Anti-Lep polyclonal antibody (CABT-L320)

Specifications


Host Species
Goat
Antibody Isotype
IgG
Species Reactivity
Mouse
Immunogen
E. coli-derived recombinant mouse Leptin/OB, Val22-Cys167, Accession #Q544U0
Conjugate
Unconjugated

Applications


Application Notes
IHC: 5-15 µg/mL, WB: 0.1 µg/mL, we recommend the following for sandwich ELISA (Capture-Detection): CABT-L320-CABT-L321
*Suggested working dilutions are given as a guide only. It is recommended that the user titrates the product for use in their own experiment using appropriate negative and positive controls.

Target


Alternative Names
FLJ94114; LEP; leptin (murine obesity homolog); leptin (obesity homolog; mouse); Leptin
Entrez Gene ID
UniProt ID

Citations


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We offer labeled antibodies using our catalogue antibody products and a broad range of intensely fluorescent dyes and labels including HRP, biotin, ALP, Alexa Fluor® dyes, DyLight® Fluor dyes, R-phycoerythrin (R-PE), at scales from less than 100 μg up to 1 g of IgG antibody. Learn More

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References


Expression of LEP, LEPR and PGC1A genes is altered in peripheral blood mononuclear cells of patients with relapsing-remitting multiple sclerosis

JOURNAL OF NEUROIMMUNOLOGY

Authors: Kolic, Ivana; Stojkovic, Ljiljana; Dincic, Evica; Jovanovic, Ivan; Stankovic, Aleksandra; Zivkovic, Maja

Leptin (LEP) may contribute to the pathogenesis of multiple sclerosis (MS) by its immunomodulatory, proinflammatory and prooxidant effects. Therefore, plasma LEP levels and mRNA expression of five genes related to the LEP signaling pathway (LEP, LEP receptor (LEPR), peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC1A), superoxide dismutase 2, tumor necrosis factor-alpha) were investigated in relapsing-remitting MS. In patients (N = 64), compared to healthy subjects (N = 62), relative LEP mRNA levels were significantly increased (p = 0,01), while LEPR and PGC1A mRNA levels were decreased (p = 0,001 and p = 0,04, respectively). Significant positive correlation was observed between LEPR mRNA levels and clinical parameters of MS progression (EDSS, MSSS).

Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy

BIOINFORMATICS

Authors: Dai, Mingwei; Wan, Xiang; Peng, Hao; Wang, Yao; Liu, Yue; Liu, Jin; Xu, Zongben; Yang, Can

Motivation A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area. Results In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (0.58%) to 68.33% (0.32%) using about 195 000 variants. Availability and implementation The LEP software is available at https://github.com/daviddaigithub/LEP. Supplementary information Supplementary data are available at Bioinformatics online.

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