Cetaceans have most likely experienced metabolic shifts since evolutionarily diverging from their terrestrial ancestors, shifts that may be reflected in the proteins such as cytochrome b that are responsible for metabolic efficiency. However, accepted statistical methods for detecting molecular adaptation are largely biased against even moderately conservative proteins because the primary criterion involves a comparison of nonsynonymous and synonymous substitution rates (dN/dS); they do not allow for the possibility that adaptation may come in the form of very few amino acid changes. We apply the MM01 model to the possible molecular adaptation of cytochrome b among cetaceans because it does not rely on a dN/dS ratio, instead evaluating positive selection in terms of the amino acid properties that comprise protein phenotypes that selection at the molecular level may act upon. We also apply the codon-degeneracy model (CDM), which focuses on evaluating overall patterns of nucleotide substitution in terms of base exchange, codon position, and synonymy to estimate the overall effect of selection. Using these relatively new models, we characterize the molecular adaptation that has occurred in the cetacean cytochrome b protein by comparing revealed amino acid replacement patterns to those found among artiodactyls, the modern terrestrial mammals found to be most closely related to cetaceans. Our findings suggest that several regions of the cetacean cytochrome b protein have experienced molecular adaptation. Also, these adaptations are spatially associated with domain structure, protein function, and the structure and function of the cytochrome bc(1) complex and its constituents. We also have found a general correlation between the results of the analytical software programs TreeSAAP (which implements the MM01 model) and CDM (which implements the codon-degeneracy model).

BackgroundEmerging knowledge of the impact of small RNAs as important cellular regulators has prompted an explosion of small transcriptome sequencing projects. Although significant progress has been made towards small RNA discovery and biogenesis in higher eukaryotes and other model organisms, knowledge in simple eukaryotes such as filamentous fungi remains limited.ResultsHere, we used 454 pyrosequencing to present a detailed analysis of the small RNA transcriptome (~ 15 - 40 nucleotides in length) from mycelia and appressoria tissues of the rice blast fungal pathogen, Magnaporthe oryzae. Small RNAs mapped to numerous nuclear and mitochondrial genomic features including repetitive elements, tRNA loci, rRNAs, protein coding genes, snRNAs and intergenic regions. For most elements, small RNAs mapped primarily to the sense strand with the exception of repetitive elements to which small RNAs mapped in the sense and antisense orientation in near equal proportions. Inspection of the small RNAs revealed a preference for U and suppression of C at position 1, particularly for antisense mapping small RNAs. In the mycelia library, small RNAs of the size 18 - 23 nt were enriched for intergenic regions and repetitive elements. Small RNAs mapping to LTR retrotransposons were classified as LTR retrotransposon-siRNAs (LTR-siRNAs). Conversely, the appressoria library had a greater proportion of 28 - 35 nt small RNAs mapping to tRNA loci, and were classified as tRNA-derived RNA fragments (tRFs). LTR-siRNAs and tRFs were independently validated by 3' RACE PCR and northern blots, respectively.ConclusionsOur findings suggest M. oryzae small RNAs differentially accumulate in vegetative and specialized-infection tissues and may play an active role in genome integrity and regulating growth and development.

Magnaporthaceae is a family of ascomycetes that includes three fungi of great economic importance: Magnaporthe oryzae, Gaeumannomyces graminis var. tritici, and Magnaporthe poae. These three fungi cause widespread disease and loss in cereal and grass crops, including rice blast disease (M. oryzae), take-all disease in wheat and other grasses (G. graminis), and summer patch disease in turf grasses (M. poae). Here, we present the finished genome sequence for M. oryzae and draft sequences for M. poae and G. graminis var. tritici. We used multiple technologies to sequence and annotate the genomes of M. oryzae, M. poae, and G. graminis var. tritici. The M. oryzae genome is now finished to seven chromosomes whereas M. poae and G. graminis var. tritici are sequenced to 40.0× and 25.0× coverage respectively. Gene models were developed by the use of multiple computational techniques and further supported by RNAseq data. In addition, we performed preliminary analysis of genome architecture and repetitive element DNA.

The basic Helix-Loop-Helix (bHLH) domain is an essential highly conserved DNA-binding domain found in many transcription factors in all eukaryotic organisms. The bHLH domain has been well studied in the Animal and Plant Kingdoms but has yet to be characterized within Fungi. Herein, we obtained and evaluated the phylogenetic relationship of 490 fungal-specific bHLH containing proteins from 55 whole genome projects composed of 49 Ascomycota and 6 Basidiomycota organisms. We identified 12 major groupings within Fungi (F1-F12); identifying conserved motifs and functions specific to each group. Several classification models were built to distinguish the 12 groups and elucidate the most discerning sites in the domain. Performance testing on these models, for correct group classification, resulted in a maximum sensitivity and specificity of 98.5% and 99.8%, respectively. We identified 12 highly discerning sites and incorporated those into a set of rules (simplified model) to classify sequences into the correct group. Conservation of amino acid sites and phylogenetic analyses established that like plant bHLH proteins, fungal bHLH-containing proteins are most closely related to animal Group B. The models used in these analyses were incorporated into a software package, the source code for which is available at www.fungalgenomics.ncsu.edu.

This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.