Project description:In this study we applied the Terminal Amine Isotopic Labeling of Substrates (TAILS) technology to characterize the natural N-terminome of newborn and adult Bothrops jararaca venoms.
Project description:While cellular transcripts encode rich information that provide key features to understand the molecular basis of snake venom variation, their presence/ abundance does not necessary imply/correlate in the translation of a functional (protein) product. In this study we carried out an analysis of the venom gland proteome of Bothrops jararaca taking into account two distinct phases of its ontogenetic development (i.e. newborn and adult specimens) and the marked sexual dimorphism recently reported on its venom proteome. Proteomic data analysis showed wider dynamic range for toxins when comparing to non-toxins and a dynamic proteome rearrangement in cellular proteins upon B. jararaca development. Differentially expressed proteins covered a number of biological pathways related to protein synthesis, including proteins related to transcription and translation, which were found to be significantly higher expressed in the newborn venom gland. Our results showed that the variation in the expression levels of cellular proteins gives rise to an even higher variation in the dynamic range of the expressed toxins. Upon ageing, the molecular constraints related to protein synthesis together with ecological traits would likely have an impact on the toxin repertoire, which, in the case of B. jararaca species, would enable the species to deal with different prey types during its lifespan.
Project description:In this work of Kisaki et al, we analyze the variation of proteome responses upon treatment of breast cancer cell lines MCF7 and MDA-MB231 with Bothrops jararaca snake venom
Project description:The clustering of mass spectra is a critical component of many proteomics applications. The clustering validation science is just as important, having evolved side by side with the clustering algorithms themselves. In this work, we build on Rieder et al. 's cluster validation framework, and we discuss the problem of selection bias in cluster validation measures; we introduce an assessment measure that is biased toward the number of peptide ion species; we introduce a cluster assessment framework for proteomics and demonstrate its importance by evaluating the performance of 8 clustering algorithms in 7 proteomics datasets, and we discuss the tradeoff between assessment measures. Finally, the validation methods presented here can be of broad applicability be-yond the clustering of mass spectra. This PRIDE entry describes in detail sample preparation, LC-MS/MS analysis, and protein identification of one of the proteomics datasets used in this work (> 10 kDa Bothrops jararaca snake venom proteome).