Sample collection and preparation of biofluids and extracts for liquid chromatography-mass spectrometry

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

The crucial role of lysine in the hepatic metabolism of growing Holstein dairy heifers as revealed by LC-MS-based untargeted metabolomics

The objective of this experiment was to evaluate the effect of supplementing rumen-protected Lys based on a Lys-deficient diet on liver metabolism in growing Holstein heifers. The experiment was conducted for 3 months with 36 Holstein heifers (initial body weight: 200 ± 9.0 kg; 7-month-old). Heifers were randomly assigned to 2 diets based on corn, soybean meal, alfalfa hay, and wheat bran: control, Lys-deficient diet (LD; 0.66% Lys in diet), and Lys-adequate diet (LA; 1.00% Lys in diet). The results showed no difference in growth performance between the 2 groups (P > 0.05). However, there was a clear trend of increasing feed conversion rate with Lys supplementation (0.05 < P < 0.01). The serum urea nitrogen concentration was significantly decreased, and the aspartate aminotransferase-to-alanine aminotransferase ratio was significantly decreased by Lys supplementation (P < 0.05). Moreover, growing heifers fed a Lys-adequate diet had lower levels of urine nitrogen excretion and higher levels of the biological value of nitrogen (P < 0.05). Metabolomic analysis revealed that 5 types of phosphatidylcholine and 3 types of ceramide were significantly increased and enriched in sphingolipid metabolism and glycerophospholipid metabolism (P < 0.05). His, Leu, and Asp levels were significantly decreased in the liver following Lys supplementation (P < 0.05). In conclusion, Lys supplementation may promote the synthesis of body tissue proteins, as evidenced by significantly decreased amino acids in the liver and urine N excretion, it also improves hepatic lipid metabolism by providing lipoprotein precursors.

Identification of gender-related metabolic disturbances in autism spectrum disorders using urinary metabolomics

Autism spectrum disorders (ASD) are a highly heterogeneous group of neurodevelopmental disorders that are more commonly diagnosed in boys than in girls. The reasons for gender differences in ASD are unknown and no definitive current evidence can explain male predominance. Therefore, in search for laboratory biomarkers responsible for ASD, a comprehensive metabolomics study was performed by metabolic profiling of urine samples in 51 ASD subjects and 51 age- and sex-matched children with typical development. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models with poor quality failed to perform the analysis based on gender in the ASD and control groups. OPLS-DA models based on single-sex samples, especially in female subjects, had better clustering between the ASD and control groups with an increase in the R² and Q² values compared with those in the whole group. Significantly increased levels of adenine, 2-Methylguanosine, creatinine, and 7alpha-hydroxytestololactone and a decrease in creatine were observed in the female ASD subjects. In particular, 7alpha-hydroxytestololactone, which has a structure similar to that of testolactone, was positively correlated with adenine (Pearson correlation coefficient, r = 0.738, p < 0.01), creatinine (r = 0.826, p < 0.01), and 2-Methylguanosine (r = 0.757, p < 0.01) and negatively correlated with creatine (r=-0.413, p < 0.05). A receiver operating characteristic curve analysis using the creatinine:creatine ratio yielded an area under the curve of 0.913 (95% CI: 0.806-1). These metabolites may be sex-related or sex-sensitive to an extent and can be valuable for identification of the molecular pathways involved in the gender bias in manifestation of ASD. The creatinine:creatine ratio has a potential to be a good predictor of ASD in the female subjects.

Metabolomics Toward Biomarker Discovery

Metabolomics has been used as practical tool in the discovery of novel biomarkers in a broad area in the clinic. The analytical platforms including nuclear magnetic resonance (NMR) and mass spectrometry (MS) can cover thousands of metabolites. With the help of multivariate data analysis, many potential biomarkers can be defined in the studies. Since metabolites stand at the end point of metabolism, it remains difficult to find novel biomarkers with good diagnostic or prognostic performance. In this chapter, we will introduce a general protocol for biomarker discovery within the scope of metabolomics using MS.