and Cry34Ab1/Cry35Ab1 durability. © 2017 Society of Chemical Industry.

Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data.

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

Automated Microarray Image Analysis Toolbox for MATLAB.

UNLABELLED: The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIA's capabilities. AVAILABILITY: The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions. CONTACT: amanda.white@pnl.gov

Comparison of fluorescence microscopy and solid-phase cytometry methods for counting bacteria in water.

Total direct counts of bacterial abundance are central in assessing the biomass and bacteriological quality of water in ecological and industrial applications. Several factors have been identified that contribute to the variability in bacterial abundance counts when using fluorescent microscopy, the most significant of which is retaining an adequate number of cells per filter to ensure an acceptable level of statistical confidence in the resulting data. Previous studies that have assessed the components of total-direct-count methods that contribute to this variance have attempted to maintain a bacterial cell abundance value per filter of approximately 10(6) cells filter(-1). In this study we have established the lower limit for the number of bacterial cells per filter at which the statistical reliability of the abundance estimate is no longer acceptable. Our results indicate that when the numbers of bacterial cells per filter were progressively reduced below 10(5), the microscopic methods increasingly overestimated the true bacterial abundance (range, 15.0 to 99.3%). The solid-phase cytometer only slightly overestimated the true bacterial abundances and was more consistent over the same range of bacterial abundances per filter (range, 8.9 to 12.5%). The solid-phase cytometer method for conducting total direct counts of bacteria was less biased and performed significantly better than any of the microscope methods. It was also found that microscopic count data from counting 5 fields on three separate filters were statistically equivalent to data from counting 20 fields on a single filter.