Fecal microbiota as a noninvasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver.

Iron is an essential trace mineral required for growth, metabolism, and immune response. Dysregulation of iron homeostasis is linked with the development and progression of various diseases. Iron accumulation is associated with inflammatory diseases and cancer, while iron deficiency leads to the growth retardation. Several studies have suggested that iron imbalance results in alteration of gut microbiota, leading to the disruption of microbial diversity, the increase of pathogen abundance, and the induction of intestinal inflammation. However, in screening studies done in the past decades, the association between the iron availability and gut microbiota has not been systemically explored. Furthermore, a noninvasive and convenient approach to determine the iron levels in tissues is lacking. In the present study, a murine model for iron dysregulation was established. 16S rRNA amplicon sequencing and bioinformatic algorithms were used to identify the key taxa. Using the key taxa identified and machine learning models, we established an easily accessible prediction model, which could accurately distinguish between iron-deprived or iron-fortified condition. This prediction model could precisely predict the iron level of the intestinal epithelial cells and the liver and could be used for early diagnosis of iron dysbiosis-related diseases, in a noninvasive manner, in the future.