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      Systems biology of host–fungus interactions: turning complexity into simplicity

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          Highlights

          ► Understanding the complexity of host–fungus interactions during commensalism. ► Genes mediating host colonization or fitness can evolve into infection-associated traits. ► Using bioinformatics to unravel functional genomics in dual-genome datasets. ► Modeling both fungal and host immune responses using network analysis tools. ► Databases and web-based resources for investigating host–pathogen interactions.

          Abstract

          Modeling interactions between fungi and their hosts at the systems level requires a molecular understanding both of how the host orchestrates immune surveillance and tolerance, and how this activation, in turn, affects fungal adaptation and survival. The transition from the commensal to pathogenic state, and the co-evolution of fungal strains within their hosts, necessitates the molecular dissection of fungal traits responsible for these interactions. There has been a dramatic increase in publically available genome-wide resources addressing fungal pathophysiology and host–fungal immunology. The integration of these existing data and emerging large-scale technologies addressing host–pathogen interactions requires novel tools to connect genome-wide data sets and theoretical approaches with experimental validation so as to identify inherent and emerging properties of host–pathogen relationships and to obtain a holistic view of infectious processes. If successful, a better understanding of the immune response in health and microbial diseases will eventually emerge and pave the way for improved therapies.

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          Most cited references59

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          Candida albicans morphogenesis and host defence: discriminating invasion from colonization.

          Candida albicans is a common fungal pathogen of humans that colonizes the skin and mucosal surfaces of most healthy individuals. Until recently, little was known about the mechanisms by which mucosal antifungal defences tolerate colonizing C. albicans but react strongly when hyphae of the same microorganism attempt to invade tissue. In this Review, we describe the properties of yeast cells and hyphae that are relevant to their interaction with the host, and the immunological mechanisms that differentially recognize colonizing versus invading C. albicans.
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            Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast

            Aneuploidy, referring here to genome contents characterized by abnormal numbers of chromosomes, has been associated with developmental defects, cancer, and adaptive evolution in experimental organisms1–9. However, it remains unresolved how aneuploidy impacts gene expression and whether aneuploidy could directly bring phenotypic variation and improved fitness over that of euploid counterparts. In this work, we designed a novel scheme to generate, through random meiotic segregation, 38 stable and fully isogenic aneuploid yeast strains with distinct karyotypes and genome contents between 1N and 3N without involving any genetic selection. Through phenotypic profiling under various growth conditions or in the presence of a panel of chemotherapeutic or antifungal drugs, we found that aneuploid strains exhibited diverse growth phenotypes, and some aneuploid strains grew better than euploid control strains under conditions suboptimal for the latter. Using quantitative mass spectrometry-based proteomics, we show that the levels of protein expression largely scale with chromosome copy numbers, following the same trend observed for the transcriptome. These results provide strong evidence that aneuploidy directly impacts gene expression at both the transcriptome and proteome levels and can generate significant phenotypic variation that could bring about fitness gains under diverse conditions. Our findings suggest that the fitness ranking between euploid and aneuploid cells is context- and karyotype-dependent, providing the basis for the notion that aneuploidy can directly underlie phenotypic evolution and cellular adaptation.
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              Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses.

              Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins, which enabled the construction of a network model consisting of 24 core regulators and 76 fine-tuners that help to explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.
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                Author and article information

                Contributors
                Journal
                Curr Opin Microbiol
                Curr. Opin. Microbiol
                Current Opinion in Microbiology
                Current Biology
                1369-5274
                1879-0364
                August 2012
                August 2012
                : 15
                : 4
                : 440-446
                Affiliations
                [1 ]Medical University of Vienna, Christian Doppler Laboratory Infection Biology, Max F. Perutz Laboratories, A-1030 Vienna, Austria
                [2 ]Department of Preclinical and Clinical Pharmacology, University of Florence, 50139 Firenze, Italy
                [3 ]Research and Innovation Centre, Edmund Mach Foundation, San Michele all’Adige, 38010, Trento, Italy
                Article
                COMICR998
                10.1016/j.mib.2012.05.001
                3501689
                22717554
                e655b6d7-ab24-4b02-9ff3-f2fb811437bd
                © 2012 Elsevier Ltd.

                This document may be redistributed and reused, subject to certain conditions.

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                Categories
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                Microbiology & Virology
                Microbiology & Virology

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