Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

High resolution mass spectrometry newborn screening applications for quantitative analysis of amino acids and acylcarnitines from dried blood spots

Amino acid and acylcarnitine first-tier newborn screening typically employs derivatized or non-derivatized sample preparation methods followed by FIA coupled to triple quadrupole (TQ) MS/MS. The low resolving power of TQ instruments results in difficulties distinguishing nominal isobaric metabolites, especially those with identical quantifying product ions such as malonylcarnitine (C3DC) and 4-hydroxybutylcarnitine (C4OH). Twenty-eight amino acids and acylcarnitines extracted from dried blood spots (DBS) were analyzed by direct injection (DI)-HRMS on a Q-Exactive Plus across available mass resolving powers in SIM, in PRM at 17,000 full width at half maximum (FWHM), and a developed SIM/PRM hybrid MS method. Most notably, quantitation of C3DC and C4OH was successful by HRMS in non-derivatized samples, thus, potentially eliminating sample derivatization requirements. Quantitation differed between SIM and PRM acquired data for several metabolites, and it was determined these quantitative differences were due to collision energy differences or kinetic isotope effects between the unlabeled metabolites and the corresponding labeled isotopologue internal standards. Overall quantitative data acquired by HRMS were similar to data acquired on TQ MS/MS platform. A proof-of-concept hybrid DI-HRMS and SIM/PRM/FullScan method was developed demonstrating the ability to hybridize targeted newborn screening with metabolomic screening.

Tracing metabolic flux through time and space with isotope labeling experiments

Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population.

Proteome Dynamics from Heavy Water Metabolic Labeling and Peptide Tandem Mass Spectrometry

Protein homeostasis (proteostasis) is a result of a dynamic equilibrium between protein synthesis and degradation. It is important for healthy cell/organ functioning and is often associated with diseases such as neurodegenerative diseases and non-Alcoholic Fatty Liver disease. Heavy water metabolic labeling, combined with liquid-chromatography and mass spectrometry (LC-MS), is a powerful approach to study proteostasis in vivo in high throughput. Traditionally, intact peptide signals are used to estimate stable isotope incorporation in time-course experiments. The time-course of label incorporation is used to extract protein decay rate constant (DRC). Intact peptide signals, computed from integration in chromatographic time and mass-to-charge ratio (m/z) domains, usually, provide an accurate estimate of label incorporation. However, sample complexity (co-elution), limited dynamic range, and low signal-to-noise ratio (S/N) may adversely interfere with the peptide signals. These artifacts complicate the DRC estimations by distorting peak shape in chromatographic time and m/z domains. Fragment ions, on the other hand, are less prone to these artifacts and are potentially well suited in aiding DRC estimations. Here, we show that the label incorporation encoded into the isotope distributions of fragment ions reflect the isotope enrichment during the metabolic labeling with heavy water. We explore the label incorporation statistics for devising practical approaches for DRC estimations.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary ¹³C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO₂ over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient ¹³CO₂ labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

BACKGROUND

Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973's fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, ¹³C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.

RESULTS

The Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO₂ conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.

CONCLUSIONS

This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO₂ assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

Assessing compartmentalized flux in lipid metabolism with isotopes

Metabolism in plants takes place across multiple cell types and within distinct organelles. The distributions equate to spatial heterogeneity; though the limited means to experimentally assess metabolism frequently involve homogenizing tissues and mixing metabolites from different locations. Most current isotope investigations of metabolism therefore lack the ability to resolve spatially distinct events. Recognition of this limitation has resulted in inspired efforts to advance metabolic flux analysis and isotopic labeling techniques. Though a number of these efforts have been applied to studies in central metabolism; recent advances in instrumentation and techniques present an untapped opportunity to make similar progress in lipid metabolism where the use of stable isotopes has been more limited. These efforts will benefit from sophisticated radiolabeling reports that continue to enrich our knowledge on lipid biosynthetic pathways and provide some direction for stable isotope experimental design and extension of MFA. Evidence for this assertion is presented through the review of several elegant stable isotope studies and by taking stock of what has been learned from radioisotope investigations when spatial aspects of metabolism were considered. The studies emphasize that glycerolipid production occurs across several locations with assembly of lipids in the ER or plastid, fatty acid biosynthesis occurring in the plastid, and the generation of acetyl-CoA and glycerol-3-phosphate taking place at multiple sites. Considering metabolism in this context underscores the cellular and subcellular organization that is important to enhanced production of glycerolipids in plants. An attempt is made to unify salient features from a number of reports into a diagrammatic model of lipid metabolism and propose where stable isotope labeling experiments and further flux analysis may help address questions in the field. This article is part of a Special Issue entitled: Plant Lipid Biology edited by Kent D. Chapman and Ivo Feussner.

Quantifying plant phenotypes with isotopic labeling & metabolic flux analysis

Analyses of metabolic flux using stable isotopes in plants have traditionally been restricted to tissues with presumed homogeneous cell populations and long metabolic steady states such as developing seeds, cell suspensions, or cultured roots and root tips. It is now possible to describe these and other metabolically more dynamic tissues such as leaves in greater detail using novel methods in mass spectrometry, isotope labeling strategies, and transient labeling-based flux analyses. Such studies are necessary for a systems level description of plant function that more closely represents biological reality, and provides insights into the genes that will need to be modified as natural resources become ever more limited and environments change.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

Tracking the metabolic pulse of plant lipid production with isotopic labeling and flux analyses: Past, present and future

Metabolism is comprised of networks of chemical transformations, organized into integrated biochemical pathways that are the basis of cellular operation, and function to sustain life. Metabolism, and thus life, is not static. The rate of metabolites transitioning through biochemical pathways (i.e., flux) determines cellular phenotypes, and is constantly changing in response to genetic or environmental perturbations. Each change evokes a response in metabolic pathway flow, and the quantification of fluxes under varied conditions helps to elucidate major and minor routes, and regulatory aspects of metabolism. To measure fluxes requires experimental methods that assess the movements and transformations of metabolites without creating artifacts. Isotopic labeling fills this role and is a long-standing experimental approach to identify pathways and quantify their metabolic relevance in different tissues or under different conditions. The application of labeling techniques to plant science is however far from reaching it potential. In light of advances in genetics and molecular biology that provide a means to alter metabolism, and given recent improvements in instrumentation, computational tools and available isotopes, the use of isotopic labeling to probe metabolism is becoming more and more powerful. We review the principal analytical methods for isotopic labeling with a focus on seminal studies of pathways and fluxes in lipid metabolism and carbon partitioning through central metabolism. Central carbon metabolic steps are directly linked to lipid production by serving to generate the precursors for fatty acid biosynthesis and lipid assembly. Additionally some of the ideas for labeling techniques that may be most applicable for lipid metabolism in the future were originally developed to investigate other aspects of central metabolism. We conclude by describing recent advances that will play an important future role in quantifying flux and metabolic operation in plant tissues.

The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.