Prospective multicenter study identifying prognostic biomarkers and microbial profiles in severe CAP using BALF, blood mNGS, and PBMC transcriptomics

Categories: Disease & Virus

May 9, 2025

Baseline characteristics of enrolled patients

A total of 89 patients were enrolled in our study, comprising 14 with mild CAP and 75 with SCAP, of whom 32 SCAP (42.67%, 32/75) patients died during the 30-day follow-up period. Their demographic and clinical characteristics are detailed in Table 1. Patients in the SCAP (death) group were older than those in the SCAP (survivor) group (69.00 ± 15.64 vs 58.56 ± 17.57, P = 0.0095). Compared to mild CAP patients, a higher proportion of males was observed in both the SCAP (survivor) (86.05% vs 42.86%, P = 0.0011) and SCAP (death) (75% vs 42.86%, P = 0.0352) group, although gender was not significantly correlated with SCAP prognosis. Interestingly, allergic history was more prevalent in SCAP (death) group compared to both mild CAP (28.13% vs 15.38%) and SCAP (survivor) patients (28.13% vs 9.52%, P = 0.0372). The incidences of comorbidities (93.75% vs 61.54% P = 0.0069) and cerebrovascular disease (31.25% vs 7.69%, P = 0.0956) were higher in SCAP (death) group than in mild CAP cases. Additionally, SCAP (death) patients had a higher rate of cerebrovascular disease (31.25% vs 7.14%, P = 0.0022) compared to SCAP (survivor) patients, suggesting it may be an indicator of worse outcomes in SCAP patients. Both the SCAP (survivor) (36.59% vs 0%, P = 0.0080) and SCAP (death) (33.33% vs 0%, P = 0.0140) groups were more likely to use non-invasive mechanical ventilation (NIPPV) compared to the mild CAP group.

Table 1 Demographic and clinical characteristics of the 89 patients enrolled with mild CAP or SCAP.

Laboratory findings at admission

As shown in Table 2, blood routine, blood biochemistry, blood electrolytes, arterial blood gas (ABG), coagulation indicators, serum immunoglobulin, inflammatory indicators, and other clinical laboratory indicators of CAP and SCAP patients were collected upon admission. As the severity of CAP increased [mild CAP → SCAP(survivor) → SCAP(death)], the levels of white blood cell count and neutrophil count, lactic acid in ABG, alanine aminotransferase (ALT), and aspartic transaminase (AST), CRP first increased and then decreased, while lymphocyte count and total protein (TP) continuously declined, P < 0.05. Additionally, laboratory indicators, including fibrin degradation product (FDP), D-dimer, PRO-BNP, CK-MB, Myoglobin, serum IgE, and β-D-1, 3-glucan (fungus) were positively correlated with the severity of CAP and poor prognosis, P < 0.05. SCAP patients had a lower level of eosinophil count, Ca2 + and serum IgM than mild CAP patients, but this indicator had no significant association with SCAP prognosis. SCAP (death) had more hypersensitive troponin I than SCAP (survivor) group.

Table 2 Laboratory Findings at Admission.

Association between CAP severity and pathogen detection rates by mNGS and CMTs

BALF mNGS, blood mNGS, and CMTs were all performed in mild CAP (n = 13), SCAP (survivor) (n = 40), SCAP (death) (n = 24) to compare the detection performance (Fig. 1). The comparison of the positive rate of pathogens in mild CAP and SCAP patients using blood and BALF mNGS or CMTs is showed in Fig. 2 and Appendix Table 1. Based on BALF mNGS data, SCAP (survivor) patients had a higher positive rate of bacteria compared to both mild CAP (82.5% vs 53.85%, P = 0.037) and SCAP (death) patients (82.5% vs 58.33%, P = 0.0341). In contrast to the above results, the positive rates of bacteria detected by CMTs increased with CAP severity [mild CAP vs SCAP (survivor) vs SCAP (death): 23.08% vs 42.5% vs 62.5%]. The detection rate of fungi by CMTs (7.69% vs 20% vs 29.17%) or BALF mNGS (15.38% vs 42.5% vs 50%) also showed a gradual increase as CAP severity worsened. Furthermore, the detection rate of fungi by blood mNGS was low and showed no significant differences among the three CAP groups (7.69% vs 7.5% vs 8.33%). The positive rate of DNA viruses detected by blood mNGS increased with disease severity (61.54% vs 80% vs 91.67%), whereas the rate of DNA viruses detected by BALF mNGS only increased in SCAP (death) patients (69.23% vs 67.5% vs 87.5%). In summary, SCAP patients, particularly SCAP (death) patients had higher rates of bacteria, fungi and DNA viruses according to a comprehensive analysis of BALF mNGS, blood mNGS, and CMTs data.

Comparison of detection performance: BALF mNGS, blood mNGS, and CMTs

As shown in Fig. 2 and Table 3, BALF mNGS significantly improved bacteria, fungi, and DNA virus detection in SCAP (survivor) patients compared to CMTs. When comparing the performance of mNGS across different sample types, we found that BALF mNGS detected more bacteria in both SCAP (survivor) (82.5% vs 47.5%, P = 0.001) and SCAP (death) patients (58.33% vs 33.33%, P = 0.0822) than blood mNGS. Similarly, the detection rate of fungi by BALF mNGS was also much higher than by blood mNGS in both SCAP (survivor) (42.5% vs 7.5%, P = 0.0003) and SCAP (death) (50% vs 8.33%, P = 0.0015) groups. The detection performance of BALF mNGS on DNA virus was much better than that of CMTs, but slightly worse than blood mNGS. Blood mNGS might be more suitable for the detection of DNA viruses than BALF mNGS, particularly in SCAP (death) patients (87.5% vs 67.5%, P = 0.0322), but it had poor detection efficiency for fungi in all CAP patients. It is disappointing that the combined application of BALF and blood mNGS did not exhibit better performance than BALF mNGS alone for detecting bacteria and fungi among CAP or SCAP patients. In conclusion, pathogen detection rates were influenced by both the testing method and disease severity stratification. BALF mNGS and CMTs significantly improved pathogen detection rates in CAP and SCAP patients. Given the different sample sources and patient conditions, the varying detection rates between methods were expected.

Table 3 Comparison of pathogen detection performance between DNA + RNA mNGS in BALF and blood samples versus CMTs among mild CAP, SCAP (survivor), SCAP (death) patients.

Different pathogen spectra in mild CAP, SCAP (survivor), and SCAP (death) patients

As shown in Fig. 3 and Appendix Table 2, the pathogen spectra identified in mild CAP, SCAP (survivor), and SCAP (death) patients varied across different diagnostic methods (BALF mNGS, blood mNGS, and CMTs). In mild CAP patients, BALF mNGS detected a broader range of bacteria, including Acinetobacter baumannii (23.08%) and Klebsiella pneumoniae (15.38%), compared to blood mNGS and CMTs. Among SCAP (survivor) patients, K. pneumoniae and A. baumannii were most frequently identified, with BALF mNGS showing the highest detection rates. In SCAP (death) patients, BALF mNGS revealed a higher prevalence of bacteria such as A. baumannii (20.83%) and Corynebacterium striatum (20.83%), while blood mNGS and CMTs detected K. pneumoniae (20.83%) and A. baumannii (25%) with a slightly different distribution.Fungal and viral spectra also differed by disease severity. In SCAP (survivor) patients, Candida glabrata (17.5%) and Candida albicans (15%) were the most common fungi detected by BALF mNGS, whereas in SCAP (death) patients, C. albicans (29.17%) and Pneumocystis jiroveci (16.67%) were predominant. Viral detection showed that Epstein-Barr virus (EBV) and Torque teno virus (TTVs) were the most frequent in both SCAP groups, with higher detection rates in blood mNGS compared to BALF mNGS.

DNA + RNA mNGS analysis identified significant pathogen-associated risk factors for severe CAP and poor prognosis (Table 4). In BALF samples, P. jiroveci (16.67% vs 2.5%, P = 0.0409) was more prevalent in fatal cases. Human cytomegalovirus (HCMV) in blood samples showed a higher detection rate in severe CAP cases (29.17% vs 7.69%, P = 0.0982). C. striatum, C. albicans, EBV, and TTVs had higher detection rates in severe cases, but these differences were not statistically significant. The results for DNA mNGS and RNA mNGS are provided separately in Appendix Table 2. Additionally, the information about co-infection conditions, read counts, and other details can be found in Appendix Tables 5–7.

Table 4 Pathogen-Associated Risk Factors for Severe CAP and Prognosis: An RNA + DNA mNGS Analysis of BALF and Blood Samples.

Concordance of mNGS with CMTs in mild CAP and SCAP patients

The positive and negative concordance rates between mNGS and CMTs are presented in Appendix Table 3. Concordance here refers to the agreement between mNGS and CMT in detecting pathogens. BALF mNGS exhibited a higher positive concordance rate with CMTs compared to blood mNGS across all three CAP groups, however, it demonstrated a lower negative concordance rate in SCAP patients relative to blood mNGS. The positive concordance rates of BALF mNGS with CMTs were 100%, 76.47%, 66.67% for bacterial detection, and 100%, 62.50%, 57.14% for fungal detection in mild CAP, SCAP (survivor), SCAP (death) patients, respectively. Additionally, the negative concordance rates of BALF mNGS to CMTs were 60%, 13.04%, 55.56% for bacterial detection and 91.67%, 65.71%, 52.94% for fungal detection across three groups. In summary, BALF mNGS demonstrates greater sensitivity in pathogen detection, albeit with reduced specificity in severe cases.

Transcriptomic differences among mild CAP, SCAP (survivor), and SCAP (death) patients

PBMC from all mild CAP, SCAP (survivor), and SCAP (death) patients at admission were used for bulk RNA-sequencing to identify potential biomarkers of CAP severity and prognosis (Fig. 1). As illustrated in Fig. 4, DEGs were analyzed through pairwise comparisons of the CAP groups. In SCAP (death) versus mild CAP, 261 DEGs were up-regulated and 170 were down-regulated, while SCAP (survivor) had only two up-regulated DEGs. Comparisons between SCAP (death) and SCAP (survivor) revealed three up-regulated and one down-regulated DEG. FOLR3 (Folate Receptor 3) and ITGA7 (integrin subunit alpha 7) were up-regulated DEGs in both SCAP groups compared to mild CAP patients, with log2FoldChange values of 4.41 and 3.39 for FOLR3 and 3.02 and 2.89 for ITGA7, respectively (adjusted P-value < 0.001). The expression of OTOF (Otoferlin), SIGLEC1 (Sialic Acid Binding Ig Like Lectin 1), CXCL10 (C-X-C motif chemokine ligand 10), and MS4A4A (Membrane Spanning 4-Domains A4A) was significantly elevated in SCAP (death) compared to both mild CAP and SCAP (survivor). Cyclin A1(CCNA1) expression was higher in SCAP (death) compared to SCAP (survivor) and mild CAP but did not differ between SCAP (survivor) and mild CAP. In conclusion, FOLR3 and ITGA7 may serve as biomarkers of CAP severity, while OTOF, SIGLEC1, MS4A4A, and CXCL10 may be valuable for predicting both disease severity and poor outcomes in CAP.

DEGs-based GO and KEGG pathway enrichment analysis

To identify key biological or molecular processes influencing CAP severity and prognosis, the identified DEGs were used to perform gene functional pathway enrichment analysis (Figs. 5 and 6). GO analysis was divided into three categories: biological process (BP), cellular component (CC), and molecular function (MF). The top up-regulated GO pathways in SCAP (survivor) patients compared to mild CAPs included folic acid transport, leukocyte migration, external side of plasma membrane, folic acid binding, and signaling receptor activity (Fig. 5A). Compared to mild CAP patients, the commonly up-regulated GO pathways in SCAP (death) primarily involved inflammatory response, extracellular region, collagen-containing extracellular matrix, extracellular space, extracellular matrix organization and RNA nuclease activity. The main down-regulated GO pathways included positive regulation of natural killer cell mediated cytotoxicity, adaptive immune response, T cell activation, immune response, external side of plasma membrane, and MHC class I protein complex binding (Fig. 5B, C). In addition, GO functional pathways, including cellular response to interleukin-17, endothelial cell activation, plasma membrane raft, CXCR3 chemokine receptor binding, and virion binding were up-regulated in SCAP(death) compared to SCAP (survivor) patients (Fig. 5D), while the down-regulated pathway was the response to corticosterone.

The most impacted pathways associated with these common DEGs were also identified based on KEGG databases. KEGG pathways such as antifolate resistance, ECM-receptor interaction, and focal adhesion were identified as activated and played crucial roles in SCAP (survivor) compared to mild cases (Fig. 6A). Complement and coagulation cascades, nitrogen metabolism, and ECM-receptor interaction were the top up-regulated KEGG pathways enriched in SCAP (death) patients compared to mild cases, while the top down-regulated KEGG pathways included hematopoietic cell lineage, cell adhesion molecules, antigen processing and presentation, primary immunodeficiency, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway (Fig. 6B, C). The up-regulated genes in SCAP (death) patients were mainly associated with Epstein-Barr virus infection, RIG-I-like receptor signaling pathway, and cytosolic DNA-sensing pathway when compared to mild patients (Fig. 6D). To further substantiate the conclusions of the pathway enrichment analysis, detailed statistical data, including p-values, adjusted p-values, gene ratios, and background ratios, have been compiled in the Appendix Tables 8 and 9.

Gene set enrichment analysis (GSEA) of KEGG and GO pathways

GSEA KEGG pathway analysis, as shown in Appendix Table 10, revealed significant pathway enrichment in SCAP (death) and SCAP (survivor) groups compared to mild CAP. Immune-related pathways, including complement activation and neutrophil extracellular trap formation, were enriched in both groups, underscoring the crucial role of immune activation in severe pneumonia. In the SCAP (death) group, pathways related to energy metabolism, such as oxidative phosphorylation and lysosome function, were significantly upregulated, indicating increased cellular energy demand and stress response. In contrast, the SCAP (survivor) group showed downregulation of immune-related pathways, such as T cell receptor signaling and FoxO signaling, pointing to the importance of immune suppression and metabolic regulation for survival. The upregulation of oxidative phosphorylation, protein synthesis, and immune regulation pathways in SCAP (death) links metabolic dysregulation and excessive immune activation to mortality.

GSEA GO analysis of SCAP (death) vs. mild CAP revealed immune suppression and metabolic dysregulation as key features of severe pneumonia, including downregulation of T cell receptor signaling and T cell co-stimulation, and upregulation of proton-driven ATP synthesis, highlighting the role of immune decline and metabolic imbalance. The death group also showed significant upregulation in stress response pathways such as complement activation and protein folding, while downregulation of RNA transport and chromatin remodeling suggests immune and repair dysfunction as key factors in mortality. In SCAP (survivor) vs. mild CAP, the survivor group exhibited significant enrichment in acute phase response and cell cycle pathways, indicating the crucial role of immune regulation and cell repair for survival. Upregulation of low-density lipoprotein binding and carrier receptor activity supports energy supply for survival. Comparing SCAP (death) with SCAP (survivor), the death group showed significant upregulation of key pathways related to energy metabolism and immune response, such as proton-driven ATP synthesis, mitochondrial electron transport, and aerobic respiration, suggesting metabolic dysregulation as a direct cause of mortality. Excessive immune activation, particularly complement activation and telomere organization, points to immune overactivation accelerating death.

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