Diagnostic challenges in peritoneal dialysis-associated peritonitis with atypical and rare pathogens: A new era of metagenomic next-generation sequencing precision diagnosis

Abstract

Peritonitis caused by atypical and rare pathogens is challenging to diagnose. Although the International Society for Peritoneal Dialysis (ISPD) guidelines significantly improve the diagnostic rate by placing peritoneal dialysis fluid into blood culture bottles, peritonitis caused by atypical pathogens, such as tuberculosis and fungi, is difficult to diagnose due to challenges in culturing these organisms using traditional methods, leading to high mortality. Metagenomic next-generation sequencing (mNGS) technology has been widely used as an accurate diagnostic technique for infectious diseases. First used in identifying and quantifying environmental micro-organisms, mNGS technology can identify rare, novel, difficult-to-detect and mixed pathogens directly from clinical samples, and has potential in predicting antibiotic resistance. This paper summarizes the application of mNGS in atypical and rare pathogens peritonitis clinical cases in recent years, and provides reference for the diagnosis of peritonitis in combination with new ISPD guidelines and diagnostic techniques. The development and principles of mNGS technology, diagnostic efficiency in peritonitis, challenges in diagnosis of atypical and rare pathogen-associated peritonitis, and application of mNGS technology are discussed in detail. The development of mNGS technology provides clinicians with powerful tools to more accurately identify and treat peritonitis. Future research needs to focus on reducing costs, improving test accessibility, and developing new bioinformatics tools to better integrate mNGS results into clinical practice.

Keywords

Peritoneal dialysis-associated peritonitis metagenomic next-generation sequencing atypical and rare pathogens

Introduction

Peritoneal dialysis (PD) as a renal replacement therapy for end-stage renal disease patients has been favored by more and more uremic patients.¹ Although advances in technology and standardization of procedures have significantly reduced the incidence of peritonitis, according to an annual peritonitis incidence statistic that includes 33 national registries, the average peritonitis incidence rate decreased from 0.6 cases per patient year (PPY) in 1992 to 0.3 cases PPY in 2019.² However, peritonitis is still one of the main reasons for PD patients to withdraw from treatment. Even in the early stages of dialysis, more than one-third of patients withdrew due to infection.³ The SONG-PD initiative also emphasizes that PD-related infection is the primary core outcome of PD and a core indicator of treatment quality.^4,5 Among the pathogens of peritonitis, the most common microorganisms are aerobic bacteria, such as Staphylococcus aureus, coagulase-negative staphylococci. Gram-positive bacteria still dominate the proportion of peritonitis, and the culture-negative rate in many regions is still higher than the ISPD recommendation of 15%—Thailand (28%), Japan (21%), the United States (16%), and Canada (16%).⁶ A recent study from Thailand suggested that up to 11% of cases may be caused by atypical pathogens of this type, including mycobacteria (2%) and fungi (9%).⁷ According to the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), fungal peritonitis accounts for only 1–3% of cases in various countries.⁶ Despite this relatively low incidence, the diagnosis of atypical pathogens in culture-negative peritonitis remains challenging due to the low abundance of atypical pathogens and the limitations of conventional culture methods. This diagnostic difficulty may result in delayed treatment, which is associated with a higher mortality rate. This type of peritonitis occurs in cases of low immunity, malnutrition, antibiotic resistance, they may not be detected in conventional bacterial cultures, or because their culture conditions are special, require longer times and specific culture conditions, and are often difficult to treat, resulting in adverse outcomes and even death of patients. While the ISPD guidelines do not explicitly define the term “atypical pathogen-associated peritonitis,” they do discuss culture-negative peritonitis and organisms that are difficult to culture using conventional methods. These organisms include Mycobacterium tuberculosis, non-tuberculous mycobacteria (NTM), and rare pathogens such as Ureaplasma, Mycoplasma, anaerobes, and viruses.⁸ Metagenomic next-generation sequencing (mNGS) technology, as a precision diagnostic tool for infectious diseases, has been widely applied. Initially used for the identification and quantification of environmental microbes, mNGS can directly detect rare, novel, hard-to-detect, and mixed pathogens from clinical samples and holds potential for predicting antibiotic resistance.⁹ Therefore, this review summarizes and discusses mNGS used in clinical cases of atypical and rare pathogen peritonitis in recent years, aiming to provide a reference for peritonitis diagnosis in combination with updated ISPD guidelines and advances in new diagnostic techniques.

Development and principle of mNGS technology

Since the advent of the first-generation sequencing technology, great progress has been made in sequencing technology through continuous iteration of sequencing technology and sequencing platform. The mNGS continues to develop rapidly in the global sequencing market due to its high throughput, rapid turnaround, broad coverage, and ability to detect pathogens without prior assumptions,¹⁰ which plays an important role in identifying complex and atypical infections (such as Chlamydia psittaci, Legionella, difficult-to-culture anaerobic bacteria and mycobacteria), zoonotic pathogens, newly emerging RNA and DNA viruses play an important role.¹¹ In previous reports, mNGS has been successfully applied to dozens of sample types such as cerebrospinal fluid, respiratory secretions, feces, urine, blood and tissues.¹² It has shown great advantages in diagnosing complex infections such as those associated with bloodstream infections, respiratory infections, bone and joint infections and encephalitis.^13–16 The metagenomic sequencing process includes sample collection, transportation, nucleic acid extraction and enrichment, library sequencing, bioinformatics analysis, and suspicious pathogen reporting. Figure 1 shows a complete flow chart for mNGS testing of dialysate from peritonitis patients. A comprehensive nucleic acid sequencing process, including wet and dry experiments. Wet experiments involve extracting nucleic acids (RNA and DNA) from samples, performing reverse transcription of RNA, preparing sequencing libraries, and sequencing itself. The dry experimental steps include base calling, data preprocessing, removal of host nucleic acid sequences, database alignment, taxonomic classification, and ultimately identification of microorganisms. The entire process, from sample processing to data analysis, aims to identify the composition of sequencing libraries, which can be targeted, DNA amplified, or both.¹⁸ Guidelines from various countries recommend minimizing any errors in the workflow in accordance with ISO (International Organization for Standardization) standards to ensure the accuracy and reliability of next-generation sequencing (NGS) in the diagnosis of infectious diseases.^19–21 As the field of clinical mNGS continues to evolve, standardized protocols, bioinformatics workflows, and reference databases are crucial for facilitating the widespread adoption of mNGS in clinical microbiology laboratories.²² The mNGS database includes reference genomes of humans and microorganisms, and the mNGS microbial genome database includes genomes of more than 12,000 bacteria, 18,000 fungi, 4600 viruses, and 100 parasites, with assembly quality reaching the standard of a complete genome or chromosome.²³ The Pathogens Metagenomics Database (PMDB) of BGI reported to include approximately 36,000 pathogens, covering a broad range of bacteria, viruses, fungi, and parasites, as well as drug-resistant genes.¹⁷ The core equipment of mNGS technology mainly includes high-throughput sequencers, which are key tools for large-scale DNA or RNA sequencing. The current mainstream second-generation sequencing platforms include Illumina, which leads with its wide application and high market share, Thermo Fisher, which has an advantage in the clinical field with its fast and easy operation, and MGI Tech Co., Ltd (MIG), which stands out in the China market.

Figure 1.

Schematic diagram of a general workflow for metagenomic next-generation sequencing for diagnostic clinical use. The detection process of mNGS in patients with peritonitis includes sample collection, transportation, nucleic acid extraction and enrichment, database sequencing, letter analysis and suspicious pathogen report. Nucleic acids of all microorganisms in dialysate were extracted and sequenced on a high-throughput sequencing platform. The PMDB of BGI reported to include approximately 36,000 pathogens, covering a broad range of bacteria, viruses, fungi, and parasites, as well as drug-resistant genes.¹⁷ mNGS: metagenomic next-generation sequencing; PMDB: Pathogens Metagenomics Database.

Diagnostic efficacy of mNGS in peritoneal dialysis associated peritonitis

The updated ISPD 2022 guidelines recommend that the blood culture bottle(s) be the preferred technique for bacterial culture of PD effluent (PDE; 1C), and if more than 15% of peritonitis cases are culture-negative, sampling and culture methods should be reviewed and improved.⁸ However, due to the presence of atypical pathogens and the problem of culture methods, the proportion of culture-negative peritonitis in some centers may even reach 40%.²⁴ The 2022 ISPD guidelines mention new technologies such as polymerase chain reaction (PCR) and 16SrRNA gene sequencing, but there is no evidence to prove that they are superior to traditional methods.^8,25 A large sample study including 511 cases showed that the sensitivity of mNGS in detecting infectious and non-infectious diseases (50.7%) was significantly higher than that of culture (35.2%).¹² The development of mNGS technology provides a new way to diagnose the difficult diagnosis of peritonitis. Table 1 summarizes recent studies on mNGS in peritonitis. In 2020, the first report of a single-center study in southern China verified the detection efficiency of mNGS for peritonitis pathogens, and the detection rate of dialysate pathogens in mNGS was significantly higher than that of traditional culture method (86.67% vs. 60.00%, P = 0.039).³¹ In another study, the sensitivity and specificity of mNGS method were 96.77% and 83.33%, and those of blood culture bottle method were 70.97% and 100%.²⁹ Compared with patients receiving antibiotic treatment, the negative rate of traditional culture method is higher. We can still use mNGS to confirm peritonitis on the day of peritonitis onset and within 2 days thereafter, cause the cell free-DNA (cfDNA) concentration in the PD fluid will increase during this period.²⁶ In another study of the diagnosis of peritonitis after antibiotic use, the mNGS method was superior to the blood culture bottle method. In the antibiotic use group, the positive rate was 92% with mNGS method and 38% with blood culture method (p = 0.016).²⁸ Moreover, the application of mNGS shortened the detection time, which was 25 (24–27) versus 89 (73–122) hours lower than that of the traditional microbial culture method (P < .001).³⁰ Although traditional culture can provide pathogen identification and drug sensitivity results within 48 h at the fastest, the rapid reporting time (about 25 h³⁰) of mNGS significantly shortens the time to diagnosis. For those patients with positive cultures, our moderately accelerated reporting time enables clinicians to adjust treatment earlier, reduce unnecessary broad-spectrum antibiotic use, and reduce the risk of superinfection and development of resistance. Finally, the ability to detect co-infections with multiple types of pathogens is that a significant advantage for mNGS, including (any type of organism, e.g., mixed infection of bacteria and virus),³⁴ about 15% of the infections found in cases using the mNGS method are multiple infections.³¹

Table 1.

Metagenomic next generation sequencing (mNGS) studies in peritonitis.

District	Year of publication	Pathogenic microorganism	Research design	Sequencing platform	Database	Case	Positive rate	Time	Cost
New York US.²⁶	2022	Typical and atypical organisms	Clinical research	lllumina Next-seq CN500^a	NCBI	17	94.12%	N/A	N/A
Anhui, China²⁷	2023	Non-tuberculous mycobacteria	Case report	N/A	NCBI	1	N/A	N/A	N/A
Beijing, China²⁸	2023	Typical and atypical organisms	Clinical research	lllumina Next-seq CN500^a	NCBI	26	92.3%	N/A	N/A
Beijing, China.²⁹	2024	Typical and atypical organisms	Clinical research	lllumina Next-Seq™550Dx^a	NCBI	37	96.8%	48H	N/A
Nanjing, China.³⁰	2023	Typical and atypical organisms	Clinical research	MGISEQ-2000^a	PMDB	18	94.4%	25H	3000RMB
Foshan, China.³¹	2022	Typical and atypical organisms	Clinical research	MGISEQ-2000^a	PMDB	30	86.7%	N/A	3000RMB
Inner Mongolia, China.³²	2024	Mycobacterium tuberculosis	Case report	N/A	N/A	1	N/A	24H	463USD
Chongqing, China.³³	2023	Ureaplasma, mycoplasma	Case report	N/A	N/A	4	N/A	N/A	N/A

*(a) Annotation: MGISEQ-2000 belongs to the platform of MGI; lllumina Next-Seq™550Dx, lllumina Next-seq CN500 belongs to the platform of Illumina; *(b) Abbreviations: NCBI, National Center for Biotechnology Information; PMDB, Pathogens Metagenomics Database; *(c) Definitions: Typical pathogens, common pathogens that cause the disease and present typical clinical features; Atypical pathogens: Less common pathogens that present atypical clinical features, such as, fungi, Mycobacterium tuberculosis;

Table 2.

Comparison of the metagenomic next-generation sequencing (mNGS) and traditional blood culture bottle method in the diagnosis of peritonitis.

Characterization	mNGS	Blood culture bottle method
Sensitivity	High, can detecting low abundance pathogens	Low
Specificity	Generally high, but potentially affected by background microbiota	High, direct pathogen isolation
Atypical pathogen detection	Effective in detecting pathogens difficult to detect with traditional methods	Limited in detecting atypical pathogens
Speed	Fast, results can be obtained within hours	Slow, results may take days to obtain
Operational	High, requires specialized bioinformatics analysis	Low, relatively simple to perform
Sample	Detects from clinical samples without handling	Sample pretreatment may be required
Antimicrobials	Not affected by antimicrobials	Antimicrobial use decreases positivity rate
Cost	High, relatively expensive test	Low, cost-effective
Resistance Testing	Can detect resistance genes, but cannot directly perform drug sensitivity testing	Can perform drug sensitivity testing to directly guide clinical use of drugs
Data Interpretation	Requires specialized knowledge, hard to interpret	Relatively intuitive and easy to interpret
Dynamic Range	Difficult to distinguish between living pathogens	Can distinguish between living pathogens

The limitations of mNGS in diagnosing peritonitis

Although mNGS has many traditional advantages, it still has some practical shortcomings. First, although the potential for cost reductions compared to traditional culture methods is large, ranging from $2000 to $4000 per sample, the high price and unfriendly health care policies discourage many patients.³⁵ The cost of a single mNGS test in Shanghai is approximately 3500 RMB, and it can increase to 4500–6500 RMB ($600–$900) for combined DNA/RNA analysis. However, costs may vary depending on the provider and the scope of the test.³⁶ Without medical insurance, it is still relatively expensive for ordinary people. In the next few years, the cost is expected to drop to 800–1000, or even lower, which will make it affordable for more patients. Despite this, the application of mNGS in certain clinical scenarios, such as severe pneumonia and neurological infections, has shown cost-effectiveness, especially in improving diagnostic accuracy and reducing medical costs.^16,37 Second, the biological activity of pathogens cannot be determined by gene sequencing. Finally, mNGS cannot be used for drug sensitivity testing, the later can only be cultured by blood routine method. Lastly, mNGS cannot directly perform susceptibility testing which still relies on traditional blood culture methods. Although mNGS can detect bacterial resistance genes and sequence them, it is reported that in bone and joint infections, the accuracy of predicting antibiotic sensitivity is only 76.5%.³⁸ Although bacterial drug resistance genes can be detected, their rapid changes–such as mutations and horizontal transfer–can lead to dynamic changes in the resistance phenotype. For example, resistance genes in some bacteria may mutate under different environmental pressures, such as exposure to antibiotics, thereby altering their resistance characteristics. This dynamism creates uncertainty in predicting the microbial resistance phenotype based solely on the presence of resistance genes.³⁹ Since the essence of mNGS is the sequencing of pathogen genetic material (nucleic acids) for diagnosis, it cannot distinguish between live and dead bacteria in the sample. Even if the nucleic acid sequences of pathogens are detected, it cannot be determined whether these pathogens have the ability to cause infection. That is, the detection of pathogen sequences does not necessarily represent actual infection, which may lead to false-positive results. However in clinical practice, the pathogen sequences detected in the mNGS test reports are combined with clinical manifestations and common sense for comprehensive judgment. Microorganisms with low reads are not considered pathogenic, and should be considered as skin colonizing bacteria, contamination, or other false-positive results. It has been reported that mNGS is not more sensitive than culture in identifying common bacteria, which is able to identify the vast majority (74%) compared to sequencing, and that the mNGS technique for detecting common bacterial infections may not be as advantageous as atypical organisms for bacteria-associated pneumonia.^12,40 It should be noted that although there is a high positive rate, both culture-positive and mNGS-negative results are found in these studies. This situation is interpreted as being due to the thick cell wall of some bacteria (Streptococcus), which makes it difficult to extract genetic material, but shows negative results. This also suggests that it is better for us to use both test methods together and verify each other. We have summarized the advantages and disadvantages of mNGS compared with traditional blood culture methods in Table 2 .

The mNGS utilization in clinical practice

China issued an expert consensus on the application of mNGS technology in critical infection detection in 2019, in which the following six indicators for mNGS detection were suggested: (1) Pathogens should be identified as soon as possible in critical cases; (2) pathogens should be identified as soon as possible for special patients such as immunosuppressed hosts, patients with underlying diseases, and patients with severe infections who have been repeatedly hospitalized; (3) patients with repeated negative traditional microbial detection techniques and poor treatment results; (4) suspected cases of new pathogens indicating possible clinical infectivity to some extent; (5) suspected cases of infection with specific pathogens; (6) chronic fever and/or infection with other clinical symptoms of unknown cause;⁴¹ These application conditions are consistent with the treatment requirements for atypical pathogens and refractory peritonitis. The guidelines from the U.S. Food and Drug Administration (FDA) have also recognized the particular value of mNGS for critically ill patients with lower respiratory tract infections and special infections such as subacute or chronic meningitis, which require special samples like bronchoalveolar lavage fluid and cerebrospinal fluid. However, the guidelines also mention that mNGS is suitable for opportunistic infections and mixed infections in immunocompromised patients, and infections with a wide variety of pathogens.⁴² Therefore, mNGS is particularly valuable in dealing with peritonitis, especially when patients experience recurrent or refractory peritonitis. The decision to use mNGS can be based on the following situations: (1) For patients with complex clinical histories: Older patients, those with a history of lupus, taking anti-rejection drugs (indicating a high risk of low immunity), or with previous episodes of peritonitis, may benefit from mNGS due to the complexity of their condition and the potential for atypical or multiple pathogens. (2) When conventional cultures are negative or inconclusive: In cases where culture results are negative or white blood cell counts in PDE do not decrease despite treatment, mNGS can provide a more comprehensive analysis to identify the causative pathogens. (3) In the presence of mixed infections or unidentified pathogens: When culture results are positive but the patient's condition does not improve, suggesting possible mixed infections or atypical pathogens, mNGS can help detect a broader range of pathogens and guide targeted treatment. (4) Post catheter removal: After the removal of the catheter, if there is encapsulation of peritoneal fluid, mNGS can play a significant role in identifying any remaining or emerging pathogens that may contribute to persistent symptoms. (5) Prior to antibiotic use: In cases where antibiotics have been administered before a formal empirical treatment regimen is established, mNGS can still detect the presence of pathogens that may have been masked by prior antibiotic exposure. In summary, mNGS should be used in clinical practice when traditional diagnostic methods fall short, especially in complex cases of PDAP, to ensure timely and accurate identification of pathogens and to inform effective treatment strategies. We have summarized the relevant timing for prioritizing the clinical application of mNGS based on the characteristics of peritonitis in Figure 2.

Figure 2.

Timing for prioritizing the clinical application of mNGS based on the characteristics of peritonitis.

Peritonitis with atypical and rare pathogens: a challenge for early diagnosis

Tuberculosis and non-tuberculosis mycobacteria

Early diagnosis of peritonitis due to tuberculosis is difficult, and although acid-fast bacilli smears are currently positive in 73% of cases, PD catheter removal is due to delayed diagnosis in approximately 53% of cases.⁴³ Although PD catheter removal does not appear to improve survival.⁴⁴ Because guidelines call for early conversion to hemodialysis and PD catheter removal of patients whose ascites leukocytes fail to fall to 100*10⁶/L by day 5 of treatment to avoid severe peritonitis complications.⁴⁵ As the gold standard for diagnosis, acid-fast bacilli take a long time to culture (often more than 6.7 weeks).⁴⁶ In addition, a meta-analysis showed that although adenosine deaminase alone often has a high specificity (0.96 (95% CI: 0.94–0.97)), the results are easily affected by non-tuberculosis factors such as liver disease.⁴⁷ Both BCG vaccination and natural infection with tuberculosis can cause a positive PPD test reaction.⁴⁸ Peritoneal biopsy is also considered one of the gold standards for the diagnosis of tuberculous peritonitis, but its use is often limited due to its traumatic nature.⁴⁹ Enzyme-Linked Immunospot assay for Interferon-gamma (IFN-γELISPOT) test is currently a useful adjunct to the diagnosis of active tuberculosis, and PD fluid test may be a more effective and accurate vehicle for the diagnosis of CAPD complicated with tuberculous peritonitis than peripheral blood.⁵⁰ The detection of M tuberculosis DNA using real-time PCR analysis including Xpert MTB/RIF technology (Molecular assays based on semi-nested real-time quantitative PCR techniques), although rapid, highly sensitive, highly specific, and simple to perform, is typically used in patients with highly suspected tuberculous peritonitis based on reported cases. And the technology usually relies on the specific nucleic acid sequence of the pathogen, these techniques require validation in the context of a high suspicion of the pathogen for a definitive diagnosis (It would be impractical and wasteful to perform this type of test on every patient in the early stages of peritonitis), thus having certain limitations in early diagnosis.^51,52 Because it is difficult to extract the DNA of M tuberculosis, the specific sequences of M tuberculosis complex including Mycobacterium bovis, Mycobacterium africanum and Mycobacterium minium are usually detected to improve the detection rate.³² In studies using mNGS for peritonitis diagnosis, 5% of peritonitis pathogens were M tuberculosis, and dialysis culture was negative, which may remind us that peritonitis caused by M tuberculosis is underestimated. Since M tuberculosis is not a common bacterium in the abdominal cavity, it is considered positive when the number of detected mycobacterial sequences is ≥1.³¹ The diagnosis of not-tuberculosis mycobacteria(NTM) peritonitis is challenging, similar to that of M tuberculosis, with symptoms and signs difficult to distinguish from conventional PD peritonitis or tuberculous peritonitis. Acid-fast stains are difficult to distinguish between M tuberculosis and NTM, and the positive rate of acid-fast staining in a study of Mycobacterium abscessus peritonitis was only 40%.⁵³ A systematic review of 57 cases of NTM peritonitis found that treatment was delayed for up to 4 weeks, and acid-fast staining was often not routinely performed at PD centers.⁵⁴ Moreover, because M abscessus tends to form biofilms, colonize and infect catheters, it is easy to be considered a culture-negative exit infection or tunnel infection, increasing the chance of misdiagnosis.⁵⁵ Studies utilizing mNGS to detect pathogens in peritonitis have identified M tuberculosis in approximately 5% of cases. Notably, NTM were not detected in these mNGS studies, which correlates with the lower incidence of NTM in peritonitis.^29,31 Although culture is still considered the gold standard for diagnosis, a recent report of metagenomic sequencing detected three species in dialysate, including Mycobacterium smegmatis, M abscessus, and Mycobacterium goodii was then identified as a pathogenic agent by final clinical presentation rather than M smegmatis isolated by conventional culture methods.²⁷ Diagnostic challenges for tuberculosis and NTM include slow growth in culture, resulting in prolonged diagnostic time, susceptibility to environmental contamination, and lack of specificity in clinical presentation, which is easily confused with peritonitis associated with conventional PD.

Fungus

Candida is the most common pathogen, accounting for 74.5% of all fungal peritonitis cases, with Candida albicans accounting for 55.7% and Candida species other than C albicans accounting for 44.3%.⁵⁶ In cohorts in southern China and North America, Candida accounts for 80%–90%.^57,58 It usually grows rapidly in culture, but other fungi may take several weeks. Especially for non-Candida species such as Aspergillus, Cryptococcus, since these species are difficult to detect by gram staining of PD permeate smears.⁵⁹ Fungal peritonitis should also be considered for culture-negative peritonitis, long-term antibiotic use, and patients with diabetes mellitus or immunocompromised individuals. Although false positives have been reported, β(1→3)-d-glucan or galactomannan may be important early diagnostic markers for such highly suspicious patients.^60,61 Early diagnosis of fungal peritonitis is important. 0% mortality has been reported if catheter removal is performed within 24 h and 31% mortality if catheter removal is delayed 24 h. Delayed catheter removal more than 24 h after diagnosis of fungal peritonitis is an independent risk factor for predicting mortality in patients with fungal peritonitis.⁵⁶ Molecular biological techniques such as PCR and RNA gene sequencing can be used to identify fungal peritonitis at an early stage. When mNGS is used to diagnose fungi, the diagnostic threshold is that the number of sequences is greater than 50, which can be considered as a positive result. Both C albicans and C albicans have reported mNGS detection.^28,29

Anaerobic bacteria

According data from Australia and New Zealand Dialysis and Transplant Registry (ANZDATA), anaerobic PD peritonitis accounts for only 0.1% of single-pathogen peritonitis, rising to 1.1% if only polymicrobial episodes are considered.⁶² In the report of Chao et al., ten cases of anaerobic PD peritonitis were reported, of which Bacteroides fragilis was the most common anaerobe species, accounting for 4 isolates, is a gram-negative anaerobe, usually associated with intestinal microbiota, but rare in peritonitis. Early diagnosis and treatment are essential to improve the prognosis of patients with anaerobic peritonitis, which has a conflicting overall prognosis with an 80% antibiotic cure rate and a relatively high mortality rate (10%), this suggests that timely diagnosis and treatment can significantly improve cure rates and reduce mortality.⁶³ Domingues et al.⁶⁴ mentioned that because anaerobic bacteria need special conditions to grow, traditional culture sometimes leads to false negative results due to insufficient conditions, while molecular diagnostic techniques such as PCR technology are not limited by these conditions.So they can reduce the occurrence of false negative results for anaerobic peritonitis. In Guangzhou, China, it was reported that mNGS technology was used to successfully diagnose and treat refractory peritonitis cases with Bacillus thetaiotaomicron and Ureaplasma parvum,this two cases dialysate cultures were negative.⁶⁵

Ureaplasma

Microorganisms of the genus Ureaplasma are a class of prokaryotes without cell walls, they can be cultured using mycoplasma culture media, they are increasingly being discovered using molecular techniques.⁶⁶ In recent years, a number of peritonitis cases have been reported in which Ureaplasma-induced peritonitis was diagnosed using 16SrRNA gene PCR or NGS technology. Among them, Ureaplasma parvum and Ureaplasma urealyticum are two common ureaplasmas that cause human genital tract infections. Infections are more common in infected women after urogenital tract operations or surgeries and have been reported to cause peritonitis many times.^33,67–69 Ureaplasma urealyticum was also found in a study (included 18 cases) on the application of mNGS diagnosis of peritonitis, indicates that the potential incidence of this pathogen is not low.³⁰ It is believed that with the promotion of technologies such as PCR, 16S sequencing and mNGS, more types of atypical pathogens can be discovered.

Viruses

Due to the particular conditions required for virus cultivation, there is currently little attention and few reports on peritonitis caused by such pathogens. In studies using mNGS in southern China, herpes simplex virus type 5 (HHV-5) and herpes simplex virus type 6B (HHV-6B) were found. In another study detecting peritonitis pathogens with mNGS, herpes simplex virus type 5 (HHV-5) and herpes simplex virus type 6A (HHV-6A) were also identified.^28,31 Given the widespread presence of herpes viruses in the population, many people carry these viruses without exhibiting symptoms. These viruses may remain dormant in the body's nerve cells and reactivate under certain conditions, such as stress, illness, or immune suppression. The detection of herpes viral DNA in samples through mNGS does not necessarily correlate with active disease. It may simply reflect a latent infection.

Conclusions

mNGS technology has significantly improved the pathogen detection rate in the diagnosis of peritonitis with atypical and rare pathogens, reducing patients' hospitalization time and medical expenses. By improving the detection rate of pathogens, reducing misdiagnosis and unnecessary use of antibiotics, and reducing the development of drug resistance, medical resources can be saved and the diagnosis of mixed infections can be made possible. Despite the current limitations such as high cost, inability to determine pathogen bioactivity, and inability to perform drug sensitivity tests, the price of mNGS testing is expected to gradually decrease with increasing market competition and further technological development, and the government reimbursement mechanism of medical insurance institutions will also be strengthened. Future research needs to focus on reducing costs, improving accessibility of testing, and developing new bioinformatics tools. Enhance clinicians' understanding and application of mNGS technology so that they can better integrate mNGS results into clinical practice.

Disclaimer

Since some references may not be directly accessible, certain claims may require further verification against primary sources. We have made every effort to ensure the accuracy of all information, but we recommend that readers consult the original sources when necessary to validate the relevant data.

Footnotes

Author contributions

DX, CX, ZZ are contributed equally to this work.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Chao Xie

Hongjian Ye

Xiao Yang

References

Yang

. Peritoneal dialysis in China: Meeting the challenge of chronic kidney failure. Am J Kidney Dis 2015; 65: 147–151.

Marshall

. A systematic review of peritoneal dialysis-related peritonitis rates over time from national or regional population-based registries and databases. Perit Dial Int 2022; 42: 39–47.

Dong

, et al. Incidence and risk factors associated with technique failure in the first year of peritoneal dialysis: A single center retrospective cohort study in Southern China. BMC Nephrol 2022; 23: 207.

Bello

Okpechi

Osman

, et al. Epidemiology of peritoneal dialysis outcomes. Nat Rev Nephrol 2022; 18: 779–793. https://doi.org/10.1038/s41581-022-00623-7

Manera

Johnson

Craig

, et al. Establishing a core outcome set for peritoneal dialysis: Report of the SONG-PD (standardized outcomes in nephrology–peritoneal dialysis) consensus workshop. Am J Kidney Dis 2020; 75: 404–412.

Perl

Fuller

Bieber

, et al. Peritoneal dialysis–related infection rates and outcomes: Results from the peritoneal dialysis outcomes and practice patterns study (PDOPPS). Am J Kidney Dis 2020; 76: 42–53.

Kanjanabuch

Chatsuwan

Udomsantisuk

, et al. Association of local unit sampling and microbiology laboratory culture practices with the ability to identify causative pathogens in peritoneal dialysis-associated peritonitis in Thailand. Kidney Int Rep 2021; 6: 1118–1129.

PK-T

Chow

Cho

, et al. ISPD Peritonitis guideline recommendations: 2022 update on prevention and treatment. Perit Dial Int 2022; 42: 110–153.

Han

, et al. mNGS in clinical microbiology laboratories: On the road to maturity. Crit Rev Microbiol 2019; 45: 668–685.

10.

Cai

Miao

, et al. High-Throughput metagenomics for identification of pathogens in the clinical settings. Small Methods 2021; 5: 2000792.

11.

Wilson

Naccache

Samayoa

, et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med 2014; 370: 2408–2417.

12.

Miao

Wang

, et al. Microbiological diagnostic performance of metagenomic next-generation sequencing when applied to clinical practice. Clin Infect Dis 2018; 67: S231–S240.

13.

Zhang

Q-C

Zhang

Q-Y

, et al. Influence factors of metagenomic next-generation sequencing negative results in diagnosed patients with spinal infection. Diagn Microbiol Infect Dis 2024; 109: 116278.

14.

Ramchandar

Gupta

, et al. Use of plasma metagenomic next-generation sequencing for pathogen identification in pediatric endocarditis. Pediatr Infect Dis J 2021; 40: 486–488.

15.

Wang

Chen

Zhang

, et al. Clinical evaluation of metagenomic next-generation sequencing in unbiased pathogen diagnosis of urinary tract infection. J Transl Med 2023; 21: 762.

16.

Jiang

Xing

Liu

, et al. Comparison and development of a metagenomic next-generation sequencing protocol for combined detection of DNA and RNA pathogens in cerebrospinal fluid. BMC Infect Dis 2022; 22:326.

17.

BGI. PMseq® Pathogenic Microorganism High-Throughput Gene Detection [EB/OL]. https://www.bgi.com/businessfield2/pmseq. [Accessed December 2024].

18.

Miller

Chiu

. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol 2018; 14: 319.

19.

ISO 15189:2022; Medical Laboratories Requirements for Quality and Competence. Geneva, Switzerland: International Organization for Standardization, 2022.

20.

Schlaberg

Chiu

Miller

, et al. Validation of metagenomic next-generation sequencing tests for universal pathogen detection. Arch Pathol Lab Med 2017; 141: 776–786.

21.

Matthijs

Souche

Alders

, et al. Guidelines for diagnostic next-generation sequencing. Eur J Hum Genet 2016; 24: 2–5.

22.

Kan

C-M

Tsang

Pei

, et al. Enhancing clinical utility: Utilization of international standards and guidelines for metagenomic sequencing in infectious disease diagnosis. Int J Mol Sci 2024; 25: 3333.

23.

Zhou

Liu

, et al. Utilizing metagenomic next-generation sequencing (mNGS) for rapid pathogen identification and to inform clinical decision-making: Results from a large real-world cohort. Infect Dis Ther 2023; 12: 1175–1187.

24.

Fahim

Hawley

McDonald

, et al. Culture-negative peritonitis in peritoneal dialysis patients in Australia: Predictors, treatment, and outcomes in 435 cases. Am J Kidney Dis 2010; 55: 690–697.

25.

Sheela Devi

Vivian Joseph Ratnam

Ramya

, et al. Detection of 16S rRNA gene for rapid identification of bacterial pathogens causing peritonitis in patients on continuous ambulatory peritoneal dialysis. Indian J Med Microbiol 2022; 40: 409–412.

26.

Burnham

Chen

Cheng

, et al. Peritoneal effluent cell-free DNA sequencing in peritoneal dialysis patients with and without peritonitis. Kidney Med 2022; 4: 100383.

27.

Chen

Zhu

Liu

, et al. Mixed infection of three nontuberculous mycobacteria species identified by metagenomic next-generation sequencing in a patient with peritoneal dialysis-associated peritonitis: A rare case report and literature review. BMC Nephrol 2023; 24: 95.

28.

Nie

Zhang

Chen

, et al. Rapid detection of pathogens of peritoneal dialysis-related peritonitis, especially in patients who have taken antibiotics, using metagenomic next-generation sequencing: A pilot study. Renal Failure 2023; 45: 2284229.

29.

Guo

S-S

Y-W

, et al. Application of metagenomic next-generation sequencing technology in the etiological diagnosis of peritoneal dialysis-associated peritonitis. Open Life Sci 2024; 19: 20220865.

30.

Zhang

Jin

Feng

, et al. [Etiological diagnostic value of metagenomic next-generation sequencing in peritoneal dialysis-related peritonitis]. Zhonghua Gan Zang Bing Za Zhi 2023; 39: 8–12.

31.

Xie

, et al. The application of metagenomic next-generation sequencing for detection of pathogens from dialysis effluent in peritoneal dialysis-associated peritonitis. Perit Dial Int 2022; 42: 585–590.

32.

R-F

Gao

W-N

, et al. Pathogenic microorganism DNA high-throughput genetic sequencing to diagnose peritoneal dialysis-associated peritonitis due to Mycobacterium tuberculosis infection. BMC Nephrol 2024; 25: 290.

33.

Wang

Yan

, et al. Mycoplasma hominis, Ureaplasma parvum, and Ureaplasma urealyticum: Hidden pathogens in peritoneal dialysis-associated peritonitis. Int J Infect Dis 2023; 131: 13–15.

34.

Simner

Miller

Carroll

. Understanding the promises and hurdles of metagenomic next-generation sequencing as a diagnostic tool for infectious diseases. Clin Infect Dis 2018; 66: 778–788.

35.

Filkins

Bryson

Miller

, et al. Navigating clinical utilization of direct-from-specimen metagenomic pathogen detection: Clinical applications, limitations, and testing recommendations. Clin Chem 2020; 66: 1381–1395.

36.

Cai

Yuan

, et al. Etiological diagnostic performance of probe capture-based targeted next-generation sequencing in bloodstream infection. J Thorac Dis 2024; 16: 2539–2549.

37.

Song

Jiang

Zong

, et al. The clinical value of mNGS of bronchoalveolar lavage fluid versus traditional microbiological tests for pathogen identification and prognosis of severe pneumonia (NT-BALF): Study protocol for a prospective multi-center randomized clinical trial. Trials 2024; 25: 276.

38.

Ruppé

Lazarevic

Girard

, et al. Clinical metagenomics of bone and joint infections: A proof of concept study. Sci Rep 2017; 7: 7718.

39.

Gosiewski

Ludwig-Galezowska

Huminska

, et al. Comprehensive detection and identification of bacterial DNA in the blood of patients with sepsis and healthy volunteers using next-generation sequencing method - the observation of DNAemia. Eur J Clin Microbiol Infect Dis 2017; 36: 329–336.

40.

Toma

Siegel

Keiser

, et al. Single-molecule long-read 16S sequencing to characterize the lung microbiome from mechanically ventilated patients with suspected pneumonia. J Clin Microbiol 2014; 52: 3913–3921.

41.

[Expert consensus for the application of metagenomic next generation sequencing in the pathogen diagnosis in clinical moderate and severe infections (first edition)]. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2020; 32: 531–536.

42.

Gauvin

Zimmermann

Kallman

. Current FDA regulatory guidance on the conduct of drug discrimination studies for NDA review: Does the scientific literature support recent recommendations? Drug Alcohol Depend 2016; 168: 307–319.

43.

Talwani

Horvath

. Tuberculous peritonitis in patients undergoing continuous ambulatory peritoneal dialysis: Case report and review. Clin Infect Dis 2000; 31: 70–75.

44.

Yang

T-Y

Tian

Y-C

Yen

T-H

, et al. Tuberculous peritonitis in patients on peritoneal dialysis: A 35-year experience from a large medical center in Northern Taiwan. Ren Fail 2023; 45: 2153064.

45.

PK-T

Szeto

Piraino

, et al. ISPD Peritonitis recommendations: 2016 update on prevention and treatment. Perit Dial Int 2016; 36: 481–508.

46.

Thomson

BKA

Vaughan

Momciu

. Mycobacterium tuberculosis peritonitis in peritoneal dialysis patients: A scoping review. Nephrology (Carlton) 2022; 27: 133–144.

47.

Shen

Y-C

Wang

Chen

, et al. Diagnostic accuracy of adenosine deaminase for tuberculous peritonitis: A meta-analysis. Arch Med Sci 2013; 9: 601–607.

48.

Ciesielski

. BCG Vaccination and the PPD test: What the clinician needs to know. J Fam Pract 1995; 40: 76–80.

49.

Chow

VC-Y

Szeto

. Indication for peritoneal biopsy in tuberculous peritonitis. Am J Surg 2003; 185: 567–573.

50.

Fan

Huang

Zhang

, et al. Value of gamma interferon enzyme-linked immunospot assay in the diagnosis of peritoneal dialysis-associated tuberculous peritonitis. Int Urol Nephrol 2022; 54: 843–849.

51.

Lye

W-C

. Rapid diagnosis of Mycobacterium tuberculous peritonitis in two continuous ambulatory peritoneal dialysis patients, using DNA amplification by polymerase chain reaction. Adv Perit Dial 2002; 18: 154–157.

52.

Kohli

Schiller

Dendukuri

, et al. Xpert® MTB/RIF assay for extrapulmonary tuberculosis and rifampicin resistance. Cochrane Database Syst Rev 2018; 8: CD012768.

53.

Karayaylali

Seyrek

Akpolat

, et al. The prevalence and clinical features of tuberculous peritonitis in CAPD patients in Turkey, report of ten cases from multi-centers. Ren Fail 2003; 25: 819–827.

54.

Song

Yan

, et al. Peritoneal dialysis-associated nontuberculous mycobacterium peritonitis: A systematic review of reported cases. Nephrol Dial Transplant 2012; 27: 1639–1644.

55.

Jheeta

Rangaiah

Clark

, et al. Mycobacterium abscessus - an uncommon, but important cause of peritoneal dialysis-associated peritonitis - case report and literature review. BMC Nephrol 2020; 21: 491.

56.

Chang

Kim

Park

, et al. Early catheter removal improves patient survival in peritoneal dialysis patients with fungal peritonitis: Results of ninety-four episodes of fungal peritonitis at a single center. Perit Dial Int 2011; 31: 60–66.

57.

Zhang

, et al. Characteristics analysis, clinical outcome and risk factors for fungal peritonitis in peritoneal dialysis patients: A 10-year case-control study. Front Med (Lausanne) 2021; 8: 774946.

58.

Mujais

. Microbiology and outcomes of peritonitis in North America. Kidney Int 2006; 70: S55–S62.

59.

Baer

Killen

Cho

, et al. Non-candidal fungal peritonitis in Far North Queensland: A case series. Perit Dial Int 2013; 33: 559–564.

60.

Worasilchai

Leelahavanichkul

Kanjanabuch

, et al. (1→3)-β-D-glucan and galactomannan testing for the diagnosis of fungal peritonitis in peritoneal dialysis patients, a pilot study. Med Mycol 2015; 53: 338–346.

61.

Chamroensakchai

Manuprasert

Leelahavanichkul

, et al. Rhodococcus induced false-positive galactomannan (GM), a biomarker of fungal presentation, in patients with peritoneal dialysis: Case reports. BMC Nephrol 2019; 20: 445.

62.

Ghali

Bannister

Brown

, et al. Microbiology and outcomes of peritonitis in Australian peritoneal dialysis patients. Perit Dial Int 2011; 31: 651–662.

63.

Chao

C-T

Lee

S-Y

Yang

W-S

, et al. Peritoneal dialysis peritonitis by anaerobic pathogens: A retrospective case series. BMC Nephrol 2013; 14: 111.

64.

Domingues

Furtado

Valério

, et al. Under-Recognized Pathogens in Peritoneal Dialysis Associated-Peritonitis: The Importance of Early Detection. 2023.

65.

Liu

Wang

, et al. Successful diagnosis and treatment of Bacillus polymyxa-induced refractory peritoneal dialysis peritonitis using mNGS: A case report. Xin Yi Xue 2025; V6.013: 453–456

66.

Masover

Perez

Matin

. Cultivation of Ureaplasma urealyticum in continuous culture. Infect Immun 1979; 23: 175–177.

67.

Yager

Ford

Boas

, et al. Ureaplasma urealyticum continuous ambulatory peritoneal dialysis-associated peritonitis diagnosed by 16S rRNA gene PCR. J Clin Microbiol 2010; 48: 4310–4312.

68.

Petri

Gemayel

El Zein

, et al. Tiny but nasty: A case report and a review of the literature on Ureaplasma parvum peritonitis. IDCases 2024; 37: e02015.

69.

Chao

Peiyi

Cuixia

, et al. Ureaplasma parvum-induced peritoneal dialysis-associated peritonitis: A case report. Chin J Nephrol 2023; 39: 471–472.