Tegegne Henok Ayalew, Savidge Tor C
Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Microbiome Center, Department of Pathology, Texas Children's Hospital, Houston, TX, USA.
Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Microbiome Center, Department of Pathology, Texas Children's Hospital, Houston, TX, USA.
Trends Pharmacol Sci. 2025 Jan;46(1):32-44. doi: 10.1016/j.tips.2024.11.007. Epub 2024 Dec 27.
The human microbiome consists of diverse microorganisms that inhabit various body sites. As these microbes are increasingly recognized as key determinants of health, there is significant interest in leveraging individual microbiome profiles for early disease detection, prevention, and drug efficacy prediction. However, the complexity of microbiome data, coupled with conflicting study outcomes, has hindered its integration into clinical practice. This challenge is partially due to demographic and technological biases that impede the development of reliable disease classifiers. Here, we examine recent advances in 16S rRNA and shotgun-metagenomics sequencing, along with bioinformatics tools designed to enhance microbiome data integration for precision diagnostics and personalized treatments. We also highlight progress in microbiome-based therapies and address the challenges of establishing causality to ensure robust diagnostics and effective treatments for complex diseases.
人类微生物组由栖息于身体各个部位的多种微生物组成。随着这些微生物越来越被视为健康的关键决定因素,人们对利用个体微生物组图谱进行疾病早期检测、预防和药物疗效预测产生了浓厚兴趣。然而,微生物组数据的复杂性以及相互矛盾的研究结果阻碍了其在临床实践中的整合。这一挑战部分归因于人口统计学和技术偏差,这些偏差阻碍了可靠疾病分类器的开发。在此,我们审视了16S rRNA和鸟枪法宏基因组测序的最新进展,以及旨在增强微生物组数据整合以实现精准诊断和个性化治疗的生物信息学工具。我们还强调了基于微生物组的疗法的进展,并探讨了建立因果关系以确保对复杂疾病进行可靠诊断和有效治疗所面临的挑战。