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Beetle (gut) juice - exploring a unique source for novel enzyme activities
Aurelie Laugraud, Paul Maclean, Sean Marshall
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Beetle (gut) juice exploring a unique source for novel enzyme activities - aurelie laugraud

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The geographic separation of New Zealand (NZ) from other major land masses for >80 million years has resulted in the evolution of unique flora and fauna. Among these are scarab beetles, whose larvae consume plant biomass including roots, wood, and humus. Like ruminant herbivores, NZ scarab beetles rely on a community of cellulolytic endosymbiotic microorganisms in their gut to convert recalcitrant plant matter into useful end products, such as energy. We hypothesized that scarab insect guts function as miniature bioreactors and are, thus, a useful model to obtain fundamental insights into the digestion of complex plant material. To begin testing this idea we employed next generation sequencing (NGS) to look for candidate genes involved in a range of enzymatic activities from the hindgut of NZ scarab larvae. Here we wish to discuss bioinformatics methods to analyse the NGS dataset. More than 100 Gbp of RNA-Seq (Illumina HiSeq, paired-end, 100bp, 400bp insert) were generated. We want to mine this data for cellulolytic genes and species/organisms present within scarab hindgut tissue. The absence of a reference genome for scarab makes this a challenging meta-transcriptomics study. We tested some of the commonly used tools available and discuss to what extend they are relevant for our dataset. A strategy combining the best parts of publicly available tools with our own adjustments is presented.

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Beetle (gut) juice exploring a unique source for novel enzyme activities - aurelie laugraud

  1. 1. Beetle (gut) juice - exploring a unique source for novel enzyme activities Aurelie Laugraud, Paul Maclean, Sean Marshall AgResearch - Lincoln, Private Bag 4749, Christchurch 8140, New Zealand Correspondence: aurelie.laugraud@agresearch.co.nz, sean.marshall@agresearch.co.nz Introduction The geographic separation of New Zealand (NZ) from other major land masses for >80 million years has resulted in the evolution of unique flora and fauna. Among these are scarab beetles, whose larvae consume plant biomass including roots, wood, and humus. Like ruminant herbivores, NZ scarab beetles rely on a community of cellulolytic endosymbiotic microorganisms in their gut to convert recalcitrant plant matter into useful end products, such as energy. We hypothesized that scarab insect guts function as miniature bioreactors and are, thus, a useful model to obtain fundamental insights into the digestion of complex plant material. To begin testing this idea we employed 100 Gbp of RNA-Seq (Illumina HiSeq, paired-end, 100bp, 400bp insert) to look for candidate genes involved in a range of enzymatic activities from the hindgut of NZ scarab larvae. Quality checking o The sequence data was first run through a workflow to remove adapters, low quality bases, and to reduce the size of the dataset for submission. o This reduced the dataset from over 100 million reads to just over 17 millions. o The initial analysis of the metatranscriptome dataset suggested that further quality control was needed. Legend : on the left : MG-RAST QC, above : EBI QC. Taxonomy The EBI (right) pipeline only handles prokaryotic annotations, while the MG-RAST (left) taxonomy annotation results include both prokaryote and eukaryote outputs. However, it will be of interest to determine the accuracy of these analyses, as the MG-RAST output has suggested the presence of primate transcripts in the sand scarab gut… Functional analysis Comparison o We have run two pipelines (MG-RAST and EBI Metagenomics) and used some in-house developed analysis. Although they look for the same characteristics (taxonomy, functional domains) they use different strategies and combining the results is very valuable to support any finding. In the near future we will also include MEGAN’s results. o Data submission for MG-RAST and EBI were quite similar. MEGAN runs locally and requires much more pre-processing. MG-RAST and MEGAN seem to give the most complete analysis. o Our approach was to assemble the transcripts using velvet/oases and trinity and then predict the functions using hmmscan with P-FAM. Improved results may be obtained if the dataset is re-run using only the transcript assemblies that have passed QC from the MG-RAST and EBI pipelines. o Future data-mining will delve deeper into the predicted The main biological question was to compare the microbial contribution to the transcript functionality using in-house AgR software combined with Interpro results and the functional analyses digestive systems of the NZ scarab and the grass grub species, which each utilize very different dietary strategies (plant root material vs. decaying driftwood). from MG-RAST. Although we have some interesting preliminary results, we have identified References • The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. Meyer, F. et al. BMC several issues that need to be overcome to adapt current metagenome analysis Bioinformatics 9, 386 (2008). • EBI Metagenomics https://www.ebi.ac.uk/metagenomics/ pipelines to allow a more complete metatransciptome analysis from these unique • Accelerated Profile HMM Searches. Eddy, S. R. PLoS Comput Biol 7, e1002195 (2011). • MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007). Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. NZ scarab digestive tract datasets. • Full-length transcriptome assembly from RNA-Seq data without a reference genome. Grabherr, M. G. et al. Nat Biotech 29, 644–652 (2011). • Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Zerbino, D. R. & Birney, E. Genome Res. 18, 821–829 (2008). Further refinements will focus on development of measurable analysis • Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. M.H. Schulz, D.R. Zerbino, M. Vingron and Ewan Birney. Bioinformatics, 2012. DOI: 10.1093/bioinformatics/bts094. • A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data C. Titus Brown et al. http://arxiv.org/abs/1203.4802 methods to allow more robust comparison and combination of result outputs • The Pfam protein families database. Punta, M. et al. Nucleic Acids Research 40, D290–D301 (2011). • InterPro in 2011: new developments in the family and domain prediction database. Hunter, S. et al. Nucleic Acids Research 40, 4725–4725 (2012). across pipelines. Conclusion

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