Metagenomic next-generation sequencing in clinical microbiology

Clinical application of targeted next-generation sequencing in severe pneumonia: a retrospective review

BACKGROUND

The precise identification of the underlying causes of infectious diseases, such as severe pneumonia, is essential, and the development of next-generation sequencing (NGS) has enhanced the effectiveness of pathogen detection. However, there is limited information on the systematic assessment of the clinical use of targeted next-generation sequencing (tNGS) in cases of severe pneumonia.

METHODS

A retrospective analysis was conducted on 130 patients with severe pneumonia treated in the ICU from June 2022 to June 2023. The consistency of the results of tNGS, metagenomics next-generation sequencing (mNGS), and culture with the clinical diagnosis was evaluated. Additionally, the results for pathogens detected by tNGS were compared with those of culture, mNGS, and quantitative reverse transcription PCR (RT-qPCR). To evaluate the efficacy of monitoring severe pneumonia, five patients with complicated infections were selected for tNGS microbiological surveillance. The tNGS and culture drug sensitisation results were then compared.

RESULTS

The tNGS results for the analysis of the 130 patients showed a concordance rate of over 70% with clinical diagnostic results. The detection of pathogenic microorganisms using tNGS was in agreement with the results of culture, mNGS, and RT-qPCR. Furthermore, the tNGS results for pathogens in the five patients monitored for complicated infections of severe pneumonia were consistent with the culture and imaging test results during treatment. The tNGS drug resistance results were in line with the drug sensitivity results in approximately 65% of the cases.

CONCLUSIONS

The application of tNGS highlights its promise and significance in assessing the effectiveness of clinical interventions and providing guidance for anti-infection therapies for severe pneumonia.

Metagenomic next-generation sequencing for rapid detection of pulmonary infection in patients with acquired immunodeficiency syndrome

BACKGROUND

Acquired immunodeficiency syndrome (AIDS) is associated with a high rate of pulmonary infections (bacteria, fungi, and viruses). To overcome the low sensitivity and long turnaround time of traditional laboratory-based diagnostic strategies, we adopted metagenomic next-generation sequencing (mNGS) technology to identify and classify pathogens.

RESULTS

This study enrolled 75 patients with AIDS and suspected pulmonary infections who were admitted to Nanning Fourth People's Hospital. Specimens were collected for traditional microbiological testing and mNGS-based diagnosis. The diagnostic yields of the two methods were compared to evaluate the diagnostic value (detection rate and turn around time) of mNGS for infections with unknown causative agent. Accordingly, 22 cases (29.3%) had a positive culture and 70 (93.3%) had positive valve mNGS results (P value < 0.0001, Chi-square test). Meanwhile, 15 patients with AIDS showed concordant results between the culture and mNGS, whereas only one 1 patient showed concordant results between Giemsa-stained smear screening and mNGS. In addition, mNGS identified multiple microbial infections (at least three pathogens) in almost 60.0% of patients with AIDS. More importantly, mNGS was able to detect a large variety of pathogens from patient tissue displaying potential infection and colonization, while culture results remained negative. There were 18 members of pathogens which were consistently detected in patients with and without AIDS.

CONCLUSIONS

In conclusion, mNGS analysis provides fast and precise pathogen detection and identification, contributing substantially to the accurate diagnosis, real-time monitoring, and treatment appropriateness of pulmonary infection in patients with AIDS.

Colonizing microbiota is associated with clinical outcomes in diabetic wound healing

With the development of society and the improvement of life quality, more than 500 million people are affected by diabetes. More than 10 % of people with diabetes will suffer from diabetic wounds, and 80 % of diabetic wounds will reoccur, so the development of new diabetic wound treatments is of great importance. The development of skin microbe research technology has gradually drawn people's attention to the complex relationship between microbes and diabetic wounds. Many studies have shown that skin microbes are associated with the outcome of diabetic wounds and can even be used as one of the indicators of wound prognosis. Skin microbes have also been found to have the potential to treat diabetic wounds. The wound colonization of different bacteria can exert opposing therapeutic effects. It is necessary to fully understand the skin microbes in diabetic wounds, which can provide valuable guidance for clinical diabetic wound treatment.

Application of metagenomic next-generation sequencing in cutaneous tuberculosis

Tuberculous infection in a skin wound is a rare but well-known condition. This study describes a child infected with tuberculosis after being wounded. Because of swelling and pain in his wrist tissue, he was admitted to the Affiliated Hospital of Jining Medical University of Shandong Province on 16 October 2021. His medical history only included a wound. He was discharged after debridement. The laboratory data were normal. Two months after surgery, his wound was still swollen and painful. Secretions from the wound were sent for metagenomic next-generation sequencing (mNGS), which revealed three reads related to the Mycobacterium tuberculosis complex group (MTBC). A diagnosis of cutaneous tuberculosis (TB) was made. The wound disappeared after anti-TB drugs were administered. This case demonstrates that, while TB presenting as a severe cutaneous wound is rare, it should be considered in the clinical diagnosis. Clinicians should also pay attention to extrapulmonary infection with MTBC in patients, particularly in some long-suffering patients, and identify the specific pathogen as soon as possible. mNGS could help to identify pathogens and facilitate early treatment, thereby improving the prognosis.

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.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

High-Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next-generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third-generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS-related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.

Liquid biopsy for infectious diseases: a focus on microbial cell-free DNA sequencing

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.