Stimulation of peripheral Kappa opioid receptors inhibits inflammatory hyperalgesia via activation of the PI3Kγ/AKT/nNOS/NO signaling pathway

<p>Abstract</p> <p>Background</p> <p>In addition to their central effects, opioids cause peripheral analgesia. There is evidence showing that peripheral activation of kappa opioid receptors (KORs) inhibits inflammatory pain. Moreover, peripheral μ-opioid receptor (MOR) activation are able to direct block PGE₂-induced ongoing hyperalgesia However, this effect was not tested for KOR selective activation. In the present study, the effect of the peripheral activation of KORs on PGE₂-induced ongoing hyperalgesia was investigated. The mechanisms involved were also evaluated.</p> <p>Results</p> <p>Local (paw) administration of U50488 (a selective KOR agonist) directly blocked, PGE₂-induced mechanical hyperalgesia in both rats and mice. This effect was reversed by treating animals with L-NMMA or N-propyl-L-arginine (a selective inhibitor of neuronal nitric oxide synthase, nNOS), suggesting involvement of the nNOS/NO pathway. U50488 peripheral effect was also dependent on stimulation of PI3Kγ/AKT because inhibitors of these kinases also reduced peripheral antinociception induced by U50488. Furthermore, U50488 lost its peripheral analgesic effect in PI3Kγ null mice. Observations made in vivo were confirmed after incubation of dorsal root ganglion cultured neurons with U50488 produced an increase in the activation of AKT as evaluated by western blot analyses of its phosphorylated form. Finally, immunofluorescence of DRG neurons revealed that KOR-expressing neurons also express PI3Kγ (≅ 43%).</p> <p>Conclusions</p> <p>The present study indicates that activation of peripheral KORs directly blocks inflammatory hyperalgesia through stimulation of the nNOS/NO signaling pathway which is probably stimulated by PI3Kγ/AKT signaling. This study extends a previously study of our group suggesting that PI3Kγ/AKT/nNOS/NO is an important analgesic pathway in primary nociceptive neurons.</p

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost