Background: Gut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly affecting the health and well-being of the host. Thus, it is important to develop a synthetic approach to study the host transcriptome and the microbiome simultaneously. Early microbial colonization in infants is critically important for directing neonatal intestinal and immune development, and is especially attractive for studying the development of human-commensal interactions. Here we report the results from a simultaneous study of the gut microbiome and host epithelial transcriptome of three-month-old exclusively breast- and formula-fed infants. Results: Variation in both host mRNA expression and the microbiome phylogenetic and functional profiles was observed between breast- and formula-fed infants. To examine the interdependent relationship between host epithelial cell gene expression and bacterial metagenomic-based profiles, the host transcriptome and functionally profiled microbiome data were subjected to novel multivariate statistical analyses. Gut microbiota metagenome virulence characteristics concurrently varied with immunity-related gene expression in epithelial cells between the formula-fed and the breast-fed infants. Conclusions: Our data provide insight into the integrated responses of the host transcriptome and microbiome to dietary substrates in the early neonatal period. We demonstrate that differences in diet can affect, via gut colonization, host expression of genes associated with the innate immune system. Furthermore, the methodology presented in this study can be adapted to assess other host-commensal and host-pathogen interactions using genomic and transcriptomic data, providing a synthetic genomics-based picture of host-commensal relationships.

The mutational genetic landscape of colorectal cancer has been extensively characterized; however, the ability of “cooperation response genes” to modulate the function of cancer “driver” genes remains largely unknown. In this study, we investigate the role of aryl hydrocarbon receptor (AhR), a ligand-activated transcription factor, in modulating oncogenic cues in the colon. We show that intestinal epithelial cell–targeted AhR knockout (KO) promotes the expansion and clonogenic capacity of colonic stem/progenitor cells harboring ApcS580/+; KrasG12D/+ mutations by upregulating Wnt signaling. The loss of AhR in the gut epithelium increased cell proliferation, reduced mouse survival rate, and promoted cecum and colon tumorigenesis in mice. Mechanistically, the antagonism of Wnt signaling induced by Lgr5 haploinsufficiency attenuated the effects of AhR KO on cecum and colon tumorigenesis. Implications: Our findings reveal that AhR signaling plays a protective role in genetically induced colon tumorigenesis at least by suppressing Wnt signaling and provides rationale for the AhR as a therapeutic target for cancer prevention and treatment.

A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.

A key goal of systems biology and translational genomics is to utilize high-throughput measurements of cellular states to develop expression-based classifiers for discriminating among different phenotypes. Recent developments of Next Generation Sequencing (NGS) technologies can facilitate classifier design by providing expression measurements for tens of thousands of genes simultaneously via the abundance of their mRNA transcripts. Because NGS technologies result in a nonlinear transformation of the actual expression distributions, their application can result in data that are less discriminative than would be the actual expression levels themselves, were they directly observable.