Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses.

BACKGROUND: Metagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity. METHODS: We evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers. RESULTS: Capture with the Twist CVRP increased sensitivity by at least 10-100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600-6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT. CONCLUSIONS: Metagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Pathogenic Detection by Metagenomic Next-generation Sequencing in Skin and Soft Tissue Infection.

BACKGROUND/AIM: Skin and soft tissue infections (SSTIs) can be life-threatening, but the conventional bacterial cultures have low sensitivity and are time-consuming. Metagenomic next-generation sequencing (mNGS) is widely used as a diagnostic tool for detecting pathogens from infection sites. However, the use of mNGS for pathogen detection in SSTIs and related research is still relatively limited. PATIENTS AND METHODS: From January 2020 to October 2021, 19 SSTI samples from 16 patients were collected in a single center (Taichung Veterans General Hospital, Taichung, Taiwan). The clinical samples were simultaneously subjected to mNGS and conventional bacterial culture methods to detect pathogens. Clinical characteristics were prospectively collected through electronic chart review. The microbiological findings from conventional bacterial culture and mNGS were analyzed and compared. RESULTS: The mNGS method detected a higher proportion of multiple pathogens in SSTIs compared to conventional bacterial culture methods. Pseudomonas spp. was among the most commonly identified Gram-negative bacilli using mNGS. Additionally, the mNGS method identified several rare pathogens in patients with SSTIs, including Granulicatella adiacens, Bacillus thuringiensis, and Bacteroides fragilis. Antimicrobial resistance genes were detected in 10 samples (52.6%) using the mNGS method, including genes for extended-spectrum beta-lactamase, Ambler class C ß-lactamases, and carbapenemase. CONCLUSION: mNGS not only plays an important role in the detection of pathogens in soft tissue infections, but also informs clinical professionals about the presence of additional microbes that may be important for treatment decisions. Further studies comparing conventional pathogen culture with the mNGS method in SSTIs are required.

Detection of Dengue Virus 1 and Mammalian Orthoreovirus 3, with Novel Reassortments, in a South African Family Returning from Thailand, 2017.

In July 2017, a family of three members, a 46-year-old male, a 45-year-old female and their 8-year-old daughter, returned to South Africa from Thailand. They presented symptoms consistent with mosquito-borne diseases, including fever, headache, severe body aches and nausea. Mosquito bites in all family members suggested recent exposure to arthropod-borne viruses. Dengue virus 1 (Genus Orthoflavivirus) was isolated (isolate no. SA397) from the serum of the 45-year-old female via intracerebral injection in neonatal mice and subsequent passage in VeroE6 cells. Phylogenetic analysis of this strain indicated close genetic identity with cosmopolitan genotype 1 DENV1 strains from Southeast Asia, assigned to major lineage K, minor lineage 1 (DENV1I_K.1), such as GZ8H (99.92%) collected in November 2018 from China, and DV1I-TM19-74 isolate (99.72%) identified in Bangkok, Thailand, in 2019. Serum samples from the 46-year-old male yielded a virus isolate that could not be confirmed as DENV1, prompting unbiased metagenomic sequencing for virus identification and characterization. Illumina sequencing identified multiple segments of a mammalian orthoreovirus (MRV), designated as Human/SA395/SA/2017. Genomic and phylogenetic analyses classified Human/SA395/SA/2017 as MRV-3 and assigned a tentative genotype, MRV-3d, based on the S1 segment. Genomic analyses suggested that Human/SA395/SA/2017 may have originated from reassortments of segments among swine, bat, and human MRVs. The closest identity of the viral attachment protein σ1 (S1) was related to a human isolate identified from Tahiti, French Polynesia, in 1960. This indicates ongoing circulation and co-circulation of Southeast Asian and Polynesian strains, but detailed knowledge is hampered by the limited availability of genomic surveillance. This case represents the rare concurrent detection of two distinct viruses with different transmission routes in the same family with similar clinical presentations. It highlights the complexity of diagnosing diseases with similar sequelae in travelers returning from tropical areas.

Identification of a Orthohantavirus strain by clinical metagenomics in Brazil.

We describe a case of a 33-year-old male presented with fever, myalgia, nausea, and asthenia for six days. The patient lived in a rural area. Initial hypotheses included arbovirus infection, viral hepatitis, and Lyme disease. Reverse transcriptase polymerase chain reaction (RT-PCR) tests for Dengue, Zika, and Chikungunya resulted negative. We were able to recover complete S, L, and M segments of virus in the Orthohantavirus genome.

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 colonization in patients with severe pneumonia was 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, patients with severe pneumonia who underwent BAL 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 with that of patients who were P jirovecii-negative. RESULTS: A total of 40% of P jirovecii-positive patients are considered to have P jirovecii colonization. The P jirovecii colonization group had a higher proportion of patients with immunosuppression and a lower lymphocyte count than the 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 patients with severe pneumonia. According to sensitivity analyses, which included or excluded codetected microorganisms, and patients not receiving trimethoprim-sulfamethoxazole treatment, similar conclusions were reached. INTERPRETATION: Immunosuppression and a reduced lymphocyte count were identified as risk factors for P jirovecii colonization in patients with non-Pneumocystis pneumonia. More frequent detection of various viruses was observed in patients colonized with P jirovecii, 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.