High-resolution metabolomics determines the mode of onset of type 2 diabetes in a 3-year prospective cohort study.

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5ß-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H­H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.

Liquid Chromatography Mass Spectrometry-Based Metabolite Pathway Analyses of Myeloma and Non-Hodgkin's Lymphoma Patients.

OBJECTIVE: This study attempted to identify altered metabolism and pathways related to non-Hodgkin's lymphoma (NHL) and myeloma patients. MATERIALS AND METHODS: In this retrospective study, we collected plasma samples from 11 patients-6 healthy controls with no evidence of any blood cancers and 5 patients with either multiple myeloma (n=3) or NHL (n=2) during the preliminary study period. Samples were analyzed using quadrupole time-of-flight liquid chromatography mass spectrometry (LC-MS). Significant features generated after statistical analyses were used for metabolomics and pathway analysis. RESULTS: Data after false discovery rate (FDR) adjustment at q=0.05 of features showed 136 for positive and 350 significant features for negative ionization mode in NHL patients as well as 262 for positive and 98 features for negative ionization mode in myeloma patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis determined that pathways such as steroid hormone biosynthesis, ABC transporters, and arginine and proline metabolism were affected in NHL patients. In myeloma patients, pyrimidine metabolism, carbon metabolism, and bile secretion pathways were potentially affected by the disease. CONCLUSION: The results have shown tremendous differences in the metabolites of healthy individuals compared to myeloma and lymphoma patients. Validation through quantitative metabolomics is encouraged, especially for the metabolites with significantly expression in blood cancer patients.

High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system.

BACKGROUND: Current available malaria diagnostic methods each have some limitations to meet the need for real-time and large-scale screening of asymptomatic and low density malaria infection at community level. It was proposed that malaria parasite-specific low molecular-weight metabolites could be used as biomarkers for the development of a malaria diagnostic tool aimed to address this diagnostic challenge. In this study, high resolution metabolomics (HRM) was employed to identify malaria parasite-specific metabolites in Plasmodium falciparum in vitro culture samples. METHODS: Supernatants were collected at 12 hours interval from 3% haematocrit in vitro 48-hour time-course asynchronized culture system of P. falciparum. Liquid chromatography coupled with high resolution mass spectrometry was applied to discover potential parasite-specific metabolites in the cell culture supernatant. A metabolome-wide association study was performed to extract metabolites using Manhattan plot with false discovery rate (FDR) and hierarchical cluster analysis. The significant metabolites based on FDR cutoff were annotated using Metlin database. Standard curves were created using corresponding chemical compounds to accurately quantify potential Plasmodium-specific metabolites in culture supernatants. RESULTS: The number of significant metabolite features was 1025 in the supernatant of the Plasmodium infected culture based on Manhattan plot with FDR q=0.05. A two way hierarchical cluster analysis showed a clear segregation of the metabolic profile of parasite infected supernatant from non-infected supernatant at four time points during the 48 hour culture. Among the 1025 annotated metabolites, the intensities of four molecules were significantly increased with culture time suggesting a positive association between the quantity of these molecules and level of parasitaemia: i) 3-methylindole, a mosquito attractant, ii) succinylacetone, a haem biosynthesis inhibitor, iii) S-methyl-L-thiocitrulline, a nitric oxide synthase inhibitor, and iv) O-arachidonoyl glycidol, a fatty acid amide hydrolase inhibitor, The highest concentrations of 3-methylindole and succinylacetone were 178 ± 18.7 pmoles at 36 hours and 157±30.5 pmoles at 48 hours respectively in parasite infected supernatant. CONCLUSION: HRM with bioinformatics identified four potential parasite-specific metabolite biomarkers using in vitro culture supernatants. Further study in malaria infected human is needed to determine presence of the molecules and its relationship with parasite densities.