Survey of benchmarks in metadata quality: Initial findings

Librar

A frequent SNP in TRIM5α strongly enhances the innate immune response against LINE-1 elements

The intracellular restriction factor TRIM5α inhibits endogenous LINE-1 retroelements. It induces innate immune signaling cascades upon sensing of cytoplasmic LINE-1 complexes, thereby underlining its importance for protecting the human genome from harmful retrotransposition events. Here, we show that a frequent SNP within the RING domain of TRIM5α, resulting in the variant H43Y, blocks LINE-1 retrotransposition with higher efficiency compared to TRIM5α WT. Upon sensing of LINE-1 complexes in the cytoplasm, TRIM5α H43Y activates both NF-κB and AP-1 signaling pathways more potently than TRIM5α WT, triggering a strong block of the LINE-1 promoter. Interestingly, the H43Y allele lost its antiviral function suggesting that its enhanced activity against endogenous LINE-1 elements is the driving force behind its maintenance within the population. Thus, our study suggests that the H43Y variant of the restriction factor and sensor TRIM5α persists within the human population since it preserves our genome from uncontrolled LINE-1 retrotransposition with higher efficiency

LC3-Associated Phagocytosis Is Required for Dendritic Cell Inflammatory Cytokine Response to Gut Commensal Yeast Saccharomyces cerevisiae

The human fungal microbiota known as mycobiota is increasingly recognized as a critical factor in human gut health and disease. Non-pathogenic commensal yeasts such as Saccharomyces cerevisiae promote homeostasis in the gut, whereas dysbiosis of the gut mycobiota is associated with inflammation. Glycan-binding receptors (lectins) are key host factors in host–mycobiota interaction in the gut. They are expressed on immune cells such as dendritic cells (DCs) and recognize fungal polysaccharides. This interaction is imperative to mount appropriate immune responses for immune homeostasis in the gut as well as clearance of fungal pathogens. Recent studies demonstrate that microtubule-associated protein light-chain 3 (LC3)-associated phagocytosis (LAP) is involved in lectin–fungi interactions. Yet, the biological impact of LAP on the lectin function remains largely elusive. In this report, we demonstrate that in mouse LAP is linked to dendritic cell-associated lectin 2 (Dectin-2), a C-type lectin specific to fungal α-mannan polysaccharide. We found that mouse Dectin-2 recognizes commensal yeast S. cerevisiae and Kazachstania unispora. Mouse bone marrow-derived DCs (BMDCs) produced inflammatory cytokines TNFα and IL-1β in response to the yeasts in a Dectin-2 and spleen tyrosine kinase (Syk)-dependent manner. We found that S. cerevisiae and K. unispora induced LAP in mouse BMDCs upon internalization. Furthermore, LC3 was activated by stimulation of BMDCs with the yeasts in a Dectin-2 and Syk-dependent manner. To address the biological impact of LAP on Dectin-2 yeast interaction, we established a knock-in mouse strain (Atg16L1E230, thereafter called E230), which BMDCs exhibit autophagy-active and LAP-negative phenotypes. When stimulated with yeasts, E230 BMDCs produced significantly less amounts of TNFα and IL-1β. Taken together, we revealed a novel link between Dectin-2 and LAP that enables host immune cells to respond to mycobiota

An Equine Model for Vaccination against a Hepacivirus: Insights into Host Responses to E2 Recombinant Protein Vaccination and Subsequent Equine Hepacivirus Inoculation

Equine hepacivirus (EqHV) is the closest known genetic homologue of hepatitis C virus. An effective prophylactic vaccine is currently not available for either of these hepaciviruses. The equine as potential surrogate model for hepacivirus vaccine studies was investigated, while equine host responses following vaccination with EqHV E2 recombinant protein and subsequent EqHV inoculation were elucidated. Four ponies received prime and booster vaccinations (recombinant protein, adjuvant) four weeks apart (day −55 and −27). Two control ponies received adjuvant only. Ponies were inoculated with EqHV RNA-positive plasma on day 0. Blood samples and liver biopsies were collected over 26 weeks (day −70 to +112). Serum analyses included detection of EqHV RNA, isotypes of E2-specific immunoglobulin G (IgG), nonstructural protein 3-specific IgG, haematology, serum biochemistry, and metabolomics. Liver tissue analyses included EqHV RNA detection, RNA sequencing, histopathology, immunohistochemistry, and fluorescent in situ hybridization. Al-though vaccination did not result in complete protective immunity against experimental EqHV inoculation, the majority of vaccinated ponies cleared the serum EqHV RNA earlier than the control ponies. The majority of vaccinated ponies appeared to recover from the EqHV-associated liver insult earlier than the control ponies. The equine model shows promise as a surrogate model for future hepacivirus vaccine research

An Equine Model for Vaccination against a Hepacivirus: Insights into Host Responses to E2 Recombinant Protein Vaccination and Subsequent Equine Hepacivirus Inoculation

Equine hepacivirus (EqHV) is the closest known genetic homologue of hepatitis C virus. An effective prophylactic vaccine is currently not available for either of these hepaciviruses. The equine as potential surrogate model for hepacivirus vaccine studies was investigated, while equine host responses following vaccination with EqHV E2 recombinant protein and subsequent EqHV inoculation were elucidated. Four ponies received prime and booster vaccinations (recombinant protein, adjuvant) four weeks apart (day −55 and −27). Two control ponies received adjuvant only. Ponies were inoculated with EqHV RNA-positive plasma on day 0. Blood samples and liver biopsies were collected over 26 weeks (day −70 to +112). Serum analyses included detection of EqHV RNA, isotypes of E2-specific immunoglobulin G (IgG), nonstructural protein 3-specific IgG, haematology, serum biochemistry, and metabolomics. Liver tissue analyses included EqHV RNA detection, RNA sequencing, histopathology, immunohistochemistry, and fluorescent in situ hybridization. Al-though vaccination did not result in complete protective immunity against experimental EqHV inoculation, the majority of vaccinated ponies cleared the serum EqHV RNA earlier than the control ponies. The majority of vaccinated ponies appeared to recover from the EqHV-associated liver insult earlier than the control ponies. The equine model shows promise as a surrogate model for future hepacivirus vaccine research

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