Detection Of Antimicrobial Resistance Genes Research Articles

Isolation and Molecular Detection of Antimicrobial Resistance Genes in Escherichia coli and Staphylococcus Species from Joint Ill Cases of Calves in Puducherry

Background: Joint ill is a septicemic polyarthritis condition due to the localization of pathogenic bacteria within the joints of young calves. The emergence of antibiotic resistant pathogen in such cases may lead to further complication in treatment. Thus, the present study was aimed to isolate and identify the bacterial pathogens and its antimicrobial resistant genes from joint ill cases in calves. Methods: A total of 31 pus swabs were collected from joint ill cases from calves and subjected for bacterial isolation and identification by phenotypic and genotypic method followed by determination of its antimicrobial resistant genes (tet and mecA gene) and antibiogram. Result: Based on colony characters, microscopic observation, biochemical tests, 17/31 (54.83%) Escherichia coli and 14/31 (45.16%) Staphylococcus aureus were isolated and identified phenotypically. All the isolates were further confirmed by polymerase chain reaction using E. coli and S. aureus species specific primers. The antimicrobial resistant genes carrying E. coli and S. aureus isolates were also detected, in which 4/17 (23.5%) isolates were positive for tet gene and 4/14 (28.57%) were positive for the mecA gene. The antibiotic susceptibility test showed that the isolates were highly sensitive to Enrofloxacin (100%) and Gentamicin (77%), but were resistant to Amoxyclav (70%) and tetracycline (50%). On conclusion, E. coli and S. aureus are the most common bacterial pathogens identified from this study. The presence of antimicrobial resistant genes (tet and mecA) and antibiogram pattern of the bacterial isolates indicating the possible treatment failure and serious public health risks in future.

Influence of Sequencing Technology on Pangenome-level Analysis and Detection of Antimicrobial Resistance Genes in ESKAPE Pathogens.

This study provides a comprehensive comparison of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing technologies in the context of antimicrobial resistance (AMR) detection in ESKAPE pathogens. By analyzing a large dataset of 1,385 whole genome sequences, the research offers valuable insights into the strengths and limitations of each approach, as well as the benefits of the novel approach of hybrid assembly. These findings have broad utility across microbiology, genomics, and infectious disease research. In particular, they apply to the work of researchers and clinicians dealing with AMR surveillance, investigation into outbreaks, and bacterial genome analysis. Given the nuance with which technological differences in genomic completeness, pangenome structure, and AMR determinant detection have been explored in this study, it is a good basis for informed method selection for future research. While the output represents an incremental advance, its significance lies in its practical implications. It thus enables researchers to take more reasonable decisions in designing genomic studies of bacterial pathogens by showing the complementarity of various sequencing approaches and their specific strengths. This could lead to more accurate and comprehensive detection of AMR, which would contribute ultimately to improved antibiotic stewardship and public health strategies. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. All the sequences used for this study are publicly accessible from GenBank, and their individual IDs are disclosed in Supplementary Table 1.

Genomic Diversity and Potential Transmission and Persistence of Salmonella in the Cambodian Vegetable Supply Chain.

S. enterica isolates (n=78) obtained from the vegetable supply chain (farms, distribution centers, markets) in two Cambodian provinces (Siem Reap, Battambang) were sequenced and analyzed. In silico identification of serotypes and detection of antimicrobial resistance genes was performed using SISTR and ABRicate, respectively. Isolates' relatedness was assessed based on high-quality single nucleotide polymorphisms (hqSNPs) identified within each serotype using the CFSAN SNP pipeline. Among 29 detected serotypes, Paratyphi B var. Java was most abundant (n=14), followed by Hvittingfoss (n=11) and Thompson (n=7). Paratyphi B var. Java was mostly found in farms (n=5) and markets (n=6), Hvittingfoss within distribution centers (n=8), and Thompson at markets (n=4) and farms (n=3). Among Paratyphi B var. Java isolates, one phylogenetic clade contained four closely related isolates (0-1 SNP difference), collected at markets in different provinces on different days. Another clade contained two isolates that differed by one SNP, one obtained from a Battambang farm and one from a Siem Reap distribution center, suggesting a broad spread of Paratyphi B var. Java in the Cambodian vegetable supply chain. Hvittingfoss isolates clustered in two clades; one contained five identical isolates, four of which were obtained in different months from the distribution center and a farm in Battambang, suggesting possible transmission among supply chain stages. The second clade contained three isolates from the Battambang distribution center that differed by 0-1 SNP and were isolated in October and November, indicating possible persistence. Lastly, among 78 analyzed isolates, 14 carried antimicrobial resistance genes and seven out of these 14 carried genes with predicted resistance to more than three classes of antibiotics. Overall, highly similar isolates of Salmonella were identified over time and at different supply chain stages, suggesting possible persistence and transmission of Salmonella within and between supply chain stages.

Survey on the Occurrence of Zoonotic Bacterial Pathogens in the Feces of Wolves (Canis lupus italicus) Collected in a Protected Area in Central Italy.

Previous investigations have explored the involvement of wolves in parasitic and viral diseases, but data on the zoonotic bacteria are limited. The aim of this study was to assess the occurrence of bacterial zoonotic agents in 16 wolf (Canis lupus italicus) fecal samples collected in a protected area in Central Italy. Campylobacter spp., Salmonella spp., Yersinia spp., Listeria monocytogenes, and Shiga Toxin-Producing Escherichia coli (STEC) were investigated by culture, while polymerase chain reaction (PCR) was employed to detect Coxiella burnetii, Mycobacterium spp., Brucella spp., and Francisella tularensis. The presence of Extended Spectrum β-Lactamase (ESBL)- and carbapenemase-producing Enterobacteriaceae was also evaluated, using selective isolation media and detection of antimicrobial resistance genes. All samples were negative for Campylobacter spp., Salmonella spp., C. burnetii, Mycobacterium spp., Brucella spp., F. tularensis, and carbapenemase-producing Enterobacteriaceae. One sample tested positive for Yersinia aldovae and three for Yersinia enterocolitica BT1A. One L. monocytogenes (serogroup IIa) and one STEC, carrying the stx1 gene, were isolated. Two ESBL isolates were detected: one Serratia fonticola, carrying blaFONA-3/6 gene, and one Escherichia coli, carrying blaCTX-M-1 gene. Both ESBL isolates were resistant to different antimicrobials and therefore classified as multi-drug-resistant. Our data suggest that wolves are potential carriers of zoonotic bacteria and may contribute to the environmental contamination through their feces.

Infection Status, Etiological Analysis of Aeromonas Spp. in Foodborne Diarrhea Patients from 2019 to 2023 in Wenzhou.

The infection status and etiological analysis of Aeromonas spp. from foodborne diarrhea patients in Wenzhou were carried out to provide the etiological basis for healthy diet and clinical treatment. Aeromonas isolates (n = 41) collected from foodborne diarrhea patients were identified using the automatic bacteriologic analyzer and mass spectrometer. Species identification, multilocus sequence typing, prediction of virulence genes, and antimicrobial resistance genes were analyzed by the data of whole genome sequencing. The antibiotic resistance of these isolates was determined using miniaturization of the broth dilution susceptibility test. A total of 1829 stool samples of diarrhea patients were collected, and the detection rate of Aeromonas spp. was 2.24% (41/1829). Moreover, Aeromonas spp. are more easily detected in warmer months (from June to August), which were identified as follows: A. veronii (53.66%, 22/41), A. caviae (21.95%, 9/41), A. hydrophila (9.76%, 4/41), A. dhakensis (4.88%, 2/41), A. rivipollensis (4.88%, 2/41), A. enteropelogenes (2.44%, 1/41), and A. media (2.44%, 1/41). All strains can be divided into 38 sequence types, 31 of which were novel, suggesting that Aeromonas spp. had high genetic diversity, multiple clones, and various sources in diarrhea patients. High number of genetic diversity and resistance were found in the Aeromonas isolates. In addition, the category distribution of the virulence genes was significantly different among the seven species of Aeromonas. Aeromonas spp. had different degrees of resistance to antibiotics, and tetracycline was the most serious, with a resistance rate of 27%. What's more, for some antimicrobial classes in silico antimicrobial resistance gene detection was highly correlated with phenotypic antimicrobial resistance patterns with an overall sensitivity of 93.3% and a specificity of 66.7%. The findings from this research highlighted the importance for development of prevention and control strategies to reduce the risk of foodborne diarrhea caused by Aeromonas spp.

Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs.

Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools-Bandage, SPAligner, and GraphAligner-in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage's potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread.

Assessing antimicrobial resistance in pasture-based dairy farms: a 15-month surveillance study in New Zealand.

Antimicrobial resistance is a global public and animal health concern. Antimicrobial resistance genes (ARGs) have been detected in dairy farm environments globally; however, few longitudinal studies have utilized shotgun metagenomics for ARG surveillance in pasture-based systems. This 15-month study aimed to undertake a baseline survey using shotgun metagenomics to assess the relative abundance and diversity of ARGs in two pasture-based dairy farm environments in New Zealand with different management practices. There was no statistically significant difference in overall ARG relative abundance between the two dairy farms (P = 0.321) during the study period. Compared with overseas data, the relative abundance of ARG copies per 16S rRNA gene in feces (0.08-0.17), effluent (0.03-0.37), soil (0.20-0.63), and bulk tank milk (0.0-0.12) samples was low. Models comparing the presence or absence of resistance classes found in >10% of all feces, effluent, and soil samples demonstrated no statistically significant associations (P > 0.05) with "season," and only multi-metal (P = 0.020) and tetracycline (P = 0.0003) resistance were significant at the "farm" level. Effluent samples harbored the most diverse ARGs, some with a recognized public health risk, whereas soil samples had the highest ARG relative abundance but without recognized health risks. This highlights the importance of considering the genomic context and risk of ARGs in metagenomic data sets. This study suggests that antimicrobial resistance on pasture-based dairy farms is low and provides essential baseline ARG surveillance data for such farming systems.IMPORTANCEAntimicrobial resistance is a global threat to human and animal health. Despite the detection of antimicrobial resistance genes (ARGs) in dairy farm environments globally, longitudinal surveillance in pasture-based systems remains limited. This study assessed the relative abundance and diversity of ARGs in two New Zealand dairy farms with different management practices and provided important baseline ARG surveillance data on pasture-based dairy farms. The overall ARG relative abundance on these two farms was low, which provides further evidence for consumers of the safety of New Zealand's export products. Effluent samples harbored the most diverse range of ARGs, some of which were classified with a recognized risk to public health, whereas soil samples had the highest ARG relative abundance; however, the soil ARGs were not classified with a recognized public health risk. This emphasizes the need to consider genomic context and risk as well as ARG relative abundance in resistome studies.

The TELCoMB Protocol for High-Sensitivity Detection of ARG-MGE Colocalizations in Complex Microbial Communities.

Understanding the genetic basis of antimicrobial resistance is crucial for developing effective mitigation strategies. One necessary step is to identify the antimicrobial resistance genes (ARGs) within a microbial population, referred to as the resistome, as well as the mobile genetic elements (MGEs) harboring ARGs. Although shotgun metagenomics has been successful in detecting ARGs and MGEs within a microbiome, it is limited by low sensitivity. Enrichment using cRNA biotinylated probes has been applied to address this limitation, enhancing the detection of rare ARGs and MGEs, especially when combined with long-read sequencing. Here, we present the TELCoMB protocol, a Snakemake workflow that elucidates resistome and mobilome composition and diversity and uncovers ARG-MGE colocalizations. The protocol supports both short- and long-read sequencing and does not require enrichment, making it versatile for various genomic data types. TELCoMB generates publication-ready figures and CSV files for comprehensive analysis, improving our understanding of antimicrobial resistance mechanisms and spread. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Installing TELCOMB Locally Alternate Protocol: Installing TELCOMB on a SLURM Cluster Basic Protocol 2: Data Preprocessing Basic Protocol 3: Calculation of Resistome Distribution and Composition Basic Protocol 4: Identification of ARG-MGE Colocalizations.

Sensitivity and specificity of Nanopore sequencing for detecting carbapenem and 3rd-generation cephalosporin-resistant Enterobacteriaceae in urine samples: Real-time simulation with public antimicrobial resistance gene database

ObjectivesTo evaluate the accuracy of beta-lactamase gene detection directly from urine samples by Nanopore sequencing. MethodsDNA was extracted from bacterial pellets in spun urine. The purified DNA was then sequenced in native form by a Nanopore sequencer (MinION) to identify the organisms and beta-lactamase genes. Results were compared to routine urine cultures and standard antimicrobial susceptibility tests (AST). ResultsWe processed 60 urine samples of which routine cultures grew Enterobacteriaceae, including 28 carbapenem-resistant (CRE), 17 extended-spectrum beta-lactamase (ESBL) or AmpC producing, and 15 non-ESBL/AmpC phenotypes. We excluded 7 samples with extremely low DNA amounts (<1 ng/μl) for a final case of 53 in total. The sensitivity of antimicrobial resistance gene detection within 6 h, the optimal duration from real-time simulation, of Nanopore sequencing for the diagnosis of carbapenem-resistant and ceftriaxone-resistant phenotypes was 73.9 % (95%CI 56.0–91.9 %) and 81.1 % (95%CI 68.5–93.7 %), while the specificity was 96.7 % (95%CI 90.2–100.0 %) and 56.3 % (95%CI 31.9–80.6 %), respectively. The median times for MinION to generate DNA reads containing carbapenemase and ESBL/AmpC genes were 93 min (IQR 17–245.5) and 99 min (IQR 31.25–269.75) after sequencing commencement, respectively. ConclusionsNanopore sequencing can identify bacterial genotypic resistance in urine and may enable clinicians to adjust antimicrobial therapy earlier than routine AST.

Overcoming Microbial Inhibition of S. Sonnei Through the Exploitation of Genomically Predicted Antibiotic Resistance Profiles for the Development of Food Enrichment Media

Linking outbreaks of Shigella spp. to specific foods is challenging due to poor selectivity of current enrichment media. We have previously shown that enrichment media, tailored to the genomically-predicted antimicrobial resistance (AMR) of Shiga toxigenic E. coli strains, enhances their isolation from foods. This study investigates the application of this approach for Shigella isolation. The AMR gene profiles of 21,908 published S. sonnei genomes indicated a high prevalence of genes conferring resistance to streptomycin (aadA, aph(3″)-Ib, aph(6)-Id, 92.8%), sulfonamides (sul1, sul2, 74.8%), and/or trimethoprim (dfrA, 96.2%). Genomic analysis and antibiotic susceptibility testing conducted with a panel of 17 outbreak-associated S. sonnei strains confirmed the correlation of AMR gene detection with resistance phenotypes. Supplementation of Shigella Broth (SB) with up to 400 µg/mL of trimethoprim or sulfadiazine did not suppress the growth of sensitive strains, whereas 100 µg/mL of streptomycin increased the selectivity of this broth. All three antibiotics increased the selectivity of modified Tryptone Soya Broth (mTSB). Based on these results, supplemented media formulations were developed and assessed by measuring the relative growth of S. sonnei in cultures coinoculated with a strain of bacteriocin-producing E. coli that is inhibitory to Shigella growth. S. sonnei was not recovered from cocultures grown in SB or mTSB without antibiotics. In contrast, media supplemented with streptomycin at 50 and 100 µg/mL, trimethoprim at 25 and 50 µg/mL, and sulfadiazine at 100 µg/mL increased the relative proportion of S. sonnei in postenrichment cultures. The enhanced recovery of resistant S. sonnei strains achieved in this study indicates that, in cases where genomic data are available for clinical S. sonnei isolates, customization of selective enrichment media based on AMR gene detection could be a valuable tool for supporting the investigation of foodborne shigellosis outbreaks.

Bacterial enrichment prior to third-generation metagenomic sequencing improves detection of BRD pathogens and genetic determinants of antimicrobial resistance in feedlot cattle.

Bovine respiratory disease (BRD) is one of the most important animal health problems in the beef industry. While bacterial culture and antimicrobial susceptibility testing have been used for diagnostic testing, the common practice of examining one isolate per species does not fully reflect the bacterial population in the sample. In contrast, a recent study with metagenomic sequencing of nasal swabs from feedlot cattle is promising in terms of bacterial pathogen identification and detection of antimicrobial resistance genes (ARGs). However, the sensitivity of metagenomic sequencing was impeded by the high proportion of host biomass in the nasal swab samples. This pilot study employed a non-selective bacterial enrichment step before nucleic acid extraction to increase the relative proportion of bacterial DNA for sequencing. Non-selective bacterial enrichment increased the proportion of bacteria relative to host sequence data, allowing increased detection of BRD pathogens compared with unenriched samples. This process also allowed for enhanced detection of ARGs with species-level resolution, including detection of ARGs for bacterial species of interest that were not targeted for culture and susceptibility testing. The long-read sequencing approach enabled ARG detection on individual bacterial reads without the need for assembly. Metagenomics following non-selective bacterial enrichment resulted in substantial agreement for four of six comparisons with culture for respiratory bacteria and substantial or better correlation with qPCR. Comparison between isolate susceptibility results and detection of ARGs was best for macrolide ARGs in Mannheimia haemolytica reads but was also substantial for sulfonamide ARGs within M. haemolytica and Pasteurella multocida reads and tetracycline ARGs in Histophilus somni reads. By increasing the proportion of bacterial DNA relative to host DNA through non-selective enrichment, we demonstrated a corresponding increase in the proportion of sequencing data identifying BRD-associated pathogens and ARGs in deep nasopharyngeal swabs from feedlot cattle using long-read metagenomic sequencing. This method shows promise as a detection strategy for BRD pathogens and ARGs and strikes a balance between processing time, input costs, and generation of on-target data. This approach could serve as a valuable tool to inform antimicrobial management for BRD and support antimicrobial stewardship.

Dental plaque microbiota sequence counts for microbial profiling and resistance genes detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications. Nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance gene (ARG) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~ 100 million), four human plaque samples, and one microbial community standard and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads, whereas abundance estimation was minimally affected, with only slight variations observed in low-abundance species. The presence of ARGs was also assessed: a total of 133 ARGs were identified. Notably, 23% of them inconsistently resulted as present or absent across downsampling datasets of the same sample. Moreover, over half of ARGs were lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low-abundance species detection, and 20 million for ARG identification.Key points• Forty million sequences are a cost-efficient solution for microbiota profiling• Fifty million sequences allow low-abundance species detection• Twenty million sequences are recommended for ARG identificationGraphical

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Molecular detection of antimicrobial resistance genes in multidrug-resistant Gram-negative bacteria isolated from clinical samples in two hospitals in Niger

Background: According to the World Health Organization (WHO), bacterial resistance to antibiotics is a global public health challenge, which is also developing in Niger. The aim of this study was to determine the prevalence of antibiotic resistance genes in Gram-negative bacilli isolated from clinical samples in the biological laboratories of two selected health facilities in Niger.&#x0D; Methodology: Clinical bacterial isolates were randomly collected from two biological laboratories of Zinder National Hospital and Niamey General Reference Hospital. These were multi-resistant Gram-negative bacteria that have been routinely isolated from pathological samples of patients. Molecular detection of resistance genes was carried out by polymerase chain reaction (PCR) amplification using specific primers. These include plasmid-mediated AmpC beta lactamase genes (blaCITM, blaDHAM, blaFOXM), ‘Cefotaxime-Munich’ type beta lactamase genes (blaCTX-M-1, blaCTX-M-2, blaCTX-M-9), KPC-type beta lactamase gene (blaKPC), Oxa-type beta lactamase gene (blaOXA-48), SHV-type beta lactamase gene (blaSHV), TEM-type beta lactamase gene (blaTEM), quinolone resistance genes (qnrA, qnrB, qnrS), and sulfonamide resistance genes (sul1, sul2, sul3).&#x0D; Results: A total of 24 strains of multidrug-resistant Gram-negative bacteria isolated from different clinical samples were analysed. The distribution of the resistance genes detected is as follows; AmpC blaCITM (n=6; 25.0%), AmpC blaDHAM (n=4; 17.0%), AmpC blaFOXM (n=0), blaCTX-M-1 (n=11; 46.0%), blaCTX-M-2 (n=0), blaCTX-M-9 (n=0), blaKPC (n=0), blaOXA-48 (n=2; 8..0%), blaSHV (n=5; 21.0%), blaTEM (n=0), qnrA (n=0), qnrB (n=5; 21.0%), qnrS (n=17; 71.0%), sul1 (n=22; 92.0%), sul2 (n=12; 50.0%), and sul3 (n=0). All isolates tested had at least two resistance genes.&#x0D; Conclusion: The results of this study provide a better understanding of the resistance situation of clinical isolates in Niger. Therefore, it is more than necessary to intensify the detection on a larger number of samples and on a national scale. This will make it possible to assess the true extent of the phenomenon and consequently guide control strategies through a national multisectoral plan.&#x0D; Contexte: Selon l'Organisation Mondiale de la Santé (OMS), la résistance bactérienne aux antibiotiques constitue un défi mondial de santé publique, qui se développe également au Niger. Le but de cette étude était de déterminer la prévalence des gènes de résistance aux antibiotiques chez les bacilles Gram négatif isolés à partir d'échantillons cliniques dans les laboratoires de biologie de deux formations sanitaires sélectionnées au Niger.&#x0D; Méthodologie: Des isolats bactériens cliniques ont été collectés de manière aléatoire dans deux laboratoires de biologie de l'Hôpital National de Zinder et de l'Hôpital Général de Référence de Niamey. Il s’agissait de bactéries Gram-négatives multirésistantes qui ont été systématiquement isolées à partir d’échantillons pathologiques de patients. La détection moléculaire des gènes de résistance a été réalisée par amplification par réaction en chaîne par polymérase (PCR) à l'aide d'amorces spécifiques. Il s'agit notamment des gènes de bêta-lactamase AmpC à médiation plasmidique (blaCITM, blaDHAM, blaFOXM), des gènes de bêta-lactamase de type «Céfotaxime-Munich» (blaCTX-M-1, blaCTX-M-2, blaCTX-M-9), du gène de bêta-lactamase de type KPC (blaKPC), du gène de bêta-lactamase de type Oxa (blaOXA-48), le gène bêta-lactamase de type SHV (blaSHV), le gène bêta-lactamase de type TEM (blaTEM), les gènes de résistance aux quinolones (qnrA, qnrB, qnrS) et les gènes de résistance aux sulfamides (sul1, sul2, sul3).&#x0D; Résultats: Au total, 24 souches de bactéries Gram-négatives multirésistantes isolées de différents échantillons cliniques ont été analysées. La répartition des gènes de résistance détectés est la suivante; AmpC blaCITM (n=6; 25,0%), AmpC blaDHAM (n=4; 17,0%), AmpC blaFOXM (n=0), blaCTX-M-1 (n=11; 46,0%), blaCTX-M-2 (n=0), blaCTX-M-9 (n=0), blaKPC (n=0), blaOXA-48 (n=2; 8,0%), blaSHV (n=5; 21,0%), blaTEM (n=0), qnrA (n=0), qnrB (n=5; 21,0%), qnrS (n=17; 71,0%), sul1 (n=22; 92,0%), sul2 (n=12; 50,0%) et sul3 (n=0). Tous les isolats testés possédaient au moins deux gènes de résistance.&#x0D; Conclusion: Les résultats de cette étude permettent de mieux comprendre la situation de résistance des isolats cliniques au Niger. Il est donc plus que nécessaire d’intensifier la détection sur un plus grand nombre d’échantillons et à l’échelle nationale. Cela permettra d'évaluer l'ampleur réelle du phénomène et par conséquent d'orienter les stratégies de lutte à travers un plan national multisectoriel.

Antimicrobial resistance in wildlife: detection of antimicrobial resistance genes in Apennine wolves (Canis lupus italicus Altobello, 1921) from Central Italy

The aim of this study was to molecularly investigate the presence of antimicrobial resistance genes (ARGs) in organ samples from 11 Apennine wolves (Canis lupus italicus) collected in Central Italy. Samples from lung, liver, spleen, kidney, tongue and intestine were investigated by PCRs targeting the following genes: tet(A), tet(B), tet(C), tet(D), tet(E), tet(G), tet(K), tet(L), tet(M), tet(O), tetA(P), tet(Q), tet(S), tet(X), sul1, sul2, sul3, blaCTX−M, blaSHV, blaTEM and mcr-1. A PCR positivity was highlighted for 13 out of the 21 tested genes; no positive results were obtained for tet(C), tet(D), tet(E), tet(G), sul3, blaCTX, blaSHV and mcr-1 genes. All 11 animals sampled showed positivity for one or more resistance genes. The results confirm the potential role of the wolf as an indicator and/or vector of antimicrobial-resistant bacteria or ARGs.

Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review.

Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.

Development and validation of multiplex real-time PCR for simultaneous detection of six bacterial pathogens causing lower respiratory tract infections and antimicrobial resistance genes

BackgroundKlebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli, Streptococcus pneumoniae and Staphylococcus aureus are major bacterial causes of lower respiratory tract infections (LRTIs) globally, leading to substantial morbidity and mortality. The rapid increase of antimicrobial resistance (AMR) in these pathogens poses significant challenges for their effective antibiotic therapy. In low-resourced settings, patients with LRTIs are prescribed antibiotics empirically while awaiting several days for culture results. Rapid pathogen and AMR gene detection could prompt optimal antibiotic use and improve outcomes.MethodsHere, we developed multiplex quantitative real-time PCR using EvaGreen dye and melting curve analysis to rapidly identify six major pathogens and fourteen AMR genes directly from respiratory samples. The reproducibility, linearity, limit of detection (LOD) of real-time PCR assays for pathogen detection were evaluated using DNA control mixes and spiked tracheal aspirate. The performance of RT-PCR assays was subsequently compared with the gold standard, conventional culture on 50 tracheal aspirate and sputum specimens of ICU patients.ResultsThe sensitivity of RT-PCR assays was 100% for K. pneumoniae, A. baumannii, P. aeruginosa, E. coli and 63.6% for S. aureus and the specificity ranged from 87.5% to 97.6%. The kappa correlation values of all pathogens between the two methods varied from 0.63 to 0.95. The limit of detection of target bacteria was 1600 CFU/ml. The quantitative results from the PCR assays demonstrated 100% concordance with quantitative culture of tracheal aspirates. Compared to culture, PCR assays exhibited higher sensitivity in detecting mixed infections and S. pneumoniae. There was a high level of concordance between the detection of AMR gene and AMR phenotype in single infections.ConclusionsOur multiplex quantitative RT-PCR assays are fast and simple, but sensitive and specific in detecting six bacterial pathogens of LRTIs and their antimicrobial resistance genes and should be further evaluated for clinical utility.

An integrated method for targeted Oxford Nanopore sequencing and automated bioinformatics for the simultaneous detection of bacteria, fungi, and ARG.

The use of metagenomics for pathogen identification in clinical practice has been limited. Here we describe a workflow to encourage the clinical utility and potential of NGS for the screening of bacteria, fungi, and antimicrobial resistance genes (ARGs). The method includes target enrichment, long-read sequencing, and automated bioinformatics. Evaluation of several tools and databases was undertaken across standard organisms (n=12), clinical isolates (n=114), and blood samples from patients with suspected bloodstream infections (n=33). The strategy used could offset the presence of host background DNA, error rates of long-read sequencing, and provide accurate and reproducible detection of pathogens. Eleven targets could be successfully tested in a single assay. Organisms could be confidently identified considering≥60% of best hits of a BLAST-based threshold of e-value 0.001 and a percent identity of>80%. For ARGs, reads with percent identity of>90% and>60% overlap of the complete gene could be confidently annotated. A kappa of 0.83 was observed compared to standard diagnostic methods. Thus, a workflow for the direct-from-sample, on-site sequencing combined with automated genomics was demonstrated to be reproducible. NGS-based technologies overcome several limitations of current day diagnostics. Highly sensitive and comprehensive methods of pathogen screening are the need of the hour. We developed a framework for reliable, on-site, screening of pathogens.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BackgroundAlthough the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.ResultsWe simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.ConclusionsThis study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Ultrasensitive detection of antimicrobial resistance genes using hybridization chain reaction employing carbon dots.

One of the top 10 global concerns include AntiMicrobial Resistance (AMR), which warrants the need to develop materials and methods for detection of AMR genes. Here, we propose a proof-of-concept approach for selective and ultrasensitive detection of AMR gene employing fluorescent carbon dots. Waste pistachio shell derived green emissive carbon dots (PCDs) with a high quantum yield of 24 were prepared via hydrothermal carbonization process and characterised using microscopic and spectroscopic techniques. The fluorescence-based Hybridization Chain Reaction (HCR) mediated sensing studies demonstrated the ability of the PCD sensor to detect AMR gene, compared to random and single mismatch DNA with a limit of detection of 16.17 pM. This strategy of waste valorization to design fluorescent probe offer excellent cost-effective and sustainable alternative for ultra-trace level detection of DNA.