Tumor-derived IFN triggers chronic pathway agonism and sensitivity to ADAR loss.

Interferons (IFNs) are cytokines that play a critical role in limiting infectious and malignant diseases 1-4 . Emerging data suggest that the strength and duration of IFN signaling can differentially impact cancer therapies, including immune checkpoint blockade 5-7 . Here, we characterize the output of IFN signaling, specifically IFN-stimulated gene (ISG) signatures, in primary tumors from The Cancer Genome Atlas. While immune infiltration correlates with the ISG signature in some primary tumors, the existence of ISG signature-positive tumors without evident infiltration of IFN-producing immune cells suggests that cancer cells per se can be a source of IFN production. Consistent with this hypothesis, analysis of patient-derived tumor xenografts propagated in immune-deficient mice shows evidence of ISG-positive tumors that correlates with expression of human type I and III IFNs derived from the cancer cells. Mechanistic studies using cell line models from the Cancer Cell Line Encyclopedia that harbor ISG signatures demonstrate that this is a by-product of a STING-dependent pathway resulting in chronic tumor-derived IFN production. This imposes a transcriptional state on the tumor, poising it to respond to the aberrant accumulation of double-stranded RNA (dsRNA) due to increased sensor levels (MDA5, RIG-I and PKR). By interrogating our functional short-hairpin RNA screen dataset across 398 cancer cell lines, we show that this ISG transcriptional state creates a novel genetic vulnerability. ISG signature-positive cancer cells are sensitive to the loss of ADAR, a dsRNA-editing enzyme that is also an ISG. A genome-wide CRISPR genetic suppressor screen reveals that the entire type I IFN pathway and the dsRNA-activated kinase, PKR, are required for the lethality induced by ADAR depletion. Therefore, tumor-derived IFN resulting in chronic signaling creates a cellular state primed to respond to dsRNA accumulation, rendering ISG-positive tumors susceptible to ADAR loss.

Genomic Analysis of Nasopharyngeal Carcinoma Reveals TME-Based Subtypes.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) associated cancer characterized by a poor prognosis and a high level of lymphocyte infiltrate. Genetic hallmarks of NPC are not completely known but include deletion of the p16 (CDKN2A) locus and mutations in NF-κB pathway components, with a relatively low total mutational load. To better understand the genetic landscape, an integrated genomic analysis was performed using a large clinical cohort of treatment-naïve NPC tumor specimens. This genomic analysis was generally concordant with previous studies; however, three subtypes of NPC were identified by differences in immune cell gene expression, prognosis, tumor cell morphology, and genetic characteristics. A gene expression signature of proliferation was poorly prognostic and associated with either higher mutation load or specific EBV gene expression patterns in a subtype-specific manner. Finally, higher levels of stromal tumor-infiltrating lymphocytes associated with good prognosis and lower expression of a WNT and TGFß pathway activation signature.Implications: This study represents the first integrated analysis of mutation, copy number, and gene expression data in NPC and suggests how tumor genetics and EBV infection influence the tumor microenvironment in this disease. These insights should be considered for guiding immunotherapy treatment strategies in this disease. Mol Cancer Res; 15(12); 1722-32. ©2017 AACR.

Inhibition of RORγT Skews TCRα Gene Rearrangement and Limits T Cell Repertoire Diversity.

Recent studies have elucidated the molecular mechanism of RORγT transcriptional regulation of Th17 differentiation and function. RORγT was initially identified as a transcription factor required for thymopoiesis by maintaining survival of CD4+CD8+ (DP) thymocytes. While RORγ antagonists are currently being developed to treat autoimmunity, it remains unclear how RORγT inhibition may impact thymocyte development. In this study, we show that in addition to regulating DP thymocytes survival, RORγT also controls genes that regulate thymocyte migration, proliferation, and T cell receptor (TCR)α selection. Strikingly, pharmacological inhibition of RORγ skews TCRα gene rearrangement, limits T cell repertoire diversity, and inhibits development of autoimmune encephalomyelitis. Thus, targeting RORγT not only inhibits Th17 cell development and function but also fundamentally alters thymic-emigrant recognition of self and foreign antigens. The analysis of RORγ inhibitors has allowed us to gain a broader perspective of the diverse function of RORγT and its impact on T cell biology.

Phenotypic responses of differentiated asthmatic human airway epithelial cultures to rhinovirus.

OBJECTIVES: Human airway epithelial cells are the principal target of human rhinovirus (HRV), a common cold pathogen that triggers the majority of asthma exacerbations. The objectives of this study were 1) to evaluate an in vitro air liquid interface cultured human airway epithelial cell model for HRV infection, and 2) to identify gene expression patterns associated with asthma intrinsically and/or after HRV infection using this model. METHODS: Air-liquid interface (ALI) human airway epithelial cell cultures were prepared from 6 asthmatic and 6 non-asthmatic donors. The effects of rhinovirus RV-A16 on ALI cultures were compared. Genome-wide gene expression changes in ALI cultures following HRV infection at 24 hours post exposure were further analyzed using RNA-seq technology. Cellular gene expression and cytokine/chemokine secretion were further evaluated by qPCR and a Luminex-based protein assay, respectively. MAIN RESULTS: ALI cultures were readily infected by HRV. RNA-seq analysis of HRV infected ALI cultures identified sets of genes associated with asthma specific viral responses. These genes are related to inflammatory pathways, epithelial structure and remodeling and cilium assembly and function, including those described previously (e.g. CCL5, CXCL10 and CX3CL1, MUC5AC, CDHR3), and novel ones that were identified for the first time in this study (e.g. CCRL1). CONCLUSIONS: ALI-cultured human airway epithelial cells challenged with HRV are a useful translational model for the study of HRV-induced responses in airway epithelial cells, given that gene expression profile using this model largely recapitulates some important patterns of gene responses in patients during clinical HRV infection. Furthermore, our data emphasize that both abnormal airway epithelial structure and inflammatory signaling are two important asthma signatures, which can be further exacerbated by HRV infection.

Pre-treatment whole blood gene expression is associated with 14-week response assessed by dynamic contrast enhanced magnetic resonance imaging in infliximab-treated rheumatoid arthritis patients.

UNLABELLED: Approximately 30% of rheumatoid arthritis patients achieve inadequate response to anti-TNF biologics. Attempts to identify molecular biomarkers predicting response have met with mixed success. This may be attributable, in part, to the variable and subjective disease assessment endpoints with large placebo effects typically used to classify patient response. Sixty-one patients with active RA despite methotrexate treatment, and with MRI-documented synovitis, were randomized to receive infliximab or placebo. Blood was collected at baseline and genome-wide transcription in whole blood was measured using microarrays. The primary endpoint in this study was determined by measuring the transfer rate constant (Ktrans) of a gadolinium-based contrast agent from plasma to synovium using MRI. Secondary endpoints included repeated clinical assessments with DAS28(CRP), and assessments of osteitis and synovitis by the RAMRIS method. Infliximab showed greater decrease from baseline in DCE-MRI Ktrans of wrist and MCP at all visits compared with placebo (P<0.001). Statistical analysis was performed to identify genes associated with treatment-specific 14-week change in Ktrans. The 256 genes identified were used to derive a gene signature score by averaging their log expression within each patient. The resulting score correlated with improvement of Ktrans in infliximab-treated patients and with deterioration of Ktrans in placebo-treated subjects. Poor responders showed high expression of activated B-cell genes whereas good responders exhibited a gene expression pattern consistent with mobilization of neutrophils and monocytes and high levels of reticulated platelets. This gene signature was significantly associated with clinical response in two previously published whole blood gene expression studies using anti-TNF therapies. These data provide support for the hypothesis that anti-TNF inadequate responders comprise a distinct molecular subtype of RA characterized by differences in pre-treatment blood mRNA expression. They also highlight the importance of placebo controls and robust, objective endpoints in biomarker discovery. TRIAL REGISTRATION: ClinicalTrials.gov NCT01313520.

Genomic and systems approaches to translational biomarker discovery in immunological diseases.

The high failure rate of new therapeutic mechanisms tested in clinical development has spurred an upsurge in research dedicated to discovering biomarker readouts that can improve decision-making. Increasingly, systems biology and genomic technologies, such as transcriptional profiling, are being leveraged to aid in the discovery of biomarker readouts. For inflammatory and immunological diseases, such as rheumatoid arthritis (RA) and asthma, progress has been made in developing biomarkers to monitor disease activity, prediction of response to therapy, and pharmacodynamic (PD) measurements. In this review, we discuss recent successes and challenges in these endeavors, highlighting the importance of human clinical studies of standard-of-care treatments in control subjects and patients with disease as the most direct path toward identifying useful translational biomarkers for clinical development.

A quantitative atlas of polyadenylation in five mammals.

We developed PolyA-seq, a strand-specific and quantitative method for high-throughput sequencing of 3' ends of polyadenylated transcripts, and used it to globally map polyadenylation (polyA) sites in 24 matched tissues in human, rhesus, dog, mouse, and rat. We show that PolyA-seq is as accurate as existing RNA sequencing (RNA-seq) approaches for digital gene expression (DGE), enabling simultaneous mapping of polyA sites and quantitative measurement of their usage. In human, we confirmed 158,533 known sites and discovered 280,857 novel sites (FDR < 2.5%). On average 10% of novel human sites were also detected in matched tissues in other species. Most novel sites represent uncharacterized alternative polyA events and extensions of known transcripts in human and mouse, but primarily delineate novel transcripts in the other three species. A total of 69.1% of known human genes that we detected have multiple polyA sites in their 3'UTRs, with 49.3% having three or more. We also detected polyadenylation of noncoding and antisense transcripts, including constitutive and tissue-specific primary microRNAs. The canonical polyA signal was strongly enriched and positionally conserved in all species. In general, usage of polyA sites is more similar within the same tissues across different species than within a species. These quantitative maps of polyA usage in evolutionarily and functionally related samples constitute a resource for understanding the regulatory mechanisms underlying alternative polyadenylation.

Sequence analysis of chromatin immunoprecipitation data for transcription factors.

Chromatin immunoprecipitation (ChIP) experiments allow the location of transcription factors to be determined across the genome. Subsequent analysis of the sequences of the identified regions allows binding to be localized at a higher resolution than can be achieved by current high-throughput experiments without sequence analysis and may provide important insight into the regulatory programs enacted by the protein of interest. In this chapter we review the tools, workflow, and common pitfalls of such analyses and recommend strategies for effective motif discovery from these data.

A quantitative model of transcriptional regulation reveals the influence of binding location on expression.

Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.

Tissue-specific transcriptional regulation has diverged significantly between human and mouse.

We demonstrate that the binding sites for highly conserved transcription factors vary extensively between human and mouse. We mapped the binding of four tissue-specific transcription factors (FOXA2, HNF1A, HNF4A and HNF6) to 4,000 orthologous gene pairs in hepatocytes purified from human and mouse livers. Despite the conserved function of these factors, from 41% to 89% of their binding events seem to be species specific. When the same protein binds the promoters of orthologous genes, approximately two-thirds of the binding sites do not align.

Foxp3 occupancy and regulation of key target genes during T-cell stimulation.

Foxp3+CD4+CD25+ regulatory T (T(reg)) cells are essential for the prevention of autoimmunity. T(reg) cells have an attenuated cytokine response to T-cell receptor stimulation, and can suppress the proliferation and effector function of neighbouring T cells. The forkhead transcription factor Foxp3 (forkhead box P3) is selectively expressed in T(reg) cells, is required for T(reg) development and function, and is sufficient to induce a T(reg) phenotype in conventional CD4+CD25- T cells. Mutations in Foxp3 cause severe, multi-organ autoimmunity in both human and mouse. FOXP3 can cooperate in a DNA-binding complex with NFAT (nuclear factor of activated T cells) to regulate the transcription of several known target genes. However, the global set of genes regulated directly by Foxp3 is not known and consequently, how this transcription factor controls the gene expression programme for T(reg) function is not understood. Here we identify Foxp3 target genes and report that many of these are key modulators of T-cell activation and function. Remarkably, the predominant, although not exclusive, effect of Foxp3 occupancy is to suppress the activation of target genes on T-cell stimulation. Foxp3 suppression of its targets appears to be crucial for the normal function of T(reg) cells, because overactive variants of some target genes are known to be associated with autoimmune disease.

High-resolution computational models of genome binding events.

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

An improved map of conserved regulatory sites for Saccharomyces cerevisiae.

BACKGROUND: The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data. RESULTS: Applying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators. CONCLUSION: Combining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.

A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data.

MOTIVATION: Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known. RESULTS: We present a powerful approach to analyzing ChIP-chip data, called THEME, that tests hypotheses concerning the sequence specificity of a protein. Hypotheses are refined using constrained local optimization. Cross-validation provides a principled standard for selecting the optimal weighting of the hypothesis and the ChIP-chip data and for choosing the best refined hypothesis. We demonstrate how to derive hypotheses for proteins from 36 domain families. Using THEME together with these hypotheses, we analyze ChIP-chip datasets for 14 human and mouse proteins. In all the cases the identified motifs are consistent with the published data with regard to the binding specificity of the proteins.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.