EGGS database: Essential Genes on Genome Scale

SEED maintains a database of microbial gene essentiality data experimentally obtained from published genome-scale gene essentiality screens (listed in Table 1). Comparative analysis of these data across multiple organisms in a rich genomic, biochemical, and phylogenetic contexts provided by the collection of annotated Subsystems greatly facilitates their interpretation and practical applications, such as, understanding of cellular networks, gene and pathway discovery, identification of novel drug targets, and strain engineering.

EGGS contents and structure

Gene essentiality data in SEED are integrated as gene attribute key-value pairs (see Help on Attributes). Each attribute Key corresponds to a single experimental data-set generated under uniform genetic and environmental conditions (briefly outlined in Table 1, follow the links to original publications for details). If two or more independent studies have been published for an organism (e.g., for E. coli, S. aureus, S. pneumoniae), several Attributes are associated with the corresponding genome in SEED. Note, that gene essentiality assertions ('Values') obtained for the same gene in different experiments ('Keys') may differ and even contradict each other. In addition, several derived Keys were generated by merging:

(i) The two datasets obtained for S. aureus to yield a Key: 'SA_essential_merged'

(ii) The two datasets obtained for S. pneumoniae: 'SP_essential_merged_Ji_Forsyth'

(iii) Gene essentiality data for 10 microbial species (a single dataset per organism, marked in Table 1) to yield a combined nonredundunt dataset 'Essential_Gene_Sets_Bacterial'

To facilitate comparative analysis of gene essentiality data in SEED, the original heterogeneous essentiality assignments have been converted to a unified format: 'essential' (E), 'nonessential' (N), with a default attribute 'undefined' (U) for all other genes. In several ambiguous cases an authors' notion of 'possibly essential gene' has been retained.

The notion of gene essentiality is meaningful only in the context of specific environmental and genetic conditions it was surveyed under (see Table 1). The specifics of technology used to generate each dataset influence gene essentiality assessments as well. The important distinction between the techniques is whether the growth of each mutant occurs clonally or in a mixed population. Although in both strategies gene 'essentiality' is deduced from the inability of a mutant cell to undergo a certain number of divisions, the passing threshold is much more stringent in mixed populations than in clonal studies. Thus, a mutant with substantially decreased fitness would be quickly selected against under the conditions of competitive outgrowth in planktonic culture, while it might still be capable of forming an isolated colony. In EGGS database E (essential gene) stands for 'essential for survival' for the datasets generated via clonal outgrowth and 'essential for fitness' for datasets generated via populational screens (Table 1).

How to use EGGS database

I. Visualization and analysis of essentiality data in Subsystem context: Essentiality data can be visualized in the biochemical and phylogenetic contexts of a Subsystem (SS) spreadsheet. This type of analysis performed across 134 metabolic Subsystems has been published by Current Opinion in Biotechnology.

To view essentiality assessments of genes in the context of SS spreadsheet click

II. Essentiality of individual genes can be viewed from a gene/protein (PEG) page. A link Attributes is available near the bottom of every PEG page. Activating this link opens a list of various attribute Keys associated with the gene or its protein product (see Help on Attributes), including gene essentiality. Column 'Key' lists all the experiments (gene essentiality datasets), in which this gene has been scored. Column 'Value' shows essentiality assessments (contradicting at times) made in each experiment. Please, note specific environmental conditions and experimental details that might have influenced each essentiality call, outlined in Table 1 and specified in the original publication.

III. To view a complete list of essential (E) or nonessential (N) genes, as well as all essentiality assignments (E, N, and U) produced in a specific experiment, open Table 1 and click on a number (corresponding to the experiment of interest) that appears in one of the columns: Essentiality assessment: ORFs total, E, N, or U. The resultant output table(s) can be sorted by any of the columns (by clicking on a heading) or searched by typing key words into a search field provided.

Table 1. Genome-scale experimentally determined bacterial gene essentiality data-sets available in SEED

back to top

Organism	SEED genome ID	Experiment	Mutagenesis		Mutant outgrowth		Essentiality assesment				Reference
			Strategy	Mutation	Strategy	Environmental conditions	ORFs total	N	E	U
BS, EC, HI, HP, MG, MT, PA, SA, SP, ST		Essential_Gene_Sets_Bacterial	Combined nonredundant dataset, includes global gene essentiality data for 10 bacterial species (a single dataset per organism, labeled with a red star below)
M.genitalium	243273.1	*MG_essential_Hutchison_2006	random	insertion	clones	Rich undefined medium SP4, 37°C, microaerobic growth in 5% CO2	482	100	382	0	[14]
S. aureus N315	158879.1	SA_essential_Ji	random	antisense RNA	clones	Rich undefined medium TSA, aerobic growth	2,600	n/a	1683	n/a	[2]
S. aureus N315	158879.1	SA_essential_Forsyth	random	antisense RNA	clones	Rich undefined medium LB+0.2% glucose, 37°C, aerobic growth	2,892	n/a	6584	n/a	[3]
S. aureus N315		*SA_essential_merged_Forsyth_and_Ji	A combined nonredundunt dataset derived from the data obtained in two similar global gene essentiality screens in S. aureus [2, 3]
H. influenzae Rd	71421.1	*HI_contribute_to_fitness_Akerley	random	insertion	population	Rich undefined medium BHI, 37°C, aerobic growth	1,657	602	670	385	[5]
S. pneumoniae R6	171101.1	SP_essential_Thanassi	targeted	insertion	clones	Rich undefined medium Todd-Hewitt, 37°C, microaerobic growth in 5% CO2	2,043	n/a	1133	1,696	[4]
S. pneumoniae R6	171101.1	SP_essential_Song	targeted	deletion	clones	Rich undefined medium Todd-Hewitt, 37°C, microaerobic growth in 5% CO2	2,043	560	1333	1,350	[13]
S. pneumoniae R6	171101.1	*SP_essential_merged	A combined nonredundunt dataset derived from the data obtained in two similar global gene essentiality screens in S. pneumoniae [4, 13]
M. tuberculosis H37Rv	83332.1	*MT_contribute_to_fitness_Rubin	random	insertion	population	Rich defined medium OADC	3,989	2,567	614	808	[6]
B. subtilis 168	224308.1	*BS_essential_Kobayashi	targeted	insertion	clones	Rich undefined medium LB, 37°C, aerobic growth	4,105	3,8305	2715	4	[7]
E. coli K-12 MG1655	83333.1	EC_contribute_to_fitness	random	insertion	population	Rich undefined medium LB, 37°C, aerobic growth	4,308	3,126	620	562	[8]
E. coli K-12 MG1655	83333.1	EC_essential_Blattner	targeted	insertion	clones	Rich undefined medium LB, 37°C, aerobic growth	4,308	2,001	n/a	n/a	[12]
E. coli K-12 BW25113	83333.1	*EC_essential_Keio	targeted	deletion	clones	Rich undefined medium LB, 37°C, aerobic growth	4,390	3,985	303	102	[15]
P. aeruginosa PAO1	208964.1	PA_candidate_essential_Jacobs1	random	insertion	clones	Rich undefined medium LB, room temp, aerobic growth	5,570	4,783	787	0	[9]
P. aeruginosa PAO1	208964.1	*PA_essential_PA14_PAO1_Liberati2	random	insertion	clones	Rich undefined medium LB, aerobic growth	5,688	4,469	3352	884	[16]
S. typhimurium LT2	99287.1	*ST_essential_Knuth	random	insertion	clones	Rich undefined medium LB, 30°C, aerobic growth	4,425	n/a	2573	n/a	[10]
H. pylori G27	85962.1	*HP_candidate_essential_Salama1	random	insertion	population	Rich undefined medium HB, 37°C, microaerobic growth in 10% CO2	1,576	1,178	344	54	[11]

References

back to top

1. Hutchison CA, Peterson SN, Gill SR, Cline RT, White O, Fraser CM, Smith HO, Venter JC: Global transposon mutagenesis and a minimal mycoplasma genome. Science 1999, 286:2165-2169.

2. Ji YD, Zhang B, Van Horn SF, Warren P, Woodnutt G, Burnham MKR, Rosenberg M: Identification of critical staphylococcal genes using conditional phenotypes generated by antisense RNA. Science 2001, 293:2266-2269.

3. Forsyth RA, Haselbeck RJ, Ohlsen KL, Yamamoto RT, Xu H, Trawick JD, Wall D, Wang LS, Brown-Driver V, Froelich JM, et al.: A genome-wide strategy for the identification of essential genes in Staphylococcus aureus. Molecular Microbiology 2002, 43:1387-1400.

4. Thanassi JA, Hartman-Neumann SL, Dougherty TJ, Dougherty BA, J. PM: Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae. Nucleic Acids Res. 2002, 30:3152-3162.

5. Akerley BJ, Rubin EJ, Novick VL, Amaya K, Judson N, Mekalanos JJ: A genome-scale analysis for identification of genes required for growth or survival of Haemophilus influenzae. Proc Natl Acad Sci U S A 2002, 99:966-971.

6. Sassetti CM, Boyd DH, Rubin EJ: Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 2003, 48:77-84.

7. Kobayashi K, Ehrlich SD, Albertini A, Amati G, Andersen KK, Arnaud M, Asai K, Ashikaga S, Aymerich S, Bessieres P, et al.: Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A 2003, 100:4678-4683.

8. Gerdes S, Scholle M, Campbell J, Balazsi G, Ravasz E, Daugherty M, Somera AL, Kyrpides N, Anderson I, Gelfand MS, et al.: Experimental determination and system-level analysis of essential genes in E. coli MG1655. J. Bacteriol. 2003, 185:5673-5684.

9. Jacobs MA, Alwood A, Thaipisuttikul I, Spencer D, Haugen E, Ernst S, Will O, Kaul R, Raymond C, Levy R, et al.: Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A 2003, 100:14339-14344.

10. Knuth K, Niesalla H, Hueck CJ, Fuchs TM: Large-scale identification of essential Salmonella genes by trapping lethal insertions. Mol Microbiol 2004, 51:1729-1744.

11. Salama NR, Shepherd B, Falkow S: Global transposon mutagenesis and essential gene analysis of Helicobacter pylori. J Bacteriol 2004, 186:7926-7935.

12. Kang Y, Durfee T, Glasner JD, Qiu Y, Frisch D, Winterberg KM, Blattner FR: Systematic mutagenesis of the Escherichia coli genome. J Bacteriol 2004, 186:4921-4930.

13. Song JH, Ko KS, Lee JY, Baek JY, Oh WS, Yoon HS, Jeong JY, Chun J: Identification of essential genes in Streptococcus pneumoniae by allelic replacement mutagenesis. Mol Cells 2005, 19:365-374.

14. Glass JI, Assad-Garcia N, Alperovich N, Yooseph S, Lewis MR, Maruf M, Hutchison CA, 3rd, Smith HO, Venter JC: Essential genes of a minimal bacterium. Proc Natl Acad Sci U S A 2006, 103:425-430.

15. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, Datsenko KA, Tomita M, Wanner BL, Mori H: Construction of Escherichia coli K-12 in-frame, single-gene knock-out mutants: the Keio collection. Mol. Systems Biol. 2006, doi:10.1038/msb4100050.

16. Liberati NT, Urbach JM, Miyata S, Lee DG, Drenkard E, Wu G, Villanueva J, Wei T, Ausubel FM: An ordered, nonredundant library of Pseudomonas aeruginosa strain PA14 transposon insertion mutants. Proc Natl Acad Sci U S A 2006.

Subsystems

back to top

Subsystems in SEED are developed and maintained by curators aiming to capture the current status of knowledge of specific biological processes (e.g. metabolic pathways or multipeptide complexes) in model species and to project this knowledge to other species via comparative genomics and metabolic reconstruction techniques (Overbeek et al., 2005). Populated subsystems are spreadsheets connecting relevant functional roles with annotated genes in hundreds of integrated genomes. Core metabolic subsystems often contain extensive notes and diagrams helping to understand topology and variations in subsystem implementation (functional variants) across a collection of diverse species. SEED Subsystem collection is available here. Examples of about 50 subsystems are available here and discussed in detail in (Overbeek et al., 2005).

Subsystem (SS) spreadsheet is used in SEED as a framework for integration of various types of data organized as gene attributes (including essentiality, gene clustering on a chromosome, virulence, microarray data, relevant publications, etc). Projection of experimentally determined essentiality assertions over a collection of subsystems in SEED opens new opportunities for data evaluation and functional interpretation:

(i) subsystems-based annotations help establish accurate gene correspondences between genomes, facilitating cross-organism comparison and projection of gene essentiality data. This approach overcomes many of the problems plaguing the common homology-based projections by exploiting the functional, genomic, and phylogenetic context of each gene, allowing for many-to-many projections and accounting for non-orthologous gene displacements;

(ii) it allows rationalization of essentiality of each gene and each functional module in the context of the organism's metabolism as a whole;

(iii) facilitates detection and reconciliation of inconsistencies in experimental data: inspection of essentiality assertions of all genes in the same pathway as a group imposes the requirement for internal data-set consistency, and for data correlation with the existing knowledge of a pathway topology, serving as built-in quality control.