Cell organisation, sulphur metabolism and ion transport-related genes are differentially expressed in Paracoccidioides brasiliensis mycelium and yeast cells

Abstract Background Mycelium-to-yeast transition in the human host is essential for pathogenicity by the fungus Paracoccidioides brasiliensis and both cell types are therefore critical to the establishment of paracoccidioidomycosis (PCM), a systemic mycosis endemic to Latin America. The infected population is of about 10 million individuals, 2% of whom will eventually develop the disease. Previously, transcriptome analysis of mycelium and yeast cells resulted in the assembly of 6,022 sequence groups. Gene expression analysis, using both in silico EST subtraction and cDNA microarray, revealed genes that were differential to yeast or mycelium, and we discussed those involved in sugar metabolism. To advance our understanding of molecular mechanisms of dimorphic transition, we performed an extended analysis of gene expression profiles using the methods mentioned above. Results In this work, continuous data mining revealed 66 new differentially expressed sequences that were MIPS(Munich Information Center for Protein Sequences)-categorised according to the cellular process in which they are presumably involved. Two well represented classes were chosen for further analysis: (i) control of cell organisation – cell wall, membrane and cytoskeleton, whose representatives were hex (encoding for a hexagonal peroxisome protein), bgl (encoding for a 1,3-β-glucosidase) in mycelium cells; and ags (an α-1,3-glucan synthase), cda (a chitin deacetylase) and vrp (a verprolin) in yeast cells; (ii) ion metabolism and transport – two genes putatively implicated in ion transport were confirmed to be highly expressed in mycelium cells – isc and ktp, respectively an iron-sulphur cluster-like protein and a cation transporter; and a putative P-type cation pump (pct) in yeast. Also, several enzymes from the cysteine de novo biosynthesis pathway were shown to be up regulated in the yeast form, including ATP sulphurylase, APS kinase and also PAPS reductase. Conclusion Taken together, these data show that several genes involved in cell organisation and ion metabolism/transport are expressed differentially along dimorphic transition. Hyper expression in yeast of the enzymes of sulphur metabolism reinforced that this metabolic pathway could be important for this process. Understanding these changes by functional analysis of such genes may lead to a better understanding of the infective process, thus providing new targets and strategies to control PCM.

Preimplantation development regulatory pathway construction through a text-mining approach

Background: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. Results: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. Conclusions: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.

A model and an algebra for semi-structured and full-text queries

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Darstellung und Charakterisierung von hyper- und hochverzweigten Methacrylaten durch Gruppenübertragungspolymerisation

ZusammenfassungDie selbstkondensierende Gruppenübertragungspolymerisation von 2-[(2-Methyl-1-triethylsiloxy-1-propenyl)oxy]ethyl-methacrylat (MTSHEMA) und die Copolymerisation mit Methylmethacrylat und tert-Butylmethacrylat wurde untersucht. Da MTSHEMA eine polymerisierbare Methacryloyl-Einheit und eine zur Initiierung einer Gruppenübertragungspolymerisation befähigte Silylketenacetal-Einheit besitzt, führt die Homopolymerisation zu hyperverzweigten und die Copolymerisation zu hochverzweigten Polymeren.Bei der Homopolymerisation von MTSHEMA konnten nur niedrige Molekulargewichte erreicht werden. Dies wird auf Nebenreaktionen der aktiven Kettenenden zurückgeführt, welche die Carbonylgruppen nucleophil angreifen und, mit der Doppelbindung Kern-Einheit reagieren. Die Copolymerisation mit Methylmethacrylat verlauft ohne Nebenreaktionen. Durch die Variation des molaren Verhältnisses von MTSHEMA zu den Comonomeren war es möglich, das Molekulargewicht, den Verzweigungsgrad und dadurch die Viskosität in Lösung zu kontrollieren. Die Bestimmung der Molekulargewichtsverteilung sämtlicher Polymere erfolgte durch Kopplung der Gelpermeationschromatographie mit einem Viskositätsdetektor und einem Vielwinkel Lichtstreu-Photometer. Die aus dem Vergleich der Viskositäten und Trägheitsradien ermittelten Schrumpfungspa-rameter lassen Schlüsse auf den Verzweigungsgrad zu.Nach den Ergebnissen der viskoelastischen Spektroskopie folgt das Verhalten der verzweigten Polymere in der Schmelze der Rouse-Theorie und deutet damit auf die Abwesenheit von Verschlaufungen hin.Durch die Copolymerisation mit tert-Butylmethacrylat und MTSHEMA konnte hochverzweigtes Poly(tert-butylmethacrylat) synthetisiert werden. Die Verseifung dieser Polymere ergab verzweigte Polymethacrylsäure.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.