Drug resistance is a public health concern that threatens to undermine decades of medical progress. ESKAPE pathogens cause most nosocomial infections, and are frequently resistant to carbapenem antibiotics, usually leaving tigecycline and colistin as the last treatment options. However, increasing tigecycline resistance and colistin’s nephrotoxicity severely restrict use of these antibiotics. We have designed antimicrobial peptides using a maximum common subgraph approach. Our best peptide (Ω76) displayed high efficacy against carbapenem and tigecycline-resistant Acinetobacter baumannii in mice. Mice treated with repeated sublethal doses of Ω76 displayed no signs of chronic toxicity. Sublethal Ω76 doses co-administered alongside sublethal colistin doses displayed no additive toxicity. These results indicate that Ω76 can potentially supplement or replace colistin, especially where nephrotoxicity is a concern. To our knowledge, no other existing antibiotics occupy this clinical niche. Mechanistically, Ω76 adopts an α-helical structure in membranes, causing rapid membrane disruption, leakage, and bacterial death.

This work casts AMP design as a computational graph optimization problem. A database of existing α-helical AMP structures has been reduced to a graphical representation. Amino acid residues are represented as nodes, and covalent/hydrogen bonds are represented as edges. We generated new graphs by optimizing the superposition of existing subgraphical motifs such that the largest number of database subgraphs was represented within our new design. This approach was used to design and experimentally characterize five peptides. Our best peptide (Ω76) displayed in vitro and in vivo efficacy against carbapenem- and tigecycline-resistant organisms and negligible in vivo toxicity at sublethal doses. Further, time-kill curves, phosphate leakage radioassays, confocal microscopy, scanning electron microscopy (SEM), microarray gene expression experiments, and nuclear magnetic resonance (NMR) spectroscopy all helped understand the mechanism of action of Ω76.

For two decades, peptide designers have attempted to improve the properties of AMPs through intuitive in cerebro and in silico approaches, both of which have yielded multiple successes. In cerebro designs typically involve increasing the positive charge, helicity, or hydrophobicity of natural AMPs or involve the de novo design of simple repeating motifs. Pexiganan ( 34 ), a lysine-rich magainin analog, is one such example of an early in cerebro design. SAAP-148 ( 35 ), created by improving the cationicity and helicity of LL-37, is a contemporary example. Other peptides with tryptophan-arginine repeat motifs ( 36 ), leucine-lysine repeat motifs ( 37 , 38 ), tryptophan-leucine-lysine repeat motifs ( 39 ), and lysine-valine disordered repeats as part of nanoengineered materials ( 40 ) have all displayed promising antimicrobial activity. Later in silico approaches have relied heavily on machine learning and optimization algorithms. Peptides designed using quantitative structure–activity relationship (QSAR) models ( 41 ), linguistic models ( 42 ), deep-learning long short-term memory (LSTM) models ( 43 ), and genetic algorithms ( 44 , 45 ) have all seen varying degrees of success. Despite these successes, AMPs have not yet been approved for clinical use. Systemic toxicity is a primary drawback ( 46 – 48 ), which restricts the use of many AMPs to topical treatment.

Antimicrobial peptides (AMPs) are ancient components of the innate immune system found across all kingdoms of life ( 19 ) and are promising candidates for the development of new drugs. Their primary mechanism of action involves incorporation into bacterial membranes through coulombic attraction, followed by membrane disruption, cytoplasmic leakage, and bacterial death. By targeting an entire cellular component rather than a specific molecule, AMPs evade the development of resistance mechanisms for single-target drugs such as carbapenems and tigecycline. Three detailed mechanisms describing AMP incorporation and disruption have been proposed: toroidal pore formation ( 20 ), barrel stave formation ( 21 ), and carpet formation ( 22 ). AMPs also have secondary mechanisms of action such as metabolic inhibition ( 23 , 24 ); inhibition of DNA ( 25 ), RNA ( 26 ), and protein synthesis ( 27 , 28 ); inhibition of translation termination ( 29 ); inhibition of septum formation ( 30 ); inhibition of cell wall synthesis ( 31 ); induction of ribosomal aggregation ( 32 ); and delocalization of membrane proteins ( 33 ).

Carbapenem class antibiotics are drugs of last resort for multidrug-resistant bacterial infections. However, resistance to carbapenems is now widespread ( 9 ), ranging from 46 to 66% across different countries ( 10 , 11 ). In A. baumannii, carbapenem resistance is caused by metallo-β-lactamases, carbapenem-hydrolyzing oxacillinases, and modified penicillin-binding proteins ( 12 ). In cases of carbapenem resistance, treatment options are usually limited to the antibiotics tigecycline and colistin ( 13 ). Unfortunately, tigecycline resistance is also rapidly increasing. One study reported tigecycline resistance in 66% of all A. baumannii isolates collected ( 14 ). In A. baumannii, multidrug efflux pumps are responsible for tigecycline resistance ( 15 ). In these cases, colistin remains the only treatment option. However, approximately half of all patients treated with colistin develop acute kidney injury ( 16 – 18 ). Because of these limited treatment options, there is a pressing need for new antibiotics to combat A. baumannii specifically and ESKAPE pathogens in general.

The emergence of drug-resistant pathogens has proven to be a grave public health problem. Worldwide, 5.3 million deaths occur annually due to antibiotic-resistant infections ( 1 ). This number can be expected to increase over time ( 2 ), especially for patients admitted to intensive care units (ICUs). Globally, a third of all ICU patients develop drug-resistant infections ( 3 ), which substantially increase patient mortality and health care costs ( 4 – 6 ). The multidrug-resistant ESKAPE pathogens, namely, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., have emerged as the leading causes of nosocomial infections. The emergence of pathogenic A. baumannii is particularly problematic and has been aided by two factors ( 7 ): its remarkable ability to uptake genetic material encoding drug resistance from the environment and its ability to survive in a hospital environment for prolonged time periods. For these reasons, A. baumannii has received a Priority-1 (critical) classification by the World Health Organization for the development of new antibiotics ( 8 ).

RESULTS

Here, we describe the computational design of Ω-family peptides using a maximum common subgraph approach. We describe the in vitro characterization of our peptides against type cultures and drug-resistant clinical isolates. We describe in vitro toxicity assays performed using human blood and cell lines, followed by in vivo toxicity assays performed on BALB/c mice. We describe in vivo efficacy experiments performed for peptide Ω76 against carbapenem- and tigecycline-resistant A. baumannii, using a BALB/c mouse model of peritoneal infection. Last, we describe multiple experiments to understand the mechanisms of action of Ω76.

Maximum common subgraphs and their utilization for AMP design Three-dimensional (3D) structures of α-helical AMPs can be reduced to graphical representations. Individual amino acid residues can be reduced to nodes. For the 20 canonical amino acids, 20 different node types exist. Similarly, inter-residue interactions can be reduced to edges. Inter-residue backbone covalent bonds (N→C) were modeled as directed edges. Similarly, inter-residue backbone hydrogen bonds (N─H→C═O) were modeled as directed edges. Therefore, any given node must contain a minimum of one edge (N→C or C→N edge) and a maximum of four edges (N→C, C→N, N─H→C═O, and C═O→N─H). Further, each node can have a maximum of one type of edge (for example, it is impossible for a single node to have two C→N edges). A dataset containing 74 α-helical structures of known AMPs extracted from the Antimicrobial Peptide Database (APD) (49) was reduced to such a graphical representation. A small dataset of AMP structures, rather than a large dataset of AMP sequences, was chosen for this study as structures are more data rich. Important subgraphical information would be absent in sequences alone. Using this dataset, we attempted to generate AMPs through maximum common subgraph matching. Our approach can be explained using a 1D analogy: Consider AMPs ABCDE and BCDEFG. A superposition of their common sequences would yield peptide ABCDEFG, where BCDE is analogous to the maximum common subgraph shared between the two peptides. Biologically, these subgraphs would be representations of AMP motifs. Because AMPs are subject to selection pressures, a motif would occur frequently only if it bestowed its parent peptide with greater antimicrobial efficacy. We anticipated that designing peptides using an energy function that encouraged the largest possible number of superposed subgraphs would therefore display enhanced antimicrobial activity. Designing AMPs sharing the largest number of maximum common subgraphs with the 74-member peptide database was performed using simulated annealing optimization. Simulated annealing is an efficient approach for finding a good approximation of the global minimum of any energy function and is extensively used for protein design (50–52). Starting with a 20-residue blank peptide template, at each iteration of the simulated annealing protocol, a residue was randomly selected and mutated. The entire template was then scanned across the peptide database to detect any subgraph matches. The template was scored on the basis of the total number of matching nodes in the peptide database. Mutations improving this score were unconditionally accepted. Mutations decreasing this score were probabilistically accepted or rejected depending on the global state of the simulated annealing protocol. Two thousand iterations were performed to exhaustively sample graph space, with each residue being mutated 100 times on average. To avoid generating homopolymeric peptides, a subgraph was defined to have a minimum of five residues per node. For clarity, a single iteration of the simulated annealing protocol is illustrated in Fig. 1. Fig. 1 An illustration of a single step of AMP design using Heligrapher, showing how the maximum common subgraph scoring scheme functions. The unique properties of α-helical graphs reduce subgraph isomorphism detection from a nondeterministic polynomial-time (NP) complete, exponential computational problem to a polynomial problem with O(m × n3) complexity, where n is the average number of residues for AMPs and m is the number of database AMPs (here, 74). The algorithm and peptide database described here have been incorporated into the Heligrapher software package. The Heligrapher database, Python source code, and usage examples have been stored on the GitHub repository (https://github.com/1337deepesh/Heligrapher) and are also provided in protocol S1. Heligrapher was used to design 1000 α-helical AMP graphs. The top 5 scoring graphical representations were converted into sequences (named Ω03→Ω93) and synthesized for this study (Table 1). All peptides appeared lysine rich and amphiphilic. The common subgraphical motifs shared between all peptides are described in fig. S1 and table S1. Table 1 The Ω-family peptides. (Top) Names, sequences, and I_scores of all Ω-family peptides synthesized for this study. Note that pexiganan was used as a toxicity control. (Bottom) Sequence alignment between Ω76 and pexiganan. Despite some similarities, the two peptides display vastly different toxicological profiles, as described in the “In vitro and in vivo toxicity of designed AMPs” section. View this table: To validate the algorithm described here, the Heligrapher energy function was inverted and used to design poor-scoring shuffled variants of Ω76 (Ω76-shuf1→4; Table 1) containing no common subgraphs. Despite having the same amino acid composition of 76, we hypothesized that these peptides would display poor antimicrobial activity as they would share no evolutionarily conserved graphical motifs with natural AMPs. The experimental characterization of these peptides is described in the “In vitro efficacy of Ω17 and Ω76 against drug-resistant clinical isolates” section.

In vitro efficacy of designed AMPs against type cultures We synthesized and experimentally characterized five peptides designed using the Heligrapher software package (Table 1). Initially, we tested these peptides against a diverse panel of pathogens of Gram-positive, Gram-negative, fungal, and mycobacterial origin. A peptide concentration range of 0.25 to 128 mg/liter was used for minimum inhibitory concentration (MIC) assays. Five peptides assayed against 30 organisms resulted in 150 MIC values provided in table S2. Designed peptides were ranked on the basis of a previously described relative scoring scheme (I_score) (43). This score was based on the number of bacterial cultures a peptide inhibited with the lowest MIC, as compared to the MICs of all other designed peptides for that given culture (Eq. 1). I _ score j = ∑ i = 1 M I { X ij = min j = 1 N ( X i ) } where : 0 ≤ i ≤ M , 0 ≤ j ≤ N (1) Here, X represents a matrix of MIC values. M represents rows that contain MIC values for a given organism. N represents columns that contain MIC values belonging to a given peptide. Note that multiple minimum MIC values can occur for any given row. Using Eq. 1, the two best performing peptides were identified to be Ω17 (I_score = 19) and Ω76 (I_score = 6). These peptides were therefore chosen for further characterization.

In vitro efficacy of Ω17 and Ω76 against drug-resistant clinical isolates The minimum bactericidal concentrations (MBCs) of peptides Ω17 and Ω76 were assayed against a panel of 64 recent clinical isolates acquired from M.S. Ramaiah Medical College, Bangalore (tables S3 and S4) (43). Many of these isolates (A. baumannii, K. pneumoniae, P. aeruginosa, and S. aureus) belonged to the ESKAPE pathogen family. Escherichia coli and coagulase-negative Staphylococci (CoNS) were also represented. Most of these isolates displayed multidrug resistance (extended-spectrum beta lactamase, methicillin, carbapenem, and tigecycline resistance). Ω17 appears to be slightly more effective against Gram-negative organisms (MBC 50 = 4 mg/liter) than against Gram-positive organisms (MBC 50 = 16 mg/liter) (Table 2, top). Ω76 was found to be effective against Gram-negative organisms only (MBC 50 = 16 mg/liter) (Table 2, bottom). Of the Gram-positive organisms tested, only CoNS displayed some inhibition when treated with Ω76 (MBC 50 = 32 mg/liter). However, Ω76 appeared to be nearly as effective against E. coli and A. baumannii isolates as compared to Ω17. Ω76 displayed an MBC 50 of 4 mg/liter for both E. coli and A. baumannii, which was only twofold higher than the Ω17 MBC 50 of 2 mg/liter for both. Table 2 MBC values for Ω17 and Ω76 against clinical isolates. (Top) MBC values for Ω17 against clinical isolates. This table depicts a frequency distribution. Taking E. coli as an example, Ω17 inhibits six isolates with an MBC of 1 mg/liter, seven isolates with an MBC of 2 mg/liter, three isolates with an MBC of 4 mg/liter, and three more isolates with an MBC of 8 mg/liter. Therefore, the median MBC value (MBC 50 ) for E. coli is 2 mg/liter. (Bottom) MBC values for Ω76 against clinical isolates. Resistance phenotypes are also mentioned. CRE, carbapenem-resistant Enterobacteriaceae; ESBL, extended-spectrum beta lactamase producers; MRSA, methicillin-resistant S. aureus; MRCN, methicillin-resistant coagulase-negative Staphylococci. View this table: Drug-resistant clinical isolates were also used to validate the Heligrapher algorithm. Four shuffled variants of Ω76 (Ω76-shuf1→4; Table 1) were designed with an inverted energy function as described in the “Maximum common subgraphs and their utilization for AMP design” section. Because of the complete absence of shared subgraphical motifs with known AMPs, Ω76-shuf1→4 were expected to have poor activity. When tested against all clinical isolates of A. baumannii, Ω76-shuf1→4 displayed MBC values significantly higher than Ω76 (table S5), thereby validating our computational approach.

In vitro and in vivo toxicity of designed AMPs Briefly, HeLa cells, HaCaT cells, and human red blood cells (RBCs) were used to assay the in vitro toxicity for Ω17 and Ω76. Survival experiments, histopathology, and blood tests were used to assay the in vivo toxicity of Ω76. In vitro cytotoxicity experiments using the HeLa and HaCaT cell lines were performed for both Ω17 and Ω76 (Fig. 2, A and B). For both peptides, the IC 50 (half maximal inhibitory concentration) against HeLa cells was >128 mg/liter. Ω76 displayed no noticeable cytotoxicity against HeLa cells even at the highest concentration tested (mean HeLa inhibition at 128 mg/liter, Ω76 = 5.9%), whereas Ω17 displayed considerable HeLa inhibition under the same conditions (mean HeLa inhibition at 128 mg/liter, Ω17 = 36.7%). Both peptides displayed negligible toxicity when tested on the HaCaT cell line. Fig. 2 In vitro and in vivo toxicity assessment of Ω76 and controls. (A) HeLa cell line toxicity for peptides Ω17 and Ω76. (B) HaCaT cell line toxicity for peptides Ω17 and Ω76. (C) Human blood hemolysis assay for peptides Ω17 and Ω76. (A to C) All experiments were performed in three to five replicates. Lines and shaded regions indicate means and SD, respectively. (D) In vivo LD 50 (median lethal dose) determination for Ω76, pexiganan, and colistin using a BALB/c mouse model. (E) Multidose cumulative toxicity determination for Ω76, pexiganan, and colistin using a BALB/c mouse model. (F) Row 1: Ω76-colistin coadministration experiment. All data relevant to this experiment have been highlighted in yellow across all panels. Row 2: Ω76 multidose survival experiment [repeated from (E) for completeness]. Rows 3 to 5: Cumulative toxicity determination for Ω76 administered at different time intervals. Row 6: Ω76 single-dose survival experiment [repeated from (D) for completeness]. BALB/c mouse kidney and liver histological sections after treatment with Ω76 and controls. (G) Kidney from untreated mouse, displaying no damage. (H) Kidney from Ω76-treated mouse, displaying no damage. (I) Kidney from colistin-treated mouse. Cast formation (red arrowheads) and tubular necrosis (green arrowhead, dislodged cellular material) are both visible. (J) Liver from untreated mouse, displaying no damage. (K) Liver from Ω76-treated mouse, displaying no damage. (L) Liver from colistin-treated mouse, displaying no damage. Scale bar, 20 μm. All raw data for this figure are provided in dataset S1. In vitro hemolysis experiments were performed using human blood (Fig. 2C). In both cases, the HB 50 (peptide concentration for 50% hemolysis) value for both peptides was >128 mg/liter. Once again, Ω76 displayed no substantial hemolysis at all concentrations tested (mean hemolysis at 128 mg/liter, Ω76 = 1.78%). However, Ω17 displayed considerable hemolysis at higher concentrations (mean hemolysis at 128 mg/liter, Ω17 = 13.91%). Because of considerable cytotoxic and hemolytic effects, Ω17 was excluded from further in vivo toxicity experiments. However, Ω17 may still find applications as a topical agent due to its strong, broad-spectrum activity. We determined the in vivo LD 50 (median lethal dose) values for Ω76, colistin, and pexiganan using BALB/c mice, using a twofold concentration gradient. All compounds were injected intraperitoneally, and mice were monitored for 5 days. LD 50 values for Ω76 (150 mg/kg), colistin (30 mg/kg), and pexiganan (60 mg/kg) were estimated by linear interpolation (Fig. 2D). The maximum sublethal doses for Ω76 (64 mg/kg), colistin (16 mg/kg), and pexiganan (32 mg/kg) were noted for use in further experiments. Ω76 appeared to be the least toxic compound tested, being 2.5× less toxic than pexiganan and 5× less toxic than colistin. Further, we performed cumulative toxicity determination experiments for Ω76, colistin, and pexiganan (Fig. 2E). In clinical settings, antibiotic treatment courses span days to weeks and may result in toxic effects that single-dose experiments fail to capture. We intraperitoneally injected 11 maximum sublethal doses of Ω76, colistin, and pexiganan in three separate cohorts. These doses were administered over 5 days at 12-hour intervals. Ten of 10 mice treated with Ω76 survived the experiment. In comparison, only 5 of 10 mice treated with colistin and 0 of 10 mice treated with pexiganan survived the experiment. Fisher’s exact test confirmed that the survival differences between the Ω76-colistin (P = 0.032) and Ω76-pexiganan (P = 10−5) cohorts were statistically significant. These results indicate that Ω76 has superior acute and cumulative toxicity characteristics in comparison to an experimental therapeutic (pexiganan) and a clinical antibiotic (colistin). Next, we investigated multidose cumulative toxicity for Ω76 by changing the time interval between doses (Fig. 2F). Three cohorts of five mice each were used. The first cohort was injected with three maximum sublethal Ω76 doses (64 mg/kg) spaced 2 hours apart. The second cohort was injected with four maximum sublethal Ω76 doses (64 mg/kg) spaced 1.5 hours apart. The third cohort was injected with five maximum sublethal Ω76 doses (64 mg/kg) spaced 1 hour apart. All mice survived for 5 days in all cohorts. These results indicate that multiple (maximum sublethal) doses of Ω76 can be administered at very short time intervals without the risk of cumulative toxicity. We investigated whether maximum sublethal doses of colistin and Ω76 could be safely coadministered. Ten mice were injected with a combined dose of colistin (16 mg/kg) and Ω76 (64 mg/kg) and monitored for 5 days, and no mortality was observed (Fig. 2F, highlighted). In contrast, a colistin dose of 32 mg/kg caused mortality in 3 of 5 mice, and a Ω76 dose of 128 mg/kg caused mortality in 2 of 5 mice (5 of 10 mice total) (Fig. 2D, highlighted). Therefore, a combined maximum sublethal dose of colistin and Ω76 is less toxic than the minimum lethal doses of colistin and Ω76 considered separately (P = 0.032, Fisher’s exact test). This indicates that Ω76 can be safely coadministered with colistin, without the concern of additive toxicity. Further, Ω76 and colistin do not negatively interact with each other. A checkerboard assay revealed a median ∑FIC of 0.5625, indicating a combined additive effect (fig. S2B) (53). Histopathology was used to confirm the lack of Ω76 toxicity at maximum sublethal doses. Liver and kidney samples were extracted from Ω76- and colistin-treated mice from Fig. 2E (survivors and nonsurvivors) and compared to untreated controls (Fig. 2, G to L). Kidney histological samples for the control (Fig. 2G) and Ω76-treated (Fig. 2H) cohorts displayed no signs of injury, with renal tubules and glomeruli appearing intact. As expected, the colistin-treated cohort displayed extensive renal damage (Fig. 2I), with prominent tubular necrosis and cast formation clearly visible. Liver histological samples for all cohorts displayed no necrosis or lipid vacuolation typically associated with liver damage (Fig. 2, J to L). These results confirm that Ω76 has no nephrotoxic or hepatotoxic activity after multiple maximum sublethal doses. Blood tests were used to further confirm the lack of Ω76 toxicity. The Ω76 cumulative toxicity experiment (Fig. 2E) was repeated with five mice, and blood was extracted at the end of 5 days. Serum creatinine, blood urea nitrogen, alanine aminotransferase, and alkaline phosphatase levels were assayed and compared to samples extracted from five untreated mice (fig. S3). In all cases, there was no significant difference between Ω76-treated and untreated cohorts. These results further confirm that multiple maximum sublethal doses of Ω76 produce no nephrotoxic or hepatotoxic effects. Fig. 3 In vivo efficacy of Ω76 using a BALB/c mouse peritoneal model of infection. (A) Protocol for the survival experiment to determine the efficacy of Ω76. (B) Protocol for peritoneal and spleen CFU estimation to determine the efficacy of Ω76. (C) Results of the survival experiment to determine the efficacy of Ω76, along with untreated, meropenem, and tigecycline controls (P values were calculated using Fisher’s exact test). (D) Results of the peritoneal CFU estimation experiment to determine the efficacy of Ω76, along with untreated, meropenem, and tigecycline controls (P values were calculated using the Welch two-sample t test). (E) Results of the spleen CFU estimation experiment to determine the efficacy of Ω76, along with untreated, meropenem, and tigecycline controls (P values were calculated using the Welch two-sample t test). All raw data for this figure are provided in dataset S1.

Ω76 successfully treats infections of carbapenem- and tigecycline-resistant A. baumannii in a mouse peritoneal model of infection We tested the in vivo efficacy of Ω76 using a BALB/c mouse peritoneal model of infection. Mice were infected with 3 × 107 colony-forming units (CFU) of a meropenem- and tigecycline-resistant A. baumannii (P1270) clinical isolate [species confirmed using 16S ribosomal RNA (rRNA) sequencing; table S6]. A. baumannii (P1270) was also deposited into the Microbial Type Culture Collection (MTCC culture number: 12889). Pilot experiments were performed to optimize Ω76 dosing (fig. S4). Four cohorts consisting of eight infected mice each were used (Fig. 3A). The first cohort was left untreated. The second cohort was treated with three doses of Ω76: 32 mg/kg (13.77 μmol/kg), 16 mg/kg, and 16 mg/kg administered at 0.5, 2.5, and 4.5 hours, respectively, after infection. The third cohort was treated with a standard dose of 13.33 mg/kg (34.76 μmol/kg) meropenem administered at 0.5 hour after infection. The fourth cohort was treated with a standard dose of 1.33 mg/kg (2.27 μmol/kg) tigecycline administered at 0.5 hour after infection. Meropenem and tigecycline doses were based on U.S. Food and Drug Administration guidelines (54, 55) for the treatment of an adult. All mice were monitored for 5 days after infection, and survival curves were plotted (Fig. 3C). Six of eight Ω76-treated mice survived for 5 days, significantly higher than the untreated survival rate of 0 of 8 (P = 0.001). In contrast, only one of eight meropenem-treated mice (P = 1) and zero of eight tigecycline-treated mice survived for 5 days. As A. baumannii (P1270) is meropenem- and tigecycline-resistant, poor performance of these drugs was expected. To confirm Ω76 efficacy, this experiment was independently replicated using uninfected and Ω76-treated cohorts using the same dosing regimen (eight mice each; fig. S5). Similar results were obtained, with six of eight of mice treated with Ω76 surviving in comparison to a one of eight survival rate of untreated mice (P = 0.015). To further demonstrate the efficacy of Ω76 against A. baumannii (P1270), we performed peritoneal and spleen CFU counts using five cohorts of BALB/c mice (five mice per cohort) infected with 3 × 107 CFU of A. baumannii (P1270). The Ω76, meropenem, and tigecycline cohorts were treated with the same dosing regimen used for the previous survival experiment (Fig. 3B). For these cohorts, all mice were euthanized 12 hours after infection. Two control cohorts were used, where mice were euthanized at 0.5 and 12 hours after infection, respectively. In all cases, peritoneal washes and spleens were extracted immediately after euthanization. Ω76 was found to significantly reduce A. baumannii (P1270) loads in both the peritoneum (P = 0.026, Fig. 3D) and spleen (P = 0.015, Fig. 3E). Ω76 reduced peritoneal loads >1000-fold, while spleen loads were reduced >100-fold. Meropenem and tigecycline did not significantly reduce A. baumannii (P1270) loads in the peritoneum (meropenem, P = 0.920; tigecycline, P = 0.847) or spleen (meropenem, P = 0.448; tigecycline, P = 0.463). Further, the mouse immune system was unable to reduce A. baumannii (P1270) loads. Peritoneal CFU counts remained constant at both 0.5 and 12 hours after infection. Spleen CFU counts increased >10-fold 12 hours after infection possibly due to our infection model, as A. baumannii (P1270) introduced peritoneally would require time to enter the bloodstream.

Ω76 localizes within and disrupts bacterial membranes, inducing small-molecule leakage, resulting in rapid bactericidal activity Confocal microscopy experiments were performed to track Ω76 during its interaction with bacterial cells (Fig. 4). E. coli (K12 MG1655) and drug-resistant A. baumannii (P1270) were used for these experiments. Fluorescein isothiocyanate (FITC)–labeled Ω76, Nile red (a lipophilic cell membrane stain), and 4′,6-diamidino-2-phenylindole (DAPI) (a nucleic acid stain) were used to treat these isolates, and they were visualized immediately after staining. Ω76 was found to colocalize with Nile red for both E. coli (K12 MG1655) and A. baumannii (P1270), indicating immediate binding to the bacterial cell membrane. Fig. 4 Confocal microscopy experiments performed on E. coli (K12 MG1655) and A. baumannii (P1270). (Top) FITC-labeled Ω76-treated E. coli. Ω76 colocalized with Nile red, indicating a membrane-binding propensity. (Bottom) FITC-labeled Ω76-treated A. baumannii (P1270). Ω76 again colocalized with Nile red, indicating a membrane-binding propensity. (Table) Pearson’s correlation coefficients given for all combinations of stains (DAPI/FITC peptide/Nile red). Better stain colocalization is denoted by higher correlation values. In both cases, the Nile red/FITC peptide pair was the most strongly correlated. Scale bar, 2 μm. Note that these images have been digitally magnified by 3× for clarity. All original images are provided in dataset S1. Colocalization for both E. coli (K12 MG1655) and A. baumannii (P1270) was quantified using Pearson’s correlation (Fig. 4, inset table). Stronger correlations indicated better colocalization. For both isolates, the strongest correlation was observed for Nile red/FITC-Ω76, confirming that the peptide selectively binds to bacterial cell membranes. Time-kill kinetic experiments (Fig. 5A) were performed in ex vivo whole human blood for Ω76, colistin, and an untreated control. Concentrations corresponding to the clinical doses of Ω76 (32 mg/liter) and colistin (5 mg/liter) (56) were used. Ω76 rapidly reduced A. baumannii (P1270) counts, causing a ≥105-fold CFU reduction in 10 min and the complete elimination of all CFU in 60 min. Colistin treatment resulted in a more modest CFU reduction of 32-fold after 60 min. As expected, no CFU reductions were observed in the untreated control. Fig. 5 The time-kill kinetics and elimination kinetics of Ω76. (A and B) Kinetic experiments showing the rapid action of Ω76 on A. baumannii (P1270). All experiments in these panels were performed in triplicate. (A) Time-kill curves performed on A. baumannii (P1270) treated with clinically relevant doses of Ω76, colistin, and an untreated control. These experiments were performed in ex vivo whole human blood. (B) PO 4 3 - 32 release radioassay to detect the leakage of small molecules upon incubation of A. baumannii (P1270) with Ω76, colistin, and an untreated control. (C) Pharmacokinetic experiments performed on mice to determine the bloodstream absorption and elimination kinetics of peritoneally injected Nselmet-Ω76. Curve fitting was performed using spline interpolation. The shaded area corresponds to Nselmet-Ω76 serum concentrations above the MBC. Inset: Fold reduction curves for Ω76, performed on A. baumannii (P1270) in ex vivo whole human blood. The fold reduction curve for Ω76 at 32 mg/liter is derived from the same data displayed in (A). The fold reduction curve for Ω76 at MBC (4 mg/liter) closely follows the trend at 32 mg/liter up to 6 min. However, Ω76 at MBC is unable to continue reducing A. baumannii (P1270) CFU counts, diverging from the 32 mg/liter trend at 8 min. For all experiments, lines and shaded regions indicate means and SD, respectively. All raw data for this figure are provided in dataset S1. A radiolabeled phosphate release assay (Fig. 5B) was performed to help understand the cause of the rapid bactericidal activity of Ω76. Phosphate was used as a model small molecule to help trace the possible leakage of essential small molecules such as K+/Na+ ions, amino acids, and sugars during membrane disruption. Treatment with Ω76 caused rapid phosphate leakage. Thirty-three percent of intracellular phosphate was lost in 10 min, rising to a 57% loss in 60 min. Treatment with colistin causes slower phosphate leakage. Ten percent of intracellular phosphate is lost in 10 min, slowly rising to a 25% loss in 60 min. As expected, the untreated control lost the least amount of phosphate, losing only 10% after 60 min. Together, these three experiments indicate that Ω76 is rapidly incorporated into the bacterial cell membrane, creating membrane disruptions that permit the rapid leakage of cytoplasmic small molecules, resulting in immediate bacterial death. We performed SEM experiments to determine the morphological changes induced by Ω76 on the bacterial cell membrane. Drug-resistant A. baumannii (P1270) displayed no morphological changes upon peptide treatment. We performed the same experiment for a sensitive strain of A. baumannii (B4505) and also observed no morphological changes (fig. S6). However, A. baumannii (B4505) protoplasts displayed some membrane irregularities at high magnifications (fig. S7). Clear membrane disruption was observed in E. coli (K12 MG1655), with prominent blebbing indicating the loss of structural cohesion of the cell membrane (fig. S8). Similar observations were recorded for Shigella flexneri (MTCC 1457), which showed membrane disruption followed by the loss of cytoplasmic contents (fig. S9). Note that large-scale membrane disruption was only observed after a 2-hour prolonged incubation period and with high concentrations of Ω76 (128 mg/liter). These results indicate that large-scale membrane damage is not a prerequisite for bacterial death, which occurs on much shorter time scales. However, these observations still confirm the direct interaction of Ω76 with bacterial membranes. Of particular interest are the in vivo killing kinetics of Ω76, especially within the bloodstream. Previously described experiments have already established the ability of Ω76 to markedly reduce A. baumannii (P1270) peritoneal and spleen CFU loads in mice (Fig. 3, D and E), indicating similar in vivo and ex vivo killing kinetics. In addition, pharmacokinetic experiments were performed (Fig. 5C) to understand the absorption and elimination kinetics of Ω76 within the mouse bloodstream. For these experiments, Ω76 was labeled with an N-terminal selenomethionine probe, which did not affect the peptide’s MBC against A. baumannii (table S5). BALB/c mice were intraperitoneally injected with Nselmet-Ω76 (70 mg/kg). At different time points, blood from individual mice was extracted via terminal cardiac puncture. The serum selenium content was then quantified via inductively coupled plasma mass spectrometry (ICP-MS), which was a direct measure of Nselmet-Ω76 concentration. We observed that Nselmet-Ω76 reached a peak serum concentration of 20 mg/liter at 4.5 min after injection and was completely eliminated 10 min after injection. The concentration of Nselmet-Ω76 in the bloodstream remained greater than the MBC (4 mg/liter) of A. baumannii (P1270) for 5.15 min. From previous time-kill experiments performed in ex vivo whole human blood (Fig. 5A; also represented in Fig. 5C, inset, in fold reduction terms), we observed that 5.15 min was sufficient for a 152-fold CFU reduction, sufficient to significantly improve survival outcomes. Of course, because of the noncumulative toxicity of Ω76 (Fig. 2, E and F), multiple doses can be administered to achieve any target CFU reduction.

The molecular response of A. baumannii to Ω76 challenge Drug-resistant A. baumannii (P1270) was challenged with Ω76 at concentrations of 0.1×, 0.25×, and 0.5× MBC [Gene Expression Omnibus (GEO) accession number: GSE116245]. Differentially expressed genes (DEGs) displaying a 1.5-fold change (up- or down-regulation) for all MBC concentrations and belonging to significantly overrepresented Gene Ontology (GO) terms were identified, as described in Methods. Using these measures, 134 genes (GO-up) were up-regulated and 62 genes (GO-down) were down-regulated upon Ω76 treatment over an MBC concentration range of 0.1 to 0.5×. Up-regulated genes (GO-up; table S7) were classified under 16 GO terms, while down-regulated genes (GO-down) were classified under 4 GO terms (GO-down; table S8). A graphical representation of the features and relationships between GO terms is provided in fig. S10. The molecular response is depicted in Fig. 6. Genes associated with 67 inner membrane proteins (and 4 outer membrane proteins) were found to be significantly up-regulated, and only 4 membrane-associated genes were down-regulated. The significant up-regulation of diverse membrane proteins may be required to compensate for Ω76-induced membrane damage. Fig. 6 The molecular response of A. baumannii (P1270) to Ω76 challenge. Up- and down-regulated (GO-up and Go-down) genes are colored green and red, respectively. For clarity, only DEGs belonging to GO terms with biological functions relevant to this study are shown. A full list of DEGs can be found in tables S4 and S5. Note that some genes can have multiple functions and belong to multiple GO terms. Note that some genes do not have corresponding gene names assigned. In these cases, the truncated Agilent ID has been used. For example, “2251” mentioned in the above figure corresponds to Agilent ID ABAYE2251. Twenty-two membrane-associated genes belonging to membrane transport proteins were up-regulated. Transporters for H 2 O, H+, K+, Na+, NH4+, Fe, and Zn ions were up-regulated, which is a response expected to compensate for Ω76-induced rapid small-molecule leakage (Fig. 5B). Transporters for organic small molecules such as α-ketoglutarate, citrate, serine, alanine, glycine, and aromatic residues were also up-regulated, indicating possible leakage of these compounds as well. Fourteen membrane-associated genes belonging to electron transport chain components—NADH (reduced form of nicotinamide adenine dinucleotide) dehydrogenase (seven genes), adenosine triphosphate (ATP) synthase (three genes), and anaerobic electron transport components (four genes)—were up-regulated. It is possible that these genes are up-regulated in response to Ω76-induced displacement of periplasmic H+ ions, which are required for ATP synthesis. Other genes belonging to diverse metabolic pathways were also found to be up-regulated, hinting at metabolic inhibitory processes. It should be noted that Ω76-induced metabolic inhibition would occur on longer time scales than simple membrane disruption and would only be relevant under circumstances where sub-MBC concentrations of Ω76 are used. Four genes (ompR, ttg2C, nlpD, and lolC) responsible for maintaining outer membrane integrity and three genes (mraY, ispU, and pal) responsible for cell wall synthesis were also up-regulated. nagZ, responsible for peptidoglycan degradation, was down-regulated. Twelve genes belonging to ribosomal proteins (5 genes for 50S subunit and 7 genes for 30S subunit) were up-regulated. Six genes involved with translation, and four genes associated with other ribosome-associated processes, were also up-regulated. Further, eight genes associated with the citric acid cycle were also up-regulated. The up-regulation of ribosomal proteins and components of the citric acid cycle may be a product of increased metabolic demands in response to Ω76 treatment. Alternatively, AMPs are known to trap ribosomal release factors (29), inhibit ribosomal protein synthesis (27, 28), and cause ribosomal aggregation (32). It is therefore conceivable that the up-regulation of ribosomal proteins is a response to ribosomal inhibition caused by Ω76. Some GO terms included poorly characterized genes (such as transcriptional regulators with no known targets for GO:0003677 and GO:0006355) or contained diverse genes with little commonality (such as enzymes with unknown reactants/products for GO:0008152, GO:0016740, GO:0005524, and GO:0005737). In these cases, the contributions of these genes to the molecular response of A. baumannii remain unclear.