Cross-Reactivity and Anti-viral Function of Dengue Capsid and NS3-Specific Memory T Cells Toward Zika Virus

Zika virus (ZIKV), a flavivirus with homology to dengue virus (DENV), is spreading to areas of DENV hyper-endemicity. Heterologous T cell immunity, whereby virus-specific memory T cells are activated by variant peptides derived from a different virus, can lead to enhanced viral clearance or diminished protective immunity and altered immunopathology. In mice, CD8+ T cells specific for DENV provide in vivo protective efficacy against subsequent ZIKV infection. In humans, contrasting studies report complete absence or varying degrees of DENV/ZIKV T cell cross-reactivity. Moreover, the impact of cross-reactive T cell recognition on the anti-viral capacity of T cells remains unclear. Here, we show that DENV-specific memory T cells display robust cross-reactive recognition of ZIKV NS3 ex vivo and after in vitro expansion in respectively n = 7/10 and n = 9/9 dengue-immune individuals tested. In contrast, cross-reactivity toward ZIKV capsid is low or absent. Cross-reactive recognition of DENV or ZIKV NS3 peptides elicits similar production of the anti-viral effector mediators IFN-γ, TNF-α, and CD107a. We identify 9 DENV/ZIKV cross-reactive epitopes, 7 of which are CD4+ and 2 are CD8+ T cell epitopes. We also show that cross-reactive CD4+ and CD8+ T cells targeting novel NS3 epitopes display anti-viral effector potential toward ZIKV-infected cells, with CD8+ T cells mediating direct lyses of these cells. Our results demonstrate that DENV NS3-specific memory T cells display anti-viral effector capacity toward ZIKV, suggesting a potential beneficial effect in humans of pre-existing T cell immunity to DENV upon ZIKV infection

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

Defining CD8+ T cell determinants during human viral infection in populations of Asian ethnicity

Abstract The identification of virus-specific CD8+ T cell determinants is a fundamental requirement for our understanding of viral disease pathogenesis. T cell epitope mapping strategies increasingly rely on algorithms that predict the binding of peptides to MHC molecules. There is, however, little information on the reliability of predictive algorithms in the context of human populations, in particular, for those expressing HLA class I molecules for which there are limited experimental data available. In this study, we evaluate the ability of NetMHCpan to predict antiviral CD8+ T cell epitopes that we identified with a traditional approach in patients of Asian ethnicity infected with Dengue virus, hepatitis B virus, or severe acute respiratory syndrome coronavirus. We experimentally demonstrate that the predictive power of algorithms defining peptide–MHC interaction directly correlates with the amount of training data on which the predictive algorithm has been constructed. These results highlight the limited applicability of the NetMHCpan algorithm for populations expressing HLA molecules for which there are little or no experimental binding data, such as those of Asian ethnicity.</jats:p

Cross-Reactivity and Anti-viral Function of Dengue Capsid and NS3-Specific Memory T Cells Toward Zika Virus

Zika virus (ZIKV), a flavivirus with homology to dengue virus (DENV), is spreading to areas of DENV hyper-endemicity. Heterologous T cell immunity, whereby virus-specific memory T cells are activated by variant peptides derived from a different virus, can lead to enhanced viral clearance or diminished protective immunity and altered immunopathology. In mice, CD8+ T cells specific for DENV provide in vivo protective efficacy against subsequent ZIKV infection. In humans, contrasting studies report complete absence or varying degrees of DENV/ZIKV T cell cross-reactivity. Moreover, the impact of cross-reactive T cell recognition on the anti-viral capacity of T cells remains unclear. Here, we show that DENV-specific memory T cells display robust cross-reactive recognition of ZIKV NS3 ex vivo and after in vitro expansion in respectively n = 7/10 and n = 9/9 dengue-immune individuals tested. In contrast, cross-reactivity toward ZIKV capsid is low or absent. Cross-reactive recognition of DENV or ZIKV NS3 peptides elicits similar production of the anti-viral effector mediators IFN-γ, TNF-α, and CD107a. We identify 9 DENV/ZIKV cross-reactive epitopes, 7 of which are CD4+ and 2 are CD8+ T cell epitopes. We also show that cross-reactive CD4+ and CD8+ T cells targeting novel NS3 epitopes display anti-viral effector potential toward ZIKV-infected cells, with CD8+ T cells mediating direct lyses of these cells. Our results demonstrate that DENV NS3-specific memory T cells display anti-viral effector capacity toward ZIKV, suggesting a potential beneficial effect in humans of pre-existing T cell immunity to DENV upon ZIKV infection

Virus-specific T lymphocytes home to the skin during natural dengue infection

10.1126/scitranslmed.aaa0526Science Translational Medicine727

Differential targeting of viral components by CD4+ versus CD8+ T lymphocytes in dengue virus infection

Dengue virus (DENV) is the principal arthropod-borne viral pathogen afflicting human populations. While repertoires of antibodies to DENV have been linked to protection or enhanced infection, the role of T lymphocytes in these processes remains poorly defined. This study provides a comprehensive overview of CD4(+) and CD8(+) T cell epitope reactivities against the DENV 2 proteome in adult patients experiencing secondary DENV infection. Dengue virus-specific T cell responses directed against an overlapping 15mer peptide library spanning the DENV 2 proteome were analyzed ex vivo by enzyme-linked immunosorbent spot assay, and recognition of individual peptides was further characterized in specific T cell lines. Thirty novel T cell epitopes were identified, 9 of which are CD4(+) and 21 are CD8(+) T cell epitopes. We observe that whereas CD8(+) T cell epitopes preferentially target nonstructural proteins (NS3 and NS5), CD4(+) epitopes are skewed toward recognition of viral components that are also targeted by B lymphocytes (envelope, capsid, and NS1). Consistently, a large proportion of dengue virus-specific CD4(+) T cells have phenotypic characteristics of circulating follicular helper T cells (CXCR5 expression and production of interleukin-21 or gamma interferon), suggesting that they are interacting with B cells in vivo. This study shows that during a dengue virus infection, the protein targets of human CD4(+) and CD8(+) T cells are largely distinct, thus highlighting key differences in the immunodominance of DENV proteins for these two cell types. This has important implications for our understanding of how the two arms of the human adaptive immune system are differentially targeted and employed as part of our response to DENV infection

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