GARS- related disease in infantile spinal muscular atrophy: Implications for diagnosis and treatment

The majority of patients with spinal muscular atrophy (SMA) identified to date harbor a biallelic exonic deletion of SMN1. However, there have been reports of SMA- like disorders that are independent of SMN1, including those due to pathogenic variants in the glycyl- tRNA synthetase gene (GARS1). We report three unrelated patients with de novo variants in GARS1 that are associated with infantile- onset SMA (iSMA). Patients were ascertained during inpatient hospital evaluations for complications of neuropathy. Evaluations were completed as indicated for clinical care and management and informed consent for publication was obtained. One newly identified, disease- associated GARS1 variant, identified in two out of three patients, was analyzed by functional studies in yeast complementation assays. Genomic analyses by exome and/or gene panel and SMN1 copy number analysis of three patients identified two previously undescribed de novo missense variants in GARS1 and excluded SMN1 as the causative gene. Functional studies in yeast revealed that one of the de novo GARS1 variants results in a loss- of- function effect, consistent with other pathogenic GARS1 alleles. In sum, the patients’ clinical presentation, assessments of previously identified GARS1 variants and functional assays in yeast suggest that the GARS1 variants described here cause iSMA. GARS1 variants have been previously associated with Charcot- Marie- Tooth disease (CMT2D) and distal SMA type V (dSMAV). Our findings expand the allelic heterogeneity of GARS- associated disease and support that severe early- onset SMA can be caused by variants in this gene. Distinguishing the SMA phenotype caused by SMN1 variants from that due to pathogenic variants in other genes such as GARS1 significantly alters approaches to treatment.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154914/1/ajmga61544_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154914/2/ajmga61544.pd

Proceedings of the 13th annual conference of INEBRIA

CITATION: Watson, R., et al. 2016. Proceedings of the 13th annual conference of INEBRIA. Addiction Science & Clinical Practice, 11:13, doi:10.1186/s13722-016-0062-9.The original publication is available at https://ascpjournal.biomedcentral.comENGLISH SUMMARY : Meeting abstracts.https://ascpjournal.biomedcentral.com/articles/10.1186/s13722-016-0062-9Publisher's versio

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

Disruption of TRF2 association, but not open or closed chromatin, at rDNA in the presence of dnTRF2.

<p>Chromatin from control/uninduced T19 cells and 30 hour dnTRF2-expressing cells was crosslinked with 1% PFA, sonicated to 100–700 bp, and immunoprecipitated with indicated antibodies: (A) H3K4me2, (B) H3K9me3, (C) UBF, and (D) TRF2. Each bar shows relative enrichment as percentage of input by ChIP-PCR. Actin and alpha satellite are control regions. alpha sat  =  alpha satellite (centromere), β-sat  =  distal beta satellite (located telomeric of rDNA), u18S  =  upstream of 18S rDNA region, 18S = 18S rDNA region, IGS  =  intergenic spacer region in rDNA repeat. Error bars show standard error of the mean. (*) indicates significant difference (<i>p</i><0.001) between control and dnTRF2 ChIP enrichment.</p

Condensin localization decreases on acrocentric short arms in the presence of dnTRF2.

<p>Combined immunostaining-FISH for (A) SMC4 (green) and rDNA (red) on metaphase chromosomes from control and 36 hour dnTRF2-expressing cells. (B) The amount of SMC4 was quantitated by measuring arbitrary fluorescence along the length of the chromosomes and plotting signal intensity as a line plot. A line begins at the p arm (0 on x-axis) and extends to the telomere of the q arm (∼150+ on x-axis). (C) The extent of rDNA and SMC4 co-localization at chromatids of metaphase chromosomes is presented in graphical format. The number of individuals chromatids examined is indicated at the top of each bar. A significant reduction in SMC4 co-localization at rDNA was observed on metaphase chromatids from cells expressing dnTRF2 for 36 hours. Scale bars in (A) are 15 micrometers.</p

dnTRF2 expression correlates with rDNA repeat array dispersion.

<p>The rDNA arrays are located on the short arms of the 5 pairs of acrocentric chromosomes. FISH on RNase-treated nuclei hybridized with an rDNA (green) PAC probe showed that rDNA, normally appearing as multiple punctate foci in the nucleus, becomes more diffuse with increased dnTRF2 expression. The T19 (dnTRF2) cell line contains ∼18 acrocentric chromosomes. Multiple short arms normally converge in the nucleus, so each foci can contain more than rDNA regions from more than one acrocentric chromosome. With increased dnTRF2 expression and telomere dysfunction, the bright foci were reduced, instead appearing as dotted or beaded tracks of fluorescent signals stretching throughout the nucleus. Pseudo-colored and gray-scale single channel images for rDNA are shown below the merged images. Scale bars equal 5 micrometers.</p

DNA damage markers appear with increased dnTRF2 expression.

<p>(A) Immunoblot for TRF2, β-actin (loading control), and H2AX-p on whole cell lysates from UV-treated (∼20 J/m²), uninduced/0-hour, 12-hour, 24-hour, 48-hour, and 72-hour dnTRF2-expressing cells. (B) Graphical representation of protein levels measured by arbitrary fluorescence units normalized to β-actin showing increased dnTRF2 protein levels with longer induction periods up to 48 hours. H2AX-p levels increased by 72 hours. (C) Immunoblot for Chk2-p and β-actin showing appearance of phosphorylated Chk2 kinase after 24 hours of dnTRF2 expression.</p

Non-random acrocentric fusion when telomeres are disrupted by various approaches.

<p>(A) Immunoblot of HT1080 whole cell lysates selected for empty vector or shTRF2 retroviral vector for 11 or 15 days with puromycin. Blot shows TRF2 protein as a doublet and β-actin as a loading control. (B) Acrocentric fusions are non-randomly induced in HT1080 cells expressing a retroviral shTRF2 construct for 10 days and 3 weeks. In addition, treatment of cells with the double-strand break inducer zeocin also results in high numbers of acrocentric fusions. asterisk (*) in graph legend denotes observations from dnTRF2 expression previously reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092432#pone.0092432-Stimpson1" target="_blank">[9]</a>.</p

Timing of nucleolar protein disruption with increasing dnTRF2 expression.

<p>(A) Immunostaining of 3D-preserved whole nuclei with nucleolar protein fibrillarin (green) and Ki-67 (red) in control and 45 hour dnTRF2 nuclei show that nucleolar morphologies changes with increased dnTRF2 expression and telomere dysfunction. (B) Induced T19 (dnTRF2-expressing) cells were analyzed at intervals over a 24-hour period using immunofluorescence with antibodies specific to fibrillarin and FLAG (to detect FLAG-tagged dnTRF2 protein - red). Nucleolar changed from a normal spherical shape to less condensed structures resembling nucleolar necklaces. (C) Quantitation of the percent of nuclei showing visibly abnormal nucleolar staining over the timecourse. Abnormal morphology (decondensed, unraveled) of nucleoli (light grey) increased as dnTRF2 was expressed for longer periods. The number of nuclei examined at each timepoint is indicated at the top of each stacked bar. After dnTRF2 expression for 24 hours, there was a statistically significant increase (asterisk) in the proportion of abnormal nucleoli compared to control cells. Scale bars equal 5 micrometers, hr =  hour.</p