The strategy of translationally fusing the two subunits of human chorionic gonadotropin (hCG) has been used to produce recombinant single chain hCG in which the C-terminus of the subunit is fused to the N-terminus without any linker using Pichia pastoris expression system. The Pichiaexpressed hCG (phCG ) attained an overall conformation similar to that of hCG, and could bind to the receptor and elicit biological response, suggesting that receptor binding and signal transduction can take place even with a molecule having blocked the C-terminus of the subunit. The carboxyl terminal of the subunit has been shown to be involved in hormone binding and signal transduction of all the heterodimeric glycoprotein hormones. However, deletion of five amino acids from the C-terminus of the subunit in the single chain hCG did not alter the overall conformation of the fusion molecule and its receptor binding ability, but led to a significant reduction in its ability to elicit biological response. These data show that these five amino acids at the C-terminus of the subunit in the single chain hCG are not absolutely essential for attaining a conformation required for receptor binding, but are essential for obtaining a full biological response.

Human chorionic gonadotropin (hCG), a heterodimeric glycoprotein hormone, is composed of an subunit noncovalently associated with the hormone-specific subunit. The objective of the present study was recombinant expression of properly folded, biologically active hCG and its subunits using an expression system that could be used for structure-function studies while providing adequate quantities of the hormone for immunocontraceptive studies. We report here expression of biologically active hCG and its subunits using a yeast expression system, Pichia pastoris. The recombinant hCG and hCG subunits were secreted into the medium and the levels of expression achieved at shake culture level were 24 and 2·7-3 mg/l secretory medium respectively.Co-expression of both subunits in the same cell resulted in secretion of heterodimeric hCG into the medium. The pichia-expressed hCG was immunologically similar to the native hormone, capable of binding to the LH receptors and stimulating a biological response in vitro. Surprisingly, the maximal response obtained was twice that obtained with the native hCG. The level of expression of hCG achieved was 12-16 mg/l secretory medium and is expected to increase several-fold in a fermentor. Thus the Pichia expression system is capable of hyperexpressing properly folded, biologically active hCG and is suitable for structurefunction studies of the hormone.

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The General Circulation Model (GCM) simulation had shown potential in yielding long-term statistical attributes of Indian precipitation and temperature which exhibit substantial inter-seasonal variation. However, GCM outputs experience substantial model structural bias that needs to be reduced prior to forcing them into hydrological models and using them in deriving insights on the impact of climate change. Traditionally, univariate bias correction approaches that can successfully yield the mean and the standard deviation of the observed variable, while ignoring the interdependence between multiple variables, are considered. Limited efforts have been made to develop bivariate bias-correction over a large region with an additional focus on the cross-correlation between two variables. Considering these, the current study suggests two objectives: (i) To apply a bivariate bias correction approach based on bivariate ranking to reduce bias in GCM historical simulation over India, (ii) To explore the potential of the proposed approach in yielding inter-seasonal variations in precipitation and temperature while also yielding the cross-correlation. This study considers three GCMs with fourteen ensemble members from the Coupled Model Intercomparison project Assessment Report-5 (CMIP5). The bivariate ranks of meteorological pairs are applied on marginal ranks till a stationary position is achieved. Results show that the bivariate approach substantially reduces bias in the mean and the standard deviation. Further, the bivariate approach performs better during non-monsoon months as compared to monsoon months in reducing the bias in the cross-correlation between precipitation and temperature as the typical negative cross-correlation structure is common during non-monsoon months. The study finds that the proposed approach successfully reproduces inter-seasonal variation in metrological variables across India.

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