Computational models to improve surveillance for cassava brown streak disease and minimize yield loss.

Cassava brown streak disease (CBSD) is a rapidly spreading viral disease that affects a major food security crop in sub-Saharan Africa. Currently, there are several proposed management interventions to minimize loss in infected fields. Field-scale data comparing the effectiveness of these interventions individually and in combination are limited and expensive to collect. Using a stochastic epidemiological model for the spread and management of CBSD in individual fields, we simulate the effectiveness of a range of management interventions. Specifically we compare the removal of diseased plants by roguing, preferential selection of planting material, deployment of virus-free 'clean seed' and pesticide on crop yield and disease status of individual fields with varying levels of whitefly density crops under low and high disease pressure. We examine management interventions for sustainable production of planting material in clean seed systems and how to improve survey protocols to identify the presence of CBSD in a field or quantify the within-field prevalence of CBSD. We also propose guidelines for practical, actionable recommendations for the deployment of management strategies in regions of sub-Saharan Africa under different disease and whitefly pressure.

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension.

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.

In vivo co-localization of enzymes on RNA scaffolds increases metabolic production in a geometrically dependent manner.

Co-localization of biochemical processes plays a key role in the directional control of metabolic fluxes toward specific products in cells. Here, we employ in vivo scaffolds made of RNA that can bind engineered proteins fused to specific RNA binding domains. This allows proteins to be co-localized on RNA scaffolds inside living Escherichia coli. We assembled a library of eight aptamers and corresponding RNA binding domains fused to partial fragments of fluorescent proteins. New scaffold designs could co-localize split green fluorescent protein fragments to produce activity as measured by cell-based fluorescence. The scaffolds consisted of either single bivalent RNAs or RNAs designed to polymerize in one or two dimensions. The new scaffolds were used to increase metabolic output from a two-enzyme pentadecane production pathway that contains a fatty aldehyde intermediate, as well as three and four enzymes in the succinate production pathway. Pentadecane synthesis depended on the geometry of enzymes on the scaffold, as determined through systematic reorientation of the acyl-ACP reductase fusion by rotation via addition of base pairs to its cognate RNA aptamer. Together, these data suggest that intra-cellular scaffolding of enzymatic reactions may enhance the direct channeling of a variety of substrates.

Computational models for improving surveillance for the early detection of direct introduction of cassava brown streak disease in Nigeria.

Cassava is a key source of calories for smallholder farmers in sub-Saharan Africa but its role as a food security crop is threatened by the cross-continental spread of cassava brown streak disease (CBSD) that causes high yield losses. In order to mitigate the impact of CBSD, it is important to minimise the delay in first detection of CBSD after introduction to a new country or state so that interventions can be deployed more effectively. Using a computational model that combines simulations of CBSD spread at both the landscape and field scales, we model the effectiveness of different country level survey strategies in Nigeria when CBSD is directly introduced. We find that the main limitation to the rapid CBSD detection in Nigeria, using the current survey strategy, is that an insufficient number of fields are surveyed in newly infected Nigerian states, not the total number of fields surveyed across the country, nor the limitation of only surveying fields near a road. We explored different strategies for geographically selecting fields to survey and found that early and consistent CBSD detection will involve confining candidate survey fields to states where CBSD has not yet been detected and where survey locations are allocated in proportion to the density of cassava crops, detects CBSD sooner, more consistently, and when the epidemic is smaller compared with distributing surveys uniformly across Nigeria.

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa.

The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world's highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world's largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale.

Expansion of the cassava brown streak pandemic in Uganda revealed by annual field survey data for 2004 to 2017.

Cassava brown streak disease (CBSD) is currently the most devastating cassava disease in eastern, central and southern Africa affecting a staple crop for over 700 million people on the continent. A major outbreak of CBSD in 2004 near Kampala rapidly spread across Uganda. In the following years, similar CBSD outbreaks were noted in countries across eastern and central Africa, and now the disease poses a threat to West Africa including Nigeria - the biggest cassava producer in the world. A comprehensive dataset with 7,627 locations, annually and consistently sampled between 2004 and 2017 was collated from historic paper and electronic records stored in Uganda. The survey comprises multiple variables including data for incidence and symptom severity of CBSD and abundance of the whitefly vector (Bemisia tabaci). This dataset provides a unique basis to characterize the epidemiology and dynamics of CBSD spread in order to inform disease surveillance and management. We also describe methods used to integrate and verify extensive field records for surveys typical of emerging epidemics in subsistence crops.

The Geographic Variation of Surveillance and Zoonotic Spillover Potential of Influenza Viruses in Domestic Poultry and Swine.

BACKGROUND: Avian and swine influenza viruses circulate worldwide and pose threats to both animal and human health. The design of global surveillance strategies is hindered by information gaps on the geospatial variation in virus emergence potential and existing surveillance efforts. METHODS: We developed a spatial framework to quantify the geographic variation in outbreak emergence potential based on indices of potential for animal-to-human and secondary human-to-human transmission. We then compared our resultant raster model of variation in emergence potential with the global distribution of recent surveillance efforts from 359105 reports of surveillance activities. RESULTS: Our framework identified regions of Southeast Asia, Eastern Europe, Central America, and sub-Saharan Africa with high potential for influenza virus spillover. In the last 15 years, however, we found that 78.43% and 49.01% of high-risk areas lacked evidence of influenza virus surveillance in swine and domestic poultry, respectively. CONCLUSIONS: Our work highlights priority areas where improved surveillance and outbreak mitigation could enhance pandemic preparedness strategies.