FINDINGS: Here, we systematically enhanced the draft genome of S. haematobium using a single-molecule and long-range DNA-sequencing approach. We achieved a major improvement in the accuracy and contiguity of the genome assembly, making it superior or comparable to assemblies for other schistosome species. We transferred curated gene models to this assembly and, using enhanced gene annotation pipelines, inferred a gene set with as many or more complete gene models as those of other well-studied schistosomes. Using conserved, single-copy orthologs, we assessed the phylogenetic position of S. haematobium in relation to other parasitic flatworms for which draft genomes were available.
CONCLUSIONS: We report a substantially enhanced genomic resource that represents a solid foundation for molecular research on S. haematobium and is poised to better underpin population and functional genomic investigations and to accelerate the search for new disease interventions.
RESULTS: More than 15,000 partial sequences were generated from the 5' and 3' ends of clones randomly selected from an E. tenella second generation merozoite full-length cDNA library. Clustering of these sequences produced 1,529 unique transcripts (UTs). Based on the transcript assembly and subsequently primer walking, 433 full-length cDNA sequences were successfully generated. These sequences varied in length, ranging from 441 bp to 3,083 bp, with an average size of 1,647 bp. Simple sequence repeat (SSR) analysis identified CAG as the most abundant trinucleotide motif, while codon usage analysis revealed that the ten most infrequently used codons in E. tenella are UAU, UGU, GUA, CAU, AUA, CGA, UUA, CUA, CGU and AGU. Subsequent analysis of the E. tenella complete coding sequences identified 25 putative secretory and 60 putative surface proteins, all of which are now rational candidates for development as recombinant vaccines or drug targets in the effort to control avian coccidiosis.
CONCLUSIONS: This paper describes the generation and characterisation of full-length cDNA sequences from E. tenella second generation merozoites and provides new insights into the E. tenella transcriptome. The data generated will be useful for the development and validation of diagnostic and control strategies for coccidiosis and will be of value in annotation of the E. tenella genome sequence.
Methods: Microarray expression dataset GSE22255 was retrieved from the Gene Expression Omnibus (GEO) database. It includes messenger ribonucleic acid (mRNA) expression data for the peripheral blood mononuclear cells of 20 controls and 20 IS patients. The bioconductor-package 'affy' was used to calculate expression and a pairwise t-test was applied to screen DEGs (P < 0.01). Further, GSEA was used to determine the enrichment of DEGs specific to gene ontology (GO) annotations.
Results: GSEA analysis revealed 21 genes to be significantly plausible gene markers, enriched in multiple pathways among all the DEGs (n = 881). Ten gene sets were found to be core enriched in specific GO annotations. JunD, NCX3 and fibroblast growth factor receptor 4 (FGFR4) were under-represented and glycoprotein M6-B (GPM6B) was persistently over-represented.
Conclusion: The identified genes are either associated with the pathophysiology of IS or they affect post-IS neuronal regeneration, thereby influencing clinical outcome. These genes should, therefore, be evaluated for their utility as suitable markers for predicting IS in clinical scenarios.
Methods: In this present study, two protein extractions methods were performed to analyze female Ae. aegyti proteome, via TCA acetone precipitation extraction method and a commercial protein extraction reagent CytoBusterTM. Then, protein identification was performed by LC-ESI-MS/MS and followed by functional protein annotation analysis.
Results: The CytoBusterTM reagent gave the highest protein yield with a mean of 475.90 µg compared to TCA acetone precipitation extraction showed 283.15 µg mean of protein. LC-ESI-MS/MS identified 1,290 and 890 proteins from the CytoBusterTM reagent and TCA acetone precipitation, respectively. When comparing the protein class categories in both methods, there were three additional categories for proteins identified using CytoBusterTM reagent. The proteins were related to scaffold/adaptor protein (PC00226), protein binding activity modulator (PC00095) and intercellular signal molecule (PC00207). In conclusion, the CytoBusterTM protein extraction reagent showed a better performance for the extraction of proteins in term of the protein yield, proteome coverage and extraction speed.