The Potential Role of Clinical Metagenomics in Infectious Diseases: Therapeutic Perspectives

Comparison of clinical metagenomics with 16S rDNA Sanger sequencing for the bacteriological diagnosis of culture-negative samples

BACKGROUND

Currently, diagnosis of bacterial infections is based on culture, possibly followed by the amplification and sequencing (Sanger method) of the 16S rDNA - encoding gene when cultures are negative. Clinical metagenomics (CMg), i.e. the sequencing of a sample's entire nucleic acids, may allow for the identification of bacteria not detected by conventional methods. Here, we tested the performance of CMg compared to 16S rDNA sequencing (Sanger) in 50 patients with suspected bacterial infection but negative cultures.

METHODS

This is a prospective cohort study. Fifty patients (73 samples) with negative culture and a 16S rDNA sequencing demand (Sanger) were recruited from two sites. On the same samples, CMg (Illumina NextSeq) was also performed and compared to 16S rDNA Sanger sequencing. Bacteria were identified using MetaPhlAn4.

RESULTS

Among the 73 samples, 20 (27 %, 17 patients) had a clinically relevant 16S rDNA Sanger sequencing result (used for patient management) while 11 (15 %, 9 patients) were considered contaminants. At the patient level, the sensitivity of CMg was 70 % (12/17) compared to 16S rDNA. In samples negative for 16S rDNA Sanger sequencing (n = 53), CMg identified clinically-relevant bacteria in 10 samples (19 %, 10 patients) with 14 additional bacteria.

CONCLUSIONS

CMg was not 100 % sensitive when compared to 16S, supporting that it may not be a suitable replacement. However, CMg did find additional bacteria in samples negative for 16S rDNA Sanger. CMg could therefore be positioned as a complementary to 16S rDNA Sanger sequencing.

What Is the Accuracy of 16S PCR Followed by Sanger Sequencing or Next-generation Sequencing in Native Vertebral Osteomyelitis? A Systematic Review and Meta-analysis

BACKGROUND

Identifying a microorganism in patients with native vertebral osteomyelitis presents diagnostic challenges. Microorganism identification through culture-based methods is constrained by prolonged processing times and sensitivity limitations. Despite the availability of molecular diagnostic techniques for identifying microorganisms in native vertebral osteomyelitis, there is considerable variability in reported sensitivity and specificity across studies, leading to uncertainty in their clinical utility.

QUESTIONS/PURPOSES

What are the sensitivity, specificity, and diagnostic odds ratios for 16S broad-range PCR followed by Sanger sequencing (16S) and metagenomic next-generation sequencing (NGS) for detecting bacteria in native vertebral osteomyelitis?

METHODS

On June 29, 2023, we searched Cochrane, Embase, Medline, and Scopus for results from January 1970 to June 2023. Included studies involved adult patients with suspected native vertebral osteomyelitis undergoing molecular diagnostics-16S bacterial broad-range PCR followed by Sanger sequencing and shotgun or targeted metagenomic NGS-for bacteria detection. Studies involving nonnative vertebral osteomyelitis and cases of brucellar, tubercular, or fungal etiology were excluded. The reference standard for the diagnosis of native vertebral osteomyelitis was a composite clinical- and investigator-defined native vertebral osteomyelitis diagnosis. Diagnostic performance was assessed using a bivariate random-effects model. Risk of bias and diagnostic applicability were evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. After a manual screening of 3403 studies, 10 studies (5 on 16S, 5 on NGS) were included in the present analysis, from which 391 patients were included from a total of 958 patients overall. Quality assessment via QUADAS-2 criteria showed moderate risk of bias and good applicability.

RESULTS

16S showed 78% (95% confidence interval [CI] 95% CI 31% to 96%) sensitivity and 94% (95% CI 73% to 99%) specificity, whereas NGS demonstrated 82% (95% CI 63% to 93%) sensitivity and 71% (95% CI 37% to 91%) specificity. In addition, the diagnostic ORs were 59 (95% CI 9 to 388) and 11 (95% CI 4 to 35) for 16S and NGS, respectively. Summary receiver operating characteristic curves showed high test performance for 16S (area under the curve for 16S 95% [95% CI 93% to 97%] and for NGS 89% [95% CI 86% to 92%]). Certainty in estimates was moderate because of sample size limitations.

CONCLUSION

This meta-analysis found moderate-to-high diagnostic performance of molecular methods on direct patient specimens for the diagnosis of native vertebral osteomyelitis. When used as a complementary test to microbiological analyses, a positive 16S result rules in the diagnosis of native vertebral osteomyelitis, while further studies are needed to understand the role of NGS in the diagnosis of native vertebral osteomyelitis. When available, these tests should be used in addition to conventional microbiology, especially in complex cases with extensively negative standard microbiological test results, to detect fastidious bacteria or to confirm the causative bacteria when their isolation and pathogenicity are unclear. A large sample size is needed in future research to understand the use of these techniques as standalone tests for diagnosis.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings

Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins.

Metagenomics Applied to the Respiratory Mycobiome in Cystic Fibrosis

Cystic fibrosis (CF) is a genetic disorder characterized by chronic microbial colonization and inflammation of the respiratory tract (RT), leading to pulmonary exacerbation (PEx) and lung damage. Although the lung bacterial microbiota has been extensively studied, the mycobiome remains understudied. However, its importance as a contributor to CF pathophysiology has been highlighted. The objective of this review is to provide an overview of the current state of knowledge regarding the mycobiome, as described through NGS-based studies, in patients with CF (pwCF).Several studies have demonstrated that the mycobiome in CF lungs is a dynamic entity, exhibiting a lower diversity and abundance than the bacterial microbiome. Nevertheless, the progression of lung damage is associated with a decrease in fungal and bacterial diversity. The core mycobiome of the RT in pwCFs is mainly composed of yeasts (Candida spp., Malassezia spp.) and molds with lower abundance. Some fungi (Aspergillus, Scedosporium/Pseudallescheria) have been demonstrated to play a role in PEx, while the involvement of others (Candida, Pneumocystis) remains uncertain. The "climax attack" ecological model has been proposed to explain the complexity and interplay of microbial populations in the RT, leading to PEx and lung damage. NGS-based studies also enable the detection of intra- and interkingdom correlations between fungi and bacteria. Further studies are required to ascertain the biological and pathophysiological relevance of these correlations. Finally, with the recent advent of CFTR modulators, our understanding of the pulmonary microbiome and mycobiome in pwCFs is about to change.

Nanopore R10.4 metagenomic detection of blaCTX-M/blaDHA antimicrobial resistance genes and their genetic environments in stool

The increasing prevalence of gut colonization with CTX-M extended-spectrum β-lactamase- and/or DHA plasmid-mediated AmpC-producing Escherichia coli is a concern. Here, we evaluate Nanopore-shotgun metagenomic sequencing (Nanopore-SMS) latest V14 chemistry to detect blaCTX-M and blaDHA genes from healthy stools. We test 25 paired samples characterized with culture-based methods (native and pre-enriched). Antimicrobial resistant genes (ARGs) are detected from reads and meta-assembled genomes (MAGs) to determine their associated genetic environments (AGEs). Sensitivity and specificity of native Nanopore-SMS are 61.1% and 100%, compared to 81.5% and 75% for pre-enriched Nanopore-SMS, respectively. Native Nanopore-SMS identifies only one sample with an AGE, whereas pre-enriched Nanopore-SMS recognizes 9/18 plasmids and 5/9 E. coli chromosomes. Pre-enriched Nanopore-SMS identifies more ARGs than native Nanopore-SMS (p < 0.001). Notably, blaCTX-Ms and blaDHAs AGEs (plasmid and chromosomes) are identified within 1 hour of sequencing. Furthermore, microbiota analyses show that pre-enriched Nanopore-SMS results in more E. coli classified reads (47% vs. 3.1%), higher differential abundance (5.69 log2 fold) and lower Shannon diversity index (p < 0.0001). Nanopore-SMS has the potential to be used for intestinal colonization screening. However, sample pre-enrichment is necessary to increase sensitivity. Further computational improvements are needed to reduce the turnaround time for clinical applications.

Analysis of pulmonary microecology and clinical characteristics of patients carrying human herpesvirus

Aim: To investigate the impact of human herpes virus (HHV) carriage on lung microbiota, and its correlation with clinical features and laboratory indicators in patients.Methods: Retrospective analysis was conducted on 30 outpatient lung infection cases, which were divided into HHV (n = 15) and non-HHV (n = 15) groups. mNGS detected microbial composition. Microbial diversity and abundance were tested using Shannon and Chao1 indices. Their relationship with laboratory indicators were explored.Results: Significant differences in microbial abundance and distribution were found between two groups (p < 0.05). Moreover, HHV group showed negative correlations (p < 0.05) between Prevotella, Porphyromonas, Streptococcus and basophil/eosinophil percentages.Conclusion: HHV carriage impacts lung microbiota, emphasizing the need for clinicians to pay attention to HHV reactivation in outpatient lung infection patients.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Shotgun metagenomic screening to improve the retrospective diagnosis of undocumented intrauterine infections on embedded placenta samples: Lessons from a brucellosis case

BACKGROUND

Microbiological diagnosis of intrauterine infections (IIU) still relies on bacteriological cultures or targeted DNA amplification lacking in sensitivity. Shotgun metagenomics (SMg) is an emerging unbiased molecular approach that makes it possible to sequence all the nucleic acids from any sample. It had never previously been used for IIU.

METHODS

We here report the case of a patient with an unexplained IIU and fetal loss that could be documented by a combined SMg/microbiological approach, leading to the diagnosis of maternal brucellosis.

RESULTS

A 31-year-old woman presented with an undocumented IIU with fetal loss at 24 weeks of gestation. Culture-based work-up failed to identify the pathogen involved. Paraffin-embedded placenta sample was retrospectively analyzed by SMg. Brucella spp nucleic acids were detected, and subacute maternal brucellosis was confirmed by targeted PCR and serological testing.

CONCLUSION

This case provides grounds for further utilization of SMg for the microbiological diagnosis of unexplained obstetrical infections.

Application of mNGS in the study of pulmonary microbiome in pneumoconiosis complicated with pulmonary infection patients and exploration of potential biomarkers

Background

Pneumoconiosis patients have a high prevalence of pulmonary infections, which can complicate diagnosis and treatment. And there is no comprehensive study of the microbiome of patients with pneumoconiosis. The application of metagenomic next-generation sequencing (mNGS) fills the gap to some extent by analyzing the lung microbiota of pneumoconiosis population while achieving accurate diagnosis.

Methods

We retrospectively analyzed 44 patients with suspected pneumoconiosis complicated with pulmonary infection between Jan 2020 and Nov 2022. Bronchoalveolar lavage fluid (BALF) specimens from 44 patients were collected and tested using the mNGS technology.

Results

Among the lung microbiome of pneumoconiosis patients with complicated pulmonary infection (P group), the most frequently detected bacteria and fungi at the genus level were Streptococcus and Aspergillus, at the species level were Streptococcus pneumoniae and Aspergillus flavus, respectively, and the most frequently detected DNA virus was Human gammaherpesvirus 4. There was no significant difference in α diversity between the P group and the non-pneumoconiosis patients complicated with pulmonary infection group (Non-P group) in pulmonary flora, while P< 0.01 for β diversity analysis, and the differential species between the two groups were Mycobacterium colombiense and Fusobacterium nucleatum. In addition, we monitored a high distribution of Malassezia and Pneumocystis in the P group, while herpes virus was detected in the majority of samples.

Conclusions

Overall, we not only revealed a comprehensive lung microbiome profile of pneumoconiosis patients, but also compared the differences between their microbiome and that of non-pneumoconiosis complicated with pulmonary infection patients. This provides a good basis for a better understanding of the relationship between pneumoconiosis and microorganisms, and for the search of potential biomarkers.

Clinical metagenomics-challenges and future prospects

Infections lacking precise diagnosis are often caused by a rare or uncharacterized pathogen, a combination of pathogens, or a known pathogen carrying undocumented or newly acquired genes. Despite medical advances in infectious disease diagnostics, many patients still experience mortality or long-term consequences due to undiagnosed or misdiagnosed infections. Thus, there is a need for an exhaustive and universal diagnostic strategy to reduce the fraction of undocumented infections. Compared to conventional diagnostics, metagenomic next-generation sequencing (mNGS) is a promising, culture-independent sequencing technology that is sensitive to detecting rare, novel, and unexpected pathogens with no preconception. Despite the fact that several studies and case reports have identified the effectiveness of mNGS in improving clinical diagnosis, there are obvious shortcomings in terms of sensitivity, specificity, costs, standardization of bioinformatic pipelines, and interpretation of findings that limit the integration of mNGS into clinical practice. Therefore, physicians must understand the potential benefits and drawbacks of mNGS when applying it to clinical practice. In this review, we will examine the current accomplishments, efficacy, and restrictions of mNGS in relation to conventional diagnostic methods. Furthermore, we will suggest potential approaches to enhance mNGS to its maximum capacity as a clinical diagnostic tool for identifying severe infections.

Diagnostic Performance and Clinical Impact of Metagenomic Next-Generation Sequencing for Pediatric Infectious Diseases

Metagenomic next-generation sequencing (mNGS) has shown promise in the diagnosis of infectious diseases in adults, while its efficacy in pediatric infections remains uncertain. We performed a retrospective analysis of 1,493 mNGS samples from pediatric patients with blood, central nervous system, and lower respiratory tract infections. The positive percent agreement (PPA) and the negative percent agreement (NPA) of mNGS were compared to conventional microbiological tests (CMT) based on clinical diagnosis. The agreement of mNGS compared to CMT, as well as the clinical impact of mNGS, were valuated. Using the clinical diagnosis as a reference, mNGS demonstrated a significantly higher overall PPA compared to CMT (53.1% [95% CI = 49.7 to 56.6%] versus 25.8% [95% CI = 22.8 to 28.9%]), while maintaining a comparable overall NPA (93.2% [95% CI = 91.3 to 95.1%] versus 97.2% [95% CI = 95.9 to 98.4%]). In septic patients under 6 years of age or with immunosuppressive status, mNGS showed a higher PPA and a comparable NPA compared to CMT. The overall PPA and NPA of mNGS compared to CMT were 75.3 and 75.0%, respectively. The majority of cases of Streptococcus pneumoniae, Streptococcus agalactiae, Mycobacterium tuberculosis complex, and Pneumocystis jirovecii infections were identified by mNGS. A positive clinical impact of 14.0% (206/1,473), a negative impact of 0.8% (11/1,473), a nonimpact of 84.7% (1,248/1,473), and an unknown impact of 0.5% (8/1,473) were observed in the mNGS results. Notably, the positive impact was greater among immunosuppressed patients than among nonimmunosuppressed individuals (67/247, 27.1% versus 139/1,226, 11.3%; P < 0.001). mNGS is valuable for pathogen detection, diagnosis, and clinical management of infections among pediatric patients. mNGS was thus effective for the diagnosis of pediatric infections, which may guide clinical management. Patients with immunosuppressive conditions benefited more from mNGS testing.

Rapid diagnostics for skin and soft tissue infections: the current landscape and future potential

PURPOSE OF REVIEW

Managing antimicrobial therapy in patients with complicated skin and soft tissue infections (SSTI) constitutes a growing challenge due to the wide spectrum of potential pathogens and resistance phenotypes. Today, microbiological documentation relies on cultural methods. This review summarizes the available evidence regarding the clinical input of rapid microbiological diagnostic tools (RMDT) and their impact on the management of antimicrobial therapy in SSTI.

RECENT FINDINGS

Accurate tools are already available for the early detection of methicillin-resistant Staphylococcus aureus (MRSA) in SSTI samples and may help avoiding or shortening empirical anti-MRSA coverage. Further research is necessary to develop and evaluate RMDT detecting group A streptococci (e.g., antigenic test) and Gram-negative pathogens (e.g., multiplex PCR assays), including through point-of-care utilization. Next-generation sequencing (NGS) methods could provide pivotal information for the stewardship of antimicrobial therapy, especially in case of polymicrobial or fungal SSTI and in the immunocompromised host; however, a shortening in the turnaround time and prospective data regarding their therapeutic input are needed to better appraise the clinical positioning of these promising approaches.

SUMMARY

The clinical input of RMDT in SSTI is currently limited due to the scarcity of available dedicated assays and the polymicrobial feature of certain cases. NGS appears as a relevant tool but requires further developments before its implementation in routine clinical practice.

Specificities of Meningitis and Meningo-Encephalitis After Kidney Transplantation: A French Retrospective Cohort Study

Kidney transplant recipients develop atypical infections in their epidemiology, presentation and outcome. Among these, meningitis and meningoencephalitis require urgent and adapted anti-infectious therapy, but published data is scarce in KTRs. The aim of this study was to describe their epidemiology, presentation and outcome, in order to improve their diagnostic and management. We performed a retrospective, multicentric cohort study in 15 French hospitals that included all 199 cases of M/ME in KTRs between 2007 and 2018 (0.9 case per 1,000 KTRs annually). Epidemiology was different from that in the general population: 20% were due to Cryptococcus neoformans, 13.5% to varicella-zoster virus, 5.5% to Mycobacterium tuberculosis, and 4.5% to Enterobacteria (half of which produced extended spectrum beta-lactamases), and 5% were Post Transplant Lymphoproliferative Disorders. Microorganisms causing M/ME in the general population were infrequent (2%, for Streptococcus pneumoniae) or absent (Neisseria meningitidis). M/ME caused by Enterobacteria, Staphylococci or filamentous fungi were associated with high and early mortality (50%-70% at 1 year). Graft survival was not associated with the etiology of M/ME, nor was impacted by immunosuppression reduction. Based on these results, we suggest international studies to adapt guidelines in order to improve the diagnosis and the probabilistic treatment of M/ME in SOTRs.

What about the bull? A systematic review about the role of males in bovine infectious infertility within cattle herds

Numerous pathogens affect cow fertility. Nevertheless, little information has been published about microorganisms associated with cattle infertility focusing on bulls. The present review offers a current analysis and highlights potential key aspects on the relevance of bulls in the emergence of infertility problems of infectious origin within herds that are still not completely determined. The present systematic review was conducted using the PubMed, Web of Science, and Scopus databases on December 9, 2022. In total, 2,224 bibliographic records were reviewed and, according to strict inclusion criteria, 38 articles were selected from 1966 to 2022, from which we ranked more than 27 different microorganisms (fungi were not identified). The most cited pathogens were BoHV (described by 26.3% of the papers), Campylobacter fetus (23.7%), Tritrichomonas foetus (18.4%), and BVDV, Ureaplasma spp., and Mycoplasma spp. (10.5% each). Despite the general trend towards an increasing number of publications about bull-infertility problems, a number of pathogens potentially transmitted through both natural breeding and seminal doses given to females and associated with infertility within herds were not ranked in the study (e.g., Chlamydia spp.). This work highlights i) the need to clearly establish the role of certain microorganisms not traditionally associated with reproductive problems in bull infertility (e.g., Staphylococcus spp. or BoHV-4) and ii) the need to perform additional studies on breeding bulls to clarify their role in infertility problems within herds. This would allow monitoring for pathogens that have gone unnoticed and those that are fastidious to diagnose and/or potentially transmitted to females.

Effect of bacterial DNA enrichment on detection and quantification of bacteria in an infected tissue model by metagenomic next-generation sequencing

Before implementing metagenomic next-generation sequencing (mNGS) in the routine diagnostic laboratory, several challenges need to be resolved. To address strengths and limitations of mNGS in bacterial detection and quantification in samples with overwhelming host DNA abundance, we used the pig muscle tissue spiked with a home-made bacterial mock community, consisting of four species from different phyla. From the spiked tissue, we extracted DNA using: (i) a procedure based on mechanical/chemical lysis (no bacterial DNA enrichment); (ii) the Ultra-Deep Microbiome Prep (Molzym) kit for bacterial DNA enrichment; and (iii) the same enrichment kit but replacing the original proteinase K treatment for tissue solubilization by a collagenases/thermolysin digestion and cell filtration. Following mNGS, we determined bacterial: 'host' read ratios and taxonomic abundance profiles. We calculated the load of each mock-community member by combining its read counts with read counts and microscopically-determined cell counts of other co-spiked bacteria. In unenriched samples, bacterial quantification and taxonomic profiling were fairly accurate but at the expense of the sensitivity of detection. The removal of 'host' DNA by the modified enrichment protocol substantially improved bacterial detection in comparison to the other two extraction procedures and generated less distorted taxonomic profiles as compared to the original enrichment protocol.

Native valve, prosthetic valve, and cardiac device-related infective endocarditis: A review and update on current innovative diagnostic and therapeutic strategies

Infective endocarditis (IE) is a life-threatening microbial infection of native and prosthetic heart valves, endocardial surface, and/or indwelling cardiac device. Prevalence of IE is increasing and mortality has not significantly improved despite technological advances. This review provides an updated overview using recent literature on the clinical presentation, diagnosis, imaging, causative pathogens, treatment, and outcomes in native valve, prosthetic valve, and cardiac device-related IE. In addition, the experimental approaches used in IE research to improve the understanding of disease mechanisms and the current diagnostic pipelines are discussed, as well as potential innovative diagnostic and therapeutic strategies. This will ultimately help towards deriving better diagnostic tools and treatments to improve IE patient outcomes.

PIMGAVir and Vir-MinION: Two Viral Metagenomic Pipelines for Complete Baseline Analysis of 2nd and 3rd Generation Data

The taxonomic classification of viral sequences is frequently used for the rapid identification of pathogens, which is a key point for when a viral outbreak occurs. Both Oxford Nanopore Technologies (ONT) MinION and the Illumina (NGS) technology provide efficient methods to detect viral pathogens. Despite the availability of many strategies and software, matching them can be a very tedious and time-consuming task. As a result, we developed PIMGAVir and Vir-MinION, two metagenomics pipelines that automatically provide the user with a complete baseline analysis. The PIMGAVir and Vir-MinION pipelines work on 2nd and 3rd generation data, respectively, and provide the user with a taxonomic classification of the reads through three strategies: assembly-based, read-based, and clustering-based. The pipelines supply the scientist with comprehensive results in graphical and textual format for future analyses. Finally, the pipelines equip the user with a stand-alone platform with dedicated and various viral databases, which is a requirement for working in field conditions without internet connection.

Prospective Comparison Between Shotgun Metagenomics and Sanger Sequencing of the 16S rRNA Gene for the Etiological Diagnosis of Infections

Bacteriological diagnosis is traditionally based on culture. However, this method may be limited by the difficulty of cultivating certain species or by prior exposure to antibiotics, which justifies the resort to molecular methods, such as Sanger sequencing of the 16S rRNA gene (Sanger 16S). Recently, shotgun metagenomics (SMg) has emerged as a powerful tool to identify a wide range of pathogenic microorganisms in numerous clinical contexts. In this study, we compared the performance of SMg to Sanger 16S for bacterial detection and identification. All patients’ samples for which Sanger 16S was requested between November 2019 and April 2020 in our institution were prospectively included. The corresponding samples were tested with a commercial 16S semi-automated method and a semi-quantitative pan-microorganism DNA- and RNA-based SMg method. Sixty-seven samples from 64 patients were analyzed. Overall, SMg was able to identify a bacterial etiology in 46.3% of cases (31/67) vs. 38.8% (26/67) with Sanger 16S. This difference reached significance when only the results obtained at the species level were compared (28/67 vs. 13/67). This study provides one of the first evidence of a significantly better performance of SMg than Sanger 16S for bacterial detection at the species level in patients with infectious diseases for whom culture-based methods have failed. This technology has the potential to replace Sanger 16S in routine practice for infectious disease diagnosis.

Mycobacterium chelonae Infection Identified by Metagenomic Next-Generation Sequencing as the Probable Cause of Acute Contained Rupture of a Biological Composite Graft-A Case Report

We present the case of a 72-year-old female patient with acute contained rupture of a biological composite graft, 21 months after replacement of the aortic valve and the ascending aorta due to an aortic dissection. Auramine-rhodamine staining of intraoperative biopsies showed acid-fast bacilli, but classical culture and molecular methods failed to identify any organism. Metagenomic analysis indicated infection with Mycobacterium&nbsp;chelonae, which was confirmed by target-specific qPCR. The complexity of the sample required a customized bioinformatics pipeline, including cleaning steps to remove sequences of human, bovine ad pig origin. Our study underlines the importance of multiple testing to increase the likelihood of pathogen identification in highly complex samples.

Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

Shotgun-metagenomics may give valuable clinical information beyond the detection of potential pathogen(s). Identification of antimicrobial resistance (AMR), virulence genes and typing directly from clinical samples has been limited due to challenges arising from incomplete genome coverage. We assessed the performance of shotgun-metagenomics on positive blood culture bottles (n = 19) with periprosthetic tissue for typing and prediction of AMR and virulence profiles in Staphylococcus aureus. We used different approaches to determine if sequence data from reads provides more information than from assembled contigs. Only 0.18% of total reads was derived from human DNA. Shotgun-metagenomics results and conventional method results were consistent in detecting S. aureus in all samples. AMR and known periprosthetic joint infection virulence genes were predicted from S. aureus. Mean coverage depth, when predicting AMR genes was 209 ×. Resistance phenotypes could be explained by genes predicted in the sample in most of the cases. The choice of bioinformatic data analysis approach clearly influenced the results, i.e. read-based analysis was more accurate for pathogen identification, while contigs seemed better for AMR profiling. Our study demonstrates high genome coverage and potential for typing and prediction of AMR and virulence profiles in S. aureus from shotgun-metagenomics data.