To complement incomplete annotations in the background database, a homology-ontology annotation retrieved by BLAST should be accompanied by an accurate subcellular localization prediction for each homologous sequence. CELLO has been shown to be helpful for the prediction of subcellular localizations of the proteins found in a proteomic data. [28] (link) Using multiple, integrated machine-learned classifiers, CELLO predicts which of four subcellular localizations in archaea and in Gram-positive bacteria, five subcellular localizations in Gram-negative bacteria, and twelve subcellular localizations in eukaryotes that the targeted protein might be found in, with the four archaeal and Gram-positive bacterial localizations being the extracellular space, the cell wall, the cytoplasmic membrane, and the cytoplasm; the five Gram-positive bacterial localizations being the extracellular space, the outer membrane, the periplasmic and cytoplasmic (inner) membranes, and the cytoplasm; and the 12 eukaryotic localizations being chloroplasts, the cytoplasm, the cytoskeleton, the endoplasmic reticulum, the extracellular/secretory space, the Golgi, lysosomes, mitochondria, the nucleus, peroxisomes, the plasma membrane, and vacuoles. Due to subcellular data increased exponentially over the years, CELLO has been trained on latest models and denoted as update version wrapping in CELLO2GO. And the resultant datasets used for prediction and evaluation is from PSORTb3.0 [23] (link).
>
Anatomy
>
Cell Component
>
Peroxisome
Peroxisome
Peroxisomes are subcellular organelles found in most eukaryotic cells.
They play a crucial role in a variety of metabolic processes, including lipid metabolism, oxidation of fatty acids, and the detoxification of hydrogen peroxide.
Peroxisomes contain enzymes that are involved in these critical functions, making them an important area of study for researchers investigating cellular metabolism and function.
This MeSH term provides a concise overview of the key features and roles of peroxisomes, helping researchers optimize their studies in this important field of biology.
They play a crucial role in a variety of metabolic processes, including lipid metabolism, oxidation of fatty acids, and the detoxification of hydrogen peroxide.
Peroxisomes contain enzymes that are involved in these critical functions, making them an important area of study for researchers investigating cellular metabolism and function.
This MeSH term provides a concise overview of the key features and roles of peroxisomes, helping researchers optimize their studies in this important field of biology.
Most cited protocols related to «Peroxisome»
Archaea
Cell Nucleus
Cell Wall
Chloroplasts
Cytoplasm
Cytoskeleton
Endoplasmic Reticulum
Eukaryota
Eukaryotic Cells
Extracellular Space
Golgi Apparatus
Gram-Positive Bacteria
Gram Negative Bacteria
Homologous Sequences
Lysosomes
Mitochondria
Periplasm
Peroxisome
Plasma Membrane
Proteins
secretion
Tissue, Membrane
Vacuole
The PPI data was collected from Saccharomyces cerevisiae core subset of database of interacting proteins (DIP) (27 (link)), version DIP_20070219. The reliability of this core subset has been tested by two methods, expression profile reliability (EPR) and paralogous verification method (PVM) (28 (link)). At the time of doing the experiments, the core subset contained 5966 interaction pairs. The protein pairs that contained a protein with <50 amino acids were removed and the remaining 5943 protein pairs comprised the final positive data set. All proteins in the data set were aligned using the multiple sequence alignment tool, cd-hit program (29 (link)). The aligned result shows that among the 5943 protein pairs, the overwhelming majority of them (5594 PPIs) have <40% pairwise sequence identity to one another. Although there are only 349 pairs with ≥40% identity in the training data set, the classifier will possibly be biased to these homologous sequence pairs.
Since the non-interacting pairs were not readily available, three strategies for constructing negative data set were used in order to compare the effects of different training data sets on the performance of the method. The first strategy has been described by Shen and colleagues (26 (link)) in detail. The non-interacting pairs were generated by randomly pairing proteins that appeared in the positive data set. Here the negative data set based on this method is called Prcp. The second is based on such an assumption that proteins occupying different subcellular localizations do not interact. The subcellular localization information of the proteins in the positive data set was extracted from Swiss-Prot (http://www.expasy.org/sprot/ ). The proteins without subcellular localization information and those denoted as ‘putative’, ‘hypothetical’ were excluded. The remaining proteins were grouped into eight subsets based on the eight main types of localization—cytoplasm, nucleus, mitochondrion, endoplasmic reticulum, golgi apparatus, peroxisome, vacuole and cytoplasm&nucleus. Each subset contained 10 proteins at least. The non-interacting pairs were generated by pairing proteins from one subset with proteins from the other subset. It must be pointed out that proteins from cytoplasm subset and nucleus subset cannot be paired with those from cytoplasm&nucleus subset. Here the negative data set based on subcellular localization information is called Psub. The two strategies must meet three requirements: (i) the non-interacting pairs cannot appear in the whole DIP yeast interacting pairs, (ii) the number of negative pairs is equal to that of positive pairs and (iii) the contribution of proteins in negative set should be as harmonious as possible (24 (link),26 (link)).
As a comparison, the third strategy was used for creating non-interacting pairs composed of artificial protein sequences. It has been demonstrated that if a sequence of one interacting pair is shuffled, then the two proteins can be deemed not to interact with each other (30 ). Thus, the negative data set was prepared by shuffling the sequences of right-side interacting pairs with k-let (k = 1,2,3) counts using the Shufflet program (31 (link)).
Since the non-interacting pairs were not readily available, three strategies for constructing negative data set were used in order to compare the effects of different training data sets on the performance of the method. The first strategy has been described by Shen and colleagues (26 (link)) in detail. The non-interacting pairs were generated by randomly pairing proteins that appeared in the positive data set. Here the negative data set based on this method is called Prcp. The second is based on such an assumption that proteins occupying different subcellular localizations do not interact. The subcellular localization information of the proteins in the positive data set was extracted from Swiss-Prot (
As a comparison, the third strategy was used for creating non-interacting pairs composed of artificial protein sequences. It has been demonstrated that if a sequence of one interacting pair is shuffled, then the two proteins can be deemed not to interact with each other (30 ). Thus, the negative data set was prepared by shuffling the sequences of right-side interacting pairs with k-let (k = 1,2,3) counts using the Shufflet program (31 (link)).
Amino Acids
Amino Acid Sequence
Cell Nucleus
Cytoplasm
Endoplasmic Reticulum
Golgi Apparatus
Homologous Sequences
Mitochondria
Peroxisome
Prepulse Inhibition
Proteins
Saccharomyces cerevisiae
Saccharomyces cerevisiae Proteins
Sequence Alignment
SET protein, human
Staphylococcal Protein A
Vacuole
We downloaded the set of crop plant proteins (barley, wheat, rice, maize) from the cropPal database24 (link) and chose those that have a subcellular localization of either ‘plastid’ (100 proteins), ‘mitochondrion’ (61 proteins), ‘nucleus’ (165 proteins), ‘peroxisome’ (11 proteins), ‘vacuole’ (18 proteins), ‘plasma membrane’ (84 proteins, ‘endoplasmic reticulum’ (43 proteins) and ‘cytosol’ (48 proteins) determined by GFP-tagging. We only kept those sequences that started with an ‘M’. For the UniProt test set, we downloaded plant proteins (taxonomy:“Viridiplantae [33090]”) that were entered after our training sets were compiled (created:[20160301 TO 20160902]) for several compartments supported by experimental evidence (“Nucleus [SL-0191]”; “Mitochondrion [SL-0173]”, “Chloroplast [SL-0049]”, “Peroxisome [SL-0204]”, “Vacuole”, “Secreted”, “Endoplasmic reticulum”, “Cytoplasm”). We manually removed those entries that localize to multiple compartments, except for the category nucleus for which we also allowed an additional cytoplasmic localization. All plant and effector test sets are available at http://localizer.csiro.au/data.html .
When evaluating performance, the number of true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FNs) were used. Sensitivity is defined as the proportion of positives that are correctly identified whereas specificity is the proportion of negatives that are correctly identified. Precision (positive predictive value, PPV, ) is a measure which captures the proportion of positive predictions that are true. Both accuracy and the Matthews Correlation Coefficient can be used to evaluate the overall performance of a method. The MCC ranges from −1 to 1, with scores of −1 corresponding to predictions in total disagreement with the observations, 0.5 to random predictions and 1 to predictions in perfect agreement with the observations. For our classifier, we count LOCALIZER predictions that are ‘chloroplast’, ‘chloroplast and possible mitochondrial’, ‘chloroplast and nucleus’ and ‘chloroplast & possible mitochondrial and nucleus’ as chloroplast predictions (same strategy for mitochondrial predictions). A protein that carries a predicted transit peptide with an additional predicted NLS might have experimental evidence only for one of those locations due to the technical hurdles of recognizing dual targeting20 (link) and should thus not necessarily be counted as a false positive prediction. A protein is counted as a nucleus prediction only if it has the category ‘nucleus’ to avoid assigning a protein to multiple predictions in the evaluation. Many plant subcellular localization methods have been published, however only a small number are available as standalone software or have the option of submitting large batch sequence files to a web server. This makes it prohibitive for researchers to use them routinely for data analysis and thus, our benchmark only includes methods that can be locally installed with ease or have a web server with a batch file submission option (Supplementary Table S2 ).
When evaluating performance, the number of true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FNs) were used. Sensitivity is defined as the proportion of positives that are correctly identified whereas specificity is the proportion of negatives that are correctly identified. Precision (positive predictive value, PPV, ) is a measure which captures the proportion of positive predictions that are true. Both accuracy and the Matthews Correlation Coefficient can be used to evaluate the overall performance of a method. The MCC ranges from −1 to 1, with scores of −1 corresponding to predictions in total disagreement with the observations, 0.5 to random predictions and 1 to predictions in perfect agreement with the observations. For our classifier, we count LOCALIZER predictions that are ‘chloroplast’, ‘chloroplast and possible mitochondrial’, ‘chloroplast and nucleus’ and ‘chloroplast & possible mitochondrial and nucleus’ as chloroplast predictions (same strategy for mitochondrial predictions). A protein that carries a predicted transit peptide with an additional predicted NLS might have experimental evidence only for one of those locations due to the technical hurdles of recognizing dual targeting20 (link) and should thus not necessarily be counted as a false positive prediction. A protein is counted as a nucleus prediction only if it has the category ‘nucleus’ to avoid assigning a protein to multiple predictions in the evaluation. Many plant subcellular localization methods have been published, however only a small number are available as standalone software or have the option of submitting large batch sequence files to a web server. This makes it prohibitive for researchers to use them routinely for data analysis and thus, our benchmark only includes methods that can be locally installed with ease or have a web server with a batch file submission option (
Cell Nucleus
Chloroplasts
Crop, Avian
Cytoplasm
Cytosol
Endoplasmic Reticulum
Green Plants
Hordeum vulgare
Hypersensitivity
Maize
Mitochondria
Nuclear Localization Signals
Peroxisome
Plant Proteins
Plants
Plasma Membrane
Plastids
Proteins
Rice
Staphylococcal Protein A
Triticum aestivum
Vacuole
Preparation of samples for transmission electron microscopy and immunogold labeling of glutathione was done with ultrathin sections on nickel grids as described in Zechmann et al. 2006 (link), 2008 (link). Small samples of the youngest fully developed leaves (about 1.5 mm2) and root tips from at least three different plants were fixed in 2.5% paraformaldehyde/0.5% glutardialdehyde in 0.06 M phosphate buffer (pH 7.2) for 90 min at room temperature (RT). Microwave fixation was performed in the same fixation solution as described above in a Polar Patent PP1000 microwave oven. Samples were fixed for two times 25 s at 300 W microwave irradiation. In between these steps, samples were gently cooled off to about 20 °C which took about 3 min. The maximum temperature of the solution, which was constantly aerated to reduce the risk of an unevenly heated solution, during fixation in the microwave oven was 30 °C. Samples fixed conventionally and with the help of microwave irradiation were then rinsed in buffer and dehydrated in increasing concentrations of acetone (50%, 70%, and 90%) at RT for 20 min at each step. Subsequently, specimens were gradually infiltrated with increasing concentrations of LR White resin (30%, 60%, and 100%; London Resin Company Ltd., Berkshire, UK) mixed with acetone (90%) and finally embedded in LR White resin and polymerized at 50 °C for 48 h in small plastic containers.
Ultrathin sections (80 nm) of the samples were blocked with 2% bovine serum albumine (BSA) in phosphate buffered saline (PBS, pH 7.2) and then treated with the primary antibody (anti-glutathione rabbit polyclonal IgG; Millipore Corp., Billerica, MA, USA) diluted 1:50 in PBS containing 1% goat serum for 2 h at RT. After a short rinse in PBS, samples were incubated with a 10 nm gold-conjugated secondary antibody (goat anti-rabbit IgG, British BioCell International, Cardiff,www.british-biocell.co.uk ) diluted 1:50 in PBS for 90 min at RT. After a short wash in PBS and distilled water, labeled grids were either immediately observed in a Philips CM10 transmission electron microscope or post-stained with uranyl-acetate (15 s).
The specificity of the immunogold-labeling procedure was tested by several negative controls. Negative controls were treated either with: (1) pre-immune serum instead of the primary antibody, (2) gold-conjugated secondary antibody (goat anti rabbit IgG) without the primary antibody, (3) non-specific secondary antibody (goat anti mouse IgG), and (4) primary antibodies pre-adsorbed with an excess of glutathione for 2 h at RT prior to labeling of the sections. For the latter, a solution containing 10 mM of glutathione was incubated with 0.5% glutardialdehyde for 1 h. The excess of glutardialdehyde was then saturated by incubation for 30 min in a solution of 1% (w/v) BSA. The resulting solution was used to saturate the glutathione-antibody for 2 h prior to its use.
Micrographs of randomly photographed immunogold-labeled sections were digitized and gold particles were counted automatically using the software package Cell D with the particle analysis tool (Olympus, Life and Material Science Europa GmbH, Hamburg, Germany). For statistical evaluation, at least four different samples were examined. A minimum of 20 (peroxisomes and vacuoles) to 60 (other cell structures) sectioned cell structures of at least 15 different cells were analyzed for gold particle density per sample. The obtained data were statistically evaluated using Statistica (Stat-Soft, USA, 2002).
Ultrathin sections (80 nm) of the samples were blocked with 2% bovine serum albumine (BSA) in phosphate buffered saline (PBS, pH 7.2) and then treated with the primary antibody (anti-glutathione rabbit polyclonal IgG; Millipore Corp., Billerica, MA, USA) diluted 1:50 in PBS containing 1% goat serum for 2 h at RT. After a short rinse in PBS, samples were incubated with a 10 nm gold-conjugated secondary antibody (goat anti-rabbit IgG, British BioCell International, Cardiff,
The specificity of the immunogold-labeling procedure was tested by several negative controls. Negative controls were treated either with: (1) pre-immune serum instead of the primary antibody, (2) gold-conjugated secondary antibody (goat anti rabbit IgG) without the primary antibody, (3) non-specific secondary antibody (goat anti mouse IgG), and (4) primary antibodies pre-adsorbed with an excess of glutathione for 2 h at RT prior to labeling of the sections. For the latter, a solution containing 10 mM of glutathione was incubated with 0.5% glutardialdehyde for 1 h. The excess of glutardialdehyde was then saturated by incubation for 30 min in a solution of 1% (w/v) BSA. The resulting solution was used to saturate the glutathione-antibody for 2 h prior to its use.
Micrographs of randomly photographed immunogold-labeled sections were digitized and gold particles were counted automatically using the software package Cell D with the particle analysis tool (Olympus, Life and Material Science Europa GmbH, Hamburg, Germany). For statistical evaluation, at least four different samples were examined. A minimum of 20 (peroxisomes and vacuoles) to 60 (other cell structures) sectioned cell structures of at least 15 different cells were analyzed for gold particle density per sample. The obtained data were statistically evaluated using Statistica (Stat-Soft, USA, 2002).
Acetone
anti-IgG
Antibodies
Cells
Cellular Structures
Glutaral
Glutathione
Goat
Gold
Immune Sera
Immunoglobulins
LR white
Microwaves
Mus
Nickel
paraform
Peroxisome
Phosphates
Plant Roots
Plants
Rabbits
Resins, Plant
Root Tip
Saline Solution
Serum
Serum Albumin, Bovine
Somatostatin-Secreting Cells
Transmission Electron Microscopy
uranyl acetate
Vacuole
We analysed the distribution of individual plasma phospholipid fatty acids and expressed them as mol%. We estimated country-specific hazard ratios (HRs) and 95% CIs for associations per one standard deviation (SD, calculated in the overall subcohort) of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression,22 (link) which allows for over-representation of cases in a case-cohort design, and pooled our findings using random-effects meta-analysis. Heterogeneity between countries was expressed as I2 values, and we used meta-regression to assess whether the heterogeneity was explained by age, BMI, or sex. We adjusted for potential confounders as follows: model 1 included age (as the underlying timescale), study centre, sex, physical activity index, smoking status, and education level. Model 2 included these parameters plus total energy intake, alcohol intake, and BMI. After recording patterns of association for the nine individual SFAs, we made a post-hoc decision to create three additional exposures based on groupings of SFAs that fit with potential biological action: group 1 (sum of the even-chain SFAs 14:0, 16:0, and 18:0) since these represent both de-novo lipogenesis and dietary intake;9 (link), 11 (link), 14 (link) group 2 (sum of the odd-chain SFAs 15:0 and 17:0) as potential sources of dairy fat;7 (link), 8 (link) and group 3 (sum of the long- or very-long-chain SFAs 20:0, 22:0, 23:0, and 24:0) since these are under-researched SFAs that might undergo distinct peroxisomal fatty acid metabolism rather than mitochondrial metabolism.25 (link) In an additional analysis, we re-grouped 23:0 into group 2 because is it also an odd-chain SFA, and removed it from group 3. Since stearoyl-CoA desaturase-1 catalyses the desaturation of 16:0 to 16:1(n-7) and of 18:0 to 18:1(n-9) through the de-novo lipogenesis pathway, we also estimated stearoyl-CoA desaturase-1 activity using product-to-precursor ratios (ratio of 16:1[n-7] to 16:0 and of 18:1[n-9] to 18:0)6 (link), 15 (link), 26 (link) and assessed each ratio for its association with incident type 2 diabetes.
In a sensitivity analysis based on model 2, we analysed the effects of adjustment for dietary variables (intakes of meat, fruit and vegetables, soft drinks, total dairy products, and carbohydrates [g/day]). We also did an analysis that further accounted for baseline HbA1C value as a covariate. To minimise the possibility of reverse causality, we also excluded 2348 people with HbA1C of 6·5% or higher at baseline or those confirmed as cases of type 2 diabetes (n=1048) within the first 2 years after baseline. Further sensitivity analyses on model 2 included adjustment for: additional potential confounders (dietary carbohydrates intake [g/day] and waist circumference [cm]); comorbidity (prevalent myocardial infarction, stroke, or cancer); and the exclusion of 723 people who were probably dietary misreporters (those with a ratio of energy intake to energy requirement in the bottom or top 1% of the distribution). We also studied the association of SFA quintiles with type 2 diabetes incidence in models 1 and 2.
We postulated that circulating SFAs would be derived from diet and through de-novo lipogenesis, and associated with carbohydrate and alcohol consumption.9 (link), 11 (link), 12 (link) Within the subcohort, we studied associations between each circulating SFA and food intakes, using Pearson correlation coefficients and 95% CI adjusted for age, sex, BMI, and energy intake. We used Stata, version 13.1 for all analyses.
In a sensitivity analysis based on model 2, we analysed the effects of adjustment for dietary variables (intakes of meat, fruit and vegetables, soft drinks, total dairy products, and carbohydrates [g/day]). We also did an analysis that further accounted for baseline HbA1C value as a covariate. To minimise the possibility of reverse causality, we also excluded 2348 people with HbA1C of 6·5% or higher at baseline or those confirmed as cases of type 2 diabetes (n=1048) within the first 2 years after baseline. Further sensitivity analyses on model 2 included adjustment for: additional potential confounders (dietary carbohydrates intake [g/day] and waist circumference [cm]); comorbidity (prevalent myocardial infarction, stroke, or cancer); and the exclusion of 723 people who were probably dietary misreporters (those with a ratio of energy intake to energy requirement in the bottom or top 1% of the distribution). We also studied the association of SFA quintiles with type 2 diabetes incidence in models 1 and 2.
We postulated that circulating SFAs would be derived from diet and through de-novo lipogenesis, and associated with carbohydrate and alcohol consumption.9 (link), 11 (link), 12 (link) Within the subcohort, we studied associations between each circulating SFA and food intakes, using Pearson correlation coefficients and 95% CI adjusted for age, sex, BMI, and energy intake. We used Stata, version 13.1 for all analyses.
Action Potentials
Biopharmaceuticals
Carbohydrates
Catalysis
Cerebrovascular Accident
Dairy Products
Diabetes Mellitus, Non-Insulin-Dependent
Diet
Eating
fatty acid desaturase 1, human
Fatty Acids
Fruit
Genetic Heterogeneity
Hypersensitivity
Lipogenesis
Malignant Neoplasms
Meat
Metabolism
Mitochondria
Myocardial Infarction
Peroxisome
Phospholipids
Plasma
Soft Drinks
Vegetables
Waist Circumference
Most recents protocols related to «Peroxisome»
Another subgroup of SHR-S, SHR-T, Wistar-S, and Wistar-T received, after the functional measurements, an overdose of ketamine + xylazine. Immediately after the respiratory arrest, the thorax was opened and the left ventricle cannulated for sterile saline perfusion (∼30 mL/min, Daigger pump, Vernon Hills IL United States) followed by modified Karnovsky solution (2.5% glutaraldehyde +2% paraformaldehyde in 0.1 M PBS, pH 7.3). Brain was removed and placed on a coronal brain matrix (72–5029, Harvard Apparatus) to obtain hypothalamic and brainstem slices. PVN, NTS, and RVLM nuclei were microdissected with the aid of a magnifying lens, using as anatomic markers the third ventricle and optic chiasma, the central canal and 4th ventricle, and, the nucleus ambiguous, raphe obscurus and inferior olive, respectively. The nuclei were immersed in a 2.5% glutaraldehyde solution for 2 h, washed in PBS and post-fixed in a 2% osmium tetroxide solution for 2 h at 4°C. Tissues were then stained overnight with uranyl acetate, dehydrated in 60% up to 100% ethanol series and immersed in pure resin. Semi-thin slices (400 nm, ultra-microtome Leica EMUC6) were obtained, placed in glass slides and stained with Toluidine Blue in order to select adequate areas for further processing. Ultra-thin slices (60 nm) were obtained with diamond knife, contrasted with 4% uranyl acetate and 0.4% lead acetate and disposed in 200 copper mesh screens.
Transverse sections of PVN, NTS, and RVLM capillaries of the 4 experimental groups were acquired in a transmission electron microscope (FEI Tecnai G20, 200 KV) and analyzed by a blind observer using the ImageJ software. The following parameters were analyzed in 9–11 capillaries/area/rat, 3 rats/experimental group: luminal and abluminal perimeter, lumen diameter, area of the endothelial cell, thickness of the basement membrane, pericytes’ coverage of capillaries, extension of capillary border between adjacent endothelial cells, the occurrence/extension of tight junctions, and, the counting of transcellular vesicles/capillary. To avoid the inclusion of non-transcytotic vesicles such as lysosomes, endosomes, peroxisomes, only the vesicles being formed at the luminal, and abluminal membranes were counted. Vesicle counting was expressed as number/capillary. Using the zoom to expand acquired images, the whole extension of capillaries was analyzed.
Transverse sections of PVN, NTS, and RVLM capillaries of the 4 experimental groups were acquired in a transmission electron microscope (FEI Tecnai G20, 200 KV) and analyzed by a blind observer using the ImageJ software. The following parameters were analyzed in 9–11 capillaries/area/rat, 3 rats/experimental group: luminal and abluminal perimeter, lumen diameter, area of the endothelial cell, thickness of the basement membrane, pericytes’ coverage of capillaries, extension of capillary border between adjacent endothelial cells, the occurrence/extension of tight junctions, and, the counting of transcellular vesicles/capillary. To avoid the inclusion of non-transcytotic vesicles such as lysosomes, endosomes, peroxisomes, only the vesicles being formed at the luminal, and abluminal membranes were counted. Vesicle counting was expressed as number/capillary. Using the zoom to expand acquired images, the whole extension of capillaries was analyzed.
Brain
Brain Stem
Capillaries
Cell Nucleus
Chest
Copper
Diamond
Drug Overdose
Endosomes
Endothelial Cells
Ethanol
Glutaral
Hypothalamus
Ketamine
lead acetate
Left Ventricles
Lens, Crystalline
Lysosomes
Membrane, Basement
Microtomy
Nucleus Raphe Obscurus
Olivary Nucleus
Optic Chiasms
Osmium Tetroxide
paraform
Perfusion
Pericytes
Perimetry
Peroxisome
Phenobarbital
Pulp Canals
Rattus norvegicus
Resins, Plant
Respiratory Rate
Saline Solution
Sterility, Reproductive
Tight Junctions
Tissue, Membrane
Tissues
Tolonium Chloride
Transcytosis
Transmission Electron Microscopy
uranyl acetate
Ventricles, Fourth
Ventricles, Third
Visually Impaired Persons
Xylazine
ChemBio3D (14.0.0.117) was used to convert 2D chemical structures to 3D structures and save them in MOL2 format from PubChem Compound. Target crystal structures were obtained from the RCSB Protein Data Bank. Singly one protein target, peroxisome proliferative activated receptor, gamma (PPARG, PDB ID:2VV4), was investigated. AutoDockTools 1.5.6 was used to convert receptors and ligands from their native formats to pdbqt formats. By deleting water molecules and adding hydrogen atoms, structures were optimized. Then, molecular docking study was performed utilizing Autodock Vina. All docking run options were set to default values according to the Genetic Algorithm. Using PyMoL, the docking results with the highest scores were visualized.
Gamma Rays
Hydrogen
Ligands
Peroxisome
Protein Targeting, Cellular
For subcellular localization, pBI221-GFP-TaCATs, pBI221-GFP-TaWD40-4B.1C and pBI221-GFP-TaWD40-4B.1T constructs were built, and each of them along with the plastid harboring peroxisome marker DsRed-SKL were introduced into wheat protoplasts by the PEG-mediated transfection83 (link). For BiFC, the coding sequence of TaWD40-4B.1C/T and TaCATs was introduced into the pUC-SPYCE(MR) and pUC-SPYNE(R)173 vector, respectively84 (link), and GUS-YFPC was constructed as the negative control. Corresponding constructs along with the plastid harboring peroxisome marker DsRed-SKL were co-transformed into wheat protoplasts using the same method. Then the protoplasts were incubated in dark at 25 °C for 16–24 h. The confocal images were finally captured using the ZEISS LSM 900 system.
Cloning Vectors
Open Reading Frames
Peroxisome
Plastids
Protoplasts
Triticum aestivum
The protoplasts co-expressing GFP-tagged proteins and RFP-SKL mentioned above were harvested and peroxisomes were isolated as described85 (link) with some modifications. Briefly, 5 × 106 intact peroxisomes were centrifuged at 5,000 × g for 1 min at 4 °C in grinding buffer (170 mM Tricine-KOH, 1 M sucrose, 1% [w/v] BSA, 2 mM EDTA, 5 mM DTT, 10 mM KCl, 1 mM MgCl2, and 1× protease inhibitor cocktail, pH 7.5) to obtain the crude extract. The supernatant was loaded onto Percoll density gradients prepared in TE buffer (20 mM Tricine-KOH and 1 mM EDTA, pH 7.5)85 (link), and centrifuged for 12 min at 13,000 × g followed by centrifugation for 20 min at 27,000 × g. After centrifugation, peroxisomes are located at the bottom of the gradients. Fractions were collected from the top and bottom of the centrifugation tubes and washed in 36% (w/w) sucrose in TE buffer and centrifuged for 30 min at 39,000 × g. The top fraction was labeled as the cytosol and the pellet as the crude peroxisome. Subsequently, the crude peroxisome was loaded onto a sucrose density gradient (2 mL 41% [w/w], 2 mL 44% [w/w], 2 mL 46% [w/w], 3 mL 49% [w/w],1 mL 51% [w/w], 1.5 mL 55% [w/w], and 1 mL 60% [w/w] in TE buffer) and centrifuged at 80,000 × g for 2 h. After centrifugation, a white band appeared at the interface of 55 and 51% sucrose. The white band was collected and labeled peroxisome extract. These fractions were resuspended and boiled in the same volume of 1× SDS-PAGE Sample Loading Buffer (Beyotime, P0015A) and subjected to immunoblot analysis using anti-GFP (Abclonal, AE012, 1:5000 (v/v)) and anti-RFP (Abclonal, AE020, 1:5000 (v/v)) antibodies.
Antibodies
Buffers
Centrifugation
Complex Extracts
Cytosol
Edetic Acid
Immunoblotting
Magnesium Chloride
Percoll
Peroxisome
Protease Inhibitors
Proteins
Protoplasts
SDS-PAGE
Sucrose
tricine
The L-lactate-producing K. phaffii GLp strain described by Melo et al. 2018 [19 (link)] was used as the parent strain for this work. The identification of the putative gene encoding the subunit 1 of the mitochondrial pyruvate carrier (MPC1) in the genome of K. phaffii was done in NCBI. The search revealed an ORF in chromosome 1 for the hypothetical protein under the accession number XM_002490794.1 located in the complementary strand (Figure 2 ). The deletion of MPC1 used a synthetic construct with 700 bp of the 3’ UTR from the leading strand right before the end of the gene (PAS_FragB_0028, Figure 2 ) and 700 bp of the 5′ UTR before the initiation codon of MPC1 (PAS_FragB_0030, Figure 2 ) as flanking regions to guide homologous recombination. The construct was composed of two expression cassettes: a selection marker cassette with the gene of the hygromycin phosphotransferase (Figure 2 , B. HygR) under the promoter of peroxisomal malate dehydrogenase [29 (link)]; and a cassette harboring the hemoglobin gene from β-proteobacteria Vitreoscilla stercoraria codon optimized for K. phaffii under the alcohol dehydrogenase 2 promoter [30 (link)]. The cassette was synthesized by GenScript (Piscataway, NJ, USA).
K. phaffii transformation was done according to Wu and Letchwork [31 (link)]. Transformants were selected in YPD (1% yeast extract, 2% peptone, and 2% dextrose) agar plates supplemented with 0.2 mg/mL hygromycin B (Sigma-Aldrich, St. Louis, MO, USA). Integration into the correct locus and the correct gene deletion was confirmed by PCR using primers listed inTable 1 .
K. phaffii transformation was done according to Wu and Letchwork [31 (link)]. Transformants were selected in YPD (1% yeast extract, 2% peptone, and 2% dextrose) agar plates supplemented with 0.2 mg/mL hygromycin B (Sigma-Aldrich, St. Louis, MO, USA). Integration into the correct locus and the correct gene deletion was confirmed by PCR using primers listed in
Agar
Alcohol Dehydrogenase II
Chromosomes
Codon
Codon, Initiator
Deletion Mutation
Gene Deletion
Genes
Genes, vif
Genome
Glucose
Hemoglobin
Homologous Recombination
hygromycin-B kinase
Hygromycin B
Lactate
Malate Dehydrogenase
Mitochondria
Oligonucleotide Primers
Parent
Peptones
Peroxisome
Proteins
Protein Subunits
Proteobacteria
Pyruvate Carrier
Saccharomyces cerevisiae
Strains
Vitreoscilla stercoraria
Top products related to «Peroxisome»
The Peroxisome Isolation Kit is a laboratory tool designed to facilitate the isolation and purification of peroxisomes from various biological samples. Peroxisomes are organelles found in eukaryotic cells and play a crucial role in various metabolic processes. The kit provides a reliable and efficient method for the extraction and separation of peroxisomes from cell homogenates or tissue samples.
Sourced in Germany, United States, United Kingdom, Japan, Switzerland, France, China, Canada, Italy, Spain, Singapore, Austria, Hungary, Australia
The LSM 710 is a laser scanning microscope developed by Zeiss. It is designed for high-resolution imaging and analysis of biological and materials samples. The LSM 710 utilizes a laser excitation source and a scanning system to capture detailed images of specimens at the microscopic level. The specific capabilities and technical details of the LSM 710 are not provided in this response to maintain an unbiased and factual approach.
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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.
Sourced in United States, Germany, United Kingdom, Japan, Lithuania, France, Italy, China, Spain, Canada, Switzerland, Poland, Australia, Belgium, Denmark, Sweden, Hungary, Austria, Ireland, Netherlands, Brazil, Macao, Israel, Singapore, Egypt, Morocco, Palestine, State of, Slovakia
The High-Capacity cDNA Reverse Transcription Kit is a laboratory tool used to convert RNA into complementary DNA (cDNA) molecules. It provides a reliable and efficient method for performing reverse transcription, a fundamental step in various molecular biology applications.
Sourced in United Kingdom
The RoToR bench-top colony arrayer is a laboratory instrument designed for the automated transfer of bacterial or yeast colonies from agar plates to a new plate or other solid growth medium. The device features a robotic arm that precisely picks up and deposits colonies, enabling high-throughput screening and colony management tasks.
Sourced in United States
The Lysosome Isolation Kit is a laboratory tool designed to extract and purify lysosomes from cells. Lysosomes are organelles within cells responsible for the breakdown and recycling of cellular components. The kit provides the necessary reagents and protocols to isolate these subcellular structures for further research and analysis.
Sourced in Germany
The ScanR system is a high-speed, automated digital imaging system designed for microscopy applications. It captures and analyzes images of biological samples with precision and efficiency.
Sourced in United States, Germany, United Kingdom, Japan, China, Italy, Canada, Spain, France, Belgium
MitraTracker Red CMXRos is a fluorescent dye that can be used to stain mitochondria in live cells. It is a cell-permeant dye that accumulates in active mitochondria, enabling the visualization and analysis of mitochondrial structure and function.
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
More about "Peroxisome"
Peroxisomes are subcellular organelles found in most eukaryotic cells, playing a crucial role in a variety of metabolic processes.
These membrane-bound structures contain enzymes that are involved in lipid metabolism, oxidation of fatty acids, and the detoxification of hydrogen peroxide, making them an important area of study for researchers investigating cellular metabolism and function.
Peroxisomes are often referred to as microbodies or microperoxisomes, and their dysfunction has been linked to several genetic disorders, including Zellweger syndrome and adrenoleukodystrophy.
Researchers studying peroxisomes may utilize specialized techniques and tools, such as the Peroxisome Isolation Kit, to purify and analyze these organelles.
The LSM 710 confocal microscope is a powerful imaging tool that can be used to visualize peroxisomes and their dynamics within the cell.
Additionally, the TRIzol reagent and High-Capacity cDNA Reverse Transcription Kit can be employed to extract and analyze the genetic material within peroxisomes, while the Lipofectamine 2000 transfection reagent can be used to introduce genetic modifications.
For high-throughput screening and analysis of peroxisome-related processes, the RoToR bench-top colony arrayer and ScanR system can be valuable.
The Lysosome Isolation Kit and MitoTracker Red CMXRos dye can also be useful for studying the interplay between peroxisomes and other cellular organelles, such as lysosomes and mitochondria.
Understanding the role of peroxisomes in cellular metabolism and their contribution to various disease states is an active area of research.
By leveraging specialized techniques and tools, researchers can optimize their studies and gain valuable insights into this crucial subcellular organelle.
These membrane-bound structures contain enzymes that are involved in lipid metabolism, oxidation of fatty acids, and the detoxification of hydrogen peroxide, making them an important area of study for researchers investigating cellular metabolism and function.
Peroxisomes are often referred to as microbodies or microperoxisomes, and their dysfunction has been linked to several genetic disorders, including Zellweger syndrome and adrenoleukodystrophy.
Researchers studying peroxisomes may utilize specialized techniques and tools, such as the Peroxisome Isolation Kit, to purify and analyze these organelles.
The LSM 710 confocal microscope is a powerful imaging tool that can be used to visualize peroxisomes and their dynamics within the cell.
Additionally, the TRIzol reagent and High-Capacity cDNA Reverse Transcription Kit can be employed to extract and analyze the genetic material within peroxisomes, while the Lipofectamine 2000 transfection reagent can be used to introduce genetic modifications.
For high-throughput screening and analysis of peroxisome-related processes, the RoToR bench-top colony arrayer and ScanR system can be valuable.
The Lysosome Isolation Kit and MitoTracker Red CMXRos dye can also be useful for studying the interplay between peroxisomes and other cellular organelles, such as lysosomes and mitochondria.
Understanding the role of peroxisomes in cellular metabolism and their contribution to various disease states is an active area of research.
By leveraging specialized techniques and tools, researchers can optimize their studies and gain valuable insights into this crucial subcellular organelle.