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Aryl hydrocarbon receptor (AhR) ligands are important for gastrointestinal health and play a role in gut inflammation and the induction of T regulatory cells, and the short chain fatty acids (SCFAs) butyrate, propionate and acetate also induce similar protective responses. Initial studies with butyrate demonstrated that this compound significantly increased expression of Ah-responsive genes such as Cyp1a1/CYP1A1 in YAMC mouse colonocytes and Caco-2 human colon cancer cell lines. Butyrate synergistically enhanced AhR ligand-induced Cyp1a1/CYP1A1 in these cells with comparable enhancement being observed for 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) and also microbiota-derived AhR ligands tryptamine, indole and 1,4-dihydroxy-2-naphthoic acid (DHNA). The effects of butyrate on enhancing induction of Cyp1b1/CYP1B1, AhR repressor (Ahrr/AhRR) and TCDD-inducible poly(ADP-ribose)polymerase (Tiparp/TiPARP) by AhR ligands were gene- and cell context-dependent with the Caco-2 cells being the most responsive cell line. Like butyrate and propionate, the prototypical hydroxyamic acid-derived histone deacetylase (HDAC) inhibitors Panobinostat and Vorinostat also enhanced AhR ligand-mediated induction and this was accompanied by enhanced histone acetylation. Acetate also enhanced basal and ligand-inducible Ah responsiveness and histone acetylation, demonstrating that acetate was an HDAC inhibitor. These results demonstrate SCFA-AhR ligand interactions in YAMC and Caco-2 cells where SCFAs synergistically enhance basal and ligand-induced expression of AhR-responsive genes.

Background Among all racial/ethnic groups, people who identify as African American/Blacks have the second highest colorectal cancer (CRC) incidence in the USA. This disparity may exist because African American/Blacks, compared to other racial/ethnic groups, have a higher prevalence of risk factors for CRC, including obesity, low fiber consumption, and higher intakes of fat and animal protein. One unexplored, underlying mechanism of this relationship is the bile acid-gut microbiome axis. High saturated fat, low fiber diets, and obesity lead to increases in tumor promoting secondary bile acids. Diets high in fiber, such as a Mediterranean diet, and intentional weight loss may reduce CRC risk by modulating the bile acid-gut microbiome axis. The purpose of this study is to test the impact of a Mediterranean diet alone, weight loss alone, or both, compared to typical diet controls on the bile acid-gut microbiome axis and CRC risk factors among African American/Blacks with obesity. Because weight loss or a Mediterranean diet alone can reduce CRC risk, we hypothesize that weight loss plus a Mediterranean diet will reduce CRC risk the most. Methods This randomized controlled lifestyle intervention will randomize 192 African American/Blacks with obesity, aged 45–75 years to one of four arms: Mediterranean diet, weight loss, weight loss plus Mediterranean diet, or typical diet controls, for 6 months (48 per arm). Data will be collected at baseline, mid-study, and study end. Primary outcomes include total circulating and fecal bile acids, taurine-conjugated bile acids, and deoxycholic acid. Secondary outcomes include body weight, body composition, dietary change, physical activity, metabolic risk, circulating cytokines, gut microbial community structure and composition, fecal short-chain fatty acids, and expression levels of genes from exfoliated intestinal cells linked to carcinogenesis. Discussion This study will be the first randomized controlled trial to examine the effects of a Mediterranean diet, weight loss, or both on bile acid metabolism, the gut microbiome, and intestinal epithelial genes associated with carcinogenesis. This approach to CRC risk reduction may be especially important among African American/Blacks given their higher risk factor profile and increased CRC incidence. Trial registration ClinicalTrials.gov NCT04753359 . Registered on 15 February 2021.

Background Overweight, obesity, and associated comorbidities are a pressing global issue among children of all ages, particularly among low-income populations. Rapid weight gain (RWG) in the first 6 months of infancy contributes to childhood obesity. Suboptimal sleep-wake patterns and gut microbiota (GM) have also been associated with childhood obesity, but little is known about their influences on early infant RWG. Sleep may alter the GM and infant metabolism, and ultimately impact obesity; however, data on the interaction between sleep-wake patterns and GM development on infant growth are scarce. In this study, we aim to investigate associations of infant sleep-wake patterns and GM development with RWG at 6 months and weight gain at 12 months. We also aim to evaluate whether temporal interactions exist between infant sleep-wake patterns and GM, and if these relations influence RWG. Methods The Snuggle Bug/ Acurrucadito study is an observational, longitudinal study investigating whether 24-h, actigraphy-assessed, sleep-wake patterns and GM development are associated with RWG among infants in their first year. Based on the Ecological Model of Growth, we propose a novel conceptual framework to incorporate sleep-wake patterns and the GM as metabolic contributors for RWG in the context of maternal-infant interactions, and familial and socio-physical environments. In total, 192 mother-infant pairs will be recruited, and sleep-wake patterns and GM development assessed at 3 and 8 weeks, and 3, 6, 9, and 12 months postpartum. Covariates including maternal and child characteristics, family and environmental factors, feeding practices and dietary intake of infants and mothers, and stool-derived metabolome and exfoliome data will be assessed. The study will apply machine learning techniques combined with logistic time-varying effect models to capture infant growth and aid in elucidating the dynamic associations between study variables and RWG. Discussion Repeated, valid, and objective assessment at clinically and developmentally meaningful intervals will provide robust measures of longitudinal sleep, GM, and growth. Project findings will provide evidence for future interventions to prevent RWG in infancy and subsequent obesity. The work also may spur the development of evidence-based guidelines to address modifiable factors that influence sleep-wake and GM development and prevent childhood obesity.

Coffee is one of the most widely consumed beverages worldwide, and epidemiology studies associate higher coffee consumption with decreased rates of mortality and decreased rates of neurological and metabolic diseases, including Parkinson’s disease and type 2 diabetes. In addition, there is also evidence that higher coffee consumption is associated with lower rates of colon and rectal cancer, as well as breast, endometrial, and other cancers, although for some of these cancers, the results are conflicting. These studies reflect the chemopreventive effects of coffee; there is also evidence that coffee consumption may be therapeutic for some forms of breast and colon cancer, and this needs to be further investigated. The mechanisms associated with the chemopreventive or chemotherapeutic effects of over 1000 individual compounds in roasted coffee are complex and may vary with different diseases. Some of these mechanisms may be related to nuclear factor erythroid 2 (Nrf2)-regulated pathways that target oxidative stress or pathways that induce reactive oxygen species to kill diseased cells (primarily therapeutic). There is evidence for the involvement of receptors which include the aryl hydrocarbon receptor (AhR) and orphan nuclear receptor 4A1 (NR4A1), as well as contributions from epigenetic pathways and the gut microbiome. Further elucidation of the mechanisms will facilitate the potential future clinical applications of coffee extracts for treating cancer and other inflammatory diseases.

Gut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly influencing the health and well-being of the host. In addition to the standard differential expression analysis of host genes to assess the complex cross-talk between environment (diet), microbiome, and host intestinal physiology, data-driven integrative approaches are needed to identify potential biomarkers of both host genes and microbial communities that characterize these interactions. Our findings demonstrate that the complementary application of univariate differential gene expression analysis and multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) and sparse Principal Components Analysis (sPCA) can be used to integrate data from both the healthy infant gut microbial community and host transcriptome (exfoliome) using stool derived exfoliated cells shed from the gut. These approaches reveal host genes and microbial functional categories related to the feeding phenotype of the infants. Our findings also confirm that combinatorial noninvasive -omic approaches provide an integrative genomics-based perspective of neonatal host-gut microbiome interactions.

The advances of modern sequencing techniques have generated an unprecedented amount of multi-omics data which provide great opportunities to quantitatively explore functional genomes from different but complementary perspectives. However, distinct modalities/sequencing technologies generate diverse types of data which greatly complicate statistical modeling because uniquely optimized methods are required for handling each type of data. In this paper, we propose a unified framework for Bayesian nonparametric matrix factorization that infers overlapping bi-clusters for multi-omics data. The proposed method adaptively discretizes different types of observations into common latent states on which cluster structures are built hierarchically. The proposed Bayesian nonparametric method is able to automatically determine the number of clusters. We demonstrate the utility of the proposed method using simulation studies and applications to a single-cell RNA-sequencing dataset, a combination of single-cell RNA-sequencing and single-cell ATAC-sequencing dataset, a bulk RNA-sequencing dataset, and a DNA methylation dataset which reveal several interesting findings that are consistent with biological literature.

The plasma membranes of all eukaryotic cells contain heterogeneous self-organising intrinsically unstable liquid ordered domains or lipid assemblies in which key signal transduction proteins are localised. These assemblies are classified as ‘lipid rafts’ (10–200 nm), which are composed mostly of cholesterol and sphingolipid microdomains and therefore do not integrate well into the fluid phospholipid bilayers. In addition, caveolae represent a subtype of lipid raft macrodomain that form flask-shaped membrane invaginations containing structural proteins, i.e. caveolins. With respect to the diverse biological effects of long-chain PUFA, increasing evidence suggests that n-3 PUFA and perhaps conjugated fatty acids uniquely alter the basic properties of cell membranes. Because of its polyunsaturation, DHA and possibly conjugated linoleic acid are sterically incompatible with sphingolipid and cholesterol and, therefore, appear to alter lipid raft behaviour and protein function. The present review examines the evidence indicating that dietary sources of n-3 PUFA can profoundly alter the biochemical make up of lipid rafts/caveolae microdomains, thereby influencing cell signalling, protein trafficking and cell cytokinetics.

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most frequently used classes of medications in the world, yet they induce an enteropathy that is associated with high morbidity and mortality. A major limitation to better understanding the pathophysiology and diagnosis of this enteropathy is the difficulty of obtaining information about the primary site of injury, namely the distal small intestine. We investigated the utility of using mRNA from exfoliated cells in stool as a means to surveil the distal small intestine in a murine model of NSAID enteropathy. Specifically, we performed RNA-Seq on exfoliated cells found in feces and compared these data to RNA-Seq from both the small intestinal mucosa and colonic mucosa of healthy control mice or those exhibiting NSAID-induced enteropathy. Global gene expression analysis, data intersection, pathway analysis, and computational approaches including linear discriminant analysis (LDA) and sparse canonical correlation analysis (CCA) were used to assess the inter-relatedness of tissue (invasive) and stool (noninvasive) datasets. These analyses revealed that the exfoliated cell transcriptome closely mirrored the transcriptome of the small intestinal mucosa. Thus, the exfoliome may serve as a non-invasive means of detecting and monitoring NSAID enteropathy (and possibly other gastrointestinal mucosal inflammatory diseases).

There is experimental evidence that dietary fish oil, which contains the n−3 fatty acid family, i.e., EPA and DHA, protects against colon tumor development, in part by increasing apoptosis. Since mitochondria can act as central executioners of apoptosis, we hypothesized that EPA and DHA incorporation into colonocyte mitochondrial membranes, owing to their high degree of unsaturation, would enhance susceptibility to damage by reactive oxygen species (ROS) generated via oxidative phosphorylation. This, in turn, would compromise mitochondrial function, thereby initiating apoptosis. To test this hypothesis, colonic crypts were isolated from rats fed either fish oil, purified n−3 fatty acid ethyl esters, or corn oil (control). Dietary lipid source had no effect on colonic mitochondrial phospholipid class mole percentages, although incorporation of EPA and DHA was associated with a reduction in n−6 fatty acids known to enhance colon tumor development, i.e., linoleic acid (LNA) and its metabolic product, arachidonic acid (ARA). Select compositional changes in major phospholipid pools were correlated to alterations in mitochondrial function as assessed by confocal microscopy. The mol% sum of LNA plus ARA in cardiolipin was inversely correlated with ROS (P=0.024). Ethanolamine glycerophospholipid ARA (P=0.046) and choline glycerophospholipid INA (P=0.033) levels were positively correlated to mitochondrial membrane potential. In contrast, ethanolamine glycerophospholipid EPA (P=0.042) and DHA (P=0.024), levels were negatively correlated to mitochondrial membrane potential. Additionally, EPA and DHA levels in choline glycerophospholipids (P=0.026) were positively correlated with caspase 3 activity. These data provide evidence in vivo indicating that dietary FPA and DHA induce compositional changes in colonic mitochondrial membrane phospholipids that facilitate appotosis.

Background Developing and evaluating new technology that enables researchers to recover gene-expression levels of colonic cells from fecal samples could be key to a non-invasive screening tool for early detection of colon cancer. The current study, to the best of our knowledge, is the first to investigate and report the reproducibility of fecal microarray data. Using the intraclass correlation coefficient (ICC) as a measure of reproducibility and the preliminary analysis of fecal and mucosal data, we assessed the reliability of mixture density estimation and the reproducibility of fecal microarray data. Using Monte Carlo-based methods, we explored whether ICC values should be modeled as a beta-mixture or transformed first and fitted with a normal-mixture. We used outcomes from bootstrapped goodness-of-fit tests to determine which approach is less sensitive toward potential violation of distributional assumptions. Results The graphical examination of both the distributions of ICC and probit-transformed ICC (PT-ICC) clearly shows that there are two components in the distributions. For ICC measurements, which are between 0 and 1, the practice in literature has been to assume that the data points are from a beta-mixture distribution. Nevertheless, in our study we show that the use of a normal-mixture modeling approach on PT-ICC could provide superior performance. Conclusions When modeling ICC values of gene expression levels, using mixture of normals in the probit-transformed (PT) scale is less sensitive toward model mis-specification than using mixture of betas. We show that a biased conclusion could be made if we follow the traditional approach and model the two sets of ICC values using the mixture of betas directly. The problematic estimation arises from the sensitivity of beta-mixtures toward model mis-specification, particularly when there are observations in the neighborhood of the the boundary points, 0 or 1. Since beta-mixture modeling is commonly used in approximating the distribution of measurements between 0 and 1, our findings have important implications beyond the findings of the current study. By using the normal-mixture approach on PT-ICC, we observed the quality of reproducible genes in fecal array data to be comparable to those in mucosal arrays.