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Establishment and analysis of a reference transcriptome for Spodoptera frugiperda.

Legeai F, Gimenez S, Duvic B, Escoubas JM, Gosselin Grenet AS, Blanc F, Cousserans F, Séninet I, Bretaudeau A, Mutuel D, Girard PA, Monsempes C, Magdelenat G, Hilliou F, Feyereisen R, Ogliastro M, Volkoff AN, Jacquin-Joly E, d'Alençon E, Nègre N, Fournier P - BMC Genomics (2014)

Bottom Line: We conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome.While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts.Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae.

View Article: PubMed Central - PubMed

Affiliation: INRA, UMR1333, DGIMI, Montpellier, France. nicolas.negre@univ-montp2.fr.

ABSTRACT

Background: Spodoptera frugiperda (Noctuidae) is a major agricultural pest throughout the American continent. The highly polyphagous larvae are frequently devastating crops of importance such as corn, sorghum, cotton and grass. In addition, the Sf9 cell line, widely used in biochemistry for in vitro protein production, is derived from S. frugiperda tissues. Many research groups are using S. frugiperda as a model organism to investigate questions such as plant adaptation, pest behavior or resistance to pesticides.

Results: In this study, we constructed a reference transcriptome assembly (Sf_TR2012b) of RNA sequences obtained from more than 35 S. frugiperda developmental time-points and tissue samples. We assessed the quality of this reference transcriptome by annotating a ubiquitous gene family--ribosomal proteins--as well as gene families that have a more constrained spatio-temporal expression and are involved in development, immunity and olfaction. We also provide a time-course of expression that we used to characterize the transcriptional regulation of the gene families studied.

Conclusion: We conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome. While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts. That allowed us to explore the spatial and temporal expression of genes and to observe that some olfactory receptors are expressed in antennae and palps but also in other non related tissues such as fat bodies. Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae.

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qPCR validation of differential expression. A. Heatmap representing the expression in rpm of each candidate transcript tested by qPCR. Genes are ordered from top to bottom from a lesser ratio between eggs and L2e samples, as measured in qPCR to a higher ratio. The red box shows the 2 genes that we used to normalize the qPCR. B. Same heatmap as in A. but showing expression as z-scores scaled by row to highlight the differential expression between eggs and L2e. C. Barplot showing the ratio measured in qPCR, using elongation factor 3 as a negative control for normalization. D. Scatter plot showing the correlation between fold changes measured by RNAseq (y axis) and ratios measured by qPCR for the tested genes. The two measurements have a correlation coefficient of 0.74. A linear regression model has been applied and is also shown on the same graph.
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Fig5: qPCR validation of differential expression. A. Heatmap representing the expression in rpm of each candidate transcript tested by qPCR. Genes are ordered from top to bottom from a lesser ratio between eggs and L2e samples, as measured in qPCR to a higher ratio. The red box shows the 2 genes that we used to normalize the qPCR. B. Same heatmap as in A. but showing expression as z-scores scaled by row to highlight the differential expression between eggs and L2e. C. Barplot showing the ratio measured in qPCR, using elongation factor 3 as a negative control for normalization. D. Scatter plot showing the correlation between fold changes measured by RNAseq (y axis) and ratios measured by qPCR for the tested genes. The two measurements have a correlation coefficient of 0.74. A linear regression model has been applied and is also shown on the same graph.

Mentions: When designing the reference transcriptome, we emphasized the production of 6 developmental time-points specific RNA libraries to be sequenced by Illumina. We were interested in the specificity of development of S. frugiperda compared to other insects. Indeed, S. frugiperda is a pest in its larval stage and resistance to common pesticides has become a particularly prevalent issue [37]. We reasoned that identifying developmental genes specific to S. frugiperda would provide the community with specific targets for the development of new strategies of pest control. To identify genes involved in the embryonic development of S. frugiperda, we focused on the comparison of 2 Illumina RNAseq libraries sequenced from eggs and L2 stage larvae RNA extracts. We used the R framework package DEseq to identify genes that are overexpressed in eggs and no longer in L2 and that are thus required only during embryonic development. 117 genes have been identified with a striking pattern of embryo only expression (Additional file 8: Figure S3). We randomly selected 16 candidates from this list to confirm by quantitative PCR that our candidates correspond to genes that are effectively transcribed in S. frugiperda and are effectively transcribed in a regulatory fashion (Figure 5A-B). We also chose 2 negative controls with high expression at all stages (elf3 and nucleolar protein 58-like) and an rbp protein (rbpL8) with non detectable expression at all stages. 15/16 tested had a significantly higher expression in embryos than in L2 larvae in qPCR (Figure 5C). In addition, we observed that our differential expression measurements by RNAseq and by qPCR are linearly correlated (Figure 5D). While some of the genes we selected are well known in other organisms to regulate development (such as even-skipped, rpd3 or ISWI), we also included genes for which no clear orthology was detected. These transcripts, such as joint2_rep_c945, joint2_rep_c7748 and joint2_rep_c1530 might represent Lepidoptera specific embryonic genes important for embryonic development, which makes them interesting targets for the development of new pest control strategies.Figure 5


Establishment and analysis of a reference transcriptome for Spodoptera frugiperda.

Legeai F, Gimenez S, Duvic B, Escoubas JM, Gosselin Grenet AS, Blanc F, Cousserans F, Séninet I, Bretaudeau A, Mutuel D, Girard PA, Monsempes C, Magdelenat G, Hilliou F, Feyereisen R, Ogliastro M, Volkoff AN, Jacquin-Joly E, d'Alençon E, Nègre N, Fournier P - BMC Genomics (2014)

qPCR validation of differential expression. A. Heatmap representing the expression in rpm of each candidate transcript tested by qPCR. Genes are ordered from top to bottom from a lesser ratio between eggs and L2e samples, as measured in qPCR to a higher ratio. The red box shows the 2 genes that we used to normalize the qPCR. B. Same heatmap as in A. but showing expression as z-scores scaled by row to highlight the differential expression between eggs and L2e. C. Barplot showing the ratio measured in qPCR, using elongation factor 3 as a negative control for normalization. D. Scatter plot showing the correlation between fold changes measured by RNAseq (y axis) and ratios measured by qPCR for the tested genes. The two measurements have a correlation coefficient of 0.74. A linear regression model has been applied and is also shown on the same graph.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4150953&req=5

Fig5: qPCR validation of differential expression. A. Heatmap representing the expression in rpm of each candidate transcript tested by qPCR. Genes are ordered from top to bottom from a lesser ratio between eggs and L2e samples, as measured in qPCR to a higher ratio. The red box shows the 2 genes that we used to normalize the qPCR. B. Same heatmap as in A. but showing expression as z-scores scaled by row to highlight the differential expression between eggs and L2e. C. Barplot showing the ratio measured in qPCR, using elongation factor 3 as a negative control for normalization. D. Scatter plot showing the correlation between fold changes measured by RNAseq (y axis) and ratios measured by qPCR for the tested genes. The two measurements have a correlation coefficient of 0.74. A linear regression model has been applied and is also shown on the same graph.
Mentions: When designing the reference transcriptome, we emphasized the production of 6 developmental time-points specific RNA libraries to be sequenced by Illumina. We were interested in the specificity of development of S. frugiperda compared to other insects. Indeed, S. frugiperda is a pest in its larval stage and resistance to common pesticides has become a particularly prevalent issue [37]. We reasoned that identifying developmental genes specific to S. frugiperda would provide the community with specific targets for the development of new strategies of pest control. To identify genes involved in the embryonic development of S. frugiperda, we focused on the comparison of 2 Illumina RNAseq libraries sequenced from eggs and L2 stage larvae RNA extracts. We used the R framework package DEseq to identify genes that are overexpressed in eggs and no longer in L2 and that are thus required only during embryonic development. 117 genes have been identified with a striking pattern of embryo only expression (Additional file 8: Figure S3). We randomly selected 16 candidates from this list to confirm by quantitative PCR that our candidates correspond to genes that are effectively transcribed in S. frugiperda and are effectively transcribed in a regulatory fashion (Figure 5A-B). We also chose 2 negative controls with high expression at all stages (elf3 and nucleolar protein 58-like) and an rbp protein (rbpL8) with non detectable expression at all stages. 15/16 tested had a significantly higher expression in embryos than in L2 larvae in qPCR (Figure 5C). In addition, we observed that our differential expression measurements by RNAseq and by qPCR are linearly correlated (Figure 5D). While some of the genes we selected are well known in other organisms to regulate development (such as even-skipped, rpd3 or ISWI), we also included genes for which no clear orthology was detected. These transcripts, such as joint2_rep_c945, joint2_rep_c7748 and joint2_rep_c1530 might represent Lepidoptera specific embryonic genes important for embryonic development, which makes them interesting targets for the development of new pest control strategies.Figure 5

Bottom Line: We conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome.While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts.Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae.

View Article: PubMed Central - PubMed

Affiliation: INRA, UMR1333, DGIMI, Montpellier, France. nicolas.negre@univ-montp2.fr.

ABSTRACT

Background: Spodoptera frugiperda (Noctuidae) is a major agricultural pest throughout the American continent. The highly polyphagous larvae are frequently devastating crops of importance such as corn, sorghum, cotton and grass. In addition, the Sf9 cell line, widely used in biochemistry for in vitro protein production, is derived from S. frugiperda tissues. Many research groups are using S. frugiperda as a model organism to investigate questions such as plant adaptation, pest behavior or resistance to pesticides.

Results: In this study, we constructed a reference transcriptome assembly (Sf_TR2012b) of RNA sequences obtained from more than 35 S. frugiperda developmental time-points and tissue samples. We assessed the quality of this reference transcriptome by annotating a ubiquitous gene family--ribosomal proteins--as well as gene families that have a more constrained spatio-temporal expression and are involved in development, immunity and olfaction. We also provide a time-course of expression that we used to characterize the transcriptional regulation of the gene families studied.

Conclusion: We conclude that the Sf_TR2012b transcriptome is a valid reference transcriptome. While its reliability decreases for the detection and annotation of genes under strong transcriptional constraint we still recover a fair percentage of tissue-specific transcripts. That allowed us to explore the spatial and temporal expression of genes and to observe that some olfactory receptors are expressed in antennae and palps but also in other non related tissues such as fat bodies. Similarly, we observed an interesting interplay of gene families involved in immunity between fat bodies and antennae.

Show MeSH
Related in: MedlinePlus