Systematic, large-scale synthetic genetic (gene-gene or epistasis) interaction screens can be used to explore genetic redundancy and pathway cross-talk. Here, we describe a high-throughput quantitative synthetic genetic array screening technology, termed eSGA that we developed for elucidating epistatic relationships and exploring genetic interaction networks in Escherichia coli.
Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10.
Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) – marked mutant alleles from engineered Hfr (High frequency of recombination) ‘donor strains’ into an ordered array of kanamycin (Kan) – marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the ‘Keio’ collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.
1. Constructing Hfr Cavalli Donor Mutant Strains by Recombineering15, 16
The steps for constructing the eSGA donor stains are described below. Briefly, we use targeted λ– Red mediated homologous recombination16 of amplified selectable DNA marker cassette fragments generated by PCR to create non-essential gene deletion mutants (section 1.1) or essential gene hypomorphic mutant donor strains (section 1.2) that are then used as ‘queries’ to define GI networks.
Note: During the technology development process, we assessed the effectiveness of using Hfr-mediated conjugative transfer for combining mutations using a well-defined Hfr donor strain (Hfr Cavalli, Hfr Hayes, and Hfr 3000). We examined (i) the ability to efficiently make donor mutants using the recombineering method pioneered by Yu et al. (2000)16, (ii) the relative efficiencies of conjugative DNA transfer of different chromosomal markers, and (iii) the effects of query gene position and chromosomal orientation relative to the Hfr transfer locus, oriT. We found that λ–Red-mediated homologous recombination efficiency was much higher in Hfr Cavalli than in Hfr Hayes or Hfr3000. If required, P1 transduction or a Psuedo Hfr strain17, can be used to create mutant donors. We have successfully adapted all these methods for making and screening large numbers of donors. Since in our trial conjugation experiments, the overall efficiency of transfer and the number of ex-conjugants observed was significantly greater with Hfr Cavalli, this particular strain background was chosen for donor construction and large-scale eSGA. All procedures for eSGA screens using Hfr Cavalli donors are described.
2. Arraying E. coli F- Recipient Mutants for eSGA Screens
3. High-density Strain Mating Procedure for Generating E. coli Double Mutants
4. Processing Data and Deriving GI Scores
Where, Svar = (varExp x (nExp-1) + varCont x (nCont -1)) / (nExp + nCont -2); varExp = the maximum variance of the normalized colony sizes for the double mutant; varCont = median of the variances in the normalized double mutant colony sizes from the reference set; nExp = number of measurements of double mutant colony sizes; nCont = the median number of experimental replicates over all the experiments; μExp = median normalized colony sizes of the double mutants; and μCont = median of normalized colony sizes for all double mutants arising from the single donor mutant strain. The S-score reflects both the statistical confidence of a putative digenic interaction as well as the biological strength of the interaction. Strong positive S-scores indicate alleviating effects, suggesting that the interacting genes participate in the same pathway9, while significant negative S-scores reflect synthetic sickness or lethality, which is often suggestive of membership in parallel redundant pathways9. For determining the pathway-level functional relationships, a single S-score for each pair of tested genes is produced from the final dataset.
Note: In our previous study9, we found that recombination tends to occur less frequently between genes within 30 kbp of each other. Therefore, for downstream analysis, following normalization and score generation, we remove interactions between genes within 30 kbp of each other. In addition to GIs themselves, functional relationships between pairs of genes can be investigated by looking at how similar the GI profiles of the two genes are. The GI profile of a gene is the set of all interactions of that gene with all other genes in the genome outside of the gene’s linkage area. Functionally similar genes tend to have higher correlations of genetic interaction profiles12, 25. Thus, by calculating correlation coefficients for all genes in the experimental dataset one can use eSGA for investigating functional relationships even between genes lying close to each other on the chromosome.
GIs reveal functional relationships between genes. Similarly, since genes in the same pathway display similar GI patterns and the GI profile similarity represents the congruency of phenotypes, we can group functionally related genes into pathways by clustering their GI profiles. Integrating GI and GI correlation networks with physical interaction information or other association data, such as genomic context (GC) relationships can also reveal the organization of higher-order functional modules that define core biological systems (and system crosstalk) in bacteria. For example, as with yeast4, 6, 7, 26, E. coli gene pairs exhibiting alleviating interactions that also have highly correlated GI profiles tend to encode proteins that are either physically associated (e.g. form a complex; Figure 4a) or that act coherently in a common biochemical pathway (e.g. components in a linear cascade). A representative example is shown in Figure 4b, where the components of the functionally redundant Isc and Suf pathways, which jointly participate in the essential Fe-S biosynthesis process, form distinct clusters that are linked together by extensive aggravating interactions (i.e. synthetic lethality). A statistical measure (e.g. hypergeometric distribution function27) can be used on GI or GI correlation data to find significant enrichment for interactions between and within pathways (Figure 4c). Cluster analysis can also be applied to functional networks derived using eSGA to predict the functions of genes lacking annotations5, 6, 9, 28-30 (Figure 4d). Since clustering algorithms vary, however, putative functional assignments determined through clustering require independent experimental verification.
Figure 1. The construction and confirmation of Hfr Cavalli non-essential single gene deletion donor strains. The panels (adapted from14) illustrate the deletion of E. coli chromosomal ORF (A) and the three primer sets used for confirming (B) the correctly generated mutant Hfr Cavalli donor strain. Amplifications from a correctly constructed deletion donor strain should produce (i) 445 bp, (ii) 309 bp, and (iii) 1.4 kb products. See protocol text for details. Click here to view larger figure.
Figure 2. The construction and confirmation strategies of essential hypomorphic donor mutant strains in an Hfr Cavalli genetic background. The panels illustrate the creation of hypomorphic mutations of essential genes through recombineering technology (A) and the primer sets (B) used for the PCR confirmation of essential gene hypomorphic mutations. See protocol text for details.
Figure 3. Schematic summary of key eSGA steps. The Hfr donor (marked with chloramphenicol resistance, CmR) and recipient F- (marked with kanamycin resistance, KanR) mutant strains are grown in 384-colony density on LB-Cm and LB-Kan plates, respectively. The donor and the recipient strains are conjugated by pinning them over each other onto an LB plate, which is then incubated overnight at 32 °C. Consequently, the conjugants are pinned onto plates containing both Kan and Cm to select double mutants. The double mutant plates are digitally imaged and the growth fitness of the colony sizes is quantitatively scored to identify aggravating and alleviating interactions.
Figure 4. Representative computational analyses of genetic interaction (GI) scores for determining pathway-level functional relationships. (A) Panel I, Distribution of correlation coefficients between the GI profiles of gene pairs encoding proteins linked by protein-protein interactions (PPI) versus randomly drawn gene pairs. The p-value was computed using the two-sample Kolmogorov-Smirnov (KS) test; Panel II, Scatter plot of correlated genetic profiles from rich media (RM) for two transporters (mdtI, mdtJ) that form a heterodimeric complex required for spermidine excretion. (B) The hierarchical clustering of the GI sub-network adapted from Butland et al.9 highlights the functional connectivity between components of the previously known Isc and Suf pathways with similar GI patterns. Pink represents aggravating (negative S-score) interactions, green represents alleviating (positive S-score) interactions and black represents absence of GI. A predicted novel component of the SUF pathway, ydhD, displays GIs with the members of the Isc pathway. (C) Pathway cross-talk recorded among cell envelope processes that are significantly enriched for aggravating or alleviating GI according to the hypergeometric enrichment analysis27. (D) A GI sub-network predicts the role of two functionally unannotated genes, yceG and yebA, in peptidoglycan splitting based on their pattern of strong alleviating interactions with well-known cell division peptidoglycan hydrolases. The figures shown in panels A, C and D are adapted from Babu et al. (2011)12. Click here to view larger figure.
We have outlined a step-wise protocol for using robotic eSGA screening to investigate bacterial gene functions at a pathway level by interrogating GI. This approach can be used to study individual genes as well as entire biological systems in E. coli. Carefully executing the experimental steps described above, including all appropriate controls, and rigorously analyzing and independently validating the GI data are key aspects for the success of eSGA in making new functional discoveries. In addition to eSGA, a conceptually similar method for studying GI in E. coli, termed GIANT-coli17, can be used to illuminate novel functional relationships that are often missed by other approaches, such as proteomic23 or phenotypic13 screens alone. Nevertheless, as with any genome-wide approach, limitations do exist on the applicability of eSGA or GIANT-coli, for example to other bacterial species. This is partly because of the unavailability of genome-wide single gene deletion mutant strain collections for most bacterial species, particularly for species where targeted mutagenesis is hindered by a low natural frequency of homologous recombination. In such cases, gene disruptions using methods such as random trackable mutagenesis-based assays like TnSeq31 can potentially be used for investigating bacterial gene functions. For an overview of emerging alternate bacterial genetic screening procedures, see review paper by Gagarinova and Emili (2012)32.
Although GI methods often reveal many interesting connections suggestive of novel mechanistic links, the integration of these data with other information such as phenotypic profiling13, physical interaction networks22, 23, and GC-inferred associations can be particularly informative. For example, as with GI networks, GC associations, which are based on the conservation of gene order (operons), bacterial gene fusions, operon recombination frequencies derived from intergenic distances of predicted operons across genomes, as well as phylogenetic profiling33 can provide additional insights into how a bacterial cell organizes protein complexes into functional pathways to mediate and coordinate major cellular processes12, 23.
E. coli is a workhorse model for understanding the molecular biology of other Gram-negative bacteria. To this end, the eSGA GI data can be used in conjunction with comparative genomics information (e.g. phylogenetic profiling, gene expression profiling) to investigate the evolutionary conservation of functional relationships discovered by eSGA across other proteobacterial species and Prokaryotic taxa. Furthermore, the interaction data generated for E. coli may be used to gain insight into the pathway architecture of microbes discovered by metagenomics, for which functional annotations are lacking. Since many genes are widely conserved across all microbes23, and because antibiotic sensitivity can be enhanced by certain genetic perturbations34, the functional relationships illuminated by eSGA may even potentially be exploited for designing innovative combination drug therapies.
The authors have nothing to disclose.
This work was supported by funds from Genome Canada, the Ontario Genomics Institute, and the Canadian Institutes of Health Research grants to J.G. and A.E. AG is a recipient of Vanier Canada Graduate Scholarship.
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Desalted custom primers | F2 and R2: 20 nt constant regions based on pKD3 sequence and 45 nt custom homology regions F2 constant region: 5′-CATATGAATATCCTCCTTA-3′ R2 constant region: 5′-TGTGTAGGCTGGAGCTGCTTC-3’S1 and S2: 27 nt constant regions for priming the amplification of the SPA-Cm cassette and 45 nt custom homology regions S1 constant region: 5’AGCTGGAGGATCCATGGAAAAGAGAAG -3′ S2 constant region: 5′- GGCCCCATATGAATATCCTCCTTAGTT -3′ KOCO-F and KOCO-C: 20 nt primers 200 bp away from the non-essential gene deletion site or the essential gene SPA-tag insertion site |
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