Fast and Accurate Multiplex Identification and Quantification of Seven Genetically Modified Soybean Lines Using Six-Color Digital PCR
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ABSTRACT:
SUBMITTER: Bogozalec Kosir A
PROVIDER: S-EPMC10670894 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:Quantification of genetically modified organisms (GMOs) in food and feed products is often required for their labelling or for tolerance thresholds. Standard-curve-based simplex quantitative polymerase chain reaction (qPCR) is the prevailing technology, which is often combined with screening analysis. With the rapidly growing number of GMOs on the world market, qPCR analysis becomes laborious and expensive. Innovative cost-effective approaches are therefore urgently needed. Here, we report the development and inter-laboratory assessment of multiplex assays to quantify GMO soybean using droplet digital PCR (ddPCR). The assays were developed to facilitate testing of foods and feed for compliance with current GMO regulations in the European Union (EU). Within the EU, the threshold for labelling is 0.9% for authorised GMOs per ingredient. Furthermore, the EU has set a technical zero tolerance limit of 0.1% for certain unauthorised GMOs. The novel multiplex ddPCR assays developed target 11 GMO soybean lines that are currently authorised, and four that are tolerated, pending authorisation in the EU. Potential significant improvements in cost efficiency are demonstrated. Performance was assessed for the critical parameters, including limits of detection and quantification, and trueness, repeatability, and robustness. Inter-laboratory performance was also determined on a number of proficiency programme and real-life samples.
| S-EPMC5561262 | biostudies-literature
Project description:Genetically modified (GM) soybeans provide a huge amount of food for human consumption and animal feed. However, the possibility of unexpected effects of transgenesis has increased food safety concerns. High-throughput sequencing profiling provides a potential approach to directly evaluate unintended effects caused by foreign genes. In this study, we performed transcriptomic analyses to evaluate differentially expressed genes (DEGs) in individual soybean tissues, including cotyledon (C), germ (G), hypocotyl (H), and radicle (R), instead of using the whole seed, from four GM and three non-GM soybean lines. A total of 3,351 DEGs were identified among the three non-GM soybean lines. When the GM lines were compared with their non-GM parents, 1,836 to 4,551 DEGs were identified. Furthermore, Gene Ontology (GO) analysis of the DEGs showed more abundant categories of GO items (199) among non-GM lines than between GM lines and the non-GM natural varieties (166). Results of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that most KEGG pathways were the same for the two types of comparisons. The study successfully employed RNA sequencing to assess the differences in gene expression among four tissues of seven soybean varieties, and the results suggest that transgenes do not induce massive transcriptomic alterations in transgenic soybeans compared with those that exist among natural varieties. This work offers empirical evidence to investigate the genomic-level disparities induced by genetic modification in soybeans, specifically focusing on seed tissues.
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Project description:We developed a multiplex pseudo-isobaric dimethyl labeling (m-pIDL) method for proteome quantification to extend the capacity of the fragment ion-based method to 6-plex by one-step dimethyl labeling with several millidalton and dalton mass differences between precursor ions and enlarging the isolation window of precursor ions to 10 m/z during data acquisition.
2019-01-22 | PXD012457 |
Project description:BackgroundThe number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult.ResultsWe propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The results showed that the R2 between the number predicted by YOLO POD and the ground truth reached 0.967, which is improved by 0.049 compared to YOLO X, while the inference time only increased by 0.08 s. Moreover, MAE, MAPE, RMSE are only 4.18, 10.0%, 6.48 respectively, the deviation is very small.ConclusionsWe have achieved the first accurate counting of soybean pods and proposed a new solution for the detection and counting of dense objects.
| S-EPMC9883929 | biostudies-literature
Project description:We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression.AvailabilityStrawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.
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Project description:OBJECTIVE:Silibinin is an antioxidant agent and is shown to have anticancer effects in different cancers including lung, breast, colorectal, liver, prostate, and kidney. There are challenges in the clinical use of silibinin. The main limitation is low solubility, poor oral absorption, and extensive hepatic metabolism. We aim to develop a High-Performance Liquid Chromatography (HPLC) sensitive method for quantification of silibinin in aqueous samples to quantify its concentration in new formulations. A reverse-phase high-performance liquid chromatography (RP-HPLC) composed of C18 column as stationary phase and the mixture of methanol (90%) and water (10%) as mobile phase. The developed method was validated based on the established guidelines. RESULTS:The retention time for silibinin was seen in 2.97 min after injection. The calibration curve was drawn and the established method demonstrated a linear ranged from 10 to 100 µg/ml, with a correlation coefficient of 0.996. The sensitivity of the developed method was 10 µg/ml. The accuracy calculated in the range of 88-105.9% and the precision (as relative standard deviation) was between 2.7 and 10.9%. These results demonstrate that the developed method can be a fast and accurate method for quantification of silibinin in aqueous samples.
| S-EPMC6854794 | biostudies-literature
Project description:IntroductionDetermination of blood volume (BV) using the dual-isotope (e.g., 99m Tc-labeled red blood cells [99m Tc-RBC] and 125 I-labeled human serum albumin [125 I-HSA]) injection method is limited in medicine due to the long isotope half-life. However, BV has been determined in laboratory settings for 100 years using the carbon monoxide (CO)-rebreathing-based procedure, which allows frequent BV measurements.MethodsWe investigated the reliability and accuracy of a semi-automated CO-rebreathing device by comparing it against the dual-isotope methodology and its ability to detect a known blood removal. In study A, BV was determined three times in ~2 h; twice using the device with rebreathing protocols lasting 2 (CO2min ) and 10 min (CO10min ) and once with the dual-isotope technique. In study B, the accuracy of the device was assessed by its ability to detect a 2% removal of BV.ResultsA good correlation was observed between both the CO-rebreathing protocols (r2 = 0.89-0.98; p < 0.001) and the dual-isotope approach (r2 = 0.89-0.95; p < 0.001). In absolute terms BV was 425 ± 263 mL and 491 ± 388 mL lower (p < 0.001) when quantified with the dual-isotope compared to the CO-rebreathing protocols. When reducing BV by 132 ± 25 mL (2%), the device quantified a lower (p < 0.001) BV of 150 ± 45 mL.ConclusionThis study emphasizes that the semi-automated device accurately determines small changes (i.e., 2%) in BV and that a high correlation with the dual-isotope methodology exists. The findings are clinically relevant owing to the method's simple and fast nature (the absence of radioactive tracers and reduced time requirements, i.e., ~15 min vs. ~180 min) and the possibility for repeated measurements within a single day.
| S-EPMC10388222 | biostudies-literature
Project description:The number of genetically modified (GM) events for canola, maize, and soybean has been steadily increasing. Real-time PCR is widely used for the detection and quantification of individual GM events. Digital PCR (dPCR) has also been used for absolute quantification of GM events. A duplex dPCR assay consisting of one reference gene and one GM event has been carried out in most cases. The detection of more than one GM event in a single assay will increase the efficiency of dPCR. The feasibility of detection and quantification of two, three, and four GM canola and soybean events at the same time was investigated at 0.1%, 1%, and 5% levels using the QX200 Droplet Digital PCR (ddPCR) system. The reference gene assay was carried out on the same plate but in different wells. For some of the assays, optimization of the probe concentrations and labels was needed for successful ddPCR. Results close to the expected result were achieved for duplex, triplex, and tetraplex ddPCR assays for GM canola events. Similar ddPCR results were also achieved for some GM soybean events with some exceptions. Overall, absolute quantification of up to four GM events at the same time improves the efficiency of GM detection.
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Project description:MicroRNAs (miRNAs) have been widely demonstrated to play fundamental roles in gene regulation in most eukaryotes. To date, there has been no study describing the miRNA composition in genetically modified organisms (GMOs). In this study, small RNAs from dry seeds of two GM soybean lines and their parental cultivars were investigated using deep sequencing technology and bioinformatic approaches. As a result, several differentially expressed gma-miRNAs were found between the GM and non-GM soybeans. Meanwhile, more differentially expressed gma-miRNAs were identified between distantly relatednon-GM soybeans, indicating that the miRNA components of soybean seeds varied among different soybean lines, including the GM and non-GM soybeans, and the extent of difference might be related to their genetic relationship. Additionally, fourteen novel gma-miRNA candidates were predicted in soybean seeds including a potential bidirectionally transcribed miRNA family with two genomic loci (gma-miR-N1). Our findings firstly provided useful data for miRNA composition in edible GM crops and also provided valuable information for soybean miRNA research.
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