Efficient Modeling of MS/MS Data for Metabolic Flux Analysis

RESEARCH ARTICLE Efficient Modeling of MS/MS Data for Metabolic Flux Analysis Naama Tepper, Tomer Shlomi* Department of Computer Science, Technion, Israel Institute of Technology, Haifa, Israel * Abstract a11111 OPEN ACCESS Citation: Tepper N, Shlomi T (2015) Efficient Modeling of MS/MS Data for Metabolic Flux Analysis. PLoS ONE 10(7): e0130213. doi:10.1371/journal. pone.0130213 Editor: Petras Dzeja, Mayo Clinic, UNITED STATES Received: February 27, 2015 Accepted: May 16, 2015 Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of E. coli. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at www.cs.technion.ac.il/~tomersh/methods.html Published: July 31, 2015 Copyright: © 2015 Tepper, Shlomi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. Introduction Metabolic flux analysis (MFA) is a method for quantifying in vivo metabolic fluxes that is commonly used to address problems in biotechnology and medicine [1–6]. It involves feeding cells with isotopic labeled nutrients (e.g. 13C labelled substrates), measuring metabolite isotopic labeling, and applying computational methods to estimate fluxes [1, 7–9]. MFA is based on the key observation that metabolite isotopic labeling patterns are uniquely determined by the distribtuion of metabolic flux in the network [10]. It is typically implemented as a non-convex optimization problem, searching for the most likely distribution of fluxes that would give rise to metabolite isotopic labeling that optimally matches experimental PLOS ONE | DOI:10.1371/journal.pone.0130213 July 31, 2015 1 / 14 Efficient Modeling of MS/MS Data for Metabolic Flux Analysis Table 1. The distribution of isotopomers of metabolite A (shown in Fig 2) within tandemers of A, g defined with respect to Aff2;3;4 2;3g . Isotopomers Tandemers 0000 [M + 0] > [m + 0] 0001 [M + 1] > [m + 0] 0010 [M + 1] > [m + 1] 0011 [M + 2] > [m + 1] 0100 [M + 1] > [m + 1] 0101 [M + 2] > [m + 1] 0110 [M + 2] > [m + 2] 0111 [M + 3] > [m + 2] 1000 [M + 0] > [m + 0] 1001 [M + 1] > [m + 0] 1010 [M + 1] > [m + 1] 1011 [M + 2] > [m + 1] 1100 [M + 1] > [m + 1] 1101 [M + 2] > [m + 1] 1110 [M + 2] > [m + 2] 1111 [M + 3] > [m + 2] Isotopomers are represented by sequences of zeroes and ones, denoting non-labeled and labeled atoms, respectively. doi:10.1371/journal.pone.0130213.t001 measurements. The running time of MFA methods becomes a major bottleneck when analyzing large-scale metabolic networks, consisting of hundreds of reactions, when repeatedly applied to compute accurate flux confidence intervals [11, 12], and in experimental design of isotopic labeling experiments [13]. The major factor that affects the performance of MFA implementations is the time required to simulate metabolite isotopic labeling for a candidate flux distribution. A distinct labeling pattern of a certain metabolite is called an isotopomer, while the distribution of abundances of all isotopomers is reffered to as, isotopomer distribution. A metabolite with n carbons has 2n distinct isotopomers; for example, as shown in Table 1, a metabolite having 4 carbons has 16 possible isotopomers. Previous studies have suggested the cumomers [14] and fluxomers [15] approaches for efficiently simulating the isotopomer distributions of all metabolites in a metabolic network given a flux vector. However, as the number of distinct isotopomers of a metabolite is exponentially dependent on the number of carbons that is has, these methods require a huge number of variables and may become computationally intractable. Measuring the complete isotopomer distribution of metabolites is technically infeasible. Instead, mass-spectrometry is typically used to measure the relative abundance of a given metabolite having different number of labeled atoms (i.e. zero labeled atoms, one, two, etc). A set of isotopomers of a certain metabolite having the same mass is referred to as massisotopomers, while the relative abundance of mass-isotopomers denoted mass-isotopomer distribution. Notably, a mass-isotopomer distribution provides limited information on positional isotopic labeling, as isotopomers with the same number of labeled atoms have the same mass regardless of their position. Mass-isotopomer distributions can be calculated given the complete isotopomer distributions (by summing the abundances of isotopomers having the same number of labeled atoms). Alternatively, they can be directly and efficiently computed via the EMU approach [1]. PLOS ONE | DOI:10.1371/journal.pone.0130213 July 31, 2015 2 / 14 Efficient Modeling of MS/MS Data for Metabolic Flux Analysis Information on the positional labeling of metabolites can be obtained by tandem mass-spectrometry (i.e. MS/MS) and was previously shown to significantly improve quantification of metabolic fluxes via MFA [12, 16–18]. It works by isolating a single parent ion from the full spectrum and measuring its mass, followed by a collision that yields product ions whose mass is also measured. It can hence be employed to derive the mass-isotopomer distribution of a (...truncated)