02546nas a2200349   4500008004100000022001400041245010800055210006900163260008000232300001400312490000800326520137300334653001401707653002001721653001501741653001201756100002501768700001701793700001901810700002001829700001901849700001901868700002601887700001801913700002101931700002101952700002301973700002901996700001602025700002402041856013102065       2014                        eng d  a0027-842400aIFN-gamma signaling maintains skin pigmentation homeostasis through regulation of melanosome maturation0 aIFNgamma signaling maintains skin pigmentation homeostasis throu  a2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USAbNATL ACAD SCIENCEScFEB  a2301-23060 v1113 a<p>Cellular homeostasis is an outcome of complex interacting processes with nonlinear feedbacks that can span distinct spatial and temporal dimensions. Skin tanning is one such dynamic response that maintains genome integrity of epidermal cells. Although pathways underlying hyperpigmentation cascade are recognized, negative feedback regulatory loops that can dampen the activated melanogenesis process are not completely understood. In this study, we delineate a regulatory role of IFN-gamma in skin pigmentation biology. We show that IFN-gamma signaling impedes maturation of the key organelle melanosome by concerted regulation of several pigmentation genes. Withdrawal of IFN-gamma signal spontaneously restores normal cellular programming. This effect in melanocytes is mediated by IFN regulatory factor-1 and is not dependent on the central regulator microphthalmia-associated transcription factor. Chronic IFN-gamma signaling shows a clear hypopigmentation phenotype in both mouse and human skin. Interestingly, IFN-gamma KO mice display a delayed recovery response to restore basal state of epidermal pigmentation after UV-induced tanning. Together, our studies delineate a new spatiotemporal role of the IFN-gamma signaling network in skin pigmentation homeostasis, which could have implications in various cutaneous depigmentary and malignant disorders.</p>10adetanning10agene regulation10ainterferon10amelanin1 aNatarajan, Vivek, T.1 aGanju, Parul1 aSingh, Archana1 aVijayan, Vinaya1 aKirty, Kritika1 aYadav, Shalini1 aPuntambekar, Shraddha1 aBajaj, Sonali1 aDani, Prachi, P.1 aKar, Hemanta, K.1 aGadgil, Chetan, J.1 aNatarajan, Krishnamurthy1 aRani, Rajni1 aGokhale, Rajesh, S.  uhttps://library.ncl.res.in/content/ifn-gamma-signaling-maintains-skin-pigmentation-homeostasis-through-regulation-melanosome-002099nas a2200181   4500008004100000022001400041245008800055210006900143260010300212300001100315490000800326520137100334100003001705700002101735700002101756700002301777856011701800       2016                        eng d  a0021-925800aIdentification of unintuitive features of sumoylation through mathematical modeling0 aIdentification of unintuitive features of sumoylation through ma  a9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USAbAMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INCcAPR  a9458-+0 v2913 a<p>Sumoylation is a multistep, multienzymatic post-translational modification in which a small ubiquitin-like modifier protein (SUMO) is attached to the target. We present the first mathematical model for sumoylation including enzyme mechanism details such as autosumoylation of E2 and multifunctional nature of SENP. Simulations and analysis reveal three nonobvious properties for the long term response, modeled as an open system: (i) the steady state sumoylation level is robust to variation in several enzyme properties; (ii) even when autosumoylation of E2 results in equal or higher activity, the target sumoylation levels are lower; and (iii) there is an optimal SENP concentration at which steady state target sumoylation level is maximum. These results are qualitatively different for a short term response modeled as a closed system, where e.g. sumoylation always decreases with increasing SENP levels. Simulations with multiple targets suggest that the available SUMO is limiting, indicating a possible explanation for the experimentally observed low fractional sumoylation. We predict qualitative differences in system responses at short post-translational and longer transcriptional time scales. We thus use this mechanism-based model to explain system properties and generate testable hypotheses for existence and mechanism of unexpected responses.</p>1 aPuntambekar, Shraddha, S.1 aNyayanit, Dimpal1 aSaxena, Priyanka1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/identification-unintuitive-features-sumoylation-through-mathematical-modeling