Genome sequence of the tsetse fly (Glossina morsitans):Vector of African trypanosomiasis

Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa. Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted protein-encoding genes led to multiple discoveries, including chromosomal integrations of bacterial (Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These genome data provide a foundation for research into trypanosomiasis prevention and yield important insights with broad implications for multiple aspects of tsetse biology.IS

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

Differential virulence and tsetse fly transmissibility of <i>Trypanosoma congolense</i> and <i>Trypanosoma brucei</i> strains

African animal trypanosomiasis causes significant economic losses in sub-Saharan African countries because of livestock mortalities and reduced productivity. Trypanosomes, the causative agents, are transmitted by tsetse flies (Glossina spp.). In the current study, we compared and contrasted the virulence characteristics of five Trypanosoma congolense and Trypanosoma brucei isolates using groups of Swiss white mice (n = 6). We further determined the vectorial capacity of Glossina pallidipes, for each of the trypanosome isolates. Results showed that the overall pre-patent (PP) periods were 8.4 ± 0.9 (range, 4–11) and 4.5 ± 0.2 (range, 4–6) for T. congolense and T. brucei isolates, respectively (p < 0.01). Despite the longer mean PP, T. congolense–infected mice exhibited a significantly (p < 0.05) shorter survival time than T. brucei–infected mice, indicating greater virulence. Differences were also noted among the individual isolates with T. congolense KETRI 2909 causing the most acute infection of the entire group with a mean ± standard error survival time of 9 ± 2.1 days. Survival time of infected tsetse flies and the proportion with mature infections at 30 days post-exposure to the infective blood meals varied among isolates, with subacute infection–causing T. congolense EATRO 1829 and chronic infection–causing T. brucei EATRO 2267 isolates showing the highest mature infection rates of 38.5% and 23.1%, respectively. Therefore, our study provides further evidence of occurrence of differences in virulence and transmissibility of eastern African trypanosome strains and has identified two, T. congolense EATRO 1829 and T. brucei EATRO 2267, as suitable for tsetse infectivity and transmissibility experiments

Spatial–Temporal Variations in Parasitological Prevalence and Host-Related Risk Factors of Camel Trypanosomiasis and Its Vectors in North Eastern Kenya: A Repeated Cross-Sectional Study

Camel trypanosomiasis (Surra) is endemic in the Horn of Africa. Understanding the spatiotemporal variations in Surra prevalence, vector dynamics, and host-related risk factors is important in developing effective control strategies. A repeated cross-sectional study was conducted to determine the Surra parasitological prevalence, livestock reservoirs, vector density/diversity, and host-related risk factors in Kenya. Random samples of 847, 1079, and 824 camels were screened at the start of the dry season, peak dry season, and during the rainy season, respectively. Blood samples were examined using the dark ground/phase contrast buffy-coat technique, and Trypanosoma species were identified based on their movement and morphology in wet and stained thin smears. Reservoir status for Trypanosoma evansi was assessed in 406 cattle and 372 goats. A rainy and dry seasons entomological surveys were conducted to determine the Surra vector abundance/diversity and spatiotemporal density changes. Surra prevalence was 7.1%, 3.4%, and 4.1% at the start of the dry season, peak dry season, and rainy season, respectively. Camel co-infections by Trypanozoon (T. evansi or Trypanosoma brucei brucei) and Trypanosoma vivax were recorded. Spatial variations in Surra prevalence were recorded at the beginning of dry (X7,N=8462=110.9, p≤0.001), peak dry (X7,N=10792=42.2, p≤0.001), and rainy (X7,N=8242=29.1, p≤0.001) seasons. The screened cattle and goats tested negative for Trypanozoon (T. evansi or T. b. brucei), while two cattle tested positive for Trypanosoma congolense. Biting fly catches were composed of a single species from Tabanus, Atylotus, Philoliche, Chrysops, and Stomoxys genera. The total catches for Philoliche, Chrysops, and Stomoxys were higher in the rainy than dry season consistent with the prevalence results. Surra remains an important camel disease in the region with its prevalence varying in space and time. Camel co-infections by Trypanozoon (T. evansi or T. b. brucei) and T. vivax necessitate proper diagnosis of suspected cases and targeted therapy

Drug resistant trypanosome stabilates stored at the Kenya Trypanosomiasis Research Institute cryobank.

<p>Other than KETRI 2538, which is molecularly characterized (not published), molecular characterization of the other isolates is not available. <b>Key</b>: *  =  these isolates were recovered from cases of treatment failure following suramin chemotherapy; a  =  stabilates which were made resistant to melarsoprol in the laboratory; EATRO  =  East African Trypanosomiasis Research Organization; KETRI  =  Kenya Trypanosomiasis Research Institute; DFMO  =  difluoromethylornithine.</p

Number of primary trypanosome stabilates collected, preserved, and stored at the Kenya Trypanosomiasis Research Institute cryobank.

<p>Number of primary trypanosome stabilates collected, preserved, and stored at the Kenya Trypanosomiasis Research Institute cryobank.</p

Trypanosome stabilates characterized using PCR, procyclic transmission test, and isoenzyme techniques.

<p><b>Key:</b> +  =  test was performed; -  =  test was not performed; numbers in parentheses  =  total number of stabilates in the cryobank.</p

Animal hosts from which various trypanosomes were isolated and stored at the Kenya Trypanosomiasis Research Institute cryobank.

<p><b>Key:</b> Tbb  =  Trypanosoma brucei brucei; Tb  =  Trypanosoma brucei; Tbr  =  Trypanosoma brucei rhodesiense; UC  =  unclassified; HNI  =  host of isolation not indicated.</p