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Development of a computing platform extensible and modular for metabolomics and metagenomics analysis: innovation with the discovery of new enzymatic activities and natural products of pharmaceutical interest derived

Grant number: 17/18922-2
Support type:Research Grants - Young Investigators Grants
Duration: March 01, 2019 - February 28, 2023
Field of knowledge:Health Sciences - Pharmacy
Principal Investigator:Ricardo Roberto da Silva
Grantee:Ricardo Roberto da Silva
Home Institution: Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Assoc. researchers:Norberto Peporine Lopes
Associated scholarship(s):19/18378-6 - Auxiliary module for discovering chemical structures in literature: data mining, BP.IC
19/05026-4 - Development of a computational platform extensible and modular for analysis of metabolomics and metagenomics experiments: innovating with the discovery of new enzymatic activities and derived natural products of interest, BP.JP

Abstract

The evolution in the data volume generated in modern omics experiments has led to the development of new analytical methods to explore complex multidimensional datasets. At the same time that the new experimental data creates conceptual challenges, the availability of a large volume of experimental data offers a great opportunity to apply technologies developed in other fields for big data management. This availability creates an opportunity to build a national platform for analysis and management of large datasets, exploring the potential of Brazilian biodiversity for the discovery of natural products and its pharmaceutical applications. It is estimated that approximately 60% of all drugs currently, registred have a natural product involved in its development and Brazilian biodiversity corresponds to about 20% of the world's biodiversity. Big data analysis allows the detection of correlations between sampled variables (e.g. metabolites) and the state of the system under study (e.g. healthy patient vs. diseased patient), which could not be observed with depth and detail which systems were analyzed in the past. Recently many methods for predicting biosynthetic pathways of natural products have been automated and made publicly available. Large scale gene predictions have been demonstrated with the application of large-scale super-expression of proteins combined to non-target metabolic, allowing the discovery of new enzymatic activities and new structures of metabolites. This approach is relatively simple and inexpensive, and has a great biotechnological potential. In addition to enabling the characterization of chemical diversity and its direct association to biosynthetic potential, it creates a route for biotechnological application with the cloning of genes that produce biochemical transformations of interest. The untargeted and large-scale nature of this approach allows analyzes of poorly characterized samples, such as microbial communities. The present project aims at the extension of a Galaxy platform instance for analysis of omics experiments. The analysis flow will feature original modules for ion detection optimization in non-targeted metabolomics experiments, matching the discovery of biosynthetic pathways and new structures of natural products. This infrastructure will be used to support strategic collaborations by implementing a heterologous expression platform in microorganisms. To enhance the platform's communitary use, exploring the use of multivariate statistical analysis applied to different biological systems, the present proposal foresees the integration of two FAPESP Thematic Projects linked to Biota Program. One of them providing support in mass spectrometry, coordinated by Prof. Dr. Norberto Peporine Lopes and another on bioprospecting of marine natural products coordinated by Prof. Dr. Leticia Costa Lotufo. In order to extend the project to the plant kingdom, we also started an interaction with the Project of the SPEC Program of FAPESP coordinated by Prof. Dr. Jonathan James Lloyd aimed at a large experimental study on the neglected Caatinga biome. These interactions will provide access to a diverse collection of big data that, together with the experiments proposed here, will be used to validate a platform integrating digital resources in multiple layers. This approach introduces a rational hardware management and flexible implementation of software on-demand that should create a new line of research that will benefit the research structure of the Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP-USP). (AU)

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