Quantitative proteomic analysis of Huh-7 cells infected with Dengue virus by label-free LC–MS

AbstractDengue is an important and growing public health problem worldwide with an estimated 100million new clinical cases annually. Currently, no licensed drug or vaccine is available. During natural infection in humans, liver cells constitute one of the main targets of dengue virus (DENV) replication. However, a clear understanding of dengue pathogenesis remains elusive. In order to gain a better reading of the cross talk between virus and host cell proteins, we used a proteomics approach to analyze the host response to DENV infection in a hepatic cell line Huh-7. Differences in proteome expression were assayed 24h post-infection using label-free LC–MS. Quantitative analysis revealed 155 differentially expressed proteins, 64 of which were up-regulated and 91 down-regulated. These results reveal an important decrease in the expression of enzymes involved in the glycolytic pathway, citrate cycle, and pyruvate metabolism. This study provides large-scale quantitative information regarding protein expression in the early stages of infection that should be useful for better compression of the pathogenesis of dengue.Biological significanceDengue infection involves alterations in the homeostasis of the host cell. Defining the interactions between virus and cell proteins should provide a better understanding of how viruses propagate and cause disease. Here, we present for the first time the proteomic analysis of hepatocytes (Huh-7 cells) infected with DENV-2 by label-free LC–MS.This article is part of a Special Issue entitled: Proteomics, mass spectrometry and peptidomics, Cancun 2013. Guest Editors: César López-Camarillo, Victoria Pando-Robles and Bronwyn Jane Barkla

Estimating mortality and disability in Peru before the COVID-19 pandemic: a systematic analysis from the Global Burden of the Disease Study 2019

"Background: Estimating and analyzing trends and patterns of health loss are essential to promote efficient resource allocation and improve Peru’s healthcare system performance. Methods: Using estimates from the Global Burden of Disease (GBD), Injuries, and Risk Factors Study (2019), we assessed mortality and disability in Peru from 1990 to 2019. We report demographic and epidemiologic trends in terms of population, life expectancy at birth (LE), mortality, incidence, prevalence, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by the major diseases and risk factors in Peru. Finally, we compared Peru with 16 countries in the Latin American (LA) region. Results: The Peruvian population reached 33.9 million inhabitants (49.9% women) in 2019. From 1990 to 2019, LE at birth increased from 69.2 (95% uncertainty interval 67.8–70.3) to 80.3 (77.2–83.2) years. This increase was driven by the decline in under-5 mortality (−80.7%) and mortality from infectious diseases in older age groups (+60 years old). The number of DALYs in 1990 was 9.2 million (8.5–10.1) and reached 7.5 million (6.1–9.0) in 2019. The proportion of DALYs due to non-communicable diseases (NCDs) increased from 38.2% in 1990 to 67.9% in 2019. The all-ages and age-standardized DALYs rates and YLLs rates decreased, but YLDs rates remained constant. In 2019, the leading causes of DALYs were neonatal disorders, lower respiratory infections (LRIs), ischemic heart disease, road injuries, and low back pain. The leading risk factors associated with DALYs in 2019 were undernutrition, high body mass index, high fasting plasma glucose, and air pollution. Before the COVID-19 pandemic, Peru experienced one of the highest LRIs-DALYs rates in the LA region. Conclusion: In the last three decades, Peru experienced significant improvements in LE and child survival and an increase in the burden of NCDs and associated disability. The Peruvian healthcare system must be redesigned to respond to this epidemiological transition. The new design should aim to reduce premature deaths and maintain healthy longevity, focusing on effective coverage and treatment of NCDs and reducing and managing the related disability.

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 age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

La importancia de la proteómica en la salud pública: The significance of proteomics in public health

La salud pública en el nuevo milenio tiene como reto integrar los avances de la genómica al derecho fundamental de la salud de todos los seres humanos. La proteómica, entendida como la disciplina científica que estudia los proteomas, es de vital importancia en la investigación en salud, ya que el conocimiento de las proteínas y moléculas efectoras de la función celular permitirá un mejor entendimiento de la fisiología humana. En este trabajo se describen los antecedentes y los conocimientos básicos del análisis proteómico basado en la espectrometría de masas y se comentan los usos de la proteómica en la búsqueda de biomarcadores para el diagnóstico y pronóstico de diferentes enfermedades, los avances en la comprensión de los trastornos crónicos y algunas enfermedades infecciosas. De manera adicional, se delinean las ventajas de la espectrometría de masas en la genotipificación de patógenos y el estudio de los polimorfismos de una sola base (SNP, por sus siglas en inglés).<br>The proteome is defined as the entirety of proteins expressed by a genome in a given time under specific physiological conditions. In an organism, the cells contain the same genome; however, they express different proteins in response to a specific micro-environment. Proteomics is responsible for the study of proteomes, using a wide range of methodological techniques. Actually, proteomics is a key tool in health research because it has made possible systematic analysis of hundreds of proteins in clinical samples with the promise of discovering new protein biomarkers for different disease conditions. Finally, proteomic strategy is a technology well-suited to provide a better understanding of systems biology and human health

Menor Resistencia Insulínica en el Obeso de Altura

It has been demonstrated that obesity produces insulin resistance, dislipidemia, high blood pressure and other metabolic alterations which are risk factors to develop atherosclerosis and cardiovascular events. On the other land, chronic expossure to high altitude increases the insulin sensibility; the blood glucose concentration, total and LDL cholesterol are lower in high altitude dwellers than in sea level inhabitants, and HDL cholesterol is higher. Therefore, it seemed interesting to study the metabolic events in the obese people living at high altitude. A total of 41 male obese, aged 20 to 60 years, were studied; 11 living at sea level (Lima, 150 m above sea level) and 30 living at high altitude (Huancayo 3200 m above sea level). Oral glucose tolerance tests consisting in 75 g were conducted in these subjects. Anthropometric measures (height, weight, body mass index, abdominal ship index) as well as blood glucose, total and HDL cholesterol, triglycerldes and insulin were measured. LDL and VLDL were calculated following the Friedewald's formula. Blood glucose, and insulin were significantly lower 2 hours after the glucose administration in the high altitude obese subjects demonstrating that obese subjects at high altitude have a lower insulin resistance, which is also supported by the higher HDL2 concentration, lower blood pressure and no hypertensive subjects. These results demonstrate that exposition to high altitude lowers the insulin resistance and the accompaning metabolic and clinical events even in obese subjects.Se ha demostrado que la obesidad produce resistencia a la insulina, dislipidemia, hipertensión arterial (HTA) y otros eventos metabólicos que favorecen la aparición de ateroesclerosis e incremento de eventos cardiovasculares. Por otro lado, la exposición a la altura incrementa la sensibilidad a la insulina, disminuye la concentración de glucosa sanguínea, colesterol total y LDL e incrementa la HDL. El presente trabajo tiene por objetivo dilucidar los cambios que se producen en el obeso expuesto a la altura. Se estudió 41 obesos, 11 de nivel del mar (Lima, 150 msnm) y 30 de altura (Huancayo- Perú, 3200 msnm), de género masculino, de 20 a 60 años de edad, a quienes se efectuó una prueba oral de tolerancia de la glucosa (75 g). Se realizaron mediciones antropométricas (talla, peso, IMC e índice cintura/cadera- ICC- ) y determinaciones en sangre de glucosa, colesterol total, HDL, triglicéridos (Tg) e insulina. El LDL y VLDL fueron calculados mediante la fórmula de Friedewald. Pese a que los obesos de altura tuvieron el IMC superior a los obesos de nivel del mar, se encontró que la glicemia e insulinemia son significativamente menores a los 120 minutos de la administración de la glucosa, indicando una menor resistencia a la insulina que, además, se sustenta en la mayor concentración de HDL2, menor PA y la ausencia de HTA. Estos resultados corroboran que la altura es un ambiente en el que disminuye la resistencia a la insulina así como los eventos clínicos y metabólicos que la acompañan, aún en sujetos obesos

Monitoring Mitochondrial Function in Aedes albopictus C6/36 Cell Line during Dengue Virus Infection

Aedes aegypti and Aedes albopictus mosquitoes are responsible for dengue virus (DENV) transmission in tropical and subtropical areas worldwide, where an estimated 3 billion people live at risk of DENV exposure. DENV-infected individuals show symptoms ranging from sub-clinical or mild to hemorrhagic fever. Infected mosquitoes do not show detectable signs of disease, even though the virus maintains a lifelong persistent infection. The interactions between viruses and host mitochondria are crucial for virus replication and pathogenicity. DENV infection in vertebrate cells modulates mitochondrial function and dynamics to facilitate viral proliferation. Here, we describe that DENV also regulates mitochondrial function and morphology in infected C6/36 mosquito cells (derived from Aedes albopictus). Our results showed that DENV infection increased ROS (reactive oxygen species) production, modulated mitochondrial transmembrane potential and induced changes in mitochondrial respiration. Furthermore, we offer the first evidence that DENV causes translocation of mitofusins to mitochondria in the C6/36 mosquito cell line. Another protein Drp-1 (Dynamin-related protein 1) did not localize to mitochondria in DENV-infected cells. This observation therefore ruled out the possibility that the abovementioned alterations in mitochondrial function are associated with mitochondrial fission. In summary, this report provides some key insights into the virus–mitochondria crosstalk in DENV infected mosquito cells

List of experimentally determined AlgU promoters of <i>P</i>. <i>aeruginosa</i> that served as a reference for generating the Position-Specific Scoring Matrix.

List of experimentally determined AlgU promoters of P. aeruginosa that served as a reference for generating the Position-Specific Scoring Matrix.</p