Bingdong Liu

Human gut microbiota can be affected by a variety of factors, including geography. This study aimed to clarify the regional specific characteristics of gut microbiota in rural residents of Xinxiang county, Henan province, with hypertension and hyperlipidemia and evaluate the association of specific gut microbiota with hypertension and hyperlipidemia clinical indices. To identify the gut microbes, 16S rRNA gene sequencing was used and a random forest disease classifier was constructed to discriminate between the gut microbiota in hypertension, hyperlipidemia, and the healthy control. Patients with hypertension and hyperlipidemia presented with marked gut microbiota dysbiosis compared to the healthy control. The gut microbiota related to hypertension and hyperlipidemia may consist of a large number of taxa, influencing each other in a complex metabolic network. Examining the top 35 genera in each group showed that Lactococcus, Alistipes, or Subdoligranulum abundances were positively correlated with systolic blood pressure (SBP) or diastolic blood pressure (DBP) in hypertensive patients with treatment-naive hypertension (n = 63). In hypertensive patients undergoing anti-hypertensive treatment (n = 104), the abundance of Megasphaera or Megamonas was positively correlated to SBP. In the hyperlipidemia group, some of the top 35 genera were significantly correlated to triglyceride, total cholesterol, and fasting blood glucose levels. This study analyzed the characteristics of the gut microbiota in patients with hypertension and/or hyperlipidemia, providing a theoretical basis for the prevention and control of hypertension and hyperlipidemia in this region.

Iron is an essential trace mineral for the growth, systemic metabolism, and immune response. Imbalance of tissue iron absorption and storage leads to various diseases. The excessive iron accumulation is associated with inflammation and cancer while iron deficiency leads to growth retardation. Studies investigated in Kenyan infants and school children suggests that both low and high iron intake result in dysbiosis of gut microbiota. This would lead to the disruption of microbial diversity, an increase of pathogen abundance and the induction of intestinal inflammation. Despite this progress, in-depth studies investigating the relationship between iron availability and gut microbiota is not completely explored. In the current study, we established a murine model to study the connection between iron and microbiota by feeding mice with either iron-deprived or -fortified diet. To identify key microbiota related to iron levels, we combined the 16S rRNA amplicon sequencing with the innovated bioinformatic algorithms, such as RDA, co-occurrence, and machine learning to identify key microbiota. Manipulation of iron levels in the diet leads to systemic iron dysregulation and dysbiosis of gut microbiota. The bioinformatic algorithms used here detect five key bacteria that correlate with systemic iron levels. Leveraging on these key microbiotas, we also established a prediction model which could precisely distinguish the individual under either iron-deprived or iron-fortified physiological condition to further prove the link between microbiota and systemic iron homeostasis. This innovated and non-invasive approach could be potentially used for the early diagnosis and therapy of iron-dysregulation related diseases, e.g. anemia, inflammatory disease, fibrosis, and cancers.