Plasma metabolite profiling reveals potential biomarkers of giant cell tumor of bone by using NMR-based metabolic profiles: A cross-sectional study

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Influence of distant tumor growth and lithium treatment on ultrastructural organization of kidney proximal tubules

Tumor growth causes significant metabolic disturbances, tissue damage and the accumulation of toxic metabolites in the blood. The kidney is an organ with highly developed capillary network and therefore can be exposed to toxic metabolites. Here, the proximal renal tubule cells were studied by immunohistochemistry and electron microscopy, on a model of hepatocellular carcinoma-29 growth in the thigh of CBA mice and lithium carbonate treatment. An increase of autophagy markers (LC3 and LAMP-1) expression was revealed under conditions of distant tumor growth and especially after lithium carbonate treatment. Under conditions of distant tumor we found decrease of numerical density of endosomes and dense apical tubules in the apical part of the cells. In the perinuclear cell compartment, there were swelling of mitochondria and a decrease in their cristae, a decrease of volume density of rough endoplasmic reticulum and the presence of autophagosomes and autolysosomes. The use of lithium carbonate led to an increase of autophagic structures volume density and of dense apical tubules numerical density in the proximal tubule cells. It is possible that the activation of autophagy by lithium can promote an increase in protein recycling in the proximal tubule cells.

Sarcoma Metabolomics: Current Horizons and Future Perspectives

The vast array of metabolic adaptations that cancer cells are capable of assuming, not only support their biosynthetic activity, but also fulfill their bioenergetic demands and keep their intracellular reduction-oxidation (redox) balance. Spotlight has recently been placed on the energy metabolism reprogramming strategies employed by cancer cells to proliferate. Knowledge regarding soft tissue and bone sarcomas metabolome is relatively sparse. Further characterization of sarcoma metabolic landscape may pave the way for diagnostic refinement and new therapeutic target identification, with benefit to sarcoma patients. This review covers the state-of-the-art knowledge on cancer metabolomics and explores in detail the most recent evidence on soft tissue and bone sarcoma metabolomics.

Integrative metabolic profile of myelodysplastic syndrome based on UHPLC-MS

Myelodysplastic syndrome (MDS) is a neoplastic disease originating from hematopoietic stem cells. Currently, hematopoietic stem cell transplantation (HSCT) is the most effective cure, although lenalidomide, azacytidine, and decitabine have been applied to relieve symptoms of MDS. The purpose of this study was to evaluate the changes in endogenous metabolites by applying a UHPLC-MS (ultra-high-performance liquid chromatography-MS) metabolomics approach and to investigate metabolic pathways related to MDS. An untargeted metabolomics approach based on UHPLC-MS in combination with multivariate data analysis, including partial least squares discrimination analysis and orthogonal partial least squares discriminant analysis, was established to investigate potential biomarkers in the plasma of MDS patients. As a result, 29 biomarkers were identified to distinguish between MDS patients, HSCT patients, and healthy controls, which were mainly related to inflammation regulation, amino acid metabolism, fatty acid metabolism, and energy metabolism. To our knowledge, this is the first time where plasma metabolomics was combined with HSCT to study the pathogenesis and therapeutic target of MDS. The identification of biomarkers and analysis of metabolic pathways could offer the possibility of discovering new therapeutic targets for MDS in the future.