Integrated analysis of messenger RNA and microRNA in patients with community- acquired pneumonia-associated sepsis

doi:10.21203/rs.3.rs-2755626/v1

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Integrated analysis of messenger RNA and microRNA in patients with community- acquired pneumonia-associated sepsis

https://doi.org/10.21203/rs.3.rs-2755626/v1

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Background: Community-acquired pneumonia (CAP) is defined as an acute lung infection involving the alveoli that occurs in a patient without recent health care exposure. A complication of CAP is severe sepsis, a syndrome of infection often accompanied by systemic inflammation and organ dysfunction. The aim of this study was to evaluate mRNA and miRNA in whole blood and to perform an integrative analysis to assess cellular signals that play a role in the pathogenesis of patients with CAP-associated sepsis.

Methods: This was a prospective, observational, single-center study of patients transported to the Department of Traumatology and Acute Critical Medicine, Graduate School of Medicine, Osaka University. Patients with CAP-associated sepsis were analyzed. The diagnosis of pneumonia was made according to the clinical findings, including blood samples and chest computed tomography scan, and the diagnosis of sepsis followed the Sepsis-3 guidelines.

Results: We included 14 critically ill patients with CAP-associated sepsis and 15 healthy control subjects (HCS). The median ages of the patient group and HCS were 78 and 55 years, and their body mass indexes were 22.8 and 21.7 kg/m², respectively. All patients were treated at the critical care center, and 11 of the 14 patients received ventilatory management. All patients survived. These 14 patients met the diagnostic criteria of Sepsis-3 and were diagnosed as having CAP-associated sepsis. Of them, 6 patients met the diagnostic criteria for septic shock. RNA sequencing showed the number of genes with up:down (upregulated:downregulated) expression variation (false discovery rate [FDR] <0.05, |log2 fold change| >1.2) to be 1209:1461 for mRNA; 51:21 for microRNA; and 646:1274 for miRNA-targeted mRNA. Canonical pathway analysis using mRNA showed activation of the PD-1 and PD-L1 cancer immunotherapy signaling pathways and inhibition of the Th1 signaling pathway as well as that using miRNA-targeted mRNAs.

Conclusions: Using integrated analysis of mRNA and miRNA, we elucidated for the first time, to our knowledge, that T-cell exhaustion occurred during the acute phase of CAP-associated sepsis and that miRNA regulated Th1 signaling and PD-1 and PD-L1 cancer immunotherapy signaling through the RNA interference action of mRNA.

mRNA

miRNA

T-cell exhaustion

Th1 signaling

PD-1 and PD-L1 cancer immunotherapy signaling

Pneumonia is a common acute respiratory infection that affects the alveoli and distal bronchial tree of the lungs [1]. According to the Global Burden of Diseases, Injuries, and Risk Factors Study, lower respiratory tract infections defined as pneumonia or bronchiolitis are a leading cause of morbidity and mortality around the world [2], and in 2019, 489 million people worldwide were affected by lower respiratory tract infections, from which 249 million died [3].

Community-acquired pneumonia (CAP) is defined as an acute lung infection involving the alveoli that occurs in a patient without recent health care exposure [4]. The diagnosis of CAP is based on the presence of select clinical features (e.g., cough, fever, sputum, production, and pleuritic chest pain) and is supported by imaging of the lung, usually by chest radiography [4–6]. One complication of CAP is severe sepsis, the syndrome of infection often accompanied by systemic inflammation and organ dysfunction [7]. In a prospective observational study by the Pneumonia Patient Outcomes Research Team (PORT), Dremsizov et al. reported that among 1339 patients hospitalized for pneumonia, severe sepsis occurred in nearly half of the patients (639 [48%]), with non-pulmonary dysfunction occurring in 520 (39%) and septic shock occurring in 61 (4.5%) patients [8].

In CAP-associated sepsis, the pathogenesis is recognized by germline-encoded pattern recognition receptors (PRRs) that recognize molecular structures known as pathogen-associated molecular patterns (PAMPs). Upon recognizing PAMPs, PRRs activate a critical signaling program that executes the first step of the host’s defense response required to kill the infectious microbes. In addition, PRRs play a role in inducing both dendritic cell maturation and the second step of host defense, adaptive immunity. The first PRRs identified were Toll-like receptors, which secrete inflammatory cytokines, type 1 interferons, chemokines, and antimicrobial peptides to facilitate inflammatory responses [9].

The release of the human genome sequence revealed that protein-coding regions account for only 1–2% of the approximately 3 billion base pairs of the genomic DNA sequence and that most of the human genome is comprised of non-coding regions such as repetitive sequences and introns that do not code for proteins [10]. RNAs include messenger RNAs (mRNAs) that are translated into proteins and non-coding RNAs (ncRNAs) that are not translated into proteins. ncRNAs are considered important regulators in gene expression networks by regulating nuclear structure and transcription in the nucleus and mRNA stability, translation, and post-translational modifications in the cytoplasm [11].

MicroRNAs (miRNAs), a type of ncRNAs, are short, single-stranded RNA fragments of 21–23 bases that contribute to the maintenance of homeostasis, regulation of cellular physiological functions, the cell cycle, differentiation, and apoptosis. miRNAs have been implicated to affect inflammatory and anti-inflammatory immune responses, such as sepsis, diabetes, tumors, chronic obstructive pulmonary disease, asthma, inflammatory bowel disease, and schizophrenia [12].

The aim of this study was to evaluate mRNA and miRNA in whole blood of patients with CAP-associated sepsis and to perform an integrative analysis to assess cellular signals that play a role in the pathogenesis of CAP.

Study design and participants

This was a prospective, observational, single-center study of patients with CAP-associated sepsis transported to the Department of Traumatology and Acute Critical Medicine, Graduate School of Medicine, Osaka University. During the six-month period from August 2020 to February 2021, 471 patients who were hospitalized and treated at the Center, from whom blood was drawn for analysis on the day of admission and their main disease was diagnosed as CAP-associated sepsis, were analyzed. The diagnosis of pneumonia was made according to clinical findings, including blood samples and chest computed tomography scans, and the definition of pneumonia.

The diagnosis of sepsis was in accordance with the Sepsis-3 criteria, i.e., that sepsis is defined as life-threatening organ dysfunction caused by an abnormal host response to infection, with a baseline change in total Sequential Organ Failure Assessment score of 2 or more points representing organ dysfunction. In addition, septic shock requires vasoconstrictors to maintain a mean blood pressure > 65 mmHg despite adequate infusion load, and the blood lactate level is > 2 mmol/L or 18 mg/dL [13]. Patient flow through the study and definitions of CAP and sepsis are shown in Fig. 1.

Clinical data

Clinical data collected from the patients’ electronic medical records by the investigators included age, sex, body mass index (BMI), Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, comorbid conditions (hypertension, diabetes, and hyperlipidemia, among others), and clinical variables (day of extubation, day of weaning off mechanical ventilation, and hospital outcome).

Library preparation and RNA sequencing

Blood for total RNA isolation of leukocytes was obtained from 14 patients with pneumonia on day 1 (admission day), and 15 healthy control subjects (HCS). Total RNA isolation of leukocytes from patients with pneumonia and HCS was performed using a PAXgene™ Blood RNA System (BD Bioscience, San Jose, CA). Full-length cDNA was generated using a SMART-Seq HT Kit (Takara Bio, Mountain View, CA). An Illumina library was prepared using a Nextera DNA Library Preparation Kit (Illumina) according to SMARTer kit instructions. The DNA libraries were then converted to libraries for DNBSEQ using MGIEasy Universal Library Conversion Kit (App-A). Sequencing was performed on a DNBSEQ-G400RS platform (MGI Tech Co., Ltd, Shenzhen, China) in 2×100 bp paired-end mode.

RNA sequence analysis

The RNA sequence (RNA-Seq) analysis was performed as shown in a previous article [14]. The sequenced reads were mapped to the human reference genome sequences (hg19) using TopHat, version 2.1.1, in combination with Bowtie2, version 2.2.8, and SAMtools, version 0.1.18. The fragments per kilobase of exons per million mapped fragments were calculated using Cufflinks, version 2.2.1. Gene-level expression raw read counts for each gene-sample combination were calculated using featureCounts of subread-2.0.0 package.

miRNA sequencing and miRNA-Seq analysis

The analysis was performed as shown in a previous article [14]. Small RNA libraries were constructed following manufacturer instructions using NEBNext Small RNA Library Prep Set for Illumina (New England Biolabs, Ipswich, MA) and sequenced on the NovaSeq 6000 platform (Illumina) in 101-bp single-end reads. Before analysis of the small RNA-Seq data, reads were trimmed the 3' adapter sequence (AGATCGGAAGAGCACACGTCT). Trimmed reads were mapped on miRBase human miRNA dataset and FANTOM5 dataset (Nature Biotechnology volume 35, pages872–878 (2017)) using miRDeep2 software. Quantification was also performed with miRDeep2 software. Relatively small RNA expression levels were calculated using the DESeq algorithm.

Statistical analysis of mRNA and miRNA

The analysis was performed with some modifications as shown in a previous article [14]. We normalized mRNA raw count data using Integrated Differential Expression and Pathway analysis, version 0.96 (iDEP.96). Normalization settings were min. CPM0.5, n libraries3. Multidimensional scaling with the cmdscale command in R was performed to compare expression between the pneumonia patients and HCS. Volcano plot analysis was performed to visualize and identify significant changes in the expression list. In this study, cut-off values were set at a false discovery rate [FDR] < 0.05 and |log2 fold change| >1.2. The raw count data for miRNAs were processed in the same manner. mRNA and miRNA differentially expressed genes were used for the subsequent analysis by Ingenuity Pathway Analysis (IPA) (QIAGEN Inc., https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/). Adjusted P values were calculated using the Benjamini-Hochberg (BH) method with multiple comparison test correction. The z-score predicts the activation state of an upstream regulator using the RNA expression pattern of the downstream state of the regulator. Regular pathways and upstream regulators are considered activated if the |z-score| is > 2, P < 0.05. The expression of predicted miRNA-targeted mRNAs was obtained from IPA using TargetScan, miRecords, and TarBase as miRNA-targeted mRNA expression databases. Canonical pathway analysis (CPA) and upstream regulator analyses were then performed using targeted mRNA expression. For comparison of miRNA gene expression between the severe CAP group and the non-severe CAP group, the presence or absence of septic shock was set as the outcome, respectively, and the Wilcoxon signed-rank test was performed.

Patient characteristics

We included 14 critically ill patients and 15 HCS. The median ages of the patients and HCS were 78 and 55 years, and their BMIs were 22.8 and 21.7 kg/m², respectively. All 14 patients were treated at an advanced critical care center, and 11 of the 14 patients received ventilatory management. All participants survived. These 14 patients met the diagnostic criteria for Sepsis-3 and were diagnosed as having CAP-associated sepsis. Among them, 6 met the diagnostic criteria for septic shock [13]. There were no significant differences between the two groups except for age. The patient characteristics are shown in Table 1.

Table 1

Patient characteristics
Characteristic	Healthy controls	Patients with CAP	P- value
Characteristic	(N = 15)	(N = 14)
Age, years, median (IQR)	55 (27–72)	78 (59–96)	< .001
Sex, male (%)	8 (50)	10 (71.4)	0.33
BMI, kg/m², median (IQR)	21.7 (18.8–29.4)	21.8 (18.8–28.7)	0.911
Comorbidities, n (%)
Diabetes	1 (6.3)	2 (14.3)
Hypertension	2 (12.5)	4 (28.6)
Hyperlipidemia	6 (37.5)	1 (7.1)
Hyperuricemia	1 (6.3)	0 (0)
Chronic heart disease	1 (6.3)	1 (7.1)
Chronic lung disease	0 (0)	1 (7.1)
Chronic kidney disease	0 (0)	0 (0)
Immunocompromised condition	0 (0)	0 (0)
Malignant neoplasm	0 (0)	4 (28.6)
Severity, n (%)
Septic shock	0 (0)	6 (42.9)
Length of hospitalization, days		23 (2–69)
Intubation required, n (%)		11 (78.6)
SOFA score, median (IQR)		5 (3–9)
APACHE II core, median (IQR)		16S (9–26)
IQR interquartile range, BMI body mass index, SOFA Sequential Organ Failure Assessment, APACHE Acute Physiology and Chronic Health Evaluation

Whole blood RNA sequencing

For RNA sequencing, 13916 mRNAs, 1797 miRNAs, and 1920 miRNA-targeted mRNAs were available for analysis. Of these, the number of genes showing up:down (upregulated:downregulated) expression variation (FDR < 0.05, |log2 fold change| >1.2) were 1209:1461 for mRNA, 51:21 for microRNA, and 646:1274 for miRNA-targeted mRNA (Fig. 2A). Multidimensional scaling showed that mRNA, miRNA, and miRNA-targeted mRNA expressions could all be used to distinguish the patient group from the HCS group (Fig. 2B).

Canonical pathway, upregulator, and signaling pathway analysis

To investigate the signaling pathways significantly involved in the pathogenesis of pneumonia, RNA sequencing results were subjected to IPA canonical signaling pathway analysis to screen and list canonical signaling pathways activated in pneumonia. CPA predicted mRNA activation of 30 pathways (the top 10 pathways are shown in Fig. 3A) and inhibition of 33 pathways (the top 10 pathways are shown in Fig. 3B). Among the activation pathways, programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) cancer immunotherapy signaling pathways showed the highest P values (BH adjusted P = 5.2E-01, z-score = 3.266), with 28 mRNAs affected in the PD-1 and PD-L1 cancer immunotherapy signaling pathways (Fig. 3C). Among the inhibited pathways, the Th1 signaling pathway showed the highest P value (BH adjusted P = 7.7E-01, z-score = -2.694), with 37 mRNAs involved in the Th1 signaling pathway (Fig. 3D). CPA of miRNA-targeted mRNAs showed activation of 6 pathways (the top 6 pathways are shown in Fig. 3E) and inactivation of 52 pathways (the top 10 pathways are shown in Fig. 3F).

Among the activated pathways, the PD-1 and PD-L1 cancer immunotherapy signaling pathways showed the highest P value (BH adjusted P = 6.6E-01, z-score = 2.985), with 26 miRNA-targeted mRNAs involved in these pathways (Fig. 3G). Among the inhibited pathways, the Th1 signaling pathway showed the highest P value (BH adjusted P = 8.9E-01, z-score = -3.024), with 34 miRNA-targeted mRNAs involved in the Th1 signaling pathway (Fig. 3H).

We used IPA to analyze upstream regulators. mRNA upstream analysis showed activated:inhibited gene expression values of 937:131 (P < 0.05). The top 20 upregulators of activation (P < 0.05) are shown in Fig. 3I, with the top three being TNF, IL1B, and ID3. The top 20 up-regulators of inactivation were miR-6882-5p, SOX4, and APOE (Fig. 3J). miRNA-targeted mRNAs showed activated:inhibited gene expression values of 1620:39 in the upstream analysis. (P < 0.05). The top 20 up-regulators of activation are shown in Fig. 3K, with the top upregulators being miR-1275, miR-486-3, and miR-4723-5p. The top 20 up-regulators of inactivation are shown in Fig. 3L, with IL7R, N6AMT1, and PAX5 as the top three.

The mRNA-miRNA integration analysis of the PD-1 and PD-L1 cancer immunotherapy signaling pathways and the Th1 signaling pathway predicted by IPA is shown in Fig. 4. PD-1 and PD-L1 cancer immunotherapy signaling involves 540 miRNAs (Fig. 4A), and Th1 signaling was predicted to involve 580 miRNAs each (Fig. 4B). Of these, miR-1275 and miR-3103-5p were consistent with the miRNAs shown in the upstream analysis. Both miRNAs are included in PD-1 and PD-L1 cancer immunotherapy signaling, with the former activating CD80 and the latter inactivating CD247.

Comparison of miRNA gene expression levels between the severe CAP and non-severe CAP groups

The results of the Wilcoxon signed-rank test between the severe CAP-associated sepsis group (with septic shock) and the non-severe CAP-associated sepsis group (without septic shock) showed significant differences for miR-148a-5p, miR-148b-5p, miR-184, miR-192-5p, miR-199a-5p, miR-221-5p, miR-30a-3p, miR-374b-5p, miR-3909, miR-4746-5p, miR-548ae-3p, and miR-6503-3p. All 12 miRNAs were included in the Th1 signaling pathway.

Nine of these 12 miRNAs (miR-148b-5p, miR-184, miR-199a-5p, miR-221-5p, miR-30a-3p, miR-374b-5p, miR-3909, miR-548ae-3p, and miR-6503-3p) were included in the PD-1 and PD-L1 cancer immunotherapy signaling pathway.

In recent years, mRNA-miRNA integration analysis has been conducted for various chronic inflammatory diseases such as breast cancer, primary myelofibrosis, and idiopathic pulmonary fibrosis, and research on miRNAs that regulate pathological conditions is ongoing [15–17]. However, there are few reports for acute inflammatory diseases, and there are no reports on integrated mRNA-miRNA analysis in patients with CAP-associated sepsis to our knowledge. In this study, we performed the first ever integrated analysis of mRNA and miRNA in CAP-associated sepsis and elucidated that miRNA causes T-cell exhaustion through the RNA interference action of mRNA.

Pathogens are recognized by the host innate immune system via an array of PRRs. These PRRs recognize molecules released by both pathogens, i.e., PAMPs and injured cells from the human host, i.e., damage-associated molecular patterns. In most cases of infection, the burden of the pathogen is diminished and eventually eliminated by the innate immune system. However, pathogen persistence will lead to dysregulation of the host immune response, and when the pathogen is persistent, the infection becomes severe, and sepsis occurs due to the poor immune response [18].

Analysis of the expression variation of mRNAs and miRNAs specifically expressed in CAP-associated sepsis compared to the HCS revealed that mRNAs, miRNAs, and miRNA-targeted mRNAs showed differences in gene expression between the HCS and the CAP patient group (Fig. 2A, B). In addition, compared to the HCS, both mRNA and miRNA target mRNA-related signals, the inflammasome pathway, MSP-RON (macrophage-stimulating protein-recepteur d'origine Nantais) Signaling in Macrophages pathway, CREB Signaling in Neuron, and Factors Promoting Cardiogenesis in Vertebrates in the CAP patients. This suggests that systemic inflammatory responses are triggered by the immune response to infectious invasion and that a variety of inflammatory signals may be fluctuating in patients with CAP-associated sepsis.

PD-1 plays an important role in suppressing immune responses and promoting self-tolerance by regulating T-cell activity, activating antigen-specific T-cell apoptosis, and inhibiting regulatory T-cell apoptosis. PD-L1 is a transmembrane protein that is considered a co-inhibitor of immune responses and binds to PD-1 to suppress the proliferation of PD-1-positive cells, inhibit cytokine secretion, and induce apoptosis [19]. The PD-1/PD-L1 pathway also inhibits immune checkpoint [15]. In recent years, activation of anti-tumor immunity by inhibiting immune checkpoints in the PD-1/PD-L1 pathway has been considered a promising approach for cancer therapy, and research is underway [20].

In contrast, PD-1 on T cells and PD-L1 on monocytes have been found to be upregulated in patients with severe sepsis and septic shock. PD-L1 signaling may play an important role in sepsis-induced immunosuppression by acting on exhausted T cells and releasing immunosuppressive molecules [18, 21–23]. Mice in which PD1-PDL1 interaction was inhibited also showed improved survival after induction of sepsis. This suggests that T-cell exhaustion may play a detrimental role [23].

Patients with sepsis have suppressed CD4 + T helper 1 (TH1) cells, TH2 cells, and TH17 cells. Postmortem studies of patients who die of sepsis show both T-cell depletion and signs of T-cell exhaustion [23]. The anti-inflammatory environment of sepsis is also the result of altered immune cell positioning. Suppression of the inflammatory Th1 cell and monocyte responses further enhances the anti-inflammatory environment by reducing the expression of proinflammatory cytokines such as TNF, IL-1β, and IL-6 [18]. Whole blood RNA-Seq analysis in this study showed that PD-1 and PD-L1 cancer immunotherapy signaling was the most activated and Th1 signaling was the most inactivated in the CAP patients compared to the HCS (Fig. 3A, B).

Upstream regulator analysis in IPA showed mRNA expression of TNF, which is involved in PD-1 and PD-L1 cancer immunotherapy signaling, and IL-6, which is involved in Th1 signaling (Fig. 3I, J), suggesting that activation of these two signals is important in the pathogenesis of pneumonia-associated sepsis This suggests that activation of the two signals is important in the pathogenesis of sepsis derived from pneumonia and is consistent with reports that PD-1 activation is accompanied by suppression of cellular functions, especially that of T cells, and inactivation of the Th1 pathway, and that strong T-cell exhaustion occurs during the acute phase of pneumonia.

There are also reports of diverse associations between infections and human leukocyte antigens (HLA), and in sepsis, an association with HLA-DQB1 has been suggested [24]. T-bet (TBX21) and GATA-3, specific transcription factors for Th1 and Th2 cells, have been reported to be significantly lower in septic patients than in normal controls [25].

PD-L1 cancer immunotherapy signaling in mRNA was significantly suppressed in the septic patients compared with the HCS, and in the heat map of the Th1 pathway, the expression of T-bet (TBX21) and GATA-3 expression in patients with CAP-associated sepsis was significantly suppressed compared with the HCS, consistent with these reports (Fig. 2C, D). The same trend can also be observed in miRNA-targeted mRNA, suggesting the involvement of miRNAs in these mechanisms (Fig. 3G, H).

Among the upstream regulators inferred from the upstream analysis, miRNAs included in the PD-1 and PD-L1 cancer immunotherapy signals were miR-1275 and miR-3103-5p. Nine types of miRNAs were also included in the same signal: miR-148b-5p, miR-184, miR-199a-5p, miR-221-5p, miR-30a-3p, miR-374b-5p, miR-3909, miR-548ae-3p, and miR-6503-3p, and these miRNAs were associated with the severity of pneumonia (Fig. 4A). Among others, elevated miR-184 is speculated to play a role as a regulator of oral and lung cancers, and its expression in this signal is consistent with the results of previous reports [26, 27]. It is also considered to be a potential biomarker not only for neoplastic lesions but also for the diagnosis of CAP [28].

There were no miRNAs applicable for upstream analysis in the Th1 signal. Twelve miRNAs in the same signal, miR-148a-5p, miR-148b-5p, miR-184, miR-192-5p, miR-199a-5p, miR-221-5p, miR-30a-3p, miR-374b-5p, miR-3909, miR-4746-5p, miR-548ae-3 p, and miR-6503-3p, were associated with severity of pneumonia (Fig. 4B). Among them, elevated hsa-miR-199-5p was found to be involved in the progression of neoplastic lesions such as lung cancer and oral squamous cell carcinoma [29, 30]. Furthermore, in a mouse model of sepsis, mmu-miR-199-5p correlated with the inflammatory response process of macrophages in innate immunity and surfactant protein D and exacerbates intestinal barrier dysfunction by inhibiting the NF-κB pathway [31, 32]. These findings suggest that elevation of hsa-miR-199-5p may also be associated with inflammation in sepsis.

MiR-192-5p upregulation has also been noted in sepsis-induced myocardial injury and acute kidney injury and was found to be elevated in sepsis cases compared to those of systemic inflammatory response syndrome [33, 34]. This suggests that the elevation of miR-199-5p identified in this study may also be associated with the severity of CAP-associated sepsis.

There are several limitations in this study. First, it is a single-center study, and second, the sample size is relatively small. Third, no classification of pneumonia by the bacteria causing the inflammation was conducted. Last, there is heterogeneity between the septic patients and the HCS. The HCS differed significantly from the septic patients in some aspects, including age.

Using integrated analysis of mRNA and miRNA, we elucidated for the first time ever that T-cell exhaustion occurred during the acute phase of CAP-associated sepsis and that miRNA regulated Th1 signaling and PD-1 and PD-L1 cancer immunotherapy signaling through the RNA interference action of mRNA.

Benjamini-Hochberg

BMI

body mass index

CAP

community-acquired pneumonia

CPA

canonical pathway analysis

FDR

false discovery rate

HCS

healthy control subjects

IPA

Ingenuity Pathway Analysis

miRNAs

microRNAs

mRNAs

messenger RNAs

non-coding RNAs

PAMPS

pathogen-associated molecular patterns

PD-1

programmed death-1

PD-L1

programmed death-ligand 1

PRRs

pattern recognition receptors

Consent to publication

Anonymized information was used, and no identifiable image was included in this study.

Competing interests

The authors declare no competing interests.

Funding

This study was supported by the Japan Agency for Medical Research and Development (grant no. 20fk0108404h0001) and The Nippon Foundation - Osaka University Project for Infectious Disease Prevention.

Authors’ contributions

SO conceived and designed this study, acquired the data, and analyzed and wrote the manuscript. HM helped with designing the study and data interpretation and conducted the literature review. YT, JY, SO, AM and YM contributed to acquiring the data. DO helped analyze the data. HO, ST, and JO conducted the literature review. All authors have read and understood the journal’s policies and believe that neither the manuscript nor the study violates any of these. All authors meet the authorship criteria detailed in the submission guidelines, and all authors agree with the content of the manuscript.

Acknowledgements

We greatly appreciate the patients, family members and healthy volunteers involved in this study. We would also like to thank all medical staff who cooperated with this study.

This study followed the principles of the Declaration of Helsinki and was approved by the institutional review board of Osaka University Hospital (approval no.: 885 [Osaka University Critical Care Consortium Novel Omix Project; Occonomix Project]). Informed consent was obtained from the patients or their relatives and the healthy volunteers for the collection of all blood samples.

Data Availability

The raw data concerning this study were submitted under Gene Expression Omnibus accession numbers GSE228542 for future access.

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No competing interests reported.

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Integrated analysis of messenger RNA and microRNA in patients with community- acquired pneumonia-associated sepsis

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Abstract

Figures

Background

Methods

Study design and participants

Clinical data

Library preparation and RNA sequencing

RNA sequence analysis

miRNA sequencing and miRNA-Seq analysis

Statistical analysis of mRNA and miRNA

Results

Patient characteristics

Whole blood RNA sequencing

Canonical pathway, upregulator, and signaling pathway analysis

Comparison of miRNA gene expression levels between the severe CAP and non-severe CAP groups

Discussion

Limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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