Infectious Disease

Alejandro Castellanos-Gonzalez of the University of Texas Medical Branch in the U.S. will use their gene silencing approach involving premade complexes of protein and small RNA to identify drug targets in the Cryptosporidium parasite, which causes severe diarrhea in young children in developing countries. Their gene silencing method involves synthesizing the so-called argonaute protein that is able to cut a single gene and attaching it to a single-strand antisense RNA that is designed to target a specific gene. This method can be easily scaled up for high-throughput drug target screens.

Boris Striepen of the University of Georgia in the U.S. will develop a new, more natural mouse model for cryptosporidiosis, which is a leading cause of severe diarrhea in children, to help identify effective treatments. Unlike previous mouse models of this disease, these mice do not need to be immune deficient as they can be infected by a natural strain of the Cryptosporidium parasite, which they previous isolated from house mice. They will genetically modify this strain so it will fluoresce and can thus be easily located in the mice and within individual cells.

Brian Reich of North Carolina State University in the U.S. will develop a software model to measure the risk of local malaria outbreaks in real-time in the Democratic Republic of Congo and identify treatment strategies for control efforts to more effectively allocate their limited resources.

Jan Mead of Emory University in the U.S. will develop a mouse model of cryptosporidiosis using human fecal transplants to mimic changes in the bacterial populations (microbiome) in the gut that occur in the human disease, which causes substantial morbidity and mortality in young children from developing countries. Drugs used to eradicate the intestinal parasite Cryptosporidium are thought to be affected by the levels and types of bacteria that populate the human gut, which is of particular importance in malnourished children who most often become infected.

Olga Tosas Auguet of the University of Oxford and collaborators at the Modernising Medical Microbiology Consortium, Guy's and St Thomas' NHS Foundation Trust, Oxford Genomics Centre and London School of Hygiene and Tropical Medicine in the United Kingdom, and Oxford Tropical Network Overseas Programs in southeast Asia and Africa, will develop a new approach for the large-scale surveillance of bacterial antibiotic resistance in low-income settings.

Thomas Churcher of Imperial College London in the United Kingdom will develop an analytical method to more accurately determine the origin of new cases of malaria in regions with low levels of the disease, which is critical for elimination efforts. Current methods are unreliable as there are no standardized criteria and they often rely solely on interviews. They will use routinely collected travel history data and disease maps to develop rigorous methods for estimating whether a case of malaria is either acquired locally or imported from another region.

Honorine Ward of Tufts Medical Center in the U.S. will develop a three-dimensional model of the human intestine for rapid screening of drugs targeting the parasite Cryptosporidium, which causes potentially lethal diarrhea in young children in developing countries. Developing drugs against Cryptosporidium has been particularly difficult, partly because of the limited understanding of the parasites behavior in the human intestine, and particularly of the effect of malnutrition, which commonly co-occurs with infection and likely contributes to disease severity.

Samuel Arnold of the University of Washington in the U.S. will develop methods to evaluate drug candidates for treating Cryptosporidium infections, which cause severe diarrhea particularly in young children from developing countries. There are no effective drugs against the Cryptosporidium parasite. This is partly because when it infects humans it becomes isolated in specific cells lining the gastrointestinal tract, which is where a drug would also need to be located at sufficient concentrations to be effective.

Kathryn Colborn of University of Colorado Denver in the U.S. will develop a statistical model to predict future outbreaks of malaria and help identify the most effective intervention strategy. Current models can help work out where and why malaria outbreaks occur rather than predicting future outbreaks. They will use supervised machine learning to develop a set of predictive algorithms using available data including weather, demographics, and malaria incidence in children under five years old from Mozambique.

Edward Thomsen of the Liverpool School of Tropical Medicine in the United Kingdom will build an open-source software platform tailored to support efforts to eliminate malaria by amalgamating desirable features from two existing disease data management platforms. The Disease Data Management System (DDMS) is an existing platform that integrates multiple datasets and supports operational decision-making through unique functionality such as automated outbreak alerts.