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Purine

Purines are a class of nitrogenous heterocyclic compounds that serve as the basic structural components of nucleic acids, such as DNA and RNA.
They play a vital role in various biological processes, including energy metabolism, cell signaling, and gene expression.
Purines can be synthesized de novo or salvaged from dietary sources.
Disorders related to purine metabolism, such as gout and Lesch-Nyhan syndrome, have significant clinical implications.
Understanding the regulation and dynamics of purine metabolism is crucial for adressing a wide range of health conditions.
Resaerchers can leverge PubComapre.ai to identify the most accurate and reproductible purine research protocols from literature, preprints, and patents, optimizing their studies with ease.

Most cited protocols related to «Purine»

1
Nucleotide Interaction Characterization in RNA
Cited 26 times
We construct a local coordinate system in the center of the six-membered rings, as shown in Figure 1a. Following this definition, the relative position and orientation between two nucleobases is described by a vector , that is conveniently expressed in cylindrical coordinates ρ, θ and (Figure 1b). Note that is invariant for rotations around the axis connecting the six-membered rings. We highlight that this definition is similar to the local referentials introduced by Gendron and Major (25 (link)). The use of a nucleotide-independent centroid makes it straightforward to compare and combine collection of position vectors deriving from different combinations of nucleobases. This is of particular importance for constructing the knowledge-based scoring function (see below). The position vector has an intuitive interpretation in terms of base-stacking and base-pairing interactions. This aspect is illustrated in Figure 1c, that shows the distribution of vectors for all neighboring bases in the crystal structure of the Haloarcula marismortui large ribosomal subunit (PDB code 1S72) (2 (link)) projected on the ρ and coordinates. In the figure, different colors correspond to different types of interactions detected by MC-annotate. Due to steric hindrance, no points are observed in a forbidden ellipsoidal region. Furthermore, almost all the base-stacking and base-pairing interactions (≈99.6%) belong to a well-defined ellipsoidal shell. It is therefore useful to introduce the anisotropic position vector
with a = 5 Å and b = 3 Å, so that pairs of bases in the interaction shell are such that . The majority of base–base contacts lying in this interaction shell are annotated either as Watson-Crick/non-Watson-Crick or as base stacking, as detailed in Table 1. Within this region we distinguish a pairing zone and a stacking zone, according to the type of featured interactions. The tri-modal histogram in Figure 1c shows that these two zones can be defined without ambiguity considering pairs such that the projection of along the axis is larger (stacking) or smaller (pairing) than 2 Å.
It is well known that the strength and nature of pairing and stacking interactions depend on the base–base distance, on the angle θ as well as on other angular parameters (e.g. twist, roll, tilt) in a non-trivial manner (24 (link),33 (link)). Such dependence can be observed in Figure 2, where the points belonging to the pairing and stacking zone of Figure 1c are projected on two separate ρ–θ planes. These distributions give an average picture containing contributions from different base pair types (purine–purine, purine–pyrimidine and pyrimidine–pyrimidine) and with weights dictated by the employed data set. Nevertheless, the observations below hold also when considering the 16 possible combinations of base pairs individually and different data sets (see Supporting Data (SD) Figrues S1–S3). In the pairing zone (Figure 2, left panel) we first observe a dominant peak centered around (ρ=5.6 Å, θ=60°), corresponding to the position of canonical Watson-Crick base pairs as well as wobble (GU) base pairs. The two other peaks correspond instead to base pairs interacting through the Hoogsteen or sugar edge (13 (link)). One can also appreciate the absence of bases in the region occupied by the sugar (190° < θ < 290°). The probability distribution in the stacking zone (Figure 2, right panel) shows a broad peak in the proximity of the origin and extending up to ρ ≈ 4 Å, which can be compared to the typical radius of the six-membered ring (≈1.4 Å). This means that partial or negligible ring overlap is very frequent in RNA structures, as also observed in a seminal paper by Bugg et al. (34 (link)). This feature is more evident in pyrimidine–pyrimidine and purine–purine pairs, for which high overlap is the exception rather than the rule (see Supplementry Figure S3), whereas overlap is systematically observed in pyrimidine–purine pairs. The fact that bases in the stacking zone are very often ‘imbricated,’ similarly to roof tiles, rather than literally stacked one on top of the other, does not imply that they are not interacting. Indeed, base–base interaction is not limited to π–π stacking but also includes electrostatic effects, London dispersion attraction, short range repulsion as well as backbone-induced effects (35 (link)).
Bottaro S., Di Palma F, & Bussi G. (2014). The role of nucleobase interactions in RNA structure and dynamics. Nucleic Acids Research, 42(21), 13306-13314.
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Publication 2014
2
Quantitative Urine Purine and Pyrimidine Analysis
Cited 25 times
For the purine and pyrimidine analysis, we operated a SCIEX 5500 Triple-Quadrupole LC-MS mass spectrometer fitted with a Turbo V ion source, online connected to an ultra-high performance liquid chromatography Agilent 1260 UHPLC system. Analyst v1.6.1 (SCIEX) was used for all SRM data acquisition, the development of the HPLC method, and the optimization of analyte-specific SRM transitions. Skyline-daily version 4.2.1.19004 was used for LC-SRM-MS data analysis and processing.
For the purine and pyrimidine analysis, urine from five mice was collected from voluntary expulsion, and 20 μL aliquots were stored at −80 °C until ready for analysis. Urine aliquots were thawed on ice and 80 μL of methanol was added containing 2-chloroadenosine (IS) at a final concentration of 2.5 μM to each 20 μL urine aliquot. The mixture was vortexed vigorously for ~30 seconds and protein precipitation was completed by incubating at −20 °C for 30 min. After this, samples were vortexed vigorously for ~30 seconds and centrifuged at 15,000 rpm for 10 min at 4 °C. An 80 μL aliquot of the supernatant was carefully removed without disturbing the pellets and transferred to an HPLC autosampler vial fitted with inserts; 2 μL were injected per HPLC-SRM-MS analysis.
Synthetic standards for compounds indicated in Table S1 were obtained from IROA (Mass Spectrometry Metabolite Library of Standards, MSMLS) or Sigma-Aldrich, St. Louis, MO. 100 μM stocks were prepared in 80% methanol and stored in −80 °C prior to use. A final standard mixture of all compounds at 5 μM (containing the internal standard/IS, 2-chloroadenosine at 2.5 μM), was prepared prior to analysis and injected at the onset of each set biological sample set. The Skyline document for urine purine/pyrimidine analysis has been uploaded to Panorama Public at https://panoramaweb.org/SkylineForSmallMolecules.url.
Adams K.J., Pratt B., Bose N., Dubois L.G., John-Williams L.S., Perrott K.M., Ky K., Kapahi P., Sharma V., MacCoss M.J., Moseley M.A., Colton C.A., MacLean B.X., Schilling B, & Thompson J.W. (2020). Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics. Journal of proteome research, 19(4), 1447-1458.
Publication 2020
2-Chloroadenosine 11-dehydrocorticosterone Biopharmaceuticals cDNA Library High-Performance Liquid Chromatographies Mass Spectrometry Methanol Mice, Laboratory Neoplasm Metastasis Pellets, Drug Proteins purine Pyrimidines Urine
3
Antifolate Drug Sensitivity Assays
Cited 23 times
RFC- and FRα-null MTXRIIOuaR2-4 (R2) Chinese hamster ovary (CHO) cells were a gift from Dr. Wayne Flintoff (University of Western Ontario)30 (link) and were cultured in α-minimal essential medium (MEM) supplemented with 10% bovine calf serum (Invitrogen, Carlsbad, CA), penicillin-streptomycin solution and glutamine at 37°C with 5% CO2. PC43-10 cells are R2 cells transfected with human RFC31 (link) and were cultured in α-MEM plus 1.5 mg/ml G418. FRα- (designated RT16) and FRβ- (designated D4) expressing CHO cells were derived from R2 cells by electroporation with FRα (obtained from Manohar Ratnam, Medical University of Ohio) and FRβ (prepared by RT-PCR; see Supplement) cDNAs in pcDNA3 vector. Cells were cloned, colonies isolated and expanded for screening by western blots (FRα) or real time RT-PCR (FRβ) (Supplement). The RT16 and D4 sublines were maintained as for PC43-10 cells. Prior to the cytotoxicity assays (see below), RT16 and D4 cells were cultured in complete folate-free RPMI 1640 (without added folate) for three days. KB human cervical cancer and IGROV1 ovarian cancer cells were purchased from American Type Culture Collection (ATCC) (Manassas, VA). Cells were routinely cultured in folate-free RPMI 1640 medium, supplemented with 10% fetal bovine serum, penicillin-streptomycin solution, 2 mM glutamine at 37°C with 5% CO2.
For growth inhibition assays, cells (CHO, KB, or IGROV1) were plated in 96 well dishes (∼5000 cells/well, total volume of 200 µl medium) with a range of inhibitors including classical antifolates and the 6-substituted pyrrolo[2,3-d]pyrimidine antifolates 1–5. The sources of the classical antifolate drugs were as follows: MTX, Drug Development Branch, National Cancer Institute (Bethesda, MD); RTX [N-(5-[N-(3,4-dihydro-2-methyl-4-oxyquinazolin-6-ylmethyl)-N-methyl-amino]-2-thienoyl)-L-glutamic acid], AstraZeneca Pharmaceuticals (Maccesfield, Cheshire, England); LMX (5,10-dideaza-5,6,7,8-tetrahydrofolate) and PMX [N-{4-[2-(2-amino-3,4-dihydro-4-oxo-7H-pyrrolo[2,3-d]pyrimidin-5-yl)ethyl]benzoyl}-L-glutamic acid] (Alimta), Eli Lilly and Co. (Indianapolis, IN); and GW1843U89 [(S)-2-(5-(((1,2-dihydro-3-methyl-1-oxo-benzo(f)quinazolin-9-yl) methyl) amino)1-oxo-2-isoindolinyl) glutaric acid], GlaxoWellcome-SmithKline Co. (Research Triangle Park, NC). The culture medium was RPMI 1640 (contains 2.3 µM folic acid) with 10% dialyzed serum and antibiotics for experiments with R2 and PC43-10 cells. For RT-16, D4, KB, and IGROV1 cells, cells were cultured in folate-free RPMI media with 10% dialyzed fetal bovine serum (Invitrogen) and antibiotics supplemented with 2 nM LCV (Drug Development Branch, National Cancer Institute, Bethesda, MD). The requirement for FR-mediated drug uptake in these assays was established in parallel incubations including 200 nM folic acid (Sigma Chemical Co., St. Louis, MO). Cells were routinely incubated for up to 96 h, and metabolically active cells (a measure of cell viability) were assayed with CellTiter-blue Cell Viability Assay (Promega) and fluorescence was measured (590 nm emission, 560 nm excitation) with a fluorescence plate reader. Data were exported from Softmax Pro software to an Excel spreadsheet for analysis and determinations of IC50s, corresponding to the drug concentrations that result in 50% loss of cell growth.
For some of the in vitro growth inhibition studies, drug treatments were performed in the presence of higher concentrations (up to 100 nM) of LCV. In others, the inhibitory effects of the antifolate inhibitors on de novo thymidylate biosynthesis (i.e., TS) and de novo purine biosynthesis (GARFTase and AICARFTase) were tested by co-incubations with thymidine (10 µM) and adenosine (60 µM). For de novo purine biosynthesis, additional protection experiments used AICA (9.6–192 µM), as a means of distinguishing inhibitory effects at GARFTase from those at AICARFTase.32 (link)
For assays of colony formation in the presence of the antifolate drugs, KB cells were harvested in log phase, diluted and 100 cells were plated into 60 mm dishes in folate-free RPMI1640 medium supplemented with 2 nM LCV, 10% dialyzed fetal bovine serum, penicillin-streptomycin, and 2 mM glutamine, in the presence of antifolate drugs. The dishes were incubated at 37°C with 5% CO2 for 10 days. At the end of the incubations, the dishes were rinsed with Dulbecco’s phosphate-buffered saline (DPBS), 5% trichloroacetic acid, borate buffer (10 mM, pH 8.8), followed by 30 min incubation in 1% methylene blue in the borate buffer. The dishes were rinsed with the borate buffer and colonies were enumerated for calculating percent colony-forming efficiency normalized to control.
To test the reversibility of colony-forming inhibition, KB cells were cultured in the presence or absence of 1 µM antifolate compounds for two days before rinsing with saline, trypsining and re-inoculating into 60 mm dishes at low and high density (200 and 400, or 2000 and 4000, cells per dish for cells cultured in absence, or presence of antifolate, respectively). The dishes were incubated for 14 days, and colonies were counted for calculating percent colony-forming efficiency.
Deng Y., Wang Y., Cherian C., Hou Z., Buck S.A., Matherly L.H, & Gangjee A. (2008). Synthesis and Discovery of High Affinity Folate Receptor-Specific Glycinamide Ribonucleotide Formyltransferase Inhibitors With Antitumor Activity. Journal of medicinal chemistry, 51(16), 5052-5063.
Publication 2008
4
Defining Nucleic Acid Base Reference System
Cited 20 times
The choice of base reference system is that established at the Tsukuba meeting (13 (link)). See the work of Lu and Olson (25 (link)) for a full discussion of the influence of such a choice. The graphical position of this reference system with respect to standard purines and pyrimidines can be found in the Tsukuba reference. In order to avoid having to give the reference system in Cartesian coordinates for each standard base, we calculate it using chosen base atoms. These are C1', N1(Y)/N9(R) and C2(Y)/C4(R) in standard bases (where Y is a pyrimidine and R is a purine). Users can change these atoms to deal with non-standard cases. For example to deal with the RNA base pseudouridine which is linked to the phosphodiester backbone through C5, the equivalent atoms would be C1', C5 and C4. For completeness, we provide our construction method: this involves the atoms forming the glycosidic bond between each base and the sugar-phosphate backbone, N1–C1' for pyrimidines and N9–C1' for purines and the normal to the mean plane of the base (termed bN below). The direction of the normal is given by the cross product (N1–C1') × (N1–C2) for pyrimidines and (N9–C1') × (N9–C4) for purines. The base reference point (termed bR below) is obtained by rotating a vector of length d (initially aligned with the N–C1' direction) clockwise by an angle τ1 around the normal vector passing through the N atom. The next vector of the reference system, pointing towards the phosphodiester backbone joined to the base (termed bL below) is obtained by a similar rotation, but using a unit vector and the angle τ2. The last vector of the reference system, pointing into the major groove, bD, is obtained from the cross product bL × bN. For the Tsukuba convention, τ1 = 141.47°, τ2 = −54.41° and d = 4.702 Å. The former Curves program used values of 132.19°, −54.51° and 4.503 Å, respectively. The major impact of this change is a movement of the base reference point towards the major groove, which means that Xdisp values (measuring the displacement of bases or base pairs along the pseudodyad with respect to the helical axis) become more positive by 0.77 Å with the new reference system. There is also a change in slide, which is more positive by 0.47 Å with the new reference. For comparisons with earlier results, Curves+ allows the user to optionally select the old reference system.
Since low resolution structures, and also snapshots from MD trajectories, may contain deformed bases, it is advisable to start by least-squares fitting (26 (link)) a standard base geometry to the atoms in the input structure before defining the base reference system. Curves+ provides the standard geometries for a number of DNA and RNA bases in a data file (standard_b.lib) that can be modified and extended by the user. Only ring atoms (plus the bound C1') need to be defined in each case. Using this data, Curves+ will automatically perform least-squares fits to the input data, but this fitting can be prevented by the user if desired.
Lavery R., Moakher M., Maddocks J.H., Petkeviciute D, & Zakrzewska K. (2009). Conformational analysis of nucleic acids revisited: Curves+. Nucleic Acids Research, 37(17), 5917-5929.
Publication 2009
5
Metabolite Identification and Pathway Analysis
Cited 17 times
Metabolites were tentatively identified using Mass Hunter Qualitative Analysis software (Agilent Technologies) and an in-house database comprising data from the Human Metabolome Database (HMDB), Lipid Maps, and Metlin as previously described65 (link). The software annotates compounds based on isotope ratios, accurate mass, chemical formulae, and scores. Elements for molecular formula generation were C, H, N, O, S, and P. A 10 ppm mass error cut-off was used with a neutral mass range up to 1700 Da and positive ions selected as H+, Na+, K+, and NH4+. Database identifications were limited to the 10 best matches based on score, charge state was limited to a maximum of two. All identifications are Metabolomics Standard Initiative level 2 based on the proposed minimum reporting standards16 (link). If no annotation through database searches was possible, molecular formulae were generated. If no formula could be generated, compounds were represented by a compound number and their retention time. Compounds represented by a formula or compound number and retention time are referred to as “unannotated” compounds. Only annotated compounds were subjected to more specific analyses; changes in unannotated compounds are only discussed for completeness and to reflect patterns of changes. MPP was used for summarization and visualization of data. Annotated compounds and their normalized abundance values were exported to Excel 2010 (Microsoft Corporation, Redmond, WA) for visualization and organization. For organization of data in Venn diagrams, a freely available online tool provided by the University of Gent, Belgium, was used66 .
Results are discussed in regard to changes in specific metabolites and metabolite groups as well as pathways assigned by MetaboAnalyst software. Annotated compounds were assigned to one of the 19 following groups: (1) Bile acids and bile acid metabolism intermediates, (2) Carbohydrates and sugars, (3) Carnitines, (4) Ceramides, glucosylceramides, and ceramide phosphoinositols, (5) Cholesterol, cholesterol esters, and intermediates, (6) Diacylglycerols, (7) Gangliosides, (8) Nucleosides, nucleotides, purine, and pyrimidine metabolism (9) Organic acids and derivatives, (10) Other, (11) Peptides, (12) Phosphatidic acids, (13) Phosphatidylcholines, (14) Phosphatidylethanolamines and phosphatidylceramides, (15) Phosphatidylinositols, (16) Phosphatidylserines, (17) Sphingomyelins, (18) Triacylglycerols, or (19) Vitamin D and derivatives. Categories were assigned dependent on HMDB super classes, classes, and subclasses or manually in case no HMDB entry was available (e.g. vitamin D metabolites). Certain categories were specified further dependent on the nature of the metabolites to enhance conciseness of designations, e.g. carnitines and phosphatidylcholines. Metabolites listed under “Other” (10) belong to the following categories with less than 3 compounds per category: amines (Palmitoleoyl-EA), aldehydes (4-aminobutyraldehyde), steroids and steroid derivates (12alpha-methylpregna-4,9(11)-diene-3,20-dione, tetrahydrodeoxycorticosterone), monoglycerols (MG(18:0e/0:0/0:0), MG(20:2(11Z,14Z)/0:0/0:0)), glycerophosphoglycerols (PG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z))), metabolites of sphingosine (N,N-dimethylsphingosine), phytosterols (plant-derived, present in small amounts in humans and animals; 22:0-Glc-stigmasterol, 22:2-Glc-stigmasterol), quaternary ammonium salts/alkaloids and derivatives (Neurine), phosphatidylglycerolphosphate (PGP(18:1(11Z)/20:3(8Z,11Z,14Z)), PGP(18:1(11Z)/22:5(7Z,10Z,13Z,16Z,19Z))), intermediates in fatty acid metabolism (2E-tetradecenoyl-CoA), fatty amides/N-acyl amides ((R)-(16,16-dimethyldocosa-cis-5,8,11,14-tetraenoyl)-1′-hydroxy-2′-propylamine). Pathway enrichment analysis was conducted based on KEGG identifiers using MetaboAnalyst19 (link). For pathway coverage compounds with KEGG ID C00107 (Dipeptide) were marked as C00012 (Peptide). Pathway analysis showed significance for selected pathways only; discussed are significantly changed pathways as well as pathways with possible relation to epileptogenesis despite their p-values > 0.05 (not significantly changed).
Heischmann S., Quinn K., Cruickshank-Quinn C., Liang L.P., Reisdorph R., Reisdorph N, & Patel M. (2016). Exploratory Metabolomics Profiling in the Kainic Acid Rat Model Reveals Depletion of 25-Hydroxyvitamin D3 during Epileptogenesis. Scientific Reports, 6, 31424.
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Publication 2016

Most recents protocols related to «Purine»

1
Determination of Purine Content in Meat and Water
At present, most researchers generally use high-performance liquid chromatography (HPLC) to detect purines and typically use strong acids to release purines for further detection [22 (link)]. In this experiment, the purine content in meat and water was determined by reversed-phase high-performance liquid chromatography (HPLC), according to the method of Kaneko [4 (link)] with some modifications. The standard solutions of hypoxanthine, adenine, xanthine, and guanine were prepared, respectively. Hypoxanthine and adenine were mainly detected. The sample was placed in a tube, 10% perchloric acid (PCA) was added, and it was then placed in boiling water for 60 min followed by ice-cooling. The pH of the resultant mixture was adjusted to 12.4 ± 0.1 with a potassium hydroxide solution. All the mixed liquid samples were then filtered using a 0.22 μm filter for further HPLC analysis.
A Waters Alliance 2695 liquid chromatography system (Waters, Milford, MA, USA), including a diode array detector (DAD) was used. The column was Waters AtlantisdC18 (4.6 mm × 250.0 mm × 5.0 µm) and analyzed at 25 °C. The mobile phase was 3% acetonitrile at a constant flow rate of 1.0 mL/min, the injection volume was 10 µL, and the UV absorbance was 254 nm. Each sample was detected in triplicate. The hypoxanthine and adenine content of samples were calculated by the standard curve and the purine removal rate was calculated according to the following formula: Purine removal rate=WrWt+Wr×100%
where Wt is the hypoxanthine and adenine content of meat (mg/100 g), and Wr is the purine content of water (mg/100 g).
Yuan J., Yang C., Cao J, & Zhang L. (2024). Effects of Low Temperature–Ultrasound–Papain (LTUP) Combined Treatments on Purine Removal from Pork Loin and Its Influence on Meat Quality and Nutritional Value. Foods, 13(8), 1215.
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Publication 2024
2
Estimating Microbial Nitrogen Supply
Microbial N supply to the small intestine was estimated by the purine derivative excretion technique [12 (link)]. Daily urinary output was collected in about 50 mL of 1 M H2SO4 to prevent ammonia-N loss (final urine pH < 3), and every 24 h, urine collection was diluted with tap water (5:1) (to prevent precipitation of uric acid during storage), filtered through glass wool and sampled. Urine samples were stored at –20 °C before analysis of purine derivatives and total N.
The amount of microbial purines absorbed was estimated from equation below [12 (link)]. where Y is the total (mmol/d) urinary excretion of purine derivatives; X is the exogenous absorbed purines (mmol/d); 0.84 is the proportion of purine derivatives excreted in the urine; 0.15 is the endogenous purine derivative excretion (mmol/d); W0.75 is the metabolic BW and 0.25 is the rate constant for the replacement of de novo synthesis of endogenous purines by exogenous purines. Daily supply of microbial N to the small intestine was estimated following the model described by Chen et al. [13 (link)], namely:
Microbial N supply (g/d) = 70X/(0.83 × 0.116 × 1000) = 0.727X, where 70 is the N content of purines (mg/mmol), 0.83 is the digestibility of microbial purines and 0.116 is the proportion of microbial N as purine N.
Valdivia-Salgado V., Flores-Santiago E.D., Ramírez-Avilés L., Segura-Correa J.C., Calzada-Marín J.M, & Ku-Vera J.C. (2024). Effect of Brosimum alicastrum Foliage on Intake, Kinetics of Fermentation and Passage and Microbial N Supply in Sheep Fed Megathyrsus maximus Hay. Animals : an Open Access Journal from MDPI, 14(8), 1144.
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Publication 2024
3
Determination of Intracellular Purines
For purine determinations, fibroblasts were detached by trypsinization, counted in a Neubauer chamber, and resuspended in 0.4 N PCA. For urine samples, 150 µl of supernatant were used and 5 µl of perchloric acid (PCA) 60% (6 N) were added to obtain a final concentration of 0.2 N PCA. The samples were kept on ice for 15 min and then centrifuged at 12,000×g for 5 min at 4 °C. Pellet obtained from fibroblast extraction was stored at − 20 °C for later protein quantification and supernatant from fibroblasts or urines was neutralized with 5 M potassium carbonate (Sigma-Aldrich, 209619) and filtered through PVDF micro spin filters (Thermo Scientific, F2517-5) via centrifugation at 10,000×g for 10 min at 4 °C. The supernatants filtered were kept at − 80 °C for HPLC determination.
HPLC coupled to an UV detector was used for purine determinations. Analytes were separated using reverse-phase ion-pair chromatography on an Atlantis T3 column (Waters, 186003729). The optimized method for nucleotides separation and quantification consist on this sequence of stepped gradient of buffer A (10 mM of ammonium acetate (Sigma-Aldrich, A1542) and 2 mM of tetrabutylammonium phosphate monobasic solution (Sigma-Aldrich, 268100) pH5) and buffer B (10 mM of ammonium phosphate (Sigma-Aldrich, 09709), 2 mM of tetrabutylammonium phosphate, and 25% of acetonitrile (J.T. Baker, 76045) pH7) as follows: 100% of buffer A for 10 min, a linear gradient of buffer B up to 75% over 10 min, 9 min at 75% buffer B, a linear gradient to 100% buffer B over 3 min, 8 min at 100% buffer B, a linear gradient to 100% A in 1 min and finally 10 min maintained at 100% buffer A. The identification of purines was made by comparing their retention times to known standards and were quantified at 254 nm in a deuterium lamp. Sample analysis was performed by EZ Chrom Elite/ELITE UV–VIS software.
To determine intracellular concentration of nucleotides, skin fibroblasts single cell volume (2 × 10–6 μl) was calculated empirically.
Escudero-Ferruz P., Ontiveros N., Cano-Estrada C., Sutcliffe D.J., Jinnah H.A., Torres R.J, & López J.M. (2024). A new physiological medium uncovers biochemical and cellular alterations in Lesch-Nyhan disease fibroblasts. Molecular Medicine, 30, 3.
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Publication 2024
4
Purine Metabolism Score for LUAD Prognosis

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Publication 2024
5
Optimizing Purine Removal in Pork Loin
Through the response surface experiments described above, the process conditions that maximized the purine removal rate of pork loin were obtained. The processing was evaluated at following. Pork loin was immersed in deionized water and no ultrasonic treatment was applied as a blank control group (WT); the simulation process of the household blanching group (JT) was as follows: a beaker containing pork loin and ultrapure water was put into a water bath and heated until the water boiled and the whole process lasted for 10 min. Purine removal rate, texture profile, cooking loss, free amino acid dissolution, soluble peptide dissolution, and in vitro protein digestibility were evaluated as indicators. The optimal response surface treatment group (CT) was compared with the home blanching treatment (HT) and the blank control treatment (WT).
Yuan J., Yang C., Cao J, & Zhang L. (2024). Effects of Low Temperature–Ultrasound–Papain (LTUP) Combined Treatments on Purine Removal from Pork Loin and Its Influence on Meat Quality and Nutritional Value. Foods, 13(8), 1215.
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Publication 2024

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6
Lipofectamine 2000 by Thermo Fisher Scientific
Sourced in United States, China, Germany, United Kingdom, Canada, Japan, France, Italy, Switzerland, Australia, Spain, Belgium, Denmark, Singapore, India, Netherlands, Sweden, New Zealand, Portugal, Poland, Israel, Lithuania, Hong Kong, Argentina, Ireland, Austria, Czechia, Cameroon, Taiwan, Province of China, Morocco
Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
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Prism 8 by GraphPad
Sourced in United States, Austria, Canada, Belgium, United Kingdom, Germany, China, Japan, Poland, Israel, Switzerland, New Zealand, Australia, Spain, Sweden
Prism 8 is a data analysis and graphing software developed by GraphPad. It is designed for researchers to visualize, analyze, and present scientific data.
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Prism 6 by GraphPad
Sourced in United States, United Kingdom, Canada, China, Germany, Japan, Belgium, Israel, Lao People's Democratic Republic, Italy, France, Austria, Sweden, Switzerland, Ireland, Finland
Prism 6 is a data analysis and graphing software developed by GraphPad. It provides tools for curve fitting, statistical analysis, and data visualization.
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Rneasy mini kit by Qiagen
Sourced in Germany, United States, United Kingdom, Netherlands, Spain, Japan, Canada, France, China, Australia, Italy, Switzerland, Sweden, Belgium, Denmark, India, Jamaica, Singapore, Poland, Lithuania, Brazil, New Zealand, Austria, Hong Kong, Portugal, Romania, Cameroon, Norway
The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
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Penicillin streptomycin by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, China, Canada, France, Japan, Australia, Switzerland, Israel, Italy, Belgium, Austria, Spain, Gabon, Ireland, New Zealand, Sweden, Netherlands, Denmark, Brazil, Macao, India, Singapore, Poland, Argentina, Cameroon, Uruguay, Morocco, Panama, Colombia, Holy See (Vatican City State), Hungary, Norway, Portugal, Mexico, Thailand, Palestine, State of, Finland, Moldova, Republic of, Jamaica, Czechia
Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.

More about "Purine"

Purines are a class of nitrogen-containing heterocyclic compounds that play a crucial role in various biological processes.
These organic compounds serve as the fundamental structural components of nucleic acids like DNA and RNA, which are essential for storing and transmitting genetic information.
Purines are involved in energy metabolism, cell signaling, and gene expression, making them pivotal in maintaining cellular functions.
Purine metabolism can occur through de novo synthesis or salvage pathways, where purines are obtained from dietary sources.
Disorders related to purine metabolism, such as gout and Lesch-Nyhan syndrome, have significant clinical implications and underscore the importance of understanding the regulation and dynamics of purine metabolism.
Researchers can leverage tools like PubCompare.ai to identify the most accurate and reproducible purine research protocols from the literature, preprints, and patents.
This can help optimize their studies and ensure the reliability of their findings.
Techniques like the EnzChek Phosphate Assay Kit, cell culture media like FBS and DMEM, RNA extraction with TRIzol reagent, and transfection using Lipofectamine 2000 are commonly employed in purine-related research.
Data analysis and visualization tools, such as Prism 8 and Prism 6, can be utilized to interpret the results of purine studies.
Additionally, the RNeasy Mini Kit can be used for RNA purification, and Penicillin/streptomycin can be added to cell culture media to prevent bacterial contamination.
By leveraging the insights and resources available, researchers can optimize their purine studies, leading to a deeper understanding of these essential compounds and their role in human health and disease.