Immunodominant T Cell Determinants of Aquaporin-4, the Autoantigen Associated with Neuromyelitis Optica

Autoantibodies that target the water channel aquaporin-4 (AQP4) in neuromyelitis optica (NMO) are IgG1, a T cell-dependent Ig subclass. However, a role for AQP4-specific T cells in this CNS inflammatory disease is not known. To evaluate their potential role in CNS autoimmunity, we have identified and characterized T cells that respond to AQP4 in C57BL/6 and SJL/J mice, two strains that are commonly studied in models of CNS inflammatory diseases. Mice were immunized with either overlapping peptides or intact hAQP4 protein encompassing the entire 323 amino acid sequence. T cell determinants identified from examination of the AQP4 peptide (p) library were located within AQP4 p21-40, p91-110, p101-120, p166-180, p231-250 and p261-280 in C57BL/6 mice, and within p11-30, p21-40, p101-120, p126-140 and p261-280 in SJL/J mice. AQP4-specific T cells were CD4+ and MHC II-restricted. In recall responses to immunization with intact AQP4, T cells responded primarily to p21-40, indicating this region contains the immunodominant T cell epitope(s) for both strains. AQP4 p21-40-primed T cells secreted both IFN-γ and IL-17. The core immunodominant AQP4 21-40 T cell determinant was mapped to residues 24-35 in C57BL/6 mice and 23-35 in SJL/J mice. Our identification of the AQP4 T cell determinants and characterization of its immunodominant determinant should permit investigators to evaluate the role of AQP4-specific T cells in vivo and to develop AQP4-targeted murine NMO models

Identification of naturally processed AQP4 determinants.

<p>AQP4 peptides were tested for their ability to induce T cell proliferative responses in intact hAQP4-primed (A) C57BL/6 and (B) SJL/J mice. Each 20-mer AQP4 peptide is indicated by first residue. Mice were immunized subcutaneously with 100 µg recombinant intact hAQP4 in CFA. 10–12 days later, lymph node cells were cultured in vitro for recall responses to the indicated human or mouse overlapping 20-mer peptides. Data are shown as stimulation indices (SI's) of mean proliferative responses in the presence of peptide (25 µg/ml) compared to the absence of antigen (background). Standard errors (+/− SEM) are shown for proliferative responses tested in triplicate. Recall to intact hAQP4 is shown for comparison.</p

Characterization of the minimal core T cell epitope within AQP4 p21-40.

<p>AQP4 p21-40-specific T cells were restimulated with truncated peptides to determine the core of the p21-40 determinant in (A, B) p21-40 specific C57BL/6 cell lines and (C, D) p21-40 specific SJL/J primary lymph node cells. Cell lines were restimulated with irradiated syngeneic splenic APC and various concentrations of p21-40 or truncated peptides. After 48 hours (cell lines) or 72 hours (LN), cultures were pulsed with ³H-thymidine and harvested 16 hours later. Data shown represent means of triplicates +/− SEM.</p

Localization of identified murine AQP4 T cell epitopes.

<p>AQP4 peptides that elicited proliferative responses in (A) C57BL/6 and (B) SJL/J mice are located within putative transmembrane and cytoplasmic domains. (C) Sequences of human (hAQP4) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015050#pone.0015050-Yang1" target="_blank">[34]</a> and murine <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015050#pone.0015050-Turtzo1" target="_blank">[17]</a> AQP4 (mAQP4). Dashes represent homologous regions between the two species.</p

Predicted I-A^b- and I-A^s- binding mAQP4 epitopes for T cell recognition.

<p>MetaSVMp, a quantitative binding affinity program that employs the immune epitope database (IEDB) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015050#pone.0015050-Hu1" target="_blank">[26]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015050#pone.0015050-Zhang1" target="_blank">[27]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015050#pone.0015050-Nielsen2" target="_blank">[30]</a>, was used to identify potential I-A^b- and I-A^s-binding of AQP4 determinants for T cell recognition. Inverse of 50% inhibition concentration values (1/IC₅₀) is plotted for I-A^b (A top panel) and I-A^s (B top panel). Peaks correspond to increased predicted binding affinity: strong binding, IC₅₀<500 nM; moderate binding, IC₅₀>500 nM and <5,000 nM, and non-binding, IC₅₀>5,000 nM. Peaks correspond to increased predicted binding affinity. The top 20% of all possible epitopes are shown for I-A^b (A bottom panel) and I-A^s (B bottom panel). MetaSVMp percentile ranks were acquired from the MetaMHC web-based application; tall peaks correspond to top-ranked predicted epitopes.</p

AQP4 p21-40 is a naturally processed immunodominant determinant of intact AQP4.

<p>(A) C57BL/6 (above) and SJL/J (below) mice were immunized subcutaneously with 100 µg recombinant intact hAQP4 in CFA. Lymph nodes were harvested at day 10–12 and cultured in the presence of various concentrations of either intact hAQP4 (left), or individual murine AQP4 peptides (right) for 4 days. Proliferation was measured by ³H-thymidine incorporation, and are presented as mean cpm +/− SEM for triplicates. (B) C57BL/6 (above) and SJL/J (below) m21-40 primed lymph node cells were assayed for cytokine production and proliferation in response to m21-40 peptide. Supernatants were collected after 72 hr for IFN-γ and IL-17A for ELISA. Data are presented as mean +/− SEM for triplicates. (C) Proliferation of C57BL/6 T cell lines specific to p21-40, p91-110, p166-180, and p261-280 were assayed following re-stimulation with various concentrations of intact hAQP4 (left) and self-peptides (right). Data are shown as stimulation indices (SI's) of triplicates of antigen proliferation over no antigen conditions (background).</p

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

BackgroundEstimates 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.Methods22 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.FindingsGlobal 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.InterpretationGlobal 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