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Obesity is associated with alterations in cholesterol and bile acid (BA) metabolism. However, the interaction among dietary intake, cholesterol absorption, and BA metabolism in patients with obesity remains unclear. We conducted a 4-wk nutritional intervention nonrandomized clinical trial with three different sequential diets for a week in the following order: regular diet (RD); high calorie, high-fat diet (HCHF), washout period on RD; and low-calorie, low-fat diet (LCLF). We provided participants with meal replacements during HCHF and LCLF diets. A total of 16 participants completed the study [

Background With pathway-specific trials in PD associated with variants in the glucocerebrosidase gene (PDGBA ) under way, we need markers that confirm the impact of genetic variants in patient-derived biofluids in order to allow patient stratification merely based on genetics and that might serve as biochemical read-out for target engagement. Objective To explore GBA-pathway-specific biomarker profiles cross-sectionally (TUEPAC-MIGAP, PPMI) and longitudinally (PPMI). Methods We measured enzyme activity of the lysosomal glucocerebrosidase, CSF levels of glucosylceramides (upstream substrate of glucocerebrosidase), CSF levels of ceramides (downstream product of glucocerebrosidase), lactosylceramides, sphingosines, sphingomyelin (by-products) and CSF levels of total α-synuclein in PDGBA patients compared to PDGBA_wildtype patients. Results Cross-sectionally in both cohorts and longitudinally in PPMI: (1) glucocerebrosidase activity was significantly lower in PDGBA compared to PDGBA_wildtype . (2) CSF levels of upstream substrates (glucosylceramides species) were higher in PDGBA compared to PDGBA_wildtype . (3) CSF levels of total α-synuclein were lower in PDGBA compared to PDGBA_wildtype . All of these findings were most pronounced in PDGBA with severe mutations (PDGBA_severe ). Cross-sectionally in TUEPAC-MIGAP and longitudinally in PPMI, CSF levels of downstream-products (ceramides) were higher in PDGBA_severe . Cross-sectionally in TUEPAC-MIGAP by-products sphinganine and sphingosine-1-phosphate and longitudinally in PPMI species of by-products lactosylceramides and sphingomyelin were higher in PDGBA_severe . Interpretation These findings confirm that GBA mutations have a relevant functional impact on biomarker profiles in patients. Bridging the gap between genetics and biochemical profiles now allows patient stratification for clinical trials merely based on mutation status. Importantly, all findings were most prominent in PDGBA with severe variants. © 2021 International Parkinson and Movement Disorder Society.

Oncogenic drivers of progression of monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) such as c-MYC have downstream effects on intracellular metabolic pathways of clonal plasma cells (PCs). Thus, extracellular environments such as the bone marrow (BM) plasma likely have unique metabolite profiles that differ from patients with MGUS compared to MM. This study utilized an untargeted metabolite and targeted complex lipid profiling of BM plasma to identify significant differences in the relative metabolite levels between patients with MGUS and MM from an exploratory cohort. This was followed by verification of some of the metabolite differences of interest by targeted quantification of the metabolites using isotopic internal standards in the exploratory cohort as well as an independent validation cohort. Significant differences were noted in the amino acid profiles such as decreased branch chain amino acids (BCAAs) and increased catabolism of tryptophan to the active kynurenine metabolite 3-hydroxy-kynurenine between patients with MGUS and MM. A decrease in the total levels of complex lipids such as phosphatidylethanolamines (PE), lactosylceramides (LCER) and phosphatidylinositols (PI) were also detected in the BM plasma samples from MM compared to MGUS patients. Thus, metabolite and complex lipid profiling of the BM plasma identifies differences in levels of metabolites and lipids between patients with MGUS and MM. This may provide insight into the possible differences of the intracellular metabolic pathways of their clonal PCs.

Background Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. Methods We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. Results We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. Conclusions Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers. Electronic supplementary material The online version of this article (10.1186/s13073-018-0586-6) contains supplementary material, which is available to authorized users.

Omega-3 polyunsaturated fatty acids (n3-PUFA) are well recognized for their potent triglyceride-lowering effects, but the potential influence of these bioactive lipids on other biological processes, particularly in the context of healthy aging, remains unknown. With the goal of gaining new insight into some less well-characterized biological effects of n3-PUFAs in healthy older adults, we performed metabolomics of fasting peripheral blood plasma collected from 12 young adults and 12 older adults before and after an open-label intervention of n3-PUFA (3.9 g/day, 2.7 g eicosapentaenoic [EPA], 1.2 g docosahexaenoic [DHA]). Proton nuclear magnetic resonance (1H-NMR) based lipoprotein subclass analysis revealed the expected reduction in total triglyceride (TG), but also demonstrated that n3-PUFA supplementation reduced very low-density lipoprotein (VLDL) particle number, modestly increased high-density lipoprotein (HDL) cholesterol, and shifted the composition of HDL subclasses. Further metabolite profiling by 1H-NMR and mass spectrometry revealed pronounced changes in phospholipids, cholesterol esters, diglycerides, and triglycerides following n3-PUFA supplementation. Furthermore, significant changes in hydroxyproline, kynurenine, and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) following n3-PUFA supplementation provide further insight into some less well-recognized biological effects of n3-PUFA supplementation, including possible effects on protein metabolism, the kynurenine pathway, and glucose metabolism.

INTRODUCTION: Patient-derived skin fibroblasts offer a unique translational model to study molecular mechanisms of multiple human diseases. Metabolomics profiling allows to track changes in a broad range of metabolites and interconnected metabolic pathways that could inform on molecular mechanisms involved in disease development and progression, and on the efficacy of therapeutic interventions. Therefore, it is important to establish standardized protocols for metabolomics analysis in human skin fibroblasts for rigorous and reliable metabolic assessment. OBJECTIVES: We aimed to develop an optimized protocol for concurrent measure of the concentration of amino acids, acylcarnitines, and components of the tricarboxylic acid (TCA) cycle in human skin fibroblasts using gas (GC) and liquid chromatography (LC) coupled with mass spectrometry (MS). METHODS: The suitability of four different methods of cell harvesting on the recovery of amino acids, acylcarnitines, and TCA cycle metabolites was established using GC/MS and LC/MS analytical platforms. For each method, metabolite stability was determined after 48 hours, two weeks and one month of storage at −80°C. RESULTS: Harvesting cells in 80% methanol solution allowed the best recovery and preservation of metabolites. Storage of samples in 80% methanol up to one month at −80°C did not significantly impact metabolite concentrations. CONCLUSION: We developed a robust workflow for metabolomics analysis in human skin fibroblasts suitable for a high-throughput multiplatform analysis. This method allows a direct side-by-side comparison of metabolic changes in samples collected at different time that could be used for studies in large patient cohorts.

While the triple tracer isotope dilution method has enabled accurate estimation of carbohydrate turnover after a mixed meal, use of the simple carbohydrate glucose as the carbohydrate source limits its translational applicability to everyday meals that typically contain complex carbohydrates. Hence, utilizing the natural enrichment of [13C]polysaccharide in commercially available grains, we devised a novel tracer method to measure postprandial complex carbohydrate turnover and indices of insulin action and β-cell function and compared the parameters to those obtained after a simple carbohydrate containing mixed meal. We studied healthy volunteers after either rice ( n = 8) or sorghum ( n = 8) and glucose ( n = 16) containing mixed meals and modified the triple tracer technique to calculate carbohydrate turnover. All meals were matched for calories and macronutrient composition. Rates of meal glucose appearance (2,658 ± 736 vs. 4,487 ± 909 μM·kg−1·2 h−1), endogenous glucose production (−835 ± 283 vs. −1,123 ± 323 μM·kg−1·2 h−1) and glucose disappearance (1,829 ± 807 vs. 3,606 ± 839 μM·kg−1·2 h−1) differed ( P < 0.01) between complex and simple carbohydrate containing meals, respectively. Interestingly, there were significant increase in indices of insulin sensitivity (32.5 ± 3.5 vs. 25.6 ± 3.2 10−5(dl·kg−1·min−2)/pM, P = 0.006) and β-cell responsivity (disposition index: 1,817 ± 234 vs. 1,236 ± 159 10−14(dl·kg−1·min−2)/pM, P < 0.005) with complex than simple carbohydrate meals. We present a novel triple tracer approach to estimate postprandial turnover of complex carbohydrate containing mixed meals. We also report higher insulin sensitivity and β-cell responsivity with complex than with simple carbohydrates in mixed meals of identical calorie and macronutrient compositions in healthy adults.

BackgroundThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.MethodsWe collected paired colon tumor and normal–adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra–subject sample correlation, and read depth. Genome–scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.ResultsWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate–producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.ConclusionsIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Figure S1. Unweighted Unifrac distances between tumor and normal-associated microbiota in dMMR and pMMR. Figure S2. Venn diagram highlighting number of microbes that overlap between tumor and normal samples in relation to MMR status (dMMR = red font, pMMR = green font, red circles = tumor samples, blue circles =normal samples). Figure S3. Microbes significantly enriched in tumor as compared to normal samples (colon tissue and mucosa) in individuals with dMMR CRC: a) Bacteroides fragilis and b) Fusobacterium nucleatum. Figure S4. Differentially abundant OTUs between patient-matched tumor and normal samples in individuals with dMMR CRC from an independent cohort. Table S1. Table S1: List of PATRIC model IDs and associated sOTUs. Table S2. Microbes identified as differentially abundant in tumor as compared to normal samples (tissue and mucosa) from individuals with dMMR or pMMR CRC. Table S3. sOTUs enriched in tumor samples (colon tissue and mucosa) as compared to normal adjacent samples in individuals with dMMR CRC. Table S4. sOTUs enriched in tumor samples (colon tissue and mucosa) as compared to normal adjacent samples in individuals with pMMR CRC. Table S5. sOTUs enriched in the proximal or distal colon (colon tissue and mucosa) of individuals with dMMR CRC. Table S6. sOTUs enriched in the proximal or distal colon (colon tissue and mucosa) of individuals with pMMR CRC. Table S7. Differentially abundant microbes in individuals with dMMR CRC from an independent cohort. Table S8. Differentially abundant microbes in individuals with pMMR CRC from an independent cohort. (PDF 2790 kb)

The production of the oncometabolite 2-hydroxyglutarate (2-HG) has been associated with c-MYC overexpression. c-MYC also regulates glutamine metabolism and drives progression of asymptomatic precursor plasma cell (PC) malignancies to symptomatic multiple myeloma (MM). However, the presence of 2-HG and its clinical significance in PC malignancies is unknown. By performing 13C stable isotope resolved metabolomics (SIRM) using U[13C6]Glucose and U[13C5]Glutamine in human myeloma cell lines (HMCLs), we show that 2-HG is produced in clonal PCs and is derived predominantly from glutamine anaplerosis into the TCA cycle. Furthermore, the 13C SIRM studies in HMCLs also demonstrate that glutamine is preferentially utilized by the TCA cycle compared with glucose. Finally, measuring the levels of 2-HG in the BM supernatant and peripheral blood plasma from patients with precursor PC malignancies such as smoldering MM (SMM) demonstrates that relatively elevated levels of 2-HG are associated with higher levels of c-MYC expression in the BM clonal PCs and with a subsequent shorter time to progression (TTP) to MM. Thus, measuring 2-HG levels in BM supernatant or peripheral blood plasma of SMM patients offers potential early identification of those patients at high risk of progression to MM, who could benefit from early therapeutic intervention.