NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

Impact of urbanization on antimicrobial resistance in soil microbial communities

Soil is one of the most important reservoirs of antibiotic resistance, global threat that needs to be addressed with the One Health approach. Despite urban parks playing a fundamental role in urban ecosystems, the diffusion, maintenance, and human impact of antibiotic-resistance genes in this substrate are still poorly addressed. To fill in this gap, we adopted a molecular and culturomics approach to study antibiotic resistance in urban parks, accounting for the environmental matrix and the level of urbanization. A higher abundance of efflux-mediated mechanisms in undisturbed environments was observed, while antibiotic alteration or inactivation, and target replacement were more abundant in areas with a higher level of urbanization, also confirmed by significant correlations with anthropogenic features of the environmental matrix. Overall, this study highlights the crucial need to monitor antibiotic resistance in urban parks' soil through a dual molecular and culturomics approach to fully understand and fight antibiotic resistance diffusion.

The role of bacteriophages in facilitating the horizontal transfer of antibiotic resistance genes in municipal wastewater treatment plants

Bacteriophages play integral roles in the ecosystem; however, their precise involvement in horizontal gene transfer and the spread of antibiotic resistance genes (ARGs) are not fully understood. In this study, a coculture system involving consortia of bacteriophages and multidrug-resistant bacteria from an aerobic tank in a municipal wastewater treatment plant (WWTP) was established to investigate the functions of bacteriophages in ARG transfer and spread. The results of the cocultivation of the MRB and bacteriophage consortia indicated that the bacterial community remained stable throughout the whole process, but the addition of bacteriophages significantly increased ARG abundance, especially in bacteriophage DNA. Nine out of the 11 identified ARGs significantly increased, indicating that more bacteriophage particles carried ARGs in the system after cocultivation. In addition, 686 plasmids were detected during cocultivation, of which only 3.36 % were identified as conjugative plasmids, which is significantly lower than the proportion found among previously published plasmids (25.2 %, totaling 14,029 plasmids). Our findings revealed that bacteriophages may play important roles in the horizontal transfer of ARGs through both bacteriophage-mediated conduction and an increase in extracellular ARGs; however, conjugative transfer may not be the main mechanism by which multidrug-resistant bacteria acquire and spread ARGs. Unlike in most previous reports, a coculture system of diverse bacteria and bacteriophages was established in this study to assess bacteriophage functions in ARG transfer and dissemination in the environment, overcoming the limitations associated with the isolation of bacteria and bacteriophages, as well as the specificity of bacteriophage hosts.

Application of next-generation sequencing to identify different pathogens

Early and precise detection and identification of various pathogens are essential for epidemiological monitoring, disease management, and reducing the prevalence of clinical infectious diseases. Traditional pathogen detection techniques, which include mass spectrometry, biochemical tests, molecular testing, and culture-based methods, are limited in application and are time-consuming. Next generation sequencing (NGS) has emerged as an essential technology for identifying pathogens. NGS is a cutting-edge sequencing method with high throughput that can create massive volumes of sequences with a broad application prospects in the field of pathogen identification and diagnosis. In this review, we introduce NGS technology in detail, summarizes the application of NGS in that identification of different pathogens, including bacteria, fungi, and viruses, and analyze the challenges and outlook for using NGS to identify clinical pathogens. Thus, this work provides a theoretical basis for NGS studies and provides evidence to support the application of NGS in distinguishing various clinical pathogens.

Strategies to Name Metallo-β-Lactamases and Number Their Amino Acid Residues

Metallo-β-lactamases (MBLs), also known as class B β-lactamases (BBLs), are Zn(II)-containing enzymes able to inactivate a broad range of β-lactams, the most commonly used antibiotics, including life-saving carbapenems. They have been known for about six decades, yet they have only gained much attention as a clinical problem for about three decades. The naming conventions of these enzymes have changed over time and followed various strategies, sometimes leading to confusion. We are summarizing the naming strategies of the currently known MBLs. These enzymes are quite diverse on the amino acid sequence level but structurally similar. Problems trying to describe conserved residues, such as Zn(II) ligands and other catalytically important residues, which have different numbers in different sequences, have led to the establishment of a standard numbering scheme for BBLs. While well intended, the standard numbering scheme is not trivial and has not been applied consistently. We revisit this standard numbering scheme and suggest some strategies for how its implementation could be made more accessible to researchers. Standard numbering facilitates the comparison of different enzymes as well as their interaction with novel antibiotics and BBL inhibitors.