Identification of proteome markers for drug-induced liver injury in zebrafish embryos.

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

COMICS: Cartoon Visualization of Omics Data in Spatial Context Using Anatomical Ontologies.

COMICS is an interactive and open-access web platform for integration and visualization of molecular expression data in anatomograms of zebrafish, carp, and mouse model systems. Anatomical ontologies are used to map omics data across experiments and between an experiment and a particular visualization in a data-dependent manner. COMICS is built on top of several existing resources. Zebrafish and mouse anatomical ontologies with their controlled vocabulary (CV) and defined hierarchy are used with the ontoCAT R package to aggregate data for comparison and visualization. Libraries from the QGIS geographical information system are used with the R packages "maps" and "maptools" to visualize and interact with molecular expression data in anatomical drawings of the model systems. COMICS allows users to upload their own data from omics experiments, using any gene or protein nomenclature they wish, as long as CV terms are used to define anatomical regions or developmental stages. Common nomenclatures such as the ZFIN gene names and UniProt accessions are provided additional support. COMICS can be used to generate publication-quality visualizations of gene and protein expression across experiments. Unlike previous tools that have used anatomical ontologies to interpret imaging data in several animal models, including zebrafish, COMICS is designed to take spatially resolved data generated by dissection or fractionation and display this data in visually clear anatomical representations rather than large data tables. COMICS is optimized for ease-of-use, with a minimalistic web interface and automatic selection of the appropriate visual representation depending on the input data.

Differentiating samples and experimental protocols by direct comparison of tandem mass spectra.

RATIONALE: Peptide tandem mass spectra can be analyzed by a number of means. They can be compared against predicted spectra of peptides derived from genome sequences, compared against previously acquired and identified spectra, or - sometimes - sequenced de novo. We recently introduced another method which compares spectra between liquid chromatography/tandem mass spectrometry (LC/MS/MS) datasets to determine the shared spectral content, and demonstrated how this can be applied in a molecular phylogenetic study using sera from human and non-human primates. We will here explore if such a method have other, serendipitous uses. METHODS: We used the existing compareMS2 algorithm without modification on a diverse set of experiments. RESULTS: First we conducted a small phylogenetic study, using (mammalian) bone samples to study old material, and human pathogens aiming to distinguish clinically important strains. Although not as straightforward as primate sera analysis, the method shows significant promise for all these applications. We also used the algorithm to compare 24 different protocols for extraction of proteins from muscle tissue. The results provided useful information in comparing protocols. Finally, we applied compareMS2 aiming for quality control of two traceable protein reference standards (troponin) used in clinical chemistry assays, by analysing the effect of storage conditions. CONCLUSIONS: The results illustrate a broad applicability of the metric based on shared tandem mass spectra between LC/MS/MS datasets for analysing protein digests in different types of experiments. There is no reason to assume that our instance of this method is optimal in any of these situations, as it makes limited or no use of accurate mass and chromatographic retention time. We propose that with further improvement and refinement, this type of analysis can be applied as a simple but informative first step in many pipelines for bottom-up tandem mass spectrometry data analysis in proteomics and other fields, comparing or analysing large numbers of samples or datasets.

Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues.

Spectral libraries provide a sensitive and accurate method for identifying peptides from tandem mass spectra, complementary to searching genome-derived databases or sequencing de novo. Their application requires comprehensive libraries including peptides from low-abundant proteins. Here we describe a method for constructing such libraries using biological differentiation to "fractionate" the proteome by harvesting adult organs and tissues and build comprehensive libraries for identifying proteins in zebrafish (Danio rerio) embryos and larvae (an important and widely used model system). Hierarchical clustering using direct comparison of spectra was used to prioritize organ selection. The resulting and publicly available library covers 14,164 proteins, significantly improved the number of peptide-spectrum matches in zebrafish developmental stages, and can be used on data from different instruments and laboratories. The library contains information on tissue and organ expression of these proteins and is also applicable for adult experiments. The approach itself is not limited to zebrafish but would work for any model system.