Synthetic biohybrid peptidoglycan oligomers enable pan-bacteria-specific labeling and imaging: in vitro and in vivo

Metabolism-triggered sensor array aided by machine learning for rapid identification of pathogens

Chemical-nose strategy has achieved certain success in the discrimination and identification of pathogens. However, this strategy usually relies on non-specific interactions, which are prone to be significantly disturbed by the change of environment thus limiting its practical usefulness. Herein, we present a novel chemical-nose sensing approach leveraging the difference in the dynamic metabolic variation during peptidoglycan metabolism among different species for rapid pathogen discrimination. Pathogens were first tethered with clickable handles through metabolic labeling at two different acidities (pH = 5 and 7) for 20 and 60 min, respectively, followed by click reaction with fluorescence up-conversion nanoparticles to generate a four-dimensional signal output. This discriminative multi-dimensional signal allowed eight types of model bacteria to be successfully classified within the training set into strains, genera, and Gram phenotypes. As the difference in signals of the four sensing channels reflects the difference in the amount/activity of enzymes involved in metabolic labeling, this strategy has good anti-interference capability, which enables precise pathogen identification within 2 h with 100% accuracy in spiked urinary samples and allows classification of unknown species out of the training set into the right phenotype. The robustness of this approach holds significant promise for its widespread application in pathogen identification and surveillance.

Chemical Nose Strategy with Metabolic Labeling and "Antibiotic-Responsive Spectrum" Enables Accurate and Rapid Pathogen Identification

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the "antibiotic-responsive spectrum" of different pathogens. d-Amino acids with click handles were metabolically incorporated into the cell wall of pathogens for further clicking with dibenzocyclooctyne-functionalized upconversion nanoparticles (DBCO-UCNPs) in the presence/absence of six types of antibiotics, which generates seven-channel sensing responses. With the assistance of machine learning algorithms, eight types of pathogens, including three types of antibiotic-resistant bacteria, can be well classified and discriminated in terms of microbial taxonomies, Gram phenotypes, and antibiotic resistance. The present metabolic-labeling-assisted strategy exhibits good anti-interference capability and improved discrimination ability rooted in the unique sensing mechanism. Sensitive identification of pathogens with 100% accuracy from artificial urinary tract infection samples at a concentration as low as 105 CFU/mL was achieved. Pathogens outside of the training set can also be discriminated well. This clearly demonstrated the potential of the present strategy in the identification of unknown pathogens in clinical samples.

Metabolic Labeling Strategy Boosted Antibacterial Efficiency for Photothermal and Photodynamic Synergistic Bacteria-Infected Wound Therapy

Pathogenic bacteria infections bring about a substantial risk to human health. Given the development of antibiotic-resistance bacteria, alternative antibacterial strategies with great inactivation efficiency and bacteria-binding ability are extremely attractive. In this work, a metabolic labeling photosensitizer, prepared by the coupling of commercial IR820 and d-propargylglycine (a type of d-amino acid, DAA) via a straightforward one-step incubation (IR820-DAA), could metabolically be incorporated into the bacterial wall via enzymatic reactions, thus enhancing antibacterial efficiency. The laser energy at 808 nm could make IR820-DAA a synergistic photothermal/photodynamic agent for efficient antibacterial therapy and wound healing. Furthermore, IR820-DAA exhibits good water solubility and biological safety for clinical translation and even possesses biofilm degradation activity toward methicillin-resistant Staphylococcus aureus (MRSA). Overall, the proposed IR820-DAA holds great promise as a nonantibiotic tool for the treatment of bacteria-related diseases and offers a blueprint for building the precise synergistic antibacterial therapeutic platform.

Smart Multifunctional Polymer Systems as Alternatives or Supplements of Antibiotics To Overcome Bacterial Resistance

In recent years, infectious diseases have again become a critical threat to global public health largely due to the challenges posed by antimicrobial resistance. Conventional antibiotics have played a crucial role in combating bacterial infections; however, their efficacy is significantly impaired by widespread drug resistance. Natural antimicrobial peptides (AMPs) and their polymeric mimics demonstrate great potential for killing bacteria with low propensity of resistance as they target the microbial membrane rather than a specific molecular target, but they are also toxic to the host eukaryotic cells. To minimize antibiotics systemic spread and the required dose that promote resistance and to advocate practical realization of the promising activity of AMPs and polymers, smart systems to target bacteria are highly sought after. This review presents bacterial recognition by various specific targeting molecules and the delivery systems of active components in supramolecules. Bacteria-induced activations of antimicrobial-based nanoformulations are also included. Recent advances in the bacteria targeting and delivery of synthetic antimicrobial agents may assist in developing new classes of highly selective antimicrobial systems which can improve bactericidal efficacy and greatly minimize the spread of bacterial resistance.

Activatable MRI probes for the specific detection of bacteria

Activatable fluorescent probes have been successfully used as molecular tools for biomedical research in the last decades. Fluorescent probes allow the detection of molecular events, providing an extraordinary platform for protein and cellular research. Nevertheless, most of the fluorescent probes reported are susceptible to interferences from endogenous fluorescence (background signal) and limited tissue penetration is expected. These drawbacks prevent the use of fluorescent tracers in the clinical setting. To overcome the limitation of fluorescent probes, we and others have developed activatable magnetic resonance probes. Herein, we report for the first time, an oligonucleotide-based probe with the capability to detect bacteria using magnetic resonance imaging (MRI). The activatable MRI probe consists of a specific oligonucleotide that targets micrococcal nuclease (MN), a nuclease derived from Staphylococcus aureus. The oligonucleotide is flanked by a superparamagnetic iron oxide nanoparticle (SPION) at one end, and by a dendron functionalized with several gadolinium complexes as enhancers, at the other end. Therefore, only upon recognition of the MRI probe by the specific bacteria is the probe activated and the MRI signal can be detected. This approach may be widely applied to detect bacterial infections or other human conditions with the potential to be translated into the clinic as an activatable contrast agent.