Expanding the phenotype associated to KMT2A variants: overlapping clinical signs between Wiedemann–Steiner and Rubinstein–Taybi syndromes

Lysine-specific methyltransferase 2A (KMT2A) is responsible for methylation of histone H3 (K4H3me) and contributes to chromatin remodeling, acting as \u201cwriter\u201d of the epigenetic machinery. Mutations in KMT2A were first reported in Wiedemann\u2013Steiner syndrome (WDSTS). More recently, KMT2A variants have been described in probands with a specific clinical diagnosis comprised in the so-called chromatinopathies. Such conditions, including WDSTS, are a group of overlapping disorders caused by mutations in genes coding for the epigenetic machinery. Among them, Rubinstein\u2013Taybi syndrome (RSTS) is mainly caused by heterozygous pathogenic variants in CREBBP or EP300. In this work, we used next generation sequencing (either by custom-made panel or by whole exome) to identify alternative causative genes in individuals with a RSTS-like phenotype negative to CREBBP and EP300 mutational screening. In six patients we identified different novel unreported variants in KMT2A gene. The identified variants are de novo in at least four out of six tested individuals and all of them display some typical RSTS phenotypic features but also WDSTS specific signs. This study reinforces the concept that germline variants affecting the epigenetic machinery lead to a shared molecular effect (alteration of the chromatin state) determining superimposable clinical conditions

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

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 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

Biomarkers discovery by peptide and protein profiling in biological fluids based on functionalized magnetic beads purification and mass spectrometry

Proteomic

Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors

Renal cell carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's life only for early stage tumors. However, some cystic and solid renal lesions cannot be confidently differentiated from clear-cell-RCC. Therefore possible markers for early detection and to distinguish malignant kidney tumors are needed. To this aim, we applied MALDI-TOF and LC-MS/MS analysis to RPC18 MB purified serum of ccRCC, non-ccRCC patients and controls. A cluster of five signals differentiate malignant tumors from benign renal masses and healthy subjects. Moreover, a combination of six ions showed the highest specificity and sensitivity to distinguish ccRCC from controls. Healthy subjects were also differentiated from non-ccRCC by three features. Peptide ratios obtained by MALDI-TOF were compared with those from label-free LC-ESI and no statistical difference was found (p > 0.05). ESI-results were linked with MALDI profiles by both TOF/TOF sequencing and MALDI FT-ICR accurate mass measurements. About 200 unique endogenous peptides, originating from 32 proteins, were identified. Among them, SDPR and ZYX were found down-expressed, while SRGN and TMSL3 were up-expressed. In conclusion, our results suggest the possibility to discriminate malignant kidney tumors based on a cluster of serum peptides. Moreover, label-free approach may represent a valid method to verify results obtained by MALDI-TOF. This article is part of a Special Issue entitled: Integrated omics

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

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