Immune and microbial signatures in immunocompetent and immunocompromised patients with pneumonia

doi:10.21203/rs.3.rs-5225770/v1

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Immune and microbial signatures in immunocompetent and immunocompromised patients with pneumonia

https://doi.org/10.21203/rs.3.rs-5225770/v1

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Background Pneumonia is a common acute respiratory infection that contributes to significant mortality and morbidity worldwide. The disruption of the airway microbiome in respiratory infection has been extensively reported. However, whether the changes in respiratory tract microbial communities during pneumonia were related to disease severity remains elusive. Herein, we aimed to investigate the correlation between the changes in airway microbiome and immune response in pneumonia patients.

Methods We performed metagenomic and metatranscriptomic sequencing on immunocompetent (ICO) and immunocompromised host (ICH) with pneumonia using bronchoalveolar lavage fluid (BALF), blood, sputum, and swab samples.

Results

Compared to ICO patients with pneumonia, ICH patients had higher Pneumonia Severity Index (PSI) score. BALF metagenomic and metatranscriptomic sequencing showed higher microbial diversity in ICH patients, while ICH patients exhibited lower microbial diversity in sputum samples. Additionally, pneumonia patients with different PSI scores exhibited different microbial communities. Relative abundance of Human Gammaherpesvirus 4 (EBV) was positively correlated with PSI score. For ICH patients, BALF metatranscriptomic sequencing found 183 up-regulated genes and 85 down-regulated genes in EBV-detected group compared with EBV not-detected group, while there was no significant difference in ICO patients, indicating that EBV might be reactivated in ICH patients, while EBV might be latent in ICO patients. In ICH patients, we observed significant down-regulation of immune related genes and interferon stimulated genes in EBV-detected group compared to the not-detected group, including CSF1R, CXCR6, IL10, IL16, and TNFRSF25. Co-occurrence network analysis found positive correlations between EBV and Citrobacter freundii or Campylobacter concisus, indicating that synergistic effects on exacerbating the severity of pneumonia might exist between EBV and these two microbes.

Conclusion EBV might be considered as a microbial signature for disease severity, which could regulate immune-related signaling pathways. Notably, we unravel that EBV presence might inhibit the immune response of hosts, reduce anti-inflammatory responses, and increase the possibilities of infections caused by other pathogens, exacerbating the pneumonia severity.

Microbiome

Microbial diversity

Pneumonia

Metatranscriptome

Immune response

Data from the World Health Organization shows that lower respiratory tract infection ranks fourth among the leading causes of death, imposing substantial social and economic burdens[1]. Pneumonia contributes significantly to global mortality and morbidity[2]. Vast differences in respiratory microbial communities between healthy patients and those affected by diseases were found, and the impacts of respiratory tract infection, proliferation of pathogenic bacteria[3–8], activation of Herpesvirus, or yeast colonization[9–12] on reshaping the respiratory tract microbiota have been highlighted. Additionally, the disruption of airway microbiome can also lead to pathogen overgrowth and increased susceptibility to infections[13]. After infections of respiratory viruses, such as influenza, adenovirus, or human rhinovirus, the incidence of bacterial pneumonia rises due to the increased colonization of pathogenic bacteria[14–17], and invasive Streptococcus pneumoniae infection can also be observed in those cases[18].

Activation of Human Gammaherpesvirus 4 (EBV) was associated with a decline in lung function and frequent acute exacerbations[19, 20], particularly in patients with chronic respiratory diseases, such as bronchiectasis and interstitial pneumonia. An increased risk of chronic obstructive pulmonary disease, pulmonary arterial hypertension, and lymphoproliferative disorders was associated with EBV[21–24]. Additionally, EBV plays an important role in the progression of respiratory infections and Candida glabrata has been reported to exacerbate the severity of COVID-19 in critically ill patients[12]. Accordingly, we hypothesized that changes in the respiratory microbial communities in patients with pneumonia could contribute to disease aggravation and poor prognosis.

However, there are few researches on comparing the changes in the overall microbial composition of the respiratory tract between immunocompetent and immunocompromised patients with pneumonia, and the understanding on whether the alterations in microbial communities are related to disease severity and poor prognosis remains limited. To validate our hypothesis, we performed metagenomic and metatranscriptomic sequencing on both immunocompetent and immunocompromised patients with pneumonia to explore the correlation of respiratory microbial communities with disease severity.

Subjects and clinical samples

According to previous study[25], consecutive patients elder than 14 years old with pneumonia admitted to the First Affiliated Hospital of Guangzhou Medical University from April 2019 to January 2020 were enrolled. According to the immune status, the patients were divided into immunocompetent (ICO) group and immunocompromised (ICH) group[25]. This study was approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2019-49). Sputum, bronchoalveolar lavage fluid (BALF), swab, and blood samples were collected for analysis.

Metagenomic and metatranscriptomic sequencing

We conducted metagenomic (metaDNA-seq) and/or metatranscriptomic sequencing (metaRNA-seq) on 339 clinical samples. DNA was extracted using QIAamp® UCP Pathogen DNA Kit (Qiagen). Human DNA was removed using Benzonase (Qiagen) and Tween20 (Sigma). Total RNA was extracted using QIAamp UCP pathogen minikit (Qiagen, Valencia, CA, USA). A total of 1 µL sample was processed with Turbo DNase (Life Technologies, USA) to deplete the host DNA background. RNA reverse transcription and amplification were conducted using Ovation RNA-Seq system (NuGEN, CA, USA). The library was constructed using Ovation Ultralow System V2 (NuGEN, CA, USA) and sequenced on Illumina Nextseq 550 platform[26].

Raw sequencing data was processed using fastp[27] to remove reads containing adapters or ambiguous “N” nucleotides and low-quality reads. All clean data were mapped to the human genome hg38 using HISAT2[28]. FeatureCounts[29] was used to quantify gene expression. Normalization and differential expression analyses were performed using the DESeq2 package[30]. Differentially expressed genes (DEGs) were determined with the adjusted p-value of < 0.05 and absolute logged fold-change (log2FC) of ≥ 2. The clusterProfiler package[31] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The immune-related genes (IRGs) list was downloaded from the Immport database[32]. According to the gene expression profile of IRGs, the Kruskal-Wallis test was used to select differential genes for further visualization using pheatmap package[33]. To infer the composition of immune cells, the CIBERSORT algorithm[34] with the original CIBERSORT gene signature file LM22 and 1000 permutations were used to examine the relative proportions of 22 invasive immune cell types.

Microbial taxonomy assignment and host-microbial network interaction

Taxonomy assignment of upper respiratory microbiome was done by Kraken 2[35]. Relative abundance was calculated using Bracken (version 2.5). Wilcoxon rank sum tests were used to evaluate the differences in α-diversity and species richness[26]. For β-diversity analysis, the principal coordinate analysis scaling (PCoA) plot was based on Jensen-Shannon divergence[36]. Non-metric multidimensional scaling (NMDS) and PERMANOVA test were used to determine the overall community composition differences. To elucidate the microbes and host co-occurrence patterns, species with frequency of more than 40% were kept for correlation calculation using the SparCC algorithm[37]. Only significant (P value < 0.05) and robust (SparCC |r| > 0.3) correlations were retained for further co-occurrence network analyses. Visualization of networks was implemented using Cytoscape[38].

Patient characteristics

A total of 115 patients with pneumonia were enrolled and divided into ICH group (n = 64) and ICO group (n = 51), respectively, according to the criteria we previously reported[25] (Table 1). Among ICH patients, hematological malignancy (34%) was the most common comorbidity, followed by solid organ malignancy (25%), immunosuppressive treatment (14%), and hematopoietic cell transplantation (14%). The median ages of ICH and ICO patients were 50 and 44 years old, respectively. A total of 339 specimens were collected from all patients. In detail, 32 BALF, 55 blood, 39 sputum, and 58 swab samples were collected from ICH patients, while 27 BALF, 43 blood, 41 sputum, and 44 swab specimens were collected from ICO patients.

Table 1

Demographic data
Clinical features		ICH	ICO
Number of cases		64	51
Sex, female		24(38%)	24(47%)
Age (mean ± SD)		50 ± 19	44 ± 20
ICH type	Solid organ transplantation	3 (5%)	NA
	Hematopoietic cell transplantation	9 (14%)	NA
	Solid-organ malignancy	16 (25%)	NA
	Hematopoietic malignancy	22 (34%)	NA
	Immunosuppressive treatment	9 (14%)	NA
	Other types of immunodeficiency disease	5 (8%)	NA
PSI score	1	2 (3%)	27 (53%)
	2	16 (25%)	17 (33%)
	3	26 (41%)	4 (8%)
	4	14 (22%)	2 (4%)
	5	6 (9%)	1 (2%)
Sample type	Balf	32	27
	Blood	55	43
	Sputum	39	41
	Swab	58	44
PSI, Pneumonia Severity index; Balf, bronchoalveolar lavage fluid; EBV, Epstein-Barr virus.

Microbial composition and diversity

Firstly, to characterize the microbial communities of ICH and ICO patients with pneumonia, the relative abundances of microbes in BALF and sputum samples were analyzed. For BALF samples, Mycoplasma pneumoniae, Pseudomonas aeruginosa, Prevotella melaninogenica, Human adenovirus 55, Rothia mucilaginosa, and Pneumocystis jirovecii were detected in both ICH and ICO patients (Figure S1A). Lower relative abundance of M. pneumoniae and higher relative abundance of P. jirovecii were found in ICH patients than in ICO patients (Figure S1B), which was consistent with our previous findings[25]. For sputum samples, the microbiota was mainly consisted of R. mucilaginosa, Lautropia mirabilis, P. melaninogenica, Veillonella parvula, Streptococcus I P16, and M. pneumoniae (Figure S1C). Compared with ICO patients, relative abundances of P. melaninogenica, M. pneumoniae, and V. dispar were lower in ICH patients (Figure S1D).

Both BALF metaDNA-seq data (Figs. 1A and 1B) and BALF metaRNA-seq data (Figs. 1C and 1D) revealed that ICH patients had higher richness and shannon indexes than ICO patients. Additionally, NMDS analysis using both BALF metaDNA-seq (Fig. 1E) and metaRNA-seq (Fig. 1F) data found clear variation in the microbiota profiles between ICH and ICO groups. However, both sputum metaDNA-seq data (Figs. 1G and 1H) and metaRNA-seq data (Figs. 1I and 1J) showed that ICH patients exhibited a significantly lower shannon index than ICO patients. Besides, both NMDS (Fig. 1K) and PCoA (Fig. 1L) showed that the total microbial compositions were much different between ICH and ICO groups.

Microbial diversity and Pneumonia Severity Index (PSI) score

PSI score is widely used for evaluating pneumonia severity, and the higher the value, the more serious the disease. The PSI score of the majority of ICH patients (n = 26, 41%) was 3, while patients with PSI score of 1 accounted for 53% (n = 27) of the ICO patients (Figure S2). Compared with patients with lower PSI scores, lower relative abundance of M. pneumoniae and higher relative abundance of P. jirovecii were found in BALF samples of patients with higher PSI scores, while lower relative abundance of P. melaninogenica and higher relative abundances of A. baumannii, Corynebacterium striatum, Streptococcus infantis, and C. glabrata were found in sputum samples (Figure S3).

The correlation between microbial diversity and PSI score was further assessed using metaDNA-seq data. For BALF samples, both richness and shannon indexes were positively correlated with PSI score (Figs. 2A and 2B). When PSI score was ≥ 2, the higher the PSI score was, the lower the richness (Fig. 2C) and shannon indexes were (Fig. 2D) in sputum samples. Furthermore, NMDS analysis using BALF metaDNA-seq data found a clear distance in the microbiota profiles between the patients with PSI score of 1 and the others (2, 3, or 4) (Fig. 2E), while microbial composition of patients with PSI score of 5 was different from that of patients with other PSI scores using sputum samples (Fig. 2F).

EBV and disease severity

Microbes with average relative abundance of > 1% detected by metaDNA-seq were selected to evaluate the association between microbial abundance and PSI score. It was observed that the relative abundances of EBV and M. pneumoniae in BALF samples, Human betaherpesvirus 5 (CMV) in blood samples, C. striatum, EBV, M. pneumoniae, and P. jirovecii in sputum samples, and A. graevenitzii, EBV, P. melaninogenica, P. nanceiensis, and S. infantis in swab samples were significantly correlated with PSI score, respectively (Fig. 3A). Positive correlations between EBV abundance and PSI score in sputum and swab samples were also found using metaRNA-seq data (Fig. 3B). Moreover, EBV abundances in ICH patients were notably higher than those in ICO patients (Figure S4).

Given the strong correlation between EBV abundance and PSI score, we further divided the enrolled patients into EBV-detected (samples, n = 112) and EBV not-detected (samples, n = 227) groups (Figure S5). ICO patients with EBV detection by BALF metaDNA-seq had high PSI scores and disease severity (Fig. 4A). Additionally, EBV detected by sputum and swab metaDNA-seq was significantly associated with high PSI score and increased disease severity (Figs. 4C and 4D). However, using BALF metaDNA-seq data, there was no significant correlation between EBV and disease severity in ICH patients (Fig. 4A), and a same trend was found in both ICO and ICH patients using blood metaDNA-seq data (Fig. 4B).

A slight difference in microbial communities was observed between EBV-detected and not-detected groups, manifested by higher relative abundance of L. mirabilis in BALF samples and lower relative abundances of M. pneumoniae and S. infantis in sputum samples, compared to EBV not-detected samples (Figure S6). Additionally, reactivated EBV has been extensively reported to regulate the host immune response and microbial composition to promote viral persistence[39–41]. The aforementioned results raised a question that whether EBV was reactivated in ICH and ICO patients. We hypothesized that combined with other microbes, EBV might play an important role in exacerbating pneumonia.

Host response

MetaRNA-seq data were further analyzed (Figs. 5 and S7). The expression of 801 genes were significantly changed in EBV-detected group compared to EBV not-detected group (Figure S7 and Table S1). For sputum samples, there were 670 up-regulated genes and 94 down-regulated genes in EBV-detected ICH patients compared with EBV not-detected group, while 157 genes and 245 genes were respectively up-regulated and down-regulated in ICO patients (Fig. 5A). For swab samples, few genes were significantly up-regulated or down-regulated in both ICH and ICO patients (Fig. 5B). For BALF samples, there were 183 up-regulated genes and 85 down-regulated genes in EBV-detected ICH patients compared with EBV not-detected group, while no significant differences were found between EBV-detected and EBV not-detected ICO patients (Fig. 5C). The above findings indicate that EBV might be reactivated in ICH patients, while EBV might be latent in ICO patients.

KEGG enrichment analysis of immune related genes (IRGs) using BALF samples (Fig. 5D) identified 18 differential pathways in ICH patients. Cytokine-cytokine receptor interaction had 30 DEGs, followed by Chemokine signaling pathway (8 DEGs) and JAK-STAT signaling pathway (7 DEGs). Furthermore, 33 IRGs (Table S2) and 40 interferon stimulated genes (ISGs) were differentially expressed (Table S3). A total of 33 IRGs were differentially down-regulated in ICH patients. CSF1R, CXCR6, IL10, IL16, and TNFRSF25 were significantly down-regulated in ICH patients. Additionally, the expression of 37 ISGs were significantly down-regulated in ICH patients, while CNP and GEM were significantly down-regulated in ICH patients.

For innate immune response, the macrophage M0 and neutrophils accounted for the majority of the cells in both ICH and ICO patients (Fig. 5E). The ratios of neutrophils, monocytes, and dendritic cells activated in EBV-detected ICH patients were lower than those in EBV not-detected ICH patients. For adaptive immune response, high ratio of T cells CD4 memory resting was detected in both ICH and ICO patients, followed by NK cells resting (Fig. 5E). Additionally, the ratios of T cells CD4 memory resting and T cells CD4 memory activated were lower in EBV-detected groups than in not-detected groups in both ICH and ICO patients. Hence, we hypothesized that presence of reactivated EBV might inhibit the immune response of hosts to increase the possibilities of infections caused by other pathogens, exacerbating the pneumonia severity in EBV-detected group.

To verify our hypothesis, co-occurrence network was further constructed to evaluate the relationship between EBV and other microorganisms. We found that EBV had strong correlations with other 48 microbial species, most of which were negative (blue line) (Fig. 6 and Table S4). Positive correlations between EBV and Citrobacter freundii or Campylobacter concisus were found, indicating that synergistic effects on exacerbating the severity of pneumonia might exist between EBV and these two microbes.

In this study, ICH patients exhibited higher alpha diversity in BALF samples and lower alpha diversity in sputum samples compared with ICO patients. Additionally, EBV was shown to be more abundant in ICH patients than in ICO patients. The detection of EBV could predict high disease severity. More DEGs were found by metaRNA-seq in ICH patients than in ICO patients, from which 33 IRGs and 40 ISGs were identified. Additionally, co-occurrence network showed strong correlations between EBV and other 48 microbial species.

Compared with ICO hosts, ICH patients are more likely to encounter with failures in timely administration of targeted antimicrobial drugs and even with higher mortality rate owing to atypical manifestations of pneumonia[42]. Lungs are accompanied with microbial succession during the progression of diseases[43–45]. In our study, we found that pneumonia patients with higher PSI score had more abundant microbiota in BALF samples, which was inconsistent with that community diversity would decrease in patients with definite pneumonia[46–50]. While, no significant differences in lung microbiota between HIV-infected and HIV-uninfected groups were observed[51]. The aforementioned results indicate that in-depth researches are needed to investigate the microbial diversity in pneumonia patients.

We demonstrated that EBV abundance in BALF samples was positively correlated with pneumonia severity. EBV infects nearly all individuals during their lifetime and persists for the duration of the individual’s life[52]. Although the carriage rate of EBV is up to 90% in adult population, the majority of people show no clinical manifestations[53]. However, it can be reactivated and proliferated in elder or ICH individuals[53–56], including patients with a variety of autoimmune diseases or cancers[57], with unsatisfactory outcomes. EBV reactivation in critically ill patients may contribute to prolonged symptoms and high mortality rates[58–60]. Coinfection with EBV increased the risk of prolonged symptoms in M. pneumoniae pneumonia[61], and was associated with fever and increased inflammation in COVID-19 patients[62]. Accordingly, the above results further indicate that EBV might be reactivated in ICH patients.

Compared with EBV not-detected group, all of IRGs identified were significantly down-regulated in EBV-detected ICH patients. The down-regulated CCL23, XCR1, CXCR6, IL16, and XCL1[63–69] would result in reduced recruitment of immune cells to the infection sites. TNFRSF25 and TNFSF4 related to mature or functional activation of immune cells[70, 71] were significantly down-regulated. Notably, significant down-regulation of CSF1R and IL10 was also found. CSF1R is closely associated with the polarization of macrophage from M0 type to M2 type, regulating inflammation levels by inhibiting the release of inflammatory factors[72]. The down-regulation of CSF1R might be associated with the increased pneumonia severity in EBV-detected ICH patients. Additionally, BCRF1 encoded by EBV is highly homologous with IL10, which can mediate B cell activation and is an important suppressor of inflammation[73]. Feedback regulation caused by EBV infection might contribute to the down-regulation of IL10, inhibiting inflammatory responses. Accordingly, we proposed that the presence of EBV might not only reduce the immune responses of host but also reduce anti-inflammatory responses, exacerbating the pneumonia severity in EBV detected ICH patients.

Similar to IRGs, most of ISGs identified were significantly down-regulated in EBV-detected ICH patients. Most of these ISGs can induce immune cell differentiation, regulate interferon secretion, and maintain cell structure[74–79]. However, the roles of these ISGs in defending against infection are unclear, potentially linked to tissue repair processes post cell damage. Down-regulation of these genes indicated that patients with EBV infection may need an extended recovery period. Emerging studies have demonstrated that distinctive microbial communities in respiratory tract exhibit effects on defending against viral infections[18, 80–82]. Immune responses to respiratory tract infection were associated with changes in microbial composition, potentially affecting subsequent immune function against secondary bacterial infection[18, 82, 83]. We also observed positive correlations between EBV and C. freundii or C. concisus, both of which are known to cause pulmonary infection. Given the pneumonia severity and the significant down-regulation of IRGs and ISGs in ICH patients with EBV presence, we unravel that EBV reactivation occurs in ICH patients with severe pneumonia, simultaneously exacerbating pneumonia severity alongside other pathogens.

Finally, we acknowledge that a significant limitation of our study is that some patients had received ineffective treatment prior to enrollment, which may have resulted in a disruption of microbial communities. Another limitation is that the clinical parameters related to immune responses, such as inflammatory cytokines and macrophages along with follow-up data, were not collected from all patients in practice, hampering us to confirm our findings about “microbiome-immune response-host” in this study and to investigate the potential of EBV in predicting prognosis.

We demonstrated that pneumonia patients with more severe conditions may have richer microbiota in BALF samples but lower microbial diversity in sputum samples. Additionally, EBV abundance was positively correlated with disease severity. Further metatranscriptomic analysis indicated that EBV might be considered as a microbial signature for disease severity, which could directly alter the airway microbiome, regulate immune-related signaling pathways and expression of IRGs to reduce anti-inflammatory responses, and increase the possibilities of infections caused by other pathogens.

BALF Bronchoalveolar lavage fluid

ICO Immunocompetent

ICH Immunocompromised host

PSI Pneumonia Severity Index

EBV Human Gammaherpesvirus 4

CMV Human betaherpesvirus 5

IRGs Immune related genes

DEGs Differentially expressed genes

ISGs Interferon stimulated genes

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

NMDS Non-metric multidimensional scaling

PCoA Principal coordinate analysis

Ethics approval and consent to participate

This study was approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2019-49), and written informed consent was obtained from all subjects or their guardians.

Consent for publication

Not applicable.

Availability of data and materials

Sequencing data were deposited in the China National GeneBank DataBase under project CNP0004620, which are publicly accessible at https://db.cngb.org/mycngbdb/submissions/project.

Competing interests

The authors declare that they have no conflict of interest.

Funding

This study was supported by the grant of State Key Laboratory of Respiratory Disease (SKLRD-Z-202224) and the China Postdoctoral Science Foundation(2023M730801)

Authors’ contributions

Y. Zhan, J. Yang and S. Li designed the experiments, and wrote the first manuscript. J. Zhou, B. Hu, Q. Du, Z. Li, W. Sun, Z. Yang, R. Chen, and F. Ye contributed to conducting the experiments and writing the final manuscript.

Acknowledgements

Not applicable.

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Immune and microbial signatures in immunocompetent and immunocompromised patients with pneumonia

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Abstract

Figures

Background

Methods

Subjects and clinical samples

Metagenomic and metatranscriptomic sequencing

Microbial taxonomy assignment and host-microbial network interaction

Results

Patient characteristics

Microbial composition and diversity

Microbial diversity and Pneumonia Severity Index (PSI) score

EBV and disease severity

Host response

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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