Nutritional Heterogeneity Among Aspergillus fumigatus Strains Has Consequences for Virulence in a Strain- and Host-Dependent Manner

Acquisition and subsequent metabolism of different carbon and nitrogen sources have been shown to play an important role in virulence attributes of the fungal pathogen Aspergillus fumigatus, such as the secretion of host tissue-damaging proteases and fungal cell wall integrity. We examined the relationship between the metabolic processes of carbon catabolite repression (CCR), nitrogen catabolite repression (NCR) and virulence in a variety of A. fumigatus clinical isolates. A considerable amount of heterogeneity with respect to the degree of CCR and NCR was observed and a positive correlation between NCR and virulence in a neutropenic mouse model of pulmonary aspergillosis (PA) was found. Isolate Afs35 was selected for further analysis and compared to the reference strain A1163, with both strains presenting the same degree of virulence in a neutropenic mouse model of PA. Afs35 metabolome analysis in physiological-relevant carbon sources indicated an accumulation of intracellular sugars that also serve as cell wall polysaccharide precursors. Genome analysis showed an accumulation of missense substitutions in the regulator of protease secretion and in genes encoding enzymes required for cell wall sugar metabolism. Based on these results, the virulence of strains Afs35 and A1163 was assessed in a triamcinolone murine model of PA and found to be significantly different, confirming the known importance of using different mouse models to assess strain-specific pathogenicity. These results highlight the importance of nitrogen metabolism for virulence and provide a detailed example of the heterogeneity that exists between A. fumigatus isolates with consequences for virulence in a strain-specific and host-dependent manner

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