Tracing metabolic fluxes using mass spectrometry: Stable isotope-resolved metabolomics in health and disease

Stable isotope tracing reveals glucose metabolism characteristics of drug-resistant B-cell acute lymphoblastic leukemia

BACKGROUND

Adult B-cell acute lymphocytic leukemia (B-ALL) is a malignant hematologic tumor characterized by the uncontrolled proliferation of B-cell lymphoblasts in the bone marrow. Despite advances in treatment, including chemotherapy and consolidation therapy, many B-ALL patients experience unfavorable prognoses due to the development of drug resistance. The precise mechanisms governing chemotherapy resistance, particularly those related to metabolic reprogramming within tumors, remain inadequately elucidated.

RESULTS

Nalm6/DOX cells exhibited significantly elevated levels of glucose, pyruvate, alanine, glutamine, and glycine compared to Nalm6 cells. Conversely, reduced levels of citrate, acetate, and leucine were observed in Nalm6/DOX cells. Upon exposure to the culture medium supplemented with tracer 13C6-glucose, the Nalm6/DOX cells showed an increase in the abundance of 13C-alanine and a decrease in the levels of 13C-lactate, indicating impaired utilization of 13C-pyruvate. Combining β-chloro-alanine (ALTi) with DOX could decrease the drug resistance phenotype of Nalm6/DOX cells. The results demonstrated that glycolysis and tricarboxylic acid cycle were suppressed in Nalm6/DOX cells, while metabolic flux through the alanine and glutamine pathways was increased. Therefore, inhibition of alanine biosynthesis in Nalm6/DOX exhibits the potential to reverse drug resistance.

SIGNIFICANCE

A new insight into the impact of metabolism on chemotherapy resistance in B-ALL has been gained through the use of stable isotope resolved metabolomics based on nuclear magnetic resonance and ultra-performance liquid chromatography/tandem mass spectrometry. This provides promising ways for the development of innovative therapeutic strategies to alleviate drug resistance and relapse in affected patients.

Using DIMet for Differential Analysis of Labeled Metabolomics Data: A Step-by-step Guide Showcasing the Glioblastoma Metabolism

Stable-isotope resolved metabolomics (SIRM) is a powerful approach for characterizing metabolic states in cells and organisms. By incorporating isotopes, such as 13C, into substrates, researchers can trace reaction rates across specific metabolic pathways. Integrating metabolomics data with gene expression profiles further enriches the analysis, as we demonstrated in our prior study on glioblastoma metabolic symbiosis. However, the bioinformatics tools for analyzing tracer metabolomics data have been limited. In this protocol, we encourage the researchers to use SIRM and transcriptomics data and to perform the downstream analysis using our software tool DIMet. Indeed, DIMet is the first comprehensive tool designed for the differential analysis of tracer metabolomics data, alongside its integration with transcriptomics data. DIMet facilitates the analysis of stable-isotope labeling and metabolic abundances, offering a streamlined approach to infer metabolic changes without requiring complex flux analysis. Its pathway-based "metabologram" visualizations effectively integrate metabolomics and transcriptomics data, offering a versatile platform capable of analyzing corrected tracer datasets across diverse systems, organisms, and isotopes. We provide detailed steps for sample preparation and data analysis using DIMet through its intuitive, web-based Galaxy interface. To showcase DIMet's capabilities, we analyzed LDHA/B knockout glioblastoma cell lines compared to controls. Accessible to all researchers through Galaxy, DIMet is free, user-friendly, and open source, making it a valuable resource for advancing metabolic research. Key features • Glioblastoma tumor spheroids in vitro replicate tumors' three-dimensional structure and natural nutrient, metabolite, and gas gradients, providing a more realistic model of tumor biology. • Joint analysis of tracer metabolomics and transcriptomics datasets provides deeper insights into the metabolic states of cells. • DIMet is a web-based tool for differential analysis and seamless integration of metabolomics and transcriptomics data, making it accessible and user-friendly. • DIMet enables researchers to infer metabolic changes, offering intuitive and visually appealing "metabologram" outputs, surpassing conventional visual representations commonly used in the field.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data

MOTIVATION

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

RESULTS

In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.

AVAILABILITY AND IMPLEMENTATION

DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

BACKGROUND

Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.

METHODS

Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.

RESULTS

Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.

CONCLUSIONS

Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.

Deciphering the essential events in the reprogramming of metabolic networks subjected to complex perturbations, including the response to pharmacological treatments in multifactorial diseases like cancer, is crucial for the design of efficient therapies. Yet, tools to infer the molecular drivers sustaining such metabolic responses remain elusive for large metabolic networks. Here we develop an efficient computational strategy that integrates measured changes at systemic and molecular levels and combines metabolic control analysis with linear programming tools to infer key molecular drivers sustaining the metabolic adaptations to complex perturbations, such as an antitumoral drug therapy. The collective behavior is approximated using linear expressions where the adaptation of systemic concentrations and fluxes to a perturbation is described as a function of the molecular reprogramming of transport and enzyme activities. Starting from measured changes in fluxes and concentrations, we identify changes in the reprogramming of transporter and enzyme activities that are required to orchestrate the metabolic adaptation of colon cancer cells to a cell cycle inhibitor.

Metabolic Study of Stable Isotope Labeled Indolinone Derivative in Hepatocyte Cell by UPLC/Q TOF MS

The aggregation process of α-synuclein (α-syn) is substantial in the pathogenesis of Parkinson's disease. Indolinone derivatives are inhibitors of α-syn aggregates and can be used as PET-based radiotracers for imaging α-syn fibrils. However, no investigations on the metabolism of indolinone derivatives have been reported until now. In the present research, a ¹³C and ¹⁵N isotope labeling strategy was developed to synthesize compound [¹³C₂,¹⁵N]-(Z)-1-(4-aminobenzyl)-3-((E)-(3-phenyl)allylidene)indolin-2-one (M0'), which was then used in a study of metabolism in hepatocytes. The metabolites were characterized using accurate mass and characteristic ion measurements. In the metabolic system, compound M0' was the main component (accounting for 97.5% of compound-related components) after incubation in hepatocytes for 3 h, which indicated that compound M0' possessed great metabolic stability. Seven metabolites have been successfully verified by UPLC/Q TOF MS in metabolic studies, including hydroxyl M0' (M1'), hydroxyl and methylated M0' (M2'), N-acetylated M0' (M3'), sulfate of hydroxyl M0' (M4'), the glucose conjugate of M0' (M5'), glucuronide conjugate of M0' (M6'), and glucuronide conjugate of hydroxyl M0' (M7'). The study on metabolism provides the important information to develop effective α-syn aggregate inhibitors and new PET-tracer-related indolinone derivatives.

Capillary Electrophoresis-High Resolution Mass Spectrometry for Measuring In Vivo Arginine Isotope Incorporation in Alzheimer's Disease Mouse Models

Immune-based metabolic reprogramming of arginine utilization in the brain contributes to the neuronal pathology associated with Alzheimer's disease (AD). To enable our long-term goals of differentiation of AD mouse model genotypes, ages, and sexes based on activity of this pathway, we describe here the novel dosing (using uniformly labeled (¹³C₆¹⁵N₄) arginine) and analysis methods using capillary electrophoresis high-resolution accurate-mass mass spectrometry for isotope tracing of metabolic products of arginine. We developed a pseudoprimed infusion-dosing regimen, using repeated injections, to achieve a steady state of uniformly labeled arginine in 135-195 min post bolus dose. Incorporation of stable isotope labeled carbon and nitrogen from uniformly labeled arginine into a host of downstream metabolites was measured in vivo in mice using serially sampled dried blood spots from the tail. In addition to the dried blood spot time course samples, total isotope incorporation into arginine-related metabolites was measured in the whole brain and plasma after 285 min. Preliminary demonstration of the technique identified differences isotope incorporation in arginine metabolites between male and female mice in a mouse-model of sporadic Alzheimer's disease (APOE4/huNOS2). The technique described herein will permit arginine pathway activity differentiation between mouse genotypes, ages, sexes, or drug treatments in order to elucidate the contribution of this pathway to Alzheimer's disease.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.