Manavi Jain, Paramveer Yadav and Priyadarshini* Pages 88 - 94 ( 7 )
Urolithiasis, which is the presence of stones in the urinary tract, has long been linked with a higher risk of causing chronic kidney diseases and associated illnesses, such as diabetes-affecting 12% of the world population. This clinical condition arises due to the supersaturation of urine and alterations in the expression of cellular and urinary proteins. The renal stone mineral composition has been well understood and incorporated as a routine part of stone removal, however, the protein composition, an essential fraction of the stone matrix has been inadequately understood and not adeptly established. Stone proteomics consists of a number of techniques including crystal analysis using X-ray diffractometry and IR spectroscopy, sample purification, identification and characterization of proteins using high throughput mass spectrometric methods. However, not many studies have utilized the data obtained from these experiments to assign functional significance to associated identified proteins. Protein network analysis using bioinformatic tools such as STRING to study protein-protein interactions will enable researchers to get better insight into stone formation mechanics. Hence, a comprehensive proteomic study of kidney stone matrix will help in deciphering protein-crystal pathways generating novel information useful for clinical application.
Urolithiasis, X-ray diffractometry, IR spectroscopy, protein-protein interaction, string, protein network analysis.
Department of Biotechnology, Jaypee Institute of Information Technology, Noida, Department of Biotechnology, Jaypee Institute of Information Technology, Noida, Department of Biotechnology, Jaypee Institute of Information Technology, Noida