Clinical characteristics and prognosis of patients with severe pneumonia with Pneumocystis jirovecii colonization: A multicenter, retrospective study.

BACKGROUND: For decades, the incidence and clinical characteristics of Pneumocystis jirovecii (P. jirovecii) colonization in patients with severe pneumonia was remained unclear. RESEARCH QUESTION: What are the clinical features and outcomes associated with P. jirovecii colonization in individuals diagnosed with severe pneumonia? STUDY DESIGN AND METHODS: In this multicenter, retrospective, matched study, severe pneumonia patients who underwent bronchoalveolar lavage clinical metagenomics from 2019 to 2023 in the ICUs of 17 medical centers were enrolled. Patients were diagnosed based on clinical metagenomics, pulmonary CT scans, and clinical presentations. Clinical data were collected retrospectively, and according to propensity score matching and Cox multivariate regression analysis, the prognosis of patients with P. jirovecii colonization was compared to that of P. jirovecii-negative patients. RESULTS: 40% of P. jirovecii positive patients are considered to have P. jirovecii colonization. P. jirovecii colonization group had a higher proportion of patients with immunosuppression and a lower lymphocyte count compared to P. jirovecii-negative group. More frequent detection of cytomegalovirus, Epstein-Barr virus, human herpesvirus-6B, human herpesvirus-7, and torque teno virus in the lungs was associated with P. jirovecii colonization than with P. jirovecii negativity. By constructing two cohorts through propensity score matching, we incorporated codetected microorganisms and clinical features into a Cox proportional hazards model and revealed that P. jirovecii colonization was an independent risk factor for mortality in severe pneumonia patients. According to sensitivity analyses, which included or excluded codetected microorganisms, as well as patients not receiving TMP-SMX treatment, similar conclusions were reached. INTERPRETATION: Immunosuppression and a reduced lymphocyte count were identified as risk factors for P. jirovecii colonization in non-PCP patients. More frequent detection of various viruses was observed in P. jirovecii colonization patients, and P. jirovecii colonization was associated with an increased 28-day mortality in patients with severe pneumonia.

Bioinformatic Approaches for Comparative Analysis of Viruses.

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need adaptations. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. This chapter offers descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. The authors also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Protocols and topics emphasize online tools (which are more accessible to users) and give the real experience of what most bioinformaticians do in day-by-day work with command-line pipelines. The topics discussed include (1) clustering related genomes, (2) whole genome multiple sequence alignments for small RNA viruses, (3) protein alignment for marker genes and species affiliation, (4) variant calling and annotation, and (5) virome analyses and pathogen identification.

Hybrid-Capture Target Enrichment in Human Pathogens: Identification, Evolution, Biosurveillance, and Genomic Epidemiology.

High-throughput sequencing (HTS) has revolutionised the field of pathogen genomics, enabling the direct recovery of pathogen genomes from clinical and environmental samples. However, pathogen nucleic acids are often overwhelmed by those of the host, requiring deep metagenomic sequencing to recover sufficient sequences for downstream analyses (e.g., identification and genome characterisation). To circumvent this, hybrid-capture target enrichment (HC) is able to enrich pathogen nucleic acids across multiple scales of divergences and taxa, depending on the panel used. In this review, we outline the applications of HC in human pathogens-bacteria, fungi, parasites and viruses-including identification, genomic epidemiology, antimicrobial resistance genotyping, and evolution. Importantly, we explored the applicability of HC to clinical metagenomics, which ultimately requires more work before it is a reliable and accurate tool for clinical diagnosis. Relatedly, the utility of HC was exemplified by COVID-19, which was used as a case study to illustrate the maturity of HC for recovering pathogen sequences. As we unravel the origins of COVID-19, zoonoses remain more relevant than ever. Therefore, the role of HC in biosurveillance studies is also highlighted in this review, which is critical in preparing us for the next pandemic. We also found that while HC is a popular tool to study viruses, it remains underutilised in parasites and fungi and, to a lesser extent, bacteria. Finally, weevaluated the future of HC with respect to bait design in the eukaryotic groups and the prospect of combining HC with long-read HTS.

Defining a metagenomic threshold for detecting low abundances of Providencia alcalifaciens in canine faecal samples.

Introduction: Acute haemorrhagic diarrhoea syndrome (AHDS) in dogs is a condition of unknown aetiology. Providencia alcalifaciens is suspected to play a role in the disease as it was commonly found in dogs suffering from AHDS during a Norwegian outbreak in 2019. The role of this bacterium as a constituent of the canine gut microbiota is unknown, hence this study set out to investigate its occurrence in healthy dogs using metagenomics. Materials and methods: To decrease the likelihood of false detection, we established a metagenomic threshold for P. alcalifaciens by spiking culture-negative stool samples with a range of bacterial dilutions and analysing these by qPCR and shotgun metagenomics. The detection limit for P. alcalifaciens was determined and used to establish a metagenomic threshold. The threshold was validated on naturally contaminated faecal samples with known cultivation status for P. alcalifaciens. Finally, the metagenomic threshold was used to determine the occurrence of P. alcalifaciens in shotgun metagenomic datasets from canine faecal samples (n=362) collected in the HUNT One Health project. Results: The metagenomic assay and qPCR had a detection limit of 1.1x103 CFU P. alcalifaciens per faecal sample, which corresponded to a Cq value of 31.4 and 569 unique k-mer counts by shotgun metagenomics. Applying this metagenomic threshold to 362 faecal metagenomic datasets from healthy dogs, P. alcalifaciens was found in only 1.1% (95% CI [0.0, 6.8]) of the samples, and then in low relative abundances (median: 0.04%; range: 0.00 to 0.81%). The sensitivity of the qPCR and shotgun metagenomics assay was low, as only 40% of culture-positive samples were also positive by qPCR and metagenomics. Discussion: Using our detection limit, the occurrence of P. alcalifaciens in faecal samples from healthy dogs was low. Given the low sensitivity of the metagenomic assay, these results do not rule out a significantly higher occurrence of this bacterium at a lower abundance.

RVFScan predicts virulence factor genes and hypervirulence of the clinical metagenome.

Bacterial infections often involve virulence factors that play a crucial role in the pathogenicity of bacteria. Accurate detection of virulence factor genes (VFGs) is essential for precise treatment and prognostic management of hypervirulent bacterial infections. However, there is a lack of rapid and accurate methods for VFG identification from the metagenomic data of clinical samples. Here, we developed a Reads-based Virulence Factors Scanner (RVFScan), an innovative user-friendly online tool that integrates a comprehensive VFG database with similarity matrix-based criteria for VFG prediction and annotation using metagenomic data without the need for assembly. RVFScan demonstrated superior performance compared to previous assembly-based and read-based VFG predictors, achieving a sensitivity of 97%, specificity of 98% and accuracy of 98%. We also conducted a large-scale analysis of 2425 clinical metagenomic datasets to investigate the utility of RVFScan, the species-specific VFG profiles and associations between VFGs and virulence phenotypes for 24 important pathogens were analyzed. By combining genomic comparisons and network analysis, we identified 53 VFGs with significantly higher abundances in hypervirulent Klebsiella pneumoniae (hvKp) than in classical K. pneumoniae. Furthermore, a cohort of 1256 samples suspected of K. pneumoniae infection demonstrated that RVFScan could identify hvKp with a sensitivity of 90%, specificity of 100% and accuracy of 98.73%, with 90% of hvKp samples consistent with clinical diagnosis (Cohen's kappa, 0.94). RVFScan has the potential to detect VFGs in low-biomass and high-complexity clinical samples using metagenomic reads without assembly. This capability facilitates the rapid identification and targeted treatment of hvKp infections and holds promise for application to other hypervirulent pathogens.

A metagenomics method for the quantitative detection of bacterial pathogens causing hospital-associated and ventilator-associated pneumonia.

IMPORTANCE: The management of ventilator-associated pneumonia and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen. To this end, we propose a metagenomic sequencing assay that includes the use of an internal sample processing control for the quantitative detection of 20 relevant bacterial species from bronchoalveolar lavage samples.

Clinical metagenomics-assisted diagnosis of relapsed HIV-associated cryptococcal meningitis.

To date, the identification of crypotococcal relapse remains clinically challenging as it often has similar manifestation with paradoxical immune reconstitution inflammatory syndrome. This study reports on the use of metagenomics assisted next generation sequencing to aid in diagnosing recurrent cryptococcal meningitis in an person living with HIV experiencing recurring symptoms, despite negative culture results for Cryptococcus neoformans in the cerebrospinal fluid. Although fungal culture was negative, when reads from metagenomic and metatranscriptomic sequencing performed on the Day 308 cerebrospinal fluid sample were mapped onto the genome from the Day 4 isolate, 589 specific reads were identified. NCBI BLAST search also revealed Cryptococcus-specific 18S/25S/28S ribosomal RNA, indicating a relapse of the disease.

Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii.

Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data. The key genetic characteristics associated with antimicrobial resistance (AMR) were selected through a least absolute shrinkage and selection operator (LASSO) regression model based on 1,942 A. baumannii genomes. The mNGS-AST prediction model was accordingly established, validated, and optimized using read simulation sequences of clinical isolates. Clinical specimens were collected to evaluate the performance of the model retrospectively and prospectively. We identified 20, 31, 24, and 3 AMR signatures of A. baumannii for imipenem, ceftazidime, cefepime, and ciprofloxacin, respectively. Four mNGS-AST models had a positive predictive value (PPV) greater than 0.97 for 230 retrospective samples, with negative predictive values (NPVs) of 100% (imipenem), 86.67% (ceftazidime), 86.67% (cefepime), and 90.91% (ciprofloxacin). Our method classified antibacterial phenotypes with an accuracy of 97.65% for imipenem, 96.57% for ceftazidime, 97.64% for cefepime, and 98.36% for ciprofloxacin. The average reporting time of mNGS-based AST was 19.1 h, in contrast to the 63.3 h for culture-based AST, thus yielding a significant reduction of 44.3 h. mNGS-AST prediction results coincided 100% with the phenotypic AST results when testing 50 prospective samples. The mNGS-based model could be used as a rapid genotypic AST approach to identify A. baumannii and predict resistance and susceptibility to antibacterials and could be applicable to other pathogens and facilitate rational antimicrobial usage.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples.

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

Contribution of Clinical Metagenomics to the Diagnosis of Bone and Joint Infections.

Bone and joint infections (BJIs) are complex infections that require precise microbiological documentation to optimize antibiotic therapy. Currently, diagnosis is based on microbiological culture, sometimes complemented by amplification and sequencing of the 16S rDNA gene. Clinical metagenomics (CMg), that is, the sequencing of the entire nucleic acids in a sample, was previously shown to identify bacteria not detected by conventional methods, but its actual contribution to the diagnosis remains to be assessed, especially with regard to 16S rDNA sequencing. In the present study, we tested the performance of CMg in 34 patients (94 samples) with suspected BJIs, as compared to culture and 16S rDNA sequencing. A total of 94 samples from 34 patients with suspicion of BJIs, recruited from two sites, were analyzed by (i) conventional culture, (ii) 16S rDNA sequencing (Sanger method), and (iii) CMg (Illumina Technology). Two negative controls were also sequenced by CMg for contamination assessment. Based on the sequencing results of negative controls, 414 out of 539 (76.7%) bacterial species detected by CMg were considered as contaminants and 125 (23.2%) as truly present. For monomicrobial infections (13 patients), the sensitivity of CMg was 83.3% as compared to culture, and 100% as compared to 16S rDNA. For polymicrobial infections (13 patients), the sensitivity of CMg was 50% compared to culture, and 100% compared to 16S rDNA. For samples negative in culture (8 patients, 21 samples), CMg detected 11 bacteria in 10 samples from 5 different patients. In 5/34 patients, CMg brought a microbiological diagnosis where conventional methods failed, and in 16/34 patients, CMg provided additional information. Finally, 99 antibiotic resistance genes were detected in 24 patients (56 samples). Provided sufficient genome coverage (87.5%), a correct inference of antibiotic susceptibility was achieved in 8/8 bacteria (100%). In conclusion, our study demonstrated that the CMg provides complementary and potentially valuable data to conventional methods of BJIs diagnosis.

Inferring antibiotic susceptibility from metagenomic data: dream or reality?

BACKGROUND: The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48 hours after sampling, at best. Recently, clinical metagenomics, the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. Clinical metagenomics indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality. OBJECTIVES: The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data. SOURCES: We conducted a narrative review using published articles available in the National Center for Biotechnology Information PubMed database. CONTENT: We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host. IMPLICATIONS: Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.

Targeted Metagenomic Sequencing-based Approach Applied to 2146 Tissue and Body Fluid Samples in Routine Clinical Practice.

BACKGROUND: The yield of next-generation sequencing (NGS) added to a Sanger sequencing-based 16S ribosomal RNA (rRNA) gene polymerase chain reaction (PCR) assay was evaluated in clinical practice for diagnosis of bacterial infection. METHODS: PCR targeting the V1 to V3 regions of the 16S rRNA gene was performed, with amplified DNA submitted to Sanger sequencing and/or NGS (Illumina MiSeq) or reported as negative, depending on the cycle threshold value. A total of 2146 normally sterile tissues or body fluids were tested between August 2020 and March 2021. Clinical sensitivity was assessed in 579 patients from whom clinical data were available. RESULTS: Compared with Sanger sequencing alone (400 positive tests), positivity increased by 87% by adding NGS (347 added positive tests). Clinical sensitivity of the assay that incorporated NGS was 53%, which was higher than culture (42%, P < .001), with an impact on clinical decision-making in 14% of infected cases. Clinical sensitivity in the subgroup that received antibiotics at sampling was 41% for culture and 63% for the sequencing assay (P < .001). CONCLUSIONS: Adding NGS to Sanger sequencing of the PCR-amplified 16S rRNA gene substantially improved test positivity. In the patient population studied, the assay was more sensitive than culture, especially in patients who had received antibiotic therapy.

Concurrent pigeon paramyxovirus-1 and Acinetobacter baumannii infection in a fatal case of pneumonia.

Pigeon paramyxovirus type 1 (PPMV-1), an antigenic variant of avian paramyxovirus type 1 (APMV-1), mainly infects pigeons. PPMV-1 genotype VI is the dominant genotype infecting pigeons in China. Human infection of avian paramyxovirus was rarely reported, and usually developed mild symptoms, such as conjunctivitis. We detected PPMV-1 in the lower respiratory sample from a fatal case with severe pneumonia; this patient aged 64 years presented cough, fever, and haemoptysis for 8 days and was admitted to hospital on Dec 26, 2020. He developed acute respiratory distress syndrome and sepsis in the following days and died of multiple organ failure on Jan 7, 2021. Sputum and blood culture reported multidrug-resistant Acinetobacter baumannii (ABA) for samples collected on days 22 and 19 post-illness, respectively. However, clinical metagenomic sequencing further reported PPMV-1 besides ABA in the bronchoalveolar lavage fluid. The PPMV-1 genome showed 99.21% identity with a Chinese strain and belonged to VI genotype by BLAST analysis. Multiple basic amino acids were observed at the cleavage site of F protein (113RKKRF117), which indicated high virulence of this PPMV-1 strain to poultry. The patient had close contact with pigeons before his illness, and PPMV-1 nucleic acid was detected from the pigeon feather. PPMV antibody was also detected in the patient serum 20 days after illness. In conclusion, concurrent PPMV-1 genotype VI.2.1.1.2.2 and ABA infection were identified in a fatal pneumonia case, and cross-species transmission of PPMV-1 may occur between infected pigeons and the human being.

Clinical Metagenomics Is Increasingly Accurate and Affordable to Detect Enteric Bacterial Pathogens in Stool.

Stool culture is the gold standard method to diagnose enteric bacterial infections; however, many clinical laboratories are transitioning to syndromic multiplex PCR panels. PCR is rapid, accurate, and affordable, yet does not yield subtyping information critical for foodborne disease surveillance. A metagenomics-based stool testing approach could simultaneously provide diagnostic and public health information. Here, we evaluated shotgun metagenomics to assess the detection of common enteric bacterial pathogens in stool. We sequenced 304 stool specimens from 285 patients alongside routine diagnostic testing for Salmonella spp., Campylobacter spp., Shigella spp., and shiga-toxin producing Escherichia coli. Five analytical approaches were assessed for pathogen detection: microbiome profiling, Kraken2, MetaPhlAn, SRST2, and KAT-SECT. Among analysis tools and databases compared, KAT-SECT analysis provided the best sensitivity and specificity for all pathogens tested compared to culture (91.2% and 96.2%, respectively). Where metagenomics detected a pathogen in culture-negative specimens, standard PCR was positive 85% of the time. The cost of metagenomics is approaching the current combined cost of PCR, reflex culture, and whole genome sequencing for pathogen detection and subtyping. As cost, speed, and analytics for single-approach metagenomics improve, it may be more routinely applied in clinical and public health laboratories.

Metagenomic Sequencing for Microbial DNA in Human Samples: Emerging Technological Advances.

Whole genome metagenomic sequencing is a powerful platform enabling the simultaneous identification of all genes from entirely different kingdoms of organisms in a complex sample. This technology has revolutionised multiple areas from microbiome research to clinical diagnoses. However, one of the major challenges of a metagenomic study is the overwhelming non-microbial DNA present in most of the host-derived specimens, which can inundate the microbial signals and reduce the sensitivity of microorganism detection. Various host DNA depletion methods to facilitate metagenomic sequencing have been developed and have received considerable attention in this context. In this review, we present an overview of current host DNA depletion approaches along with explanations of their underlying principles, advantages and disadvantages. We also discuss their applications in laboratory microbiome research and clinical diagnoses and, finally, we envisage the direction of the further perfection of metagenomic sequencing in samples with overabundant host DNA.

The Role of Metagenomics and Next-Generation Sequencing in Infectious Disease Diagnosis.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) for pathogen detection is becoming increasingly available as a method to identify pathogens in cases of suspected infection. mNGS analyzes the nucleic acid content of patient samples with high-throughput sequencing technologies to detect and characterize microorganism DNA and/or RNA. This unbiased approach to organism detection enables diagnosis of a broad spectrum of infection types and can identify more potential pathogens than any single conventional test. This can lead to improved ability to diagnose patients, although there remains concern regarding contamination and detection of nonclinically significant organisms. CONTENT: We describe the laboratory approach to mNGS testing and highlight multiple considerations that affect diagnostic performance. We also summarize recent literature investigating the diagnostic performance of mNGS assays for a variety of infection types and recommend further studies to evaluate the improvement in clinical outcomes and cost-effectiveness of mNGS testing. SUMMARY: The majority of studies demonstrate that mNGS has sensitivity similar to specific PCR assays and will identify more potential pathogens than conventional methods. While many of these additional organism detections correlate with the expected pathogen spectrum based on patient presentations, there are relatively few formal studies demonstrating whether these are true-positive infections and benefits to clinical outcomes. Reduced specificity due to contamination and clinically nonsignificant organism detections remains a major concern, emphasizing the importance of careful interpretation of the organism pathogenicity and potential association with the clinical syndrome. Further research is needed to determine the possible improvement in clinical outcomes and cost-effectiveness of mNGS testing.

Future potential of metagenomics in microbiology laboratories.

Rapid and sensitive diagnostic strategies are necessary for patient care and public health. Most of the current conventional microbiological assays detect only a restricted panel of pathogens at a time or require a microbe to be successfully cultured from a sample. Clinical metagenomics next-generation sequencing (mNGS) has the potential to unbiasedly detect all pathogens in a sample, increasing the sensitivity for detection and enabling the discovery of unknown infectious agents. High expectations have been built around mNGS; however, this technique is far from widely available. This review highlights the advances and currently available options in terms of costs, turnaround time, sensitivity, specificity, validation, and reproducibility of mNGS as a diagnostic tool in clinical microbiology laboratories. The need for a novel diagnostic tool to increase the sensitivity of microbial diagnostics is clear. mNGS has the potential to revolutionize clinical microbiology. However, its role as a diagnostic tool has yet to be widely established, which is crucial for successfully implementing the technique. A clear definition of diagnostic algorithms that include mNGS is vital to show clinical utility. Similarly to real-time PCR, mNGS will one day become a vital tool in any testing algorithm.

Clinical metagenomics assessments improve diagnosis and outcomes in community-acquired pneumonia.

BACKGROUND: Identifying the causes of community-acquired pneumonia (CAP) is challenging due to the disease's complex etiology and the limitations of traditional microbiological diagnostic methods. Recent advances in next generation sequencing (NGS)-based metagenomics allow pan-pathogen detection in a single assay, and may have significant advantages over culture-based techniques. RESULTS: We conducted a cohort study of 159 CAP patients to assess the diagnostic performance of a clinical metagenomics assay and its impact on clinical management and patient outcomes. When compared to other techniques, clinical metagenomics detected more pathogens in more CAP cases, and identified a substantial number of polymicrobial infections. Moreover, metagenomics results led to changes in or confirmation of clinical management in 35 of 59 cases; these 35 cases also had significantly improved patient outcomes. CONCLUSIONS: Clinical metagenomics could be a valuable tool for the diagnosis and treatment of CAP. TRIAL REGISTRATION: Trial registration number with the Chinese Clinical Trial Registry: ChiCTR2100043628 .

Rapid metagenomics for diagnosis of bloodstream and respiratory tract nosocomial infections: current status and future prospects.

Introduction: Nosocomial infections represent a major problem for the health-care systems worldwide. Currently, diagnosis relies on microbiological culture, which is slow and has poor sensitivity. While waiting for a diagnosis, patients are treated with empiric broad spectrum antimicrobials, which are often inappropriate for the infecting pathogen. This results in poor patient outcomes, poor antimicrobial stewardship and increased costs for health-care systems.Areas covered: Clinical metagenomics (CMg), the application of metagenomic sequencing for the diagnosis of infection, has the potential to become a viable alternative to culture that can offer rapid results with high accuracy. In this article, we review current CMg methods for the diagnosis of nosocomial bloodstream (BSI) and lower respiratory-tract infections (LRTI).Expert opinion: CMg approaches are more accurate in LRTI compared to BSI. This is because BSIs are caused by low pathogen numbers in a high background of human cells. To overcome this, most approaches focus on cell-free DNA, but, to date, these tests are not accurate enough yet to replace blood culture. The higher pathogen numbers in LRTI samples make this a more suitable for CMg and accurate approaches have been developed, which are likely to be implemented in hospitals within the next 2-5 years.