Memory of Inflammation in Regulatory T Cells.

Eukaryotic cells can "remember" transient encounters with a wide range of stimuli, inducing lasting states of altered responsiveness. Regulatory T (Treg) cells are a specialized lineage of suppressive CD4 T cells that act as critical negative regulators of inflammation in various biological contexts. Treg cells exposed to inflammatory conditions acquire strongly enhanced suppressive function. Using inducible genetic tracing, we analyzed the long-term stability of activation-induced transcriptional, epigenomic, and functional changes in Treg cells. We found that the inflammation-experienced Treg cell population reversed many activation-induced changes and lost its enhanced suppressive function over time. The "memory-less" potentiation of Treg suppressor function may help avoid a state of generalized immunosuppression that could otherwise result from repeated activation.

A Distinct Function of Regulatory T Cells in Tissue Protection.

Regulatory 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.

Control of the inheritance of regulatory T cell identity by a cis element in the Foxp3 locus.

In 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.

Extrathymic generation of regulatory T cells in placental mammals mitigates maternal-fetal conflict.

Regulatory 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.

Foxp3 exploits a pre-existent enhancer landscape for regulatory T cell lineage specification.

Regulatory T (Treg) cells, whose identity and function are defined by the transcription factor Foxp3, are indispensable for immune homeostasis. It is unclear whether Foxp3 exerts its Treg lineage specification function through active modification of the chromatin landscape and establishment of new enhancers or by exploiting a pre-existing enhancer landscape. Analysis of the chromatin accessibility of Foxp3-bound enhancers in Treg and Foxp3-negative T cells showed that Foxp3 was bound overwhelmingly to preaccessible enhancers occupied by its cofactors in precursor cells or a structurally related predecessor. Furthermore, the bulk of Foxp3-bound Treg cell enhancers lacking in Foxp3(-) CD4(+) cells became accessible upon T cell receptor activation prior to Foxp3 expression, and only a small subset associated with several functionally important genes were exclusively Treg cell specific. Thus, in a late cellular differentiation process, Foxp3 defines Treg cell functionality in an "opportunistic" manner by largely exploiting the preformed enhancer network instead of establishing a new enhancer landscape.

Continuous requirement for the TCR in regulatory T cell function.

Foxp3(+) regulatory T cells (T(reg) cells) maintain immunological tolerance, and their deficiency results in fatal multiorgan autoimmunity. Although heightened signaling via the T cell antigen receptor (TCR) is critical for the differentiation of T(reg) cells, the role of TCR signaling in T(reg) cell function remains largely unknown. Here we demonstrated that inducible ablation of the TCR resulted in T(reg) cell dysfunction that could not be attributed to impaired expression of the transcription factor Foxp3, decreased expression of T(reg) cell signature genes or altered ability to sense and consume interleukin 2 (IL-2). Instead, TCR signaling was required for maintaining the expression of a limited subset of genes comprising 25% of the activated T(reg) cell transcriptional signature. Our results reveal a critical role for the TCR in the suppressor capacity of T(reg) cells.

Inflammation-induced repression of chromatin bound by the transcription factor Foxp3 in regulatory T cells.

The transcription factor Foxp3 is indispensable for the ability of regulatory T cells (Treg cells) to suppress fatal inflammation. Here we characterized the role of Foxp3 in chromatin remodeling and the regulation of gene expression in actively suppressive Treg cells in an inflammatory setting. Although genome-wide occupancy of regulatory elements in DNA by Foxp3 was similar in resting Treg cells and those activated in vivo, Foxp3-bound enhancer elements in the DNA were poised for repression only in activated Treg cells. Following activation, Foxp3-bound sites showed diminished accessibility of chromatin and selective deposition of histone H3 trimethylated at Lys27 (H3K27me3), which was associated with recruitment of the histone methyltransferase Ezh2 and downregulation of the expression of nearby genes. Thus, Foxp3 poises its targets for repression by facilitating the formation of repressive chromatin in Treg cells upon their activation in response to inflammatory cues.

Transcription factor Foxp3 and its protein partners form a complex regulatory network.

The transcription factor Foxp3 is indispensible for the differentiation and function of regulatory T cells (T(reg) cells). To gain insights into the molecular mechanisms of Foxp3-mediated gene expression, we purified Foxp3 complexes and explored their composition. Biochemical and mass-spectrometric analyses revealed that Foxp3 forms multiprotein complexes of 400-800 kDa or larger and identified 361 associated proteins, ∼30% of which were transcription related. Foxp3 directly regulated expression of a large proportion of the genes encoding its cofactors. Some transcription factor partners of Foxp3 facilitated its expression. Functional analysis of the cooperation of Foxp3 with one such partner, GATA-3, provided additional evidence for a network of transcriptional regulation afforded by Foxp3 and its associates to control distinct aspects of T(reg) cell biology.

Identification of NK Cell Subpopulations That Differentiate HIV-Infected Subject Cohorts with Diverse Levels of Virus Control.

HIV infection is controlled immunologically in a small subset of infected individuals without antiretroviral therapy (ART), though the mechanism of control is unclear. CD8+ T cells are a critical component of HIV control in many immunological controllers. NK cells are also believed to have a role in controlling HIV infection, though their role is less well characterized. We used mass cytometry to simultaneously measure the levels of expression of 24 surface markers on peripheral NK cells from HIV-infected subjects with various degrees of HIV natural control; we then used machine learning to identify NK cell subpopulations that differentiate HIV controllers from noncontrollers. Using CITRUS (cluster identification, characterization, and regression), we identified 3 NK cell subpopulations that differentiated subjects with chronic HIV viremia (viremic noncontrollers [VNC]) from individuals with undetectable HIV viremia without ART (elite controllers [EC]). In a parallel approach, we identified 11 NK cell subpopulations that differentiated HIV-infected subject groups using k-means clustering after dimensionality reduction by t-neighbor stochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA). Among these additional 11 subpopulations, the frequencies of 5 correlated with HIV DNA levels; importantly, significance was retained in 2 subpopulations in analyses that included only cohorts without detectable viremia. By comparing the surface marker expression patterns of all identified subpopulations, we revealed that the CD11b+ CD57- CD161+ Siglec-7+ subpopulation of CD56dim CD16+ NK cells are more abundant in EC and HIV-negative controls than in VNC and that the frequency of these cells correlated with HIV DNA levels. We hypothesize that this population may have a role in immunological control of HIV infection.IMPORTANCE HIV infection results in the establishment of a stable reservoir of latently infected cells; ART is usually required to keep viral replication under control and disease progression at bay, though a small subset of HIV-infected subjects can control HIV infection without ART through immunological mechanisms. In this study, we sought to identify subpopulations of NK cells that may be involved in the natural immunological control of HIV infection. We used mass cytometry to measure surface marker expression on peripheral NK cells. Using two distinct semisupervised machine learning approaches, we identified a CD11b+ CD57- CD161+ Siglec-7+ subpopulation of CD56dim CD16+ NK cells that differentiates HIV controllers from noncontrollers. These cells can be sorted out for future functional studies to assess their potential role in the immunological control of HIV infection.

Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity.

BACKGROUND: The immune system undergoes a myriad of changes with age. While it is known that antibody-secreting plasma and long-lived memory B cells change with age, it remains unclear how the binding profile of the circulating antibody repertoire is impacted. RESULTS: To understand humoral immunity changes with respect to age, we characterized serum antibody binding to high density peptide microarrays in a diverse cohort of 1675 donors. We discovered thousands of peptides that bind antibodies in age-dependent fashion, many of which contain di-serine motifs. Peptide binding profiles were aggregated into an "immune age" by a machine learning regression model that was highly correlated with chronological age. Applying this regression model to previously-unobserved donors, we found that a donor's predicted immune age is longitudinally consistent over years, suggesting it could be a robust long-term biomarker of humoral immune ageing. Finally, we assayed serum from donors with autoimmune disease and found a significant association between "accelerated immune ageing" and autoimmune disease activity. CONCLUSIONS: The circulating antibody repertoire has increased binding to thousands of di-serine peptide containing peptides in older donors, which can be represented as an immune age. Increased immune age is associated with autoimmune disease, acute inflammatory disease severity, and may be a broadly relevant biomarker of immune function in health, disease, and therapeutic intervention.

The tumor virus landscape of AIDS-related lymphomas.

Immunodeficiency dramatically increases susceptibility to cancer as a result of reduced immune surveillance and enhanced opportunities for virus-mediated oncogenesis. Although AIDS-related lymphomas (ARLs) are frequently associated with known oncogenic viruses, many cases contain no known transforming virus. To discover novel transforming viruses, we profiled a set of ARL samples using whole transcriptome sequencing. We determined that Epstein-Barr virus (EBV) was the only virus detected in the tumor samples of this cohort, suggesting that if unidentified pathogens exist in this disease, they are present in <10% of cases or undetectable by our methods. To evaluate the role of EBV in ARL pathogenesis, we analyzed viral gene expression and found highly heterogeneous patterns of viral transcription across samples. We also found significant heterogeneity of viral antigen expression across a large cohort, with many patient samples presenting with restricted type I viral latency, indicating that EBV latency proteins are under increased immunosurveillance in the post-combined antiretroviral therapies era. Furthermore, EBV infection of lymphoma cells in HIV-positive individuals was associated with a distinct host gene expression program. These findings provide insight into the joint host-virus regulatory network of primary ARL tumor samples and expand our understanding of virus-associated oncogenesis. Our findings may also have therapeutic implications, as treatment may be personalized to target specific viral and virus-associated host processes that are only present in a subset of patients.

Sequence and chromatin determinants of cell-type-specific transcription factor binding.

Gene regulatory programs in distinct cell types are maintained in large part through the cell-type-specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type-specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line-specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type-specific binding patterns, four display distinct cell-type-specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type-specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type-specific binding profiles.

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.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

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/ approximately aarvey/repeatmap.

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.

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.

Detection of genomic islands via segmental genome heterogeneity.

While the recognition of genomic islands can be a powerful mechanism for identifying genes that distinguish related bacteria, few methods have been developed to identify them specifically. Rather, identification of islands often begins with cataloging individual genes likely to have been recently introduced into the genome; regions with many putative alien genes are then examined for other features suggestive of recent acquisition of a large genomic region. When few phylogenetic relatives are available, the identification of alien genes relies on their atypical features relative to the bulk of the genes in the genome. The weakness of these 'bottom-up' approaches lies in the difficulty in identifying robustly those genes which are atypical, or phylogenetically restricted, due to recent foreign ancestry. Herein, we apply an alternative 'top-down' approach where bacterial genomes are recursively divided into progressively smaller regions, each with uniform composition. In this way, large chromosomal regions with atypical features are identified with high confidence due to the simultaneous analysis of multiple genes. This approach is based on a generalized divergence measure to quantify the compositional difference between segments in a hypothesis-testing framework. We tested the proposed genome island prediction algorithm on both artificial chimeric genomes and genuine bacterial genomes.

Heme controls the regulation of protein tyrosine kinases Jak2 and Src.

Protein tyrosine kinases play key roles in many molecular and cellular processes in diverse living organisms. Their proper functioning is crucial for the normal growth, development, and health in humans, whereas their dysfunction can cause serious diseases, including various cancers. As such, intense studies have been performed to understand the molecular mechanisms by which the activities of protein tyrosine kinases are regulated in mammalian cells. Particularly, small molecules that can modulate the activity of tyrosine kinases are of great importance for discovering therapeutic drug candidates for numerous diseases. Notably, heme cannot only serve as a prosthetic group for hemoglobins and enzymes, but it also is a small signaling molecule that can control the activity of diverse signaling and regulatory proteins. Using a computational search, we found that a group of non-membrane spanning tyrosine kinases contains one or more CP motifs that can potentially bind to heme and mediate heme regulation. We then used experimental approaches to determine whether heme can affect the activity of any of these tyrosine kinases. We found that heme indeed affects the phosphorylation of key tyrosine residues in Jak2 and Src, and is therefore able to modulate Jak2 and Src activity. Further experiments showed that Jak2 and Src bind to heme and that the presence of heme alters the sensitivity of Jak2 and Src to trypsin digestion. These results suggest that heme actively interacts with Jak2 and Src and alters their conformation.

ResBoost: characterizing and predicting catalytic residues in enzymes.

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.