Time-Dependent Multi-Omics Integration in Sepsis-Associated Liver Dysfunction

Introduction

Sepsis, especially severe cases, causes multiple organ dysfunction due to systemic infection, resulting in up to 5 million deaths globally each year. Traditionally, Sepsis-Associated Liver Dysfunction (SALD) was considered a condition accompanied by jaundice and hyperbilirubinemia. With deeper research, it has been discovered that liver dysfunction occurs in the early stages of sepsis, but there is currently no specific treatment for this disease.

In recent years, Multi-Omics Technologies have rapidly developed, such as genomics, transcriptomics, proteomics, and metabolomics, significantly promoting the development of personalized medicine. However, single omics analysis often only provides information on one level of biological complexity. Therefore, integrating multi-omics data may bring new insights into the molecular mechanisms of complex diseases.

Research Background and Objectives

Given the limitations of existing research methods, this study aims to explore the molecular mechanisms in sepsis-associated liver dysfunction models through Time-Dependent Multi-Omics Integration (TDMI). This paper is the first to use the TDMI method, which is more effective than single omics analysis in associating the pathophysiological processes of sepsis with clinical outcomes.

Authors and Publication Information

This article was written by Ann-Yae Na, Hyojin Lee, Eun Ki Min, et al., affiliated with institutions such as the College of Pharmacy, Kyungpook National University, Korea, and the Department of Environmental Engineering, Seoul National University of Science and Technology. The paper was published in April 2023 in the journal “Genomics Proteomics Bioinformatics”.

Research Methods

Overall Process

This study designed multi-omics data integration experiments at multiple time points to clarify the molecular mechanisms in the early stages of sepsis. The research mainly included the acquisition and analysis of transcriptomics, phosphoproteomics, proteomics, and metabolomics data. The specific process is as follows:

1. Sample Processing and Data Acquisition:

   • Introduced sepsis model through cecal ligation and puncture (CLP) in 7-week-old male ICR mice.
   • Samples were taken at 0 hours, 4 hours, 6 hours, 15 hours, and 18 hours to obtain blood and liver tissue samples.

2. Detection and Analysis Methods:

   • Used immunoblotting to detect changes in cytokine levels.
   • Utilized phosphoproteomic, transcriptomic, and metabolite data collected at 4 hours, 6 hours, and 18 hours for subsequent analysis.

3. Experimental Design:

   • Used various omics technologies: LC-MS for protein and metabolite analysis, RNA-seq for transcriptome analysis, TiO2 enrichment technology for phosphoprotein analysis.
   • Analyzed data using statistical tools such as Pearson correlation coefficient, Fisher’s test, and mixed linear models.

Data Integration and Analysis

1. Limitations of Single Omics Analysis:

   • Single omics providing information on a single level of each biological entity cannot independently explain complex disease mechanisms.
   • For example, metabolomics may provide information on endogenous product levels but cannot comprehensively reveal biological properties such as genetic variations and post-transcriptional modifications of these metabolites.

2. Time-Dependent Multi-Omics Integration:

   • Conducted across multiple “Omics” datasets at multiple time points to explore changes in molecular mechanisms during disease progression.
   • Used XMwas program v0.55 for comprehensive network analysis of multi-omics data, setting a correlation threshold of 0.4 and selecting relevant pathways.

Research Results

1. Identification of Important Molecules:

   • Phosphoproteomics data indicated that protein phosphorylation levels change over time during sepsis progression, for example, significantly increased phosphorylation of pyruvate dehydrogenase E1 subunit in the 4-hour group.
   • In transcriptomics analysis, Apoe gene expression significantly increased in the 6-hour CLP group, indicating its key regulatory role in early sepsis.
   • Proteomics data in the 18-hour CLP group showed significantly upregulated SERPINA3N protein levels, possibly related to inflammatory responses.

2. Multi-Omics Data Analysis and Pathway Integration:

   • After comprehensive analysis of data at various time points, the Toll-like receptor 4 (TLR4) pathway was finally confirmed to be associated with SALS.
   • The experiment verified the expression level changes of genes related to the TLR4 pathway through qPCR.

3. Validation and Further Research:

   • Used real-time quantitative PCR (qRT-PCR) to validate the expression levels of multiple genes related to the TLR4 pathway.
   • Results showed that genes with significant expression in SALS validated the results of the TDMI method, revealing the key role of the TLR4 pathway in sepsis progression.

Discussion and Conclusion

Through the specified multi-omics integration method, this study identified a clear pathophysiological pathway - the TLR4 pathway, demonstrating the advantages of TDMI in revealing complex disease mechanisms. Additionally, the paradigm of time-dependent multi-omics data integration in this study provides an ideal framework for profound biological interpretation of multi-omics data.

Research Significance and Prospects

The integrated analysis of multi-omics data provides new tools for basic biology and medical research, capable of revealing potential disease mechanisms and guiding potential therapeutic strategies. Future research still needs to explore multi-omics changes in other organs related to sepsis (such as lungs, kidneys, etc.) to comprehensively understand multiple organ dysfunction syndrome.

This research provides a new perspective and method for studying complex pathological mechanisms, with important scientific value and application potential.