MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes

Background: Tracing stable isotopes, such as 13 C using various mass spectrometry (MS) methods provides a valuable information necessary for the study of biochemical processes in cells. However, extracting such information requires special care, such as a correction for naturally occurring isotopes, or overlapping mass spectra of various components of the cell culture medium. Developing a method for a correction of overlapping peaks is the primary objective of this study. Results: Our computer program-MIDcor (free at https://github.com/seliv55/mid_correct) written in the R programming language, corrects the raw MS spectra both for the naturally occurring isotopes and for the overlapping of peaks corresponding to various substances. To this end, the mass spectra of unlabeled metabolites measured in two media are necessary: in a minimal medium containing only derivatized metabolites and chemicals for derivatization, and in a complete cell incubated medium. The MIDcor program calculates the difference (D)between the theoretical and experimentally measured spectra of metabolites containing only the naturally occurring isotopes. The result of comparison of D in the two media determines a way of deciphering the true spectra. (1) If D in the complete medium is greater than that in the minimal medium in at least one peak, then unchanged D is subtracted from the raw spectra of the labeled metabolite. (2) If D does not depend on the medium, then the spectrum probably overlaps with a derivatized fragment of the same metabolite, and D is modified proportionally to the metabolite labeling. The program automatically reaches a decision regarding the way of correction. For some metabolites/fragments in the case (2)D was found to decrease when the tested substance was 13 C labeled, and this isotopic effect also can be corrected automatically, if the user provides a measured spectrum of the substance in which the 13 C labeling is known a priori. Conclusion: Using the developed program improves the reliability of stable isotope tracer data analysis

A Flexible Tool to Correct Superimposed Mass Isotopologue Distributions in GC-APCI-MS Flux Experiments

The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.SALSA (School of Analytical Sciences Adlershof, Albert-Einstein-Straße 5, 12489 Berlin, Germany)Peer Reviewe

MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes

Background: Tracing stable isotopes, such as 13 C using various mass spectrometry (MS) methods provides a valuable information necessary for the study of biochemical processes in cells. However, extracting such information requires special care, such as a correction for naturally occurring isotopes, or overlapping mass spectra of various components of the cell culture medium. Developing a method for a correction of overlapping peaks is the primary objective of this study. Results: Our computer program-MIDcor (free at https://github.com/seliv55/mid_correct) written in the R programming language, corrects the raw MS spectra both for the naturally occurring isotopes and for the overlapping of peaks corresponding to various substances. To this end, the mass spectra of unlabeled metabolites measured in two media are necessary: in a minimal medium containing only derivatized metabolites and chemicals for derivatization, and in a complete cell incubated medium. The MIDcor program calculates the difference (D)between the theoretical and experimentally measured spectra of metabolites containing only the naturally occurring isotopes. The result of comparison of D in the two media determines a way of deciphering the true spectra. (1) If D in the complete medium is greater than that in the minimal medium in at least one peak, then unchanged D is subtracted from the raw spectra of the labeled metabolite. (2) If D does not depend on the medium, then the spectrum probably overlaps with a derivatized fragment of the same metabolite, and D is modified proportionally to the metabolite labeling. The program automatically reaches a decision regarding the way of correction. For some metabolites/fragments in the case (2)D was found to decrease when the tested substance was 13 C labeled, and this isotopic effect also can be corrected automatically, if the user provides a measured spectrum of the substance in which the 13 C labeling is known a priori. Conclusion: Using the developed program improves the reliability of stable isotope tracer data analysis

Computational hypothesis generation with genome-side metabolic reconstructions: in-silico prediction of metabolic changes in the freshwater model organism Daphnia to environmental stressors

Computational toxicology is an emerging, multidisciplinary field that uses in-silico modelling techniques to predict and understand how biological organisms interact with pollutants and environmental stressors. Genome-wide metabolic reconstruction (GWMR) is an in-silico modelling technique that aims to represent the metabolic capabilities of an organism. Daphnia is an emerging model species for environmental omics whose underlying biology is still being uncovered. Creating a metabolic reconstruction of Daphnia and applying it in an environmental computational toxicology setting has the potential to aid in understanding its interaction with environmental stressors. Here, the fist GWMR of D. magna is presented, which is built using METRONOME, a newly developed tool for automated GWMR of new genome sequences. Active module identification allows for omics data sets to be integrated into in-silico models and uses optimisation algorithms to find hot-spots within networks that represent areas that are significantly impacted based on a toxicogenomic transcriptomics dataset. Here, a method that uses the active modules approach in a predictive capacity for computational hypothesis generation is introduced to predict unknown metabolic responses to environmentally relevant human-induced stressors. A computational workflow is presented that takes a new genome sequence, builds a GWMR and integrates gene expression data to make predictions of metabolic effects. The aim is to introduce an element of hypothesis generation into the untargeted metabolomics experimental workflow. A study to validate this approach using D. magna as the target organism is presented, which uses untargeted Liquid-Chromatography Mass Spectrometry (LC-MS) to make metabolomics measurements. A software tool MUSCLE is presented that uses multi-objective closed-loop evolutionary optimisation to automatically develop LC-MS instrument methods and is used here to develop the analytical method