8 research outputs found
Application of IVDr NMR spectroscopy to stratify Parkinson’s disease with absolute quantitation of blood serum metabolites and lipoproteins
No full textAbstract The challenge of early detection and stratification in Parkinson’s disease (PD) is urgent due to the current emergence of mechanism-based disease-modifying treatments. In here, metabolomic and lipidomic parameters obtained by a standardized and targeted in vitro diagnostic research (IVDr) platform have a significant potential to address therapy-related questions and generate improved biomarker panels. Our study aimed to use IVDr nuclear magnetic resonance (NMR) spectroscopy to quantify metabolites and lipoproteins in PD blood serum from different cohorts to stratify metabolically driven subtypes of idiopathic and genetic PD. Serum aliquots from three neurodegeneration biobank cohorts (287 samples in total, including 62 PD patient samples with GBA mutation, 98/43 PD patient samples of early/late stages of disease duration, 20 PD samples from patients with mutations in recessive PD genes and some smaller subgroups of mitochondrial and double mutation cases) were prepared and analyzed with IVDr NMR spectroscopy, covering 39 blood serum metabolites and 112 lipoprotein parameters. Uni- and multivariate statistics were used to identify metabolism-driven changes under consideration of typical confounders such as age, sex and disease duration and set into context with clinical biomarkers such as CSF concentrations of alpha-synuclein, neurofilament light chain, and tau protein. Based on the different PD subgroups we performed a total of eight different comparisons. Highlights from these comparisons include increased citrate and dimethylglycine with a decrease of creatinine and methionine in healthy controls and early PD group compared to GBA, PD late and recessive PD. We furthermore identified decreased HDL-3 free cholesterol in genetic PD cases compared to sporadic subject samples (sum of the PD early and PD late groups). Considering medication, we found that the levodopa equivalent daily dose (LEDD) is mostly positively correlated with tyrosine and citrate in sporadic PD compared to pyruvate and phenylalanine in genetic PD. Cerebrospinal fluid levels of alpha-synuclein were negatively correlated with alanine. Further metabolites and lipoproteins with discriminatory power for double mutation PD cases involved ornithine, 2-aminobutyrate and 2-hydroxybutyrate as well as for mitochondrial phenotypes via LDL phospholipid, apolipoprotein and cholesterol subfractions. Quantitative IVDr NMR serum spectroscopy is able to stratify PD patient samples of different etiology and can contribute to a wider understanding of the underlying metabolism-driven alterations e.g. in energy, amino acid, and lipoprotein metabolism. Though our overall cohort was large, major confounders such as age, sex and medication have a strong impact. That is why absolute quantification and detailed patient knowledge about metabolic confounders, is a premise for future translation of NMR serum spectroscopy to routine PD diagnostics
Quantitative Metabolomics and Lipoprotein Analysis of PDAC Patients Suggests Serum Marker Categories for Pancreatic Function, Pancreatectomy, Cancer Metabolism, and Systemic Disturbances
No full textStratification of ovarian cancer borderline from high-grade serous carcinoma patients by quantitative serum NMR spectroscopy of metabolites, lipoproteins, and inflammatory markers
No full textStratification of ovarian cancer borderline from high-grade serous carcinoma patients by quantitative serum NMR spectroscopy of metabolites, lipoproteins, and inflammatory markers
No full textBackground: Traditional diagnosis is based on histology or clinical-stage classification which provides no information on tumor metabolism and inflammation, which, however, are both hallmarks of cancer and are directly associated with prognosis and severity. This project was an exploratory approach to profile metabolites, lipoproteins, and inflammation parameters (glycoprotein A and glycoprotein B) of borderline ovarian tumor (BOT) and high-grade serous ovarian cancer (HGSOC) for identifying additional useful serum markers and stratifying ovarian cancer patients in the future.Methods: This project included 201 serum samples of which 50 were received from BOT and 151 from high-grade serous ovarian cancer (HGSOC), respectively. All the serum samples were validated and phenotyped by 1H-NMR-based metabolomics with in vitro diagnostics research (IVDr) standard operating procedures generating quantitative data on 38 metabolites, 112 lipoprotein parameters, and 5 inflammation markers. Uni- and multivariate statistics were applied to identify NMR-based alterations. Moreover, biomarker analysis was carried out with all NMR parameters and CA-125.Results: Ketone bodies, glutamate, 2-hydroxybutyrate, glucose, glycerol, and phenylalanine levels were significantly higher in HGSOC, while the same tumors showed significantly lower levels of alanine and histidine. Furthermore, alanine and histidine and formic acid decreased and increased, respectively, over the clinical stages. Inflammatory markers glycoproteins A and B (GlycA and GlycB) increased significantly over the clinical stages and were higher in HGSOC, alongside significant changes in lipoproteins. Lipoprotein subfractions of VLDLs, IDLs, and LDLs increased significantly in HGSOC and over the clinical stages, while total plasma apolipoprotein A1 and A2 and a subfraction of HDLs decreased significantly over the clinical stages. Additionally, LDL triglycerides significantly increased in advanced ovarian cancer. In biomarker analysis, glycoprotein inflammation biomarkers behaved in the same way as the established clinical biomarker CA-125. Moreover, CA-125/GlycA, CA-125/GlycB, and CA-125/Glycs are potential biomarkers for diagnosis, prognosis, and treatment response of epithelial ovarian cancer (EOC). Last, the quantitative inflammatory parameters clearly displayed unique patterns of metabolites, lipoproteins, and CA-125 in BOT and HGSOC with clinical stages I–IV.Conclusion:1H-NMR-based metabolomics with commercial IVDr assays could detect and identify altered metabolites and lipoproteins relevant to EOC development and progression and show that inflammation (based on glycoproteins) increased along with malignancy. As inflammation is a hallmark of cancer, glycoproteins, thereof, are promising future serum biomarkers for the diagnosis, prognosis, and treatment response of EOC. This was supported by the definition and stratification of three different inflammatory serum classes which characterize specific alternations in metabolites, lipoproteins, and CA-125, implicating that future diagnosis could be refined not only by diagnosed histology and/or clinical stages but also by glycoprotein classes
Acidic ascites inhibits ovarian cancer cell proliferation and correlates with the metabolomic, lipidomic and inflammatory phenotype of human patients
No full textBACKGROUND: The poor prognosis of ovarian cancer patients is strongly related to peritoneal metastasis with the production of malignant ascites. However, it remains largely unclear how ascites in the peritoneal cavity influences tumor metabolism and recurrence. This study is an explorative approach aimed at for a deeper molecular and physical–chemical characterization of malignant ascites and to investigate their effect on in vitro ovarian cancer cell proliferation. METHODS: This study included 10 malignant ascites specimens from patients undergoing ovarian cancer resection. Ascites samples were deeply phenotyped by (1)H-NMR based metabolomics, blood-gas analyzer based gas flow analysis and flow cytomertry based a 13-plex cytokine panel. Characteristics of tumor cells were investigated in a 3D spheroid model by SEM and metabolic activity, adhesion, anti-apoptosis, migratory ability evaluated by MTT assay, adhesion assay, flowcytometry and scratch assay. The effect of different pH values was assessed by adding 10% malignant ascites to the test samples. RESULTS: The overall extracellular (peritoneal) environment was alkaline, with pH of ascites at stage II-III = 7.51 ± 0.16, and stage IV = 7.78 ± 0.16. Ovarian cancer spheroids grew rapidly in a slightly alkaline environment. Decreasing pH of the cell culture medium suppressed tumor features, metabolic activity, adhesion, anti-apoptosis, and migratory ability. However, 10% ascites could prevent tumor cells from being affected by acidic pH. Metabolomics analysis identified stage IV patients had significantly higher concentrations of alanine, isoleucine, phenylalanine, and glutamine than stage II-III patients, while stage II-III patients had significantly higher concentrations of 3-hydroxybutyrate. pH was positively correlated with acetate, and acetate positively correlated with lipid compounds. IL-8 was positively correlated with lipid metabolites and acetate. Glutathione and carnitine were negatively correlated with cytokines IL-6 and chemokines (IL-8 & MCP-1). CONCLUSION: Alkaline malignant ascites facilitated ovarian cancer progression. Additionally, deep ascites phenotyping by metabolomics and cytokine investigations allows for a refined stratification of ovarian cancer patients. These findings contribute to the understanding of ascites pathology in ovarian cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03763-3
Identification and impact of microbiota-derived metabolites in ascites of ovarian and gastrointestinal cancer
No full textAbstract Background Malignant ascites is a common complication of advanced ovarian cancer (OC) and gastrointestinal cancer (GI), significantly impacting metastasis, quality of life, and survival. Increased intestinal permeability can lead to blood or lymphatic infiltration and microbial translocation from the gastrointestinal or uterine tract. This study aimed to identify microbiota-derived metabolites in ascites from OC (stages II-III and IV) and GI patients, assessing their roles in tumor progression. Methods Malignant ascites samples from 18 OC and GI patients were analyzed using a four-dimensional (4D) untargeted metabolomics approach combining reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) with trapped ion mobility spectrometry time-of-flight mass spectrometry (timsTOF-MS). Additonally, a targeted flow cytometry-based cytokine panel was used to screen for inflammatory markers. Non-endogenous, microbiota-derived metabolites were identified through the Human Microbial Metabolome Database (MiMeDB). Results OC stage IV exhibited metabolic profiles similar to GI cancers, while OC stage II-III differed significantly. Stage IV OC patients exhibited higher levels of 11 typically microbiome-derived metabolites, including 1-methylhistidine, 3-hydroxyanthranilic acid, 4-pyridoxic acid, biliverdin, butyryl-L-carnitine, hydroxypropionic acid, indole, lysophosphatidylinositol 18:1 (LPI 18:1), mevalonic acid, N-acetyl-L-phenylalanine, and nudifloramide, and lower levels of 5 metabolites, including benzyl alcohol, naringenin, o-cresol, octadecanedioic acid, and phenol, compared to stage II–III. Correlation analysis revealed positive associations between IL-10 and metabolites such as glucosamine and LPCs, while MCP-1 positively correlated with benzyl alcohol and phenol. Conclusion 4D metabolomics revealed distinct metabolic signatures in OC and GI ascites, highlighting microbiota-derived metabolites involved in lipid metabolism and inflammation. Metabolites like 3-hydroxyanthranilic acid, indole, and naringenin may serve as markers of disease progression and underscore the microbiota’s role in shaping malignant ascites and tumor biology
Quantitative Metabolomics and Lipoprotein Analysis of PDAC Patients Suggests Serum Marker Categories for Pancreatic Function, Pancreatectomy, Cancer Metabolism, and Systemic Disturbances
No full textPancreatic ductal adenocarcinoma (PDAC) is difficult
to diagnose
in the early stages and lacks reliable biomarkers. The scope of this
project was to establish quantitative nuclear magnetic resonance (NMR)
spectroscopy to comprehensively study blood serum alterations in PDAC
patients. Serum samples from 34 PDAC patients obtained before and
after pancreatectomy as well as 83 age- and sex-matched control samples
from healthy donors were analyzed with in vitro diagnostics
research (IVDr) proton NMR spectroscopy at 600 MHz. Uni- and multivariate
statistics were applied to identify significant biofluid alterations.
We identified 29 significantly changed metabolites and 98 lipoproteins
when comparing serum from healthy controls with those of PDAC patients.
The most prominent features were assigned to (i) markers of pancreatic
function (e.g., glucose and blood triglycerides), (ii) markers related
to surgery (e.g., ketone bodies and blood cholesterols), (iii) PDAC-associated
markers (e.g., amino acids and creatine), and (iv) markers for systemic
disturbances in PDAC (e.g., gut metabolites DMG, TMAO, DMSO2, and liver lipoproteins). Quantitative serum NMR spectroscopy is
suited as a diagnostic tool to investigate PDAC. Remarkably, 2-hydroxybutyrate
(2-HB) as a previously suggested marker for insulin resistance was
found in extraordinarily high levels only after pancreatectomy, suggesting
this metabolite is the strongest marker for pancreatic loss of function
Application of IVDr NMR spectroscopy to stratify Parkinson's disease with absolute quantitation of blood serum metabolites and lipoproteins
No full textThe challenge of early detection and stratification in Parkinson's disease (PD) is urgent due to the current emergence of mechanism-based disease-modifying treatments. In here, metabolomic and lipidomic parameters obtained by a standardized and targeted in vitro diagnostic research (IVDr) platform have a significant potential to address therapy-related questions and generate improved biomarker panels. Our study aimed to use IVDr nuclear magnetic resonance (NMR) spectroscopy to quantify metabolites and lipoproteins in PD blood serum from different cohorts to stratify metabolically driven subtypes of idiopathic and genetic PD. Serum aliquots from three neurodegeneration biobank cohorts (287 samples in total, including 62 PD patient samples with GBA mutation, 98/43 PD patient samples of early/late stages of disease duration, 20 PD samples from patients with mutations in recessive PD genes and some smaller subgroups of mitochondrial and double mutation cases) were prepared and analyzed with IVDr NMR spectroscopy, covering 39 blood serum metabolites and 112 lipoprotein parameters. Uni- and multivariate statistics were used to identify metabolism-driven changes under consideration of typical confounders such as age, sex and disease duration and set into context with clinical biomarkers such as CSF concentrations of alpha-synuclein, neurofilament light chain, and tau protein. Based on the different PD subgroups we performed a total of eight different comparisons. Highlights from these comparisons include increased citrate and dimethylglycine with a decrease of creatinine and methionine in healthy controls and early PD group compared to GBA, PD late and recessive PD. We furthermore identified decreased HDL-3 free cholesterol in genetic PD cases compared to sporadic subject samples (sum of the PD early and PD late groups). Considering medication, we found that the levodopa equivalent daily dose (LEDD) is mostly positively correlated with tyrosine and citrate in sporadic PD compared to pyruvate and phenylalanine in genetic PD. Cerebrospinal fluid levels of alpha-synuclein were negatively correlated with alanine. Further metabolites and lipoproteins with discriminatory power for double mutation PD cases involved ornithine, 2-aminobutyrate and 2-hydroxybutyrate as well as for mitochondrial phenotypes via LDL phospholipid, apolipoprotein and cholesterol subfractions. Quantitative IVDr NMR serum spectroscopy is able to stratify PD patient samples of different etiology and can contribute to a wider understanding of the underlying metabolism-driven alterations e.g. in energy, amino acid, and lipoprotein metabolism. Though our overall cohort was large, major confounders such as age, sex and medication have a strong impact. That is why absolute quantification and detailed patient knowledge about metabolic confounders, is a premise for future translation of NMR serum spectroscopy to routine PD diagnostics.</p
