Project description:The fungal pathogen Histoplasma capsulatum is thought to be the most common cause of fungal respiratory infections in immunocompetent humans, yet little is known about its biology. Here we provide the first genome-wide studies to experimentally validate its genome annotation. A functional interrogation of the Histoplasma genome provides critical support for continued investigation into the biology and pathogenesis of H. capsulatum and related fungi. We employed a three-pronged approach to provide a functional annotation for the H. capsulatum G217B strain. First, we probed high-density tiling arrays with labeled cDNAs from cells grown under diverse conditions. These data defined 6,172 transcriptionally active regions (TARs), providing validation of 6,008 gene predictions. Interestingly, 22% of these predictions showed evidence of anti-sense transcription. Additionally, we detected transcription of 264 novel genes not present in the original gene predictions. To further enrich our analysis, we incorporated expression data from whole-genome oligonucleotide microarrays. These expression data included profiling under growth conditions that were not represented in the tiling experiment, and validated an additional 2,249 gene predictions. Finally, we compared the G217B gene predictions to other available fungal genomes, and observed that an additional 254 gene predictions had an ortholog in a different fungal species, suggesting that they represent genuine coding sequences. These analyses yielded a high confidence set of validated gene predictions for H. capsulatum. The transcript sets resulting from this study are a valuable resource for further experimental characterization of this ubiquitous fungal pathogen. The data is available for interactive exploration at http://histo.ucsf.edu. The non-redundant genome sequence of Histoplasma capsulatum G217B was tiled over a set of 93 CombiMatrix arrays, which were then hybridized with labeled cDNA samples from yeast-form (red channel) or mycelial-form (green channel) Histoplasma. Due to low yields from the mycelial samples, only the red channel intensities were analyzed (and the red foreground intensities are, therefore, reported in the VALUE column for each sample). Rather than normalizing intensities across arrays, each probe was evaluated as detected or undetected relative to the negative control intensities on the corresponding array, and the density of detected probes as a function of genome position was used for the remaining analysis.

Project description:Pitkanen2014 - Metabolic reconstruction of Histoplasma capsulatum using CoReCo This model was reconstructed with the CoReCo method (Comparative ReConstruction of genome-scale metabolic networks) from protein sequence and phylogeny data. This model is described in the article: Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species Esa Pitkänen, Paula Jouhten, Jian Hou, Muhammad Fahad Syed, Peter Blomberg, Jana Kludas, Merja Oja, Liisa Holm, Merja Penttilä, Juho Rousu, Mikko Arvas PLoS Comput Biol. 2014 Feb 6;10(2):e1003465 Abstract: We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. This model is hosted on BioModels Database and identified by: MODEL1302010022 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Histoplasma

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure