Enhanced detection of carbapenemase-producing Enterobacteriaceae by an optimized phenol red assay

Staphylococcus aureus colonizing the skin microbiota of adults with severe atopic dermatitis exhibits genomic diversity and convergence in biofilm traits

Atopic dermatitis (AD) is a chronic inflammatory skin disorder exacerbated by Staphylococcus aureus colonization. The specific factors that drive S. aureus overgrowth and persistence in AD remain poorly understood. This study analyzed skin barrier functions and microbiome diversity in lesional (LE) and non-lesional (NL) forearm sites of individuals with severe AD compared to healthy control subjects (HS). Notable differences were found in transepidermal water loss, stratum corneum hydration, and microbiome composition. Cutibacterium was more prevalent in HS, while S. aureus and S. lugdunensis were predominantly found in AD LE skin. The results highlighted that microbial balance depends on inter-species competition. Specifically, network analysis at the genus level demonstrated that overall bacterial correlations were higher in HS, indicating a more stable microbial community. Notably, network analysis at the species level revealed that S. aureus engaged in competitive interactions in NL and LE but not in HS. Whole-genome sequencing (WGS) showed considerable genetic diversity among S. aureus strains from AD. Despite this variability, the isolates exhibited convergence in key phenotypic traits such as adhesion and biofilm formation, which are crucial for microbial persistence. These common phenotypes suggest an adaptive evolution, driven by competition in the AD skin microenvironment, of S. aureus and underscoring the interplay between genetic diversity and phenotypic convergence in microbial adaptation.

A New Perspective on the Antimicrobial Mechanism of Berberine Hydrochloride Against Staphylococcus aureus Revealed by Untargeted Metabolomic Studies

Berberine hydrochloride (BBR) is a natural product widely used in clinical medicine and animal production. It has a variety of antimicrobial effects, but its complex antimicrobial mechanism has not been clarified. This study aimed to discover the metabolic markers and gain a new perspective on the antibacterial mechanism of BBR. The effects of different inhibitory concentrations of BBR on the survival and growth of standard strain Staphylococcus aureus ATCC 25923 were analyzed by the bacteriostatic activity test. Differences in intracellular metabolites of S. aureus following 19 μg/ml BBR exposure for 1 h were investigated by combining non-targeted metabolomics techniques of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). The results showed that the minimum inhibitory concentration of BBR against S. aureus was 51 μg/ml. A total of 368 and 3,454 putative metabolites were identified by GC-MS and LC-MS analyses, respectively. Principal component analysis showed the separation of intracellular metabolite profiles between BBR-exposed samples and non-exposed controls. Pathway activity profiling analysis indicated a global inhibition of metabolisms by BBR exposure, while enhancement was also found in nucleic acid metabolism, amino sugar, and nucleotide sugar metabolism. Several metabolic markers were screened out mainly based on their variable importance of projection values. Two pyridine dicarboxylic acids were significantly downregulated, suggesting the reduction of stress resistance. The oxidized phospholipid (PHOOA-PE) was accumulated, while lipid antioxidant gamma-tocopherol was decreased, and farnesyl PP, the synthetic precursor of another antioxidant (staphyloxanthin), was decreased below the detection threshold. This evidence indicates that BBR reduced the antioxidant capacity of S. aureus. Accumulation of the precursors (UDP-GlcNAc, CDP-ribitol, and CDP-glycerol) and downregulation of the key metabolite D-Ala-D-Ala suggest the inhibition of cell wall synthesis, especially the peptidoglycan synthesis. Metabolites involved in the shikimate pathway (such as 3-dehydroshikimate) and downstream aromatic amino acid synthesis were disturbed. This study provides the first metabolomics information on the antibacterial mechanism of BBR against S. aureus. The key metabolic markers screened in this study suggest that the shikimate pathway, staphyloxanthin synthesis, and peptidoglycan biosynthesis are new directions for further study of BBR antibacterial mechanism in the future.

AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning

A rapid and accurate detection of carbapenemase-producing Gram-negative bacteria (CPGNB) has an immediate demand in the clinic. Here, we developed and validated a method for rapid detection of CPGNB using Blue-Carba combined with deep learning (designated as AI-Blue-Carba). The optimum bacterial suspension concentration and detection wavelength were determined using a Multimode Plate Reader and integrated with deep learning modeling. We examined 160 carbapenemase-producing and non-carbapenemase-producing bacteria using the Blue-Carba test and a series of time and optical density values were obtained to build and validate the machine models. Subsequently, a simplified model was re-evaluated by descending the dataset from 13 time points to 2 time points. The best suitable bacterial concentration was determined to be 1.5 optical density (OD) and the optimum detection wavelength for AI-Blue-Carba was set as 615 nm. Among the 2 models (LRM and LSTM), the LSTM model generated the higher ROC-AUC value. Moreover, the simplified LSTM model trained by short time points (0–15 min) did not impair the accuracy of LSTM model. Compared with the traditional Blue-Carba, the AI-Blue-Carba method has a sensitivity of 95.3% and a specificity of 95.7% at 15 min, which is a rapid and accurate method to detect CPGNB.

One System for All: Is Mass Spectrometry a Future Alternative for Conventional Antibiotic Susceptibility Testing?

The two main pillars of clinical microbiological diagnostics are the identification of potentially pathogenic microorganisms from patient samples and the testing for antibiotic susceptibility (AST) to allow efficient treatment with active antimicrobial agents. While routine microbial species identification is increasingly performed with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), routine AST still largely relies on conventional and molecular techniques such as broth microdilution or disk and gradient diffusion tests, PCR and automated variants thereof. However, shortly after the introduction of MALDI-TOF MS based routine identification, first attempts to perform AST on the same instruments were reported. Today, a number of different approaches to perform AST with MALDI-TOF MS and other MS techniques have been proposed, some restricted to particular microbial taxa and resistance mechanisms while others being more generic. Further, while some of the methods are in a stage of proof of principles, others are already commercialized. In this review we discuss the different principal approaches of mass spectrometry based AST and evaluate the advantages and disadvantages compared to conventional and molecular techniques. At present, the possibility that MS will soon become a routine tool for AST seems unlikely – still, the same was true for routine microbial identification a mere 15 years ago.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?