Identification and analysis of anti-HDL scFv-antibodies obtained from phage display based synthetic antibody library

Autor: Janita Lövgren, Nina Sirkka, Matti Jauhiainen, Priyanka Negi, Eeva-Christine Brockmann, Jari Metso, Paivi Malmi, Kim Pettersson, Tuomas Huovinen, Urpo Lamminmäki
Rok vydání: 2015
Předmět:
Zdroj: Clinical biochemistry. 49(6)
ISSN: 1873-2933
Popis: Objective In epidemiological studies plasma high density lipoprotein cholesterol (HDL-C) levels are found to correlate inversely with atherosclerotic cardiovascular events. HDL consists of different subpopulations and they vary in their anti-atherogenic properties. The aim of this study is to isolate coronary artery disease (CAD) specific anti-HDL scFv-antibodies. Design and methods To obtain CAD specific HDL binders, we used phage displayed synthetic antibody libraries to enrich specific antibodies against HDL isolated from CAD patients. The antibodies were affinity purified. Their capability to recognize apolipoproteins A-I and A-II, various HDL forms differing in lipid/protein ratios and plasma HDL, was studied using time-resolved fluorescence based immunoassay. Results Using different selection strategies and immunoassay based screening we obtained altogether 1200 clones displaying HDL binding activity. By sequencing 337, we identified 264 unique antibodies against HDL. A set of 61 antibodies were selected for further analysis. We found a variety of antibodies with different binding profiles, including apoA-I binding antibodies either in lipid-dependent or lipid-independent manner and binders against apoA-II. Several antibodies were able to discriminate between HDL derived from CAD patients and healthy controls. A majority of the antibodies were immunoreactive with HDL in plasma. Conclusion The novel HDL recognizing antibodies isolated from synthetic antibody phage library have displayed interesting HDL-binding characteristics suggesting that, in addition to use as research tools, a part of them might be useful for the development of diagnostic methods for CAD risk assessment.
Databáze: OpenAIRE