Metabolic arsenal of giant viruses: host hijack or self-use?

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Belhaouari, D., De Souza, G., Lamb, D., Kelly, S., Goldstone, J., Stegeman, J., Colson, P., La Scola, B., & Aherfi, S. Metabolic arsenal of giant viruses: host hijack or self-use? ELife, 11, (2022): e78674, https://doi.org/10.7554/elife.78674.Viruses generally are defined as lacking the fundamental properties of living organisms in that they do not harbor an energy metabolism system or protein synthesis machinery. However, the discovery of giant viruses of amoeba has fundamentally challenged this view because of their exceptional genome properties, particle sizes and encoding of the enzyme machinery for some steps of protein synthesis. Although giant viruses are not able to replicate autonomously and still require a host for their multiplication, numerous metabolic genes involved in energy production have been recently detected in giant virus genomes from many environments. These findings have further blurred the boundaries that separate viruses and living organisms. Herein, we summarize information concerning genes and proteins involved in cellular metabolic pathways and their orthologues that have, surprisingly, been discovered in giant viruses. The remarkable diversity of metabolic genes described in giant viruses include genes encoding enzymes involved in glycolysis, gluconeogenesis, tricarboxylic acid cycle, photosynthesis, and β-oxidation. These viral genes are thought to have been acquired from diverse biological sources through lateral gene transfer early in the evolution of Nucleo-Cytoplasmic Large DNA Viruses, or in some cases more recently. It was assumed that viruses are capable of hijacking host metabolic networks. But the giant virus auxiliary metabolic genes also may represent another form of host metabolism manipulation, by expanding the catalytic capabilities of the host cells especially in harsh environments, providing the infected host cells with a selective evolutionary advantage compared to non-infected cells and hence favoring the viral replication. However, the mechanism of these genes' functionality remains unclear to date.Royal Society - David C. Lamb Woods Hole Center for Oceans and Human Health - John J. Stegeman National Institutes of Health (P01ES021923) - John J. Stegeman National Science Foundation (OCE-1314642) - John J. Stegeman Agence Nationale de la Recherche ("Investments for the Future" program Méditerranée-Infection 10-IAHU-03) Djamal Brahim Belhaouari Gabriel Augusto Pires De Souza Philippe Colson Sarah Aherf

Angiotensin II Receptor Blockers (ARBs Antihypertensive Agents) Increase Replication of SARS-CoV-2 in Vero E6 Cells

Several comorbidities, including hypertension, have been associated with an increased risk of developing severe disease during SARS-CoV-2 infection. Angiotensin II receptor blockers (ARBs) are currently some of the most widely-used drugs to control blood pressure by acting on the angiotensin II type 1 receptor (AT1R). ARBs have been reported to trigger the modulation of the angiotensin I converting enzyme 2 (ACE2), the receptor used by the virus to penetrate susceptible cells, raising concern that such treatments may promote virus capture and increase their viral load in patients receiving ARBs therapy. In this in vitro study, we reviewed the effect of ARBs on ACE2 and AT1R expression and investigated whether treatment of permissive ACE2+/AT1R+ Vero E6 cells with ARBs alters SARS-CoV-2 replication in vitro in an angiotensin II-free system. After treating the cells with the ARBs, we observed an approximate 50% relative increase in SARS-CoV-2 production in infected Vero E6 cells that correlates with the ARBs-induced up-regulation of ACE2 expression. From this data, we believe that the use of ARBs in hypertensive patients infected by SARS-CoV-2 should be carefully evaluated

MODELOS DE MACHINE LEARNING APLICADOS NA ESTIMAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA DO PLANALTO OCIDENTAL PAULISTA

Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique.La evapotranspiración depende de la interacción entre las variables meteorológicas (radiación solar, temperatura del aire, precipitación, humedad relativa y velocidad del viento) y las condiciones fitosanitarias de los cultivos agrícolas. Es complejo construir mediciones confiables de evapotranspiración debido a los altos costos de implementar técnicas micrometeorológicas, además de las dificultades en la operación y mantenimiento de los equipos necesarios. El objetivo de esta investigación fue modelar la evapotranspiración de referencia a través de técnicas de aprendizaje automático en datos climáticos de 30 estaciones meteorológicas automáticas en el Planalto Ocidental Paulista, Estado de São Paulo, Brasil, en el período 2013-2017. Se realizó una comparación del rendimiento estadístico entre las técnicas utilizadas, donde el mejor rendimiento del modelo EToMLP4 (rRMSE = 0,62%), seguido de EToANFIS4 (rRMSE = 0,75%), EToSVM4 (rRMSE = 1,19%) y EToGRNN4 (rRMSE = 11,05 %). Las medidas de desempeño de la base de validación muestran que los modelos propuestos son capaces de estimar la evapotranspiración de referencia, con énfasis en la técnica MPL.A evapotranspiração depende da interação entre variáveis meteorológicas (radiação solar, temperatura do ar, precipitação, umidade relativa do ar e velocidade do vento) e condições fitossanitárias das culturas agrícolas. É complexo construir medidas confiáveis de evapotranspiração devido aos elevados custos para implantação de técnicas micrometeorológicas, além de dificuldades na operação e manutenção dos equipamentos necessários. O propósito desta pesquisa foi modelar a evapotranspiração de referência (ETo) por meio de técnicas de machine learning em dados climáticos de 30 estações meteorológicas automáticas do Planalto Ocidental Paulista, Estado de São Paulo, Brasil, no período de 2013-2017. Uma comparação do desempenho estatístico entre as técnicas utilizadas foi realizada onde constatou-se melhor desempenho do modelo EToMLP4 (rRMSE = 0.62%), seguido por EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) e EToGRNN4 (rRMSE = 11.05%). Medidas de performance da base de validação evidenciam que os modelos propostos são aptos à estimativa da evapotranspiração de referência com destaque para a técnica MPL. Palavras-chave: evapotranspiração; modelagem matemática; aprendizagem de máquina.   Machine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulista   ABSTRACT: Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique. Keywords: evapotranspiration; modeling; machine learning

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

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran\u27s eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R$^{2}$ = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R$^{2}$ = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

AimAmazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types.LocationAmazonia.TaxonAngiosperms (Magnoliids; Monocots; Eudicots).MethodsData for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny.ResultsIn the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types.Main ConclusionNumerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution