Mice with dysfunctional TGF-ß signaling develop altered intestinal microbiome and colorectal cancer resistant to 5FU.

Emerging data show a rise in colorectal cancer (CRC) incidence in young men and women that is often chemoresistant. One potential risk factor is an alteration in the microbiome. Here, we investigated the role of TGF-ß signaling on the intestinal microbiome and the efficacy of chemotherapy for CRC induced by azoxymethane and dextran sodium sulfate in mice. We used two genotypes of TGF-ß-signaling-deficient mice (Smad4+/- and Smad4+/-Sptbn1+/-), which developed CRC with similar phenotypes and had similar alterations in the intestinal microbiome. Using these mice, we evaluated the intestinal microbiome and determined the effect of dysfunctional TGF-ß signaling on the response to the chemotherapeutic agent 5-Fluoro-uracil (5FU) after induction of CRC. Using shotgun metagenomic sequencing, we determined gut microbiota composition in mice with CRC and found reduced amounts of beneficial species of Bacteroides and Parabacteroides in the mutants compared to the wild-type (WT) mice. Furthermore, the mutant mice with CRC were resistant to 5FU. Whereas the abundances of E. boltae, B.dorei, Lachnoclostridium sp., and Mordavella sp. were significantly reduced in mice with CRC, these species only recovered to basal amounts after 5FU treatment in WT mice, suggesting that the alterations in the intestinal microbiome resulting from compromised TGF-ß signaling impaired the response to 5FU. These findings could have implications for inhibiting the TGF-ß pathway in the treatment of CRC or other cancers.

Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application.

The scientific community currently defines the human microbiome as all the bacteria, viruses, fungi, archaea, and eukaryotes that occupy the human body. When considering the variable locations, composition, diversity, and abundance of our microbial symbionts, the sheer volume of microorganisms reaches hundreds of trillions. With the onset of next generation sequencing (NGS), also known as high-throughput sequencing (HTS) technologies, the barriers to studying the human microbiome lowered significantly, making in-depth microbiome research accessible. Certain locations on the human body, such as the gastrointestinal, oral, nasal, and skin microbiomes have been heavily studied through community-focused projects like the Human Microbiome Project (HMP). In particular, the gastrointestinal microbiome (GM) has received significant attention due to links to neurological, immunological, and metabolic diseases, as well as cancer. Though HTS technologies allow deeper exploration of the GM, data informing the functional characteristics of microbiota and resulting effects on human function or disease are still sparse. This void is compounded by microbiome variability observed among humans through factors like genetics, environment, diet, metabolic activity, and even exercise; making GM research inherently difficult to study. This chapter describes an interdisciplinary approach to GM research with the goal of mitigating the hindrances of translating findings into a clinical setting. By applying tools and knowledge from microbiology, metagenomics, bioinformatics, machine learning, predictive modeling, and clinical study data from children with treatment-resistant epilepsy, we describe a proof-of-concept approach to clinical translation and precision application of GM research.