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

['Héloise Rytter', 'Université De Paris', 'Paris', 'Inserm - Cnrs Umr', 'Institut Necker-Enfants Malades. Team', 'Pathogénie Des Infections Systémiques', 'Anne Jamet', 'Jason Ziveri', 'Elodie Ramond', 'Mathieu Coureuil']

Date: 2021-08

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.

Metabolic pathways are intimately associated with the virulence of pathogenic bacteria and are therefore interesting antibacterial targets. Here we focused on the role of the pentose phosphate pathway (PPP) in the intracellular life cycle of the bacterium Francisella novicida. We were able to show, by combining genetic and imaging approaches, that this pathway was a major contributor to the intracellular survival of the bacterium. Complementary proteomics and metabolomics approaches revealed that PPP was at the crossroads of many other metabolic pathways. This work has thus allowed us to better understand the key role played by the PPP in the survival of Francisella in its intracellular niche.

Funding: These studies were supported by INSERM, CNRS and Université de Paris. Jason Ziveri and Héloise Rytter were supported by a fellowship from the “Délégation Générale à l’Armement”. Elodie Ramond was supported by the Agence Nationale de la Recherche (ANR-15-CE15-0017 StopBugEntry). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All relevant data are within the paper and its Supporting information files except for the mass spectrometry proteomics data that have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD023550.

Copyright: © 2021 Rytter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Here, we performed a functional analysis of the four PPP mutants (tktA, rpe, talA and rpiA) and a global approach based on the analysis of their proteo-metabolomic features to highlight PPP-related pathways and potential biological hubs. The data presented suggest a biphasic bacterial regulation mode between glycolysis and PPP during intracellular multiplication, and reveal previously unrecognized links between PPP and other metabolic pathways.

Transketolase, which is central to the non-oxidative branch of the PPP, can produce either R-5P or Sedoheptulose-7-phosphate (S-7P) in response to available metabolite concentrations. Ribulose-5-phosphate (Ru-5P), can be converted either into xylulose-5- phosphate (Xyl-5-P) by Rpe or R-5P by RpiA, respectively. Finally, TalA catalyzes the conversion of S-7P and GA-3P to erythrose-4-phosphate (E-4P) and fructose-6-phosphate (F-6P). Overall, the different precursors synthesized by the non-oxidative branch of the PPP contribute to multiple important functions of the bacterial cell, including biosynthesis of lipopolysaccharide (LPS), aromatic amino acids and nucleic acid precursors.

The pentose phosphate pathway (PPP) constitutes, with glycolysis, a major pathway for glucose catabolism. However, its contribution to bacterial metabolic adaptation and especially its importance in bacterial pathogenesis, remains largely unexplored. The PPP is composed of two branches, an oxidative and a non-oxidative branch [ 11 ]. Glucose flux through the oxidative branch produces NADPH, an essential reductant in anabolic processes. The non-oxidative branch generates the five-carbon sugar Ribose-5P (R-5P) from glucose and can be reversibly converted into glycolytic intermediates such as glyceraldehyde 3P (GA-3P) and Fructose-6P (F-6P). Francisella, which lacks the oxidative branch of the PPP, is equipped with a complete non-oxidative branch composed of the four enzymes: tktA (FTN_1333, encoding transketolase), rpiA (FTN_1185, encoding ribose 5-phosphate isomerase), rpe (FTN_1221, encoding ribulose phosphate 3-epimerase) and talA (FTN_0781, encoding transaldolase) ( S1 Fig ). These four genes are scattered along the F. novicida chromosome and each of them is present in a single copy.

Francisella virulence is tightly linked to its capacity to multiply exclusively in the cytosolic compartment of infected cells, and in particular in macrophages in vivo. Cytosolic pathogens, that notably include Listeria monocytogenes and Shigella flexneri, often require the utilization of multiple host-derived nutrients [ 5 – 7 ] and hexoses are generally their preferred carbon and energy sources. The capacity of Francisella to multiply in the host cytosol is controlled by multiple regulatory circuits [ 8 ], connected to metabolism. In particular, we have shown that gluconeogenesis was essential for Francisella intracellular multiplication [ 9 , 10 ], allowing host-derived substrates such as amino acid, pyruvate and glycerol to be used as carbon, nitrogen and energy sources.

Francisella tularensis is the causative agent of the zoonotic disease tularemia [ 1 ]. This facultative intracellular bacterial pathogen is able to infect numerous cell types but is thought to replicate and disseminate mainly in macrophages in vivo [ 2 ]. The four major subspecies (subsp) of F. tularensis currently listed are the subsps: tularensis, holarctica, mediasiatica and novicida (the latter is also called F. novicida). These subsps differ in their virulence and geographical origin [ 3 ] but all cause a fulminant disease in mice that is similar to tularemia in humans [ 4 ]. Although F. novicida is rarely pathogenic in humans, its genome shares a high degree of nucleotide sequence conservation with the human pathogenic subsp tularensis and is thus widely used as a model to study highly virulent subspecies.

Results

Transketolase, a conserved enzyme of the PPP Transketolase enzymes are ubiquitously expressed in eukaryotes and bacteria. In bacteria, they allow the production of precursors required for the synthesis of nucleotides and certain amino acids as well as for LPS synthesis (S1 Fig). Francisella genomes possess a unique transketolase-encoding gene (here designated tktA). The transketolase TktA of F. novicida (FTN_1333) is a 663 amino acid long protein that shows 41% to 57% amino acid sequence identity with its orthologs in other pathogenic bacterial species (eg. it shares 55.6% and 41% amino acid identity with the transketolases of Legionella pneumophila and Mycobacterium tuberculosis, respectively). It should be noted that several bacteria express multiple isoforms of transketolases. For example, Escherichia coli has two genes (tktA and tktB), Salmonella typhimurium has three genes (tktA, tktB, and tktC), encoding transketolases with different enzymatic properties, and Citrobacter rodentium genomes encode up to six transketolase isoforms. In F. tularensis species, tktA is the first gene of a highly conserved operon [10] and precedes genes (gapA, pgK, pyK and fba, respectively) involved in glycolysis/gluconeogenesis (Fig 1A and 1B). The organization of the four first genes of this operon (tktA-pyK) is conserved in both L. pneumophila [12] and Coxiella burnetii species (S2 Fig and S1 Table). Several other pathogenic bacterial species, such as Bordetella pertussis and Brucella melitensis, also have tktA, gapA and pgk genes in the same genetic cluster and with the same organization but lack the pyk and fba genes. The species with most medical relevance were chosen to be depicted in the figure showing the tktA operon genetic context (S2 Fig). Notably, in most Burkholderia species (including B. multivorans, B. pseudomallei, B. thailandensis, …), the tktA and gapA genes are adjacent and in the same orientation, suggesting that they belong to the same transcriptional unit, whereas the pgk, pyk, and fba genes cluster is located in a distinct region of the chromosome. PPT PowerPoint slide

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larger image TIFF original image Download: 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). https://doi.org/10.1371/journal.ppat.1009326.g001 These observations prompted us to first quantify the transcription of the two consecutive F. novicida genes tktA and gapA by qRT-PCR, in wild-type (WT) bacteria grown in chemically defined medium (CDM) [13]. Transcription of each gene was found to be approximately similar in the presence of glucose or glycerol, suggesting that their expression is not controlled by these carbon sources. (Fig 1C). The expression of the gapA gene was consistently higher than that of the tktA gene with both carbon sources. Of note, gapA gene expression appeared to be higher in late exponential phase (OD 600nm of 1–1.2) than in early exponential phase (OD 600nm of 0.5), while tktA gene expression remained unchanged. Transcription of tktA and gapA genes was next quantified in J774.1-infected macrophages during a 48 h-period (Fig 1D). Expression of both genes progressively increased during the first 24 h of infection (corresponding to the active phase of intracellular bacterial multiplication), and dropped at 48 h. As in CDM, gapA gene expression was significantly higher than that of tktA at all time-points tested (approximately 5-fold higher), suggesting that gapA possesses its own promoter. Although RT-PCR analyses have shown that the genes tktA and gapA were co-transcribed [10], a promoter prediction analysis of the sequence immediately upstream of the gapA gene (BPROM, executed on-line at www.softberry.com) identified putative -35 and -10 promoter elements, sharing significant homology to the consensus site recognized by the general sigma factor σ70 (S1 Fig).

Dynamics of macrophage infection of the PPP mutants We then used imaging approaches to characterize, at the single cell level, the impact of the four mutations in the PPP. We first monitored by confocal immunofluorescence microscopy the subcellular localization of the four PPP mutants, using GFP-labeled bacteria, and the late phagosomal marker LAMP-1 (Fig 4A and 4B). As expected, the Δfpi negative control strain showed a high colocalization with LAMP-1 at all time points tested (up to 80% at 24 h), confirming that it remained trapped in phagosomes. At 1 h, the % colocalization with LAMP-1 did not exceed 25% with WT and ΔtalA strains; was approximately 36% for the ΔrpiA, and Δrpe mutant strains; and 60% for ΔtktA. Thus, at this early stage, for all PPP mutants, the majority of bacteria had already escaped from the phagosomal compartment, with the exception of ΔtktA which exhibited a slight escape defect. At 10 h and 24 h, the % colocalization with LAMP-1 remained below 10% with WT and ΔtalA strains. Confirming their presence in the cytosolic compartment, the ΔrpiA, Δrpe and ΔtktA mutants showed less than 25% colocalization with LAMP-1 at 10 h (17.9%,10.9% and 22.3% respectively); and less than 15% at 24 h (7.4%, 10.7%, 12.1%, respectively) (Fig 4B). PPT PowerPoint slide

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larger image TIFF original image Download: 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). https://doi.org/10.1371/journal.ppat.1009326.g004 We also quantified the percentage of infected cells at 10 h and 24 h (Fig 4C). With the WT and ΔtalA mutant strains, almost 70% of cells were infected at 10 h, while this percentage was below 20% for the three mutants. At 24 h, the percentage of infected cells was above 80% for WT and ΔtalA mutant strains, and increased to 42% and 27% for ΔrpiA and Δrpe, respectively, but remained unchanged with ΔtktA. To precise these data, we further quantified the number of GFP-positive spots per infected cell, by counting a total of 100 infected cells per strain, at 10h and 24 h (Fig 4D and 4E). Comparable results were recorded with WT and ΔtalA strains. At both time-points tested, a broad distribution of the number of GFP-positive spots was recorded. At 10 h, these ranged from 1 to more than 100 spots per infected cell (with a majority of infected cells having less than 20 spots). At 24 h, the number of cells bearing more than 60 spots further increased, in particular with ΔtalA. In contrast, with the three other PPP mutants, the number of GFP-positive spots recorded was below 15 per infected cell at 10 h and reached 74 and 54 for for ΔrpiA and Δ rpe, respectively; but remained below 43 for ΔtktA, at 24h. Altogether, these results suggest that the observed intracellular growth defects of PPP ΔrpiA, Δrpe, and ΔtktA mutants, is primarily a consequence of their inability to cope with the cytosolic compartment environment of infected macrophages. We next wished to follow and quantify the dynamics of intracellular multiplication of the PPP mutants by time-lapse video microscopy, using a fully automated microscope (Incucyte 531 S3, Essen BioScience). J774–1 macrophages with red nuclei (here designated J774.1 red ) were infected with GFP-expressing bacteria at an MOI of 100 and infection was followed over a 48 h-period, in 96-well plates (Figs 5 and S7 and S1–S5 Movies). We first quantified the total number of J774.1 red cells over time to evaluate the impact of bacterial infection on cell viability (see Materials and methods). This assay showed that cells were able to continuously multiply at least during the 40 first hours and were not affected by bacterial infection (Fig 5A). Intracellular bacterial multiplication was next followed by monitoring the total green area intensity (reflecting multiplication of the GFP-positive bacteria) over time translated on graph line by using the Incucyte S3 software (Fig 5B). Multiplication of the ΔtktA, Δrpe and ΔrpiA mutants was affected to variable extents. The ΔtktA mutant was again the most severely affected whereas the ΔtalA mutant showed wild-type multiplication. PPT PowerPoint slide

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larger image TIFF original image Download: 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. https://doi.org/10.1371/journal.ppat.1009326.g005 We then used a machine learning approach (see Data analyses, Materials and methods; S8 Fig) to quantify from the video microscopy data: i) the total number of cells, at each time point, and ii) for each cell, at each time point, the presence of a GFP signal (bacteria) and the GFP area (bacterial multiplication). The total number of J774.1 red cells infected with wild-type bacteria increased during the first 10 h of infection, then reached a plateau until 40h and increased again between 40 and 48 h (Fig 5C). This increase was faster and continuous with the ΔtalA mutant than with the wild-type strain. In contrast with the three PPP mutants, the total number of J774.1 red cells infected with GFP-expressing bacteria progressively decreased with time, suggesting a continuous elimination of these mutant bacteria similar to that of the Δfpi negative control strain. At selected time points (10 h, 24 h, and 48 h), the amount of GFP signal detected was accurately quantified (GFP area, see Materials and methods). This analysis revealed that after 10 h, more than 85% of the GFP-positive cells contained few bacteria as indicated by a measured GFP area of less than 200 pixels for all strains tested (estimation range of 1 to 10 bacteria per cell; Fig 5D). However, in 1–2% of cells infected with ΔrpiA and Δrpe mutants, bacterial multiplication had occurred, as quantified by measured GFP areas of 200–400 pixels. As expected, wild-type bacteria showed a greater capacity for intracellular multiplication, with measured GFP areas in the 200–400, 400–600 and up to the 600-800-pixel ranges. Remarkably, the ΔtalA mutant also showed a heterogeneous and even broader bacterial intracellular multiplication capacity than the wild-type strain. After 48 h, 40% of the cells infected with the ΔtalA mutant showed a GFP area >200 pixels, as compared to only 15% with the wild-type strain. Of note, after 24 h, cells infected with ΔrpiA and Δrpe mutants were still identified in the 200–400 pixels range (1–2% of the cells) and for the ΔrpiA mutant approximately 2% of the cells were in the >600 pixels range, revealing an active multiplication of this mutant in a limited subset of cells. Overall, these analyses revealed that ΔtktA, Δrpe and ΔrpiA mutants had impaired intracellular multiplication until 30 h after infection. The Δrpe and ΔrpiA mutants then resumed growth in a limited subset of cells, reaching up to 30% that of WT. A very modest intracellular multiplication of the ΔtktA mutant could also be visualized between 40 h and 48 h (Fig 5 and S1–S5 Movies). The ΔtalA mutant showed wild-type or even improved intracellular multiplication-dissemination, in all the conditions tested.

Virulence assay in the adult fly The impact of PPP mutations was then assessed in vivo. For this purpose, we used the adult fly model, a simple animal model that has been used previously to assess the attenuation of virulence of Francisella mutants [17,21–24]. We wanted to know if the effect of the mutants on fly survival depended on bacterial proliferation. Therefore, we monitored the survival of flies after infection and bacterial multiplication in infected flies. Adult male flies were infected with wild-type F. novicida (WT), or ΔtktA, ΔrpiA, Δrpe, ΔtalA, and Δfpi isogenic mutants (see Materials and methods). Fly survival was monitored over a 10-day period, using 20 adult male Drosophila per bacterial strain. 100% of the flies infected with the WT strain or the ΔtalA mutant strain died within 6–7 days while 100% of the flies infected with the Δfpi mutant strain survived. The ΔtktA, ΔrpiA and Δrpe mutants were only slightly less virulent than the WT and killed 90% of the flies after 10 days (S9A Fig). Hence, in spite of showing impaired intracellular multiplication, the ΔtktA, ΔrpiA and Δrpe mutants retained most of their virulence properties in this model. To examine bacterial growth within the adult fly, we used a total of 8 flies per bacterial strain and each assay was performed in triplicate. The average number of bacteria (CFUs) recovered from flies 2 h after pricking varied between 4x103 and 8x104 bacteria/8 flies. By day 4 post-infection, bacterial growth reached 5x107 bacteria/8 flies for the WT strain as well as for the three PPP mutants, whereas the counts of the Δfpi mutant remained below 9x106 bacteria/8 flies. For WT and ΔtalA strains, CFUs were not monitored after day 4 due to insufficient number of surviving flies (< 24). By day 6 post-infection, bacterial growth reached 1 x 108 bacteria/8 flies for the three PPP mutants, whereas the CFUs of the Δfpi mutant (control) reached only 9x106 bacteria/8 flies. For ΔrpiA, ΔrpE, ΔtktA strains, CFUs were not monitored after day 6 due to insufficient number of surviving flies (<24). By day 8 post-infection, the CFUs of the Δfpi mutant (control) remained approximately 1 x107 bacteria/8 flies. Of note, in this model, the Δfpi mutant was also able to multiply until day 6 and persisted in infected flies at day 8 even if it did not cause any death (S9B Fig).

Invalidation of PPP key enzymes leads to common variations of the global proteomic profile Having determined the impact of PPP gene inactivation on F. novicida intracellular survival and virulence, we then wished to further our understanding of the consequences of the mutations on bacterial physiology and to identify possible links between PPP and other metabolic pathways. To this end, we performed a whole cell proteomic analysis, combined with a global metabolomic analysis of the PPP mutants. Whole cell proteomic approaches provide a global view of the post-transcriptional alterations caused by a mutation. Metabolomic approaches bring complementary information on the metabolic changes generated by these alterations and are particularly relevant when studying mutations in enzymatic pathways. We first performed a whole-cell comparative nanoLC-MS/MS proteomic analysis of WT F. novicida and the three mutant strains that showed impaired intracellular growth (ΔtktA, Δrpe and ΔrpiA). As a control we also performed the whole cell comparative analysis of WT F. novicida and of the ΔtalA mutant that did not show any intracellular growth defect (Fig 6). Whole cell protein samples were prepared from bacteria grown at 37°C in rich medium (TSB). Each strain was analyzed in three independent biological replicates (see Materials and methods). PPT PowerPoint slide

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larger image TIFF original image Download: 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 OD 600 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). https://doi.org/10.1371/journal.ppat.1009326.g006 In all cases, invalidation of the gene, and therefore absence of the protein, induced a strong modulation of the global amount of protein compared to the WT. Overall, 1 502 proteins were identified across samples. Of those, we retained 1 381 proteins confidently quantified in at least one condition, covering 80% of the proteome on 1 722 proteins predicted to be encoded by the F. novicida U112 genome. We performed a t-test (FDR<0.05) showing that a large number of proteins was impacted by the deletions: 835 proteins by ΔtktA, 690 proteins by ΔrpiA, 372 by Δrpe and 237 by ΔtalA (S3 Table). As expected, for each mutant, the proteins deleted from the genome were found as absent in the corresponding mutant. Of note, in all three mutants ΔtktA, ΔrpiA and Δrpe, expression of the TalA protein was found increased. A common set of 145 proteins was modulated in the four mutants. However, an additional set of 137 proteins was modulated in ΔtktA, ΔrpiA, Δrpe mutants but not in the ΔtalA mutant which might be involved in their impaired intracellular behavior (Fig 6 and S3 Table). The vast majority of these proteins was following the same modulation pattern, with 73 upregulated and 64 downregulated in the three mutant strains. Enrichment pathway analysis, performed using Kegg mapper (https://www.genome.jp/kegg/mapper.html), highlighted perturbation of proteins mainly involved in metabolic pathways, including the PPP (with 17 proteins downregulated and 34 upregulated) and biosynthesis of secondary metabolites and cofactors (with 23 proteins downregulated and 20 upregulated), suggesting a complex modulation of these pathways. Of note, the amplitude of protein modulation, in terms of fold changes, was stronger in ΔrpiA and ΔtktA, than in Δrpe. Interestingly, most of the proteins encoded by the fatty acid degradation locus fad (S10 Fig), were up- regulated in the three mutants but not in the ΔtalA control strain (Fig 6). These results were confirmed by qRT-PCR, indicating that the impact of tktA gene inactivation on Fad proteins expression occurred already at the transcriptional level. Indeed, a ca. 3-fold increased expression of most of the genes of the fad operon was recorded in exponentially-grown bacteria in K3 medium (OD 600 0.5), in the mutant compared to WT. In late exponential phase (OD 600 1), this increase reached up to 6-fold that of WT (S10 Fig). Altogether these data strongly suggested a yet unanticipated link between PPP and fatty acid metabolism.

Metabolomic analyses of the PPP mutants As for proteomic analyses, bacteria were grown at 37°C in TSB to an OD 600 of 0.5. Each strain was analyzed in three independent biological replicates (see Materials and methods). Inactivation of the tktA gene resulted in important changes in global metabolome (50 metabolites are presented in Fig 7). Forty metabolite levels changed significantly (23 down and 17 up). An increase of metabolites belonging to different chemical classes was recorded, including notably amino acids, nucleotides and sugars. The glucose oxidation intermediates were significantly affected with marked accumulation of glucose, the glycolytic intermediate F- 1,6P, as well as the PPP metabolite Ribose-P. It also yielded the accumulation of dihydroxyacetone phosphate (DHAP), a metabolite tightly associated to the glycolytic/gluconeogenic pathways. Indeed, DHAP is a breakdown product of fructose 1,6- bisphosphate (F-1,6P) by the enzyme fructose biphosphate aldolase (FBA) and can be converted to GA-3P by the enzyme triose phosphate isomerase. PPT PowerPoint slide

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larger image TIFF original image Download: 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 OD 600 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. https://doi.org/10.1371/journal.ppat.1009326.g007 On the contrary, tktA inactivation was accompanied by decreased concentrations of the PPP intermediate sedoheptulose-7P (S-7P) as well as of the glycolytic end product pyruvate. Of note, our lab recently showed that in the Gram-positive pathogen Staphylococcus aureus, inactivation of tktA also led to deregulation of whole-cell metabolism [25] and, as in Francisella, to accumulation of R-5P and decreased amounts of S-7P. In E. coli, mutations in transketolase have been shown to lead to an accumulation of DHAP, a precursor of the highly toxic compound methylglyoxal (MG; [26]). To exclude a possible toxic effect of DHAP-derived MG production, we evaluated the sensitivity of the ΔtktA mutant to increasing concentrations of MG compared to WT F. novicida and E. coli K12 (S11 Fig). The ΔtktA mutant strain appeared to be even more resistant to MG treatment than the WT strain. Therefore, although this mutant produces increased amounts of MG, it is very unlikely to impact bacterial multiplication. Consistent with what was observed with ΔtktA, inactivation of rpiA resulted in the accumulation of the glycolytic intermediate G-6P, and Ribose-P. However, unlike ΔtktA, inactivation of ΔrpiA resulted in increased levels of S-7P. rpiA inactivation was also accompanied by decreased production of the glycolytic intermediate GA-3P. rpe inactivation resulted in similar effect as rpiA with accumulation of S-7P and glucose. In addition, rpe inactivation resulted in decreased pyruvate. Overall ΔrpiA and Δrpe inactivation manifested a similar metabolic phenotype that was distinct from that of the ΔtktA mutant. In addition, all the mutants shared a relative decrease of long chain fatty acids (LCFAs), such as: i) palmitic and stearic acids (16 and 18 carbon, saturated LCFAs, respectively), in both ΔtktA and ΔrpiA mutants; ii) palmitoleic acid (16 carbon, monounsaturated LCFA), in Δrpe and ΔrpiA mutants; or iii) oleic acid (18 carbon, monounsaturated FA), in ΔtktA and Δrpe mutants. Hence, metabolic profiling of mutants inactivated in the PPP showed common traits with alterations in the relative amounts of similar glycolytic intermediates and fatty acids. These data are also consistent with proteomic and transcriptomic analysis showing an upregulation of the whole fad operon and further confirm the link between the PPP and fatty acid metabolism.

[END]

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