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Transcriptome sequencing of Eucalyptus camaldulensis seedlings subjected to water stress reveals functional single nucleotide polymorphisms and genes under selection.

Thumma BR, Sharma N, Southerton SG - BMC Genomics (2012)

Bottom Line: Of the 287 positively selected genes, ninety genes showed differential expression and 27 SNPs from 17 positively selected genes showed differential allelic expression between treatments.Correlation of allelic expression of several SNPs with total gene expression indicates that these variants may be the cis-acting variants or in linkage disequilibrium with such variants.Enrichment of apoptosis and cell death gene categories among the positively selected genes reveals the past selection pressures experienced by the populations used in this study.

View Article: PubMed Central - HTML - PubMed

Affiliation: CSIRO Plant Industry, Clunies Ross Street, Acton, ACT, Australia. reddy.thumma@csiro.au

ABSTRACT

Background: Water stress limits plant survival and production in many parts of the world. Identification of genes and alleles responding to water stress conditions is important in breeding plants better adapted to drought. Currently there are no studies examining the transcriptome wide gene and allelic expression patterns under water stress conditions. We used RNA sequencing (RNA-seq) to identify the candidate genes and alleles and to explore the evolutionary signatures of selection.

Results: We studied the effect of water stress on gene expression in Eucalyptus camaldulensis seedlings derived from three natural populations. We used reference-guided transcriptome mapping to study gene expression. Several genes showed differential expression between control and stress conditions. Gene ontology (GO) enrichment tests revealed up-regulation of 140 stress-related gene categories and down-regulation of 35 metabolic and cell wall organisation gene categories. More than 190,000 single nucleotide polymorphisms (SNPs) were detected and 2737 of these showed differential allelic expression. Allelic expression of 52% of these variants was correlated with differential gene expression. Signatures of selection patterns were studied by estimating the proportion of nonsynonymous to synonymous substitution rates (Ka/Ks). The average Ka/Ks ratio among the 13,719 genes was 0.39 indicating that most of the genes are under purifying selection. Among the positively selected genes (Ka/Ks > 1.5) apoptosis and cell death categories were enriched. Of the 287 positively selected genes, ninety genes showed differential expression and 27 SNPs from 17 positively selected genes showed differential allelic expression between treatments.

Conclusions: Correlation of allelic expression of several SNPs with total gene expression indicates that these variants may be the cis-acting variants or in linkage disequilibrium with such variants. Enrichment of apoptosis and cell death gene categories among the positively selected genes reveals the past selection pressures experienced by the populations used in this study.

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Multi-dimensional scaling (MDS) plot of gene expression of 12 RNA-seq libraries. S0 and C0 were samples collected at the beginning of the treatment. Control samples (C1) and stressed samples (S1) collected at the end of the treatment; K = Katherine, P = Petford, M = Mount Isa.
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Figure 2: Multi-dimensional scaling (MDS) plot of gene expression of 12 RNA-seq libraries. S0 and C0 were samples collected at the beginning of the treatment. Control samples (C1) and stressed samples (S1) collected at the end of the treatment; K = Katherine, P = Petford, M = Mount Isa.

Mentions: Reads from all the 12 libraries were mapped against the Eucalyptus reference genome sequence to generate gene annotations using the TopHat and Cufflinks packages. A total of 32,474 transcripts were predicted including a large number of alternatively spliced transcripts. The identity of the transcripts was investigated by BLAST searches against the Arabidopsis protein database. This analysis revealed 15,538 unique genes from the total transcripts. Read counts mapping to the gene annotations generated by reference-guided transcriptome mapping were used for testing differential expression of the genes between control (C1) and stress (S1) treatments using the edgeR package. Before testing for differential expression, diagnostic tests were performed to test the consistency of the data between the populations. A high correlation was observed in gene expression between the three populations from a given treatment as measured by the read counts. The Pearson’s correlation coefficient between the read counts of the three populations before stress treatment (total six libraries 3 from S0 and three from C0) ranged from 0.94 to 0.99 and the correlation coefficient between the three populations of control plants at the end of the experiment (C1) ranged from 0.93 to 0.95. Similarly in the stress treatment (S1) the correlation coefficients between the populations ranged from 0.94 to 0.97 (Additional file 3). This is further reflected in clustering analysis. Multi-dimensional scaling (MDS) plot of the count data clearly separated the 12 libraries into three groups (Figure 2). The six libraries from the three populations before treatment (S0 and C0) were clustered together. Similarly the three populations of control plants (C1) at the end of the treatment clustered together while populations from the stress treatment (S1) formed another cluster. As there is a high degree of similarity between the populations from a treatment, reads from each population from a treatment were used as biological replicates in testing for differential expression.


Transcriptome sequencing of Eucalyptus camaldulensis seedlings subjected to water stress reveals functional single nucleotide polymorphisms and genes under selection.

Thumma BR, Sharma N, Southerton SG - BMC Genomics (2012)

Multi-dimensional scaling (MDS) plot of gene expression of 12 RNA-seq libraries. S0 and C0 were samples collected at the beginning of the treatment. Control samples (C1) and stressed samples (S1) collected at the end of the treatment; K = Katherine, P = Petford, M = Mount Isa.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472208&req=5

Figure 2: Multi-dimensional scaling (MDS) plot of gene expression of 12 RNA-seq libraries. S0 and C0 were samples collected at the beginning of the treatment. Control samples (C1) and stressed samples (S1) collected at the end of the treatment; K = Katherine, P = Petford, M = Mount Isa.
Mentions: Reads from all the 12 libraries were mapped against the Eucalyptus reference genome sequence to generate gene annotations using the TopHat and Cufflinks packages. A total of 32,474 transcripts were predicted including a large number of alternatively spliced transcripts. The identity of the transcripts was investigated by BLAST searches against the Arabidopsis protein database. This analysis revealed 15,538 unique genes from the total transcripts. Read counts mapping to the gene annotations generated by reference-guided transcriptome mapping were used for testing differential expression of the genes between control (C1) and stress (S1) treatments using the edgeR package. Before testing for differential expression, diagnostic tests were performed to test the consistency of the data between the populations. A high correlation was observed in gene expression between the three populations from a given treatment as measured by the read counts. The Pearson’s correlation coefficient between the read counts of the three populations before stress treatment (total six libraries 3 from S0 and three from C0) ranged from 0.94 to 0.99 and the correlation coefficient between the three populations of control plants at the end of the experiment (C1) ranged from 0.93 to 0.95. Similarly in the stress treatment (S1) the correlation coefficients between the populations ranged from 0.94 to 0.97 (Additional file 3). This is further reflected in clustering analysis. Multi-dimensional scaling (MDS) plot of the count data clearly separated the 12 libraries into three groups (Figure 2). The six libraries from the three populations before treatment (S0 and C0) were clustered together. Similarly the three populations of control plants (C1) at the end of the treatment clustered together while populations from the stress treatment (S1) formed another cluster. As there is a high degree of similarity between the populations from a treatment, reads from each population from a treatment were used as biological replicates in testing for differential expression.

Bottom Line: Of the 287 positively selected genes, ninety genes showed differential expression and 27 SNPs from 17 positively selected genes showed differential allelic expression between treatments.Correlation of allelic expression of several SNPs with total gene expression indicates that these variants may be the cis-acting variants or in linkage disequilibrium with such variants.Enrichment of apoptosis and cell death gene categories among the positively selected genes reveals the past selection pressures experienced by the populations used in this study.

View Article: PubMed Central - HTML - PubMed

Affiliation: CSIRO Plant Industry, Clunies Ross Street, Acton, ACT, Australia. reddy.thumma@csiro.au

ABSTRACT

Background: Water stress limits plant survival and production in many parts of the world. Identification of genes and alleles responding to water stress conditions is important in breeding plants better adapted to drought. Currently there are no studies examining the transcriptome wide gene and allelic expression patterns under water stress conditions. We used RNA sequencing (RNA-seq) to identify the candidate genes and alleles and to explore the evolutionary signatures of selection.

Results: We studied the effect of water stress on gene expression in Eucalyptus camaldulensis seedlings derived from three natural populations. We used reference-guided transcriptome mapping to study gene expression. Several genes showed differential expression between control and stress conditions. Gene ontology (GO) enrichment tests revealed up-regulation of 140 stress-related gene categories and down-regulation of 35 metabolic and cell wall organisation gene categories. More than 190,000 single nucleotide polymorphisms (SNPs) were detected and 2737 of these showed differential allelic expression. Allelic expression of 52% of these variants was correlated with differential gene expression. Signatures of selection patterns were studied by estimating the proportion of nonsynonymous to synonymous substitution rates (Ka/Ks). The average Ka/Ks ratio among the 13,719 genes was 0.39 indicating that most of the genes are under purifying selection. Among the positively selected genes (Ka/Ks > 1.5) apoptosis and cell death categories were enriched. Of the 287 positively selected genes, ninety genes showed differential expression and 27 SNPs from 17 positively selected genes showed differential allelic expression between treatments.

Conclusions: Correlation of allelic expression of several SNPs with total gene expression indicates that these variants may be the cis-acting variants or in linkage disequilibrium with such variants. Enrichment of apoptosis and cell death gene categories among the positively selected genes reveals the past selection pressures experienced by the populations used in this study.

Show MeSH
Related in: MedlinePlus