Microbial communities inhabiting various human body niches are intricately intertwined with the dynamics of health and disease. The WHO has emphasized through global surveillance that the crisis of pathogen drug resistance is escalating, narrowing treatment options and posing a severe threat to public health. The gut microbiota also plays a key role in disease occurrence and progression. By leveraging artificial intelligence (AI) models to mine the correlation patterns between gut microbial features and diseases, high-precision risk prediction models can be constructed to achieve early disease warning and precise assessment. In the field of vaccine design, especially for pandemic-causing pathogens, combining with data-driven optimization of antigen design and adjustment of vaccination protocols can effectively enhance vaccine efficacy and reduce the risk of large-scale outbreaks. The synergistic development of three core directions-drug resistance analysis, disease risk prediction models, and vaccine design-collectively promotes the advancement of precision diagnosis and treatment.
The overarching goal of this Research Topic is to comprehensively decipher the complex relationship between microbial communities and human health and disease. Currently, the escalating prevalence of pathogen resistance, insufficient precision of disease risk prediction models, and limitations in vaccine development pose multiple challenges to public health. To address these issues, we aim to: 1) systematically map the resistance spectrum of pathogens within microbial communities using genomic and metagenomic technologies to reveal resistance mechanisms; 2) integrate multi-omics data (e.g., gut microbiota) and clinical information to construct disease risk prediction models covering diverse scenarios (e.g., infection risk, metabolic disorders), enhancing early warning capabilities; 3) independently conduct vaccine design research targeting key antigenic targets in microbial communities to break through bottlenecks in traditional vaccine antigen screening. The ultimate objective is to establish an independent yet collaborative disease prevention and control system integrating resistance mechanism analysis, dynamic risk warning, and immune intervention.
We mainly focus on the role of microbiomes in disease diagnosis and treatment. We invite contributions exploring the pathogen resistance spectrum, global drug resistance dissemination and monitoring. Studies on developing risk models leveraging gut microbiome and other microbial omics data for disease prediction are highly encouraged. Additionally, research on innovative vaccine design targeting microbiome components is welcome. We seek original research articles, reviews, and perspectives that advance these areas, fostering interdisciplinary discussions on translating microbiome insights into clinical applications.
Microbial communities inhabiting various human body niches are intricately intertwined with the dynamics of health and disease. The WHO has emphasized through global surveillance that the crisis of pathogen drug resistance is escalating, narrowing treatment options and posing a severe threat to public health. The gut microbiota also plays a key role in disease occurrence and progression. By leveraging artificial intelligence (AI) models to mine the correlation patterns between gut microbial features and diseases, high-precision risk prediction models can be constructed to achieve early disease warning and precise assessment. In the field of vaccine design, especially for pandemic-causing pathogens, combining with data-driven optimization of antigen design and adjustment of vaccination protocols can effectively enhance vaccine efficacy and reduce the risk of large-scale outbreaks. The synergistic development of three core directions-drug resistance analysis, disease risk prediction models, and vaccine design-collectively promotes the advancement of precision diagnosis and treatment.
The overarching goal of this Research Topic is to comprehensively decipher the complex relationship between microbial communities and human health and disease. Currently, the escalating prevalence of pathogen resistance, insufficient precision of disease risk prediction models, and limitations in vaccine development pose multiple challenges to public health. To address these issues, we aim to: 1) systematically map the resistance spectrum of pathogens within microbial communities using genomic and metagenomic technologies to reveal resistance mechanisms; 2) integrate multi-omics data (e.g., gut microbiota) and clinical information to construct disease risk prediction models covering diverse scenarios (e.g., infection risk, metabolic disorders), enhancing early warning capabilities; 3) independently conduct vaccine design research targeting key antigenic targets in microbial communities to break through bottlenecks in traditional vaccine antigen screening. The ultimate objective is to establish an independent yet collaborative disease prevention and control system integrating resistance mechanism analysis, dynamic risk warning, and immune intervention.
We mainly focus on the role of microbiomes in disease diagnosis and treatment. We invite contributions exploring the pathogen resistance spectrum, global drug resistance dissemination and monitoring. Studies on developing risk models leveraging gut microbiome and other microbial omics data for disease prediction are highly encouraged. Additionally, research on innovative vaccine design targeting microbiome components is welcome. We seek original research articles, reviews, and perspectives that advance these areas, fostering interdisciplinary discussions on translating microbiome insights into clinical applications.