Figure 1

Identification of upregulated gametocyte transcripts using differential expression analysis. (a) The eight RNA Seq libraries were subjected to pair-wise hierarchical clustering between the vectors of read counts for all pairs of samples. Heat-mapping of Pearson’s correlation coefficient scores reveal similarities between the transcriptome profiles of the different samples, with white (Pearson R =1.0) being the most similar and red (Pearson R = 0.0) being the least similar. Adjacent dendograms reveal clustering of the biological replicates within the same developmental stages, including gametocytes (gam), merozoites (mer) and sporozoites (spor). (b, c) Normalised read counts of individual genes calculated for gametocytes have been plotted against those for merozoites (b) and sporozoites (c). Gene transcripts calculated to be upregulated in gametocytes compared with merozoites or sporozoites reside between dotted red or green dotted-lines, respectively. The dotted blue lines indicate equal levels of transcript abundance between samples. For the sake of clarity, the axis limits have been set to a maximum normalised count of 10,000, inevitably excluding some highly expressed gene transcripts. (d) Summary of the DE analysis of gametocytes compared with merozoites (G vs M) or sporozoites (G vs S) showing the percentage of genes upregulated (blue) or downregulated (red) in gametocytes, of all predicted E. tenella genes. (e) Venn diagram revealing the overlap of genes whose transcript levels were upregulated in gametocytes compared with either merozoites (left, G > M) or sporozoites (right, G > S). A total of 863 upregulated gametocyte transcripts genes were identified in this overlapping region.