Principal Response Curve Analysis of Arthropod Community Abundance Data with Sparse Subsets

Principal response curve (PRC) analysis was applied to an assessment of the ecological impact of the genetically-modified (GM), insect-resistant, cotton MON 88702 on predatory Hemiptera communities in the field. The field community was represented by ten taxa collected ten times across the season at six sites, in which individual taxa were not observed in at least 25% of the time (unique site x collection combinations). These complete absences and those nearly so, called sparse subsets of the data in this investigation, were the result of geoclimatic and seasonal variations, which are both independent of the treatment effect for which the PRC analysis is intended. If the sparse subsets were included in the analysis, the treatment effect would be underestimated. Here, a modified analysis is proposed to remove those sparse subsets and to be performed on the incomplete data. In the application to MON 88702, four components (PRC1-4) were significant at the 5% level by the modified method, when more than 50% of the data were excluded due to no- or low responses, and five (PRC1-5) by the classical method. While PRC1-2 was highly consistent between two methods, PRC3-5 was largely different because of sparse subsets of the data. Differences in results between two methods demonstrate that excluding sparse subsets prevented the bias in the estimation of the treatment effect and the relationship with the community from confounding with the environmental variation that caused the sparse data. In this regard, the modification should be considered as a supplement of the classical PRC analysis and recommended when abundance data have sparse subsets

Probing Protein Dynamics in Neuronal Nitric Oxide Synthase by Quantitative Cross-Linking Mass Spectrometry

Nitric oxide synthase (NOS) is responsible for biosynthesis of nitric oxide (NO), an important signaling molecule controlling diverse physiological processes such as neurotransmission and vasodilation. Neuronal NOS (nNOS) is a calmodulin (CaM)-controlled enzyme. In the absence of CaM, several nNOS-unique control elements, along with NADP+ binding, suppress electron transfer across the NOS domains. CaM binding relieves the inhibitory factors to promote the electron transport required for NO production. The regulatory dynamics of nNOS control elements are critical to governing NO signaling, yet mechanistic questions remain because the intrinsic dynamics of NOS thwart traditional structural biology approaches. Here, we employ cross-linking mass spectrometry (XL MS) to probe regulatory dynamics in nNOS, focusing on the CaM-responsive control elements. Quantitative cross-linking revealed conformational changes differentiating the nNOS reductase (nNOSred) alone, nNOSred with NADP+, nNOS-CaM, and nNOS-CaM with NADP+. We observed distinct effects of CaM vs. NADP+ on cross-linking patterns in nNOSred. CaM induces striking global changes while the impact of NADP+ is primarily localized to the NADPH-binding subdomain. Moreover, CaM increases the abundance of intra-nNOS cross-links that are related to the formation of the inter-CaM-nNOS cross-links. Taken together, these XL MS results demonstrate that CaM and NADP+ site-specifically alter the nNOS conformational landscape.This is a manuscript of an article published as Jiang, Ting, Guanghua Wan, Haikun Zhang, Yadav Prasad Gyawali, Eric S. Underbakke, and Changjian Feng. "Probing protein dynamics in neuronal nitric oxide synthase by quantitative cross-linking mass spectrometry." Biochemistry 62, no. 15 (2023): 2232-2237. doi:10.1021/acs.biochem.3c00245. Copyright © 2023 American Chemical Society

Significance of Photosynthetic Characters in the Evolution of Asian Gnetum (Gnetales)

Gnetum is a genus in the Gnetales that has a unique but ambiguous placement within seed plant phylogeny. Previous studies have shown that Gnetum has lower values of photosynthetic characters than those of other seed plants, but few Gnetum species have been studied, and those that have been studied are restricted to narrow taxonomic and geographic ranges. In addition, the mechanism underlying the lower values of photosynthetic characters in Gnetum remains poorly understood. Here, we investigated the photosynthetic characters of a Chinese lianoid species, i.e., Gnetum parvifolium, and co-occurring woody angiosperms growing in the wild, as well as seedlings of five Chinese Gnetum species cultivated in a greenhouse. The five Gnetum species had considerably lower values for photosynthesis parameters (net photosynthetic rate, transpiration rate, intercellular CO2 concentration, and stomatal conductance) than those of other seed plant representatives. Interrelated analyses revealed that the low photosynthetic capacity may be an intrinsic property of Gnetum, and may be associated with its evolutionary history. Comparison of the chloroplast genomes (cpDNAs) of Gnetum with those of other seed plant representatives revealed that 17 coding genes are absent from the cpDNAs of all species of Gnetum. This lack of multiple functional genes from the cpDNAs probably leads to the low photosynthetic rates of Gnetum. Our results provide a new perspective on the evolutionary history of the Gnetales, and on the ecophysiological and genomic attributes of tropical biomes in general. These results could also be useful for the breeding and cultivation of Gnetum

Comparative analysis of genetically-modified crops: Part 1. Conditional difference testing with a given genetic background.

The European Food Safety Authority (EFSA) mandates two sets of statistical tests in the comparative assessment of a genetically-modified (GM) crop: difference testing to demonstrate whether the GM crop is different from its appropriate non-traited control; and equivalence testing to demonstrate whether it is equivalent to conventional references with an history-of-safe-use. The equivalence testing method prescribed by EFSA confounds the so-called GM trait effect with genotypic differences between the reference varieties and non-traited control. Critically, these genotypic differences, which we define as a 'control background effect', are the result of conventional plant breeding. Thus, the result of EFSA equivalence testing often has little or nothing to do with the GM trait effect, which should be the sole focus of the comparative assessment. Here, an integrated method is introduced for both difference and equivalence testing that considers the differences of the three genotype groups (GM, control, and references) as a two-dimensional random variable. A novel statistical model is proposed, called the trait model, that treats the effects of the GM and control materials as fixed for their difference, and as random for their common background. For significance testing, the covariance structure of the three genotype groups is utilized to decompose the differences into the trait effect and the control background effect. The trait difference is then derived as a conditional mean, given the background effect. The comparative assessment can then focus on the conditional mean difference, which is independent of the control background effect. Furthermore, the trait model is flexible enough to include various types of genotype-by-environment (G×E) interactions inherent to the experimental design of the trial. Numerical evaluations and simulations show that this new method is substantially more efficient than the current EFSA method in reducing both Type I and Type II errors (protecting both the consumer and producer risk) after the background effect is removed from the test statistic, and successfully addresses two major criticisms (i.e. statistical model lack of G×E, and study-specific equivalence criterion) that have been raised