Background: Beta-amyloid (Abeta) and tau protein in cerebrospinal fluid (CSF) are established diagnostic biomarkers for Alzheimer’s disease (AD). However, these biomarkers may not the only ones existing parameters that reflect Alzheimer’s disease neuropathological change. The use of quantitative metabolomics approach could provide novel insights into dementia progression and identify key metabolic alterations in CSF and serum.Methods: In the present study, we quantified a set of 45 metabolites in CSF (71 patients) and 27 in serum (76 patients) in patients with mild cognitive impairment (MCI), AD, and controls using nuclear magnetic resonance (NMR)-based metabolomics.Results: We found significantly reduced CSF (1.32-fold, p = 0.0195) and serum (1.47-fold, p = 0.0484) levels of the ketone body acetoacetate in AD and MCI patients. Additionally, we found decreased levels (1.20-fold, p = 0.0438) of the branched-chain amino acid (BCAA) valine in the CSF of AD patients with increased valine degradation pathway metabolites (such as 3-hydroxyisobutyrate and α-ketoisovalerate). Moreover, we discovered that CSF 2-hydroxybutyrate is dramatically reduced in the MCI patient group (1.23-fold, p = 0.039). On the other hand, vitamin C (ascorbate) was significantly raised in CSF of these patients (p = 0.008). We also identified altered CSF protein content, 1,5-anhydrosorbitol and fructose as further metabolic shifts distinguishing AD from MCI. Significantly decreased serum levels of the amino acid ornithine were seen in the AD dementia group when compared to healthy controls (1.36-fold, p = 0.011). When investigating the effect of sex, we found for AD males the sign of decreased 2-hydroxybutyrate and acetoacetate in CSF while for AD females increased serum creatinine was identified.Conclusion: Quantitative NMR metabolomics of CSF and serum was able to efficiently identify metabolic changes associated with dementia groups of MCI and AD patients. Further, we showed strong correlations between these changes and well-established metabolomic and clinical indicators like Abeta.

Background: Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.Methods: This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes. Results: Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.Conclusion: The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Background: Braak’s hypothesis states that sporadic Parkinson’s disease (PD) follows a specific progression of pathology from the peripheral to the central nervous system, and this progression can be monitored by detecting the accumulation of alpha-Synuclein (α-Syn) protein. Consequently, there is growing interest in understanding how the gut (commensal) microbiome can regulate α-Syn accumulation, as this could potentially lead to PD. Methods: We used 16S rRNA and shotgun sequencing to characterise microbial diversity. ¹H-NMR was employed to understand the metabolite production and intestinal inflammation estimated using ELISA and RNA-sequencing from feces and the intestinal epithelial layer respectively. The Na⁺ channel current and gut permeability were measured using an Ussing chamber. Immunohistochemistry and immunofluorescence imaging were applied to detect the α-Syn protein. LC-MS/MS was used for characterization of proteins from metabolite treated neuronal cells. Finally, Metascape and Ingenuity Pathway Analysis (IPA) bioinformatics tools were used for identification of dysregulated pathways. Results: We studied a transgenic (TG) rat model overexpressing the human SNCA gene and found that a progressive gut microbial composition alteration characterized by the reduction of Firmicutes to Bacteroidetes ratio could be detected in the young TG rats. Interestingly, this ratio then increased with ageing. The dynamics of Lactobacillus and Alistipes were monitored and reduced Lactobacillus and increased Alistipes abundance was discerned in ageing TG rats. Additionally, the SNCA gene overexpression resulted in gut α-Syn protein expression and increased with advanced age. Further, older TG animals had increased intestinal inflammation, decreased Na⁺ current and a robust alteration in metabolite production characterized by the increase of succinate levels in feces and serum. Manipulation of the gut bacteria by short-term antibiotic cocktail treatment revealed a complete loss of short-chain fatty acids and a reduction in succinate levels. Although antibiotic cocktail treatment did not change α-Syn expression in the enteric nervous system of the colon, however, reduced α-Syn expression was detected in the olfactory bulbs (forebrain) of the TG rats. Conclusion: Our data emphasize that the gut microbiome dysbiosis synchronous with ageing leads to a specific alteration of gut metabolites and can be modulated by antibiotics which may affect PD pathology.

The blood–brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-β-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-β or conditioned medium from TGF-β secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-β notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-β-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes.

Alzheimer’s disease (AD) is the most common form of dementia in the elderly and has been associated with changes in lipoprotein metabolism. We performed quantitative lipoprotein analysis in a local cohort of cognitively impaired elderly and control subjects using standardized nuclear magnetic resonance (NMR) spectroscopy. A commercially available quantitative NMR-based assay covering 112 lipoprotein main and subtype variables was used to investigate blood serum samples from a moderate cohort size of 161 persons (71 female, 90 male), including measures of quality control. Additionally, clinical metadata and cerebrospinal fluid AD biomarkers were collected and used for analysis. High-density lipoprotein (HDL) HDL-4 subfraction levels were mostly high in female individuals with mild cognitive impairment (MCI), followed by AD. Low-density lipoprotein (LDL) LDL-2 cholesterol was slightly elevated in male AD patients. HDL-2 apolipoprotein Apo-A1, HDL-2 phospholipids, and HDL-3 triglycerides were highly abundant in AD and MCI women compared to men. When considering clinical biomarkers (Aβ, tau), very low-density lipoprotein (VLDL) VLDL-1 and intermediate-density lipoprotein (IDL) triglycerides were substantially higher in AD compared to MCI. In addition, triglyceride levels correlated positively with dementia. Different lipoprotein serum patterns were identified for AD, MCI, and control subjects. Interestingly, HDL-4 and LDL-2 cholesterol parameters revealed strong gender-specific changes in the context of AD-driven dementia. As gender-based comparisons were based on smaller sub-groups with a low n-number, several statistical findings did not meet the significance threshold for multiple comparisons testing. Still, our finding suggests that serum HDL-4 parameters and various triglycerides correlate positively with AD pathology which could be a read-out of extended lipids traveling through the blood-brain barrier, supporting amyloid plaque formation processes. Thereof, we see herein a proof of concept that this quantitative NMR-based lipoprotein assay can generate important and highly interesting data for refined AD diagnosis and patient stratification, especially when larger cohorts are available.

The discovery of the oncometabolite 2-hydroxyglutarate in isocitrate dehydrogenase 1–mutated ( IDH1 -mutated) tumor entities affirmed the role of metabolism in cancer. However, large databases with tissue metabolites that are modulated by IDH1 mutation remain an area of development. Here, we present an unprecedented and valuable resource for tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and tumor treatments in 101 tissue samples from 73 diffuse glioma patients (24 astrocytoma, 17 oligodendroglioma, 32 glioblastoma), investigated by NMR-based metabolomics and supported by RNA-Seq. We discovered comparison-specific metabolites and pathways modulated by IDH1 ( IDH1 mutation status cohort) and tumor entity. The Longitudinal investigation cohort provides metabolic profiles of untreated and corresponding treated glioma samples at first progression. Most interestingly, univariate and multivariate cox regressions and Kaplan-Meier analyses revealed that tissue metabolites correlate with progression-free and overall survival. Thus, this study introduces potentially novel candidate prognostic and surrogate metabolite biomarkers for future prospective clinical studies, aiming at further refining patient stratification in diffuse glioma. Furthermore, our data will facilitate the generation of so-far–unanticipated hypotheses for experimental studies to advance our molecular understanding of glioma biology.