9 research outputs found
NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor
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202091.pdf (publisher's version ) (Open Access
Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets
Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S
Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease
Genome-wide association studies (GWASs) have identified genetic loci associated with the risk of Alzheimer's disease (AD), but the molecular mechanisms by which they confer risk are largely unknown. We conducted a metabolome-wide association study (MWAS) of AD-associated loci from GWASs using untargeted metabolic profiling (metabolomics) by ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). We identified an association of lactosylceramides (LacCer) with AD-related single-nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0 × 10-5 to 1.3 × 10-44). We showed that plasma LacCer concentrations are associated with cognitive performance and genetically modified levels of LacCer are associated with AD risk. We then showed that concentrations of sphingomyelins, ceramides, and hexosylceramides were altered in brain tissue from Abca7 knockout mice, compared with wild type (WT) (P = 0.049-1.4 × 10-5), but not in a mouse model of amyloidosis. Furthermore, activation of microglia increases intracellular concentrations of hexosylceramides in part through induction in the expression of sphingosine kinase, an enzyme with a high control coefficient for sphingolipid and ceramide synthesis. Our work suggests that the risk for AD arising from functional variations in ABCA7 is mediated at least in part through ceramides. Modulation of their metabolism or downstream signaling may offer new therapeutic opportunities for AD
Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted <sup>1</sup>H NMR Metabolic Profiling
<sup>1</sup>H NMR spectroscopy of
biofluids generates reproducible
data allowing detection and quantification of small molecules in large
population cohorts. Statistical models to analyze such data are now
well-established, and the use of univariate metabolome wide association
studies (MWAS) investigating the spectral features separately has
emerged as a computationally efficient and interpretable alternative
to multivariate models. The MWAS rely on the accurate estimation of
a metabolome wide significance level (MWSL) to be applied to control
the family wise error rate. Subsequent interpretation requires efficient
visualization and formal feature annotation, which, in-turn, call
for efficient prioritization of spectral variables of interest. Using
human serum <sup>1</sup>H NMR spectroscopic profiles from 3948 participants
from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed
a series of MWAS for serum levels of glucose. We first propose an
extension of the conventional MWSL that yields stable estimates of
the MWSL across the different model parameterizations and distributional
features of the outcome. We propose both efficient visualization methods
and a strategy based on subsampling and internal validation to prioritize
the associations. Our work proposes and illustrates practical and
scalable solutions to facilitate the implementation of the MWAS approach
and improve interpretation in large cohort studies
Workflow for Integrated Processing of Multicohort Untargeted <sup>1</sup>H NMR Metabolomics Data in Large-Scale Metabolic Epidemiology
Large-scale
metabolomics studies involving thousands of samples
present multiple challenges in data analysis, particularly when an
untargeted platform is used. Studies with multiple cohorts and analysis
platforms exacerbate existing problems such as peak alignment and
normalization. Therefore, there is a need for robust processing pipelines
that can ensure reliable data for statistical analysis. The COMBI-BIO
project incorporates serum from ∼8000 individuals, in three
cohorts, profiled by six assays in two phases using both <sup>1</sup>H NMR and UPLC–MS. Here we present the COMBI-BIO NMR analysis
pipeline and demonstrate its fitness for purpose using representative
quality control (QC) samples. NMR spectra were first aligned and normalized.
After eliminating interfering signals, outliers identified using Hotelling’s <i>T</i><sup>2</sup> were removed and a cohort/phase adjustment
was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment
of the NMR data was shown to increase the correlation-based alignment
quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586
for NOESY, showing that the improvement was present across both large
and small peaks. End-to-end quality assessment of the pipeline was
achieved using Hotelling’s <i>T</i><sup>2</sup> distributions.
For CPMG spectra, the interquartile range decreased from 1.425 in
raw QC data to 0.679 in processed spectra, while the corresponding
change for NOESY spectra was from 0.795 to 0.636, indicating an improvement
in precision following processing. PCA indicated that gross phase
and cohort differences were no longer present. These results illustrate
that the pipeline produces robust and reproducible data, successfully
addressing the methodological challenges of this large multifaceted
study