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

Trypanosoma cruzi interaction with host tissues modulate the composition of large extracellular vesicles

Abstract Trypanosoma cruzi is the protozoan that causes Chagas disease (CD), an endemic parasitosis in Latin America distributed around the globe. If CD is not treated in acute phase, the parasite remains silent for years in the host's tissues in a chronic form, which may progress to cardiac, digestive or neurological manifestations. Recently, studies indicated that the gastrointestinal tract represents an important reservoir for T. cruzi in the chronic phase. During interaction T. cruzi and host cells release extracellular vesicles (EVs) that modulates the immune system and infection, but the dynamics of secretion of host and parasite molecules through these EVs is not understood. Now, we used two cell lines: mouse myoblast cell line C2C12, and human intestinal epithelial cell line Caco-2to simulate the environments found by the parasite in the host. We isolated large EVs (LEVs) from the interaction of T. cruzi CL Brener and Dm28c/C2C12 and Caco-2 cells upon 2 and 24 h of infection. Our data showed that at two hours there is a strong cellular response mediated by EVs, both in the number, variety and enrichment/targeting of proteins found in LEVs for diverse functions. Qualitative and quantitative analysis showed that proteins exported in LEVs of C2C12 and Caco-2 have different patterns. We found a predominance of host proteins at early infection. The parasite-host cell interaction induces a switch in the functionality of proteins carried by LEVs and a heterogeneous response depending on the tissues analyzed. Protein–protein interaction analysis showed that cytoplasmic and mitochondrial homologues of the same parasite protein, tryparedoxin peroxidase, were differentially packaged in LEVs, also impacting the interacting molecule of this protein in the host. These data provide new evidence that the interaction with T. cruzi leads to a rapid tissue response through the release of LEVs, reflecting the enrichment of some proteins that could modulate the infection environment

Coenzyme Q10 supplementation acts as antioxidant on dystrophic muscle cells

Increased oxidative stress is a frequent feature in Duchenne muscular dystrophy (DMD). High reactive oxygen species (ROS) levels, associated with altered enzyme antioxidant activity, have been reported in dystrophic patients and mdx mice, an experimental model of DMD. In this study, we investigated the effects of coenzyme Q10 (CoQ10) on oxidative stress marker levels and calcium concentration in primary cultures of dystrophic muscle cells from mdx mice. Primary cultures of skeletal muscle cells from C57BL/10 and mdx mice were treated with coenzyme Q10 (5 μM) for 24 h. The untreated mdx and C57BL/10 muscle cells were used as controls. The MTT and live/dead cell assays showed that CoQ10 presented no cytotoxic effect on normal and dystrophic muscle cells. Intracellular calcium concentration, H2O2 production, 4-HNE, and SOD-2 levels were higher in mdx muscle cells. No significant difference in the catalase, GPx, and Gr levels was found between experimental groups. This study demonstrated that CoQ10 treatment was able to reduce levels of oxidative stress markers, such as H2O2, acting as an antioxidant, as well as decreasing abnormal intracellular calcium influx in dystrophic muscles cells. This study demonstrated that CoQ10 treatment was able to reduce levels of oxidative stress markers, such as H2O2, acting as an antioxidant, as well as decreasing abnormal intracellular calcium influx in dystrophic muscles cells. Our findings also suggest that the decrease of oxidative stress reduces the need for upregulation of antioxidant pathways, such as SOD and GSH24611751185CAPES - Coordenação de Aperfeiçoamento de Pessoal e Nível SuperiorCNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo001Não temNão temThis study was financed in part by the Coordenação de Pessoal de Nivel Superior Brasil, (CAPES)–Finance Code 001, Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), CNPq and FAEPEX. L.H.R.M., A.R.F., and R.D.M. were the recipient of a FAPESP fellowship. D.S.M, T.A.H., and C.C.L are the recipient of a CAPES fellowshi

Integrative Analysis of the Ethanol Tolerance of Saccharomyces cerevisiae.

Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here

Spatial non-stationarity in the distribution of fish species richness of tropical streams

Diversity gradients are observed for various groups of organisms. For fishes in streams, the water-energy, productivity, and temporal heterogeneity hypotheses can explain richness patterns. The relationship between species diversity and the variables that represent these hypotheses is generally linear and stationary, that is, the effect of each of those variables is constant throughout a geographically defined area. But the assumption of spatial stationarity has not yet been tested on a great number of diversity gradients. Therefore, we aimed to quantify the spatial stationarity in the relationships between fish species richness in small stream (653 streams) located throughout Brazil, and the water-energy, productivity, and temporal heterogeneity hypotheses using a geographically weighted regression—GWR. There was a conspicuous absence of spatial stationarity in fish species richness. Furthermore, water-energy dynamics represented a possible metabolic restriction acting on the community structuring of fish species richness in streams. This mechanism separated the fish fauna into two regions: (i) The Amazonian region, characterized by a stable climate and populations that are less resistant to climatic variation; and (ii) The central region, featured by greater ranges of temperature and fish populations that are resistant to climatic variation