Identificação e caracterização de um análogo de gene de resistência (AGR) da família de Caricaceae Dumort

The majority of cloned resistance (R) genes characterized so far contain a nucleotide-binding site (NBS) and a leucine-rich repeat (LRR) domain, where highly conserved motifs are found. Resistance genes analogs (RGAs) are genetic markers obtained by a PCR-based strategy using degenerated oligonucleotide primers drawn from these highly conserved "motifs". This strategy has the advantage of the high degree of structural and amino acid sequence conservation that is observed in R genes. The objective of the present study was to search for RGAs in Carica papaya L. and Vasconcellea cauliflora Jacq. A. DC. Out of three combinations of primers tested, only one resulted in amplification. The amplified product was cloned in pCR2.1TOPO and than sequenced using M13 forward and reverse primers. Forty-eight clones were sequenced from each species. The 96 sequences generated for each species were cleaned of vector sequences and clustered using CAP3 assembler. From the GENEBANK, one RGA was identified in C. papaya showing a BlastX e-value of 2x10-61 to the gb|AAP45165.1| putative disease resistant protein RGA3 (Solanum bulbocastanum). To the extent of our knowledge this is the first report of a RGA in the Caricaceae Dumort family. Preliminary structural studies were performed to further characterize this putative NBS-LRR type protein. Efforts to search for other RGAs in papaya should continue, mostly to provide basis for the development of transgenic papaya with resistance to diseases.A maioria dos genes de resistência (R) clonados e caracterizados até o momento contém domínios NBS (nucleotide binding site) e LRR (leucine-rich repeat). Dentro destes domínios, encontram-se "motifs" altamente conservados. Análogos de genes de resistência (RGAs) são marcadores genéticos obtidos por uma estratégia, baseada em PCR, que usa primers degenerados desenhados a partir desses "motifs" altamente conservados dos genes R. Esta estratégia possui a vantagem do elevado grau de conservação da estrutura e seqüência dos aminoácidos observados nos genes R. O objetivo do presente estudo foi realizar uma busca por RGAs em Carica papaya L. e Vasconcellea cauliflora Jacq. A. DC. De três combinações de primers avaliadas, somente uma obteve sucesso na amplificação. O produto da amplificação foi então clonado em pCR2.1TOPO e seqüenciado utilizando os primers universais M13 forward e reverse. Quarenta e oito clones foram seqüenciados de cada espécie vegetal. Das 96 seqüências geradas para cada espécie, retiraram-se as seqüências do vetor e, em seguida, as mesmas foram agrupadas utilizando o programa "CAP3 assembler". A partir do GENEBANK, foi identificado um RGA em C. papaya apresentando um BlastX e-value de 2x10-61 com o "gb|AAP45165.1| putative disease resistant protein RGA3 (Solanum bulbocastanum)". Na extensão do nosso conhecimento, este é o primeiro relato de um RGA na família Caricaceae Dumort. Estudos preliminares de estrutura foram realizados visando à maior caracterização deste potencial "NBS-LRR type protein". Esforços para encontrar novos análogos de genes de resistência devem continuar, principalmente para fornecer bases para o desenvolvimento de plantas de mamão transgênicas com resistência a doenças

Post-genomic analysis of Monosporascus cannonballus and Macrophomina phaseolina - potential target selection

Monosporascus cannonballus Pollack & Uecker and Macrophomina phaseolina Tassi (Goid) are phytopathogenic fungi responsible for causing "root rot and vine decline" in melon (Cucumis melo L.). Currently, cultural management practices are predominantly employed to control these pathogens, as the use of pesticides not only has detrimental environmental impacts but has also proven ineffective against them. These fungi have already undergone molecular characterization, and their genomes are now available, enabling the targeted search for protein targets. Therefore, this study aimed to identify novel target proteins that can serve as a foundation for the development of fungicides for effectively managing these pathogens. The genomes of M. cannonballus (assembly ASM415492v1) and M. phaseolina (assembly ASM2087553v1) were subjected to comprehensive analysis, filtration, and comparison. The proteomes of both fungi were clustered based on functional criteria, including putative and hypothetical functions, cell localization, and function-structure relationships. The selection process for homologs in the fungal genomes included a structural search. In the case of M. cannonballus, a total of 17,518 proteins were re-annotated, and among them, 13 candidate targets were identified. As for M. phaseolina, 30,226 initial proteins were analyzed, leading to the identification of 10 potential target proteins. This study thus provides new insights into the molecular functions of these potential targets, with the further validation of inhibitors through experimental methods holding promise for expanding our knowledge in this area

Transcriptional profiles of the human pathogenic fungus paracoccidioides brasiliensis in mycelium and yeast cells

This work was supported by MCT, CNPq, CAPES, FUB, UFG, and FUNDECT-MS. PbGenome Network: Alda Maria T. Ferreira, Alessandra Dantas, Alessandra J. Baptista, Alexandre M. Bailão, Ana Lídia Bonato, André C. Amaral, Bruno S. Daher, Camila M. Silva, Christiane S. Costa, Clayton L. Borges, Cléber O. Soares, Cristina M. Junta, Daniel A. S. Anjos, Edans F. O. Sandes, Eduardo A. Donadi, Elza T. Sakamoto-Hojo, Flábio R. Araújo, Flávia C. Albuquerque, Gina C. Oliveira, João Ricardo M. Almeida, Juliana C. Oliveira, Kláudia G. Jorge, Larissa Fernandes, Lorena S. Derengowski, Luís Artur M. Bataus, Marcus A. M. Araújo, Marcus K. Inoue, Marlene T. De-Souza, Mauro F. Almeida, Nádia S. Parachin, Nadya S. Castro, Odair P. Martins, Patrícia L. N. Costa, Paula Sandrin-Garcia, Renata B. A. Soares, Stephano S. Mello, and Viviane C. B. ReisParacoccidioides brasiliensis is the causative agent of paracoccidioidomycosis, a disease that affects 10 million individuals in Latin America. This report depicts the results of the analysis of 6,022 assembled groups from mycelium and yeast phase expressed sequence tags, covering about 80% of the estimated genome of this dimorphic, thermo-regulated fungus. The data provide a comprehensive view of the fungal metabolism, including overexpressed transcripts, stage-specific genes, and also those that are up- or down-regulated as assessed by in silico electronic subtraction and cDNA microarrays. Also, a significant differential expression pattern in mycelium and yeast cells was detected, which was confirmed by Northern blot analysis, providing insights into differential metabolic adaptations. The overall transcriptome analysis provided information about sequences related to the cell cycle, stress response, drug resistance, and signal transduction pathways of the pathogen. Novel P. brasiliensis genes have been identified, probably corresponding to proteins that should be addressed as virulence factor candidates and potential new drug targets

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

Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase—a potential target to reduce Fusarium head blight disease

International audienceFusarium head blight (FHB) is one of the most destructive diseases of wheat and other cereals worldwide. During infection, the Fusarium fungi produce mycotoxins that represent a high risk to human and animal health. Developing small-molecule inhibitors to specifically reduce mycotoxin levels would be highly beneficial since current treatments unspecifically target the Fusarium pathogen. Culmorin possesses a well-known important synergistically virulence role among mycotoxins, and longiborneol synthase appears to be a key enzyme for its synthesis, thus making longiborneol synthase a particularly interesting target. This study aims to discover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure-based virtual screening investigation. A longiborneol synthase structural model is first built using homology modeling, followed by molecular dynamics simulations that provided the required input for a protein-ligand ensemble docking procedure. From this strategy, the three most interesting compounds (hits) were selected among the 25 top-ranked docked compounds from a library of 15,000 drug-like compounds. These putative inhibitors of longiborneol synthase provide a sound starting point for further studies involving molecular modeling coupled to biochemical experiments. This process could eventually lead to the development of novel approaches to reduce mycotoxin contamination in harvested grai

Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening

International audienceFusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHBoccurs in Europe, North America and around the world causing significant losses inproduction and endangers human and animal health. In this article, we provide thestrategic steps for the specific target selection for the phytopathogen system wheat-Fusarium graminearum. The economic impact of FHB leads to the need for innovation.Currently used fungicides have been shown to be effective over the years, butrecently cereal infecting Fusaria have developed resistance. Our work presents a newperspective on target selection to allow the development of new fungicides. We developedan innovative approach combining both genomic analysis and molecularmodeling to increase the discovery for new chemical compounds with both safetyand low environmental impact. Our protein targets selection revealed 13 candidateswith high specificity, essentiality and potentially assayable with a favorable accessibilityto drug activity. Among them, three proteins: trichodiene synthase, endoglucanase-5 and ERG6 were selected for deeper structural analyses to identify new putativefungicides. Overall, the bioinformatics filtering for novel protein targets appliedfor agricultural purposes is a response to the demand for chemical crop protection.The availability of the genome, secretome and PHI-base allowed the enrichment ofthe search that combined experimental data in planta . The homology modeling andmolecular dynamics simulations allowed the acquisition of three robust and stableconformers. From this step, approximately ten thousand compounds have been virtuallyscreened against three candidates. Forty-five top-ranked compounds were selectedfrom docking results as presenting better interactions and energy at the bindingpockets and no toxicity. These compounds may act as inhibitors and lead to thedevelopment of new fungicides