Melbourne Medical School Collected Works - Research Publications

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Start date: 2013 × End date: 2014 × Author: Lopez Campos, Guillermo ×

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    Global Transcriptome Analysis of Lactococcus garvieae Strains in Response to Temperature
    Aguado-Urda, M ; Gibello, A ; Blanco, MDM ; Fernandez-Garayzabal, JF ; Lopez-Alonso, V ; Lopez-Campos, GH ; van Schaik, W (PUBLIC LIBRARY SCIENCE, 2013-11-04)
    Lactococcus garvieae is an important fish and an opportunistic human pathogen. The genomic sequences of several L. garvieae strains have been recently published, opening the possibility of global studies on the biology of this pathogen. In this study, a whole genome DNA microarray of two strains of L. garvieae was designed and validated. This DNA microarray was used to investigate the effects of growth temperature (18°C and 37°C) on the transcriptome of two clinical strains of L. garvieae that were isolated from fish (Lg8831) and from a human case of septicemia (Lg21881). The transcriptome profiles evidenced a strain-specific response to temperature, which was more evident at 18°C. Among the most significant findings, Lg8831 was found to up-regulate at 18°C several genes encoding different cold-shock and cold-induced proteins involved in an efficient adaptive response of this strain to low-temperature conditions. Another relevant result was the description, for the first time, of respiratory metabolism in L. garvieae, whose gene expression regulation was temperature-dependent in Lg21881. This study provides new insights about how environmental factors such as temperature can affect L. garvieae gene expression. These data could improve our understanding of the regulatory networks and adaptive biology of this important pathogen.
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    Mapping Biomedical Vocabularies: A Semi-Automated Term Matching Approach
    Ofoghi, B ; Lopez-Campos, GH ; Martin Sanchez, FJ ; Verspoor, K ; Mantas, J ; Househ, MS ; Hasman, A (IOS PRESS, 2014)
    Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i.e., different terms in multiple vocabularies referring to the same entity) between the vocabularies increase. Therefore, there is a need for automatically aligning vocabularies. In this paper, we explore and propose new methods for detecting probable matches between two vocabularies. The methods build upon existing string similarity functions, enhancing these functions for the context of semi-automated vocabulary matching.