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Garg, M.

Publications and source records attributed to Garg, M..

2 recordsLinked to original sources

PSP2, a gene encoding RGG-motif protein, is a novel suppressor of clathrin heavy chain deficiency

Clathrin, made up of the heavy- and light-chains, constitutes one of the most abundant protein in vesicles involved in intracellular protein trafficking and endocytosis. YPR129W, which encodes RGG-motif containing translation repressor was identified as a part of multi-gene construct (SCD6) that suppressed clathrin deficiency. However, the contribution of YPR129W alone in suppressing clathrin deficiency has not been documented. In this study we identify YPR129W as a necessary and sufficient gene in a multigene construct SCD6 that suppresses clathrin deficiency. Importantly, we identify cytoplasmic RGG-motif protein encoding gene PSP2 as a novel suppressor of clathrin deficiency. Three other RGG-motif protein encoding genes SBP1, DED1 and GBP2 do not suppress clathrin deficiency. DHH1, a DEAD-box RNA helicase with translation repression activity also fails to rescue clathrin deficiency. -factor secretion assay suggests that suppression of clathrin deficiency by SCD6 and PSP2 is not mediated by the rescue of the trans-Golgi network (TGN) protein sorting defect observed in the absence of CHC1. Detailed domain analysis of the two suppressors reveals that the RGG-motif of both Scd6 and Psp2 is important for suppressing clathrin deficiency. Additionally, the Lsm domain deletion as well as the arginine to alanine mutation in the arginine methylation defective (AMD) mutant render Scd6 defective in suppressing clathrin deficiency. Overall based on our results using SCD6 and PSP2 proteins, we identify a novel role of RGG-motif in suppressing clathrin deficiency. Since both the suppressors are RNA-binding granule-resident proteins, this study opens an exciting avenue for exploring the connection between clathrin function and cytoplasmic RNA metabolism.

cell biology

Tumour gene expression signature in primary melanoma predicts long-term outcomes: A prospective multicentre study

PurposePredicting outcomes after resection of primary melanoma remains crude, primarily based on tumour thickness. We explored gene expression signatures for their ability to better predict outcomes. MethodsDifferential expression analysis of 194 primary melanomas resected from patients who either developed distant metastasis (n=89) or did not (n=105) was performed. We identified 121 metastasis-associated genes that were included in our prognostic signature, "Cam_121". Several machine learning classification models were trained using nested leave- one-out cross validation (LOOCV) to test the signatures capacity to predict metastases, as well as regression models to predict survival. The prognostic accuracy was externally validated in two independent datasets. ResultsCam_121 performed significantly better in predicting distant metastases than any of the models trained with the clinical covariates alone (pAccuracy=4.92x10-3), as well as those trained with two published prognostic signatures. Cam_121 expression score was strongly associated with progression-free survival (HR=1.7, p=3.44x10-6), overall survival (HR=1.73, p=7.71x10-6) and melanoma-specific survival (HR=1.59, p=0.02). Cam_121 expression score also negatively correlated with measures of immune cell infiltration ({rho}=-0.73, p<2.2x10-16), with a higher score representing reduced tumour lymphocytic infiltration and a higher absolute 5-year risk of death in stage II melanoma. ConclusionsThe Cam_121 primary melanoma gene expression signature outperformed currently available alternatives in predicting the risk of distant recurrence. The signature confirmed (using unbiased approaches) the central prognostic importance of immune cell infiltration in long-term patient outcomes and could be used to identify stage II melanoma patients at highest risk of metastases and poor survival who might benefit most from adjuvant therapies. Translational relevancePredicting outcomes after resection of primary melanoma is currently based on traditional histopathological staging, however survival outcomes within these disease stages varies markedly. Since adjuvant systemic therapies are now being used routinely, accurate prognostic information is needed to better risk stratify patients and avoid unnecessary use of high cost, potentially harmful drugs, as well as to inform future adjuvant strategies. The Cam_121 gene expression signature appears to have this capability and warrants evaluation in prospective clinical trials.

bioinformatics