The complete genome sequence of the gastric pathogen Helicobacter pylori

Tomb, Jean-F.; White, Owen; Kerlavage, Anthony R.; Clayton, Rebecca A.; Sutton, Granger; Fleischmann, Robert; Ketchum, Karen A.; Klenk, Hans Peter; Gill, Steven R.; Dougherty, Brian; Nelson, Karen E.; Quackenbush, John; Zhou, Lixin; Kirkness, Ewen F.; Peterson, Scott N.; Loftus, Brendan J.; Richardson, D.; Dodson, Robert J.; Khalak, Hanif; Glodek, Anna; McKenney, Keith; Fitzegerald, Lisa M.; Lee, Norman; Adams, Mark D.; Hickey, Erin K.; Berg, Douglas E.; Gocayne, Jeanine D.; Utterback, Teresa; Peterson, Jeremy; Kelley, Jenny M.; Cotton, Matthew; Weidman, Janice M.; Fujii, Claire; Bowman, Cheryl; Watthey, Larry; Wallin, Erik; Hayes, William; Borodovsky, Mark; Karp, Peter D.; Smith, Hamilton O.; Fraser, Claire M.; Venter, J. Craig

doi:10.1038/41483

Cited by 3,272 publications

(3,301 citation statements)

References 37 publications

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“…We propose that this is a reasonable estimate of the number of membrane proteins in each of these organisms. Note that these values are significantly different from values reported by other authors in their analyses of genomes (Tomb et al, 1997).…”

Section: Resultscontrasting

confidence: 93%

How many membrane proteins are there?

1998

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One of the basic issues that arises in functional genomics is the ability to predict the subcellular location of proteins that are deduced from gene and genome sequencing. In particular, one would like to be able to readily specify those proteins that are soluble and those that are inserted in a membrane. Traditional methods of distinguishing between these two locations have relied on extensive, time-consuming biochemical studies. The alternative approach has been to make inferences based on a visual search of the amino acid sequences of presumed gene products for stretches of hydrophobic amino acids. This numerical, sequence-based approach is usually seen as a first approximation pending more reliable biochemical data. The recent availability of large and complete sequence data sets for several organisms allows us to determine just how accurate such a numerical approach could be, and to attempt to minimize and quantify the error involved. We have optimized a statistical approach to protein location determination. Using our approach, we have determined that surprisingly few proteins are misallocated using the numerical method. We also examine the biological implications of the success of this technique.Keywords: computer modeling; discriminant analysis; hydropathy; membrane proteins; statistical methods Experimental methods of determination of subcellular protein location are accurate but time-consuming. Hydropathy analysis (Kyte & Doolittle, 1982) has often been used to deduce subcellular localization of proteins in the absence of experimental data. However, although visual inspection of hydropathy plots can be useful in predicting the topology of known integral membrane proteins, it is ineffective as an accurate predictor of the location of a protein.To discriminate between integral and peripheral membrane proteins, Klein et al. (1985) generated a single number, maxH, the average hydropathy of the most hydrophobic protein segment of a given length for a given protein using a given hydropathy scale. In the interest of clarity, we will refer to this number as the "maxH value," while using the term "maxH segment" to refer to the hydrophobic peptide segment to which it belongs. This was then applied to a set of known integral and peripheral membrane proteins in a training set. A discriminator function was generated that assigned a probability of being an integral membrane protein to a given value of maxH. This function was then used to analyze a similar set of known proteins, the tester set. It was determined that the Kyte-Doolittle hydropathy scale and a window length of 17 residues gave the best resolution of membrane and soluble proteins in the tester set. We have found that the method used by Klein et al. (1985) is still generally useful, but that the actual functions provided in their paper are not, having been derived at a time when very few proteins were both sequenced and characterized. ResultsWe discovered the need for a new discriminator when we attempted to apply the functions described by Klei...

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Section: Resultscontrasting

confidence: 93%

How many membrane proteins are there?

1998

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“…Despite this fact, we observe that the pathway for glycolysis (4) has a high RPI in H. pylori, and this indicates that some translational bias in this organism is nevertheless present. In this respect, note that glucose appears to be the only carbohydrate utilized by this bacterium (Tomb et al 1997) and that rapid growth with a doubling time of about 50 min has been reported (Andersen et al 1997). Our analysis also indicates the thioredoxin pathway (40) to have a rather high RPI value (see Fig.…”

Section: Discussionmentioning

confidence: 71%

Insights on the Evolution of Metabolic Networks of Unicellular Translationally Biased Organisms from Transcriptomic Data and Sequence Analysis

Carbone

Madden

2005

J Mol Evol

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Abstract. Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

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“…Bacteria present very diverse evolutionary strategies linked to pathogenesis, such as: pathogenicity islands in Salmonella and E. coli [17], and Vibrio cholerae [18], gene uptake systems in V. cholerae [19], tandem repeats for phase variation in Haemophilus influenzae [20], tandem repeats linked with contingency loci in H. pylori [10,21], and long repeats for antigenic variation in Mycoplasma [9,22], B. burgdorferi [23], and M. tuberculosis [12].…”

Section: Adaptation Of Pathogensmentioning

confidence: 99%

“…In recent years, the analysis of completely sequenced genomes revealed that long repeats are ubiquitous in archaea and eubacteria [8][9][10][11][12]. Although many of these repeats still have unknown roles, our knowledge of their functions has increased significantly with the recent genomic exploratory analysis.…”

Section: Introductionmentioning

confidence: 99%