Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea.
Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Genomics Proteomics Bioinformatics. 2023 Dec;21(6):1101-1116. doi: 10.1016/j.gpb.2023.04.002. Epub 2023 Apr 20.
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.
最近开发的允许分析每个单一组学的技术为正在进行的疾病过程提供了公正的见解。然而,指定随后的整合策略的研究设计仍然具有挑战性,这些整合策略可以将脓毒症病理生理学和临床结果联系起来。在这里,我们在与脓毒症相关的肝功能障碍(SALD)模型中进行了时间依赖性多组学整合(TDMI)。我们成功地推断出 Toll 样受体 4(TLR4)途径与 SALD 的关系。尽管 TLR4 是脓毒症进展的关键因素,但在单组学分析中没有指定,而仅在 TDMI 分析中指定。这一发现表明,就探索 SALD 的潜在病理生理机制而言,基于 TDMI 的方法比单组学分析更具优势。此外,基于 TDMI 的方法可以成为多组学数据集进行深入生物学解释的理想范例,这可能会为基础生物学、健康和疾病带来新的见解,从而为治疗策略确定有前途的候选者。