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      A Phenotypic Profile of the Candida albicans Regulatory Network

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          Abstract

          Candida albicans is a normal resident of the gastrointestinal tract and also the most prevalent fungal pathogen of humans. It last shared a common ancestor with the model yeast Saccharomyces cerevisiae over 300 million years ago. We describe a collection of 143 genetically matched strains of C. albicans, each of which has been deleted for a specific transcriptional regulator. This collection represents a large fraction of the non-essential transcription circuitry. A phenotypic profile for each mutant was developed using a screen of 55 growth conditions. The results identify the biological roles of many individual transcriptional regulators; for many, this work represents the first description of their functions. For example, a quarter of the strains showed altered colony formation, a phenotype reflecting transitions among yeast, pseudohyphal, and hyphal cell forms. These transitions, which have been closely linked to pathogenesis, have been extensively studied, yet our work nearly doubles the number of transcriptional regulators known to influence them. As a second example, nearly a quarter of the knockout strains affected sensitivity to commonly used antifungal drugs; although a few transcriptional regulators have previously been implicated in susceptibility to these drugs, our work indicates many additional mechanisms of sensitivity and resistance. Finally, our results inform how transcriptional networks evolve. Comparison with the existing S. cerevisiae data (supplemented by additional S. cerevisiae experiments reported here) allows the first systematic analysis of phenotypic conservation by orthologous transcriptional regulators over a large evolutionary distance. We find that, despite the many specific wiring changes documented between these species, the general phenotypes of orthologous transcriptional regulator knockouts are largely conserved. These observations support the idea that many wiring changes affect the detailed architecture of the circuit, but not its overall output.

          Author Summary

          A key goal in the understanding of the biology of an organism is the description of the regulatory networks that control the expression of its genes. Changes in gene expression result in new cellular phenotypes that can be acted upon by evolutionary forces to influence the configuration of these networks. We have developed a phenotypic description of the transcriptional regulatory networks of the major fungal pathogen of humans, Candida albicans, by individually deleting genes that encode transcriptional regulators and observing the resulting phenotypes in a variety of environmental conditions. This approach provides insight into the biological roles of many previously uncharacterized regulators, and allows us to assign groups of regulators to specific biological roles, many of which are relevant to pathogenesis. For example, we identified groups of regulators that influenced sensitivity to antifungal drugs, the ability to acquire iron (a challenge for organisms in a human host), and the ability to form complex multi-cellular colonies. Our results also allow us to analyze how the phenotypes associated with transcriptional regulators change as organisms diverge. A comparison of C. albicans data with that from the well-characterized yeast S. cerevisiae revealed strong phenotypic conservation between related transcriptional regulators, despite the more than 300 million years which separate the species.

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          Most cited references60

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          Transcriptional regulatory networks in Saccharomyces cerevisiae.

          We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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            Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

            Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location. Copyright 2001 Academic Press.
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              SGD: Saccharomyces Genome Database.

              J. Cherry (1998)
              The Saccharomyces Genome Database (SGD) provides Internet access to the complete Saccharomyces cerevisiae genomic sequence, its genes and their products, the phenotypes of its mutants, and the literature supporting these data. The amount of information and the number of features provided by SGD have increased greatly following the release of the S.cerevisiae genomic sequence, which is currently the only complete sequence of a eukaryotic genome. SGD aids researchers by providing not only basic information, but also tools such as sequence similarity searching that lead to detailed information about features of the genome and relationships between genes. SGD presents information using a variety of user-friendly, dynamically created graphical displays illustrating physical, genetic and sequence feature maps. SGD can be accessed via the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                December 2009
                December 2009
                24 December 2009
                : 5
                : 12
                : e1000783
                Affiliations
                [1 ]Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America
                [2 ]Department of Medicine, Infectious Diseases Division, University of California San Francisco, San Francisco, California, United States of America
                [3 ]Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
                The University of North Carolina at Chapel Hill, United States of America
                Author notes

                Conceived and designed the experiments: ORH. Performed the experiments: ORH JD. Analyzed the data: ORH ADJ. Contributed reagents/materials/analysis tools: ORH SMN. Wrote the paper: ORH ADJ.

                Article
                09-PLGE-RA-1732R2
                10.1371/journal.pgen.1000783
                2790342
                20041210
                cdb38423-8c10-4789-b966-87b7e66474a5
                Homann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 12 October 2009
                : 22 November 2009
                Page count
                Pages: 12
                Categories
                Research Article
                Evolutionary Biology/Evolutionary and Comparative Genetics
                Evolutionary Biology/Morphogenesis and Cell Biology
                Genetics and Genomics/Gene Expression
                Genetics and Genomics/Gene Function
                Infectious Diseases/Fungal Infections

                Genetics
                Genetics

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