Molecular toolbox for the identification of unknown genetically modified organisms

Synsor: a tool for alignment-free detection of engineered DNA sequences

DNA sequences of nearly any desired composition, length, and function can be synthesized to alter the biology of an organism for purposes ranging from the bioproduction of therapeutic compounds to invasive pest control. Yet despite offering many great benefits, engineered DNA poses a risk due to their possible misuse or abuse by malicious actors, or their unintentional introduction into the environment. Monitoring the presence of engineered DNA in biological or environmental systems is therefore crucial for routine and timely detection of emerging biological threats, and for improving public acceptance of genetic technologies. To address this, we developed Synsor, a tool for identifying engineered DNA sequences in high-throughput sequencing data. Synsor leverages the k-mer signature differences between naturally occurring and engineered DNA sequences and uses an artificial neural network to classify whether a DNA sequence is natural or engineered. By querying suspected sequences against the model, Synsor can identify sequences that are likely to have been engineered. Using natural plasmid and engineered vector sequences, we showed that Synsor identifies engineered DNA with >99% accuracy. We demonstrate how Synsor can be used to detect potential genetically engineered organisms and locate where engineered DNA is being introduced into the environment by analysing genomic and metagenomic data from yeast and wastewater samples, respectively. Synsor is therefore a powerful tool that will streamline the process of identifying engineered DNA in poorly characterized biological or environmental systems, thereby allowing for enhanced monitoring of emerging biological threats.

A nanomaterial-free and thionine labeling-based lateral flow immunoassay for rapid and visual detection of the transgenic CP4-EPSPS protein

Nanomaterial-based lateral flow immunoassays (LFIAs) have been widely used for the on-site detection of genetically modified components. However, the practical applications are often limited by the complex matrix, such as in red samples. In this study, a thionine (Thi) labeling-based LFIA was developed for the first time to detect CP4-EPSPS protein. The optimal labeling concentration of Thi was 0.5 mg/mL, and the antibody could be rapidly coupled to Thi in 10 min. The visual limit of detection (vLOD) levels for transgenic soybean, sugar beet, and cotton containing the CP4-EPSPS protein reached 0.05%, 0.1%, and 0.1%, respectively, and had no interference from other proteins. After storage at 4 °C for three months, the LFIA sensitivity remained unchanged and showed good stability. This method could be used to screen and detect a variety of transgenic crops containing the CP4-EPSPS protein, and the results were consistent with the current standard assay. This study pioneered the development of an immunochromatographic method using Thi as a marker and applied it to the detection of the CP4-EPSPS protein in herbicide-tolerant transgenic crops. This provides a new method for the rapid immunoassay of Thi as a dye and has good prospects for practical application.

Highly sensitive SERS assay of genetically modified organisms in maize via a nanoflower substrate coupled with hybridization chain reaction amplification

A novel biosensor based on a high-density “hot spot” SERS substrate coupled with HCR amplification strategy was developed for the ultrasensitive detection of genetically modified organisms in maize.

Statistical modeling of STR capillary electrophoresis signal

BACKGROUND

In order to isolate an individual's genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited. The signal can be further compounded by the presence of noise and PCR artifacts such as stutter which can mask or mimic biological alleles. Because manual interpretation methods cannot comprehensively account for such nuances, it would be valuable to develop a signal model that can effectively characterize the various components of STR signal independent of a priori knowledge of the quantity or quality of DNA.

RESULTS

First, we seek to mathematically characterize the quality of the profile by measuring changes in the signal with respect to amplicon size. Next, we examine the noise, allele, and stutter components of the signal and develop distinct models for each. Using cross-validation and model selection, we identify a model that can be effectively utilized for downstream interpretation. Finally, we show an implementation of the model in NOCIt, a software system that calculates the a posteriori probability distribution on the number of contributors.

CONCLUSION

The model was selected using a large, diverse set of DNA samples obtained from 144 different laboratory conditions; with DNA amounts ranging from a single copy of DNA to hundreds of copies, and the quality of the profiles ranging from pristine to highly degraded. Implemented in NOCIt, the model enables a probabilisitc approach to estimating the number of contributors to complex, environmental samples.

Current perspectives on genetically modified crops and detection methods

Genetically modified (GM) crops are the fastest adopted commodities in the agribiotech industry. This market penetration should provide a sustainable basis for ensuring food supply for growing global populations. The successful completion of two decades of commercial GM crop production (1996-2015) is underscored by the increasing rate of adoption of genetic engineering technology by farmers worldwide. With the advent of introduction of multiple traits stacked together in GM crops for combined herbicide tolerance, insect resistance, drought tolerance or disease resistance, the requirement of reliable and sensitive detection methods for tracing and labeling genetically modified organisms in the food/feed chain has become increasingly important. In addition, several countries have established threshold levels for GM content which trigger legally binding labeling schemes. The labeling of GM crops is mandatory in many countries (such as China, EU, Russia, Australia, New Zealand, Brazil, Israel, Saudi Arabia, Korea, Chile, Philippines, Indonesia, Thailand), whereas in Canada, Hong Kong, USA, South Africa, and Argentina voluntary labeling schemes operate. The rapid adoption of GM crops has increased controversies, and mitigating these issues pertaining to the implementation of effective regulatory measures for the detection of GM crops is essential. DNA-based detection methods have been successfully employed, while the whole genome sequencing using next-generation sequencing (NGS) technologies provides an advanced means for detecting genetically modified organisms and foods/feeds in GM crops. This review article describes the current status of GM crop commercialization and discusses the benefits and shortcomings of common and advanced detection systems for GMs in foods and animal feeds.

An integrated strategy combining DNA walking and NGS to detect GMOs

Recently, we developed a DNA walking system for the detection and characterization of a broad spectrum of GMOs in routine analysis of food/feed matrices. Here, we present a new version with improved throughput and sensitivity by coupling the DNA walking system to Pacific Bioscience® Next-generation sequencing technology. The performance of the new strategy was thoroughly assessed through several assays. First, we tested its detection and identification capability on grains with high or low GMO content. Second, the potential impacts of food processing were investigated using rice noodle samples. Finally, GMO mixtures and a real-life sample were analyzed to illustrate the applicability of the proposed strategy in routine GMO analysis. In all tested samples, the presence of multiple GMOs was unambiguously proven by the characterization of transgene flanking regions and the combinations of elements that are typical for transgene constructs.

Data in support of the detection of genetically modified organisms (GMOs) in food and feed samples

Food and feed samples were randomly collected from different sources, including local and imported materials from the Syrian local market. These included maize, barley, soybean, fresh food samples and raw material. GMO detection was conducted by PCR and nested PCR-based techniques using specific primers for the most used foreign DNA commonly used in genetic transformation procedures, i.e., 35S promoter, T-nos, epsps, cryIA(b) gene and nptII gene. The results revealed for the first time in Syria the presence of GM foods and feeds with glyphosate-resistant trait of P35S promoter and NOS terminator in the imported soybean samples with high frequency (5 out of the 6 imported soybean samples). While, tests showed negative results for the local samples. Also, tests revealed existence of GMOs in two imported maize samples detecting the presence of 35S promoter and nos terminator. Nested PCR results using two sets of primers confirmed our data. The methods applied in the brief data are based on DNA analysis by Polymerase Chain Reaction (PCR). This technique is specific, practical, reproducible and sensitive enough to detect up to 0.1% GMO in food and/or feedstuffs. Furthermore, all of the techniques mentioned are economic and can be applied in Syria and other developing countries. For all these reasons, the DNA-based analysis methods were chosen and preferred over protein-based analysis.

Assessment of a direct hybridization microarray strategy for comprehensive monitoring of genetically modified organisms (GMOs)

Detection of GMO material in crop and food samples is the primary step in GMO monitoring and regulation, with the increasing number of GM events in the world market requiring detection solutions with high multiplexing capacity. In this study, we test the suitability of a high-density oligonucleotide microarray platform for direct, quantitative detection of GMOs found in the Turkish feed market. We tested 1830 different 60nt probes designed to cover the GM cassettes from 12 different GM cultivars (3 soya, 9 maize), as well as plant species-specific and contamination controls, and developed a data analysis method aiming to provide maximum throughput and sensitivity. The system was able specifically to identify each cultivar, and in 10/12 cases was sensitive enough to detect GMO DNA at concentrations of ⩽1%. These GMOs could also be quantified using the microarray, as their fluorescence signals increased linearly with GMO concentration.

Current and new approaches in GMO detection: challenges and solutions

In many countries, genetically modified organisms (GMO) legislations have been established in order to guarantee the traceability of food/feed products on the market and to protect the consumer freedom of choice. Therefore, several GMO detection strategies, mainly based on DNA, have been developed to implement these legislations. Due to its numerous advantages, the quantitative PCR (qPCR) is the method of choice for the enforcement laboratories in GMO routine analysis. However, given the increasing number and diversity of GMO developed and put on the market around the world, some technical hurdles could be encountered with the qPCR technology, mainly owing to its inherent properties. To address these challenges, alternative GMO detection methods have been developed, allowing faster detections of single GM target (e.g., loop-mediated isothermal amplification), simultaneous detections of multiple GM targets (e.g., PCR capillary gel electrophoresis, microarray, and Luminex), more accurate quantification of GM targets (e.g., digital PCR), or characterization of partially known (e.g., DNA walking and Next Generation Sequencing (NGS)) or unknown (e.g., NGS) GMO. The benefits and drawbacks of these methods are discussed in this review.

Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing

Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.

Evalution of DNA extraction methods in order to monitor genetically modified materials in soy foodstuffs and feeds commercialised in Turkey by multiplex real-time PCR

BACKGROUND

Soybean is one of the most important biotech crops, widely used as an ingredient in both foodstuffs and feed. DNA extraction methods have been evaluated to detect the presence of genetically modified (GM) materials in soya-containing food and feed products commercialised in Turkey.

RESULTS

All extraction methods performed well for the majority of soya foods and feed products analysed. However, the most successful method varied between different products; the Foodproof, Genespin and the cetyltrimethylammonium bromide (CTAB) methods each produced the highest DNA yield and purity for different soya foodstuffs and feeds. Of the samples tested, 20% were positive for the presence of at least two GM elements (35S/NOS) while 11% contained an additional GM element (35S/NOS/FMV). Of the tested products, animal feeds showed a larger prevalence of GM material (50%) than the soya-containing foodstuffs (13%).

CONCLUSION

The best performing extraction methods proved to be the Foodproof, Genespin and CTAB methods for soya-containing food and feed products. The results obtained herein clearly demonstrate the presence of GM soybean in the Turkish market, and that the Foodproof GMO Screening Kit provides reliable screening of soy-containing food and feed products.

Validation of a sensitive DNA walking strategy to characterise unauthorised GMOs using model food matrices mimicking common rice products

To identify unauthorised GMOs in food and feed matrices, an integrated approach has recently been developed targeting pCAMBIA family vectors, highly present in transgenic plants. Their presence is first assessed by qPCR screening and is subsequently confirmed by characterising the transgene flanking regions, using DNA walking. Here, the DNA walking performance has been thoroughly tested for the first time, regarding the targeted DNA quality and quantity. Several assays, on model food matrices mimicking common rice products, have allowed to determine the limit of detection as well as the potential effects of food mixture and processing. This detection system allows the identification of transgenic insertions as low as 10 HGEs and was not affected by the presence of untargeted DNA. Moreover, despite the clear impact of food processing on DNA quality, this method was able to cope with degraded DNA. Given its specificity, sensitivity, reliability, applicability and practicability, the proposed approach is a key detection tool, easily implementable in enforcement laboratories.

GMOseek: a user friendly tool for optimized GMO testing

Background

With the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called "matrix approach" to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the "matrix approach" is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data.

Results

The developed GMOseek software is designed to support decision making in all the phases of routine GMO laboratory testing, including the interpretation of wet-lab results. The tool makes use of a tabulated matrix of GM events and their genetic elements, of the laboratory analysis history and the available information about the sample at hand. The tool uses an optimization approach to suggest the most suited screening assays for the given sample. The practical GMOseek user interface allows the user to customize the search for a cost-efficient combination of screening assays to be employed on a given sample. It further guides the user to select appropriate analyses to determine the presence of individual GM events in the analyzed sample, and it helps taking a final decision regarding the GMO composition in the sample. GMOseek can also be used to evaluate new, previously unused GMO screening targets and to estimate the profitability of developing new GMO screening methods.

Conclusion

The presented freely available software tool offers the GMO testing laboratories the possibility to select combinations of assays (e.g. quantitative real-time PCR tests) needed for their task, by allowing the expert to express his/her preferences in terms of multiplexing and cost. The utility of GMOseek is exemplified by analyzing selected food, feed and seed samples from a national reference laboratory for GMO testing and by comparing its performance to existing tools which use the matrix approach. GMOseek proves superior when tested on real samples in terms of GMO coverage and cost efficiency of its screening strategies, including its capacity of simple interpretation of the testing results.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-258) contains supplementary material, which is available to authorized users.

A high-throughput method for GMO multi-detection using a microfluidic dynamic array

The ever-increasing production of genetically modified crops generates a demand for high-throughput DNA-based methods for the enforcement of genetically modified organisms (GMO) labelling requirements. The application of standard real-time PCR will become increasingly costly with the growth of the number of GMOs that is potentially present in an individual sample. The present work presents the results of an innovative approach in genetically modified crops analysis by DNA based methods, which is the use of a microfluidic dynamic array as a high throughput multi-detection system. In order to evaluate the system, six test samples with an increasing degree of complexity were prepared, preamplified and subsequently analysed in the Fluidigm system. Twenty-eight assays targeting different DNA elements, GM events and species-specific reference genes were used in the experiment. The large majority of the assays tested presented expected results. The power of low level detection was assessed and elements present at concentrations as low as 0.06 % were successfully detected. The approach proposed in this work presents the Fluidigm system as a suitable and promising platform for GMO multi-detection.

Characterization of GM events by insert knowledge adapted re-sequencing approaches

Detection methods and data from molecular characterization of genetically modified (GM) events are needed by stakeholders of public risk assessors and regulators. Generally, the molecular characteristics of GM events are incomprehensively revealed by current approaches and biased towards detecting transformation vector derived sequences. GM events are classified based on available knowledge of the sequences of vectors and inserts (insert knowledge). Herein we present three insert knowledge-adapted approaches for characterization GM events (TT51-1 and T1c-19 rice as examples) based on paired-end re-sequencing with the advantages of comprehensiveness, accuracy, and automation. The comprehensive molecular characteristics of two rice events were revealed with additional unintended insertions comparing with the results from PCR and Southern blotting. Comprehensive transgene characterization of TT51-1 and T1c-19 is shown to be independent of a priori knowledge of the insert and vector sequences employing the developed approaches. This provides an opportunity to identify and characterize also unknown GM events.

An innovative and integrated approach based on DNA walking to identify unauthorised GMOs

In the coming years, the frequency of unauthorised genetically modified organisms (GMOs) being present in the European food and feed chain will increase significantly. Therefore, we have developed a strategy to identify unauthorised GMOs containing a pCAMBIA family vector, frequently present in transgenic plants. This integrated approach is performed in two successive steps on Bt rice grains. First, the potential presence of unauthorised GMOs is assessed by the qPCR SYBR®Green technology targeting the terminator 35S pCAMBIA element. Second, its presence is confirmed via the characterisation of the junction between the transgenic cassette and the rice genome. To this end, a DNA walking strategy is applied using a first reverse primer followed by two semi-nested PCR rounds using primers that are each time nested to the previous reverse primer. This approach allows to rapidly identify the transgene flanking region and can easily be implemented by the enforcement laboratories.

The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants

BACKGROUND

Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information.

DESCRIPTION

The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available.

CONCLUSIONS

The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms.

Relative quantification in seed GMO analysis: state of art and bottlenecks

Reliable quantitative methods are needed to comply with current EU regulations on the mandatory labeling of genetically modified organisms (GMOs) and GMO-derived food and feed products with a minimum GMO content of 0.9 %. The implementation of EU Commission Recommendation 2004/787/EC on technical guidance for sampling and detection which meant as a helpful tool for the practical implementation of EC Regulation 1830/2003, which states that "the results of quantitative analysis should be expressed as the number of target DNA sequences per target taxon specific sequences calculated in terms of haploid genomes". This has led to an intense debate on the type of calibrator best suitable for GMO quantification. The main question addressed in this review is whether reference materials and calibrators should be matrix based or whether pure DNA analytes should be used for relative quantification in GMO analysis. The state of the art, including the advantages and drawbacks, of using DNA plasmid (compared to genomic DNA reference materials) as calibrators, is widely described. In addition, the influence of the genetic structure of seeds on real-time PCR quantitative results obtained for seed lots is discussed. The specific composition of a seed kernel, the mode of inheritance, and the ploidy level ensure that there is discordance between a GMO % expressed as a haploid genome equivalent and a GMO % based on numbers of seeds. This means that a threshold fixed as a percentage of seeds cannot be used as such for RT-PCR. All critical points that affect the expression of the GMO content in seeds are discussed in this paper.

How to deal with the upcoming challenges in GMO detection in food and feed

Biotech crops are the fastest adopted crop technology in the history of modern agriculture. The commercialisation of GMO is in many countries strictly regulated laying down the need for traceability and labelling. To comply with these legislations, detection methods are needed. To date, GM events have been developed by the introduction of a transgenic insert (i.e., promoter, coding sequence, terminator) into the plant genome and real-time PCR is the detection method of choice. However, new types of genetic elements will be used to construct new GMO and new crops will be transformed. Additionally, the presence of unauthorised GMO in food and feed samples might increase in the near future. To enable enforcement laboratories to continue detecting all GM events and to obtain an idea of the possible presence of unauthorised GMO in a food and feed sample, an intensive screening will become necessary. A pragmatic, cost-effective, and time-saving approach is presented here together with an overview of the evolution of the GMO and the upcoming needs.

Development and validation of real-time PCR screening methods for detection of cry1A.105 and cry2Ab2 genes in genetically modified organisms

Primers and probes were developed for the element-specific detection of cry1A.105 and cry2Ab2 genes, based on their DNA sequence as present in GM maize MON89034. Cry genes are present in many genetically modified (GM) plants and they are important targets for developing GMO element-specific detection methods. Element-specific methods can be of use to screen for the presence of GMOs in food and feed supply chains. Moreover, a combination of GMO elements may indicate the potential presence of unapproved GMOs (UGMs). Primer-probe combinations were evaluated in terms of specificity, efficiency and limit of detection. Except for specificity, the complete experiment was performed in 9 PCR runs, on 9 different days and by testing 8 DNA concentrations. The results showed a high specificity and efficiency for cry1A.105 and cry2Ab2 detection. The limit of detection was between 0.05 and 0.01 ng DNA per PCR reaction for both assays. These data confirm the applicability of these new primer-probe combinations for element detection that can contribute to the screening for GM and UGM crops in food and feed samples.