Limits...
Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms.

van Opijnen T, Bodi KL, Camilli A - Nat. Methods (2009)

Bottom Line: These changes are used to calculate each mutant's fitness.A genome-wide screen for genetic interactions of five query genes identified both alleviating and aggravating interactions that could be divided into seven distinct categories.Owing to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute, and Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, Massachusetts, USA.

ABSTRACT
Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing the flanking regions en masse. These changes are used to calculate each mutant's fitness. Using this approach, we determined fitness for each gene of Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions of five query genes identified both alleviating and aggravating interactions that could be divided into seven distinct categories. Owing to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

Show MeSH

Related in: MedlinePlus

Validation of genetic interactions. (a) Fitness (± s.e.m.) of seven genes either knocked out singly or in combination with ccpA are compared between the 1×1 method (blue) and Tn-seq (yellow). The expected multiplicative fitness for each double gene knockout is depicted in green (expected fitness was determined by multiplying ccpA fitness [0.84±0.04 s.e.m.] with fitness measured for the other gene). Numbers underneath the graph refer to Streptococcus pneumoniae TIGR4 gene numbers (SP_number). (b) A detail of the genetic interaction network showing five of the validated genetic interactions and their specific interactions. (c) A sub-network from the larger genetic interaction network showing four PTS genes including two of the validated genetic interactions (SP_0476 and SP_1185), β-galactosidase (SP_0648) and a gene with unknown function (SP_0475). Interaction colors, node colors and numbers are as in Figure 4.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2957483&req=5

Figure 5: Validation of genetic interactions. (a) Fitness (± s.e.m.) of seven genes either knocked out singly or in combination with ccpA are compared between the 1×1 method (blue) and Tn-seq (yellow). The expected multiplicative fitness for each double gene knockout is depicted in green (expected fitness was determined by multiplying ccpA fitness [0.84±0.04 s.e.m.] with fitness measured for the other gene). Numbers underneath the graph refer to Streptococcus pneumoniae TIGR4 gene numbers (SP_number). (b) A detail of the genetic interaction network showing five of the validated genetic interactions and their specific interactions. (c) A sub-network from the larger genetic interaction network showing four PTS genes including two of the validated genetic interactions (SP_0476 and SP_1185), β-galactosidase (SP_0648) and a gene with unknown function (SP_0475). Interaction colors, node colors and numbers are as in Figure 4.

Mentions: To validate the genetic interactions identified by Tn-Seq we deleted seven genes by replacement with a drug marker both in the wild type and ccpA backgrounds. Fitness values for all 14 mutant strains were confirmed by 1×1 competitions based on a student’s t-test with Bonferroni correction for multiple testing (Fig. 5a). Specifically, alleviating interactions were confirmed for SP_2001 (TCS05), ABC transporters SP_1957 and SP_0720 and metabolism gene SP_1123, and an antagonistic interaction was confirmed for amino acid metabolism gene SP_1029 (Fig. 5b). In addition, two interactions were confirmed between ccpA and the PTS genes SP_0476 (lacF-1) and SP_1185 (lacE-2), which are part of a small sub-network including four PTS genes, the hydrolase bgaA (SP_0648) and an uncharacterized gene SP_0475 (Fig. 5c).


Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms.

van Opijnen T, Bodi KL, Camilli A - Nat. Methods (2009)

Validation of genetic interactions. (a) Fitness (± s.e.m.) of seven genes either knocked out singly or in combination with ccpA are compared between the 1×1 method (blue) and Tn-seq (yellow). The expected multiplicative fitness for each double gene knockout is depicted in green (expected fitness was determined by multiplying ccpA fitness [0.84±0.04 s.e.m.] with fitness measured for the other gene). Numbers underneath the graph refer to Streptococcus pneumoniae TIGR4 gene numbers (SP_number). (b) A detail of the genetic interaction network showing five of the validated genetic interactions and their specific interactions. (c) A sub-network from the larger genetic interaction network showing four PTS genes including two of the validated genetic interactions (SP_0476 and SP_1185), β-galactosidase (SP_0648) and a gene with unknown function (SP_0475). Interaction colors, node colors and numbers are as in Figure 4.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957483&req=5

Figure 5: Validation of genetic interactions. (a) Fitness (± s.e.m.) of seven genes either knocked out singly or in combination with ccpA are compared between the 1×1 method (blue) and Tn-seq (yellow). The expected multiplicative fitness for each double gene knockout is depicted in green (expected fitness was determined by multiplying ccpA fitness [0.84±0.04 s.e.m.] with fitness measured for the other gene). Numbers underneath the graph refer to Streptococcus pneumoniae TIGR4 gene numbers (SP_number). (b) A detail of the genetic interaction network showing five of the validated genetic interactions and their specific interactions. (c) A sub-network from the larger genetic interaction network showing four PTS genes including two of the validated genetic interactions (SP_0476 and SP_1185), β-galactosidase (SP_0648) and a gene with unknown function (SP_0475). Interaction colors, node colors and numbers are as in Figure 4.
Mentions: To validate the genetic interactions identified by Tn-Seq we deleted seven genes by replacement with a drug marker both in the wild type and ccpA backgrounds. Fitness values for all 14 mutant strains were confirmed by 1×1 competitions based on a student’s t-test with Bonferroni correction for multiple testing (Fig. 5a). Specifically, alleviating interactions were confirmed for SP_2001 (TCS05), ABC transporters SP_1957 and SP_0720 and metabolism gene SP_1123, and an antagonistic interaction was confirmed for amino acid metabolism gene SP_1029 (Fig. 5b). In addition, two interactions were confirmed between ccpA and the PTS genes SP_0476 (lacF-1) and SP_1185 (lacE-2), which are part of a small sub-network including four PTS genes, the hydrolase bgaA (SP_0648) and an uncharacterized gene SP_0475 (Fig. 5c).

Bottom Line: These changes are used to calculate each mutant's fitness.A genome-wide screen for genetic interactions of five query genes identified both alleviating and aggravating interactions that could be divided into seven distinct categories.Owing to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute, and Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, Massachusetts, USA.

ABSTRACT
Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing the flanking regions en masse. These changes are used to calculate each mutant's fitness. Using this approach, we determined fitness for each gene of Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions of five query genes identified both alleviating and aggravating interactions that could be divided into seven distinct categories. Owing to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

Show MeSH
Related in: MedlinePlus