Background

The Escherichia coli response regulator NarL controls transcription of genes involved in nitrate respiration during anaerobiosis. NarL consists of two domains joined by a linker that wraps around the interdomain interface. Phosphorylation of the NarL N-terminal receiver domain (RD) releases the, otherwise sequestered, C-terminal output domain (OD) that subsequently binds specific DNA promoter sites to repress or activate gene expression. The aim of this study is to investigate the extent to which the NarL OD and RD function independently to regulate transcription, and the affect of the linker on OD function.

Results

NarL OD constructs containing different linker segments were examined for their ability to repress frdA-lacZ or activate narG-lacZ reporter fusion genes. These in vivo expression assays revealed that the NarL OD, in the absence or presence of linker helix α6, constitutively repressed frdA-lacZ expression regardless of nitrate availability. However, the presence of the linker loop α5-α6 reversed this repression and also showed impaired DNA binding in vitro. The OD alone could not activate narG-lacZ expression; this activity required the presence of the NarL RD. A footprint assay demonstrated that the NarL OD only partially bound recognition sites at the narG promoter, and the binding affinity was increased by the presence of the phosphorylated RD. Analytical ultracentrifugation used to examine domain oligomerization showed that the NarL RD forms dimers in solution while the OD is monomeric.

Conclusions

The NarL RD operates as an on-off switch to occlude or release the OD in a nitrate-responsive manner, but has additional roles to directly stimulate transcription at promoters for which the OD lacks independent function. One such role of the RD is to enhance the DNA binding affinity of the OD to target promoter sites. The data also imply that NarL phosphorylation results in RD dimerization and in the separation of the entire linker region from the OD.

Zinc is an essential micronutrient that promotes normal growth, development and immune function. In the context of persistent dietary zinc inadequacies, large-scale food fortification can help fill the gap between intake and requirements. Burkina Faso mandates wheat flour fortification with iron and folic acid. We used activity-based cost modelling to estimate the cost of adding zinc to the country's wheat flour fortification standard assuming (1) no change in compliance with the national standard, and (2) a substantial improvement in compliance. We used household food consumption data to model effective coverage, that is, the number of women of reproductive age (WRA) predicted to achieve adequate zinc density (zinc intake/1000 kcal) with the addition of fortification to diets. Without interventions, the prevalence of inadequate dietary zinc density was ~35.5%. With no change in compliance, the annual average incremental cost of adding zinc to fortified wheat flour was $10,347, which would effectively cover <1% of WRA at an incremental cost of ~$0.54/WRA effectively covered. Improving compliance added ~$300,000/year to the cost of the fortification programme without zinc; including zinc added another ~$78,000/year but only reduced inadequate intake among WRA by 3.6% at an incremental cost of ~$0.45/WRA effectively covered. Although the incremental cost of adding zinc to wheat flour is low ($0.01/wheat flour consumer/year), given low levels of wheat flour consumption, zinc fortification of wheat flour alone contributes marginally to, but will not fully close, the dietary zinc gap. Future research should explore potential contributions of zinc to a broader set of delivery vehicles.

Nutrient reference values (NRVs) for zinc set by several expert groups differ widely and may affect the predicted prevalence of inadequate zinc intake. We examined this possibility using NRVs published by four different authorities and nationally representative dietary intake data collected among children aged 12-59 months and women in Cameroon. Usual zinc intake was estimated from 24 h recall data using the National Cancer Institute method. Prevalences of total zinc intake below the dietary requirement and of "absorbable zinc intake" below the physiological requirement were estimated using NRVs published by the World Health Organization (WHO), US Institute of Medicine (IOM), International Zinc Nutrition Consultative Group (IZiNCG), and European Food Safety Authority (EFSA). The prevalence of inadequate zinc intake ranged from 10% (IZiNCG-physiological requirement, 95% CI 7-13%) to 81% (EFSA-physiological requirement, 95% CI 78-84%) among children and 9% (WHO-physiological requirement, 95% CI 8-11.0%) to 94% (IOM-physiological requirement, 95% CI 92-95%) among women These differences in the prevalence of inadequate intake translated into sizeable differences in the predicted benefit and cost-effectiveness of zinc fortification programs. Depending on the NRVs applied, assessments differ regarding the need for and design of zinc fortification programs. Efforts are needed to harmonize NRVs for zinc.

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.