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REG1A protein, human

REG1A, also known as regenerating islet-derived protein 1 alpha, is a secreted protein that plays a role in pancreatic islet cell regeneration and function.
It is expressed in the exocrine and endocrine pancreas and has been implicated in the pathogenesis of diabetes and pancreatitis.
Reserach into REG1A protein can provide insights into pancreatic health and disease processes.
PubCompare.ai offers a powerful AI-driven tool to streamline REG1A protein research by effortlessly locating the best protocols and products from scientific literature, pre-prings, and patents.
This innovative platform can help unlcok new discoveries and advance the understanding of this important pancreatic protein.

Most cited protocols related to «REG1A protein, human»

1
SAXS Data Collection and Analysis
Cited 113 times
All SAXS data collection was performed at the SIBYLS beamline, an international user facility. An application for experiments is accessible at www.bl1231.als.lbl.gov. The data collection strategy has been designed to minimize errors due to instrumentation, radiation damage and concentration dependant phenomenon. The strategy applied depended on available stock concentration and size of the protein. SAXS data were collected on 3 serial dilutions of each sample preparation starting at a maximum 10 and a minimum of 1 mg/ml. Sample loading for data collection for each protein proceeded in the following order, lowest concentration, middle concentration, highest concentration followed by a final buffer measurement. The sample cell was washed between protein solutions using a mild detergent soak for 1 minute followed by 3 rinses with buffer solution. The subtraction of buffer collected before the sample was compared to buffer collected after each sample to insure the subtraction process was not subject to instrument variations. Data was collected from two short and one long X-ray exposure for each protein sample. The short exposures were compared against one another to identify whether significant radiation damage occurred on this time scale. The beam size at the sample is 4×1mm and converges at the detector to a 100×100µm spot. The large beam size at the sample spreads radiation over the entire sample greatly reducing radiation damage. Concentrations were compared against one another to determine whether concentration dependant structure factors contributed to the data. In two cases minor concentration dependence was observed and corrected by extrapolating behavior to zero concentration9 using code developed in house. A final scattering curve used for analysis was created for each sample (Supplementary Figure 2).
For most samples only 1 Å X-rays were used. Short exposures were 0.5 seconds while the long exposures were 5 seconds. However for proteins with long dimensions such as PF1548, 1.5 Å wavelength was also used to better define the maximum distances. Short and long exposures were 4 and 40 seconds respectively. All data were collected at room temperature.
Hura G.L., Menon A.L., Hammel M., Rambo R.P., Poole FL I.I., Tsutakawa S.E., Jenney FE J.r., Classen S., Frankel K.A., Hopkins R.C., Yang S.J., Scott J.W., Dillard B.D., Adams M.W, & Tainer J.A. (2009). Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nature methods, 6(8), 606-612.
Publication 2009
Buffers Cells Detergents Electromagnetic Radiation Neoplasm Metastasis Proteins Radiography REG1A protein, human Technique, Dilution X-Rays, Diagnostic
2
SAXS Data Collection and Analysis
Cited 108 times
All SAXS data collection was performed at the SIBYLS beamline, an international user facility. An application for experiments is accessible at www.bl1231.als.lbl.gov. The data collection strategy has been designed to minimize errors due to instrumentation, radiation damage and concentration dependant phenomenon. The strategy applied depended on available stock concentration and size of the protein. SAXS data were collected on 3 serial dilutions of each sample preparation starting at a maximum 10 and a minimum of 1 mg/ml. Sample loading for data collection for each protein proceeded in the following order, lowest concentration, middle concentration, highest concentration followed by a final buffer measurement. The sample cell was washed between protein solutions using a mild detergent soak for 1 minute followed by 3 rinses with buffer solution. The subtraction of buffer collected before the sample was compared to buffer collected after each sample to insure the subtraction process was not subject to instrument variations. Data was collected from two short and one long X-ray exposure for each protein sample. The short exposures were compared against one another to identify whether significant radiation damage occurred on this time scale. The beam size at the sample is 4×1mm and converges at the detector to a 100×100µm spot. The large beam size at the sample spreads radiation over the entire sample greatly reducing radiation damage. Concentrations were compared against one another to determine whether concentration dependant structure factors contributed to the data. In two cases minor concentration dependence was observed and corrected by extrapolating behavior to zero concentration9 using code developed in house. A final scattering curve used for analysis was created for each sample (Supplementary Figure 2).
For most samples only 1 Å X-rays were used. Short exposures were 0.5 seconds while the long exposures were 5 seconds. However for proteins with long dimensions such as PF1548, 1.5 Å wavelength was also used to better define the maximum distances. Short and long exposures were 4 and 40 seconds respectively. All data were collected at room temperature.
Hura G.L., Menon A.L., Hammel M., Rambo R.P., Poole FL I.I., Tsutakawa S.E., Jenney FE J.r., Classen S., Frankel K.A., Hopkins R.C., Yang S.J., Scott J.W., Dillard B.D., Adams M.W, & Tainer J.A. (2009). Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nature methods, 6(8), 606-612.
Publication 2009
Buffers Cells Detergents Electromagnetic Radiation Neoplasm Metastasis Proteins Radiography REG1A protein, human Technique, Dilution X-Rays, Diagnostic
3
Quantifying Intracellular Protein Localization
Cited 20 times

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Publication 2018
Cells Cytoplasm Fluorescence Light Microscopy Microscopy, Confocal Microscopy, Fluorescence Proteins REG1A protein, human Staphylococcal Protein A
4
Validating Cell Deconvolution Signatures
Cited 18 times
Three external datasets were collected to validate our signature matrices (for RNA-Seq and microarray deconvolution) (Figure S9). 1) The dataset from Zimmermann et al. (2016) (link) provides both flow cytometry data and RNA-Seq data and the data are available through ImmPort (http://www.immport.org) with accession number SDY67. Cell types proportions were retrieved from their B cell, T regs and innate flow cytometry panels. 2) The dataset from Newman et al. (2015) (link) was downloaded from GEO: GSE65133. The dataset provides both microarray data and cell type proportions. 3) For the dataset of Mohanty et al. (2015) (link), we downloaded the microarray data from GEO: GSE9654, and flow cytometry data from ImmPort, accession number SDY404. We analyzed the T cells and B cells panels (L1 and L4, respectively).
The three external datasets contain both PBMC transcriptomic data and flow cytometry data where cell type proportions can be obtained in relation to the PBMC fraction of blood. All the flow cytometry proportion extracted are available in Table S6.
Monaco G., Lee B., Xu W., Mustafah S., Hwang Y.Y., Carré C., Burdin N., Visan L., Ceccarelli M., Poidinger M., Zippelius A., Pedro de Magalhães J, & Larbi A. (2019). RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Reports, 26(6), 1627-1640.e7.
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Publication 2019
B-Lymphocytes BLOOD Cells Flow Cytometry Gene Expression Profiling Microarray Analysis REG1A protein, human RNA-Seq T-Lymphocyte
5
Automated Keyword Annotation Similarity
Cited 16 times
Most keywords with which UniProt proteins are annotated were originally defined manually by database curators. They are automatically transferred to homologous proteins according to various rules developed within UniProt (1 (link)). The keyword annotation similarity between two proteins x, y with keyword lists Kx and Ky is defined in the exact same way as the GO annotation similarity while defining sim(a, b) = I(a = b), with indicator function I( · ). This yields sim(x, y) = 2|Kx∩Ky|/(|Kx| + |Ky|). The keywords in the UniProt knowledge base describe functional features in categories such as molecular function, domain, biological process, ligand and cellular component. We ignore keyword categories technical term and coding sequence diversity, and keywords provided by the UniProt automatic annotation team that do not describe biological functions.
Mirdita M., von den Driesch L., Galiez C., Martin M.J., Söding J, & Steinegger M. (2016). Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Research, 45(Database issue), D170-D176.
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Publication 2016
Biological Processes Cellular Structures Ligands Open Reading Frames Proteins REG1A protein, human

Most recents protocols related to «REG1A protein, human»

1
Evaluating Mutation Effects on Kinase and TF Activities
The effects of mutations on the kinase and TF activities were assessed by associating the activity of a given protein with the mutational status of the same protein or other proteins it might interact throughout the cellular regulatory networks. First, we built a binary mutation matrix M where the index Mij corresponds to 1 if the sample i has a mutation in gene j and 0 otherwise. To do that, we selected the mutations classified as frameshift and in‐frame Indels, missense, nonsense and stop codon loss. Given the proteins X and Y, the association between the activity of Y (Yact) and the mutational status of X (Xmut) was assessed across samples by fitting a linear model that took into account possible confounding effects: Yact=β0+β1Study+β2Xmut+ɛ where Yact represents the activity of protein Y, β0 the intercept, β1 the regression coefficient for the covariate experimental study, β2 the regression coefficient for the mutational status of X and ɛ the noise term. This model was applied to assess the effect of Xmut on the activity of the same protein (Xact ∼ Xmut) and on the activity of other proteins (Yact ∼ Xmut). The P‐values from the coefficients of Xmut (β2) were calculated using the t‐statistic over a Student's t‐distribution and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. The linear models and respective statistics were calculated using the lm and p.adjust R functions.
The associations were performed with the genes mutated in more than 20 samples and with the protein activities estimated in at least 10 samples. An association between a pair Yact ∼ Xmut or Xact ∼ Xmut was performed if Xmut was mutated in at least five of all the samples in the pair. Regarding the Yact ∼ Xmut associations, we tested 520,938 pairs between 208 kinases and 3,590 genes and 1,048,216 pairs between 292 TFs and 3,590 genes. In relation to the Xact ∼ Xmut associations, we tested 40 pairs and 64 pairs with the kinases and TFs, respectively.
Sousa A., Dugourd A., Memon D., Petursson B., Petsalaki E., Saez‐Rodriguez J, & Beltrao P. (2023). Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival. Molecular Systems Biology, 19(3), e10631.
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Publication 2023
Cells Codon, Terminator Frameshift Mutation Genes INDEL Mutation Matrix-M Mutation Mutation, Nonsense Phosphotransferases Proteins Reading Frames REG1A protein, human Staphylococcal Protein A
2
SRPK1 Inhibitor Treatment of K562 and Kasumi-1 Cells
K562 and Kasumi-1 cell lines were purchased from the European Collection of Authenticated Cell Cultures (ECACC) and cultured using RPMI-1640 culture medium with L-glutamine (Sigma Aldrich, United Kingdom), further supplemented with 10% foetal bovine serum (FBS; Sigma Aldrich) for K562 cells and 20% FBS for Kasumi-1 cells. Cells were used between passages 6–19, seeded at densities of 5 × 105–1 × 106 in T25 flasks and were sub-cultured every 48 h. Cell lines were incubated in 5% CO2 at 37°C.
K562 and Kasumi-1 cells were treated with SRPK1 specific small molecule inhibitors 5-methyl-N-(2-(morpholin-4-yl)-5-(tri-fluoromethyl)phenyl) furan-2-carboxamide, commonly known as SR Protein Inhibitor X (SPHINX) which was purchased from Enamine (Kiev, Ukraine). SPHINX was dissolved in DMSO (Sigma Aldrich) at a stock concentration of 25 mM. Cells (1 × 106/ml) were seeded in a T25 flask for each treatment which was performed in duplicate. Cells were incubated with 10 nM-10 μM SPHINX for up to 72 h.
Wodi C., Belali T., Morse R., Porazinski S, & Ladomery M. (2023). SPHINX-Based Combination Therapy as a Potential Novel Treatment Strategy for Acute Myeloid Leukaemia. British Journal of Biomedical Science, 80, 11041.
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Publication 2023
Cell Culture Techniques Cell Lines Cells Culture Media Europeans furan Glutamine inhibitors K562 Cells REG1A protein, human Sulfoxide, Dimethyl
3
Survival Analysis of Stage III Esophageal Cancer
Surveillance, Epidemiology, and End Results (SEER) 18 Regs custom Data (1975–2016) is the data source for this retrospective study. We identified patients diagnosed stage III esophageal cancer from 2004 to 2015 (Fig. 1). Patients included in this exploration had to meet all of the criteria over 18 years of age, tumor size <600 mm, diagnosed with stage III esophageal cancer. The exclusion criteria were as follows not first malignant primary indicator, complete data could not be obtained, patients diagnosed by autopsy. We extracted the following data from the SEER database: age, primary site of tumor, histologic, race, gender, chemotherapy history, T stage, N stage, surgery history, radiation history, tumor size, marital status, radiation sequence with surgery, and follow-up information. We employed OS and cancer-specific survival rate (CSS) as survival rate times analyzed in this investigation. OS is the survival rate time from the day of diagnosis to the day of death from any cause or last follow-up. CSS is an OS measure excluding other causes of death. In this research, we adopted the AJCC 6th edition TNM staging. We use data from public databases and was exempt from institutional review board approval.
Yang W., Yang Y., Ma X., Ma M, & Han B. (2023). Surgery and postoperative radiotherapy affect the prognosis of esophageal cancer: A SEER analysis. Medicine, 102(9), e32925.
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Publication 2023
Autopsy Diagnosis Esophageal Cancer Ethics Committees, Research Gender Malignant Neoplasms Neoplasms Operative Surgical Procedures Patients Pharmacotherapy Radiosurgery Radiotherapy REG1A protein, human
4
HIV Infant Treatment Feasibility Study
The PEHSS cohort was a feasibility study to investigate different approaches of starting ART in infants infected with HIV (5 (link), 44 (link)) that was undertaken in 2002–2005, prior to implementation of universal ART in all infants infected with HIV. We evaluated participants randomized to arm B, who received immediate ART from birth to 12 months followed by ATI. ART was restarted after ATI according to the prevailing 2003 WHO and South African national guidelines. To investigate T cell markers associated with disease progression or time to meeting ART criteria, we used PBMCs from 13 participants who were virally suppressed from whom samples were available up to 3 months before ATI. Multiple samples from 2 study participants, PS-021-C and PS-114-C, were obtained for the longitudinal study.
In addition, we investigated the immunophenotype of PD-1–expressing CD8+ T cells in older children and young adults from previously described cohorts in sub-Saharan Africa. PSPs were defined as children who were infected with HIV, ART-naive, and older than 5 years with CD4+ T cell counts of more 350 cells/mm3 and more than 20%; pediatric progressors were defined as having CD4+ T cell counts of less than 350 cells/mm3 or less than 20%. Viremic adults (viremic adults) consisted of horizontally infected ART-naive individuals with chronic infection matched by sex. The fourth group consisted of HIV-exposed uninfected children matched by age and sex. No data were available on viral load and drug suppression for the mothers of these children. In analyses done after ART initiation, samples were selected from the time point after 1 to 2 years of viral suppression.
Vieira V., Lim N., Singh A., Leitman E., Dsouza R., Adland E., Muenchhoff M., Roider J., Marin Lopez M., Carabelli J., Giandhari J., Groll A., Jooste P., Prado J.G., Thobakgale C., Dong K., Kiepiela P., Prendergast A.J., Tudor-Williams G., Frater J., Walker B.D., Ndung’u T., Ramsuran V., Leslie A., Kløverpris H.N, & Goulder P. (2023). Slow progression of pediatric HIV associates with early CD8+ T cell PD-1 expression and a stem-like phenotype. JCI Insight, 8(3), e156049.
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Publication 2023
Adult Birth CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Child Chronic Infection Disease Progression Immunophenotyping Infant Mothers Pharmaceutical Preparations REG1A protein, human Southern African People T-Lymphocyte Viremia Young Adult
5
Comprehensive HIVDEP CRISPR Library Construction
The HIVDEP sgRNA library is composed of 525 genes (4,191 sgRNAs). The top scoring 368 genes (as determined by -log10 MAGeCK score) of the genome-wide screen were included in the HIVDEP library. Additional unique top scoring genes with <10%FDR from HuEpi and PIKA screens were added to the HIVDEP library that contained an additional 131 genes. NR5A1, NHLRC4, SFTPA2, ZNF768, MYL10, GIMAP5, SPG21, CHSY3, ZNF25, REG1A, and ATXN3 were manually selected as nonessential genes (18 (link)) that were neither enriched nor depleted in the genome-wide screen. Six new sgRNAs were designed using two algorithms, GUIDES (52 (link)) and CHOPCHOP (53 (link)). We also manually included other known dependency factors, including CCR5 and PAPSS1. A total of 212 nontargeting controls were designed using GUIDES and included. The HIVDEP sgRNA library was synthesized (Twist Biosciences) and cloned into HIV-CRISPR. Oligo pools were amplified using Herculase II Fusion DNA polymerase (Agilent; 600677) combined with 1 ng of pooled oligonucleotide template, primers ArrayF and ArrayR (ArrayF primer: TAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCG and ArrayR primer: ACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTC TAGCTCTAAAAC), an annealing temperature of 59°C, an extension time of 20 s, and 25 cycles. Following PCR amplification, a 140-bp amplicon was gel-purified and cloned into BsmBI (NEB; R0580) digested HIVCRISPR using Gibson Assembly (NEB; E2611S). Each Gibson reaction was carried out at 50°C for 60 min. Drop dialysis was performed on each Gibson reaction according to the manufacturer’s protocol using a Type-VS Millipore membrane (VSWP 02500). Then, 5 μL of the reaction was used to transform 25 μL of Endura electrocompetent cells (Lucigen; 60242-2) according to the manufacturer’s protocol using a Gene Pulser (Bio-Rad). To ensure adequate representation, sufficient parallel transformations were performed and plated onto carbenicillin containing LB agarose 245 × 245 mm plates (Thermo Fisher) at 492-times the total number of oligonucleotides of each library pool. After overnight growth at 37°C, colonies were scraped off, pelleted, and used for plasmid DNA preps using the endotoxin-free Nucleobond plasmid midiprep kit (TaKaRa Bio; 740422.10). The HIVDEP library was sequenced and contains all 4,191 sgRNAs included in the synthesis (GEO Data set, accession numbers GSM6945645 and GSM6945646).
Montoya V.R., Ready T.M., Felton A., Fine S.R., OhAinle M, & Emerman M. (2023). A Virus-Packageable CRISPR System Identifies Host Dependency Factors Co-Opted by Multiple HIV-1 Strains. mBio, 14(1), e00009-23.
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Publication 2023
Anabolism Carbenicillin CCR5 protein, human Cells Clustered Regularly Interspaced Short Palindromic Repeats Dialysis DNA-Directed DNA Polymerase DNA Library Endotoxins Genes Genome MAST Syndrome NR5A1 protein, human Ochotona Oligonucleotide Primers Oligonucleotides Plasmids REG1A protein, human Sepharose Tissue, Membrane

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1
Cd4 positive selection antibody cocktail by Miltenyi Biotec
The CD4 positive selection antibody cocktail is a laboratory product designed for the isolation and enrichment of CD4-positive cells from complex cell samples. The cocktail contains a mixture of antibodies that specifically bind to the CD4 surface marker, allowing for the efficient separation and purification of CD4-expressing cells. This product is intended for use in research and laboratory applications that require the isolation of CD4-positive cell populations.
2
Dimethyl sulfoxide (dmso) by Merck Group
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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Staphtype software by Ridom
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Ridom StaphType software is a computer-based application designed for the molecular typing of Staphylococcus aureus. The software analyzes genomic data to identify and classify different strains of the bacterium.
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Protease inhibitor cocktail by Roche
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Protease inhibitor cocktail is a laboratory reagent used to inhibit the activity of proteases, which are enzymes that break down proteins. It is commonly used in protein extraction and purification procedures to prevent protein degradation.
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Alexa fluor 647 coupled antibody against γh2a x protein by BD
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The Alexa Fluor 647-coupled antibody is a specific antibody that binds to the γH2A.X protein. The Alexa Fluor 647 dye is attached to the antibody, allowing for fluorescent detection and visualization of the γH2A.X protein.
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Sybyl x 2 by Tripos
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SYBYL-X 2.0 is a molecular modeling software suite developed by Tripos. It provides tools for the visualization, analysis, and manipulation of molecular structures. The software supports a variety of file formats and enables users to perform tasks such as molecular docking, conformational analysis, and pharmacophore modeling.
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Foxp3 staining buffer set by Thermo Fisher Scientific
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The Foxp3 Staining Buffer Set is a laboratory reagent used for the intracellular staining of the Foxp3 transcription factor, which is a marker for regulatory T cells. The kit includes the necessary buffers and solutions to perform this specific staining procedure.
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Tools by AutoDock
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AutoDock Tools is a software suite designed to perform molecular docking simulations. It provides a graphical user interface (GUI) for preparing input files, running docking calculations, and analyzing the results. The core function of AutoDock Tools is to predict the preferred binding orientations and affinities between a small molecule and a target protein.

More about "REG1A protein, human"

Regenerating Islet-Derived Protein 1 Alpha (REG1A) is a secreted protein that plays a crucial role in pancreatic islet cell regeneration and function.
It is also known as Islet Cell Regeneration Protein 1 Alpha or Pancreatitis-Associated Protein 1.
REG1A is expressed in both the exocrine and endocrine compartments of the pancreas and has been implicated in the pathogenesis of diabetes and pancreatitis.
Conducting research on the REG1A protein can provide valuable insights into the complex processes underlying pancreatic health and disease.
PubCompare.ai, an innovative AI-driven platform, offers a powerful tool to streamline and enhance REG1A protein research.
This platform can effortlessly locate the best protocols and products from scientific literature, pre-prints, and patents, helping researchers unlock new discoveries and advance the understanding of this important pancreatic protein.
By leveraging the insights gained from the MeSH term description, researchers can explore related concepts and techniques to further their investigations.
For instance, the use of CD4 positive selection antibody cocktails, DMSO, Ridom StaphType software, protease inhibitor cocktails, Tween 20, Sybyl/Sketch modules, Alexa Fluor 647-coupled antibodies against γH2A.X protein, SYBYL-X 2.0, and Foxp3 Staining Buffer Sets may all contribute to a more comprehensive understanding of REG1A and its role in pancreatic health and disease processes.
Ultimately, the integration of these tools and techniques, combined with the power of PubCompare.ai's AI-driven comparisons, can streamline the research process and unlock new avenues for advancing the knowledge of the REG1A protein and its implications for human health.
Reserach into this key pancreatic protein can lead to groundbreaking discoveries and improved treatment strategies for diabetes, pancreatitis, and other related conditions.