The pentose phosphate pathway constitutes a major metabolic hub in pathogenic Francisella

Abstract

Metabolic pathways are now considered as intrinsic virulence attributes of pathogenic bacteria and thus represent potential targets for antibacterial strategies. Here we focused on the role of the pentose phosphate pathway (PPP) and its connections with other metabolic pathways in the pathophysiology of Francisella novicida. The involvement of the PPP in the intracellular life cycle of Francisella was first demonstrated by studying PPP inactivating mutants. Indeed, we observed that inactivation of the tktA, rpiA or rpe genes severely impaired intramacrophage multiplication during the first 24 hours. However, time-lapse video microscopy demonstrated that rpiA and rpe mutants were able to resume late intracellular multiplication. To better understand the links between PPP and other metabolic networks in the bacterium, we also performed an extensive proteo-metabolomic analysis of these mutants. We show that the PPP constitutes a major bacterial metabolic hub with multiple connections to glycolysis, the tricarboxylic acid cycle and other pathways, such as fatty acid degradation and sulfur metabolism. Altogether our study highlights how PPP plays a key role in the pathogenesis and growth of Francisella in its intracellular niche.

Fig 1. Transcriptional analysis of tktA and gapA genes.

(A) Schematic organization of the tktA operon in F. novicida and Legionella pneumophila. The terms PPP and Glycolysis on top, indicate the genes involved in either the PPP or the Glycolytic/gluconeogenic pathways. The last gene of the Francisella locus is absent in the Legionella locus (fba). The dotted blue arrows indicate the predicted transcriptional units. In Legionella, in the absence of the CsrA regulatory protein, transcription is interrupted after tktA (circled sign -) whereas in the presence of CsrA, transcription resumes till the end of the operon (circled sign +). (B) The enzymatic reactions corresponding to TktA and GapA enzymes are shown as green balls on a schematic depiction of the PPP and glycolytic pathways. (C) qRT-PCR analysis of tktA and gapA genes in WT F. novicida, grown in CDM supplemented either with glucose or glycerol, in exponential (Early, white labels) and stationary phase of growth (Late, black labels). (D) qRT-PCR analysis of tktA and gapA genes in WT F. novicida, over a 24 hour-period of intracellular growth in J774-1 macrophages. *, P <0.01 (as determined by Student’s t test).

Fig 2. Growth of the PPP mutants in liquid culture.

Bacterial growth was monitored in CDM supplemented with various carbon sources: Glc (glucose), Gly (glycerol), Rib (ribose) and Fru (fructose), at a final concentration of 25 mM; or Cas (casamino acids), and Pep (peptone) at a final concentration of 0.2%. Bacterial growth was also monitored in two complex media: TSB (trypic soya broth) and K3 (Shaedler K3 medium). Stationary-phase bacterial cultures of wild-type F. novicida (WT), ΔtktA, ΔrpE, ΔrpiA, ΔtalA and Δfpi mutants were diluted to a final OD_600nm of 0.1, in 20 mL broth. Every hour, the OD_600nm of the culture was measured, during a 24 h-period.

Fig 3. Intracellular multiplication of the PPP mutants.

Kinetics of intracellular multiplication of the mutants was monitored in J774.1 macrophages over a 24 h-period in DMEM supplemented with either (A) Glucose (Glc), or (B) Glycerol (Gly), and compared to that in the wild-type F. novicida (WT). A Δfpi mutant strain was used as a negative control. *, P <0.05; **, P <0.01; ***, P <0.001 (compared to WT strain; as determined by two-way ANOVA test).

Fig 4. Subcellular localization of the PPP mutants.

Glucose-grown J774.1 were infected for 1 h with wild-type F. novicida (WT), ΔrpiA, Δrpe, ΔtktA, ΔtalA, or ΔFPI strains and their co-localization with the phagosomal marker LAMP-1 was observed by confocal microscopy 1h, 10h and 24 h, after beginning of the experiment. (A) Scale bars at the bottom right of each panel correspond to 5 μm. J774.1 were stained for F. tularensis (green), LAMP-1 (red), and host DNA (blue, DAPI stained). (B) Quantification of bacteria/phagosome colocalization in glucose-grown J774.1 macrophages. Mean and SD of triplicate wells. ***p<0,0001 (compared to ΔFPI strain; as determined by ANOVA one-way test). (C) Percentage of infected cells at 10 and 24 h was quantified by using imageJ software. At least 1 000 cells per condition were counted. ***p<0,0001 (compared to WT strain; as determined by ANOVA one-way test). The number of GFP-positive spots per infected cell was quantified at 10h (D) and 24 h (E) by using the Icy Software. We analyzed at least 100 infected cells for each condition. ***p<0,0001 (compared to WT strain; as determined by ANOVA one-way test).

Fig 5. Time lapse video microscopy analyses of the PPP mutants.

J774.1_red cells (expressing mKate2 nuclear-restricted red fluorescent protein) were infected with bacteria expressing the green fluorescent protein (GFP), in DMEM supplemented with glucose (MOI = 100). One hour after infection, cells were washed several times with gentamicin-containing medium (10 μg mL^-1) to remove extracellular bacteria. A gentamicin concentration of 10 μg mL^-1 was then maintained throughout the experiment. Images were acquired every 20 min using the IncuCyte S3 live cell imaging system (Essen BioScience) over a 48-h period. The kinetics of intracellular multiplication of PPP mutants were monitored by Incucyte S3 software (Incucyte Live-Cell Analysis System, Sartorius). F. novicida WT is shown in blue; the negative control strain Δfpi, in cyan; ΔrpiA, in red; Δrpe in yellow; ΔtktA, in orange; and ΔtalA, in green. (A) Total number of J774.1_red cells was determined by counting the number of red nuclei per image every 20 minutes. An image taken 1h post-infection generally contains between 50 and 100 cells. Results are presented as the mean and standard deviation of sixteen images. (B) Bacterial multiplication (GFP-expressing bacteria) was monitored by checking the total area of green particles (i.e., GFP-expressing bacteria) in each image (containing between 50 and 250 cells) every 20 min. Multiplication was translated into graphical lines using the Incucyte S3 software. Results are presented as the mean and standard deviation of sixteen images. (C) The total number of GFP-positive cells (i.e., infected with GFP-expressing bacteria) was monitored by quantifying the percentage of cells with red nuclei associated with at least one detectable green intensity signal in one image (containing between 50 and 250 cells). Results are presented as the mean and interpolation curve of eight images. (D) At selected times (10 h, 24 h, and 48 h), the number of GFP-positive cells was decomposed into 5 categories based on the area of the detected GFP signal (in pixels; px): i) 0–200; px ii) 200–400; px, iii) 400–600; px; iv) 600–800; and >800; px; and corresponding to increasingly infected cells. The data presented correspond to the total number of cells in 8 images.

Fig 6. Differential proteomes of WT and ΔtktA, Δrpe and ΔrpiA mutants.

Bacteria were cultured in TSB and collected during exponential phase of growth (at OD₆₀₀ of 0.5). The proteomes of the four PPP mutants ΔtktA, ΔrpiA, Δrpe and ΔtalA, was compared to that of WT F. novicida. Volcano plot representing the statistical comparison of the protein LFQ intensities of each mutant versus WT. Inner volcano was established using S0 = 0.1, FDR = 0.05 and the outer volcano using S0 = 0.1, FDR = 0.01. The abscissa reports the fold change in logarithmic scale (difference), the ordinate the–log(pvalue). Proteins undergoing the same modulation in ΔtktA, Δrpe and ΔrpiA mutants but not in ΔtalA are highlighted in color (blue and red for decreased increased in the mutant, respectively).

Fig 7. Comparison of metabolite profiles of WT and ΔtktA, Δrpe and ΔrpiA mutants.

Bacteria were cultured in TSB and collected during exponential phase of growth (at OD₆₀₀ of 0.5). Heatmap visualization and hierarchical clustering analysis of the metabolite profiling in each mutant compared to WT F. novicida. Upper part, heatmaps showing the top 50 (ΔtktA, Δrpe) or top 30 (ΔrpiA) most changing compounds. Three biological replicates, performed for each sample, are presented. Rows: metabolites; columns: samples; color key indicates metabolite relative concentration value (blue: lowest; red: highest). The arrows, to the right of each heatmap, pinpoint the metabolites related to the PPP or glycolytic pathways. Lower part, the position of the metabolites related to the PPP or glycolytic pathways is shown on a schematic depiction of the pathways.

Fig 8. Correlation network derived from rCCA between metabolites and proteins from Francisella.

(A) WT/ΔtktA; (B) WT/ΔrpiA; (C) WT/Δrpe.