PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.

BACKGROUND: Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins. RESULTS: We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates. CONCLUSIONS: Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset.

BACKGROUND: Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics. RESULTS: We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods. CONCLUSIONS: The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Tbc1d1 mutation in lean mouse strain confers leanness and protects from diet-induced obesity.

We previously identified Nob1 as a quantitative trait locus for high-fat diet-induced obesity and diabetes in genome-wide scans of outcross populations of obese and lean mouse strains. Additional crossbreeding experiments indicated that Nob1 represents an obesity suppressor from the lean Swiss Jim Lambert (SJL) strain. Here we identify a SJL-specific mutation in the Tbc1d1 gene that results in a truncated protein lacking the TBC Rab-GTPase-activating protein domain. TBC1D1, which has been recently linked to human obesity, is related to the insulin signaling protein AS160 and is predominantly expressed in skeletal muscle. Knockdown of TBC1D1 in skeletal muscle cells increased fatty acid uptake and oxidation, whereas overexpression of TBC1D1 had the opposite effect. Recombinant congenic mice lacking TBC1D1 showed reduced body weight, decreased respiratory quotient, increased fatty acid oxidation and reduced glucose uptake in isolated skeletal muscle. Our data strongly suggest that mutation of Tbc1d1 suppresses high-fat diet-induced obesity by increasing lipid use in skeletal muscle.