> Physiology > Molecular Function > Glycolysis

Glycolysis

Glycolysis is the metabolic pathway that converts glucose into pyruvate and energy in the form of ATP.
It is a fundamental process in cellular respiration, occurring in the cytoplasm of both prokaryotic and eukaryotic cells.
Glycolysis involves a series of enzymatic reactions that break down glucose, producing two molecules of pyruvate, two ATP, and two NADH.
This pathway is highly regulated and can be optimized for research purposes using advanced AI-driven tools like PubCompare.ai.
PubCompare.ai helps researchers easily locate the most accurate and reproducible glycolysis protocols from literature, preprints, and patents, streamlining research and improving accuracy.

Most cited protocols related to «Glycolysis»

Constrained Metabolic Flux Analysis

TMFA was performed largely as originally described.14 (link) Measured metabolite concentrations (Supplementary Table 3 online) were used to constrain the TMFA model. For compounds that were quantitated in glucose-grown cells, but not glycerol- or acetate-grown cells, the upper bound for the concentration in the glycerol- or acetate-fed cells was set to 10 times the measured upper bound of the 95% confidence interval in glucose-grown cells. Unmeasured compounds were assumed to be between 1 µM and 20 mM in concentration, except for 1,3-diphosphoglycerate (27), which was assumed to be between 1 µM and 50 mM. The increased upper bound was necessary in order to allow glycolytic flux in glucose- and glycerol-fed cultures.

Bennett B.D., Kimball E.H., Gao M., Osterhout R., Van Dien S.J, & Rabinowitz J.D. (2009). Absolute Metabolite Concentrations and Implied Enzyme Active Site Occupancy in Escherichia coli. Nature chemical biology, 5(8), 593-599.

Publication 2009

Acetate Cells Glucose Glycerin Glycolysis

Absolute Protein Quantification and Stoichiometry

The absolute protein concentrations determined for 41 glycolytic proteins were aligned with the summed protein intensities as provided by the Progenesis LC-MS software (v4.0, Nonlinear Dynamics Limited) divided by the number of expected tryptic peptides as recently specified4 (link),25 (link). The thus generated models were applied to estimate absolute protein levels for all quantified proteins in the CID and HCD dataset, respectively, and the expected errors were calculated by bootstrapping25 (link) (Supplementary Fig. 5). To control for variations in protein extraction efficiency, which was lower for stationary phase samples, we used the total protein mass per cell (that is the summed masses of all quantified proteins) accurately determined in triplicates for the glucose experiment by our LC-MS approach (Supplementary Fig. 5A) and, assuming that the volumetric protein concentration is condition independent54 (link), we adjusted the total protein mass per cell for each condition according to the precisely measured cellular volumes (Supplementary Table 23 and Supplementary Note 3) determined previously26 (link). Due to the higher number of quantified membrane proteins, higher number of growth conditions included and the analysis in biological triplicates (Supplementary Fig. 4), protein quantities obtained from data set 2 were employed for all quantitative analysis carry out in this study. Data generated in data set 1 was only included in the qualitative analysis of identified protein modifications illustrated in Table 1 and Fig. 5A and B.
To assess the technical and biological variability of our label-free protein quantification approach, we performed duplicate SRM and shotgun LC-MS analyses of three independent biological samples grown in glucose media and chemostat µ=0.5 and correlated the protein abundances determined by our data analysis pipeline (Supplementary Fig. 7). Besides, stoichiometries were determined for quantified components of protein complexes with known subunit composition (Supplementary Table 27).

Schmidt A., Kochanowski K., Vedelaar S., Ahrné E., Volkmer B., Callipo L., Knoops K., Bauer M., Aebersold R, & Heinemann M. (2015). The quantitative and condition-dependent Escherichia coli proteome. Nature biotechnology, 34(1), 104-110.

Publication 2015

Biopharmaceuticals Gene Products, Protein Glucose Glycolysis Growth Disorders Membrane Proteins Peptides Post-Translational Protein Processing Proteins Protein Subunits Proto-Oncogene Mas Trypsin

Constrained Metabolic Flux Analysis

Publication 2009

Acetate Cells Glucose Glycerin Glycolysis

Comprehensive Metabolomic Profiling for CVD

A high-throughput NMR metabolomics platform8 was used for the quantification of 68 lipid and abundant metabolite measures from baseline serum samples of the FINRISK, SABRE, and BWHHS cohorts. All metabolites were measured in a single experimental setup, which allows for the simultaneous quantification of both routine lipids, total lipid concentrations of 14 lipoprotein subclasses, fatty acid composition such as monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA), various glycolysis precursors, ketone bodies and amino acids in absolute concentration units (Supplemental Table 1).^{8 (link)} The targeted metabolite profiling therefore includes both known metabolic risk factors and metabolites from multiple physiological pathways, which have not previously been examined in relation to CVD risk in large population studies. The 68 metabolite measures were assessed for association with incident CVD events using a hypothesis-generating biomarker discovery approach with subsequent replication in two independent cohorts. Spearman’s correlations of the metabolites are shown in Supplemental Figure 1. The NMR metabolomics platform has previously been used in various epidemiological studies^{9 ,10 (link),16 (link),17 (link),20 (link)–22 (link),31 (link),32 (link)}, details of the experimentation have been described^{9 ,24 (link)}, and the method has recently been reviewed.^{8 (link),19 (link)}A subset of 679 serum samples from the FINRISK study were additionally profiled with liquid-chromatography mass spectrometry (LC-MS) using the Metabolon platform^{33 (link)} in a case-cohort design for comparison of biomarker associations with incident CVD (expanded methods online). The biomarker associations were further compared with those obtained by LC-MS-based profiling of the Framingham Offspring Study (fifth examination cycle, n=2289 fasting plasma samples), as described in detail previously.^{13 (link),14 (link)} Since several fatty acid biomarkers were not measured by LC-MS, the quantification was analytically confirmed by comparing NMR and gas chromatography in the Cardiovascular Risk in Young Finns Study (YFS, n=2193 fasting serum samples).^{34 (link)} Metabolite profiling data collected at two-time points in YFS9 was further used to examine associations of dietary intake with the circulating biomarkers, and tracking of concentrations within the same individuals over 6 years.

Würtz P., Havulinna A.S., Soininen P., Tynkkynen T., Prieto-Merino D., Tillin T., Ghorbani A., Artati A., Wang Q., Tiainen M., Kangas A.J., Kettunen J., Kaikkonen J., Mikkilä V., Jula A., Kähönen M., Lehtimäki T., Lawlor D.A., Gaunt T.R., Hughes A.D., Sattar N., Illig T., Adamski J., Wang T.J., Perola M., Ripatti S., Vasan R.S., Raitakari O.T., Gerszten R.E., Casas J.P., Chaturvedi N., Ala-Korpela M, & Salomaa V. (2015). Metabolite Profiling and Cardiovascular Event Risk: A Prospective Study of Three Population-Based Cohorts. Circulation, 131(9), 774-785.

Publication 2015

Amino Acids Biological Markers DNA Replication Fatty Acids Fatty Acids, Monounsaturated Gas Chromatography Glycolysis Ketone Bodies Lipids Lipoproteins Liquid Chromatography Mass Spectrometry physiology Plasma Polyunsaturated Fatty Acids Serum

Comprehensive NMR Metabolomics in Diverse Cohorts

This work is an extension of our previous GWA-metabolomics study, in which the
quantitative high-throughput NMR metabolomics platform, used to quantify human
blood metabolites, was applied4 (link). In this study, we have utilized
the same platform to quantify 123 metabolite measures that represent a broad
molecular signature of systemic metabolism. The metabolite set covers multiple
metabolic pathways, including lipoprotein lipids and subclasses, fatty acids as
well as amino acids and glycolysis precursors. Most of the NMR-based
metabolomics analyses were performed with the comprehensive quantitative
serum/plasma platform described originally by Soininen et al.24 (link) and reviewed recently25 (link). This same platform was
used here to analyse samples in Estonian Genome Center of University of Tartu
Cohort (EGCUT), Finnish Twin Cohort, a subsample of FINRISK 1997 (FR97), Genetic
Predisposition of Coronary Heart Disease in Patients Verified with Coronary
Angiogram (COROGENE), Genetics of METabolic Syndrome, Helsinki Birth Cohort
Study (HBCS), Cooperative Health Research in the Region of Augsburg (KORA),
Northern Finland Birth Cohort 1966 (NFBC 1966), FINRISK subsample of incident
cardiovascular cases and controls (PredictCVD), EGCUT sub-cohort (PROTE) and
YFS. Metabolite-specific untransformed distributions and descriptive summary
statistics from the largest cohort, NFBC 1966, are presented in Supplementary Fig. 3. Chemical shifts and
the coefficients of variation for inter-assay variability are presented in Supplementary Data 3 for each
metabolite. Here, the study was extended with Erasmus Rucphen Family Study
(ERF), Leiden Longevity Study (LLS) and Netherlands Twin Register (NTR) cohorts
for which the small-molecule information was available from another NMR-based
method (Supplementary Table 2 for
details)26 (link). Metabolite-specific untransformed distributions
and descriptive summary statistics for these measures from the ERF cohort are
given in Supplementary Fig. 4.
Chemical shifts and the coefficients of variation for inter-assay variability
are presented in Supplementary Table
7. The sample material was mostly serum, except for EGCUT, PROTE, NTR
and LLS in which the sample material was EDTA-plasma. The ERF cohort had
additional lipoprotein measures available through the method developed by Bruker
Ltd. (https://www.bruker.com/fileadmin/user_upload/8-PDF-Docs/MagneticResonance/NMR/brochures/lipo-analysis_apps.pdf).
The terminology of this method utilized for lipoprotein analyses in ERF was
matched based on the lipoprotein particle size with the comprehensive
quantitative serum/plasma platform to enable meta-analyses. The vast majority of
blood samples were fasting, however, if a study did not have overnight fasting
samples, we corrected the fasting time effect by using R package gam and fitting
a smoothed spline to adjust for fasting. All metabolites were first adjusted for
age, sex, time from last meal, if applicable, and ten first principal components
from genomic data and the resulting residuals were transformed to normal
distribution by inverse rank-based normal transformation.

Kettunen J., Demirkan A., Würtz P., Draisma H.H., Haller T., Rawal R., Vaarhorst A., Kangas A.J., Lyytikäinen L.P., Pirinen M., Pool R., Sarin A.P., Soininen P., Tukiainen T., Wang Q., Tiainen M., Tynkkynen T., Amin N., Zeller T., Beekman M., Deelen J., van Dijk K.W., Esko T., Hottenga J.J., van Leeuwen E.M., Lehtimäki T., Mihailov E., Rose R.J., de Craen A.J., Gieger C., Kähönen M., Perola M., Blankenberg S., Savolainen M.J., Verhoeven A., Viikari J., Willemsen G., Boomsma D.I., van Duijn C.M., Eriksson J., Jula A., Järvelin M.R., Kaprio J., Metspalu A., Raitakari O., Salomaa V., Slagboom P.E., Waldenberger M., Ripatti S, & Ala-Korpela M. (2016). Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nature Communications, 7, 11122.

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Publication 2016

Amino Acids, Acidic Biological Assay Birth Cohort Childbirth CTSB protein, human Edetic Acid Extended Family Genome Genome-Wide Association Study Glycolysis Heart Disease, Coronary Hereditary Diseases HMGA2 protein, human Lipids Lipoproteins Metabolism Patients Plasma Serum Twins

Most recents protocols related to «Glycolysis»

Metabolomic Analysis of TCA and Glycolysis

Tricarboxylic acid cycle and Glycolysis cycle metabolites were identified by using 5 mM ammonium acetate in water as buffer PH 9.9 (A) and 100% acetonitrile as a buffer (B) using Luna 3 µM NH2 100 A⁰ Chromatography column (Phenomenex, Torrance, CA). The Gradient used: 0–20 min-80% B (Flow rate 0.2 ml/min); 20–20.10 min- 80% to 2% B; 20.10–25 min-2% B(Flow rate 0.3 ml/min); 25–30 min 80% B (Flowrate 0.35 ml/min); 30–35 min-80%B (Flow rate 0.4 ml/min); 35–38 min 80% B (Flow rate 0.4 ml/min); followed by re-equilibration at the end of the gradient to the initial starting condition 80% B a Flow rate of 0.2 ml/min. All the identified metabolites were normalized by spiked internal standard (Mohammed et al., 2020c (link)).

Alghetaa H., Mohammed A., Singh N., Wilson K., Cai G., Putluri N., Nagarkatti M, & Nagarkatti P. (2023). Resveratrol attenuates staphylococcal enterotoxin B-activated immune cell metabolism via upregulation of miR-100 and suppression of mTOR signaling pathway. Frontiers in Pharmacology, 14, 1106733.

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Publication 2023

acetonitrile ammonium acetate Buffers Chromatography Citric Acid Cycle Glycolysis

Andrographolide Modulates Metabolic Pathways

The known target gene list of AG was downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/#query=andrographolide) and analyzed using Cytoscape's JEPPETTO plugin (https://apps.cytoscape.org/apps/jepetto). Kyoto Encyclopedia of Genes and Genomes analysis (https://www.genome.jp/kegg/) identified several cancerous, metabolic and p53 signaling pathways as AG-related pathways. Interaction between genes and chemicals by AG were analyzed using BioCarta Pathways Dataset (https://maayanlab.cloud/Harmonizome/search?t=all&q=andrographolide). The GSE74769 microarray dataset (obtained from the Gene Expression Omnibus database; http://www.ncbi.nlm.nih.gov/geo/) was examined to identify a potential link between glycolysis and AG. The correlation between PDK mRNA expression and AG cytotoxicity in the cells was performed using Spearman's correlation analysis.

Yang E.S., Do Y., Cheon S.Y., Kim B., Ling J., Cho M.K., Kim T., Bae S.J, & Ha K.T. (2023). Andrographolide suppresses aerobic glycolysis and induces apoptotic cell death by inhibiting pyruvate dehydrogenase kinase 1 expression. Oncology Reports, 49(4), 72.

Publication 2023

andrographolide Cells CTSB protein, human Cytotoxin Genes Genome Glycolysis Malignant Neoplasms Microarray Analysis RNA, Messenger Signal Transduction Pathways

Cellular Bioenergetics Profiling Using Seahorse

Seahorse XF Cell Mito Stress Test kit (Agilent Technologies, Inc.) was used to determine the O₂ consumption rate (OCR) and Seahorse XF Glycolysis Stress Test kit was used to examine the extracellular acidification rate (ECAR), as previously described (19 (link)). The transfected SAOS-2 cells (1x10⁵) were plated onto a Seahorse XF-96 cell culture microplate. Cells were next equilibrated with XF Base media (Agilent Technologies Deutschland GmbH) at 37˚C for 1 h in an incubator lacking CO₂ and then serum-starved for 1 h in glucose-free media-containing treatments (Invitrogen; Thermo Fisher Scientific, Inc.). A total of 1 mM oligomycin, 1 mM p-trifluoromethoxy carbonyl cyanide phenylhydrazone (MilliporeSigma), 2 mM antimycin A (MilliporeSigma) and 2 mM rotenone (MilliporeSigma) were added to each well at 37˚C overnight to detect the OCR. For the measurement of ECAR, each well contained 10 mM glucose, 1 mM oligomycin (MilliporeSigma) and 80 mM 2-deoxyglucose (MilliporeSigma) at 37˚C overnight. A Seahorse XF-96 analyzer (Agilent Technologies, Inc.) was used to detect the samples and data were assessed using Seahorse XFe24 Wave version 2.2 software (Agilent Technologies, Inc.).

Li R., He H, & He X. (2023). APOC1 promotes the progression of osteosarcoma by binding to MTCH2. Experimental and Therapeutic Medicine, 25(4), 163.

Publication 2023

2-Deoxyglucose Antimycin A carbonyl cyanide phenylhydrazone Cell Culture Techniques Cells Exercise Tests Glucose Glycolysis Mitomycin Oligomycins Ovalocytosis, Malaysian-Melanesian-Filipino Type Rotenone Seahorses Serum

Expanded 13C Metabolic Flux Analysis

For MFA, we established a combined model for glycolysis, the PPP and TCA cycle, which has been previously described and utilized in Stifel et al. (43 (link)). It predicts ¹³C mass distributions on metabolites based on flow rates of the metabolic system by utilizing the EMU concept (40 (link), 55 (link)–57 (link)) and was implemented in RStan [R interface to Stan, a tool for Bayesian analysis (58 )]. Comparing predictions for ¹³C mass distributions with the corresponding GC/MS measurements (section 2.6) using sampling-based Bayesian statistics allowed for identifying suitable fluxes within the network. It further estimated how the precision in measurements affects the precision of estimated fluxes, including standard deviations and confidence intervals. Conveniently, unidentifiable fluxes can be recognized by wide confidence ranges.
Our PPP estimation is built on the same method as the one used by Lee, Katz, and Rognstad (59 (link), 60 (link)) that is based on the assumption that PPP utilization can be represented as a shift in the label (‘carbon scrambling’) of the top carbon atoms of PPP metabolites. For this approach, usually only the m+1/m+2 ratio on lactate would be used as a proxy for triose labeling using a 1,2-¹³C₂-labeled glucose input, but we expanded the method so that the complete CMD of the full metabolite as well as the CMD of the lactate fragment across carbon 2 and 3 were taken into account. The model firstly estimated relative fluxes from GC/MS measurements and subsequently utilized ¹³CO₂ production and the secretion of lactate into the medium to transform these relative fluxes into absolute values. The parallel tracer setup of 1,2-¹³C₂-labeled glucose, ¹³C₆-labeled glucose, and ¹³C₅-labeled glutamine enabled improved flux determination, as the estimated fluxes must apply to sets of measurements obtained from each tracer. The details of the metabolic model are available in the Supplements.

Wolfschmitt E.M., Hogg M., Vogt J.A., Zink F., Wachter U., Hezel F., Zhang X., Hoffmann A., Gröger M., Hartmann C., Gässler H., Datzmann T., Merz T., Hellmann A., Kranz C., Calzia E., Radermacher P, & Messerer D.A. (2023). The effect of sodium thiosulfate on immune cell metabolism during porcine hemorrhage and resuscitation. Frontiers in Immunology, 14, 1125594.

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Publication 2023

Carbon Citric Acid Cycle Dietary Supplements Gas Chromatography-Mass Spectrometry Glucose Glutamine Glycolysis Lactates secretion Trioses

Bioenergetic Assessment of Cardiomyocytes

We used XF Cell Mito Stress Test and XF Glycolytic Rate Assay kit to measure the oxygen consumption rate (OCR) for the mitochondrial respiratory activity and proton efflux rate (PER) for the glycolytic levels in the cardiomyocytes, by using a Seahorse XFe96 Extracellular Flux Analyzer (Agilent, CA). Cells (45,000) were plated into an Xfe96 cell culture microplate (Agilent) containing RPMI/B27 supplemented with 10% FBS and 10 μM ROCK inhibitor. After 48 h to allow recovery, we conducted the metabolic profiling using the XFe96 Seahorse analyzer with two kits according to the manufacture’s manual. Briefly, 1 day prior to the experiment, the Xfe96 sensor cartridges were hydrated in XF calibrator solution and incubated overnight at 37°C in a non-CO₂ incubator. 1 hour prior to the experiment, the cells were incubated at 37°C (non-CO₂) in 200 μl of Seahorse assay medium, containing XF base medium supplemented 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose (pH 7.4). OCR was measured with sequential injections of 2 μM oligomycin, 2 μM FCCP and each 0.5 μM of rotenone/antimycin A. PER was measured with sequential injections of 0.5 μM of rotenone/antimycin A and 50 mM of 2-deoxy-D-glucose (2-DG). Data were normalized by fluorescence of cell viability using PrestoBlue reagent (Thermo Fisher).

Zhang P., Liu Y., Li C., Stine L.D., Wang P.H., Turnbull M.W., Wu H, & Liu Q. (2023). Ectopic expression of SARS-CoV-2 S and ORF-9B proteins alters metabolic profiles and impairs contractile function in cardiomyocytes. Frontiers in Cell and Developmental Biology, 11, 1110271.

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Publication 2023

Antimycin A Biological Assay Carbonyl Cyanide p-Trifluoromethoxyphenylhydrazone Cell Culture Techniques Cells Cell Survival Exercise Tests Fluorescence Glucose Glutamine Glycolysis Mitochondrial Inheritance Mitomycin Myocytes, Cardiac Oligomycins Oxygen Consumption oxytocin, 1-desamino-(O-Et-Tyr)(2)- Protons Pyruvate Respiratory Rate Rotenone Seahorses

Top products related to «Glycolysis»

Xf96 extracellular flux analyzer by Agilent Technologies

Sourced in United States

The XF96 Extracellular Flux Analyzer is a laboratory instrument designed to measure the metabolic activity of cells in a high-throughput manner. The device is capable of simultaneously assessing the oxygen consumption rate and extracellular acidification rate of cells, providing insights into their respiratory and glycolytic activity.

Seahorse xf glycolysis stress test kit by Agilent Technologies

Sourced in United States, China

The Seahorse XF Glycolysis Stress Test Kit is a lab equipment product from Agilent Technologies. It is designed to measure the glycolytic function of cells in real-time.

Seahorse xfe96 analyzer by Agilent Technologies

Sourced in United States, Germany

The Seahorse XFe96 Analyzer is a high-throughput instrument designed for real-time measurement of cellular metabolism. The analyzer uses microplates to assess oxygen consumption rate and extracellular acidification rate, providing insights into cellular bioenergetics.

Seahorse xf cell mito stress test kit by Agilent Technologies

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The Seahorse XF Cell Mito Stress Test Kit is a laboratory equipment product designed to measure mitochondrial function in live cells. It provides real-time analysis of key parameters such as oxygen consumption rate and extracellular acidification rate, which are indicators of cellular respiration and metabolic activity.

Oligomycin by Merck Group

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Oligomycin is a laboratory product manufactured by Merck Group. It functions as an inhibitor of the mitochondrial F1F0-ATP synthase enzyme complex, which is responsible for the synthesis of adenosine triphosphate (ATP) in cells. Oligomycin is commonly used in research applications to study cellular bioenergetics and mitochondrial function.

Xf24 extracellular flux analyzer by Agilent Technologies

Sourced in United States, France

The XF24 Extracellular Flux Analyzer is a lab equipment product from Agilent Technologies. It is designed to measure the oxygen consumption rate and extracellular acidification rate of cells in real-time.

Xf glycolysis stress test kit by Agilent Technologies

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The XF Glycolysis Stress Test Kit is a laboratory equipment product designed to measure the glycolytic function of cells. It provides real-time analysis of cellular metabolic parameters, including glycolysis and glycolytic capacity.

Glycolysis stress test kit by Agilent Technologies

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The Glycolysis Stress Test Kit is a laboratory equipment product designed to measure and analyze cellular glycolysis, a fundamental metabolic process. This kit provides the necessary tools and reagents to assess the glycolytic capacity and function of cells in a controlled experimental setting.

Seahorse xfe96 extracellular flux analyzer by Agilent Technologies

Sourced in United States, Germany

The Seahorse XFe96 Extracellular Flux Analyzer is a laboratory instrument designed to measure the metabolic activity of cells. It provides real-time analysis of cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in a 96-well microplate format.

Seahorse xf96 extracellular flux analyzer by Agilent Technologies

Sourced in United States

The Seahorse XF96 Extracellular Flux Analyzer is a laboratory instrument designed to measure the rate of oxygen consumption and extracellular acidification in live cells. It provides real-time, high-throughput analysis of cellular metabolism.

What are the key steps involved in the glycolysis process?

Glycolysis is a series of enzymatic reactions that break down glucose into two molecules of pyruvate. The key steps include: 1. Glucose phosphorylation - Glucose is converted to glucose-6-phosphate using the enzyme hexokinase. 2. Isomerization - Glucose-6-phosphate is converted to fructose-6-phosphate. 3. Phosphofructokinase reaction - Fructose-6-phosphate is converted to fructose-1,6-bisphosphate. 4. Cleavage - Fructose-1,6-bisphosphate is cleaved into two 3-carbon molecules, glyceraldehyde-3-phosphate. 5. Oxidation and ATP production - The glyceraldehyde-3-phosphate molecules are oxidized, producing two ATP and two NADH per glucose molecule.

How can PubCompare.ai help optimize glycolysis protocols for research?

PubCompare.ai is a powerful tool that can help researchers optimize glycolysis protocols in several ways: 1. It allows you to efficiently screen the protocol literature to identify the most effective and reproducible glycolysis procedures. 2. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for your specific research goals. 3. By leveraging the platform's Critical Insights, you can pinpoint the most important factors that impact glycolysis protocol performance, improving your chances of achieving accurate and reproducible results.

Are there any common challenges or variations in using glycolysis protocols?

Yes, there are a few common challenges and variations in using glycolysis protocols: 1. Enzyme activity and cofactor requirements - Glycolysis involves several enzymes, each with specific activity and cofactor needs. Ensuring optimal enzyme function is crucial for efficient glycolysis. 2. Substrate availability - The availability of glucose or other substrates can impact the rate and efficiency of glycolysis. This is an important consideration when designing experiments. 3. Regulation and optimization - Glycolysis is a highly regulated pathway, with various control points that can be modulated for research purposes. Understaning these regulatory mechanisms can help optimize glycolysis for specific applications. 4. Cell type and organism variations - Glycolysis can vary slightly between cell types and organisms due to differences in enzyme isoforms and regulatory mechanisms. Choosing the right protocol for your system is important.

How can glycolysis be used in different research applications?

Glycolysis has a wide range of research applications: 1. Cellular energetics - Studying glycolysis is crucial for understanding cellular energy metabolism and ATP production. 2. Disease modeling - Dysregulation of glycolysis is implicated in various disease states, such as cancer and metabolic disorders. Glycolysis protocols can be used to model these disease processes. 3. Biotechnology - Engineered strains of microorganisms with optimized glycolysis pathways are used to produce valuable chemicals and biofuels at industrial scales. 4. Biofuel production - Glycolysis is a key step in the conversion of biomass to biofuels like ethanol, making it an important process for renewable energy research. 5. Drug discovery - Glycolysis inhibitors are being investigated as potential therapeutic agents, especially in oncology. Accurate glycolysis protocols are needed to test these drugs.

More about "Glycolysis"

Glycolysis is a fundamental metabolic pathway that converts glucose into pyruvate, generating energy in the form of ATP.
This process is central to cellular respiration and occurs in the cytoplasm of both prokaryotic and eukaryotic cells.
The glycolytic pathway involves a series of enzymatic reactions that break down glucose, producing two molecules of pyruvate, two ATP, and two NADH.
Glycolysis is a highly regulated process that can be optimized for research purposes using advanced AI-driven tools like PubCompare.ai.
This platform helps researchers easily locate the most accurate and reproducible glycolysis protocols from literature, preprints, and patents, streamlining the research process and improving accuracy.
The Seahorse XF Glycolysis Stress Test Kit and the Seahorse XF Cell Mito Stress Test Kit are powerful tools that can be used in conjunction with the Seahorse XF96 and XFe96 Extracellular Flux Analyzers to measure glycolytic activity and mitochondrial function in cells.
These instruments provide valuable insights into cellular metabolism and can be used to support glycolysis research.
Oligomycin, a known inhibitor of ATP synthase, can also be utilized in glycolysis studies to investigate the cellular response to changes in ATP production.
The Seahorse XF24 Extracellular Flux Analyzer and the XF Glycolysis Stress Test Kit are additional tools that can be employed to measure glycolytic parameters and optimize glycolysis research.
By leveraging these advanced technologies and AI-driven platforms, researchers can enhance their understanding of glycolysis, optimize experimental protocols, and achieve more accurate and reproducible results in their studies.