Intracellular Metabolite Concentrations Research Articles

Zic-HILIC MS/MS Method for NADomics Provides Novel Insights into Redox Homeostasis in Escherichia coli BL21 Under Microaerobic and Anaerobic Conditions

Background: Nicotinamide adenine dinucleotide (NAD+), its precursors, and its derivatives (collectively NADome) play a crucial role in cellular processes and maintain redox homeostasis. Understanding the dynamics of these metabolic pools and redox reactions can provide valuable insights into metabolic functions, especially cellular regulation and stress response mechanisms. The accurate quantification of these metabolites is challenging due to the interconversion between the redox forms. Methods: Our laboratory previously developed a zwitterionic hydrophilic interaction liquid chromatography (zic-HILIC)–tandem mass spectrometry method for the quantification of five essential pyridine nucleotides, including NAD+ derivatives and it’s reduced forms, with 13C isotope dilution and matrix-matched calibration. In this study, we have improved the performance of the chromatographic method and expanded its scope to twelve analytes for a comprehensive view of NAD+ biosynthesis and utilization. The analytical method was validated and applied to investigate Escherichia coli BL21 under varying oxygen supplies including aerobic, microaerobic, and anaerobic conditions. Conclusions: The intracellular absolute metabolite concentrations ranged over four orders of magnitude with NAD+ as the highest abundant, while its precursors were much less abundant. The composition of the NADome at oxygen-limited conditions aligned more with that in the anaerobic conditions rather than in the aerobic phase. Overall, the NADome was quite homeostatic and E. coli rapidly, but in a minor way, adapted the metabolic activity to the challenging shift in the growth conditions and achieved redox balance. Our findings demonstrate that the zic-HILIC-MS/MS method is sensitive, accurate, robust, and high-throughput, providing valuable insights into NAD+ metabolism and the potential significance of these metabolites in various biological contexts.

Alveolar and Bone Marrow-derived Macrophages Differ in Metabolism and Glutamine Utilization.

Changes in metabolic activity are key regulators of macrophage activity. Pro-inflammatory macrophages upregulate glycolysis, which promotes an inflammatory phenotype, whereas pro-repair macrophages rely upon oxidative metabolism and glutaminolysis to support their activity. Work to understand how metabolism regulates macrophage phenotype has been done primarily in macrophage cell lines and bone marrow-derived macrophages (BMDM). Our study sought to understand changes in metabolic activity of murine tissue-resident alveolar macrophages (AM) in response to LPS stimulation and to contrast them to BMDM. These studies also determined the contribution of glutamine metabolism using the glutamine inhibitor, DON. We found that compared to BMDM, AM have higher rates of oxygen consumption and contain a higher concentration of intracellular metabolites involved in fatty acid oxidation. In response to LPS, BMDM but not AM increased rates of glycolysis. Inhibition of glutamine metabolism using DON altered the metabolic activity of AM but not BMDM. Within AM, glutamine inhibition led to increases in intracellular metabolites involved in glycolysis, the TCA cycle, fatty acid oxidation, and amino acid metabolism. Glutamine inhibition also altered the metabolic response to LPS within AM but not BMDM. Our data reveal striking differences in the metabolic activity of AM and BMDM.

Abstract 2887: Impact of ionizing radiation on the energy metabolism of normal and tumor cells in the brain

Abstract This study aims to analyze irradiation-induced metabolic alterations in glioblastoma cells and astrocytes to enhance understanding of cellular responses and facilitate the development of more effective radiotherapeutic strategies. Metabolic reprogramming of tumor cells is considered one of the hallmarks of cancer. Studies have shown that abnormal activation of oncogenes and cancer-related signaling pathways, as well as the inactivation of tumor suppressor genes can induce metabolic reprogramming. These metabolic aberrations facilitate rapid proliferation, continuous growth, invasion, metastasis, and immune evasion. Following the application of radiotherapy, the activity of several metabolic pathways significantly changes, potentially leading to the development of radioresistance. However, a differential effect of ionizing radiation on tumor and normal cells is not well understood. Hence, quantifying irradiation-induced metabolic changes in glioblastoma cells and astrocytes is of high importance. We have developed a novel approach to measure dynamic metabolite changes upon irradiation, in vivo, in situ, and in real-time. This tool combines imaging of recently developed fluorescent biosensors via 2-photon microscopy and millimeter-scaled irradiation. Murine glioblastoma cells and astrocytes were generated with stable expression of biosensors for the quantification of intracellular concentrations of various metabolites, including lactate. Cytosolic concentrations of lactate depend on the balance between glycolytic production, mitochondrial consumption of pyruvate and lactate, and the exchange with the extracellular lactate pool via monocarboxylate transporters. Many cancer cells are characterized by an excessive conversion of glucose to lactate even under normoxic conditions. We quantified this so-called Warburg Effect in glioblastoma cells and astrocytes as the ratio between basal lactate production and the lactate increase after OXPHOS inhibition and determined irradiation-induced metabolic changes on the single-cell level. Complementarily, we used metabolomics and Seahorse analyses before and after irradiation. Our data indicate that irradiation increases oxygen consumption in glioblastoma cells but not in astrocytes. Also, we established an orthotopic glioblastoma mouse model via injection of sensor-expressing tumor cells for in vivo measurements of metabolite changes during and after irradiation. These data are expected to significantly advance the understanding of the cellular response to irradiation on a molecular level in situ and in real-time. This knowledge will help to develop combination treatments and increase the efficiency of radiotherapy. Citation Format: Marvin Kreuzer, Pascal Imseng, Bruno Weber, Martin Pruschy. Impact of ionizing radiation on the energy metabolism of normal and tumor cells in the brain [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2887.

The Noncanonical Functions of Metabolites in Tumor Progression.

Metabolic reprogramming has emerged as a prominent hallmark of cancer, characterized by substantial alterations in nutrient uptake and intracellular metabolic pathways. Consequently, intracellular metabolite concentrations undergo significant changes which can contribute to tumorigenesis through diverse mechanisms. Beyond their classical roles in regulating metabolic pathway flux, metabolites exhibit noncanonical functions that play a crucial role in tumor progression. In this review, we delve into the nonclassical functions of metabolites in the context of tumor progression, with a particular focus on their capacity to modulate gene expression and cell signaling. Furthermore, we discuss the potential exploitation of these nonclassical functions in the enhancement of cancer therapy.

Epigenetic Targeting to Overcome Radioresistance in Head and Neck Cancer.

(1) Background: The sensitivity of head and neck squamous cell carcinoma (HNSCC) to ionizing radiation, among others, is determined by the number of cells with high clonogenic potential and stem-like features. These cellular characteristics are dynamically regulated in response to treatment and may lead to an enrichment of radioresistant cells with a cancer stem cell (CSC) phenotype. Epigenetic mechanisms, particularly DNA and histone methylation, are key regulators of gene-specific transcription and cellular plasticity. Therefore, we hypothesized that specific epigenetic targeting may prevent irradiation-induced plasticity and may sensitize HNSCC cells to radiotherapy. (2) Methods: We compared the DNA methylome and intracellular concentrations of tricarboxylic acid cycle metabolites in radioresistant FaDu and Cal33 cell lines with their parental controls, as well as aldehyde dehydrogenase (ALDH)-positive CSCs with negative controls. Moreover, we conducted a screen of a chemical library targeting enzymes involved in epigenetic regulation in combination with irradiation and analyzed the clonogenic potential, sphere formation, and DNA repair capacity to identify compounds with both radiosensitizing and CSC-targeting potential. (3) Results: We identified the histone demethylase inhibitor GSK-J1, which targets UTX (KDM6A) and JMJD3 (KDM6B), leading to increased H3K27 trimethylation, heterochromatin formation, and gene silencing. The clonogenic survival assay after siRNA-mediated knock-down of both genes radiosensitized Cal33 and SAS cell lines. Moreover, high KDM6A expression in tissue sections of patients with HNSCC was associated with improved locoregional control after primary (n = 137) and post-operative (n = 187) radio/chemotherapy. Conversely, high KDM6B expression was a prognostic factor for reduced overall survival. (4) Conclusions: Within this study, we investigated cellular and molecular mechanisms underlying irradiation-induced cellular plasticity, a key inducer of radioresistance, with a focus on epigenetic alterations. We identified UTX (KDM6A) as a putative prognostic and therapeutic target for HNSCC patients treated with radiotherapy.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors.

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Modeling heterogeneity in a cell culture using a coupled population balance-oxidative stress model

Population balance models (PBMs) were formulated to describe the evolution in time of the cell population in terms of growth and oxidative stress. Flow cytometry data was used to gain insight into the distribution of important quantities (e.g., cell size, intracellular concentrations of metabolites) over the entire cell population. A coupled population balance-oxidative stress model was developed to predict distributions in cell size and intracellular glutamate, ROS, NADPH and NADP+ concentrations in shake flask cultures of B. pertussis. The major advantage of using a PBM is that it accounts for the distributions and can predict the heterogeneity of the cell population with respect to experimental conditions that are averaged out in bulk models. When comparing the coupled population balance – oxidative stress model to the bulk oxidative stress model, it is apparent that the PBM provides much better predictions of the intracellular ROS concentration. We hypothesize that due to the nonlinear relations between cell growth and oxidative stress, intracellular and cell surface quantities can be better modeled with population balance models.

Quantification of Thiopurine Metabolites in Human Erythrocytes by Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS).

The thiopurine drugs, azathioprine, mercaptopurine, and thioguanine, are widely used in the treatment of several malignant and nonmalignant diseases. These inactive prodrugs undergo extensive metabolism to form active cytotoxic metabolites, which act mainly by incorporating into DNA and affecting cell replication. Thiopurine methyltransferase is a highly variable cytosolic enzyme that catalyzes the S-methylation of the thiopurine bases-an inactivating pathway. Patients with low-activity variants of TPMT can be affected by pronounced pharmacologic effects when receiving thiopurine medications. Clinical studies have reported significant interpatient variability in intracellular thiopurine metabolite concentrations in patients receiving thiopurine therapy. In this chapter, we present an LC-MS/MS method to monitor the thiopurine metabolites: 6-thioguanine nucleotides and 6-methylmercaptopurine derivatives in human erythrocytes. This method utilizes acid hydrolysis to release the bases and improves upon previously published procedures by utilizing stable isotope internal standards and a more efficient chromatographic separation.

FRI607 SGLT2 Inhibition Improves Muscle Oxidative Capacity And Its Relationship With Cardiac Performance In Patients With T2D And Heart Failure

Abstract Disclosure: Y. Qin: None. N.D. Sanchez: None. A.J. Moody: None. F.M. Acosta: None. S. Neppala: None. M. Brown: None. H. Honka: None. B. Todd: None. L.A. Cruz Moreno: None. A.R. Stepanenko: None. S.E. Espinoza: None. N. Musi: None. C. Triplitt: None. G. Clarke: None. E. Cersosimo: None. R.A. DeFronzo: None. C. Solis-Herrera: None. Cardioprotective benefits of SGLT2 inhibitors (SGLT2i) have been largely thought to be coupled to a physiologic increase in plasma ketones, with much interest in the role of adaptive changes in cardiac fuel source in subjects with T2D and HF. Given the changes in the global dynamics of bioenergetics with SGLT2i, and the proven pleiotropic effects of ketone bodies, suggest that, in addition to their cardioprotective function, SGLT2i may influence other organ systems. In particular, how skeletal muscle (SkM), or cardiopulmonary function, respond to these global bioenergetics changes remains largely unknown. A pilot randomized-controlled clinical study was designed to examine the effects of SGLT2i on SkM bioenergetics (31P MRS), & cardiopulmonary function (CPET), and its association with left ventricular cardiac function (MRI). Six subjects (Age=59±2.6, BMI=33.3±1.1, A1c=7.1±0.3) with T2D and HF (<50%) were randomized empagliflozin 25mg/day (SGLT2i) vs. placebo (control) in a 2:1 fashion for 12 weeks. Imaging and clinical evaluations were performed pre- and post-intervention. The SGLT2i group improved EF (28.7 vs. 38.8, p=0.1). SkM 31P MRS at rest, measuring the relative intracellular concentrations of high-energy metabolites of phospholipid breakdown, reduced with SGLT2i, suggesting better energy utilization by the SkM. 31P MRS-after exercise, showed significant improvements in SkM oxidative capacity (KPCR*[PCr], 0.6 to 0.8, p=0.03), with SGLT2i.During CPET testing, the ventilatory anaerobic threshold (VT), with SGLT2i, showed a significant (p=0.05) increase in VT time, suggesting an increase in the time in aerobiosis in these subjects. The minute ventilation-to-carbon dioxide output slope (a strong predictor of cardiopulmonary complications/death) was improved in the SGLT2i group vs. placebo, which could be associated with the CV benefit observed with these agents. Additionally, patient self-reported outcomes (Promis) showed a trend of improvement in the SGLT2i group. In summary, we demonstrate novel and exciting preliminary findings that in patients with T2D & HF, SGLT2i-associated benefits are not isolated to the heart and may also expand to the SkM, including improved oxidative capacity, energy utilization, and SkM recovery post-exercise. With improvements in HF prognostic markers and patient self-reported outcomes. These original findings may represent novel mechanisms contributing to the cardiac benefits of SGLT2i. Presentation: Friday, June 16, 2023

In vivo calibration of genetically encoded metabolite biosensors must account for metabolite metabolism during calibration and cellular volume.

Isotopic assays of brain glucose utilization rates have been used for more than four decades to establish relationships between energetics, functional activity, and neurotransmitter cycling. Limitations of these methods include the relatively long time (1-60 min) for the determination of labeled metabolite levels and the lack of cellular resolution. Identification and quantification of fuels for neurons and astrocytes that support activation and higher brain functions are a major, unresolved issues. Glycolysis is preferentially up-regulated during activation even though oxygen level and supply are adequate, causing lactate concentrations to quickly rise during alerting, sensory processing, cognitive tasks, and memory consolidation. However, the fate of lactate (rapid release from brain or cell-cell shuttling coupled with local oxidation) is long disputed. Genetically encoded biosensors can determine intracellular metabolite concentrations and report real-time lactate level responses to sensory, behavioral, and biochemical challenges at the cellular level. Kinetics and time courses of cellular lactate concentration changes are informative, but accurate biosensor calibration is required for quantitative comparisons of lactate levels in astrocytes and neurons. An in vivo calibration procedure for the Laconic lactate biosensor involves intracellular lactate depletion by intravenous pyruvate-mediated trans-acceleration of lactate efflux followed by sensor saturation by intravenous infusion of high doses of lactate plus ammonium chloride. In the present paper, the validity of this procedure is questioned because rapid lactate-pyruvate interconversion in blood, preferential neuronal oxidation of both monocarboxylates, on-going glycolytic metabolism, and cellular volumes were not taken into account. Calibration pitfalls for the Laconic lactate biosensor also apply to other metabolite biosensors that are standardized in vivo by infusion of substrates that can be metabolized in peripheral tissues. We discuss how technical shortcomings negate the conclusion that Laconic sensor calibrations support the existence of an in vivo astrocyte-neuron lactate concentration gradient linked to lactate shuttling from astrocytes to neurons to fuel neuronal activity.

Monitoring intracellular metabolite concentrations by moving horizon estimation based on kinetic modeling

The biotechnology industry can significantly benefit from new paradigms such as smart manufacturing, digitalization and quality-by-design to render more competitive and robust processes. Real-time monitoring of key process parameters and performance indicators can facilitate the transition toward smart biomanufacturing. Since cells are typically used to catalyze biotechnological processes, online monitoring of the cell's health and intracellular metabolic status is of interest to the bioprocess industry. However, intracellular monitoring is challenging due to the intrinsic physical limitation imposed by the cell wall/membrane and the long time required for analytical measurements. This work outlines how soft sensors based on moving horizon estimation allow inferring the intracellular metabolite concentrations in bioprocesses, even for advanced or complex metabolic systems. For example, it can be applied for monitoring metabolic cybergenetic systems, whereby metabolic pathways are dynamically regulated via genetic circuits and external inputs. The moving horizon estimator uses a kinetic model of the central carbon and energy metabolism that describes the dynamics of the intracellular metabolites. We underline the use of moving horizon estimation considering the anaerobic fermentation of Escherichia coli with optogenetic regulation of the adenosine triphosphate turnover. With the information on the extracellular substrate, product and biomass concentrations, we could reconstruct the internal cell's metabolic state with a good performance.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Riboswitch-mediated regulation of riboflavin biosynthesis genes in prokaryotes.

Prokaryotic organisms frequently use riboswitches to quantify intracellular metabolite concentration via high-affinity metabolite receptors. Riboswitches possess a metabolite-sensing system that controls gene regulation in a cis-acting fashion at the initiation of transcriptional/translational level by binding with a specific metabolite and controlling various biochemical pathways. Riboswitch binds with flavin mononucleotide (FMN), a phosphorylated form of riboflavin and controls gene expression involved in riboflavin biosynthesis and transport pathway. The first step of the riboflavin biosynthesis pathway is initiated by the conversion of guanine nucleotide triphosphate (GTP), which is an intermediate of the purine biosynthesis pathway. An alternative pentose phosphate pathway of riboflavin biosynthesis includes the enzymatic conversion of ribulose-5-phosphate into 3, 4 dihydroxy-2-butanone-4-phosphates by DHBP synthase. The product of ribAB interferes with both GTP cyclohydrolase II as well as DHBP synthase activities, which catalyze the cleavage of GTP and converts DHBP Ribu5P in the initial steps of both riboflavin biosynthesis branches. Riboswitches are located in the 5' untranslated region (5' UTR) of messenger RNAs and contain an aptamer domain (highly conserved in sequence) where metabolite binding leads to a conformational change in an aptamer domain, which modulate the regulation of gene expression located on bacterial mRNA. In this review, we focus on how riboswitch regulates the riboflavin biosynthesis pathway in Bacillus subtilis and Lactobacillus plantarum.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

An experimental and in silico analysis of Lacticaseibacillus paracasei isolated from whey shows an association between lactate production and amino acid catabolism.

The production of lactic acid from agroindustry waste products, such as whey, heavily relies on microorganisms within the genusLactobacillus. In this work, a genome-scale metabolic model was implemented from Vinay-Lara (iLca334_548), improved adding some enzymatic reactions and used to analyse metabolic fluxes ofLacticaseibacillus paracasei, which is aLactobacillusstrain isolated from whey used in the large-scale production of lactic acid. Overall, the highest rate of lactic acid productivity was 2.9 g l-1h-1, which equates to a dilution rate of 0.125 h-1, when continuous culture conditions were established. Restrictions on lactic acid production caused by exchange reactions, complex culture medium and intracellular metabolite concentrations were considered and included in the model. In total, theiLca334_548 model consisted of 1046 reactions and 959 metabolites, and flow balance analysis better predicted lactate flux than biomass. The distribution of fluxes exhibited an increase in lactate formation as biomass decreased. This finding is supported by the reactions carried out by glyceraldehyde 3-phosphate dehydrogenase, pyruvate formate lyase and ribose-5-phosphate isomerase, corroborating the modelled phenotype with experimental data. In conclusion, there is potential for the improvement of lactate production in a complex media by amino acid catabolism, especially when lactate is derived from pyruvate.

Extracellular Lactate Acts as a Metabolic Checkpoint and Shapes Monocyte Function Time Dependently.

Elevated blood lactate levels are frequently found in critically ill patients and thought to result from tissue hypoperfusion and cellular oxygen shortage. Considering the close relationship between immune cell function and intracellular metabolism, lactate is more than a glycolytic waste molecule but able to regulate the immune response. Our aim was to elucidate the temporal and mechanistic effect of extracellular lactate on monocytes. To this end, primary human monocytes and the human monocytic cell line MonoMac6 were stimulated with various toll-like-receptor agonists after priming with Na-L-lactate under constant pH conditions. As readout, cytokine production was measured, real-time assessment of intracellular energy pathways was performed, and intracellular metabolite concentrations were determined. Irrespective of the immunogenic stimulus, short-term Na-lactate-priming strongly reduced cytokine production capacity. Lactate and hexoses accumulated intracellularly and, together with a decreased glycolytic flux, indicate a lactate-triggered impairment of glycolysis. To counteract intracellular hyperglycemia, glucose is shunted into the branching polyol pathway, leading to sorbitol accumulation. In contrast, long-term priming with Na-L-lactate induced cellular adaption and abolished the suppressive effect. This lactate tolerance is characterized by a decreased cellular respiration due to a reduced complex-I activity. Our results indicate that exogenous lactate shapes monocyte function by altering the intracellular energy metabolism and acts as a metabolic checkpoint of monocyte activation.

The Rate of Cellular Energy Production of Muscle Cells Is Attenuated By Carnitine Intracellular Deficiency Caused By Imatinib Treatment

Introduction: Previous works identified that imatinib intake through the carnitine-specific OCTN2 (SLC22A5) transporter resulted in a significant decrease of carnitine intracellular concentrations in chronic myeloid leukemia (CML) and muscle cell lines. On contrary, even high doses of carnitine in preincubation did not influence imatinib cell intake capacity. Specifically performed inhibition of OCTN2 activity by vinorelbine resulted in block of carnitine cell intake, while imatinib intake was only slightly reduced (13-30%). This observation is in line with the knowledge that imatinib is transported also through other known SLC transporters. OCTN2 transporter is the major transporter for carnitine, an essential compound in cell energy metabolism. Presented work follows a hypothesis that non-equal competition between imatinib and carnitine intake through OCTN2 can lead to the carnitine intracellular deficiency, which can be in CML patients manifested by a disruption of skeletal muscle mitochondrial density and cause side effects like fatigue, muscle pain and cramps reported up to 80% of patients treated with imatinib (Kekale et al., 2015).Methods: Muscle cell HTB-153 (human rhabdomyosarcoma, ATCC HS 729), CML cell line KCL-22 (DSMZ ACC 519) were used for in vitro experiments. Intracellular concentration of imatinib, carnitine and metabolites were measured by chromatographic separation using XBridge Amide column (50x2.1mm, 3.5µm; Waters, Milford (MA), USA) and ZIC-pHILIC column (50x2.1mm, 5 µm; Merck, Darmstadt, Germany) coupled to tandem mass spectrometer (QTRAP 4000; Sciex, USA).Results: Carnitine, resp. L-carnitine transports long-chain fatty acids to mitochondria and its high rate is required especially in energetically demanding tissues such as skeletal and cardiac muscles. The concentrations of citric acid cycle (CAC) metabolites (citrate, malate, alpha-ketoglutarate, succinate, fumarate, 2-hydroxyglutarate, cis-aconitate), glycolysis (phosphoenolpyruvate, 3- phosphoglycerate, lactate), production of ATP, ADP and AMP were measured in HTB-153 cells 3 and 24 hours after imatinib treatment in vitro. The significant decrease of malate (CAC), lactate (glycolysis) and ATP levels were found at both time points after imatinib treatment compared to baseline. The same observations were found in KCL-22, which was used for comparison as BCR-ABL1 positive cell line. Additionally, significant decrease of succinate and 2-hydroxyglutarate (CAC) was detected in KCL-22 after imatinib treatment. Next, HTB-153 was incubated with imatinib (1-8 µM) for 24 hours and carnitine (8 µM) was supplied for last 3 hours of incubation, i.e., after 21 hours of imatinib treatment start. No significant changes were found in any metabolites of CAC and glycolysis. Production of ATP, ADP and AMP was not changed as well.Conclusions: Imatinib treatment of muscle (rhabdomyosarcoma) and CML cell lines caused a significant decrease of intracellular concentrations of carnitine. Significant decrease of ATP levels and of certain metabolites of CAC and glycolysis outlined that cells struggle from attenuated mitochondria energy production after imatinib treatment. This has not happened, if carnitine was supplied to the culture for final 3 hours of 24 hours incubation with imatinib. Observed data strongly support the hypothesis that decreased carnitine intake to the muscle cells due to competition with imatinib transport through OCTN2 attenuated mitochondria energy production. Interestingly, the clinical trial NCT03426722 (Chae H et al. 2019) showed that L-carnitine could effectively relieve imatinib-related muscle cramps and significantly increase QoL in patients with advanced gastrointestinal stromal tumor.Supported by GACR18-18407S, MZCR00023736 DisclosuresNo relevant conflicts of interest to declare.

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach.

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO2 fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of 13C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

Mechanistic insights into ureolysis mediated calcite precipitation

Microbially induced calcite precipitation (MICP) has applications in self-healing concrete, soil consolidation and bio-grouting. Development of models of processes involved in MICP is a step towards gaining mechanistic insights and building reliable predictive models of MICP. This work deals with understanding the reactions and transport processes involved in ureolysis mediated calcite precipitation in Sporosarcina pasteurii (S. pasteurii). Particularly, a simple structured model that describes urea uptake, ureolysis, ammonium assimilation and ammonium excretion processes is developed. The structured model describes extra-cellular processes like transport processes and important intra-cellular reactions involved in ureolysis. The model parameters are estimated using measured concentrations of extracellular and intracellular metabolites during experiments. In this work, The maximum specific growth rate and yield coefficient of S. pasteurii are found to be 0.78 d−1 and 4.93 gDW/g, respectively. The maximum urease activity of 1.79 μmoles/min/gDW biomass was observed during the fourth day of growth when grown in minimal media. It is shown that the urea uptake can be described by a first-order kinetics suggesting active transport. The estimated parameter of rate constant of first-order kinetics is 9.8 × 10−2d−1. On the other hand, the ammonium ion excretion takes place through free diffusion. The estimated parameter of permeability coefficient for ammonium ion diffusion is 9.2 × 10−3 m d−1.