Calcium Channel Blocker D-\u3cem\u3ecis\u3c/em\u3e-Diltiazem Does Not Slow Retinal Degeneration in the \u3cem\u3ePDE6B\u3c/em\u3e Mutant \u3cem\u3ercd1\u3c/em\u3e Canine Model of Retinitis Pigmentosa

Purpose: D-cis-diltiazem, a calcium channel blocker, has been reported to enhance photoreceptor survival in the rd mouse, a model of retinitis pigmentosa (RP) resulting from mutation of the PDE6B gene. We tested the hypothesis that diltiazem treatment would similarly rescue the canine rcd1 model of RP, which is also caused by a null mutation in the PDE6B gene. Methods: D-cis-diltiazem was delivered orally twice daily to rcd1 affected dogs beginning at 4 weeks of age; untreated age-matched rcd1 dogs served as controls. At 14 weeks, electroretinograms (ERG) were performed on all animals; 14 dogs were euthanized at this age, and 2 dogs at 25 weeks of age. Eyes were enucleated, fixed, and processed for routine histological examination. Results: No significant differences were found in ERG or histopathologic parameters between diltiazem-treated and untreated rcd1 dogs. Neither rcd1 group showed a rod b-wave; ERGs evoked by single white flashes (dark- or light-adapted) and flicker were also identical between groups. Similarly, treated and untreated animals did not differ in the degree of preservation of the photoreceptor layer, confirmed in cell counts within the outer nuclear layer. Conclusions: Treatment of rcd1 affected dogs with D-cis-diltiazem did not modify the photoreceptor disease when results were assessed using either ERG or histopathologic criteria. The positive photoreceptor-rescue effect of calcium channel blockers reported in the rd mouse was thus not generalizable to another species with retinal degeneration due to mutation in the PDE6B gene. Caution needs to be exerted in extrapolation to the comparable human forms of RP

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph)

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )

Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets

Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score (∼6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 × 10‾⁴⁹), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications.This work was funded by a Medical Research Council (MRC) strategic award to M.D.T., I.P.H., D.S. and L.V.W. (MC_PC_12010). This research has been conducted using the UK Biobank Resource under application 648. This article presents independent research funded partially by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the UK Department of Health. This research used the ALICE and SPECTRE High-Performance Computing Facilities at the University of Leicester. Additional acknowledgments and funding details can be found in the Supplementary Note

Author Correction: MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Correction to: Scientific Data, published online 25 May 2022 The order of the column headings was incorrect in Table 1, meaning values were listed under the wrong descriptions. This has been corrected in the pdf and HTML versions of the article

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )

Phenotypic and functional translation of IL1RL1 locus polymorphisms in lung tissue and asthmatic airway epithelium.

The IL1RL1 (ST2) gene locus is robustly associated with asthma; however, the contribution of single nucleotide polymorphisms (SNPs) in this locus to specific asthma subtypes and the functional mechanisms underlying these associations remain to be defined. We tested for association between IL1RL1 region SNPs and characteristics of asthma as defined by clinical and immunological measures and addressed functional effects of these genetic variants in lung tissue and airway epithelium. Utilizing 4 independent cohorts (Lifelines, Dutch Asthma GWAS [DAG], Genetics of Asthma Severity and Phenotypes [GASP], and Manchester Asthma and Allergy Study [MAAS]) and resequencing data, we identified 3 key signals associated with asthma features. Investigations in lung tissue and primary bronchial epithelial cells identified context-dependent relationships between the signals and IL1RL1 mRNA and soluble protein expression. This was also observed for asthma-associated IL1RL1 nonsynonymous coding TIR domain SNPs. Bronchial epithelial cell cultures from asthma patients, exposed to exacerbation-relevant stimulations, revealed modulatory effects for all 4 signals on IL1RL1 mRNA and/or protein expression, suggesting SNP-environment interactions. The IL1RL1 TIR signaling domain haplotype affected IL-33-driven NF-κB signaling, while not interfering with TLR signaling. In summary, we identify that IL1RL1 genetic signals potentially contribute to severe and eosinophilic phenotypes in asthma, as well as provide initial mechanistic insight, including genetic regulation of IL1RL1 isoform expression and receptor signaling