Bayesian ^{13}CMetabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the NonOxidative Pentose Phosphate Pathway Supports Phagocytosis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Preparation of RPMI Tracer Medium
2.3. Isolation of Granulocytes from Whole Blood
2.4. Phagocytosis Assay
2.5. Isotopic Tracer Experiments
2.6. Extraction of Intracellular Metabolites
2.7. Derivatization and GC−MS Analysis of Intracellular Metabolites
2.8. Metabolic Network Model and Metabolic Flux Analysis
2.8.1. Glossary
 C1, C2, …, C6: Designation of the position for the carbons of a metabolite.
 ${c}_{1},{c}_{2},\dots {,c}_{Nc}$: Isolated labeling on the carbons of a fragment metabolite.
 CMD: Normalized carbon mass distribution of a fragment with the elements ${r}_{i}$, where $i$ denotes the mass offset or number of simultaneously labeled carbons.
 MID: Normalized mass isotopomer distribution of a fragment without correction for naturally occurring isotopes of its elements (e.g., H, C, N, O, and Si). Values are expressed as mol%.
 $N$: Number of carbon labeling positions of a fragment metabolite.
2.8.2. ^{13}CPositional Labeling Approach
2.8.3. Accurate Assessment of the Selected Fragments for ^{13}CMFA
 $n$: number of carbons
 ${m}_{n}$: fractional abundance of each isotopomer of a fragment (M+0 to M+(n+1))
 ${A}_{n}:$ peak area of an individual mass isotopomer
 ${\sum}_{i}A$: sum of all signal areas
2.8.4. Bayesian Modeling for ^{13}CMFA
2.8.5. Stoichiometric Flux Restraint Analysis of the Metabolic Network
2.8.6. Evaluation of the Bayesian ^{13}CMFA
2.8.7. Evaluation of Flux Precision for Monitored Fragments
2.9. Principal Component Analysis
3. Results and Discussion
3.1. GCMS Analysis of Intracellular Metabolites as Their EthyloximeTrimethylsilyl Derivatives
3.2. Evaluation of ^{13}CIsotopomer Mass Distribution Based on Established Models and Concepts
3.2.1. Characterization of Glucose Pathway Utilization Based on Interpretation of ^{13}C Labeling Patterns
3.2.2. Estimation of Fractional Contribution Using the [U^{13}C]glucose Tracer
3.2.3. ^{13}CPositional Labeling Analysis of G6P Indicates an Input of Unlabeled S7P into the NonOxidative PPP
3.3. Validation and Evaluation of our Bayesian ^{13}CMFA Performance
3.3.1. Validation of the Proposed ^{13}CMFA Approach: Accuracy and Precision Were Comparable with StateoftheArt Software Packages
3.3.2. Selection of ^{13}CLabeled Glucose Tracers for ^{13}CMFA of Glycolysis and PPP
3.3.3. GC−MS Fragments of G6P Improve Flux Precision of the Glucose Metabolism
3.4. Glucose Metabolism of Granulocytes: Identification of Metabolic Patterns and Their Regulation
3.4.1. The NonOxidative PPP Promotes Ribose5Phosphate Biosynthesis in Granulocytes
3.4.2. A “HardWired” PPP Network Gives Insight into Cellular Mechanisms
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Metabolite  m/z  Carbon Atoms  Molecular Formula 

Glucose  568  C1−C6  C_{22}H_{54}O_{6}NSi_{5} 
319  C3−C6  C_{13}H_{31}O_{3}Si_{3}  
G6P  720  C1−C6  C_{25}H_{63}O_{9}NSi_{6}P 
471  C3−C6  C_{16}H_{40}O_{6}Si_{4}P  
357  C5−C6  C_{11}H_{30}O_{5}Si_{3}P  
X5P, Ru5P, R5P  618  C1−C5  C_{21}H_{53}O_{8}NSi_{5}P 
R5P  459  C3−C5  C_{15}H_{40}O_{6}Si_{4}P 
DHAP  414  C1−C3  C_{13}H_{33}O_{6}NSi_{3}P 
3PG  459  C1−C3  C_{14}H_{36}O_{7}Si_{4}P 
Metabolic Flux  PC1 (60%)  PC2 (27%)  PC3 (13%)  

glycolysis  ∆GPI  −57.2  6.6  
∆Q2  −10.0  −15.0  
Q11  −32.7  
oxidative PPP  Z3  63.1  
nonoxidative PPP  ∆TAL  24.4  −3.2  
∆TKT1  24.8  −3.8  5.2  
∆TKT2  24.4  −3.2  
Input S7P  −3.0  
R5P loss (Q4)  −10.1  17.7  −8.0 
Novel GC−MSBased Bayesian ^{13}CMFA  Alternative Methods  Advantage of Our Proposed Method  

Extraction and Derivatization for GC−MS analysis  water/methanol/acetonitrile extraction one derivatization: glucose/sugar phosphates, incl. triosephosphates (EtOXTMS)  methanol/chloroform/water extraction [47] acid hydrolysis of glycogen and RNA [47] two derivatizations: sugar (aldonitrile propionate), triose phosphates (TBDMS) [47] 

Chromatography  baseline separation H6P with a total run time of 15 min  baseline separation H6P with a total run time of 31 min (GC−NCI−MS) [16] coelution of H6P without derivatization (LC−MS) [3] 

MS  detection of multiple fragments of sugar phosphates and glucose (GC−MS)  detection of the entire carbon skeleton of sugar phosphates (LC−MS/GC−NCI−MS) [3,11,16] 

Tracer experiments  parallel tracer experiments  singletracer experiment [3] 

Formal analysis  flux constraint analysis as a central element analysis of the feasible flux space  “black box” or integrated into software algorithms (INCA software, Bayesian MFA) [31,46,50] 

Bayesian ^{13}CMFA vs. optimization approach  detection of multiple optima that could be caused by measurement errors  single set of flux values that best fit the measurement data 

full uncertainty distribution of fluxes  only upper and lower confidence interval 
 
pairwise correlation of fluxes with their joint confidence regions  may not be integrated into wellestablished MFA software (i.e., INCA) 

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Hogg, M.; Wolfschmitt, E.M.; Wachter, U.; Zink, F.; Radermacher, P.; Vogt, J.A. Bayesian ^{13}CMetabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the NonOxidative Pentose Phosphate Pathway Supports Phagocytosis. Metabolites 2024, 14, 24. https://doi.org/10.3390/metabo14010024
Hogg M, Wolfschmitt EM, Wachter U, Zink F, Radermacher P, Vogt JA. Bayesian ^{13}CMetabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the NonOxidative Pentose Phosphate Pathway Supports Phagocytosis. Metabolites. 2024; 14(1):24. https://doi.org/10.3390/metabo14010024
Chicago/Turabian StyleHogg, Melanie, EvaMaria Wolfschmitt, Ulrich Wachter, Fabian Zink, Peter Radermacher, and Josef Albert Vogt. 2024. "Bayesian ^{13}CMetabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the NonOxidative Pentose Phosphate Pathway Supports Phagocytosis" Metabolites 14, no. 1: 24. https://doi.org/10.3390/metabo14010024