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

Genomics Proteomics Bioinformatics

Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea; BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Kyungpook National University, Daegu 41566, Republic of Korea; School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea. Electronic address:

Published: December 2023

AI Article Synopsis

  • Recent advances in single omics technologies have enhanced our understanding of disease but highlight the difficulty of integrating these findings to fully grasp sepsis pathophysiology and its clinical outcomes.
  • A study utilizing a time-dependent multi-omics integration (TDMI) approach on a sepsis-associated liver dysfunction model linked the TLR4 pathway to SALD, demonstrating the limitations of single-omics analyses in identifying key factors in sepsis progression.
  • The TDMI approach is positioned as a superior method for discovering biological mechanisms in multi-omics datasets, potentially leading to innovative therapeutic strategies and better insights into health and disease.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11082264PMC
http://dx.doi.org/10.1016/j.gpb.2023.04.002DOI Listing

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