Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, The Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
Correspondence
David W. Ussery
(dave{at}cbs.dtu.dk)
Genomes of the month
There have been six bacterial genomes published since the last Genome Update column was written. All of this month's new genomes, listed in Table 1, come from genera for which there are multiple sequenced genomes already present in the databases. Brucella abortus and Chlamydophila abortus can both cause abortion in animals, as their names imply, although via different mechanisms. Two more Staphylococcus genomes have been reported (Staphylococcus aureus and Staphylococcus epidermidis), as well as genomes of Salmonella enterica SCB67 (Chiu et al., 2005
) and Wolbachia species TRS (Foster et al., 2005
). From a broader perspective, it should be noted that there are many genera (indeed many bacterial phyla) which have no representative genomes sequenced and it is hoped that at least some of the future genomes being sequenced will be more reflective of the biological diversity in the environment.
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Most infections caused by staphylococci are due to Staphylococcus aureus. Nevertheless, the incidence of infections due to Staphylococcus epidermidis and other coagulase-negative staphylococci has been steadily increasing in recent years. S. aureus is responsible for numerous hospital- and community-acquired infections, whereas infections with S. epidermidis are often associated with implanted medical devices. Gill et al. (2005) have sequenced the
2·8 Mb genome of S. aureus (strain COL), an early methicillin-resistant (MRSA) isolate, and the genome of S. epidermidis (strain RP62a,
2·6 Mb) and have conducted comparative analysis of these two species and other staphylococcal genomes to investigate their evolution and their resistance. S. aureus and S. epidermidis share a core set of 1681 ORFs. Their virulence and resistance attributes might be due to gene transfer between staphylococci and low-GC-content Gram-positive bacteria. Integrated plasmids in S. epidermidis containing genes encoding resistance to cadmium and species-specific LPXTG surface proteins, and a novel genome island, which can be a potential S. epidermidis virulence factor, were also identified, but a significant observation was the evidence for gene transfer between staphylococci and bacilli. The cap operon, a major virulence factor in Bacillus anthracis, has integrated the genomes of S. epidermidis strain RP62a and ATCC 12228, possibly via plasmid-mediated gene transfer.
Method of the month prediction of membrane proteins
All living cells are encapsulated by at least one membrane. In previous Genome Updates we have described prediction of the translocation machinery and various methods for the prediction of the proteins that are translocated over the cellular membrane. This month we will discuss methods for prediction of proteins that are embedded in the membrane. Membrane proteins have a significant number of roles in cellular metabolism and cell stability.
The majority of membrane proteins have a trans-membrane -helix domain, but a minority have a
-barrel domain. Outer-membrane proteins of Gram-negative bacteria are often
-barrel proteins.
Most prediction methods have dealt with prediction of -helical domains and their topology. We highly recommend TMHMM which is one of the most used prediction tools for identifying transmembrane proteins and their topology (Krogh et al., 2001
). Many other methods are available, such as HMMTOP (Tusnady & Simon, 2001
) and MEMSAT (Jones et al., 1994
). The performance of transmembrane helix predictors has been reviewed (Möller et al., 2001
). For
-barrel membrane proteins, only two methods are available: BOMP (Berven et al., 2004
) and PRED-TMBB (Bagos et al., 2004
).
One would not expect the fraction of membrane proteins to differ significantly among different phyla. All prokaryotes need membrane transporters in order to take up metabolites and essential ions from the surroundings. As can be seen in Fig. 1, the fraction of membrane proteins in the proteome of different bacteria ranges from 15 to 20 % in the majority of phyla. It is worth mentioning that in some phyla the distribution range is quite large. For example, in Actinobacteria, the fraction of membrane proteins predicted using TMHMM ranges from less than 10 % in Mycobacterium leprae to 21 % in Cornyebacterium glutamicum. However, one should remember that the genome of Mycobacterium leprae contains many pseudogenes and hence the fraction of the total in the case of this genome might have a different biological meaning than for other bacterial genomes containing few pseudogenes as part of the total gene count. The panel on the left shows the data plotted as a fraction of the predicted proteins containing transmembrane helices, normalized to the total number of proteins encoded in the genome. However, since some genomes are smaller than others, and we have shown previously that the mean genome length of some phyla can be different, we have also plotted the data in terms of the total number of transmembrane helices, without dividing by the total number of proteins in a given proteome. Again, in the example of the Actinobacteria, the range can be seen to differ in the plot on the right (unnormalized) compared to the fraction of the total, plotted on the left. In this case, the distribution is more evenly distributed, with a mean of around 600 proteins, although there are two outliers, with around 1370 predicted transmembrane proteins these are the two Streptomyces genomes, which are quite large, containing about 7500 encoded proteins. In addition, the Firmicutes, with only one membrane, have a larger fraction of transmembrane proteins than the Proteobacteria, which have two membranes. One might expect the opposite trend here, although when the total number of proteins is examined in the right panel, the distributions seem to overlap more closely. It appears from this figure that most free-living bacteria contain roughly 400500 transmembrane proteins.
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Supplemental web pages
Additional web pages containing supplemental material related to this article can be accessed from www.cbs.dtu.dk/services/GenomeAtlas/suppl/GenUp017/
Acknowledgements
This work was supported by a grant from the Danish Center for Scientific Computing.
REFERENCES
Bagos, P. G., Liakopoulos, T. D., Spyropoulos, I. C. & Hamodrakas, S. J. (2004). PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins. Nucleic Acids Res 32, W400W404.
Berven, F. S., Flikka, K., Jensen, H. B. & Eidhammer, I. (2004). BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria. Nucleic Acids Res 32, W394W399.
Chiu, C. H., Tang, P., Chu, C., Hu, S., Bao, Q., Yu, J., Chou, Y. Y., Wang, H. S. & Lee, Y. S. (2005). The genome sequence of Salmonella enterica serovar Choleraesuis, a highly invasive and resistant zoonotic pathogen. Nucleic Acids Res 33, 16901698.
Foster, J., Ganatra, M., Kamal, I. & 23 other authors (2005). The Wolbachia genome of Brugia malayi: endosymbiont evolution within a human pathogenic nematode. PLoS Biol 3, e121.[CrossRef][Medline]
Gill, S. R., Fouts, D. E., Archer, G. L. & 26 other authors (2005). Insights on evolution of virulence and resistance from the complete genome analysis of an early methicillin-resistant Staphylococcus aureus strain and a biofilm-producing methicillin-resistant Staphylococcus epidermidis strain. J Bacteriol 187, 24262438.
Halling, S. M., Peterson-Burch, B. D., Bricker, B. J., Zuerner, R. L., Qing, Z., Li, L. L., Kapur, V., Alt, D. P. & Olsen, S. C. (2005). Completion of the genome sequence of Brucella abortus and comparison to the highly similar genomes of Brucella melitensis and Brucella suis. J Bacteriol 187, 27152726.
Jones, D. T., Taylor, W. R. & Thornton, J. M. (1994). A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry 33, 30383049.[CrossRef][Medline]
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305, 567580.[CrossRef][Medline]
Möller, S., Croning, M. D. & Apweiler, R. (2001). Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17, 646653.
Thomson, N. R., Yeats, C., Bell, K. & 17 other authors (2005). The Chlamydophila abortus genome sequence reveals an array of variable proteins that contribute to interspecies variation. Genome Res 15, 629640.
Tusnady, G. E. & Simon, I. (2001). The HMMTOP transmembrane topology prediction server. Bioinformatics 17, 849850.
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