Biology subjects

Kastenmüller, G.

Publications and source records attributed to Kastenmüller, G..

7 recordsLinked to original sources

bioRxiv2024

Interlaboratory comparison of standardised metabolomics and lipidomics analyses in human and rodent blood using the MxP(R) Quant 500 kit

Metabolomics and lipidomics are pivotal in understanding phenotypic variations beyond genomics. However, quantification and comparability of mass spectrometry (MS)-derived data are challenging. Standardised assays can enhance data comparability, enabling applications in multi-center epidemiological and clinical studies. Here we evaluated the performance and reproducibility of the MxP(R) Quant 500 kit across 14 laboratories. The kit allows quantification of 634 different metabolites from 26 compound classes using triple quadrupole MS. Each laboratory analysed twelve samples, including human plasma and serum, lipaemic plasma, NIST SRM 1950, and mouse and rat plasma, in triplicates. 505 out of the 634 metabolites were measurable above the limit of detection in all laboratories, while eight metabolites were undetectable in our study. Out of the 505 metabolites, 412 were observed in both human and rodent samples. Overall, the kit exhibited high reproducibility with a median coefficient of variation (CV) of 14.3 %. CVs in NIST SRM 1950 reference plasma were below 25 % and 10 % for 494 and 138 metabolites, respectively. To facilitate further inspection of reproducibility for any compound, we provide detailed results from the in-depth evaluation of reproducibility across concentration ranges using Deming regression. Interlaboratory reproducibility was similar across sample types, with some species-, matrix-, and phenotype-specific differences due to variations in concentration ranges. Comparisons with previous studies on the performance of MS-based kits (including the AbsoluteIDQ p180 and the Lipidyzer) revealed good concordance of reproducibility results and measured absolute concentrations in NIST SRM 1950 for most metabolites, making the MxP(R) Quant 500 kit a relevant tool to apply metabolomics and lipidomics in multi-center studies.

biochemistry↗

bioRxiv2023

The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

biochemistry↗

bioRxiv2021

The landscape of metabolic brain alterations in Alzheimer's disease

INTRODUCTIONAlzheimers disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in brain has been missing. METHODSWe metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures. RESULTSWe observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than {beta}-amyloid pathology. DISCUSSIONThis work provides a comprehensive reference map of metabolic brain changes in AD which lays the foundation for future mechanistic follow-up studies.

neuroscience↗

bioRxiv2020

Serum Metabolites Associate with Brain Amyloid Beta Deposition, Cognitive Dysfunction, and Alzheimers Disease Progression

RATIONALEMetabolomics in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort provides a powerful tool for mapping biochemical changes in AD, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-{beta} deposition in AD. OBJECTIVESWe examined 140 serum metabolites and their associations with brain amyloid-{beta} deposition, cognition, and conversion from mild cognitive impairment (MCI) to AD. FINDINGSSerum-based targeted metabolite levels were measured in 1,531 ADNI participants. We performed association analysis of metabolites with brain amyloid-{beta} deposition measured from [18F] Florbetapir PET scans. We identified nine metabolites as significantly associated with amyloid-{beta} deposition after FDR-based multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-{beta} accumulation. However, higher levels of seven phosphatidylcholines (PC) were associated with increased amyloid deposition. In addition, PC ae C44:4 was significantly associated with cognition and conversion from MCI to AD dementia. CONCLUSIONPerturbations in PC and acylcarnitine metabolism may play a role in features intrinsic to AD including amyloid-{beta} deposition and cognitive performance.

neuroscience↗

bioRxiv2020

Genetic architecture of host proteins interacting with SARS-CoV-2

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid in silico assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

genomics↗

bioRxiv2019

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are commonly used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the significance of the underlying correlation coefficients. A statistical cutoff, however, is not guaranteed to capture biological reality, and heavily depends on dataset properties such as sample size. We here propose an alternative, innovative approach to address the problem of network reconstruction. Specifically, we developed a cutoff selection algorithm that maximizes the agreement to a given ground truth. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. The optimal network outperforms networks obtained with statistical cutoffs and is robust with respect to sample size. Importantly, we can show that even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach on an untargeted metabolomics and a transcriptomics dataset from The Cancer Genome Atlas (TCGA). For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for the optimization. Overall, this paper shows that using prior information for correlation network inference is superior to using regular statistical cutoffs, even if the prior information is incomplete or partially inaccurate.

bioinformatics↗

bioRxiv2019

Characterization of bulk phosphatidylcholine compositions in human plasma using side-chain resolving lipidomics

Kit-based assays, such as AbsoluteIDQ p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally relevant information on the detailed fatty acid side chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side chain resolving Lipidyzer platform, analyzing 223 samples in parallel to the AbsoluteIDQ. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological datasets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

epidemiology↗