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A Comparison of transgenic and wild type soybean seeds: analysis of transcriptome profiles using RNA-Seq.

Lambirth KC, Whaley AM, Blakley IC, Schlueter JA, Bost KL, Loraine AE, Piller KJ - BMC Biotechnol. (2015)

Bottom Line: However, the effects of expressing recombinant protein at high levels on bean physiology are not well understood.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. kclambirth@uncc.edu.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Venn diagrams of differentially expressed genes between edgeR and cufflinks. Numbers of genes that are up and down-regulated in both edgeR and cufflinks are shown for 764 (a), ST111 (b), and ST77 (c) lines. Total differentially expressed genes for each line determined by each program and the number of shared genes for each is shown in (d). Numbers of differentially expressed (DE) genes shared between each line from the edgeR results are illustrated in (e). Significance was defined by an FDR of 0.01
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Fig5: Venn diagrams of differentially expressed genes between edgeR and cufflinks. Numbers of genes that are up and down-regulated in both edgeR and cufflinks are shown for 764 (a), ST111 (b), and ST77 (c) lines. Total differentially expressed genes for each line determined by each program and the number of shared genes for each is shown in (d). Numbers of differentially expressed (DE) genes shared between each line from the edgeR results are illustrated in (e). Significance was defined by an FDR of 0.01

Mentions: Cufflinks software was used in addition to edgeR to investigate differential expression and revealed similar differences in gene expression. Cufflinks reported 47 upregulated and 28 downregulated genes in ST77, 744 upregulated and 361 downregulated genes in ST111 and 1249 upregulated and 843 downregulated genes in 764. Volcano plots were constructed from the results and are shown in Fig. 4. These plots show the relationship between fold change and statistical significance of differentially expressed genes. Note that there is >20-fold difference in the number of differentially expressed (DE) genes between ST77 and 764. Thus, it is clear from both the edgeR and cufflinks results that while there were significant differences in all three transgenic events, differences were the most substantial in line 764 relative to the wild type controls. The results of these two programs are illustrated in Fig. 5. The Venn diagrams (Fig. 5a–c) indicate the number of up and downregulated genes identified by each program separately and together, while the bar chart (Fig. 5d) shows the total number of upregulated and downregulated genes as well as the portion of shared genes identified from each program. Five genes were differentially expressed in all three transgenic lines, including Glyma.12G136600 (protein kinase), Glyma.13G171200 (ribosomal RNA protein-7 related), Glyma.01G103100 (branched chain alpha-keto acid decarboxylase E1 beta subunit), and two genes with no functional annotation information (Glyma.07G207000, Glyma.13G011800). Glyma.01G103100 and Glyma.13G171200 showed no commonality between the three events in the direction of altered expression; however Glyma.01G103100, Glyma.07G207000, and Glyma 13.G011800 were upregulated in all three transgenics. Fig. 5e shows the number of common DE genes shared between each of the three lines based on the edgeR results. A list of all shared differentially expressed genes between all events is available in the git repository file “Diffexpoverlap” under the “DiffExp” directory.Fig. 4


A Comparison of transgenic and wild type soybean seeds: analysis of transcriptome profiles using RNA-Seq.

Lambirth KC, Whaley AM, Blakley IC, Schlueter JA, Bost KL, Loraine AE, Piller KJ - BMC Biotechnol. (2015)

Venn diagrams of differentially expressed genes between edgeR and cufflinks. Numbers of genes that are up and down-regulated in both edgeR and cufflinks are shown for 764 (a), ST111 (b), and ST77 (c) lines. Total differentially expressed genes for each line determined by each program and the number of shared genes for each is shown in (d). Numbers of differentially expressed (DE) genes shared between each line from the edgeR results are illustrated in (e). Significance was defined by an FDR of 0.01
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4591623&req=5

Fig5: Venn diagrams of differentially expressed genes between edgeR and cufflinks. Numbers of genes that are up and down-regulated in both edgeR and cufflinks are shown for 764 (a), ST111 (b), and ST77 (c) lines. Total differentially expressed genes for each line determined by each program and the number of shared genes for each is shown in (d). Numbers of differentially expressed (DE) genes shared between each line from the edgeR results are illustrated in (e). Significance was defined by an FDR of 0.01
Mentions: Cufflinks software was used in addition to edgeR to investigate differential expression and revealed similar differences in gene expression. Cufflinks reported 47 upregulated and 28 downregulated genes in ST77, 744 upregulated and 361 downregulated genes in ST111 and 1249 upregulated and 843 downregulated genes in 764. Volcano plots were constructed from the results and are shown in Fig. 4. These plots show the relationship between fold change and statistical significance of differentially expressed genes. Note that there is >20-fold difference in the number of differentially expressed (DE) genes between ST77 and 764. Thus, it is clear from both the edgeR and cufflinks results that while there were significant differences in all three transgenic events, differences were the most substantial in line 764 relative to the wild type controls. The results of these two programs are illustrated in Fig. 5. The Venn diagrams (Fig. 5a–c) indicate the number of up and downregulated genes identified by each program separately and together, while the bar chart (Fig. 5d) shows the total number of upregulated and downregulated genes as well as the portion of shared genes identified from each program. Five genes were differentially expressed in all three transgenic lines, including Glyma.12G136600 (protein kinase), Glyma.13G171200 (ribosomal RNA protein-7 related), Glyma.01G103100 (branched chain alpha-keto acid decarboxylase E1 beta subunit), and two genes with no functional annotation information (Glyma.07G207000, Glyma.13G011800). Glyma.01G103100 and Glyma.13G171200 showed no commonality between the three events in the direction of altered expression; however Glyma.01G103100, Glyma.07G207000, and Glyma 13.G011800 were upregulated in all three transgenics. Fig. 5e shows the number of common DE genes shared between each of the three lines based on the edgeR results. A list of all shared differentially expressed genes between all events is available in the git repository file “Diffexpoverlap” under the “DiffExp” directory.Fig. 4

Bottom Line: However, the effects of expressing recombinant protein at high levels on bean physiology are not well understood.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. kclambirth@uncc.edu.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus