Target MRNA Abundance Dilutes MicroRNA and SiRNA Activity

Post-transcriptional regulation by microRNAs and siRNAs depends not only on characteristics of individual binding sites in target mRNA molecules, but also on system-level properties such as overall molecular concentrations. We hypothesize that an intracellular pool of microRNAs/siRNAs faced with a larger number of available predicted target transcripts will downregulate each individual target gene to a lesser extent. To test this hypothesis, we analyzed mRNA expression change from 178 microRNA and siRNA transfection experiments in two cell lines. We find that downregulation of particular genes mediated by microRNAs and siRNAs indeed varies with the total concentration of available target transcripts. We conclude that to interpret and design experiments involving gene regulation by small RNAs, global properties, such as target mRNA abundance, need to be considered in addition to local determinants. We propose that analysis of microRNA/siRNA targeting would benefit from a more quantitative definition, rather than simple categorization of genes as ‘target’ or ‘not a target.’ Our results are important for understanding microRNA regulation and may also have implications for siRNA design and small RNA therapeutics

Extrathymic Generation of Regulatory T Cells in Placental Mammals Mitigates Maternal-Fetal Conflict

SummaryRegulatory T (Treg) cells, whose differentiation and function are controlled by X chromosome-encoded transcription factor Foxp3, are generated in the thymus (tTreg) and extrathymically (peripheral, pTreg), and their deficiency results in fatal autoimmunity. Here, we demonstrate that a Foxp3 enhancer, conserved noncoding sequence 1 (CNS1), essential for pTreg but dispensable for tTreg cell generation, is present only in placental mammals. CNS1 is largely composed of mammalian-wide interspersed repeats (MIR) that have undergone retrotransposition during early mammalian radiation. During pregnancy, pTreg cells specific to a model paternal alloantigen were generated in a CNS1-dependent manner and accumulated in the placenta. Furthermore, when mated with allogeneic, but not syngeneic, males, CNS1-deficient females showed increased fetal resorption accompanied by increased immune cell infiltration and defective remodeling of spiral arteries. Our results suggest that, during evolution, a CNS1-dependent mechanism of extrathymic differentiation of Treg cells emerged in placental animals to enforce maternal-fetal tolerance

Control of the Inheritance of Regulatory T Cell Identity by a cis Element in the Foxp3 Locus

SummaryIn multicellular organisms, specialized functions are delegated to distinct cell types whose identity and functional integrity are maintained upon challenge. However, little is known about the mechanisms enabling lineage inheritance and their biological implications. Regulatory T (Treg) cells, which express the transcription factor Foxp3, suppress fatal autoimmunity throughout the lifespan of animals. Here, we show that a dedicated Foxp3 intronic element CNS2 maintains Treg cell lineage identity by acting as a sensor of the essential Treg cell growth factor IL-2 and its downstream target STAT5. CNS2 sustains Foxp3 expression during division of mature Treg cells when IL-2 is limiting and counteracts proinflammatory cytokine signaling that leads to the loss of Foxp3. CNS2-mediated stable inheritance of Foxp3 expression is critical for adequate suppression of diverse types of chronic inflammation by Treg cells and prevents their differentiation into inflammatory effector cells. The described mechanism may represent a general principle of the inheritance of differentiated cell states

An Atlas of the Epstein-Barr Virus Transcriptome and Epigenome Reveals Host-Virus Regulatory Interactions

SummaryEpstein-Barr virus (EBV), which is associated with multiple human tumors, persists as a minichromosome in the nucleus of B lymphocytes and induces malignancies through incompletely understood mechanisms. Here, we present a large-scale functional genomic analysis of EBV. Our experimentally generated nucleosome positioning maps and viral protein binding data were integrated with over 700 publicly available high-throughput sequencing data sets for human lymphoblastoid cell lines mapped to the EBV genome. We found that viral lytic genes are coexpressed with cellular cancer-associated pathways, suggesting that the lytic cycle may play an unexpected role in virus-mediated oncogenesis. Host regulators of viral oncogene expression and chromosome structure were identified and validated, revealing a role for the B cell-specific protein Pax5 in viral gene regulation and the cohesin complex in regulating higher order chromatin structure. Our findings provide a deeper understanding of latent viral persistence in oncogenesis and establish a valuable viral genomics resource for future exploration

Modulation of enhancer looping and differential gene targeting by Epstein-Barr virus transcription factors directs cellular reprogramming

Epstein-Barr virus (EBV) epigenetically reprogrammes B-lymphocytes to drive immortalization and facilitate viral persistence. Host-cell transcription is perturbed principally through the actions of EBV EBNA 2, 3A, 3B and 3C, with cellular genes deregulated by specific combinations of these EBNAs through unknown mechanisms. Comparing human genome binding by these viral transcription factors, we discovered that 25% of binding sites were shared by EBNA 2 and the EBNA 3s and were located predominantly in enhancers. Moreover, 80% of potential EBNA 3A, 3B or 3C target genes were also targeted by EBNA 2, implicating extensive interplay between EBNA 2 and 3 proteins in cellular reprogramming. Investigating shared enhancer sites neighbouring two new targets (WEE1 and CTBP2) we discovered that EBNA 3 proteins repress transcription by modulating enhancer-promoter loop formation to establish repressive chromatin hubs or prevent assembly of active hubs. Re-ChIP analysis revealed that EBNA 2 and 3 proteins do not bind simultaneously at shared sites but compete for binding thereby modulating enhancer-promoter interactions. At an EBNA 3-only intergenic enhancer site between ADAM28 and ADAMDEC1 EBNA 3C was also able to independently direct epigenetic repression of both genes through enhancer-promoter looping. Significantly, studying shared or unique EBNA 3 binding sites at WEE1, CTBP2, ITGAL (LFA-1 alpha chain), BCL2L11 (Bim) and the ADAMs, we also discovered that different sets of EBNA 3 proteins bind regulatory elements in a gene and cell-type specific manner. Binding profiles correlated with the effects of individual EBNA 3 proteins on the expression of these genes, providing a molecular basis for the targeting of different sets of cellular genes by the EBNA 3s. Our results therefore highlight the influence of the genomic and cellular context in determining the specificity of gene deregulation by EBV and provide a paradigm for host-cell reprogramming through modulation of enhancer-promoter interactions by viral transcription factors

A Distinct Function of Regulatory T Cells in Tissue Protection

SummaryRegulatory T (Treg) cells suppress immune responses to a broad range of non-microbial and microbial antigens and indirectly limit immune inflammation-inflicted tissue damage by employing multiple mechanisms of suppression. Here, we demonstrate that selective Treg cell deficiency in amphiregulin leads to severe acute lung damage and decreased blood oxygen concentration during influenza virus infection without any measureable alterations in Treg cell suppressor function, antiviral immune responses, or viral load. This tissue repair modality is mobilized in Treg cells in response to inflammatory mediator IL-18 or alarmin IL-33, but not by TCR signaling that is required for suppressor function. These results suggest that, during infectious lung injury, Treg cells have a major direct and non-redundant role in tissue repair and maintenance—distinct from their role in suppression of immune responses and inflammation—and that these two essential Treg cell functions are invoked by separable cues

Minimizing off-target signals in RNA fluorescent in situ hybridization

Fluorescent in situ hybridization (FISH) techniques are becoming extremely sensitive, to the point where individual RNA or DNA molecules can be detected with small probes. At this level of sensitivity, the elimination of ‘off-target’ hybridization is of crucial importance, but typical probes used for RNA and DNA FISH contain sequences repeated elsewhere in the genome. We find that very short (e.g. 20 nt) perfect repeated sequences within much longer probes (e.g. 350–1500 nt) can produce significant off-target signals. The extent of noise is surprising given the long length of the probes and the short length of non-specific regions. When we removed the small regions of repeated sequence from either short or long probes, we find that the signal-to-noise ratio is increased by orders of magnitude, putting us in a regime where fluorescent signals can be considered to be a quantitative measure of target transcript numbers. As the majority of genes in complex organisms contain repeated k-mers, we provide genome-wide annotations of k-mer-uniqueness at http://cbio.mskcc.org/∼aarvey/repeatmap

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding

ResBoost: characterizing and predicting catalytic residues in enzymes

Abstract Background Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. Results We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). Conclusion ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA