scholarly journals Integrative Genetics Analysis of Juvenile Idiopathic Arthritis Identifies Novel Loci

2020 ◽  
Author(s):  
Yun R. Li ◽  
Jin Li ◽  
Joseph T. Glessner ◽  
Jie Yang ◽  
Michael E. March ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Clinical Presentation ◽  
Immune Cell ◽  
Drug Repositioning ◽  
Cell Types ◽  
Disease Phenotypes ◽  
Genome Wide ◽  
Immune Mediated ◽  
Mhc Locus ◽  
Shared Genetic

Juvenile Idiopathic Arthritis (JIA) is the most common type of arthritis among children, encompassing a highly heterogeneous group of immune-mediated joint disorders, being classified into seven subtypes based on clinical presentation. To systematically understand the distinct and shared genetic underpinnings of JIA subtypes, we conducted a heterogeneity-sensitive GWAS encompassing a total of 1245 JIA cases classified into 7 subtypes and 9250 controls. In addition to the MHC locus, we uncovered 16 genome-wide significant loci, among which 15 were shared between at least two JIA subtypes, including 11 novel loci. Functional annotation indicates that candidate genes at these loci are expressed in diverse immune cell types. Further, based on the association results, the 7 JIA subtypes were classified into two groups, reflecting their autoimmune vs autoinflammatory nature. Our results suggest a common genetic mechanism underlying these subtypes in spite of their different clinical disease phenotypes, and that there may be drug repositioning opportunities for rare JIA subtypes.

The Intersection of Metformin and Inflammation

2021 ◽  
Author(s):  
Leena P. Bharath ◽  
Barbara S. Nikolajczyk
Keyword(s):  
Mitochondrial Function ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Remarkable Effect ◽  
Tumor Onset ◽  
Age Related ◽  
Immune Mediated ◽  
Autoimmune Conditions ◽  
Near Term

The biguanide metformin is the most commonly used antidiabetic drug. Recent studies show that metformin not only improves chronic inflammation by improving metabolic parameters but also has a direct anti-inflammatory effect. In light of these findings, it is essential to identify the inflammatory pathways targeted by metformin to develop a comprehensive understanding of the mechanisms of action of this drug. Commonly accepted mechanisms of metformin action include AMPK activation and inhibition of mTOR pathways, which are evaluated in multiple diseases. Additionally, metformin's action on mitochondrial function and cellular homeostasis processes such as autophagy, is of particular interest because of the importance of these mechanisms in maintaining cellular health. Both dysregulated mitochondria and failure of the autophagy pathways, the latter of which impair clearance of dysfunctional, damaged, or excess organelles, affect cellular health drastically and can trigger the onset of metabolic and age-related diseases. Immune cells are the fundamental cell types that govern the health of an organism. Thus, dysregulation of autophagy or mitochondrial function in immune cells has a remarkable effect on susceptibility to infections, response to vaccination, tumor onset, and the development of inflammatory and autoimmune conditions. Here we summarize the latest research on metformin's regulation of immune cell mitochondrial function and autophagy as evidence that new clinical trials on metformin with primary outcomes related to the immune system should be considered to treat immune-mediated diseases over the near term.

scholarly journals Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types

2017 ◽  
Vol 49 (4) ◽  
pp. 600-605 ◽  
Author(s):  
Sung Chun ◽  
Alexandra Casparino ◽  
Nikolaos A Patsopoulos ◽  
Damien C Croteau-Chonka ◽  
Benjamin A Raby ◽  
...  
Keyword(s):  
Autoimmune Disease ◽  
Immune Cell ◽  
Cell Types ◽  
Genetic Effects ◽  
Statistical Evidence ◽  
Shared Genetic

scholarly journals Integrative Genome-Wide Analysis of Long Noncoding RNAs in Diverse Immune Cell Types of Melanoma Patients

2018 ◽  
Vol 78 (15) ◽  
pp. 4411-4423 ◽  
Author(s):  
Lei Wang ◽  
Sara J. Felts ◽  
Virginia P. Van Keulen ◽  
Adam D. Scheid ◽  
Matthew S. Block ◽  
...  
Keyword(s):  
Immune Cell ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Long Noncoding Rnas ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Melanoma Patients

scholarly journals Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for twelve immune-mediated diseases

2020 ◽  
Author(s):  
Kousik Kundu ◽  
Alice L. Mann ◽  
Manuel Tardaguila ◽  
Stephen Watt ◽  
Hannes Ponstingl ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Meta Analysis ◽  
Cell Types ◽  
Genetic Associations ◽  
Sequencing Data ◽  
Immune Mediated ◽  
Causal Genes ◽  
Causal Variants

AbstractThe identification of causal genetic variants for common diseases improves understanding of disease biology. Here we use data from the BLUEPRINT project to identify regulatory quantitative trait loci (QTL) for three primary human immune cell types and use these to fine-map putative causal variants for twelve immune-mediated diseases. We identify 340 unique, non major histocompatibility complex (MHC) disease loci that colocalise with high (>98%) posterior probability with regulatory QTLs, and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping applied to regulatory QTLs yields smaller credible set sizes and higher posterior probabilities for candidate causal variants compared to disease summary statistics. We also describe a systematic under-representation of insertion/deletion (INDEL) polymorphisms in credible sets derived from publicly available disease meta-analysis when compared to QTLs based on genome-sequencing data. Overall, our findings suggest that fine-mapping applied to disease-colocalising regulatory QTLs can enhance the discovery of putative causal disease variants and provide insights into the underlying causal genes and molecular mechanisms.

scholarly journals Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes

2019 ◽  
Author(s):  
CS Thom ◽  
BF Voight
Keyword(s):  
Progenitor Cells ◽  
Genetic Relationships ◽  
Genetic Associations ◽  
Crohns Disease ◽  
Erythroid Progenitor ◽  
End Points ◽  
Disease Phenotypes ◽  
Genome Wide ◽  
Genetic Signal ◽  
Shared Genetic

AbstractBackgroundGenetic associations link hematopoietic traits and disease end-points, but most causal variants and genes underlying these relationships are unknown. Here, we used genetic colocalization to nominate loci and genes related to shared genetic signal for hematopoietic, cardiovascular, autoimmune, neuropsychiatric and cancer phenotypes.ResultsOur findings recapitulate developmental hematopoietic lineage relationships, identify loci associating traits with causal genetic relationships, and reveal novel associations. Out of 2706 loci with genome-wide significant signal for at least 1 blood trait, we identified 1779 unique sites (66%) with shared genetic signal for 2+ hematologic traits at a false discovery rate <5%. We could assign some sites to specific developmental cell types during hematopoiesis based on affected traits, including those likely to impact hematopoietic progenitor cells and/or megakaryocyte-erythroid progenitor cells. Through an expanded analysis of 70 human traits, we define 2+ colocalizing traits at 2007 loci from an analysis of 9852 sites (20%) containing genome-wide significant signal for at least 1 GWAS trait. In addition to variants and genes underlying shared genetic signal between blood traits and disease phenotypes that had been previously related through mendelian randomization studies, we define loci and related genes underlying shared signal between eosinophil count and eczema. We also identified colocalizing signals in a number of clinically relevant coding mutations, including in sites linking PTPN22 with Crohns disease, NIPA with coronary artery disease and platelet trait variation, and the hemochromatosis gene HFE with altered lipid levels. Finally, we anticipate potential off-target effects on blood traits related novel therapeutic targets, including TRAIL.ConclusionsOur findings provide a road map for gene validation experiments and novel therapeutics related to hematopoietic development, and offer a rationale for pleiotropic interactions between hematopoietic loci and disease end-points.

scholarly journals FitHiChIP: Identification of significant chromatin contacts from HiChIP data

2018 ◽  
Author(s):  
Sourya Bhattacharyya ◽  
Vivek Chandra ◽  
Pandurangan Vijayanand ◽  
Ferhat Ay
Keyword(s):  
Immune Cell ◽  
Cell Types ◽  
Contact Analysis ◽  
Computational Method ◽  
Genomic Distance ◽  
Regulatory Regions ◽  
Uniform Coverage ◽  
Link Type ◽  
Genome Wide

Here we describe FitHiChIP (github.com/ay-lab/FitHiChIP), a computational method for identifying chromatin contacts among regulatory regions such as en-hancers and promoters from HiChIP/PLAC-seq data. FitHiChIP jointly models the non-uniform coverage and genomic distance scaling of HiChIP data, captures previously validated enhancer interactions for several genes including MYC and TP53, and recovers contacts genome-wide that are supported by ChIA-PET, pro-moter capture Hi-C and Hi-C data. FitHiChIP also provides a framework for differential contact analysis as showcased in a comparison of HiChIP data we have generated for two distinct immune cell types.

scholarly journals OTME-5. Meningioma liquid biopsy specimens exhibit contrasting immune-cell landscapes across methylation-subtypes and estimated recurrence risk subgroups

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii14-ii14
Author(s):  
Grayson Herrgott ◽  
Ruicong She ◽  
Thais Sabedot ◽  
Michael Wells ◽  
Karam Asmaro ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Random Forest ◽  
Liquid Biopsy ◽  
Immune Cell ◽  
Cell Types ◽  
Recurrence Risk ◽  
Random Forest Classifier ◽  
Serum Samples ◽  
Biopsy Specimens ◽  
Genome Wide

Abstract Background Tumor-infiltrating immune cell compositions have been previously correlated to encouragement or inhibition of tumor growth. This association highlights immune-landscape profiling through non-invasive methods as a crucial step in approaches to treatment of patients with meningioma (MNG), a prevalent primary intracranial tumor. Genome-wide DNA methylation patterns can aid in definition and assessment of cell compositions in liquid biopsy serum specimens, and allow for development of machine-learning models with predictive capabilities. Methods We profiled the cfDNA methylome (EPIC array) in liquid biopsy specimens from patients with MNG (n = 63) and nontumor controls (n = 6). We conducted both unsupervised epigenome-wide and supervised analyses of the meningioma methylome. Estimation of immune cell composition was conducted using Python-based methodology, where a reference methylome atlas of chosen cell types (B-cells, CD4- and CD8T-cells, neutrophils, natural killer cells, monocytes, cortical neuron, vascular endothelial cells, and healthy meninge) was used to deconvolute the MNG samples. Recurrence risk was estimated using an existing methylation-based Random-Forest classifier previously reported and validated, adapted to our serum-based cohort through employment of translatable meningioma subgroup-specific methylation markers (differentially methylated probes). Results We identified four distinct genome-wide methylation subgroups (k-clusters) of MNG which presented differential tumor micro-environments across all cell types investigated. Application of the DNA methylation-based Random-Forest classifier allowed for categorization of primary MNG serum samples into estimated recurrence-risk subgroups. Significantly contrasting micro-environments for the subgroups were observed across several cell-types, with those MNG more likely to recur displaying depletion in cell types reported to improve anti-tumoral response in many tumors (e.g. T-Cells). Conclusions DNA methylation based deconvolution allowed for detection of contrasting tumor microenvironment compositions across MNG methylation subtypes and recurrence-risk estimation subgroups. These results suggest that microenvironment profiling can be informative of probable tumor behavior and prognostic outcomes, helping guide therapeutic approaches towards treatment of patients with MNG.

scholarly journals Genetic architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications

2016 ◽  
Vol 76 (5) ◽  
pp. 906-913 ◽  
Author(s):  
Michael J Ombrello ◽  
Victoria L Arthur ◽  
Elaine F Remmers ◽  
Anne Hinks ◽  
Ioanna Tachmazidou ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Genetic Risk ◽  
Genetic Architecture ◽  
Systemic Juvenile Idiopathic Arthritis ◽  
Disease Process ◽  
Chromosome 1 ◽  
Childhood Arthritis ◽  
Genome Wide ◽  
A Genome ◽  
Shared Genetic

ObjectivesJuvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA.MethodsWe performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes.ResultsThe major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes.ConclusionsThe lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.

scholarly journals Comparative analysis of genome-wide DNA methylation identifies patterns that associate with conserved transcriptional programs in osteosarcoma

2020 ◽  
Author(s):  
Lauren J. Mills ◽  
Milcah C. Scott ◽  
Pankti Shah ◽  
Anne R. Cunanan ◽  
Archana Deshpande ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Large Scale ◽  
Immune Cell ◽  
Cell Types ◽  
Structural Variations ◽  
Canine Osteosarcoma ◽  
Genome Wide ◽  
Highly Correlated ◽  
Murine Osteosarcoma ◽  
Methylation Patterns

AbstractOsteosarcoma is an aggressive tumor of the bone that primarily affects young adults and adolescents. Osteosarcoma is characterized by genomic chaos and heterogeneity. While inactivation of tumor suppressor p53 TP53 is nearly universal other high frequency mutations or structural variations have not been identified. Despite this genomic heterogeneity, key conserved transcriptional programs associated with survival have been identified across human, canine and induced murine osteosarcoma. The epigenomic landscape, including DNA methylation, plays a key role in establishing transcriptional programs in all cell types. The role of epigenetic dysregulation has been studied in a variety of cancers but has yet to be explored at scale in osteosarcoma. Here we examined genome-wide DNA methylation patterns in 24 human and 44 canine osteosarcoma samples identifying groups of highly correlated DNA methylation marks in human and canine osteosarcoma samples. We also link specific DNA methylation patterns to key transcriptional programs in both human and canine osteosarcoma. Building on previous work, we built a DNA methylation-based measure for the presence and abundance of various immune cell types in osteosarcoma. Finally, we determined that the underlying state of the tumor, and not changes in cell composition, were the main driver of differences in DNA methylation across the human and canine samples.SignificanceThis is the first large scale study of DNA methylation in osteosarcoma and lays the ground work for the exploration of DNA methylation programs that help establish conserved transcriptional programs in the context of different genomic landscapes.

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