Dataset Information

Parallel deep transcriptome and proteome analysis of zebrafish larvae.

ABSTRACT: BACKGROUND: Sensitivity and throughput of transcriptomic and proteomic technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNA-seq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and for any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in embryos of zebrafish, which is an upcoming model in disease studies. RESULTS: We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 18,230 genes. Gene expression was also found to correlate with the abundance of 963 distinct proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. CONCLUSIONS: By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories in zebrafish. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies.

SUBMITTER: Palmblad M

PROVIDER: S-EPMC4016144 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Transcriptome analysis is a powerful tool to obtain large amount genome-scale gene expression profiles. Despite its extensive usage to diverse biological problems in the last decade, transcriptomic researches approaching the zebrafish embryonic development have been very limited. Several recent studies have made great progress in this direction, yet the large gap still exists, especially regarding to the transcriptome dynamics of embryonic stages from early gastrulation onwards. Here, we present a comprehensive analysis about the transcriptomes of 9 different stages covering 7 major periods (cleavage, blastula, gastrula, segmentation, pharyngula, hatching and early larval stage) in zebrafish development, by recruiting the RNA-sequencing technology. We detected the expression for at least 24,065 genes in at least one of the 9 stages. We identified 16,130 genes that were significantly differentially expressed between stages and were subsequently classified into six clusters. Each revealed gene cluster had distinct expression patterns and characteristic functional pathways, providing a framework for the understanding of the developmental transcriptome dynamics. Over 4000 genes were identified as preferentially expressed in one of the stages, which could be of high relevance to stage-specific developmental and molecular events. Among the 68 transcription factor families active during development, most had enhanced average expression levels and thus might be crucial for embryogenesis, whereas the inactivation of the other families was likely required by the activation of the zygotic genome. We discussed our RNA-seq data together with previous findings about the Wnt signaling pathway and some other genes with known functions, to show how our data could be used to advance our understanding about these developmental functional elements. Our study provides ample information for further study about the molecular and cellular mechanisms underlying vertebrate development.

Project description:BackgroundParasitoid insects manipulate their hosts' physiology by injecting various factors into their host upon parasitization. Transcriptomic approaches provide a powerful approach to study insect host-parasitoid interactions at the molecular level. In order to investigate the effects of parasitization by an ichneumonid wasp (Diadegma semiclausum) on the host (Plutella xylostella), the larval transcriptome profile was analyzed using a short-read deep sequencing method (Illumina). Symbiotic polydnaviruses (PDVs) associated with ichneumonid parasitoids, known as ichnoviruses, play significant roles in host immune suppression and developmental regulation. In the current study, D. semiclausum ichnovirus (DsIV) genes expressed in P. xylostella were identified and their sequences compared with other reported PDVs. Five of these genes encode proteins of unknown identity, that have not previously been reported.ResultsDe novo assembly of cDNA sequence data generated 172,660 contigs between 100 and 10000 bp in length; with 35% of > 200 bp in length. Parasitization had significant impacts on expression levels of 928 identified insect host transcripts. Gene ontology data illustrated that the majority of the differentially expressed genes are involved in binding, catalytic activity, and metabolic and cellular processes. In addition, the results show that transcription levels of antimicrobial peptides, such as gloverin, cecropin E and lysozyme, were up-regulated after parasitism. Expression of ichnovirus genes were detected in parasitized larvae with 19 unique sequences identified from five PDV gene families including vankyrin, viral innexin, repeat elements, a cysteine-rich motif, and polar residue rich protein. Vankyrin 1 and repeat element 1 genes showed the highest transcription levels among the DsIV genes.ConclusionThis study provides detailed information on differential expression of P. xylostella larval genes following parasitization, DsIV genes expressed in the host and also improves our current understanding of this host-parasitoid interaction.

Dataset Information

Parallel deep transcriptome and proteome analysis of zebrafish larvae.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets