Biomedical subjects

Ana Paula Oliveira

Publications and source records attributed to Ana Paula Oliveira.

4 recordsLinked to original sources

PubMed2007

A systems biology approach to study glucose repression in the yeast Saccharomyces cerevisiae.

Glucose repression in the yeast Saccharomyces cerevisiae has evolved as a complex regulatory system involving several different pathways. There are two main pathways involved in signal transduction. One has a role in glucose sensing and regulation of glucose transport, while another takes part in repression of a wide range of genes involved in utilization of alternative carbon sources. In this work, we applied a systems biology approach to study the interaction between these two pathways. Through genome-wide transcription analysis of strains with disruption of HXK2, GRR1, MIG1, the combination of MIG1 and MIG2, and the parental strain, we identified 393 genes to have significantly changed expression levels. To identify co-regulation patterns in the different strains we applied principal component analysis. Disruption of either GRR1 or HXK2 were both found to have profound effects on transcription of genes related to TCA cycle and respiration, as well as ATP synthesis coupled proton transport, all displaying an increased expression. The hxk2Delta strain showed reduced overflow metabolism towards ethanol relative to the parental strain. We also used a genome-scale metabolic model to identify reporter metabolites, and found that there is a high degree of consistency between the identified reporter metabolites and the physiological effects observed in the different mutants. Our systems biology approach points to close interaction between the two pathways, and our metabolism driven analysis of transcription data may find a wider application for analysis of cross-talk between different pathways involved in regulation of metabolism.

Gene Expression Profiling↗

PubMed2006

Near-infrared Raman spectroscopy for oral carcinoma diagnosis.

OBJECTIVE: Fourier-transform (FT)-Raman spectroscopy has been used to explore the changes in the vibrational bands of normal, dysplastic (DE) and squamous cell carcinoma (SCC) tissues. BACKGROUND DATA: Raman spectroscopy has been applied as a diagnostic tool for the detection of cancers, due to its sensitivity to the changes in molecular composition and conformation that occurs in malignant tissues. The detection of weak Raman signals from biotissues becomes easier by FT-Raman due to fluorescence suppression. METHODS: A carcinogen (7,12-dimethybenz[a]anthracene [DMBA]) was applied daily in the oral pouch of 21 hamster to induce oral carcinoma. After 14 weeks, the fragments of squamous cell carcinomas and oral normal tissue were collected and analyzed by FT-Raman spectroscopy, using a 1064-nm Nd:YAG laser line as an excitation source. A total of 123 spectra were obtained and divided in normal and malignant tissue groups, and analyzed statistically through principal components analysis (PCA) and classified using Mahalanobis distance. RESULTS: Major differences between normal and malignant spectra seem to arise from the composition, conformational, and structural changes of proteins, and possible increase of its content in malignant epithelia. An algorithm based on PCA was able to separate the samples into two groups--normal and carcinoma. For the algorithm training group, 91% sensitivity and 69% specificity were observed, while the prospective group had 100% sensitivity and 55% specificity. CONCLUSION: The algorithm based on PCA has the potential for classifying Raman spectra and can be useful for detection of dysplastic and malign oral lesion.

Algorithms↗

PubMed2006

Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans.

BACKGROUND: Aspergillus nidulans (the asexual form of Emericella nidulans) is a model organism for aspergilli, which are an important group of filamentous fungi that encompasses human and plant pathogens as well as industrial cell factories. Aspergilli have a highly diversified metabolism and, because of their medical, agricultural and biotechnological importance, it would be valuable to have an understanding of how their metabolism is regulated. We therefore conducted a genome-wide transcription analysis of A. nidulans grown on three different carbon sources (glucose, glycerol, and ethanol) with the objective of identifying global regulatory structures. Furthermore, we reconstructed the complete metabolic network of this organism, which resulted in linking 666 genes to metabolic functions, as well as assigning metabolic roles to 472 genes that were previously uncharacterized. RESULTS: Through combination of the reconstructed metabolic network and the transcription data, we identified subnetwork structures that pointed to coordinated regulation of genes that are involved in many different parts of the metabolism. Thus, for a shift from glucose to ethanol, we identified coordinated regulation of the complete pathway for oxidation of ethanol, as well as upregulation of gluconeogenesis and downregulation of glycolysis and the pentose phosphate pathway. Furthermore, on change in carbon source from glucose to ethanol, the cells shift from using the pentose phosphate pathway as the major source of NADPH (nicotinamide adenine dinucleotide phosphatase, reduced form) for biosynthesis to use of the malic enzyme. CONCLUSION: Our analysis indicates that some of the genes are regulated by common transcription factors, making it possible to establish new putative links between known transcription factors and genes through clustering.

Aspergillus nidulans↗

PubMed2005

Modeling Lactococcus lactis using a genome-scale flux model.

BACKGROUND: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. RESULTS: The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. CONCLUSION: The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

Bacteria, Anaerobic↗