Whole-genome sequencing in the prediction of antimicrobial resistance

Multidrug-Resistant Sepsis: A Critical Healthcare Challenge

Sepsis globally accounts for an alarming annual toll of 48.9 million cases, resulting in 11 million deaths, and inflicts an economic burden of approximately USD 38 billion on the United States healthcare system. The rise of multidrug-resistant organisms (MDROs) has elevated the urgency surrounding the management of multidrug-resistant (MDR) sepsis, evolving into a critical global health concern. This review aims to provide a comprehensive overview of the current epidemiology of (MDR) sepsis and its associated healthcare challenges, particularly in critically ill hospitalized patients. Highlighted findings demonstrated the complex nature of (MDR) sepsis pathophysiology and the resulting immune responses, which significantly hinder sepsis treatment. Studies also revealed that aging, antibiotic overuse or abuse, inadequate empiric antibiotic therapy, and underlying comorbidities contribute significantly to recurrent sepsis, thereby leading to septic shock, multi-organ failure, and ultimately immune paralysis, which all contribute to high mortality rates among sepsis patients. Moreover, studies confirmed a correlation between elevated readmission rates and an increased risk of cognitive and organ dysfunction among sepsis patients, amplifying hospital-associated costs. To mitigate the impact of sepsis burden, researchers have directed their efforts towards innovative diagnostic methods like point-of-care testing (POCT) devices for rapid, accurate, and particularly bedside detection of sepsis; however, these methods are currently limited to detecting only a few resistance biomarkers, thus warranting further exploration. Numerous interventions have also been introduced to treat MDR sepsis, including combination therapy with antibiotics from two different classes and precision therapy, which involves personalized treatment strategies tailored to individual needs. Finally, addressing MDR-associated healthcare challenges at regional levels based on local pathogen resistance patterns emerges as a critical strategy for effective sepsis treatment and minimizing adverse effects.

Antimicrobial Multidrug Resistance: Clinical Implications for Infection Management in Critically Ill Patients

The increasing incidence of antimicrobial resistance (AMR) worldwide represents a serious threat in the management of sepsis. Due to resistance to the most common antimicrobials prescribed, multidrug-resistant (MDR) pathogens have been associated with delays in adequate antimicrobial therapy leading to significant increases in mortality, along with prolonged hospital length of stay (LOS) and increases in healthcare costs. In response to MDR infections and the delay of microbiological results, broad-spectrum antibiotics are frequently used in empirical antimicrobial therapy. This can contribute to the overuse and misuse of antibiotics, further promoting the development of resistance. Multiple measures have been suggested to combat AMR. This review will focus on describing the epidemiology and trends concerning MDR pathogens. Additionally, it will explore the crucial aspects of identifying patients susceptible to MDR infections and optimizing antimicrobial drug dosing, which are both pivotal considerations in the fight against AMR. Expert commentary: The increasing AMR in ICUs worldwide makes the empirical antibiotic therapy challenging in septic patients. An AMR surveillance program together with improvements in MDR identification based on patient risk stratification and molecular rapid diagnostic tools may further help tailoring antimicrobial therapies and avoid unnecessary broad-spectrum antibiotics. Continuous infusions of antibiotics, therapeutic drug monitoring (TDM)-based dosing regimens and combination therapy may contribute to optimizing antimicrobial therapy and limiting the emergence of resistance.

Antibiotic Resistance and Probiotics: Knowledge Gaps, Market Overview and Preliminary Screening

Probiotics are among those products, the use of which is increasing, and they are available primarily as food/dietary supplements, as well as in the form of medicines. This study aims to assess the attitudes and practices of health professionals and students of health sciences, give a short overview of the probiotics currently on the market, and conduct a screening of five food supplements and one drug with respect to antibiotic resistance. Nearly half of the respondents in our survey state that probiotics have no side effects, while only 6.3% believe that the use of probiotics can lead to antibiotic resistance. In addition, more than 40% of the participants throw unused probiotics into municipal waste. The market analysis results indicate that probiotic products on the Serbian market have highly variable CFU counts, while the declared health claims cover numerous beneficial health effects, and they are sometimes even registered as medicines. Lactobacilli are frequently present in probiotic supplements, and are sold in pharmacies and online. The experimental results showed that antibiotic resistance is present in different types of lactobacilli in probiotic products. The risk of using probiotics, regardless of their beneficial health effects, should be taken into account in the future. An update to the regulations governing probiotics, including a stipulation for antimicrobial resistance (AMR) testing, should be established, and guidelines for their proper use and disposal put into place.

KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs

The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for species-specific genes extraction and classification, which can output the results through an iterative keyword matching algorithm. It can not only extract and classify the amino acid sequences, but also the nucleotide sequences, and it has proved to be fast and efficient for microbial analysis. Analysis of the ancient Wood Ljungdahl (WL) pathway through the KEGG_Extractor reveals that ~226 archaeal strains contained the WL pathway-related genes. Most of them were Methanococcus maripaludis, Methanosarcina mazei and members of the Methanobacterium, Thermococcus and Methanosarcina genus. Using the KEGG_Extractor, the ARWL database was constructed, which had a high accuracy and complement. This tool helps to link genes with the KEGG pathway and promote the reconstruction of molecular networks. Availability and implementation: KEGG_Extractor is freely available from the GitHub.

Paving the way for precise diagnostics of antimicrobial resistant bacteria

The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.

Application of Nanopore Sequencing (MinION) for the Analysis of Bacteriome and Resistome of Bean Sprouts

The aspiration these days is to apply rapid methods for parallel analysis of bacteriome and resistome of food samples to increase food safety and prevent antibiotic resistance genes (ARGs) spreading. In this work, we used nanopore sequencing (NS) to determine the diversity and dynamics of the microbiome and resistome in two types of bean sprouts. We proved that NS provided an easy, quick, and reliable way to identify the microbiome and resistome of a food sample also. The species diversity obtained by NS and by cultivation methods with MALDI-TOF MS identification was comparable. In both samples, before and after cultivation (30 °C, 48 h), the dominant part of bacteriome formed Gammaproteobacteria (Enterobacteriaceae, Erwiniaceae, Pseudomonadaceae, Moraxellaceae) and then Firmicutes (Streptococcaceae). The diversity and abundance of single ARGs groups were comparable for both samples despite bacteriome differences. More than 50% of the detected ARGs alignments were mutations conferring resistance to aminoglycosides (16S rRNA), resistance to fluoroquinolones (gyrA, gyrB, parC, parD) and elfamycin (EF-Tu). ARGs encoding efflux pumps formed more than 30% of the detected alignments. Beta-lactamases were represented by many variants, but were less abundant.

Full pathogen characterisation: species identification including the detection of virulence factors and antibiotic resistance genes via multiplex DNA-assays

Antibiotic resistances progressively cause treatment failures, and their spreading dynamics reached an alarming level. Some strains have already been classified as highly critical, e.g. the ones summarised by the acronym ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.). To restrain this trend and enable effective medication, as much information as possible must be obtained in the least possible time. Here, we present a DNA microarray-based assay that screens for the most important sepsis-relevant 44 pathogenic species, 360 virulence factors (mediate pathogenicity in otherwise non-pathogenic strains), and 409 antibiotic resistance genes in parallel. The assay was evaluated with 14 multidrug resistant strains, including all ESKAPE pathogens, mainly obtained from clinical isolates. We used a cost-efficient ligation-based detection platform designed to emulate the highly specific multiplex detection of padlock probes. Results could be obtained within one day, requiring approximately 4 h for amplification, application to the microarray, and detection.

Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance

Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database – Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene- and coverage- dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicin- and two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.

Characterization and Transcriptome Studies of Autoinducer Synthase Gene from Multidrug Resistant Acinetobacter baumannii Strain 863

Quorum sensing (QS) is a cell-to-cell communication system that uses autoinducers as signaling molecules to enable inter-species and intra-species interactions in response to external stimuli according to the population density. QS allows bacteria such as Acinetobacter baumannii to react rapidly in response to environmental changes and hence, increase the chances of survival. A. baumannii is one of the causative agents in hospital-acquired infections and the number of cases has increased remarkably in the past decade. In this study, A. baumannii strain 863, a multidrug-resistant pathogen, was found to exhibit QS activity by producing N-acyl homoserine lactone. We identified the autoinducer synthase gene, which we named abaI, by performing whole genome sequencing analysis of A. baumannii strain 863. Using high resolution tandem triple quadrupole mass spectrometry, we reported that abaI of A. baumannii strain 863 produced 3-hydroxy-dodecanoyl-homoserine lactone. A gene deletion mutant was constructed, which confirmed the functionality of abaI. A growth defect was observed in the QS-deficient mutant strain. Transcriptome profiling was performed to determine the possible genes regulated by QS. Four groups of genes that showed differential expression were discovered, namely those involved in carbon source metabolism, energy production, stress response and the translation process.

Genus-Wide Assessment of Antibiotic Resistance in Lactobacillus spp

Lactobacillus species are widely used as probiotics and starter cultures for a variety of foods, supported by a long history of safe usage. Although more than 35 species meet the European Food Safety Authority (EFSA) criteria for qualified presumption of safety status, the safety of Lactobacillus species and their carriage of antibiotic resistance (AR) genes is under continuing ad hoc review. To comprehensively update the identification of AR in the genus Lactobacillus, we determined the antibiotic susceptibility patterns of 182 Lactobacillus type strains and compared these phenotypes to their genotypes based on genome-wide annotations of AR genes. Resistances to trimethoprim, vancomycin, and kanamycin were the most common phenotypes. A combination of homology-based screening and manual annotation identified genes encoding resistance to aminoglycosides (20 sequences), tetracycline (18), erythromycin (6), clindamycin (60), and chloramphenicol (42). In particular, the genes aac(3) and lsa, involved in resistance to aminoglycosides and clindamycin, respectively, were found in Lactobacillus spp. Acquired determinants predicted to code for tetracycline and erythromycin resistance were detected in Lactobacillus ingluviei, Lactobacillus amylophilus, and Lactobacillus amylotrophicus, flanked in the genome by mobile genetic elements with potential for horizontal transfer.IMPORTANCELactobacillus species are generally considered to be nonpathogenic and are used in a wide variety of foods and products for humans and animals. However, many of the species examined in this study have antibiotic resistance levels which exceed those recommended by the EFSA, suggesting that these cutoff values should be reexamined in light of the genetic basis for resistance discussed here. Our data provide evidence for rationally revising the regulatory guidelines for safety assessment of lactobacilli entering the food chain as starter cultures, food preservatives, or probiotics and will facilitate comprehensive genotype-based assessment of strains for safety screening.

Molecular Methods for Detection of Antimicrobial Resistance

The increase in bacteria harboring antimicrobial resistance (AMR) is a global problem because there is a paucity of antibiotics available to treat multidrug-resistant bacterial infections in humans and animals. Detection of AMR present in bacteria that may pose a threat to veterinary and public health is routinely performed using standardized phenotypic methods. Molecular methods are often used in addition to phenotypic methods but are set to replace them in many laboratories due to the greater speed and accuracy they provide in detecting the underlying genetic mechanism(s) for AMR. In this article we describe some of the common molecular methods currently used for detection of AMR genes. These include PCR, DNA microarray, whole-genome sequencing and metagenomics, and matrix-assisted laser desorption ionization-time of flight mass spectrometry. The strengths and weaknesses of these methods are discussed, especially in the context of implementing them for routine surveillance activities on a global scale for mitigating the risk posed by AMR worldwide. Based on current popularity and ease of use, PCR and single-isolate whole-genome sequencing seem irreplaceable.

Recent Advances in Molecular Diagnosis of Pseudomonasaeruginosa Infection by State-of-the-Art Genotyping Techniques

Pseudomonas aeruginosa is a rod-shaped Gram-negative bacterium which is notably known as a pathogen in humans, animals, and plants. Infections caused by P. aeruginosa especially in hospitalized patients are often life-threatening and rapidly increasing worldwide throughout the years. Recently, multidrug-resistant P. aeruginosa has taken a toll on humans' health due to the inefficiency of antimicrobial agents. Therefore, the rapid and advanced diagnostic techniques to accurately detect this bacterium particularly in clinical samples are indeed necessary to ensure timely and effective treatments and to prevent outbreaks. This review aims to discuss most recent of state-of-the-art molecular diagnostic techniques enabling fast and accurate detection and identification of P. aeruginosa based on well-developed genotyping techniques, e.g., polymerase chain reaction, pulse-field gel electrophoresis, and next generation sequencing. The advantages and limitations of each of the methods are also reviewed.