Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Authors: OH, VERA-KHLARA - Jeju National University; Li, Robert

Submitted to: Advanced Science
Publication Type: Literature Review
Publication Acceptance Date: 11/1/2024
Publication Date: 11/13/2024
Citation: Oh, V.S., Li, R.W. 2024. Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine. Briefings in Bioinformatics. 11(47). Article e2400458. https://doi.org/10.1002/advs.202400458.
DOI: https://doi.org/10.1002/advs.202400458

Interpretive Summary: To better understand multi-dimensional etiological complexity in dynamic processes of diseases, it is essential to identify feature-based signatures precisely and longitudinally during the dysregulation of a phenotype of interest. Dynamic microbiome data from either 16S-targeted marker gene surveys or metagenomics are routinely obtained. Meta-strategies in microbial research are typically generated by using different sequencing dates, laboratories, and studies. Data aggregation in distinct layers of feature types such as microbes, metabolites, genes, and other entities is common. In addition, integrated joint modeling approaches have been proposed, including data integration. Recently, several research groups have developed dynamic microbiome-specific methods including AI and ML tools. However, to date, there are no gold-standard approaches for complete analytical protocols with well-validated methods that can be directly applied to the more complex architectures of dynamically and longitudinally measured microbiome data or their accompanying meta-omics data in personalized and precision medicine studies. In this review, we discussed the landscape of dynamic methods, including the current challenges and strategies to enhance meta-strategies for the future of personalized and precision medicine.

Technical Abstract: Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.