Intestinal epigenomic alterations are associated with a dysregulated nutrient absorption phenotype in obesity.

Obesity is an epidemic with myriad health effects, but little is understood regarding individual obese phenotypes and how they may respond to therapy. Epigenetic changes associated with obesity have been detected in blood, liver, pancreas, and adipose tissues. Previous work found that dietary glucose hyperabsorption occurs in some obese subjects, but detailed transcriptional or epigenomic features of the intestine associated with this phenotype are unknown. This study evaluated differentially expressed genes and relative chromatin accessibility in intestinal organoids established from donors classified as lean, obese, or obese hyperabsorptive by body mass index and glucose transport assays. Transcriptomic analysis indicated that obese hyperabsorptive subjects have significantly upregulated dietary nutrient absorption proteins and downregulated type I interferon targets. Chromatin accessibility and transcription factor footprinting suggested that enhanced binding of HNF4G promotes the obese hyperabsorption phenotype. Quantitative PCR assessment in a larger subject cohort suggested that intestinal epithelial expression of CUBN, GIP, and SLC2A5 have high correlation with hyperabsorption. The obese hyperabsorption phenotype is characterized by transcriptional changes that support increased nutrient uptake and may be driven by differentially accessible chromatin. Recognizing unique intestinal phenotypes in obesity provides new perspective in considering therapeutic targets and options to manage the disease.

Lactic acid induces transcriptional repression of macrophage inflammatory response via histone acetylation.

Lactic acid has emerged as an important modulator of immune cell function. It can be produced by both gut microbiota and the host metabolism at homeostasis and during disease states. The production of lactic acid in the gut microenvironment is vital for tissue homeostasis. In the present study, we examined how lactic acid integrates cellular metabolism to shape the epigenome of macrophages during pro-inflammatory response. We found that lactic acid serves as a primary fuel source to promote histone H3K27 acetylation, which allows the expression of immunosuppressive gene program including Nr4a1. Consequently, macrophage pro-inflammatory function was transcriptionally repressed. Furthermore, the histone acetylation induced by lactic acid promotes a form of long-term immunosuppression ("trained immunosuppression"). Pre-exposure to lactic acid induces lipopolysaccharide tolerance. These findings thus indicate that lactic acid sensing and its effect on chromatin remodeling in macrophages represent a key homeostatic mechanism that can provide a tolerogenic tissue microenvironment.

Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples.

BACKGROUND: Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. RESULTS: As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a "C > T|G > A" mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. CONCLUSIONS: Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip.

AIMS: To compare the performance of four sequence-based and one microarray methods for DNA methylation profiling. METHODS: DNA from two cell lines were profiled by reduced representation bisulfite sequencing, methyl capture sequencing (SS-Meth Seq), NimbleGen SeqCapEpi CpGiant(Nimblegen MethSeq), methylated DNA immunoprecipitation (MeDIP) and the Human Methylation 450 Bead Chip (Meth450K). RESULTS & CONCLUSION: Despite differences in genome-wide coverage, high correlation and concordance were observed between different methods. Significant overlap of differentially methylated regions was identified between sequenced-based platforms. MeDIP provided the best coverage for the whole genome and gene body regions, while RRBS and Nimblegen MethSeq were superior for CpGs in CpG islands and promoters. Methylation analyses can be achieved by any of the five methods but understanding their differences may better address the research question being posed.