Despite the broad-spectrum antimicrobial activities of silver, its internal usage is restricted, owing to the toxicity. Strategies to enhance its efficacy are highly desirable but rely heavily on the understanding of its molecular mechanism of action. However, up to now, no direct silver-targeting proteins have been mined at a proteome-wide scale, which hinders systemic studies on the biological pathways interrupted by silver. Herein, we build up a unique system, namely liquid chromatography gel electrophoresis inductively coupled plasma mass spectrometry (LC-GE-ICP-MS), allowing 34 proteins directly bound by silver ions to be identified in Escherichia coli . By using integrated omic approaches, including metalloproteomics, metabolomics, bioinformatics, and systemic biology, we delineated the first dynamic antimicrobial actions of silver (Ag + ) in E. coli , i.e., it primarily damages multiple enzymes in glycolysis and tricarboxylic acid (TCA) cycle, leading to the stalling of the oxidative branch of the TCA cycle and an adaptive metabolic divergence to the reductive glyoxylate pathway. It then further damages the adaptive glyoxylate pathway and suppresses the cellular oxidative stress responses, causing systemic damages and death of the bacterium. To harness these novel findings, we coadministrated metabolites involved in the Krebs cycles with Ag + and found that they can significantly potentiate the efficacy of silver both in vitro and in an animal model. Our study reveals the comprehensive and dynamic mechanisms of Ag + toxicity in E . coli cells and offers a novel and general approach for deciphering molecular mechanisms of metallodrugs in various pathogens and cells to facilitate the development of new therapeutics.

Urease as a potential target of antimicrobial drugs has received considerable attention given its versatile roles in microbial infection. Development of effective urease inhibitors, however, is a significant challenge due to the deeply buried active site and highly specific substrate of a bacterial urease. Conventionally, urease inhibitors are designed by either targeting the active site or mimicking substrate of urease, which is not efficient. Up to now, only one effective inhibitor—acetohydroxamic acid (AHA)—is clinically available, but it has adverse side effects. Herein, we demonstrate that a clinically used drug, colloidal bismuth subcitrate, utilizes an unusual way to inhibit urease activity, i.e., disruption of urease maturation process via functional perturbation of a metallochaperone, UreG. Similar phenomena were also observed in various pathogenic bacteria, suggesting that UreG may serve as a general target for design of new types of urease inhibitors. Using Helicobacter pylori UreG as a showcase, by virtual screening combined with experimental validation, we show that two compounds targeting UreG also efficiently inhibited urease activity with inhibitory concentration (IC)50 values of micromolar level, resulting in attenuated virulence of the pathogen. We further demonstrate the efficacy of the compounds in a mammalian cell infection model. This study opens up a new opportunity for the design of more effective urease inhibitors and clearly indicates that metallochaperones involved in the maturation of important microbial metalloenzymes serve as new targets for devising a new type of antimicrobial drugs.

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The mechanisms of action of arsenic trioxide (ATO), a clincally used drug for the treatment of acute promyelocytic leukemia, have been actively studied mainly through characterization of individual putative protein...

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Significance Superbugs carrying a mobile colistin resistance gene ( mcr ) are jeopardizing the clinical efficacy of the last-line antibiotic colistin. The development of MCR inhibitors is urgently required to cope with antibiotic-resistance emergencies. Here, we show that silver (Ag + ) fully restores the susceptibility of mcr-1 –carrying superbugs against colistin both in vitro and in vivo. We found an unprecedented tetra-silver center in the active-site pocket of MCR-1 through the substitution of the essential Zn 2+ ions in the intact enzyme, leading to the prevention of substrate binding (i.e. the dysfunction of MCR-1 in transferring phosphorylethanolamine to lipid A). Importantly, the ability of Ag + to suppress resistance evolution extends the lifespan of currently used antibiotics, providing a strategy to treat infections by mcr -positive bacteria.

Metalloproteins account for nearly one-third of proteins in proteomes. To date, the identification of metalloproteins relies mainly on protein purification and the subsequent characterization of bound metals, which often leads to losses of metal ions bound weakly and transiently. Herein, we developed a strategy to visualize and subsequently identify endogenous metalloproteins and metal-binding proteins in living cells via integration of fluorescence imaging with proteomics. We synthesized a "metal-tunable" fluorescent probe (denoted as M-TRACER) that rapidly enters cells to target proteins with 4-40 fold fluorescence enhancements. By using Ni-TRACER as an example, we demonstrate the feasibility of tracking Ni-binding proteins in vitro, while cellular small molecules exhibit negligible interference on the labeling. We identified 44 Ni-binding proteins from microbes using Helicobacter pylori as a showcase. We further applied Cu-TRACER to mammalian cells and found 54 Cu-binding proteins. The strategy we report here provides a great opportunity to track various endogenous metallo-proteomes and to mine potential targets of metallodrugs.