Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of rat live from Sprague Dawley animals - a complementary proteomics and transcriptomics approach sheds light on drug detoxifying proteins of the endoplasmic reticulum

ABSTRACT: The hepatic endoplasmic reticulum contains a series of enzymes that oxidize and conjugate lipid and steroids. Together, these enzymes form a molecular machine that plays key roles in the metabolism of both endogenous and xenobiotic compounds. To characterize this molecular machine, we used quantitative proteomics to assess the frequency of occurrence of detected peptides within each primary sequence, leading to the assessment of the relative abundances of 137 of these proteins. These 137 proteins include over 38 different cytochrome P450s, 11 glucuronosyltransferases, and 9 carboxylesterases. Our sensitive approach was able to detect P450 allelic isoforms which differ by only a single amino acid and clearly resolved 4 splice variants of the glucuronosyltransferases. We identified a cytosolically-exposed DID motif for 3 cytochrome P450s that were located with high abundance in the Golgi apparatus as well the lack of a C-terminal HXEL motif for the sole carboxylesterase highly abundant in the Golgi. Using gene expression microarrays, we also characterized the hepatic transcriptome, and a comparison of proteomics and transcriptomics indicated a requirement for both technologies in order to provide insight into protein families of drug detoxification. In this way, a major hurdle of hepatotoxicity related to drug development may be overcome. Experiment Overall Design: Four replicate Sprague-Dawley rats were assessed, each on an individual microarray.

ORGANISM(S): Rattus norvegicus

SUBMITTER: Paul Boutros

PROVIDER: E-GEOD-9226 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

Dataset Information

Transcription profiling of rat live from Sprague Dawley animals - a complementary proteomics and transcriptomics approach sheds light on drug detoxifying proteins of the endoplasmic reticulum

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