Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice
Biswajit Mishra, Anindya Basu, Fadi Shehadeh, LewisOscar Felix, Sai Sundeep Kollala, Yashpal Singh Chhonker, Mandar T. Naik, Charilaos Dellis, Liyang Zhang, Narchonai Ganesan, Daryl J. Murry, Jianhua Gu, Michael B. Sherman, Frederick M. Ausubel, Paul P. Sotiriadis, Eleftherios Mylonakis
Biswajit Mishra, Anindya Basu, Fadi Shehadeh, LewisOscar Felix, Sai Sundeep Kollala, Yashpal Singh Chhonker, Mandar T. Naik, Charilaos Dellis, Liyang Zhang, Narchonai Ganesan, Daryl J. Murry, Jianhua Gu, Michael B. Sherman, Frederick M. Ausubel, Paul P. Sotiriadis, Eleftherios Mylonakis
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Research Article Infectious disease Microbiology

Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice

Abstract

As antimicrobial resistance rises, new antibacterial candidates are urgently needed. Using sequence space information from over 14,743 functional antimicrobial peptides (AMPs), we improved the antimicrobial properties of citropin 1.1, an AMP with weak antimethicillin resistant Staphylococcus aureus (MRSA) activity, producing a short and potent antistaphylococcal peptide, CIT-8 (13 residues). At 40 μg/mL, CIT-8 eradicated 1 × 108 drug-resistant MRSA and vancomycin resistant S. aureus (VRSA) persister cells within 30 minutes of exposure and reduced the number of viable biofilm cells of MRSA and VRSA by 3 log10 and 4 log10 in established biofilms, respectively. CIT-8 (at 32 μg/mL) depolarized and permeated the S. aureus MW2 membrane. In a mouse model of MRSA skin infection, CIT-8 (2% w/w in petroleum jelly) significantly reduced the bacterial burden by 2.3 log10 (P < 0.0001). Our methodology accelerated AMP design by combining traditional peptide design strategies, such as truncation, substitution, and structure-guided alteration, with machine learning–backed sequence optimization.

Authors

Biswajit Mishra, Anindya Basu, Fadi Shehadeh, LewisOscar Felix, Sai Sundeep Kollala, Yashpal Singh Chhonker, Mandar T. Naik, Charilaos Dellis, Liyang Zhang, Narchonai Ganesan, Daryl J. Murry, Jianhua Gu, Michael B. Sherman, Frederick M. Ausubel, Paul P. Sotiriadis, Eleftherios Mylonakis

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Figure 5

Mechanism of action of CIT-8 and associated stress response by MRSA.

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Mechanism of action of CIT-8 and associated stress response by MRSA. (A)...
(A) Fluorescence-based, DIBAC4(3)-assisted S. aureus MW2 membrane depolarization caused by CIT-8 peptide (at 32 μg/mL) monitored for 40 minutes after peptide exposure (n = 3). (B and C) Fluorescence-based membrane permeability of S. aureus MW2 treated with CIT-8 (4–32 μg/mL), untreated bacteria (UT), vancomycin (Vanc), and melittin (Mel) at 32 μg/mL after 60 minutes, assessed using (B) PI and (C) SYTOX Green fluorescence (n = 4, ****P < 0.0001 by 1-way ANOVA followed by Dunnett’s multiple comparison test). (D) ATP release from S. aureus MW2 upon CIT-8 (at 32 μg/mL) interaction for 30 minutes (* denotes P < 0.05 by Student’s t-test, unpaired 2-tailed). (E) Cryo-EM image of control S. aureus MW2. (F) Cryo-EM image of S. aureus MW2 treated with CIT-8 at 80 μg/mL for 60 minutes (green arrows indicate membrane perturbation). (G) SEM image of control S. aureus MW2. (H) SEM image of S. aureus MW2 treated with CIT-8 at 40 μg/mL for 60 minutes (white arrows indicate membrane blebbing). (I) RNA-seq–derived differential gene expression (DGE) of significantly upregulated genes (n = 2 samples, P < 0.05, calculated using DESeq2 (76) in S. aureus MW2 by CIT-8 (at 2 μg/mL) treated for 30 minutes. (J) Pathway analysis of the targeted metabolome of S. aureus MW2 treated with peptide CIT-8 at 4 μg/mL for 30 minutes, revealing significant alterations in key stress and metabolic pathways (n = 3, significant metabolite in pathways were determined by their P < 0.05 obtained by Student’s t test, unpaired, 2-tailed). (K) Stress responsive vitamin B6 pathway in S. aureus MW2, indicating key regulatory genes (pdxT and pdxS revelated by our RNA-seq analysis) and metabolite (Erythrose 4-phosphate, identified by our targeted metabolomics analysis) positions in the pathway. Scale bars: 100 nm (E and F); 400 nm (G and H).