Aminael Sánchez-Rodríguez

Are there keystone mycorrhizal fungi associated to tropical epiphytic orchids

In epiphytic orchids, distinctive groups of fungi are involved in the symbiotic association. However, little is known about the factors that determine the mycorrhizal community structure. Here, we analyzed the orchid mycorrhizal fungi communities associated with three sympatric Cymbidieae epiphytic tropical orchids (Cyrtochilum flexuosum, Cyrtochilum myanthum, and Maxillaria calantha) at two sites located within the mountain rainforest of southern Ecuador. To characterize these communities at each orchid population, the ITS2 region was analyzed by Illumina MiSeq technology. Fifty-five mycorrhizal fungi operational taxonomic units (OTUs) putatively attributed to members of Serendipitaceae, Ceratobasidiaceae and Tulasnellaceae were identified. Significant differences in mycorrhizal communities were detected between the three sympatric orchid species as well as among sites/populations. Interestingly, some mycorrhizal OTUs overlapped among orchid populations. Our results suggested that populations of studied epiphytic orchids have site-adjusted mycorrhizal communities structured around keystone fungal species. Interaction with multiple mycorrhizal fungi could favor orchid site occurrence and co-existence among several orchid species.

Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains

Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domains substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was implemented in the NRPSpredictor and in a first step uses the physicochemical fingerprint of the 34 residues lining the active site of the phenylalanine-adenylation domain of gramicidin synthetase A [PDB ID 1 amu] to derive a feature vector. Homologous positions were extracted for A-domains with known and unknown substrate specificities and turned into feature vectors. At the same time, A-domains with known specificities towards similar substrates were clustered by physicochemical properties of amino acids (AA). In a second step, support vector machines (SVMs) were optimized from feature vectors of characterized A-domains in each of the resulting clusters. Later, SVMs were used in the variant of TSVMs that integrate a fraction of uncharacterized A-domains during training to predict unknown specificities. Finally, uncharacterized A-domains were scored by each of the constructed alignment-free models (TSVM) representing each substrate specificity resulting from the clustering. The model producing the largest score for the uncharacterized A-domain assigns the substrate specificity to it (Rausch et al., Nucleic Acids Res 33:5799-5808, 2005).

A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents

Gastric cancer is the third leading cause of cancer-related mortality worldwide and despite advances in prevention, diagnosis and therapy, it is still regarded as a global health concern. The efficacy of the therapies for gastric cancer is limited by a poor response to currently available therapeutic regimens. One of the reasons that may explain these poor clinical outcomes is the highly heterogeneous nature of this disease. In this sense, it is essential to discover new molecular agents capable of targeting various gastric cancer subtypes simultaneously. Here, we present a multi-objective approach for the ligand-based virtual screening discovery of chemical compounds simultaneously active against the gastric cancer cell lines AGS, NCI-N87 and SNU-1. The proposed approach relays in a novel methodology based on the development of ensemble models for the bioactivity prediction against each individual gastric cancer cell line. The methodology includes the aggregation of one ensemble per cell line using a desirability-based algorithm into virtual screening protocols. Our research leads to the proposal of a multi-targeted virtual screening protocol able to achieve high enrichment of known chemicals with anti-gastric cancer activity. Specifically, our results indicate that, using the proposed protocol, it is possible to retrieve almost 20 more times multi-targeted compounds in the first 1% of the ranked list than what is expected from a uniform distribution of the active ones in the virtual screening database. More importantly, the proposed protocol attains an outstanding initial enrichment of known multi-targeted anti-gastric cancer agents.

Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands

BACKGROUND Virtual methodologies have become essential components of the drug discovery pipeline. Specifically, structure-based drug design methodologies exploit the 3D structure of molecular targets to discover new drug candidates through molecular docking. Recently, dual target ligands of the Adenosine A2A Receptor and Monoamine Oxidase B enzyme have been proposed as effective therapies for the treatment of Parkinsons disease. METHODS In this paper we propose a structure-based methodology, which is extensively validated, for the discovery of dual Adenosine A2A Receptor/Monoamine Oxidase B ligands. This methodology involves molecular docking studies against both receptors and the evaluation of different scoring functions fusion strategies for maximizing the initial virtual screening enrichment of known dual ligands. RESULTS The developed methodology provides high values of enrichment of known ligands, which outperform that of the individual scoring functions. At the same time, the obtained ensemble can be translated in a sequence of steps that should be followed to maximize the enrichment of dual target Adenosine A2A Receptor antagonists and Monoamine Oxidase B inhibitors. CONCLUSION Information relative to docking scores to both targets have to be combined for achieving high dual ligands enrichment. Combining the rankings derived from different scoring functions proved to be a valuable strategy for improving the enrichment relative to single scoring function in virtual screening experiments.

Shifts in leaf litter breakdown along a forest-pasture-urban gradient in Andean streams.

Abstract Tropical montane ecosystems of the Andes are critically threatened by a rapid land‐use change which can potentially affect stream variables, aquatic communities, and ecosystem processes such as leaf litter breakdown. However, these effects have not been sufficiently investigated in the Andean region and at high altitude locations in general. Here, we studied the influence of land use (forest–pasture–urban) on stream physico‐chemical variables (e.g., water temperature, nutrient concentration, and pH), aquatic communities (macroinvertebrates and aquatic fungi) and leaf litter breakdown rates in Andean streams (southern Ecuador), and how variation in those stream physico‐chemical variables affect macroinvertebrates and fungi related to leaf litter breakdown. We found that pH, water temperature, and nutrient concentration increased along the land‐use gradient. Macroinvertebrate communities were significantly different between land uses. Shredder richness and abundance were lower in pasture than forest sites and totally absent in urban sites, and fungal richness and biomass were higher in forest sites than in pasture and urban sites. Leaf litter breakdown rates became slower as riparian land use changed from natural to anthropogenically disturbed conditions and were largely determined by pH, water temperature, phosphate concentration, fungal activity, and single species of leaf‐shredding invertebrates. Our findings provide evidence that leaf litter breakdown in Andean streams is sensitive to riparian land‐use change, with urban streams being the most affected. In addition, this study highlights the role of fungal biomass and shredder species (Phylloicus; Trichoptera and Anchytarsus; Coleoptera) on leaf litter breakdown in Andean streams and the contribution of aquatic fungi in supporting this ecosystem process when shredders are absent or present low abundance in streams affected by urbanization. Finally, we summarize important implications in terms of managing of native vegetation and riparian buffers to promote ecological integrity and functioning of tropical Andean stream ecosystems.

Assessing the effect of pretreatments on the structure and functionality of microbial communities for the bioconversion of microalgae to biogas

Microalgae biomethanization is driven by anaerobic sludge associated microorganisms and is generally limited by the incomplete hydrolysis of the microalgae cell wall, which results in a low availability of microalgal biomass for the methanogenic community. The application of enzymatic pretreatments, e.g., with hydrolytic enzymes, is among the strategies used to work around the incomplete hydrolysis of the microalgae cell wall. Despite the proven efficacy of these pretreatments in increasing biomethanization, the changes that a given pretreatment may cause to the anaerobic sludge associated microorganisms during biomethanization are still unknown. This study evaluated the changes in the expression of the metatranscriptome of anaerobic sludge associated microorganisms during Chlorella sorokiniana biomethanization without pretreatment (WP) (control) and pretreated with commercial cellulase in order to increase the solubilization of the microalgal organic matter. Pretreated microalgal biomass experienced significant increases in biogas the production. The metatranscriptomic analysis of control samples showed functionally active microalgae cells, a bacterial community dominated by γ- and δ-proteobacteria, and a methanogenic community dominated by Methanospirillum hungatei. In contrast, pretreated samples were characterized by the absence of active microalgae cells and a bacteria population dominated by species of the Clostridia class. These differences are also related to the differential activation of metabolic pathways e.g., those associated with the degradation of organic matter during its biomethanization.

Chemoinformatics profiling of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold

Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson´s disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine derivatives and benzothiazinones). Methods: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold. Results: The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAO-B/A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches. Conclusion: The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAO-B inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson´s disease.

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

BackgroundPreeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis.MethodsWe firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways.ResultsThe consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism.ConclusionOur results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.