Phylogenetic analysis reveals a high level of speciation in the Paracoccidioides genus.

Paracoccidioidomycosis (PCM) is a systemic disease endemic to most of Latin America, with greatest impact in rural areas. The taxonomic status of one of the best studied Paracoccidioides isolates (Pb01) as P. brasiliensis remains unresolved due to its genomic differences from the other three previously described phylogenetic species (S1, PS2 and PS3; Carrero et al., 2008. Fungal Genet. Biol. 45, 605). Using the genealogic concordance method of phylogenetic species recognition (GCPSR) via maximum parsimony and Bayesian analysis, we identified a clade of 17 genotypically similar isolates, including Pb01, which are distinct from the S1/PS2/P3 clade. Consistent with GCPSR, this "Pb01-like" group can be considered a new phylogenetic species, since it is strongly supported by all independent and concatenated genealogies. "Pb01-like" species exhibit great sequence and morphological divergence from the S1/PS2/PS3 species clade, and we estimate that these groups last shared a common ancestor approximately 32 million years ago. In addition, recombination analysis revealed independent events inside both main groups suggesting reproductive isolation. Consequently, we recommend the formal description of the "Pb01-like" cluster as the new species Paracoccidioides lutzii, a tribute to Adolpho Lutz, discoverer of P. brasiliensis in 1908.

Predicting antimicrobial peptides from eukaryotic genomes: in silico strategies to develop antibiotics.

A remarkable and intriguing challenge for the modern medicine consists in the development of alternative therapies to avoid the problem of microbial resistance. The cationic antimicrobial peptides present a promise to be used to develop more efficient drugs applied to human health. The in silico analysis of genomic databases is a strategy utilized to predict peptides of therapeutic interest. Once the main antimicrobial peptides' physical-chemical properties are already known, the correlation of those features to search on these databases is a tool to shorten identifying new antibiotics. This study reports the identification of antimicrobial peptides by theoretical analyses by scanning the Paracoccidioides brasiliensis transcriptome and the human genome databases. The identified sequences were synthesized and investigated for hemocompatibility and also antimicrobial activity. Two peptides presented antifungal activity against Candida albicans. Furthermore, three peptides exhibited antibacterial effects against Staphylococcus aureus and Escherichia coli; finally one of them presented high potential to kill both pathogens with superior activity in comparison to chloramphenicol. None of them showed toxicity to mammalian cells. In silico structural analyses were performed in order to better understand function-structure relation, clearly demonstrating the necessity of cationic peptide surfaces and the exposition of hydrophobic amino acid residues. In summary, our results suggest that the use of computational programs in order to identify and evaluate antimicrobial peptides from genomic databases is a remarkable tool that could be used to abbreviate the search of peptides with biotechnological potential from natural resources.