Different Conformations of Phosphatase and Tensin Homolog, Deleted on Chromosome 10 (PTEN) Protein within the Nucleus and Cytoplasm of Neurons

PTEN is a critical gene involved in the regulation of many cellular processes. The product of this gene has dual phosphatase activity and is able to dephosphorylate the 5′ end of the phosphatidylinositol (3,4,5)-trisphosphate. Within the cellular nucleus, this protein has been associated with regulation of the expression of many genes, although the mechanism of this regulation remains unclear. In this paper, two specific oligonucleotide aptamers were developed and selected, using the SELEX procedure, according to their ability to detect the PTEN protein in different subcellular compartments of neurons. While one aptamer was able to detect PTEN in the nucleus, the other recognized PTEN in the cytoplasm. The recognition pattern of PTEN by both aptamers was confirmed using antibodies in western blots of the proteins purified from mouse cerebellar homogenates and subcellular fractions. Additionally, we demonstrated that the two aptamers recognized different epitopes of the target peptide. The results presented here could not be fully explained by the canonical phosphatase structure of PTEN, suggesting the existence of different conformations of phosphatase in the nucleus and the cytoplasm

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

Simultaneous recognition of PTEN peptide by aptamers and antibodies.

<p>The results of precipitation are shown in terms of net amounts of molecules acquired through extrapolations from the standard curve of quantification. The backgrounds considered (PTENz7 and PTENz14) were attained through incubation and co-purification of the aptamers and antibodies in the absence of peptides. Quadruplicate samples showed the positive binding of PTENz14 to the peptide (PTENz14+Peptide). Findings also confirmed the blockade of PTENz7 recognition by the antibodies (PTENz7+Peptide). Extrapolations from the standard curve of quantification show 1.8×10¹¹ molecules of PTENz14 aptamer were bound to the peptide and co-precipitated with the antibodies attached to Sepharose – nearly a 20-fold increase with respect to the background (P<0.0001).</p

Aptamer's selection using PTEN recombinant protein and PTEN synthetic peptide.

<p><b>A</b>. In these assays, selected oligonucleotides were separated from the GST-PTEN target, indicated above the graph, and quantified by means of real-time PCR. The effect of amplification of the library on the quantity of oligonucleotides obtained is illustrated in this graph. The number of molecules, as indicated on the y axis, is obtained through extrapolation of the standard curve, while the number of cycles applied to the library used is indicated on the x axis. The three assays show a significant increase compared with the library without amplification, used as control. Libraries were amplified through PCR separately for each assay. Standard deviation is indicated using bars on each column. <b>B</b>. Results for assays as described above, using the synthetic peptide of PTEN as target, instead of GST-PTEN. Libraries were separated in aliquots from a larger pool that was amplified through PCR. <b>C</b>. Specificity of the oligonucleotides obtained. Aliquots of the selected oligonucleotides were amplified again through PCR, separated, and split into four dilutions. The filter binding assays were performed with different concentrations for four separate selections of aptamers, and each selection is indicated at the bottom of the graph. The gradient of concentration follows the slope of the triangle and the standard deviation is indicated using bars on each column. <b>D</b>. Affinity of the aptamers obtained. Autoradiography of radioactive aptamers bound to specific targets. The aptamer PTENz7 recognized the recombinant PTEN protein and the peptide without binding to the GST-Anp32e recombinant protein in filter binding assays. <b>E</b>. Selected oligonucleotides developed with 25 cycles of library amplification, using the synthetic peptide of PTEN as target, were cloned into vectors and sequenced. Plasmids from six different clones were labeled by PCR containing ATP-gamma-³⁵PO₄, incubated, and their affinity was measured in Scatchard binding assays. The example shown is the oligonucleotide from clone PTENz7, and the affinity constant measured corresponds to 75 nM, using GST-PTEN protein at a final concentration of 25 µM. At the bottom of the graph the autoradiographic results from the radioactive aptamers retained in the filter binding assays are shown. The concentration of the aptamers used corresponds to the measurements illustrated within the graph. On the x axis, the concentration of PTENz7 in nM is provided. On the y axis, the ratio between bound and free aptamers is shown.</p

Scheme of PTEN structure.

<p>A representation of the PTEN structure (PDB: 1DR5) was obtained by means of the PyMol program. PTEN domains corresponding to the phosphatase domain (PTPs) and the regulatory domain (C2) are displayed in this graphical representation. The peptide selected as target was contained on the PTP domain and was shown in blue color.</p

PTEN recognition in Western blot assays and predicted PTEN aptamer conformations.

<p><b>A</b>. Protein pattern expression in <i>E. coli</i> of the recombinant proteins GST (E. coli) and GST-PTEN (+PTEN) in conjunction with the affinity column-purified proteins (GST and +PTEN). The identity was confirmed by binding of the goat antibody (Ab), resulting in similar patterns to the cloned aptamers derived from peptide (PTENz7 and PTENz14). <b>B</b>. Western blot using aptamers PTENz7 and PTENz14 (z7 and z14) and antibodies (Ab Rb) developed with peptide in the cerebellar homogenates. The synthetic aptamer strand of Anp32e was used as a negative control, as well as the omission of aptamers (–). The arrow indicates the most intense band corresponding to the 55-kDa PTEN isoform. <b>C</b>. Western blot detection made as in (B) using aptamers and antibodies developed against the GST- PTEN recombinant protein. The arrows indicate the band of 55 kDa and two bands of greater size, possibly due to ubiquitin-modified PTEN. D. PTEN aptamer conformations: Sequences from PTENz7 (left column) or PTENz14 (right column) were analyzed for internal interactions and the best three predicted structures (mFOLD, <a href="http://www.idtdna.com/Scitools/Applications/mFold" target="_blank">http://www.idtdna.com/Scitools/Applications/mFold</a>) were presented in this panel. For the more stable conformation of PTENz7, a differential free energy of Gibbs of −19.04 kcal.mole-1 was observed and for PTENz14 this value was −17.3 kcal.mole-1.</p

Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

Background: The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods: For this analysis, we constructed burden-weighted dose–response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings: The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15–39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0–0) and 0·603 (0·400–1·00) standard drinks per day, and the NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0–0·403) to 1·87 (0·500–3·30) standard drinks per day and an NDE that ranged between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3–65·4) were aged 15–39 years and 76·9% (73·0–81·3) were male. Interpretation: There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. Funding: Bill & Melinda Gates Foundation

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

BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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