Mass Isotopomer Research Articles

The Effect of Flavonoids and Topiramate on Glucose Carbon Metabolism in a HepG2 Steatosis Cell Culture Model: A Stable Isotope Study

Background: Insufficient treatment options are available for metabolic dysfunction-associated steatotic liver disease (MASLD). Flavonoids and topiramate have been studied for weight loss but need investigation into their effects on liver metabolism. This study’s aim was to examine the effects of flavonoids or topiramate on glucose metabolic carbon flux in a cell culture model of steatosis. Methods: Steatosis was induced in HepG2 cells through exposure to oleic acid (OA, 0.5 mml/L) conjugated to bovine serum albumin (2:1). Additionally, 50% U13C-glucose was supplied in the medium as a stable isotope tracer. Cells were treated with DMSO, 10 μM of naringenin, morin, silibinin, or topiramate (44 μM) for 72 h. A non-steatotic, untreated HepG2 cell control was included. Cell extracts were analyzed by gas chromatography/mass spectrometry and mass isotopomer distribution analysis for glycogen synthesis, de novo fatty acid synthesis, tricarboxylic acid (TCA) cycle activity, and ribose synthesis. Groups were compared by ANOVA with Tukey’s pair-wise testing. Results: Compared to untreated HepG2 controls, OA-exposed steatotic cells exhibited increased lipid accumulation by ORO staining (1.6-fold), enhanced palmitate de novo synthesis, reduced pyruvate carboxylase/pyruvate dehydrogenase (PC/PDH) ratio, and reduced ribose synthesis. Treatment with topiramate or silibinin ameliorated the lipid accumulation (1.3-fold) and mitigated enhancement of de novo synthesis. Morin-treated cells exhibited enhanced de novo synthesis but suppressed ribose synthesis. Conclusions: Potential mechanisms of reduced lipid accumulation by topiramate and silibinin may include suppression of palmitate de novo synthesis and a relative decrease in carbon flux through the PDH pathway. Further studies are needed on potential utility in MASLD based on their specific metabolic effects.

Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.

Labeling with deuterium oxide (D2O) has emerged as one of the preferred approaches for measuring the synthesis of individual proteins in vivo. In these experiments, the synthesis rates of proteins are determined by modeling mass shifts in peptides during the labeling period. This modeling depends on a theoretical maximum enrichment determined by the number of labeling sites (NEH) of each amino acid in the peptide sequence. Currently, NEH is determined from one set of published values. However, it has been demonstrated that NEH can differ between species and potentially tissues. The goal of this work was to determine the number of NEH for each amino acid within a given experiment to capture the conditions unique to that experiment. We used four methods to compute the NEH values. To test these approaches, we used two publicly available data sets. In a de novo approach, we compute NEH values and the label enrichment from the abundances of three mass isotopomers. The other three methods use the complete isotope profiles and body water enrichment in deuterium as an input parameter. They determine the NEH values by (1) minimizing the residual sum of squares, (2) from the mole percent excess of labeling, and (3) the time course profile of the depletion of the relative isotope abundance of monoisotope. In the test samples, the method using residual sum of squares performed the best. The methods are implemented in a tool for determining the NEH for each amino acid within a given experiment to use in the determination of protein synthesis rates using D2O.

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection.

We describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25-82%.

PyMIDA: A Graphical User Interface for Mass Isotopomer Distribution Analysis.

Mass isotopomer distribution analysis (MIDA) is an analytical technique that measures the synthesis rate of biological polymers using combinatorial probabilities and stable isotope labeling. Over the past few decades, this method has been developed and applied to a wide range of uses that have increased our understanding of metabolism and the etiology and monitoring of disease. There is currently no publicly available piece of software for performing MIDA calculations in a targeted manner without its functionality being limited to a specific use case. We present a cross-platform Python graphical user interface implementation for research to obtain kinetic parameters easily from stable-isotope labeling studies and provide the code and user manual on GitHub.

Gas chromatography-mass spectrometry selected ion monitoring and gas chromatography-tandem mass spectrometry selected reaction monitoring analyses of mono-, di- and tri-unsaturated C25 highly branched isoprenoid alkene biomarkers in sea ice and sediment samples: A comparative study.

The efficiency of selected ion monitoring (SIM) and selected reaction monitoring (SRM) analyses for the quantification of three mono-, di- and tri-unsaturated highly branched isoprenoid (HBI) alkenes (IP25 , IPSO25 and HBI III, respectively), often used as proxies for the occurrence of Arctic and Antarctic sea ice or the adjacent open waters, was compared. Gas chromatography (GC)-mass spectrometry (MS)/SIM and GC/MS/MS/SRM analyses were carried out on dilute solutions made from purified standards of these three HBIs, and then on hydrocarbon fractions of several sediment and sea ice sample extracts. More efficient and specific SRM transitions were selected after collision-induced dissociation of each precursor ion at different collision energies. SRM analysis avoided any overestimation of IP25 resulting from the contribution of the coeluting 13 C mass isotopomer of IPSO25 (M+ ˙ + 2) to the SIM target ion. In contrast, SRM analysis is less reliable for IPSO25 quantification in cases where several regio-isomers are present, likely due to intense double bond migrations following electron impact. In the case of HBI III, SRM analysis constitutes a potentially suitable alternative to SIM analysis, especially in terms of improving limit of detection. Despite the intense migrations of HBI double bonds under electron ionization, the selected SRM transitions should be more suitable than SIM target ions for IP25 and HBI III quantification in complex hydrocarbon fractions of natural samples. However, the advantage is less evident for IPSO25 due to the presence of numerous regio-isomers.

Development of a rapid and highly accurate method for 13C tracer-based metabolomics and its application on a hydrogenotrophic methanogen.

Microfluidic capillary electrophoresis-mass spectrometry (CE-MS) is a rapid and highly accurate method to determine isotopomer patterns in isotopically labeled compounds. Here, we developed a novel method for tracer-based metabolomics using CE-MS for underivatized proteinogenic amino acids. The method consisting of a ZipChip CE system and a high-resolution Orbitrap Fusion Tribrid mass spectrometer allows us to obtain highly accurate data from 1μl of 100nmol/l amino acids comparable to a mere 1 [Formula: see text] 104-105 prokaryotic cells. To validate the capability of the CE-MS method, we analyzed 16 protein-derived amino acids from a methanogenic archaeon Methanothermobacter thermautotrophicus as a model organism, and the mass spectra showed sharp peaks with low mass errors and background noise. Tracer-based metabolome analysis was then performed to identify the central carbon metabolism in M. thermautotrophicus using 13C-labeled substrates. The mass isotopomer distributions of serine, aspartate, and glutamate revealed the occurrence of both the Wood-Ljungdahl pathway and an incomplete reductive tricarboxylic acid cycle for carbon fixation. In addition, biosynthesis pathways of 15 amino acids were constructed based on the mass isotopomer distributions of the detected protein-derived amino acids, genomic information, and public databases. Among them, the presence of alternative enzymes of alanine dehydrogenase, ornithine cyclodeaminase, and homoserine kinase was suggested in the biosynthesis pathways of alanine, proline, and threonine, respectively. To our knowledge, the novel 13C tracer-based metabolomics using CE-MS can be considered the most efficient method to identify central carbon metabolism and amino acid biosynthesis pathways and is applicable to any kind of isolated microbe.

Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID).

The analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here, we present PIRAMID (Program for Integration and Rapid Analysis of Mass Isotopomer Distributions), a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface-driven program to automate the extraction of isotopic information from mass spectrometry (MS) datasets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e. 2H, 13C, 15N, 18O, 34S). MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available. All the data presented in this publication are available under the "Help_menu" folder of the PIRAMID software.

THU123 Knockout Of Renal Glut2 Gene Activates The Hypothalamic-pituitary-adrenal Axis To Increase Glucose Production In Mice

Abstract Disclosure: T.S. Faniyan: None. J. Robles: None. X. Zhang: None. S. Bathina: None. P.S. Brookes: None. R.J. Perry: None. K.H. Chhabra: None. We recently reported the contribution of renal GLUT2 (glucose transporter 2) to systemic glucose homeostasis using a mouse model in which we can knockout renal Glut2 gene at a desired time. Renal Glut2 knockout mice have improved glucose tolerance and are protected from high-fat diet or streptozocin-induced diabetes because they excrete excess blood glucose in urine. Interestingly, despite the profound loss of glucose in urine, renal Glut2 knockout mice have normal fasting blood glucose levels. These findings indicate that an increase in endogenous glucose production may be compensating for the loss of glucose in urine. To determine the mechanisms responsible for evoking the increase in endogenous glucose production, we focused on studying the hypothalamic-pituitary-adrenal (HPA) axis because of its established role in elevating blood glucose levels. We used ELISA to measure circulating adrenocorticotrophic hormone (ACTH) and corticosterone, which are major hormones of the HPA axis. Moreover, we used fluorescence in situ hybridization to assess the expression of hypothalamic corticotropin releasing hormone gene (Crh). We also performed in vitro metabolomics analysis of the mouse liver using mass spectrometry. Finally, we measured glucose production from pyruvate in vivo in the liver and kidney using mass isotopomer distribution analysis. Renal Glut2 knockout mice had increased (p<0.05) levels of hypothalamic Crh (100 ±13 vs 142 ±9 %, control vs experimental), circulating ACTH (120 ±8 vs 213 ±12 pg/ml), and corticosterone (30.4 ±5.5 vs 115 ±15.2 ng/ml). In addition, we found that metabolites such as hepatic glucose-6-phosphate (19.2 ±1.5 vs 15 ±1 arbitrary unit, control vs experimental), glucose-1-phosphate (20 ±1.5 vs 15.5 ±1), and fructose-6-phosphate (16 ±1.2 vs 12.4 ±0.8) were decreased in renal Glut2 knockout mice. We further observed that glucose production was increased in both the liver (15.1 ±0.4 vs 18.4 ±0.5 mg/kg/min) and kidney (1.7 ±0.2 vs 2.6 ±0.3 mg/kg/min) in renal Glut2 knockout mice. Our results demonstrate that loss of renal Glut2 causes hyperactivation of the HPA axis coupled with decrease in hepatic glucose metabolism and increase in hepatic as well as renal glucose production. These findings may explain how the renal Glut2 knockout mice prevent hypoglycemia despite massive glycosuria. Altogether, our study identifies a novel crosstalk between the kidneys, hypothalamus, and adrenal glands to regulate systemic glucose homeostasis. This information may be useful in either optimizing the efficacy of drugs that enhance glycosuria to treat diabetes or addressing the side effects of such drugs on the HPA axis. Presentation: Thursday, June 15, 2023

Measurement of gluconeogenesis by 2H2O labeling and mass isotopomer distribution analysis

The gluconeogenesis pathway, which converts nonsugar molecules into glucose, is critical for maintaining glucose homeostasis. Techniques that measure flux through this pathway are invaluable for studying metabolic diseases such as diabetes that are associated with dysregulation of this pathway. We introduce a new method that measures fractional gluconeogenesis by heavy water labeling and gas chromatographic-mass spectrometric analysis. This technique circumvents cumbersome benchwork or inference of positionality from mass spectra. The enrichment and pattern of deuterium label on glucose is quantified by use of mass isotopomer distribution analysis, which informs on how much of glucose-6-phosphate-derived glucose comes from the gluconeogenesis (GNG) pathway. We use an invivo model of the GNG pathway that is based on previously published models but offers a new approach to calculating GNG pathway and subpathway contributions using combinatorial probabilities. We demonstrated that this method accurately quantifies fractional GNG through experiments that perturb flux through the pathway and by probing analytical sensitivity. While this method was developed in mice, the results suggest that it is translatable to humans in a clinical setting.

Dual strategy for 13C-Metabolic flux analysis of central carbon and energy metabolism in Mammalian cells based on LC-isoMRM-MS

Central carbon and energy metabolism are the most concerned metabolic pathways in 13C-Metabolic flux analysis (13C-MFA). However, some α-keto acids, ribonucleoside triphosphate (NTPs) and deoxyribonucleoside triphosphate (dNTPs) involved in central carbon and energy metabolism pathways were unstable or reactive, leading to inaccurate metabolic flux analysis. To achieve accurate 13C-MFA of central carbon and energy metabolism, we proposed a dual strategy for the detection of 101 metabolites in glucose metabolism pathways. N-Methylphenylethylamine (MPEA) was utilized for derivatization of 4 carboxyl (α-keto acids) and 8 phosphate metabolites (NTPs and dNTPs). After derivatization, the MPEA derivatives were investigated to be stable for 4 weeks under 4 °C and detected with high intensity in ∼104 cells. On the other hand, we analyzed an additional 89 metabolites in central carbon and energy metabolic pathways were directly analyzed by liquid chromatography tandem mass spectrometry (LC-MRM-MS). The limit of detection (LODs) of our method were as low as 0.05 ng/mL and the linear range was at least two orders of magnitude with determination coefficient (R2) > 0.9701. The relative standard divisions (RSDs) of intra- and inter-day of 95% metabolites were below 20%. In addition, the isotope list of 82 detected metabolites in central carbon and energy metabolism were generated according to isotopologues and isotopomers for each metabolite resulting from 13C incorporation. Accurate assessment of mass isotopomer distributions (MIDs) of intracellular 13C-labeled metabolites was achieved in [U–13C]-glucose cultured HepG2 cells by our dual strategy. Finally, we performed MID analysis of 101 metabolites in central carbon and energy metabolism. Overall, this dual method is reproducible and robust for application on 13C-MFA and has a great potential for studying clinical isotope labeled samples.

Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling

Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.

13C metabolic flux analysis clarifies distinct metabolic phenotypes of cancer cell spheroid mimicking tumor hypoxia

Cancer cells adapt their intracellular energy metabolism to the oxygen-deprived tumor microenvironment (TME) to ensure tumor progression. This adaptive mechanism has focused attention on the metabolic phenotypes of tumor cells under hypoxic TME for developing novel cancer therapies. Although widely used monolayer (2D) culture does not fully reflect in vivo hypoxic TME, spheroid (3D) culture can produce a milieu similar to the TME in vivo. However, how different metabolic phenotypes are expressed in 3D cultures mimicking tumor hypoxia compared with 2D cultures under hypoxia remains unclear. To address this issue, we investigated the metabolic phenotypes of 2D- and 3D-cultured cancer cells by 13C-metabolic flux analysis (13C-MFA). Principal component analysis of 13C mass isotopomer distributions clearly demonstrated distinct metabolic phenotypes of 3D-cultured cells. 13C-MFA clarified that 3D culture significantly upregulated pyruvate carboxylase flux in line with the pyruvate carboxylase protein expression level. On the other hand, 3D culture downregulated glutaminolytic flux. Consistent with our findings, 3D-cultured cells are more resistant to a glutaminase inhibitor than 2D-cultured cells. This study suggests the importance of considering the metabolic characteristics of the particular in vitro model used for research on cancer metabolism.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses.

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-13C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their 12C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

Protein turnover models for LC-MS data of heavy water metabolic labeling.

Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.

UHAS-MIDA Software Package: Mass Isotopomer Distribution Analysis-Applying the Inverse Matrix for the Determination of Stable Isotopes of Chromium Distribution during Red Blood Cell Labelling

Clinical assessment of fluid volume status in children during malaria can be taxing and often inaccurate. During malaria, changes in fluid volume are rather multifarious and estimating this parameter, especially in sick children is very challenging for clinicians who frequently rely on indices such as long capillary refill times, tachycardia, central venous pressure and decreased urine volume as guides. Here, we present the UHAS-MIDA, an open-source software tool that calculates the red blood cell (RBC) concentration and blood volume during malaria in children determined using a stable isotope of chromium (53Cr as the label) by gas chromatography-mass spectrometry in selective ion monitoring (GC/MS-SIM) analysis. A key component involves the determination of the compositions of the most abundant naturally occurring isotopes of Cr (50Cr, 52Cr, 53Cr), and converting the proportions into a 3 × 3 matrix. To estimate unknown proportions of chromium isotopic mixtures from the measured abundances of three ions, an inverse matrix was calculated. The inverse together with several inputs is then used to calculate the corrected MS ion abundances. Thus, we constructed the software tool UHAS- MIDA using HTML, CSS/Bootstrap, JavaScript, and PHP scripting languages. The tool enables the user to efficiently determine RBC concentration and fluid volume. The source code, binary packages and associated materials for UHAS-MIDA are freely available at https://github.com/bentil078/Abaye-et-al_UHASmida

Mammary Development in Gilts at One Week Postnatal Is Related to Plasma Lysine Concentration at 24 h after Birth, but Not Colostrum Dose.

Simple SummaryA relationship exists between a female’s early nutritional environment and her ability to produce milk when she lactates as an adult. Colostrum is the first milk available to neonates after birth. We hypothesized that differing levels of colostrum stimulate differences in very early mammary development. Despite differences in weight at 24 h and 7 days, mammary morphological development and DNA content was not found to be different between gilts fed a high versus low dose of colostrum. The rate of mammary gland protein and DNA synthesis over the first week was not different between the groups. Circulating levels of amino acids were determined after 24 h of colostrum feeding, and levels of circulating lysine were found to be related to average daily gain and mammary DNA synthetic rate. Moreover, the level of lysine was related to a lower ratio of DNA to protein synthesis, suggesting that higher lysine favored cell division versus differentiation (by leaving the cell cycle). Further studies are needed in this area.Perinatal nutrition affects future milk production. The number of mammary epithelial cells affect milk production capacity. Therefore, it was hypothesized that the level of colostrum intake affects the proliferation rate and the total number of mammary epithelial cells in the gland. The ratio of newly synthesized protein to newly synthesized DNA reflects the relative amount of cellular differentiation to cell division. The study objective was to determine the relationship between the level of colostrum intake and 24 h-level of circulating amino acid, glucose and insulin with mammary parenchyma histological features, cell division and protein synthesis over the first week postnatal. One of two standardized doses of a homogenate colostrum sample, 10% (n = 8) and 20% (n = 8) of birth bodyweight, was fed to gilts over the first 24 h postnatal. Gilts were administered deuterium oxide immediately after birth and daily to label newly synthesized DNA and proteins. Gilts were euthanized on postnatal day seven, and DNA and protein were isolated from mammary parenchyma. DNA and protein fractional synthesis (f) and fractional synthetic rate (FSR) were calculated using mass isotopomer distribution analysis. The ratio of protein f and FSR to DNA f and FSR were calculated and used to indicate the relative amounts of differentiation to cell division. Mammary morphological development was also analyzed by measuring the parenchymal epithelial area and the stromal and epithelial proliferation index on postnatal day seven. Colostrum dose was not related to any of the variables used to evaluate mammary development. However, plasma lysine levels at 24 h postnatal were positively related to average daily gain (ADG; r = 0.54, p = 0.05), DNA f (r = 0.57; p = 0.03) and DNA FSR (r = 0.57; p = 0.03) in mammary parenchyma. Plasma lysine was inversely related to the ratio of protein to DNA f and FSR (r = −0.56; p = 0.04). ADG was related to the parenchymal epithelial area and DNA and protein f and FSR (p < 0.05). These relationships support the idea that the nutritional environment affects early mammary development and that higher lysine levels in the perinatal period favored a greater degree of cell division versus differentiation in mammary of neonatal pigs and thus, warrant further investigations.

MP33-17 A GLUTAMINASE ISOFORM SWITCH DRIVES THERAPEUTIC RESISTANCE AND DISEASE PROGRESSION OF PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology I (MP33)1 Sep 2021MP33-17 A GLUTAMINASE ISOFORM SWITCH DRIVES THERAPEUTIC RESISTANCE AND DISEASE PROGRESSION OF PROSTATE CANCER Lingfan Xu Lingfan XuLingfan Xu More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002042.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer is one of the most common cancer type in men. Although hormonal therapy, which targets the androgen receptor (AR) signaling, is efficacious initially, tumor recurrence will eventually recur. Better understanding the mechanism of therapy resistance and disease progression and therefore exploiting new therapeutic target is a pressing unmet issue. METHODS: We employed mass spectrometry and isotopomer tracing technology to determine metabolic flux and reprogramming in different stage of prostate cancer (primary, castration-resistant and neuroendocrine- transdifferentiated). Immunohistochemical staining on human tissue microarrays was used to determine the expression of the two isoforms of glutaminase (GLS1) across a panel of tissues diagnosed with different histology results. Genetic (e.g short hairpin RNA, crispr/cas9) and pharmacological (GLS1 inhibitor, CB-839) inhibition were employed to assess the biological function of GLS1 as well as its two splicing variants in prostate cancer. Animal models were established to longitudinally simulate the nature history of disease progression and drug therapeutic outcomes were also evaluated in vivo. RESULTS: We observe that hormonal therapy-resistant prostate cancer (including castration-resistant prostate cancer, CRPC and neuroendocrine prostate cancer, NEPC) is extremely addicted to glutamine instead of androgen, which results in the failure of AR-directed therapies. Furthermore, GLS1, the key enzyme for the glutamine metabolism, has two functionally different splicing variants (KGA and GAC), which are differentially expressed primary and recurrent stage of prostate cancer. KGA is the dominant isoform in hormone-sensitive prostate cancer whereas GAC, the much more enzymatically potent isoform, predominates in CRPC and NEPC. Furthermore, this isoform switch of GLS1 is evidently observed longitudinally in animal models along with tumor recurrence after being castrated. GLS1 selective inhibitor, CB-839, shows great inhibitory effect preferentially on GAC-dominant prostate cancer variants. CONCLUSIONS: An isoform switch of GLS1, from KGA to GAC, drives prostate cancer to be resistant to hormonal therapy, but dependent on glutamine due to the hyper capability of GAC for glutamine utilization. Therefore, GAC would be an ideal target independently of AR for those androgen/AR-indifferent prostate cancer patients. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e613-e613 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lingfan Xu More articles by this author Expand All Advertisement Loading ...

Measurement of lipogenic flux by deuterium resolved mass spectrometry

De novo lipogenesis (DNL) is disrupted in a wide range of human disease. Thus, quantification of DNL may provide insight into mechanisms and guide interventions if it can be performed rapidly and noninvasively. DNL flux is commonly measured by 2H incorporation into fatty acids following deuterated water (2H2O) administration. However, the sensitivity of this approach is limited by the natural abundance of 13C, which masks detection of 2H by mass spectrometry. Here we report that high-resolution Orbitrap gas-chromatography mass-spectrometry resolves 2H and 13C fatty acid mass isotopomers, allowing DNL to be quantified using lower 2H2O doses and shorter experimental periods than previously possible. Serial measurements over 24-hrs in mice detects the nocturnal activation of DNL and matches a 3H-water method in mice with genetic activation of DNL. Most importantly, DNL is detected in overnight-fasted humans in less than an hour and is responsive to feeding during a 4-h study. Thus, 2H specific MS provides the ability to study DNL in settings that are currently impractical.

Thirty years development of ¹³C metabolic flux analysis: a review

¹³C metabolic flux analysis (¹³C-MFA) enables the precise quantification of intracellular metabolic reaction rates by analyzing the distribution of mass isotopomers of proteinogenic amino acids or intracellular metabolites through ¹³C labeling experiments. ¹³C-MFA has received much attention as it can help systematically understand cellular metabolic characteristics, guide metabolic engineering design and gain mechanistic insights into pathophysiology. This article reviews the advances of ¹³C-MFA in the past 30 years and discusses its potential and future perspective, with a focus on its application in industrial biotechnology and biomedicine.