Humanized Rag1−/−γc−/− Mice Support Multilineage Hematopoiesis and Are Susceptible to HIV-1 Infection via Systemic and Vaginal Routes

Several new immunodeficient mouse models for human cell engraftment have recently been introduced that include the Rag2−/−γc−/−, NOD/SCID, NOD/SCIDγc−/− and NOD/SCIDβ2m−/− strains. Transplantation of these mice with CD34+ human hematopoietic stem cells leads to prolonged engraftment, multilineage hematopoiesis and the capacity to generate human immune responses against a variety of antigens. However, the various mouse strains used and different methods of engrafting human cells are beginning to illustrate strain specific variations in engraftment levels, duration and longevity of mouse life span. In these proof-of-concept studies we evaluated the Balb/c-Rag1−/−γ−/− strain for engraftment by human fetal liver derived CD34+ hematopoietic cells using the same protocol found to be effective for Balb/c-Rag2−/−γc−/− mice. We demonstrate that these mice can be efficiently engrafted and show multilineage human hematopoiesis with human cells populating different lymphoid organs. Generation of human cells continues beyond a year and production of human immunoglobulins is noted. Infection with HIV-1 leads to chronic viremia with a resultant CD4 T cell loss. To mimic the predominant sexual viral transmission, we challenged humanized Rag1−/−γc−/− mice with HIV-1 via vaginal route which also resulted in chronic viremia and helper T cell loss. Thus these mice can be further exploited for studying human pathogens that infect the human hematopoietic system in an in vivo setting

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

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

Comparison of Rag1^−/−γc^−/− versus Rag2^−/−γc^−/− mice for generation of humanized mice (Hu Mice).

<p>Comparison of Rag1^−/−γc^−/− versus Rag2^−/−γc^−/− mice for generation of humanized mice (Hu Mice).</p

Immunohistochemical staining of human hematopoietic cells in lymphoid organs.

<p>Tissue sections of lymphoid organs (spleen, lymph node, and thymus) from humanized Rag1^−/−γc^−/− mice were stained for the presence of human leukocytes. CD45⁺ leukocytes (A,B,C), CD68⁺ macrophages/dendritic cells (D,E,F), CD3⁺ T cells (G,H,I), CD4⁺ helper T cells (J,K,L), CD8⁺ cytotoxic T cells (M,N,O) and CD20⁺ B cells (P,Q,R) were detected in each of the organs assayed.</p

Human CD45 cell engraftment levels in humanized Rag1^−/−γc^−/− mice.

<p>Human CD34 cell reconstituted mice were bled at 12 weeks post-engraftment. RBCs were lysed and the white blood cell fraction was stained for human panleukocyte CD45 marker and FACS analyzed. The level of human cell engraftment for each mouse is depicted.</p

FACS analysis of multilineage human hematopoiesis in humanized Rag1^−/−γc^−/− mice.

<p>Single cell suspensions were made from spleen, bone marrow, lymph node and thymus of humanized Rag1^−/−γc^−/− mice and were stained with different antibodies to detect human hematopoietic cell sub-sets. Human anti-CD45 antibody was used to define human leukocytes. From this population human CD3+ T cells (A) as well as CD4+ and CD8+ T cell subsets were identified in the lymph node, spleen and thymus (B). Dendritic cells were identified by their lack of lineage staining (CD3, CD19, CD14, CD16, CD20 and CD56) and expression of HLA DR (C). Both myeloid (CD11c) and plasmacytoid (CD123) dendritic cells (D) were identified in lymphoid organs as were CD14 expressing monocytes (E) and two subsets of CD19 and CD20 expressing B cells (F).</p

Human antibody production in humanized Rag1^−/−γc^−/− mice.

<p>Blood was drawn at 16 weeks post human CD34 cell engraftment and sera were analyzed by ELISA to detect different human immunoglobulin classes.</p

Humanized Rag1^−/−γc^−/− mice are permissive to HIV-1 infection by vaginal route and show CD4 T cell decline.

<p>Mice were infected by vaginal route with R5 BaL HIV-1. Blood was collected weekly and viral RNA extracted from the plasma fraction. Viral RNA loads were determined by Q-RT-PCR as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020169#s2" target="_blank">methods</a>. Levels of CD4 T cells were monitored on a weekly basis by FACS to determine their decline. Baseline values for each of the mice were established prior to infection as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020169#s2" target="_blank">Methods</a>. A. RNA viral loads. B. CD4 T cell levels.</p