Long-Chain and Very Long-Chain Ceramides Mediate Doxorubicin-Induced Toxicity and Fibrosis

Doxorubicin (Dox) is a chemotherapeutic agent with cardiotoxicity associated with profibrotic effects. Dox increases ceramide levels with pro-inflammatory effects, cell death, and fibrosis. The purpose of our study was to identify the underlying ceramide signaling pathways. We aimed to characterize the downstream effects on cell survival, metabolism, and fibrosis. Human fibroblasts (hFSF) were treated with 0.7 µM of Dox or transgenically overexpressed ceramide synthase 2 (FLAG-CerS2). Furthermore, cells were pre-treated with MitoTempo (MT) (2 h, 20 µM) or Fumonisin B1 (FuB) (4 h, 100 µM). Protein expression was measured by Western blot or immunofluorescence (IF). Ceramide levels were determined with mass spectroscopy (MS). Visualizations were conducted using laser scanning microscopy (LSM) or electron microscopy. Mitochondrial activity was measured using seahorse analysis. Dox and CerS2 overexpression increased CerS2 protein expression. Coherently, ceramides were elevated with the highest peak for C24:0. Ceramide- induced mitochondrial ROS production was reduced with MT or FuB preincubation. Mitochondrial homeostasis was reduced and accompanied by reduced ATP production. Our data show that the increase in pro-inflammatory ceramides is an essential contributor to Dox side-effects. The accumulation of ceramides resulted in a lipotoxic shift and subsequently mitochondrial structural and functional damage, which was partially reversible following inhibition of ceramide synthesis

Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence

[EN] This paper proposes an interpolation model for monthly rainfall in large areas of complex orography. It has been implemented in the Iberian Peninsula (continental territories of Spain and Portugal), Balearic and Canary Islands covering a territory of almost 600.000km(2). To do this a data set that comprises a total number of 11,822 monthly precipitation series has been created (11,042 provided by the Spanish Meteorological Agency and 780 provided by the National Water Resources Information System of the Portuguese Water Institute). The data set covers the period from October 1940 until September 2005. The interpolation model has been based on the assumption of two different components on monthly precipitation. The first component reflects local and seasonal characteristics and 24 different mean monthly precipitation maps (12) and SDs maps (12) compose it. It considers the varying influence of physiographic variables such as altitude and orientation. The second precipitation component reflects the synoptic pattern that dominated each month of the series and it is composed by series of anomalies of monthly precipitation (780). Anomalies have been interpolated by means of ordinary kriging once local spatial continuity was assumed. Gridded maps of each variable have been developed at 200m resolution following a hybrid methodology that implements two different interpolation techniques. The first technique applies a regression analysis to derive maps depending on altitude and orientation; the second one is a weighting technique to consider the non-linearity of the precipitation/altitude dependence. Cross validation has been applied to estimate the goodness of both techniques. Results show an average annual precipitation of 655mm/year. Although this figure is only 4% less than the estimate of MAGRAMA (2004), regional and local differences are highlighted when the spatial distribution is considered. The model constitutes a comprehensive implementation considering the availability of historical records and the need of avoiding slow calculations in large territories.Ministry of Economy, Industry and Competitiveness, Grant/Award Number: CGL2014-52571-RÁlvarez-Rodríguez, J.; Llasat, M.; Estrela Monreal, T. (2019). Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence. International Journal of Climatology. 39(10):3962-3975. https://doi.org/10.1002/joc.6051S396239753910AEMET.2011Atlas Climático Ibérico. (Iberian Climate Atlas) VV.AA. Agencia Estatal de Meteorología. Ministerio de Medio Ambiente. ISBN: 978‐84‐7837‐079‐5. Available at:http://www.aemet.es/documentos/es/conocermas/publicaciones/Atlas-climatologico/Atlas.pdf[Accessed 14th February 2018]Álvarez‐Rodríguez J.2011.Estimación de la distribución espacial de la precipitación en zonas montañosas mediante métodos geoestadísticos (Analysis of spatial distribution of precipitation in mountainous areas by means of geostatistical analysis). PhD Thesis. Polytechnic University of Madrid Higher Technical School of Civil EngineeringÁlvarez-Rodríguez, J., Llasat, M. C., & Estrela, T. (2017). Analysis of geographic and orographic influence in Spanish monthly precipitation. International Journal of Climatology, 37, 350-362. doi:10.1002/joc.5007Barros, A. P., Kim, G., Williams, E., & Nesbitt, S. W. (2004). Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Natural Hazards and Earth System Sciences, 4(1), 29-51. doi:10.5194/nhess-4-29-2004Barstad, I., Grabowski, W. W., & Smolarkiewicz, P. K. (2007). Characteristics of large-scale orographic precipitation: Evaluation of linear model in idealized problems. Journal of Hydrology, 340(1-2), 78-90. doi:10.1016/j.jhydrol.2007.04.005Creutin, J. D., & Obled, C. (1982). Objective analyses and mapping techniques for rainfall fields: An objective comparison. Water Resources Research, 18(2), 413-431. doi:10.1029/wr018i002p00413Daly, C., Neilson, R. P., & Phillips, D. L. (1994). A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology, 33(2), 140-158. doi:10.1175/1520-0450(1994)0332.0.co;2Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., … Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031-2064. doi:10.1002/joc.1688Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., & Swift, L. W. (2017). High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset. International Journal of Climatology, 37, 124-137. doi:10.1002/joc.4986Dhar, O. N., & Nandargi, S. (2004). Rainfall distribution over the Arunachal Pradesh Himalayas. Weather, 59(6), 155-157. doi:10.1256/wea.87.03Falivene, O., Cabrera, L., Tolosana-Delgado, R., & Sáez, A. (2010). Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example. Computers & Geosciences, 36(4), 512-519. doi:10.1016/j.cageo.2009.09.015Fiering, B., & Jackson, B. (1971). Synthetic Streamflows. Water Resources Monograph. doi:10.1029/wm001Gambolati, G., & Volpi, G. (1979). A conceptual deterministic analysis of the kriging technique in hydrology. Water Resources Research, 15(3), 625-629. doi:10.1029/wr015i003p00625Gómez-Hernández, J. J., Cassiraga, E. F., Guardiola-Albert, C., & Rodríguez, J. Á. (2001). Incorporating Information from a Digital Elevation Model for Improving the Areal Estimation of Rainfall. geoENV III — Geostatistics for Environmental Applications, 67-78. doi:10.1007/978-94-010-0810-5_6Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1-2), 113-129. doi:10.1016/s0022-1694(00)00144-xHanson, C. L. (1982). DISTRIBUTION AND STOCHASTIC GENERATION OF ANNUAL AND MONTHLY PRECIPITATION ON A MOUNTAINOUS WATERSHED IN SOUTHWEST IDAHO. Journal of the American Water Resources Association, 18(5), 875-883. doi:10.1111/j.1752-1688.1982.tb00085.xLloyd, C. D. (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. Journal of Hydrology, 308(1-4), 128-150. doi:10.1016/j.jhydrol.2004.10.026Marquı́nez, J., Lastra, J., & Garcı́a, P. (2003). Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis. Journal of Hydrology, 270(1-2), 1-11. doi:10.1016/s0022-1694(02)00110-5Martínez-Cob, A. (1996). Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology, 174(1-2), 19-35. doi:10.1016/0022-1694(95)02755-6Mitáš, L., & Mitášová, H. (1988). General variational approach to the interpolation problem. Computers & Mathematics with Applications, 16(12), 983-992. doi:10.1016/0898-1221(88)90255-6Naoum, S., & Tsanis, I. K. (2004). Orographic Precipitation Modeling with Multiple Linear Regression. Journal of Hydrologic Engineering, 9(2), 79-102. doi:10.1061/(asce)1084-0699(2004)9:2(79)Ninyerola, M., Pons, X., & Roure, J. M. (2006). Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System. Theoretical and Applied Climatology, 89(3-4), 195-209. doi:10.1007/s00704-006-0264-2Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691. doi:10.1016/j.cageo.2004.03.012Rotunno, R., & Ferretti, R. (2001). Mechanisms of Intense Alpine Rainfall. Journal of the Atmospheric Sciences, 58(13), 1732-1749. doi:10.1175/1520-0469(2001)0582.0.co;2Singh, P., Ramasastri, K. S., & Kumar, N. (1995). Topographical Influence on Precipitation Distribution in Different Ranges of Western Himalayas. Hydrology Research, 26(4-5), 259-284. doi:10.2166/nh.1995.0015Tabios, G. Q., & Salas, J. D. (1985). A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION. Journal of the American Water Resources Association, 21(3), 365-380. doi:10.1111/j.1752-1688.1985.tb00147.xTHIESSEN, A. H. (1911). PRECIPITATION AVERAGES FOR LARGE AREAS. 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Targeted delivery of a phosphoinositide 3-kinase γ inhibitor to restore organ function in sepsis

Jaundice, the clinical hallmark of infection-associated liver dysfunction, reflects altered membrane organization of the canalicular pole of hepatocytes and portends poor outcomes. Mice lacking phosphoinositide 3-kinase-γ (PI3Kγ) are protected against membrane disintegration and hepatic excretory dysfunction. However, they exhibit a severe immune defect that hinders neutrophil recruitment to sites of infection. To exploit the therapeutic potential of PI3Kγ inhibition in sepsis, a targeted approach to deliver drugs to hepatic parenchymal cells without compromising other cells, in particular immune cells, seems warranted. Here, we demonstrate that nanocarriers functionalized through DY-635, a fluorescent polymethine dye, and a ligand of organic anion transporters can selectively deliver therapeutics to hepatic parenchymal cells. Applying this strategy to a murine model of sepsis, we observed the PI3Kγ-dependent restoration of biliary canalicular architecture, maintained excretory liver function, and improved survival without impairing host defense mechanisms. This strategy carries the potential to expand targeted nanomedicines to disease entities with systemic inflammation and concomitantly impaired barrier functionality

Sphingosine 1-phosphate lyase ablation disrupts presynaptic architecture and function via an ubiquitin- proteasome mediated mechanism

The bioactive lipid sphingosine 1-phosphate (S1P) is a degradation product of sphingolipids that are particularly abundant in neurons. We have shown previously that neuronal S1P accumulation is toxic leading to ER-stress and an increase in intracellular calcium. To clarify the neuronal function of S1P, we generated brain-specific knockout mouse models in which S1P-lyase (SPL), the enzyme responsible for irreversible S1P cleavage was inactivated. Constitutive ablation of SPL in the brain (SPL(fl/fl/Nes)) but not postnatal neuronal forebrain-restricted SPL deletion (SPL(fl/fl/CaMK)) caused marked accumulation of S1P. Hence, altered presynaptic architecture including a significant decrease in number and density of synaptic vesicles, decreased expression of several presynaptic proteins, and impaired synaptic short term plasticity were observed in hippocampal neurons from SPL(fl/fl/Nes) mice. Accordingly, these mice displayed cognitive deficits. At the molecular level, an activation of the ubiquitin-proteasome system (UPS) was detected which resulted in a decreased expression of the deubiquitinating enzyme USP14 and several presynaptic proteins. Upon inhibition of proteasomal activity, USP14 levels, expression of presynaptic proteins and synaptic function were restored. These findings identify S1P metabolism as a novel player in modulating synaptic architecture and plasticity

Cell-surface residence of sphingosine 1-phosphate receptor 1 on lymphocytes determines lymphocyte egress kinetics

The sphingosine 1-phosphate receptor 1 (S1P1) promotes lymphocyte egress from lymphoid organs. Previous work showed that agonist-induced internalization of this G protein–coupled receptor correlates with inhibition of lymphocyte egress and results in lymphopenia. However, it is unclear if S1P1 internalization is necessary for this effect. We characterize a knockin mouse (S1p1rS5A/S5A) in which the C-terminal serine-rich S1P1 motif, which is important for S1P1 internalization but dispensable for S1P1 signaling, is mutated. T cells expressing the mutant S1P1 showed delayed S1P1 internalization and defective desensitization after agonist stimulation. Mutant mice exhibited significantly delayed lymphopenia after S1P1 agonist administration or disruption of the vascular S1P gradient. Adoptive transfer experiments demonstrated that mutant S1P1 expression in lymphocytes, rather than endothelial cells, facilitated this delay in lymphopenia. Thus, cell-surface residency of S1P1 on T cells is a primary determinant of lymphocyte egress kinetics in vivo

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field