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      Phylogenetically Driven Sequencing of Extremely Halophilic Archaea Reveals Strategies for Static and Dynamic Osmo-response

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Organisms across the tree of life use a variety of mechanisms to respond to stress-inducing fluctuations in osmotic conditions. Cellular response mechanisms and phenotypes associated with osmoadaptation also play important roles in bacterial virulence, human health, agricultural production and many other biological systems. To improve understanding of osmoadaptive strategies, we have generated 59 high-quality draft genomes for the haloarchaea (a euryarchaeal clade whose members thrive in hypersaline environments and routinely experience drastic changes in environmental salinity) and analyzed these new genomes in combination with those from 21 previously sequenced haloarchaeal isolates. We propose a generalized model for haloarchaeal management of cytoplasmic osmolarity in response to osmotic shifts, where potassium accumulation and sodium expulsion during osmotic upshock are accomplished via secondary transport using the proton gradient as an energy source, and potassium loss during downshock is via a combination of secondary transport and non-specific ion loss through mechanosensitive channels. We also propose new mechanisms for magnesium and chloride accumulation. We describe the expansion and differentiation of haloarchaeal general transcription factor families, including two novel expansions of the TATA-binding protein family, and discuss their potential for enabling rapid adaptation to environmental fluxes. We challenge a recent high-profile proposal regarding the evolutionary origins of the haloarchaea by showing that inclusion of additional genomes significantly reduces support for a proposed large-scale horizontal gene transfer into the ancestral haloarchaeon from the bacterial domain. The combination of broad (17 genera) and deep (≥5 species in four genera) sampling of a phenotypically unified clade has enabled us to uncover both highly conserved and specialized features of osmoadaptation. Finally, we demonstrate the broad utility of such datasets, for metagenomics, improvements to automated gene annotation and investigations of evolutionary processes.

          Author Summary

          The ability to adjust to changing osmotic conditions (osmoadaptation) is crucial to the survival of organisms across the tree of life. However, significant gaps still exist in our understanding of this important phenomenon. To help fill some of these gaps, we have produced high-quality draft genomes for 59 osmoadaptation “experts” (extreme halophiles of the euryarchaeal family Halobacteriaceae). We describe the dispersal of osmoadaptive protein families across the haloarchaeal evolutionary tree. We use this data to suggest a generalized model for haloarchaeal ion transport in response to changing osmotic conditions, including proposed new mechanisms for magnesium and chloride accumulation. We describe the evolutionary expansion and differentiation of haloarchaeal general transcription factor families and discuss their potential for enabling rapid adaptation to environmental fluxes. Lastly, we challenge a recent high-profile proposal regarding the evolutionary origins of the haloarchaea by showing that inclusion of additional genomes significantly reduces support for a proposed large-scale horizontal gene transfer into the ancestral haloarchaeon from the bacterial domain. This result highlights the power of our dataset for making evolutionary inferences, a feature which will make it useful to the broader evolutionary community. We distribute our genomic dataset through a user-friendly graphical interface.

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          Most cited references 76

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            A new generation of homology search tools based on probabilistic inference.

             Sean R. Eddy (2009)
            Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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              GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes.

              We present 'gene prediction improvement pipeline' (GenePRIMP; http://geneprimp.jgi-psf.org/), a computational process that performs evidence-based evaluation of gene models in prokaryotic genomes and reports anomalies including inconsistent start sites, missed genes and split genes. We found that manual curation of gene models using the anomaly reports generated by GenePRIMP improved their quality, and demonstrate the applicability of GenePRIMP in improving finishing quality and comparing different genome-sequencing and annotation technologies.
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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
                November 2014
                13 November 2014
                : 10
                : 11
                Affiliations
                [1 ]Microbiology Graduate Group, University of California, Davis, Davis, California, United States of America
                [2 ]Genome Center, University of California, Davis, Davis, California, United States of America
                [3 ]Department of Biomedical Engineering, University of California, Davis, Davis, California, United States of America
                [4 ]Proteome Software, Portland, Oregon, United States of America
                [5 ]Department of Energy Joint Genomes Institute, Walnut Creek, California, United States of America
                [6 ]Department of Earth and Planetary Sciences, University of California, Davis, Davis, California, United States of America
                [7 ]Université Grenoble Alpes, Institut de Biologie Structurale, Grenoble, France
                [8 ]Centre National de la Recherche Scientifique, Institut de Biologie Structurale, Grenoble, France
                [9 ]Commissariat à l'énergie Atomique et aux Énergies Alternatives, Département du Science du Vivant, Institut de Biologie Structurale, Grenoble, France
                [10 ]ithree Institute, University of Technology, Sydney, Australia
                University of Illinois at Urbana-Champaign, United States of America
                Author notes

                PMS is currently an employee of Proteome Software, Portland, Oregon, USA. However, this employment began after initial submission of this manuscript. The authors declare that no competing interests exist.

                Conceived and designed the experiments: PMS EAB DW MK DM JAE MTF. Performed the experiments: DW MK PMS EAB DL AIY AED AT. Analyzed the data: AED AT DW MK DM PMS EAB. Contributed reagents/materials/analysis tools: JAE MTF. Wrote the paper: EAB PMS. Prepared libraries: DL AIY. Assembled genomes and did initial annotation: AED AT. Conducted phylogenetic analyses: DW MK. Assisted with ion transport and proteome acidification analyses: DM. Conducted pangenome, proteome acidification, genera comparisons and HGT analyses: PMS. Conducted core genome, GC content, GTF expansion and osmoadaptation analyses: EAB.

                Article
                PGENETICS-D-14-01695
                10.1371/journal.pgen.1004784
                4230888
                25393412

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                Page count
                Pages: 23
                Funding
                Funding for this project was provided by the National Science Foundation, grant number 0949453. 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
                Astrobiology
                Computational Biology
                Comparative Genomics
                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Bacterial Evolution
                Evolutionary Genetics
                Molecular Evolution
                Genetics
                Genomics
                Microbial Genomics
                Bacterial Genomics
                Microbiology
                Archaean Biology
                Archaeal Evolution
                Archaeal Physiology
                Bacteriology
                Microbial Physiology
                Ecology and Environmental Sciences
                Extremophiles
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All raw read files are available from the European Nucleotide Archive database. All assemblies and PGAAP annotations are available through the National Center for Biotechnology Information databases. Accession numbers for both databases are listed in Table S2. Additional data files are available through the Data Dryad digital repository (doi: 10.5061/dryad.1546n) as noted in the text.

                Genetics

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