Targeted mass spectrometry methods for detecting oxidative post-translational modifications

Could ornithine supplementation be beneficial to prevent the formation of pro-atherogenic carbamylated low-density lipoprotein (c-LDL) particles?

Carbamylation (or carbamoylation) is a non-enzymatic post-translational modification process of lysine residues and protein N-termini, which occurs throughout the lifespan of both various plasma proteins and low-density lipoprotein (LDL) particles. Carbamylation results from the binding of isocyanates spontaneously derived from high levels of blood urea, environmental pollutants, nutritional sources and leads to the formation of potentially atherogenic carbamylated-LDL (c-LDL) particles. The carbamylation of LDL apolipoproteins is associated unfavorable downstream effects. Ornithine is a non-proteinogenic amino acid, which plays a central role at the urea cycle function. The primary use of ornithine in supplements is to support athletic performance, liver function and wound recovery. Ornithine is structurally highly similar to lysine, and is only one carbon atom shorter in its side-chain. Therefore, we hypothesize that supplemented ornithine could compete with ε-amino groups of lysine residues found in apolipoproteins of native LDL particles in their binding to isocyanates and decrease c-LDL formation. This issue still remains unresolved in current literature and needs to be elucidated in experimental studies.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.