@article {44, title = {Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 10}, pages = {1366-1374}, abstract = {

To define potentially causal variants for autoimmune disease, we fine-mapped 76 rheumatoid arthritis (11,475 cases, 15,870 controls) and type 1 diabetes loci (9,334 cases, 11,111 controls). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89\% of common variants. We defined credible sets of <=5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

}, keywords = {Alleles, Arthritis, Rheumatoid, Case-Control Studies, CD28 Antigens, Chromosome Mapping, CTLA-4 Antigen, Diabetes Mellitus, Type 1, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Jurkat Cells, Mutation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA, Long Noncoding, Tumor Necrosis Factor alpha-Induced Protein 3}, issn = {1546-1718}, doi = {10.1038/s41588-018-0216-7}, author = {Westra, Harm-Jan and Mart{\'\i}nez-Bonet, Marta and Onengut-Gumuscu, Suna and Lee, Annette and Luo, Yang and Teslovich, Nikola and Worthington, Jane and Martin, Javier and Huizinga, Tom and Klareskog, Lars and Rantapaa-Dahlqvist, Solbritt and Chen, Wei-Min and Quinlan, Aaron and Todd, John A and Eyre, Steve and Nigrovic, Peter A and Gregersen, Peter K and Rich, Stephen S and Raychaudhuri, Soumya} } @article {31, title = {Functional genomics of stromal cells in chronic inflammatory diseases.}, journal = {Curr Opin Rheumatol}, volume = {30}, year = {2018}, month = {2018 Jan}, pages = {65-71}, abstract = {

PURPOSE OF REVIEW: Stroma is a broad term referring to the connective tissue matrix in which other cells reside. It is composed of diverse cell types with functions such as extracellular matrix maintenance, blood and lymph vessel development, and effector cell recruitment. The tissue microenvironment is determined by the molecular characteristics and relative abundances of different stromal cells such as fibroblasts, endothelial cells, pericytes, and mesenchymal precursor cells. Stromal cell heterogeneity is explained by embryonic developmental lineage, stages of differentiation to other cell types, and activation states. Interaction between immune and stromal cell types is critical to wound healing, cancer, and a wide range of inflammatory diseases. Here, we review recent studies of inflammatory diseases that use functional genomics and single-cell technologies to identify and characterize stromal cell types associated with pathogenesis.

RECENT FINDINGS: High dimensional strategies using mRNA sequencing, mass cytometry, and fluorescence activated cell-sorting with fresh primary tissue samples are producing detailed views of what is happening in diseased tissue in rheumatoid arthritis, inflammatory bowel disease, and cancer. Fibroblasts positive for CD90 (Thy-1) are enriched in the synovium of rheumatoid arthritis patients. Single-cell RNA-seq studies will lead to more discoveries about the stroma in the near future.

SUMMARY: Stromal cells form the microenvironment of inflamed and diseased tissues. Functional genomics is producing an increasingly detailed view of subsets of stromal cells with pathogenic functions in rheumatic diseases and cancer. Future genomics studies will discover disease mechanisms by perturbing molecular pathways with chemokines and therapies known to affect patient outcomes. Functional genomics studies with large sample sizes of patient tissues will identify patient subsets with different disease phenotypes or treatment responses.

}, keywords = {Arthritis, Rheumatoid, Cell Differentiation, Chemokines, Endothelial Cells, Fibroblasts, Flow Cytometry, Genomics, Humans, Inflammatory Bowel Diseases, Mesenchymal Stem Cells, Pericytes, Rheumatic Diseases, RNA, Messenger, Sequence Analysis, RNA, Single-Cell Analysis, Stromal Cells, Synovial Membrane, Thy-1 Antigens}, issn = {1531-6963}, doi = {10.1097/BOR.0000000000000455}, author = {Slowikowski, Kamil and Wei, Kevin and Brenner, Michael B and Raychaudhuri, Soumya} } @article {17, title = {Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis.}, journal = {Curr Opin Immunol}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {27-36}, abstract = {

CD4+ T cells have been long known to play an important role in the pathogenesis of rheumatoid arthritis (RA), but the specific cell populations and states that drive the disease have been challenging to identify with low dimensional single cell data and bulk assays. The advent of high dimensional single cell technologies-like single cell RNA-seq or mass cytometry-has offered promise to defining key populations, but brings new methodological and statistical challenges. Recent single cell profiling studies have revealed a broad diversity of cell types among CD4+ T cells, identifying novel populations that are expanded or altered in RA. Here, we will review recent findings on CD4+ T cell heterogeneity and RA that have come from single cell profiling studies and discuss the best practices for conducting these studies.

}, keywords = {Animals, Arthritis, Rheumatoid, Blood Cells, CD4-Positive T-Lymphocytes, Datasets as Topic, Flow Cytometry, Humans, Immunophenotyping, Organ Specificity, Single-Cell Analysis, T-Lymphocyte Subsets}, issn = {1879-0372}, doi = {10.1016/j.coi.2017.08.005}, author = {Fonseka, Chamith Y and Rao, Deepak A and Raychaudhuri, Soumya} }