key: cord-0079438-169laiak authors: Qian, Mengjia; Zhu, Bijun; Zhan, Yanxia; Wang, Lingyan; Shen, Qi; Zhang, Miaomiao; Yue, Lei; Wu, Duojiao; Chen, Hao; Wang, Xiangdong; Cheng, Yunfeng title: Analysis of Negative Results of Metagenomics Next-Generation Sequencing in Clinical Practice date: 2022-05-16 journal: Front Cell Infect Microbiol DOI: 10.3389/fcimb.2022.892076 sha: 35c8be67ba689436525bbae0841ceee1673072a2 doc_id: 79438 cord_uid: 169laiak BACKGROUND: Metagenomics next-generation sequencing (mNGS) has been increasingly used in the clinic, which provides a powerful tool for the etiological diagnosis of infectious diseases. Precise treatment can be carried out according to the positive mNGS results. However, the role of negative results of mNGS remains poorly defined in clinical practice. METHODS: The results of 1,021 samples from patients who received the mNGS test at Zhongshan Hospital, Fudan University, between January 2019 and December 2019 were analyzed. RESULTS: There were 308 samples (30.17%) of negative results included in the current study. The top 2 types of negative samples were blood (130/308) and tissue (63/308), which also accounted for the highest negative proportion in diseases. Sputum and bronchoalveolar lavage fluid (BALF) were more likely to have positive results. In false-negative results (defined as negative in mNGS test but reported positive in other sample types or assays), 118 samples were found when compared to regular microbiological assays. The negative predictive value (NPV) of mNGS was 95.79% [95%CI, 93.8%–97.8%] as compared to culture and smear. Mycobacterium, Aspergillus, and Mycoplasma ranked as the top 3 microorganisms on the undetected pathogen list. CONCLUSIONS: The present data indicate that when the mNGS test is negative, the negative prediction accuracy rate of the original specimen is significant. However, other laboratory assays results and clinical presentations should always be carefully considered prior to drawing a diagnosis. Infectious diseases are caused by a variety of pathogens, which lead to systemic or local inflammation (Honda and Littman, 2012) with significant mortalities (Lozano et al., 2012) . As the clinical manifestations of infectious diseases are diverse, an accurate clinical diagnosis is challenging when a pathogen is unknown (Simner et al., 2018) . With the development of gene detection technology, metagenomics next-generation sequencing (mNGS) has been increasingly used in the clinic, which provides a powerful tool for the etiological diagnosis of infectious diseases (Chiu and Miller, 2019) . As it directly extracts all microbial nucleic acids from clinical samples, mNGS theoretically meets the detection needs of all microorganisms in one detection (Goldberg et al., 2015; Li et al., 2021) . Through unbiased and full coverage sequencing technology, the acquired sequence information is compared with a microbial database to obtain the specific species and relative content of microorganisms (Miao et al., 2018) . Compared with the conventional culture assay, mNGS is less time-consuming, not limited by culture conditions, and capable of detecting those pathogens that are difficult to culture (Kitsios et al., 2018) . When the mNGS test is positive, precise treatment could be carried out soundly according to the actual clinical situation. However, the role of negative results of mNGS remains poorly defined in clinical practice. As such, the present study is aimed to evaluate the clinical value of negative mNGS results and to provide evidence for its proper applications. A total of 1,021 samples from hospitalized patients who received mNGS test at Zhongshan Hospital, Fudan University, from January 2019 to December 2019 were collected and reviewed. Results of regular clinical assays of these samples were also collected at the same time. The study was approved by the institutional review board of Zhongshan Hospital, Fudan University (#B2021-694R). Written informed consent was obtained from each patient prior to the enrollment. Samples were collected at standard procedures according to their various types including blood, sputum, bronchoalveolar lavage fluid (BALF), and tissue. Nucleic acid was extracted and fragmented into DNA fragments, and then these fragments were end-repaired, barcoded, and amplified to be qualified libraries. Libraries were sequenced on the BGISEQ-50 platform. Sequencing data were compared to human references, then human sequence reads were removed, and nonhuman sequence reads were further filtered and compared to databases (bacteria, fungi, and parasite databases). Final data including stringent mapped reads number (SMRN), coverage rate (CovRate), relative abundance (Re_Abu), and sequencing depth were analyzed and reported. Continuous data are shown as mean values ± SE. Student's t-test was used to measure the differences in variables, where appropriate. Data analysis was performed using SPSS 16.0 software. p-Values <0.05 were considered significant, and all tests were two-tailed. A total of 1,021 samples were included in the current study, with 713 samples (69.83%) of positive results and 308 samples (30.17%) of negative results ( Table 1) . Reports that only with colonization or background microorganisms were considered negative. The sample type distribution of all negative samples is listed in Figure 1A . The most common negative sample type was blood (130/308, 42.2%). Tissue, including lung tissue, bone tissue, lymph node tissue, valve tissue, and skin tissue, ranked second (63/308, 20.5%). Other sample types included BALF, sputum, pleural fluid, cerebrospinal fluid (CSF), ascetic fluid, pericardial fluid, pus, swab, joint fluid, and drainage fluid. The negative rate was calculated in each sample type (sample numbers >10) and shown in Figure 1B . Pleural fluid (54.55%), ascetic fluid (42.86%), and blood (38.12%) ranked the top 3, and pus (9.68%) ranked the last. Blood and tissue accounted for the highest negative proportion in different common infectious diseases as listed in Figure 2 . For instance, blood accounted for 79.27% (65/82) in a fever of unknown origin and 67.74% (21/31) in digestive system diseases. Tissue accounted for 90% (9/10) in lymph node enlargement and 69.23% (9/13) in orthopedic diseases. In order to study the influence of sample type on mNGS results, true negative (TN) and false negative (FN) were defined. Laboratory results that were negative in all sample types and all assays were defined as TN, and laboratory results that were positive in some sample types or some assays were defined as FN. Conventional assays included in our study were culture, smear, TSPOT.TB (TSPOT), cryptococcal antigen (CrAg), immunoassay, 1-3-b-D-glucan test (G test), and galactomannan test (GM test). In addition, parasite detection was all negative in all samples suspected of parasite infection, so it was not listed. All the results of other conventional assays were collected and sampled at the same time as mNGS. However, the pairedculture test was the basic premise of the comparison. Therefore, 23 cases were excluded because they had no pairedculture results. The remaining 285 cases were further classified in Table 2 according to TN and FN. Common colonization pathogens detected in conventional assays were also considered to be negative results. According to the definition and statistical results, TN was 58.6% (167/285) and FN was 41.4% (118/285). FN positively differed among culture and smear in the same specimen, culture, smear, and mNGS of other sample types and other assays as listed in Table 3 . One case might have several positive results. As shown in Table 3 , there were 26 culture positive cases, 1 smear positive case, and 22 mNGS positive cases in other sample types. In terms of blood and tissue that had high negative rates, respiratory samples including sputum and BALF were more likely to have positive results (Figure 4) . In addition to culture, other assays commonly used in clinics include TSPOT for Mycobacterium tuberculosis, CrAg for Cryptococcus, G test and GM test for fungus, and immunoassay for mycoplasma, Legionella, etc. There were 48 cases that were positive in the TSPOT test, but in the mNGS test, no Mycobacterium was detected in these cases, even in the database. G test and GM test were effectively used to detect Aspergillus , and immunoassay was effective for Mycoplasma detection (Parker et al., 2018) . These three pathogens were in the top 3 in the undetected pathogen list shown in Figure 5 . In addition, there were 3 cases reported positive in CrAg and 1 case reported Cryptococcus positive in blood culture. Furthermore, there was a case of Rickettsia detection in immunoassay, and no sequence was detected in mNGS. In order to further exclude the influence of FN from the laboratory, we collected the experimental data of all the negative samples. The data collected cover the whole process of mNGS, including DNA concentration, library concentration, total sequence, and host sequence ratio. Bur for some sample types, since the total amount of the sample type is small, it is not statistically significant and is not included. The comparison of blood, tissue, BALF, sputum, and pleural fluid is shown in Figure 6 . In the DNA extraction step, DNA concentration was significantly lower in FN samples than in TN samples in sputum. In the library construction step, library concentration was lower in FN samples than in TN samples in blood. However, in the last laboratory step, sequencing, neither the total sequence nor the host sequence ratio had a significant difference between TN and FN. In addition, infection indexes include leukocyte (WBC), neutrophil (Neut), lymphocyte (Lymph), eosinophilia (EO), Creactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and erythrocyte sedimentation rate (ESR) of all the negative samples were collected. The disease groups with insufficient data for statistical analysis were removed. The comparison of WBC, Neut, Lymph, and EO between TN and FN is shown in Figure 7A , and CRP, PCT, IL-6, and ESR are shown in FIGURE 2 | Distribution of negative mNGS cases in diseases. The distribution of negative mNGS cases in different diseases including fever of unknown origin, respiratory diseases, cardiovascular diseases, digestive system diseases, renal diseases, brain diseases, orthopedic diseases, hematological diseases, rheumatic diseases, skin infection, and lymph node enlargement. BALF, bronchoalveolar lavage fluid; CSF, cerebrospinal fluid; mNGS, metagenomics next-generation sequencing. In the present study, 1,021 mNGS reports were analyzed to evaluate the role of mNGS in clinical practice, in particular when the results were negative. The overall negative rate was 30.17%. Blood and tissue had the most frequent negative reports, accounting for 42.21% and 20.45%, respectively. According to the negative rate of all samples, blood (38.12%) and tissue (31.66%) also accounted for the third and fourth place of the negative rate. In addition, according to the distribution of diseases, the negative proportion of blood and tissue in various diseases was greater than that of other sample types. Although the negative result rates of blood and tissue were high, compared with culture and smear, the NPV of mNGS reached 95.79% (95%CI, 93.8%-97.8%) in the current study, which implies that when a negative mNGS result is reported, the negative prediction accuracy rate of the original specimen is high. The NPV of another study was 100% (95%CI, 71.7%-100%) for bacteria, 72.7% (95%CI, 39.3%-92.7%) for fungi, and 100% (95%CI, 76%-100%) for M. tuberculosis in mNGS vs. culture and smear . However, when other assays, such as TSPOT, were added to the NPV comparison, the proportion of undetected M. tuberculosis increased significantly. In our study, only 2 cases were cultured M. tuberculosis positive in the same specimen, 2 cases were culture positive, and 1 case was smear positive in other sample types; however, 48 cases were TSPOT positive. A positive TSPOT may merely due to the manifestation of old tuberculosis (Ma et al., 2019) , especially when its mNGS test is negative. Hence, it is recommended that when a specific infection is suspected in the clinic, other proper assays should also be used as combined diagnostic measures. Some pathogens with low SMRN or SMRNG were excluded in the report, and some pathogens were identified as colonized or background microorganisms. When the report is negative and there is a suspected specific pathogen infection in the clinic, it can be traced back to the original database for a query. The causes of FN both in the clinic and in the laboratory were analyzed. The first is the selection of sample types in the clinic. Clinicians should have a comprehensive consideration of the FIGURE 3 | Classification of negative mNGS cases. In total, 308 negative mNGS cases were divided into no paired-culture result, paired-culture negative, and paired-culture positive. Twelve paired-culture positive cases were further divided into report included, report not included, and not detected. mNGS, metagenomics next-generation sequencing. ) and BALF (20.89%) were much lower than those of other sample types in our study. Moreover, the false-negative samples of blood and tissue were reanalyzed, and the data showed that when both culture and mNGS were negative when sputum and BALF samples were sent for detection, the positive rate would be significantly increased. Furthermore, in the case of unknown pathogen infection, selecting multiple sample types for combined detection is recommended (Wang et al., 2020) . Second, the correlation between the status of infection and FN was explored. Complete blood count, CRP, PCT, IL-6, and ESR were collected on the day of examination of mNGS to determine the differences between FN and TN. There were no statistical differences between these two groups, which was consistent with another study (Duan et al., 2021) . The data of the infection indexes showed that the samples with a clinical diagnosis of respiratory disease tended to be non-infectious diseases in mNGS samples. Therefore, the differential diagnosis ( Third, the experimental data were compared to analyze the false-negative results in the experiment. There were differences between FN and TN groups in DNA concentration of sputum and library concentration of blood. When there is lower DNA concentration in sputum samples and lower library concentration in blood emerged, we should be vigilant. The experimental steps should first make sure that each step is double-checked. When it turned into a negative report, it should be returned to the clinic, and clinicians should be reminded to make a comprehensive diagnosis and judgment. There were no statistical differences between FN and TN groups in the sequencing step. Therefore, increasing the amount of data is meaningless for false-negative samples. Moreover, the host sequence ratio had no reference significance in the judgment of false negatives. Furthermore, Mycobacterium and Aspergillus were found to be the two most undetected pathogens. A possible explanation is that the cell walls of these pathogens were both thick and elaborate (Alderwick et al., 2015; Gow et al., 2017) . It implies that before DNA extraction, precaution should be observed for the step of cell wall broken to obtain optical results. Some limitations exist in our study. First, the definition of negative and positive in the study is limited to laboratory results, without clinical verification. Second, the laboratory results of bacteria, fungi, and parasites were analyzed in this study. Although viruses can be divided into DNA viruses and RNA viruses, the mNGS test in the current study only collected the result of DNA, as RNA viruses would degrade and could not be detected (Chen et al., 2020) . Therefore, virus research was not included in the present study. Third, since prior antibiotic exposure has been reported to have less impact on the results of the mNGS test, the impact of antibiotics was not examined in study (Miao et al., 2018) . In conclusion, the present study showed that when the mNGS result is negative, the negative prediction accuracy rate of the original specimen is significant. However, other laboratory results and clinical presentations should always be carefully considered prior to the diagnosis. The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors. The studies involving human participants were reviewed and approved by Zhongshan Hospital, Fudan University. The patients/participants provided their written informed consent to participate in this study. The Mycobacterial Cell Wall-Peptidoglycan and Arabinogalactan. 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BZ, YZ, LW, QS, MZ, and LY performed the experiments and analyzed the data. MQ, DW, HC, XW, and YC performed the literature review and drafted and revised the manuscript. All authors read and approved the final manuscript. This study was conducted at the Institute of Clinical Science of Zhongshan Hospital, Fudan University. We sincerely thank all the staff and participants for their important contributions.