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Cited by 38Respiratory Support and MechanismsSepsis Diagnosis and TreatmentAdvanced Proteomics Techniques and Applications

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Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

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Lin, Mengna, Xu, Feixiang, Sun, Jian, Song, Jianfeng, Shen, Yao, Lu, Su, Ding, Hailin, Lan, Lulu, Chen, Chen, Ma, Wen, Wu, Xueling, Song, Zhenju, Wang, Weibing·2024·Critical Care
DOI 10.1186/s13054-024-05000-3PMC PMC11218270
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Abstract

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet 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 and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers 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, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the 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 particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the 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), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to 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, offering valuable insights into the prognosis of individuals with ARDS.

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Fig. 1 Study design overview and summary of the serum proteomic analysis of ARDS, disease control, and health control. A Overview of the serum proteomic and metabolic workflow, including cohort construction (survived ARDS: N = 54, deceased ARDS: N = 76, disease control: N = 33, and healthy control: N = 33), serum validation cohort (survived ARDS: N = 98 and deceased ARDS: N = 85) and data analysis (proteomic and metabolic data). B A number of proteins were identified and quantified in three groups. C The top five most abundant proteins are labeled. D The clustering tree of each sample
Fig. 1 Study design overview and summary of the serum proteomic analysis of ARDS, disease control, and health control. A Overview of the serum proteomic and metabolic workflow, including cohort construction (survived ARDS: N = 54, deceased ARDS: N = 76, disease control: N = 33, and healthy control: N = 33), serum validation cohort (survived ARDS: N = 98 and deceased ARDS: N = 85) and data analysis (proteomic and metabolic data). B A number of proteins were identified and quantified in three groups. C The top five most abundant proteins are labeled. D The clustering tree of each sampleCC BY · PubMed Central
Fig. 2 Differentially abundant proteins and functional alterations in ARDS. A PCA discriminates ARDS patients from non-ARDS subjects. The % value indicates the explained variance. Volcano plot displaying differentially abundant proteins between ARDS vs. HC B and ARDS vs. DC C. Each dot represents a protein, with red dots for proteins significantly upregulated in ARDS and blue dots for proteins significantly downregulated in ARDS. P values were calculated using the R package ‘limma’ and adjusted using the Benjamini–Hochberg method. Two-sided P values were calculated. Fold change ≥ 2.0 and FDR-adjusted P < 0.05 is considered statistically significant. D–E KEGG pathway enrichments of ARDS compared with HC and DC groups, respectively. F A Venn diagram elucidates the distribution of DAP across the ARDS, DC, and HC groups. G The heatmap showed the 16 overlapped down-regulated proteins in ARDS. H A chordal graph maps the interaction between 9 common pathways and their associated proteins
Fig. 2 Differentially abundant proteins and functional alterations in ARDS. A PCA discriminates ARDS patients from non-ARDS subjects. The % value indicates the explained variance. Volcano plot displaying differentially abundant proteins between ARDS vs. HC B and ARDS vs. DC C. Each dot represents a protein, with red dots for proteins significantly upregulated in ARDS and blue dots for proteins significantly downregulated in ARDS. P values were calculated using the R package ‘limma’ and adjusted using the Benjamini–Hochberg method. Two-sided P values were calculated. Fold change ≥ 2.0 and FDR-adjusted P < 0.05 is considered statistically significant. D–E KEGG pathway enrichments of ARDS compared with HC and DC groups, respectively. F A Venn diagram elucidates the distribution of DAP across the ARDS, DC, and HC groups. G The heatmap showed the 16 overlapped down-regulated proteins in ARDS. H A chordal graph maps the interaction between 9 common pathways and their associated proteinsCC BY · PubMed Central
Fig. 3 The metabolic analysis and its interaction with proteomics in ARDS in the discovery cohort. A–C Partial least squares discriminant analysis (PLS-DA) showed the separation of ARDS patients from HC and DC groups. D Dot plot of enriched KEGG pathways identified by differentially abundant metabolites. Dot size indicates the number of enriched proteins, colors represent the significance of enrichment (P-value). E Cross-talk network of the significantly enriched pathway by integrating both proteomics and metabolomics. Blue dots represent proteins, green dots are metabolites, and orange dots are enriched pathways. Sphingolipid signaling pathway was highlighted in red. F The relative abundance of proteins and metabolites in sphingolipid signaling pathway across ARDS, HC, and DC groups
Fig. 3 The metabolic analysis and its interaction with proteomics in ARDS in the discovery cohort. A–C Partial least squares discriminant analysis (PLS-DA) showed the separation of ARDS patients from HC and DC groups. D Dot plot of enriched KEGG pathways identified by differentially abundant metabolites. Dot size indicates the number of enriched proteins, colors represent the significance of enrichment (P-value). E Cross-talk network of the significantly enriched pathway by integrating both proteomics and metabolomics. Blue dots represent proteins, green dots are metabolites, and orange dots are enriched pathways. Sphingolipid signaling pathway was highlighted in red. F The relative abundance of proteins and metabolites in sphingolipid signaling pathway across ARDS, HC, and DC groupsCC BY · PubMed Central
Fig. 4 The multi-omics analysis compared deceased ARDS with survived ARDS. A PCA plot displays the proteome separation in deceased and survived ARDS. Each dot represents a sample, with red dots for deceased ARDS samples and green dots for survived ARDS samples. B Volcano plot displaying differentially abundant proteins between deceased and survived ARDS. P values were adjusted using the Benjamini–Hochberg method. C GSEA analysis showed up- and down-regulated pathways in deceased ARDS compared with survived ARDS. D A heatmap showed representative proteins linked to the pathways highlighted in C. The mean difference (deceased ARDS–survived ARDS) of proteins in pathways larger than 0.1 were kept. E Left: PPI network analysis of DAPs between deceased ARDS and survived ARDS. The significance threshold was defined as P < 0.05. The sliding ball chart on the right shows the network degree and log2FC of the corresponding proteins in the PPI network. (F) PLS-DA score plots between deceased and survived ARDS for metabolomics. G The Sankey diagram showed the enriched pathways and their associated DAMs. H The abundance of LysoPC (O-18:0/0:0), LysoPC (15:0/0:0), and S1P between deceased and survived ARDS groups. Statistical significance was determined using the FDR-adjusted P-value. P values were calculated by Wilcoxon’s rank sum test. I Correlations between metabolites mentioned in H and the SOFA scores. The correlation coefficients (simplified as R) and rho values of correlations are indicated
Fig. 4 The multi-omics analysis compared deceased ARDS with survived ARDS. A PCA plot displays the proteome separation in deceased and survived ARDS. Each dot represents a sample, with red dots for deceased ARDS samples and green dots for survived ARDS samples. B Volcano plot displaying differentially abundant proteins between deceased and survived ARDS. P values were adjusted using the Benjamini–Hochberg method. C GSEA analysis showed up- and down-regulated pathways in deceased ARDS compared with survived ARDS. D A heatmap showed representative proteins linked to the pathways highlighted in C. The mean difference (deceased ARDS–survived ARDS) of proteins in pathways larger than 0.1 were kept. E Left: PPI network analysis of DAPs between deceased ARDS and survived ARDS. The significance threshold was defined as P < 0.05. The sliding ball chart on the right shows the network degree and log2FC of the corresponding proteins in the PPI network. (F) PLS-DA score plots between deceased and survived ARDS for metabolomics. G The Sankey diagram showed the enriched pathways and their associated DAMs. H The abundance of LysoPC (O-18:0/0:0), LysoPC (15:0/0:0), and S1P between deceased and survived ARDS groups. Statistical significance was determined using the FDR-adjusted P-value. P values were calculated by Wilcoxon’s rank sum test. I Correlations between metabolites mentioned in H and the SOFA scores. The correlation coefficients (simplified as R) and rho values of correlations are indicatedCC BY · PubMed Central
Fig. 5 Prognostic model development and validation for ARDS. A The flowchart details the methodology for screening candidate biomarkers. B The expression levels of eight proteins between the deceased and survived ARDS in both discovery (the upper panel) and validation cohorts (the lower panel). Receiver operating characteristic curve of protein C, clinical D, and combined model E in the discovery cohort and the validation cohort F–H. The 95% confidence interval is shown between brackets. The sensitivity and specificity of the Glm model were also provided. AUC, area under the curve
Fig. 5 Prognostic model development and validation for ARDS. A The flowchart details the methodology for screening candidate biomarkers. B The expression levels of eight proteins between the deceased and survived ARDS in both discovery (the upper panel) and validation cohorts (the lower panel). Receiver operating characteristic curve of protein C, clinical D, and combined model E in the discovery cohort and the validation cohort F–H. The 95% confidence interval is shown between brackets. The sensitivity and specificity of the Glm model were also provided. AUC, area under the curveCC BY · PubMed Central

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Metadata

Authors
Lin, M., Xu, F., Sun, J., Song, J., Shen, Y., Lu, S., Ding, H., Lan, L., Chen, C., Ma, W., Wu, X., Song, Z., & Wang, W.
Affiliations
  • Fudan University (CN)
  • Shanghai Medical College of Fudan University (CN)
  • Shanghai Jiao Tong University (CN)
Corresponding author
  • Wu, Xueling
  • Song, Zhenju
  • Wang, Weibing
ORCID
Venue
Critical Care
Publisher
Springer Science and Business Media LLC
Published
2024-07-02
Volume
28
Issue
1
Pages
213-213
ISSN
1364-8535 (ISSN-L)
DOI
10.1186/s13054-024-05000-3
PMC
PMC11218270
OpenAlex
W4400258367
Cited by
38
References
1
Citations by year
2026: 25 · 2025: 10 · 2024: 3
FWCI
11.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Critical Care (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Key Research and Development Program of China (2021YFC2501800)
  • National Natural Science Foundation of China (82072214)
  • Science and Technology of Shanghai Committee (23Y31900100)
  • Shanghai Municipal Science and Technology Major Project (ZD2021CY001)
  • Shanghai New Three-year Action Plan for Public Health (GWVI-11.1-03)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory Support and Mechanisms
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords
acute respiratory distress syndrome, serum biomarkers, multi-omics integration, proteomics, metabolomics, sphingolipid signaling, sphingosine-1-phosphate, MEK1, glycolysis, unfolded protein response, protein-protein interaction network, VCAM-1, LDHB, MBL2, low back pain, early prediction model, host response
MeSH terms
Multiomics, Aged, Aged, 80 and over, Blood Proteins, Female, Humans, Male, Middle Aged, Prognosis, Respiratory Distress Syndrome, Cohort Studies, Biomarkers, Proteomics, Metabolomics

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