Linking the aryl hydrocarbon receptor with altered DNA methylation patterns and developmentally induced aberrant antiviral CD8+ T cell responses.

Successfully fighting infection requires a properly tuned immune system. Recent epidemiological studies link exposure to pollutants that bind the aryl hydrocarbon receptor (AHR) during development with poorer immune responses later in life. Yet, how developmental triggering of AHR durably alters immune cell function remains unknown. Using a mouse model, we show that developmental activation of AHR leads to long-lasting reduction in the response of CD8(+) T cells during influenza virus infection, cells critical for resolving primary infection. Combining genome-wide approaches, we demonstrate that developmental activation alters DNA methylation and gene expression patterns in isolated CD8(+) T cells prior to and during infection. Altered transcriptional profiles in CD8(+) T cells from developmentally exposed mice reflect changes in pathways involved in proliferation and immunoregulation, with an overall pattern that bears hallmarks of T cell exhaustion. Developmental exposure also changed DNA methylation across the genome, but differences were most pronounced following infection, where we observed inverse correlation between promoter methylation and gene expression. This points to altered regulation of DNA methylation as one mechanism by which AHR causes durable changes in T cell function. Discovering that distinct gene sets and pathways were differentially changed in developmentally exposed mice prior to and after infection further reveals that the process of CD8(+) T cell activation is rendered fundamentally different by early life AHR signaling. These findings reveal a novel role for AHR in the developing immune system: regulating DNA methylation and gene expression as T cells respond to infection later in life.

groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

BACKGROUND: Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. RESULTS: Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. CONCLUSIONS: Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

Dynamic reorganization of the AC16 cardiomyocyte transcriptome in response to TNFα signaling revealed by integrated genomic analyses.

BACKGROUND: Defining cell type-specific transcriptomes in mammals can be challenging, especially for unannotated regions of the genome. We have developed an analytical pipeline called groHMM for annotating primary transcripts using global nuclear run-on sequencing (GRO-seq) data. Herein, we use this pipeline to characterize the transcriptome of an immortalized adult human ventricular cardiomyocyte cell line (AC16) in response to signaling by tumor necrosis factor alpha (TNFα), which is controlled in part by NF-κB, a key transcriptional regulator of inflammation. A unique aspect of this work is the use of the RNA polymerase II (Pol II) inhibitor α-amanitin, which we used to define a set of RNA polymerase I and III (Pol I and Pol III) transcripts. RESULTS: Using groHMM, we identified ~30,000 coding and non-coding transcribed regions in AC16 cells, which includes a set of unique Pol I and Pol III primary transcripts. Many of these transcripts have not been annotated previously, including enhancer RNAs originating from NF-κB binding sites. In addition, we observed that AC16 cells rapidly and dynamically reorganize their transcriptomes in response to TNFα stimulation in an NF-κB-dependent manner, switching from a basal state to a proinflammatory state affecting a spectrum of cardiac-associated protein-coding and non-coding genes. Moreover, we observed distinct Pol II dynamics for up- and downregulated genes, with a rapid release of Pol II into productive elongation for TNFα-stimulated genes. As expected, the TNFα-induced changes in the AC16 transcriptome resulted in corresponding changes in cognate mRNA and protein levels in a similar manner, but with delayed kinetics. CONCLUSIONS: Our studies illustrate how computational genomics can be used to characterize the signal-regulated transcriptome in biologically relevant cell types, providing new information about how the human genome is organized, transcribed and regulated. In addition, they show how α-amanitin can be used to reveal the Pol I and Pol III transcriptome. Furthermore, they shed new light on the regulation of the cardiomyocyte transcriptome in response to a proinflammatory signal and help to clarify the link between inflammation and cardiomyocyte function at the transcriptional level.

Characterization of basal and estrogen-regulated antisense transcription in breast cancer cells: Role in regulating sense transcription.

Estrogen-responsive breast cancer cells exhibit both basal and estrogen-regulated transcriptional programs, which lead to the transcription of many different transcription units (i.e., genes), including those that produce coding and non-coding sense (e.g., mRNA, lncRNA) and antisense (i.e., asRNA) transcripts. We have previously characterized the global basal and estrogen-regulated transcriptomes in estrogen receptor alpha (ERα)-positive MCF-7 breast cancer cells. Herein, we have mined genomic data to define three classes of antisense transcription in MCF-7 cells based on where their antisense transcription termination sites reside relative to their cognate sense mRNA and lncRNA genes. These three classes differ in their response to estrogen treatment, the enrichment of a number of genomic features associated with active promoters (H3K4me3, RNA polymerase II, open chromatin architecture), and the biological functions of their cognate sense genes as analyzed by DAVID gene ontology. We further characterized two estrogen-regulated antisense transcripts arising from the MYC gene in MCF-7 cells, showing that these antisense transcripts are 5'-capped, 3'-polyadenylated, and localized to different compartments of the cell. Together, our analyses have revealed distinct classes of antisense transcription correlated to different biological processes and response to estrogen stimulation, uncovering another layer of hormone-regulated gene regulation.

Distinct Roles for BET Family Members in Estrogen Receptor α Enhancer Function and Gene Regulation in Breast Cancer Cells.

The bromodomain family member proteins (BRD; BET proteins) are key coregulators for estrogen receptor alpha (ERα)-mediated transcriptional enhancers. The use of BRD-selective inhibitors has gained much attention as a potential treatment for various solid tumors, including ER-positive breast cancers. However, the roles of individual BET family members have largely remained unexplored. Here, we describe the role of BRDs in estrogen (E2)-dependent gene expression in ERα-positive breast cancer cells. We observed that chemical inhibition of BET family proteins with JQ1 impairs E2-regulated gene expression and growth in breast cancer cells. In addition, RNAi-mediated depletion of each BET family member (BRDs 2, 3, and 4) revealed partially redundant roles at ERα enhancers and for target gene transcription. Furthermore, we found a unique role of BRD3 as a molecular sensor of total BET family protein levels and activity through compensatory control of its own protein levels. Finally, we observed that BRD3 is recruited to a subset of ERα-binding sites (ERBS) that are enriched for active enhancer features, located in clusters of ERBSs likely functioning as "super enhancers," and associated with highly E2-responsive genes. Collectively, our results illustrate a critical and specific role for BET family members in ERα-dependent gene transcription. IMPLICATIONS: BRD3 is recruited to and controls the activity of a subset ERα transcriptional enhancers, providing a therapeutic opportunity to target BRD3 with BET inhibitors in ERα-positive breast cancers.

An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks.

BACKGROUND: Metabolomics, petroleum and biodiesel chemistry, biomarker discovery, and other fields which rely on high-resolution profiling of complex chemical mixtures generate datasets which contain millions of detector intensity readings, each uniquely addressed along dimensions of time (e.g., retention time of chemicals on a chromatographic column), a spectral value (e.g., mass-to-charge ratio of ions derived from chemicals), and the analytical run number. They also must rely on data preprocessing techniques. In particular, inter-run variance in the retention time of chemical species poses a significant hurdle that must be cleared before feature extraction, data reduction, and knowledge discovery can ensue. Alignment methods, for calibrating retention reportedly (and in our experience) can misalign matching chemicals, falsely align distinct ones, be unduly sensitive to chosen values of input parameters, and result in distortions of peak shape and area. RESULTS: We present an iterative block-shifting approach for retention-time calibration that detects chromatographic features and qualifies them by retention time, spectrum, and the effect of their inclusion on the quality of alignment itself. Mass chromatograms are aligned pairwise to one selected as a reference. In tests using a 45-run GC-MS experiment, block-shifting reduced the absolute deviation of retention by greater than 30-fold. It compared favourably to COW and XCMS with respect to alignment, and was markedly superior in preservation of peak area. CONCLUSION: Iterative block-shifting is an attractive method to align GC-MS mass chromatograms that is also generalizable to other two-dimensional techniques such as HPLC-MS.

In vitro analysis of the impact of enrofloxacin residues on the human intestinal microbiota using H-NMR spectroscopy.

Exposure of humans to antimicrobial residues in food-producing animals may alter the intestinal microbiota which could result in a potential risk to human health. To determine the effect of enrofloxacin on the human intestinal microbiota, fecal suspensions (25%) were cultured in the presence of 0.06-5 µg/ml enrofloxacin. The bacterial community was analyzed by plating on selective culture media, pyrosequencing and nuclear magnetic resonance (NMR) spectroscopy. Pyrosequencing analysis of 16S rRNA genes and viable counts on Bacteroides sp., Enterococcus sp., and Bifidobacterium sp. selection medium indicated that there were no significant changes in the bacteria numbers at the selected enrofloxacin concentrations (0.06, 0.1, and 1 µg/ml) relative to the control samples after a 48 h incubation. NMR analysis showed remarkably similar spectra in cultures treated with 0.06, 0.1, and 1 µg/ml enrofloxacin, with some slight differences in peak heights. However, hierarchical clustering analysis indicated significant differences in metabolite concentrations between the control and those samples treated with 1 µg/ml enrofloxacin. Leucine, phenylalanine, proline, and 2-oxovalerate were positively correlated with the concentration of enrofloxacin. NMR analysis is a potentially useful tool to monitor changes of the human intestinal microbiota, in addition to traditional culture methods and pyrosequencing.

Reordering hierarchical tree based on bilateral symmetric distance.

BACKGROUND: In microarray data analysis, hierarchical clustering (HC) is often used to group samples or genes according to their gene expression profiles to study their associations. In a typical HC, nested clustering structures can be quickly identified in a tree. The relationship between objects is lost, however, because clusters rather than individual objects are compared. This results in a tree that is hard to interpret. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes an ordering method, HC-SYM, which minimizes bilateral symmetric distance of two adjacent clusters in a tree so that similar objects in the clusters are located in the cluster boundaries. The performance of HC-SYM was evaluated by both supervised and unsupervised approaches and compared favourably with other ordering methods. CONCLUSIONS/SIGNIFICANCE: The intuitive relationship between objects and flexibility of the HC-SYM method can be very helpful in the exploratory analysis of not only microarray data but also similar high-dimensional data.

Modulation of lipid biosynthesis contributes to stress resistance and longevity of C. elegans mutants.

Many lifespan-modulating genes are involved in either generation of oxidative substrates and end-products, or their detoxification and removal. Among such metabolites, only lipoperoxides have the ability to produce free-radical chain reactions. For this study, fatty-acid profiles were compared across a panel of C. elegans mutants that span a tenfold range of longevities in a uniform genetic background. Two lipid structural properties correlated extremely well with lifespan in these worms: fatty-acid chain length and susceptibility to oxidation both decreased sharply in the longest-lived mutants (affecting the insulinlike-signaling pathway). This suggested a functional model in which longevity benefits from a reduction in lipid peroxidation substrates, offset by a coordinate decline in fatty-acid chain length to maintain membrane fluidity. This model was tested by disrupting the underlying steps in lipid biosynthesis, using RNAi knockdown to deplete transcripts of genes involved in fatty-acid metabolism. These interventions produced effects on longevity that were fully consistent with the functions and abundances of their products. Most knockdowns also produced concordant effects on survival of hydrogen peroxide stress, which can trigger lipoperoxide chain reactions.