From genes to population: effects of toxicants on Enchytraeus albidus

Nowadays, a systems biology approach is both a challenge as well as believed to be the ideal form of understanding the organisms’ mechanisms of response. Responses at different levels of biological organization should be integrated to better understand the mechanisms, and hence predict the effects of stress agents, usable in broader contexts. The main aim of this thesis was to evaluate the underlying mechanisms of Enchytraeus albidus responses to chemical stressors. Therefore, there was a large investment on the gene library enrichment for this species, as explained ahead. Overall, effects of chemicals from two different groups (metals and pesticides) were assessed at different levels of biological organization: from genes and biochemical biomarkers to population endpoints. Selected chemicals were: 1) the metals cadmium and zinc; 2) the insecticide dimethoate, the herbicide atrazine and the fungicide carbendazim. At the gene and sub-cellular level, the effects of time and dosage were also adressed. Traditional ecotoxicological tests - survival, reproduction and avoidance behavior - indicated that pesticides were more toxic than metals. Avoidance behaviour is extremely important from an ecological point of view, but not recommended to use for risk assessment purposes. The oxidative stress related experiment showed that metals induced significant effects on several antioxidant enzyme activities and substrate levels, as well as oxidative damage on the membrane cells. To increase the potential of our molecular tool to assess transcriptional responses, the existing cDNA library was enriched with metal and pesticide responding genes, using Suppression Subtractive Hybridization (SSH). With the sequencing information obtained, an improved Agilent custom oligonucleotide microarray was developed and an EST database, including all existing molecular data on E. albidus, was made publicly available as an interactive tool to access information. With this microarray tool, most interesting and novel information on the mechanisms of chemical toxicity was obtained, with the identification of common and specific key pathways affected by each compound. The obtained results allowed the identification of mechanisms of action for the tested compounds in E. albidus, some of which are in line with the ones known for mammals, suggesting across species conserved modes of action and underlining the usefulness of this soil invertebrate as a model species. In general, biochemical and molecular responses were influenced by time of exposure and chemical dosage and these allowed to see the evolution of events. Cellular energy allocation results confirmed the gene expression evidences of an increased energetic expenditure, which can partially explain the decrease on the reproductive output, verified at a later stage. Correlations found throughout this thesis between effects at the different levels of biological organization have further improved our knowledge on the toxicity of metals and pesticides in this species.

Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition - a crucial initial task - with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.