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Innovative Fatty Acid-Guided Biosensor Design for Neutrophil Gelatinase, a Prognostic and Diagnostic Biomarker for
Kaustubh Jumle1, Priya Paliwal1, Mohamed A M Ali2
1Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India.
Insights
Researchers developed a novel fatty acid biosensor for detecting Neutrophil Gelatinase-Associated Lipocalin (NGAL), a key biomarker for early Chronic Kidney Disease (CKD) detection. This cost-effective method offers a promising new approach for diagnosing CKD.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Biochemistry
Background:
- Chronic kidney disease (CKD) affects 850 million globally, ranking as the 5th leading cause of years of life lost.
- Neutrophil gelatinase-associated lipocalin (NGAL) is a validated prognostic biomarker for pre-clinical CKD stages.
- Current CKD diagnostics involve blood/urine analysis and ultrasound, necessitating improved early detection methods.
Purpose of the Study:
- To explore fatty acids as biorecognition elements for selective Neutrophil Gelatinase-Associated Lipocalin (NGAL) capture.
- To develop and validate a novel biosensor platform for NGAL detection for improved Chronic Kidney Disease (CKD) diagnostics.
- To compare the analytical performance of various electrochemical techniques for NGAL quantification.
Main Methods:
- Computational analysis including molecular docking and dynamics simulations to assess fatty acid-lipocalin interactions.
- Fabrication and testing of a biosensor utilizing fatty acids for NGAL capture.
- Comparative analysis of fluorescence and electrochemical detection methods, including square-wave voltammetry, differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectroscopy.
Main Results:
- Linoleic acid demonstrated the most favorable binding affinity and stability with NGAL among the tested fatty acids.
- Differential pulse voltammetry (DPV) exhibited superior analytical performance for NGAL detection.
- The DPV method achieved a low limit of detection (LOD) of 0.05 ng/mL and high sensitivity (23.2 µA/cm2/pg).
Conclusions:
- Fatty acids, particularly linoleic acid, can serve as effective biorecognition elements for NGAL detection.
- A novel fatty acid-based biosensor platform offers a cost-effective and robust approach for NGAL detection.
- This study presents a promising new avenue for early Chronic Kidney Disease (CKD) diagnostics.
Abstract:
Chronic kidney disease (CKD) afflicts 850 million people worldwide, with an estimate that it is the 5th highest cause of years of life lost (YLLs). Standard confirmatory procedures for disease are blood and urine analysis with ultrasound for confirmation. Neutrophil gelatinase-associated lipocalin (NGAL) has been established as a prognostic biomarker, especially for the pre-clinical stages of CKD, thus presenting itself as a dependable predictor of the progression. With the aim of designing diagnostics, fatty acids were explored as potential biorecognition elements for the selective capture of NGAL. Three fatty acids-linoleic acid, arachidonic acid, and retinoic acid-were shortlisted as plausible candidates based on their known affinity toward lipocalin family proteins. Docking followed by molecular dynamics simulations were employed to evaluate the binding affinity and stability of each complex. Among them, linoleic acid exhibited the most favorable interaction, as evidenced by the lowest binding free energy. Subsequently, fluorescence and electrochemical techniques-square-wave voltammetry, differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS)-were systematically compared for qualitative and quantitative checking of the accuracy of NGAL detection. Amongst the electrochemical techniques, differential pulse voltammetry DPV demonstrated superior analytical performance with an LOD of 0.05 ng/mL with a sensitivity of 23.2 µA/cm2/pg. To the best of our knowledge, this is the first report of a fatty acid-based biosensor platform for NGAL detection, presenting a novel approach for CKD diagnostics. The sensitivity obtained is comparable with available NGAL detection methods yet cost-effective and robust.
