Application value of blood metagenomic next-generation sequencing in patients with connective tissue diseases

Deep RNA sequencing of muscle tissue reveals absence of viral signatures in dermatomyositis

Objective: To explore a possible connection between active viral infections and manifestation of dermatomyositis (DM). Methods: Skeletal muscle biopsies were analyzed from patients diagnosed with juvenile (n=10) and adult (n=12) DM. Adult DM patients harbored autoantibodies against either TIF-1γ (n=7) or MDA5 (n=5). Additionally, we investigated skeletal muscle biopsies from non-diseased controls (NDC, n=5). We used an unbiased high-throughput RNA sequencing (HTS) approach to detect viral sequences. To further increase sequencing depth, a host depletion approach was applied. Results: In this observational study, no relevant viral sequences were detected either by native sequencing or after host depletion. The absence of detectable viral sequences makes an active viral infection of the muscle tissue unlikely to be the cause of DM in our cohorts. Discussion: Type I interferons (IFN) play a major role in the pathogenesis of both juvenile and adult DM. The IFN response is remarkably conserved between DM subtypes classified by specific autoantibodies. Certain acute viral infections are accompanied by a prominent type I IFN response involving similar downstream mechanisms as in DM. Aiming to elucidate the pathogenesis of DM in skeletal muscle tissue, we used deep RNA sequencing and a host depletion approach to detect possible causative viruses.

Use of metagenomic next-generation sequencing for diagnosis of peritonitis in end-stage liver disease

Background: Conventional methods are low in positive rates and time-consuming for ascites pathogen detection in patients with end-stage liver disease (ESLD). With many advantages, metagenomic next-generation sequencing (mNGS) may be a good alternative method. However, the related studies are still lacking. Methods: Ascites from 50 ESLD patients were sampled for pathogen detection using mNGS and conventional methods (culture and polymorphonuclear neutrophils detection) in this prospective observational study. Results: Forty-two samples were detected positive using mNGS. 29 strains of bacteria, 11 strains of fungi, and 9 strains of viruses were detected. 46% of patients were detected to be co-infected with 2 or more pathogens by mNGS. Moreover, mNGS showed similar and high positive rates in ESLD patients with different clinical characteristics. Compared to conventional methods, mNGS had higher positivity rates (84% vs. 20%, P<0.001), sensitivity (45.2% vs. 23.8%, P=0.039), broader pathogen spectrum, shorter detection time (24 hours vs. 3-7 days), but lower specificity (25% vs 100%, P = 0.010). Furthermore, compared to conventional methods, mNGS showed similar consistence with final diagnosis (42% vs. 36%, P=0.539). Conclusions: mNGS may be a good supplement for conventional methods and helpful to early etiological diagnosis of peritonitis, and thus improve ESLD patients' survival.

Validation of metagenomic next-generation sequencing of bronchoalveolar lavage fluid for diagnosis of suspected pulmonary infections in patients with systemic autoimmune rheumatic diseases receiving immunosuppressant therapy

Background

The accuracy and sensitivity of conventional microbiological tests (CMTs) are insufficient to identify opportunistic pathogens in patients with systemic autoimmune rheumatic diseases (SARDs). The study aimed to assess the usefulness of metagenomic next-generation sequencing (mNGS) vs. CMTs for the diagnosis of pulmonary infections in patients with SARDs receiving immunosuppressant therapy.

Methods

The medical records of 40 patients with pulmonary infections and SARDs treated with immunosuppressants or corticosteroids were reviewed retrospectively. Bronchoalveolar lavage fluid (BALF) samples were collected from all patients and examined by mNGS and CMTs. Diagnostic values of the CMTs and mNGS were compared with the clinical composite diagnosis as the reference standard.

Results

Of the 40 patients included for analysis, 37 (92.5%) were diagnosed with pulmonary infections and 3 (7.5%) with non-infectious diseases, of which two were considered primary diseases and one an asthma attack. In total, 15 pathogens (7 bacteria, 5 fungi, and 3 viruses) were detected by CMTs as compared to 58 (36 bacteria, 12 fungi, and 10 viruses) by mNGS. Diagnostic accuracy of mNGS was superior to that of the CMTs for the detection of co-infections with bacteria and fungi (95 vs. 53%, respectively, p < 0.01), and for the detection of single infections with fungi (97.5 vs. 55%, respectively, p < 0.01). Of the 31 patients diagnosed with co-infections, 4 (12.9%) were positive for two pathogens and 27 (87.1%) for three or more. The detection rate of co-infection was significantly higher for mNGS than CMTs (95 vs. 16%, respectively, p < 0.01).

Conclusion

The accuracy of mNGS was superior to that of the CMTs for the diagnosis of pulmonary infections in patients with SARDs treated with immunosuppressants. The rapid diagnosis by mNGS can ensure timely adjustment of treatment regimens to improve diagnosis and outcomes.

Metagenomic next-generation sequencing shotgun for the diagnosis of infection in connective tissue diseases: A retrospective study

Objective

Patients with connective tissue diseases (CTDs) are at high risk of infection due to various reasons. The purpose of the study was to investigate the infection diagnosis value of metagenomic next-generation sequencing (mNGS) shotgun in CTDs to guide the use of anti-infective therapy more quickly and accurately.

Methods

In this retrospective study, a total of 103 patients with CTDs admitted with suspected infection between December 2018 and September 2021 were assessed using mNGS as well as conventional microbiological tests (CMT).

Results

Among these 103 patients, 65 were confirmed to have an infection (Group I) and 38 had no infection (Group II). mNGS reached a sensitivity of 92.31% in diagnosing pathogens in Group I. Moreover, mNGS showed good performance in identifying mixed infection. In all infection types, lung infection was the most common. mNGS also played an important role in detecting Pneumocystis jirovecii, which was associated with low CD4+ T-cell counts inextricably.

Conclusion

mNGS is a useful tool with outstanding diagnostic potential in identifying pathogens in patients with CTDs and conduce to provide guidance in clinical practice.