Microarray-based method for detection of unknown genetic modifications

<p>Abstract</p> <p>Background</p> <p>Due to the increased use of genetic modifications in crop improvement, there is a need to develop effective methods for the detection of both known and unknown transgene constructs in plants. We have developed a strategy for detection and characterization of unknown genetic modifications and we present a proof of concept for this method using <it>Arabidopsis thaliana </it>and <it>Oryza sativa </it>(rice). The approach relies on direct hybridization of total genomic DNA to high density microarrays designed to have probes tiled throughout a set of reference sequences.</p> <p>Results</p> <p>We show that by using arrays with 25 basepair probes covering both strands of a set of 235 vectors (2 million basepairs) we can detect transgene sequences in transformed lines of <it>A. thaliana </it>and rice without prior knowledge about the transformation vectors or the T-DNA constructs used to generate the studied plants.</p> <p>Conclusion</p> <p>The approach should allow the user to detect the presence of transgene sequences and get sufficient information for further characterization of unknown genetic constructs in plants. The only requirements are access to a small amount of pure transgene plant material, that the genetic construct in question is above a certain size (here ≥ 140 basepairs) and that parts of the construct shows some degree of sequence similarity with published genetic elements.</p

Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction

<p>Abstract</p> <p>Background</p> <p>When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown.</p> <p>Results</p> <p>We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of <it>Arabidopsis thaliana </it>and published EST datasets from commercially relevant species (rice and papaya).</p> <p>Conclusion</p> <p>We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs.</p

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.Litao Yang, Congmao Wang, Arne Holst-Jensen, Dany Morisset, Yongjun Lin and Dabing Zhan

Near-normalization of glycaemic control with glucagon-like peptide-1 receptor agonist treatment combined with exercise in patients with type 2 diabetes

AIMS: To investigate the effects of exercise in combination with a glucagon‐like peptide‐1 receptor agonist (GLP‐1RA), liraglutide, or placebo for the treatment of type 2 diabetes. METHODS: Thirty‐three overweight, dysregulated and sedentary patients with type 2 diabetes were randomly allocated to 16 weeks of either exercise and liraglutide or exercise and placebo. Both groups had three supervised 60‐minute training sessions per week including spinning and resistance training. RESULTS: Glycated haemoglobin (HbA1c) levels dropped by a mean ± standard deviation of 2.0% ± 1.2% (from 8.2% ± 1.4%) in the exercise plus liraglutide group vs 0.3% ± 0.9% (from 8.0% ± 1.2%) in the exercise plus placebo group ( P < .001), and body weight was reduced more with liraglutide (−3.4 ± 2.9 kg vs −1.6 ± 2.3 kg; P < .001). Compared with baseline, similar reductions were seen in body fat (exercise plus liraglutide: −2.5% ± 1.4% [ P < .001]; exercise plus placebo: −2.2% ± 1.9% [ P < .001]) and similar increases were observed in maximum oxygen uptake (exercise plus liraglutide: 0.5 ± 0.5 L O(2)/min [ P < .001]; exercise plus placebo: 0.4 ± 0.4 L O(2)/min [ P = .002]). Greater reductions in fasting plasma glucose (−3.4 ± 2.3 mM vs −0.3 ± 2.6 mM, P < .001) and systolic blood pressure (−5.4 ± 7.4 mm Hg vs −0.6 ± 11.1 mm Hg, P < .01) were seen with exercise plus liraglutide vs exercise plus placebo. The two groups experienced similar increases in quality of life during the intervention. CONCLUSIONS: In obese patients with type 2 diabetes, exercise combined with GLP‐1RA treatment near‐normalized HbA1c levels and caused a robust weight loss when compared with placebo. These results suggest that a combination of exercise and GLP‐1RA treatment is effective in type 2 diabetes