Glutathione (GSH) is the most abundant cellular tripeptide (L-γglutamate-L-cysteinyl-glycine) which is as critical as oxygen and water. This low molecular mass antioxidant has a very high intracellular concentration that ranges from 1-10 mM and reaches extreme concentration points in malignant cell types. This defender of the cell is compartmentalized in mitochondria, nucleus, peroxisomes, endoplasmic reticulum (ER), and cytosol where it is synthesized. The enzymes involved in GSH redox cycling are important for both cellular free radical and nonradical detoxification. The present review article is covering the crucial roles of GSH to combat oxidative/nitrosative stress related to different diseases/disorders and possible drug designing for new therapies.

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S-nitrosylation of proteins occurs as a consequence of the derivatization of cysteine thiols with nitric oxide (NO) and is often associated with diseases and protein malfunction. Aberrant S-nitrosylation, in addition to other genetic and epigenetic factors, has gained rapid importance as a prime cause of various metabolic, respiratory, and cardiac disorders, with a major emphasis on cancer and neurodegeneration. The S-nitrosoproteome, a term used to collectively refer to the diverse and dynamic repertoire of S-nitrosylated proteins, is relatively less explored in the field of redox biochemistry, in contrast to other covalently modified versions of the same set of proteins. Advancing research is gradually unveiling the enormous clinical importance of S-nitrosylation in the etiology of diseases and is opening up new avenues of prompt diagnosis that harness this phenomenon. Ever since the discovery of the two robust and highly conserved S-nitrosoglutathione reductase and thioredoxin systems as candidate denitrosylases, years of rampant speculation centered around the identification of specific substrates and other candidate denitrosylases, subcellular localization of both substrates and denitrosylases, the position of susceptible thiols, mechanisms of S-denitrosylation under basal and stimulus-dependent conditions, impact on protein conformation and function, and extrapolating these findings towards the understanding of diseases, aging and the development of novel therapeutic strategies. However, newer insights in the ever-expanding field of redox biology reveal distinct gaps in exploring the crucial crosstalk between the redoxins/major denitrosylase systems. Clarifying the importance of the functional overlap of the glutaredoxin, glutathione, and thioredoxin systems and examining their complementary functions as denitrosylases and antioxidant enzymatic defense systems are essential prerequisites for devising a rationale that could aid in predicting the extent of cell survival under high oxidative/nitrosative stress while taking into account the existence of the alternative and compensatory regulatory mechanisms. This review thus attempts to highlight major gaps in our understanding of the robust cellular redox regulation system, which is upheld by the concerted efforts of various denitrosylases and antioxidants.

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The focus of this paper is on regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are subject to classification errors. Latent variable is used to model the binary response. The joint distribution is expressed as a product of the marginal distribution of the continuous response and the conditional distribution of the binary response given the continuous response. Models are proposed to incorporate the measurement error and/or classification errors. Likelihood based analysis is performed to estimate the regression parameters of interest. Theoretical studies are made to find the bias of the likelihood estimates of the model parameters. An extensive simulation study is carried out to investigate the effect of ignoring classification errors and/or measurement error on the estimates of the model parameters. The methodology is illustrated with a data set obtained by conducting a small scale survey.