Probiotics are defined as live microorganisms that can exert beneficial effects on human health in the gut and extra-intestinal sites when consumed in sufficient quantity. Recent advancements in the study of the human microbiome coupled with the development of high-throughput platforms, have significantly enhanced our understanding of probiotic activity. Multi-omics approaches including: metagenomics, metabolomics, (meta)transcriptomics, and (meta)proteomics, have facilitated the study of complex probiotic-microbiome-host interactions at multiple levels. In parallel, several bioinformatic tools and pipelines have been developed to streamline the analysis of the big bulk of data generated from these studies. Accordingly, the incorporation of artificial intelligence and machine learning algorithms has enabled the accurate prediction of the in situ and in vivo probiotic behavior in microbial communities and host niches.
This Research Topic aims to cover the latest developments in probiotic research research through the lens of multi-omics approaches. Specifically, its focus is to provide mechanistic insight into the probiotic phenotype and the health-promoting activity of strains derived from different sources such as fermented products and environmental samples, using cutting-edge platforms, novel bioinformatic tools and pipelines. Furthermore, this Research Topic aims at advancing our knowledge on the probiotic-host and probiotic-microbiota interface. Detailed knowledge of these interactions can lead to targeted applications leading to host-, strain- and disease- specific supplements with increased efficiency.
We welcome Original Research, Review, Mini Review and Systematic Review articles focusing on:
• Application of bioinformatic tools and pipelines for big data analysis in probiotic research;
• The integration of artificial intelligence and modeling algorithms for the prediction of probiotic host adaptation and activity;
• Construction of databases containing probiotic microorganisms;
• Multi-omics technologies to study probiotic action and probiotic-host interactions in the gut and distal sites such as gut-skin and gut-brain axis;
• Genomics, including comparative and functional genomics, of new probiotic strains.
Probiotics are defined as live microorganisms that can exert beneficial effects on human health in the gut and extra-intestinal sites when consumed in sufficient quantity. Recent advancements in the study of the human microbiome coupled with the development of high-throughput platforms, have significantly enhanced our understanding of probiotic activity. Multi-omics approaches including: metagenomics, metabolomics, (meta)transcriptomics, and (meta)proteomics, have facilitated the study of complex probiotic-microbiome-host interactions at multiple levels. In parallel, several bioinformatic tools and pipelines have been developed to streamline the analysis of the big bulk of data generated from these studies. Accordingly, the incorporation of artificial intelligence and machine learning algorithms has enabled the accurate prediction of the in situ and in vivo probiotic behavior in microbial communities and host niches.
This Research Topic aims to cover the latest developments in probiotic research research through the lens of multi-omics approaches. Specifically, its focus is to provide mechanistic insight into the probiotic phenotype and the health-promoting activity of strains derived from different sources such as fermented products and environmental samples, using cutting-edge platforms, novel bioinformatic tools and pipelines. Furthermore, this Research Topic aims at advancing our knowledge on the probiotic-host and probiotic-microbiota interface. Detailed knowledge of these interactions can lead to targeted applications leading to host-, strain- and disease- specific supplements with increased efficiency.
We welcome Original Research, Review, Mini Review and Systematic Review articles focusing on:
• Application of bioinformatic tools and pipelines for big data analysis in probiotic research;
• The integration of artificial intelligence and modeling algorithms for the prediction of probiotic host adaptation and activity;
• Construction of databases containing probiotic microorganisms;
• Multi-omics technologies to study probiotic action and probiotic-host interactions in the gut and distal sites such as gut-skin and gut-brain axis;
• Genomics, including comparative and functional genomics, of new probiotic strains.
