Please note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA) profiles, unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied.Due to the success of the Research Topic
'Untangle the Broad Connections and Tight Interactions Between Human Microbiota and Complex Diseases Through Data-Driven Approaches' and the rapidly evolving subject area, we are pleased to launch volume II of this collection.
It is well known that microorganisms are ubiquitous in the environment and occupy almost all habitats in animals and humans. Microbiota in the human body mainly consists of bacteria, fungi, viruses, archaea, protists, etc. With the advancements in sequencing technologies and mass spectrometry instruments, together with their in-depth applications in the dissection of the human microbiota, more and more evidence has shown that microorganisms play very important roles in physiological functions and are closely related to various complex diseases in human beings.
This has led to an insightful understanding of underlying disease mechanisms from microbial perspectives. Therefore, elucidation of the microbiota-disease association will be of great help for understanding the pathogenesis of human diseases, promoting early diagnosis, and improving precision medicine. For example, in the human gut, the vast majority of gut microbes not only synthesize essential amino acids and vitamins but also facilitate the digestion of indigestible components of the human diet like plant polysaccharides. When gut microbial communities change, people are likely to suffer from related digestive system diseases. If changes in intestinal microbes can be detected in time and given corresponding treatment, the workload of later clinical diagnosis and treatment will be greatly reduced.
Although current technologies have already helped us identify many previously unexpected connections between the microbiota and diseases, such as cancer, autoinflammatory diseases, metabolic syndromes, digestive system diseases, cardiovascular diseases, and central nervous system disorders, the present level of knowledge is still limited. It is rather difficult to analyze the existing meta-omics data in-depth due to the lack of competent algorithms and bioinformatics tools, which leads to a narrow understanding of the microbiome-disease association and severely limits the development of the association mechanisms. Therefore, efficient computational tools and novel algorithms are urgently needed to be applied in the microbiota-disease association analysis, to promote the application of microbial analysis in clinical settings for the diagnosis, treatment, and prevention of complex human diseases. In addition, downstream experimental validations of the computational discoveries in terms of the associations between microbiota and diseases are urgently needed to promote the real-world application of the meta-omics analysis. Therefore, studies with a combination of experimental and computational methods for interrogating these intriguing associations are also desirable.
This Research Topic welcomes the contribution of Original Research, Review, Mini Review, Methods, Technology and Code, and Perspectives articles focused on relevant research in the exciting, but still very challenging, field of human microbiota-disease associations. Topics of particular interest include, but are not limited to, the following subtopics:
• Novel pipelines, workflows, web servers, and software for the analysis of meta-omics data
• Computational analysis and experimental validation of the associations between human microbiota and complex diseases at all levels
• Computational simulation and modeling for the dynamic interactions between human microbiota and complex disease.
• Predictions, identifications, and validations of bacterial species and metabolites as biomarkers for clinical diagnostics of complex diseases
• Applications of microbial omics techniques in facilitating clinical diagnostics of human diseases
• Overturning conventional cognitions or existing models of microbiota-disease associations in human beings