Statistical models for the analysis of protein expression changes by stable isotope labeling are still poorly developed, particularly for data obtained by 16

Mass spectrometry (MS) is a technique of paramount importance in Proteomics, and developments in this field have been possible owing to novel MS instrumentation, experimental strategies, and bioinformatics tools. Today it is possible to identify and determine relative expression levels of thousands of proteins in a biological system by MS analysis of peptides produced by proteolytic digestion. In some situations, however, the precise characterization of a particular peptide species in a very complex peptide mixture is needed. While single-fragment ion-based scanning modes such as selected ion reaction monitoring (SIRM) or consecutive reaction monitoring (CRM) may be highly sensitive, they do not produce MS/MS information and their actual specificity must be determined in advance, a prerequisite that is not usually met in a basic research context. In such cases, the MS detector may be programmed to perform continuous MS/MS spectra on the peptide ion of interest in order to obtain structural information. This selected MS/MS ion monitoring (SMIM) mode has a number of advantages that are fully exploited by MS detectors that, like the linear ion trap, are characterized by high scanning speeds. In this work, we show some applications of this technique in the context of biological studies. These results were obtained by selecting an appropriate combination of scans according to the purpose of each one of these research scenarios. They include highly specific identification of proteins present in low amounts, characterization and relative quantification of post-translational modifications such as phosphorylation and S-nitrosylation and species-specific peptide identification.

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Protein-protein interactions are an important mechanism for the regulation of enzyme function allowing metabolite channeling, crosstalk between pathways or the introduction of post-translational modifications. Therefore, a number of high-throughput studies have been carried out to shed light on the protein networks established under different pathophysiological settings. Surprisingly, this type of information is quite limited for enzymes of intermediary metabolism such as betaine homocysteine S-methyltransferase, despite its high hepatic abundancy and its role in homocysteine metabolism. Here, we have taken advantage of two approaches, affinity purification combined with mass spectrometry and yeast two-hybrid, to further uncover the array of interactions of betaine homocysteine S-methyltransferase in normal liver of Rattus norvegicus. A total of 131 non-redundant putative interaction targets were identified, out of which 20 were selected for further validation by coimmunoprecipitation. Interaction targets validated by two different methods include: S-methylmethionine homocysteine methyltransferase or betaine homocysteine methyltransferase 2, methionine adenosyltransferases α1 and α2, cAMP-dependent protein kinase catalytic subunit alpha, 4-hydroxyphenylpyruvic acid dioxygenase and aldolase b. Network analysis identified 122 nodes and 165 edges, as well as a limited number of KEGG pathways that comprise: the biosynthesis of amino acids, cysteine and methionine metabolism, the spliceosome and metabolic pathways. These results further expand the connections within the hepatic methionine cycle and suggest putative cross-talks with additional metabolic pathways that deserve additional research.