DNA extraction of microbial DNA directly from infected tissue: an optimized protocol for use in nanopore sequencing. - Related Documents




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477801.0000DNA extraction of microbial DNA directly from infected tissue: an optimized protocol for use in nanopore sequencing. Identification of bacteria causing tissue infections can be comprehensive and, in the cases of non- or slow-growing bacteria, near impossible with conventional methods. Performing shotgun metagenomic sequencing on bacterial DNA extracted directly from the infected tissue may improve time to diagnosis and targeted treatment considerably. However, infected tissue consists mainly of human DNA (hDNA) which hampers bacterial identification. In this proof of concept study, we present a modified version of the Ultra-Deep Microbiome Prep kit for DNA extraction procedure, removing additional human DNA. Tissue biopsies from 3 patients with orthopedic implant-related infections containing varying degrees of Staphylococcus aureus were included. Subsequent DNA shotgun metagenomic sequencing using Oxford Nanopore Technologies' (ONT) MinION platform and ONTs EPI2ME WIMP and ARMA bioinformatic workflows for microbe and antibiotic resistance genes identification, respectively. The modified DNA extraction protocol led to an additional ~10-fold reduction of human DNA while preserving S. aureus DNA. Including the DNA sequencing and bioinformatics analyses, the presented protocol has the potential of identifying the infection-causing pathogen in infected tissue within 7 hours after biopsy. However, due to low number of S. aureus reads, positive identification of antibiotic resistance genes was not possible.202032076089
507510.9996Fast and Economic Microarray-Based Detection of Species-, Resistance-, and Virulence-Associated Genes in Clinical Strains of Vancomycin-Resistant Enterococci (VRE). Today, there is a continuous worldwide battle against antimicrobial resistance (AMR) and that includes vancomycin-resistant enterococci (VRE). Methods that can adequately and quickly detect transmission chains in outbreaks are needed to trace and manage this problem fast and cost-effectively. In this study, DNA-microarray-based technology was developed for this purpose. It commenced with the bioinformatic design of specific oligonucleotide sequences to obtain amplification primers and hybridization probes. Microarrays were manufactured using these synthesized oligonucleotides. A highly parallel and stringent labeling and hybridization protocol was developed and employed using isolated genomic DNA from previously sequenced (referenced) clinical VRE strains for optimal sensitivity and specificity. Microarray results showed the detection of virulence, resistance, and species-specific genes in the VRE strains. Theoretical predictions of the microarray results were also derived from the sequences of the same VRE strain and were compared to array results while optimizing protocols until the microarray result and theoretical predictions were a match. The study concludes that DNA microarray technology can be used to quickly, accurately, and economically detect specifically and massively parallel target genes in enterococci.202439409516
494320.9996Targeted sequencing of Enterobacterales bacteria using CRISPR-Cas9 enrichment and Oxford Nanopore Technologies. Sequencing DNA directly from patient samples enables faster pathogen characterization compared to traditional culture-based approaches, but often yields insufficient sequence data for effective downstream analysis. CRISPR-Cas9 enrichment is designed to improve the yield of low abundance sequences but has not been thoroughly explored with Oxford Nanopore Technologies (ONT) for use in clinical bacterial epidemiology. We designed CRISPR-Cas9 guide RNAs to enrich the human pathogen Klebsiella pneumoniae, by targeting multi-locus sequence type (MLST) and transfer RNA (tRNA) genes, as well as common antimicrobial resistance (AMR) genes and the resistance-associated integron gene intI1. We validated enrichment performance in 20 K. pneumoniae isolates, finding that guides generated successful enrichment across all conserved sites except for one AMR gene in two isolates. Enrichment of MLST genes led to a correct allele call in all seven loci for 8 out of 10 isolates that had depth of 30× or more in these regions. We then compared enriched and unenriched sequencing of three human fecal samples spiked with K. pneumoniae at varying abundance. Enriched sequencing generated 56× and 11.3× the number of AMR and MLST reads, respectively, compared to unenriched sequencing, and required approximately one-third of the computational storage space. Targeting the intI1 gene often led to detection of 10-20 proximal resistance genes due to the long reads produced by ONT sequencing. We demonstrated that CRISPR-Cas9 enrichment combined with ONT sequencing enabled improved genomic characterization outcomes over unenriched sequencing of patient samples. This method could be used to inform infection control strategies by identifying patients colonized with high-risk strains. IMPORTANCE: Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen Klebsiella pneumoniae and improve recovery from human stool samples. We found our enrichment method to significantly outperform traditional methods, generating far more data originating from our target genes. Additionally, we developed new computational techniques to further enhance the analysis, providing a thorough method for characterizing pathogens from complex biological samples.202539772804
494130.9996BacCapSeq: a Platform for Diagnosis and Characterization of Bacterial Infections. We report a platform that increases the sensitivity of high-throughput sequencing for detection and characterization of bacteria, virulence determinants, and antimicrobial resistance (AMR) genes. The system uses a probe set comprised of 4.2 million oligonucleotides based on the Pathosystems Resource Integration Center (PATRIC) database, the Comprehensive Antibiotic Resistance Database (CARD), and the Virulence Factor Database (VFDB), representing 307 bacterial species that include all known human-pathogenic species, known antimicrobial resistance genes, and known virulence factors, respectively. The use of bacterial capture sequencing (BacCapSeq) resulted in an up to 1,000-fold increase in bacterial reads from blood samples and lowered the limit of detection by 1 to 2 orders of magnitude compared to conventional unbiased high-throughput sequencing, down to a level comparable to that of agent-specific real-time PCR with as few as 5 million total reads generated per sample. It detected not only the presence of AMR genes but also biomarkers for AMR that included both constitutive and differentially expressed transcripts.IMPORTANCE BacCapSeq is a method for differential diagnosis of bacterial infections and defining antimicrobial sensitivity profiles that has the potential to reduce morbidity and mortality, health care costs, and the inappropriate use of antibiotics that contributes to the development of antimicrobial resistance.201830352937
511140.9996Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.201931597945
507650.9996Diagnostic microarray for human and animal bacterial diseases and their virulence and antimicrobial resistance genes. Rapid diagnosis and treatment of disease is often based on the identification and characterization of causative agents derived from phenotypic characteristics. Current methods can be laborious and time-consuming, often requiring many skilled personnel and a large amount of lab space. The objective of our study was to develop a spotted microarray for rapid identification and characterization of bacterial pathogens and their antimicrobial resistance genes. Our spotted microarray consists of 489 70mer probes that detect 40 bacterial pathogens of medical, veterinary and zoonotic importance (including 15 NIAID Category A, B and C pathogens); associated genes that encode resistance for antimicrobial and metal resistance; and DNA elements that are important for horizontal gene transfer among bacteria. High specificity and reliability of the microarray was achieved for bacterial pathogens of animal and human importance by validating MDR pathogenic bacteria as pure cultures or by following their inoculation in complex and highly organic sample matrices, such as soil and manure.201020035807
493960.9996Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. OBJECTIVES: The introduction of metagenomic sequencing to diagnostic microbiology has been hampered by slowness, cost and complexity. We explored whether MinION nanopore sequencing could accelerate diagnosis and resistance profiling, using complicated urinary tract infections as an exemplar. METHODS: Bacterial DNA was enriched from clinical urines (n = 10) and from healthy urines 'spiked' with multiresistant Escherichia coli (n = 5), then sequenced by MinION. Sequences were analysed using external databases and bioinformatic pipelines or, ultimately, using integrated real-time analysis applications. Results were compared with Illumina data and resistance phenotypes. RESULTS: MinION correctly identified pathogens without culture and, among 55 acquired resistance genes detected in the cultivated bacteria by Illumina sequencing, 51 were found by MinION sequencing directly from the urines; with three of the four failures in an early run with low genome coverage. Resistance-conferring mutations and allelic variants were not reliably identified. CONCLUSIONS: MinION sequencing comprehensively identified pathogens and acquired resistance genes from urine in a timeframe similar to PCR (4 h from sample to result). Bioinformatic pipeline optimization is needed to better detect resistances conferred by point mutations. Metagenomic-sequencing-based diagnosis will enable clinicians to adjust antimicrobial therapy before the second dose of a typical (i.e. every 8 h) antibiotic.201727667325
493570.9996Three Distinct Annotation Platforms Differ in Detection of Antimicrobial Resistance Genes in Long-Read, Short-Read, and Hybrid Sequences Derived from Total Genomic DNA or from Purified Plasmid DNA. Recent advances and lower costs in rapid high-throughput sequencing have engendered hope that whole genome sequencing (WGS) might afford complete resistome characterization in bacterial isolates. WGS is particularly useful for the clinical characterization of fastidious and slow-growing bacteria. Despite its potential, several challenges should be addressed before adopting WGS to detect antimicrobial resistance (AMR) genes in the clinical laboratory. Here, with three distinct ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), different approaches were compared to identify best practices for detecting AMR genes, including: total genomic DNA and plasmid DNA extractions, the solo assembly of Illumina short-reads and of Oxford Nanopore Technologies (ONT) long-reads, two hybrid assembly pipelines, and three in silico AMR databases. We also determined the susceptibility of each strain to 21 antimicrobials. We found that all AMR genes detected in pure plasmid DNA were also detectable in total genomic DNA, indicating that, at least in these three enterobacterial genera, the purification of plasmid DNA was not necessary to detect plasmid-borne AMR genes. Illumina short-reads used with ONT long-reads in either hybrid or polished assemblies of total genomic DNA enhanced the sensitivity and accuracy of AMR gene detection. Phenotypic susceptibility closely corresponded with genotypes identified by sequencing; however, the three AMR databases differed significantly in distinguishing mobile dedicated AMR genes from non-mobile chromosomal housekeeping genes in which rare spontaneous resistance mutations might occur. This study indicates that each method employed in a WGS workflow has an impact on the detection of AMR genes. A combination of short- and long-reads, followed by at least three different AMR databases, should be used for the consistent detection of such genes. Further, an additional step for plasmid DNA purification and sequencing may not be necessary. This study reveals the need for standardized biochemical and informatic procedures and database resources for consistent, reliable AMR genotyping to take full advantage of WGS in order to expedite patient treatment and track AMR genes within the hospital and community.202236290058
462980.9996Screening and in silico characterization of prophages in Helicobacter pylori clinical strains. The increase of antibiotic resistance calls for alternatives to control Helicobacter pylori, a Gram-negative bacterium associated with various gastric diseases. Bacteriophages (phages) can be highly effective in the treatment of pathogenic bacteria. Here, we developed a method to identify prophages in H. pylori genomes aiming at their future use in therapy. A polymerase chain reaction (PCR)-based technique tested five primer pairs on 74 clinical H. pylori strains. After the PCR screening, 14 strains most likely to carry prophages were fully sequenced. After that, a more holistic approach was taken by studying the complete genome of the strains. This study allowed us to identify 12 intact prophage sequences, which were then characterized concerning their morphology, virulence, and antibiotic-resistance genes. To understand the variability of prophages, a phylogenetic analysis using the sequences of all H. pylori phages reported to date was performed. Overall, we increased the efficiency of identifying complete prophages to 54.1 %. Genes with homology to potential virulence factors were identified in some new prophages. Phylogenetic analysis revealed a close relationship among H. pylori-phages, although there are phages with different geographical origins. This study provides a deeper understanding of H. pylori-phages, providing valuable insights into their potential use in therapy.202539368610
507990.9995Development of a Rapid, Culture-Free, Universal Microbial Identification System Using Internal Transcribed Spacer Targeting Primers. The indiscriminate administration of broad-spectrum antibiotics is a primary contributor to the increasing prevalence of antibiotic resistance. Unfortunately, culture, the gold standard for bacterial identification is a time intensive process. Due to this extended diagnostic period, broad-spectrum antibiotics are generally prescribed to prevent poor outcomes. To overcome the deficits of culture-based methods, we have developed a rapid universal bacterial identification system. The platform uses a unique universal polymerase chain reaction primer set that targets the internal transcribed spacer regions between conserved bacterial genes, creating a distinguishable amplicon signature for every bacterial species. Bioinformatic simulation demonstrates that nearly every bacteria in a set of 45 commonly isolated pathogenic species can be uniquely identified using this approach. We experimentally confirmed these predictions on a representative set of pathogenic bacterial species. We further showed that the system can determine the corresponding concentration of each pathogen. Finally, we validated performance in clinical urinary tract infection samples.202539503259
4623100.9995Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes. Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome.201931611361
5124110.9995Oxford nanopore long-read sequencing enables the generation of complete bacterial and plasmid genomes without short-read sequencing. INTRODUCTION: Genome-based analysis is crucial in monitoring antibiotic-resistant bacteria (ARB)and antibiotic-resistance genes (ARGs). Short-read sequencing is typically used to obtain incomplete draft genomes, while long-read sequencing can obtain genomes of multidrug resistance (MDR) plasmids and track the transmission of plasmid-borne antimicrobial resistance genes in bacteria. However, long-read sequencing suffers from low-accuracy base calling, and short-read sequencing is often required to improve genome accuracy. This increases costs and turnaround time. METHODS: In this study, a novel ONT sequencing method is described, which uses the latest ONT chemistry with improved accuracy to assemble genomes of MDR strains and plasmids from long-read sequencing data only. Three strains of Salmonella carrying MDR plasmids were sequenced using the ONT SQK-LSK114 kit with flow cell R10.4.1, and de novo genome assembly was performed with average read accuracy (Q > 10) of 98.9%. RESULTS AND DISCUSSION: For a 5-Mb-long bacterial genome, finished genome sequences with accuracy of >99.99% could be obtained at 75× sequencing coverage depth using Flye and Medaka software. Thus, this new ONT method greatly improves base-calling accuracy, allowing for the de novo assembly of high-quality finished bacterial or plasmid genomes without the need for short-read sequencing. This saves both money and time and supports the application of ONT data in critical genome-based epidemiological analyses. The novel ONT approach described in this study can take the place of traditional combination genome assembly based on short- and long-read sequencing, enabling pangenomic analyses based on high-quality complete bacterial and plasmid genomes to monitor the spread of antibiotic-resistant bacteria and antibiotic resistance genes.202337256057
4942120.9995Nanopore-based enrichment of antimicrobial resistance genes - a case-based study. Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we used real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compared its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then modelled the influence of several variables on the enrichment results and predicted the large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we showed how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.202336949817
5826130.9995Rapid and accurate sepsis diagnostics via a novel probe-based multiplex real-time PCR system. Sepsis is a critical clinical emergency that requires prompt diagnosis and intervention. Its prevalence has increased due to the aging population and increased antibiotic resistance. Early identification and the use of innovative technologies are crucial for improving patient outcomes. Modern methodologies are needed to minimize the turnaround time for diagnosis and improve outcomes. Rapid diagnostic tests and multiplex PCR are effective but have limitations in identifying a range of pathogens and target genes. Our study evaluated two novel probe-based multiplex real-time PCR systems: the SEPSI ID and SEPSI DR panels. These systems can quickly identify bacterial and fungal pathogens, alongside antibiotic resistance genes. The assays cover 29 microorganisms (gram-negative bacteria, gram-positive bacteria, yeast, and mold species), alongside 23 resistance genes and four virulence factors. A streamlined workflow uses 2 µL of broth from positive blood cultures (BCs) without nucleic acid extraction and provides results in approximately 1 h. We present the results from an evaluation of 228 BCs and 22 isolates previously characterized by whole-genome sequencing. In comparison to the reference methods, the SEPSI ID panel demonstrated a sensitivity of 96.88%, a specificity of 100%, and a PPV of 100%, whereas the SEPSI DR panel showed a sensitivity of 97.8%, a PPV of 89.7%, and a specificity of 96.7%. Both panels also identified additional pathogens and resistance-related targets not detected by conventional methods. This assay shows promise for rapidly and accurately diagnosing sepsis. Future studies should validate its performance in various clinical settings to enhance sepsis management and improve patient outcomes.IMPORTANCEWe present a new diagnostic method that enables the quick and precise identification of pathogens and resistance genes from positive blood cultures, eliminating the need for nucleic acid extraction. This technique can also be used on fresh pathogen cultures. It has the potential to greatly improve treatment protocols, leading to better patient outcomes, more responsible antibiotic use, and more efficient management of healthcare resources.202541025980
5105140.9995Emerging insights of Staphylococcus spp. in human mastitis. Human mastitis represents a prevalent and intricate condition that significantly challenges breastfeeding women, often exacerbated by pathogenic bacteria such as Staphylococcus aureus. A deep understanding of the interplay between human mastitis, the breast milk microbiome, and causative agents is imperative. This understanding must focus on the bacterium's virulence and resistance genes, which critically influence the severity and persistence of mastitis. Current methods for detecting these genes, including Polymerase Chain Reaction (PCR), 16S rRNA gene sequencing, shotgun metagenomic sequencing, multiplex PCR, whole genome sequencing (WGS), loop-mediated isothermal amplification (LAMP), CRISPR-based assays, and microarray technology, are vital in elucidating bacterial pathogenicity and resistance profiles. However, advanced attention is required to refine diagnostic techniques, enabling earlier detection and more effective therapeutic approaches for human mastitis. The involvement of Staphylococcus aureus in human infection should be a prime focus, especially in women's health, which deals directly with neonates. Essential virulence genes in Staphylococcus species are instrumental in infection mechanisms and antibiotic resistance, serving as potential targets for personalized treatments. Thus, this review focuses on Staphylococcusaureus-induced mastitis, examining its virulence factors and detection techniques to advance diagnostic and therapeutic strategies.202540349998
4631150.9995Genome Analysis of an Enterococcal Prophage, Entfac.MY. BACKGROUND: Bacteriophages are bacterial parasites. Unlike lytic bacteriophages, lysogenic bacteriophages do not multiply immediately after entering the host cells and may integrate their genomes into the bacterial genomes as prophages. Prophages can include various phenotypic and genotypic effects on the host bacteria. Enterococcus spp. are Gram-positive bacteria that cause infections in humans and animals. In recent decades, these bacteria have become resistant to various antimicrobials, including vancomycin. The aim of this study was to analyze genome of an enterococcal prophage. METHODS: In this study, Enterococcus faecium EntfacYE was isolated from biological samples and its genome was analyzed using next-generation sequencing method. RESULTS: Overall, 254 prophage genes were identified in the bacterial genome. The prophage included 39 housekeeping, 41 replication and regulation, 80 structural and packaging, and 48 lysis genes. Moreover, 46 genes with unknown functions were identified. All genes were annotated in DNA Data Bank of Japan. CONCLUSION: In general, most prophage genes were linked to packaging and structure (31.5%) gene group. However, genes with unknown functions included a high proportion (18.11%), which indicated necessity of further analyses. Genomic analysis of the prophages can be effective in better understanding of their roles in development of bacterial resistance to antibiotics. Moreover, identification and study of prophages can help researchers develop genetic engineering tools and novel infection therapies.202236061127
5106160.9995Metagenomic diagnostics for the simultaneous detection of multiple pathogens in human stool specimens from Côte d'Ivoire: a proof-of-concept study. BACKGROUND: The intestinal microbiome is a complex community and its role in influencing human health is poorly understood. While conventional microbiology commonly attributes digestive disorders to a single microorganism, a metagenomic approach can detect multiple pathogens simultaneously and might elucidate the role of microbial communities in the pathogenesis of intestinal diseases. We present a proof-of-concept that a shotgun metagenomic approach provides useful information on the diverse composition of intestinal pathogens and antimicrobial resistance profiles in human stool samples. METHODS: In October 2012, we obtained stool specimens from patients with persistent diarrhea in south Côte d'Ivoire. Four stool samples were purposefully selected and subjected to microscopy, multiplex polymerase chain reaction (PCR), and a metagenomic approach. For the latter, we employed the National Center for Biotechnology Information nucleotide database and screened for 36 pathogenic organisms (bacteria, helminths, intestinal protozoa, and viruses) that may cause digestive disorders. We further characterized the bacterial population and the prevailing resistance patterns by comparing our metagenomic datasets with a genome-specific marker database and with a comprehensive antibiotic resistance database. RESULTS: In the four patients, the metagenomic approach identified between eight and 11 pathogen classes that potentially cause digestive disorders. For bacterial pathogens, the diagnostic agreement between multiplex PCR and metagenomics was high; yet, metagenomics diagnosed several bacteria not detected by multiplex PCR. In contrast, some of the helminth and intestinal protozoa infections detected by microscopy were missed by metagenomics. The antimicrobial resistance analysis revealed the presence of genes conferring resistance to several commonly used antibiotics. CONCLUSIONS: A metagenomic approach provides detailed information on the presence and diversity of pathogenic organisms in human stool samples. Metagenomic studies allow for in-depth molecular characterization such as the antimicrobial resistance status, which may be useful to develop setting-specific treatment algorithms. While metagenomic approaches remain challenging, the benefits of gaining new insights into intestinal microbial communities call for a broader application in epidemiologic studies. TRIAL REGISTRATION: ISRCTN86951400.201626391184
4624170.9995Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the 'distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings.201728205635
4916180.9995A clinical metagenomic study of biopsies from Mexican endophthalmitis patients reveals the presence of complex bacterial communities and a diversity of resistance genes. Infectious endophthalmitis is a severe ophthalmic emergency. This infection can be caused by bacteria and fungi. For efficient treatment, the administration of antimicrobial drugs to which the microbes are susceptible is essential. The aim of this study was to identify micro-organisms in biopsies of Mexican endophthalmitis patients using metagenomic next-generation sequencing and determine which antibiotic resistance genes were present in the biopsy samples. In this prospective case study, 19 endophthalmitis patients were recruited. Samples of vitreous or aqueous humour were extracted for DNA extraction for metagenomic next-generation sequencing. Analysis of the sequencing results revealed the presence of a wide variety of bacteria in the biopsies. Resistome analysis showed that homologues of antibiotic resistance genes were present in several biopsy samples. Genes possibly conferring resistance to ceftazidime and vancomycin were detected in addition to various genes encoding efflux pumps. Our findings contrast with the widespread opinion that only one or a few bacterial strains are present in the infected tissues of endophthalmitis patients. These diverse communities might host many of the resistance genes that were detected, which can further complicate the infections.202439045243
5113190.9995Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.202134882354