Abstract
Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene ...identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes
SERTAD4
and
GLT8D2
. We also perform GWAS in the DO for a wide-range of bone traits and identify
Qsox1
as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.
Aims
We aimed to identify long‐term HbA1c trajectories and examine associated characteristics in an observational, childhood‐onset (<17 years) type 1 diabetes cohort.
Methods
Data are from the ...Epidemiology of Diabetes Complications study, comprising 405 participants with ≥2 of seven possible HbA1c measurements over follow‐up (1988–2013) and available DNA (baseline mean diabetes duration 21 years, 53% men). HbA1c trajectories were estimated using latent class growth models. Baseline and change in participant characteristics were compared across trajectories.
Results
Five HbA1c trajectories were identified: low (51%), intermediate stable (22%), improved (19%), high stable (6%), and worsened (2%; not included in analyses). Age, diabetes duration, diabetes onset age, and sex did not differ across trajectories. Characteristics did not differ significantly between intermediate stable and low trajectories at baseline, though albumin excretion rate (AER, p = 0.0002) and estimated glomerular filtration rate (eGFR, p = 0.001) worsened slightly more in intermediate stable over time. Improved and high stable trajectories had higher baseline LDL‐c (p = 0.002 and 0.003, respectively). Improved trajectory increased median self‐monitoring of blood glucose from <1 to 3.5 times/day (p < 0.0001) and had larger LDL‐c improvement (p = 0.004) but greater worsening of AER (p < 0.0001) and eGFR (p < 0.0001) than low. The A allele of rs12970134 (near MC4R) was associated with improved (p = 0.0003) or high stable (p = 0.001) HbA1c trajectory, both patterns with high baseline HbA1c.
Conclusions
Long‐term HbA1c trajectories were primarily associated with modifiable factors in this type 1 diabetes cohort. The intermediate stable pattern had a risk factor profile that suggests some protection against adverse metabolic effects of chronic hyperglycaemia, warranting further study.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin‐secreting beta cells in the pancreas. This process results in life‐long dependence on exogenous insulin for ...survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre‐clinical through to clinically apparent disease.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Abstract
Osteoporosis affects millions worldwide and is often caused by osteoclast induced bone loss. Here, we identify the cytoplasmic protein ELMO1 as an important ‘signaling node’ in osteoclasts. ...We note that
ELMO1
SNPs associate with bone abnormalities in humans, and that ELMO1 deletion in mice reduces bone loss in four in vivo models: osteoprotegerin deficiency, ovariectomy, and two types of inflammatory arthritis. Our transcriptomic analyses coupled with CRISPR/Cas9 genetic deletion identify
Elmo1
associated regulators of osteoclast function, including cathepsin G and myeloperoxidase. Further, we define the ‘ELMO1 interactome’ in osteoclasts via proteomics and reveal proteins required for bone degradation. ELMO1 also contributes to osteoclast sealing zone on bone-like surfaces and distribution of osteoclast-specific proteases. Finally, a 3D structure-based ELMO1 inhibitory peptide reduces bone resorption in wild type osteoclasts. Collectively, we identify ELMO1 as a signaling hub that regulates osteoclast function and bone loss, with relevance to osteoporosis and arthritis.
Oligodendrocyte progenitor cells (OPCs) account for about 5% of total brain and spinal cord cells, giving rise to myelinating oligodendrocytes that provide electrical insulation to neurons of the ...CNS. OPCs have also recently been shown to regulate inflammatory responses and glial scar formation, suggesting functions that extend beyond myelination. Low-density lipoprotein receptor-related protein 1 (LRP1) is a multifaceted phagocytic receptor that is highly expressed in several CNS cell types, including OPCs. Here, we have generated an oligodendroglia-specific knockout of LRP1, which presents with normal myelin development, but is associated with better outcomes in two animal models of demyelination (EAE and cuprizone). At a mechanistic level, LRP1 did not directly affect OPC differentiation into mature oligodendrocytes. Instead, animals lacking LRP1 in OPCs in the demyelinating CNS were characterized by a robust dampening of inflammation. In particular, LRP1-deficient OPCs presented with impaired antigen cross-presentation machinery, suggesting a failure to propagate the inflammatory response and thus promoting faster myelin repair and neuroprotection. Our study places OPCs as major regulators of neuroinflammation in an LRP1-dependent fashion.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Human leukocyte antigen (HLA) genes confer substantial risk for autoimmune diseases on a log-additive scale. Here we speculated that differences in autoantigen-binding repertoires between a ...heterozygote's two expressed HLA variants might result in additional non-additive risk effects. We tested the non-additive disease contributions of classical HLA alleles in patients and matched controls for five common autoimmune diseases: rheumatoid arthritis (ncases = 5,337), type 1 diabetes (T1D; ncases = 5,567), psoriasis vulgaris (ncases = 3,089), idiopathic achalasia (ncases = 727) and celiac disease (ncases = 11,115). In four of the five diseases, we observed highly significant, non-additive dominance effects (rheumatoid arthritis, P = 2.5 × 10(-12); T1D, P = 2.4 × 10(-10); psoriasis, P = 5.9 × 10(-6); celiac disease, P = 1.2 × 10(-87)). In three of these diseases, the non-additive dominance effects were explained by interactions between specific classical HLA alleles (rheumatoid arthritis, P = 1.8 × 10(-3); T1D, P = 8.6 × 10(-27); celiac disease, P = 6.0 × 10(-100)). These interactions generally increased disease risk and explained moderate but significant fractions of phenotypic variance (rheumatoid arthritis, 1.4%; T1D, 4.0%; celiac disease, 4.1%) beyond a simple additive model.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UILJ, UKNU, UL, UM, UPUK
DNA sequence variation within human leukocyte antigen (HLA) genes mediate susceptibility to a wide range of human diseases. The complex genetic structure of the major histocompatibility complex (MHC) ...makes it difficult, however, to collect genotyping data in large cohorts. Long-range linkage disequilibrium between HLA loci and SNP markers across the major histocompatibility complex (MHC) region offers an alternative approach through imputation to interrogate HLA variation in existing GWAS data sets. Here we describe a computational strategy, SNP2HLA, to impute classical alleles and amino acid polymorphisms at class I (HLA-A, -B, -C) and class II (-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) loci. To characterize performance of SNP2HLA, we constructed two European ancestry reference panels, one based on data collected in HapMap-CEPH pedigrees (90 individuals) and another based on data collected by the Type 1 Diabetes Genetics Consortium (T1DGC, 5,225 individuals). We imputed HLA alleles in an independent data set from the British 1958 Birth Cohort (N = 918) with gold standard four-digit HLA types and SNPs genotyped using the Affymetrix GeneChip 500 K and Illumina Immunochip microarrays. We demonstrate that the sample size of the reference panel, rather than SNP density of the genotyping platform, is critical to achieve high imputation accuracy. Using the larger T1DGC reference panel, the average accuracy at four-digit resolution is 94.7% using the low-density Affymetrix GeneChip 500 K, and 96.7% using the high-density Illumina Immunochip. For amino acid polymorphisms within HLA genes, we achieve 98.6% and 99.3% accuracy using the Affymetrix GeneChip 500 K and Illumina Immunochip, respectively. Finally, we demonstrate how imputation and association testing at amino acid resolution can facilitate fine-mapping of primary MHC association signals, giving a specific example from type 1 diabetes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Professional phagocytes (such as macrophages) and non-professional phagocytes (such as epithelial cells) clear billions of apoptotic cells and particles on a daily basis. Although professional and ...non-professional macrophages reside in proximity in most tissues, whether they communicate with each other during cell clearance, and how this might affect inflammation, is not known. Here we show that macrophages, through the release of a soluble growth factor and microvesicles, alter the type of particles engulfed by non-professional phagocytes and influence their inflammatory response. During phagocytosis of apoptotic cells or in response to inflammation-associated cytokines, macrophages released insulin-like growth factor 1 (IGF-1). The binding of IGF-1 to its receptor on non-professional phagocytes redirected their phagocytosis, such that uptake of larger apoptotic cells was reduced whereas engulfment of microvesicles was increased. IGF-1 did not alter engulfment by macrophages. Macrophages also released microvesicles, whose uptake by epithelial cells was enhanced by IGF-1 and led to decreased inflammatory responses by epithelial cells. Consistent with these observations, deletion of IGF-1 receptor in airway epithelial cells led to exacerbated lung inflammation after allergen exposure. These genetic and functional studies reveal that IGF-1- and microvesicle-dependent communication between macrophages and epithelial cells can critically influence the magnitude of tissue inflammation in vivo.
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IJS, KISLJ, NUK, SBMB, UL, UM, UPUK
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