This review will summarize and discuss the current biological understanding of the motile eukaryotic flagellum, as posed out by recent advances enabled by post-genomics and proteomics approaches. The organelle, which is crucial for motility, survival, differentiation, reproduction, division and feeding, among other activities, of many eukaryotes, is a great example of a natural nanomachine assembled mostly by proteins (around 350-650 of them) that have been conserved throughout eukaryotic evolution. Flagellar proteins are discussed in terms of their arrangement on to the axoneme, the canonical “9+2” microtubule pattern, and also motor and sensorial elements that have been detected by recent proteomic analyses in organisms such as Chlamydomonas reinhardtii, sea urchin, and trypanosomatids. Such findings can be remarkably matched up to important discoveries in vertebrate and mammalian types as diverse as sperm cells, ciliated kidney epithelia, respiratory and oviductal cilia, and neuro-epithelia, among others. Here we will focus on some exciting work regarding eukaryotic flagellar proteins, particularly using the flagellar proteome of C. reinhardtii as a reference map for exploring motility in function, dysfunction and pathogenic flagellates. The reference map for the eukaryotic flagellar proteome consists of 652 proteins that include known structural and intraflagellar transport (IFT) proteins, less well-characterized signal transduction proteins and flagellar associated proteins (FAPs), besides almost two hundred unannotated conserved proteins, which lately have been the subject of intense investigation and of our present examination.

Resumo: O gênero Burkholderia constitui mais de 40 espécies, incluindo genomovares entre bactérias identificadas anteriormente como parte do complexo B. cepacia (Bcc), além de B. mallei (agente causal do mormo) e a B. pseudomallei (agente causal da melioidose ou pseudomormo). B. mallei e B. pseudomallei foram escolhidos como alvos deste trabalho exatamente pela incrível capacidade zoonótica (de certo modo, compartilhada na letalidade potencial para humanos e animais) e risco iminente à saúde pública em geral, assim como também por serem potenciais agentes bioterroristas, principalmente pela habilidade de infecção por aerossóis e a inexistência de vacinas efetivas. Ressalta-se, aqui, o caráter de re-emergência das zoonoses em geral, não só no Brasil, mas no mundo inteiro, em que aproximadamente 75% das doenças infecciosas humanas recém-emergentes são de origem animal; com a incrível porcentagem de cerca 60% de todos os patógenos humanos serem, em essência, zoonóticos. Apesar da relativa antiguidade das duas doenças, pouco se sabe sobre os detalhes e mecanismos de virulência e patogenicidade em mormo e melioidose. Neste trabalho foram usadas vários recursos e ferramentas de Bioinformática para investigar genes e produtos gênicos putativos em cromossomos e replicons seqüenciados dos genomas de B. mallei e B. pseudomallei, numa abordagem patogenômica visando a identificação de genes representativos de fatores de

Abstract. The multi-relational data mining (MRDM) approach looks for patterns that involve multiple tables from a relational database made of complex/structured objects whose normalized representation does require multiple tables. We have applied MRDM methods (relational association rule discovery and probabilistic relational models) with hidden Markov models (HMMs) and Viterbi algorithm (VA) to mine tetratricopeptide repeat (TPR), pentatricopeptide (PPR) and half-a-TPR (HAT) in genomes of pathogenic protozoa Leishmania. TPR is a protein-protein interaction module and TPRcontaining proteins (TPRPs) act as scaffolds for the assembly of different multiprotein complexes. Our aim is to build a great panel of the TPR-like superfamily of Leishmania. Distributed relational state representations for complex stochastic processes were applied to identification, clustering and classification of Leishmania genes and we were able to detect putative 104 TPRPs, 36 PPRPs and 08 HATPs, comprising the TPR-like superfamily. We have also compared currently available resources (Pfam, SMART, SUPER-FAMILY and TPRpred) with our approach (MRDM/HMM/VA).

Combining different types of data from multiple databases (DBs) is a key feature in bioinformatics, particularly due to the problem that each of these DB resources usually contains different subsets of biological knowledge and only answers questions in its domain, nether helping with questions that span domain boundaries nor considering them. As bioinformatics DBs grow in size and as biological questions grow in scope, better solutions will inevitably consist in preserving the autonomy and diversity of DBs and developing new systems to offer an integrated and transparent access to existing distributed data sources (DS). In this paper, we present a decision support system (DSS), called FlagelLink, to provide access to a set of distributed information about a particular domain (the flagellum, a cellular organelle responsible for motility). It employs useful bioinformatics tools (such as BLAST, MUSCLE, HMMER, etc) in an exclusive data warehouse (DW) through terminology and ontology resources (semantic-driven) to maintain an actual DSS for a specific knowledge domain. FlagelLink (available at http://flagellink.nugen.uece.br/flagellink) has a unified, ondemand integration approach that merges the identified ontological knowledge (which means a defined number of test cases and scenarios of genes and proteins all involved in flagellar activities) with traditional and ontology-based information integration techniques.