Metagenomic next-generation sequencing (mNGS) is increasingly being applied in clinical laboratories for unbiased culture-independent diagnosis. Whether it can be a next routine pathogen identification tool has become a topic of concern. We review the current implementation of this new technology for infectious disease diagnostics and discuss the feasibility of transforming mNGS into a routine diagnostic test. Since 2008, numerous studies from over 20 countries have revealed the practicality of mNGS in the work-up of undiagnosed infectious diseases. mNGS performs well in identifying rare, novel, difficult-to-detect and coinfected pathogens directly from clinical samples and presents great potential in resistance prediction by sequencing the antibiotic resistance genes, providing new diagnostic evidence that can be used to guide treatment options and improve antibiotic stewardship. Many physicians recognized mNGS as a last resort method to address clinical infection problems. Although several hurdles, such as workflow validation, quality control, method standardisation, and data interpretation, remain before mNGS can be implemented routinely in clinical laboratories, they are temporary and can be overcome by rapidly evolving technologies. With more validated workflows, lower cost and turnaround time, and simplified interpretation criteria, mNGS will be widely accepted in clinical practice. Overall, mNGS is transforming the landscape of clinical microbiology laboratories, and to ensure that it is properly utilised in clinical diagnosis, both physicians and microbiologists should have a thorough understanding of the power and limitations of this method.

Metagenomic next-generation sequencing (mNGS) of microbial cell-free DNA (mcfDNA sequencing) is becoming an attractive diagnostic modality for infectious diseases, allowing broad-range pathogen detection, noninvasive sampling, and rapid diagnosis. At this key juncture in the translation of metagenomics into clinical practice, an integrative perspective is needed to understand the significance of emerging mcfDNA sequencing technology. In this review, we summarized the actual performance of the mcfDNA sequencing tests recently used in health care settings for the diagnosis of a variety of infectious diseases and further focused on the practice considerations (challenges and solutions) for improving the accuracy and clinical relevance of the results produced by this evolving technique. Such knowledge will be helpful for physicians, microbiologists and researchers to understand what is going on in this quickly progressing field of non-invasive pathogen diagnosis by mcfDNA sequencing and promote the routine implementation of this technique in the diagnosis of infectious disease.

Microbiome research based on high-throughput sequencing has grown exponentially in recent years, but methodological variations can easily undermine the reproducibility across studies. To systematically evaluate the comparability of sequencing results of 16S rRNA gene sequencing (16Ss)- and shotgun metagenomic sequencing (SMs)-based microbial community profiling in laboratories under routine conditions. We designed a multicenter study across 35 participating laboratories in China using designed mock communities and homogenized fecal samples. A wide range of practices and approaches was reported by the participating laboratories. The observed microbial compositions of the mock communities in 46.2% (12/26) of the 16Ss and 82.6% (19/23) of the SMs laboratories had significant correlations with the expected result (Spearman r>0.59, P <0.05). The results from laboratories with near-identical protocols showed slight interlaboratory deviations. However, a high degree of interlaboratory deviation was found in the observed abundances of specific taxa, such as Bacteroides spp. (range: 0.3%-53.5%), Enterococci spp. (range: 0.8%-43.9%) and Fusobacterium spp. (range: 0.1%-39.8%). SMs performed better than 16Ss in detecting low-abundance bacteria (B. bifidum). The differences in DNA extraction methods, amplified regions and bioinformatics analysis tools (taxonomic classifiers and database) were important factors causing interlaboratory deviations. Addressing laboratory contamination is an urgent task because various sources of unexpected microbes were found in negative control samples. Well-defined control samples, such as the mock communities in this study, should be routinely used in microbiome research for monitoring potential biases. The findings in this study will provide guidance in the choice of more reasonable operating procedures to minimize potential methodological biases in revealing human microbiota composition.

Diarrhoea caused by Escherichia coli (E. coli) is a major health problem in developing countries. Diarrhoeagenic E. coli (DEC) strains are divided into six pathotypes: enteropathogenic E. coli (EPEC), Shiga toxin-producing (or enterohemorrhagic) E. coli (STEC), enterotoxigenic E. coli (ETEC), enteroaggregative E. coli (EAEC), enteroinvasive E. coli (EIEC) and diffusely adherent E. coli (DAEC) [ 1 x 1 Kaper, JB, Nataro, JP, and Mobley, HL. Pathogenic Escherichia coli. Nat Rev Microbiol. 2004; 2: 123–140 Crossref | PubMed | Scopus (2136) | Google Scholar See all References , 2 x 2 Nataro, JP and Kaper, JB. Diarrheagenic Escherichia coli. Clin Microbiol Rev. 1998; 11: 142–201 PubMed | Google Scholar See all References ]. Global epidemics and outbreaks of all DEC types except DAEC are frequently reported [ 2 x 2 Nataro, JP and Kaper, JB. Diarrheagenic Escherichia coli. Clin Microbiol Rev. 1998; 11: 142–201 PubMed | Google Scholar See all References 2 ]. From May to July of 2011, a large-scale outbreak of DEC caused 3816 documented infections (including 54 deaths) in Germany, 845 of which (22%) involved haemolytic uraemic syndrome (HUS). The outbreak strain was characterized as an extended-spectrum β-lactamase (ESBL)-expressing E. coli strain of serotype O104:H4 with virulence factors of both EHEC and EAEC [ 3 x 3 Frank, C, Werber, D, Cramer, JP et al. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. N Engl J Med. 2011; 365: 1771–1780 Crossref | PubMed | Scopus (642) | Google Scholar See all References 3 ]. China has always been a hotspot of such epidemics. For example, diarrhoeal epidemics caused by E. coli O157:H7 were reported in Jiangsu and Anhui provinces in 1999, leading to 20 000 cases of infection and 195 cases of HUS, with 177 deaths [ 4 x 4 Luo, P, Chen, HZ, Zeng, M et al. Preparation and biological characterization of monoclonal antibody against Shiga toxinIIA1 subunit of enterohemorrhagic E. coli O157:H7. Acta Academiae Medicinae Militaris Tertiae. 2008; 30: 698–701 Google Scholar See all References 4 ]. DEC, the major pathogen causing diarrhoea [ 2 x 2 Nataro, JP and Kaper, JB. Diarrheagenic Escherichia coli. Clin Microbiol Rev. 1998; 11: 142–201 PubMed | Google Scholar See all References 2 ], is primarily spread by water and food and grows in warm, moist places. Although southeastern China has always been a hotspot for diarrhoea, with potential risks of DEC epidemics, there is a lack of DEC surveillance data. In this study, we chose the southeastern region as a representative region for diarrhoeal epidemics in China to investigate the epidemiological and aetiological characteristics of DEC.

Nanopore sequencing, known for real-time analysis, shows promise for rapid clinical infection diagnosis but lacks effective assays for bloodstream infections (BSIs). We prospectively assessed the performance of a novel nanopore targeted sequencing (NTS) assay in identifying pathogens and predicting antibiotic resistance in BSIs, analyzing 387 blood samples from December 2021 to April 2023. The positivity rate for NTS (69.5%, 269/387) nearly matches that of metagenomic next-generation sequencing (mNGS) (74.7%, 289/387; p=0.128) and surpasses the positivity rate of conventional blood culture (BC) (33.9%, 131/387; p<0.01). Frequent pathogens detected by NTS included Klebsiella pneumoniae (n=54), Pseudomonas aeruginosa (n=36), Escherichia coli (n=36), Enterococcus faecium(n=30), Acinetobacter baumannii(n=26), Staphylococcus aureus(n=23), and Human cytomegalovirus (n=37). Against a composite BSI diagnostic standard, NTS demonstrated a sensitivity and specificity of 84.0% (95% CI 79.5%-87.7%) and 90.1% (95% CI 81.7%-88.5%), respectively. The concordance between NTS and mNGS results (the percentage of total cases where both either detected BSI-related pathogens or were both negative) was 90.2% (359/387), whereas the consistency between NTS and BC was only 60.2% (233/387). In 80.6% (50/62) of the samples with identical pathogens identified by both NTS tests and BCs, the genotypic resistance identified by NTS correlated with culture-confirmed phenotypic resistance. Using NTS, 95% of samples can be tested and analyzed in approximately 7hours, allowing for early patient diagnosis. NTS is rapid, sensitive, and efficient for detecting BSIs and drug-resistant genes, making it a potential preferred diagnostic tool for early infection identification in critically ill patients.