Quantitative Imaging of Bacteriophage Amplification for Rapid Detection of Bacteria in Model Foods

A phage amplification-assisted SEA-CRISPR/Cas12a system for viable bacteria detection

Rapid and accurate detection of viable bacteria is essential for the clinical diagnosis of urinary tract infections (UTIs) and for making effective therapeutic decisions. However, most current molecular diagnostic techniques are unable to differentiate between viable and non-viable bacteria. In this study, we introduce a novel isothermal platform that integrates strand exchange amplification (SEA) with the CRISPR/Cas12a system, thereby enhancing both the sensitivity and specificity of the assay and achieving detection of phage DNA at concentrations as low as 4 × 102 copies per μL. Moreover, the incorporation of phages facilitates the specific recognition of viable bacteria and amplifies the initial signal through the inherent specificity and propagation properties of these phages. By employing the phage-assisted SEA-Cas12a approach, we successfully detected viable bacteria in human urine samples without the necessity of DNA extraction within 3.5 hours, achieving a detection limit of 103 CFU per mL. Considering its speed, accuracy, and independence from specialized equipment, this platform demonstrates significant potential as a robust tool for the rapid detection of various pathogens in resource-limited settings, thereby facilitating timely clinical management of UTI patients.

Detection of Escherichia coli Using Bacteriophage T7 and Analysis of Excitation‑Emission Matrix Fluorescence Spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

Antibiotic Resistance: Do We Need Only Cutting-Edge Methods, or Can New Visions Such as One Health Be More Useful for Learning from Nature?

Antibiotic resistance is an increasing global problem for public health, and focusing on biofilms has provided further insights into resistance evolution in bacteria. Resistance is innate in many bacterial species, and many antibiotics are derived from natural molecules of soil microorganisms. Is it possible that nature can help control AMR diffusion? In this review, an analysis of resistance mechanisms is summarized, and an excursus of the different approaches to challenging resistance spread based on natural processes is presented as "lessons from Nature". On the "host side", immunotherapy strategies for bacterial infections have a long history before antibiotics, but continuous new inputs through biotechnology advances are enlarging their applications, efficacy, and safety. Antimicrobial peptides and monoclonal antibodies are considered for controlling antibiotic resistance. Understanding the biology of natural predators is providing new, effective, and safe ways to combat resistant bacteria. As natural enemies, bacteriophages were used to treat severe infections before the discovery of antibiotics, marginalized during the antibiotic era, and revitalized upon the diffusion of multi-resistance. Finally, sociopolitical aspects such as education, global action, and climate change are also considered as important tools for tackling antibiotic resistance from the One Health perspective.

Bio-based antimicrobial compositions and sensing technologies to improve food safety

Microbial contamination of food products is a significant challenge that impacts food safety and quality. This review focuses on bio-based technologies for enhancing the decontamination of raw foods during postharvest processing, preventing cross-contamination, and rapidly detecting microbial risks. The bio-based antimicrobial compositions include bio-based antimicrobial delivery systems and coatings. The antimicrobial delivery systems are developed using cell-based carriers, microbubbles, and lipid-based colloidal particles. The antimicrobial coatings are engineered by incorporating biopolymers with conventional antimicrobials or cell-based antimicrobial carriers. The bio-based sensing approaches focus on replacing antibodies with more stable and cost-effective bio-receptors, including antimicrobial peptides, bacteriophages, DNAzymes, and engineered liposomes. Together, these approaches can reduce microbial contamination risks and enhance the in-situ detection of microbes.