Differential G protein activation by the long and short isoforms of the dopamine D2 receptor.

BACKGROUND AND PURPOSE: The dopamine D2 receptor is expressed as a short (D2S) and a long (D2L) isoform with 29 additional amino acids in the third intracellular loop. The D2S isoform shows higher presynaptic expression than the D2L isoform, and decreased D2S expression has recently been linked to an increased risk for schizophrenia. Here, we present the first investigation, at receptor isoform level, of kinetic differences in the G protein activation profiles of the D2S, compared with the D2L isoform. EXPERIMENTAL APPROACH: We employed a NanoBRET-based approach to G protein dissociation to interrogate the time-resolved coupling profile of 3×HA-tagged D2L and D2S to Gαi/o/z proteins in vitro. KEY RESULTS: Using dopamine as a D2 receptor agonist, we observed a more pronounced activation of Gαo and Gαz than Gαi proteins by D2L compared with D2S. This differentiation was not observed for D2S, which activated Gαo and Gαz with lower efficacy than D2L. These signalling differences were preserved on second messenger level and were not due to differences in receptor expression. Expanding to a set of seven full and partial D2 receptor agonists showed these effects were not restricted to dopamine but rather a mutual, receptor-associated property. Contrasting this trend, we found that D2S activated G proteins faster than D2L upon full receptor activation. CONCLUSION AND IMPLICATIONS: The findings highlight that both D2L and D2S are mechanistically able to activate all non-visual Gαi/o proteins. Thereby, they add to previous reports about isoform-specificity to certain Gαi/o proteins observed in specific cell types.

Nuclear morphology is a deep learning biomarker of cellular senescence.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accuracy, and investigate murine astrocytes, murine neurons, and fibroblasts with premature aging in culture. After generalizing our approach, the predictor recognizes higher rates of senescence in p21-positive and ethynyl-2'-deoxyuridine (EdU)-negative nuclei in tissues and shows an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating medical records reveals that higher rates of senescent cells correspond to decreased rates of malignant neoplasms and increased rates of osteoporosis, osteoarthritis, hypertension and cerebral infarction. In sum, we show that morphological alterations of the nucleus can serve as a deep learning predictor of senescence that is applicable across tissues and species and is associated with health outcomes in humans.