A Nanocomposite Dynamic Covalent Cross-Linked Hydrogel Loaded with Fusidic Acid for Treating Antibiotic-Resistant Infected Wounds.

Methicillin-resistant Staphylococcus aureus (MRSA) is associated with high levels of morbidity and is considered a difficult-to-treat infection, often requiring nonstandard treatment regimens and antibiotics. Since over 40% of the emerging antibiotic compounds have insufficient solubility that limits their bioavailability and thus efficacy through oral or intravenous administration, it is crucial that alternative drug delivery products be developed for wound care applications. Existing effective treatments for soft tissue MRSA infections, such as fusidic acid (FA), which is typically administered orally, could also benefit from alternative routes of administration to improve local efficacy and bioavailability while reducing the required therapeutic dose. Herein, we report an antimicrobial poly(oligoethylene glycol methacrylate) (POEGMA)-based composite hydrogel loaded with fusidic acid-encapsulating self-assembled polylactic acid-b-poly(oligo(ethylene glycol) methyl ether methacrylate) (PLA-POEGMA) nanoparticles for the treatment of MRSA-infected skin wounds. The inclusion of the self-assembled nanoparticles (380 nm diameter when loaded with fusidic acid) does not alter the favorable mechanical properties and stability of the hydrogel in the context of its use as a wound dressing, while fusidic acid (FA) can be released from the hydrogel over ∼10 h via a diffusion-controlled mechanism. The antimicrobial studies demonstrate a clear zone of inhibition in vitro and a 1-2 order of magnitude inhibition of bacterial growth in vivo in an MRSA-infected full-thickness excisional murine wound model even at very low antibiotic doses. Our approach thus can both circumvent challenges in the local delivery of hydrophobic antimicrobial compounds and directly deliver antimicrobials into the wound to effectively combat methicillin-resistant infections using a fraction of the drug dose required using other clinically relevant strategies.

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. 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 antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Engineering orthogonal human O-linked glycoprotein biosynthesis in bacteria.

A major objective of synthetic glycobiology is to re-engineer existing cellular glycosylation pathways from the top down or construct non-natural ones from the bottom up for new and useful purposes. Here, we have developed a set of orthogonal pathways for eukaryotic O-linked protein glycosylation in Escherichia coli that installed the cancer-associated mucin-type glycans Tn, T, sialyl-Tn and sialyl-T onto serine residues in acceptor motifs derived from different human O-glycoproteins. These same glycoengineered bacteria were used to supply crude cell extracts enriched with glycosylation machinery that permitted cell-free construction of O-glycoproteins in a one-pot reaction. In addition, O-glycosylation-competent bacteria were able to generate an antigenically authentic Tn-MUC1 glycoform that exhibited reactivity with antibody 5E5, which specifically recognizes cancer-associated glycoforms of MUC1. We anticipate that the orthogonal glycoprotein biosynthesis pathways developed here will provide facile access to structurally diverse O-glycoforms for a range of important scientific and therapeutic applications.