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Background: Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome. Methods: We performed a prospective cohort study of 19 treatment-naïve pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients' pretreatment samples. Results: Patients with Crohn's disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn's disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status. Conclusions: Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment. [ABSTRACT FROM AUTHOR]

Background Methylation on the fifth position of cytosine (5-mC) is an essential epigenetic mark that is linked to both normal neurodevelopment and neurological diseases. The recent identification of another modified form of cytosine, 5-hydroxymethylcytosine (5-hmC), in both stem cells and post-mitotic neurons, raises new questions as to the role of this base in mediating epigenetic effects. Genomic studies of these marks using model systems are limited, particularly with array-based tools, because the standard method of detecting DNA methylation cannot distinguish between 5-mC and 5-hmC and most methods have been developed to only survey the human genome. Results We show that non-human data generated using the optimization of a widely used human DNA methylation array, designed only to detect 5-mC, reproducibly distinguishes tissue types within and between chimpanzee, rhesus, and mouse, with correlations near the human DNA level (R2 > 0.99). Genome-wide methylation analysis, using this approach, reveals 6,102 differentially methylated loci between rhesus placental and fetal tissues with pathways analysis significantly overrepresented for developmental processes. Restricting the analysis to oncogenes and tumor suppressor genes finds 76 differentially methylated loci, suggesting that rhesus placental tissue carries a cancer epigenetic signature. Similarly, adapting the assay to detect 5-hmC finds highly reproducible 5-hmC levels within human, rhesus, and mouse brain tissue that is species-specific with a hierarchical abundance among the three species (human > rhesus >> mouse). Annotation of 5-hmC with respect to gene structure reveals a significant prevalence in the 3'UTR and an association with chromatin-related ontological terms, suggesting an epigenetic feedback loop mechanism for 5-hmC. Conclusions Together, these data show that this array-based methylation assay is generalizable to all mammals for the detection of both 5-mC and 5-hmC, greatly improving the utility of mammalian model systems to study the role of epigenetics in human health, disease, and evolution. [ABSTRACT FROM AUTHOR]

Background: Fragile X syndrome (FXS) is a common form of inherited intellectual disability caused by an expansion of CGG repeats located in the 5' untranslated region (UTR) of the FMR1 gene, which leads to hypermethylation and silencing of this locus. Although a dramatic increase in DNA methylation of the FMR1 full mutation allele is well documented, the extent to which these changes affect DNA methylation throughout the rest of the genome has gone unexplored. Methods: Here we examined genome-wide methylation in both peripheral blood (N = 62) and induced pluripotent stem cells (iPSCs; N = 10) from FXS individuals and controls. Results: We not only found the expected significant DNA methylation differences in the FMR1 promoter and 50 UTR, we also saw that these changes inverse in the FMR1 gene body. Importantly, we found no other differentially methylated loci throughout the remainder of the genome, indicating the aberrant methylation of FMR1 in FXS is locus-specific. Conclusions: This study provides a comprehensive methylation profile of FXS and helps refine our understanding of the mechanisms behind FMR1 silencing. [ABSTRACT FROM AUTHOR]

Background: Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset. Results: By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters. Conclusion: We have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes), known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code) are freely available under the GPL license. The source code is available [see Additional File 1] as additional material, and the latest version can be obtained at http://www4.ncsu.edu/~pchopra/landmarks.html. The code is under active development to incorporate new clustering methods and analysis. [ABSTRACT FROM AUTHOR]

Background: Methylation on the fifth position of cytosine (5-mC) is an essential epigenetic mark that is linked to both normal neurodevelopment and neurological diseases. The recent identification of another modified form of cytosine, 5-hydroxymethylcytosine (5-hmC), in both stem cells and post-mitotic neurons, raises new questions as to the role of this base in mediating epigenetic effects. Genomic studies of these marks using model systems are limited, particularly with array-based tools, because the standard method of detecting DNA methylation cannot distinguish between 5-mC and 5-hmC and most methods have been developed to only survey the human genome., Results: We show that non-human data generated using the optimization of a widely used human DNA methylation array, designed only to detect 5-mC, reproducibly distinguishes tissue types within and between chimpanzee, rhesus, and mouse, with correlations near the human DNA level (R(2) > 0.99). Genome-wide methylation analysis, using this approach, reveals 6,102 differentially methylated loci between rhesus placental and fetal tissues with pathways analysis significantly overrepresented for developmental processes. Restricting the analysis to oncogenes and tumor suppressor genes finds 76 differentially methylated loci, suggesting that rhesus placental tissue carries a cancer epigenetic signature. Similarly, adapting the assay to detect 5-hmC finds highly reproducible 5-hmC levels within human, rhesus, and mouse brain tissue that is species-specific with a hierarchical abundance among the three species (human > rhesus >> mouse). Annotation of 5-hmC with respect to gene structure reveals a significant prevalence in the 3'UTR and an association with chromatin-related ontological terms, suggesting an epigenetic feedback loop mechanism for 5-hmC., Conclusions: Together, these data show that this array-based methylation assay is generalizable to all mammals for the detection of both 5-mC and 5-hmC, greatly improving the utility of mammalian model systems to study the role of epigenetics in human health, disease, and evolution.