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1.  Common ancestry of iron oxide- and iron-sulfide-based biomineralization in magnetotactic bacteria 
The ISME Journal  2011;5(10):1634-1640.
Magnetosomes are prokaryotic organelles produced by magnetotactic bacteria that consist of nanometer-sized magnetite (Fe3O4) or/and greigite (Fe3S4) magnetic crystals enveloped by a lipid bilayer membrane. In magnetite-producing magnetotactic bacteria, proteins present in the magnetosome membrane modulate biomineralization of the magnetite crystal. In these microorganisms, genes that encode for magnetosome membrane proteins as well as genes involved in the construction of the magnetite magnetosome chain, the mam and mms genes, are organized within a genomic island. However, partially because there are presently no greigite-producing magnetotactic bacteria in pure culture, little is known regarding the greigite biomineralization process in these organisms including whether similar genes are involved in the process. Here using culture-independent techniques, we now show that mam genes involved in the production of magnetite magnetosomes are also present in greigite-producing magnetotactic bacteria. This finding suggest that the biomineralization of magnetite and greigite did not have evolve independently (that is, magnetotaxis is polyphyletic) as once suggested. Instead, results presented here are consistent with a model in which the ability to biomineralize magnetosomes and the possession of the mam genes was acquired by bacteria from a common ancestor, that is, the magnetotactic trait is monophyletic.
PMCID: PMC3176509  PMID: 21509043
biomineralization evolution; greigite; magnetite; magnetotactic bacteria; magnetosome; horizontal gene transfer
2.  AtlasT4SS: A curated database for type IV secretion systems 
BMC Microbiology  2012;12:172.
The type IV secretion system (T4SS) can be classified as a large family of macromolecule transporter systems, divided into three recognized sub-families, according to the well-known functions. The major sub-family is the conjugation system, which allows transfer of genetic material, such as a nucleoprotein, via cell contact among bacteria. Also, the conjugation system can transfer genetic material from bacteria to eukaryotic cells; such is the case with the T-DNA transfer of Agrobacterium tumefaciens to host plant cells. The system of effector protein transport constitutes the second sub-family, and the third one corresponds to the DNA uptake/release system. Genome analyses have revealed numerous T4SS in Bacteria and Archaea. The purpose of this work was to organize, classify, and integrate the T4SS data into a single database, called AtlasT4SS - the first public database devoted exclusively to this prokaryotic secretion system.
The AtlasT4SS is a manual curated database that describes a large number of proteins related to the type IV secretion system reported so far in Gram-negative and Gram-positive bacteria, as well as in Archaea. The database was created using the RDBMS MySQL and the Catalyst Framework based in the Perl programming language and using the Model-View-Controller (MVC) design pattern for Web. The current version holds a comprehensive collection of 1,617 T4SS proteins from 58 Bacteria (49 Gram-negative and 9 Gram-Positive), one Archaea and 11 plasmids. By applying the bi-directional best hit (BBH) relationship in pairwise genome comparison, it was possible to obtain a core set of 134 clusters of orthologous genes encoding T4SS proteins.
In our database we present one way of classifying orthologous groups of T4SSs in a hierarchical classification scheme with three levels. The first level comprises four classes that are based on the organization of genetic determinants, shared homologies, and evolutionary relationships: (i) F-T4SS, (ii) P-T4SS, (iii) I-T4SS, and (iv) GI-T4SS. The second level designates a specific well-known protein families otherwise an uncharacterized protein family. Finally, in the third level, each protein of an ortholog cluster is classified according to its involvement in a specific cellular process. AtlasT4SS database is open access and is available at
PMCID: PMC3489848  PMID: 22876890
3.  Distinct patterns of somatic alterations in a lymphoblastoid and a tumor genome derived from the same individual 
Nucleic Acids Research  2011;39(14):6056-6068.
Although patterns of somatic alterations have been reported for tumor genomes, little is known on how they compare with alterations present in non-tumor genomes. A comparison of the two would be crucial to better characterize the genetic alterations driving tumorigenesis. We sequenced the genomes of a lymphoblastoid (HCC1954BL) and a breast tumor (HCC1954) cell line derived from the same patient and compared the somatic alterations present in both. The lymphoblastoid genome presents a comparable number and similar spectrum of nucleotide substitutions to that found in the tumor genome. However, a significant difference in the ratio of non-synonymous to synonymous substitutions was observed between both genomes (P = 0.031). Protein–protein interaction analysis revealed that mutations in the tumor genome preferentially affect hub-genes (P = 0.0017) and are co-selected to present synergistic functions (P < 0.0001). KEGG analysis showed that in the tumor genome most mutated genes were organized into signaling pathways related to tumorigenesis. No such organization or synergy was observed in the lymphoblastoid genome. Our results indicate that endogenous mutagens and replication errors can generate the overall number of mutations required to drive tumorigenesis and that it is the combination rather than the frequency of mutations that is crucial to complete tumorigenic transformation.
PMCID: PMC3152357  PMID: 21493686
4.  Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps 
BMC Microbiology  2008;8:101.
Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only ~50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms.
We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested.
The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts.
PMCID: PMC2453137  PMID: 18565227
5.  Swine and Poultry Pathogens: the Complete Genome Sequences of Two Strains of Mycoplasma hyopneumoniae and a Strain of Mycoplasma synoviae†  
Vasconcelos, Ana Tereza R. | Ferreira, Henrique B. | Bizarro, Cristiano V. | Bonatto, Sandro L. | Carvalho, Marcos O. | Pinto, Paulo M. | Almeida, Darcy F. | Almeida, Luiz G. P. | Almeida, Rosana | Alves-Filho, Leonardo | Assunção, Enedina N. | Azevedo, Vasco A. C. | Bogo, Maurício R. | Brigido, Marcelo M. | Brocchi, Marcelo | Burity, Helio A. | Camargo, Anamaria A. | Camargo, Sandro S. | Carepo, Marta S. | Carraro, Dirce M. | de Mattos Cascardo, Júlio C. | Castro, Luiza A. | Cavalcanti, Gisele | Chemale, Gustavo | Collevatti, Rosane G. | Cunha, Cristina W. | Dallagiovanna, Bruno | Dambrós, Bibiana P. | Dellagostin, Odir A. | Falcão, Clarissa | Fantinatti-Garboggini, Fabiana | Felipe, Maria S. S. | Fiorentin, Laurimar | Franco, Gloria R. | Freitas, Nara S. A. | Frías, Diego | Grangeiro, Thalles B. | Grisard, Edmundo C. | Guimarães, Claudia T. | Hungria, Mariangela | Jardim, Sílvia N. | Krieger, Marco A. | Laurino, Jomar P. | Lima, Lucymara F. A. | Lopes, Maryellen I. | Loreto, Élgion L. S. | Madeira, Humberto M. F. | Manfio, Gilson P. | Maranhão, Andrea Q. | Martinkovics, Christyanne T. | Medeiros, Sílvia R. B. | Moreira, Miguel A. M. | Neiva, Márcia | Ramalho-Neto, Cicero E. | Nicolás, Marisa F. | Oliveira, Sergio C. | Paixão, Roger F. C. | Pedrosa, Fábio O. | Pena, Sérgio D. J. | Pereira, Maristela | Pereira-Ferrari, Lilian | Piffer, Itamar | Pinto, Luciano S. | Potrich, Deise P. | Salim, Anna C. M. | Santos, Fabrício R. | Schmitt, Renata | Schneider, Maria P. C. | Schrank, Augusto | Schrank, Irene S. | Schuck, Adriana F. | Seuanez, Hector N. | Silva, Denise W. | Silva, Rosane | Silva, Sérgio C. | Soares, Célia M. A. | Souza, Kelly R. L. | Souza, Rangel C. | Staats, Charley C. | Steffens, Maria B. R. | Teixeira, Santuza M. R. | Urmenyi, Turan P. | Vainstein, Marilene H. | Zuccherato, Luciana W. | Simpson, Andrew J. G. | Zaha, Arnaldo
Journal of Bacteriology  2005;187(16):5568-5577.
This work reports the results of analyses of three complete mycoplasma genomes, a pathogenic (7448) and a nonpathogenic (J) strain of the swine pathogen Mycoplasma hyopneumoniae and a strain of the avian pathogen Mycoplasma synoviae; the genome sizes of the three strains were 920,079 bp, 897,405 bp, and 799,476 bp, respectively. These genomes were compared with other sequenced mycoplasma genomes reported in the literature to examine several aspects of mycoplasma evolution. Strain-specific regions, including integrative and conjugal elements, and genome rearrangements and alterations in adhesin sequences were observed in the M. hyopneumoniae strains, and all of these were potentially related to pathogenicity. Genomic comparisons revealed that reduction in genome size implied loss of redundant metabolic pathways, with maintenance of alternative routes in different species. Horizontal gene transfer was consistently observed between M. synoviae and Mycoplasma gallisepticum. Our analyses indicated a likely transfer event of hemagglutinin-coding DNA sequences from M. gallisepticum to M. synoviae.
PMCID: PMC1196056  PMID: 16077101

Results 1-5 (5)