Martin Karplus of Harvard University in the U.S. will integrate informatics and artificial intelligence approaches to design stable, synthetic antigens based on the viral hemagglutinin (HA) protein to be used as a universal influenza vaccine. Seasonal influenza causes substantial morbidity worldwide, and the development of a universal vaccine is a global health priority. The current HA-based vaccines do not provide broad coverage against multiple strains, and must be administered annually because of the high mutability of the virus. To address this, they will use a new approach to develop vaccines that elicit broadly-neutralizing antibodies effective against many different influenza strains, including avian, swine, and human varieties. First, they will assemble the publicly-available DNA sequences of the HA gene of avian, swine, and human influenza A strains, and use homology modeling with existing protein structures as templates to create a library of antigens that together are predicted to both maintain the immune system’s memory of influenza while enabling a rapid immune response to future seasonal or pandemic strains. The antigen library will be optimized by performing affinity maturation simulations initiated from known germline precursors of broadly-neutralizing antibodies, and combining the results with machine learning, to predict which antigen cocktails will produce antibodies with the greatest breadth of reactivity. The most promising of these immunization cocktails will be efficacy-tested in small animal models.

Peter Kwong of the Vaccine Research Center, National Institute of Allergy and Infectious Diseases in the U.S. will use an informatics-based approach to identify influenza virus epitopes that are especially suited to induce a strong and broad immune response, being conserved, accessible, and with a specific structural flexibility, and develop them as vaccine targets. Influenza is highly contagious and can cause severe illness, particularly among the young, elderly, and those with pre-existing conditions. Current vaccines are only partially effective, protect against single strains, and are generally ineffective against pandemic strains. Starting with three exposed influenza viral proteins, hemagglutinin (HA), neuraminidase (NA), and matrix protein 2 ectodomain (M2e), they will identify and rank candidate epitopes by determining their structural flexibility using all-atom molecular dynamic simulations, and by evaluating their conservation and surface accessibility, to promote recognition by the immune system and the generation of antibodies that target different viral strains. Candidate epitopes will be tested for antigenicity in vitro, and the best used to immunize mice to characterize the antibody response. After optimizing the vaccine regimen, they will ultimately evaluate the ability of the candidate vaccines to provide protection against diverse influenza strains in multiple animal models.

Dr. Yoshihiro Kawaoka of the University of Tokyo in Japan will develop broadly effective influenza vaccines by mixing together epitopes of conserved fragments of the viral hemagglutinin (HA) protein, which only elicit a weak immune response, together with millions of different, non-naturally occurring fragments that elicit a strong immune response, to induce broadly cross-reacting antibodies. Influenza is of world-wide concern severely impacting public health and the global economy. Tens of millions of reported cases result in tens of thousands of deaths annually in the U.S. alone, and the rapid spread of the virus between countries causes epidemic or pandemic outbreaks. Traditional vaccines are directed towards selected epitopes in the head region of the viral HA protein because they elicit a strong immune response. However, these regions are frequently mutated, rendering the vaccines useless. Vaccines directed towards more conserved epitopes only elicit a weak immune response, but this can be strengthened using HA proteins previously unseen by the immune system. They will produce a library of millions of HA epitopes that contain artificial mutations in the immune-dominant regions while preserving the conserved regions. This should focus the production of highly reactive antibodies against the conserved HA epitopes, which will eliminate a wider range of influenza strains. They will test this using single and repeat immunizations of different mixes in ferrets. Once optimized, the vaccination strategy will be tested in ferrets pre-exposed to influenza virus to mimic the human situation. The result will be a single vaccination that protects against a wider range of influenza strains than traditional vaccines.

Alice McHardy of the Helmholtz Centre for Infection Research in Germany will use a computational approach to engineer a more stable, neuraminidase (NA)-like antigen for use in influenza vaccines to increase both the duration and the breadth of protection against multiple influenza strains. Seasonal influenza viruses are constantly mutating, and current vaccines designed to target the variable, but strongly immunogenic, surface antigen, hemagglutinin (HA), must be frequently replaced. Vaccines designed to target another immunogenic antigen, neuraminidase (NA), fail to elicit a strong immune response likely due to the instability of the NA protein during the manufacturing process. They will combine genetic, structural, and evolutionary data to design an NA-like antigen with epitopes conserved across at least two influenza subtypes to provide protection against a broader range of influenza strains. The protein will be optimized for stability and immunogenicity, and then tested in mouse and ferret influenza infection models. The antigen will be co-formulated with c-di-AMP (known to improve responsiveness to vaccines in vulnerable populations including infants and the elderly) as the adjuvant. The expected result of this project is an antigen that provides increased duration and breadth of immune protection against multiple strains of influenza.

David Hughes of Pennsylvania State University, John Corbett of aWhere, and Rhiannan Price of DigitalGlobe, in the U.S. will develop a software platform comprising prediction algorithms that leverage artificial intelligence to predict where and when plant diseases and pests will occur from weather and satellite data to alert farmers to check their crops. Pests and diseases are moving targets, however most current surveillance methods monitor only their presence or absence. Predicting when and where they are likely to occur would be more valuable for preventing them. This has recently been made possible by studies on how environmental factors influence the emergence and behaviour of crop pests and diseases. They will use a systems approach that incorporates these new predictors along with historical data and couples them with an artificial intelligence component that learns from ground observations recorded using smartphones to improve accuracy. They will combine their existing agricultural intelligence platform and smartphone application with their prototype predictive model and test their approach with maize and cassava crops on farms across seven different counties in Kenya. The platform will produce location-specific forecasts that can be acted upon immediately by farmers.

Yingda Xie of Rutgers, The State University of NJ and JoAnne Flynn of the University of Pittsburgh, both in the U.S., will develop a non-invasive approach for testing candidate anti-tuberculosis compounds in animal models and patients using positron emission tomography-x-ray computed tomography (PET/CT). Tuberculosis (TB) is a leading cause of death in developing countries, and rates are sustained by the causative bacterium, Mycobacterium tuberculosis, developing resistance to current drugs. To circumvent this, new drugs are being designed to target human cells and proteins rather than those of the bacteria. To test these drugs, new tools are also needed to monitor TB in patients. 18 Fluorodeoxyglucose (FDG)-PET/CT is a non-invasive imaging tool that uses radioactively-labelled glucose to light up areas of metabolic activity in the body such as the lesions formed by M. tuberculosis and immune cells that play a critical role in infection. They have histopathological sections and cell and chemical data of TB lesions from non-human primate models and will use them to quantify the different lesions. Then, by using the available PET-CT scans of the lesions, they will search for quantitative signatures that can predict a specific type of lesion. The accuracy of these PET/CT signatures will be tested in a separate group of animals. Their study will reveal details of the TB immune response across different lesions, which could help design new treatments, and the signatures can be used to test the activity of new drug candidates in animal models and humans.

Eric Kaduru of KadAfrica Estate Limited and John Onekalit of the Kitgum Concerned Women's Association both in Uganda will provide a 12-month, integrated life skills and agricultural training program along with land and seedlings to young refugee women out of school in Uganda to begin their own sustainable passion fruit farming cooperatives. Uganda has accepted many refugees, but also has the world's youngest population and very high unemployment. As a result, particularly girls have very limited employment opportunities if they even manage to finish school, and instead often work in the sex industry or immediately get married. This in turn leads to increased teenage pregnancies and rates of HIV. They will provide a cooperative start-up kit including training in sustainable passion fruit farming, which has a guaranteed market, access to land through a community-based land lease model, and passion fruit seedlings. The girls will also be taught about finance, nutrition, and sexual and reproductive health. They will test their approach in two refugee camps with 180 adolescent girls between 14- and 22-years old who are out of school. The success of the project will be assessed in terms of the effect on poverty and health.

Elena Levashina of the Max Planck Institute for Infection Biology in Germany and Kelly Lee of the University of Washington in the U.S. will use cryoelectron tomography to image the three-dimensional ultrastructure of a protein on the surface of the malaria-causing parasite Plasmodium falciparum to help design better vaccines. Malaria kills half a million people annually, but there are still no highly effective vaccines available. One of the parasite's coat proteins, CSP, is a prime target for vaccine development. However, not much is known about its natural structure on the live parasite, which is how inhibitory antibodies produced in response to a vaccine will be able to recognize and destroy it. One of the best ways to observe the natural structure of a protein at high resolution is to immobilize it in non-crystalline ice and image it under very low (< −150 °C) temperatures. They will develop protocols to isolate large numbers of highly pure parasites directly from mosquitoes and carefully cryopreserve them on grids to maintain their natural form and enable clearer imaging. They will also use this high-resolution imaging technique to study how antibody binding affects the parasite. Their results will help design new vaccines that can produce highly active, inhibitory antibodies.

Sophie Mower of The Centre for Global Equality in the United Kingdom will establish a collaborative program for technology students at Bahir Dar University in Ethiopia using expertise and support from a number of other centers in Africa and beyond to provide training and financial resources for them to research and develop their own innovative solutions to local challenges. Students at the university work on creative solutions such as mapping applications particularly in areas of agriculture and health. However, advancing their ideas is limited by the lack of professional networks and material resources. They will provide a dedicated space at the university and work with the Centre for Global Equality (CGE) and companies in the Cambridge Cluster, both in the United Kingdom, and an African innovation hub. Together, they will provide training for 30 students on co-creating their solutions with end-users to increase their impact. They will also hold an ideation hackathon to generate design ideas, and award $2000 to the six most competitive projects. These will be supported through to prototype development using methods from an incubator approach established at the CGE.

Mohlopheni Marakalala of the Africa Health Research Institute in South Africa will study the role of specific proteins associated with immune cell death in tuberculosis patients to better understand how the disease progresses and help develop new diagnostics and therapies. Tuberculosis (TB) is a bacterial disease that causes 1.5 million deaths per year, mostly in poor countries. Understanding how the human immune system responds to TB infection could help develop more effective, host-targeted treatments. Granulomas - tissues that form as a result of inflammation - are commonly seen in the lungs of TB patients. Their characteristics change as the disease progresses and they can cause severe lung damage. Granulomas are thought to be formed by the death of white blood cells called neutrophils, which are also abundant in the airways of patients. He will study granulomas isolated from patients at different stages of the disease to identify proteins linked to neutrophil cell death and see if they are linked with lung damage and disease progression. He will then determine whether the levels of these proteins in the blood can be used as disease biomarkers for the early detection of TB. Lastly, he will use molecular genetic techniques to reduce the level of these proteins in neutrophils and evaluate the effect on TB infection to see if the approach could be exploited as a potential therapy.

Caroline Stefani of the Benaroya Research Institute at Virginia Mason and Yongxing (Leon) Zhao of Carnegie Mellon University both in the U.S. will build an imaging platform combining expansion microbiology and confocal virtual reality to visualize complex host-pathogen interactions in infected tissues to help develop new diagnostics and therapeutics. It is the molecular interactions between the host and the pathogen, both in tissues and inside cells, that ultimately dictate whether an infection takes hold or is destroyed. Identifying these interactions could help develop new treatments. However, they remain difficult to study in sufficient resolution. They have developed a new method for three-dimensional visualization of confocal microscopy images using commercial virtual reality technology to pinpoint the subcellular localization of host-pathogen interactions. They will combine this with a new technique, expansion microscopy optimized for microbiology (ExMicro), which visualizes nanoscale details of dozens of different molecules in infected tissue by embedding it in a polyacrylate-based polymer that can be expanded in pure water to improve resolution. They will develop protocols and software to optimize both methods for studying host-pathogen interactions, and build a platform to share their new toolset with the scientific community.

Alison Bentley of the National Institute of Agricultural Botany and Ari Sadanandom of the University of Durham both in the United Kingdom will examine whether a new molecular link that they found explaining the increase in plant diseases (biotic factors) associated with high nutrient levels (abiotic factors) can be exploited to maximize wheat crop yield with minimal negative impact on the environment. Wheat, one of the first domesticated food crops, has been grown for over 10,000 years and is critically important to global food supply. Traditionally, crop yields are maximized by applying nitrogen fertilizer to stimulate growth, and fungicides and pesticides to prevent disease. These approaches are expensive and can harm the environment. Another complication is that increasing amounts of nitrogen fertilizer also increases the occurrence of disease. They have identified a group of transcription factors (TFs) - proteins that control expression of specific genes – that appear to protect plants against the fungus Septoria specifically under varying nitrogen levels. To investigate this, they will create transgenic wheat to increase or decrease each TF and explore the effect on disease resistance and growth in different concentrations of nitrogen. Understanding this relationship will allow them to boost plant resistance to disease under high growth conditions, and thereby optimize crop yield with maximal economic gain and minimal environmental impact.