Targeted disruption of inducible nitric oxide synthase protects against aging, S-nitrosation, and insulin resistance in muscle of male mice

Accumulating evidence has demonstrated that S-nitrosation of proteins plays a critical role in several human diseases. Here, we explored the role of inducible nitric oxide synthase (iNOS) in the S-nitrosation of proteins involved in the early steps of the insulin-signaling pathway and insulin resistance in the skeletal muscle of aged mice. Aging increased iNOS expression and S-nitrosation of major proteins involved in insulin signaling, thereby reducing insulin sensitivity in skeletal muscle. Conversely, aged iNOS-null mice were protected from S-nitrosation–induced insulin resistance. Moreover, pharmacological treatment with an iNOS inhibitor and acute exercise reduced iNOS-induced S-nitrosation and increased insulin sensitivity in the muscle of aged animals. These findings indicate that the insulin resistance observed in aged mice is mainly mediated through the S-nitrosation of the insulin-signaling pathway

Alguns dados sobre a Fauna entomológica da ilha das Flores - Açores

IV Expedição Científica do Departamento de Biologia - Flores 1989Com este trabalho, realizado em Julho de 1989 nas Flores - a ilha mais ocidental do Arquipélago dos Açores -, acrescentaram-se onze espécies de Lepidópteros à lista referenciada para aquela ilha, pertencendo uma à família Lycaenidae (Lampides boeticus L.), oito a familia Noctuidae (Agrotis ipsilon HFN., Brotolomia meticulosa L., Chrysodeixis chalcites ESPER., Heliothis armigera HBN., Noctua atlantica WARREN, Noctua pronuba L., Peridroma saucia HBN., Sesamia nonagrioides LEF.), uma à família Nymphalidae (Vanessa atalanta L.) e uma a família Pyralidae (Glyphodes unionalis HBN.). Entre os demais insectos, foram identificadas cerca de duas dezenas e meia de espécies, distribuídas pelas Ordens Dermaptera, Orthoptera, Dictyoptera, Heteroptera, Homoptera, Coleoptera, Neuroptera, Diptera, Hymenoptera e Collembola. Salienta-se ainda a importância, do ponto de vista agronómico, das pragas Mythimna unipuncta (HAWORTH) e Xestia c-nigrun L. naquela ilha.RÉSUMÉ: Avec ce travail, réalisé en Juillet 1989 a Flores - l'île plus occidental de l'archipel des Açores, onze espèces de Lépidoptères ont été ajoutées à la liste des espèces connus pour cette île, dont une appartient a la famille Lycaenidae (Lampides boelicus L.), huit à la famille Noctuidae (Agrotis ipsilon HFN., Brotolomia meticulosa L. Chrysodeicis chalcites ESPER., Heliothis armigera HBN., Noctua atlantica WARREN, Noctua pronuba L., Peridroma saucia HBN., Sesamia nonagrioides LEF.), une à la famille Nymphalidae (Vanessa atalanta L.) et une à la famille Pyralidae (Glyphodes unionalis HBN.). Parmi les autres insects ont été identifiés environ deux dizaines et demie d'espèces, lesquelles sont réparties par les Ordres Dermaptera, Orthoptera, Dictyoptera, Heteroptera, Homoptera, Coleoptera, Neuroptera, Diptera, Hymenoptera et Collembola. On remarque I'importance, du point de vue agronomique, des ravageurs Mythimna unipuncra (HAWORTH) et Xestia c-nigrum L. dans cette île

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio