期刊论文详细信息
BMC Biotechnology
A Comparison of transgenic and wild type soybean seeds: analysis of transcriptome profiles using RNA-Seq
Kevin C. Lambirth1  Adam M. Whaley3  Ivory C. Blakley2  Jessica A. Schlueter3  Kenneth L. Bost1  Ann E. Loraine2  Kenneth J. Piller1 
[1] Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte 28223, NC, USA
[2] Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, Kannapolis 28081, NC, USA
[3] Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte 28223, NC, USA
关键词: Equivalence;    Glycine max;    Biologics;    Next generation sequencing;    Gene expression;    Pharmaceuticals;    Biotechnology;    Transcriptomics;   
Others  :  1228278
DOI  :  10.1186/s12896-015-0207-z
 received in 2015-03-06, accepted in 2015-09-22,  发布年份 2015
【 摘 要 】

Background

Soybean (Glycine max) has been bred for thousands of years to produce seeds rich in protein for human and animal consumption, making them an appealing bioreactor for producing valuable recombinant proteins at high levels. However, the effects of expressing recombinant protein at high levels on bean physiology are not well understood. To address this, we investigated whether gene expression within transgenic soybean seed tissue is altered when large amounts of recombinant proteins are being produced and stored exclusively in the seeds. We used RNA-Seq to survey gene expression in three transgenic soybean lines expressing recombinant protein at levels representing up to 1.61 % of total protein in seed tissues. The three lines included: ST77, expressing human thyroglobulin protein (hTG), ST111, expressing human myelin basic protein (hMBP), and 764, expressing a mutant, nontoxic form of a staphylococcal subunit vaccine protein (mSEB). All lines selected for analysis were homozygous and contained a single copy of the transgene.

Methods

Each transgenic soybean seed was screened for transgene presence and recombinant protein expression via PCR and western blotting.  Whole seed mRNA was extracted and cDNA libraries constructed for Illumina sequencing.  Following alignment to the soybean reference genome, differential gene expression analysis was conducted using edgeR and cufflinks.  Functional analysis of differentially expressed genes was carried out using the gene ontology analysis tool AgriGO.

Results

The transcriptomes of nine seeds from each transgenic line were sequenced and compared with wild type seeds. Native soybean gene expression was significantly altered in line 764 (mSEB) with more than 3000 genes being upregulated or downregulated. ST77 (hTG) and ST111 (hMBP) had significantly less differences with 52 and 307 differentially expressed genes respectively. Gene ontology enrichment analysis found that the upregulated genes in the 764 line were annotated with functions related to endopeptidase inhibitors and protein synthesis, but suppressed expression of genes annotated to the nuclear pore and to protein transport. No significant gene ontology terms were detected in ST77, and only a few genes involved in photosynthesis and thylakoid functions were downregulated in ST111. Despite these differences, transgenic plants and seeds appeared phenotypically similar to non-transgenic controls. There was no correlation between recombinant protein expression level and the quantity of differentially expressed genes detected.

Conclusions

Measurable unscripted gene expression changes were detected in the seed transcriptomes of all three transgenic soybean lines analyzed, with line 764 being substantially altered. Differences detected at the transcript level may be due to T-DNA insert locations, random mutations following transformation or direct effects of the recombinant protein itself, or a combination of these. The physiological consequences of such changes remain unknown.

【 授权许可】

   
2015 Lambirth et al.

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【 参考文献 】
  • [1]Liu K. Soybeans: chemistry, technology, and utilization. 1st ed. Springer, US; 1997.
  • [2]Hudson LC, Lambirth KC, Bost KL, Piller KJ. Advancements in transgenic soy: from field to bedside. In: Board JE, editor. A comprehensive survey of international soybean research - genetics, physiology, agronomy and nitrogen relationships. InTech Open: InTech; 2013. p. 447-474
  • [3]Bazalo GR, Joshi AV, Germak J. Comparison of human growth hormone products’ cost in pediatric and adult patients. A budgetary impact model. Manag Care. 2007; 16:45-51.
  • [4]Franklin SL, Geffner ME. Growth hormone: the expansion of available products and indications. Endocrinol Metab Clin North Am. 2009; 38:587-611.
  • [5]Powell R, Hudson LC, Lambirth KC, Luth D, Wang K, Bost KL et al.. Recombinant expression of homodimeric 660 kDa human thyroglobulin in soybean seeds: an alternative source of human thyroglobulin. Plant Cell Rep. 2011; 30:1327-38.
  • [6]Hudson LC, Bost KL, Piller KJ. Optimizing recombinant protein expression in soybean. In: Sudaric A, editor. Soybean - molecular aspects of breeding. InTech Open: InTech; 2011. p. 19-42
  • [7]Oakes JL, Bost KL, Piller KJ. Stability of a soybean seed-derived vaccine antigen following long-term storage, processing and transport in the absence of a cold chain. J Sci Food Agr. 2009; 89:2191-9.
  • [8]Bost KL, Lambirth KC, Hudson LC, Piller KJ. Soybean-derived thyroglobulin as an analyte specific reagent for in vitro diagnostic tests and devices. In: Advances in medicine and biology. Berhardt LV, editor. Nova Biomedical, New York; 2014: p.23-40.
  • [9]Hudson LC, Seabolt BS, Odle J, Bost KL, Stahl CH, Piller KJ. Sublethal staphylococcal enterotoxin B challenge model in pigs to evaluate protection following immunization with a soybean-derived vaccine. Clin Vaccine Immunol. 2013; 20:24-32.
  • [10]Bost KL, Piller KJ. Protein expression systems: why soybean seeds? In: Sudaric A, editor. Soybean - molecular aspects of breeding. Intech Open: InTech; 2011. p. 3-18
  • [11]Hudson LC, Garg R, Bost KL, Piller KJ. Soybean seeds: a practical host for the production of functional subunit vaccines. Biomed Res Int. 2014; doi:10.1155/2014/340804
  • [12]Piller KJ, Clemente TE, Jun SM, Petty CC, Sato S, Pascual DW et al.. Expression and immunogenicity of an Escherichia coli K99 fimbriae subunit antigen in soybean. Planta. 2005; 222:6-18.
  • [13]Ding SH, Huang LY, Wang YD, Sun HC, Xiang ZH. High-level expression of basic fibroblast growth factor in transgenic soybean seeds and characterization of its biological activity. Biotechnol Lett. 2006; 28:869-75.
  • [14]Herman RA, Ladics GS. Endogenous allergen upregulation: transgenic vs. traditionally bred crops. Food Chem Toxicol. 2011; 49:2667-9.
  • [15]Simo C, Ibanez C, Valdes A, Cifuentes A, Garcia-Canas V. Metabolomics of genetically modified crops. Int J Mol Sci. 2014; doi:10.3390/ijms151018941.
  • [16]Zhang X, Zhao P, Wu K, Zhang Y, Peng M, Liu Z. Compositional equivalency of RNAi-mediated virus-resistant transgenic soybean and its nontransgenic counterpart. J Agric Food Chem. 2014; 62:4475-9.
  • [17]Cheng KC, Beaulieu J, Iquira E, Belzile FJ, Fortin MG, Stromvik MV. Effect of transgenes on global gene expression in soybean is within the natural range of variation of conventional cultivars. J Agric Food Chem. 2008; 56:3057-67.
  • [18]Beale MH, Ward JL, Baker JM. Establishing substantial equivalence: metabolomics. Methods Mol Biol. 2009; 478:289-303.
  • [19]Batista R, Saibo N, Lourenco T, Oliveira MM. Microarray analyses reveal that plant mutagenesis may induce more transcriptomic changes than transgene insertion. Proc Natl Acad Sci U S A. 2008; 105:3640-5.
  • [20]Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ et al.. Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J. 2006; 4:369-80.
  • [21]Ricroch AE, Berge JB, Kuntz M. Evaluation of genetically engineered crops using transcriptomic, proteomic, and metabolomic profiling techniques. Plant Physiol. 2011; 155:1752-61.
  • [22]Catchpole GS, Beckmann M, Enot DP, Mondhe M, Zywicki B, Taylor J et al.. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci U S A. 2005; 102:14458-62.
  • [23]Baker JM, Hawkins ND, Ward JL, Lovegrove A, Napier JA, Shewry PR et al.. A metabolomic study of substantial equivalence of field-grown genetically modified wheat. Plant Biotechnol J. 2006; 4:381-92.
  • [24]Kusano M, Redestig H, Hirai T, Oikawa A, Matsuda F, Fukushima A, et al. Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment. PloS One. 2011; doi:10.1371/journal.pone.0016989.
  • [25]Natarajan S, Luthria D, Bae H, Lakshman D, Mitra A. Transgenic soybeans and soybean protein analysis: an overview. J Agric Food Chem. 2013; 61:11736-43.
  • [26]Lepping MD, Herman RA, Potts BL. Compositional equivalence of DAS-444O6-6 (AAD-12 + 2mEPSPS + PAT) herbicide-tolerant soybean and nontransgenic soybean. J Agric Food Chem. 2013; 61:11180-90.
  • [27]Barbosa H, Arruda SC, Azevedo R, Arruda MZ. New insights on proteomics of transgenic soybean seeds: evaluation of differential expressions of enzymes and proteins. Anal Bioanal Chem. 2012; 402:299-314.
  • [28]Snell C, Bernheim A, Bergé J-B, Kuntz M, Pascal G, Paris A et al.. Assessment of the health impact of GM plant diets in long-term and multigenerational animal feeding trials: A literature review. Food Chem Toxicol. 2012; 50:1134-48.
  • [29]Pitzschke A, Hirt H. New insights into an old story: Agrobacterium-induced tumour formation in plants by plant transformation. EMBO J. 2010; 29:1021-32.
  • [30]Houshyani B, van der Krol AR, Bino RJ, Bouwmeester HJ. Assessment of pleiotropic transcriptome perturbations in Arabidopsis engineered for indirect insect defence. BMC Plant Biol. 2014; 14:170. BioMed Central Full Text
  • [31]Kuiper HA, Kok EJ, Engel KH. Exploitation of molecular profiling techniques for GM food safety assessment. Curr Opin Biotechnol. 2003; 14:238-43.
  • [32]Rynda-Apple A, Huarte E, Maddaloni M, Callis G, Skyberg JA, Pascual DW. Active immunization using a single dose immunotherapeutic abates established EAE via IL-10 and regulatory T cells. Eur J Immunol. 2011; 41:313-23.
  • [33]Paz MM, Martinez JC, Kalvig AB, Fonger TM, Wang K. Improved cotyledonary node method using an alternative explant derived from mature seed for efficient Agrobacterium-mediated soybean transformation. Plant Cell Rep. 2006; 25:206-13.
  • [34]Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W et al.. Genome sequence of the palaeopolyploid soybean. Nature. 2010; 463:178-83.
  • [35]Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009; 25:1105-11.
  • [36]Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 2014; doi:10.1093/nar/gkt1069.
  • [37]Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012; doi:10.1093/nar/gkr944.
  • [38]Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database C. The sequence read archive. Nucleic Acids Res. 2011; doi:10.1093/nar/gkq1019
  • [39]Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30:923-30.
  • [40]Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26:139-40.
  • [41]Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995; 57:289-300.
  • [42]Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR et al.. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7:562-78.
  • [43]Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002; 30:207-10.
  • [44]Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010; doi:10.1186/gb-2010-11-2-r14.
  • [45]Du Z, Zhou X, Ling Y, Zhang Z, Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010; doi:10.1093/nar/gkq310.
  • [46]Nicol JW, Helt GA, Blanchard SG, Raja A, Loraine AE. The integrated genome browser: free software for distribution and exploration of genome-scale datasets. Bioinformatics. 2009; 25:2730-1.
  • [47]Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlen M et al.. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012; 40:10084-97.
  • [48]Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot. 2012; 99:248-56.
  • [49]Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013; doi:10.1186/gb-2013-14-9-r95.
  • [50]Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015; 16:59-70.
  • [51]Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013; doi:10.1186/1471-2105-14-91.
  • [52]Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N et al.. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003; 34:374-8.
  • [53]Rang A, Linke B, Jansen B. Detection of RNA variants transcribed from the transgene in roundup ready soybean. Eur Food Res Technol. 2005; 220:438-43.
  • [54]Ichikawa T, Nakazawa M, Kawashima M, Muto S, Gohda K, Suzuki K et al.. Sequence database of 1172 T-DNA insertion sites in Arabidopsis activation-tagging lines that showed phenotypes in T1 generation. Plant J. 2003; 36:421-9.
  • [55]Schoen DJ, David JL, Bataillon TM. Deleterious mutation accumulation and the regeneration of genetic resources. Proc Natl Acad Sci U S A. 1998; 95:394-9.
  • [56]Molinier J, Ries G, Zipfel C, Hohn B. Transgeneration memory of stress in plants. Nature. 2006; 442:1046-9.
  • [57]Forsbach A, Schubert D, Lechtenberg B, Gils M, Schmidt R. A comprehensive characterization of single-copy T-DNA insertions in the Arabidopsis thaliana genome. Plant Mol Biol. 2003; 52:161-76.
  • [58]Latham JR, Wilson AK, Steinbrecher RA. The mutational consequences of plant transformation. J Biomed Biotechnol. 2006; doi:10.1155/JBB/2006/25376.
  • [59]Vaucheret H, Beclin C, Elmayan T, Feuerbach F, Godon C, Morel JB et al.. Transgene-induced gene silencing in plants. Plant J. 1998; 16:651-9.
  • [60]Beers E, Woffenden B, Zhao C. Plant proteolytic enzymes: possible roles during programmed cell deat. In: Programmed cell death in higher plants. Lam E, Fukuda H, Greenberg J, editors. Springer, Netherlands; 2000: p.155-71.
  • [61]Solomon M, Belenghi B, Delledonne M, Menachem E, Levine A. The involvement of cysteine proteases and protease inhibitor genes in the regulation of programmed cell death in plants. Plant Cell. 1999; 11:431-44.
  • [62]Botella MA, Xu Y, Prabha TN, Zhao Y, Narasimhan ML, Wilson KA et al.. Differential expression of soybean cysteine proteinase inhibitor genes during development and in response to wounding and methyl jasmonate. Plant Physiol. 1996; 112:1201-10.
  • [63]Antão CM, Malcata FX. Plant serine proteases: biochemical, physiological and molecular features. Plant Physiol Biochem. 2005; 43:637-50.
  • [64]Singh A, Meena M, Kumar D, Dubey AK, Hassan I. Structural and functional analysis of various globulin proteins from soy seed. Crit Rev Food Sci Nutr. 2015; 55:1491-502.
  • [65]Russell DA, Spatola LA, Dian T, Paradkar VM, Dufield DR, Carroll JA et al.. Host limits to accurate human growth hormone production in multiple plant systems. Biotechnol Bioeng. 2005; 89:775-82.
  • [66]Jones SI, Gonzalez DO, Vodkin LO. Flux of transcript patterns during soybean seed development. BMC Genomics. 2010; doi:10.1186/1471-2164-11-136.
  • [67]Gallardo K, Firnhaber C, Zuber H, Hericher D, Belghazi M, Henry C et al.. A combined proteome and transcriptome analysis of developing Medicago truncatula seeds: evidence for metabolic specialization of maternal and filial tissues. Mol Cell Proteomics. 2007; 6:2165-79.
  • [68]Jones SI, Vodkin LO. Using RNA-Seq to profile soybean seed development from fertilization to maturity. PloS One. 2013; doi:10.1371/journal.pone.0059270.
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