MicroRNAs (miRNAs) are a class of small noncoding RNAs that have important regulatory roles in multicellular organisms. However, miRNA has never been identified experimentally in protist. Direct cloning of 438 expressed miRNA tags by microRNA serial analysis of gene expression from the parasitic protist Trichomonas vaginalis identified nine candidate miRNAs. Bioinformatics analysis of the corresponding genomic region revealed that these miRNA candidates contain a classical stem-loop-stem structure of pre-microRNAs. Analysis of the 20 nt long mature tva-miR-001 showed that it is an intergenic miRNA located at the scaffold DS113596. Tva-miR-001 was differentially expressed in the trophozoite, pseudocyst and amoeboid stages. Based on the experimental results of the present study, we provided solid evidence that protist possesses a miRNA regulating network comparable with multicellular organisms for the first time.

Trichomoniasis caused by Trichomonas vaginalis is the most common sexual transmitted infection in the world. The 170-MB genome of this protozoan contains 60,000 genes, the largest number of genes ever identified in protozoan. High-throughput expression sequenced tag analysis showed that at least 4,000 genes were expressed in the trophozoite stage. In the present study, we use two-dimensional electrophoresis combined with matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis to profile, identify, and characterize proteins expressed in the trophozoite stage of T. vaginalis. A total of 247 spots representing 164 different proteins were identified. The identified proteins with known sequence or motif/domain homologies were further classified into groups according to their biological functions. Among them, proteins related to carbohydrate metabolism represented the most abundant category in the T. vaginalis proteome. This study presented the most extensive proteomic analysis of T. vaginalis to date and provided a reference proteome database for future comparative proteomic studies.

BackgroundNext-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared.ResultsHere, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours.ConclusionsIn this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.