Deletion of Genes Encoding Arginase Improves Use of "Heavy" Isotope-Labeled Arginine for Mass Spectrometry in Fission Yeast

Review of arginase as a promising biocatalyst: characteristics, preparation, applications and future challenges

As a committed step in the urea cycle, arginase cleaves l-arginine to form l-ornithine and urea. l-Ornithine is essential to: cell proliferation, collagen formation and other physiological functions, while the urea cycle itself converts highly toxic ammonia to urea for excretion. Recently, arginase was exploited as an efficient catalyst for the environmentally friendly synthesis of l-ornithine, an abundant nonprotein amino acid that is widely employed as a food supplement and nutrition product. It was also proposed as an arginine-reducing agent in order to treat arginase deficiency and to be a means of depleting arginine to treat arginine auxotrophic tumors. Targeting arginase inhibitors of the arginase/ornithine pathway offers great promise as a therapy for: cardiovascular, central nervous system diseases and cancers with high arginase expression. In this review, recent advances in the characteristics, structure, catalytic mechanism and preparation of arginase were summarized, with a focus being placed on the biotechnical and medical applications of arginase. In particular, perspectives have been presented on the challenges and opportunities for the environmentally friendly utilization of arginase during l-ornithine production and in therapies.

Antunes I, Dömling A, H. Elsinga P. Arginase as a Potential Biomarker of Disease Progression: A Molecular Imaging Perspective

Arginase is a widely known enzyme of the urea cycle that catalyzes the hydrolysis of L-arginine to L-ornithine and urea. The action of arginase goes beyond the boundaries of hepatic ureogenic function, being widespread through most tissues. Two arginase isoforms coexist, the type I (Arg1) predominantly expressed in the liver and the type II (Arg2) expressed throughout extrahepatic tissues. By producing L-ornithine while competing with nitric oxide synthase (NOS) for the same substrate (L-arginine), arginase can influence the endogenous levels of polyamines, proline, and NO•. Several pathophysiological processes may deregulate arginase/NOS balance, disturbing the homeostasis and functionality of the organism. Upregulated arginase expression is associated with several pathological processes that can range from cardiovascular, immune-mediated, and tumorigenic conditions to neurodegenerative disorders. Thus, arginase is a potential biomarker of disease progression and severity and has recently been the subject of research studies regarding the therapeutic efficacy of arginase inhibitors. This review gives a comprehensive overview of the pathophysiological role of arginase and the current state of development of arginase inhibitors, discussing the potential of arginase as a molecular imaging biomarker and stimulating the development of novel specific and high-affinity arginase imaging probes.

Quantitative Proteomic Analysis in Candida albicans Using SILAC-Based Mass Spectrometry

Stable isotope labelling by amino acids in cell culture (SILAC) in conjunction with MS analysis is a sensitive and reliable technique for quantifying relative differences in protein abundance and posttranslational modifications between cell populations. We develop and utilise SILAC-MS workflows for quantitative proteomics in the fungal pathogen Candida albicans. Arginine metabolism provides important cues for escaping host defences during pathogenesis, which limits the use of auxotrophs in Candida research. Our strategy eliminates the need for engineering arginine auxotrophs for SILAC experiments and allows the use of ARG4 as selectable marker during strain construction. Cells that are auxotrophic for lysine are successfully labelled with both lysine and arginine stable isotopes. We find that prototrophic C. albicans preferentially uses exogenous arginine and down-regulates internal production, which allow it to achieve high incorporation rates. However, similar to other yeast, C. albicans is able to metabolise heavy arginine to heavy proline, which compromised the accuracy of protein quantification. A computational method is developed to correct for the incorporation of heavy proline. In addition, we utilise the developed SILAC labelling in C. albicans for the global quantitative proteomic analysis of a strain expressing a phosphatase-dead mutant Cdc14^PD .

Construction, Growth, and Harvesting of Fission Yeast Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) Strains

Stable isotope labeling by amino acids in cell culture (SILAC) enables the relative quantification of protein amounts and posttranslational modifications in complex biological samples through the use of stable heavy isotope-labeled amino acids. Here we describe methods for the application of SILAC to fission yeast Schizosaccharomyces pombe using either labeled lysine or a combination of labeled lysine and labeled arginine. The latter approach is more complicated than the use of labeled lysine alone but may yield a more comprehensive (phospho)proteomic analysis. The protocol includes methods for construction of SILAC-compatible strains, growth of cultures in labeled medium, cell harvesting, and protein extraction.

PyQuant: A Versatile Framework for Analysis of Quantitative Mass Spectrometry Data

Quantitative mass spectrometry data necessitates an analytical pipeline that captures the accuracy and comprehensiveness of the experiments. Currently, data analysis is often coupled to specific software packages, which restricts the analysis to a given workflow and precludes a more thorough characterization of the data by other complementary tools. To address this, we have developed PyQuant, a cross-platform mass spectrometry data quantification application that is compatible with existing frameworks and can be used as a stand-alone quantification tool. PyQuant supports most types of quantitative mass spectrometry data including SILAC, NeuCode, (15)N, (13)C, or (18)O and chemical methods such as iTRAQ or TMT and provides the option of adding custom labeling strategies. In addition, PyQuant can perform specialized analyses such as quantifying isotopically labeled samples where the label has been metabolized into other amino acids and targeted quantification of selected ions independent of spectral assignment. PyQuant is capable of quantifying search results from popular proteomic frameworks such as MaxQuant, Proteome Discoverer, and the Trans-Proteomic Pipeline in addition to several standalone search engines. We have found that PyQuant routinely quantifies a greater proportion of spectral assignments, with increases ranging from 25-45% in this study. Finally, PyQuant is capable of complementing spectral assignments between replicates to quantify ions missed because of lack of MS/MS fragmentation or that were omitted because of issues such as spectra quality or false discovery rates. This results in an increase of biologically useful data available for interpretation. In summary, PyQuant is a flexible mass spectrometry data quantification platform that is capable of interfacing with a variety of existing formats and is highly customizable, which permits easy configuration for custom analysis.