Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis
Ruichen Li
University of Shanghai for Science and Technology, Shanghai, China
Naval Medical Center, Naval Medical University, Shanghai, China
Search for more papers by this authorQiushi Wang
Department of Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University, Shandong, China
Search for more papers by this authorRu Gao
University of Shanghai for Science and Technology, Shanghai, China
Naval Medical Center, Naval Medical University, Shanghai, China
Search for more papers by this authorRutao Shen
The National Center for Liver Cancer, Naval Medical University, Shanghai, China
Search for more papers by this authorQihao Wang
University of Shanghai for Science and Technology, Shanghai, China
Search for more papers by this authorXiuliang Cui
The National Center for Liver Cancer, Naval Medical University, Shanghai, China
Search for more papers by this authorCorresponding Author
Zhiming Jiang
Department of Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University, Shandong, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorCorresponding Author
Lijie Zhang
Department of Information, Changhai Hospital, Naval Medical University, Shanghai, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorCorresponding Author
Jingjing Fang
Naval Medical Center, Naval Medical University, Shanghai, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorRuichen Li
University of Shanghai for Science and Technology, Shanghai, China
Naval Medical Center, Naval Medical University, Shanghai, China
Search for more papers by this authorQiushi Wang
Department of Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University, Shandong, China
Search for more papers by this authorRu Gao
University of Shanghai for Science and Technology, Shanghai, China
Naval Medical Center, Naval Medical University, Shanghai, China
Search for more papers by this authorRutao Shen
The National Center for Liver Cancer, Naval Medical University, Shanghai, China
Search for more papers by this authorQihao Wang
University of Shanghai for Science and Technology, Shanghai, China
Search for more papers by this authorXiuliang Cui
The National Center for Liver Cancer, Naval Medical University, Shanghai, China
Search for more papers by this authorCorresponding Author
Zhiming Jiang
Department of Critical Care Medicine, The First Affiliated Hospital of Shandong First Medical University, Shandong, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorCorresponding Author
Lijie Zhang
Department of Information, Changhai Hospital, Naval Medical University, Shanghai, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorCorresponding Author
Jingjing Fang
Naval Medical Center, Naval Medical University, Shanghai, China
Correspondence:
Zhiming Jiang ([email protected])
Lijie Zhang ([email protected])
Jingjing Fang ([email protected])
Search for more papers by this authorFunding: This study was funded by the National Natural Science Foundation of China (82172877).
Ruichen Li, Qiushi Wang and Ru Gao contributed equally to this work.
ABSTRACT
Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artificial intelligence model, to evaluate the gene importance for sepsis. About 688 important genes were identified, and these genes were enriched in pathways involved in inflammation and immune regulation, such as the PI3K-Akt signalling pathway, necroptosis and the NF-κB signalling pathway. We further selected differentially expressed genes both at bulk and single-cell levels and found TIMP1, GSTO1 and MYL6 exhibited significant different expressions in multiple cell types. Moreover, the expression levels of these 3 genes were correlated with the abundance of important immune cells, such as M-MDSC cells. Further analysis demonstrated that these three genes were highly expressed in sepsis patients with worse outcomes, such as severe, non-survived and shock sepsis patients. Using a drug repositioning strategy, we found navitoclax, curcumin and rotenone could down-regulate and bind to these genes. In conclusion, TIMP1, GSTO1 and MYL6 may serve as promising biomarkers and targets for sepsis treatment.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supporting Information
| Filename | Description |
|---|---|
| cep70031-sup-0001-TableS1.xlsxExcel 2007 spreadsheet , 441 KB |
Table S1. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
References
- 1M. Singer, C. S. Deutschman, C. W. Seymour, et al., “The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),” JAMA 315, no. 8 (2016): 801–810, https://doi.org/10.1001/jama.2016.0287.
- 2K. E. Rudd, S. C. Johnson, K. M. Agesa, et al., “Global, Regional, and National Sepsis Incidence and Mortality, 1990–2017: Analysis for the Global Burden of Disease Study,” Lancet 395, no. 10219 (2020): 200–211, https://doi.org/10.1016/S0140-6736(19)32989-7.
- 3H. C. Prescott and D. C. Angus, “Enhancing Recovery From Sepsis: A Review,” JAMA 319, no. 1 (2018): 62–75, https://doi.org/10.1001/jama.2017.17687.
- 4T. M. Pelaia, M. Shojaei, and A. S. McLean, “The Role of Transcriptomics in Redefining Critical Illness,” Critical Care 27, no. 1 (2023): 89, https://doi.org/10.1186/s13054-023-04364-2.
- 5J. A. Herberg, M. Kaforou, V. J. Wright, et al., “Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children,” JAMA 316, no. 8 (2016): 835–845, https://doi.org/10.1001/jama.2016.11236.
- 6A. Baghela, O. M. Pena, A. H. Lee, et al., “Predicting Sepsis Severity at First Clinical Presentation: The Role of Endotypes and Mechanistic Signatures,” eBioMedicine 75 (2022): 103776, https://doi.org/10.1016/j.ebiom.2021.103776.
- 7A. J. Kwok, A. Allcock, R. C. Ferreira, et al., “Neutrophils and Emergency Granulopoiesis Drive Immune Suppression and an Extreme Response Endotype During Sepsis,” Nature Immunology 24, no. 5 (2023): 767–779, https://doi.org/10.1038/s41590-023-01490-5.
- 8J. L. Katzman, U. Shaham, A. Cloninger, J. Bates, T. Jiang, and Y. Kluger, “DeepSurv: Personalized Treatment Recommender System Using a Cox Proportional Hazards Deep Neural Network,” BMC Medical Research Methodology 18, no. 1 (2018): 24, https://doi.org/10.1186/s12874-018-0482-1.
- 9E. Choi, M. T. Bahadori, J. A. Kulas, et al., “ RETAIN: An Interpretable Predictive Model for Healthcare Using Reverse Time Attention Mechanism,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS'16 Curran Associates Inc., 2016), 3512–3520.
- 10E. Withnell, X. Zhang, K. Sun, and Y. Guo, “XOmiVAE: An Interpretable Deep Learning Model for Cancer Classification Using High-Dimensional Omics Data,” Briefings in Bioinformatics 22, no. 6 (2021): bbab315, https://doi.org/10.1093/bib/bbab315.
- 11V. Bourgeais, F. Zehraoui, M. Ben Hamdoune, and B. Hanczar, “Deep GONet: Self-Explainable Deep Neural Network Based on Gene Ontology for Phenotype Prediction From Gene Expression Data,” BMC Bioinformatics 22, no. Suppl 10 (2021): 455, https://doi.org/10.1186/s12859-021-04370-7.
- 12H. A. Elmarakeby, J. Hwang, R. Arafeh, et al., “Biologically Informed Deep Neural Network for Prostate Cancer Discovery,” Nature 598, no. 7880 (2021): 348–352, https://doi.org/10.1038/s41586-021-03922-4.
- 13A. Shrikumar, P. Greenside, A. Shcherbina, et al., “Not Just a Black Box: Learning Important Features Through Propagating Activation Differences,” 2017, https://doi.org/10.48550/arXiv.1605.01713.
10.48550/arXiv.1605.01713 Google Scholar
- 14Y. Zhang and B. Ning, “Signaling Pathways and Intervention Therapies in Sepsis,” Signal Transduction and Targeted Therapy 6, no. 1 (2021): 1–36, https://doi.org/10.1038/s41392-021-00816-9.
- 15H. Li, K. Li, Y. Xing, et al., “Phenylephrine Attenuated Sepsis-Induced Cardiac Inflammation and Mitochondrial Injury Through an Effect on the PI3K/Akt Signaling Pathway,” Journal of Cardiovascular Pharmacology 73, no. 3 (2019): 186–194, https://doi.org/10.1097/FJC.0000000000000651.
- 16D. Bertheloot, E. Latz, and B. S. Franklin, “Necroptosis, Pyroptosis and Apoptosis: An Intricate Game of Cell Death,” Cellular & Molecular Immunology 18, no. 5 (2021): 1106–1121, https://doi.org/10.1038/s41423-020-00630-3.
- 17T. Liu, L. Zhang, D. Joo, and S.-C. Sun, “NF-κB Signaling in Inflammation,” Signal Transduction and Targeted Therapy 2, no. 1 (2017): 17023, https://doi.org/10.1038/sigtrans.2017.23.
- 18Y.-R. Miao, Q. Zhang, Q. Lei, et al., “ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and Its Application in Cancer Immunotherapy,” Advanced Science 7, no. 7 (2020): 1902880, https://doi.org/10.1002/advs.201902880.
- 19A. Subramanian, R. Narayan, S. M. Corsello, et al., “A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles,” Cell 171, no. 6 (2017): 1437–1452.e17, https://doi.org/10.1016/j.cell.2017.10.049.
- 20S. K. Burley, C. Bhikadiya, C. Bi, et al., “RCSB Protein Data Bank (RCSB.Org): Delivery of Experimentally-Determined PDB Structures Alongside One Million Computed Structure Models of Proteins From Artificial Intelligence/Machine Learning,” Nucleic Acids Research 51, no. D1 (2023): D488–D508, https://doi.org/10.1093/nar/gkac1077.
- 21J. Abramson, J. Adler, J. Dunger, et al., “Accurate Structure Prediction of Biomolecular Interactions With AlphaFold 3,” Nature 630, no. 8016 (2024): 493–500, https://doi.org/10.1038/s41586-024-07487-w.
- 22J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli, “AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings,” Journal of Chemical Information and Modeling 61, no. 8 (2021): 3891–3898, https://doi.org/10.1021/acs.jcim.1c00203.
- 23B. Schoeps, J. Frädrich, and A. Krüger, “Cut Loose TIMP-1: An Emerging Cytokine in Inflammation,” Trends in Cell Biology 33, no. 5 (2023): 413–426, https://doi.org/10.1016/j.tcb.2022.08.005.
- 24L. Lorente, M. Martín, F. Plasencia, et al., “The 372 T/C Genetic Polymorphism of TIMP-1 Is Associated With Serum Levels of TIMP-1 and Survival in Patients With Severe Sepsis,” Critical Care 17, no. 3 (2013): R94, https://doi.org/10.1186/cc12739.
- 25S. Zhu, M. Ashok, J. Li, et al., “Spermine Protects Mice Against Lethal Sepsis Partly by Attenuating Surrogate Inflammatory Markers,” Molecular Medicine 15, no. 7-8 (2009): 275–282, https://doi.org/10.2119/molmed.2009.00062.
- 26S. Li, L. Wang, Z. Xu, et al., “ASC Deglutathionylation Is a Checkpoint for NLRP3 Inflammasome Activation,” Journal of Experimental Medicine 218, no. 9 (2021): e20202637, https://doi.org/10.1084/jem.20202637.
- 27D. Menon, R. Coll, L. A. J. O'Neill, and P. G. Board, “GSTO1-1 Modulates Metabolism in Macrophages Activated Through the LPS and TLR4 Pathway,” Journal of Cell Science 128, no. 10 (2015): 1982–1990, https://doi.org/10.1242/jcs.167858.
- 28A. Zhang, X. Wang, W. Lin, H. Zhu, and J. Pan, “Identification and Verification of Disulfidptosis-Related Genes in Sepsis-Induced Acute Lung Injury,” Frontiers in Medicine 11 (2024): 1430252, https://doi.org/10.3389/fmed.2024.1430252.
- 29M. Yoshinari, Y. Nishibata, S. Masuda, et al., “Low Disease Activity of Microscopic Polyangiitis in Patients With Anti-Myosin Light Chain 6 Antibody That Disrupts Actin Rearrangement Necessary for Neutrophil Extracellular Trap Formation,” Arthritis Research & Therapy 24, no. 1 (2022): 274, https://doi.org/10.1186/s13075-022-02974-9.
- 30O.-S. Kwon, W. Kim, H.-J. Cha, and H. Lee, “In Silico Drug Repositioning: From Large-Scale Transcriptome Data to Therapeutics,” Archives of Pharmacal Research 42, no. 10 (2019): 879–889, https://doi.org/10.1007/s12272-019-01176-3.
- 31Y. Zhang, L. Tang, J. Lu, L. M. Xu, B. L. Cheng, and J. Y. Xiong, “ABT-263 Enhanced Bacterial Phagocytosis of Macrophages in Aged Mouse Through Beclin-1-Dependent Autophagy,” BMC Geriatrics 21, no. 1 (2021): 225, https://doi.org/10.1186/s12877-021-02173-2.
- 32N. N. Mohamad Anuar, N. S. Nor Hisam, S. L. Liew, and A. Ugusman, “Clinical Review: Navitoclax as a Pro-Apoptotic and Anti-Fibrotic Agent,” Frontiers in Pharmacology 11 (2020): 564108, https://doi.org/10.3389/fphar.2020.564108.
- 33M. A. Rahman, A. A. Shuvo, A. K. Bepari, et al., “Curcumin Improves D-Galactose and Normal-Aging Associated Memory Impairment in Mice: In Vivo and In Silico-Based Studies,” PLoS One 17, no. 6 (2022): e0270123, https://doi.org/10.1371/journal.pone.0270123.
- 34L. Chen, Y. Lu, L. Zhao, et al., “Curcumin Attenuates Sepsis-Induced Acute Organ Dysfunction by Preventing Inflammation and Enhancing the Suppressive Function of Tregs,” International Immunopharmacology 61 (2018): 1–7, https://doi.org/10.1016/j.intimp.2018.04.041.
- 35H. Xu, J. Chen, X. Si, et al., “PKR Inhibition Mediates Endotoxin Tolerance in Macrophages Through Inactivation of PI3K/AKT Signaling,” Molecular Medicine Reports 17, no. 6 (2018): 8548–8556, https://doi.org/10.3892/mmr.2018.8869.
- 36H. R. Wong, N. Cvijanovich, R. Lin, et al., “Identification of Pediatric Septic Shock Subclasses Based on Genome-Wide Expression Profiling,” BMC Medicine 7 (2009): 34, https://doi.org/10.1186/1741-7015-7-34.
- 37J. Park, I. Munagala, H. Xu, et al., “Interferon Signature in the Blood in Inflammatory Common Variable Immune Deficiency,” PLoS One 8, no. 9 (2013): e74893, https://doi.org/10.1371/journal.pone.0074893.
- 38B. P. Scicluna, F. Uhel, L. A. van Vught, et al., “The Leukocyte Non-Coding RNA Landscape in Critically Ill Patients With Sepsis,” eLife 9 (2020): e58597, https://doi.org/10.7554/eLife.58597.
- 39Y. Wu, L. Zhang, Y. Zhang, Y. Zhen, and S. Liu, “Bioinformatics Analysis to Screen for Critical Genes Between Survived and Non-Survived Patients With Sepsis,” Molecular Medicine Reports 18, no. 4 (2018): 3737–3743, https://doi.org/10.3892/mmr.2018.9408.
- 40V. Herwanto, B. Tang, Y. Wang, et al., “Blood Transcriptome Analysis of Patients With Uncomplicated Bacterial Infection and Sepsis,” BMC Research Notes 14, no. 1 (2021): 76, https://doi.org/10.1186/s13104-021-05488-w.
- 41E. L. Tsalik, R. J. Langley, D. L. Dinwiddie, et al., “An Integrated Transcriptome and Expressed Variant Analysis of Sepsis Survival and Death,” Genome Medicine 6, no. 11 (2014): 111, https://doi.org/10.1186/s13073-014-0111-5.
- 42M. Gillespie, B. Jassal, R. Stephan, et al., “The Reactome Pathway Knowledgebase 2022,” Nucleic Acids Research 50, no. D1 (2022): D687–D692, https://doi.org/10.1093/nar/gkab1028.
- 43M. E. Ritchie, B. Phipson, D. Wu, et al., “Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies,” Nucleic Acids Research 43, no. 7 (2015): e47, https://doi.org/10.1093/nar/gkv007.
- 44M. I. Love, W. Huber, and S. Anders, “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data With DESeq2,” Genome Biology 15, no. 12 (2014): 550, https://doi.org/10.1186/s13059-014-0550-8.
- 45T. Wu, E. Hu, S. Xu, et al., “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data,” Innovation (Camb) 2, no. 3 (2021): 100141, https://doi.org/10.1016/j.xinn.2021.100141.
- 46A. Das, G. Ariyakumar, N. Gupta, et al., “Identifying Immune Signatures of Sepsis to Increase Diagnostic Accuracy in Very Preterm Babies,” Nature Communications 15, no. 1 (2024): 388, https://doi.org/10.1038/s41467-023-44387-5.
- 47X. Qiu, J. Li, J. Bonenfant, et al., “Dynamic Changes in Human Single-Cell Transcriptional Signatures During Fatal Sepsis,” Journal of Leukocyte Biology 110, no. 6 (2021): 1253–1268, https://doi.org/10.1002/JLB.5MA0721-825R.
- 48D. B. Darden, X. Dong, M. A. Brusko, et al., “A Novel Single Cell RNA-Seq Analysis of Non-Myeloid Circulating Cells in Late Sepsis,” Frontiers in Immunology 12 (2021): 696536, https://doi.org/10.3389/fimmu.2021.696536.
- 49R. Satija, J. A. Farrell, D. Gennert, A. F. Schier, and A. Regev, “Spatial Reconstruction of Single-Cell Gene Expression Data,” Nature Biotechnology 33, no. 5 (2015): 495–502, https://doi.org/10.1038/nbt.3192.
