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      The Temperature Dependent Proteomic Analysis of Thermotoga maritima

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          Abstract

          Thermotoga maritima ( T. maritima) is a typical thermophile, and its proteome response to environmental temperature changes has yet to be explored. This study aims to uncover the temperature-dependent proteins of T. maritima using comparative proteomic approach. T. maritima was cultured under four temperatures, 60°C, 70°C, 80°C and 90°C, and the bacterial proteins were extracted and electrophoresed in two-dimensional mode. After analysis of gel images, a total of 224 spots, either cytoplasm or membrane, were defined as temperature-dependent. Of these spots, 75 unique bacterial proteins were identified using MALDI TOF/TOF MS. As is well known, the chaperone proteins such as heat shock protein 60 and elongation factor Tu, were up-regulated in abundance due to increased temperature. However, several temperature-dependent proteins of T. maritima responded very differently when compared to responses of the thermophile T. tengcongensis. Intriguingly, a number of proteins involved in central carbohydrate metabolism were significantly up-regulated at higher temperature. Their corresponding mRNA levels were elevated accordingly. The increase in abundance of several key enzymes indicates that a number of central carbohydrate metabolism pathways of T. maritima are activated at higher temperatures.

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          Evidence for lateral gene transfer between Archaea and bacteria from genome sequence of Thermotoga maritima.

          The 1,860,725-base-pair genome of Thermotoga maritima MSB8 contains 1,877 predicted coding regions, 1,014 (54%) of which have functional assignments and 863 (46%) of which are of unknown function. Genome analysis reveals numerous pathways involved in degradation of sugars and plant polysaccharides, and 108 genes that have orthologues only in the genomes of other thermophilic Eubacteria and Archaea. Of the Eubacteria sequenced to date, T. maritima has the highest percentage (24%) of genes that are most similar to archaeal genes. Eighty-one archaeal-like genes are clustered in 15 regions of the T. maritima genome that range in size from 4 to 20 kilobases. Conservation of gene order between T. maritima and Archaea in many of the clustered regions suggests that lateral gene transfer may have occurred between thermophilic Eubacteria and Archaea.
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            Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline.

            Structural genomics is emerging as a principal approach to define protein structure-function relationships. To apply this approach on a genomic scale, novel methods and technologies must be developed to determine large numbers of structures. We describe the design and implementation of a high-throughput structural genomics pipeline and its application to the proteome of the thermophilic bacterium Thermotoga maritima. By using this pipeline, we successfully cloned and attempted expression of 1,376 of the predicted 1,877 genes (73%) and have identified crystallization conditions for 432 proteins, comprising 23% of the T. maritima proteome. Representative structures from TM0423 glycerol dehydrogenase and TM0449 thymidylate synthase-complementing protein are presented as examples of final outputs from the pipeline.
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              Carbohydrate-induced differential gene expression patterns in the hyperthermophilic bacterium Thermotoga maritima.

              The hyperthermophilic bacterium Thermotoga maritima MSB8 was grown on a variety of carbohydrates to determine the influence of carbon and energy source on differential gene expression. Despite the fact that T. maritima has been phylogenetically characterized as a primitive microorganism from an evolutionary perspective, results here suggest that it has versatile and discriminating mechanisms for regulating and effecting complex carbohydrate utilization. Growth of T. maritima on monosaccharides was found to be slower than growth on polysaccharides, although growth to cell densities of 10(8) to 10(9) cells/ml was observed on all carbohydrates tested. Differential expression of genes encoding carbohydrate-active proteins encoded in the T. maritima genome was followed using a targeted cDNA microarray in conjunction with mixed model statistical analysis. Coordinated regulation of genes responding to specific carbohydrates was noted. Although glucose generally repressed expression of all glycoside hydrolase genes, other sugars induced or repressed these genes to varying extents. Expression profiles of most endo-acting glycoside hydrolase genes correlated well with their reported biochemical properties, although exo-acting glycoside hydrolase genes displayed less specific expression patterns. Genes encoding selected putative ABC sugar transporters were found to respond to specific carbohydrates, and in some cases putative oligopeptide transporter genes were also found to respond to specific sugar substrates. Several genes encoding putative transcriptional regulators were expressed during growth on specific sugars, thus suggesting functional assignments. The transcriptional response of T. maritima to specific carbohydrate growth substrates indicated that sugar backbone- and linkage-specific regulatory networks are operational in this organism during the uptake and utilization of carbohydrate substrates. Furthermore, the wide ranging collection of such networks in T. maritima suggests that this organism is capable of adapting to a variety of growth environments containing carbohydrate growth substrates.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                5 October 2012
                : 7
                : 10
                : e46463
                Affiliations
                [1 ]Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China
                [2 ]The Department of Medicine, University of Louisville, Louisville, Kentucky, United States of America
                Lawrence Berkeley National Laboratory, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Performed the experiments: ZW. Analyzed the data: ZW SL. Contributed reagents/materials/analysis tools: WT QW XB ZC. Wrote the paper: SL ZW WT. Gave useful suggestions: JZ NX. Helped with the experiment: JZ NX.

                Article
                PONE-D-12-16750
                10.1371/journal.pone.0046463
                3465335
                23071576
                56c9823c-4d52-44f9-961b-1352127dadf8
                Copyright @ 2012

                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
                : 11 June 2012
                : 30 August 2012
                Page count
                Pages: 10
                Funding
                The work was supported by the grants of the National Basic Research Program of China (2010CB912703) (2007CB707801) and the National Natural Science Foundation of China (30770467). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biotechnology
                Applied Microbiology
                Computational Biology
                Molecular Genetics
                Gene Expression
                Microbiology
                Microbial Ecology
                Microbial Physiology
                Molecular Cell Biology
                Cellular Stress Responses
                Proteomics
                Protein Abundance

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                Uncategorized

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