Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies

Elemental Composition determination based on MS

Motivation: Identification of metabolites is essential for its use as biomarkers, for research in systems biology, and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High resolution mass spectrometry, which provides the exact mass, together with common constraint-rules, for rejecting false proposed elemental compositions, can not always provide one unique elemental composition solution. Results: The Multi-stage Elemental Formula (MEF) tool is presented in this paper to enable the correct assignment of elemental composition to compounds, their fragment ions, and neutral losses that originate from the molecular ion by using multi-stage mass spectrometry (MSn). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MSn data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MSn tree (the width and the depth of the tree) was addressed. This method makes an important step towards semi-automatic de novo identification of metabolites using MSn data

Metabolite identification using automated comparison of high-resolution multistage mass spectral trees

Multistage mass spectrometry (MS n) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS n data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS n spectra acquired from 549 metabolites using Orbitrap FTMS n. For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS n spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS n spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS n spectral libraries that can be used for searching and matching experimental MS n spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies. © 2012 American Chemical Society