A Novel Inactivated Intranasal Respiratory Syncytial Virus Vaccine Promotes Viral Clearance without Th2 Associated Vaccine-Enhanced Disease

Respiratory syncytial virus (RSV) is a leading cause of bronchiolitis and pneumonia in young children worldwide, and no vaccine is currently available. Inactivated RSV vaccines tested in the 1960's led to vaccine-enhanced disease upon viral challenge, which has undermined RSV vaccine development. RSV infection is increasingly being recognized as an important pathogen in the elderly, as well as other individuals with compromised pulmonary immunity. A safe and effective inactivated RSV vaccine would be of tremendous therapeutic benefit to many of these populations.In these preclinical studies, a mouse model was utilized to assess the efficacy of a novel, nanoemulsion-adjuvanted, inactivated mucosal RSV vaccine. Our results demonstrate that NE-RSV immunization induced durable, RSV-specific humoral responses, both systemically and in the lungs. Vaccinated mice exhibited increased protection against subsequent live viral challenge, which was associated with an enhanced Th1/Th17 response. In these studies, NE-RSV vaccinated mice displayed no evidence of Th2 mediated immunopotentiation, as has been previously described for other inactivated RSV vaccines.These studies indicate that nanoemulsion-based inactivated RSV vaccination can augment viral-specific immunity, decrease mucus production and increase viral clearance, without evidence of Th2 immune mediated pathology

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

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

A Sodium-Translocating Module Linking Succinate Production to Formation of Membrane Potential in Prevotella bryantii

Ruminants such as cattle and sheep depend on the breakdown of carbohydrates from plant-based feedstuff which is accomplished by the microbial community in the rumen. Roughly 40% of the rumen microbiota belong to the family of which ferment sugars to organic acids such as acetate, propionate as well as succinate. These substrates are important nutrients for the ruminant. In a metaproteome analysis of the rumen of cattle, proteins that are homologous to the Na -translocating NADH:quinone oxidoreductase (NQR) and the quinone:fumarate reductase (QFR) were identified in different species. Here we show that fumarate reduction to succinate in anaerobically growing is coupled to chemiosmotic energy conservation by a supercomplex composed of NQR and QFR. This odium-translocating ADH:umarate oxidoeductase (SNFR) supercomplex was enriched by BN-PAGE and characterized by in-gel enzyme activity staining and mass spectrometry. High NADH oxidation (850 nmol min mg), quinone reduction (490 nmol min mg) and fumarate reduction (1200 nmol min mg) activities, together with high expression levels, demonstrate that SNFR represents a charge-separating unit in . Absorption spectroscopy of SNFR exposed to different substrates revealed intramolecular electron transfer from the FAD cofactor in NQR to heme cofactors in QFR. SNFR catalyzed the stoichiometric conversion of NADH and fumarate to NAD and succinate. We propose that the regeneration of NAD in is intimately linked to the build-up of an electrochemical gradient which powers ATP synthesis by electron transport phosphorylation. Feeding strategies for ruminants are designed to optimize nutrient efficiency for animals and to prevent energy losses like enhanced methane production. Key to this are the fermentative reactions of the rumen microbiota, dominated by sp. We show that succinate formation by is coupled to NADH oxidation and sodium-gradient formation by a newly described supercomplex consisting of Na-translocating NADH:quinone oxidoreductase (NQR) and fumarate reductase (QFR), representing the odium-translocating ADH:umarate oxidoeductase (SNFR) supercomplex. SNFR is the major charge-separating module, generating an electrochemical sodium gradient in . Our findings offer clues to the observation that use of fumarate as feed additive does not significantly increase succinate production, or decrease methanogenesis, by the microbial community in the rumen