After the initiation of Human Microbiome Project in 2008, various biostatistic and bioinformatic tools for data analysis and computational methods have been developed and applied to microbiome studies. In this review and perspective, we discuss the research and statistical hypotheses in gut microbiome studies, focusing on mechanistic concepts that underlie the complex relationships among host, microbiome, and environment. We review the current available statistic tools and highlight recent progress of newly developed statistical methods and models. Given the current challenges and limitations in biostatistic approaches and tools, we discuss the future direction in developing statistical methods and models for the microbiome studies.

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Vitamin D exerts regulatory roles via vitamin D receptor (VDR) in mucosal immunity, host defense, and inflammation involving host factors and microbiome. Human Vdr gene variation shapes the microbiome and VDR deletion leads to dysbiosis. Low VDR expression and diminished vitamin D/VDR signaling are observed in colon cancer. Nevertheless, how intestinal epithelial VDR is involved in tumorigenesis through gut microbiota remains unknown. We hypothesized that intestinal VDR protects mice against dysbiosis via modulating the JAK/STAT pathway in tumorigenesis. To test our hypothesis, we used an azoxymethane/Dextran Sulfate Sodium-induced cancer model in intestinal VDR conditional knockout (VDRΔIEC) mice, cell cultures, stem-cell derived colonoids, and human colon cancer samples. VDRΔIEC mice have higher numbers of tumors with location shifted from distal to proximal colon. Fecal microbiota analysis showed that VDR deletion leads to bacterial profile shift from normal to susceptible carcinogenesis. We found enhanced bacterial staining in mouse and human tumors. Microbial metabolites from VDRΔIEC mice showed elevated secondary bile acids, consistent with the observations in human CRC. We further identified that VDR protein bound to the Jak2 promoter, suggesting that VDR transcriptionally regulated Jak2. The JAK/STAT pathway is critical in intestinal and microbial homeostasis. Fecal samples from VDRΔIEC mice activate the STAT3 signaling in human and mouse organoids. Lack of VDR led to hyperfunction of Jak2 in respond to intestinal dysbiosis. A JAK/STAT inhibitor abolished the microbiome-induced activation of STAT3. We provide insights into the mechanism of VDR dysfunction leading to dysbiosis and tumorigenesis. It indicates a new target - microbiome and VDR for prevention of cancer.

Microbiota derived metabolites act as chemical messengers that elicit a profound impact on host physiology. Vitamin D receptor (VDR) is a key genetic factor for shaping the host microbiome. However, it remains unclear how microbial metabolites are altered in the absence of VDR. We investigated metabolites from mice with tissue-specific deletion of VDR in intestinal epithelial cells or myeloid cells. Conditional VDR deletion severely changed metabolites specifically produced from carbohydrate, protein, lipid, and bile acid metabolism. Eighty-four out of 765 biochemicals were significantly altered due to the Vdr status, and 530 significant changes were due to the high-fat diet intervention. The impact of diet was more prominent due to loss of VDR as indicated by the differences in metabolites generated from energy expenditure, tri-carboxylic acid cycle, tocopherol, polyamine metabolism, and bile acids. The effect of HFD was more pronounced in female mice after VDR deletion. Interestingly, the expression levels of farnesoid X receptor in liver and intestine were significantly increased after intestinal epithelial VDR deletion and were further increased by the high-fat diet. Our study highlights the gender differences, tissue specificity, and potential gut-liver-microbiome axis mediated by VDR that might trigger downstream metabolic disorders.