Mutations in the U2 snRNA geneRNU2-2Pcause a severe neurodevelopmental disorder with prominent epilepsy

Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy Daniel Greene , Koenraad De Wispelaere , Jon Lees , Andrea Katrinecz , Sonia Pascoal , Emma Hales , View ORCID Profile Marta Codina-Solà , View ORCID Profile Irene Valenzuela , View ORCID Profile Eduardo F. Tizzano , Giles Atton , Deirdre Donnelly , Nicola Foulds , Joanna Jarvis , Shane McKee , Michael O’Donoghue , Mohnish Suri , Pradeep Vasudevan , Kathy Stirrups , Natasha P. Morgan , View ORCID Profile Kathleen Freson , View ORCID Profile Andrew D. Mumford , View ORCID Profile Ernest Turro doi: https://doi.org/10.1101/2024.09.03.24312863 Daniel Greene 1 Department of Medicine, University of Cambridge , Cambridge, UK 2 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Koenraad De Wispelaere 2 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY, USA 3 Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jon Lees 4 School of Cellular and Molecular Medicine, University of Bristol , Bristol, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Katrinecz 5 NIHR BioResource, Cambridge University Hospitals , Cambridge Biomedical Campus, Cambridge, UK 6 Department of Haematology, School of Clinical Medicine, University of Cambridge , Cambridge Biomedical Campus, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sonia Pascoal 5 NIHR BioResource, Cambridge University Hospitals , Cambridge Biomedical Campus, Cambridge, UK 6 Department of Haematology, School of Clinical Medicine, University of Cambridge , Cambridge Biomedical Campus, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma Hales 5 NIHR BioResource, Cambridge University Hospitals , Cambridge Biomedical Campus, Cambridge, UK 6 Department of Haematology, School of Clinical Medicine, University of Cambridge , Cambridge Biomedical Campus, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marta Codina-Solà 7 Department of Clinical and Molecular Genetics, Hospital Universitari Vall d’Hebron , Barcelona, Catalonia, Spain 8 Medicine Genetics Group Vall d’Hebron Research Institute , Barcelona, Catalonia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marta Codina-Solà Irene Valenzuela 7 Department of Clinical and Molecular Genetics, Hospital Universitari Vall d’Hebron , Barcelona, Catalonia, Spain 8 Medicine Genetics Group Vall d’Hebron Research Institute , Barcelona, Catalonia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Irene Valenzuela Eduardo F. Tizzano 7 Department of Clinical and Molecular Genetics, Hospital Universitari Vall d’Hebron , Barcelona, Catalonia, Spain 8 Medicine Genetics Group Vall d’Hebron Research Institute , Barcelona, Catalonia, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eduardo F. Tizzano Giles Atton 9 Wessex Clinical Genetics Service, University Hospital Southampton NHS Foundation Trust , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Deirdre Donnelly 10 Genetic Medicine, Belfast City Hospital , Belfast, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicola Foulds 9 Wessex Clinical Genetics Service, University Hospital Southampton NHS Foundation Trust , Southampton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joanna Jarvis 11 Clinical Genetics Unit, Birmingham Womens’ Hospital , Birmingham, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shane McKee 10 Genetic Medicine, Belfast City Hospital , Belfast, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael O’Donoghue 12 Neurology, Nottingham University Hospital NHS Trust , Nottingham, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohnish Suri 13 Clinical Genetics, Nottingham University Hospital NHS Trust , Nottingham, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pradeep Vasudevan 14 Clinical Genetics, University Hospitals of Leicester NHS Trust , Leicester, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathy Stirrups 5 NIHR BioResource, Cambridge University Hospitals , Cambridge Biomedical Campus, Cambridge, UK 6 Department of Haematology, School of Clinical Medicine, University of Cambridge , Cambridge Biomedical Campus, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natasha P. Morgan 5 NIHR BioResource, Cambridge University Hospitals , Cambridge Biomedical Campus, Cambridge, UK 6 Department of Haematology, School of Clinical Medicine, University of Cambridge , Cambridge Biomedical Campus, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen Freson 3 Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kathleen Freson Andrew D. Mumford 4 School of Cellular and Molecular Medicine, University of Bristol , Bristol, United Kingdom 15 NHS South West Genomic Medicine Service Alliance , Bristol, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew D. Mumford Ernest Turro 1 Department of Medicine, University of Cambridge , Cambridge, UK 2 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY, USA 16 Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai , New York, NY, USA 17 Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ernest Turro For correspondence: ernest.turro{at}mssm.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract The major spliceosome comprises the five snRNAs U1, U2, U4, U5 and U6. We recently showed that mutations in RNU4- 2, which encodes U4 snRNA, cause one of the most prevalent monogenic neurodevelopmental disorders. Here, we report that recurrent germline mutations in RNU2-2P , a 191bp gene encoding U2 snRNA, are responsible for a related disorder. By genetic association, we implicated recurrent de novo single nucleotide mutations at nucleotide positions 4 and 35 of RNU2-2P among nine cases. We replicated this finding in six additional cases, bringing the total to 15. The disorder is characterized by intellectual disability, neurodevelopmental delay, autistic behavior, microcephaly, hypotonia, epilepsy and hyperventilation. All cases display a severe and complex seizure phenotype. Our findings cement the role of major spliceosomal snRNAs in the etiologies of neurodevelopmental disorders. Over 1,400 genes have been established as etiological for neurodevelopmental disorders (NDDs) featuring intellectual disability, of which only 10 are non-coding 1 . Three of these ten non-coding genes— RNU4ATAC , RNU12 and RNU4-2 —encode small nuclear RNAs (snRNAs) that play crucial roles in orchestrating gene splicing. Variants in RNU4ATAC are responsible for microcephalic osteodysplastic primordial dwarfism, type I (MOPDI) 2 , Roifman syndrome 3 and Lowry syndrome 4 , while variants in RNU12 cause early onset cerebellar ataxia 5 . These pathologies are inherited in an autosomal recessive manner. Both RNU4ATAC and RNU12 encode components of the minor spliceosome, a molecular complex that orchestrates splicing for about 0.35% of all splice junctions in humans 6 . However, more than 99% of introns are spliced by the major spliceosome. Recently, we reported that de novo mutations in RNU4-2 , which encodes the U4 snRNA component of the major spliceosome, cause one of the most prevalent monogenic NDDs 7 . The discovery was later published independently by a separate group 8 . To explore whether other non-coding genes might also be causal for NDDs, we performed a refined statistical analysis of the 100,000 Genomes Project (100KGP) data in the National Genomic Research Library (NGRL) 9 . Following a previously described approach 7 , 10 , we applied the BeviMed genetic association method 11 to compare rare variant genotypes in 41,132 non-coding genes between 7,452 unrelated, unexplained cases annotated as having a neurodevelopmental abnormality (NDA) and 43,727 unrelated participants without an NDA. Importantly, while our previous analyses filtered out SNVs having a combined annotation-dependent depletion (CADD) score <10, our present analysis relaxed this threshold as, in non-coding genes, it has been shown that CADD scores are a poor discriminant of variant pathogenicity 12 . Our analysis yielded only two genes with a posterior probability of association (PPA) with NDA >0.5. RNU4-2 , which encodes the snRNA U4, had a PPA≈1 7 , and RNU2-2P , which encodes the snRNA U2, had a PPA=0.97. The novel association with RNU2-2P depended on inclusion of variants with CADD scores ≤ 10 ( Extended Data Fig. 1 ). Conditional on the association, two variants, at nucleotide positions 4 and 35, had a BeviMed posterior probability of pathogenicity (PPP) >0.5 ( Fig. 1a ). The 9 NDA cases with either of the variants had a significantly greater phenotypic homogeneity based on Human Phenotype Ontology (HPO) terms than expected under random selection of 9 NDA cases from unexplained and unrelated NDA study participants in the 100KGP ( Fig. 1b ), supporting causality for a distinct NDD. Although RNU2-2P is annotated as a pseudogene in bioinformatics databases 13 , it is the most highly expressed of the 71 known homologs of U2 across tissues, including brain and non-brain tissues, blood cells ( Fig. 1c , Extended Data Fig. 2 ) and cell lines 14 . Moreover, RNU2-2P resides in a 5’ untranslated exon of WDR74 that had previously been identified as being enriched for hotspot mutations in cancer, although the existence of RNU2-2P at that locus was not known at the time 15 . More recently, it has been shown that RNU2-2P is a functional gene that is transcribed independently of WDR74 and carries recurrent somatic mutations (n.28C>T and n.28C>G) that drive B-cell derived tumors, prostate cancers and pancreatic cancers 16 . Download figure Open in new tab Extended Data Fig. 1 | Effect on PPAs of relaxing the CADD score threshold. PPAs between non-coding genes and NDA, with and without filtering out variants with a CADD score <10. Download figure Open in new tab Extended Data Fig. 2 | Expression heatmaps of U2 homologs. Mean log expression levels of the 71 U2 homologs in adult tissues sampled by GTEx (top) and four blood cell types sampled by the NIHR BioResource – Rare Disease RNA Phenotyping Study (bottom). The dynamic ranges of the two datasets differ because GTEx used a poly-A selection protocol 31 , which suppresses expression estimates of almost all non-coding genes, while the NIHR BioResource used a ribosomal RNA depletion protocol, which only suppresses the expression estimates of ribosomal RNA genes. Download figure Open in new tab Fig. 1 | Discovery and replication of RNU2-2P as an etiological gene for a novel NDD. a , BeviMed PPAs between each of RNU4-2 and RNU2-2P and NDA. All other non-coding genes had a PPA 0.5: n.4G>A and n.35A>G. b , Distribution of phenotypic homogeneity scores (see Methods) for randomly selected sets of nine participants chosen from the 9,112 unrelated NDA cases. The actual score for the nine ID cases with one of the two RNU2-2P variants having a PPP >0.5 is indicated with a red line. c , Bar plot of the mean (over brain and non-brain tissues in GTEx) of the mean (over samples) log expression of each of the 71 annotated U2 snRNA gene homologs. The bars are ordered by the sum over tissue categories. The bar corresponding to RNU2-2P is highlighted in red, showing it is by far the most highly expressed homolog. d , Above, for each of the 191 bases of the RNU2-2P gene, the number of participants with a rare allele at that position, stratified by affection status and inheritance information of the carried rare allele. The bases corresponding to the two variants with a PPP>0.5 are indicated with green arrows. The black bars indicate the number of distinct alternate alleles in gnomAD at each position (multiple insertions and multiple deletions at a given position each count as one). Underneath, the data corresponding to nucleotide positions 1 to 41 are shown in greater detail: above and below the reverse complement of the RNU2-2P sequence (Seq.), the alternate alleles in 100KGP participants and the distinct alleles in gnomAD are shown, respectively, where ‘+’ indicates insertions and diagonal blue dash indicates variants that fail QC in gnomAD. e , Pedigrees for participants with a rare alternate allele n.4 or n.35 of RNU2-2P . Pedigrees used for discovery have a “G” prefix and are labeled in black. Pedigrees used for replication in the 100KGP, in the NBR or in the GMS have an “M”, “N” or “S” prefix, respectively, and are labeled in blue. The two germline variants with a high PPP, n.4G>A and n.35A>G, are located in a genomic locus spanning a region of approximately 40 nucleotides at the 5’ end of the 191bp RNU2-2P gene. The locus has a markedly reduced density of variation in gnomAD 17 , which is consistent with the effects of negative selection ( Fig. 1d ). Published secondary structure data of the U2 snRNA show that n.4 is a crucial interactor of U6 within the pre-B and B complexes, while n.35 binds the branch sites of introns 18 ( Extended Data Fig. 3 ). Trio sequencing of four of the five cases with n.4G>A and three of the four cases with n.35A>G showed the variants were de novo in each case. A variant with a different alternate allele at nucleotide 35, n.35A>T, was called in eight unaffected participants but failed quality control (QC) in gnomAD ( Fig. 1d ). Analysis of whole-genome sequencing (WGS) allele-specific coverage and Sanger sequencing data suggested that n.35A>G is a germline variant, but that n.35A>T is a recurring somatic mosaic variant. This somatic variant is observed only in individuals over the age of 40, which is consistent with clonal hematopoiesis ( Extended Data Fig. 4 ). Download figure Open in new tab Extended Data Fig. 3 | Location of the pathogenic variants in U2 snRNA within the major spliceosome. Assembly of the spliceosome A complex is initiated by binding of the intronic 5’ splice site (5’SS) to the U1 snRNA and the intronic branch site sequence to the U2 snRNA through Watson-Crick pairing of cognate ribonucleotides. The branch site sequence is depicted as the human YUNAY consensus motif (Y=C or T; N=any ribonucleotide), which interacts with the GUAG sequence at positions 33 to 36 in the U2 snRNA (depicted in red) 18 . The spliceosome pre-B complex is formed by incorporation of the U4/U6.U5 tri-small nuclear ribonucleoprotein (snRNP) complex that contains the U4, U5 and U6 snRNAs. This requires interactions between U5 snRNA and the 5’ and 3’ exons 32 and further interactions between nucleotides near the 3’ end of the U6 snRNA and a cognate CGCUUCUCG sequence (nucleotides 3–11) close to the 5’ end of the U2 snRNA (depicted in blue) 33 . Tethering of U4/U6.U5 tri-snRNP to U2 within the spliceosome pre-B complex enables displacement of U1 to enable a new interaction between U6 snRNA with the 5’SS and reconfiguration of U4/U6.U5 tri-snRNP to form the catalytically active spliceosome B complex, which is a prerequisite for the splicing reaction 34 . The critical U6 snRNA region that interacts with the intronic 5’SS 35 is maintained in correct orientation by conserved regions in the adjacent U4 snRNA (depicted in orange) which are the sites of destabilizing variants responsible for the recently described RNU4-2 syndrome 7 . The variants responsible for RNU2-2P syndrome occur at critical interaction sites between U2 snRNA near n.4 and U6 snRNA and between U2 snRNA near n.35 and intronic branch sites. These interactions are necessary for intron recognition and the correct assembly of the catalytically active spliceosome B complex 36 . Download figure Open in new tab Extended Data Fig. 4 | Mosaicism analysis. a , For each of the three rare variants at positions n.4 and n.35 of RNU2-2P called in the discovery collection, truncated bar charts showing the distribution of the proportions of reads supporting the alternate allele over participants, partitioned into 0% and all left-open intervals of size 4% up to 100%. In contrast to n.4G>A and n.35A>G, the reads in the eight participants with the n.35A>T heterozygous call exhibit a strong skew in favor of the reference allele. Furthermore, seven participants with a homozygous reference call at n.35 have at least 8% of aligned reads at that position supporting the ‘T’ allele, suggesting that n.35A>T is not a germline variant, but rather a low-frequency somatic mosaic variant. b , Histogram of age at enrollment of participants in the discovery collection. The purple points show the age at enrollment of study participants with at least 8% of aligned reads supporting the ‘T’ allele at n.35. These participants are significantly older than expected by chance ( P =1.3×10 -3 , Kolmogorov-Smirnoff test). To comply with Genomics England’s rules on identifiability, all ages of at least 95 years are included in the same x =95 bin. c , Sanger sequencing traces from an NDA case (in pedigree N1) with the n.4G>A call, an unaffected participant with the n.35A>T call and a control with neither call, showing that n.4G>A is a germline variant while n.35A>T is a likely somatic mosaic variant. To replicate our findings, we examined three additional rare disease collections: a component of the 100KGP not included in the discovery dataset (10,373 participants, of which 1,736 have an NDA), the NIHR BioResource-Rare Diseases (NBR) 19 (7,388 participants, of which 731 have an NDA), and the UK’s Genomic Medicine Service (GMS) data (32,030 participants, of which 6,469 have an NDA). We identified a further six cases in these replication collections with de novo variants and no unaffected carriers of either variant. Four cases had n.4G>A, one case had n.35A>G and one case had a different alternate allele at nucleotide 35: n.35A>C. Although this case was the only individual harboring n.35A>C, modeling of the interactions between U2 snRNA and canonical branch site sequences suggested that n.35A>C has a destabilizing effect on binding that is greater than that of the n.35A>G variant observed in five cases and in many cases similar in magnitude to that of the n.4G>A variant with respect to its cognate partner U6 ( Extended Data Fig. 5 ). All of these variants were called confidently by WGS ( Extended Data Fig. 6 ). In the 100KGP, the prevalence of cases with these RNU2-2P mutations was five times smaller than that of RNU4-2 syndrome, yet still more prevalent than all but 29 of the ~1,400 known etiological genes for intellectual disability ( Extended Data Fig. 7 ). Download figure Open in new tab Extended Data Fig. 5 | Predicted effects of the mutants on duplex binding stability. a , Differential binding stability (ΔΔG) values between U2 and U6 for the A4 mutant allele compared to the reference G4 allele and between U2 and each of 16 branch site sequences consistent with the human YUNAY motif. Each of the substitutions reduces the predicted free energies of association relative to the corresponding reference allele. b , For each of the alleles observed at n.4 of RNU2-2P (the reference G4 and the mutant A4), a graphical representation of Watson-Crick interactions between the U6-interacting region in U4 (encompassing UCGCU at n.2–6) and the corresponding U6 snRNA region. Hydrogen bonding between cognate nucleotides is depicted with dotted lines. c , For each of the germline alleles observed at n.35 (the reference A35 and the mutant G35 and C35 alleles), a graphical representation of Watson-Crick interactions between the branch site recognition region in U4 (GUAG at n.33–36) and an example branch site sequence (CUUAU). Hydrogen bonding between cognate nucleotides is depicted with dotted lines. Download figure Open in new tab Extended Data Fig. 6 | Read pileups in the replication collections. Sequencing read pileups for cases identified in the replication collections. The reads supporting the reference allele are in blue and those supporting the variant allele are in red. Download figure Open in new tab Extended Data Fig. 7 | Prevalence in the 100KGP. Out of the 9,112 NDA-coded cases in the 100KGP, the number solved through pathogenic (P) or likely pathogenic (LP) variants in a gene, provided at least 11 cases were diagnosed. In the case of RNU2-2P , the number of NDA-coded cases with one of the two recurring de novo variants is shown instead of the number solved with P/LP variants. Analysis of the HPO terms assigned to the nine uniformly phenotyped 100KGP cases revealed that 100% had ‘Intellectual disability’ and ‘Global developmental delay’, 89% had ‘Delayed speech and language development’, 78% had ‘Motor delay’ and 56% had ‘Autistic behavior’, in line with frequencies in NDA cases generally ( Fig. 2 ). However, certain terms were enriched in RNU2-2P cases: ‘Seizure’ was present in 89% of cases (vs. 27% in other NDA cases, Bonferroni-adjusted (BA) P =2.44×10 -3 ), ‘Microcephaly’ in 78% of cases (vs. 18%, BA P =1.62×10 -3 ), ‘Generalized hypotonia’ in 56% of cases (vs. 13%, BA P =3.56×10 -2 ), ‘Severe global developmental delay’ in 44% (vs. 2.7%, BA P =8.89×10 -4 ) and ‘Hyperventilation’ in 33% of cases (vs. 0.16%, BA P =7.56×10 -6 ). No HPO terms were significantly underrepresented in the RNU2-2P cases. Of the terms that were enriched amongst cases with RNU4-2 syndrome, ‘Seizure’, ‘Microcephaly’, ‘Generalized hypotonia’ were also enriched in RNU2-2P cases. Download figure Open in new tab Fig. 2 | Phenotypic and mechanistic characterization. a , Graph showing the ‘is-a’ relationships among HPO terms present in at least three of the nine NDA-coded RNU2-2P cases in the discovery collection or significantly enriched among them relative to 9,112 unrelated NDA-coded participants of the 100KGP. The significantly overrepresented terms are highlighted. For each term, the number of cases with the term and the percentage that number represents out of nine is shown. For each overrepresented term, the proportion of NDA-coded participants with the term and the proportion of NDA-coded RNU2-2P cases with the term are represented as the horizontal coordinate of the base and the head of an arrow, respectively. * Only eight of the nine (89%) of the cases had the ‘Seizure’ HPO term in the NGRL. However, epilepsy was confirmed in the case without the HPO term by inspecting the case’s electronic health record. b , Clinical photographs of the cases from pedigrees G1, G4 and S3. The cases share the common features of long palpebral fissures with slight eversion of the lateral lower lids, long eyelashes, broad nasal root, large low set ears, wide mouth and wide spaced teeth. The approximate ages of the cases when the photographs were taken are shown. However, ‘Severe global developmental delay’ and ‘Hyperventilation’ were only enriched in RNU2-2P cases, suggesting that these may be differentiating phenotypic features. Strikingly, three RNU2-2P cases were coded with the seldom used ‘Hyperventilation’ term by three independent clinicians. Detailed clinical vignettes for the cases in pedigrees G1, G2, G4, M2 and S3 are provided in Supplementary Information . These indicate that the neurodevelopmental phenotype caused by the RNU2-2P variants typically manifests from three to six months of age but is progressive, frequently severe and is accompanied by characteristic dysmorphic features ( Fig. 2b ). All the cases displayed prominent epilepsy, usually from the first few months of life, and seizures were severe and pharmaco-resistant. Seizures were characteristically complex and included spasms, tonic, tonic clonic, myoclonic and absence types, classified in some probands as Lennox-Gastaut syndrome. These features distinguish the RNU2-2P cases from previously reported cases with RNU4-2 syndrome, in which the developmental phenotype is reported as less severe, the dysmorphic features different and epilepsy typically later in onset, less severe and more commonly focal 7 , 8 , 20 . Extraordinarily, case M2 also harbored a de novo truncating variant in SPEN predicted to cause Radio-Tartalgia syndrome. 21 However, the case had short stature (<0.4th centile) and microcephaly (≪0.4th centile), which are not characteristic of Radio-Tartalgia syndrome, whilst also having a morphology that more closely resembled other RNU2-2P patients than Radio-Tartalgia patients. Analysis of uniquely aligned reads at heterozygous sites in whole blood RNA-seq data revealed that both alleles of RNU2-2P are expressed robustly in cases ( Extended Data Fig. 8 ). However, gene expression analysis comparing the five cases to 495 unrelated unexplained participants with an NDA did not reveal numbers of differentially expressed genes or differentially used splice junctions above what was expected under random permutation of case labels, suggesting that transcriptomic analysis of other tissue types will be required to uncover the underlying molecular mediator of disease. Download figure Open in new tab Extended Data Fig. 8 | Allele-specific expression of RNU2-2P in cases. Coverage of RNA-seq reads from whole blood aligned to the genome near RNU2-2P in five cases. The allelic balance at heterozygous sites is shown in red. The locations of the mutant alleles at n.4 and n.35 are indicated with green arrows. The aligned reads overlapping heterozygous sites show that both alleles are expressed robustly in the cases in pedigrees G6, M1 and S3. The cases in pedigrees G1 and G5 were heterozygous only at n.4, where coverage was too low to assess allele-specific expression. Mutant RNU2-2P mice do not exist but, interestingly, mice with a homozygous 5bp deletion in the U2 ortholog Rnu2-8 present with ataxia and neurodegeneration 22 . Transcriptomic analysis of the mutant cerebellum detected alternative splicing, mainly increased retention of short introns. While it remains unclear how this splicing defect causes neuron death, it has been hypothesized that several retained introns contain premature translation termination codons that might trigger the nonsense-mediated mRNA-decay (NMD) pathway. Interestingly, we and others have shown that the human disorders caused by recessive inheritance of variants in RNU4ATAC and RNU12 result in minor intron retention in blood cells and fibroblasts 2 , 3 , 5 , 23 . In contrast, we have been unable to detect a significant and reproducible large-scale intron retention defect in either blood cells or fibroblasts of patients with dominant germline variants in the major spliceosome genes RNU4-2 and RNU2-2P. Although a recent study described systematic disruption of 5’ splice site usage in whole blood of some patients with de novo RNU4-2 variants 8 , RNA-seq of fibroblasts in a separate case study could not detect any defect in splicing 20 . Moreover, transcriptomic analysis of primary hematological tumors and cell lines infected with vectors expressing the U2 c.28C>T RNU2-2P mutation did not reveal any significant differences in splicing 16 . Therefore, further studies are required to understand how RNU4-2 and RNU2-2P mutations affect splicing. It might be that, in contrast to recessive disorders, it is challenging to detect splicing defects in these newly discovered dominant disorders because wild type transcripts are expressed in combination with misspliced transcripts from the same gene that are subjected to NMD. In certain cell types, the effects of NMD might be overcome such that the overall expression levels of mRNAs remain unchanged, due to rapid mRNA turnover and dosage compensation 24 . However, certain cell types, such as stem cells, which we have not yet been able to study, might be more sensitive to high NMD dosage than differentiated cells. Neuronal stem cell and mouse models of RNU4-2 and RNU2-2P pathologies will be needed to resolve these mechanistic questions. Author contributions D.G. conducted statistical and bioinformatic analyses and cowrote the paper. K. D. W. analyzed RNA-seq data, generated expression heatmaps and made the illustration showing molecular interactions. J.L. modeled free energies of association. A.K. processed NBR RNA-seq data. S.P. performed PCR and Sanger sequencing. E.H. oversaw recruitment to the NBR RNA-seq project. M.C-S., I.V. and E.F.T. designed primers, selected cases for sequencing and provided early access to detailed phenotype data on RNU4-2 cases for comparative analysis. G.A., D.D., N.F., J.J., S.McK., M.O’D., M.S., and P.V. obtained consent and provided detailed phenotype information. M.O’D. also provided expert clinical interpretation. K.S. and N.M. oversaw the NBR RNA-seq study. K.F. provided biological interpretation and cowrote the paper. A.M. coordinated clinical contacts, provided clinical and biological interpretation and cowrote the paper. E.T. oversaw the study and cowrote the paper. Competing interests The authors declare no competing interests. Methods Enrollment The enrollment criteria for participants in the NGRL are available from the Genomics England website 25 . The enrollment criteria for NBR participants are given in ref. 19 . Genetic association analysis The genetic association analysis was conducted as described previously 7 , 10 , except that variants were not thresholded on CADD score. Cases comprised all of the 9,112 unrelated cases in the 100KGP included in the merged variant call format (VCF) file provided by the 100KGP who were annotated with the NDA HPO, while the controls comprised all of the 40,937 unrelated participants in the merged VCF who were not assigned the NDA term. Phenotypic homogeneity analysis To assess the phenotypic homogeneity of the nine participants in the discovery collection with n.4G>A or n.35A>G in RNU2-2P , we computed a phenotype homogeneity score for that group with respect to unexplained and unrelated NDA study participants. We calculated this score using the ‘get_sim_grid’ and ‘get_sim_p’ functions from the ontologySimilarity R package, 26 as previously described 7 . We then obtained a Monte Carlo P -value as the proportion of random sets of nine unexplained unrelated NDA cases having a homogeneity that was greater than or equal to the homogeneity score of the group carrying either of the RNU2-2P variants. Analysis of HPO terms To identify enriched or depleted HPO terms among the nine NDA-annotated cases with n.4G>A or n.35A>G in RNU2-2P in the discovery collection, compared with unrelated NDA-coded participants without either of these two variants, we computed P -values of association using Fisher’s two-sided exact test. We only tested enrichment for terms attached to at least three of the nine cases and which belonged to the set of non-redundant terms at each level of frequency among the cases. To account for multiple comparisons, we adjusted the P -values by multiplying them by the number of tests. An adjusted P -value <0.05 was deemed statistically significant. To visualize both common and distinctive HPO terms for RNU2-2P cases, we selected terms that were either statistically significant or present in at least 50% of the cases, removed redundant terms at each level of frequency among the nine cases, and arranged the terms along with a non-redundant set of ancestral terms as a directed acyclic graph of is-a relations. These analyses were conducted using the ontologyX R packages 26 . Analysis of expression levels of U2 snRNA homologs We downloaded transcript-level expression estimates from the GTEx portal, which were generated by genome alignment with STAR v2.5.3a and quantification with RSEM v1.3.0, accounting for alignments of reads to multiple regions of the reference genome. We summed the transcript-level expression estimates within genes by summing over isoforms. We then normalized the gene expression estimates using the TMM method and transformed them to FPKM values. Finally, we compute the mean log(FPKM + 1) within each tissue type. The NIHR BioResource – Rare Disease RNA Phenotyping Study is a multicentre multiomics study of approximately 1,000 patients. It consists of RNA sequencing and proteomics of platelets, neutrophils, monocytes and CD4+ T-cells with WGS for each participant where possible. We aligned the NIHR BioResource blood cell RNA-seq data to the genome using STAR v2.7.10a and quantified expression estimates with RSEM v1.3.1. The gene level expression estimates were computed as described above for each blood cell type. Mosaicism analysis To compute the proportions of WGS reads supporting alternate alleles, we extracted the sequencing depth and the number of reads supporting each alternate allele at n.4 and n.35 of RNU2-2P from BAM files using ‘samtools mpileup’ (samtools version 1.16.1) with default settings. Sanger sequencing We used the following primers to amplify genomic DNA containing the RNU2-2P gene prior to Sanger sequencing: forward primer: CCAATCCCAGGATCCTAAAAA; reverse primer: GAAGACCACATGGAGATACTACG. The amplified fragments correspond to chr11:62841419– 62842071 in version GRCh38 of the human reference genome. Modeling free energies of association We calculated the free energy of duplex formation ΔG 27 of duplex formation with U6 and with branch site sequences for wildtype and mutant U2 using the RNA.fold_compound.eval_structure function in the ViennaRNA (v2.6.4) python package. This allowed us to calculate the difference in stability change on mutation, ΔΔG. For U2-2P, we already know from the literature the U2 RNA duplex pairings formed with the branch point and U6 sequences (allowing us to pre-specify the structure and evaluate ΔG from this). RNA-seq data analysis We performed QC on RNA-seq data derived from the whole blood of 5,546 participants in the NGRL as follows. Based on visual inspection of QC parameter distributions, we filtered out samples with a percentage of RNA fragments larger than 200 bases (as measured by an Agilent Tapestation 4200) ≤ 65%, a total read count outside the range (108M, 592M), a genome mapping rate <0.85 or a high-quality read rate <0.9 (where reads were deemed to be of high quality if they aligned as proper pairs, had fewer than seven mismatches and had a mapping quality ≥ 60). After QC filtering, 5,165 samples remained for analysis, including the five cases with implicated variants in RNU2-2P . We assessed allele-specific expression in cases by counting genome-aligned RNA-seq reads overlapping heterozygous sites using ‘samtools mpileup’ (samtools version 1.16.1) with default settings. We selected 500 samples for differential gene expression and splice junction usage analysis by taking the five cases and 495 samples selected at random from those passing the QC criteria, and which belonged to unrelated NDA-coded individuals presently unexplained. We used DESeq2 28 (version 1.44) to conduct both differential gene expression analysis and differential splice junction usage analysis. For differential gene expression analysis, we took the transcript quantifications generated by the Salmon software 29 and aggregated them by gene using the ‘tximport’ BioConductor package 30 . For differential splice junction usage analysis we used the read counts of spliced reads supporting the junctions of the introns of the canonical transcripts of genes in Ensembl version 104. We only analyzed junctions that were observed (i.e., supported by at least one spliced read) in at least 99% of the 500 samples. Ethics Participants of the 100KGP, the 100KGP Pilot Project and the GMS were enrolled to the National Genomic Research Library under a protocol approved by the East of England– Cambridge Central Research Ethics Committee (ref: 20/EE/0035). We obtained written informed consent to publish additional clinical data from a subset of the affected cases in the NGRL following local best practices. NBR participants were enrolled under a protocol approved by the East of England Cambridge South Research Ethics Committee (ref. 13/EE/0325). Data availability Genetic and phenotypic data for the 100KGP study participants, the 100KGP Pilot study participants and the GMS participants are available through the Genomics England Research Environment via the application at https://www.genomicsengland.co.uk/join-a-gecip-domain . Data pertaining to: WGS data were obtained for 78,132 100KGP participants, 4,054 100KGP Pilot participants, 32,030 GMS participants (v3) and 13,037 NBR participants; HPO phenotype data from the ‘rare_diseases_participant_phenotype’ table (Main Programme v14), ‘observation’ table (GMS v3) and ‘hpo’ table (Rare Diseases Pilot v3); Specific Disease class data from the ‘rare_diseases_participant_disease’ table (Main Programme v13); ICD10 codes from the ‘hes_apc’ table (Main Programme v13); pedigree information from the ‘rare_diseases_pedigree_member’ table (Main Programme v13), ‘referral_participant’ table (GMS v3), and ‘pedigree’ table (Rare Diseases Pilot v3); explained/unexplained status of cases from the ‘gmc_exit_questionnaire’ tables (Main Programme v18, GMS v3). Accession codes for NBR data are given in ref. 19 . CADD v.1.5 ( https://cadd.gs.washington.edu/ ), gnomAD v.3.0 ( https://gnomad.broadinstitute.org/ ) and Ensembl v.104 ( http://may2021.archive.ensembl.org/index.html ) were used for variant annotation. Expression data for U2 snRNA homologs were extracted from the file ‘GTEx_Analysis_2017-06-05_v8_RSEMv1.3.0_transcript_expected_count.gct.gz’ available from the GTEx Portal. Data presented in this paper were requested from the Genomics England Airlock on August 13, 2024 at 3:39am British Summer Time (BST). The manuscript was submitted to the Genomics England Publication Committee on August 21, 2024 at 23:51 BST and approved for submission on August 27, 2024 at 15:52 BST. Code availability Software packages rsvr 1.0, bcftools 1.16, samtools 1.9 and perl 5 were used to build the non-coding 100KGP Rareservoir. The Rareservoir software is available from https://github.com/turrogroup/rsvr . All R packages listed in the manuscript are available via the Comprehensive R Archive Network site ( https://cran.r-project.org/ ) or Bioconductor ( https://bioconductor.org ). The ViennaRNA 2.6.4 package is available from the Python Package Index ( https://pypi.org ). Supplementary Information Case vignettes Redacted for medRxiv. Acknowledgements This research was made possible through access to data in the National Genomic Research Library, which is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The National Genomic Research Library holds data provided by patients and collected by the NHS as part of their care and data collected as part of their participation in research. The National Genomic Research Library is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centers, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. K.F. was supported by Katholieke Universiteit (KU) Leuven Special Research Fund (BOF) (C14/19/096 and C14/23/121), Research Foundation – Flanders (G072921N) and NIH award R01HL161365. K.D.W. was supported by the Belgian American Education Foundation and NIH award R01HL161365. A.M. was supported by NIH award R01HL161365. D.G., and E.T. were supported by NIH awards R01HL161365 and R03HD111492 and E.T. was further supported by the Lowy Foundation USA. Footnotes ↵ * Joint authors. References 1. ↵ Martin AR et al. PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels . Nat Genet . 2019 ; 51 ( 11 ): 1560 – 1565 . OpenUrl CrossRef PubMed 2. ↵ He H et al. Mutations in U4atac snRNA, a component of the minor spliceosome, in the developmental disorder MOPD I . Science . 2011 ; 332 ( 6026 ): 238 – 240 . OpenUrl Abstract / FREE Full Text 3. ↵ Merico D et al. Compound heterozygous mutations in the noncoding RNU4ATAC cause Roifman Syndrome by disrupting minor intron splicing . Nat Commun . 2015 ; 6 : 8718 . OpenUrl CrossRef PubMed 4. ↵ Farach LS et al. The expanding phenotype of RNU4ATAC pathogenic variants to Lowry Wood syndrome . Am J Med Genet A . 2018 ; 176 ( 2 ): 465 – 469 . OpenUrl CrossRef 5. ↵ Elsaid MF et al. Mutation in noncoding RNA RNU12 causes early onset cerebellar ataxia . Ann Neurol . 2017 ; 81 ( 1 ): 68 – 78 . OpenUrl CrossRef 6. ↵ Levine A , Durbin R . A computational scan for U12-dependent introns in the human genome sequence . Nucleic Acids Res . 2001 ; 29 ( 19 ): 4006 – 4013 . OpenUrl CrossRef PubMed Web of Science 7. ↵ Greene D et al. Mutations in the U4 snRNA gene RNU4-2 cause one of the most prevalent monogenic neurodevelopmental disorders . Nat Med . 2024 ; 30 ( 8 ): 2165 – 2169 . OpenUrl 8. ↵ Chen Y et al. De novo variants in the RNU4-2 snRNA cause a frequent neurodevelopmental syndrome . Nature . 2024 . 9. ↵ The National Genomics Research Library v5.1, Genomics England. doi: 10.6084/m9.figshare.4530893 10. ↵ Greene D et al. Genetic association analysis of 77,539 genomes reveals rare disease etiologies . Nat Med . 2023 ; 29 ( 3 ): 679 – 688 . OpenUrl CrossRef 11. ↵ Greene D , Richardson S , Turro E . A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases . Am J Hum Genet . 2017 ; 101 ( 1 ): 104 – 114 . OpenUrl 12. ↵ Mather CA et al. CADD score has limited clinical validity for the identification of pathogenic variants in noncoding regions in a hereditary cancer panel . Genet Med . 2016 ; 18 ( 12 ): 1269 – 1275 . OpenUrl CrossRef 13. ↵ Ensembl genome browser; https://ensembl.org 14. ↵ Mabin JW , Lewis PW , Brow DA , Dvinge H . Human spliceosomal snRNA sequence variants generate variant spliceosomes . RNA . 2021 ; 27 ( 10 ): 1186 – 1203 . OpenUrl Abstract / FREE Full Text 15. ↵ Weinhold N et al. Genome-wide analysis of noncoding regulatory mutations in cancer . Nat Genet . 2014 ; 46 ( 11 ): 1160 – 1165 . OpenUrl CrossRef PubMed 16. ↵ Oz P et al. PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2 . NPJ Genom Med . 2022 ; 7 ( 1 ): 19 . OpenUrl 17. ↵ Chen S et al. A genomic mutational constraint map using variation in 76,156 human genomes . Nature . 2024 ; 625 ( 7993 ): 92 – 100 . OpenUrl 18. ↵ Xie J , Wang L , Lin RJ . Variations of intronic branchpoint motif: identification and functional implications in splicing and disease . Commun Biol . 2023 ; 6 ( 1 ): 1142 . OpenUrl 19. ↵ Turro E et al. Whole-genome sequencing of patients with rare diseases in a national health system . Nature . 2020 ; 583 ( 7814 ): 96 – 102 . OpenUrl CrossRef PubMed 20. ↵ Schot R et al. Re-analysis of whole genome sequencing ends a diagnostic odyssey: Case report of an RNU4-2 related neurodevelopmental disorder . Clin Genet . 2024 . 21. ↵ Radio FC et al. SPEN haploinsufficiency causes a neurodevelopmental disorder overlapping proximal 1p36 deletion syndrome with an episignature of X chromosomes in females . Am J Hum Genet . 2021 ; 108 ( 3 ): 502 – 516 . OpenUrl 22. ↵ Jia Y , Mu JC , Ackerman SL . Mutation of a U2 snRNA gene causes global disruption of alternative splicing and neurodegeneration . Cell . 2012 ; 148 ( 1-2 ): 296 – 308 . OpenUrl CrossRef PubMed Web of Science 23. ↵ Heremans J et al. Abnormal differentiation of B cells and megakaryocytes in patients with Roifman syndrome . J Allergy Clin Immunol . 2018 ; 142 ( 2 ): 630 – 646 . OpenUrl CrossRef PubMed 24. ↵ Lindeboom RGH , Supek F , Lehner B . The rules and impact of nonsense-mediated mRNA decay in human cancers . Nat Genet . 2016 ; 48 ( 10 ): 1112 – 8 . OpenUrl CrossRef PubMed References 25. ↵ 100,000 Genomes Project Rare Disease Elligibility Criteria ( 2018 ); https://files.genomicsengland.co.uk/forms/Rare-Disease-Eligibility-Criteria.pdf 26. ↵ Greene D , Richardson S , Turro E . ontologyX: a suite of R packages for working with ontological data . Bioinformatics . 2017 ; 33 ( 7 ): 1104 – 1106 . OpenUrl 27. ↵ Tinoco I , Uhlenbeck OC , Levine MD . Estimation of secondary structure in ribonucleic acids . Nature . 1971 ; 230 ( 5293 ): 362 – 367 . OpenUrl CrossRef PubMed Web of Science 28. ↵ Love MI , Huber W , Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol . 2014 ; 15 ( 12 ): 550 . OpenUrl CrossRef PubMed 29. ↵ Patro R et al. Salmon provides fast and bias-aware quantification of transcript expression . Nat Methods . 2017 ; 14 ( 4 ): 417 – 419 . OpenUrl CrossRef PubMed 30. ↵ Soneson C , Love MI , Robinson MD . Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences . F1000Res . 2015 ; 4 : 1521 . OpenUrl 31. ↵ Aguet F et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues . Science . 2020 ; 369 ( 6509 ): 1318 – 1330 . OpenUrl Abstract / FREE Full Text 32. ↵ Choi S , Cho N , Kim KK . The implications of alternative pre-mRNA splicing in cell signal transduction . Exp Mol Med . 2023 ; 55 ( 4 ): 755 – 766 . OpenUrl 33. ↵ Rhode BM , Hartmuth K , Westhof E , Hrmann R . Proximity of conserved U6 and U2 snRNA elements to the 5’ splice site region in activated spliceosomes . EMBO J . 2006 ; 25 ( 11 ): 2475 – 2486 . OpenUrl Abstract / FREE Full Text 34. ↵ Wilkinson ME , Charenton C , Nagai K . RNA Splicing by the Spliceosome . Annu Rev Biochem . 2020 ; 89 : 359 – 388 . OpenUrl 35. ↵ Zhang Z et al. Cryo-EM analyses of dimerized spliceosomes provide new insights into the functions of B complex proteins . EMBO J . 2024 ; 43 ( 6 ): 1065 – 1088 . OpenUrl 36. ↵ Boesler C et al. A spliceosome intermediate with loosely associated tri-snRNP accumulates in the absence of Prp28 ATPase activity . Nat Commun . 2016 ; 7 : 11997 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted September 04, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy Daniel Greene , Koenraad De Wispelaere , Jon Lees , Andrea Katrinecz , Sonia Pascoal , Emma Hales , Marta Codina-Solà , Irene Valenzuela , Eduardo F. Tizzano , Giles Atton , Deirdre Donnelly , Nicola Foulds , Joanna Jarvis , Shane McKee , Michael O’Donoghue , Mohnish Suri , Pradeep Vasudevan , Kathy Stirrups , Natasha P. Morgan , Kathleen Freson , Andrew D. Mumford , Ernest Turro medRxiv 2024.09.03.24312863; doi: https://doi.org/10.1101/2024.09.03.24312863 Share This Article: Copy Citation Tools Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy Daniel Greene , Koenraad De Wispelaere , Jon Lees , Andrea Katrinecz , Sonia Pascoal , Emma Hales , Marta Codina-Solà , Irene Valenzuela , Eduardo F. Tizzano , Giles Atton , Deirdre Donnelly , Nicola Foulds , Joanna Jarvis , Shane McKee , Michael O’Donoghue , Mohnish Suri , Pradeep Vasudevan , Kathy Stirrups , Natasha P. Morgan , Kathleen Freson , Andrew D. Mumford , Ernest Turro medRxiv 2024.09.03.24312863; doi: https://doi.org/10.1101/2024.09.03.24312863 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6621) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4558) Health Policy (1372) Health Systems and Quality Improvement (1616) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6635) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (100) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0378b516ffa58f4',t:'MTc4MDA3OTE1Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();