Detection of genetically modified plant products by protein strip testing: an evaluation of real-life samples

The determination of the presence of genetically modified plant material by the detection of expressed genetically engineered proteins using lateral flow protein strip tests has been evaluated in different matrices. The presence of five major genetically engineered proteins (CP4-EPSPS, CryIAb, Cry9C, PAT/pat and PAT/bar protein) was detected at low levels in seeds, seed/leaf powder and leaf tissue from genetically modified soy, maize or oilseed rape. A comparison between &quot;protein strip test&quot; (PST) and &quot;polymerase chain reaction&quot; (PCR) analysis of genetically modified food/feed samples demonstrates complementarities of both techniques. -® Springer-Verlag 2007</p

Filtering Genes for Cluster and Network Analysis

<p>Abstract</p> <p>Background</p> <p>Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias.</p> <p>Results</p> <p>This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks.</p> <p>Conclusion</p> <p>The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.</p

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Non-destructive evaluation techniques and what they tell us about wood property variation

To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance

Use of pJANUS™-02-001 as a calibrator plasmid for Roundup Ready soybean event GTS-40-3-2 detection: an interlaboratory trial assessment

Owing to the labelling requirements of food and feed products containing materials derived from genetically modified organisms, quantitative detection methods have to be developed for this purpose, including the necessary certified reference materials and calibrator standards. To date, for most genetically modified organisms authorized in the European Union, certified reference materials derived from seed powders are being developed. Here, an assessment has been made on the feasibility of using plasmid DNA as an alternative calibrator for the quantitative detection of genetically modified organisms. For this, a dual-target plasmid, designated as pJANUS™-02-001, comprising part of a junction region of genetically modified soybean event GTS-40-3-2 and the endogenous soybean-specific lectin gene was constructed. The dynamic range, efficiency and limit of detection for the soybean event GTS-40-3-2 real-time quantitative polymerase chain reaction (Q-PCR) system described by Terry et al. (J AOAC Int 85(4):938–944, 2002) were shown to be similar for in house produced homozygous genomic DNA from leaf tissue of soybean event GTS-40-3-2 and for plasmid pJANUS™-02-001 DNA backgrounds. The performance of this real-time Q-PCR system using both types of DNA templates as calibrator standards in quantitative DNA analysis was further assessed in an interlaboratory trial. Statistical analysis and fuzzy-logic-based interpretation were performed on critical method parameters (as defined by the European Network of GMO Laboratories and the Community Reference Laboratory for GM Food and Feed guidelines) and demonstrated that the plasmid pJANUS™-02-001 DNA represents a valuable alternative to genomic DNA as a calibrator for the quantification of soybean event GTS-40-3-2 in food and feed products

A theoretical introduction to “Combinatory SYBR®Green qPCR Screening”, a matrix-based approach for the detection of materials derived from genetically modified plants

The detection of genetically modified (GM) materials in food and feed products is a complex multi-step analytical process invoking screening, identification, and often quantification of the genetically modified organisms (GMO) present in a sample. “Combinatory qPCR SYBR®Green screening” (CoSYPS) is a matrix-based approach for determining the presence of GM plant materials in products. The CoSYPS decision-support system (DSS) interprets the analytical results of SYBR®GREEN qPCR analysis based on four values: the Ct- and Tm values and the LOD and LOQ for each method. A theoretical explanation of the different concepts applied in CoSYPS analysis is given (GMO Universe, “Prime number tracing”, matrix/combinatory approach) and documented using the RoundUp Ready soy GTS40-3-2 as an example. By applying a limited set of SYBR®GREEN qPCR methods and through application of a newly developed “prime number”-based algorithm, the nature of subsets of corresponding GMO in a sample can be determined. Together, these analyses provide guidance for semi-quantitative estimation of GMO presence in a food and feed product