A novel mutation in intron 11 donor splice site, responsible of a rare genotype in thyroglobulin gene by altering the pre-mRNA splincing process: Cell expression and bioinformatic analysis

Thyroglobulin (TG) is a homodimeric glycoprotein synthesized by the thyroid gland. To date, two hundred twenty-seven variations of the TG gene have been identified in humans. Thyroid dyshormonogenesis due to TG gene mutations have an estimated incidence of approximately 1 in 100,000 newborns. The clinical spectrum ranges from euthyroid to mild or severe hypothyroidism. The purpose of the present study was to identify and characterize new variants in the TG gene. We report an Argentine patient with congenital hypothyroidism, enlarged thyroid gland and low levels of serum TG. Sequencing of DNA, expression of chimeric minigenes as well as bioinformatics analysis were performed. DNA sequencing identified the presence of compound heterozygous mutations in the TG gene: the maternal mutation consists of a c.3001+5G > A, whereas the paternal mutation consists of p.Arg296*. Minigen analysis of the variant c.3001+5A performed in HeLa, CV1 and Hek293T cell lines, showed a total lack of transcript expression. So, in order to validate that the loss of expression was caused by such variation, site-directed mutagenesis was performed on the mutated clone, which previously had a pSPL3 vector change, to give rise to a wild-type clone c.3001+5G, endorsing that the mutation c.3001+5G > A is the cause of the total lack of expression. In conclusion, we demonstrate that the c.3001+5G > A mutation causes a rare genotype, altering the splicing of the pre-mRNA. This work contributes to elucidating the molecular bases of TG defects associated with congenital hypothyroidism and expands our knowledge in relation to the pathologic roles of the position 5 in the donor splice site.Fil: Gomes Pio, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Molina, Maricel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Siffo, Sofía. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Chiesa, Ana Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; ArgentinaFil: Rivolta, Carina Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Targovnik, Hector Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentin

Mutational screening of the TPO and DUOX2 genes in Argentinian children with congenital hypothyroidism due to thyroid dyshormonogenesis

[Purpose]: Primary congenital hypothyroidism (CH) is the most common endocrine disease in children and one of the preventable causes of both cognitive and motor deficits. We present a genetic and bioinformatics investigation of rational clinical design in 17 Argentine patients suspected of CH due to thyroid dyshormonogenesis (TDH). [Methods]: Next-Generation Sequencing approach was used to identify variants in Thyroid Peroxidase (TPO) and Dual Oxidase 2 (DUOX2) genes. A custom panel targeting 7 genes associated with TDH [(TPO), Iodothyrosine Deiodinase I (IYD), Solute Carrier Family 26 Member 4 (SLC26A4), Thyroglobulin (TG), DUOX2, Dual Oxidase Maturation Factor 2 (DUOXA2), Solute Carrier Family 5 Member 5 (SLC5A5)] and 4 associated with thyroid dysembryogenesis [PAX8, FOXE1, NKX2-1, Thyroid Stimulating Hormone Receptor (TSHR)] has been designed. Additionally, bioinformatic analysis and structural modeling were carried out to predict the disease-causing potential variants. [Results]: Four novel variants have been identified, two in TPO: c.2749-2 A > C and c.2752_2753delAG, [p.Ser918Cysfs*62] and two variants in DUOX2 gene: c.425 C > G [p.Pro142Arg] and c.2695delC [p.Gln899Serfs*21]. Eighteen identified TPO, DUOX2 and IYD variants were previously described. We identified potentially pahogenic biallelic variants in TPO and DUOX2 in 7 and 2 patients, respectively. We also detected a potentially pathogenic monoallelic variant in TPO and DUOX2 in 7 and 1 patients respectively. [Conclusions]: 22 variants have been identified associated with TDH. All described novel mutations occur in domains important for protein structure and function, predicting the TDH phenotype.This study was funded by grants from the Fondo para la Investigación Científica y Tecnológica (FONCyT-ANPCyT-MINCyT, PICT 2014-1193 to CMR, PICT 2015-1811 and PICT-2018-02146 to H.M.T.), CONICET (PIP 2015-11220150100499 to C.M.R.), Universidad de Buenos Aires (UBACyT 2016-20020150100099BA and 2020-20020190100050BA to C.M.R.) and Fondo de Investigación Sanitaria/FEDER (PI16/01920 and PI20/01589 to R.G.-S.)

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Analysis of novel thyroid peroxidase gene variants from the gnomad database using in silico bioinformatics algorithms and literature review

Thyroid peroxidase (TPO) is a thyroid-specific enzyme that plays a key role in thyroid hormones biosynthesis and is the major autoantigen in Hashimoto’s disease, the most common organ-specific autoimmune disease. TPO catalyzes both iodination and coupling of iodotyrosine residues within the thyroglobulin molecule. Variants in TPO gene cause congenital hypothyroidism (CH) by iodide organification defect and are commonly inherited in an autosomal recessive manner. In the present study, we report a detailed analysis and bioinformatic prediction of the TPO variants reported in the Genome Aggregation Database (gnomAD) v2.1.1. 456 variants from unrelated individuals were analized using prediction tools such us PROVEAN, SIFT, PolyPhen-2, Fsplice, among others. The proportion of missense cysteine, nonsense, frameshift, and splice acceptor/donor variants were analyzed in each ethnic group included in the gnomAD v2.1.1 dataset. The results showed a clear predominance of frameshift variants in the East Asian (82%) and European (Finnish) (75%) population, whereas the splice site variants predominate in African/African Americans (99.46%), Other (96%), Latino/Admixed American (94%), South Asian (86%), European (Non-Finnish) (56%) and Ashkenazi Jewish (56%) populations with a significant p value <0.0001***. The analysis of the distribution of the variants revealed that most missense variants identified in the An peroxidase domain map in exon 8, followed by exons 11, 7 and 9. In total, 183 novel TPO variants were described (13 missense cysteine’s variants, 158 missense variants involving the An peroxidase domain and 12 splicing variants) which were not reported in the literature and that would have deleterious effects on prediction programs. The estimated prevalence of heterozygous carriers of the potentially damaging variants was 1:77. In conclusion, we provide an updated and curated reference source of new TPO variants for application in clinical diagnosis and genetic counseling.Fil: Molina, Maricel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; ArgentinaFil: Gomes Pio, Mauricio. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Scheps, Karen. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; ArgentinaFil: Adrover, Ezequiela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Abelleyro, Miguel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Targovnik, Hector Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; ArgentinaFil: Rivolta, Carina Marcela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaLXVII Reunión Anual de la Sociedad Argentina de Investigación Clínica; LXX Reunión Anual de la Sociedad Argentina de Inmunología & 3er Congreso Franco Argentino de Inmunología y Reunión Anual 2022 de la Sociedad Argentina de FisiologíaMar del PlataArgentinaSociedad Argentina de Investigación ClínicaSociedad Argentina de InmunologíaSociedad Argentina de Fisiologí

Curating the gnomAD database: Report of novel variants in the thyrogobulin gene using in silico bioinformatics algorithms

Thyroglobulin (TG) is a large glycosylated protein of 2767 amino acids, secreted by the thyrocytes into the follicular lumen. It plays an essential role in the process of thyroid hormone synthesis. TG gene variants lead to permanent congenital hypothyroidism. In the present work, we report a detailed population and bioinformatic prediction analyses of the TG variants indexed in the Genome Aggregation Database (gnomAD). The results showed a clear predominance of nonsense variants in the European (Finnish), European (Non-Finnish) and Ashkenazi Jewish ethnic groups, whereas the splice site variants predominate in South Asian and African/African-American populations. In total, 282 novel TG variants were described (47 missense involving the wild-type cysteine residues, 177 missense located in the ChEL domain and 58 splice site variants) which were not reported in the literature and that would have deleterious effects in prediction programs. In the gnomAD population, the estimated prevalence of heterozygous carriers of the potentially damaging variants was 1:320. In conclusion, we provide an updated and curated reference source for the diagnosis of thyroid disease, mainly to congenital hypothyroidism due to TG deficiency. The identification and characterization of TG variants is undoubtedly a valuable approach to study the TG structure/function relations and an important tool for clinical diagnosis and genetic counseling.Fil: Gomes Pio, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Siffo, Sofía. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Scheps, Karen. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Molina, Maricel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Adrover, Ezequiela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Abelleyro, Miguel Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Rivolta, Carina Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; ArgentinaFil: Targovnik, Hector Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Microbiología; Argentin

p.L571P in the linker domain of rat thyroglobulin causes intracellular retention

Thyroglobulin (TG), a large glycosylated protein secreted by thyrocytes into the thyroid follicular lumen, plays an essential role in thyroid hormone biosynthesis. Rattus norvegicus TG (rTG) is encoded by a large single copy gene, 186-kb long, located on chromosome 7 composed of 48 exons encoding a 8461-kb mRNA. Although the TG gene displays sequence variability, many missense mutations do not impose any adverse effect on the TG protein, whereas other nucleotide substitutions may affect its TG stability and/or TG intracellular trafficking. In order to gain a further understanding of the protein domains regulating its intracellular fate, we cloned a full-length cDNA from rTG into the pcDNA6/V5-His B expression vector. However, transient expression of the cDNA in HEK293T cells showed that the encoded protein was not a wild-type molecule, as it was unable to be secreted in the culture supernatant. Sequencing analyses revealed three random mutations, which accidentally emerged during the course of cloning: c.1712T>C [p.L571P] in the linker domain (amino acid positions 360 to 604), c.2027A>G [p.Q676R] in TG type 1–6 repeat and c.2720A>G [p.Q907R] in the TG type 1–7 repeat. Expression of cDNAs encoding a combination of two mutations [p.Q676R-p.Q907R], [p.L571P-p.Q907R] or [p.L571P-p.Q676R] indicated that any TG bearing the p.L571P substitution was trapped intracellularly. Indeed, we expressed the single point mutant p.L571P and confirmed that this point mutation was sufficient to cause intracellular retention of mutant TG in HEK293T cells. Endo H analysis showed that the p.L571P mutant is completely sensitive to the enzyme, whereas the will-type TG acquires full N-glycan modifications in Golgi apparatus. This data suggest that the p.L571P mutant contains the mannose-type N-glycan, that was added at the first stage of glycosylation. Complex-type N-glycan formation in the Golgi apparatus does not occur, consistent with defective endoplasmic reticulum exit of the mutant TG. Moreover, predictive analysis of the 3D linker domain showed that the p.L571P mutation would result in a significant protein conformational change. In conclusion, our studies identified a novel amino acid residue within the linker domain of TG associated with its conformational maturation and intracellular trafficking.Fil: Citterio, Cintia Eliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Siffo, Sofía. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Moya, Christian M.. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología. Cátedra de Genética y Biología Molecular; ArgentinaFil: Gomes Pio, Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Molina, Maricel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Scheps, Karen. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Arvan, Peter. University of Michigan; Estados UnidosFil: Rey, Osvaldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Rivolta, Carina Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Targovnik, Hector Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentin

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

BackgroundFuture trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050.MethodsUsing forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline.FindingsIn the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]).InterpretationGlobally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions.FundingBill & Melinda Gates Foundation.</p