Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

BACKGROUND: Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. METHODS: We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). FINDINGS: Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4-61·9) in 1980 to 71·8 years (71·5-72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7-17·4), to 62·6 years (56·5-70·2). Total deaths increased by 4·1% (2·6-5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8-18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6-16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9-14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1-44·6), malaria (43·1%, 34·7-51·8), neonatal preterm birth complications (29·8%, 24·8-34·9), and maternal disorders (29·1%, 19·3-37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years o

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013

Background: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods: Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990-2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings: All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8-58·5) of deaths and 41·6% (40·1-43·0) of DALYs. Risks quantified account for 87·9% (86·5-89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation: Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography–year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4–61·9) in 1980 to 71·8 years (71·5–72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7–17·4), to 62·6 years (56·5–70·2). Total deaths increased by 4·1% (2·6–5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8–18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6–16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9–14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1–44·6), malaria (43·1%, 34·7–51·8), neonatal preterm birth complications (29·8%, 24·8–34·9), and maternal disorders (29·1%, 19·3–37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000–183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000–532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Funding Bill & Melinda Gates Foundation

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980�2015: a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14�294 geography�year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61·7 years (95 uncertainty interval 61·4�61·9) in 1980 to 71·8 years (71·5�72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7�17·4), to 62·6 years (56·5�70·2). Total deaths increased by 4·1 (2·6�5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0 (15·8�18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1 (12·6�16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1 (11·9�14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1, 39·1�44·6), malaria (43·1, 34·7�51·8), neonatal preterm birth complications (29·8, 24·8�34·9), and maternal disorders (29·1, 19·3�37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146�000 deaths, 118�000�183�000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393�000 deaths, 228�000�532�000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost YLLs) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Funding Bill & Melinda Gates Foundation. © 2016 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY licens

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61.7 years (95% uncertainty interval 61.4-61.9) in 1980 to 71.8 years (71.5-72.2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11.3 years (3.7-17.4), to 62.6 years (56.5-70.2). Total deaths increased by 4.1% (2.6-5.6) from 2005 to 2015, rising to 55.8 million (54.9 million to 56.6 million) in 2015, but age-standardised death rates fell by 17.0% (15.8-18.1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14.1% (12.6-16.0) to 39.8 million (39.2 million to 40.5 million) in 2015, whereas age-standardised rates decreased by 13.1% (11.9-14.3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42.1%, 39.1-44.6), malaria (43.1%, 34.7-51.8), neonatal preterm birth complications (29.8%, 24.8-34.9), and maternal disorders (29.1%, 19.3-37.1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Mechanistic Investigations to Enhance Carotenoid Content and Composition in Rice Endosperm

El meu projecte de recerca es centra en les investigacions per comprendre millor el mecanisme d'acumulació de carotenoides en call i endosperma d'arròs. Un dels principals objectius és comprendre millor el coll d'ampolla de la via biosintètica dels carotenoides, analitzant els gens implicats en l'acumulació de carotenoides i la seva funcionalitat per utilitzar-los posteriorment en aplicacions d'enginyeria metabòlica. Els objectius generals eren: 1) reconstituir una via nativa de carotenoides a l'endosperma d'arròs definint el nombre mínim de gens necessaris per acumular β-carotè; 2) restaurar l'expressió d'OsPSY1 a l'endosperma mitjançant la reactivació del seu promotor; 3) provar si els gens del factor de divisió del cloroplast en combinació amb els gens carotenogènics milloren l'acumulació de carotenoides a l'endosperma d'arròs. El PSY1 fa el primer pas de la via biosintètica dels carotenoides. A l'endosperma d'arròs, el promotor de PSY1 no està actiu, per la qual cosa la primera part del meu treball ha estat definir el nombre mínim de gens necessaris per acumular β-carotè a l'endosperma d'arròs. Als primers experiments, es van transformar plantes d'arròs amb OsPSY1 i OsPDS. Els resultats van indicar que la combinació d'aquests dos gens condueix a l'acumulació de β-carotè al call d'arròs. Tanmateix, la co-transformació dels gens subsegüents (OsZDS, OsZISO i OsCRTISO) serà important per a la plena comprensió de cadascun dels gens i la funció en l'acumulació de carotenoides a l'endosperma de l'arròs. El treball futur ha de centrar-se en generar una població de plantes d'arròs transgèniques amb la combinació de tots els gens esmentats per disseccionar la funció de cada gen a la ruta metabòlica. La segona part del meu projecte es va centrar en la reactivació del promotor de l'OSPSY1 específicament a l'endosperma de l'arròs. Per assolir aquest objectiu, identifiquem sis possibles elements cis-reguladors específics de l'endosperma a la regió promotora d'OsPSY1: tres caixes p de prolamina, un motiu AACA, un motiu similar a GCN4 i una caixa opaca 2 (caixa O2). Per provar la nostra hipòtesi, construïm dos promotors sintètics de OsPSY1 amb 6 motius i 4 motius corregits per a l'expressió de GFP. La versió de tipus salvatge del promotor OsPSY1 controlant l'expressió de GFP es va utilitzar com a control negatiu. Els resultats van confirmar que GFP s'expressava en gran mesura i que la proteïna GFP s'acumulava i es detectava a les línies de tripa d'arròs transgènic que contenien el promotor OsPSY1 corregit amb 6 i 4 motius. Tot plegat, aquests resultats confirmen que el promotor corregit d'OsPSY1 va activar l'expressió del gen. Els elements reguladors cis tenen el potencial de regular OsPSY1, i quan són presents poden activar el promotor d'OsPSY1, recolzant fortament el seu paper en l'expressió d'OsPSY1 a l'endosperma de l'arròs. A més, descrivim una tercera estratègia per entendre el paper dels factors de divisió del cloroplast AtPDV1 i AtARC3 a l'acumulació de carotenoides a l'endosperma de l'arròs. Una de les nostres estratègies va ser combinar els gens de divisió del cloroplast (AtARC3 i AtPDV1) amb gens carotenogènics (ZmPSY1 i PaCRTI). Aquesta estratègia no va donar lloc a diferències significatives en l'acumulació de carotenoides i la quantitat de cromoplasts. A la segona estratègia combinem els factors de divisió de cloroplasts (AtARC3 i AtPDV1) amb AtOrHis per potenciar l'acumulació de carotenoides a l'endosperm de l'arròs. Demostrem la co-transformació de l'arròs amb els tres gens (AtARC3, AtPDV1 i AtOrHis). També confirmem l'acumulació d'ARNm per als tres gens al call d'arròs. Tot i així, estem lluny d'identificar tots els factors necessaris que controlen la divisió i el nombre de cromoplasts. Seran necessaris més experiments per identificar i caracteritzar les proteïnes que s'associen a aquests factors per tal de determinar-ne el paper precís en l'acumulació de carotenoides a l'endosperma de l'arròs.Mi proyecto de investigación se centra en las investigaciones para comprender mejor el mecanismo de acumulación de carotenoides en callo y endospermo de arroz. Uno de los principales objetivos es comprender mejor el cuello de botella de la vía biosintética de los carotenoides, analizando los genes implicados en la acumulación de carotenoides y su funcionalidad para su posterior uso en aplicaciones de ingeniería metabólica. Los objetivos generales eran: 1) reconstituir una vía nativa de carotenoides en el endospermo de arroz definiendo el número mínimo de genes necesarios para acumular β-caroteno; 2) restaurar la expresión de OsPSY1 en el endospermo mediante la reactivación de su promotor; 3) probar si los genes del factor de división del cloroplasto en combinación con los genes carotenogénicos mejoran la acumulación de carotenoides en el endospermo de arroz. El PSY1 realiza el primer paso de la vía biosintética de los carotenoides. En el endospermo de arroz, el promotor de PSY1 no está activo, por lo que la primera parte de mi trabajo ha sido definir el número mínimo de genes necesarios para acumular β-caroteno en el endospermo de arroz. En los primeros experimentos, se transformaron plantas de arroz con OsPSY1 y OsPDS. Los resultados indicaron que la combinación de estos dos genes conduce a la acumulación de β-caroteno en el callo de arroz. Sin embargo, la co-transformación de los genes subsiguientes (OsZDS, OsZISO y OsCRTISO) será importante para la plena comprensión de cada uno de los genes y la función en la acumulación de carotenoides en el endospermo del arroz. El trabajo futuro debe centrarse en la generación de una población de plantas de arroz transgénicas con la combinación de todos los genes mencionados para diseccionar la función de cada gen en la ruta metabólica. La segunda parte de mi proyecto se centró en la reactivación del promotor del OsPSY1 específicamente en el endospermo del arroz. Para lograr este objetivo, identificamos seis posibles elementos cis-reguladores específicos del endospermo en la región promotora de OsPSY1: tres cajas p de prolamina, un motivo AACA, un motivo similar a GCN4 y una caja opaca 2 (caja O2). Para probar nuestra hipótesis, construimos dos promotores sintéticos de OsPSY1 con 6 motivos y 4 motivos corregidos para la expresión de GFP. La versión de tipo salvaje del promotor OsPSY1 controlando la expresión de GFP se utilizó como control negativo. Los resultados confirmaron que GFP se expresaba en gran medida y que la proteína GFP se acumulaba y se detectaba en las líneas de callos de arroz transgénico que contenían el promotor OsPSY1 corregido con 6 y 4 motivos. En conjunto, estos resultados confirman que el promotor corregido de OsPSY1 activó la expresión del gen. Los elementos reguladores cis tienen el potencial de regular OsPSY1, y cuando están presentes pueden activar el promotor de OsPSY1, apoyando fuertemente su papel en la expresión de OsPSY1 en el endospermo del arroz. Además, describimos una tercera estrategia para entender el papel de los factores de división del cloroplasto AtPDV1 y AtARC3 en la acumulación de carotenoides en el endospermo del arroz. Una de nuestras estrategias fue combinar los genes de división del cloroplasto (AtARC3 y AtPDV1) con genes carotenogénicos (ZmPSY1 y PaCRTI). Esta estrategia no dio lugar a diferencias significativas en la acumulación de carotenoides y en la cantidad de cromoplastos. En la segunda estrategia combinamos los factores de división de cloroplastos (AtARC3 y AtPDV1) con AtOrHis para potenciar la acumulación de carotenoides en el endospermo del arroz. Demostramos la co-transformación del arroz con los tres genes (AtARC3, AtPDV1 y AtOrHis). También confirmamos la acumulación de ARNm para los tres genes en el callo de arroz. Sin embargo, estamos lejos de identificar todos los factores necesarios que controlan la división y el número de cromoplastos. Serán necesarios más experimentos para identificar y caracterizar las proteínas que se asocian a estos factores con el fin de determinar su papel preciso en la acumulación de carotenoides en el endospermo del arroz.My research project focused on investigations to better understand the mechanism of carotenoid accumulation in rice callus and endosperm. A major aim is to better understand the bottleneck of carotenoid biosynthetic pathway, by analyzing genes involved in carotenoid accumulation and their functionality for further use in metabolic engineering applications. The overall aims were to: 1) reconstitute a native carotenoid pathway in the rice endosperm by defining the minimum number of genes necessary to accumulate β-carotene; 2) restoring OsPSY1 expression in the endosperm by re-activation of the promoter; 3) test if chloroplast division factor genes in combination with carotenogenic genes enhance carotenoid accumulation in rice endosperm. PSY1 commits the first step of the carotenoid biosynthetic pathway. In rice endosperm, the PSY1 promoter is not active so the first part of my work has been to define the minimum number of necessary genes to accumulate β-carotene in rice endosperm. In early experiments, rice plants were transformed with OsPSY1 and OsPDS. The results indicated that the combination of these two genes leads to the accumulation of β-carotene in rice callus. However, co-transformation of the subsequent genes (OsZDS, OsZISO and OsCRTISO) will be important for the full understanding of each genes and the function in the accumulation of carotenoid in rice endosperm. Future work needs to focus on generating a population of transgenic rice plants with the combination of all the above genes in order to dissect the function of each gene in the pathway. The second part of my project was focus on the reactivation of the OsPSY1 gene promoter specifically in rice endosperm. To achieve this aim, we identified six potential endosperm specific cis-regulatory elements in the promoter region of OsPSY1: three prolamin p-boxes, one AACA motif, one GCN4-like motif and an Opaque 2 box (O2 box). To test our hypothesis, we constructed two synthetic OsPSY1 promoters with 6-motif and 4-motif corrected driving GFP. The wild type version of OsPSY1 promoter under the control of GFP was used as a negative control. The results confirmed that GFP was highly expressed and the GFP protein was accumulated and detected in the transgenic rice callus lines containing the corrected 6- and 4-motif OsPSY1 promoter. Taken together, these results confirm that the corrected OsPSY1 promoter activated gene expression. The cis-regulatory elements have the potential to regulate OsPSY1, and when present they can activate the OsPSY1 promoter, strongly supporting their role in the expression of OsPSY1 in rice endosperm. Moreover, we describe a third strategy to understand the role of the plastid division factors AtPDV1 and AtARC3 in the accumulation of carotenoids in rice endosperm. One of our strategies was to combine the plastid division genes (AtARC3 and AtPDV1) with carotenogenic genes (ZmPSY1 and PaCRTI). This strategy did not result in any significant differences in carotenoid accumulation and chromoplast quantity. In the second strategy we combined the plastid division factors (AtARC3 and AtPDV1) with AtOrHis in order to enhance carotenoid accumulation in the rice endosperm. We demonstrate co-transformation of rice with the three genes (AtARC3, AtPDV1 and AtOrHis). Also we confirmed mRNA accumulation for the three genes in rice callus. However, we are far from identifying all the necessary factors controlling chromoplast division and number. Further experiments will be required to identify and characterize proteins that associate with these factors in order to determine their precise role in the accumulation of carotenoids in the rice endosperm