Novel Time-dependent Multi-omics Integration in Sepsis-associated Liver Dysfunction

Overview

Journal Genomics Proteomics Bioinformatics

Specialty Biology

Date 2023 Apr 21

PMID 37084954

Authors

Ann-Yae Na

Hyojin Lee

Eun Ki Min

Sanjita Paudel

So Young Choi

Hyunchae Sim

Kwang-Hyeon Liu

Ki-Tae Kim

Jong-Sup Bae

Sangkyu Lee

Affiliations

Soon will be listed here.

Abstract

The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes. However, it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes. Here, we conducted a time-dependent multi-omics integration (TDMI) in a sepsis-associated liver dysfunction (SALD) model. We successfully deduced the relation of the Toll-like receptor 4 (TLR4) pathway with SALD. Although TLR4 is a critical factor in sepsis progression, it is not specified in single-omics analyses but only in the TDMI analysis. This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD. Furthermore, TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology, health, and diseases, thus allowing the identification of promising candidates for therapeutic strategies.

References

Guven-Maiorov E, Keskin O, Gursoy A, VanWaes C, Chen Z, Tsai C . The Architecture of the TIR Domain Signalosome in the Toll-like Receptor-4 Signaling Pathway. Sci Rep. 2015; 5:13128. PMC: 4544004. DOI: 10.1038/srep13128. View

Marshall J . New translational research provides insights into liver dysfunction in sepsis. PLoS Med. 2012; 9(11):e1001341. PMC: 3496662. DOI: 10.1371/journal.pmed.1001341. View

Min S, Lee B, Yoon S . Deep learning in bioinformatics. Brief Bioinform. 2016; 18(5):851-869. DOI: 10.1093/bib/bbw068. View

Alaish S, Timmons J, Smith A, Buzza M, Murphy E, Zhao A . CANDIDATE GENES FOR LIMITING CHOLESTATIC INTESTINAL INJURY IDENTIFIED BY GENE EXPRESSION PROFILING. Physiol Rep. 2013; 1(4). PMC: 3808870. DOI: 10.1002/phy2.73. View

Ebong S, Call D, Nemzek J, Bolgos G, NEWCOMB D, Remick D . Immunopathologic alterations in murine models of sepsis of increasing severity. Infect Immun. 1999; 67(12):6603-10. PMC: 97073. DOI: 10.1128/IAI.67.12.6603-6610.1999. View

Kempthorne C, Nielsen A, Wilson D, McNulty J, Cameron R, Liscombe D . Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae. Phytochemistry. 2021; 187:112747. DOI: 10.1016/j.phytochem.2021.112747. View

Buescher J, Driggers E . Integration of omics: more than the sum of its parts. Cancer Metab. 2016; 4:4. PMC: 4761192. DOI: 10.1186/s40170-016-0143-y. View

Hasin Y, Seldin M, Lusis A . Multi-omics approaches to disease. Genome Biol. 2017; 18(1):83. PMC: 5418815. DOI: 10.1186/s13059-017-1215-1. View

Cavill R, Jennen D, Kleinjans J, Briede J . Transcriptomic and metabolomic data integration. Brief Bioinform. 2015; 17(5):891-901. DOI: 10.1093/bib/bbv090. View

10.

Canzler S, Schor J, Busch W, Schubert K, Rolle-Kampczyk U, Seitz H . Prospects and challenges of multi-omics data integration in toxicology. Arch Toxicol. 2020; 94(2):371-388. DOI: 10.1007/s00204-020-02656-y. View

11.

Uppal K, Ma C, Go Y, Jones D, Wren J . xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics. 2017; 34(4):701-702. PMC: 5860615. DOI: 10.1093/bioinformatics/btx656. View

12.

Liu Y, Ding J, Reynolds L, Lohman K, Register T, de la Fuente A . Methylomics of gene expression in human monocytes. Hum Mol Genet. 2013; 22(24):5065-74. PMC: 3836482. DOI: 10.1093/hmg/ddt356. View

13.

Boehme M, Guenther M, Stahr A, Liebmann M, Jaenisch N, Witte O . Impact of indomethacin on neuroinflammation and hippocampal neurogenesis in aged mice. Neurosci Lett. 2014; 572:7-12. DOI: 10.1016/j.neulet.2014.04.043. View

14.

Wang F, Li Z, Song T, Jia Y, Qi L, Ren L . Proteomics study on the effect of silybin on cardiomyopathy in obese mice. Sci Rep. 2021; 11(1):7136. PMC: 8009917. DOI: 10.1038/s41598-021-86717-x. View

15.

Guo J, Zhao J, Liu R, Yu J, Zhang M, Wang H . Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms. Metabolomics. 2021; 17(4):38. DOI: 10.1007/s11306-021-01788-1. View

16.

Ten Have G, Engelen M, Wolfe R, Deutz N . Phenylalanine isotope pulse method to measure effect of sepsis on protein breakdown and membrane transport in the pig. Am J Physiol Endocrinol Metab. 2017; 312(6):E519-E529. PMC: 5494580. DOI: 10.1152/ajpendo.00351.2016. View

17.

Chiarla C, Giovannini I, Miggiano G . Plasma amino acids of the transsulfuration pathway and plasma lactate in septic patients. Clin Ter. 2017; 168(2):e72-e76. DOI: 10.7417/CT.2017.1986. View

18.

Rardin M, Wiley S, Naviaux R, Murphy A, Dixon J . Monitoring phosphorylation of the pyruvate dehydrogenase complex. Anal Biochem. 2009; 389(2):157-64. PMC: 2713743. DOI: 10.1016/j.ab.2009.03.040. View

19.

Graw S, Chappell K, Washam C, Gies A, Bird J, Robeson 2nd M . Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics. 2020; 17(2):170-185. PMC: 8058243. DOI: 10.1039/d0mo00041h. View

20.

Wang C, Deng L, Hong M, Akkaraju G, Inoue J, Chen Z . TAK1 is a ubiquitin-dependent kinase of MKK and IKK. Nature. 2001; 412(6844):346-51. DOI: 10.1038/35085597. View