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Chapter 17 : Identification of Virulence Genes in Silico: Infectious Disease Genomics

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Abstract:

Microbial genomics is a great equalizer, which promises to stimulate research and advance knowledge of the more obscure, but not necessarily unimportant, microbes to a greater extent than the better-studied systems. Some methods are better than others at identifying virulence factors, but are not easily applied to organisms without good genetic systems. The initial identification of virulence factors of from database homologies yielded relatively few candidates. It is evolutionarily removed from the well-studied proteobacteria or gram-positive organisms, so it is likely to have less homology to virulence factors from these groups. One approach to obtain additional information without relying on homology is comparative genomics. The differences identified by this approach confirm the importance of the genes and identify which genes contribute to the differences between these two infections. Genomes are mosaic for a variety of features, ranging from base composition to clustering of virulence genes (pathogenicity islands). The whole genome sequence with predicted coding sequences allows any gene to be cloned. Finally, production of partial polypeptides can be used to test efficacy as a vaccine if a suitable model is available. Whole-genome sequencing is having profound effects on the understanding of mechanisms of infection, the identification of virulence factors, and the development of diagnostics and vaccines. Methods to identify virulence factors can rely on classic genetic approaches as well as homology searches and other genome-wide analyses such as comparative genomics.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17

Key Concept Ranking

Shotgun Sequencing
0.47236878
Treponema denticola
0.4661421
Treponema pallidum
0.4661421
Treponema denticola
0.4661421
Treponema pallidum
0.4661421
Treponema denticola
0.4661421
Treponema pallidum
0.4661421
0.47236878
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Figures

Image of FIGURE 1
FIGURE 1

Current genome projects. The biological world, represented by the phylogeny deduced from 16S rRNA, with the eubacteria (home to microbial pathogens) emphasized. The circles represent public genome projects, i.e., those that release their data and have been funded, started, and/or completed. The data are taken from a number of tallies present on the Internet, such as those at www.tigr.org/tdb/mdb/mdb.html and www.ncbi.nlm.nih.gov/Entrez/Genome/org.html.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 2
FIGURE 2

Increase in genome projects. Data such as those in Fig. 1 that were collected at several times are summarized.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 3
FIGURE 3

Stages in sequencing a bacterial genome. In the initial shotgun sequencing phase (A), a random library is prepared and each clone is sequenced from one or both ends, producing a collection of random sequences (dotted arrows). These are assembled into a provisional consensus sequence (B). In addition, a few large insert clones (such as clones in phage lambda) are prepared and the ends of their inserts are sequenced (solid arrows) and assembled with the short insert clones. The inner section of these inserts is not sequenced. Resequencing of selected regions is performed in the finishing stage (C), including the inner regions of large insert clones.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 4
FIGURE 4

The genome of . Summary of the analysis of the genome sequence. The range in genome size is due to the presence of frameshifts: depending on whether and how these are corrected, one arrives at a slightly different genome length. Note that the frameshifts could be due to several sources, such as sequencing errors, decaying genes, genes regulated by translational frameshifting, or contingency genes ( ). Hypothetical proteins are those entries in databases that have no assigned function and have thus not been demonstrated to be functional. The total number of annotated genes include protein-coding genes as well as nontranslated RNAs (tRNA, rRNA, e.g.).

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 5
FIGURE 5

Candidate virulence factors of based on sequence homology.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 6
FIGURE 6

Whole genome fingerprinting. The sequence was divided into 75 intervals ranging from 5 to 28 kb, with overlaps ranging from 0.2 to 1 kb. Each interval can be amplified by PCR in both and strains. The amplified fragments are digested with a restriction enzyme to generate the fingerprint, allowing whole genomes to be compared.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 7
FIGURE 7

Applications for heterologous expression of genes.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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Image of FIGURE 8
FIGURE 8

Comparison of methods for antigen identification.

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17
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References

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Tables

Generic image for table
TABLE 1

The TlyC family of putative hemolysins

Citation: Weinstock G, Sodergren E, Smajs D, Norris S. 2000. Identification of Virulence Genes in Silico: Infectious Disease Genomics, p 251-261. In Brogden K, Roth J, Stanton T, Bolin C, Minion F, Wannemuehler M (ed), Virulence Mechanisms of Bacterial Pathogens, Third Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818111.ch17

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