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BackgroundAggregation of unfolded proteins occurs mainly through the exposed hydrophobic surfaces. Any mechanism of inhibition of this aggregation should explain the prevention of these hydrophobic interactions. Though arginine is prevalently used as an aggregation suppressor, its mechanism of action is not clearly understood. We propose a mechanism based on the hydrophobic interactions of arginine.MethodologyWe have analyzed arginine solution for its hydrotropic effect by pyrene solubility and the presence of hydrophobic environment by 1-anilino-8-naphthalene sulfonic acid fluorescence. Mass spectroscopic analyses show that arginine forms molecular clusters in the gas phase and the cluster composition is dependent on the solution conditions. Light scattering studies indicate that arginine exists as clusters in solution. In the presence of arginine, the reverse phase chromatographic elution profile of Alzheimer's amyloid beta 1-42 (Aβ1-42) peptide is modified. Changes in the hydrodynamic volume of Aβ1-42 in the presence of arginine measured by size exclusion chromatography show that arginine binds to Aβ1-42. Arginine increases the solubility of Aβ1-42 peptide in aqueous medium. It decreases the aggregation of Aβ1-42 as observed by atomic force microscopy.ConclusionsBased on our experimental results we propose that molecular clusters of arginine in aqueous solutions display a hydrophobic surface by the alignment of its three methylene groups. The hydrophobic surfaces present on the proteins interact with the hydrophobic surface presented by the arginine clusters. The masking of hydrophobic surface inhibits protein-protein aggregation. This mechanism is also responsible for the hydrotropic effect of arginine on various compounds. It is also explained why other amino acids fail to inhibit the protein aggregation.

Ovarian cancer is the seventh most common gynaecologic malignancy seen in women. Majority of the patients with ovarian cancer are diagnosed at the advanced stage making prognosis poor. The standard management of advanced ovarian cancer includes tumour debulking surgery followed by chemotherapy. Various types of chemotherapeutic regimens have been used to treat advanced ovarian cancer, but the most promising and the currently used standard first-line treatment is carboplatin and paclitaxel. Despite improved clinical response and survival to this combination of chemotherapy, numerous patients either undergo relapse or succumb to the disease as a result of chemotherapy resistance. To understand this phenomenon at a cellular level, various macromolecules such as DNA, messenger RNA and proteins have been developed as biomarkers for chemotherapy response. This review comprehensively summarizes the problem that pertains to chemotherapy resistance in advanced ovarian cancer and provides a good overview of the various biomarkers that have been developed in this field.

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Mammary gland is made up of a branching network of ducts that end in alveoli. Terminally differentiated mammary epithelial cells (MECs) constitute the innermost layer of aveoli. They are milk-secreting cuboidal cells that secrete milk proteins during lactation. Little is known about the expression profile of proteins in the metabolically active MECs during lactation or their functional role in the lactation process. In the present investigation, we have reported the proteome map of MECs in lactating cows using 2DE MALDI-TOF/TOF MS and 1D-Gel-LC-MS/MS. MECs were isolated from milk using immunomagnetic beads and confirmed by RT-PCR and Western blotting. The 1D-Gel-LC-MS/MS and 2DE-MS/MS based approaches led to identification of 431 and 134 proteins, respectively, with a total of 497 unique proteins. Proteins identified in this study were clustered into functional groups using bioinformatics tools. Pathway analysis of the identified proteins revealed 28 pathways (p < 0.05) providing evidence for involvement of various proteins in lactation function. This study further provides experimental evidence for the presence of many proteins that have been predicted in annotated bovine genome. The data generated further provide a set of bovine MEC-specific proteins that will help the researchers to understand the molecular events taking place during lactation.