Sequencing-based methods and resources to study antimicrobial resistance

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.

Dual elemental doping activated signaling pathway of angiogenesis and defective heterojunction engineering for effective therapy of MRSA-infected wounds

Multi-drug resistant bacterial infections pose a significant threat to human health. Thus, the development of effective bactericidal strategies is a pressing concern. In this study, a ternary heterostructure (Zn-CN/P-GO/BiS) comprised of Zn-doped graphite phase carbon nitride (g-C3N4), phosphorous-doped graphene oxide (GO) and bismuth sulphide (Bi2S3) is constructed for efficiently treating methicillin-resistant Staphylococcus aureus (MRSA)-infected wound. Zn doping-induced defect sites in g-C3N4 results in a reduced band gap (ΔE) and a smaller energy gap (ΔEST) between the singlet state S1 and triplet state T1, which favours two-photon excitation and accelerates electron transfer. Furthermore, the formation of an internal electric field at the ternary heterogeneous interface optimizes the charge transfer pathway, inhibits the recombination of electron-hole pairs, improves the photodynamic effect of g-C3N4, and enhances its catalytic performance. Therefore, the Zn-CN/P-GO/BiS significantly augments the production of reactive oxygen species and heat under 808 nm NIR (0.67 W cm-2) irradiation, leading to the elimination of 99.60% ± 0.07% MRSA within 20 min. Additionally, the release of essential trace elements (Zn and P) promotes wound healing by activating hypoxia-inducible factor-1 (HIF-1) and peroxisome proliferator-activated receptors (PPAR) signaling pathways. This work provides unique insight into the rapid antibacterial applications of trace element doping and two-photon excitation.

Molecular rapid diagnostic testing for bloodstream infections: Nanopore targeted sequencing with pathogen-specific primers

BACKGROUND

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs).

METHODS

We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023.

RESULTS

The positivity rate for NTS (69.5 %, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7 %, 289/387; p = 0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9 %, 131/387; p < 0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n = 54), Pseudomonas aeruginosa (n = 36), Escherichia coli (n = 36), Enterococcus faecium(n = 30), Acinetobacter baumannii(n = 26), Staphylococcus aureus(n = 23), and Human cytomegalovirus (n = 37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0 % (95 % CI 79.5 %-87.7 %) and 90.1 % (95 % CI 81.7 %-88.5 %), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2 % (359/387), whereas the consistency between NTS and BC was only 60.2 % (233/387). In 80.6 % (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95 % of samples can be tested and analyzed in approximately 7 h, allowing for early patient diagnosis.

CONCLUSIONS

NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences

BACKGROUND

Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.

RESULTS

In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.

CONCLUSIONS

ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Role of RelA-synthesized (p)ppGpp and ROS-induced mutagenesis in de novo acquisition of antibiotic resistance in E. coli

The stringent response of bacteria to starvation and stress also fulfills a role in addressing the threat of antibiotics. Within this stringent response, (p)ppGpp, synthesized by RelA or SpoT, functions as a global alarmone. However, the effect of this (p)ppGpp on resistance development is poorly understood. Here, we show that knockout of relA or rpoS curtails resistance development against bactericidal antibiotics. The emergence of mutated genes associated with starvation and (p)ppGpp, among others, indicates the activation of stringent responses. The growth rate is decreased in ΔrelA-resistant strains due to the reduced ability to synthesize (p)ppGpp and the persistence of deacylated tRNA impeding protein synthesis. Sluggish cellular activity causes decreased production of reactive oxygen species (ROS), thereby reducing oxidative damage, leading to weakened DNA mismatch repair, potentially reducing the generation of mutations. These findings offer new targets for mitigating antibiotic resistance development, potentially achieved through inhibiting (p)ppGpp or ROS synthesis.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes

MOTIVATION

The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance.

RESULTS

In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs.

AVAILABILITY AND IMPLEMENTATION

The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation

BACKGROUND

The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection.

OBJECTIVE

In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed.

METHODS

The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients' admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model's predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning-based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic.

RESULTS

The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance.

CONCLUSIONS

The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Rapid detection of mexX in Pseudomonas aeruginosa based on CRISPR-Cas13a coupled with recombinase polymerase amplification

The principal pathogen responsible for chronic urinary tract infections, immunocompromised hosts, and cystic fibrosis patients is Pseudomonas aeruginosa, which is difficult to eradicate. Due to the extensive use of antibiotics, multidrug-resistant P. aeruginosa has evolved, complicating clinical therapy. Therefore, a rapid and efficient approach for detecting P. aeruginosa strains and their resistance genes is necessary for early clinical diagnosis and appropriate treatment. This study combines recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats-association protein 13a (CRISPR-Cas13a) to establish a one-tube and two-step reaction systems for detecting the mexX gene in P. aeruginosa. The test times for one-tube and two-step RPA-Cas13a methods were 5 and 40 min (including a 30 min RPA amplification reaction), respectively. Both methods outperform Quantitative Real-time Polymerase Chain Reactions (qRT-PCR) and traditional PCR. The limit of detection (LoD) of P. aeruginosa genome in one-tube and two-step RPA-Cas13a is 10 aM and 1 aM, respectively. Meanwhile, the designed primers have a high specificity for P. aeruginosa mexX gene. These two methods were also verified with actual samples isolated from industrial settings and demonstrated great accuracy. Furthermore, the results of the two-step RPA-Cas13a assay could also be visualized using a commercial lateral flow dipstick with a LoD of 10 fM, which is a useful adjunt to the gold-standard qRT-PCR assay in field detection. Taken together, the procedure developed in this study using RPA and CRISPR-Cas13a provides a simple and fast way for detecting resistance genes.

Metabolism-Triggered Plasmonic Nanosensor for Bacterial Detection and Antimicrobial Susceptibility Testing of Clinical Isolates

Antimicrobial resistance (AMR) is predicted to become the leading cause of death worldwide in the coming decades. Rapid and on-site antibiotic susceptibility testing (AST) is crucial for guiding appropriate antibiotic choices to combat AMR. With this in mind, we have designed a simple and efficient plasmonic nanosensor consisting of Cu2+ and cysteine-modified AuNP (Au/Cys) that utilizes the metabolic activity of bacteria toward Cu2+ for bacterial detection and AST. When Cu2+ is present, it induces the aggregation of Au/Cys. However, in the presence of bacteria, Cu2+ is metabolized to varying extents, resulting in distinct levels of aggregation. Moreover, the metabolic activity of bacteria can be influenced by their antibiotic susceptibility, allowing us to differentiate between susceptible and resistant strains through direct color changes from the Cu2+-Au/Cys platform over approximately 3 h. These color changes can be easily detected using naked-eye observation, smartphone analysis, or absorption readout. We have validated the platform using four clinical isolates and six types of antibiotics, demonstrating a clinical sensitivity and specificity of 95.8%. Given its simplicity, low cost, high speed, and high accuracy, the plasmonic nanosensor holds great potential for point-of-care detection of antibiotic susceptibility across various settings.

Metagenomics reveals the temporal dynamics of the rumen resistome and microbiome in goat kids

BACKGROUND

The gut microbiome of domestic animals carries antibiotic resistance genes (ARGs) which can be transmitted to the environment and humans, resulting in challenges of antibiotic resistance. Although it has been reported that the rumen microbiome of ruminants may be a reservoir of ARGs, the factors affecting the temporal dynamics of the rumen resistome are still unclear. Here, we collected rumen content samples of goats at 1, 7, 14, 28, 42, 56, 70, and 84 days of age, analyzed their microbiome and resistome profiles using metagenomics, and assessed the temporal dynamics of the rumen resistome in goats at the early stage of life under a conventional feeding system.

RESULTS

In our results, the rumen resistome of goat kids contained ARGs to 41 classes, and the richness of ARGs decreased with age. Four antibiotic compound types of ARGs, including drugs, biocides, metals, and multi-compounds, were found during milk feeding, while only drug types of ARGs were observed after supplementation with starter feed. The specific ARGs for each age and their temporal dynamics were characterized, and the network inference model revealed that the interactions among ARGs were related to age. A strong correlation between the profiles of rumen resistome and microbiome was found using Procrustes analysis. Ruminal Escherichia coli within Proteobacteria phylum was the main carrier of ARGs in goats consuming colostrum, while Prevotella ruminicola and Fibrobacter succinogenes associated with cellulose degradation were the carriers of ARGs after starter supplementation. Milk consumption was likely a source of rumen ARGs, and the changes in the rumen resistome with age were correlated with the microbiome modulation by starter supplementation.

CONCLUSIONS

Our data revealed that the temporal dynamics of the rumen resistome are associated with the microbiome, and the reservoir of ARGs in the rumen during early life is likely related to age and diet. It may be a feasible strategy to reduce the rumen and its downstream dissemination of ARGs in ruminants through early-life dietary intervention. Video Abstract.

Altered microbiota, antimicrobial resistance genes, and functional enzyme profiles in the rumen of yak calves fed with milk replacer

IMPORTANCE

Yaks, as ruminants inhabiting high-altitude environments, possess a distinct rumen microbiome and are resistant to extreme living conditions. This study investigated the microbiota, resistome, and functional gene profiles in the rumen of yaks fed milk or milk replacer (MR), providing insights into the regulation of the rumen microbiome and the intervention of antimicrobial resistance in yaks through dietary methods. The abundance of Prevotella members increased significantly in response to MR. Tetracycline resistance was the most predominant. The rumen of yaks contained multiple antimicrobial resistance genes (ARGs) originating from different bacteria, which could be driven by MR, and these ARGs displayed intricate and complex interactions. MR also induced changes in functional genes. The enzymes associated with fiber degradation and butyrate metabolism were activated and showed close correlations with Prevotella members and butyrate concentration. This study allows us to deeply understand the ruminal microbiome and ARGs of yaks and their relationship with rumen bacteria in response to different milk sources.

Reactive oxygen species accelerate de novo acquisition of antibiotic resistance in E. coli

Reactive oxygen species (ROS) produced as a secondary effect of bactericidal antibiotics are hypothesized to play a role in killing bacteria. If correct, ROS may play a role in development of de novo resistance. Here we report that single-gene knockout strains with reduced ROS scavenging exhibited enhanced ROS accumulation and more rapid acquisition of resistance when exposed to sublethal levels of bactericidal antibiotics. Consistent with this observation, the ROS scavenger thiourea in the medium decelerated resistance development. Thiourea downregulated the transcriptional level of error-prone DNA polymerase and DNA glycosylase MutM, which counters the incorporation and accumulation of 8-hydroxy-2'-deoxyguanosine (8-HOdG) in the genome. The level of 8-HOdG significantly increased following incubation with bactericidal antibiotics but decreased after treatment with the ROS scavenger thiourea. These observations suggest that in E. coli sublethal levels of ROS stimulate de novo development of resistance, providing a mechanistic basis for hormetic responses induced by antibiotics.

Metagenomic identification of pathogens and antimicrobial-resistant genes in bacterial positive blood cultures by nanopore sequencing

Nanopore sequencing workflows have attracted increasing attention owing to their fast, real-time, and convenient portability. Positive blood culture samples were collected from patients with bacterial bloodstream infection and tested by nanopore sequencing. This study compared the sequencing results for pathogen taxonomic profiling and antimicrobial resistance genes to those of species identification and phenotypic drug susceptibility using traditional microbiology testing. A total of 37 bacterial positive blood culture results of strain genotyping by nanopore sequencing were consistent with those of mass spectrometry. Among them, one mixed infection of bacteria and fungi was identified using nanopore sequencing and confirmatory quantitative polymerase chain reaction. The amount of sequencing data was 21.89 ± 8.46 MB for species identification, and 1.0 MB microbial strain data enabled accurate determination. Data volumes greater than or equal to 94.6 MB nearly covered all the antimicrobial resistance genes of the bacteria in our study. In addition, the results of the antimicrobial resistance genes were compared with those of phenotypic drug susceptibility testing for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Therefore, the nanopore sequencing platform for rapid identification of causing pathogens and relevant antimicrobial resistance genes complementary to conventional blood culture outcomes may optimize antimicrobial stewardship management for patients with bacterial bloodstream infection.

Development of a novel integrated isothermal amplification system for detection of bacteria-spiked blood samples

Bloodstream infection (BSI) caused by bacteria is highly pathogenic and lethal, and easily develops whole-body inflammatory state. Immediate identification of disease-causing bacteria can improve patient prognosis. Traditional testing methods are not only time-consuming, but such tests are limited to laboratories. Recombinase polymerase amplification combined with lateral flow dipstick (RPA-LFD) holds great promise for rapid nucleic acid detection, but the uncapping operation after amplification easily contaminates laboratories. Therefore, the establishment of a more effective integrated isothermal amplification system has become an urgent problem to be solved. In this study, we designed and fabricated a hermetically sealed integrated isothermal amplification system. Combining with this system, a set of RPA-LFD assays for detecting S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI were established and evaluated. The whole process could be completed in less than 15 min and the results can be visualized by the naked eye. The developed RPA-LFD assays displayed a good sensitivity, and no cross-reactivity was observed in seven similar bacterial genera. The results obtained with 60 clinical samples indicated that the developed RPA-LFD assays had high specifcity and sensitivity for identifying S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI. In conclusion, our results showed that the developed RPA-LFD assay is an alternative to existing PCR-based methods for detection of S. aureus, K. peneumoniae, P. aeruginosa, and H. influenza in BSI in primary hospitals.

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

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.

Metagenomic analysis of ecological niche overlap and community collapse in microbiome dynamics

Species utilizing the same resources often fail to coexist for extended periods of time. Such competitive exclusion mechanisms potentially underly microbiome dynamics, causing breakdowns of communities composed of species with similar genetic backgrounds of resource utilization. Although genes responsible for competitive exclusion among a small number of species have been investigated in pioneering studies, it remains a major challenge to integrate genomics and ecology for understanding stable coexistence in species-rich communities. Here, we examine whether community-scale analyses of functional gene redundancy can provide a useful platform for interpreting and predicting collapse of bacterial communities. Through 110-day time-series of experimental microbiome dynamics, we analyzed the metagenome-assembled genomes of co-occurring bacterial species. We then inferred ecological niche space based on the multivariate analysis of the genome compositions. The analysis allowed us to evaluate potential shifts in the level of niche overlap between species through time. We hypothesized that community-scale pressure of competitive exclusion could be evaluated by quantifying overlap of genetically determined resource-use profiles (metabolic pathway profiles) among coexisting species. We found that the degree of community compositional changes observed in the experimental microbiome was correlated with the magnitude of gene-repertoire overlaps among bacterial species, although the causation between the two variables deserves future extensive research. The metagenome-based analysis of genetic potential for competitive exclusion will help us forecast major events in microbiome dynamics such as sudden community collapse (i.e., dysbiosis).

Revolutionizing Medical Microbiology: How Molecular and Genomic Approaches Are Changing Diagnostic Techniques

Molecular and genomic approaches have revolutionized medical microbiology by offering faster and more accurate diagnostic techniques for infectious diseases. Traditional methods, which include culturing microbes and biochemical testing, are time-consuming and may not detect antibiotic-resistant strains. In contrast, molecular and genomic methods, including polymerase chain reaction (PCR)-based techniques and whole-genome sequencing, provide rapid and precise detection of pathogens, early-stage diseases, and antibiotic-resistant strains. These approaches have advantages such as high sensitivity and specificity, the potential for targeted therapies, and personalized medicine. However, implementing molecular and genomic techniques faces challenges related to cost, equipment, expertise, and data analysis. Ethical and legal considerations regarding patient privacy and genetic data usage also arise. Nonetheless, the future of medical microbiology lies in the widespread adoption of molecular and genomic approaches, which can lead to improved patient outcomes and the identification of antibiotic-resistant strains. Continued advancements, education, and exploration of ethical implications are necessary to fully harness the potential of molecular and genomic techniques in medical microbiology.

Microbiota of long-term indwelling hemodialysis catheters during renal transplantation perioperative period: a cross-sectional metagenomic microbial community analysis

Background: Catheter-related infection (CRI) is a major complication in patients undergoing hemodialysis. The lack of high-throughput research on catheter-related microbiota makes it difficult to predict the occurrence of CRI. Thus, this study aimed to delineate the microbial structure and diversity landscape of hemodialysis catheter tips among patients during the perioperative period of kidney transplantation (KTx) and provide insights into predicting the occurrence of CRI.Methods: Forty patients at the Department of Transplantation undergoing hemodialysis catheter removal were prospectively included. Samples, including catheter tip, catheter outlet skin swab, catheter blood, peripheral blood, oropharynx swab, and midstream urine, from the separate pre- and post-KTx groups were collected and analyzed using metagenomic next-generation sequencing (mNGS). All the catheter tips and blood samples were cultured conventionally.Results: The positive detection rates for bacteria using mNGS and traditional culture were 97.09% (200/206) and 2.65% (3/113), respectively. Low antibiotic-sensitivity biofilms with colonized bacteria were detected at the catheter tip. In asymptomatic patients, no statistically significant difference was observed in the catheter tip microbial composition and diversity between the pre- and post-KTx group. The catheter tip microbial composition and diversity were associated with fasting blood glucose levels. Microorganisms at the catheter tip most likely originated from catheter outlet skin and peripheral blood.Conclusions: The long-term colonization microbiota at the catheter tip is in a relatively stable state and is not readily influenced by KTx. It does not act as the source of infection in all CRIs, but could reflect hematogenous infection to some extent.

Polymyxin resistance caused by large-scale genomic inversion due to IS26 intramolecular translocation in Klebsiella pneumoniae

Polymyxin B and polymyxin E (colistin) are presently considered the last line of defense against human infections caused by multidrug-resistant Gram-negative organisms such as carbapenemase-producer Enterobacterales, Acinetobacter baumannii, and Klebsiella pneumoniae. Yet resistance to this last-line drugs is a major public health threat and is rapidly increasing. Polymyxin S2 (S2) is a polymyxin B analogue previously synthesized in our institute with obviously high antibacterial activity and lower toxicity than polymyxin B and colistin. To predict the possible resistant mechanism of S2 for wide clinical application, we experimentally induced bacterial resistant mutants and studied the preliminary resistance mechanisms. Mut-S, a resistant mutant of K. pneumoniae ATCC BAA-2146 (Kpn2146) induced by S2, was analyzed by whole genome sequencing, transcriptomics, mass spectrometry and complementation experiment. Surprisingly, large-scale genomic inversion (LSGI) of approximately 1.1 Mbp in the chromosome caused by IS26 mediated intramolecular transposition was found in Mut-S, which led to mgrB truncation, lipid A modification and hence S2 resistance. The resistance can be complemented by plasmid carrying intact mgrB. The same mechanism was also found in polymyxin B and colistin induced drug-resistant mutants of Kpn2146 (Mut-B and Mut-E, respectively). This is the first report of polymyxin resistance caused by IS26 intramolecular transposition mediated mgrB truncation in chromosome in K. pneumoniae. The findings broaden our scope of knowledge for polymyxin resistance and enriched our understanding of how bacteria can manage to survive in the presence of antibiotics.

Characterization and Molecular Mechanism of Aminoglycoside-6-Adenyl Transferase Associated with Aminoglycoside Resistance from Elizabethkingia meningoseptica

Purpose

Elizabethkingia meningoseptica (EM) is a multi-drug-resistant bacterium of global concern for its role in nosocomial infection and is generally resistant to aminoglycoside antibiotics. In the whole genome of an EM strain (FMS-007), an aminoglycoside-6-adenyl transferase gene (ant(6)FMS-007) was predicted. This study aimed to characterize the biochemical function of ANT(6)FMS-007 and analyze the relationship between genotype and phenotype of ant(6) in clinical EM isolates, so as to provide evidence for clinical precision drug use. This study could establish a method for the verification of known or unknown functionally resistant genes.

Methods

A total of 42 EM clinical isolates were collected from clinical departments during 2015-2023. The phenotype of aminoglycoside antibiotics was analyzed by broth microdilution (BMD) and Kirby-Bauer (K-B) methods. The whole-length ant(6) from EM clinical isolates was analyzed by polymerase chain reaction (PCR) and sequencing. The biochemical function of predictive ANT(6)FMS-007 from the FMS-007 whole genome was identified by 3D plate experiment and mass spectrometry analysis. Candidate active sites were predicted by multi-species sequence alignment and molecular docking, and other important sites were identified in the comparison of ant(6) genotypes and phenotypes of EM clinical isolates. Drug susceptibility test was used to verify the function of these sites.

Results

The predictive ANT(6)FMS-007 protein could inactivate STR by modifying STR with ATP to form STR-AMP. Four active sites (Asp-38, Asp-42, Lys-95, and Lys-213) of ANT(6)FMS-007 were identified. Thirty-one EM clinical isolates (74%) carried the ant(6) gene. Eight EM clinical isolates containing the ant(6) gene had MIC values (<=32μg/mL) lower by at least 16-fold than FMS-007 (512μg/mL) for STR, and N59H and K204Q were the common mutations in the ant(6) gene.

Conclusion

This assay verified the biochemical function of the predictive gene ant(6)FMS-007 and could provide an alternative method to study resistant gene function in multi-drug-resistant bacteria. The inconsistency between genotype and phenotype of resistant genes indicated that the combination of resistance gene detection and functional analysis could better provide precision medicine for clinical use.

LSPR-Based Biosensing Enables the Detection of Antimicrobial Resistance Genes

The development of rapid, simple, and accurate bioassays for the detection of nucleic acids has received increasing demand in recent years. Here, localized surface plasmon resonance (LSPR) spectroscopy for the detection of an antimicrobial resistance gene, sulfhydryl variable β-lactamase (blaSHV), which confers resistance against a broad spectrum of β-lactam antibiotics is used. By performing limit of detection experiments, a 23 nucleotide (nt) long deoxyribonucleic acid (DNA) sequence down to 25 nm was detected, whereby the signal intensity is inversely correlated with sequence length (23, 43, 63, and 100 nt). In addition to endpoint measurements of hybridization events, the setup also allowed to monitor the hybridization events in real-time, and consequently enabled to extract kinetic parameters of the studied binding reaction. Performing LSPR measurements using single nucleotide polymorphism (SNP) variants of blaSHV revealed that these sequences can be distinguished from the fully complementary sequence. The possibility to distinguish such sequences is of utmost importance in clinical environments, as it allows to identify mutations essential for enzyme function and thus, is crucial for the correct treatment with antibiotics. Taken together, this system provides a robust, label-free, and cost-efficient analytical tool for the detection of nucleic acids and will enable the surveillance of antimicrobial resistance determinants.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Antimicrobial resistance gene lack in tick-borne pathogenic bacteria

Tick-borne infections, including those of bacterial origin, are significant public health issues. Antimicrobial resistance (AMR), which is one of the most pressing health challenges of our time, is driven by specific genetic determinants, primarily by the antimicrobial resistance genes (ARGs) of bacteria. In our work, we investigated the occurrence of ARGs in the genomes of tick-borne bacterial species that can cause human infections. For this purpose, we processed short/long reads of 1550 bacterial isolates of the genera Anaplasma (n = 20), Bartonella (n = 131), Borrelia (n = 311), Coxiella (n = 73), Ehrlichia (n = 13), Francisella (n = 959) and Rickettsia (n = 43) generated by second/third generation sequencing that have been freely accessible at the NCBI SRA repository. From Francisella tularensis, 98.9% of the samples contained the FTU-1 beta-lactamase gene. However, it is part of the F. tularensis representative genome as well. Furthermore, 16.3% of them contained additional ARGs. Only 2.2% of isolates from other genera (Bartonella: 2, Coxiella: 8, Ehrlichia: 1, Rickettsia: 2) contained any ARG. We found that the odds of ARG occurrence in Coxiella samples were significantly higher in isolates related to farm animals than from other sources. Our results describe a surprising lack of ARGs in these bacteria and suggest that Coxiella species in farm animal settings could play a role in the spread of AMR.

pH Window for High Selectivity of Ionizable Antimicrobial Polymers toward Bacteria

Antimicrobial polymers exhibit great potential for treating drug-resistant bacteria; however, designing antimicrobial polymers that can selectively kill bacteria and cause relatively low toxicity to normal tissues/cells remains a key challenge. Here, we report a pH window for ionizable polymers that exhibit high selectivity toward bacteria. Ionizable polymer PC6A showed the greatest selectivity (131.6) at pH 7.4, exhibiting low hemolytic activity and high antimicrobial activity against bacteria, whereas a very high or low protonation degree (PD) produced relatively low selectivity (≤35.6). Bactericidal mechanism of PC6A primarily comprised membrane lysis without inducing drug resistance even after consecutive incubation for 32 passages. Furthermore, PC6A demonstrated synergistic effects in combination with antibiotics at pH 7.4. Hence, this study provides a strategy for designing selective antimicrobial polymers.

[Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance]

Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.

Selection of Antibiotic-Resistant Bacterial Strains and Identification of Genomic Alterations by Whole-Genome Sequencing: Using Staphylococcus aureus and Oxacillin Resistance as an Example

Here, we discuss methods for the selection of antibiotic-resistant bacteria and the use of high-throughput whole-genome sequencing for the identification of the underlying mutations. We comment on sample requirements and the choice of specific DNA preparation methods depending on the strain used and briefly introduce a workflow we use for the selection of Staphylococcus aureus strains with increased oxacillin resistance and identification of genomic alterations.

Bacterial defences: mechanisms, evolution and antimicrobial resistance

Throughout their evolutionary history, bacteria have faced diverse threats from other microorganisms, including competing bacteria, bacteriophages and predators. In response to these threats, they have evolved sophisticated defence mechanisms that today also protect bacteria against antibiotics and other therapies. In this Review, we explore the protective strategies of bacteria, including the mechanisms, evolution and clinical implications of these ancient defences. We also review the countermeasures that attackers have evolved to overcome bacterial defences. We argue that understanding how bacteria defend themselves in nature is important for the development of new therapies and for minimizing resistance evolution.

A Rapid Antimicrobial Resistance Diagnostic Platform for Staphylococcus aureus Using Recombinase Polymerase Amplification

Antimicrobial resistance (AMR) has posed a global threat to public health. The Staphylococcus aureus strains have especially developed AMR to practically all antimicrobial medications. There is an unmet need for rapid and accurate detection of the S. aureus AMR. In this study, we developed two versions of recombinase polymerase amplification (RPA), the fluorescent signal monitoring and lateral flow dipstick, for detecting the clinically relevant AMR genes retained by S. aureus isolates and simultaneously identifying such isolates at the species level. The sensitivity and specificity were validated with clinical samples. Our results showed that this RPA tool was able to detect antibiotic resistance for all the 54 collected S. aureus isolates with high sensitivity, specificity, and accuracy (all higher than 92%). Moreover, results of the RPA tool are 100% consistent with that of PCR. In sum, we successfully developed a rapid and accurate AMR diagnostic platform for S. aureus. The RPA might be used as an effective diagnostic test in clinical microbiology laboratories to improve the design and application of antibiotic therapy. IMPORTANCE Staphylococcus aureus is a species of Staphylococcus and belongs to Gram-positive. Meanwhile, S. aureus remains one of the most common nosocomial and community-acquired infections, causing blood flow, skin, soft tissue, and lower respiratory tract infections. The identification of the particular nuc gene and the other eight genes of drug-resistant S. aureus can reliably and quickly diagnose the illness, allowing doctors to prescribe treatment regimens sooner. The detection target in this work is a particular gene of S. aureus, and a POCT is built to simultaneously recognize S. aureus and analyze genes representing four common antibiotic families. We developed and assessed a rapid and on-site diagnostic platform for the specific and sensitive detection of S. aureus. This method allows the determination of S. aureus infection and 10 different AMR genes representing four different families of antibiotics within 40 min. It was easily adaptable in low-resource circumstances and professional-lacking circumstances. It should be supported in overcoming the continuous difficulty of drug-resistant S. aureus infections, which is a shortage of diagnostic tools that can swiftly detect infectious bacteria and numerous antibiotic resistance indicators.

TB-ML-a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis

Motivation

Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test or reproduce published models.

Results

We packaged a number of published and unpublished ML models for predicting AMR of M.tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard, we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic.

Availability and implementation

TB-ML contains a simple Docker API written in Python and is available at https://github.com/jodyphelan/tb-ml. Example Docker containers for resistance prediction and corresponding data pre-processing as well as a tutorial on how to create new containers for TB-ML are available at https://tb-ml.github.io/tb-ml-containers/.

Contact

jody.phelan@lshtm.ac.uk.

Genomic Epidemiological Analysis of Antimicrobial-Resistant Bacteria with Nanopore Sequencing

Antimicrobial-resistant (AMR) bacterial infections caused by clinically important bacteria, including ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and mycobacteria (Mycobacterium tuberculosis and nontuberculous mycobacteria), have become a global public health threat. Their epidemic and pandemic clones often accumulate useful accessory genes in their genomes, such as AMR genes (ARGs) and virulence factor genes (VFGs). This process is facilitated by horizontal gene transfer among microbial communities via mobile genetic elements (MGEs), such as plasmids and phages. Nanopore long-read sequencing allows easy and inexpensive analysis of complex bacterial genome structures, although some aspects of sequencing data calculation and genome analysis methods are not systematically understood. Here we describe the latest and most recommended experimental and bioinformatics methods available for the construction of complete bacterial genomes from nanopore sequencing data and the detection and classification of genotypes of bacterial chromosomes, ARGs, VFGs, plasmids, and other MGEs based on their genomic sequences for genomic epidemiological analysis of AMR bacteria.

Antimicrobial susceptibility test and antimicrobial resistance gene detection of extracellular enzyme bacteria isolated from tilapia (Oreochromis niloticus) for probiotic candidates

Background and Aim: Antimicrobial resistance (AMR) is a global problem that can increase mortality and morbidity rates and adversely affect health. Therefore, AMR control must be carried out in various sectors, including the fisheries sector, using probiotics. Bacteria can become resistant to antibiotics, including bacteria used for probiotics. This study aimed to isolate bacteria as potential producers of extracellular enzymes, phenotypic characterization, and antibiotic-resistant gene patterns. Materials and Methods: In this study, 459 bacterial isolates were isolated from the stomach of tilapia in Indonesia. Tilapia was obtained from Sukabumi, Ciamis, Serang, Banjarnegara, Jayapura, Sorong, Manokwari Selatan, Takalar, Lampung, Batam, and Mandiangin. Enzymatic bacteria were identified. An antimicrobial susceptibility test was conducted by agar disk diffusion, and genotypic detection of encoding genes was performed using a molecular method. Results: This study obtained 137 isolates (29.84%) that can produce extracellular enzymes. The highest number of E-sensitive isolates was found, including 130 isolates (94.89%). Six isolates (6/137) can produce four enzymes (amylase, protease, cellulose, and lipase), and they were sensitive to antibiotics. A total of 99 isolates can produce extracellular enzymes, and they were sensitive to antibiotics. Such isolates serve as a consortium of probiotic candidates. The isolates that are resistant to oxytetracycline (OT), erythromycin (E), tetracycline (TE), and enrofloxacin (ENR) included 15 isolates (10.95%), seven isolates (5.11%), three isolates (2.19%), and one isolate (0.73%), respectively. In addition, four isolates (2.92%) were detected as multidrug-resistant. The tet(A) gene obtained the highest result of detection of resistance genes in isolates that were intermediate and resistant to TE and OT. Isolates that serve as ENR intermediates have a high qnr(S) resistance gene. Conclusion: The data in this study provide the latest update that bacteria can serve as a consortium of potential probiotics with antibiotic-resistant genes for the treatment of fish. Bacteria that are intermediate to antibiotics may contain resistance genes. The results of this study will improve the policy of probiotic standards in Indonesia.

Antimicrobial susceptibility study and molecular epidemiology of ceftazidime/avibactam against Pseudomonas aeruginosa collected from clinical patients in PR China (2004-2021)

Introduction. The increasing prevalence of multidrug-resistant (MDR) Pseudomonas aeruginosa worldwide is a significant global public health concern. Ceftazidime/avibactam (CZA) has been considered a novel promising β-lactam/β-lactamase inhibitor combination antibiotic against difficult-to-treat P. aeruginosa isolates. Big data studies on CZA susceptibility against P. aeruginosa have been limited.Gap statement. Production of metallo-β-lactamases was the most prevalent resistance mechanism for P. aeruginosa against CZA.Aim. To assess the in vitro activity of CZA against P. aeruginosa strains and the relevant resistance mechanisms.Methodology. One thousand three hundred and sixty-three P. aeruginosa isolates were collected from 2004 to 2021. Antimicrobial susceptibility testing was carried out for commonly used antipseudomonal drugs via the broth microdilution method. Polymerase chain reaction (PCR) or whole-genome sequencing were performed to analyse the most common carbapenemase genes. Molecular epidemiology was analysed by uploading the sequencing data to the Center for Genomic Epidemiology website.Results. Antimicrobial susceptibility testing showed that CZA and lipopeptides are the most active antibiotics against P. aeruginosa isolates. PCR and genome sequencing revealed that the most prevalent resistance mechanism for P. aeruginosa against CZA was the production of metallo-β-lactamases. None of the bla _PDC mutations were found to be associated with avibactam resistance.Conclusion. Our findings revealed that CZA and lipopeptides are the most active antibiotics against P. aeruginosa isolates. The most prevalent resistance mechanism for P. aeruginosa against CZA was the production of metallo-β-lactamases, and none of the bla _PDC mutations were found to be associated with avibactam resistance.

Transiently silent acquired antimicrobial resistance: an emerging challenge in susceptibility testing

Acquisition and expression of antimicrobial resistance (AMR) mechanisms in bacteria are often associated with a fitness cost. Thus, evolutionary adaptation and fitness cost compensation may support the advance of subpopulations with a silent resistance phenotype when the antibiotic selection pressure is absent. However, reports are emerging on the transient nature of silent acquired AMR, describing genetic alterations that can change the expression of these determinants to a clinically relevant level of resistance, and the association with breakthrough infections causing treatment failures. This phenomenon of transiently silent acquired AMR (tsaAMR) is likely to increase, considering the overall expansion of acquired AMR in bacterial pathogens. Moreover, the augmented use of genotypic methods in combination with conventional phenotypic antimicrobial susceptibility testing (AST) will increasingly enable the detection of genotype and phenotype discrepancy. This review defines tsaAMR as acquired antimicrobial resistance genes with a corresponding phenotype within the wild-type distribution or below the clinical breakpoint for susceptibility for which genetic alterations can mediate expression to a clinically relevant level of resistance. References to in vivo resistance development and therapeutic failures caused by selected resistant subpopulations of tsaAMR in Gram-positive and Gram-negative pathogens are given. We also describe the underlying molecular mechanisms, including alterations in the expression, reading frame or copy number of AMR determinants, and discuss the clinical relevance concerning challenges for conventional AST.

Application of metagenomic next-generation sequencing in the diagnosis of urinary tract infection in patients undergoing cutaneous ureterostomy

Objective

Urinary tract infection (UTI) is an inflammatory response of the urothelium to bacterial invasion and is a common complication in patients with cutaneous ureterostomy (CU). For such patients, accurate and efficient identification of pathogens remains a challenge. The aim of this study included exploring utility of metagenomic next-generation sequencing (mNGS) in assisting microbiological diagnosis of UTI among patients undergoing CU, identifying promising cytokine or microorganism biomarkers, revealing microbiome diversity change and compare virulence factors (VFs) and antibiotic resistance genes (ARGs) after infection.

Methods

We performed a case-control study of 50 consecutive CU patients from December 2020 to January 2021. According to the clinical diagnostic criteria, samples were divided into infected group and uninfected group and difference of urine culture, cytokines, microorganism, ARGs and VFs were compared between the two groups.

Results

Inflammatory responses were more serious in infected group, as evidenced by a significant increase in IFN-α (p=0.031), IL-1β (0.023) and IL-6 (p=0.018). Clinical culture shows that there is higher positive rate in infected group for most clinical pathogens like Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Candida auris etc. and the top three pathogens with positive frequencies were E. coli, K. pneumoniae, and Enterococcus faecalis. Benchmarking clinical culture, the total sensitivity is 91.4% and specificity is 76.3% for mNGS. As for mNGS, there was no significant difference in microbiome α- diversity between infected and uninfected group. Three species biomarkers including Citrobacter freundii, Klebsiella oxytoca, and Enterobacter cloacae are enriched in infected group based on Lefse. E. cloacae were significantly correlated with IL-6 and IL-10. K. oxytoca were significantly correlated with IL-1β. Besides, the unweighted gene number and weighted gene abundance of VFs or ARGs are significantly higher in infected group. Notablely, ARGs belonging to fluoroquinolones, betalatmas, fosfomycin, phenicol, phenolic compound abundance is significantly higher in infected group which may have bad effect on clinical treatment for patients.

Conclusion

mNGS, along with urine culture, will provide comprehensive and efficient reference for the diagnosis of UTI in patients with CU and allow us to monitor microbial changes in urine of these patients. Moreover, cytokines (IL-6, IL-1β, and IFN-a) or microorganisms like C. freundii, K. oxytoca or E. cloacae are promising biomarkers for building effective UTI diagnostic model of patients with CU and seriously the VFs and ARGs abundance increase in infected group may play bad effect on clinical treatment.

Extensively drug-resistant Acinetobacter baumannii: role of conjugative plasmids in transferring resistance

Acinetobacter baumannii is one of the most successful pathogens that can cause difficult-to-treat nosocomial infections. Outbreaks and infections caused by multi-drug resistant A. baumannii are prevalent worldwide, with only a few antibiotics are currently available for treatments. Plasmids represent an ideal vehicle for acquiring and transferring resistance genes in A. baumannii. Five extensively drug-resistant A. baumannii clinical isolates from three major Jordanian hospitals were fully sequenced. Whole-Genome Sequences (WGS) were used to study the antimicrobial resistance and virulence genes, sequence types, and phylogenetic relationship of the isolates. Plasmids were characterized In-silico, followed by conjugation, and plasmid curing experiments. Eight plasmids were recovered; resistance plasmids carrying either aminoglycosides or sulfonamide genes were detected. Chromosomal resistance genes included bla_OXA-66, bla_OXA-91, and bla_OXA-23, and the detected virulence factors were involved in biofilm formation, adhesion, and many other mechanisms. Conjugation and plasmid curing experiments resulted in the transfer or loss of several resistance phenotypes. Plasmid profiling along with phylogenetic analyses revealed high similarities between two A. baumannii isolates recovered from two different intensive care units (ICU). The high similarities between the isolates of the study, especially the two ICU isolates, suggest that there is a common A. baumannii strain prevailing in different ICU wards in Jordanian hospitals. Three resistance genes were plasmid-borne, and the transfer of the resistance phenotype emphasizes the role and importance of conjugative plasmids in spreading resistance among A. baumannii clinical strains.

Deterministic Effect of pH on Shaping Soil Resistome Revealed by Metagenomic Analysis

Soil is recognized as the major reservoir of antibiotic resistance genes (ARGs), harboring the most diverse naturally evolved ARGs on the planet. Multidrug resistance genes are a class of ARGs, and their high prevalence in natural soil ecosystems has recently raised concerns. Since most of these genes express proton motive force (PMF) driven efflux pumps, studying whether soil pH is a determinant for the selection of multidrug efflux pump genes and thus shaping the soil resistome are of great interest. In this study, we collected 108 soils with pH values ranging from 4.37 to 9.69 from multiple ecosystems and profiled the composition of ARGs for metagenomes and metagenome-assembled genomes. We observed the multidrug efflux pump genes enriched in the acidic soil resistome, and their abundances have significant soil pH dependence. This reflects the benefits of high soil proton activity on the multidrug efflux pump genes, especially for the PMF-driven inner membrane transferase. In addition, we preliminary indicate the putative microbial participants in pH shaping the soil resistome by applying ecological analyzing tools such as stepwise regression and random forest model fitting. The decisive influence of proton activity on shaping the resistome is more impactful than any other examined factors, and as the consequence, we revisited the influence of edaphic factors on the soil resistome; i.e., the deterministic selection of resistance mechanisms by edaphic factors could lead to the bottom-up shaping of the ARG composition. Such natural developing mechanisms of the resistome are herein suggested to be considered in assessing human-driven ARG transmissions.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Functional Metagenomics to Study Antibiotic Resistance

The construction and screening of metagenomic expression libraries have a great potential to identify novel genes with desired functions. Here, we describe metagenomic library preparation from fecal DNA, screening of libraries for antibiotic resistance genes (ARGs), massively parallel DNA sequencing of the enriched DNA fragments, and a computational pipeline for high-throughput assembly and annotation of functionally selected DNA.

The K-mer antibiotic resistance gene variant analyzer (KARGVA)

Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer - KARGVA - an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2 and 86.6% accuracy within 1 and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.

The application of targeted nanopore sequencing for the identification of pathogens and resistance genes in lower respiratory tract infections

Objectives

Lower respiratory tract infections (LRTIs) are one of the causes of mortality among infectious diseases. Microbial cultures commonly used in clinical practice are time-consuming, have poor sensitivity to unculturable and polymicrobial patterns, and are inadequate to guide timely and accurate antibiotic therapy. We investigated the feasibility of targeted nanopore sequencing (TNPseq) for the identification of pathogen and antimicrobial resistance (AMR) genes across suspected patients with LRTIs. TNPseq is a novel approach, which was improved based on nanopore sequencing for the identification of bacterial and fungal infections of clinical relevance.

Methods

This prospective study recruited 146 patients suspected of having LRTIs and with a median age of 61 years. The potential pathogens in these patients were detected by both TNPseq and the traditional culture workups. We compared the performance between the two methods among 146 LRTIs-related specimens. AMR genes were also detected by TNPseq to prompt the proper utilization of antibiotics.

Results

At least one pathogen was detected in 133 (91.1%) samples by TNPseq, but only 37 (25.3%) samples contained positive isolates among 146 cultured specimens. TNPseq possessed higher sensitivity than the conventional culture method (91.1 vs. 25.3%, P < 0.001) in identifying pathogens. It detected more samples with bacterial infections (P < 0.001) and mixed infections (P < 0.001) compared with the clinical culture tests. The most frequent AMR gene identified by TNPseq was bla _TEM (n = 29), followed by bla _SHV (n = 4), bla _KPC (n = 2), bla _CTX-M (n = 2), and mecA (n = 2). Furthermore, TNPseq discovered five possible multi-drug resistance specimens.

Conclusion

TNPseq is efficient to identify pathogens early, thus assisting physicians to conduct timely and precise treatment for patients with suspected LRTIs.

Multiplexed Target Enrichment Enables Efficient and In-Depth Analysis of Antimicrobial Resistome in Metagenomes

Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny fraction of metagenomic content, which makes it difficult to investigate low-abundance ARGs in various environmental settings. Thus, a highly sensitive, accurate, and comprehensive method is needed to describe ARG profiles in complex metagenomic samples. In this study, we established a high-throughput sequencing method based on targeted amplification, which could simultaneously detect ARGs (n = 251), mobile genetic element genes (n = 8), and metal resistance genes (n = 19) in metagenomes. The performance of amplicon sequencing was compared with traditional metagenomic shotgun sequencing (MetaSeq). A total of 1421 primer pairs were designed, achieving extremely high coverage of target genes. The amplicon sequencing significantly improved the recovery of target ARGs (~9 × 104-fold), with higher sensitivity and diversity, less cost, and computation burden. Furthermore, targeted enrichment allows deep scanning of single nucleotide polymorphisms (SNPs), and elevated SNPs detection was shown in this study. We further performed this approach for 48 environmental samples (37 feces, 20 soils, and 7 sewage) and 16 clinical samples. All samples tested in this study showed high diversity and recovery of targeted genes. Our results demonstrated that the approach could be applied to various metagenomic samples and served as an efficient tool in the surveillance and evolution assessment of ARGs. Access to the resistome using the enrichment method validated in this study enabled the capture of low-abundance resistomes while being less costly and time-consuming, which can greatly advance our understanding of local and global resistome dynamics. IMPORTANCE ARGs, an increasing global threat to human health, can be transferred into health-related microorganisms in the environment by horizontal gene transfer, posing a serious threat to public health. Advancing profiling methods are needed for monitoring and predicting the potential risks of ARGs in metagenomes. Our study described a customized amplicon sequencing assay that could enable a high-throughput, targeted, in-depth analysis of ARGs and detect a low-abundance portion of resistomes. This method could serve as an efficient tool to assess the variation and evolution of specific ARGs in the clinical and natural environment.

Genomic Insight into Shimazuella Soli Sp. Nov. Isolated from Soil and Its Putative Novel Class II Lasso Peptide

The strain designated as AN120528^T was isolated from farmland soil in South Korea. This strain grows well on R2A medium at 28 °C. The cells are an off-white colour and have no hyphae. The phylogenetic analysis indicated that the strain is a member of the genus Shimazuella with a 98.11% similarity to Shimazuella alba KC615^T and a 97.05% similarity to S. kribbensis KCTC 9933^T, respectively. The strain AN120528^T shares common chemotaxonomic features with the other two type strains in the genus. It has MK-9 (H₄) and MK-10 (H₄) as its predominant menaquinones. The major fatty acids are iso-C_14:0, iso-C_15:0, anteiso-C_15:0 and iso-C_16:0. Diphosphatidylglycerol (DPG), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), lipids (L), and aminolipids (AL) were identified as the major cellular polar lipids. Analysis of the peptidoglycan showed the presence of meso-diaminopimelic acid. Whole-genome sequencing revealed that the genome of the strain is approximately 3.3 Mbp in size. The strain showed a 77.5% average nucleotide identity (ANI) with S. alba KC615^T. The genomic DNA (gDNA) G + C content is 39.0%. Based on polyphasic taxonomy analysis, it is proposed that this strain, AN120528^T, represents a novel species in the genus Shimazuella, designated as Shimazuella soli sp. nov. The type stain is AN120528^T (=KCTC 39810^T = DSM 103571^T). Furthermore, shimazuellin I, a new 15-amino-acid peptide, was discovered in the AN120528^T through genome mining; it has the features of a lasso peptide, containing eight amino acids (-G-Q-G-G-S-N-N-D-) that form a macrolactam ring and seven amino acids (-D-G-W-Y-H-S-K-) that form a tail.

Contamination of Proteus mirabilis harbouring various clinically important antimicrobial resistance genes in retail meat and aquatic products from food markets in China

Proteus mirabilis is an opportunistic pathogen frequently associated with nosocomial infection and food poisoning cases. Contamination of P. mirabilis in retail meat products may be important transmission routes for human infection with P. mirabilis. In this study a total of 89 P. mirabilis strains were isolated from 347 samples in 14 food markets in China and subjected to whole-genome sequencing. Phylogenetic analysis showed that all 89 strains were divided into 81 different clones (SNPs >5), indicating high genetic diversity of P. mirabilis in food markets. Antimicrobial susceptibility testing showed that 81 (91.01%) strains displayed multidrug resistance profiles. Seventy-three different resistance genes (or variants) were found, including various clinically important antimicrobial resistance genes aac(6')-Ib-cr (77.53%), bla _CTX-M (39.33%), fosA3 (30.34%), as well as multiresistance gene cfr (4.50%), tigecycline resistance gene cluster tmexCD3-toprJ1 (4.50%) and carbapenemase gene bla _NDM-1 (1.12%). Diverse genetic elements including Tn7 transposon, plasmid, SXT/R391 integrative conjugative element were associated with the horizontal transfer of cfr. tmexCD3-toprJ1 and bla _NDM-1 were located on ICEPmiChnJZ26 and Salmonella genomic island 1, respectively. Our study emphasized high contamination of P. mirabilis harbouring various clinically important antimicrobial resistance genes in retail meat and aquatic products, which might be an important issue in terms of food safety and human health.

Genome-resolved insight into the reservoir of antibiotic resistance genes in aquatic microbial community

Aquatic microbial communities are an important reservoir of antibiotic resistance genes (ARGs). However, distribution and diversity of different ARG categories in environmental microbes with different ecological strategies is not yet well studied. Despite the potential exposure of the southern part of the Caspian Sea to the release of antibiotics, little is known about its natural resistome profile. We used a combination of Hidden Markov model (HMM), homology alignment and a deep learning approach for comprehensive screening of the diversity and distribution of ARGs in the Caspian Sea metagenomes at genome resolution. Detected ARGs were classified into five antibiotic resistance categories including prevention of access to target (44%), modification/protection of targets (30%), direct modification of antibiotics (22%), stress resistance (3%), and metal resistance (1%). The 102 detected ARG containing metagenome-assembled genomes of the Caspian Sea were dominated by representatives of Acidimicrobiia, Gammaproteobacteria, and Actinobacteria classes. Comparative analysis revealed that the highly abundant, oligotrophic, and genome streamlined representatives of taxa Acidimicrobiia and Actinobacteria modify the antibiotic target via mutation to develop antibiotic resistance rather than carrying extra resistance genes. Our results help with understanding how the encoded resistance categories of each genome are aligned with its ecological strategies.

Immunomodulatory biomaterials for implant-associated infections: from conventional to advanced therapeutic strategies

Implant-associated infection (IAI) is increasingly emerging as a serious threat with the massive application of biomaterials. Bacteria attached to the surface of implants are often difficult to remove and exhibit high resistance to bactericides. In the quest for novel antimicrobial strategies, conventional antimicrobial materials often fail to exert their function because they tend to focus on direct bactericidal activity while neglecting the modulation of immune systems. The inflammatory response induced by host immune cells was thought to be a detrimental force impeding wound healing. However, the immune system has recently received increasing attention as a vital player in the host's defense against infection. Anti-infective strategies based on the modulation of host immune defenses are emerging as a field of interest. This review explains the importance of the immune system in combating infections and describes current advanced immune-enhanced anti-infection strategies. First, the characteristics of traditional/conventional implant biomaterials and the reasons for the difficulty of bacterial clearance in IAI were reviewed. Second, the importance of immune cells in the battle against bacteria is elucidated. Then, we discuss how to design biomaterials that activate the defense function of immune cells to enhance the antimicrobial potential. Based on the key premise of restoring proper host-protective immunity, varying advanced immune-enhanced antimicrobial strategies were discussed. Finally, current issues and perspectives in this field were offered. This review will provide scientific guidance to enhance the development of advanced anti-infective biomaterials.

Impact of international travel and diarrhea on gut microbiome and resistome dynamics

International travel contributes to the global spread of antimicrobial resistance. Travelers' diarrhea exacerbates the risk of acquiring multidrug-resistant organisms and can lead to persistent gastrointestinal disturbance post-travel. However, little is known about the impact of diarrhea on travelers' gut microbiomes, and the dynamics of these changes throughout travel. Here, we assembled a cohort of 159 international students visiting the Andean city of Cusco, Peru and applied next-generation sequencing techniques to 718 longitudinally-collected stool samples. We find that gut microbiome composition changed significantly throughout travel, but taxonomic diversity remained stable. However, diarrhea disrupted this stability and resulted in an increased abundance of antimicrobial resistance genes that can remain high for weeks. We also identified taxa differentially abundant between diarrheal and non-diarrheal samples, which were used to develop a classification model that distinguishes between these disease states. Additionally, we sequenced the genomes of 212 diarrheagenic Escherichia coli isolates and found those from travelers who experienced diarrhea encoded more antimicrobial resistance genes than those who did not. In this work, we find the gut microbiomes of international travelers' are resilient to dysbiosis; however, they are also susceptible to colonization by multidrug-resistant bacteria, a risk that is more pronounced in travelers with diarrhea.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Host-dependent resistance of Group A Streptococcus to sulfamethoxazole mediated by a horizontally-acquired reduced folate transporter

Described antimicrobial resistance mechanisms enable bacteria to avoid the direct effects of antibiotics and can be monitored by in vitro susceptibility testing and genetic methods. Here we describe a mechanism of sulfamethoxazole resistance that requires a host metabolite for activity. Using a combination of in vitro evolution and metabolic rescue experiments, we identify an energy-coupling factor (ECF) transporter S component gene (thfT) that enables Group A Streptococcus to acquire extracellular reduced folate compounds. ThfT likely expands the substrate specificity of an endogenous ECF transporter to acquire reduced folate compounds directly from the host, thereby bypassing the inhibition of folate biosynthesis by sulfamethoxazole. As such, ThfT is a functional equivalent of eukaryotic folate uptake pathways that confers very high levels of resistance to sulfamethoxazole, yet remains undetectable when Group A Streptococcus is grown in the absence of reduced folates. Our study highlights the need to understand how antibiotic susceptibility of pathogens might function during infections to identify additional mechanisms of resistance and reduce ineffective antibiotic use and treatment failures, which in turn further contribute to the spread of antimicrobial resistance genes amongst bacterial pathogens.

Sequence-based detection and typing procedures for Burkholderia mallei: Assessment and prospects

Although glanders has been eradicated in most of the developed world, the disease still persists in various countries such as Brazil, India, Pakistan, Bangladesh, Nepal, Iran, Bahrain, UAE and Turkey. It is one of the notifiable diseases listed by the World Organization for Animal Health. Occurrence of glanders imposes restriction on equestrian events and restricts equine movement, thus causing economic losses to equine industry. The genetic diversity and global distribution of the causing agent, Burkholderia (B.) mallei, have not been assessed in detail and are complicated by the high clonality of this organism. Among the identification and typing methods, PCR-based methods for distinguishing B. mallei from its close relative B. pseudomallei as well as genotyping using tandem repeat regions (MLVA) are established. The advent and continuous advancement of the sequencing techniques and the reconstruction of closed genomes enable the development of genome guided epidemiological tools. For achieving a higher genomic resolution, genotyping methods based on whole genome sequencing data can be employed, like genome-wide single nucleotide polymorphisms. One of the limitations in obtaining complete genomic sequences for further molecular characterization of B. mallei is its high GC content. In this review, we aim to provide an overview of the widely used detection and typing methods for B. mallei and illustrate gaps that still require development. The genomic features of Burkholderia, their high homology and clonality will be first described from a comparative genomics perspective. Then, the commonly used molecular detection (PCR systems) and typing systems (e.g., multilocus sequence typing, variable number of tandem repeat analysis) will be presented and put in perspective with recently developed genomic methods. Also, the increasing availability of B. mallei genomic sequences and evolution of the sequencing methods offers exciting prospects for further refinement of B. mallei typing, that could overcome the difficulties presently encountered with this particular bacterium.