RESULTS: Both laboratory approaches yielded complete mtDNA genomes from M. f. fascicularis with high accuracy and/or coverage. According to our phylogenetic reconstructions, M. f. fascicularis initially diverged into two clades 1.70 million years ago (Ma), with one including haplotypes from mainland Southeast Asia, the Malay Peninsula and North Sumatra (Clade A) and the other, haplotypes from the islands of Bangka, Java, Borneo, Timor, and the Philippines (Clade B). The three geographical populations of Clade A appear as paraphyletic groups, while local populations of Clade B form monophyletic clades with the exception of a Philippine individual which is nested within the Borneo clade. Further, in Clade B the branching pattern among main clades/lineages remains largely unresolved, most likely due to their relatively rapid diversification 0.93-0.84 Ma.
CONCLUSIONS: Both laboratory methods have proven to be powerful to generate complete mtDNA genome data with similarly high accuracy, with the DNA-capture and high-throughput sequencing approach as the most promising and only practical option to obtain such data from highly degraded DNA, in time and with relatively low costs. The application of complete mtDNA genomes yields new insights into the evolutionary history of M. f. fascicularis by providing a more robust phylogeny and more reliable divergence age estimations than earlier studies.
RESULTS: Two fungal isolates (UM 1400 and UM 1020) from human specimens were identified as Daldinia eschscholtzii by morphological features and ITS-based phylogenetic analysis. Both genomes were similar in size with 10,822 predicted genes in UM 1400 (35.8 Mb) and 11,120 predicted genes in UM 1020 (35.5 Mb). A total of 751 gene families were shared among both UM isolates, including gene families associated with fungus-host interactions. In the CAZyme comparative analysis, both genomes were found to contain arrays of CAZyme related to plant cell wall degradation. Genes encoding secreted peptidases were found in the genomes, which encode for the peptidases involved in the degradation of structural proteins in plant cell wall. In addition, arrays of secondary metabolite backbone genes were identified in both genomes, indicating of their potential to produce bioactive secondary metabolites. Both genomes also contained an abundance of gene encoding signaling components, with three proposed MAPK cascades involved in cell wall integrity, osmoregulation, and mating/filamentation. Besides genomic evidence for degrading capability, both isolates also harbored an array of genes encoding stress response proteins that are potentially significant for adaptation to living in the hostile environments.
CONCLUSIONS: Our genomic studies provide further information for the biological understanding of the D. eschscholtzii and suggest that these wood-decaying fungi are also equipped for adaptation to adverse environments in the human host.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.