G6PD Deficiency at Sumba in Eastern Indonesia Is Prevalent, Diverse and Severe: Implications for Primaquine Therapy against Relapsing Vivax Malaria

Safe treatment of Plasmodium vivax requires diagnosis of both the infection and status of erythrocytic glucose-6-phosphate dehydrogenase (G6PD) activity because hypnozoitocidal therapy against relapse requires primaquine, which causes a mild to severe acute hemolytic anemia in G6PD deficient patients. Many national malaria control programs recommend primaquine therapy without G6PD screening but with monitoring due to a broad lack of G6PD deficiency screening capacity. The degree of risk in doing so hinges upon the level of residual G6PD activity among the variants present in any given area. We conducted studies on Sumba Island in eastern Indonesia in order to assess the potential threat posed by primaquine therapy without G6PD screening. We sampled 2,033 residents of three separate districts in western Sumba for quantitative G6PD activity and 104 (5.1%) were phenotypically deficient (\u3c4.6U/gHb; median normal 10U/gHb). The villages were in two distinct ecosystems, coastal and inland. A positive correlation occurred between the prevalence of malaria and G6PD deficiency: 5.9% coastal versus inland 0.2% for malaria (P\u3c0.001), and 6.7% and 3.1% for G6PD deficiency (P\u3c0.001) at coastal and inland sites, respectively. The dominant genotypes of G6PD deficiency were Vanua Lava, Viangchan, and Chatham, accounting for 98.5%of the 70 samples genotyped. Subjects expressing the dominant genotypes all had less than 10% of normal enzyme activities and were thus considered severe variants. Blind administration of anti-relapse primaquine therapy at Sumba would likely impose risk of serious harm

Improved estimation of gut passage time considerably affects trait-based dispersal models

1. Animals are important vectors for transporting seeds, nutrients and microbes across landscapes. However, models that quantify the magnitude of these ecosystem services across a broad range of taxa often rely on generalised mass-based scaling parameters for gut passage time. This relationship is weak and fundamentally breaks down when considering individual species, indicating that current models may incorrectly attribute or estimate the magnitude of dispersal. 2. We collated a large dataset of gut passage time for endothermic animals measured using undigested markers (n = 391 species). For each species, we compiled trait data, including body mass, morphology, gut physiology, diet and phylogeny. We then compared the ability of five statistical models (constant, generalised least squares, phylogenetic generalised least squares, general linear model and random forest) to estimate the time of first marker appearance (transit time; TT) and mean marker retention time (MRT) for particle and solute markers in mammals and birds separately. 3. For mammals, we found that the inclusion of additional traits appreciably reduced the median root-mean squared error across all markers in a leave-one-out cross validation. For birds, however, additional traits did not significantly improve our ability to predict gut passage time across markers. This may have occurred due to the smaller number of bird species included in our analysis or the absence of important explanatory factors such as differences in gastrointestinal morphology. 4. Using the MRTparticle random forest model from this study, we updated two trait-based dispersal models for seed and nutrient movement by mammals. The magnitude of dispersal in our updated predictions ranged from 66% to 176% of the original model formulation for different scenarios, highlighting the importance of gut passage time for dispersal models. Furthermore, the contribution by individual or groups of species was found sizeably altered in our updated models. 5.Future modelling studies of dispersal by mammals, for which empirical estimates of gut passage time are absent, will benefit from predicting gut passage time using statistical models that incorporate traits including animal morphology, diet and gut physiology

Data from: Mycorrhizal symbioses influence the trophic structure of the Serengeti

It is known that tropical grasslands such as Serengeti host large populations of arbuscular mycorrhizal (AM) fungi and that they respond to abiotic and biotic factors. It is also known that AM symbioses are important for the uptake of essential plant nutrients, which, in turn, influences the biomass and nutritional quality of herbivores and their predators. The purpose of this study was to investigate the influence of AM symbioses on the biomass of different trophic levels of an ecosystem. To do this, we first measured the neutral lipid fatty acid biomarker 16:1ω5 to estimate the biomass of AM fungi in a long-term grazing exclusion experiment. Then, we used model selection of Bayesian linear regressions to infer the primary factors that influence AM fungal biomass. Using model selection of different combinations of soil characteristics, we selected the best model using the leave-one-out cross-validation information criterion. Finally, we used the Madingley model to simulate the influence of AM fungi on higher trophic levels. We combined spatially explicit information about soil phosphorus and AM fungal biomass to explore the emergent patterns of the Serengeti resulting from AM symbioses. Our Bayesian analysis indicated that total soil phosphorus was the strongest predictor of AM fungal biomass, and there were significant interactions with grazing. Arbuscular mycorrhizal fungal biomass is lowest in soil where phosphorus is limited and increases with increasing phosphorus concentration. Biomass was also significantly higher in plots that were not grazed. The Madingley model indicated that nutritional benefits of AM symbioses maintain a substantial proportion of the biomass across all trophic levels. Synthesis. Our analysis shows that inputs of phosphorus through arbuscular mycorrhizal symbioses substantially increase the ability of plants to grow and maintain nutritional quality, cascading through the biomass of consumers and predators in the ecosystem. Although they account for less than 1% of the total modelled biomass, the predicted nutritional benefit provided by arbuscular mycorrhizal fungi increased the biomass of macro-organisms in the Serengeti by 48%. When considering the management of biodiversity, future ecosystem models should account for the influence of arbuscular mycorrhizal fungi on all trophic levels

Improved estimation of gut passage time considerably affects trait‐based dispersal models

1. Animals are important vectors for transporting seeds, nutrients and microbes across landscapes. However, models that quantify the magnitude of these ecosystem services across a broad range of taxa often rely on generalised mass-based scaling parameters for gut passage time. This relationship is weak and fundamentally breaks down when considering individual species, indicating that current models may incorrectly attribute or estimate the magnitude of dispersal. 2. We collated a large dataset of gut passage time for endothermic animals measured using undigested markers (n = 391 species). For each species, we compiled trait data, including body mass, morphology, gut physiology, diet and phylogeny. We then compared the ability of five statistical models (constant, generalised least squares, phylogenetic generalised least squares, general linear model and random forest) to estimate the time of first marker appearance (transit time; TT) and mean marker retention time (MRT) for particle and solute markers in mammals and birds separately. 3. For mammals, we found that the inclusion of additional traits appreciably reduced the median root-mean squared error across all markers in a leave-one-out cross validation. For birds, however, additional traits did not significantly improve our ability to predict gut passage time across markers. This may have occurred due to the smaller number of bird species included in our analysis or the absence of important explanatory factors such as differences in gastrointestinal morphology. 4. Using the MRTparticle random forest model from this study, we updated two trait-based dispersal models for seed and nutrient movement by mammals. The magnitude of dispersal in our updated predictions ranged from 66% to 176% of the original model formulation for different scenarios, highlighting the importance of gut passage time for dispersal models. Furthermore, the contribution by individual or groups of species was found sizeably altered in our updated models. 5. Future modelling studies of dispersal by mammals, for which empirical estimates of gut passage time are absent, will benefit from predicting gut passage time using statistical models that incorporate traits including animal morphology, diet and gut physiology

Final Mapping File for Dryad - FINAL_MAPPING_FILE.csv

Metadata for six sites with grazed and ungrazed treatments. Data include bulk density, clay, silt, and sand percent, pH, plant species richness, phosphorus, nitrogen, soil organic matter, AM fungal abundance (NLFA), mean annual rainfall, and concentrations of Fe and Ca

CJ200528120617_2

Additional file 3. Additional figure S1–S8

Demographic, malaria and G6PDd prevalence data by gender and ecosystem in western Sumba.

<p>n = sample number.</p><p>¹ Population number was obtained from central agency statistic from each district.</p><p>² Microscopy diagnosed.</p><p>³ One subject experienced mixed infection of <i>P</i>. <i>falciparum</i> and <i>P</i>. <i>vivax</i>.</p><p>⁴ Hb < 10 g/dl.</p><p>⁵ G6PD activities ≤4.6 U/gHb.</p><p>⁶ Samples screened as G6PD deficient but declined, absent or failed to be DNA extracted.</p><p>Demographic, malaria and G6PDd prevalence data by gender and ecosystem in western Sumba.</p

A) Schematic work flow of the study in western Sumba in 2012 and B) map of G6PD deficiency and malaria prevalences in study sites in Sumba.

<p>Small inlet showed the map of Indonesia and where Sumba island is located.</p