4 research outputs found

    ALPK1 missense pathogenic variant in five families leads to ROSAH syndrome, an ocular multisystem autosomal dominant disorder

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    Purpose: To identify the molecular cause in five unrelated families with a distinct autosomal dominant ocular systemic disorder we called ROSAH syndrome due to clinical features of retinal dystrophy, optic nerve edema, splenomegaly, anhidrosis, and migraine headache. Methods: Independent discovery exome and genome sequencing in families 1, 2, and 3, and confirmation in families 4 and 5. Expression of wild-type messenger RNA and protein in human and mouse tissues and cell lines. Ciliary assays in fibroblasts from affected and unaffected family members. Results: We found the heterozygous missense variant in the ɑkinase gene, ALPK1, (c.710C>T, [p.Thr237Met]), segregated with disease in all five families. All patients shared the ROSAH phenotype with additional low-grade ocular inflammation, pancytopenia, recurrent infections, and mild renal impairment in some. ALPK1 was notably expressed in retina, retinal pigment epithelium, and optic nerve, with immunofluorescence indicating localization to the basal body of the connecting cilium of the photoreceptors, and presence in the sweat glands. Immunocytofluorescence revealed expression at the centrioles and spindle poles during metaphase, and at the base of the primary cilium. Affected family member fibroblasts demonstrated defective ciliogenesis. Conclusion: Heterozygosity for ALPK1, p.Thr237Met leads to ROSAH syndrome, an autosomal dominant ocular systemic disorder

    Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

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    The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    Strange hadron collectivity in pPb and PbPb collisions

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    International audienceThe collective behavior of KS0 {\textrm{K}}_{\textrm{S}}^0 and Λ/Λ \Lambda /\overline{\Lambda} strange hadrons is studied by measuring the elliptic azimuthal anisotropy (v2_{2}) using the scalar-product and multiparticle correlation methods. Proton-lead (pPb) collisions at a nucleon-nucleon center-of-mass energy sNN \sqrt{s_{\textrm{NN}}} = 8.16 TeV and lead-lead (PbPb) collisions at sNN \sqrt{s_{\textrm{NN}}} = 5.02 TeV collected by the CMS experiment at the LHC are investigated. Nonflow effects in the pPb collisions are studied by using a subevent cumulant analysis and by excluding events where a jet with transverse momentum greater than 20 GeV is present. The strange hadron v2_{2} values extracted in pPb collisions via the four- and six-particle correlation method are found to be nearly identical, suggesting the collective behavior. Comparisons of the pPb and PbPb results for both strange hadrons and charged particles illustrate how event-by-event flow fluctuations depend on the system size.[graphic not available: see fulltext
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