Abstract
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Osteoarticular infections (OAI) are a significant cause of morbidity and mortality. Cultures and serology are some of the gold standards for identifying infection but are often unable to provide a timely diagnosis or a diagnosis at all.
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Genetic testing offers capabilities that other modalities lack. Polymerase chain reaction has multiple versions with various costs and turnaround times. This technology has become implemented in multiple pediatric center OAI diagnostic protocols. There is sufficient literature documenting effectiveness in certain clinical situations, especially with fastidious organism diagnosis, but significant limitation still exists.
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Metagenomic next-generation sequencing is an unbiased or hypothesis-free modality with the capability to detect the genetic material of bacteria, viruses, parasites, fungi, and humans from a single sample.
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Potential benefits include pathogen identification unaffected by antimicrobial administration, detection of fastidious organisms more quickly, delineation of pathogens in polymicrobial infections, antimicrobial susceptibility, and avoidance of invasive procedures.
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It is a resource-intensive modality with little standardization of the complex processes. Appropriate use and definitive clinical impact have yet to be determined.
Introduction
Infectious disease has been and continues to be one of the world's leading causes of morbidity and mortality (1, 2). Osteoarticular infections (OAI) in pediatrics are relatively common, with an incidence of 1–20 per 10,000, depending on the region (3, 4). Pathogen identification is important for diagnosis and treatment and is reported to alter patient management in up to 85% of cases (5, 6). Most treatment algorithms place a high value on identifying the pathogen and antimicrobial sensitivities using culture and molecular genetics (3, 4, 5, 7). Culture-based modalities have been the gold standard for the diagnosis and treatment of musculoskeletal infection (8, 9); however, cultures have significant limitations due to high rates of false negatives and the extensive length of time for results. A systematic review of culture methods in pediatric OAI shows the blood and tissue culture positivity rates are 21.5–44% and 55.4%, respectively (7). These limitations can significantly impact patient care, particularly in critically ill patients (10, 11). Culture-negative cases often lead to a longer course of empiric antibiotics which are reported to be inadequate in up to 20% of cases with added side effects (4, 6, 12, 13, 14, 15). In a study published by Spyridakis et al. (13), 89 of 129 (69%) cases of septic arthritis were culture-negative and failed initial empiric antibiotic treatment 9% of the time.
Genetic testing is being used more commonly as an alternative to traditional cultures in orthopedic OAI. This includes polymerase chain reaction (PCR) with its associated variations and next-generation sequencing (NGS) with its associated variations which are expanded upon in a recent article by Indelli et al. (16). PCR has the advantage of improved sensitivity and detection of fastidious organisms, and the results are less affected by antibiotic administration. A limitation associated with PCR is the ability to identify limited types of pathogens that the PCR variation is designed for, which often requires some clinical suspicion to best direct the test. Timing of results, inability to detect antibiotic sensitivity, and cost burden are further limitations (2, 9, 16, 17, 18, 19, 20, 21). Furthermore, studies demonstrate that despite increased sensitivity, PCR can have a substantial false positive rate (22, 23, 24, 25, 26, 27, 28). Metagenomic NGS (mNGS) is a newer technology in the clinical setting and has the potential to overcome some of the limitations of PCR but lacks standardization and data confirming clinical impact.
Multiplex PCR is a relatively cheap and fast technique, but to detect multiple pathogens, you need to design and run different PCR reactions; it also requires specific primers for each target. mNGS is relatively expensive and time-consuming because it is a more complex process that involves several steps such as library preparation, sequencing, and bioinformatics analysis. However, it produces a more comprehensive view of the sample's genetic makeup. A comparison of the two is provided in Table 1. Wang et al. (29) published a paper comparing broad-range PCR and mNGS for the diagnosis of prosthetic joint infection (PJI). The joint fluid PCR sensitivity was 82.2%, mNGS 95.6%, and culture 77.8%. Specificity for all three was similar at 94.4%. mNGS was able to identify a pathogen in four fungal infections and one polymicrobial infection not identified by PCR. Niles et al. (30) performed a retrospective review of 60 patients, most of whom were immunocompromised, comparing mNGS to conventional testing of culture and PCR. The authors found that mNGS provided very similar results to conventional testing but often slower. They state that mNGS did not alter management in the majority of cases and provided minimal diagnostic value with additional cost to conventional testing.
Comparison of PCR and mNGS.
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NGS, next-generation sequencing; PCR, polymerase chain reaction.
Metagenomic next-generation sequencing
NGS allows multiple diverse DNA molecules to be sequenced in parallel to generate billions of series of nitrogenous bases that make up complementary DNA. These DNA series are referred to as reads and can be 75–10,000s base pairs in length. These reads are created with fluorescently labeled nucleotides that produce an optical readout providing the genetic information of organisms in the sample. The deeper or longer these reads are, the greater the test's sensitivity. The diversity, amount of data, and acquisition speed far surpass its predecessor of Sanger sequencing, which is still being used (2, 20, 31, 32, 33, 34, 35). Targeted NGS identifies a type or class of organism of particular interest and often combines PCR into the process (2, 9, 20, 32). mNGS is a method of testing for nucleic acids that overcomes some limitations of culture, molecular techniques, and targeted NGS. mNGS has been used in various applications over the past 15 years. However, its role in the clinical treatment of infectious disease has become much more of a focus over the last 5 years (2, 9, 16, 20, 32, 36, 37, 38). mNGS is considered unbiased or referred to as shotgun sequencing because it requires no prior knowledge of the patient's clinical scenario and it has the capability to detect every form of DNA and RNA from a chosen sample (2, 9, 20, 32, 39, 40). This affords the ability of a single test to detect bacteria, viruses, parasites, fungi, and human genetic material from just one sample, possibly noninvasively. There is promising data on various tissues, such as synovial fluid, blood, and cerebral spinal fluid (5, 9, 32, 41, 42, 43, 44, 45, 46, 47, 48). Numerous steps are involved with multiple variations at each step that are often platform and laboratory specific. A nice review of various platforms is provided in a recent paper by Indelli et al. (16). Detailed steps of performing mNGS are reported elsewhere (20, 49, 50, 51, 52, 53) and are beyond the scope of this review, but we feel it is necessary to have a basic understanding of the process to better appreciate the future potential and limitations of the technology.
How it works
There are multiple types of sequencing platforms available with different benefits and limitations. Some of them are illumina dye sequencing, ion torrent sequencing, and nanopore sequencing. The nanopore sequencing platforms differ in methodology and are faster and more portable with a tradeoff of increased sequencing errors and less data output (2, 20, 32). Nevertheless, the beneficial applications have already been displayed in multiple laboratory and clinical studies (19, 54, 55, 56, 57, 58). A large percentage of published papers use an illumina dye sequencing platform produced by Illumina (San Diego, CA, USA), so a general overview of the process used on one of these platforms is presented.
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Specimen procurement: These can be various tissues, but all require precise steps to procure and stabilize the sample (2, 20, 32).
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Nucleic acid extraction: The technique used for this step is highly variable and dependent upon the sample type, but there are often multiple kits available for each sample type. Extraction kits are often different for DNA and RNA, and both may be used for a given sample depending on desired information. A DNA extraction kit would be used to identify the causative agent, while an RNA extraction kit would be used to study the gene expression of the pathogen. DNA extraction kits typically use a combination of physical and chemical methods to separate DNA from other cellular components, such as proteins and lipids (2, 20, 32, 49).
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Sequencing library preparation: During this step, multiple processes aim to obtain a sizeable clonal cluster of DNA or RNA that covers all genomes in the sample, correlating with their prevalence. Sometimes reverse transcriptase is used in this step to obtain DNA from RNA. Often amplification and other targeting techniques such as PCR are employed to improve the test's sensitivity; however, this introduces bias. Host DNA is subtracted in this step or the bioinformatics step, and there are pros and cons for each method (2, 20, 31, 32, 34, 35, 59).
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Sequencing steps: This is different depending on the platform used, but for this explanation, once the library of DNA/RNA is built, the complementary fluorescently labeled DNA attaches one nucleotide per cycle, and an optical readout is produced in the form of A, G, T, or C (2, 20, 31, 32, 34, 35, 59).
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Bioinformatic analysis: Over the last few years, multiple computational pipelines for mNGS analysis have been developed, and most are similar. The first step is usually a preprocessing step where essentially, the sequences are cleaned up by removing low-quality areas. During the second step, the human genome is removed if not already done so. The third step involves aligning the sequences to a pathogen database to determine taxonomic classification. The fourth step is to organize and statistically analyze the data and present it in a clinically applicable format (2, 20, 31, 32, 34, 35, 59).
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Interpretation of results: There is no one standardized method of interpreting results, but an in-depth knowledge of all aspects of the process is best, and this often requires communication among multiple experts in bioinformatics, the laboratory setting, and the clinical setting (2, 9, 20, 32, 49).
Multiple reproductions of graphical overviews are provided in Figs. 1, 2, and 3.
Schematic of the workflow of mNGS reproduced with permission from Gu et al. (32).
Citation: EFORT Open Reviews 8, 4; 10.1530/EOR-22-0136
mNGS workflow reproduced with permission from Govender et al. (9).
Citation: EFORT Open Reviews 8, 4; 10.1530/EOR-22-0136
A typical mNGS bioinformatics pipeline reproduced with permission from Chiu et al. (20).
Citation: EFORT Open Reviews 8, 4; 10.1530/EOR-22-0136
Benefits
There are multiple benefits of mNGS, starting with the above-mentioned unbiased approach. Often infection etiology is unknown, and there may be a wide array of different pathogens, especially in immunocompromised patients with opportunistic infections that may be fungal, parasitic, or bacterial. mNGS is the only single test that can delineate between all pathogens (2, 20, 28, 32, 60). Additionally, some fastidious organisms take weeks to grow on culture, and mNGS has the potential to provide results within days (5, 20, 32, 61). Often, especially in immunocompromised individuals, infections can be polymicrobial, and mNGS can provide quantitative data on the organism concentration to help delineate which pathogens contribute to the disease process (20, 32, 62, 63, 64). Along similar lines, the human genetic material in the sample provides valuable information about the host’s immune response. This helps delineate if the microorganisms detected are pathogenic, active or latent, dead or alive, and chronic or acute. The presence of host genetic biomarkers associated with the expression of specific immune-modulating components such as cytokines, chemokines, and interferons is associated with different pathogenic processes (2, 20, 65, 66, 67, 68, 69, 70). Different computational methods are used to analyze the genetic material in a sample to determine the host's immune response. These methods include studying gene expression with RNA sequencing, analyzing metabolic pathways, and using machine learning techniques to identify patterns in the data (20, 71). mNGS can identify specific genes associated with antimicrobial resistance or pathogen virulence (9, 20, 32, 72, 73, 74, 75). Metabolomics analysis is used to identify changes in the host's metabolic pathways that may be associated with different pathogenic processes (76, 77). Network and pathway analysis can help to identify the interactions between the host, pathogens, and commensal organisms and how these interactions may be affecting the host's immune response (78, 79). However, it is important to note that interpreting these results can be complex and often requires multiple methods to get a comprehensive understanding of the host's immune response. A benefit of the above techniques is that early administration of broad-spectrum antibiotics does not alter the results, which is not the case with culture-based studies (2, 9, 20, 32, 61).
Another major benefit, particularly in pediatric patients, is the potential to avoid invasive diagnostic techniques such as lumbar puncture and surgery. A form of mNGS called cell-free NGS (cfNGS) can detect pathogen DNA in the patient's bloodstream even when an infection is confined to a specific location. mNGS often involves obtaining tissue samples from the area of infection followed by extraction of nucleic acids from the cells within this tissue. cfNGS involves a peripheral blood draw that sequences cell-free DNA (cfDNA) present in the host bloodstream from already naturally lysed pathogenic cells (16, 80). The cfDNA fragments have a short half-life in the circulating blood, so rapidly dividing oncologic and pathogenic cells produce increased and quantifiable amounts of cfDNA fragments valuable in the diagnosis and prognosis of pathogenic processes (16, 20, 80, 81). The mildly invasive nature and quantifiable measurements allow multiple samples to be obtained linearly in hospital and office settings to monitor disease processes. Rossoff et al. (82) retrospectively reviewed 100 cfNGS in pediatric patients to determine if the test provided clinically relevant information. As high as 70% (70/100) of the tests were positive, with 80% (56/70) of those being clinically relevant. They went on to note that 14 of the clinically relevant positive tests had negative results from conventional testing. They concluded that mNGS produced a higher diagnostic yield than invasive diagnostics, and 34 of those procedures could have been potentially avoided with cfNGS results alone. Echeverria et al. (80) studied 53 patients with confirmed PJI who had cfNGS performed on peripheral blood prior to operative intervention. cfNGS detected a pathogen on average 3 days faster than other testing and identified a pathogen in 4/7 culture-negative cases. The patients also had cfNGS performed on peripheral blood drawn at follow-up, which showed decreasing pathogen levels. They concluded that mNGS improved organism identification from 87 to 94% and may offer a novel method to monitor patient clearance of infection. Blauwkamp et al. (50) published a large prospective study on cfNGS and their data suggest benefits outweigh the limitations. There have also been studies of cfNGS showing limited benefit. Niles et al. (83) performed a retrospective review of 169 pediatric patients who underwent cfNGS and found that cfNGS had a negative clinical impact in 5.3% (9/169), a positive impact in 12.4% (21/169), and no impact in 82.2% (139/169) of patients. The authors concluded that cfNGS had some clinical impact on immunocompromised patients but had limited clinical impact when ordered for all patients. Hogan et al. (44) arrived at similar findings.
Limitations
One of the first limitations of mNGS is that reference databases have to be updated continually to improve sensitivity, and if new reference sequences become inadvertently contaminated with other species, this could lead to a cascade of inappropriate diagnoses and treatment (2, 20, 32). The fact that mNGS is unbiased and extremely sensitive to any nucleic acid in the sample poses multiple difficulties. Microbial contaminants introduced during any stage of the process make it difficult to distinguish between pathogenic and nonpathogenic organisms (2, 32, 49, 84). mNGS laboratories have to maintain an extremely high workflow quality to prevent contamination and this requires stringent quality control testing (32, 47, 85). Additionally, most of the samples are host nucleic acids, significantly decreasing sensitivity unless mitigated by various host depletion methods and targeted sequencing methods. Identifying the pathogenic microorganism of polymicrobial samples can be challenging. In general, these methods require some level of computational expertise and interpretation of the results. It is important to note that no single method is perfect and it is often necessary to combine multiple approaches such as comparing the results with a database of known pathogens, using computational tools to assemble the sequencing reads into groups and identifying patterns that are commonly found in pathogens, using functional gene markers such as virulence genes or antibiotic resistance genes that are commonly found in pathogens, and grouping sequencing reads together that likely originate from the same microorganism (2, 19, 32, 59, 63, 64, 86).
The logistics of correctly procuring a sample, transporting it to the lab, and accurately performing the tests require substantial resources. At the time of collection, DNA and RNA stabilizers are often used to prevent degradation by host and environmental enzymes. DNA is a double-stranded molecule that is stable and can be stored for long periods. RNA, on the other hand, is a single-stranded molecule that is not as stable and degrades quickly, so RNA extraction kits typically include steps to stabilize the RNA, such as adding a preservative or freezing the samples (32, 47, 85). At this point, turnaround time and cost are highly variable but reported times range from 6 h to 2 weeks and prices range from $130 to 2500 (2, 5, 9, 35, 50, 84, 87, 88). The majority of mNGS samples are shipped to an outside lab adding to the time and expense, but the initial overhead of creating an onsite capable lab will range from hundreds of thousands to millions of dollars (2, 89). Torchia et al. (90) published a cost-effective analysis in 2019 comparing NGS vs culture for diagnosing PJI. They found NGS cost-effective in cases with high pretest probability, but there was a high amount of variability between sequencing platforms. They concluded that NGS testing should be reserved for specific clinical situations. In 2018, Chai et al. (91) conducted a cost–benefit analysis on mNGS in a fever of unknown origin. They found that mNGS should not be the first-line diagnostics for a fever of unknown origin as a cost-saving measure but should be used as a supplement in the appropriate context.
The last step of the process is data interpretation and clinical application, which lacks standardization. As of 2021, there is no Food and Drug Administration (FDA)-approved infectious disease mNGS assay (84). However, the US FDA did produce a draft guidance document in 2016 (92). Much of the data interpretation requires an in-depth knowledge of every step, such as sequencing platform, lab environment, and bioinformatics system (2, 9, 20, 47, 84). Due to the vast amount of data, some mNGS platforms have the disadvantage of barcode index switching, and this could lead to cross-contamination of samples and inaccurate results (32, 93). Another difficulty is the storage and transfer of massive amounts of patient data in a secure and accessible way. As mentioned earlier, multiple experts need to collaborate to interpret mNGS results accurately, and this requires a complex setup of hardware and software to do this efficiently and securely (2, 9, 20, 35, 94, 95). Figure 4 is a reproduction focused on the challenges of mNGS.
Challenges to routine deployment of mNGS in the clinical setting reproduced with permission from Chiu et al. (20).
Citation: EFORT Open Reviews 8, 4; 10.1530/EOR-22-0136
Clinical use
Multiple studies have been published in the literature implementing mNGS to identify pathogens in various clinical settings. Govender et al. (9) published a systematic review and meta-analysis on mNGS in infectious diseases in 2021. Their analysis includes studies that obtained samples from blood, urine, respiratory tract, cerebrospinal fluid, synovial fluid, orthopedic sonication, intraocular fluid, and cardiac tissue. Sensitivity in the most reported sample types was 84% in orthopedic fluid (n = 297), 90% in blood (n = 288), and 75% in CSF (n = 133). Specificity was 67% in orthopedic fluid (n = 224), 86% in blood (n = 533), and 96% in CSF (n = 314). Four studies included in the systematic review that made drug susceptibility predictions contained a large amount of heterogeneity. mNGS made a categorical prediction 88% of the time in reference to antimicrobial-resistant or susceptible pathogens. Twenty-four percent of the resistant samples were not classified as resistant by mNGS, and 5% of the samples were predicted as resistant when they were not. They concluded that with much more research, mNGS has the potential to become the next frontier of clinical microbiology and will likely be included in the clinicians' armamentarium for treating infectious diseases. Multiple other studies have addressed clinical impact, but all conclude that more research is needed to determine this definitively (5, 30, 50, 82, 83, 96, 97, 98).
Pediatrics
Various papers report on the effectiveness and positive clinical impact of PCR in the diagnosis of pediatric OAI, especially when the pathogen is Kingella kingae (7, 21, 99, 100, 101). Papers report up to a 10-fold increase in K. kingae detection rate (102, 103, 104). PCR is largely incorporated into all pediatric center diagnostic protocols but still has various limitations that necessitate the continued use of culture-based diagnostics and further advancement of other genetic modalities such as mNGS. Data on the use of mNGS in pediatric OAI is much more limited with only one study being found. Ramchandar et al. (5) performed a study comparing mNGS to culture and comparing mNGS to culture with PCR on 42 children with acute OAI. The mNGS group identified a pathogen in 61.9% (26/42) of subjects as compared to 45.2% (19/42) by operative culture and 57.1% (24/42) by culture/PCR. There were four instances where mNGS detected a probable pathogen when culture/PCR was negative, giving the culture/PCR group 4 FN. These organisms were two cases of Neisseria gonorrhoeae arthritis, one case of Brevundimonas vesicularis osteomyelitis, and one case of K. kingae osteomyelitis. mNGS produced one FP and two FN. The FP was a non-pathological host organism, and the two FN were both Borrelia burgdorferi identified by PCR only. In two MRSA cases, the mNGS and culture/PCR groups both identified the mecA gene. The authors concluded that mNGS compared to culture and culture/PCR produced similar diagnostic yields and did not significantly impact clinical care. They also noted that PCR might be more sensitive than mNGS when testing for Lyme disease, and mNGS can be of benefit in detecting fastidious organisms.
Although the use of mNGS in pediatric OAI literature is limited, there are many other pediatric studies showing promise with several types of infections. Graff et al. (98) published a review that encompassed 18 case reports and 13 cohorts and case controls, utilizing mNGS in diagnosing pediatric meningitis and encephalitis. The authors concluded that mNGS has the potential to impact clinical care positively but, at this point, should be used in parallel with conventional testing. Edward et al. (84) published a review focusing on pediatrics. They discussed multiple papers on pulmonary infection that reached conclusions of improved antimicrobial stewardship, less invasive procedures, and greater sensitivity, particularly in fastidious organisms (30, 47, 82, 105, 106, 107, 108). They reviewed two studies on endocarditis, one of which mNGS significantly altered clinical management (109) and the other it did not (97). They reported on two papers discussing the use in immunocompromised patients, which found results to be clinically relevant in most cases (110, 111). Furthermore, Edwards et al. (84) discussed the lack of literature on pediatric OAI. The authors of the review conclude that there is optimism and potential surrounding mNGS in infectious diseases, but many limitations have to be overcome, with much more research needed.
Adult OAI
There is much more adult literature on the use of mNGS in OAI, which is very promising for the future of its use in pediatric OAI. Tang et al. (112) published a recent systematic review on mNGS in PJI. They included 9 papers (28, 29, 42, 43, 113, 114, 115, 116, 117), including 541 PJI patients and 466 non‐PJI patients. The overall methodological quality of the included papers was low, with a significant bias found in all papers. mNGS sensitivity and specificity was 63–96% and 73–100%, respectively. mNGS identification rate in culture-negative PJI was 82–100% in six studies and 9–31% in the remaining three. The positive predictive value was 71–100% and the negative predictive value was 74–95%. In PJIs that had antibiotic administration prior to the sample being obtained, the mNGS identification rate was 74.05–92.31%. The authors concluded that mNGS demonstrates clinical significance in diagnosing PJI, particularly in culture-negative PJI and PJI with early antibiotic administration. They say that mNGS should not replace other traditional diagnostic tools, and higher quality research is needed to determine the most appropriate use of mNGS. A recent paper produced by Indelli et al. (16) reviews the current adult PJI literature.
Conclusion
The use of genetic testing in pediatric and adult OAI and other infectious diseases has a significant impact on patient care. PCR is used commonly in pediatric OAI to diagnose fastidious organisms such as K. kingae and culture-negative infections. Its use has increased as a response to positive results over the last 15 years. At this point, mNGS documented use in pediatric OAI is extremely limited, but its use in adult OAI is beneficial in culture-negative cases, antibiotic selection, and timelier acquisition of results. Compared to mNGS, PCR has more clinical data showing its effectiveness and it is more accessible but has limitations. mNGS poses the potential to overcome some limitations of PCR and clinical data over the last 5 years has increased substantially in adult PJI and pediatrics outside of orthopedics. The reported benefits in the literature include a hypothesis-free diagnostic tool, the ability to detect the majority of pathogenic organisms in a single sample, the detection of fastidious organisms, the detection of pathogenic organisms unidentified by other culture-based and molecular-based techniques, improved antimicrobial selection, and lower occurrence of invasive procedures. Currently, there is little standardization, and it is a resource-intensive testing modality with an undefined clinical impact. Further studies are needed to elucidate the complete clinical benefit. The current knowledge and understanding of the very basics of this modality and its role in clinical medicine are lacking across the orthopedic community as a whole. This paper aims to introduce the basics of mNGS with the potential benefits and limitations and with the accompanying evidence to foster more research in the clinical setting, particularly in pediatric orthopedics.
ICMJE conflict of interest statement
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding statement
This work did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.
Acknowledgements
The authors would like to acknowledge MountainView Regional Medical Center Orthopedic Surgery Residency program for use of their facilities during the production of this paper.
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