Artificial Intelligence in Battle against Antimicrobial Resistance: Opportunities and Challenges

Due to the overuse and abuse of antibiotics, antimicrobial resistance (AMR) poses a serious risk to socioeconomic development and public health. A paradigm shift is required to address this dilemma, and artificial intelligence (AI) appears as a possible remedy. AI, including machine learning (ML) and deep learning (DL), has demonstrated significant promise in several medical research fields, especially in the fight against AMR. Applications of AI in AMR use cutting-edge computational methods to analyze gene expression and whole-genome sequencing data, assisting in discovering infectious disease etiology and disease subtypes. These AI-driven systems have several advantages over more conventional ones, including less need for human involvement, more accuracy, and lower costs. However, they also encounter difficulties, such as inconsistent performance across datasets, with data volume critically influencing model efficacy. The accessibility and expense of high-throughput sequencing data, particularly next-generation sequencing data, also pose challenges to the wider application of AI models for AMR investigation. Despite these difficulties, AI has significant promise in the fight against AMR, and its advantages and disadvantages must be carefully considered in order to build successful tactics for dealing with this urgent worldwide problem. We assess research papers on AMR analysis using AI on various datasets and contrast the effectiveness of various AI models. We thoroughly reviewed the DL models used up to this point in the field of AMR, and we additionally discussed the challenges that come with deploying these approaches. This paper offers a thorough overview of AI's applications in AMR analysis, highlighting both its benefits and drawbacks

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement

Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance

Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which bcombines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species

Quorum sensing: An imperative longevity weapon in bacteria

Bacterial cells exhibit a complex pattern of co-operative behaviour as shown by their capacity to communicate amongst each other. Quorum sensing (QS) is a generic term used for bacterial cell-to-cell communication which secures survival of its species. Many QS bacteria produce and release autoinducers like acyl-homoserine lactone-signaling molecules to regulate cell population density. Different species of bacteria utilize different QS molecules to regulate its gene expression. A free-living marine bacterium, Vibrio harveyi, uses two QS system to control the density-dependent expression of bioluminescence (lux), commonly classified as sensor and autoinducer system. In Pseudomonas aeruginosa, QS not only controls virulence factor production but also biofilm formation. It is comprised two hierarchically organised systems, each consisting of an autoinducer synthetase (LasI/RhlI) and a corresponding regulator protein (LasR/RhlR). Biofilms produced by Pseudomonas, under control of QS, are ubiquitous in nature and contribute towards colonizations in patients of cystic fibrosis. Other organisms like Haemophilus influenzae and Streptococcus also utilize QS mechanism to control virulence in otitis and endocarditic decay. Overall, QS plays a major role in controlling bacterial economy. It is a simple, practical and effective mechanism of production and control. If the concentration of enzyme is critical, bacteria can sense it and perform a prompt activation or repression of certain target genes for controlling its environment. This review focuses on the QS mechanisms and their role in the survival of few important bacterial species

Phylogeny and potential virulence of cryptic clade Escherichia coli species complex isolates derived from an arable field trial

Analysis of Escherichia coli taxonomy has expanded into a species-complex with the identification of divergent cryptic clades. A key question is the evolutionary trajectory of these clades and their relationship to isolates of clinical or veterinary importance. Since they have some environmental association, we screened a collection of E. coli isolated from a long-term spring barley field trial for their presence. While most isolates clustered into the enteric-clade, four of them clustered into Clade-V, and one in Clade-IV. The Clade -V isolates shared >96% intra-clade average nucleotide sequence identity but <91% with other clades. Although pan-genomics analysis confirmed their taxonomy as Clade -V (E. marmotae), retrospective phylogroup PCR did not discriminate them correctly. Differences in metabolic and adherence gene alleles occurred in the Clade -V isolates compared to E. coli sensu scricto. They also encoded the bacteriophage phage-associated cyto-lethal distending toxin (CDT) and antimicrobial resistance (AMR) genes, including an ESBL, blaOXA-453. Thus, the isolate collection encompassed a genetic diversity, and included cryptic clade isolates that encode potential virulence factors. The analysis has determined the phylogenetic relationship of cryptic clade isolates with E. coli sensu scricto and indicates a potential for horizontal transfer of virulence factors

Future challenges to microbial food safety

Despite significant efforts by all parties involved, there is still a considerable burden of foodborne illness, in which micro-organisms play a prominent role. Microbes can enter the food chain at different steps, are highly versatile and can adapt to the environment allowing survival, growth and production of toxic compounds. This sets them apart from chemical agents and thus their study from food toxicology. We summarize the discussions of a conference organized by the Dutch Food and Consumer Products Safety Authority and the European Food Safety Authority. The goal of the conference was to discuss new challenges to food safety that are caused by micro-organisms as well as strategies and methodologies to counter these. Management of food safety is based on generally accepted principles of Hazard Analysis Critical Control Points and of Good Manufacturing Practices. However, a more pro-active, science-based approach is required, starting with the ability to predict where problems might arise by applying the risk analysis framework. Developments that may influence food safety in the future occur on different scales (from global to molecular) and in different time frames (from decades to less than a minute). This necessitates development of new risk assessment approaches, taking the impact of different drivers of change into account. We provide an overview of drivers that may affect food safety and their potential impact on foodborne pathogens and human disease risks. We conclude that many drivers may result in increased food safety risks, requiring active governmental policy setting and anticipation by food industries whereas other drivers may decrease food safety risks. Monitoring of contamination in the food chain, combined with surveillance of human illness and epidemiological investigations of outbreaks and sporadic cases continue to be important sources of information. New approaches in human illness surveillance include the use of molecular markers for improved outbreak detection and source attribution, sero-epidemiology and disease burden estimation. Current developments in molecular techniques make it possible to rapidly assemble information on the genome of various isolates of microbial species of concern. Such information can be used to develop new tracking and tracing methods, and to investigate the behavior of micro-organisms under environmentally relevant stress conditions. These novel tools and insight need to be applied to objectives for food safety strategies, as well as to models that predict microbial behavior. In addition, the increasing complexity of the global food systems necessitates improved communication between all parties involved: scientists, risk assessors and risk managers, as well as consumer

Molecular Tools for the Study of Resistance to Disinfectants

Disinfectants, antiseptics, and sanitizers are crucial for hygiene standards and disease control, as recently emphasized by the SARS-CoV-2 (COVID-19) pandemic. With the foreshadowing of antibiotic resistance, new cutting-edge technologies and innovative methodology need to be applied to prevent the latest emerging antimicrobial resistance crisis, resistance to disinfectants. Disinfectant resistance is a relatively novel field of study, and although some molecular mechanisms have been elucidated, little is known about complex mechanisms, cross-resistance with antibiotics, and the existence of resistance biomarkers. Fortunately, great advances have been made in the field of sequencing technology and bioinformatics. Although there are many limitations to this technology, various “omics” approaches to disinfectant resistance will be crucial in directing environment-specific disinfection programs. In addition, the vast amounts of data generated by sequencing technologies can be applied by artificial intelligence (AI) models to identify key disinfectant resistance markers and in the surveillance of disinfectant resistance genes. A combination of these approaches will be crucial in identifying new disinfectant resistance mechanisms, in monitoring resistant populations, and in identifying cellular targets for new disinfectant formulations. These molecular tools will be vital in the battle against disinfectant resistance, the latest development in the antimicrobial resistance crisis