Exploring chemical conversions in metabolic networks by tracing atoms transitions

Dissertação de mestrado em Bioinformática

Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

PhD thesis in Bioengineering

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

In vitro and in vivo studies of temozolomide loading in zeolite structures as drug delivery systems for glioblastoma

Zeolites Y (faujasite) and MOR (mordonite) were used as hosts for temozolomide (TMZ), a current good-standard chemotherapeutic agent used in the treatment of glioblastoma brain tumors. TMZ was loaded into zeolites by liquid-phase adsorption at controlled pH. FTIR, 1H NMR, MS, SEM, UV/vis and chemical analysis demonstrated the successful loading of TMZ into zeolite hosts. The hydrolysis of TMZ in MTIC (TMZ metabolite) after the preparation of drug delivery systems (DDS) was observed in simulated body fluid. The effect of zeolites and DDS were evaluated on the viability of glioblastoma cell lines. Unloaded Y zeolite presented toxicity to cancer cells in contrast to MOR. In accordance, the best results in potentiation of the TMZ effect was obtained with MOR. We found that mordonite loaded with 0.026 mmol of TMZ was able to decrease the half maximal inhibitory concentrations (IC50) at least 3-fold in comparison to free temozolomide both in vitro and in vivo.

Determinação de psicofármacos em cabelo humano por técnicas cromatográficas hifenadas: um método alternativo não-invasivo

Dissertação de mestrado em Técnicas de Caracterização e Análise Química

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.