Gene discovery from a pilot study of the transcriptomes from three diverse microbial eukaryotes: Corallomyxa tenera, Chilodonella uncinata, and Subulatomonas tetraspora

Abstract

Background: Characterizing genome-scale data from diverse eukaryotes is essential for gene discovery and for inferring major transitions across the eukaryotic tree of life. Yet, the bulk of eukaryotic diversity remains undersampled, particularly for free-living microbial lineages. Analysis of transcriptome data generated from high throughput (e.g. 454) sequencing of mRNAs provides an efficient way to characterize genes from diverse eukaryotes. Results: Here we report analyses of RNA-Seq data from the rhizarian net-like amoeba Corallomyxa tenera, the ciliate Chilodonella uncinata and a recently-described genus representing a novel major clade of eukaryotes, Subulatomonas tetraspora. We generated 16,983, 11,529 and 10,630 contigs plus single reads for these taxa respectively. Given that these organisms cannot be cultured axenically, we developed custom scripts to remove bacterial contaminants through an iterative BLAST based protocol and we then identified expressed genes using BLAST2GO [1;2]. This approach yielded a large number of genes with eukaryotic homologs, as well as numerous novel genes. To assess our approach and to explore the resulting sequences, we searched for genes involved in anaerobic metabolism, RNAi and meiosis. Further, we report the results of a preliminary phylogenomic analysis including these organisms. Conclusions: We characterized the transcriptomes of three phylogenetically diverse eukaryotes. After applying several filters to ensure the retention of only high-quality, non-contaminant data, we identified numerous sequences that can be used for gene discovery and phylogenomics. We found candidate genes involved in RNAi, meiosis, and anaerobic metabolism, and generated phylogenies that place the target taxa in positions predicted by previous analyses. This work supports the use of high throughput approaches for assessing features of non-model organisms, even in instances when species cannot be cultured axenically or grown to large numbers.

GENOME STRUCTURE DRIVES PATTERNS OF GENE FAMILY EVOLUTION IN CILIATES, A CASE STUDY USINGCHILODONELLA UNCINATA(PROTISTA, CILIOPHORA, PHYLLOPHARYNGEA)

“…The lack of homologs in other eukaryotes suggests that these genes are limited to C. uncinata and its relatives. Our interpretation of patterns here are limited by the relatively low coverage of Pol transcriptome data (Grant et al 2012) and the evidence that copy numbers of the expressed gene family members vary considerably (Bellec and Katz 2012). Based on these limitations, we predict that many more alternatively processed gene families may exist in C. uncinata.…”

“…of IESs = the number of unprocessed internal excised sequences (IES) that are contained in the sequences. Transcriptome data are from Grant et al ().…”

“…MAC = macronuclear genome; reads = the number of reads of each haplotype in the transcriptome (Grant et al ); clones = the number of clones from PCR reactions, which is a very rough proxy of copy number in macronuclear genome (Bellec and Katz ).…”

Comparative transcriptomics of Entelegyne spiders (Araneae, Entelegynae), with emphasis on molecular evolution of orphan genes

“…is similar to findings from previous transcriptomic analyses of a variety of different taxa. These include studies of other spiders (e.g., [31,33,76]), other arachnids (e.g., [8,83,84]), other metazoans (e.g.,[53,85,86]), and even non-metazoans (e.g., [87,88]). One previously suggested explanation for these similarities (e.g., [7]) is that transcriptome assemblies from short-read RNA-Seq analyses are biased toward the capture of highly expressed, conserved housekeeping genes.…”

Evolution of bacterial recombinase A ( recA ) in eukaryotes explained by addition of genomic data of key microbial lineages

“…We passed the assembled transcriptome data through a series of QC steps to remove rRNA and bacterial contaminants [14]. The obtained reads were assembled, using Trinity RNA-Seq de novo assembly TRINITY software [15].…”