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A proof-of-concept of lateral flow based luteinizing hormone detection in urine for ovulation prediction in buffaloes

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Abstract

We present a method for the detection of luteinizing hormone (LH) in buffalo urine by using gold nanoparticles (AuNPs) conjugated with novel anti-peptide antibodies against LH (anti LHP) in lateral flow assay format. Buffalo LH is an important reproductive hormone and is a chemically complex glycoprotein. Its surge release precedes ovulation and therefore detecting LH has implications in identifying the ovulation event. Any sensor thus developed for sensing LH may have the potential for predicting ovulation and hence can assist herd managers in making decisions on the timing of artificial insemination. Recombinant LH production is time consuming, difficult and costly. Hence, we identified an epitope peptide sequence in buffalo LH and raised antibodies against it. The chemically synthesized peptide and antibodies were used for developing the sensor. The gold nanoparticles and conjugates were characterized through physicochemical methods which confirmed the binding of peptides and antibodies to the gold nanoparticles. A qualitative ELISA for sensing LH was developed based on competitive binding of gold nanoparticles conjugated with the epitope peptide and LH towards the anti-peptide antibodies against LH. We also further explored the detection of LH in buffalo urine using the gold nanoparticle-LHP conjugate (AuNP-LHP) in dipstick format. These experiments provided a proof-of-concept towards applicability of the LH based sensor for ovulation prediction in buffaloes.

Graphical abstract: A proof-of-concept of lateral flow based luteinizing hormone detection in urine for ovulation prediction in buffaloes

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Article information


Submitted
16 Apr 2020
Accepted
28 May 2020
First published
03 Jun 2020

Anal. Methods, 2020, Advance Article
Article type
Paper

A proof-of-concept of lateral flow based luteinizing hormone detection in urine for ovulation prediction in buffaloes

V. Nayan, E. S. Sinha, S. K. Onteru and D. Singh, Anal. Methods, 2020, Advance Article , DOI: 10.1039/D0AY00787K

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