Paracoccidioides is a fungal pathogen and the cause of paracoccidioidomycosis, a health-threatening human systemic mycosis endemic to Latin America. Infection by Paracoccidioides, a dimorphic fungus in the order Onygenales, is coupled with a thermally regulated transition from a soil-dwelling filamentous form to a yeast-like pathogenic form. To better understand the genetic basis of growth and pathogenicity in Paracoccidioides, we sequenced the genomes of two strains of Paracoccidioides brasiliensis (Pb03 and Pb18) and one strain of Paracoccidioides lutzii (Pb01). These genomes range in size from 29.1 Mb to 32.9 Mb and encode 7,610 to 8,130 genes. To enable genetic studies, we mapped 94% of the P. brasiliensis Pb18 assembly onto five chromosomes. We characterized gene family content across Onygenales and related fungi, and within Paracoccidioides we found expansions of the fungal-specific kinase family FunK1. Additionally, the Onygenales have lost many genes involved in carbohydrate metabolism and fewer genes involved in protein metabolism, resulting in a higher ratio of proteases to carbohydrate active enzymes in the Onygenales than their relatives. To determine if gene content correlated with growth on different substrates, we screened the non-pathogenic onygenale Uncinocarpus reesii, which has orthologs for 91% of Paracoccidioides metabolic genes, for growth on 190 carbon sources. U. reesii showed growth on a limited range of carbohydrates, primarily basic plant sugars and cell wall components; this suggests that Onygenales, including dimorphic fungi, can degrade cellulosic plant material in the soil. In addition, U. reesii grew on gelatin and a wide range of dipeptides and amino acids, indicating a preference for proteinaceous growth substrates over carbohydrates, which may enable these fungi to also degrade animal biomass. These capabilities for degrading plant and animal substrates suggest a duality in lifestyle that could enable pathogenic species of Onygenales to transfer from soil to animal hosts. Paracoccidioides sp. are fungal pathogens that cause paracoccidioidomycosis in humans. They are part of a larger group of dimorphic fungi causing pulmonary infections in immunocompetent people, whereas many other fungi cause opportunistic infections. We sequenced the genomes of two strains of Paracoccidioides brasiliensis and one strain of the closely related species Paracoccidioides lutzii, and compared them to other fungal genomes. We found gene family expansions specific to Paracoccidioides, including the fungal-specific kinase family. By contrast we found that dimorphic fungi as a group have lost many genes involved in carbohydrate metabolism but retained most proteases. As the growth substrates for dimorphic fungi have not been well characterized, we tested a non-pathogenic relative, Uncinocarpus reesii, for growth on 190 carbon sources. We found that U. reesii is capable of growth on a limited set of carbohydrates, but grows more rapidly on a wide range of dipeptides and amino acids. Our analysis suggests that this genetic and phenotypic preference may underlie the ability of the dimorphic fungi to infect and grow on animal hosts.

Paracoccidioides brasiliensis is the causative agent of paracoccidioidomycosis, a disease that affects 10 million individuals in Latin America. This report depicts the results of the analysis of 6,022 assembled groups from mycelium and yeast phase expressed sequence tags, covering about 80% of the estimated genome of this dimorphic, thermo-regulated fungus. The data provide a comprehensive view of the fungal metabolism, including overexpressed transcripts, stage-specific genes, and also those that are up- or down-regulated as assessed by in silico electronic subtraction and cDNA microarrays. Also, a significant differential expression pattern in mycelium and yeast cells was detected, which was confirmed by Northern blot analysis, providing insights into differential metabolic adaptations. The overall transcriptome analysis provided information about sequences related to the cell cycle, stress response, drug resistance, and signal transduction pathways of the pathogen. Novel P. brasiliensis genes have been identified, probably corresponding to proteins that should be addressed as virulence factor candidates and potential new drug targets.

Paracoccidioides brasiliensis is a pathogenic fungus that undergoes a temperature‐dependent cell morphology change from mycelium (22° C) to yeast (36° C). It is assumed that this morphological transition correlates with the infection of the human host. Our goal was to identify genes expressed in the mycelium (M) and yeast (Y) forms by EST sequencing in order to generate a partial map of the fungus transcriptome. Individual EST sequences were clustered by the CAP3 program and annotated using Blastx similarity analysis and InterPro Scan. Three different databases, GenBank nr, COG (clusters of orthologous groups) and GO (gene ontology) were used for annotation. A total of 3938 (Y = 1654 and M = 2274) ESTs were sequenced and clustered into 597 contigs and 1563 singlets, making up a total of 2160 genes, which possibly represent one‐quarter of the complete gene repertoire in P. brasiliensis. From this total, 1040 were successfully annotated and 894 could be classified in 18 functional COG categories as follows: cellular metabolism (44%); information storage and processing (25%); cellular processes—cell division, posttranslational modifications, among others (19%); and genes of unknown functions (12%). Computer analysis enabled us to identify some genes potentially involved in the dimorphic transition and drug resistance. Furthermore, computer subtraction analysis revealed several genes possibly expressed in stage‐specific forms of P. brasiliensis. Further analysis of these genes may provide new insights into the pathology and differentiation of P. brasiliensis. All EST sequences have been deposited in GenBank under Accession Nos CA580326–CA584263. Copyright © 2003 John Wiley & Sons, Ltd.

In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts. The proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs. This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively. The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.