Î²-lactam Biotransformations Activate Innate Immunity - The Journal of

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#-lactam Biotransformations Activate Innate Immunity Joonseok Oh, Jaymin Patel, Hyun Bong Park, and Jason M. Crawford J. Org. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.joc.8b00241 • Publication Date (Web): 04 Apr 2018 Downloaded from http://pubs.acs.org on April 4, 2018

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The Journal of Organic Chemistry

β-Lactam Biotransformations Activate Innate Immunity Joonseok Oh,†,‡ Jaymin Patel, ‡,§ Hyun Bong Park,†,‡, and Jason M. Crawford*,†,‡,∥ †

Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States Chemical Biology Institute, Yale University, West Haven, Connecticut 06516, United States § Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, United States ∥Department of Microbial Pathogenesis, Yale School of Medicine, New Haven, Connecticut 06536, United States ‡

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TOC

ABSTRACT: Antibiotics are widely prescribed to treat bacterial infections, but many of these drugs also affect patient immune responses. While the molecular mechanisms regulating these diverse immunomodulatory interactions are largely unknown, recent studies support two primary models: 1) antibiotics can alter immune function by directly interacting with human targets; and/or 2) antibiotics can indirectly affect immune responses via alteration of the human microbiota composition. Here, we describe results that could support a third model in which a non-immunostimulatory antibiotic can be biotransformed by human microbiota members into an immunostimulatory product that lacks antibacterial activity. Specifically, we identified, characterized, and semi-synthesized new biotransformation products derived from the β-lactams amoxicillin and ampicillin, antibiotics regularly prescribed in the clinic. The drug metabolism products were identified in bacterial cultures harboring β-lactamase, a common resistance determinant. One of the amoxicillin biotransformation products activated innate immunity, as assessed by NF-κB signaling in human leukemic monocytes, whereas amoxicillin itself exhibited no effect. Amoxicillin has previously been shown to have minimal long-term impact on human microbiota composition in clinical trial studies. Taken together, our results could support a broader immunomodulatory mechanism whereby antibiotics could indirectly regulate immune function in a stable, microbiome-dependent manner.


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INTRODUCTION Since the development of the β-lactam penicillin, antibiotics have saved countless lives from bacterial infections.1 However, the emergence of resistance to all major classes of FDA-approved antibiotics demands better control over the development of drug resistant pathogens2 and the maintenance of an antibiotic discovery and development pipeline.3 It also demands a better understanding of how antibiotics affect host immune responses.4 Indeed, select antibiotics are known to modulate the immune system.4,5 The mechanisms of these immunomodulatory interactions are complex and largely unknown. Confounding these studies, antibiotics can alter the composition of the human microbiota, the collection of microbes found in and on the human body.6,7 In the context of host-microbiota relationships, antibiotics have been shown to facilitate the proliferation of select, drug resistant, and immunostimulatory gut bacteria that exacerbate inflammatory disease symptoms8,9 and to deplete gut bacterial diversity leading to deleterious immunomodulatory consequences in early-life immune system development and in adult immunotherapies.6,10-12 These are examples of microbiome-dependent mechanisms, in which the microbial community structure significantly changes (e.g., dysbiosis) upon antibiotic treatment leading to alternative host/patient outcomes. Conversely, select antibiotics have been shown to directly regulate host immune responses in germ-free mouse models, supporting additional microbiome-independent mechanisms.13,14 In the global drug market, average annual sales of β-lactam antibiotics amount to approximately 15 billon USD, accounting for about 65% of antimicrobial drug sales.15 These broad-spectrum antibiotics are classified by their core heterocyclic structures: penams, penems, carbapenems, cephems, clavams, and monobactams (Figure 1).16,17 Their biomedical importance and structural complexity stemming from their central 2-azetidinone pharmacophore have 3 ACS Paragon Plus Environment


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inspired chemists and biologists alike.17 Their well-studied antibacterial modes of action are associated with enzymes involved in the synthesis of bacterial peptidoglycan.16

However,

research examining β-lactam-induced modulation of host immune responses has demonstrated variable results from little to no significant relevance observed to substantial responses.5-7 In mouse models, low-dose penicillin (a penam) treatment from birth altered expression of immunity genes, and the authors proposed that this response could contribute to immune system development via microbiota alteration.6,7 Additionally, cefodizime (a cephem) augmented immune functions of natural killer cells and phagocytic activity of monocytes and macrophages in immunocompromised animal models.5 S

S

penam

penem

N O carbapenem

e.g., amoxicillin e.g., penicillin

e.g., faropenem

e.g., imipenem

N

N

O

O

S

O N

NH

N

O

O

O clavam

cephem

e.g. , clavulanic acid

e.g., cefaclor

monobactam (2-azetidinone) e.g., aztreonam

Figure 1. Core structures of representative β-lactam antibiotics with noted examples in the clinic.

In our ongoing studies on the identification of metabolites from the gammaproteobacteria Photorhabdus asymbiotica, we identified four new β-lactam-dependent – penams ampicillin and amoxicillin – small molecule biotransformation products featuring a phenylpyrazine motif. P. asymbiotica is an entomopathogen that can also cause systemic human infections and biosynthesize a spectrum of known and uncharacterized secondary metabolites.18-21 Among these pathways,

the bacterium

encodes

genes

for simple carbapenem


biosynthesis, and

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correspondingly, β-lactamase drug resistance.16 The biotransformation products characterized in this study were also identified in the cultures of β-lactamase-encoding Escherichia coli, a common member of the human gut microbiota.22 β-lactamase is encoded in a variety of human microbiome members and drug-resistant pathogens,2,23 suggesting that a variety of bacteria may contribute to their production. Indeed, extended-spectrum β-lactamase-producing pathogens, such as Klebsiella pneumoniae and E. coli resistant to 3rd generation cephalosporins, have been found globally since the early 1980s, and their plasmid-associated resistance determinant can be transferred to other diverse bacterial community members.24 The structures of the four new βlactam-derived metabolites were elucidated using spectroscopic and computational techniques. They were also produced through a biomimetic, semi-synthetic approach. While the biotransformation metabolites lacked antibacterial activity, one of the amoxicillin-derived products activated innate immunity, as assessed in a human leukemic monocyte NF- κB reporter cell line.

RESULTS AND DISCUSSION In the process of characterizing bioactive metabolites from P. asymbiotica ATCC43949 in a hemolymph-mimetic medium (HMM)25,26 supplemented with ampicillin (25 µg/mL / 70 µM), two compounds (1 and 2), possessing interesting UV-visible chromophores (Figure S1) and identical molecular weights, were detected by LC-MS (Figure 2 and Figure S2). The target compounds were purified through solvent extraction and chromatographic techniques (See Experimental Details). The respective HRESIMS spectra of compounds 1 and 2 displayed protonated ions at m/z 346.1227 and 346.1218 (calcd [M + H]+, m/z 346.1220)(Figure S3), which supported their shared neutral molecular formulas as C17H19N3O3S. The 1D NMR spectra of



compounds 1 and 2 shared significant similarities excluding only a few 1H and

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13

C resonances

(Table 1, Figure S4–S14), suggesting that the products were diastereomers. Their molecular formulas, distinctive

13

C chemical shifts of their C-3 positions, and HMBC cross-peaks from

secondary methyl motifs to C-2 and C-3, and from H-6 to C-5 allowed the establishment of their 2-dimethylthiazolidine architectures (Figure 3). Another HMBC cross-peak was observed from H-3 to a carboxylic acid carbon, determining C-3 connectivity of this moiety to the heterocyclic system (Figure 3). A mono-substituted phenyl moiety was also identified based on characteristic coupling patterns and chemical shifts in the 1D NMR data.

Figure 2. Detection of compounds 1–4 using E. colibla+ and P. asymbiotica (PA) resistant to β-lactam antibiotics. AMP, ampicillin; AMX, amoxicillin; HMM, hemolymph mimetic medium; and n.d., not detected. The mean integrated area values from extracted ion chromatograms for compounds 1–4 were used to render the graphs, and error bars represent the standard deviation.

Given the deductive molecular weight indicating the remaining four degrees of unsaturation, along with the HMBC cross-peaks from the ortho-aromatic protons to C-9 and from H-12 to a carbonyl carbon (C-8), facilitated construction of the 3-phenylpyrazin-2(1H)-one sub-structure. This heterocyclic system was connected to the aforementioned 2-dimethylthiazolidine via the C6 ACS Paragon Plus Environment

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6 methylene based on the COSY correlation from H-6 to H-5 comprising the thiazolidine moiety and the HMBC cross-peaks from H-6 to C-8 and C-12 on the conjugated heterocycle. These data supported the structures of new metabolites 1 and 2 as shown in Figure 3. Table 1. 1H NMR and 13C Spectroscopic Data of Compounds 1–4 (25 °C, Methanol-d4)

Position 2 3 5

6 8 9 11 12 13 14, 18 15, 17 16 2-Me (α) 2-Me (β) 3-COOH

1 δH, mult. (J in Hz) 3.47, br s 4.96, dd (8.0, 3.8) 4.28, dd (14.0, 8.0); 4.40, dd (14.0, 4.0)

7.46, d (4.2) 7.64, d (4.2) 8.14, dd (6.7, 3.0) 7.41, m 7.41, m 1.62, s 1.29, s

δC 58.6 76.2 63.7

53.1 155.9 153.0 122.9 129.7 135.8 128.6 127.4 129.3 27.5 28.1 172.6

2 δH, mult. (J in Hz) 3.62, br s 4.97, dd (9.7, 4.5) 3.91, dd (14.0, 9.6); 4.15, dd (13.8, 4.6)

7.43 (4.2) 7.56 (4.2) 8.12, dd (6.8, 2.7) 7.41, m 7.41, m 1.66, s 1.24, s

3 δH, mult. (J in Hz)

δC 60.0 74.0

3.38, s 4.92, dd (8.1, 4.1) 4.25, dd (14.0, 8.3); 4.37, dd (14.0, 4.2)

62.6

55.3 155.6 152.2 122.4 130.8 135.9

7.37, d (4.3) 7.50, d (4.3)

δC 58.7 77.3 63.7

53.1 156.0 152.8 122.7 128.5 127.2

4 δH, mult. (J in Hz) 3.77, s 4.97, dd (9.4, 4.1) 3.92, dd (13.8, 9.5); 4.13, dd (13.8, 4.2)

7.36, d (4.2) 7.46, d (4.2)

δC 59.4 72.3 62.8

55.0 155.7 152.3 122.3 129.4 127.3

128.4

8.08, d (8.8)

130.4

8.05, d (8.8)

130.3

127.5 129.2 26.9 27.1 174.0

6.80, d (8.8)

114.2 159.0 28.2 27.7 173.5

6.80, d (8.7)

114.2 158.9 26.8 26.5 172.0

1.62, s 1.29, s

1.65, s 1.24, s

Product configurations were supported using 2D ROESY NMR analyses as shown in Figure 4. The ROE correlation from H-3 to H-5 was observed in 1, establishing a synconfiguration, while the correlation was absent in 2. Instead, H-3 and H-5 in 2 exhibited ROE correlations with their respective diastereotopic methyl protons, supporting an anti-configuration. The structures of compounds 1 and 2 were reminiscent of ampicillin, which was supplemented in the medium, implying that compounds 1 and 2 were β-lactam-derived. These new metabolites were not merely β-lactam-degradation products, as their carbon composition was higher than the starting material.



(A)

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O

O OH

OH 11

N 9

18 17

R

13

8

H

3

HN 5

6

S

17

15

R

1R=H 3 R = OH

(B)

16

4

12

N 18

2

O

14

16

N

11

4

12

H

9

HN

N

13

8

14

O

15

3

5

S

6

2 R=H 4 R = OH

O

O

OH

OH N

HN

N

HN N

2

N S

O 1 and 2

HO

S

O 3 and 4 COSY HMBC

Figure 3. Structures of 1–4 generated via biotransformation from investigated penams (A) and Key 2D NMR correlations for structural elucidation (B).

As a plausible, biologically-relevant two-carbon source, glyoxal was added to pure ampicillin in neutral H2O, and indeed, 1 and 2 were detected as the major products over time. Their proposed biotransformation pathway is shown in Figure 5. In this proposal, ampicillin is hydrolyzed into two diastereomeric penicilloic acids, varying the configuration of C-5, which is known to be highly vulnerable to epimerization in a broad pH range (2.5–13).27,28 The dialdehyde commonly produced via bacterial oxidation/peroxidation of glucose, nucleic acids, and lipids19,20 then condenses with the two nucleophilic amino groups of the hydrolyzed ampicillin (the β-lactamase product) to form the observed diazine motif (Figure 5). These data support that the metabolites are biotransformed in P. asymbiotica cultures via β-lactamase (or hydrolytic)

ring

opening,

glyoxal

condensation,

and

decarboxylation.

The

same

biotransformation products are formed in β-lactamase-encoding E. coli (E. colibla+) cultures under similar conditions (Figure 2). Glyoxal represents a common byproduct of metabolism,29,30 suggesting that the phenomenon could be general.


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Figure 4. Stereochemical investigations of compounds 1 and 2 (A) and 3 and 4 (B). The key NOE correlations are signified with yellow-dotted lines and in (B), predicted interproton distances are shown on corresponding NOE correlations.

Amoxicillin is a structurally related and more clinically-significant penam than ampicillin, so we also challenged this β-lactam precursor to P. asymbiotica and E. colibla+ and indeed, it underwent a similar biotransformation as expected to produce new metabolites 3 and 4 (Figure 2, Figure S15 and S16). In the biomimetic semi-synthesis of these metabolites, amoxicillin was relatively stable to glyoxal treatment in aqueous conditions at neutral pH, but under basic conditions (pH ~10) to promote β-lactam ring-opening (the β-lactamase product), target compounds 3 and 4 were produced as the major products (Figure S16). In early time points, peaks consistent with diastereomeric penicilloic acids were detected as intermediates by LC/MS (Figure S16). The final products were fully characterized by HRESIMS and NMR (Figure 3 and Table 1; Figure S17–S31). 9 ACS Paragon Plus Environment


Figure 5. Proposed biotransformation pathway for the formation of metabolites 1–4.

Because metabolite 3 had biological activity in our assays (see below), we elected to further characterize the 3D solution structures of 3 versus 4. The 3D structures were assigned using 1DNOESY followed by interproton distance analyses with peak amplitude normalization for improved cross-relaxation (PANIC).31,32 The NOE signal from the well-resolved 1H NMR

resonances H-11 and H-12 were chosen as the reference signal, and H-5 resonances of 3 and 4 were selectively irradiated using the 1DNOESY pulse to deduce interproton distances from H-3 to H-5 (Figure S23 and S24 for 3; Figure S29 and S30 for 4). The measured interproton distance of 3 was 2.6 Å, whereas 4 was 3.8 Å, verifying the respective syn- and anti-configurations for the two compounds. These NOE-derived spatial distances were computationally confirmed with reference to a reported protocol:32 a conformational search with the syn- and anti-configured diastereomers, corresponding to the proposed stereostructures for 3 and 4 based upon NOE analysis, identified two predominant conformers for each diastereomer with the hydroxycarbonyl motif occupying a pseudo-equatorial (3a and 4a) or pseudo-axial (3b and 4b) orientation on the thiazolidine moiety. The Boltzmann populations at the MMFF field of 3a and 3b were 99.9% and 0.1%, and 4a and 4b were 91.3% and 8.7%, implying that each pseudo-equatorial 10 ACS Paragon Plus Environment

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conformation occupying the heterocycle was significantly more stable than their counterparts. The calculated interproton distances from H-3 to H-5 for 3a and 3b were 2.5 and 3.9 Å and those for 4a and 4b were 3.7 and 3.8 Å. Each predicted distance was Boltzmann-averaged to 2.5 and 3.7 Å, exhibiting a match with those measured from the PANIC analysis, confirming the relative stereochemical assignments and supporting dominant conformers in solution. The differences in these configuration and conformations are important for biological activity (See below). With the predominant epimerization of C-5 known in penicilloic acids (pH range, 2.5–13)27,28 and the inherited configuration of C-3 as S from their parental β-lactam substrates, the dominant absolute configurations of 1–4 were proposed as shown in Figure 1. Predicted and experimental ECD spectra were not completely conclusive, presumably due to the flexible nature of the stereocenters in close proximity to the chromophores (Figure S32, Table S1–S4). Consequently, we conducted chiral derivatization to access potential minor epimerization at C-3 and ultimately to establish their absolute configuration.33 The mixture of 1 and 2 and another mixture of 3 and 4 were subjected to chiral derivatization in order to generate the corresponding (R)- and (S)-1(9-anthryl)-2,2,2-trifluoroethyl (AT) esters, and only two diastereomeric esters for each mixture were detected by LC/MS and multidimensional NMR (Figure S33–S61). Upon purification of each diastereomer, the evaluation of differences in the chemical shifts between (S)- and (R)-AT esters of compounds 1–4 revealed that 1H resonances of dimethyl moieties in (S)-AT esters were shielded (∆δSR < 0) whereas those of pyrazine functionalities in the counterparts were shielded (∆δSR > 0) (Figure 6, Table S5). These data established the inherited 3S configuration and precluded minor epimerization at C-3 under the conditions of our experiments.



Figure 6. Determination of the absolute configuration of C-3 using 1H chemical shift values of (S)- and (R)-AT esters of compounds 1–4 Metabolites 1–4 were initially examined for antimicrobial activities against Bacillus subtilis (Gram-positive), E. coli (Gram-negative), and Saccharomyces cerevisiae (yeast/fungus), but no activity was observed at concentrations up to 100 µM (data not shown). We next examined the four metabolites for immunostimulatory activity via NF-κB signaling in human leukemic monocytes (THP-1 reporter cells).

The THP-1 cells were individually treated with the

metabolites, a negative DMSO control, and a potent toll-like receptor 4 (TLR4) agonist lipopolysaccharide (LPS) positive control. Addition of 5 µg/mL LPS robustly activated NF-κB, and 3 similarly activated the immune response in a dose-dependent manner at or above 10 µM (Figure 7). The other metabolites and their parental β-lactam substrates did not lead to significant activation (Figure 7). These data indicate that the antibiotic amoxicillin is converted to an inactive antibacterial with strong immunostimulatory activity in the presence of bacteria harboring the β-lactamase resistance determinant.


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Figure 7. THP-1 reporter cells were individually treated with compounds for 16 hours. Activation of NFκB induces the production of a secreted alkaline phosphatase (SEAP) reporter. SEAP activity, after QUANTI-BlueTM detection, was measured via absorbance at 620 nm. Activities were normalized to the positive control LPS.

CONCLUSION While antibiotic-dependent host immunomodulation is a well-known biological phenomenon at the phenotypic level, the underlying molecular mechanisms are complex and largely unknown.5 These interactions are known to occur directly at the host level – microbiome-independent mechanisms13,14 – and they are known to occur indirectly at the gut microbe level – microbiomedependent mechanisms.8,12,34 Interestingly, in amoxicillin clinical trial studies, minimal longterm effects were observed for gut microbiota composition compared to control patients.35 Our studies here in combination with the data of previous clinical trials suggest that amoxicillin could indirectly activate innate immunity (via 3) in a stable, microbiome-dependent manner. We expect that this immunological example will be one of many as the human microbiota field transitions



from taxonomic analysis to better defining host-bacteria interactions at the biochemical and molecular levels. Future studies will need to determine the specific molecular target(s) of 3 for NF-κB activation and assess the physiological relevance and concentration of this novel agonist in animal models.

EXPERIMENTAL SECTION General Experimental Procedures. UV/Vis spectra were obtained on an Agilent 1260 Infinity system equipped with a photo diode array (PDA) detector (Agilent Technologies, CA, USA). Full NMR data sets were recorded on an Agilent 600 MHz NMR spectrometer (DD2) equipped with an inverse cold probe (3 mm). Flash column chromatography was carried out on Lichroprep reverse phase (RP)-18 (40–63 mm, Merck, NJ, USA) or a Biotage-Isolera One (Biotage, Charlotte, NC, USA) equipped with a Biotage SNAP cartridge (KP-C18-HS 120 g). LC-MS analysis was performed on an Agilent 1260 Infinity system using a Phenomenex Luna C18(2) (100 Å) 5 µm (4.6 × 150 mm) (Phenomenex, CA, USA) column and a PDA detector, coupled with a quadrupole electrospray ionization mass spectrometry instrument (Agilent 6120). Separation and isolation of compounds 1–4 were carried out with an Agilent Prepstar HPLC system with an Agilent Polaris C18-A 5 µm (21.2 × 250 mm) column, a Phenomenex Luna C18(2) (100 Å) 10 µm (10.0 × 250 mm) column, and an Agilent Zorbax RX-SIL 5 µm (9.4 × 250 mm) column. High-resolution ESI-MS (HR-ESI-MS) data were collected on an Agilent iFunnel 6550 quadrupole time-of-flight (QTOF) MS instrument fitted with an electrospray ionization (ESI) source coupled to an Agilent 1290 Infinity HPLC system. Experimental ECD spectra were recorded on a Chirascan CD spectrometer (Applied Photophysics, Inc., MA, USA). Ampicillin,


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amoxicillin, and glyoxal were purchased from Fisher Scientific (NH, USA) and used without purification. XAD-7 HP resins were also obtained from Fisher Scientific. Initial Cultivation for Identification of Compounds 1 and 2. A 5 mL Luria Bertani (LB) liquid culture supplemented with 25 µg/mL ampicillin was initiated by inoculation of a single colony of P. asymbiotica. Upon overnight growth at 30 °C and 250 rpm under aerobic conditions, the culture was used to inoculate 6 × 5 mL fresh LB cultures, and incubated at 30 °C and 250 rpm overnight. The HMM medium was prepared with reference to a previous study25 and each seed culture was utilized to inoculate 1 × 6 L cultures containing HMM supplemented with 25 µg/mL ampicillin. These cultures were grown for two days using similar parameters and then centrifuged at 14,000 g (r.t.) for 30 min. XAD-7 HP resins (20 g/L) were added to the cleared supernatants and the mixtures were incubated for 2 h at 30 °C and 200 rpm. The pooled filtered resins were extracted with MeOH and acetone (6 L each) for 2 h at 30 °C, and the extract was filtered and evaporated under reduced pressure to yield the crude material (~10.0 g). Isolation and Purification of Molecules. The crude extract (10.0 g) was chromatographed over a Biotage SNAP cartridge (KP-C18-HS 120 g) with a programmed gradient elution (0→100% MeOH in water for 1 h) to generate 10, 6-min fractions (Fr.1–Fr.10). In Fr.5, Compounds 1 and 2 were detected in Fr.5 by LC-MS analysis, and they were further purified using prep RP HPLC equipped with an Agilent Polaris C18-A column (5→50% MeCN in water with 0.01% TFA over 60 min, 8 mL/min, 1 min automated fraction collection). Among 60 fractions (Fr.5.1–Fr.5.60), Fr.5.24 to Fr.5.26 contained the target compounds, and they were combined for further fractionation via semi-prep HPLC (Phenomenex Luna C18(2)), with a gradient program (5→100% MeCN in water with 0.01% TFA over 60 min, 4 mL/min) to garner compounds 1 (tR = 24 min, 0.8 mg) and 2 (tR = 26 min, 0.5 mg). Under the conditions of these experiments, the two 15 ACS Paragon Plus Environment


compounds underwent epimerization. Thus, these epimerized mixtures were subjected to semiprep normal phase (NP) HPLC using a silica column (Agilent Zorbax RX-SIL), eluting with DCM and MeOH (0–50% MeOH in DCM over 30 min, 4 mL/min) to ultimately purify compounds 1 (tR = 13 min) and 2 (11 min). Compounds 3 and 4 were semi-synthesized via a method elaborated in the section “Semi-synthesis of Compounds 1–4” below and purified in a similar fashion as compounds 1 and 2. Compounds 3 and 4 were collected in Fr. 5 on the 1) crude fractionation step, detected in Fr.5.23 and Fr.5.24 on the 2) prep RP HPLC step, eluted at their respective retention times of 21 and 22 min on the 3) semi-prep RP HPLC step, and ultimately purified with the retention times of 15 and 14 min on the 4) semi-prep NP HPLC step. Compound 1. Yellow amorphous powder; UV-vis (MeOH) see Figure S1; positive HR-ESIMS [M+H]+ at m/z 346.1227 (Figure S3)(calcd for C17H20N3O3S, 346.1220); ECD (MeOH) see Figure S32; 1H and 13C NMR see Table 1. Compound 2. Yellow amorphous powder; UV-vis (MeOH) see Figure S1; positive HR-ESIMS [M+H]+ at m/z 346.1218 (Figure S3)(calcd for C17H20N3O3S, 346.1220); ECD (MeOH) see Figure S32; 1H and 13C NMR see Table 1. Compound 3. Yellow amorphous gum; UV-vis (MeOH) see Figure S15; positive HR-ESI-MS [M+H]+ at m/z 362.1168 (Figure S18)(calcd for C17H20N3O4S, 362.1169); ECD (MeOH) see Figure S32; 1H and 13C NMR see Table 1. Compound 4. Yellow amorphous gum; UV-vis (MeOH) see Figure S15; positive HR-ESI-MS [M+H]+ at m/z 362.1172 (Figure S18)(calcd for C17H20N3O4S, 362.1169); ECD (MeOH) see Figure S32; 1H and 13C NMR see Table 1. Semi-synthesis of Compounds 1–4. To semi-synthesize 1 and 2, ampicillin (100 mg) and glyoxal (500 µL) were added to 100 mL of H2O (neutral) and incubated at 40 °C. The reaction 16 ACS Paragon Plus Environment

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was monitored by LC-MS, and after overnight incubation, the reaction was complete. For the generation of 3 and 4, amoxicillin (120 mg) with the dialdehyde (500 µL) was treated in an identical manner as for ampicillin, except the pH of the reaction solvent was adjusted to ~10 to facilitate β-lactam hydrolysis. Compounds 3 and 4 were similarly isolated after overnight reactions. All reaction mixtures were dried and subjected to the purification approach described in section “Isolation and Purification of Molecules” to obtain pure metabolites. The reaction yields for semi-synthetic 1 and 2 from ampicillin were ~2–3% and those for 3 and 4 from amoxicillin were ~1–2% under the conditions of our experiments. Detection of Compounds 1–4 in E. colibla+ and P. asymbiotica. The β-lactam resistant E. coli (E. colibla+) used in this study was constructed by transforming the β-lactamase-expressing pUC19 plasmid into E. coli MG1655. Each single colony of wild type P. asymbiotica and E. colibla+ was picked and inoculated into a 5 mL LB liquid culture. After overnight growth at 30 °C and 250 rpm under aerobic conditions, each culture (25 µL) was transferred to 3 × 5 mL HMM medium containing 25 µg/mL of ampicillin or amoxicillin. Cultures were incubated at 30 °C and 250 rpm for 2 d, and centrifuged at 3,000 rpm (r.t.) for 30 min. Then, XAD-7 HP resins were added to the cleared supernatants and incubated for 2 h at 30 °C and 200 rpm. The filtered resins were extracted with MeOH and acetone (6 mL each) for 2 h at 30 °C. Each extract was evaporated, the residue was dissolved in100 µL MeOH, and the resuspension was filtered for HR-ESI-MS analysis (Agilent iFunnel 6550 QTOF). Cultures of P. asymbiotica or E. colibla+ in HMM for the negative controls lacking β-lactam were similarly prepared. The calculated protonated HR-ESI-MS signals for compounds 1–4, i.e., m/z 346.1220 for 1 and 2; m/z 362.1169 for 3 and 4, in the culture extracts were extracted using a mass error window of 10 ppm (Agilent



MassHunter Qualitative Analysis B.06.00). The resulting ion counts were integrated and visualized in Figure 2, utilizing GraphPad Prism 7.01 (GraphPad Software, Inc., CA, USA). NMR Parameters and PANIC Analysis.32 All NMR experiments were performed using the standard Agilent pulse library, and for 2D heteronuclear and ROESY NMR experiments, their gradient adiabatic versions were utilized. The 1DNOESY for PANIC analysis was implemented using double-pulse field gradient spin-echo NOE (DPFGSENOE) excitation sculpted selective sequence incorporated with a zero-quantum filter element (500 ms mixing time, 2 s relaxation time, 64 scans). The resonances for H-12 in compounds 3 and 4 were selectively irradiated using 1DNOESY pulse and the integrations for H-12 were normalized into -1000 as an arbitrary number. The generated NOE intensities for H-11 were then integrated with reference to -1000 (Figure S23 and S24 for 3 and Figure S29 and S30 for 4). Each normalized integration value (NOEreference) and the interproton distances, for H-11–H-12 in 3 and 4 being 2.5 Å (rreference), were used to solve an equation below for calibrating interproton distances between H-3 and H-5 (runknown). The NOEunknown values for the two diastereomers were acquired by NOE-integrations of H-3 upon selective irradiation of H-5 and normalization of the NOE integration values into 1000. Equation: NOEunknown/NOEreference = (rreference)6/(runknown)6 ECD Calculations.36,37 All conformational searches in this study were conducted utilizing the Macromodel (version 9.9, Schrodinger LLC) module in a mixed torsional/low-mode sampling in the MMFF force field. The searches were initially completed in the gas phase with a 50 kJ/mol energy window and a maximum of 10,000 steps for a thorough exploration of stable conformers. The Polak−Ribiere conjugate gradient method was chosen for a minimization process in 10,000 maximum iterations and a 0.001 kJ (mol Å)−1 convergence threshold on the root-mean-square 18 ACS Paragon Plus Environment

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gradient. Among the conformers identified at the MMFF force file, the most stable conformers for each compound were used for computed interproton distance analysis and shown in 3D images rendered with Pymol (1.7.x, Open Source) (Figure 4). Conformers within the upper 10 kJ/mol energy window were selected and their geometries were optimized at the B3LYP/6– 31G(d) level using polarizable continuum solvation model (PCM) and a dielectric constant representing MeOH (Table S2). 36,37 36,37 These optimized structures were employed for excited state DFT calculations in the PCM mode, and the excitation energies and rotational strengths of these conformers were Boltzmann-averaged based upon the calculated Gibbs free energies (Table S2)36,37 and fitted to the Gaussian functions to simulate ECD curves (SpecDis).38 Chiral Derivatization for Absolute Configuration Assignment.39 To solutions of 1–4 (3 mg) in CH2Cl2 (500 µL) were added (R)- and (S)-1-(9-anthryl)-2,2,2-trifluoroethanol (13 mg). 1Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC, hydrochloride) (25 mg), Et3N (13 µL), and DMAP (8 mg) were added to the solution and the reaction was kept at r.t. for 12 –24 h. The reaction mixture was purified using HPLC and the purified yields were 20–30%. NF-κB Activation Assay. THP1-Dual reporter cells (InvivoGen, CA, USA) were grown in RPMI 1640 (Life Technologies, CA, USA) + 5% heat inactivated FBS (Life Technologies) at 37 °C in 5% CO2. No antibiotics were used while passaging. At the 3rd passage, 2 ×105 cells in 200 µL media were inoculated in each well of a 96-well plate. Cells were incubated with compounds for 16 hours. Activation of NF-κB in these reporter cells results in the secretion of alkaline phosphatase (SEAP) into the medium. SEAP levels were quantified by mixing 20 µL of cell culture with 180 µL of QUANTI-BlueTM detection reagent, incubating overnight at 37 °C, and measuring absorbance at 620 nm. Activation relative to the potent agonist LPS was plotted.



ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: Full NMR data sets of compounds 1–4 and computational details.

AUTHOR INFORMATION Corresponding Author *[email protected] ORCID Jaymin Patel: 0000-0002-4380-4805 Hyun Bong Park: 0000-0002-2986-3833 Jason M. Crawford: 0000-0002-7583-1242

ACKNOWLEDGMENTS We recognize Ms. W.Y. Cho, Dr. A. Healy, and Dr. C.S. Kim (Yale University) for initial bioassay screening, and advice on chiral derivatization and structural assignments. We thank Dr. D. Ferreira (University of Mississippi) for discussions regarding ECD. We thank Dr. C.P. Butts and Ms. S. Zhong (University of Bristol) and Agilent Spinsights for practical advice regarding 1DNOESY experiments and PANIC analysis. We also thank the Yale Center for Research Computing, especially Dr. A. Sherman, for guidance and use of the research computing infrastructure. This work was supported by the Burroughs Wellcome Foundation (1016720). The lab also acknowledges support from the Camille and Henry Dreyfus Foundation (TC-17-011), the Damon Runyon Cancer Research Foundation (DRR-39-16), the National Institutes of Health (1DP2-CA186575), and Yale University.


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