Differences in 5'untranslated regions highlight the importance of translational regulation of dosage sensitive genes

Background: Untranslated regions (UTRs) are important mediators of post-transcriptional regulation. The length of UTRs and the composition of regulatory elements within them are known to vary substantially across genes, but little is known about the reasons for this variation in humans. Here, we set out to determine whether this variation, specifically in 5’UTRs, correlates with gene dosage sensitivity. Results: We investigate 5’UTR length, the number of alternative transcription start sites, the potential for alternative splicing, the number and type of upstream open reading frames (uORFs) and the propensity of 5’UTRs to form secondary structures. We explore how these elements vary by gene tolerance to loss-of-function (LoF; using the LOEUF metric), and in genes where changes in dosage are known to cause disease. We show that LOEUF correlates with 5’UTR length and complexity. Genes that are most intolerant to LoF have longer 5’UTRs, greater TSS diversity, and more upstream regulatory elements than their LoF tolerant counterparts. We show that these differences are evident in disease gene-sets, but not in recessive developmental disorder genes where LoF of a single allele is tolerated. Conclusions: Our results confirm the importance of post-transcriptional regulation through 5'UTRs in tight regulation of mRNA and protein levels, particularly for genes where changes in dosage are deleterious and lead to disease. Finally, to support gene-based investigation we release a web-based browser tool, VuTR, that supports exploration of the composition of individual 5'UTRs and the impact of genetic variation within them

De novo variants in the RNU4-2 snRNA cause a frequent neurodevelopmental syndrome

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here we identify the non-coding RNA RNU4-2 as a syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 base pair region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals in whom it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to RNU4-1 and other U4 homologues. Using RNA sequencing, we show how 5′ splice-site use is systematically disrupted in individuals with RNU4-2 variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 base pair region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

BACKGROUND: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. METHODS: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. FINDINGS: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96-1·28). INTERPRETATION: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme

Additional file 1 of Differences in 5'untranslated regions highlight the importance of translational regulation of dosage sensitive genes

Additional file 1: Supplementary Figures

De novo variants in the <i>RNU4-2</i> snRNA cause a frequent neurodevelopmental syndrome

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here, we identify the non-coding RNA RNU4-2 as a syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 bp region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and Stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals where it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to RNU4-1 and other U4 homologs. Using RNA-sequencing, we show how 5’ splice site usage is systematically disrupted in individuals with RNU4-2 variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 bp region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide

De novo variants in the non-coding spliceosomal snRNA gene RNU4-2 are a frequent cause of syndromic neurodevelopmental disorders.

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes 1 . Increasingly, large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here, we identify the non-coding RNA RNU4-2 as a novel syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome 2 . We identify an 18 bp region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and Stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 119 individuals with NDD. The vast majority of individuals (77.3%) have the same highly recurrent single base-pair insertion (n.64_65insT). We estimate that variants in this region explain 0.41% of individuals with NDD. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to its contiguous counterpart RNU4-1 and other U4 homologs, supporting RNU4-2 's role as the primary U4 transcript in the brain. Overall, this work underscores the importance of non-coding genes in rare disorders. It will provide a diagnosis to thousands of individuals with NDD worldwide and pave the way for the development of effective treatments for these individuals. </p

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future

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