Abstract
Sepsis is a complex systemic inflammatory syndrome that currently lacks stable and specific biomarkers. Multi-omics integration combined with machine learning and single-cell analysis offers new approaches for elucidating molecular mechanisms and nominating candidate regulators for further validation. Establish an integrated analytical framework combining transcriptomics, proteomics, metabolomics, and single-cell transcriptomics to identify sepsis-associated candidates and generate testable hypotheses for downstream mechanistic and translational studies. A multi-omics integrative framework was established by combining transcriptomic, proteomic, and metabolomic data. Weighted gene co-expression network analysis (WGCNA) was first applied to identify modules significantly associated with sepsis, and key candidate genes were further refined using LASSO regression and SVM-RFE algorithms. Compare differentially expressed transcripts and proteins through quadrant analysis. Further leverage open-access proteome-phenome resources to assess the association between priority target proteins and sepsis-related phenotypes/disease risk. Metabolomic profiling was conducted to identify metabolites significantly correlated with the core genes and their enriched pathways. A joint gene–metabolite classification model was then constructed using logistic regression, support vector machines, and random forests, with predictive performance evaluated using ROC curve analysis. LASSO regression was used to visualize coefficient shrinkage and effect direction, while random forest–based feature importance quantified the relative contribution of each variable. Finally, leveraging the ITCM database, an in silico screening was performed to identify natural compounds with potential regulatory effects on TPR and ERN1 expression. TPR and ERN1 were prioritized as key candidate biomarkers associated with sepsis. External validation in GEO cohorts and analyses of protein–disease risk association databases further supported their reproducible disease association in external datasets, motivating further validation of their prognostic/diagnostic potential. Non-targeted metabolomics screening identified 136 differentially expressed metabolites associated with them, primarily enriched in glycerophospholipid metabolism and fatty acid-related pathways. Metabolite-gene correlation integration linked these metabolic alterations to TPR/ERN1, suggesting their potential involvement in immune-metabolic pathway remodeling associated with sepsis heterogeneity. Single-cell transcriptomics provided cellular origin-level support, showing enrichment of TPR and ERN1 expression in immune subpopulations, including monocyte-macrophages and NK cells. A joint gene-metabolite model constructed using a fixed feature set (2 genes + 5 metabolites) demonstrated strong discriminatory potential in an internally derived cohort, though external validation in independent, multi-omics matched cohorts remains urgently needed. Finally, exploratory computational screening based on the ITCM database identified several potential active monomers in traditional Chinese medicine that may influence TPR or ERN1 expression, providing hypothesis-generating clues for subsequent mechanistic studies and drug translation. By integrating multi-omics data, TPR and ERN1 were identified as sepsis-associated candidate biomarkers and linked to immune-metabolic alterations associated with immune cells. The combined gene-metabolite signature panel and exploratory compound screening provide a hypothesis-generating framework for downstream experimental validation and clinical study design.
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Data availability
Histological data from 21 sepsis patients and 10 healthy volunteers are available through the Chinese National GeneBank DataBase (CNGBdb) at db.cngb.org/ under accession number CNP0002611 (permanent link). Public transcriptomic datasets used in this study are available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE65682, GSE163151, GSE232753, GSE95233, GSE69063, GSE243217, GSE236713, GSE185263, GSE154918, GSE134347, and GSE100159. Additional datasets generated and/or analyzed during the current study are included in the Supplementary Information files. The code and scripts are available upon request from the corresponding author.
References
Angus, DC, Bindman, AB, (2022). Achieving Diagnostic Excellence for Sepsis. JAMA-J AM MED ASSOC, 327 JAMA-J AM MED ASSOC. https://doi.org/10.1001/jama.2021.23916
Rudd, K. E., Johnson, S. C., Agesa, K. M., Shackelford, K. A., Tsoi, D., Kievlan, D. R., Colombara, D. V., Ikuta, K. S., Kissoon, N., Finfer, S., Fleischmann-Struzek, C., Machado, F. R., Reinhart, K. K., Rowan, K., Seymour, C. W., Watson, R. S., West, T. E., Marinho, F., Hay, S. I., Lozano, R., … Naghavi, M. (2020). Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the Global Burden of Disease Study. Lancet (London, England), 395(10219), 200–211. https://doi.org/10.1016/S0140-6736(19)32989-7
Jacob, S. T., Banura, P., Baeten, J. M., Moore, C. C., Meya, D., Nakiyingi, L., Burke, R., Horton, C. L., Iga, B., Wald, A., Reynolds, S. J., Mayanja-Kizza, H., Scheld, W. M., & Promoting Resource-Limited Interventions for Sepsis Management in Uganda Study Group. The impact of early monitored management on survival in hospitalized adult Ugandan patients with severe sepsis: A prospective intervention study*. Crit. Care Med. 40(7), 2050–2058. https://doi.org/10.1097/CCM.0b013e31824e65d7 (2012).
Gupta, R. G., Hartigan, S. M., Kashiouris, M. G., Sessler, C. N. & Bearman, G. M. Early goal-directed resuscitation of patients with septic shock: Current evidence and future directions. Critical care (London, England) 19(1), 286 (2015).
Song, L. et al. Post-translational modifications in sepsis-induced organ dysfunction: Mechanisms and implications. Front. Immunol. 15, 1461051. https://doi.org/10.3389/fimmu.2024.1461051 (2024).
Schuurman, A. R., Chouchane, O., Butler, J. M., Peters-Sengers, H., Joosten, S., Brands, X., Haak, B. W., Otto, N. A., Uhel, F., Klarenbeek, A., van Linge, C. C., van Kampen, A., Pras-Raves, M., van Weeghel, M., van Eijk, M., Ferraz, M. J., Faber, D. R., de Vos, A., Scicluna, B. P., Vaz, F. M., … van der Poll, T. (2024). The shifting lipidomic landscape of blood monocytes and neutrophils during pneumonia. JCI insight, 9(4), e164400. https://doi.org/10.1172/jci.insight.164400
Birner, C. et al. Lipid Metabolism Disorders as Diagnostic Biosignatures in Sepsis. Infectious disease reports 16(5), 806–819. https://doi.org/10.3390/idr16050062 (2024).
Liu, J., Zhou, G., Wang, X. & Liu, D. Metabolic reprogramming consequences of sepsis: Adaptations and contradictions. Cellular and molecular life sciences : CMLS 79(8), 456. https://doi.org/10.1007/s00018-022-04490-0 (2022).
Assis, P. A., Allen, R. M., Schaller, M. A., Kunkel, S. L., & Bermick, J. R. (2024). Metabolic reprogramming and dysregulated IL-17 production impairs CD4 T cell function post sepsis. iScience, 27(7), 110114. https://doi.org/10.1016/j.isci.2024.110114
Wang, Y. et al. Immunophenotyping of Peripheral Blood Mononuclear Cells in Septic Shock Patients With High-Dimensional Flow Cytometry Analysis Reveals Two Subgroups With Differential Responses to Immunostimulant Drugs. Front. Immunol. 12, 634127. https://doi.org/10.3389/fimmu.2021.634127 (2021).
Hartman, E. et al. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nat. Commun. 14(1), 5359. https://doi.org/10.1038/s41467-023-41146-4 (2023).
Bomrah, S. et al. A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: A step towards explainability. Critical care (London, England) 28(1), 180. https://doi.org/10.1186/s13054-024-04948-6 (2024).
Lee, Y. S., Han, S., Lee, Y. E., Cho, J., Choi, Y. K., Yoon, S. Y., Oh, D. K., Lee, S. Y., Park, M. H., Lim, C. M., Moon, J. Y., & Korean Sepsis Alliance (KSA) Investigators (2024). Development and validation of an interpretable model for predicting sepsis mortality across care settings. Scientific reports, 14(1), 13637. https://doi.org/10.1038/s41598-024-64463-0
Antcliffe, D. B., Burnham, K. L., Al-Beidh, F., Santhakumaran, S., Brett, S. J., Hinds, C. J., Ashby, D., Knight, J. C., & Gordon, A. C. (2019). Transcriptomic Signatures in Sepsis and a Differential Response to Steroids. From the VANISH Randomized Trial. American journal of respiratory and critical care medicine, 199(8), 980–986. https://doi.org/10.1164/rccm.201807-1419OC
Tsou, C. C. et al. DIA-Umpire: Comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12(3), 258–264. https://doi.org/10.1038/nmeth.3255 (2015).
Wishart, DS., Feunang, YD., Marcu, A., Guo, AC., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., Sayeeda, Z., Lo, E., Assempour, N., Berjanskii, M., Singhal, S., Arndt, D., Liang, Y., Badran, H., Grant, J., Serra-Cayuela, A., Liu, Y., Mandal, R., Neveu, V., Pon, A., Knox, C., Wilson, M., Manach, C., Scalbert, A, (2018). HMDB 4.0: The human metabolome database for 2018. NUCLEIC ACIDS RES, 46 (D1), D608-D617.
Li, S. et al. Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis. Sci Rep 14(1), 23130. https://doi.org/10.1038/s41598-024-74040-0 (2024).
Li, Y., Wang, C. & Chen, M. Metabolomics-based study of potential biomarkers of sepsis. Sci Rep 13(1), 585. https://doi.org/10.1038/s41598-022-24878-z (2023).
Li, S. et al. Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis. Sci. Rep. 14(1), 23130. https://doi.org/10.1038/s41598-024-74040-0 (2024).
Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 9, 559. https://doi.org/10.1186/1471-2105-9-559 (2008).
Choi, M. et al. MSstats: An R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30(17), 2524–2526. https://doi.org/10.1093/bioinformatics/btu305 (2014).
Xue, Z., Zhu, T., Zhang, F., Zhang, C., Xiang, N., Qian, L., Yi, X., Sun, Y., Liu, W., Cai, X., Wang, L., Dai, X., Yue, L., Li, L., Pham, TV., Piersma, SR., Xiao, Q., Luo, M., Lu, C., Zhu, J., Zhao, Y., Wang, G., Xiao, J., Liu, T., Liu, Z., He, Y., Wu, Q., Gong, T., Zhu, J., Zheng, Z., Ye, J., Li, Y., Jimenez, CR., A, J., Guo, T, (2023). DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins. Patterns (N Y), 4 (7), 100792. https://doi.org/10.1016/j.patter.2023.100792
Mehnert, M. et al. Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes. Nat Commun 11(1), 3563. https://doi.org/10.1038/s41467-020-17387-y (2020).
Mu, H. et al. OmicShare tools: A zero-code interactive online platform for biological data analysis and visualization. Imeta 3(5), e228. https://doi.org/10.1002/imt2.228 (2024).
Friedman, J., Hastie, T. & Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 33(1), 1–22 (2010).
Wang, Q. & Liu, X. Screening of feature genes in distinguishing different types of breast cancer using support vector machine. Onco. Targets. Ther. 8, 2311–2317 (2015).
Xu, J. H., Chang, W. H., Fu, H. W., Yuan, T. & Chen, P. The mRNA, miRNA and lncRNA networks in hepatocellular carcinoma: An integrative transcriptomic analysis from Gene Expression Omnibus. Mol. Med. Rep. 17(5), 6472–6482. https://doi.org/10.3892/mmr.2018.8694 (2018).
Deng, Y. T. et al. Atlas of the plasma proteome in health and disease in 53,026 adults. Cell 188(1), 253-271.e7. https://doi.org/10.1016/j.cell.2024.10.045 (2024).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 16(5), 284–287. https://doi.org/10.1089/omi.2011.0118 (2012).
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44(D1), D457–D462. https://doi.org/10.1093/nar/gkv1070 (2016).
Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. P NATL ACAD SCI USA 102(43), 15545–15550. https://doi.org/10.1073/pnas.0506580102 (2005).
Zheng, X. et al. Predictive modeling of mortality risk in septic shock patients based on supervised machine learning algorithm. Chinese Emergency Medicine for Critical Illness 36(4), 345–352. https://doi.org/10.3760/cma.j.cn121430-20230930-00832 (2024).
Tian, S., Zhang, J., Yuan, S., Wang, Q., Lv, C., Wang, J., Fang, J., Fu, L., Yang, J., Zu, X., Zhao, J., & Zhang, W. (2023). Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Briefings in bioinformatics, 24(2), bbad027. https://doi.org/10.1093/bib/bbad027
ZHANG Cuicui, QIN Tingting, MENG Zhaoting, LI Kai. Research advances in abnormal lipid metabolism of lung cancer. China Oncology, 2021, 31(1): 76–80.
Dou, H. et al. Oxidized Phospholipids Promote NETosis and Arterial Thrombosis in LNK(SH2B3) Deficiency. Circulation 144(24), 1940–1954. https://doi.org/10.1161/CIRCULATIONAHA.121.056414 (2021).
Wang, B, Wu, L, Chen, J, Dong, L, Chen, C, Wen, Z, Hu, J, Fleming, I, Wang, DW, (2021). Metabolism pathways of arachidonic acids: Mechanisms and potential therapeutic targets. SIGNAL TRANSDUCT TAR, 6 SIGNAL TRANSDUCT TAR. https://doi.org/10.1038/s41392-020-00443-w
O'Flaherty, J. T., Lees, C. J., Miller, C. H., McCall, C. E., Lewis, J. C., Love, S. H., & Wykle, R. L. (1981). Selective desensitization of neutrophils: Further studies with 1-O-alkyl-sn-glycero-3-phosphocholine analogues. Journal of immunology (Baltimore, Md. : 1950), 127(2), 731–737.
Krull, S., Thyberg, J., Björkroth, B., Rackwitz, H. R. & Cordes, V. C. Nucleoporins as components of the nuclear pore complex core structure and TPR as the architectural element of the nuclear basket. Mol. Biol. Cell 15(9), 4261–4277. https://doi.org/10.1091/mbc.e04-03-0165 (2004).
Krull, S. et al. Protein Tpr is required for establishing nuclear pore-associated zones of heterochromatin exclusion. EMBO J. 29(10), 1659–1673. https://doi.org/10.1038/emboj.2010.54 (2010).
Raices, M. & D’Angelo, M. A. Nuclear pore complex composition: A new regulator of tissue-specific and developmental functions. Nat. Rev. Mol. Cell Biol. 13(11), 687–699. https://doi.org/10.1038/nrm3461 (2012).
Hetz, C., Zhang, K. & Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. Nat. Rev. Mol. Cell Biol. 21(8), 421–438. https://doi.org/10.1038/s41580-020-0250-z (2020).
Sheppard, T. Unfolded protein response: Letting go of stress NAT CHEM BIOL 10(11), 877–877. https://doi.org/10.1038/nchembio.1676 (2014).
Hetz, C., Martinon, F., Rodriguez, D. & Glimcher, L. H. The unfolded protein response: Integrating stress signals through the stress sensor IRE1α. Physiol. Rev. 91(4), 1219–1243. https://doi.org/10.1152/physrev.00001.2011 (2011).
Eckerle, M. et al. Metabolomics as a Driver in Advancing Precision Medicine in Sepsis. Pharmacotherapy 37(9), 1023–1032. https://doi.org/10.1002/phar.1974 (2017).
Xiao, X., Ye, M., Xu, Y. & Jiang, C. Zhonghua wei zhong bing ji jiu yi xue 32(3), 382–384. https://doi.org/10.3760/cma.j.cn121430-20200226-00238 (2020).
Li, P., Li, X., Wu, Y., Li, M., & Wang, X. (2017). A novel AMPK activator hernandezine inhibits LPS-induced TNFα production. Oncotarget, 8(40), 67218–67226. https://doi.org/10.18632/oncotarget.18365
Yu, T. et al. Hernandezine alleviates inflammatory bowel disease by enhancing gut barrier integrity via the AMPK/NRF2 pathway and regulating immune-metabolic balance. Int Immunopharmacol 163, 115230. https://doi.org/10.1016/j.intimp.2025.115230 (2025).
Córdova-Isaza, A., Jiménez-Mármol, S., Guerra, Y., Salas-Sarduy, E, (2023). Enzyme Inhibitors from Gorgonians and Soft Corals. Mar Drugs, 21 (2),
Yaxuan, S. I. & Yong, Y. A. N. G. Metabolic Regulation and Intervention Strategies of Tumor Infiltrating T Cells. Progress in Pharmaceutical Sciences 47(1), 56–65. https://doi.org/10.20053/j.issn1001-5094.2023.01.006 (2023).
Liang, J., Li, T., Zhao, J., Wang, C. & Sun, H. Current understanding of the human microbiome in glioma. Front Oncol 12, 781741. https://doi.org/10.3389/fonc.2022.781741 (2022).
Li, F., Zhao, Y., Wei, L., Li, S. & Liu, J. Tumor-infiltrating Treg, MDSC, and IDO expression associated with outcomes of neoadjuvant chemotherapy of breast cancer. Cancer Biol. Ther. 19(8), 695–705. https://doi.org/10.1080/15384047.2018.1450116 (2018).
Rocha, S. M. et al. Histamine induces microglia activation and dopaminergic neuronal toxicity via H1 receptor activation. J. Neuroinflammation 13(1), 137. https://doi.org/10.1186/s12974-016-0600-0 (2016).
Yuan, X., Zhu, J., Kang, Q., He, X., & Guo, D. (2019). Protective Effect of Hesperidin Against Sepsis-Induced Lung Injury by Inducing the Heat-Stable Protein 70 (Hsp70)/Toll-Like Receptor 4 (TLR4)/ Myeloid Differentiation Primary Response 88 (MyD88) Pathway. Medical science monitor : International medical journal of experimental and clinical research, 25, 107–114. https://doi.org/10.12659/MSM.912490
Ny, V., Houška, M., Pavela, R. & Tříska, J. Potential benefits of incorporating Astragalus membranaceus into the diet of people undergoing disease treatment: An overview. Journal of Functional Foods 77, 104339 (2021).
Lee, J. H., Koo, T. H., Hwang, B. Y. & Lee, J. J. Kaurane diterpene, kamebakaurin, inhibits NF-kappa B by directly targeting the DNA-binding activity of p50 and blocks the expression of antiapoptotic NF-kappa B target genes. J. Biol. Chem. 277(21), 18411–18420. https://doi.org/10.1074/jbc.M201368200 (2002).
Zhou Y, Su X, Wang C, Alarfaj AA, Hajinur Hirad A. Toosendanin Attenuates Mycoplasma pneumonia Induced Pneumonia (MPP) in Mice via Inhibiting NF-κB-Mediated Inflammatory Response: In vivo and silico Studies. Dose-Response. 2025;23(3). https://doi.org/10.1177/15593258251367619
Mo, C. et al. The crosstalk between Nrf2 and AMPK signal pathways is important for the anti-inflammatory effect of berberine in LPS-stimulated macrophages and endotoxin-shocked mice. Antioxid. Redox Signal. 20(4), 574–588. https://doi.org/10.1089/ars.2012.5116 (2014).
Li, C., Liu, K., Liu, S., Aerqin, Q. & Wu, X. Role of Ginkgolides in the Inflammatory Immune Response of Neurological Diseases: A Review of Current Literatures. Front Syst Neurosci. 31(14), 45. https://doi.org/10.3389/fnsys.2020.00045.PMID:32848639;PMCID:PMC7411855 (2020).
Zhang, J. et al. Cantharidin: A double-edged sword in medicine and toxicology. Front. Pharmacol. 16, 1644186. https://doi.org/10.3389/fphar.2025.1644186 (2025).
Acknowledgements
We thank the open-access proteome-phenome resource database established by Huashan Hospital of Fudan University (https://proteome-phenome-atlas.com/) and BGI for guidance on single-cell sequencing.
Funding
Financial support for this research was provided by the Science and Technology Bureau of Luzhou City (Grant No. 2024LZXNYDJ038), the Sichuan Provincial Medical Research Project (Grant No. S20250001), and the Sichuan Science and Technology Department Project (Grant No. 2026YFHZ0136).
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X.L. performed the data analysis, prepared the figures, and drafted the manuscript. G.K. was responsible for sample collection and data curation. Y.H. contributed to the initial study conception and design. M.C. reviewed and edited the manuscript and provided overall supervision. All authors read and approved the final manuscript.
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This investigation was performed in full alignment with the Declaration of Helsinki. The ethics committee of the Affiliated Hospital of Southwest Medical University approved the study protocol (Ethical Approval No. ky2018029). The trial is registered under ChiCTR1900021261.
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Li, X., Ke, G., Hu, Y. et al. A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37342-z
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DOI: https://doi.org/10.1038/s41598-026-37342-z
