2.3.1. Quantitative differential transcriptional analyses among P. vivax stages

Recently completed studies of the transcriptome of P. vivax for SPZ activation (Roth et al., 2018), as well as liver (Gural et al., 2018) and asexual blood stages (Zhu et al., 2016), support comparative transcriptomic study of SPZs, their biology and transcriptional regulation over the P. vivax life-cycle. Data for activated SPZs from Roth et al. (2018) had significantly lower depth coverage, with ~0.03 to 0.6 M reads mapping to the P. vivax P01 mRNA transcripts; compared with 0.7 to 15.3 M, 2.4 to 10.6 M and 18.7 to 57.6 M mapped reads for salivary SPZ, liver stages (Gural et al., 2018) and asexual blood stages (Zhu et al., 2016), respectively (Supplementary Figs. S3 and S4). This lower coverage could not be compensated for through data normalization and therefore data from Roth et al. (2018) was not included in our quantitative analyses, although qualitatively, many of the highly transcribed genes in Roth et al. (2018) SPZs were among the highly transcribed genes in salivary SPZs from the present study. The remaining RNAseq data presents an analytical challenge in that each (SPZ, liver stage and blood stage) is produced in a separate study and may be influenced by technical batch effects. To address this, we first examined P. vivax transcripts in a previous microarray study of multiple P. vivax life-cycle stages (Westenberger et al., 2010), including SPZs and several blood stages, to identify genes that were the most transcriptionally stable across the life-cycle. We identified ~160 genes with low transcriptional variability between SPZs and blood stages that covered the breadth of transcript abundance levels in Westenberger et al. (2010). These include ribosomal proteins, histones, translation initiation complex proteins and various chaperones (see Supplementary Fig. S5). We assessed transcription of these 160 genes among the current and recently published RNA-seq data for P. vivax and all were of similarly low variability (Supplementary Fig. S6). This suggests that any batch effect between the studies is sufficiently lower than the biological differences between each life-cycle stage, allowing informative comparisons.

To define transcripts that were up-regulated in SPZs relative to blood stages, we remapped raw reads representing early (18-24 h p.i.), mid (30-40 h p.i.) and late (42-46 h p.i.) P. vivax blood stage infections from Zhu et al. (2016) to the P. vivax P01 transcripts using RSEM and quality controlled using Qualimap, as per Section 2.3. Differential transcription between P. vivax salivary gland SPZs and mixed blood stages (Zhu et al., 2016) was assessed using EC data in EdgeR (Nikolayeva and Robinson, 2014) and limma (Ritchie et al., 2015) (differential transcription cut-off: ≥ 2-fold change in counts per million (CPM) and a False Discovery Rate (FDR) ≤ 0.05). Pearson Chi squared tests were used to detect over-represented Pfam domains and Gene Ontology (GO) terms among differentially transcribed genes in SPZs (Bonferroni-corrected P < 0.05), based on gene annotations in PlasmoDB (release v29).

We also compared transcription of the SPZ stages with recently published liver stage data from Gural et al. (2018) as per the SPZ to blood stage comparisons above, with the following modifications: (i) EC values were normalized using the ‘upper quartile’ method instead of Trimmed mean of M-value (TMM) normalization, (ii) differential transcription was assessed using a quasi-likelihood generalize linear model (instead of a linear model) and (iii) an FDR threshold for significance of ≤ 0.01 was used instead of ≤ 0.05. These differences related to specific attributes of the liver stage dataset, particularly the small number of replicates (n = 2) per experimental condition. Data visualization and interactive R-shiny plots were produced in R using the ggplot2 (Wickham and Chang, 2008; https://cran.r-project.org/web/packages/ggplot2/index.html), gplots (heatmap.2) (Warnes et al., 2009; https://github.com/cran/gplots) and Glimma (Law et al., 2016) packages.