Differential transcriptomic responses of Biomphalaria glabrata (Gastropoda, Mollusca) to bacteria and metazoan parasites, Schistosoma mansoni and Echinostoma paraensei (Digenea, Platyhelminthes)

Abstract

A 70-mer-oligonucleotide-based microarray (1152 features) that emphasizes stress and immune responses factors was constructed to study transcriptomic responses of the snail Biomphalaria glabrata to different immune challenges. In addition to sequences with relevant putative ID and Gene Ontology (GO) annotation, the array features non-immune factors and unknown B. glabrata ESTs for functional gene discovery. The transcription profiles of B. glabrata (3 biological replicates, each a pool of 5 snails) were recorded at 12h post-wounding, exposure to Gram negative or Gram positive bacteria (Escherichia coli and Micrococcus luteus, respectively), or infection with compatible trematode parasites (Schistosoma mansoni or Echinostoma paraensei, 20 miracidia/snail), relative to controls, using universal reference RNA. The data were subjected to Significance Analysis for Microarrays (SAM), with a false positive rate (FPR) <or=10%. Wounding yielded a modest differential expression profile (27 up/21 down) with affected features mostly dissimilar from other treatments. Partially overlapping, yet distinct expression profiles were recorded from snails challenged with E. coli (83 up/20 down) or M. luteus (120 up/42 down), mostly showing up-regulation of defense and stress-related features. Significantly altered expression of selected immune features indicates that B. glabrata detects and responds differently to compatible trematodes. Echinostoma paraensei infection was associated mostly with down-regulation of many (immune-) transcripts (42 up/68 down), whereas S. mansoni exposure yielded a preponderance of up-regulated features (140 up/23 down), with only few known immune genes affected. These observations may reflect the divergent strategies developed by trematodes during their evolution as specialized pathogens of snails to negate host defense responses. Clearly, the immune defenses of B. glabrata distinguish and respond differently to various immune challenges.

Figure 1. Reproducibility of the B. glabrata oligo-based microarray

Three different arrays were hybridized with cDNA probes derived from RNA of three independent pools of untreated control B. glabrata (10–12mm shell diameter, n=5/pool). Pairwise comparisons showed that the signals obtained for features of the array (expressed as log₂ ratio of experimental versus universal RNA reference) grouped closely along a 45° angle line (R² values ranging from 0.82 to 0.94). This demonstrates that biological replicate samples yield highly similar signals between array hybridizations. C1, C2, C3 designate the cDNA probes produced with different biological replicates.

Figure 2. Transcriptional responses of B. glabrata to experimental challenges

The number of features that were up- or down-regulated at 12 hours after challenge is shown for each experimental treatment. False Positive Rate ≤ 10%. Features with a (putative) ID are numbered in black bars, white bars indicate unknown (novel) sequences.

Figure 3. Response of B. glabrata to wounding compared to other challenges

Transcripts significantly increased (a) or decreased (b) at 12 hours post wounding were modest in number and encompassed different sequences in relation to the features affected by challenge with bacteria and trematodes (shown combined). For wounding, the total number of differentially expressed features is shown with the number features with unknown function in brackets. False Positive Rate ≤ 10%.

Figure 4. Comparison of transcription profiles of B. glabrata after challenge with bacteria or digenetic trematodes

These Venn diagrams show the number of shared and unique features that were up-regulated (a) or down-regulated (b) at 12 hour post exposure to E.coli (Gram negative), M. luteus (Gram positive), E. paraensei and S. mansoni. Each challenge yielded a distinct transcriptome. Note the absence of overlap in features with decreased expression in response to E. paraensei and S. mansoni. The numbers in the Venn diagram represent total number of differentially expressed features with the number of features with unknown function shown in brackets. False Positive Rate ≤ 10%.

Figure 5. Impact of different challenges on categories of immune or stress response features

The number of differentially expressed features that was assigned to an immune or stress response category (based on putative ID and GO annotation) is presented for each separate experimental treatment. False Positive Rate ≤ 10%. Bacterial challenge evoked transcriptional responses with mostly increased expression. The responses of B. glabrata to the compatible digenean parasites E. paraensei and S. mansoni include many down-regulated features, likely reflecting less effective immune responses.

Figure 6. Differential expression validated by reverse transcripton quantitative PCR

RT-qPCR was applied as independent means to validate differential expression as detected by the microarray approach. Primers were designed for selected features and qPCR was performed with the same templates that were used to generate the cDNA probes for the microarray experiments. While the results tended to indicate greater fold change in expression, RT-qPCR results did correlate closely to the trends observed in transcript expression for all the array treatments.