Journal of Clinical Infectious Diseases & Practice
Open Access

Metagenomic next-generation sequencing (mNGS); Infectious disease diagnosis; Pathogen detection; Genomic sequencing; Diagnostic challenges; Culture-independent diagnostics

Introduction

Infectious diseases remain a leading cause of morbidity and mortality worldwide, requiring timely and accurate diagnosis for effective treatment. Traditional diagnostic methods, such as culture-based techniques and polymerase chain reaction (PCR), are often limited in scope, speed, and sensitivity. Metagenomic Next-Generation Sequencing (mNGS) offers a promising alternative, enabling the unbiased identification of pathogens in a single test, without the need for prior knowledge of the organism [1]. This revolutionary tool can detect bacteria, viruses, fungi, and parasites in a culture-independent manner, providing crucial diagnostic insights, particularly in cases where conventional methods fail. However, despite its potential, there are significant hurdles in the clinical application of mNGS, including issues of cost, data analysis, and clinical interpretation.

Materials and Methods

Study Design: This review synthesizes existing literature on mNGS as a diagnostic tool for infectious diseases. A systematic analysis of clinical trials, case studies, and research articles published between 2010 and 2023 was performed to assess the performance of mNGS. Peer-reviewed journals, clinical reports, and databases such as PubMed, Scopus, and Google Scholar were used to gather relevant studies on mNGS and infectious disease diagnostics. Both primary research articles and comprehensive reviews were included. Studies were selected based on the use of mNGS for diagnosing bacterial, viral, fungal, or parasitic infections in clinical settings [2]. Inclusion criteria also focused on studies discussing the challenges and limitations of mNGS in routine diagnostics. A qualitative synthesis was conducted to identify the common themes regarding the benefits and challenges of mNGS. The focus was on the diagnostic accuracy, pathogen detection breadth, clinical utility, and cost-effectiveness of mNGS [3]. The challenges highlighted in the analysis include bioinformatic complexity, the need for standardized interpretation protocols, false positives due to contamination, and the lack of clear regulatory and clinical guidelines for the integration of mNGS into diagnostic workflows.

Discussion

Metagenomic Next-Generation Sequencing (mNGS) represents a significant advancement in the diagnosis of infectious diseases, offering a culture-independent method for detecting a wide variety of pathogens in a single test. Unlike traditional diagnostic tools such as cultures or polymerase chain reaction (PCR), which are often limited to specific pathogens or are constrained by the availability of prior knowledge, mNGS provides an unbiased approach capable of detecting bacteria, viruses, fungi, and parasites simultaneously [4]. This ability to generate broad-spectrum diagnostic data has made mNGS particularly valuable in cases of unknown or atypical infections, where conventional methods often fail. Despite these advantages, several challenges hinder the widespread clinical adoption of mNGS. One of the primary concerns is the high cost associated with sequencing, which includes both the actual sequencing process and the downstream data analysis [5]. The costs are often prohibitive for routine diagnostic use, especially in resource-limited settings. As sequencing technology becomes more affordable, there is hope that the financial burden will decrease, allowing for broader access to mNGS diagnostics. Another significant hurdle is the complexity of bioinformatics analysis. The sheer volume of data generated by mNGS requires advanced computational tools for alignment, filtering, and interpretation. Differentiating clinically relevant pathogens from background noise, such as commensal organisms or contaminants, presents a major challenge. Currently, there is no universally accepted standard for interpreting mNGS data in the clinical context [6]. The risk of false positives, often due to contamination, can lead to unnecessary treatments or interventions. Conversely, false negatives may occur if the sequencing depth is insufficient to detect low-abundance pathogens.

Data interpretation further complicates the clinical use of mNGS. Clinicians may struggle to determine the clinical significance of detected organisms, especially in polymicrobial samples. While mNGS can detect a wide range of organisms, it does not necessarily provide information on antibiotic resistance or virulence, which are critical factors in guiding treatment decisions [7]. For mNGS to be a reliable diagnostic tool, bioinformatic pipelines need to evolve to provide more actionable insights, including resistance gene detection and better differentiation of pathogenic from non-pathogenic organisms. The lack of standardized guidelines for the clinical implementation of mNGS is another obstacle. Clinical laboratories currently lack uniform protocols for sample preparation, sequencing, and data interpretation, making it difficult to compare results across different institutions [8]. This variability can affect the reliability and reproducibility of mNGS-based diagnostics. Developing regulatory frameworks and standard operating procedures for mNGS use in clinical microbiology will be crucial for its broader adoption. Despite these challenges, mNGS has shown promise in several clinical settings. For example, it has been particularly useful in identifying pathogens in cases of encephalitis or sepsis where traditional methods failed [9]. In such instances, mNGS provided timely and critical diagnostic information, allowing clinicians to tailor treatment plans accordingly. As more studies validate the utility of mNGS in diagnosing infectious diseases, the technology may become more integrated into clinical practice.

Moving forward, collaboration between bioinformaticians, microbiologists, and clinicians will be essential to enhance the clinical utility of mNGS. Innovations in sequencing technology, such as faster and cheaper sequencing methods, and advances in bioinformatics tools capable of interpreting complex data sets more efficiently, will also be key in overcoming current limitations [10]. Furthermore, prospective clinical trials that assess the impact of mNGS on patient outcomes, particularly in complex or refractory infections, are needed to provide robust evidence for its routine clinical use. In conclusion, mNGS has the potential to revolutionize infectious disease diagnosis by offering a comprehensive, rapid, and culture-independent diagnostic tool. However, for it to achieve its full clinical potential, challenges related to cost, bioinformatics, data interpretation, and standardization must be addressed. With ongoing advancements and interdisciplinary efforts, mNGS could become a cornerstone of modern infectious disease diagnostics, improving patient care by enabling faster and more accurate pathogen detection.

Conclusion

Metagenomic Next-Generation Sequencing represents a breakthrough in the field of infectious disease diagnostics, with the ability to detect a wide range of pathogens from a single sample. While its advantages in sensitivity and scope are clear, significant hurdles must be addressed for its clinical implementation. These include the high cost of testing, challenges in bioinformatic data interpretation, and the need for standardized clinical guidelines. Future research should focus on overcoming these barriers, developing cost-effective solutions, and refining analytical tools to ensure mNGS becomes a routine part of clinical diagnostics for infectious diseases. Its potential to revolutionize patient care in both routine and complex cases of infection makes it a powerful tool for the future of medicine.

Acknowledgement

None

Conflict of Interest

None

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