Optimization of CoaD Inhibitors against Gram-Negative Organisms

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Optimization of CoaD inhibitors against Gramnegative organisms through targeted metabolomics Christopher M Rath, Bret M Benton, Javier De Vicente, Joseph E Drumm, Mei Geng, Cindy Li, Robert J Moreau, Xiaoyu Shen, Colin K Skepper, Micah Steffek, Kenneth Takeoka, Lisha Wang, Jun-Rong Wei, Wenjian Xu, Qiong Zhang, and Brian Y Feng ACS Infect. Dis., Just Accepted Manuscript • DOI: 10.1021/acsinfecdis.7b00214 • Publication Date (Web): 15 Dec 2017 Downloaded from http://pubs.acs.org on December 16, 2017

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ACS Infectious Diseases

Optimization of CoaD inhibitors against Gram-negative organisms through targeted metabolomics Christopher M. Rath, Bret M. Benton, Javier de Vicente, Joseph E. Drumm, Mei Geng, Cindy Li, Robert J. Moreau, Xiaoyu Shen, Colin K. Skepper, Micah Steffek, Kenneth Takeoka, Lisha Wang, Jun-Rong Wei, Wenjian Xu, Qiong Zhang, and Brian Y. Feng Novartis Institutes for BioMedical Research, 5300 Chiron Way, Emeryville, California 94608, United States E-mail: [email protected] Drug-resistant Gram-negative bacteria are of increasing concern worldwide. Novel antibiotics are needed, but their development is complicated by the requirement to simultaneously optimize molecules for target affinity and cellular potency, which can result in divergent structure-activity relationships (SARs). These challenges were exemplified during our attempts to optimize CoaD inhibitors identified through a biochemical screen. To facilitate lead optimization, we developed mass spectroscopy assays based on the hypothesis that levels of CoA metabolites would reflect the cellular enzymatic activity of CoaD. Using these methods, we were able to monitor the effects of cellular enzyme inhibition at compound concentrations up to 100-fold below the minimum inhibitory concentration (MIC), a common metric of growth inhibition. Furthermore, we generated a panel of efflux pump mutants to dissect the susceptibility of a representative CoaD inhibitor to efflux. These approaches allowed for a nuanced understanding of the permeability and efflux liabilities of the series and helped guide optimization efforts to achieve measureable MICs against wild-type E. coli . Keywords. CoaD, PPAT, metabolomics, efflux, Gram-negative, permeability, antibiotics

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The spread of antibiotic resistance among bacterial pathogens has become a global public health crisis. Isolates have been described with acquired genetic traits that provide resistance to all clinically used antibiotics.1 Of particular concern are multidrug-resistant members of the ESKAPE group of pathogens2 (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). These organisms threaten routine medical procedures, such as surgery, chemotherapy, and childbirth. New agents unaffected by existing mechanisms of resistance are needed to treat these infections. The coenzyme A (CoA-SH) biosynthesis pathway is a potential target for novel antimicrobials (Figure 1a).3–6 CoA-SH is essential across all domains of life, acts as a co-factor for many enzymatic reactions, and serves as a donor for the 4’-phosphopantetheine prosthetic group that facilitates acyl carrier protein (ACP)-dependent shuttling of activated acyl intermediates.7,8 These intermediates are involved in many processes, including the citric acid cycle, fatty acid biosynthesis, and membrane biogenesis; CoA-SH-derived metabolites are key components in the cell wall and both the inner and outer membranes of Gram-negative bacteria.9 In most organisms, CoA-SH is synthesized in five steps from pantothenic acid, also referred to as vitamin B5.5 CoaA (also known as PanK) catalyzes phosphorylation of pantothenic acid, and the bifunctional enzyme CoaB/C (also known as PPCS) transfers a cysteine group onto 4’-phosphopantothenate before decarboxylation to form 4’phosphopantetheine. CoaD (also known as PPAT) adenylates 4’-phosphopantetheine to form dephospho-CoA, which is then phosphorylated by CoaE (also known as DPCK) to produce CoA-SH. There is growing evidence that supports targeting CoA biosynthesis as strategy for antibiotic therapy. Previous work, comprehensively reviewed elsewhere,10 documents that whereas the pantothenic acid transporter PanF makes the pantothenic acid biosynthesis


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pathway dispensable in E.coli,10–12 the genes involved in biosynthesis of CoA from pantothenic acid are essential. Recently, it was also shown that CoaB/C was essential in a mouse model of M. tuberculosis infection.4 Small molecule inhibitors of CoA biosynthesis have also been described; Inhibitors of CoaA, the rate-limiting step in CoA synthesis,13 were recently reported with activity against Mycobacterium tuberculosis.14 Pantothenic acid analogues such as Npentylpantothenamide are recognized as substrates by the entire CoA biosynthetic pathway and are thought to form toxic acyl-ACP species that inhibit fatty acid biosynthesis.15–18 A related compound, the natural product CJ-15,801 is a pantothenic acid analog accepted as a substrate by CoaA; the product of that reaction inhibits CoaB/C.19 Identification of a natural product targeting this pathway suggests that other antimetabolites targeting CoA biosynthesis may exist; recent work describes novel methods to screen for this type of bioactivity.20,21 Rationallydesigned substrate mimetics were also reported to inhibit CoaB/C, though they lack cellular activity.22 CoaD, the penultimate step in the pathway, has also been targeted10,23–26 and recently investigators at AstraZeneca described compounds with activity against Gram-positive bacteria and some efficacy in vivo.27 To date, however, no on-target CoaD inhibitors have been documented that inhibit the growth of wild-type (WT) Gram-negative organisms. Of the enzymes in the pathway, CoaD is an especially attractive target because it is structurally well-conserved among bacterial species, dissimilar to the closest human orthologue, and unable to be bypassed via supplementation with pantethine or CoA-SH.28,29 To identify chemical starting points for CoaD inhibitors, we used a fragment-based approach to identify multiple scaffolds that inhibited the enzyme.30,31

Subsequently, we found that the biggest

challenge in optimizing these inhibitors was bridging the gap between in vitro enzyme inhibition and cellular activity. We attribute this largely to the effectiveness of the Gram-negative permeability barrier, long known to contribute to the intrinsic insusceptibility of these organisms to antibiotics.32 The LPS-laden outer membrane restricts hydrophobic compound influx, while



the phospholipid inner membrane limits polar compound entry. Xenobiotics that manage to pass beyond these membranes must also avoid multiple redundant efflux pumps in order to accumulate in the cell. Thus, optimization for cellular activity requires simultaneous tuning of target affinity, permeability, and efflux pump recognition. We sought to address this challenge by designing a sensitive assay that could measure target inhibition in cells below the threshold where growth inhibition could be observed. We hypothesized that levels of CoA metabolites could serve as proxies for the cellular enzymatic activity of CoaD. This is consistent with a recent report documenting the depletion of CoA metabolites due to genetic silencing of coaBC in M. tuberculosis.4 Others have previously described methods for measuring the cellular concentrations of CoA-SH and related metabolites33–37. We adapted these targeted metabolomics approaches to specifically measure the metabolites involved in CoA biosynthesis in response to compound treatment. We developed methods using liquid chromatography mass spectroscopy (LC-MS), as well as an automated solid-phase extraction mass spectroscopy platform (SPE-MS). Subsequently, we used the SPE-MS assay to characterize the output of our lead optimization campaign, which largely focused on molecules constructed from triazolopyrimidinone, triazolopyrimidine or azabenzimidazole scaffolds. This provided a window into the cellular disposition of the compounds, and was up to 100-fold more sensitive than the MIC assays used to measure growth inhibition. The information provided by this assay, in combination with robust structural and microbiological data described elsewhere (including selection of target mutations),31 facilitated optimization of the series exemplified by compounds 1-8 (Table 1). This series includes the first CoaD inhibitors with measurable MICs against a WT Gram-negative organism.


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Results and Discussion. A cellular assay for CoaD inhibition. Originally identified in a fragment-based screen, our CoaD inhibitor scaffold was initially optimized using biochemical assays and a robust crystallography system.30,31 Potency improved rapidly, surpassing the sensitivity limit for the enzyme assay, which corresponded to an IC50 of approximately 12 nM. For a more precise measure of affinity we relied on an SPR-based binding assay. However, even as affinity improved, the initial molecules did not consistently affect bacterial growth. To better explore SARs that encompassed both target engagement and cellular inhibitor accumulation, we sought to develop a cell-based assay for CoaD function. We hypothesized that inhibition of CoaD should have a measureable effect on the cellular concentration of CoA metabolites (Figure 1A). To test this, we developed two methods to measure CoA metabolites in cells; one using an LCMS approach and the other using a high-throughput SPE-MS platform. Using LC-MS, we initially set out to measure dephospho-CoA (CoA-DP, enzymatic product of CoaD), CoA-SH, and acetyl-CoA (CoA-Ac). Preliminary chromatography conditions were based on published metabolomics methods,38 and optimized using pure standards (Supplementary Methods). Our optimized methods were able to readily detect CoA-SH and CoA-Ac in cellular extracts of rapidly growing E. coli, and these analytes were chromatographically well-resolved with respect to pure standards. (Figure 1B). CoA-DP was not robustly detectable, in keeping with reports that CoA-DP represents only a small fraction of the intracellular CoA metabolite pool.39-40 The concentrations of CoA-SH and CoA-Ac were consistent with previous reports.41-42 Overall, the LC-MS method provided reliable quantitation of CoA-SH and CoA-Ac, but was hindered by throughput as each sample required a six minute LC separation method.



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The SPE-MS approach is less sensitive than LC-MS, but much higher throughput.43 On our instrument, one sample could be processed approximately every 8 seconds (Figure 1C). The increased throughput of the SPE-MS assay facilitated optimization of other assay Table 1. Metabolite-guided optimization of CoaD inhibitors.

E. coli enzyme IC50 (µM)

E. coli SPR Kd (µM)

E. coli - ∆tolC CoA-SH depletionEC5 a 0 (µM)

E. coli-∆tolC MIC (µM), (µg/mL)

E. coli WT CoA-SH depletion a EC50 (µM)

E. coli WT MIC (µM), (µg/mL)

1

0.31

N.D.

1.78

82.92 (32)

>200

>331.69 (>128)

2

0.021

0.0039

0.096

9.73 (4)

41.17

>311.51 (>128)

3

0.0066

N.D.

0.14

0.56 (0.25)

2.73

71.60 (32)

4

0.0039

0.00082

0.0089

0.26 (0.125)

1.48

67.09 (32)

5

0.037

0.009

0.59

48.66 (16)

>200

>389.29 (>128)

6

0.056

0.0101

0.37

12.83 (4)

4.76

>410.52 (>128)

7

0.0132

0.011

0.45

17.72 (8)

>200

>283.50 (>128)

8

0.011

0.00183

0.024

0.58 (0.25)

11.9

>294.59 (>128)

Compound Number

Compound


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a

Determined using SPE-MS assayparameters,

such as the culture incubation period yielding maximal

CoA metabolite levels. CoA metabolite levels were measured at several time points during early log-phase growth, and MS intensities were normalized to an internal standard (CoA-IS, Figure 1D). At early time points, CoA-Ac levels were high, dropping quickly over the first hour of monitoring. The consequences of high initial levels of CoA-Ac, followed by a rapid drop off are not known, although the literature suggests that this may be related to changes in bacterial metabolism during the different phases of growth.40 In contrast, at early time points, CoA-SH levels were low, rising continuously over the course of the incubation. We chose an incubation period of three hours as the interval over which to measure CoA-SH, as it presented a balance between signal-over-background and incubation time. CoA metabolite changes can be detected following compound treatment. Compounds 1 is a novel CoaD inhibitor with sub-micromolar affinity for the purified enzyme, but no detectable MIC in WT E. coli (Table 1). We tested the effects of compound 1 on CoA metabolite levels using both LC-MS and SPE-MS platforms. We found that compound 1 treatment lowered CoA metabolite levels in efflux-deficient E. coli-∆tolC cells, and that the EC50 values for depletion were within a two-fold range between platforms (Figure 1E). We also observed that the EC50 for compound 1-induced metabolite depletion is two orders of magnitude below the EC50 of growth inhibition, as determined by measuring the OD600 of the cultures at the time of harvest. This suggested that it might be possible to detect changes in CoaD function at compound concentrations below those that affect cell growth. To further characterize our cellular assay, we treated E. coli with the fatty-acid biosynthesis inhibitors triclosan, cerulenin and 6-(2,6-dibromophenyl)pyrido[2,3-d]pyrimidine2,7-diamine (ACCCi) a recently-described inhibitor of acetyl-CoA carboxylase, the first committed step of fatty acid biosynthesis.44,45 We also tested gatifloxacin (targeting DNA biosynthesis) and trimethoprim (targeting folate biosynthesis). Cerulenin, triclosan and



gatifloxacin all caused measureable depletions in CoA-SH levels, whereas ACCCi and trimethoprim had no effect (Table 2). CoA-Ac levels were not affected by this panel of nonCoaD inhibitors. The behavior of this small panel of reference antibiotics was somewhat surprising. Whereas treatment with fatty acid biosynthesis inhibitors such as triclosan and cerulenin might reasonably be expected to perturb CoA metabolite levels, the lack of activity by ACCCi indicates that CoA-SH levels—for the experimental conditions described here—are not a general indicator for function of fatty-acid biosynthesis. Futhermore, observation of Gatifloxacin activity suggests that CoA-SH levels may also reflect growth inhibition. However, the lack of an effect due to trimethoprim treatment suggests a more complicated relationship. Gatifloxacininduced depletion of CoA-SH may be tied to the rapid cidality caused by the fluoroquinolone mechanism. To better understand the dependence of cell growth on CoaD inhibitor activity, we employed a strain of E. coli in which the bacterial coaD gene was deleted and complemented with the nearest human orthologue (coaSY). These cells grew normally in the absence of CoaD inhibitor treatment and were equally susceptible to a range of reference antibiotics as the parental strain.31 Furthermore, MIC experiments carried out in this strain indicated that replacing the bacterial enzyme with the human orthologue was sufficient to rescue the growth inhibition caused by this series of CoaD inhibitors.31 In this cell line, compounds 1, 2 and 4 caused no depletion of CoA metabolite levels (compound 3 was not tested). Importantly, none of these compounds inhibited the biochemical function of CoaSY when tested at a maximum concentration of 200 µM. Conversely, the effects of the fatty-acid biosynthesis inhibitors, as well as gatifloxacin and ACCCi, on CoA metabolite levels were unchanged compared to their effects on WT cells. In addition, we observed that only in the case of CoaD inhibitors did depletion of CoA metabolites precede growth inhibition. We also observed that, under these assay conditions, only CoaD inhibitors appeared to robustly deplete CoA-Ac in a dose-dependent


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Table 2. Effects of CoaD inhibitors and reference antibiotics on CoA metabolite levels. Growth Inhibition EC50 a (µM) 16

SPE-MS

LC-MS

Target CoA-SH CoA-Ac CoA-SH CoA-Ac (Pathway) Cell Line EC50 (µM) EC50 (µM) EC50 (µM) EC50 (µM) CoaD E. coli-∆tolC 1.65 2.11 1.81 2.32 Compound 1 (CoA b >200 >200 >200 >200 >200 biosynthesis) E. coli-∆tolC, coaSY CoaD E. coli-∆tolC 1.3 0.15 0.15 0.16 0.18 Compound 2 (CoA b >200 >200 >200 >200 >200 biosynthesis) E. coli-∆tolC, coaSY CoaD E. coli-∆tolC 0.123 200 >200 biosynthesis) E. coli-∆tolC, coaSY FabB E. coli-∆tolC 39.7 97.23 >200 75.83 >200 Cerulenin (Fatty acid b 36.2 80.49 >200 104.22 >200 biosynthesis) E. coli-∆tolC, coaSY FabI E. coli-∆tolC 0.023 0.044 >20 0.044 >20 Triclosan (Fatty acid b 0.013 0.017 >20 0.013 >20 biosynthesis) E. coli-∆tolC, coaSY AccC E. coli-∆tolC 0.12 >200 >200 >200 >200 ACCCi (CoA b E. coli-∆tolC, coaSY 0.085 >200 >200 >200 >200 metabolism) GyrAB, E. coli-∆tolC 0.60 1.049 >200 1.19 >200 ParCE Gatifloxacin b (DNA E. coli-∆tolC, coaSY 0.27 0.895 >200 1.17 >200 biosynthesis) FolA E. coli-∆tolC 3.6 >200 >200 >200 >200 Trimethoprim (Folate b 2.8 >200 >200 >200 >200 Biosynthesis) E. coli-∆tolC, coaSY a b Determined from OD600 measured after 3 hours of growth in the presence of inhibitor. coaSY is the human orthologue of bacterial coaD. Compound

fashion. Discordance between the levels of CoA-SH and CoA-Ac may be related to the compound incubation period we chose for our assays. Overall, these results suggest that levels of CoA metabolites can serve as a useful measure of cellular CoaD inhibition.

Metabolite-guided optimization. Routine characterization of CoaD inhibitors included biochemical assays against the bacterial CoaD orthologues from E. coli, P. aeruginosa as well as the human orthologue, CoaSY. MICs were measured in a variety of sensitized and WT bacterial organisms, and metabolite depletion was measured in both WT E. coli and E. coli∆tolC. In comparing the data across assays for 240 compounds, we observed a strong correlation between biochemical potency and CoA metabolite depletion in E. coli-∆tolC cells (Figure 2A), especially for compounds with target affinity below 1 µM. Additionally, the EC50



values for CoA-SH depletion were highly correlated with growth inhibition as measured in MIC experiments, forming a trend along a line approximating 1/100th of the MIC (Figure 2B). We also observed that a number of compounds which lack measureable MICs in WT E. coli exhibited detectable depletion of CoA metabolites (Figure 2C), suggesting that further optimization would result in growth inhibition. Taken together, these data supported the hypothesis that inhibition of E. coli CoaD leads to a decrease in the cellular concentration of CoA-SH and CoA-Ac, which in turn leads to inhibition of bacterial growth. Integration of these data allowed us to simultaneously test hypotheses with regard to target affinity and cellular target engagement. For instance, the scaffold elaborations exemplified in compounds 1-4 (Table 1) were guided mainly by structural biology and drove biochemical affinity to nanomolar levels.31 To test the effects of scaffold modifications, we generated several related molecules with identical substituents but with modifications to the core ring system of the molecule, exemplified by compounds 5-8. We observed that despite similar biochemical affinities (compounds 5 and 6 have IC50s of 37 and 56 nM, respectively), the scaffold modification distinguishing these compounds caused a large change in the WT EC50 of CoA–SH depletion, (going from >200 µM to 4.76 µM, respectively). This can also be observed in compounds 7 and 8, where the WT EC50 of CoA-SH depletion fell from >200 µM to 11.9 µM. As a result, we conjectured that the parent scaffold of compounds 5 and 7, containing a pyrimidinone moiety, was intrinsically less able to accumulate in cells. Indeed, none of the 30 derivatives of this scaffold were able to measurably affect metabolite levels in WT cells, even though similar analogues from different scaffolds had readily detectable cellular activity. However, changing the core was not a panacea; although the WT metabolite EC50 of compound 8 is at least 20-fold lower than compound 7, and compound 8 has significantly improved affinity and E. coli-∆tolC metabolite EC50 compared to compound 7, we found that compound 8 is less potent in the WT metabolite assay than compound 6, even though compound 6 is weaker in the


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biochemical assay (11.9 and 4.76 µM, respectively). This suggests that even as we were able to reduce the liabilities of the scaffold, elaboration created new permeability and efflux liabilities. This held true across the series, with TolC-dependent efflux contributing to between 10- and 150-fold decreases in cellular potency (Figure 2d). Using these types of analyses, we were able to isolate divergent SARs among permeability, efflux, and target affinity, thereby greatly adding to our understanding of the chemical series. Cellular accumulation of compound 4 is impacted by outer membrane permeability and efflux pump activity in multiple Gram-negative organisms. Having observed that the activity of CoaD inhibitors was better in efflux-deficient E. coli, we wanted to better understand the balance between permeability and efflux liabilities for these compounds. We focused on compound 4, one of the more potent inhibitors against WT E. coli. Initially, we measured MICs for compound 4 across a panel of Gram-negative strains with outer membrane permeability or efflux defects (Table 3). These experiments yielded several observations. In E. coli, perturbations to the outer membrane, such as those seen in the E. coli-imp421346 strain, led to a 64-fold improvement in susceptibility relative to the WT parent strain. Perturbations to efflux caused a slightly more pronounced effect; individual loss of the AcrAB efflux system improved susceptibility to compound 4 by 32-fold, while cells lacking the tolC gene, which is a critical component of at least nine efflux pumps, showed a 256-fold increase in susceptibility. In contrast, gatifloxacin , a well-studied inhibitor of DNA synthesis, was 4-fold more active in all sensitized strains. For P. aeruginosa the trends are less clear due to off-scale MIC measurements in comparator WT strains. In the P. aeruginosa-Z61 strain, which has both a permeabilized outer membrane as well as a premature stop codon in the oprM gene47, MICs were improved by ≥4-fold, whereas deletion of the MexAB efflux system improved potency by ≥ 8-fold. Gatifloxacin displayed similar properties; MICs were improved 16-fold against the P.



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aeruginosa-Z61 strain and 8-fold against the P. aeruginosa-∆mexAB strain. Klebsiella pneumoniae susceptibility was also dependent on efflux, shifting from >64 µg/mL in WT bacteria Table 3. Antibacterial activity of compound 4 against outer membrane-defective or effluxdeficient strains of bacteria.

Organism (strain)

Phenotype

Compound 4 MIC (µg/mL)

Gatifloxacin MIC (µg/mL)

E. coli (ATCC25922)

WT

64

0.015

E. coli (BW25113)

WT

32

0.015

E. coli (BW25113)-∆acrB

No acrAB-dependent efflux

1

0.004

E. coli (BW25113)-∆tolC

No tolC-dependent efflux

0.125

0.004

E. coli (MCR106)

WT

64

0.015

E. coli (MCR106)-imp4213

Outer membrane permeability defect

1

0.004

P. aeruginosa (PAO1)

WT

>128

0.5

P. aeruginosa (PAO1)-∆mexAB-oprM

No mexAB-dependent efflux

16

0.06

P. aeruginosa-Z61 (ATCC35151)

Outer membrane permeability defect

32

0.03

K. pneumoniae (ATCC43816)

WT

>64

0.06

K. pneumoniae (ATCC43816)-∆tolC

No tolC-dependent efflux

4

0.008

K. pneumoniae (ATCC43816)-∆acrB

No acrAB-dependent efflux

4

0.008

to 4 µg/mL in cells deficient for AcrAB or TolC. This suggests that AcrAB may be the dominant TolC-dependent efflux pump recognizing compound 4 in Klebsiella. Gatifloxacin showed similar results, improving 8-fold in both the tolC and acrAB mutant strains. To further investigate the individual effects of influx and efflux on cellular activity, we measured MICs of 13 molecules from the CoaD inhibitor series in E. coli-∆tolC cells in the presence and absence of 50 µg/mL Polymyxin-B nonapeptide (PMBN), a known chemical permeabilizing agent.48,49 We observed that 10 of the 13 CoaD inhibitors tested showed no


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significant (>2-fold) activity changes in the presence of PMBN (Figure 3). However, large molecules such as vancomycin, erythromycin, and rifampicin benefited from co-treatment with PMBN, consistent with the hypothesis that these molecules are excluded from E. coli by the outer membrane.50,51 These data suggest that efflux plays a larger role than OM permeability in limiting the cytoplasmic concentration of compound 4. This is also consistent with the strong correlation between biochemical affinity and metabolite depletion in efflux-deficient cells. To better understand how the various TolC-dependent pumps may be contributing to the 256-fold difference between WT and E. coli-∆tolC MICs for compound 4, we constructed a strain of E. coli with all nine TolC-partner pumps inactivated.52 This strain served as the parent for a panel of strains each complemented with one functioning efflux system, and we measured the MIC for compound 4 across this panel. As a caveat, we did not assess the expression levels of the pump components in this panel, so pump expression may differ amongst strains as well from WT E. coli. Against the parental strain, compound 4 exhibited an MIC similar to that measured in E. coli-∆tolC cells. Testing in each mutant strain indicated that three of the nine efflux pumps recognized compound 4: AcrAB, AcrEF, and MdtEF (Figure 4). When we tested several known antibiotics against this panel, we observed other profiles in the data set as well. For instance, novobiocin was recognized by AcrAB and AcrEF, but not substantially recognized by MdtF. On the other hand, tetracycline was affected by several pump systems, but to a lesser degree (≤ 4-fold). The profile of compound 4 against this panel was most similar to erythromycin and argyrin B53 (Supplementary Table 1). These results highlight the promiscuous nature of efflux recognition in E. coli and suggest that complex efflux liabilities can be de-convoluted and perhaps individually optimized. Canvassing the entire corpus of inhibitors in this series with the pump complementation panel may allow us to derive SARs that enable optimization away from efflux liabilities. Alternatively, early preference could be given to scaffolds or molecules that are recognized by fewer pumps,



potentially facilitating efflux avoidance. We imagine that combining a sensitive assay, such as our SPE-MS approach, with individual contributions of efflux systems could lead to a deeper understanding of chemical basis for efflux substrate recognition. Dynamics of CoaD inhibition. Previous studies of E. coli β-alanine auxotrophs suggest that depletion of CoA metabolites should result in growth arrest (stasis);39 similar results were reported using the fatty acid biosynthesis inhibitor cerulenin.54 In contrast, the aforementioned ACCCi was reported to be bactericidal in Haemophilus influenzae45 and a recent report described genetic depletion of coaBC to be cidal in Mycobacterium tuberculosis.4 Using our assay for CoA metabolite levels, we sought to understand the kinetics of CoA depletion in response to CoaD inhibition. We measured metabolite depletion over a time course of treatment with compound 4 at different concentrations (Figure 5A and B). As expected, addition of compound 4 caused concentration-dependent depletion of CoA-SH. Furthermore, cellular concentrations of both CoA-SH and CoA-Ac fell to background levels within 120 minutes at concentrations of compound 4 ≥0.125 µg/mL (Figure 5C). From these data, it appears the rate of depletion of CoA-SH and CoA-Ac did not substantially increase between concentrations of 0.25 and 4 µM, suggesting cellular target occupancy had reached a maximum. Furthermore, we performed a time-kill experiment to measure the effect of compound 4 treatment on cell viability over time (Figure 5D). These data show that at ≥ 4X multiples of MIC, compound 4 treatment resulted in bacteriostasis. At these concentrations, growth had largely stopped by 120 minutes, although we observed some growth prior to that point. We hypothesize that with CoA biosynthesis blocked by an inhibitor, existing cellular stores of CoA metabolites were sufficient to support cell viability for up to 120 minutes under these conditions. This general hypothesis is consistent with the results of the SPE-MS time course, although the assays were carried out under different inoculum conditions and so cannot be directly compared. Other metabolic targets, such as


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folate inhibitors, exhibit a similar lag in growth inhibition when tested in time-kill studies.55 Further study may describe in greater detail how CoA biosynthesis inhibition leads to cell death. Given the ubiquity of CoA metabolites in metabolism, and the observation that multiple nodes in the CoA biosynthesis pathway are subject to feedback regulation, it seems likely that there are numerous consequences of CoA metabolite depletion, some of which may depend on environmental factors such as nutrient availability. Expanded metabolomic profiling,4 perhaps utilizing other known inhibitors (such as pantothenic acid analogues and the ACCCi molecule) may clarify more precisely how bacterial physiology is affected by CoA metabolite depletion. To conclude, a key challenge facing target-based discovery of novel Gram-negative antibiotics is the simultaneous optimization of molecules for potency and cellular accumulation, and our experience with CoaD inhibitors bears this out. Here, we suggest two strategies that may be useful in addressing this problem. First, the sensitivity of our targeted metabolomics assays allowed us to measure the on-target bioactivity of compounds too weak to inhibit cell growth. This greatly expanded the data available to understand bioactivity in WT cells, where few compounds produced measureable growth inhibition. Comparison of these data across cell lines with differing efflux capacity allowed us to simultaneously study the effects of chemical optimizations on potency, permeation and efflux. This enabled us to focus our chemistry efforts on designing compounds with a good balance of these properties, ultimately resulting in multiple compounds with measureable MICs against WT E. coli. In addition, this work strongly supports the hypothesis that inhibition of CoaD leads to a depletion of CoA and CoA-Ac, which in turn leads to a loss of E. coli viability. This series of compounds30,31 also suggests that it is possible to target the bacterial enzyme without appreciably affecting the orthologous human enzyme. The second strategy we employed utilized a strain panel with tailored efflux perturbations to achieve a granular understanding of efflux pump recognition for a key representative of the compound series. Wider application of these—or similar—approaches may yet allow us to



improve the inhibitors we have described here, though optimizing compounds to minimize efflux pump recognition is a difficult task. Although not every target will be amenable to the development of sensitive cellular assays, it may be through study of these types of systems that we will gain a deeper understanding of the chemical determinants of Gram-negative permeability. Materials and Methods. Bacterial strains, plasmids and growth conditions. Details regarding the strains and plasmids used in this study, as well as their construction are listed in the Supplementary Methods. All strains were cultured in cation-adjusted Mueller-Hinton broth (CAMHB; Becton Dickinson, Sparks, MD, #212322) unless otherwise noted. Kanamycin (K1377; 25 µg/mL), arabinose (A3256; 0.2%), (Sigma-Aldrich, St. Louis, MO), ampicillin (C2130; 30 µg/mL in agar media, 100 µg/mL in broth media) (Teknova, Hollister, CA) and gentamicin (G3632, 10 µg/mL) were included in growth media where indicated. P. aeruginosa PAO1 and K1119 were gifts from Keith Poole (Queen’s University, CA). E. coli strains BW25133, ∆tolC and ∆acrB were obtained as part of the Keio strain collection56 and are available from the Coli Genetic Stock Center (http://cgsc2.biology.yale.edu/). ATCC strains were purchased from American Type Culture Collection (https://www.atcc.org/). JWK0002, JWK0079 and JWK0080 were made in this study. Standards for mass spectroscopy. Standards of CoA-SH, CoA-Ac, and CoA-DP were obtained from Sigma. The CoA internal standard was synthesized by reacting 100 mg (130 µmol) CoASH with 240 mg (1.3 mmol) iodoacetamide in the dark for one hour in 5 mL of 0.3 mg/mL ammonium bicarbonate. The excess iodoacetamide was quenched by addition of 300 mg of DTT, which was reacted at 37oC for 90 minutes. Any excess DTT or unreacted CoA-SH was then scavenged by passing it through 3 x 5 mL slurry of thiolsepharose resin. The slurry was prepared by mixing 2 g of thiolsepharose resin in 20 mL of 0.3 mg/mL ammonium bicarbonate.


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The flow through, consisting of CoA-IS in 0.3 mg/mL ammonium bicarbonate with quenched iodoacetamide, was then lyophilized for three days to allow all materials except the final product to evaporate. The final product was stored at -20oC, either as a powder or as a 10 mM stock in 0.1% formic acid. The internal standard exhibits linear response to dose over the assay concentration range (Supplementary Methods). Biochemical assays. All enzymes were expressed with C-terminal His tags and purified from E. coli using nickel chelate affinity chromatography. All biochemical reactions were carried out in a buffer consisting of 50 mM Tris pH 7.5, 50 mM KCl, 5 mM DTT, 1 mM MgCl2, 0.01% (w/v) BSA, 0.01% (w/v) P20. 4’-Phosphopantetheine, the substrate for CoaD, was synthesized biosynthetically by incubating 12 µM E. coli CoaA enzyme with 10 mM ATP (V703, Promega) and 5 mM pantethine (P2125, Sigma) overnight at room temperature. After confirming that the reaction had run to completion by measuring ATP concentration, it was subsequently aliquoted and used as a crude mixture. CoaD activity was measured by coupling the production of pyrophosphate from the CoaD reaction to the production of inorganic phosphate by inorganic pyrophosphatase. Inorganic phosphate was then quantified using the Biomol Green reagent (BML-AK111, Enzo Life Siences) and measuring the UV absorbance at 620 nm. Reactions were assembled by incubating 24 nM E. coli CoaD, 200 µM ATP and compound together in assay buffer for five minutes, and initiated by the addition of 20 uM 4’-phosphopantetheine and 78 nM pyrophosphatase. In the case of the human orthologue, 13.6 nM CoaSY was substituted for CoaD. Reactions were allowed to proceed for 30 minutes at room temperature before being stopped through addition of a volume of Biomol Green reagent equal to the reaction volume. Compounds were delivered in sufficient concentrations to result in a final concentration of DMSO in each reaction of less than 5%. Chemical Biotinylation. Purified CoaD was diluted to a concentration of 10 µM in PBS. A stock concentration of 1 mM EZ-Link Sulfo-NHS-LC-LC-Biotin (ThermoFisher) was prepared in water.



The EZ-Link Sulfo-NHS-LC-LC-Biotin was mixed with the protein solution at equimolar amounts (10 µM) and the reaction was allowed to proceed at room temperature for one hour. The reaction was stopped through buffer exchange into PBS using a Zeba desalting spin column (ThermoFisher). The desalted solution was then dialyzed to further remove any free EZ-Link Sulfo-NHS-LC-LC-Biotin which may interfere with any subsequent binding assays. SPR Binding Assays. Biotinylated CoaD was immobilized to a Biacore SA chip and any unbound streptavidin was blocked with biocytin (Sigma-Aldrich). Compounds were tested individually at varying concentrations in running buffer (50 mM HEPES pH 7.0, 150 mM KCl, 1 mM TCEP, 0.05% Tween 20, 2% DMSO) at 20°C. Kinetic constants were calculated from the resulting sensorgrams using either multi-cycle kinetics by Biacore (Flow rate: 100 µL/min) or the OneStep (Flow rate: 150 µL/min) method on the SensiQ platform (Quinn, 2012). The lengths of association and dissociation times were varied depending on the potency of the test compound. The top compound concentration was determined by the estimated potency and tested in a 2fold dilution series. All sensor chips were monitored for loss of activity through the injection of a control compound which in general retained greater than 75% of the activity over the course of a 30 hour experiment. Analysis of the binding curves and determination of the kinetic parameters were fit to 1:1 binding models using evaluation software (Version 2.0, Biacore or QDat, SensiQ). Inoculum and sample preparation for metabolite assays. Unless mentioned otherwise, inocula were prepared as follows: frozen glycerol stocks were used to inoculate CAMHB. These cultures were grown in a shaking incubator at 37°C overnight. The next day, overnight cultures were diluted in fresh media to a density corresponding to OD600 = 0.05 and grown in a shaking incubator at 220 rpm and 37°C to a density corresponding to OD600 = 0.2. Metabolite Assay.100 µL of inoculant was added to warm 96-well polypropylene microtiter plates containing compounds spotted as 2 µL drops from DMSO stocks, sealed with a


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breathable film and incubated at 37°C for 3 hours in a shaking incubator. Plates were then sealed and frozen at -80oC. After at least two hours, plates were removed from the -80oC freezer, thawed for an hour at room temperature, unsealed, then 100 µL of acetonitrile with 1 mM DTT was added to each well. Plates were sealed with foil and shaken for 20 minutes on a plate shaker. Plates were then spun in a swinging bucket centrifuge for 15 minutes at 4,000 x g at 4oC. 100 µL of supernatant were then transferred to a new 96-well plate, where it was diluted 2x with LC buffer A (10 mM tributylamine with 15 mM acetic acid in water with 3% methanol) containing 10 µM CoA internal standard. Plates were then sealed with foil, and frozen at -80oC. Plates were thawed at room temperature for at least 60 minutes. They were then spun at 1,000 x g at room temperature for 2 minutes. LC-MS metabolite assays. Assay plates were analyzed on a Waters Acquity LC with a Sciex 5500 triple quadrupole mass spectrometer. The instrument was run at a flow rate of 0.35 mL/ min with a Waters HSS 1.8 micron 2 x 100 mm column. A 20 µL injection volume was utilized, and samples were kept at 4oC while the column heater was kept at 50oC. Pump A utilized LC buffer A, whereas pump B utilized acetonitrile. The gradient for the 6 minute method consisted of 0% B from 0-0.5 minutes, then ramping to 95% B from 0.5-3.5 minutes, a hold at 95% from 3.4-4.2 minutes, and a return to 0% B from 4.2-4.3 minutes. MS source settings were 20 units CUR, 8 units CAD, -4500 V IS, 450oC TEM, 35 units GS1, and 40 units GS2. MRM Q1/Q3 transition settings with 75 ms dwell time consisted: of 766/408 m/z for CoA-SH, 808/408 m/z for CoA-Ac, and 823/408 m/z for CoA-IS. DP and CE settings for the metabolites were -100/-43 V, -150/-48 V, and -150/-48 V respectively. EP and CXP were 10 V and -21 V for all transitions. SPE-MS assay. Assay plates were analyzed on either a RapidFire 300 or a RapidFire 360 automated SPE system connected to a Sciex 5500 triple quadrupole mass spectrometer.



RapidFire settings including flow rates of 1.5 mL / minute for pump 1, and 1.25 mL / minute for pumps 2 and 3. Pump 1 was run with LC buffer A and LC buffer B for pumps 2 and 3 (75% acetonitrile). RapidFire cycle times consisted of: 0.6 seconds maximum aspiration, 3 seconds load, 4 seconds elute, and 1 second re-equilibration. A phenyl column (Agilent #G9208A) was employed for SPE. The instrument was programmed to run two wash steps between each compound titration. MS source settings were 20 units CUR, 8 units CAD, -4500 V IS, 550oC TEM, 50 units GS1, and 50 units GS2. MRM Q1/Q3 transition settings with 33 ms dwell time consisted of consisted: of 766/408 m/z for CoA-SH, 808/408 m/z for CoA-Ac, and 823/408 m/z for CoA-IS. DP and CE settings for the metabolites were -100/-43 V, -150/-48 V, and -150/-48 V respectively. EP and CXP were -10 V and -21 V for all transitions. SRM transition settings are described in the Supplementary Methods. Time-course of CoA metabolite depletion. Samples were prepared and analyzed as above, with the exception of samples at time points shorter than 2 hours, which were concentrated 10 fold to make samples comparable in cell density to later time points. The measurements from these samples were normalized accordingly. Antibacterial activity testing. Antibacterial activity was assessed using a broth microdilution assay following the recommended methodology of the Clinical and Laboratory Standards Institute (CLSI)57. In brief, fresh bacterial overnight colony growth was resuspended in sterile saline, adjusted to a 0.5 McFarland turbidity standard and then diluted 1:200 into CAMHB to yield a final target inoculum of 5x105 colony-forming units (CFU)/mL. Two-fold serial dilutions of compounds were prepared in 100% dimethyl sulfoxide (DMSO) at 100-fold the highest final assay concentration; the resulting dilution series of compounds were diluted 1:10 with sterile water. Assay microtiter plates, which contained 10 µL of 10-fold final concentration of compound per well, were inoculated with a volume of 90 µL of bacterial inoculum, sealed in a plastic bag to prevent moisture loss and incubated for 20 hours at 35°C in ambient air.


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Following incubation, assay plates were monitored for bacterial growth with a SPECTRAmax380 microtiter plate reader (Molecular Devices, Sunnyvale, CA) at 600 nm, as well as by visual observation with a reading mirror. The MIC is defined as the lowest concentration of antibiotic at which the visible growth of the organism is completely inhibited. Performance of the assay was monitored by testing gatifloxacin against laboratory quality control strains in accordance with guidelines of the CLSI58. Time-kill procedure. Time-kill experiments were performed in CAMHB according to CLSIdefined methodology58 against E. coli strain NB27177. Antibiotics were added to the culture medium at concentrations equivalent to multiples of the MIC for E. coli-∆tolC. Media tubes were inoculated with early log-phase culture of E. coli-∆tolC, which was diluted to yield a final cell density of 5x105-1x106 CFU/mL. Samples taken at inoculation constituted the 0-hour time point. Cultures were then incubated at 37°C in ambient air with constant agitation using an orbital shaker (Innova 43, New Brunswick Scientific, Enfield, CT) for 24 hours and were sampled at 2, 4, 8 and 24 hours with careful mixing of each culture prior to sampling. Viable cell counts were determined by performing 10-fold serial dilutions in sterile saline; 100 µL of undiluted and diluted samples were applied directly on Mueller-Hinton agar (MHA, Difco; 22520) using sterile glass beads. Colonies were counted after incubation for 18 to 24 hours at 35°C in ambient air. Supporting Information. The Supporting Information is free of charge on the ACS publications website. It contains: Effects of tolC-dependent efflux pumps on reference antibiotic sensitivity (Supplementary Table 1), Supplementary Methods and reagents used in strain construction (Supplementary Methods, Supplementary Table 2, Supplementary Table 3), Settings used for mass spectroscopy and standard curves (Supplementary Table 4, Supplementary Table 5, Supplementary Figure 1, Supplementary Figure 2). Abbreviations. Structure-activity relationship (SAR). Minimum inhibitory concentration (MIC). Coenzyme A (CoA-SH). Wild-type (WT). Liquid-chromatography mass spectroscopy (LC-MS). Solid-phase extraction mass spectroscopy (SPE-MS). Dephospho-CoA (CoA-DP). Acetyl-CoA (CoA-Ac). CoA internal standard (CoA-IS). Acetyl-CoA Carboxylase inhibitor (ACCCi). Acyl carrier protein (ACP). Cation-adjusted Mueller-Hinton (CAMHB). Polymyxin-B nonapeptide (PMBN).



Author Contributions. CR developed MS methods, designed experiments, generated data, interpreted results. BB designed experiments and interpreted results. JDV led the chemistry effort. JED, RM, XS, CS, WX and QZ designed, synthesized and purified compounds described in the manuscript. MG, CL, MS and LW designed experiments, generated data and interpreted results. KT and JRW designed and created strains used in the manuscript. BYF designed experiments, generated data, interpreted results and wrote the manuscript. Conflict of Interest statement. The authors declare the following competing financial interest(s): BB, CL, RM, XS, CS, LW, JRW and BYF are current employees of, receive a salary from, and may hold restricted stock units in Novartis. Acknowledgements. We would like to thank Laura McDowell, Jennifer Leeds, Heinz Moser and Catherine Jones for critical reading of the manuscript. We would like to thank William Sawyer for LCMS support. References. (1) Boucher, H. W., Talbot, G. H., Bradley, J. S., Edwards, J. E., Gilbert, D., Rice, L. B., Scheld, M., Spellberg, B., and Bartlett, J. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin. Infect. Dis. (2009) 48, 1–12, DOI:10.1086/595011. (2) Rice, L. B. Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE. J. Infect. Dis. (2008) 197, 1079–81, DOI:10.1086/533452. (3) Ambady, A., Awasthy, D., Yadav, R., Basuthkar, S., Seshadri, K., and Sharma, U. Evaluation of CoA biosynthesis proteins of Mycobacterium tuberculosis as potential drug targets. Tuberculosis (2012) 92, 521–528, DOI:10.1016/j.tube.2012.08.001. (4) Evans, J. C., Trujillo, C., Wang, Z., Eoh, H., Ehrt, S., Schnappinger, D., Boshoff, H. I. M., Rhee, K. Y., Barry, C. E., and Mizrahi, V. Validation of CoaBC as a Bactericidal Target in the Coenzyme A Pathway of Mycobacterium tuberculosis. ACS Infect. Dis. (2016) 2, 958–968, DOI:10.1021/acsinfecdis.6b00150. (5) Leonardi, R., Zhang, Y. M., Rock, C. O., and Jackowski, S. Coenzyme A: Back in action. Prog. Lipid Res. (2005) 44, 125–153, DOI:10.1016/j.plipres.2005.04.001. (6) Spry, C., Kirk, K., and Saliba, K. J. Coenzyme A biosynthesis: An antimicrobial drug target. FEMS Microbiol. Rev. (2008) 32, 56–106, DOI:10.1111/j.1574-6976.2007.00093.x. (7) Bray, M. S., and Young, M. E. Regulation of fatty acid metabolism by cell autonomous circadian clocks: Time to fatten up on information? J. Biol. Chem. (2011) 286, 11883–11889, DOI:10.1074/jbc.R110.214643. (8) Lambalot, R. H., and Walsh, C. T. Cloning, Overproduction, and Characterization of the Escherichia coli Holo-acyl Carrier Protein Synthase. J. Biol. Chem. (1995) 270, 24658–24661, DOI:10.1074/jbc.270.42.24658. (9) Begley, T. P., Kinsland, C., and Strauss, E. The biosynthesis of coenzyme A in bacteria. Vitam. Horm. (2001) 61, 157–71. (10) Spry, C., Kirk, K., and Saliba, K. J. Coenzyme A biosynthesis: An antimicrobial drug target. FEMS Microbiol. Rev. (2008) 32, 56–106, DOI:10.1111/j.1574-6976.2007.00093.x. (11) Vallari, D. S., and Rock, C. O. Pantothenate transport in Escherichia coli. J. Bacteriol.


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