Upon isolation of transposon mutants, single primer colony PCR is a robust, high-throughput method to identify the site of transposon insertion for each mutant. This approach derives from a typical nested PCR protocol but here a single primer hybridizes specifically and/or non-specifically to the template DNA depending on the stringency of the annealing temperature (Figure 1A). The typical PCR products consist of multiple DNA fragments, most of which are specific (Figure 1B). The use of a different sequencing primer that anneals right upstream of the transposon ITR, and downstream of the sequence recognized by the amplification primer provides specificity for the sequencing step (Figure 1C). Automated software for sequence analysis aligns the obtained sequences to the Coxiella genome providing the exact site of transposon insertions (Figure 1C). All transposon insertions can be then annotated on the Coxiella genome (Figure 1D).
Each Coxiella mutant is isolated and amplified axenically in ACCM-2 medium prior to either storage or screening. Figure 2 illustrates an example of 38 transposon mutants in 16 dot/icmCoxiella genes (Figure 2A). To assess the viability of Coxiella mutants, axenic growth curves are obtained by sampling bacterial cultures for 7 days post inoculation and applying the bacterial concentration assay described in 1.5 (Figurie 2B). Amplified mutants are then incubated with epithelial cells, in triplicate 96-well plates for 7 days. All Coxiella mutants generated being GFP-tagged, intracellular growth curves are obtained by measuring the GFP fluorescence intensity of every well, every 24 hr, and plotting the measured values as a function of time (Figure 2C).
Intracellular growth curves provide quantitative analysis of the phenotypes associated with every transposon insertion in the Coxiella genome. To add qualitative information about the same transposon mutants, we opted for automated image acquisition and analysis. Seven days post infection, plates are fixed, processed for immunofluorescence as described in 4 and analyzed using an automated, epifluorescence microscope as described in 5. Automated image analysis software such as CellProfiler (Broad Institute, www.cellprofiler.com) processes the acquired channels independently and segments identified objects for comparative analysis (Figure 3). This allows the identification and morphological characterization of host cell nuclei, cell contours, lysosomes and Coxiella colonies (Figure 3 top panels). Correlating Coxiella colonies with cells and lysosomes allows the identification and specific morphological analysis of Coxiella-containing vacuoles (which are LAMP1 positive, Figure 3 bottom left panel). Correlating Coxiella colonies with host cell contours allows the identification and specific morphological analysis of infected cells (Figure 3 bottom center panel). Finally, the 4 channels are merged for illustration and quality control purposes (Figure 3 bottom right panel).
Data obtained from automated image analysis can be plotted against each other to obtain “multi-phenotypic scatter plots”. As an example, in Figure 4A the average area (in μm2) of Coxiella colonies is plotted against the number of colonies per cell (Figure 4A), in order to identify mutations that affect intracellular replication of Coxiella (replication phenotype) and/or the capacity of bacteria to invade host cells (internalization phenotype). Statistical analysis was used to define regions in the resulting scatter plot corresponding to mild (-4 < Z-score ≤ -2) and severe (Z-score ≤ -4) phenotypes. Mutants were observed in 3 main clusters: mutations that resulted in a defective intracellular growth of Coxiella were grouped in the left-most part of the plot (Figure 4A, pink and red dots); mutations that affected Coxiella internalization in cells were grouped in the bottom part of the plot (Figure 4A, light and dark blue dots) and finally, green dots in the right-most region of the plot correspond to mutations resulting in non-significant phenotypes (Z-score > -2). Importantly, mutants that fail to replicate but are still able to invade host cells, are detected after 7 days of infection as single bacteria, or small colonies, adjacent to host cell nuclei (Figure 4C, second panel). Hence, the size of Coxiella “colonies” will be significantly affected but the number of infected cells will not vary as compared to WT Coxiella-infected cells. On the contrary, mutations that affect the capacity of Coxiella to invade host cells result in a decrease in the number of colonies/cell. When this number is significantly below 1, it indicates that, on average, there is a decrease in the overall number of infected cells. Alternatively, the average area (in μm2) of Coxiella colonies can be plotted against the number of host cells surviving infection (Figure 4B), to identify mutations that confer cytotoxicity to Coxiella (cytotoxic phenotype). As above, statistical analysis was used to define regions in the resulting scatter plot corresponding to mild (-4 < Z-score ≤ -2) and severe (Z-score ≤ -4) phenotypes. Most screened mutations did not significantly affect cell survival, regardless of bacterial replication within host cells (Figure 3B green dots). 37 mutations mildly affected host cell survival (Figure 3B, light red dots), and 7 mutations were particularly detrimental to host cell survival (Figure 3B, dark red dots). Please note that additional parameters obtained by the automated image analysis procedure can be used to derive other charts, according to experimental needs.

Figure 1: Sequencing and annotation of Coxiella transposon mutants. (A) Single primer colony PCR is used to amplify DNA fragments containing the site of transposon insertion. An amplification primer (Amp) is used both as a specific and non-specific primer depending on the stringency of annealing temperature. (B) Typical result of single primer colony PCR. Each reaction produces a number of fragments of variable size, some of which contain the transposon insertion site; some others are randomly amplified as by-products of the low stringency PCR cycle. (C) The use of a sequencing primer (Seq) that hybridizes to the transposon sequence allows the sequencing of the fragments of interest. (D) Sequence analysis software allows the automatic annotation of transposon insertions on the bacterial genome. Please click here to view a larger version of this figure.

Figure 2: Axenic and intracellular growth of Coxiella transposon mutants. (A) In the pilot screen, we have isolated, sequenced and screened 38 transposon mutants in 16 core genes of the Coxiella dot/icm secretion system (indicated in red). (B) In order to assess the viability of each transposon mutant, the growth of each isolate in axenic culture medium is monitored over 8 days using a fluorescently tagged DNA intercalating agent. (C) Each mutant is then used to infect epithelial cells. As the transposon possesses a GFP cassette, intracellular bacterial growth is monitored over 7 days of infection by following the variations of GFP fluorescence associated with Coxiella replication, using a microplate reader. Please click here to view a larger version of this figure.

Figure 3: Automated image analysis of Coxiella infections. An automated epifluorescence microscope is used to image 21 positions per well of triplicate 96-well plates. The image analysis software segments objects in each acquired channels for quantification and analysis. In all cases, objects touching the border of the images are excluded. (A) The Hoechst channel is used to identify host cell nuclei (circled in green). (B) These are used as seeds to identify host cell contours in the Cy3 channel (the position of nuclei is circled in blue, cell contours are in green). (C) The Cy5 channel is used to identify LAMP1-positive compartments (circled in green); only the objects included in the previously identified cell contours (in red) are retained for image analysis. (D) The GFP channel is used to identify Coxiella colonies (circled in green). (E) Correlating Coxiella colonies with LAMP1-positive compartments allows the identification of Coxiella-containing vacuoles (CCVs); only the objects included in the previously identified cell contours (in green) are retained for image analysis. (F) Correlating Coxiella colonies with cell contours allows the identification of infected cells (pseudocolored). (G) Images acquired in the 4 fluorescence channels (corresponding to Coxiella colonies (green), host cell nuclei (blue), host cell plasma membrane (grey), LAMP1-positive compartments (red)) are merged and used for illustration and quality control. Scale bars 10 µm. Please click here to view a larger version of this figure.

Figure 4: Large-scale identification of Coxiella factors involved in host/pathogen interactions. (A) The average area (in μm2) of Coxiella colonies is plotted against the relative number of colonies per cell, to identify replication and internalization phenotypes of interest. Green dots represent phenotypes that deviate from WT Coxiella by a Z-score > -2 (not significant). Pink and light blue dots represent replication and internalization phenotypes respectively, with a Z-score between -2 and -4 (mild phenotypes). Red and dark blue dots represent phenotypes with a Z-score ≤ -4 (strong phenotypes). (B) The average area (in μm2) of Coxiella colonies was plotted against the total number of cells (infected and not infected) that survived 7 days of infection to estimate the cytotoxic effect resulting from transposon insertions. Green dots represent phenotypes that deviate from WT Coxiella by a Z-score > -2 (not significant). Pink dots represent cytotoxic phenotypes with a Z-score between -2 and -4 (mild phenotypes). Red dots represent cytotoxic phenotypes with a Z-score ≤ -4 (strong phenotypes). Arrows indicate mutants illustrated by the corresponding lowercase letter in C. (C) Representative images of replication, internalization and cytotoxic phenotypes. In all cases, host cell nuclei are in red, Coxiella colonies are in green. Scale bars 50 µm. Please click here to view a larger version of this figure.