Emily R. Miraldi

An IL-23R/IL-22 circuit regulates epithelial serum amyloid A to promote local effector Th17 responses

RORγt(+) Th17 cells are important for mucosal defenses but also contribute to autoimmune disease. They accumulate in the intestine in response to microbiota and produce IL-17 cytokines. Segmented filamentous bacteria (SFB) are Th17-inducing commensals that potentiate autoimmunity in mice. RORγt(+) T cells were induced in mesenteric lymph nodes early after SFB colonization and distributed across different segments of the gastrointestinal tract. However, robust IL-17A production was restricted to the ileum, where SFB makes direct contact with the epithelium and induces serum amyloid A proteins 1 and 2 (SAA1/2), which promote local IL-17A expression in RORγt(+) T cells. We identified an SFB-dependent role of type 3 innate lymphoid cells (ILC3), which secreted IL-22 that induced epithelial SAA production in a Stat3-dependent manner. This highlights the critical role of tissue microenvironment in activating effector functions of committed Th17 cells, which may have important implications for how these cells contribute to inflammatory disease.

CX3CR1+ mononuclear phagocytes support colitis-associated innate lymphoid cell production of IL-22

Intestinal CX3CR1+ mononuclear phagocytes regulate ILC3 in vivo in response to colitis associated microbial signals.

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

DDX5 and its associated lncRNA Rmrp modulate TH17 cell effector functions

T helper 17 (TH17) lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear receptor. Here we identify the RNA helicase DEAD-box protein 5 (DDX5) as a RORγt partner that coordinates transcription of selective TH17 genes, and is required for TH17-mediated inflammatory pathologies. Surprisingly, the ability of DDX5 to interact with RORγt and coactivate its targets depends on intrinsic RNA helicase activity and binding of a conserved nuclear long noncoding RNA (lncRNA), Rmrp, which is mutated in patients with cartilage-hair hypoplasia. A targeted Rmrp gene mutation in mice, corresponding to a gene mutation in cartilage-hair hypoplasia patients, altered lncRNA chromatin occupancy, and reduced the DDX5–RORγt interaction and RORγt target gene transcription. Elucidation of the link between Rmrp and the DDX5–RORγt complex reveals a role for RNA helicases and lncRNAs in tissue-specific transcriptional regulation, and provides new opportunities for therapeutic intervention in TH17-dependent diseases.

Phosphotyrosine signaling analysis of site-specific mutations on EGFRvIII identifies determinants governing glioblastoma cell growth

To evaluate the role of individual EGFR phosphorylation sites in activating components of the cellular signaling network we have performed a mass spectrometry-based analysis of the phosphotyrosine network downstream of site-specific EGFRvIII mutants, enabling quantification of network-level effects of site-specific point mutations. Mutation at Y845, Y1068 or Y1148 resulted in diminished receptor phosphorylation, while mutation at Y1173 led to increased phosphorylation on multiple EGFRvIII residues. Altered phosphorylation at the receptor was recapitulated in downstream signaling network activation levels, with Y1173F mutation leading to increased phosphorylation throughout the network. Computational modeling of GBM cell growth as a function of network phosphorylation levels highlights the Erk pathway as crucial for regulating EGFRvIII-driven U87MG GBM cell behavior, with the unexpected finding that Erk1/2 is negatively correlated to GBM cell growth. Genetic manipulation of this pathway supports the model, demonstrating that EGFRvIII-expressing U87MG GBM cells are sensitive to Erk activation levels. Additionally, we developed a model describing glioblastoma cell growth based on a reduced set of phosphoproteins, which represent potential candidates for future development as therapeutic targets for EGFRvIII-positive glioblastoma patients.

Critical role of IRF1 and BATF in forming chromatin landscape during type 1 regulatory cell differentiation

Type 1 regulatory T cells (Tr1 cells) are induced by interleukin-27 (IL-27) and have critical roles in the control of autoimmunity and resolution of inflammation. We found that the transcription factors IRF1 and BATF were induced early on after treatment with IL-27 and were required for the differentiation and function of Tr1 cells in vitro and in vivo. Epigenetic and transcriptional analyses revealed that both transcription factors influenced chromatin accessibility and expression of the genes required for Tr1 cell function. IRF1 and BATF deficiencies uniquely altered the chromatin landscape, suggesting that these factors serve a pioneering function during Tr1 cell differentiation.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or “bait”) that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

A Damage-Independent Role for 53BP1 that Impacts Break Order and Igh Architecture during Class Switch Recombination

SUMMARY During class switch recombination (CSR), B cells replace the Igh Cμ or δ exons with another down-stream constant region exon (CH), altering the anti-body isotype. CSR occurs through the introduction of AID-mediated double-strand breaks (DSBs) in switch regions and subsequent ligation of broken ends. Here, we developed an assay to investigate the dynamics of DSB formation in individual cells. We demonstrate that the upstream switch region Sμ is first targeted during recombination and that the mechanism underlying this control relies on 53BP1. Surprisingly, regulation of break order occurs through residual binding of 53BP1 to chromatin before the introduction of damage and independent of its established role in DNA repair. Using chromosome conformation capture, we show that 53BP1 mediates changes in chromatin architecture that affect break order. Finally, our results explain how changes in Igh architecture in the absence of 53BP1 could promote inversional rearrangements that compromise CSR.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).