A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains

Distinguishing Pathovars from Nonpathovars: Escherichia coli

Escherichia coli is one of the most well-adapted and pathogenically versatile bacterial organisms. It causes a variety of human infections, including gastrointestinal illnesses and extraintestinal infections. It is also part of the intestinal commensal flora of humans and other mammals. Groups of E. coli that cause diarrhea are often described as intestinal pathogenic E. coli (IPEC), while those that cause infections outside of the gut are called extraintestinal pathogenic E. coli (ExPEC). IPEC can cause a variety of diarrheal illnesses as well as extraintestinal syndromes such as hemolytic-uremic syndrome. ExPEC cause urinary tract infections, bloodstream infection, sepsis, and neonatal meningitis. IPEC and ExPEC have thus come to be referred to as pathogenic variants of E. coli or pathovars. While IPEC can be distinguished from commensal E. coli based on their characteristic virulence factors responsible for their associated clinical manifestations, ExPEC cannot be so easily distinguished. IPEC most likely have reservoirs outside of the human intestine but it is unclear if ExPEC represent nothing more than commensal E. coli that breach a sterile barrier to cause extraintestinal infections. This question has become more complicated by the advent of whole genome sequencing (WGS) that has raised a new question about the taxonomic characterization of E. coli based on traditional clinical microbiologic and phylogenetic methods. This review discusses how molecular epidemiologic approaches have been used to address these questions, and how answers to these questions may contribute to our better understanding of the epidemiology of infections caused by E. coli. *This article is part of a curated collection.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers

A hierarchical machine learning (HML) framework is presented that uses a small dataset to learn and predict the dominant build parameters necessary to print high-fidelity 3D features of alginate hydrogels. We examine the 3D printing of soft hydrogel forms printed with the freeform reversible embedding of suspended hydrogel method based on a CAD file that isolated the single-strand diameter and shape fidelity of printed alginate. Combinations of system variables ranging from print speed, flow rate, ink concentration to nozzle diameter were systematically varied to generate a small dataset of 48 prints. Prints were imaged and scored according to their dimensional similarity to the CAD file, and high print fidelity was defined as prints with less than 10% error from the CAD file. As a part of the HML framework, statistical inference was performed, using the least absolute shrinkage and selection operator to find the dominant variables that drive the error in the final prints. Model fit between the system parameters and print score was elucidated and improved by a parameterized middle layer of variable relationships which showed good performance between the predicted and observed data (R² = 0.643). Optimization allowed for the prediction of build parameters that gave rise to high-fidelity prints of the measured features. A trade-off was identified when optimizing for the fidelity of different features printed within the same construct, showing the need for complex predictive design tools. A combination of known and discovered relationships was used to generate process maps for the 3D bioprinting designer that show error minimums based on the chosen input variables. Our approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.

Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes!

BACKGROUND

Next-generation sequencing (NGS) technologies are being used to predict antimicrobial resistance. The field is evolving rapidly and transitioning out of the research setting into clinical use. Clinical laboratories are evaluating the accuracy and utility of genomic resistance prediction, including methods for NGS, downstream bioinformatic pipeline components, and the clinical settings in which this type of testing should be offered.

CONTENT

We describe genomic sequencing as it pertains to predicting antimicrobial resistance in clinical isolates and samples. We elaborate on current methodologies and workflows to perform this testing and summarize the current state of genomic resistance prediction in clinical settings. To highlight this aspect, we include 3 medically relevant microorganism exemplars: Mycobacterium tuberculosis, Staphylococcus aureus, and Neisseria gonorrhoeae. Last, we discuss the future of genomic-based resistance detection in clinical microbiology laboratories.

SUMMARY

Antimicrobial resistance prediction by genomic approaches is in its infancy for routine patient care. Genomic approaches have already added value to the current diagnostic testing landscape in specific circumstances and will play an increasingly important role in diagnostic microbiology. Future advancements will shorten turnaround time, reduce costs, and improve our analysis and interpretation of clinically actionable results.

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.

Interest of bacterial pangenome analyses in clinical microbiology

Thanks to the progress and decreasing costs in genome sequencing technologies, more than 250,000 bacterial genomes are currently available in public databases, covering most, if not all, of the major human-associated phylogenetic groups of these microorganisms, pathogenic or not. In addition, for many of them, sequences from several strains of a given species are available, thus enabling to evaluate their genetic diversity and study their evolution. In addition, the significant cost reduction of bacterial whole genome sequencing as well as the rapid increase in the number of available bacterial genomes have prompted the development of pangenomic software tools. The study of bacterial pangenome has many applications in clinical microbiology. It can unveil the pathogenic potential and ability of bacteria to resist antimicrobials as well identify specific sequences and predict antigenic epitopes that allow molecular or serologic assays and vaccines to be designed. Bacterial pangenome constitutes a powerful method for understanding the history of human bacteria and relating these findings to diagnosis in clinical microbiology laboratories in order to optimize patient management.

Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data

Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance.IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

PRAP: Pan Resistome analysis pipeline

Background

Antibiotic resistance genes (ARGs) can spread among pathogens via horizontal gene transfer, resulting in imparities in their distribution even within the same species. Therefore, a pan-genome approach to analyzing resistomes is necessary for thoroughly characterizing patterns of ARGs distribution within particular pathogen populations. Software tools are readily available for either ARGs identification or pan-genome analysis, but few exist to combine the two functions.

Results

We developed Pan Resistome Analysis Pipeline (PRAP) for the rapid identification of antibiotic resistance genes from various formats of whole genome sequences based on the CARD or ResFinder databases. Detailed annotations were used to analyze pan-resistome features and characterize distributions of ARGs. The contribution of different alleles to antibiotic resistance was predicted by a random forest classifier. Results of analysis were presented in browsable files along with a variety of visualization options. We demonstrated the performance of PRAP by analyzing the genomes of 26 Salmonella enterica isolates from Shanghai, China.

Conclusions

PRAP was effective for identifying ARGs and visualizing pan-resistome features, therefore facilitating pan-genomic investigation of ARGs. This tool has the ability to further excavate potential relationships between antibiotic resistance genes and their phenotypic traits.

Gene Transmission in the One Health Microbiosphere and the Channels of Antimicrobial Resistance

Antibiotic resistance is a field in which the concept of One Health can best be illustrated. One Health is based on the definition of communication spaces among diverse environments. Antibiotic resistance is encoded by genes, however, these genes are propagated in mobile genetic elements (MGEs), circulating among bacterial species and clones that are integrated into the multiple microbiotas of humans, animals, food, sewage, soil, and water environments, the One Health microbiosphere. The dynamics and evolution of antibiotic resistance depend on the communication networks linking all these ecological, biological, and genetic entities. These communications occur by environmental overlapping and merging, a critical issue in countries with poor sanitation, but also favored by the homogenizing power of globalization. The overwhelming increase in the population of highly uniform food animals has contributed to the parallel increase in the absolute size of their microbiotas, consequently enhancing the possibility of microbiome merging between humans and animals. Microbial communities coalescence might lead to shared microbiomes in which the spread of antibiotic resistance (of human, animal, or environmental origin) is facilitated. Intermicrobiome communication is exerted by shuttle bacterial species (or clones within species) belonging to generalist taxa, able to multiply in the microbiomes of various hosts, including humans, animals, and plants. Their integration into local genetic exchange communities fosters antibiotic resistance gene flow, following the channels of accessory genome exchange among bacterial species. These channels delineate a topology of gene circulation, including dense clusters of species with frequent historical and recent exchanges. The ecological compatibility of these species, sharing the same niches and environments, determines the exchange possibilities. In summary, the fertility of the One Health approach to antibiotic resistance depends on the progress of understanding multihierarchical systems, encompassing communications among environments (macro/microaggregates), among microbiotas (communities), among bacterial species (clones), and communications among MGEs.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.