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Energetic Metabolism Targeted Panel

Evaluating Oxidative Stress, Mitochondrial Function, and Inflammation to Score Complex Metabolic Changes Associated with Ageing

Maintaining optimal metabolic health is more crucial than ever. That's where the Crescendo approach steps in, revolutionizing the way we assess and manage our metabolic well-being. By combining cutting-edge analytical techniques, advanced data analysis, and personalized scoring systems, Crescendo sets a new standard for reliable metabolic assessment.

Methodological Approach

Crescendo Care applies LC-MS/MS analysis of metabolite concentrations in the TCA cycle and metabolic flux analysis as a powerful tool to score the complex aging-related metabolic perturbations, based on oxidative stress, mitochondrial function, and inflammation study

The assessment of metabolic flux within the tricarboxylic acid (TCA) cycle, also known as the Krebs cycle, and associated pathways, such as ketogenesis, necessitates the quantification of the rates at which metabolites are interconverted within these metabolic pathways.

Fig.1 depicts the Krebs cycle, while Fig.2 illustrates the interconnectivity between this metabolic cycle and other pathways, such as glycolysis, lipid metabolism, and amino acid metabolism.

Figure 1_TCA cycle

Figure 2_mitochondria metabolism

The synergistic application of LC-MS/MS analysis of metabolite concentrations in the TCA cycle and metabolic flux analysis provides a powerful tool for elucidating complex metabolic networks and identifying metabolic perturbations, potential biomarkers or therapeutic targets in various disease states.

The advantages of this combined methodology are as follows:

  • Comprehensive assessment: LC-MS/MS allows for the simultaneous quantification of multiple metabolites involved in the citric acid cycle and related pathways. By measuring metabolite concentrations, researchers can obtain a snapshot of the metabolic state of the cell or organism under different conditions.
  • Identification of Metabolic Perturbations: The combination of these techniques enables the detection of subtle changes in metabolite concentrations and flux distributions, which may be indicative of underlying metabolic disorders or adaptations to various physiological or pathological conditions or environmental factors. LC-MS/MS analysis can uncover changes in metabolite levels that could potentially function as biomarkers for metabolic dysfunction.
  • Elucidation of regulatory mechanisms: Integrating metabolite concentration data with flux analysis allows for the identification of potential regulatory points within the TCA cycle, such as rate-limiting enzymes or allosteric modulators, which may play crucial roles in controlling metabolic homeostasis
  • Mapping Metabolite Fluxes: Metabolic flux analysis enables the mapping of metabolic flux distributions through the citric acid cycle and correlated pathways. By estimating fluxes through individual reactions, researchers can identify key metabolic nodes that are altered under specific conditions.
  • Validation of Metabolic Models: Metabolic flux analysis relies on mathematical models of cellular metabolism, which often involve simplifications and assumptions. Integrating experimental data from LC-MS/MS analysis with metabolic flux analysis can help validate these models and refine their predictions.
  • Identification of Metabolic Targets: The combination of LC-MS/MS analysis and metabolic flux analysis enables the identification of potential metabolic targets for therapeutic intervention. By elucidating metabolic pathways that are dysregulated in disease states, it becomes possible to nominate candidate targets for drug discovery or metabolic engineering strategies.

Why the Crescendo Approach sets a new standard for a Reliable Metabolic Assessment

The Crescendo methodology approach facilitates the practical implementation of our proprietary analytical diagnostic methods across multiple domains. This enables our Personalized Scoring System, which provides a tailored assessment of an individual's metabolic health.

Utilizing LC-MS/MS for Metabolite Profiling: LC-MS/MS serves as a valuable tool for quantifying the concentrations of diverse metabolites implicated in oxidative stress responses, including antioxidants, reactive oxygen species (ROS), and metabolites linked to energy metabolism. Through the comparative analysis of metabolite profiles between individuals experiencing oxidative stress and healthy controls, it becomes possible to discern distinctive alterations indicative of the condition.

Metabolic Flux Analysis (MFA) for Flux Distribution Estimation: MFA involves the estimation of metabolic flux distributions in cellular metabolic networks. By integrating experimental data with mathematical models, MFA can provide insights into how metabolic pathways are rewired in response to oxidative stress. For example, MFA can reveal changes in fluxes through pathways involved in antioxidant defense, energy metabolism, and redox homeostasis.

 

Identification of Metabolic Signatures: By combining LC-MS/MS data with MFA results, researchers can identify metabolic signatures that are indicative of oxidative stress. This may include alterations in the fluxes of specific metabolites or metabolic pathways, as well as changes in the concentrations of key metabolites associated with oxidative damage or antioxidant responses.

Scoring System for Oxidative Stress: Based on the combined analysis of LC-MS/MS and MFA data, researchers can develop a scoring system or biomarker panel to quantitatively assess the degree of oxidative stress in patients. This scoring system may incorporate information from multiple metabolites and fluxes, providing a comprehensive measure of metabolic dysregulation associated with oxidative stress.

Clinical Applications: The developed scoring system can be applied in clinical settings to diagnose oxidative stress, monitor disease progression, and evaluate the efficacy of therapeutic interventions. By providing quantitative measures of metabolic alterations, this approach can aid clinicians in personalized patient management and treatment optimization.

Overall, combining LC-MS/MS analysis with Metabolic Flux Analysis enables the identification of metabolic signatures associated with oxidative stress and the development of comprehensive scoring systems for assessing oxidative stress in patients. The clinical application of this approach can significantly improve the diagnosis, monitoring, and management of oxidative stress-related diseases,

References

  • Luo, B., Groenke, K., Takors, R., Wandrey, C., & Oldiges, M. (2007). Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography-mass spectrometry. Journal of Chromatography A, 1147(2), 153-164. doi:10.1016/j.chroma.2007.02.034
  • Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51-78. doi:10.1002/mas.20108
  • Wiechert, W. (2001). 13C Metabolic Flux Analysis. Metabolic Engineering, 3(3), 195-206. doi:10.1006/mben.2001.0187
  • Nagrath, D., Sahai, V., & Sen, S. (2009). Analysis of metabolic networks using 13C isotopic tracers. Current Opinion in Chemical Biology, 13(5-6), 522-528. doi:10.1016/j.cbpa.2009.08.009
  • Zamboni, N., Fendt, S. M., Ruhl, M., & Sauer, U. (2009). 13C-based metabolic flux analysis. Nature Protocols, 4, 878-892. doi:10.1038/nprot.2009.58
  • Yuan, M., Breitkopf, S. B., Yang, X., & Asara, J. M. (2012). A positive/negative ion–switching, targeted mass spectrometry–based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nature Protocols, 7, 872-881. doi:10.1038/nprot.2012.024
  • Kell, D. B. (2006). Systems biology, metabolic modeling and metabolomics in drug discovery and development. Drug Discovery Today, 11(23-24), 1085-1092. doi:10.1016/j.drudis.2006.10.006
  • Tanaka, Y., & Alexopoulos, S. P. (2018). Metabolomics and oxidative stress in psychiatric disorders. Current Topics in Medicinal Chemistry, 18(12), 1001-1009. doi:10.2174/1568026618666181206143016
  • Antoniewicz, M. R., Kelleher, J. K., & Stephanopoulos, G. (2007). Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis. Analytical Chemistry, 79(19), 7554-7559. doi:10.1021/ac070889i
  • Millard, P., Smallbone, K., Mendes, P., & Kell, D. B. (2017). Metabolic flux analysis: an overview of theory and practice. Methods in Molecular Biology, 985, 59-97. doi:10.1007/978-1-62703-299-5_4

What is included on the Crescendo Metabolic Wellness profile

The Crescendo Metabolic Wellness Profile is a comprehensive assessment tool designed to evaluate an individual's metabolic health status, focusing on oxidative stress, mitochondrial function, and inflammation. It’s a customizable profile, including:

Mithocondrial function_TCA Cycle

Citric acid, Isocitrate, α-ketoglutarate, Succinyl-CoA, Succinate, Fumarate, Malate, Oxaloacetate, NAD/NADH, FAD/FADH2, ATP, ADP, AMP, Acetyl-CoA, Pyruvic acid, Lactic acid, Aspartic acid.

 Oxidative stress

GSH, GSSG, Cysteine, Cystine, N-acetyl-cysteine, Homocysteine, Homocystine, Taurine, Serine, Glycine, Glutamic acid, Methionine, Cysteine hydrodisulfide, Cysteine hydrotrisulfide, Cystine trisulfide, Cystine tetrasulfide, Glutathione hydrodisulfide, Glutathione hydrotrisulfide, Glutathione trisulfide, Glutathione tetrasulfide, Sulfite, Thiosulfite, N-acetyl-cysteine disulfide.

 Inflammation

(±)10-HdoHE, (±)11-HdoHE, (±)11,12-DHET, (±)11(12)-EET, (±)11(12)-EET ethanolamide, (±)12,13-DiHOME, (±)13-HdoHE, (±)14-HdoHE, (±)14,15-DHET, (±)14,15-DiHETE, (±)14(15)-EET, (±)14(15)-EET ethanolamide, (±)16-HdoHE, (±)16-HETE, (±)17-HdoHE, (±)17-HETE, (±)17,18-DiHETE, (±)18-HETE, (±)20-HdoHE, (±)4-HdoHE, (±)5-HEDE, (±)5,6-DHET, (±)5,6-DHET-lactone, (±)5(6)-EET, (±)5(6)-EET ethanolamide, (±)7-HdoHE, (±)8-HdoHE, (±)8,9-DHET, (±)8(9)-EET ethanolamide, (±)9-HETE, (±)9-HpODE, (±)9,10-DiHOME, 10(S),17(S)-DiHDoHE, 11-dehydro Thromboxane B₂, 11-trans LTC₄, 11-trans LTE₄, 11(S)-HETE, 11β Prostaglandin  F₂ₐ, 11β+13,14-dihydrc-15-keto Prostaglandin F₂ₐ, 12-(S)HHTrE, 12-oxo LTB₄, 12-OxoETE, 12(13)-EpOME, 12(S)-HEPE, 12(S)-HETE, 12(S)-HpEPE, 12(S)-HpETE, 13-OxoOE, 13,14-dihydro 15-keto Prostaglandin  D₂, 13,14-dihydro-15-keto Prostaglandin  F₂ₐ PGFI, 13,14-dihydro-15-keto Prostaglandin E₂, 13,14-dihydro-15-keto Prostaglandin J₂, 13,14-dihydro-15-keto-tetranor Prostaglandin F₁β, 13,14-dihydro-15keto-tetranor-Prostaglandin F₁ₐ, 13,14-dihydro-keto-tetranor Prostaglandin E₂, 13,14-keto-tetranor Prostaglandin D₂, 13(S)-HODE, 13(S)-HOTrE, 13(S)-HpODE, 13(S)-HpODE, 14,15-LTC₄, Eoxin C₄, EXC₄, 14,15-LTE4, Eoxin E₄, 15-deoxy-delya12,14-PGJ₂, 15-keto Prostaglandin E₂, 15-keto Prostaglandn F₂ₐ, 15-OxoEDE, 15(S)-HEPE, 15(S)-HETE, 15(S)-HETrE, 15(S)-HpEPE, 15(S)-HpETE, 17(18)-HpETE, 18-carboxy dinor LTB₄, 18-carboxy dinor LTB₄,18(S)-HEPE, 19(S)-HETE, 1a,1b-dihomo-Prostaglandin F₂ₐ, 2,3-dinor-8-iso Prostaglandin F₂ₐ, 20-carboxy arachidonic acid, 20-carboxy LTB₄, 20-HETE, 20-hydroxy LTB₄, 20-hydroxy LTB₄, 20-hydroxy Prostaglandin E₂, 20-hydroxy Prostaglandin F₂ₐ, 5(S)-HEPE, 5(S)-HETE, 5(S)-HpEPE, 5(S)-HpETE, 5(S),14(R)-LXB₄, 5(S),15(S)-DiHETE, 5(S),6(R)-Lipoxin A₄, 5(S),6(S)-Lipoxin A₄, 6-trans LTB₄, 6,15-dlketo-13,14-dhydro Prostaglandn F₁ₐ, 8-iso Prostaglandin A₁, 8-iso Prostaglandin A₂, 8-iso Prostaglandin E-7, 8-iso Prostaglandin E₁, 8-iso Prostaglandin F₁ₐ, 8-iso Prostaglandin F₂ₐ, 8-iso Prostaglandin F₃ₐ, 8-iso-13,14-dihydro-15-keto Prostaglandin F₂ₐ, 8-iso-15-keto Prostaglandin F₂ₐ, 8-iso-15(R)-Prostaglandin F₂ₐ, 8,12-iso-iPF₂ₐ-Vl 1,5-lactone, 8(S)-HETE, 8(S),15(S)-DiHETE, 9-OxoODE, 9(10)-EpOME, 9(S)-HODE, 9(S)-HOTrE, Eicosapentaenolic acid (EPA), iPF₂ₐ-IV, Leukotriene B₄, Leukotriene C₄, Leukotriene D₄, Leukotriene E₄, Leukotriene F₄, Lipoxin A₅, LTB4 Ethanolamide, Lyso-PAF C-16, N-acetyl LTE₄, OEA (oleoyilethanolamide), PAF C-16, PGD₂ Ethanolamide, PGE₁ Ethanolamide, PGE₂ Ethanolamide, PGF₂ₐ Ethanolamide, Prostaglandin A₁, Prostaglandin A₂, Prostaglandin B₂, Prostaglandin D₁, Prostaglandin D₂, Prostaglandin D₃, Prostaglandin E₁, Prostaglandin E₂, Prostaglandin E₃, Prostaglandin F₂ₐ, Prostaglandin F₂ₐ, Prostaglandin F₃ₐ, Prostaglandin J₂, Prostaglandin K₂, Resolvin D₁, Resolvin D₁, Tetranor-PGDM, Tetranor-PGEM, Tetranor-PGFM, Thromboxane B₁, Thromboxane B₂, Thromboxane B₃