Integrative Multi-Omics Analysis Unravels the Host Response Landscape and Reveals a Serum Protein Panel for Early Prognosis Prediction for ARDS

doi:10.21203/rs.3.rs-4368726/v1

Background

Multidimensional biological mechanisms underpinning Acute Respiratory Distress Syndrome (ARDS) continue to be elucidated, and novel early biomarkers for ARDS prognosis remain to be identified.

Methods

We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control (DC) and healthy control (HC) groups. 28-day prognosis biomarkers of ARDS were screened by the LASSO method, fold change, and Boruta algorithm in the discovery cohort. We verified the serum candidate biomarkers by Parallel Reaction Monitoring (PRM) targeted Mass Spectrometry (MS) on an external validation cohort. Machine learning models were applied to explore the biomarker panel of ARDS prognosis.

Results

In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, the distinct proteomic and metabolic signatures can differentiate ARDS from both control groups. Pathway analysis identified the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. Within this pathway, MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions. Additionally, the metabolite sphingosine 1-phosphate (S1P) was found to be closely associated with ARDS and its prognosis. Our research further highlights essential pathways driving deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In which, GAPDH and ENO1, the critical enzymes in glycolysis, showed the largest interaction degree in protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), as confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy over the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008).

Interpretation

Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, which can provide clues relevant to the prognosis of individuals with ARDS.

Proteomics

Metabolomics

Multi-omics

ARDS

Machine learning

Prognosis model

ARDS represents a prevalent manifestation of critical illness, arising from severe infections, major injuries, or the inhalation of harmful irritants[1]. Research indicates that ARDS affects approximately 10% of patients in intensive care units (ICUs), with mortality reaching up to 46% and even 70% during the coronavirus disease 2019 (COVID-19) pandemic[2]. Consequently, seeking effective treatments and precise prognostic methods become a priority to enhance ARDS patient outcomes, the need becomes even more intensified by the COVID-19 crisis. However, the prediction of ARDS prognosis in the early days after ARDS onset is challenging because of the high variability in the underlying mechanisms of ARDS. The systemic response alterations observed in ARDS can be attributed to a multifaceted array of factors, including pathogen and injury exposure, genetic susceptibility, and immune responses[3]. These factors may influence protein activity and the downstream metabolites that drive disease progression[4–6]. Therefore, it is important to investigate the potential association between host-derived proteins and metabolites circulating in the bloodstream with the pathogenesis and advancement of ARDS.

The integration of multi-omics approaches, particularly the combination of proteomics and metabolomics, elucidates the interactions across different biological system layers, as demonstrated in studies on cardiomyopathy[7], non‑small cell lung cancer[8], hepatitis C infection[9] et al.. Previous studies have used serum proteins and/or metabolites to investigate infectious disease. For example, one study identified a set of proteins capable of accurately distinguishing and predicting COVID-19 outcomes[5]. Jacob M. et al. leveraged proteomics and metabolomics to identify early predictive and pathogenic signatures of Staphylococcus aureus bacteremia[10]. Yi Wang and his colleagues investigated new diagnosis biomarkers and potential mechanism involved in the pediatric severe community-acquired pneumonia using proteomics combined with metabolomics[11]. However, the interplay between proteomic and metabolomic profiles in the progression of adult ARDS and their collective role from a holistic perspective remains underexplored. Moreover, blood biomarkers have increasingly attracted significant interest in ARDS investigations in recent years[12], showing promise in enhancing diagnosis, prognostication, and management strategies for ARDS, either independently or in conjunction with physiological parameters.

An integrated proteome and metabolome could pave new pathways for understanding ARDS. In this study, we employed proteomics and metabolomics to examine the host response in ARDS serum samples within a discovery cohort comprising ARDS onset, disease controls, and healthy controls. This approach enabled a comprehensive profiling of biological characteristics and potential mechanisms associated with ARDS and its prognosis. Subsequently, we screened the candidate biomarkers of ARDS prognosis based on machine learning methods. Ultimately, we employed the Parallel Reaction Monitoring (PRM) method to validate the prognosis biomarkers at ARDS onset in an independent cohort. The multi-omic analysis generated in this study provided a global overview of the molecular changes, which may provide useful insight into the therapy and prognosis of ARDS.

Ethical approval

The study involving human participants was reviewed and approved by the Ethical Committee of Zhongshan Hospital, Fudan University, China (Ethical approval number: B2023-029R), Renji Hospital, Shanghai Jiaotong University School of Medicine, China (No. LY2023-096-B), Minhang Hospital, Fudan University, China (No. 2022-041-01K), and Pudong Hospital, Fudan University, China (No. WZ-22). Written informed consent to participate in this study was provided by the participants or their relatives.

Collections and preparation of clinical specimen

Discovery cohort

Single-center observational study. ARDS patients were recruited from the ICU of Zhongshan Hospital between December 2022 and September 2023. Serum specimens were collected within 48 hours following the onset of ARDS. A comprehensive analysis of clinical data was performed, utilizing patient medical records to gather information on routine blood tests, liver function tests (including alanine aminotransferase [ALT] and aspartate aminotransferase [AST]), total and conjugated bilirubin (STB and CB), renal function indicators (blood urea nitrogen [BUN] and creatinine [Cr]), albumin [Alb], C-reactive protein [CRP)], procalcitonin [PCT], and coagulation profiles (fibrinogen [Fg], prothrombin time [PT], activated partial thromboplastin time [APTT], and D-dimer). The Sepsis-related Organ Failure Assessment (SOFA) score and P/F ratio were evaluated concurrently. Comorbidities were also collected. For comparative analysis, disease controls (DC) and healthy controls (HC) were recruited and matched with the ARDS cohort based on age and sex. The DC group consisted of patients who had risk factors (sepsis, pneumonia et al.) of ARDS but did not progress to ARDS during their hospital stay, with serum samples collected within the first 48 hours of ICU admission. Serum samples from healthy volunteers were obtained from the Health Check Center of Zhongshan Hospital, ensuring a robust control framework for the study.

Validation cohort

Multi-center validation. Validation samples were from the ICUs of other three hospitals. Serum samples from patients diagnosed with ARDS were collected within 48 hours after the onset of ARDS.

All venous blood was collected from participants and processed within 8 hours to isolate serum. The serum was separated by centrifugation at 300×g for 10 min and stored at −80℃ until testing.

Proteomics sequencing and data preprocessing

Proteins were enriched from serum using magnetic nanomaterials[13]. After magnetic separation, beads were washed with Reagent G and incubated with serum at 37°C for 1 hour. Post-incubation, beads were thrice washed with Reagent G to retain enriched proteins. For digestion, beads were treated with Reagent A, followed by heating with Reagent B at 95°C for 5 minutes. Upon returning to room temperature, the mixture was digested enzymatically with Reagents C and D at 37°C for 2 hours. Digestion was terminated using Reagent E, and the supernatant was collected post-centrifugation for desalting. Peptide desalting involved activating a desalting column with methanol, conditioning with Condition Buffer, and equilibrating with Wash Buffer, each step followed by centrifugation. The peptides were loaded onto the column, washed, and eluted with Elution Buffer, resulting in a 60 µL concentrated sample. Before mass spectrometry, each sample was mixed with iRT standard (Biognosys, ThermoFisher) in a volume ratio of 1:20 (iRT: sample). The iRT standard kit, comprising 11 synthetic peptides not found in nature, is designed for optimized stability, sensitivity, and retention time, making it an ideal internal standard for chromatographic calibration and quantitative quality control without influencing the test samples. The mass spectrometer was operated in data-independent acquisition (DIA) mode. The DIA raw data were processed using Spectronaut Pulsar 17.5 software (Biognosys) against the Uniprot-Homo sapiens-9606-2023.2.1.fasta database.

Metabolomics sequencing and data preprocessing

A Dionex Ultimate 3000 RS UHPLC fitted with Q-Exactive plus quadrupole-Orbitrap mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. The original liquid chromatography coupled with high-resolution mass spectrometry (LC-MS) data was processed by software Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK) for baseline filtering, peak identification, integral, retention time correction, peak alignment, and normalization. The main parameters of 5 ppm precursor tolerance, 10 ppm product tolerance, and 5% production threshold were applied. Compound identification was based on the precise mass-to-charge ratio (M/z), secondary fragments, and isotopic distribution using The Human Metabolome Database (HMDB), Lipidmaps (V2.3), Metlin, EMDB, PMDB, and self-built databases to do qualitative analysis. The details of data preprocessing were in Supporting Information.

Validation of candidate biomarkers using PRM assay

To evaluate the candidate biomarkers of prognosis, a unique PRM assay was generated, incorporating as many candidate proteins as possible. For the proteome profiling samples, the peptide was examined utilizing a Q Exactive HF-X Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific), integrated with a state-of-the-art high-performance liquid chromatography system (EASY nLC 1200, Thermo Fisher Scientific). The experimental details of PRM assay were in Supporting Information.

Data processing of the PRM results was conducted using Skyline software. This involved sequential importation of the spectral library and PRM detection data. Parameters set during the import of spectral library data included a false discovery rate (FDR) of 1%, a precursor ion length range of 6-25 amino acids, charge states of 2 or 3 for precursor ions, and 1 or 2 for fragment ions, with a selection of y, p, and b ion types for fragment ions. The ion mass tolerance was set to 0.05 Da, and a maximum of 7 fragment ions were analyzed. The quantification results for targeted ions were exported for further analysis.

Bioinformatic analysis

Proteomics data quality control and pre-processing

The proteomics data analysis encompassed the following key procedures. Step 1: Screen proteins that have unique peptides ≥ 1 for further analysis. Step 2: proteins with more than 50% null values in three groups were excluded. Step 3: for proteins whose effective value ≥50% in one group, the empty values were filled with the mean value of the group. The remaining missing values were filled using half of the sample's minimum value. Step 4: high-quality proteins were retained by Log2 transformation and z-normalization for subsequent data analysis. Specifically, in total 2669 proteins were collected for downstream statistical and bioinformatics analysis.

Screening of differentially abundant proteins (DAP), differentially abundant metabolites (DAM), and enrichment analysis

Protein abundance changes in different sample groups were conducted through principal component analysis (PCA). For circulating proteomic data from the discovery cohort, the fold change (FC) value was derived from the ratio of ARDS to non-ARDS cases. Statistically significant differentially abundant proteins (DAPs) and differentially abundant metabolites (DAMs) were identified based on the criteria of FC ≥ 2 or FC ≤ -2, and P < 0.05. P-values were adjusted for false discovery rate (FDR) using Benjamini and Hochberg. To further investigate potential pathological and biological mechanisms associated with ARDS, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The functional enrichment analysis of KEGG was performed with the ClusterProfiler package in R. KEGG pathway database (https://www.kegg.jp/kegg/pathway.html) was applied for metabolites pathway enrichment analysis. Ggplot2 packages and Cytoscape[14] were used to achieve Venn diagram, heatmap, and network visualization.

Protein-protein interaction network analysis

The protein-protein interactions (PPIs) were obtained from the STRING database[15]. Differentially abundant proteins (P-value < 0.05) were mapped to PPIs to generate the DAP PPI network in ARDS. The Cytoscape software[14] was used to visualize the network. The Cytoscape plugin cytoHubba[16] was utilized to calculate the degree in the PPI network.

GSEA analysis

For the GSEA enrichment analysis, we utilized the Molecular Signatures Database (MSigDB), specifically focusing on the KEGG gene set. We designated an FDR threshold of 0.05 as the demarcation for statistical significance. The process for computing the Normalized Enrichment Score (NES) within the GSEA framework entails the prioritization of proteins based on their statistical relevance, ranging from the most to the least significant, succeeded by the examination of the distribution pattern of the proteins associated with each gene set throughout the prioritized list. The integrated abundance of proteins was then calculated by utilizing the ClusterProfiler R package[17].

Constructing a prognostic model based on the early serum proteome

We constructed three classification models to predict adverse outcomes of ARDS: first, screened candidate proteins were used to construct a protein-based model. Second, to compare the protein model with current clinical practice, a clinical risk model was constructed and optimized. The clinical parameters were selected by the LASSO method among lab tests, SOFA, P/F ratio, and comorbidity. A third combined model was formed by stacking the clinical prognostic parameters with the protein parameters. Five machine learning methods, Naïve Bayes (Bayes), Random forest (RF), Generalized Linear Model (Glm), Supporting Vector machine (SVM), and Gradient Boosting Machine (GBM) were used in the three classification models. Discrimination performance was assessed using the receiver operating characteristic (ROC) curve with an area under the curve (AUC), sensitivity, and specificity. The DeLong test was strategically utilized to statistically compare the AUCs across different models, providing a robust assessment of their discriminative capabilities. To prevent overfitting, ‘10-fold cross-validation’ was employed to assess the performance of machine learning methods.

Statistical Analysis

Mann–Whitney U tests or Kruskal–Wallis tests were used for comparisons of continuous variables, whereas Chi-square tests were used for categorical variables. Correlation analysis was assessed by Spearman correlation. A P value of < 0.05 (two-tailed) was considered statistically significant Benjamini-Hochberg correction for multiple testing was applied as appropriate.

Participants characteristics

For the discovery cohort, the demographic and clinical characteristics of 130 ARDS cases, 33 healthy controls (HC), and 33 disease controls (DC) are presented in Table 1. At baseline, the mean age of the ARDS group was 72.5 (SD, 11.4) years, and 74.6% were male. Gender distribution and age were matched in DC and HC with ARDS (Table 1), while most of the lab tests were different between ARDS and DC groups. At the onset of ARDS, non-survivors showed decreased P/F ratio, PLT, and Alb levels, alongside increased SOFA scores, BUN, and AST, compared to the survivors. No significant differences were observed in other clinical indicators and comorbidities. We acquired serum proteome profiles of all participants (n =196) using a DIA strategy and global metabolome by the LC-MS method. An overview of proteomics and metabolomics workflow can be found in Fig. 1A. The independent prospective validation cohort incorporating 183 early ARDS patients with 85 deceased and 98 survived (Fig. 1A and Table S1). The differences in baseline characteristics between surviving and deceased ARDS patients were nearly consistent across the discovery and validation cohorts.

Altered serum proteomic profiling in the ARDS group and its biological features

After filtering the low abundant proteins, 2669 high-quality proteins were collected for the data analysis (Additional file 1). The median protein number for 196 samples was 2471 (Fig. 1B). The apolipoproteins, such as APOA1 and APOB were the most abundant proteins for each group (Fig. 1C). The clustering tree indicated the appropriate pre-processing method in this study (Fig. 1D).

Our preliminary analysis employed principal component analysis (PCA) to explore the clustering patterns among the groups (Fig. 2A and Fig. S1A-S1C), which successfully demonstrated a distinct separation between ARDS samples and those from HC or DC groups. A comprehensive differential expression analysis revealed that 1069, 319, and 511 proteins were uniquely altered in ARDS vs. HC, ARDS vs. DC, and DC vs. HC groups, respectively (Fig. 2B-2C and Fig. S1D, as detailed in Additional file 2). These findings demonstrated the progressive features of serum protein alterations correlating with the severity of the disease. A critical subset of 214 differentially abundant proteins emerged as consistently regulated across ARDS, with 16 proteins showing persistent downregulation and 198 showing upregulation in comparisons of both ARDS vs. DC and ARDS vs. HC (Fig. 2F, Additional file 3). This pattern highlights a distinctive protein signature characteristic of ARDS. Among the downregulated proteins, Fetuin B (FETUB) was an intriguing link between metabolism and bacteremia and can now be classified as biomarkers of SaB mortality[10]; Paraoxonase 1 (PON1), negatively associated with higher mortality of sepsis[18], underlines significance in ARDS pathology compared to DC and HC groups (Fig. 2G). Conversely, certain proteins, including Surfactant Protein D (SFTPD) and Signal Transducers and Activators of Transcription 3 (STAT3) et al., were found elevated (Additional file 3). Moreover, SFTPD, a circulating epithelial markers[19] and STAT3, an activator of macrophages and neutrophils[20], were implicated as potential key contributors to the pathogenesis of ARDS, offering novel insights into its molecular underpinnings.

Pathway analysis for distinguishing ARDS from non-ARDS controls at the protein level

Pathway analysis at the protein level identified nine distinct pathways that were specifically modulated in ARDS (Fig. 2D-2E and Fig. S2). Notably, the oxidative phosphorylation pathway emerged as significantly regulated in comparisons of ARDS with both DC (NES = 1.615, P = 0.001) and HC (NES = 1.582, P < 0.001), underscoring its pivotal role in the energetic metabolism associated with ARDS (comprehensive pathway listings are available in Additional file 4).

In addition to oxidative phosphorylation, key biological processes such as the spliceosome and proteasome pathways were upregulated in ARDS (Spliceosome: ARDS vs. DC, NES = 1.474, P = 0.007; ARDS vs. HC, NES = 1.500, P < 0.001. Proteasome: ARDS vs. DC, NES = 1.471, P = 0.011; ARDS vs. HC, NES = 1.534, P = 0.003). Signaling pathways, including mTOR, FoxO, VEGF, and sphingolipid signaling, also showed significant upregulation in ARDS relative to both DC and HC, indicating the complexity of molecular alterations in ARDS, including immune system, cell signaling and lipid mechanism. An exploration of unique pathways between ARDS and HC revealed an upregulation of processes related to amino acid degradation, autophagy, endocytosis, and the TNF signaling pathway, alongside restricted focal adhesions in ARDS (Fig. 2D and Fig. S2).

Fig. 2H illustrates the enriched proteins within these nine overlapping pathways, all of which exhibited increased serum levels in ARDS cases. Among the highlighted proteins, Succinate Dehydrogenase Complex (SDHB), involved in mitochondrial function, and various components of the vacuolar ATPase family (ATP6V1D, ATP5MG, ATP6V1H) were implicated in oxidative phosphorylation. Moreover, proteins such as Mitogen-Activated Protein Kinase Kinase 1 (MAP2K1), Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1), and Non-Receptor Tyrosine Kinase (SRC), associated with the VEGF signaling pathway, were significantly upregulated in ARDS, illustrating a comprehensive network of molecular interactions contributing to the pathogenesis and progression of ARDS.

Metabolites LysoPCs were decreased in ARDS compared with non-ARDS controls

In our metabolomic investigation, we cataloged a comprehensive array of 3331 metabolites, encompassing amino acids, lipids, and other critical serum metabolites, as detailed in Additional file 5. The PCA plots, showcased in Fig. 3A-3C, revealed a pronounced metabolic differentiation of the ARDS in comparison to both DC and HC groups. This distinct separation revealed the role of metabolic dysregulation in the etiology of ARDS.

In our comparative metabolomics study, we identified 214, 113, and 206 differentially abundant metabolites (DAMs) in the ARDS vs. HC, ARDS vs. DC, and DC vs. HC, respectively (Additional file 6). Among these, lysophosphatidylcholine emerged as the most markedly altered metabolites in ARDS, exemplified by significant decreased in LysoPC (0:0/18:2(9Z,12Z)) (ARDS vs. HC: VIP = 22.5, P = 0.0003; ARDS vs. DC: VIP = 27.7, P = 0.017) and LysoPC (0:0/18:0) (ARDS vs. HC: VIP = 13.4, P < 0.0001; ARDS vs. DC: VIP = 9.5, P = 0.0003) (Additional file 6). Further exploration through the KEGG pathway analysis of these DAMs revealed the profound impact of ARDS on metabolic processes. This analysis notably highlighted the modulation of the sphingolipid signaling pathway and sphingolipid metabolism, which were distinctively observed in ARDS as compared to the non-ARDS groups (Fig. 3D).

Cross‑talk between proteomics and metabolomics implicated sphingolipid signaling pathway as a hub pathway in ARDS

To elucidate the pathogenetic mechanisms underlying ARDS, we undertook an integrated analysis of proteomics and metabolomics datasets. This approach enabled us to dissect the multifaceted changes characterizing dysregulated biological functions associated with ARDS. A significant finding from our study was the identification of the sphingolipid signaling pathway as a key regulatory axis, influenced at both the protein and metabolite levels, pinpointing it as a hub pathway in ARDS pathogenesis (Fig. 3D and Table S2). Further network analysis demonstrated the critical role of the sphingolipid signaling pathway, integrating it centrally within the ARDS-unique regulatory network (Fig. 3E and Fig. S3-S4). Specifically, critical regulatory proteins such as BCL2 Associated X (BAX), BH3 Interacting Domain Death Agonist (BID), PIK3R1, MAP2K1, and NF-κB Subunit (RELA) exhibited upregulation in ARDS (Fig. 3F). It can be seen that MAP2K1, the hub protein in network, interacts sphingolipid signaling pathway with several important pathways, including parathyroid hormone synthesis secretion and action, Apelin signaling pathway, choline metabolism, phospholipase D signaling pathway, Fc gamma R-mediated phagocytosis and apoptosis. BAX and BID mediate interactions linking the sphingolipid signaling pathway with necroptosis and apoptosis, and RELA associates with the pathway in apoptosis. PIK3R1 connects the sphingolipid signaling pathway to Fc gamma R-mediated phagocytosis, apoptosis, phospholipase D signaling pathway and choline metabolism (Fig. 3E).

Concurrently, metabolomic analysis revealed differential expression of key sphingolipids. S1P showed reduced levels, whereas sphingosine exhibited elevated levels in ARDS compared to both DC and HC groups (Fig. 3F). Importantly, S1P interacts sphingolipid signaling pathway with several signaling pathways, including Apelin, calcium and phospholipase D signaling pathways.

Dysregulation of immune response, energy metabolism, hematopoiesis, and signaling pathway in deceased patients at the ARDS onset

To further investigate the proteomic alterations associated with different prognoses in ARDS, we conducted a comparison analysis between deceased and survived ARDS. PCA elucidated that the first two principal components accounted for 14.2% and 5.8% of the variance between the deceased and survived ARDS (Fig. 4A). Differential expression analysis identified 40 proteins with significant variations (│fold change│≥ 1.5 and FDR < 0.05), suggesting distinctive protein profiles that may serve as early indicators of prognosis in ARDS (Table S3). The most significantly altered proteins between deceased and survived ARDS were Radixin (RDX) and Moesin (MSN) which are important in linking actin to the plasma membrane (Fig. 4B). Subsequent GSEA revealed several highly ranked molecular pathways markedly enriched across various biological themes such as energy metabolism, immune response, proteasome function, gap junction communication, calcium signaling, and hematopoietic cell lineage differentiation (Fig. 4C). Remarkably, pathways like the unfolded protein response (NES = 1.74, P = 0.04), proteasome (NES = 1.84, P = 0.02), glycolysis/gluconeogenesis (NES = 1.62, P = 0.1), and interferon-α response (NES = 1.63, P = 0.1) showed positive associations with the deceased ARDS group. Conversely, pathways associated with hematopoietic cell lineage (NES = -2.06, P = 0.01), gap junctions (NES = -1.85, P = 0.03), calcium signaling (NES = -1.78, P = 0.05), and CD4⁺ T cell activity (NES = -2.05, P = 0.04) were more prominent in patients who survived ARDS in the early phase.

Importantly, to identify proteins that may play crucial roles in deceased ARDS, we constructed the PPI network of a panel of 429 differentially abundant proteins (P < 0.05). A PPI network comprising 423 nodes and 11675 edges was obtained (Additional file 7). The network topology was further analyzed to identify hub proteins, with a particular focus on their degree of connectivity. To refine our selection of key protein candidates, we spotlighted the top 25 proteins exhibiting the highest connectivity degrees. Among these, Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH), Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1), and Enolase 1 (ENO1) emerged as central hub proteins within the network (Fig. 4D-4E). This finding is in concordance with previous GSEA results, since GAPDH and ENO1 as critical enzymes in glycolysis; moreover, HSP90AA1 was the house-keeping protein that aids protein folding and has intrinsic ATPase activity.

Metabolites LysoPCs and S1P were negatively correlated with the prognosis of ARDS

We initially assessed the clustering of the metabolomics data using PCA (Fig. 4F). The PCA plot demonstrated, at the global metabolomic level, a slight separation of the ARDS-deceased and survived groups. Pathway analysis was performed at 45 DAMs with annotation by KEGG (Fig. 4G and Additional file 8). The DAMs were most enriched in signaling pathways, such as the sphingolipid signaling pathway, phospholipase D signaling pathway, calcium signaling pathway, and Apelin signaling pathway. Importantly, lysophosphatidylcholine LysoPC (O-18:0/0:0), LysoPC (15:0/0:0), and S1P were prominently featured within these pathways. Furthermore, the levels of LysoPC and S1P were significantly reduced in the ARDS-deceased group, highlighting their potential involvement in the fatal progression of ARDS (Fig. 4H). More importantly, a correlation analysis between these three metabolites and the severity of ARDS, as quantified by the SOFA score, revealed negative correlations (R = -0.47 for LysoPC (O-18:0/0:0); R = -0.50 for LysoPC (15:0/0:0); R = -0.36 for S1P) (Fig. 4I), suggesting the potential metabolome mechanism underlying the heightened mortality risk in ARDS non-survivors.

Biomarker panel for early prediction of ARDS prognosis

In order to early predict the prognosis of ARDS, we further construct prognosis model. LASSO (Fig. S5A-S5B) and Boruta methods were used to screen the candidate proteins in the discovery cohort, then combining with 40 DAPs, 36 candidate biomarkers with unique peptides ≥3 were proposed for further targeted proteomics analysis. Finally, 31 proteins with 174 peptides were targeted by PRM assay in the external validation cohort (Additional file 9), a total of 22 candidate proteins with peptides ≥2 were kept, in which 8 proteins maintaining consistent significance in both discovery and validation cohorts (Fig. 5A-5B). Among the eight proteins, six proteins were significantly upregulated in deceased patients in both discovery and validation cohorts, including Vascular Cell Adhesion Molecule 1 (VCAM1), Lactate Dehydrogenase B (LDHB), Moesin (MSN), Filaggrin 2 (FLG2), Lamin A/C (LMNA), and Lipopolysaccharide Binding Protein (LBP), while the other two proteins, Transgelin 2 (TAGLN2) and Mannose Binding Lectin 2 (MBL2) were consistently downregulated in the deceased ARDS group (Fig. 5B). We selected these markers as an eight-protein marker panel for early identification of deceased patients of ARDS.

In the discovery cohort, we first compared five state-of-the-art machine learning classifiers using the protein model, The Glm model was the final classifier due to its overall superior performance, evidenced by a 10-fold cross-validated ROC–AUC of 0.893 (95% CI 0.837-0.949) and a sensitivity of 0.920 (Fig. 5C). In comparison, the clinical risk model comprised of SOFA score, P/F ratio and PLT from LASSO feature selection (Fig. S5C-S5D) yielded in a ROC-AUC of 0.784 (95% CI 0.703-0.866) and a sensitivity of 0.885 (Fig. 5D). Combination of parameters in both models resulted in a ROC-AUC of 0.890 (95% CI 0.834-0.946) and a sensitivity of 0.931 (Fig. 5E). The protein model performed significantly better than the clinical risk model (Delong test, P < 0.001), whereas the combination of both models did not exhibit statistical superiority over the protein model alone (Delong test, P = 0.970). Subsequent validation of the Glm classifier using the protein model resulted in an ROC-AUC of 0.802 (95% CI 0.739-0.865) and a sensitivity of 0.835 (Fig. 5F) in the external cohort, whereas the clinical risk model resulted in an ROC-AUC of 0.738 (95% CI 0.655-0.811) and a sensitivity of 0.788 (Fig. 5G). The combination of both models led to an ROC-AUC of 0.844 (95% CI 0.786-0.902) (Fig. 5H). In the validation cohort, the protein model also outperformed the clinical risk model (Delong test, P = 0.008), a combination of both models was superior to the protein model alone (Delong test, P = 0.006).

In this study, we have combined proteomics and metabolomics to investigate the differences in the proteome, metabolome, and related biological pathways between ARDS and non-ARDS controls, as well as deceased-ARDS vs. survived-ARDS. To our knowledge, this is the first study to combine proteomic and metabolomic data obtained from adult ARDS and non-ARDS controls. Our study also identified 8 proteins, which are early candidates to identify deceased ARDS cases from survived ARDS. These candidates and constructed prognosis model were validated in an independent external cohort.

We identified significant differences in ARDS patients and non-ARDS controls in terms of proteins and metabolites. This integrative analysis offers a global perspective on the proteomic and metabolic landscapes of ARDS, shedding light on sphingolipid signaling pathway as pivotal in the pathophysiology of ARDS, specifically highlighting the role of S1P, a molecule synthesized through the phosphorylation of sphingosine. Previous study emphasized S1P as a potential biomarker in sepsis: decreased serum S1P levels proved to be a stronger predictor of septic shock[21], which was consistent with our results that S1P negatively correlates with the disease severity of ARDS. These findings suggest pivotal metabolic shifts and disruptions that may contribute to the development and progression of ARDS, offering insights into potential metabolic biomarkers. S1P is a bioactive phosphosphingolipid with pleiotropic roles in many aspects of vasculature biology including angiogenesis, barrier integrity[22], and inflammation[23]. In addition, we observed a distinct pattern between ARDS and DC/HC groups at the level of sphingosine. In ARDS patients, sphingosine was upregulated as compared to non-ARDS controls, underlying the activation of S1P phosphatases, which are rate-limiting enzymes for reversing S1P into sphingosine. The prospect of new strategies on ARDS patients by interfering with S1P is exciting and could potentially reduce morbidity and mortality in patients with severe lung disorders.

Furthermore, our results align with a recent study by Manicone, Anne M. et al.[24], which identified the MEK1/2 pathway as a key regulator of macrophage reparative properties, the administration of MEK inhibition after LPS-induced acute lung injury led to improved recovery of weight and fewer neutrophils in the alveolar compartment. Similarly, in our study, ARDS has higher levels of MAP2K1 (i.e. MEK1), and MAP2K1 engaged in the sphingolipid signaling pathway was identified the hub protein in proteome-metabolome cross-talk network, therefore MEK inhibition has important therapeutic implications for ARDS in disrupting sphingolipid signaling pathway and its up- or down-stream biological process. Meanwhile, we also pointed another protein PIK3R1 widely engaged in the sphingolipid signaling pathway, may be a new therapeutic target of ARDS.

In addition to our findings of altered sphingolipid signaling pathway in ARDS, we also uncovered dysregulated cell death pathways, such as apoptosis and necroptosis. Our results demonstrated the proteins BID and BAX interact apoptosis/necroptosis with sphingolipid signaling pathway and were upregulated in ARDS compared non-ARDS groups. Previous researches showed higher blood concentrations of BAX are associate with mortality of sepsis[25] and an increase of BID renders cells more susceptible to apoptosis during sepsis[26]. Thus, BAX and BID may be important regulators of cell death from sphingolipid signaling pathway involved in exacerbating severe disease, such as sepsis and ARDS.

Moreover, our analysis presented a comprehensive view of proteomic alterations distinguishing deceased ARDS from survived ARDS, the elucidation of the functional roles and molecular mechanisms of these proteins in ARDS lays the groundwork for potential therapeutic innovations targeting energy metabolism, such as glycolysis. One previous study showed that the increase of proinflammatory cytokines including pro-IL-1β and TNF-α preceded the activation of glycolysis in macrophages during LPS-induced ALI, suggesting that glycolysis maybe induced by inflammation[27]. In COVID-19 ARDS, T cells are exhausted and skewed towards glycolysis, with a concomitant reduction in mitochondrial dependence, probably as a result of reduced oxygenation of the pulmonary tissue[28, 29]. Consistently, we identified upregulated glycolysis, as well as upregulated interferon-α response and activated CD4⁺ T cell, which elucidated the glycolysis coordinated with inflammation in ARDS, aside from generating energy and producing building blocks for cellular survival and signaling transduction[30].

Notably, the concentrations of LysoPC species such as LysoPC (O-18:0/0:0) and LysoPC (15:0/0:0) in the deceased ARDS groups were decreased compared with the survived group. LysoPC is a lipid mediator derived from membrane phosphatidylcholine, and it is known to contribute to inflammation by increasing chemokine production and activating endothelium, neutrophils, monocytes, macrophages, and lymphocytes[31]. However, the role of LysoPCs in ARDS has not yet been clearly elucidated. In sepsis patients, serum LysoPC levels were lower than those in controls, and among them, LysoPC (16:0) and LysoPC (18:0) were decreased[32]. Lower concentrations of serum or plasma LysoPC predicted worse outcomes[33]. Our findings align with these observations, revealing a comparable reduction in serum LysoPC levels in ARDS, akin to that observed in sepsis. Therefore, LysoPCs could be important metabolite markers for differentiating the diagnosis and prognosis of ARDS, which needs further validation and mechanism exploration.

In this study, we observed a moderate baseline prediction model based on clinical variables of SOFA, P/F ratio, and PLT but the modeling performance was dramatically increased by incorporating eight serum protein biomarkers. These findings suggest that our serum biomarkers and prognosis model could indeed be a promising prognosis predictor of ARDS patients compared to traditional clinical models. In an independent validation cohort, the model was also able to identify deceased ARDS at the early phase with a sensitivity of 0.871, and AUC of 0.844. Therefore, we proposed that facile and scalable analyses of serum proteins based on serum proteomics could be used to prescreen high-risk populations for deceased ARDS. Interestingly, a previous study revealed VCAM1 overexpressed on endothelial cell-derived extracellular vesicles during sepsis, facilitating the activation of the NF-κB pathway by interacting with integrin subunit alpha 4 (ITGA4) on the monocyte surface thereby regulating monocyte differentiation[34]. Furthermore, the biomarker MBL2, also corroborated by previous investigations, emerges as a critical determinant of mortality risk among patients with severe pneumococcal infections who exhibit MBL deficiency[35], highlighting the pathogenic significance of this innate immune defense protein. Other candidate biomarkers, LDHB was engaged in the glycolysis[36], MSN was expressed in the endothelial cell and was important for cell-cell recognition and signaling and for cell movement[37]. LBP is the LPS binding protein, LBP-LPS complex initiates a signal cascade which triggers the secretion of pro-inflammatory cytokines[38]. Abnormal LMNA results in nuclear structural abnormalities and mesenchymal tissue damage[39]; FLG2 was associated with epidermal maturation[40]; TAGLN2, functions as a binding protein to actin to facilitate the formation of intracellular cytoskeleton structures[41]. These biomarkers can categorize into several groups reflecting distinct pathological processes: epithelial injury, endothelial damage, imbalanced immune system, mitochondrial dysfunction and disrupt cytoskeleton, which engaged in the overall ARDS progress.

We acknowledged the limitations of the present study. First, further studies are needed to explore the underlying mechanisms of how these eight proteins contribute to the adverse prognosis of ARDS, focusing on their specific roles within the immune and pathology systems. Second, the prediction model was not accessible in point-of-care testing yet, which further needs the development of biological technology. Third, the longitudinal multi-omics was needed to explore the dynamic features of ARDS and dynamic levels of prognosis biomarkers.

In conclusion, this comprehensive study highlights the importance of the sphingolipid signaling pathway in unraveling the complex etiology and pathogenesis of ARDS. Protein MAP2K1 and metabolite S1P play the critical roles in this pathway. We show that a panel of eight proteins is superior to a clinical prognostic model in predicting ARDS-deceased events in both the discovery and validation cohort. These findings have significant implications for risk assessment and potential guidance of therapeutic strategies in the management of ARDS.

Acknowledgments

We thank all the participants. We gratefully acknowledge the LuMing Biological Technology Co., Ltd. (Shanghai) for the support of 4D-DIA proteomic and global metabolomic sequencing. We also acknowledged the iProteome Biological Technology Co., Ltd. (Shanghai) for the support of PRM assay.

Author contributions

SZJ and WWB conceived the study; LMN, WWB, and SZJ designed the study; WWB, SZJ and WXL supervised this project; SJ, SJF, SY, LS, DHL, LLL, CC, MW performed the processing of samples and generation of data. LMN and XFX analyzed the data; LMN and XFX drafted the original paper; WWB, SZJ and WXL reviewed the paper. All authors read and approved the final manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2021YFC2501800, 2023YFC0872500 and 2023YFC3043507), the National Natural Science Foundation of China (82072214), and the Science and Technology of Shanghai Committee (23Y31900100 and 21MC1930400). This work also was supported by the Shanghai Municipal Science and Technology Major Project (ZD2021CY001), and Shanghai New Three-year Action Plan for Public Health (GWVI-11.1-03).

Availability of data and materials

Data and research materials used in this study are available upon request to qualified researchers for purposes of replication, further analysis, and academic collaboration.

Ethics approval and consent to participate

The lead author and guarantor certify that this manuscript presents a truthful, precise, and complete report of the study conducted; it ensures no significant facets of the research have been excluded, and any deviations from the initial study design and registration are thoroughly accounted for and elucidated.

Competing interests

The authors declare no competing interests.

Author details

¹Shanghai Institute of Infectious Disease and Biosecurity，Fudan University, Shanghai, China;

²Department of Emergency Medicine, Zhongshan Hospital, Fudan University, Shanghai, China;

³Department of Emergency Medicine, Minhang Hospital, Fudan University, Shanghai, China;

⁴Department of Respiratory Medicine, Pudong Hospital, Fudan University, Shanghai, China;

⁵School of Public Health, Fudan University, Shanghai, China;

⁶Department of Respiratory Medicine, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China;

⁷Institute of Emergency Rescue and Critical Care, Fudan University, Shanghai, China

Chang YJ, Yoo HJ, Kim SJ, Lee KH, Lim CM, Hong SB, Koh Y, Huh JW: A targeted metabolomics approach for sepsis-induced ARDS and its subphenotypes. Crit Care 2023, 27(1).
Ñamendys-Silva SA, Gutiérrez-Villaseñor A, Romero-González JP: Hospital mortality in mechanically ventilated COVID-19 patients in Mexico. Intensive care medicine 2020, 46(11):2086-2088.
Kneyber MCJ, Khemani RG, Bhalla A, Blokpoel RGT, Cruces P, Dahmer MK, Emeriaud G, Grunwell J, Ilia S, Katira BH et al: Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome. Lancet Resp Med 2023, 11(2):197-212.
Shen B, Yi X, Sun YT, Bi XJ, Du JP, Zhang C, Quan S, Zhang FF, Sun R, Qian LJ et al: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell 2020, 182(1):59-+.
Shu T, Ning WS, Wu D, Xu JQ, Han QQ, Huang MH, Zou XJ, Yang QY, Yuan Y, Bie YY et al: Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19. Immunity 2020, 53(5):1108-+.
Gorman EA, O'Kane CM, McAuley DF: Acute Respiratory Distress Syndrome 2022 2 Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet 2022, 400(10358):1157-1170.
Li MB, Parker BL, Pearson E, Hunter B, Cao J, Koay YC, Guneratne O, James DE, Yang J, Lal S et al: Core functional nodes and sex-specific pathways in human ischaemic and dilated cardiomyopathy. Nat Commun 2020, 11(1).
Xu RH, Wang JR, Zhu QQ, Zou C, Wei ZH, Wang H, Ding Z, Meng MJ, Wei HM, Xia SJ et al: Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer. Biomark Res 2023, 11(1).
Ali RO, Quinn GM, Umarova R, Haddad JA, Zhang GY, Townsend EC, Scheuing L, Hill KL, Gewirtz M, Rampertaap S et al: Longitudinal multi-omics analyses of the gut-liver axis reveals metabolic dysregulation in hepatitis C infection and cirrhosis. Nat Microbiol 2023, 8(1):12-+.
Wozniak JM, Mills RH, Olson J, Caldera JR, Sepich-Poore GD, Carrillo-Terrazas M, Tsai CM, Vargas F, Knight R, Dorrestein PC et al: Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures. Cell 2020, 182(5):1311-1327 e1314.
Wang Y, Huang XL, Li F, Jia XB, Jia N, Fu J, Liu S, Zhang J, Ge HY, Huang SY et al: Serum-integrated omics reveal the host response landscape for severe pediatric community-acquired pneumonia. Crit Care 2023, 27(1).
Cao SR, Li HQ, Xin JY, Jin ZH, Zhang ZY, Li JW, Zhu YK, Su L, Huang PP, Jiang L et al: Identification of genetic profile and biomarkers involved in acute respiratory distress syndrome. Intensive care medicine 2023.
Blume JE, Manning WC, Troiano G, Hornburg D, Figa M, Hesterberg L, Platt TL, Zhao XY, Cuaresma RA, Everley PA et al: Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun 2020, 11(1).
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13(11):2498-2504.
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P et al: The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets (vol 49, pg D605, 2021). Nucleic Acids Res 2021, 49(18):10800-10800.
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY: : identifying hub objects and sub-networks from complex interactome. Bmc Syst Biol 2014, 8.
Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-Seq data. Bmc Bioinformatics 2013, 14.
Bojic S, Kotur-Stevuljevic J, Kalezic N, Jelic-Ivanovic Z, Stefanovic A, Palibrk I, Memon L, Kalaba Z, Stojanovic M, Simic-Ogrizovic S: Low paraoxonase 1 activity predicts mortality in surgical patients with sepsis. Disease markers 2014, 2014:427378.
Lin HS, Liu QSJ, Zhao L, Liu ZQ, Cui HH, Li PH, Fan HJ, Guo LQ: Circulating Pulmonary-Originated Epithelial Biomarkers for Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis. Int J Mol Sci 2023, 24(7).
Zhao JP, Yu H, Liu YD, Gibson SA, Yan ZQ, Xu X, Gaggar A, Li PK, Li CL, Wei S et al: Protective effect of suppressing STAT3 activity in LPS-induced acute lung injury. Am J Physiol-Lung C 2016, 311(5):L868-L880.
Winkler MS, Nierhaus A, Holzmann M, Mudersbach E, Bauer A, Robbe L, Zahrte C, Geffken M, Peine S, Schwedhelm E et al: Decreased serum concentrations of sphingosine-1-phosphate in sepsis. Crit Care 2015, 19.
Piotti A, Novelli D, Meessen JMTA, Ferlicca D, Coppolecchia S, Marino A, Salati G, Savioli M, Grasselli G, Bellani G et al: Endothelial damage in septic shock patients as evidenced by circulating syndecan-1, sphingosine-1-phosphate and soluble VE-cadherin: a substudy of ALBIOS. Crit Care 2021, 25(1).
Natarajan V, Dudek SM, Jacobson JR, Moreno-Vinasco L, Huang LS, Abassi T, Mathew B, Zhao YT, Wang LC, Bittman R et al: Sphingosine-1-Phosphate, FTY720, and Sphingosine-1-Phosphate Receptors in the Pathobiology of Acute Lung Injury. Am J Resp Cell Mol 2013, 49(1):6-17.
Long ME, Eddy WE, Gong KQ, Lovelace-Macon LL, McMahan RS, Charron J, Liles WC, Manicone AM: MEK1/2 Inhibition Promotes Macrophage Reparative Properties. J Immunol 2017, 198(2):862-872.
Lorente L, Martín MM, Ortiz-López R, González-Rivero AF, Jiménez A: Blood concentrations of proapoctotic Bax are associated with mortality of septic patients. Anaesth Crit Care Pa 2023, 42(5).
Turrel-Davin F, Guignant C, Lepape A, Mougin B, Monneret G, Venet F: Upregulation of the pro-apoptotic genes BID and FAS in septic shock patients. Crit Care 2010, 14(4).
Ning L, Zou SS, Bo W, Lin HQ: Targeting immunometabolism against acute lung injury. Clin Immunol 2023, 249.
Karagiannis F, Peukert K, Surace L, Michla M, Nikolka F, Fox M, Weiss P, Feuerborn C, Maier P, Schulz S et al: Impaired ketogenesis ties metabolism to T cell dysfunction in COVID-19. Nature 2022, 609(7928):801-+.
Osuchowski MF, Winkler MS, Skirecki T, Cajander S, Shankar-Hari M, Lachmann G, Monneret G, Venet F, Bauer M, Brunkhorst FM et al: The COVID-19 puzzle: deciphering pathophysiology and phenotypes of a new disease entity. Lancet Resp Med 2021, 9(6):622-642.
O'Neill LAJ, Kishton RJ, Rathmell J: A guide to immunometabolism for immunologists. Nat Rev Immunol 2016, 16(9):553-565.
Amunugama K, Pike DP, Ford DA: The lipid biology of sepsis. J Lipid Res 2021, 62.
Drobnik W, Liebisch G, Audebert FX, Fröhlich D, Glück T, Vogel P, Rothe G, Schmitz G: Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients. J Lipid Res 2003, 44(4):754-761.
Park DW, Kwak DS, Park YY, Chang Y, Huh JW, Lim CM, Koh Y, Song DK, Hong SB: Impact of serial measurements of lysophosphatidylcholine on 28-day mortality prediction in patients admitted to the intensive care unit with severe sepsis or septic shock. J Crit Care 2014, 29(5).
Wang L, Tang Y, Tang JJ, Liu X, Zi SF, Li SL, Chen HB, Liu AR, Huang W, Xie JF et al: Endothelial cell-derived extracellular vesicles expressing surface VCAM1 promote sepsis-related acute lung injury by targeting and reprogramming monocytes. J Extracell Vesicles 2024, 13(3).
Eisen DP, Dean MM, Boermeester MA, Fidler KJ, Gordon AC, Kronborg G, Kun JFJ, Lau YL, Payeras A, Valdimarsson H et al: Low serum mannose-binding lectin level increases the risk of death due to pneumococcal infection. Clin Infect Dis 2008, 47(4):510-516.
Beltinger C: LDHA and LDHB are dispensable for aerobic glycolysis in neuroblastoma cells while promoting their aggressiveness. J Biol Chem 2019, 294(1):66-66.
Lagresle-Peyrou C, Luce S, Ouchani F, Soheili TS, Sadek H, Chouteau M, Durand A, Pic I, Majewski J, Brouzes C et al: X-linked primary immunodeficiency associated with hemizygous mutations in the moesin (MSN) gene. J Allergy Clin Immun 2016, 138(6):1681-+.
Gabarin RS, Li MS, Zimmel PA, Marshall JC, Li YM, Zhang HB: Intracellular and Extracellular Lipopolysaccharide Signaling in Sepsis: Avenues for Novel Therapeutic Strategies. J Innate Immun 2021, 13(6):323-332.
He LG, Liao JQ, Liu Z, Wang T, Zhou Y, Wang TF, Lei BY, Zhou GQ: Multi-omic analysis of mandibuloacral dysplasia type A patient iPSC-derived MSC senescence reveals miR-311 as a novel biomarker for MSC senescence. Hum Mol Genet 2023, 32(19):2872-2886.
Agren M, Litman T, Eriksen JO, Schjerling P, Bzorek M, Gjerdrum LMR: Gene Expression Linked to Reepithelialization of Human Skin Wounds. Int J Mol Sci 2022, 23(24).
Wang LD, Tan H, Huang YL, Guo MY, Dong YX, Liu CX, Zhao H, Liu Z: TAGLN2 promotes papillary thyroid carcinoma invasion via the Rap1/PI3K/AKT axis. Endocr-Relat Cancer 2023, 30(1).

Table 1. Baseline characteristics of the discovery cohort.

	ARDS group (N=130)	Survived ARDS (n=54)	Deceased ARDS (n=76)	Disease control (N=33)	Healthy control (N=33)	P1	P2
Age (mean, SD)	72.5 (11.4)	72.2 (11.8)	73.7 (11.2)	72.1 (11.6)	72.0 (8.2)	0.883	0.792
Male (n, %)	97 (74.6)	41 (75.9)	56 (73.7)	25 (75.8)	25 (75.8)	0.774	0.893
SOFA	6 [4-9]	5 [4-7]	6 [5-9]	3 [2-3]	/	<0.001***	<0.001***
*P/F ratio	109 [81-151]	122 [95-200]	98 [73-138]	265 [203-354]	/	<0.001***	<0.001***
RBC (*10¹²/L)	3.66 [2.90-4.11]	3.55 [2.88-4.00]	3.71 [2.95-4.16]	4.10 [3.77-4.50]	/	0.367	0.001**
Hb (g/L)	111.0 [90.0-128.0]	108.5 [85.8-128.0]	111.5 [91.3-127.8]	127.5 [113.3-137.3]	/	0.657	0.003**
WBC (*10⁹/L)	10.35 [7.65-14.30]	9.83 [7.56-12.32]	10.41 [7.66-15.04]	7.2 [5.4-11.2]	/	0.676	0.005**
NEU (*10⁹/L)	9.30 [6.60-12.45]	8.80 [6.38-10.65]	9.55 [6.70-13.28]	5.3 [3.7-9.7]	/	0.352	0.001**
LYM (*10⁹/L)	0.4 [0.2-0.6]	0.4 [0.3-0.8]	0.4 [0.2-0.6]	0.8 [0.5-1.1]	/	0.234	<0.001***
PLT (*10⁹/L)	153.5 [82.5-229.3]	189.5 [126.8-249.5]	129.0 [68.0-205.0]	191.0 [151.5-254.0]	/	0.005**	0.011*
ALT (U/L)	27.0 [18.0-45.3]	29.5 [19.8-54.8]	25.5 [17.0-44.0]	30.0 [22.0-41.0]	/	0.561	0.385
AST (U/L)	29.0 [20.0-47.0]	27.0 [20.0-34.0]	34.0 [21.0-54.0]	28.0 [21.0-50.8]	/	0.041*	0.970
STB (µmol/L)	11.4 [7.6-19.2]	10.8 [7.7-17.6]	12.3 [7.4-20.1]	9.9 [6.7-11.7]	/	0.931	0.051
CB (µmol/L)	4.9 [3.1-8.4]	4.5 [3.2-6.5]	5.5 [3.1-8.6]	3.9 [3.0-5.2]	/	0.938	0.006**
BUN (mmol/L)	12.15 [9.18-20.98]	11.55 [9.45-15.85]	12.8 [8.6-24.38]	6.9 [5.5-9.0]	/	0.049*	<0.001***
Cr (µmol/L)	90.0 [64.8-128.8]	81.0 [63.5-111.8]	96.0 [67.5-169.8]	77.5 [62.5-97.8]	/	0.097	0.194
Alb (g/L)	31.0 [28.0-34.0]	32.0 [29.0-35.3]	30.0 [28.0-32.0]	34.0 [32.0-37.0]	/	0.006**	<0.001***
CRP (mg/L)	60.3 [19.6-90.0]	55.1 [8.6-68.3]	68.9 [26.3-98.3]	23.9 [4.4-62.4]	/	0.109	0.019*
PCT (ng/mL)	0.29 [0.11-0.61]	0.23 [0.08-0.76]	0.30 [0.11-0.56]	0.07 [0.03-0.19]	/	0.568	<0.001***
Fg (mg/dL)	419.5 [278.3-540.3]	410.5 [249.8-560.3]	427.0 [301.0-538.0]	469.0 [269.0-537.0]	/	0.921	0.618
PT (second)	13.3 [12.5-14.7]	13.1 [12.2-14.0]	13.5 [12.7-15.1]	12.6 [12.0-13.3]	/	0.079	0.003**
APTT (second)	29.3 [26.4-34.3]	29.4 [25.6-34.2]	29.3 [27.0-34.3]	27.6 [25.9-30.9]	/	0.944	0.072
D-dimer (mg/L)	4.22 [1.75-10.7]	3.27 [1.42-7.13]	4.41 [2.02-11.50]	1.13 [0.57-3.31]	/	0.158	<0.001***
Hypertension (n, %)	87 (66.9)	38 (70.4)	49 (64.5)	23 (69.7)	/	0.607	0.762
Coronary heart disease (n, %)	30 (23.1)	12 (22.2)	18 (23.7)	7 (21.2)	/	1.000	0.820
Diabetes (n, %)	50 (38.5)	15 (27.8)	35 (46.1)	14 (42.4)	/	0.054	0.749
Cerebrovascular disease (n, %)	23 (17.7)	11 (20.4)	12 (15.8)	2 (6.1)	/	0.659	0.099
Immunosuppression (n, %)	13 (10.0)	6 (11.1)	7 ( 9.2)	1 (3.0)	/	0.953	0.203

P1: Comparison between survived ARDS and deceased ARDS; P2: Comparison between ARDS group and disease control.

*P < 0.05, **P < 0.01, ***P < 0.001.

No competing interests reported.

Integrative Multi-Omics Analysis Unravels the Host Response Landscape and Reveals a Serum Protein Panel for Early Prognosis Prediction for ARDS

Status:

Version 1

Abstract

Background

Methods

Results

Interpretation

Figures

Introduction

Material and methods

Results

Discussion

Declarations

References

Table 1

Additional Declarations

Supplementary Files

Status:

Version 1