Salmonella Within Macrophages--An Extreme Host-Pathogen Interface: Small Molecule Inhibitors of Bacterial Efflux and the Roles of Bacterial Lipid Metabolism and Mammalian Co-Culture During Infection

Pathogens withstand extreme host environments during infection. Salmonella enterica serovar Typhimurium survives within host macrophages, immune cells intended to phagocytose and destroy pathogens. Through decades of study, we know much about how Salmonella endures this challenging niche. I leveraged this host-pathogen interface to identify small molecules that disrupt Salmonella infection of macrophages. I developed a medium-throughput fluorescence microscopy-based screening assay and image analysis pipeline to quantify intracellular bacterial load. With this platform, I identified 300 small molecules that reduce Salmonella infection of macrophages. Of the top 60 hits, I characterized three compounds that inhibit bacterial efflux pumps and sensitize Salmonella to host antimicrobial peptides. This result highlights the importance of bacterial efflux pumps in defense against host antimicrobials, and validates efflux pumps as a therapeutic target to treat infection. I also characterized the antimicrobial activity of clomipramine, a clinically used tricyclic antidepressant. I found that anti-Salmonella activity was unrelated to clomipramine’s canonical inhibition of the serotonin reuptake transporter, and that clomipramine may activate host autophagy to clear bacteria. However, clomipramine was ineffective against Salmonella infection in vivo, which limits the possibility of repurposing this drug as an antimicrobial. Together, these studies exemplify the complexity of Salmonella infection of macrophages in how many possible pathways can be modulated by drugs to disrupt this host-pathogen interface.Recent studies have established the roles that host and bacterial heterogeneity play in the progression and outcome of infection. I identified a unique macrophage phenotype that governs the use of lipids by Salmonella. Only within pro-inflammatory amino-acid-supplemented macrophages was lipid metabolism important for Salmonella infection. Further, only a subset of bacteria utilized lipids within these macrophages, highlighting that even in a specialized macrophage, individual Salmonella employ unique nutritional strategies. Finally, I investigated the effects of co-culturing leukocytes with infected macrophages on Salmonella infection. I found co-culturing erythrocytes or T cells altered activation, iron homeostasis, and nitric oxide levels, with the net effect of increasing Salmonella replication within macrophages. Thus, the macrophage niche is highly diverse and influenced by many factors. Together, my studies illustrate the complexity and uniqueness of the extreme Salmonella-macrophage host-pathogen interface

A cell-based infection assay identifies efflux pump modulators that reduce bacterial intracellular load

<div><p>Bacterial efflux pumps transport small molecules from the cytoplasm or periplasm outside the cell. Efflux pump activity is typically increased in multi-drug resistant (MDR) pathogens; chemicals that inhibit efflux pumps may have potential for antibiotic development. Using an in-cell screen, we identified three efflux pump modulators (EPMs) from a drug diversity library. The screening platform uses macrophages infected with the human Gram-negative pathogen <i>Salmonella enterica (Salmonella)</i> to identify small molecules that prevent bacterial replication or survival within the host environment. A secondary screen for hit compounds that increase the accumulation of an efflux pump substrate, Hoechst 33342, identified three small molecules with activity comparable to the known efflux pump inhibitor PAβN (Phe-Arg β-naphthylamide). The three putative EPMs demonstrated significant antibacterial activity against <i>Salmonella</i> within primary and cell culture macrophages and within a human epithelial cell line. Unlike traditional antibiotics, the three compounds did not inhibit bacterial growth in standard microbiological media. The three compounds prevented energy-dependent efflux pump activity in <i>Salmonella</i> and bound the AcrB subunit of the AcrAB-TolC efflux system with K_Ds in the micromolar range. Moreover, the EPMs display antibacterial synergy with antimicrobial peptides, a class of host innate immune defense molecules present in body fluids and cells. The EPMs also had synergistic activity with antibiotics exported by AcrAB-TolC in broth and in macrophages and inhibited efflux pump activity in MDR Gram-negative ESKAPE clinical isolates. Thus, an in-cell screening approach identified EPMs that synergize with innate immunity to kill bacteria and have potential for development as adjuvants to antibiotics.</p></div

The three hit compounds decrease bacterial load of <i>Salmonella</i> in mammalian cells.

<p>(A-C) Monitoring of bacterial load by GFP (SAFIRE) or CFU in RAW264.7 cells. (A) Representative micrographs of cells in 96-well plates infected with <i>sifB</i>::<i>GFP Salmonella</i>. Two hours after infection cells were treated with the indicated compound [25 μM] for 16 hours. (B) Dose response curve for SAFIRE and (C) CFU; keys includes IC₅₀ values. (D-E) Monitoring of bacterial load by GFP (SAFIRE) in HeLa cells infected with <i>Salmonella</i> expressing <i>rpsM</i>::<i>GFP</i> and treated with the indicated compound [25 μM]. (F) Monitoring of bacterial load by CFU in BMDMs treated with compounds [25 μM]. Mean and SEM from three independent biological replicates. The nonlinear curve fitting (B, C) is constrained using uninfected cells as the minimum and DMSO-treated cells as the maximum. (E, F) * <i>p</i> < 0.05; ** <i>p</i> < 0.01, *** <i>p</i> < 0.001 compared to DMSO by one-way ANOVA with Dunnett’s post-test.</p

EPMs do not disrupt bacterial inner or outer membranes.

<p>(A,B) <i>Salmonella</i> treated with DMSO or EPMs [100 μM] but not CCCP [1 mM] acquire TMRM staining within 30 minutes. (A) Representative data from one of three independent experiments. (B) Median fluorescence intensity from three experiments normalized to unstained control (0). (C) Disk diffusion assays; the radius of the zone of growth inhibition after 16 hours of exposure to compound across a dose range. Black lines, semilog fit for the combined antibiotic data; gray lines, semilog fit for CCCP and PAβN; dotted lines, limit of detection (disk radius). Average of two measurements from each image captured from one experiment representative of two independent experiments. (D,E) Nitrocefin access to the periplasm as monitored by nitrocefin [100 μM] hydrolysis in the presence of the indicated concentrations of compounds. (D) Absorbance 486 nm of <i>bla+ Salmonella</i> normalized to <i>bla- Salmonella</i>. Data is representative of at least two independent biological replicates. (E) Slope of the linear region of the A₄₈₆ plot from at least three experiments. Data is normalized to A₄₈₆/minute. * <i>p</i> < 0.05, *** <i>p</i> < 0.001, **** <i>p</i> < 0.0001 by one-way ANOVA with Dunnett’s post-test.</p

EPMs synergize with antimicrobial peptides.

<p><i>Salmonella</i> was grown in M9-based defined media in the presence of <b>(A-D</b>) polymyxin B (5 μg/ml; 1/8 MIC) or <b>(E-H)</b> LL37 (5 μg/ml; 1/8 MIC) and EPMs (PAβN, 500 μM; EPMs, 25 μM). Mean and SD of triplicate samples from one representative experiment of three independent biological replicates. DMSO, polymyxin B and LL37 curves repeat across graphs.</p

EPM35 and EPM43 block efflux of Nile red from ESKAPE MDR clinical isolates.

<p>(A-D) Defined strains obtained from BEI resources were examined for Nile red retention after glucose addition in the presence of the indicated compound. Data for each sample were normalized to the initial fluorescence (100%). Dose response curves at seven minutes after glucose addition. Mean of three biological replicates performed in duplicate. * <i>p</i> < 0.05; ** <i>p</i> < 0.01; *** <i>p</i> < 0.001; ****<i>p</i> < 0.0001 compared to DMSO + glucose by one-way ANOVA and Dunnett’s multiple comparison post-test.</p

The hit compounds reduce the survival of MDR <i>Salmonella</i> in macrophages.

<p>Monitoring of bacterial load by CFU in RAW264.7 cells infected for two hours with the strain of <i>Salmonella</i> shown and then treated with the indicated compound for 16 hours, followed by macrophage lysis and plating for CFU. A) SL1344 Wild Type <i>Salmonella</i>, B) Clinical <i>Salmonella</i> isolate S10801, (C) SL1344 with <i>macAB</i>::kan, <i>acrAB</i>::kan or <i>tolC</i>::cm, respectively. Geometric mean of four biological replicates. Upper lines, mean CFU/well of wild-type SL1344 with DMSO treatment; lower lines, limit of detection. * <i>p</i> < 0.05, ** <i>p</i> < 0.01; *** <i>p</i> < 0.001, **** <i>p</i> < 0.0001 relative to DMSO, one-way ANOVA with Dunnett’s post-test.</p

Bacterial strains.

<p>Bacterial strains.</p

Screening platform (SAFIRE) used to identify EPMs.

<p>(A) Schematic of screening methodology. (B) Representative micrographs of infected macrophages from DMSO-treated wells. Upper left is a field with 522 macrophages; remaining images are the indicated channels zoomed in on the boxed region. Scale bars are 50 μm. (C) Representative micrographs of infected macrophages treated with rifampicin [2 μg/mL] or of uninfected macrophages treated with DMSO. (D) Distribution of B-scores and <i>p</i>-values for 14,400 compounds from the Maybridge HitFinder^TM v11 library. The locations of the three hit compounds (EPM30, EPM35 and EPM43) and of chloramphenicol (Cm), which was identified from the library, are shown.</p

EPMs enhance activity of erythromycin and ciprofloxacin against <i>Salmonella</i> in macrophages.

<p>RAW 264.7 macrophages were infected with <i>Salmonella</i> and treated with a dose range of (A) erythromycin or (B) ciprofloxacin and with DMSO or the indicated concentration of an EPM. At 18 hours post infection samples were processed for fluorescence microscopy as described. Data were normalized to treatment with DMSO and antibiotic vehicle (100%). Key: black, DMSO; red, EPM30; green, EPM35; blue, EPM43; gray, calculated additivity of the antibiotic and the corresponding EPM using the formula (100 –([percent inhibition EPM] + [percent inhibition antibiotic])), where percent inhibition is calculated as 100 –[percent of DMSO]. Data are mean + SEM of three biological replicates. * <i>p</i> < 0.05; ** <i>p</i> < 0.01; *** <i>p</i> < 0.001; **** <i>p</i> < 0.0001 of EPM treatment versus calculated additivity by one-way ANOVA with Sidak’s multiple comparison test.</p