Abstract
BackgroundRecombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database.ResultsThe NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and beta-conglycinin chains.The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified.ConclusionsNanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.
Highlights
Recombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns
The experiment revealed 113 proteins, of which 87% were replicated 4 times, as shown in Figure 1B and Table 1. These results far exceed the minimum protein identification quality compared to other proteomic data, such as those obtained from the 2DE technique, in which only 10 to 20% of the identified proteins exhibit a coverage greater than 30% [14,20]
NanoUPLC-MSE experiments are a viable choice as a proteomic pipeline for soybean protein detection
Summary
Soybean [Glycine max (L) Merrill] is one of the most important leguminous crops in the world with a vital importance to the economies of many countries. Efforts have been undertaken to improve soybean crop yields To this end, genetic engineering has been extensively used to develop soybean plants with abiotic and biotic resistance or tolerance [2]. Genetic engineering has been extensively used to develop soybean plants with abiotic and biotic resistance or tolerance [2] Both the quantity of grain produced and the nutritional content of the grain are critical; the production of highly nutritional seeds of many important crops is currently a focus of research [3,4,5]. Database comparisons are typically performed using peptide mass fingerprinting [19,20], and quantization is performed by gel image intensity evaluation or by protein tagging [21,22] All of these stages of 2DE are timeconsuming and can produce inconsistent results. We present a statistical assessment of soybean seeds using NanoUPLC-MSE proteomic experiments and provide a comparison with the theoretical tryptic digestion of sequences from the Uniprot[29,30] soybean database
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