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      Analysis of the Genome and Transcriptome of Cryptococcus neoformans var. grubii Reveals Complex RNA Expression and Microevolution Leading to Virulence Attenuation

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      1 , 2 , * , 3 , 4 , 5 , 6 , 6 , 7 , 8 , 5 , 9 , 10 , 11 , 5 , 3 , 4 , 5 , 9 , 12 , 13 , 1 , 2 , 13 , 14 , 5 , 15 , 16 , 17 , 13 , 18 , 12 , 17 , 14 , 19 , 1 , 2 , 5 , 20 , 4 , 9 , 17 , 5 , 1 , 2 , 21 , 13 , 9 , 11 , 22 , 12 , 16 , 5 , 23 , 24 , 6 , 5 , * , 3 , 13 , * , 5 , *
      PLoS Genetics
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          Abstract

          Cryptococcus neoformans is a pathogenic basidiomycetous yeast responsible for more than 600,000 deaths each year. It occurs as two serotypes (A and D) representing two varieties (i.e. grubii and neoformans, respectively). Here, we sequenced the genome and performed an RNA-Seq-based analysis of the C. neoformans var. grubii transcriptome structure. We determined the chromosomal locations, analyzed the sequence/structural features of the centromeres, and identified origins of replication. The genome was annotated based on automated and manual curation. More than 40,000 introns populating more than 99% of the expressed genes were identified. Although most of these introns are located in the coding DNA sequences (CDS), over 2,000 introns in the untranslated regions (UTRs) were also identified. Poly(A)-containing reads were employed to locate the polyadenylation sites of more than 80% of the genes. Examination of the sequences around these sites revealed a new poly(A)-site-associated motif (AUGHAH). In addition, 1,197 miscRNAs were identified. These miscRNAs can be spliced and/or polyadenylated, but do not appear to have obvious coding capacities. Finally, this genome sequence enabled a comparative analysis of strain H99 variants obtained after laboratory passage. The spectrum of mutations identified provides insights into the genetics underlying the micro-evolution of a laboratory strain, and identifies mutations involved in stress responses, mating efficiency, and virulence.

          Author Summary

          Cryptococcus neoformans var. grubii is a major human pathogen responsible for deadly meningoencephalitis in immunocompromised patients. Here, we report the sequencing and annotation of its genome. Evidence for extensive intron splicing, antisense transcription, non-coding RNAs, and alternative polyadenylation indicates the potential for highly intricate regulation of gene expression in this opportunistic pathogen. In addition, detailed molecular, genetic, and genomic studies were performed to characterize structural features of the genome, including centromeres and origins of replication. Finally, the phenotypic and genome re-sequencing analysis of a collection of isolates of the reference H99 strain resulting from laboratory passage revealed that microevolutionary processes during in vitro culturing of pathogenic fungi can impact virulence.

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          Most cited references131

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                April 2014
                17 April 2014
                : 10
                : 4
                : e1004261
                Affiliations
                [1 ]Institut Pasteur, Unité Biologie et Pathogénicité Fongiques, Département Génomes et Génétique, Paris, France
                [2 ]INRA, USC2019, Paris, France
                [3 ]University of Queensland, School of Chemistry and Molecular Biosciences, Brisbane, Queensland, Australia
                [4 ]Institut Pasteur, Plate-forme Transcriptome et Epigénome, Département Génomes et Génétique, Paris, France
                [5 ]Duke University Medical Center, Department of Molecular Genetics and Microbiology, Durham, North Carolina, United States of America
                [6 ]Jawaharlal Nehru Centre for Advanced Scientific Research, Molecular Biology and Genetics Unit, Bangalore, India
                [7 ]Genotypic Technology Private Limited, Bangalore, India
                [8 ]Institut Pasteur, Unité Biologie Cellulaire du Parasitisme, Département Biologie Cellulaire et Infection, Paris, France
                [9 ]INRA, UMR 1319 Micalis, Jouy-en-Josas, France
                [10 ]Yonsei University, Center for Fungal Pathogenesis, Department of Biotechnology, Seoul, Republic of Korea
                [11 ]Rutgers New Jersey Medical School, Department of Microbiology and Molecular Genetics, Newark, New Jersey, United States of America
                [12 ]Washington University School of Medicine, Department of Molecular Microbiology, St. Louis, Missouri, United States of America
                [13 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                [14 ]University of Virginia, Department of Biochemistry and Molecular Genetics, Charlottesville, Virginia, United States of America
                [15 ]California Institute of Technology, Division of Biology, Pasadena, California, United States of America
                [16 ]University of Missouri-Kansas City, School of Biological Sciences, Division of Cell Biology and Biophysics, Kansas City, Missouri, United States of America
                [17 ]Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
                [18 ]Clemson University, Department of Genetics and Biochemistry, Clemson, South Carolina, United States of America
                [19 ]University of North Carolina, Department of Genetics, Chapel Hill, North Carolina, United States of America
                [20 ]University of Minnesota, Microbiology Department, Minneapolis, Minnesota, United States of America
                [21 ]University of Queensland, School of Mathematics and Physics, Brisbane, Queensland, Australia
                [22 ]Duke University Medical Center, Duke Department of Medicine and Molecular Genetics and Microbiology, Durham, North Carolina, United States of America
                [23 ]University of California, Department of Plant Pathology & Microbiology, Riverside, California, United States of America
                [24 ]Michael Smith Laboratories, Department of Microbiology and Immunology, Vancouver, British Columbia, Canada
                Oregon State University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GJ YSB MM LDM PAM CN CSN JRP JKL AI JESt JWK KS JH JAF CAC FSD. Performed the experiments: KLO DP EJB GC VY CCH RBB FB WC YC EWLC JYC AFA CG KJG JG SGH SG JLH YPH GI SJ CDK WL FM KN CP TR JESc SS CW IAW QZ LK NM. Analyzed the data: GJ CN CSN JKL AI JWK KS JH JAF CAC FSD. Wrote the paper: GJ KLO DP CSN AI JWK KS JH JAF CAC FSD.

                [¤]

                Current address: Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America.

                Article
                PGENETICS-D-13-02581
                10.1371/journal.pgen.1004261
                3990503
                24743168
                083a9d76-7854-4505-ac73-61d5ccd6518d
                Copyright @ 2014

                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
                : 19 September 2013
                : 7 February 2014
                Page count
                Pages: 26
                Funding
                This work was supported by the National Human Genome Research Institute, grant number U54HG003067 to the Broad Institute. This project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No.:HHSN272200900018C. This work was supported by a grant from ANR (2010-BLAN-1620-01 program YeastIntrons) to GJ. This work was also supported by NIH/NIAID R37 award AI39115-16 and R01 award AI50113-10 (JH), NIH/NIAID R21 award AI094364 (AI), and by NIH/NINDS R01 grant NS042263 (FSD). The intramural support of JNCASR to KS for the centromere work is highly acknowledged. The work on replication origins was funded by a Burroughs Wellcome Scholar Award in Molecular Pathogenic Mycology awarded to CSN. The work on microevolution was funded by the NHMRC Project Grant 455980 (JAF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biotechnology
                Genetic Engineering
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Genome Expression Analysis
                Comparative Genomics
                Genome Evolution
                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Genetics
                Genomics
                Microbiology
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Genome Sequencing
                Mycology
                Fungal Evolution

                Genetics
                Genetics

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