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Golgi membrane protein 1 (GOLM1) is a Golgi-resident type 2 transmembrane protein known to be overexpressed in several cancers, including he-patocellular carcinoma (HCC), as well as in viral in-fections. However, the role of GOLM1 in lipid metabolism remains enigmatic. In this study, we employed siRNA-mediated GOLM1 depletion in Huh -7 HCC cells to study the role of GOLM1 in lipid metabolism. Mass spectrometric lipidomic analysis in GOLM1 knockdown cells showed an aberrant accu-mulation of sphingolipids, such as ceramides, hex-osylceramides, dihexosylceramides, sphinganine, sphingosine, and ceramide phosphate, along with cholesteryl esters. Furthermore, we observed a reduction in phosphatidylethanolamines and lyso-phosphatidylethanolamines. In addition, Seahorse extracellular flux analysis indicated a reduction in mitochondrial oxygen consumption rate upon GOLM1 depletion. Finally, alterations in Golgi struc-ture and distribution were observed both by electron microscopy imaging and immunofluorescence mi-croscopy analysis. Importantly, we found that GOLM1 depletion also affected cell proliferation and cell cycle progression in Huh-7 HCC cells. The Golgi structural defects induced by GOLM1 reduction might potentially affect the trafficking of proteins and lipids leading to distorted intracellular lipid ho-meostasis, which may result in organelle dysfunction and altered cell growth. In conclusion, we demon-strate that GOLM1 depletion affects sphingolipid metabolism, mitochondrial function, Golgi structure, and proliferation of HCC cells.

Large-scale interdependent teleconnections influence precipitation at various spatio-temporal scales. Selecting the relevant climate indices based on geographical location is important. Therefore, this study focuses on the spatial multivariate selection of climate indices influencing precipitation variability over India, using the partial least square regression and variable importance of projection technique. 17 climate indices and gridded precipitation dataset (0.25 × 0.25°) from the Indian Meteorological Department for 1951–2020 at a monthly scale are considered. Results show that among all the indices, Nino 4, Nino 1 + 2, Trans Nino Index, Atlantic Multidecadal Oscillation (AMO), quasi-biennial oscillation (QBO), Arctic oscillation (AO), and North Atlantic Oscillation (NAO) have a significant influence on precipitation over India. Further, within homogenous regions, it is found that the Southern Oscillation Index and Nino 3.4 are selected majorly in the South Peninsular compared to other regions. The NAO/AO show a similar pattern and was found to be relevant in the Northeast region (>89%). AMO is selected mainly in Northwest, and West Central (>80%), AMO and QBO at about 70% of grid locations over Central Northeast India. It is to be noted that the number of climate indices identified varies spatially across the study region. Overall, the study highlights identifying the relevant climate indices would aid in developing improved predictive and parsimonious models for agriculture planning and water resources management

Extreme precipitation events are increasing due to climate change and leading to frequent flooding and severe droughts. These events vary in both space and time and are positively correlated with the climate teleconnections representing the oscillations of the ocean-atmospheric system. However, large numbers of climate signals and the precipitation response may vary at certain time lags with each climate indices. This study identifies time lags for climate indices using cross-correlation analysis between extreme precipitation and climate indices. These time-lagged climate indices are used as a covariate to fit a non-stationary generalized extreme value (NS-GEV) model over Monsoon Asia. The best NS-GEV model among non-stationary models is selected based on Akaike information criteria (AICc). Results show that the correlation between precipitation and different climate indices is spatially non-uniform. Incorporating time lag climate indices as covariate improves the performance of the non-stationary models. This study helps in understanding the teleconnections influencing the variation of extreme precipitation in a non-stationary framework and to revise the infrastructure designs and flood risk assessment.