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Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii

ABSTRACT: Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug-resistance due to its robust outer membrane and its ability to acquire and retain extracellular DNA. Moreover, it can survive for prolonged durations on surfaces and is resistant to desiccation. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new chemical matter with antibacterial activity against this burdensome pathogen. Here, we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a deep neural network with this growth inhibition dataset and performed predictions on the Drug Repurposing Hub for structurally novel molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii, which could overcome intrinsic and acquired resistance mechanisms in clinical isolates. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE, a functionally conserved protein that contributes to shuttling lipoproteins from the inner membrane to the outer membrane. Moreover, abaucin was able to control an A. baumannii infection in a murine wound model. Together, this work highlights the utility of machine learning in discovering new antibiotics and describes a promising lead with narrow-spectrum activity against a challenging Gram-negative pathogen.

ORGANISM(S): Acinetobacter baumannii ATCC 17978

PROVIDER: GSE214305 | GEO | 2023/02/01

REPOSITORIES: GEO

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Project description:The discovery of antibiotics has led to the effective treatment of bacterial infections that were otherwise fatal and has had a transformative effect on modern medicine. Teixobactin is an unusual depsipeptide natural product that was recently discovered from a previously unculturable soil bacterium which is active against several Gram-positive pathogens, including methicillin- resistant Staphylococcus aureus and vancomycin-resistant Enterococci. One of the key attractive features of teixobactin as an antibiotic lead is that resistance could not be generated in a laboratory setting against S. aureus. This is proposed to be a result of the mechanism of action that involves binding to essential bacterial cell wall synthesis building blocks lipid II and lipid III.In the present study, metabolomics was used to investigate the potential metabolic pathways and mode of action involved in mechanisms of antibacterial activity and bacterial killing of the synthetic teixobactin analog Leu10-teixobactin against an MRSA strain, S. aureus ATCC700699. The metabolomes of S. aureus ATCC700699 cells were compared at 1, 3 and 6 h following treatment with Leu10-teixobactin (0.5 µg/ml, i.e. 0.5x MIC) and the untreated controls. Leu10-teixobactin significantly perturbed bacterial membrane lipids (glycerophospholipids and fatty acids), peptidoglycan (lipid I and II) metabolism and cell wall teichoic acid (lipid III) biosynthesis as early as after 1 h of treatment reflecting an initial activity on the cell envelope. Concordant with its time-dependent antibacterial killing action, Leu10-teixobactin caused more perturbations in the levels of key intermediates in pathways of amino-sugar and nucleotide-sugar metabolism and their downstream peptidoglycan and teichoic acid biosynthesis at 3 and 6 h. Significant perturbations in arginine metabolism and interrelated tricarboxylic acid cycle, histidine metabolism, pantothenate and Coenzyme A biosynthesis were also observed at 3 and 6 h.To conclude, this is the first study to provide novel metabolomics mechanistic information which lends to support the development of teixobactin as an antibacterial drug for the treatment of multi-drug resistant Gram-positive infections.

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Deep learning-guided discovery of a narrow-spectrum antibiotic against Acinetobacter baumannii

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