19 research outputs found
Biochemical and genetic analysis of RNA processing and decay
L'expression des gènes est le conduit par lequel l'information génétique est traduite dans les phénotypes cellulaires. Récemment, il a été démontré que le programme de l'expression des gènes dans les cellules de mammifères est régi, au moins en partie par l'expression d'ARN double brin court (ARNdb). Ce mode de régulation des gènes est influencé par un grand groupe de protéines de liaison à l'ARN double brin qui peuvent soit stabiliser ou déclencher la dégradation de l'ARN double brin. En effet, les ribonucléases (RNases) spécifiques à l'ARN double brin jouent un rôle important dans l'expression des gènes. Dans la plupart des eucaryotes, les membres de la famille des RNase Ill spécifiques ~ l'ARNdb déclenchent la dégradation de l'ARN et initient la réponse immune de la cellule. Un défaut dans l'activité de la RNase Ill (DICER) inhibe l'expression des gènes et favorise le développement du cancer. D'autre part, la surexpression de la RNase Ill bloque l'infection virale. Cependant, très peu est connu sur la fonction de gestion domestique des RNases Ill chez les eucaryotes et le mécanisme par lequel ils font la distinction entre les espèces d'ARN cellulaire et l'infection
virale. Cette thèse pave la voie sur la manière dont les ARNdbs sont choisis pour être clivés et démontre leur contribution dans le mécanisme de l'ARN en utilisant la levure comme modèle d'étude. Initialement, les déterminants de réactivité de la RNase Ill chez la levure (Rnt1 p) ont été identifiés in vitro et utilisés pour étudier l'impact global de Rnt1 p sur la maturation des ARNs noncodants. Les résultats indiquent que Rnt1 p est nécessaire pour la maturation de tous les petits ARN nucléolaires (snoRNAs) impliqués dans la méthylation de l'ARNr et ils identifient un nouveau rôle de Rnt1 p dans la maturation des snoRNAs introniques. Il a été démontré que le clivage de Rnt1 p contribue à coordonner l'expression de certaines protéines ribosomales et des snoRNA contenus dans leurs introns. La maturation du snoRNA à partir' de l'ARN prémessager bloque l'expression du gène hôte, alors qu'en retardant la maturation du snoRNA, celle-ci se séroule sur l'intron excisé ce qui permet l'expression des deux gènes. De cette façon, la cellule peut coordonner soigneusement la quantité de protéines ribosomales et de snoRNAs requises pour la biogénèse des ribosomes. En outre, l'analyse globale de la maturation des snoRNAs a identifié de nouveaux signaux de clivage de Rnt1 p qui ne présentent pas un motif de séquence conservé.Abstract: Gene expression is the conduit by which genetic information is connected into cellular phenotypes. Recently, it was shown that gene expression in mammalian cells is governed, at least in part, by the expression of short double stranded RNA (dsRNA). This mode of gene regulation is influenced by a large group of dsRNA binding proteins that could either stabilize or trigger the degradation of dsRNA. Indeed, double stranded RNA (dsRNA) specific ribonucleases (RNases) play an important role in regulating gene expression. In most eukaryotes, members of the dsRNA specific RNase III family trigger RNA degradation and initiate cellular immune response. Disruption of human . RNase III (Dicer) deregulates fetal gene expression and promotes the development of cancer. However, very little is known about the housekeeping function of eukaryotic RNase III and the mechanism by which they distinguish between exogenous and endogenous cellular RNA species. This thesis elucidates how dsRNAs are selected for cleavage and demonstrates their contribution to RNA metabolism in yeast as model eukaryote. Initially, the reactivity determinants of yeast RNase III (Rnt1p) were identified in vitro and used to study the global impact of Rnt1p on the processing of non-coding RNA. The results indicate that Rnt1p is required for the processing of all small nucleolar RNAs (snoRNAs) involved in rRNA methylation and identify a new role of Rnt1p in the processing of intronic snoRNAs. It was shown that Rnt1p cleavage helps to coordinate the expression of some ribosomal protein genes hosting intronic snoRNAs. Direct snoRNA processing from the pre-mRNA blocks the expression of the host gene, while delayed snoRNA processing from the excised intron allows the expression of both genes. In this way, the cell can carefully calibrate the amount of snoRNA and ribosomal proteins required for ribosome biogenesis. In addition, a global analysis of snoRNA processing identified new forms of Rnt1p cleavage signals that do not exhibit a conserved sequence motif but instead use a new RNA fold to recruit the enzyme to the cleavage site. This finding led to the conclusion that Rnt1p may use a wide combination of structural motifs to identify its substrates and thus increases the theoretical number of potential degradation targets in vivo . To evaluate this possibility, a new search for snoRNA independent Rnt1p cleavage targets was performed. Interestingly, many Rnt1p cleavage signals were identified in intergenic regions devoid of known RNA transcripts. In vivo , it was shown that Rnt1p induce the termination of non-polyadenylated transcripts and functions as a surveillance mechanism for transcription read-through. This finding directly links Rnt1p to the transcription machinery and provides a new mechanism for polyadenylation independent transcription termination. Together the work described in this thesis presents an example of how eukaryotic RNase III may identify its substrates and present a case study where transcription, RNA processing and stability are linked
Yeast RNase III triggers polyadenylation-independent transcription termination
Transcription termination of messenger RNA (mRNA) is normally achieved by polyadenylation followed by Rat1p-dependent 5'-3' exoribonuleolytic degradation of the downstream transcript. Here we show that the yeast ortholog of the dsRNA-specific ribonuclease III (Rnt1p) may trigger Rat1p-dependent termination of RNA transcripts that fail to terminate near polyadenylation signals. Rnt1p cleavage sites were found downstream of several genes, and the deletion of RNT1 resulted in transcription readthrough. Inactivation of Rat1p impaired Rnt1p-dependent termination and resulted in the accumulation of 3' end cleavage products. These results support a model for transcription termination in which cotranscriptional cleavage by Rnt1p provides access for exoribonucleases in the absence of polyadenylation signals.This work was supported by a grant from
the Canadian Institute of Health Research. S. A. is a Chercheur Boursier National of the
Fonds de la Recherche en Santé du Québec. F.R. holds a New Investigator Award from the
Canadian Institute of Health Research. P-É.J. holds a post-doctoral award from the IRCM
training program in cancer research funded by the CIHR. J.-R.L is a research fellow of the
Terry Fox Foundation through an award from the National Cancer Institute of Canada
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation
Biochemical and genetic analysis of RNA processing and decay
Gene expression is the conduit by which genetic information is connected into cellular phenotypes. Recently, it was shown that gene expression in mammalian cells is governed, at least in part, by the expression of short double stranded RNA (dsRNA). This mode of gene regulation is influenced by a large group of dsRNA binding proteins that could either stabilize or trigger the degradation of dsRNA. Indeed, double stranded RNA (dsRNA) specific ribonucleases (RNases) play an important role in regulating gene expression. In most eukaryotes, members of the dsRNA specific RNase III family trigger RNA degradation and initiate cellular immune response. Disruption of human . RNase III (Dicer) deregulates fetal gene expression and promotes the development of cancer. However, very little is known about the housekeeping function of eukaryotic RNase III and the mechanism by which they distinguish between exogenous and endogenous cellular RNA species. This thesis elucidates how dsRNAs are selected for cleavage and demonstrates their contribution to RNA metabolism in yeast as model eukaryote. Initially, the reactivity determinants of yeast RNase III (Rnt1p) were identified in vitro and used to study the global impact of Rnt1p on the processing of non-coding RNA. The results indicate that Rnt1p is required for the processing of all small nucleolar RNAs (snoRNAs) involved in rRNA methylation and identify a new role of Rnt1p in the processing of intronic snoRNAs. It was shown that Rnt1p cleavage helps to coordinate the expression of some ribosomal protein genes hosting intronic snoRNAs. Direct snoRNA processing from the pre-mRNA blocks the expression of the host gene, while delayed snoRNA processing from the excised intron allows the expression of both genes. In this way, the cell can carefully calibrate the amount of snoRNA and ribosomal proteins required for ribosome biogenesis. In addition, a global analysis of snoRNA processing identified new forms of Rnt1p cleavage signals that do not exhibit a conserved sequence motif but instead use a new RNA fold to recruit the enzyme to the cleavage site. This finding led to the conclusion that Rnt1p may use a wide combination of structural motifs to identify its substrates and thus increases the theoretical number of potential degradation targets in vivo . To evaluate this possibility, a new search for snoRNA independent Rnt1p cleavage targets was performed. Interestingly, many Rnt1p cleavage signals were identified in intergenic regions devoid of known RNA transcripts. In vivo , it was shown that Rnt1p induce the termination of non-polyadenylated transcripts and functions as a surveillance mechanism for transcription read-through. This finding directly links Rnt1p to the transcription machinery and provides a new mechanism for polyadenylation independent transcription termination. Together the work described in this thesis presents an example of how eukaryotic RNase III may identify its substrates and present a case study where transcription, RNA processing and stability are linked
Genome-Wide Prediction and Analysis of Yeast RNase III-Dependent snoRNA Processing Signals
In Saccharomyces cerevisiae, the maturation of both pre-rRNA and pre-small nucleolar RNAs (pre-snoRNAs) involves common factors, thereby providing a potential mechanism for the coregulation of snoRNA and rRNA synthesis. In this study, we examined the global impact of the double-stranded-RNA-specific RNase Rnt1p, which is required for pre-rRNA processing, on the maturation of all known snoRNAs. In silico searches for Rnt1p cleavage signals, and genome-wide analysis of the Rnt1p-dependent expression profile, identified seven new Rnt1p substrates. Interestingly, two of the newly identified Rnt1p-dependent snoRNAs, snR39 and snR59, are located in the introns of the ribosomal protein genes RPL7A and RPL7B. In vitro and in vivo experiments indicated that snR39 is normally processed from the lariat of RPL7A, suggesting that the expressions of RPL7A and snR39 are linked. In contrast, snR59 is produced by a direct cleavage of the RPL7B pre-mRNA, indicating that a single pre-mRNA transcript cannot be spliced to produce a mature RPL7B mRNA and processed by Rnt1p to produce a mature snR59 simultaneously. The results presented here reveal a new role of yeast RNase III in the processing of intron-encoded snoRNAs that permits independent regulation of the host mRNA and its associated snoRNA
The RNA catabolic enzymes Rex4p, Rnt1p, and Dbr1p show genetic interaction with trans-acting factors involved in processing of ITS1 in Saccharomyces cerevisiae pre-rRNA
Eukaryotes have two types of ribosomes containing either 5.8S(L) or 5.8S(S) rRNA that are produced by alternative pre-rRNA processing. The exact processing pathway for the minor 5.8S(L) rRNA species is poorly documented. We have previously shown that the trans-acting factor Rrp5p and the RNA exonuclease Rex4p genetically interact to influence the ratio between the two forms of 5.8S rRNA in the yeast Saccharomyces cerevisiae. Here we report a further analysis of ITS1 processing in various yeast mutants that reveals genetic interactions between, on the one hand, Rrp5p and RNase MRP, the endonuclease required for 5.8S(S) rRNA synthesis, and, on the other, Rex4p, the RNase III homolog Rnt1p, and the debranching enzyme Dbr1p. Yeast cells carrying a temperature-sensitive mutation in RNase MRP (rrp2-1) exhibit a pre-rRNA processing phenotype very similar to that of the previously studied rrp5-33 mutant: ITS2 processing precedes ITS1 processing, 5.8S(L) rRNA becomes the major species, and ITS1 is processed at the recently reported novel site A4 located midway between sites A2 and A3. As in the rrp5-Δ3 mutant, all of these phenotypical processing features disappear upon inactivation of the REX4 gene. Moreover, inactivation of the DBR1 gene in rrp2-1, or the RNT1 gene in rrp5-Δ3 mutant cells also negates the effects of the original mutation on pre-rRNA processing. These data link a total of three RNA catabolic enzymes, Rex4p, Rnt1p, and Dbr1p, to ITS1 processing and the relative production of 5.8S(S) and 5.8S(L) rRNA. A possible model for the indirect involvement of the three enzymes in yeast pre-rRNA processing is discussed
Dataset Figure_5: Whi5-GFP intensity versus size and time in a synchronous G1 population
Whi5-GFP intensity as a function of time for the different FOV of elutriated cells. This dataset contains: 1. Raw TIF_images_NADH – These are autofluorescence images taken at each time point for the purposes of calculating cell size. Time points were every 10 minutes except at 30 minutes which had to be discarded due to poor focus. 2. Raw TIF_images_WHI5 – These raw image files correspond to images of Whi5-GFP excited at 1000 nm fpr all 11 time points of different FOV obtained at 3 different z positions (1-3) (0, -0.5 um, + 0.5 um). Whi5-GFP intensity values for each nucleus were taken for the z-position that gave the highest intensity for each nucleus. 3. NADHDATA_with_time plot.xlsx is the analysis of the raw images of auto-fluorescence exciting at 750 nm. The only relevant information for the Figure is in Colume C sheet 1. It is the cell area in total pixels. 4. Whi5Data_BestFocusPlanes_All_FOV.xlsx is the analysis of the Whi5-GFP images for Whi5-GFP intensity vs time and size for each time point (which corresponds to a different FOV)