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Biocontainment of genetically modified organisms by synthetic protein design.

ABSTRACT: Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. Here we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.?A) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass their biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. This work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.

SUBMITTER: Mandell DJ

PROVIDER: S-EPMC4422498 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Biocontainment of genetically modified organisms by synthetic protein design.

Mandell Daniel J DJ Lajoie Marc J MJ Mee Michael T MT Takeuchi Ryo R Kuznetsov Gleb G Norville Julie E JE Gregg Christopher J CJ Stoddard Barry L BL Church George M GM

Nature 20150121 7537

Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. Here we computationa ...[more]

PMID: 25607366

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Project description:Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Project description:BACKGROUND: According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called 'minimum required performance limit' (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. RESULTS: We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section 'Availability of supporting data' for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. CONCLUSIONS: The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in -but not limited to- the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations seem to be a reasonable minimum number to narrow down the possible spread of results.

Project description:Gene drive organisms are a recent development created by using methods of genetic engineering; they inherit genetic constructs that are passed on to future generations with a higher probability than with Mendelian inheritance. There are some specific challenges inherent to the environmental risk assessment (ERA) of genetically engineered (GE) gene drive organisms because subsequent generations of these GE organisms might show effects that were not observed or intended in the former generations. Unintended effects can emerge from interaction of the gene drive construct with the heterogeneous genetic background of natural populations and/or be triggered by changing environmental conditions. This is especially relevant in the case of gene drives with invasive characteristics and typically takes dozens of generations to render the desired effect. Under these circumstances, "next generation effects" can substantially increase the spatial and temporal complexity associated with a high level of uncertainty in ERA. To deal with these problems, we suggest the introduction of a new additional step in the ERA of GE gene drive organisms that takes 3 criteria into account: the biology of the target organisms, their naturally occurring interactions with the environment (biotic and abiotic), and their intended biological characteristics introduced by genetic engineering. These 3 criteria are merged to form an additional step in ERA, combining specific "knowns" and integrating areas of "known unknowns" and uncertainties, with the aim of assessing the spatiotemporal controllability of GE gene drive organisms. The establishment of assessing spatiotemporal controllability can be used to define so-called "cut-off criteria" in the risk analysis of GE gene drive organisms: If it is likely that GE gene drive organisms escape spatiotemporal controllability, the risk assessment cannot be sufficiently reliable because it is not conclusive. Under such circumstances, the environmental release of the GE gene drive organisms would not be compatible with the precautionary principle (PP). Integr Environ Assess Manag 2020;00:1-14. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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Biocontainment of genetically modified organisms by synthetic protein design.

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Biocontainment of genetically modified organisms by synthetic protein design.

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