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In addition, it was also hypothesized that participants would present high fat oxidation rates i. Twenty-eight competitively trained male runners volunteered to participate in the study Table 1.
Participants identified as either recreationally trained endurance e. Standard exclusion criteria have previously been defined in Costa et al. In addition, participants were also excluded if reporting adhering to macronutrient modification dietary practices e.
All participants reported having some exposure in consuming carbohydrate i. As part of standard experimental procedures, participants refrained from strenuous exercise in the days leading up to i. Baseline stature and BM were measured, and body fat mass determined using multi-frequency bioelectrical impedance analysis mBCASeca, Ecomed, Hamburg, Germany.
Criteria for attaining V̇ O 2max included the participants reaching volitional exhaustion e. During the incremental exercise test, two fans were placed one meter from the treadmill at a dual fan speed of Whole-body total carbohydrate and fat oxidation rates were calculated from the last min of each increment, using non-protein respiratory quotient values as published by Péronnet and Massicotte :.
As part of exercise gastroenterology intervention studies reported elsewhere Costa et al. Protocol 1 P1; Costa et al. Exercise-associated BM loss and post-exercise P Osmol were 2. Protocol 2 P2; Gaskell et al. Exercise-associated BM loss and post-exercise P Osmol were 1.
To avoid participant duplication bias, one participant was fully removed from the data set due to participation in P1. Protocol 3 P3; McCubbin et al. Exercise-associated BM loss was 2.
A validated and reliability checked, exercise specific, modified visual analogue mVAS scale was used to assess gut discomfort, total-GIS, upper-GIS i.
Participants were educated and advised to complete the GIS rating scale as follows: 1—4 indicative of mild GIS i. If no GIS were reported by participants, this was recorded as a 0, and subsequently no GIS severity rating was assessed.
Considering GIS, such as regurgitation and defecation, results in complete or temporary reduction or cessation of exercise, these GIS are presented as 0 and 10 rating only. Additionally, a point Likert-type rating scale was used to quantify self-reported perceptive feeding tolerance, with 0 indicating no tolerance to 10 indicating extremely high tolerance five indicative of moderate tolerance; Miall et al.
For consistency, all measurements and samples were recorded and collected before carbohydrate feeding at each respective time point. Breath samples were collected in accordance with clinical gastroenterology guidelines Bate et al. The breath sample was collected on the 2nd expiration.
Breath H 2 determination on P2 was used to detect gastrointestinal transit in accordance with orocecal transit time procedures Gaskell et al. Confirmation of adequate statistical power a priori for the primary research is previously described Costa et al. Participants and researchers at the time of data collection were unaware that the combined metadata would be used for analysis of feeding tolerance, GIS, blood glucose availability, and whole-body total carbohydrate and fat oxidation rates in response to various exertional stress protocols with differing carbohydrate feeding regimes during exercise.
Based on the statistical test, mean, SD, and effect size, and applying a standard alpha 0. All data were checked for normal distribution Shapiro—Wilk test of normality by calculating skewness and kurtosis coefficients.
General linear mixed model with post hoc was used to determine differences in oxidation rates during the incremental exercise test. Variables with singular data points were examined using independent sample t tests or nonparametric Mann—Whitney U test, when appropriate.
Statistics were analyzed using SPSS statistical software V. Participant characteristics between exercise protocols are depicted in Table 1. MFO observed in the incremental exercise test was 0.
No significant differences were observed between exercise protocol groups. Whole-body energy expenditure and whole-body total carbohydrate and fat oxidation rates during steady-state exercise are presented in Figure 1. No significant correlations were observed between whole-body total carbohydrate and fat oxidation rates during steady-state and end of exercise with BM, training volume, and fitness status V̇ O 2max.
No significant correlations were observed between whole-body total carbohydrate oxidation during steady-state or end of exercise with absolute dietary energy, protein, carbohydrate, and fat intake. Figure 1. Blood glucose responses are presented in Figure 2.
Figure 2. No significant changes were observed for breath H 2 responses to the carbohydrate feeding intervention during exercise. Gastrointestinal symptoms are presented in Figure 3. Figure 3. Gut discomfort Atotal Bupper Cand lower D gastrointestinal symptom severity rated on a exercise specific mVAS for gastrointestinal symptoms Gaskell et al.
Feeding tolerance markers are presented in Figure 4. Figure 4. Feeding tolerance markers, including interest in food Ainterest in drink Btolerance to food Ctolerance to drink Dappetite Eand thirst Frated on a point Likert-type rating scale Miall et al.
The current study aimed to utilize metadata from previously published research to explore: 1 fuel kinetics of endurance and ultra-endurance runners in response to an incremental exercise test to volitional exhaustion and 2 gastrointestinal feeding tolerance and GIS, glucose availability, and whole-body total carbohydrate and fat oxidation rates, in response to differing carbohydrate intake protocols during prolonged strenuous exercise protocols in competitively trained male endurance and ultra-endurance runners consuming a habitual mixed macronutrient diet.
Firstly, the data show a vast range of MFO rates during both the incremental exercise test i. These data suggest that a high whole-body total fat oxidation rate can be attained without dietary carbohydrate abstinence or restriction, and even when carbohydrate is provided during prolonged endurance exercise i.
Previous research has explored whole-body total carbohydrate and fat oxidation rates in the post-prandial period. These results, however, are in accordance with an ultra-endurance exercise cycling protocol i.
The whole-body total fat oxidation rates observed in the current study are surprising considering that the consumption of carbohydrate in the hours prior to strenuous endurance exercise e.
Moreover, unlike the majority of previous investigations, a unique aspect to the current experimental procedures i. This dual feeding scenario mirrors real-life practices of athletes, which is generally an uncommon application in experimental designs exploring fuel kinetics in athlete populations, but of high translational research relevance.
The authors acknowledge that the current data set uses metadata extrapolated from previously published research that focused on markers of exercise-induced gastrointestinal syndrome EIGS; Costa et al. Therefore, a limitation of the current study was the inability to statistically compare data between exercise test protocols i.
Nevertheless, sufficient statistical power e. Moreover, the authors acknowledge that the current study did not assess exogenous carbohydrate oxidation using the 13 C stable isotope method, aligned with whole-body total carbohydrate oxidation.
Although this information may have provided an insight into the magnitude to which consumed carbohydrates during exercise contributed to fuel provisions, such analysis was outside the scope of the primary research outcomes and raises two key discussion points for not warranting such analysis: 1 methodological limitation of applying the 13 C stable isotope method for detecting exogenous carbohydrate oxidation that includes experimental preparation — glycogen depletion exercise protocol e.
In the current study, the observations provide evidence of high mean MFO 0. Interestingly, the mean value obtained for MFO in the current cohort 0. It is important to note, that previous research has reported lower MFO and Fat max when an incremental exercise test is performed in the post-prandial state compared to fasted Bergman and Brooks, ; Achten et al.
Taken together, the results from the current study suggest large athlete group variation in MFO and Fat max in response to an incremental exercise test and also suggest the current group of endurance and ultra-endurance runners present high MFO and Fat max compared with previous groups, despite consuming a habitual mixed macronutrient diet and performing the incremental exercise test in the post-prandial state.
In the current data set, mean whole-body total fat oxidation rates of 0.
: Glucose availability| The Big Picture: Checking Your Blood Glucose | Nature , — All authors contributed to the various aspects of the manuscript preparation and review. None of the tested proteins were able to co-immunoprecipitate AMPK. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY. Importantly, the results in this paper are always compared to controls with 4. Wu, G. Reprints and permissions. |
| Publication types | The avaiilability sequences, basepair Glucose availability to basepair downstream aavilability the transcription start site avqilability used for Natural solutions for high cholesterol putative Glucoose and the SLCs includeded in BIA nutritional assessment tool study. g-h Natural solutions for high cholesterol, Glucose and amino acids regulate mTOR localization to the lysosome consistent with glucose regulating the Rag-GTPase pathway upstream of mTORC1. Mfsd2a is a transporter for the essential omega-3 fatty acid docosahexaenoic acid. Pilotte, I. The next question was to consider how energy deficits, such as limiting glucose availability, might be integrated with innate immunity. |
| Diabetes Tests | CDC | Nutrients Liguori C, Ruffini R, Olivola E et al Cerebral glucose metabolism in idiopathic REM sleep behavior disorder is different from tau-related and α-synuclein-related neurodegenerative disorders: a brain [18F]FDG PET study. Nguyen, L. Islas-Espinoza AM, Campos-Rodriguez C, San Juan ER Thalidomide protects against acute pentylenetetrazol and pilocarpine-induced seizures in mice. Kim HY, Kim JY, un KG et al Alien hand syndrome after epilepsia partialis continua: FDG PET and MRI studies. |
| Increased glucose availability sensitizes pancreatic cancer to chemotherapy | Nature Communications | Kim, S. g Gluclse glucose levels and Glucoose area availabi,ity the curve AUC during pyruvate tolerance Natural solutions for high cholesterol. Glucose feeds the TCA cycle via circulating lactate. costa monash. RF aided in the planning of experiments, interpretation of results, performed cell analyzing, drafted parts and proofing of the manuscript. The resulting peak table was further analyzed via MetaboLyzer. |
| Access options | For rescue experiments, cell-permeable GSH L-Glutathione reduced, Sigma, G , NAC N-Acetyl-L-cysteine, Sigma, no. A , BSO L-Buthionine-sulfoximine, Sigma, no. B , and GCLC overexpressing plasmids Origene, mouse no. MC , human no. SC were used. Chemotherapies included gemcitabine gemcitabine hydrochloride, Sigma, no. G , oxaliplatin Sigma, O , irinotecan Sigma, no. I , and 5-FU Sigma, no. Nuclear and cytoplasmic extraction was performed using NE-PER nuclear and cytoplasmic extraction reagents ThermoFisher Scientific, no. Oligos were obtained from ThermoFisher Scientific with the following ID numbers: GCLC human no. siRNA transfections were performed using Lipofectamine ThermoFisher Scientific, no. GCLC knockout was performed using a guide RNA Sigma, no. A negative control plasmid NegativeControl1 was used in isogenic cells. Plasmid transfections were performed with lipofectamine Clones from the parental MiaPaCa-2 cell line were expanded for verification of GCLC knockout with RT-qPCR and Western blotting. Total RNA was extracted using PureLink RNA isolation Life Technologies, no. PCR reactions were performed in triplicate using ThermoFisher Scientific primers with the following ID numbers: GCLC human no. RT-qPCR acquisition was captured using a Bio-Rad CFX96 and analyzed using Bio-Rad CFX Manager 2. RNA quality was assessed using an Agilent Bioanalyzer Agilent Technologies. RNA sequencing was performed using bp paired-end format on a NovaSeq Illumina sequencer. FastQC was used to assess RNA-seq quality and TrimGalore was used for adapter and quality trimming. RNA-seq reads were mapped against hg38 using STAR v2. Total protein was extracted with RIPA buffer Pierce, no. NW and transferred to polyvinylidene difluoride membranes. Membranes were probed with antibodies against GCLC Proteintech, no. SC, dilution. Blots were probed with secondary antibodies customized for the Odyssey Imaging system Secondary antibodies RD Goat anti-Mouse IgG Li-COR, no. The density of blots was quantified using Image Studio Software v. Samples were preserved in formalin and embedded in paraffin followed by GCLC Proteintech, no. SC, dilution , cleaved caspase-3 Asp Cell Signaling, no. Samples were prepared with formalin and embedded in paraffin, followed by and immunolabeling. Cell viability was estimated by DNA quantitation using the PicoGreen dsDNA assay Life Technologies, no. P or through cell counting using Trypan blue ThermoFisher Scientific, no. Cells — per well were plated in six-well plates. Culture medium was not changed during experiments unless indicated. To measure lipid ROS, a lipid peroxidation assay Cayman Chemical, no. Readouts were normalized to cell number or protein content. Tumor fragments were weighed and homogenized using Folch method chloroform-methanol. For fatty acid measurements, the chloroform phase containing TG-bound fatty acids was hydrolyzed using alkaline hydrolysis. Fatty acids were converted to their methyl esters and analyzed by GC-MS. Fatty acids were quantified using a fatty acid standard. Fatty acids were derivatized using two-step derivatization. First, keto- and aldehyde groups were protected by the reaction with MOX methoxylamine-HCl in pyridine, overnight at room temperature. The resulting MOX-TMS derivatives were analyzed by GC-MS. Masses were monitored via the SIM acquisition mode. Metabolomics data were analyzed using the MSD ChemStation Software, version: F. Metabolite counts were normalized using gamma-hydroxybutyrate. Untargeted metabolomics was performed using LC-MS. Three-microliter aliquots taken from each sample were pooled and the QC standard was analyzed every 6th injection. In addition, we collected MS2-level data on representative control and treated samples. The min gradient used is given below. The resolution of the MS2 scans were taken at a stepped NCE energy of The spectral features were log-transformed and further analyzed via MetaboLyzer 1 using 0. The resulting peak table was further analyzed via MetaboLyzer. First the data was normalized to protein concentration in each sample. The relative abundance values for each spectral feature were then calculated with respect to a labeled internal standard betaine-d9. The ion presence threshold was then set at 0. All p-values were corrected via the Benjamini-Hochberg step-up procedure for false discovery rate FDR correction. The data were then utilized for PCA, putative identification assignment, and pathway enrichment analysis via KEGG. All experiments involving mice were approved by the Case Western Reserve University Institutional Animal Care Regulations and Use Committee Mice were received standard chow and nutrient-free bedding. S starting two weeks before cancer cell implantation. The peripheral glucose levels were measured using glucometer Alphatrak 2 by tail clipping. The blood glucose levels in streptozotocin-treated mice were titrated to a non-toxic range with daily subcutaneously injections of long-acting insulin glargine Fisher Scientific, no. Patient-derived xenograft samples were purchased from The Jackson Laboratory no. TM and propagated in nude mice. Body weights were measured weekly. Based on our IACUC protocol, the maximal tumor burden is mm 3 and tumor volume in our animal was not exceeded. Equal numbers of male and female mice were used. Briefly, a 0. Mice with confirmed tumors were then randomized to the indicated treatment conditions. For rescue studies, BSO 4. We retrospectively identified patients who presented with metastatic PDAC at University Hospitals Cleveland Medical Center and stratified them according to the usage of chemotherapy vs. supportive care. This study was approved by the Institutional Review Board IRB at University Hospitals Cleveland Medical Center STUDY Informed consent was waived by the IRB because the study was retrospective. The gender, number and age of participants in this study are provided in Supplementary Tables. We utilized raw glucose values extracted from electronic medical records to determine the glycemic status for both cohorts. Glucose values were analyzed across two-time intervals: pre-diagnosis obtained within the days preceding PDAC diagnosis and during the treatment period based on the chemotherapy initiation date. Identical thresholds were used for patients who did not receive chemotherapy. Both pre- and postdiagnosis treatment values were utilized for stratification of the supportive care cohort due to the limited number of glucose values available. These stratification parameters are in line with American Diabetes Association criteria for diagnosis of diabetes based on random glucose levels Survival distributions were estimated using Kaplan-Meier estimation and compared by log-rank tests. Pairwise comparison of tumor growth trajectories employed longitudinal mixed models with random intercept and with time viewed as categorical. Box-Cox transformations log or square root were used if supported by residual and normal probability plots. For multiple comparisons adjustment, the Holm method was adopted StataSE v The Nelson-Aalen estimate was used to graphically depict the cumulative hazard of death over time. Multivariable Cox proportional hazards regression was used to identify factors associated with overall survival, defined as the time from diagnosis to death or last follow-up. Variables included in multivariable models were those considered to be clinically relevant. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. RNA sequencing data were deposited into Gene Expression Omnibus with accession number GSE Metabolomics data have been deposited in the EMBL-EBI MetaboLights database with accession no. Source data for Figs. Source data are provided with this paper. Morgan, R. The contribution of cytotoxic chemotherapy to 5-year survival in adult malignancies. Article Google Scholar. Kou et al. Conroy, T. et al. Med , — Article CAS PubMed Google Scholar. Von, Hoff et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. Siegel, R. Cancer statistics, CA Cancer J. PMID: Article PubMed Google Scholar. Olive, K. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science , — Article ADS CAS PubMed PubMed Central Google Scholar. Nywening, T. Targeting both tumour-associated CXCR2. Gut 67 , — Pishvaian, M. Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial. Lancet Oncol. Article CAS PubMed PubMed Central Google Scholar. Whatcott, C. Desmoplasia in Primary Tumors and Metastatic Lesions of Pancreatic Cancer. Cancer Res. Article CAS Google Scholar. Provenzano, P. Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer cell 21 , — Koong, A. Pancreatic tumors show high levels of hypoxia. Int J. Kamphorst, J. Human pancreatic cancer tumors are nutrient poor and tumor cells actively scavenge extracellular protein. Sullivan, M. Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability. Elife 8 , e Vaziri-Gohar, A. Limited nutrient availability in the tumor microenvironment renders pancreatic tumors sensitive to allosteric IDH1 inhibitors. Cancer 3 , — Sousa, C. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature , — Hui, S. Glucose feeds the TCA cycle via circulating lactate. Article ADS PubMed PubMed Central Google Scholar. Vaziri-Gohar et al. Metabolic Dependencies in Pancreatic Cancer. Article PubMed PubMed Central Google Scholar. Halbrook, C. Employing metabolism to improve the diagnosis and treatment of pancreatic cancer. Cancer Cell 31 , 5—19 Rossignol, R. Energy substrate modulates mitochondrial structure and oxidative capacity in cancer cells. Son, J. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Zarei, M. Posttranscriptional Upregulation of IDH1 by HuR Establishes a Powerful Survival Phenotype in Pancreatic Cancer Cells. Hong, Y. Nuclear factor erythroid-derived 2 -like 2 regulates drug resistance in pancreatic cancer cells. Pancreas 39 May , — Danai, L. Altered exocrine function can drive adipose wasting in early pancreatic cancer. Graham, M. The streptozotocin-induced diabetic nude mouse model: differences between animals from different sources. CAS PubMed PubMed Central Google Scholar. Sangeetha, P. Increase in free radical generation and lipid peroxidation following chemotherapy in patients with cancer. Free Radic. Med 8 , 15—19 Yang, H. The role of cellular reactive oxygen species in cancer chemotherapy. Brody, D. Dixon Complex HuR function in pancreatic cancer cells. Wiley Interdiscip. RNA 9 , e Song, Im-S. Delayed mechanism for induction of gamma-glutamylcysteine synthetase heavy subunit mRNA stability by oxidative stress involving p38 mitogen-activated protein kinase signaling. Wu, G. Glutathione metabolism and its implications for health. Traverso, N. Role of glutathione in cancer progression and chemoresistance. Med Cell Longev. Viable cells were counted using trypan blue exclusion. Cells were washed in PBS before incubation for 15 min at 37°C in glucose uptake buffer 8. After the 37°C incubation, μl of cells was added to the DOG layer and pulsed for various lengths of time. The reaction was stopped by centrifugation at 14, rpm for 10 min. Cytoscint ICN; 2 ml was added to each vial and counted using an LS scintillation counter Beckman Coulter. Each sample was measured in triplicate. Statistical analysis t test was performed using Microsoft Excel. Measurement of glycolysis is based on the generation and diffusion of [ 3 H] 2 O during the dehydration reaction mediated by enolase. The glycolytic rate is calculated from the ratio of diffused to undiffused radioactivity. Briefly, 10 6 cells were washed with PBS and preincubated in Krebs buffer mM NaCl, 2 mM KCl, 25 mM NaHCO 3 , 2 mM CaCl 2 , 0. Cells were pelleted, resuspended in 0. As controls, cell-free samples background fraction and 1 μCi of [ 3 H] 2 O PerkinElmer in 0. Triplicate samples were lysed with 0. Each tube was placed in a scintillation vial containing 0. Vials were tightly capped and sealed. Scintillation mixture was added to each vial, and samples were counted for 1 min on an LS scintillation counter. Lactate produced from cells stimulated for 24 h was determined from supernatant using lactate reagent Sigma-Aldrich; 5 μl of cell supernatant in 0. Concentrations were determined based on lactate standard Sigma-Aldrich. In each well of a six-well plate, 2. Cells were lysed as described by Cham et al. For unstimulated cells, 2. Cytosolic lysates from 2. Ab against total ERK was purchased from Zymed Laboratories, Ab against total JNK was obtained from BD Pharmingen, and Ab against total p38 MAPK was obtained from Santa Cruz Biotechnology. Individual bands were quantified using UN-SCAN-IT software Silk Scientific and were normalized against a loading control. Statistical analysis paired t test was performed using Microsoft Excel. For APC stimulation, 5 × 10 5 mitomycin C-treated P mastocytoma cells transfected with B7. Supernatants were collected after 20 h of stimulation and analyzed for IL-2 or IFN-γ by ELISA using Ab pairs from BD Pharmingen. Survival rates of T cells stimulated for 24 h under glucose-depriving conditions were determined by dual annexin V-FITC BD Pharmingen and propidium iodide staining. T cells 10 6 were stimulated for 24 h with beads coated with 2C11 and PV-1, as described above, in a well plate. Survival rate was assessed by the percentage of cells that stained negatively for both annexin V and propidium iodide, as determined by flow cytometry. Total RNA was extracted from cells using TRIzol Invitrogen Life Technologies. Effector cells 2. Nuclei were isolated from 5 × 10 6 stimulated effector cells, according to the method described by Schreiber et al. The protein content of nuclear extracts was measured using a DC Protein Assay kit Bio-Rad. Approximately 5 μg of protein was used for each analysis. Probes were designed with overhang sequences for radiolabeling purposes. Complementary single-stranded oligonucleotides were annealed in TEN buffer mM Tris pH 7. Double-stranded probes ng were labeled 30 μCi of each [ 32 P]dNTP and Klenow fragment New England Biolabs. RNA was isolated and converted to cDNA. Actin mRNA was used as a loading control. The expression of aldolase C and α-enolase was substantially higher in effector cells, whereas β-actin was expressed comparably Fig. A , Effector cells express higher protein levels of glycolytic enzymes, aldolase C, and α-enolase, as determined by Western blotting. Expression of actin was used as an indicator of equivalent loading. B , Resting effector cells have greater glucose uptake compared with naive cells. The rate of glucose uptake was determined by the amount of [1,2- 3 H]DG cpm in each sample. Data are representative of at least three independent experiments. Statistics compare naive vs effector T cells. C , Effector cells have a higher glycolytic rate compared with naive cells. D , Stimulated effector cells produce significant levels of lactate. Similar results were observed in at least two independent experiments. To explore glucose-dependent metabolism in these differentiation states, [ 3 H]2-DG uptake was measured. Similarly, the glycolytic rate was substantially higher in effector cells Fig. The conversion of pyruvate to lactate enables cells to generate additional ATP from glucose without dependence on oxygen. To determine whether glucose was predominantly being metabolized anaerobically, we measured lactate production released into the supernatant. In resting cells, lactate production was undetectable data not shown. However, lactate generation after stimulation with anti-CD3 and anti-CD28 Abs was readily detected from effector 2C cells, whereas that detected from naive cells was little above background Fig. We hypothesized that glucose might be essential for a subset of functions unique to effector cells. Specifically, we reasoned that production of IFN-γ made at significantly higher levels by effector cells might be inhibited by limiting glucose availability, whereas production of IL-2 made by both naive and effector cells might be unaffected. Glucose deprivation was achieved in two ways. Second, 2-DG, an analog of glucose that blocks hexokinase, phosphoglucoisomerase, and phosphoglucomutase 16 , was added to DMEM containing 25 mM d -glucose. IFN-γ production also was more sensitive than IL-2 production to inhibition by 2-DG when stimulated with either anti-CD3 and anti-CD28 mAbs Fig. IFN-γ production is more sensitive to glucose depletion than IL-2 production. Cytokine production was determined by ELISA. Data are representative of six independent experiments. Data are representative of 13 independent experiments. C , Effector 2C T cells were stimulated with mitomycin C-treated PB7. Data are representative of two independent experiments. All data are representative of at least three independent experiments. Paired t test was performed, comparing IFN-γ production under 25 or 0 mM glucose conditions. To determine that this preferential inhibition was not unique to primed 2C TCR transgenic T cells, bulk T cells collected from an MLR and a long-term CTL clone were analyzed. The same preferential inhibition of IFN-γ production over IL-2 production was observed by bulk T cells stimulated with anti-CD3- and anti-CD28 Ab-coated beads Fig. It was conceivable that stimulating T cells under glucose-depriving conditions may have caused the cells to undergo apoptosis. It was of interest to determine the mechanism by which glucose deprivation was inhibiting IFN-γ production. To determine whether a proximal TCR signaling defect could be implicated, PMA and ionomycin were used to stimulate cytokine production. However, IFN-γ production in response to PMA and ionomycin was still blocked by elimination of glucose from the medium Fig. As with the other stimuli, IL-2 production in response to PMA and ionomycin was little affected by glucose deprivation. Data are representative of three independent experiments. B , IL and IL were used to induce IFN-γ production by effector 2C T cells in the presence of 2-DG. Cytokine production was assayed by ELISA. S , stimulated; U , unstimulated. After 6 h, total RNA was isolated and used to assess steady-state cytokine mRNA levels by RPA. RNA was isolated and analyzed by RPA. Data are representative of four independent experiments. After 4 h of stimulation, actinomycin D was added time zero. Cells were collected at the indicated times after the addition of actinomycin D. At extended time points, L32 and GAPDH mRNA also diminished; therefore, 28S mRNA was used as a loading control. An alternative physiologic stimulus for inducing IFN-γ production by T cells is the combination of the cytokines IL and IL Signaling through ILR and ILR uses distinct proximal pathways from TCR signaling, converging at the point of p38 MAPK and NF-κB Increasing amounts of 2-DG also inhibited IFN-γ production induced by IL and IL Fig. To determine at what level IFN-γ production was being affected by glucose deprivation, IL-2 and IFN-γ mRNA were examined by RPA. Stimulation for 6 h with anti-CD3- and anti-CDcoated beads in the absence of glucose revealed diminished levels of IFN-γ mRNA, but minimal change in IL-2 mRNA Fig. Kinetic analysis revealed that early induction of IFN-γ mRNA was intact, but the levels began to decrease 4—7 h after stimulation in the presence of 2-DG, suggesting that IFN-γ mRNA expression was not being sustained Fig. This difference became even more striking at later time points. Because decreased mRNA levels could be a result of decreased transcription or increased degradation, we used actinomycin D to block further transcription and assessed the half-life of IFN-γ transcripts in the presence or absence of 2-DG. Actinomycin D was added 4 h after stimulation, and cells were harvested at various times for mRNA analysis by RPA. We observed that the rate of IFN-γ mRNA decay followed the same kinetics with or without addition of 2-DG Fig. This indicates that IFN-γ mRNA degradation was not augmented by glucose deprivation, implying an effect at the level of diminished transcription. The inhibitory effect of glucose deprivation on IFN-γ production in response to PMA and ionomycin focused attention on downstream effectors. However, phosphorylation of p38 MAPK Fig. A , To address whether glucose deprivation affects activation of MAPKs, effector cells were stimulated in the presence 50 mM or the absence of 2-DG for the indicated time periods. Whole cell lysates were then prepared to examine kinase activity by Western blotting using phospho-specific Abs. Data are representative of at least four independent experiments. B , Effector cells were stimulated in the presence 25 mM or the absence of 2-DG for the indicated time periods. Nuclear lysates were then prepared to examine transcription factor DNA binding activity by gel-shift analysis. Gel shifts using an OCT-1 probe were performed as an indicator of protein loading. IFN-γ production was undetectable from cells stimulated in 25 mM 2-DG data not shown. C , mRNA levels of T-bet and eomesodermin were still induced despite the presence of 2-DG. Transcript levels were determined by semiquantitative RT-PCR. A key downstream target of p38 MAPK is ATF-2, a binding site for which has been described in the IFN-γ promoter Other transcription factors have been implicated in regulating the IFN-γ gene. Additionally, STAT4 DNA binding activity was still present even in cells stimulated under 2-DG conditions data not shown. Moreover, mRNA induction of the T-box family transcription factors T-bet and eomesodermin was not affected by 2-DG Fig. We next considered a model in which an uncharacterized putative regulator might be required for maintaining long-term IFN-γ transcriptional activity. In mammalian cells, protein synthesis can be regulated by the kinase, mammalian target of rapamycin, and its substrates, p70S6K and 4E-BP1. Recent work has suggested that activation of these kinases is dependent on nutrient availability in HEK cells We investigated whether p70S6K was activated in effector 2C cells and whether this activation was inhibited by 2-DG. Phosphorylation of p70S6K was induced by anti-CD3 Ab alone, and this phosphorylation was not induced further by anti-CD28 Ab data not shown. Interestingly, the addition of 2-DG inhibited p70S6K phosphorylation in a dose-dependent fashion Fig. By contrast, phosphorylation of neither the related ribosomal kinase p90RSK nor ERK was inhibited. Thus, phosphorylation of p70S6K and 4E-BP1, but not that of multiple additional kinases activated downstream from TCR engagement, was suppressed by glucose deprivation. Protein synthesis is required for the expression of IFN-γ mRNA. Phosphorylation of p70S6K is reduced A and delayed B in the presence of 2-DG, as determined by Western blotting. A , Effector cells were stimulated for 3 h. B , Effector cells were stimulated in 50 mM 2-DG. In some cases, the same membrane was stripped and reprobed for the expression of other proteins. C , Cycloheximide blocks IFN-γ mRNA. Cytokine mRNA was determined by RPA. Total RNA was isolated and used to measure cytokine mRNA levels by RPA. Because p70S6K is involved in regulated protein translation, we explored the possibility that de novo protein synthesis was required for the expression of the IFN -γ gene, but not the IL-2 gene. To address this possibility, effector 2C cells were stimulated with anti-CD3 and anti-CD28 Abs in the presence or the absence of cycloheximide, and IL-2 and IFN-γ mRNA levels were assessed by RPA. To determine at what time point protein synthesis is required for IFN-γ transcription, cycloheximide was added at various times during a h stimulation. Addition of cycloheximide at early time points 2—6 h after stimulation blocked further IFN-γ transcription, but addition at later times had a minimal effect Fig. We observed that glucose-dependent metabolism is critical for IFN-γ, but not IL-2, production, the latter cytokine being produced by both naive and effector T cells. Other aspects of glucose-dependent metabolism in hemopoietic cells have been reported. Frauwirth et al. Growth factor withdrawal resulted in rapid down-regulation of the glucose transporter Glut1, which was also linked to cell death 11 , In addition, glucokinase was found in a multiordered complex with the proapoptotic Bcl-2 family member BAD in hepatocytes Collectively, these observations establish a connection between glucose metabolism and regulated apoptotic death. Although we did not observe increased apoptosis in our model under conditions of glucose deprivation after 24 h, this may be because of concomitant CD28 costimulation that up-regulates expression of antiapoptotic molecules However, increased cell death did not explain the short term effects of glucose deprivation observed in our current study. Although previous experiments have shown high glucose use by PHA-stimulated peripheral T cells 25 , our current results show high glucose-dependent metabolism in resting nonproliferating effector cells, dissociating the glucose-dependent phenotype from the process of proliferation. In vivo administration of 2-DG has been shown to inhibit secretion of IFN-γ, TNF-α, IL-1β, and IL-6, whereas production of IL-2 and IL-4 was not affected In another report, Miller et al. Together, these studies show that 2-DG can elicit either positive or negative effects on cellular immunity. Although the effect of 2-DG in those systems could be mediated through other molecules e. In our current study we found no defect in DNA binding of transcription factors known to regulate IFN-γ gene expression; however, molecular regulation of the IFN-γ gene is incompletely understood. By contrast, transgenic T cells expressing GFP driven by a minimal 3. An A1C below 5. This measures your blood sugar after an overnight fast not eating. This measures your blood sugar before and after you drink a liquid that contains glucose. Source: American Diabetes Association. If your doctor thinks you have type 1 diabetes, your blood may also tested for autoantibodies substances that indicate your body is attacking itself that are often present in type 1 diabetes but not in type 2 diabetes. You may have your urine tested for ketones produced when your body burns fat for energy , which also indicate type 1 diabetes instead of type 2 diabetes. Gestational diabetes is diagnosed using blood tests. If your risk is higher for getting gestational diabetes due to having more risk factors , your doctor may test you earlier. Results can differ depending on the size of the glucose drink and how often your blood sugar is tested. Ask your doctor what your test results mean. If your test results show you have prediabetes, ask your doctor or nurse if the lifestyle change program offered through the CDC-led National Diabetes Prevention Program is available in your community. You can also search for an online or in-person program. |
Glucose availability -
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Targeted profiling for CoAs and carnitines in electrospray ionization-positive mode by the RP chromatographic method employed a gradient containing water solvent A and acetonitrile ACN, solvent B, with both solvents containing 0.
Separation of metabolites was performed on a Zorbax Eclipse XDBC18 column 50 × 4. Glycolysis and TCA metabolites were separated by normal phase chromatography using solvents containing water solvent A , solvent A modified by the addition of 5 mM ammonium acetate pH 9.
The binary pump flow rate was 0. The flow rate was gradually increased during the separation from 0. Metabolites were separated on a Luna Amino NH2 column 4 µm, A 2.
All the columns used in this study were washed and reconditioned after every 50 injections. The chromatographic separation of non-lipid metabolites was performed using either reverse phase separation or normal phase online with the unbiased profiling platform based on a SL Rapid resolution LC and a triple Quadrupole mass spectrometer Agilent Technologies, Santa Clara, CA.
Lipidomics required a Shimadzu CTOA Nexera X2 UHPLC coupled with TripleTOF equipped with a Turbo VTM ion source AB Sciex, Concord, Canada. Using a dual electrospray ionization source, the samples were independently examined in both positive and negative ionization modes. The data acquisition during the analysis was controlled using the Mass Hunter workstation data acquisition software.
Mouse liver lipids were extracted using a modified Bligh-Dyer method. Briefly, 50 mg of crushed tissue sample from mouse whole liver was used. The organic layer was collected, followed by a complete drying procedure under nitrogen.
For lipid separation, 5 ml of the lipid extract was injected into a 1. The column heater temperature was set at 55°C. The column was equilibrated for 3 min and run at a flow rate of 0. The raw data in. mgf format were converted using ProteoWizard software.
The NIST MS PepSearch Program was used to search the converted files against LipidBlast libraries. The minimum match factor at was set for the downstream data processing. The raw data files were searched against this in-house library of known lipids with mass and retention time using Multiquant 1.
The lipid species identified in the positive or negative ion modes were analyzed separately using relative abundance of peak spectra for the downstream analyses. The identified lipids were quantified by normalizing against their respective internal standard. Lipid extracts are separated on an Acquity UPLC CSH C18 1.
In positive ion mode, sphingolipids are detected using dynamic multiple reaction monitoring dMRM. Injection volume is 2 μl, and the samples are analyzed in a randomized order with the pooled QC sample injection eight times throughout the sample queue.
Post-time is 5 min, and the flow rate is 0. Results from LC-MS experiments are collected using Agilent Mass Hunter Workstation and analyzed using the software package Agilent Mass Hunter Quant B.
Ceramide and lipid species are quantitated based on peak area ratios to the standards added to the extracts. To determine the relative abundance of FAD and FMN in mouse liver tissue, extracts were prepared and analyzed by high-resolution mass spectrometry HRMS.
Approximately 20—30 mg of tissue were pulverized in liquid nitrogen then homogenized with a Precellys Tissue Homogenizer. Samples were centrifuged at 17, × g for 5 min at 4°C, supernatants were transferred to clean tubes, followed by evaporation under vacuum. The total run time was 50 min.
To increase desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative mode and imported to Thermo Trace Finder software for final analysis.
Relative abundance was normalized by tissue weight. To determine the relative abundance of ubiquinone oxidized CoQ10 , ubiquinol reduced CoQ10 , ubiquinone-9 CoQ9 , and ubiquinol-9 reduced CoQ9 in mouse liver samples, extracts were prepared and analyzed by Thermo Scientific TSQ triple quadrupole mass spectrometer coupled with a Dionex UltiMate HPLC system.
Tissue extracts were vortexed, centrifuged at 17, × g for 5 min at 4°C, and supernatants were transferred to clean autosampler vials. The mobile phase was methanol containing 5 mM ammonium formate.
Separations of CoQ9, CoQ10, reduced-CoQ9, and reduced-CoQ10 were achieved on a Kinetex 2. The mass spectrometer was operated in the MRM positive ion electrospray mode with the following transitions.
Raw data files were imported to Thermo Trace Finder software for final analysis. All measurements were taken from distinct biological samples.
Unless otherwise noted, all statistical analyses were performed using GraphPad Prism version 9 and tests described in the figure legends.
In the case of multiple groups, a one- or two-way ANOVA with post-hoc tests were used to determine statistical significance. When only two groups were compared, nonparametric Mann—Whitney tests were used to determine statistical significance.
Gene expression and metabolomic heatmaps were plotted as Z-scores using R 4. The species-by-species t -test was applied for metabolomics data to identify the top differentially regulated metabolites that passed the nominal threshold p-values. For multiple comparisons, the Sidak procedure was used for false discovery rate FDR correction.
Statistical analysis of energy balance was performed by ANCOVA with lean body mass as a co-variate Mina et al. No statistical method was used to predetermine sample size. Unblinded analysis of histology was performed by the investigators. All data are expressed as mean ± SEM, unless otherwise specified.
This study did not generate new unique reagents. The authors declare that reagents utilized are available upon reasonable request to the corresponding author.
All data generated or analyzed during this study are included in the manuscript and supporting files. RNA-sequencing data have been deposited in GEO under accession code GSE Numerical data used to generate the figures are attached as source data files to each figure. Figure 5 - Source Data 2 and Figure 6 - Source Data 2 contain Western blot scans.
Our editorial process produces two outputs: i public reviews designed to be posted alongside the preprint for the benefit of readers; ii feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.
Thank you for submitting your article "Vitamin B2 enables peroxisome proliferator-activated receptor a regulation of fasting glucose availability" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by David James as the Senior Editor.
The reviewers have opted to remain anonymous. The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. However, energy expenditure EE was not actually calculated to support these two statements.
RER should be calculated to reflect glucose homeostasis and metabolic flexibility. The question is why? What is then the role of FAD for energy balance? Importantly, does B2D induce lipodystrophy in a manner similar to IEMs?
Thus, assessing body weight and body composition phenotypes requires a careful analysis of the various factors that might affect these phenotypes.
If changes in caloric intake or energy expenditure are observed before the change in body weight or fat mass, it is more likely that one can attribute the difference to these changes. The authors state that FAD is required for hepatic glucose production during fasting.
However, the data presented in Figure 2a show that liver FAD displays the opposite h rhythm to liver glucose. It is not ideal to show hepatic glucose level, as the liver secrete glucose into the circulation. According to previous publications, liver FAD levels usually coincide with blood glucose.
Normalization to the total protein level would be more suitable. In Figure 4b, it is surprising to observe that the body weights of the mice are significantly different at week 0 before any diet interventions.
The authors should provide an explanation for this data. Another question is why this experiment used 9 weeks of diet while other experiments used 4 weeks of diet. Consistency in presenting the data would help in comparing the results of different experiments.
Figure 5d shows a representative protein blotted for each complex Total OXPHOS Rodent WB Antibody Cocktail, ab , which unfortunately does not represent the whole complex. Thus, the authors cannot conclude that B2D and fenofibrate lacked meaningful impacts on relative levels of oxidative phosphorylation proteins.
It would be surprising that, if PPARa activity is impacted, some genes encoding various subunits of these complexes would not change as many are known as direct targets of PPARa. Nonetheless, the authors compared 4 groups together in Figure 5a, 5b, 5c, 5d, 5e, and 6e.
Are they different samples? If not, this analysis is flawed. In Figure 2b, the authors perform an "HGP" hepatic glucose production.
However, in the methods, they state that they inject radioactive glucose, and then measure plasma glucose. This experiment would test glucose consumption and not production. Next to this panel, there is a pyruvate tolerance test which is the correct experiment. Is a the HGP experimental methods a typo and they actually used radioactive pyruvate?
Or did they b actually measure glucose consumption by injecting radioactive glucose? If a , please correct this typo. If b , test glucose production by injecting radioactive pyruvate rather than radioactive glucose if possible. Thank you for the suggestion.
We included measurements of energy expenditure and RER in the revised manuscript, which support our conclusion that increased energy expenditure does not account for the weight differences between B2D and control.
Please see revised Figures 1, 3, and Supplemental Figures 1 and 2. Our findings show that B2D lowers energy expenditure and reduces body weight but we were unable to detect changes in food intake. To our knowledge, almost no studies explored the requirements for FAD in energy balance in a comprehensive, mechanistic way.
This lack of attention to a critical electron transfer cofactor and regulator of diverse flavoproteins motivated our study. We also searched extensively for energy expenditure analysis of animal models and people of IEMs resembling B2D.
Only a handful of studies addressed the issue. IEMs that arise from mutations in genes encoding mitochondrial FAD transfer enzymes for FAO are often identified by ectopic fat distribution and liver disease J Inherit Metab Dis —, People with long-chain fatty acid oxidation disorders LC-FAODs , including mutations in the FAD-dependent protein ACADVL, show similar energy expenditure phenotypes as B2D.
Energy expenditure is significantly lower among subjects with LC-FAODs compared to the reference population in the National Health and Nutrition Examination Survey Mol Genet Metab , These observations suggest responses to B2D are relevant to studying IEMs defined by mutations in FAD-dependent metabolic enzymes.
In new efforts to explore energy balance in this model, we examined markers of thermogenesis. Control diet and B2D showed similar brown adipose tissue BAT morphology and gene expression patterns Supplemental Figure 3.
Overall our results show that B2D causes an energy conservation phenotype with reduced energy expenditure. It remains unclear whether this is a primary or compensatory change in response to B2D. We have clarified this point in the discussion. We agree that body weight changes represent complex outcomes that require careful analysis.
In our studies, we follow the most contemporary analysis protocols, including the use of CalR Cell Metab , to determine whether energy expenditure variables change as a result of the diet. For this phenotype, body mass variables without normalization to lean body mass were accounted for in our statistical analyses to report the energy expenditure data accurately.
We cannot find other reported mouse models of IEM where energy expenditure was analyzed by CalR to identify body-weight independent effects. As we now discuss in the manuscript revised Discussion section , we note that B2D effects resemble energy expenditure measurements for people with IEM Mol Genet Metab , To further address the reviewer's concern, we performed measurements of food intake during the first week of B2D Author response image 1.
One week of B2D did not impact food intake. We know additional experiments and equipment time will be needed to address the complex energy balance effects of B2D, and this will be the focus of our future work.
a Cumulative food intake. b ad libitum blood glucose. Mann-Whitney test for a and b. This level varies among mouse strains. The majority of bioavailable riboflavin is converted to FAD and excess is excreted in urine as FMN N Engl J Med , In our revision, we expanded our comments on the riboflavin requirements to support mouse body weight gain and placed our observations within the nutritional studies performed almost 80 years ago.
Thank you for the excellent suggestion. Revised Figure 2 includes liver FAD levels in the fasted and re-fed state, which demonstrates liver FAD levels coincide with blood glucose levels.
The problem with total protein is that it cannot be measured accurately after metabolite extraction, whereas total DNA, tissue weight, and total signal can all be measured accurately after metabolite extraction.
Thank you for pointing this out. Nonetheless, we acknowledge the previous representation of body weight change may cause confusion. Animals were randomly assigned to control or B2D and we did not attempt to match initial body weights, therefore any differences in initial body weight occurred by chance.
In the process of re-assessing the statistics for Figure 4D, we do not find body weights are different at week 0 or week 1. Regarding the experimental design, we exposed mice to B2D long enough to durably establish steady-state weight gain effects before fenofibrate exposure.
We used malnutrition J Hepatol , , Ppara knockout Gut , , cancer cachexia Proc Natl Acad Sci USA E—E, , and other riboflavin-depletion studies Ann Neurol , as guides to design the B2D experiments and fenofibrate interventions. The revised methods section cites these studies.
We agree and edited the text accordingly to note the OXPHOS antibody only detects a fraction of mitochondrial proteins. In the discussion, we noted that fibrates show a minor impact on OXPHOS complexes only at high concentrations compared to other lipid and glucose lowering agents, such as statins and thiazolidinediones Toxicol Appl Pharmacol , We appreciate your input.
Although the diet treatments are different, we feel inclusion of these contrasts remains important for the study. The fenofibrate treatments uncover new gene and metabolic programs beyond B2D alone, including greater activation of the integrated stress response.
The text and figure legends have been revised to clarify sampling times for these experiments. To perform new experiments and associated mass spectrometry analysis, we would need to repeat experiments on four additional groups of mice and home in on appropriate times for sampling. This is non-trivial and beyond the scope of the current project.
We clarified the description of the glucose metabolism data in Figure 2 and separated the pyruvate tolerance test and the hepatic glucose production panels. Our lab performed studies of infused not injected radiolabeled glucose to assess basal hepatic glucose production at steady state.
The revised Materials and methods includes additional details of the protocol. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
This work was funded by the Nancy Chang, PhD Award for Research Excellence at Baylor College of Medicine, American Diabetes Association IBS and NIH R01DK to SMH. We thank the Gene Vector Core at Baylor College of Medicine for vectors and support.
Other BCM core services received support from NCI P30CA Human Tissue Acquisition and Pathology, Metabolomics, and Integrated Microscopy. This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.
All of the animals were handled according to approved institutional animal care and use committee IACUC protocols AN of the Baylor College of Medicine.
Every effort was made to minimize suffering. This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited. Article citation count generated by polling the highest count across the following sources: Crossref , PubMed Central , Scopus.
Apicomplexans are ubiquitous intracellular parasites of animals. These parasites use a programmed sequence of secretory events to find, invade, and then re-engineer their host cells to enable parasite growth and proliferation.
The secretory organelles micronemes and rhoptries mediate the first steps of invasion. After invasion, a second secretion programme drives host cell remodelling and occurs from dense granules.
The site s of dense granule exocytosis, however, has been unknown. In Toxoplasma gondii , small subapical annular structures that are embedded in the IMC have been observed, but the role or significance of these apical annuli to plasma membrane function has also been unknown.
Here, we determined that integral membrane proteins of the plasma membrane occur specifically at these apical annular sites, that these proteins include SNARE proteins, and that the apical annuli are sites of vesicle fusion and exocytosis.
Specifically, we show that dense granules require these structures for the secretion of their cargo proteins. When secretion is perturbed at the apical annuli, parasite growth is strongly impaired.
The apical annuli, therefore, represent a second type of IMC-embedded structure to the apical complex that is specialised for protein secretion, and reveal that in Toxoplasma there is a physical separation of the processes of pre- and post-invasion secretion that mediate host-parasite interactions.
Cellular metabolism plays an essential role in the regrowth and regeneration of a neuron following physical injury. Yet, our knowledge of the specific metabolic pathways that are beneficial to neuron regeneration remains sparse.
Previously, we have shown that modulation of O-linked β-N-acetylglucosamine O-GlcNAc signaling, a ubiquitous post-translational modification that acts as a cellular nutrient sensor, can significantly enhance in vivo neuron regeneration.
Here, we define the specific metabolic pathway by which O-GlcNAc transferase ogt-1 loss of function mediates increased regenerative outgrowth. Performing in vivo laser axotomy and measuring subsequent regeneration of individual neurons in C.
elegans , we find that glycolysis, serine synthesis pathway SSP , one-carbon metabolism OCM , and the downstream transsulfuration metabolic pathway TSP are all essential in this process. Testing downstream branches of this pathway, we find that enhanced regeneration is dependent only on the vitamin B12 independent shunt pathway.
These results are further supported by RNA sequencing that reveals dramatic transcriptional changes by the ogt-1 mutation, in the genes involved in glycolysis, OCM, TSP, and ATP metabolism.
Strikingly, the beneficial effects of the ogt-1 mutation can be recapitulated by simple metabolic supplementation of the OCM metabolite methionine in wild-type animals. Taken together, these data unearth the metabolic pathways involved in the increased regenerative capacity of a damaged neuron in ogt-1 animals and highlight the therapeutic possibilities of OCM and its related pathways in the treatment of neuronal injury.
Mitochondrial membrane potential directly powers many critical functions of mitochondria, including ATP production, mitochondrial protein import, and metabolite transport. Its loss is a cardinal feature of aging and mitochondrial diseases, and cells closely monitor membrane potential as an indicator of mitochondrial health.
Given its central importance, it is logical that cells would modulate mitochondrial membrane potential in response to demand and environmental cues, but there has been little exploration of this question.
We report that loss of the Sit4 protein phosphatase in yeast increases mitochondrial membrane potential, both by inducing the electron transport chain and the phosphate starvation response. Indeed, a similarly elevated mitochondrial membrane potential is also elicited simply by phosphate starvation or by abrogation of the Phodependent phosphate sensing pathway.
We also demonstrate that this connection between phosphate limitation and enhancement of mitochondrial membrane potential is observed in primary and immortalized mammalian cells as well as in Drosophila. These data suggest that mitochondrial membrane potential is subject to environmental stimuli and intracellular signaling regulation and raise the possibility for therapeutic enhancement of mitochondrial function even in defective mitochondria.
Share this article Doi. Cite this article Peter M Masschelin Pradip Saha Scott A Ochsner Aaron R Cox Kang Ho Kim Jessica B Felix Robert Sharp Xin Li Lin Tan Jun Hyoung Park Liping Wang Vasanta Putluri Philip L Lorenzi Alli M Nuotio-Antar Zheng Sun Benny Abraham Kaipparettu Nagireddy Putluri David D Moore Scott A Summers Neil J McKenna Sean M Hartig Vitamin B2 enables regulation of fasting glucose availability.
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Figure 1 with 3 supplements see all. Download asset Open asset. Figure 1—source data 1 Numerical data presented in Figure 1. Excel spreadsheet containing raw data for panels presented in the figure.
Download elifefig1-data1-v1. Figure 2. Figure 2—source data 1 Numerical data presented in Figure 2. Download elifefig2-data1-v1. Download elifefig2-data2-v1. Download elifefig2-data3-v1. Figure 3 with 1 supplement see all. Figure 3—source data 1 Numerical data presented in Figure 3.
Download elifefig3-data1-v1. B The table summarize the p -values. The total number of genes responding to 12 h of starvation was higher. Nine putative transporters Slc22b2 , Mfsd8 , Mfsd11 , Slc22b3 , Slc33a2 , Slc37a3 , Mfsd1 , and Mfsd14b , and two known transporters, Slc6a7, Slc25a3 showed changes in expression levels, Figure 3B.
Of these, Slc22b2 and Slc22b3 where the only ones affected both by 3 h and 12 h starvation with Slc22b2 downregulated at both time points and Slc22b3 downregulated at 3 h and upregulated after 12 h of starvation.
While most reverted back to control levels after refeeding with glucose, Slc22b2 stayed downregulated even after refeeding and Slc25a3 stayed upregulated. Refeeding 12 h starved cells did upregulate expression of one additional putative transporter, Slc59a2 , as well as a lactate transporter, Slc16a3.
In addition to SLCs and putative transporters, more general metabolism and stress markers were monitored: Glyceraldehydephosphate dehydrogenase Gapdh , an important enzyme in glycolysis and energy production Ramzan et al. Of these, Gsk3b and Nrf2 were affected at 3 h of glucose starvation, and while Nrf 2 reverted to control levels, Gsk3b stayed downregulated after glucose was refed to the culture.
Gapdh was affected not by starvation, but by refeeding 3 h starved cells. After 12 h of starvation, Nrf2 , was once again upregulated, while no mRNA expression could be found after refeeding, indicated by the gray box in Figure 3A.
Refeeding cells after 12 h of starvation downregulated p To ensure that glucose starvation and refeeding affected the metabolism in the primary cortex cultures, total mTOR was measured after 3 and 12 h glucose starvation and after refeeding for 12 h. During starvation for 3 and 12 h, the total mTOR protein expressions were downregulated and after the cells were refed with D -glucose, the expression of total mTOR went back to normal, Supplementary Figure 1B.
To monitor if the glucose starvation resulted in any protein expression or localization changes, ICC was performed for those targets for which there were verified antibodies available, Figures 4 — 6.
As to not introduce bias in the evaluation of fluorescent signal and to be able to analyze a large sample size, a pipeline in CellProfiler was used to evaluate changes in fluorescent intensities and position in the cell compared to DAPI nucleus staining and Pan neuronal marker, staining soma, axons and dendrites of neurons.
Fluorescent intensity in the cytoplasm was compared between control and starved or control and refed, Figure 4 , and representative ICC images used during the analysis are found in Figures 5 , 6. The transporters that were monitored were all found in neurons.
The staining was monitored in the whole cell, the outer perimeter including projections and the core soma excluding nucleus. Figure 4. Changes in protein expression of putative SLCs subjected to glucose starvation and glucose starvation and refeeding.
Primary cortex cultures subjected to glucose starvation S and glucose starvation and refeeding SR for 3 and 12 h were used for immunocytochemistry ICC. In total, six putative SLCs were investigated where at least one antibody has been verified previously and shown to work on primary cortex cultures; MFSD1, MFSD4A, MFSD11, MFSD14A, MFSD14B and UNC93A.
Images were taken using the same exposure settings for all pictures for each transporter. For each group, 6—10 pictures were taken including several cells. Each graph contain data regarding the mean fluorescent intensity for whole orange , outer apricot , and core yellow staining.
No difference in expression was observed for MFSD1 after A glucose S nor B glucose SR. C MFSD4A was found to be downregulated in all three compartments after 3 and 12 h S, except for the core compartment after 12 h.
D No alteration was found after 3 h SR, no data was collected for 12 h samples in the same experiment. No difference in immunostaining was observed for MFSD11 after E 12 h S, no data collected for 3 h S. F Reduction in immunostaining was observed in the outer and core compartment after 3 h SR, and increased immunostaining was found after 12 h SR.
G The immunostaining of MFSD14A was induced in the core compartment after 3 h S, while induced in all three compartments after 12 h S. H Meanwhile after 3 h SR, MFSD14A staining was increased after 3 h in all three compartments, and still increased in the outer and core compartments after 12 h SR.
I No differences were found for MFSD14B after glucose starvation, while J the immunostaining was reduced after 3 h SR in the whole and outer compartments, and after 12 h SR in the core. K UNC93A immunostaining was reduced after 12 h S in all three compartments, while L no differences were observed in cells subjected to 3 and 12 h SR.
Figure 5. Representative ICC images of the putative SLCs subjected to glucose starvation used for measuring changes in protein expression. Primary cortex cultures subjected to glucose starvation S for 3 and 12 h were used for immunocytochemistry.
In total, six putative SLCs, where one antibody has been verified previously and shown to stain primary cortex cultures Perland et al. Images were taken using the same exposure settings for all pictures for each transporter, scale bar represents 20 μm.
The putative SLC is labeled in green FITC , the neuronal marker PAN in red cyto and the nucleus marker DAPI in blue. The panel consists of the representative ICC images of each target for both the I control and the II S condition; A MFSD1 3 h, B MFSD1 12 h, C MFSD4A 3 h, D MFSD4A 12 h, E MFSD11 12 h, F MFSD14A 3 h, G MFSD14A 12 h, H MFSD14B 3 h, I MFSD14B 12 h, J UNC93A 3 h, and K UNC93A 12 h.
Figure 6. Representative ICC images of the putative SLCs subjected to glucose starvation and refeeding used for measuring alterations in protein expression. Primary cortex cultures subjected to glucose starvation and refeeding SR for 3 and 12 h were used for immunocytochemistry.
Six putative SLCs were stained for: MFSD1, MFSD4A, MFSD11, MFSD14A, MFSD14B, and UNC93A. The panel consists of the representative ICC images of each target for both the I control and the II SR condition; A MFSD1 3 h, B MFSD1 12 h, C MFSD4A 3 h, D MFSD11 3 h, E MFSD11 12 h, F MFSD14A 3 h, G MFSD14A 12h, H MFSD14B 3h, I MFSD14B 12 h, J UNC93A 3 h, and K UNC93A 12 h.
For MFSD1, where mRNA upregulation could be seen after 12 h of glucose starvation, no alteration in protein expression was found to have taken place yet, while for MFSD4A Slc60a1 , protein expression was downregulated in the whole cell at both 3 and 12 h, Figure 4C , while no change in mRNA expression was seen at the same time point, Figure 3.
MFSD11 staining intensity remained the same in the whole cell, however, a localization shift was seen after starvation and refeeding after both 3 and 12 h, Figure 4F.
MFSD14A, which was transcriptionally upregulated after 3 h of starvation, had increased staining at 12 h in whole cell as well as the core and outer portions of the cell. Starving and refeeding glucose to cells strongly increased protein expression of MFSD14A in the whole, core and outer portions of the cells, Figures 4F,G.
Refeeding glucose to starved cells likewise affected MFSD14B compared with controls, but in the opposite direction, with lower fluorescent intensity in the whole and outer portion of the cells at 3 h, and the core of the cell at 12 h, Figure 4J.
Staining for UNC93A was lower after 12 h of starvation in all areas of the cells, while staining was at the same levels as controls after refeeding, Figures 4K,L.
To monitor if changes in gene expression also occurs as an effect of sugar availability in a multicellular model organism, we turned to the well-studied D. melanogaster Jennings, Not all putative SLCs included in the study were conserved in flies, however, a few of them had a clear orthologue to the human and mouse sequences: Mfsd1 mrva and CG , Mfsd6 jef , Mfsd8 Cln7 , Slc22a32 rtet , Mfsd11 CG , Mfsd14a CG , and Unc93a CG General markers known to be involved in metabolism and sugar pathways were also studied; Akh , AkhR Galikova et al.
Adult male flies were subjected to 0 h controls , 3 h and 12 h of starvation as well as refeeding after 3 h and 12 h of starvation. A majority of the SLC and putative SLC orthologues, as well as metabolic targets CG, Akh , jef, AkhR , InR , CG , and CG were affected after 3 h of starvation and were mostly downregulated except for CG which was upregulated.
Refeeding returned the expression back to normal for CG, jef and CG , as well as for metabolic target Akh. Two targets, AkhR and InR , both receptors involved in glucose homeostasis, remained downregulated even after refeeding the starved flies.
Eight targets were affected by 12 h of starvation, and only two Gapdh1 and CG were upregulated, while the rest were downregulated. Refeeding starved flies after 12 h of starvation caused normalization of Gapdh1, Akh and jef.
Meanwhile AkhR , InR and rtet were still downregulated after refeeding, and CG still upregulated. Cln7 switched from being downregulated after 12 h of starvation to upregulated after refeeding, compared with the control. No significant changes in expression were found for Ilp5 , Ilp6 , Gapdh2 , mrva , and CG , Figure 7.
For four of the new SLCs and putative transporters, a response was seen both in glucose starved cell cultures and starved adult flies. Mfsd1 CG , Mfsd8 Cln7 , Mfsd11 CG , and Mfsd14a orthologue CG were all affected by starvation, summary in Figure 12A.
Figure 7. Gene regulation of putative SLCs and genes involved in metabolism in D. Samples were used to measure the mRNA expression via qRT-PCR and the expression was normalized against three stable housekeeping genes.
The control was set to 1 and the mRNA expression for each group is relative to the control group 0 h of starvation. A The heatmap display alterations in gene expression for 0 h S, 3 h S, 12 h S, 3 h SR and 12 h SR flies.
Glucose starvation of cortex cultures yielded changes in expression for some of the new SLCs and putative transporters, but not all, and by far fewer than were expected based on the presence of glucose sensing motifs.
Total glucose starvation is a high stress metabolic state for cell cultures that rely heavily on glucose for energy. Likewise, total starvation for adult flies represents a high stress state where not only glucose metabolism is affected.
In primary cortex cultures, glucose deprivation affected more than double the targets than glucose starvation did. Eighteen of 33 targets were affected after 3 h and 16 targets after 12 h of glucose deprivation. Mfsd14a, Mfsd14b , and Unc93a were upregulated at 3 h of deprivation, while Gsk3, Mfsd13a, Slc22b4, Slc22b2, Slc16a3, p53, Slc6a7, Slc37a3, Slc22b3, Slc22b1, Gclc , Nrf2 , Slc22a32, Mfsd11 , and Rheb where all downregulated, Figures 8A,B and Supplementary Figures 5 , 6.
After 12 h of glucose deprivation upregulated gene expression was seen for: Slc33a2, Mfsd9, Mfsd1, Mfsd14a, Mfsd14b, Gapdh , Slc25a3, Gclc, Slc22a32, Mfsd11, Slc60a1, Rheb , and the important neuronal glucose transporter Slc2a3. Downregulation was seen for just three targets, Slc6a7, Slc37a3 and Perk.
Figure 8. The mRNA and protein expression regulation of putative SLCs and genes connected to glucose metabolism and stress after glucose deprivation. Primary cortex cells from mice were subjected to glucose deprivation D for 3 and 12 h.
The genes and experiments were hierachical clustered. A The heatmap display alterations in gene expression for 3 and 12 h glucose deprived cells compared with its corresponding control.
B The table summarize the p-values. Primary cortex cultures subjected to glucose deprivation for 3 and 12 h were used for immunocytochemistry ICC. Six putative SLCs were investigated; MFSD1, MFSD4A, MFSD11, MFSD14A, MFSD14B, and UNC93A. Images were taken using the same exposure settings for all pictures.
For each group, 6—10 pictures were taken. Identification of cellular compartments, nuclei based on DAPI staining and whole neuronal cytoplasm based on Pan-neuronal staining were performed.
C No differences in immunostaining was observed for MFSD1, MFSD11, MFSD14A and MFSD14B, while the immunostainings for MFSD4A and UNC93A were induced in all three compartments after 3 h glucose deprivation. A few new SLCs and putative transporter genes responded both at 3 and at 12 h, and for both Slc22a32 and Mfsd11 , gene expression decreased after 3 h of glucose deprivation but increased beyond controls after 12 h again.
In contrast, the gene expression of both Mfsd14a and Mfsd14b significantly increased at 3 h and stayed upregulated even at 12 h, compared with controls. The known transporters, Slc6a7 and Slc37a3 were both downregulated at both time points. Putative SLC genes that did not respond to the glucose deprivation in the media were: Unc93b1 , Mfsd6, Slc59a2, Slc61a1, Mfsd8 , and Slc59a2 , Figure 8 , and overview in Figures 11 , For a few of the putative transporters, antibodies yielding good quality staining were found and fluorescent intensity was again monitored using the CellProfiler pipeline, Figure 8C , with representative ICC images used for the analysis in Figure 9.
MFSD1 expression, which had an upregulated gene expression after 12 h of deprivation, was seen to increase in the outer portions of neurons after 12 h of glucose deprivation, Figure 8C. MFSD4A, also with an increased gene expression Slc60a1 after 12 h of deprivation, had higher fluorescent staining in the whole, outer and core parts of the cells already after 3 h.
MFSD11 staining was found to increase in the core of neurons after 3 h of deprivation, as did the expression of UNC93A. UNC93A staining was also seen to increase in the whole and outer parts of the neurons after just 3 h of deprivation, mirroring the increased gene expression seen at 3 h.
For both MFSD14A and MFSD14B, no change in protein expression could be seen, Figure 8C , while the gene expression was increased for both transporters at both time points, Figure 8A.
Figure 9. Representative ICC images of the putative SLCs subjected to glucose derpivation used for measuring changes in protein expression. Primary cortex cells from mice were subjected to glucose deprivation D for 3 and 12 h were used for immunocytochemistry. MFSD1, MFSD4A, MFSD11, MFSD14A, MFSD14B, and UNC93A were stained for.
The panel consists of the representative ICC images of each target displaying both the I control and the II glucose deprivation; A MFSD1 3 h, B MFSD1 12 h, C MFSD4A 3 h, D MFSD4A 12 h, E MFSD11 3 h, F MFSD11 12 h, G MFSD14A 3 h, H MFSD14A 12h, I MFSD14B 3h, J MFSD14B 12 h, K UNC93A 3 h, and L UNC93A 12 h.
Focusing on effects caused by sugars and not just general starvation, adult males were feed various concentrations of sugars and yeast to mimic fluctuating sugar availability in the body. Decreasing both sugar and yeast to a low-calorie diet, increased the expression of CG the orthologue to Mfsd11 , while decreasing both Gapdh1 and CG the orthologue to Mfsd14a , Figure Decreasing just the sugar content while keeping the protein content at normal level, produced the highest gene expression shift seen yet, with nine of 15 targets affected and all of the genes affected were upregulated, Figure Interestingly, increasing just the sugar content only affected two genes, CG and Gapdh1 , Figures 10 , 12 B.
CG orthologue to Mfsd11 showed changed expression in all three diets compared with controls, increased in both a general low-calorie diet and a low sugar diet while decreased in the high sugar diet. Figure Gene regulation of putative SLCs and metabolic genes after feeding fruit flies different diets.
The mRNA expression was measured via qRT-PCR and the expression was normalized against three stable housekeeping genes. The control was set to 1 and the mRNA expression for each group is relative to the control group A The heatmap display the alteration in gene expression for flies subjected to low calorie, low sugar, high sugar and control diet.
One model was made from mouse primary cortex cultures, and the other was adult male D. The cell cultures were composed of a mixture of cells, where the presence of both astrocytes and neurons were verified, and our findings are compared with normal culturing protocols where non-physiological concentrations of glucose 4.
However, the energy consumption of multicellular organisms is regulated by hormones and polypeptides e. Using these two systems together, information of both specific and overall regulation of these putative transporters was collected and while we did find comparable reactions in both systems for some targets, some were different.
Based on the glycemic need of the neurons, most protocols suggest a glucose concentration of 4. Interestingly, this concentration has been shown to sustain neuronal metabolism as well Kleman et al.
Importantly, the results in this paper are always compared to controls with 4. Hence, the results might not mirror the regulation in the brain during glucose deprivation Sunwoldt et al.
The newly identified SLC and putative transporters that were included here have been found to be mostly neuronal Perland et al. Glucose is mainly used to provide the neurons with one highly important product, energy in the form of ATP, a task performed in the cytosol and within the mitochondria Jonckheere et al.
The ATP is used to fuel action potentials but also to maintain ion gradients and neuronal membrane potentials. In addition, glucose is used to both drive the production, and as a precursor for, biosynthesis of neurotransmitters Dienel, ; Harris et al.
A majority of the SLCs act as cotransporters or exchangers, where ions are needed to maintain the transport, meaning that both nutrient and ion levels affect their activity and function Cesar-Razquin et al.
In addition, since these reactions take place at different sites of the cells, transport through membranes are needed not just for glucose, but for additional substrates and byproducts of glucose metabolism.
Based on the gene regulation changes found in both primary cortex cultures and in adult D. melanogaster , subjected to no or low glucose availability, many of the newly identified SLCs and putative transporters have a potential to be involved in nutrient intake and maybe also metabolism.
However, the magnitude of up- and downregulation presented in Figure 3A does not match the level of significance in Figure 3B. This is not surprising giving the rather high expression variance for some of the targets not evident in the heatmaps but affecting the conclusions that can be drawn from the gene regulation changes.
A higher n number might have resolved this issue. Moreover, due to the lack of knowledge about exact cellular localization and transported substrate of many of the SLCs included in this study, more work is necessary to pin-point their exact contribution. An overview of the targets, orthologues, and gene regulation after glucose starvation, deprivation, and different diets can be found in Figures 11 , Not all new SLCs and putative transporters were found to be conserved in the fly, but for 14 of the targets included, one orthologue was found.
Perhaps the conservation between these 14 implies a general basal function for cells. For one of these, no mRNA expression was detected in the adult flies included in our study.
Similarly, some of the transporters were not found to be expressed in the mouse primary cortex cultures that were used lack of information indicated by gray squares in Figure 11A. Equating mouse primary cortex cultures and adult flies is not a balanced comparison, but it does reveal that some transporters, and putative transporters, do react in a similar fashion.
This was especially apparent when comparing glucose deprived primary cortex culture and the low sugar diets in the flies. Summarizing of the results from in vitro and in vivo experiments.
Red box indicate upregulation, blue box indicate downregulation, white box illustrates no regulation observed and gray box display where information is not obtained.
B Tabulated summary of metabolic and stress targets in primary cortex cells and fruit flies. Illustrating similarities between starved and deprived of the results from in vitro and in vivo experiments. A Venn diagram displaying similarities and dissimilarities in gene regulation between starved and deprived cortex cultures and fruit fly.
B Venn diagram illustrating gene regulation similarities and dissimilarities between the diets low calorie, low sugar, and high sugar performed in D. Upregulation in cells and flies due to low glucose availability were observed for Mfsd1 and orthologue CG , Slc22a32 orthologues rtet , Mfsd11 orthologue CG , Mfsd14a and Mfsd14b with common orthologue CG , and Unc93a with orthologue CG , Figures 7 , 10 , with summary in Figure All of these, apart from Mfsd11 , were only upregulated in D.
melanogaster by the low sugar diet, not the low-calorie diet, which suggest that they are possibly regulated by factors that response to sugar levels rather than an overall low level of nutrient.
The responsiveness of the putative transporters included in our study, became more evident by re-introducing glucose to the cell cultures or refeeding the flies after total starvation.
A few normalized their expression to control levels after introduction of glucose, for summary see Figures 13A,B. A 3-h lack of glucose in cell media, or a complete lack of food for adult flies, followed by refeeding normalized the expression of Slc60a2, Mfsd14a and CG back down to normal levels.
Conversely, Unc93a, Unc93b1, Slc22b2, Slc22b3, CG and jef were all downregulated and then restored after refeeding. This indicates a rather quick and dynamic modulation due to energy availability. After 12 h of starvation and subsequent refeeding, Mfsd1, Slc32a2, Mfsd14b, Slc22b3, Slc6a7 , and Slc37a3 were all upregulated and then stabilized.
Mfsd8, Slc22a32, Mfsd11 and jef were instead downregulated and then returned to normal levels. Graphical illustration of gene regulation in in vitro and in vivo.
The gene regulation for each putative SLC and metabolic target measured in both primary cortex cultures and fruit flies were summarized in two graphical illustrations; one for each timepoint; no regulation black , no regulation at 12 h S and upregulation at 12 h SR purple , upregulation for both experimental groups red , no regulation at 12 h S and downregulation at 12 h SR yellow , downregulation at 12 h S and no regulation at 12 h SR orange , downregulation at 12 h S and upregulation at 12 h SR light blue , downregulation for both experimental groups blue.
From predictions of modulatory binding sites upstream of the transcription site, Figure 2 missing , Mfsd1 was predicted to contain one of the highest number of carbohydrate sensing motifs docking sequences, with over nine sites of Mlx , Mlxip and Mlxipl , key regulators of metabolic adaptations to glucose Stoeckman et al.
The other transporters seen to modulate their gene expression likewise had predicted carbohydrate sensing motifs in their promotor regions, though not as numerous as Mfsd1. The cellular localization of MFSD1 are contradicting, studies done in mice found it to be a lysosomal protein Massa Lopez et al.
MFSD1 has been found to be tightly connected to Glycosylated Lysosomal Membrane Protein GLMP where the loss of MFSD1 causes severe liver disease in MFSD1-knockout mice. Despite the clear physiological effect of the loss of Mfsd1 , the precise function or transported substrate was not found even though several different amino acids were tested Massa Lopez et al.
Slc22a32 previously named Mfsd10 before inclusion into the Slc22 family was also one of the strongly regulated targets in our study and has an unknown function but a structure that resembles that of other transporters.
Recently, Mfsd10 has been identified as a protein expressed at the nuclear envelope, possibly involved in transport of toxic material out from the nuclear environment Cheng et al. In our experiments, Slc22a32 Mfsd10 was downregulated during glucose starvation in cortex cells, and in starved adult flies, while upregulated during glucose deprivation in both cells and flies.
Mfsd11 CG was the only target upregulated by both a low sugar and low-calorie diet in the flies, and conversely, downregulated by a high sugar diet.
Mfsd11 was also predicted to have the highest number of gene modulation sites associated with whole body energy, Figure 2. A general role in metabolism is a high probability. Recently, MFSD11 was identified as a novel candidate linked to intellectual disability Anazi et al.
Mfsd6 with orthologue jef , was the only target that was not altered in our primary cortex cultures but downregulated in starved adult flies.
Mfsd6 has been found to be downregulated in whole brain slices from mice fed a high calorie diet as well as in brains from mice placed on starvation Bagchi et al.
While Mfsd6 expression was found in our cells, other factors available in a whole biological system that are not present in a cell culture are needed for its regulation. However, it might also be un-affected by glucose availability, but rather depend on another macronutrient. Two putative transporters whose expression were found to be highly dynamic in our setting were Mfsd14a and Mfsd14b , along with their common orthologue CG , Figures 11 — They were both upregulation due to glucose starvation in cell cultures while they were both downregulated in starved flies.
They were both upregulated during low glucose conditions glucose deprivation in the cell cultures, and after low sugar diet in the fly, while no change was seen after a low calorie or high sugar diets.
The gene expression was normalized after refeeding. The function of MFSD14A and MFSD14B is unclear, however, MFSD14A has been linked to infertility in male mice due to a marked reduction of spermatozoa possibly due to faulty glycosylation Doran et al.
Interestingly, the protein expression of MFSD14A was upregulated in the glucose starved cells as well as after glucose refeeding.
MFSD14A was the only putative transporter that is suggested to be intracellularly that displayed an increase in the whole cytoplasm, suggesting that this transporter, not only increase in expression, but also potentially move from its location.
The Golgi apparatus harbors mechanisms that can sense nutrient availability and regulate cellular processes through O-GlcNAcylation of proteins, and re-stacking of the Golgi apparatus can aid in autophagy Zhang and Wang, The increased protein expression seen for MFSD14A after starvation, in Figure 4 , could be due to a restacking of the Golgi apparatus and increased autophagy taking place during starvation.
Similar gene regulation changes were seen for members of the SV2 family, Slc22b1, Slc22b2, Slc22b3 and its relative Slc22b4. These genes code for synaptic vesicle glycoproteins, present on synaptic vesicle of neurons Buckley and Kelly, ; Bajjalieh et al.
All isoforms were seen to respond in a similar fashion during glucose deprivation in the cell cultures, where a transient downregulation was seen at 3 h and normal mRNA levels found at 12 h, perhaps due to a slower vesicle recycling needed during low glucose availability.
During total glucose starvation, the major isoform Slc22b1 , was not affected. SV2A, the protein of Slc22b1 , is known to harbor important function during normal neurotransmission but has in recent years also been implicated as a galactose transporter Madeo et al.
SV2B Slc22b2 and SV2C Slc22b3 were however both affected by a total lack of glucose. The function of these two proteins is not as vigorously studied as that of SV2A, however, they also perform functions connected to synaptic transmission.
ATP binding and modulating of transport function has been found for the glucose transporter Glut1 SLC2A1 , where ATP binding affects transport activity Levine et al. The putative transporter UNC93A has previously been found to react to nutrient availability, both in mice subjected to complete starvation for 24 h and in cell cultures subjected to amino acid starvation Ceder et al.
Our results suggest that UNC93A expression is also affected by glucose availability. However, the mRNA expression in both mice cell cultures and fly were not affected by starvation, but rather by deprivation upregulated and glucose refeeding after starvation downregulated in cells and flies.
UNC93A has been suggested to be a regulator of Twik-related acid potassium 1 TASK 1 channels in C. elegans de la Cruz et al. The difference in regulation between no glucose and low glucose could point to this function.
Hence, the upregulation during deprivation could be due to e. Subjecting mouse embryonic cortex cultures to glucose starvation or deprivation also affected general metabolism targets, as did starvation and nutrient restriction in the flies. Nrf2 , involved with antioxidant defense mechanisms Itoh et al.
Glucose starvation and deprivation also downregulated general responses in pathways coupled to metabolisms, such as Gsk3b , important in glycogen metabolism and insulin sensing Cohen and Goedert, while Rheb , involved in energy sensing Dibble and Cantley, was only affected by glucose deprivation in the cell cultures.
Also, more affected by low levels of glucose rather than none, was Gapdh , where an upregulation was seen after 12 h Seidler, melanogaster , two variants of Gapdh are present and Gapdh1 was upregulated after total starvation in the adult flies, while downregulation was seen after the low calorie and high sugar diet, summary in Figure 11B.
Gapdh2 , meanwhile, was only upregulated during low sugar conditions. These alterations serve as a verification that the time points and glucose levels used in the set up are sufficiently long to elicit a response in the two models used.
For the primary cortex cultures, deprivation seems to induce more targets involved in metabolism than starvation, correlating with the higher number of putative transporters also affected by this condition.
A known neuronal glucose transporter that was also affected by glucose deprivation, rather than no glucose, was Slc2a3. The regulation of Slc2a3 due to glucose deprivation had already been shown in primary neuronal cultures from rat embryos, where cultures deprived for 48 h caused a 4-fold increase of gene expression Nagamatsu et al.
Here, gene expression of Slc2a3 increased after 12 h, while no difference could be seen after 3 h. For this transporter, 3 h of glucose deprivation is too short to elicit a response, while 12 h is just enough.
This could well be true for other putative transporters included here, meaning that even though some transporters were not found to react in this setting, they might well be involved in glucose metabolism and regulated by it. Several known SLCs that were monitored also increased by a lack of glucose in the cortex cell cultures, Slc6a7, Slc25a3 , Slc37a3 , and Slc16a3.
These SLCs are not directly involved in glucose transport but connected to general metabolism and delivers information about how other SLCs react to no or low glucose and nutrient levels.
For example, SLC6A7 transports proline, an important molecule for several cellular aspects e. The increase in expression observed for Slc6a7 could be a secondary effect due to the glucose starvation i.
Our findings demonstrate how tightly connected and regulated the metabolic pathways are within both unicellular and multicellular life-forms. Several genes are regulated simultaneously maybe to activate alternative metabolic pathways as a response to fluctuating nutrient levels or just as a response of other upstream genes.
The recently classified and putative SLCs that we have monitored in this study have a probability of being necessary for normal neuronal function during fluctuating energy and glucose availability.
However, establishing their exact core function during these glucose conditions remains. The fact that almost one third of transporters and transporter-related proteins remain orphans with unknown, or contradictive, location and function, establishes the need for further research about them to fully understand their mechanistic role and their impact on cellular conditions.
The datasets that contain protein sequences and the script presented in this study can be found in online repositories. All images used for protein expression and localization analysis can be received upon request to the corresponding author.
MC planned and performed diet experiments in primary cells, RNA extractions, cDNA synthesis, primer design, qPCR, ICC and imaging, diet experiments in D. melanogaster , cDNA synthesis and qPCR on flies, phylogenetic and promoter analysis.
Using metadata from previously published research, this Caffeine addiction symptoms sought Avsilability explore: 1 whole-body Glucosr carbohydrate Goucose fat Natural solutions for high cholesterol rates of endurance e. Indirect calorimetry was used to determine availabilitt fat oxidation MFO in the incremental exercise and carbohydrate and fat oxidation rates during steady-state running. Gastrointestinal symptoms GISbreath hydrogen H 2and blood glucose responses were measured throughout the steady-state running protocols. Despite high variability between participants, high rates of MFO [mean range : 0. Whole-body total fat oxidation rate was 0. Glucose is a main energy avaikability Natural solutions for high cholesterol neurons in brain Glucose availability limited storage Farm-fresh ingredients and retina where it is stored in glial Glucose availability cells and supplied upon demand. Availabilit availability abailability visual function are related. Human positron emission tomography studies indicate increased blood flow and glucose metabolic rate in primary visual cortex during stimulation, with retinotopic distribution. Retinal electrophysiology covaries with glucose concentration in in vitro models as well as in humans, at comparable concentrations in the physiological range. The interactions between retinal electrophysiology notably the electroretinogram b-wave and glucose metabolism appear more stringent than for cortical evoked responses.
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