Targeting the TCA cycle can ameliorate widespread axonal energy deficiency in neuroinflammatory lesions

Inflammation in the central nervous system can impair the function of neuronal mitochondria and contributes to axon degeneration in the common neuroinflammatory disease multiple sclerosis (MS). Here we combine cell-type-specific mitochondrial proteomics with in vivo biosensor imaging to dissect how inflammation alters the molecular composition and functional capacity of neuronal mitochondria. We show that neuroinflammatory lesions in the mouse spinal cord cause widespread and persisting axonal ATP deficiency, which precedes mitochondrial oxidation and calcium overload. This axonal energy deficiency is associated with impaired electron transport chain function, but also an upstream imbalance of tricarboxylic acid (TCA) cycle enzymes, with several, including key rate-limiting, enzymes being depleted in neuronal mitochondria in experimental models and in MS lesions. Notably, viral overexpression of individual TCA enzymes can ameliorate the axonal energy deficits in neuroinflammatory lesions, suggesting that TCA cycle dysfunction in MS may be amendable to therapy

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

Consequences of the Lack of TNFR1 in Ouabain Response in the Hippocampus of C57BL/6J Mice

Ouabain is a cardiac glycoside that has a protective effect against neuroinflammation at low doses through Na+/K+-ATPase signaling and that can activate tumor necrosis factor (TNF) in the brain. TNF plays an essential role in neuroinflammation and regulates glutamate receptors by acting on two different receptors (tumor necrosis factor receptor 1 [TNFR1] and TNFR2) that have distinct functions and expression. The activation of constitutively and ubiquitously expressed TNFR1 leads to the expression of pro-inflammatory cytokines. Thus, this study aimed to elucidate the effects of ouabain in a TNFR1 knockout (KO) mouse model. Interestingly, the hippocampus of TNFR1 KO mice showed a basal increase in both TNFR2 membrane expression and brain-derived neurotrophic factor (BDNF) release, suggesting a compensatory mechanism. Moreover, ouabain activated TNF-α-converting enzyme/a disintegrin and metalloprotease 17 (TACE/ADAM17), decreased N-methyl-D-aspartate (NMDA) receptor subunit 2A (NR2A) expression, and induced anxiety-like behavior in both genotype animals, independent of the presence of TNFR1. However, ouabain induced an increase in interleukin (IL)-1β in the hippocampus, a decrease in IL-6 in serum, and an increase in NMDA receptor subunit 1 (NR1) only in wild-type (WT) mice, indicating that TNFR1 or TNFR2 expression may be important for some effects of ouabain. Collectively, our results indicate a connection between ouabain signaling and TNFR1, with the effect of ouabain partially dependent on TNFR1