1. Combining the tsetse fly genome with disease control Lessons from triatomine bugs: Chagas disease control SNP diversity Cool phylogenomics Tsetse fly 1 2 3

2. Sympatric speciation Vector-borne transmission in Trypanosoma cruzi Triatomine bugs (Rhodnius sp.) Palms Triatomines evolved with the formation of South America 95 MYA* * Gaunt and Miles (2002) reviewed by Science

3. Sylvatic hosts of T. cruzi

4. Basis of the Southern cone initiative: Triatoma infestans - a key vector in Argentina, Bolivia, Brazil, Chile, Paraguay, Uruguay and southern Peru - Domiciliated (domesticated) - Susceptible to insecticide (adults and nymphs) - Insecticide control is cheap A domesticated vector has nowhere to hide

5. Many deaths resulting from a genetically isolated vector population A simple solution……. Chris Schofield 

6. Apparent distribution of Triatoma infestans 1982 2002 The success of targeted vector control Chris Schofield 

7. Control Initiatives Objectives 2. Interrupt vectorial transmission 1. Interrupt transfusional transmission The Southern Cone Project Chris Schofield 

8. ?? PATTEC Lake Victoria Basin Initiative The Tsetse Belt Tanzania Kenya Uganda The problem Not a continuous inter-breeding population but distribution of specie and sub- species populations

9. What might the tsetse genome look like? EST clustering pipelines from the current tsetse library databases ( midgut, salivary gland, and fatbody) Identified one SNP every 654 base pairs (Pi = 0.0015) The mosquito genome gives 1 SNP every 785 bp for cds (Pi = 0.0013) and 1/627 overall Far higher than in Drosophila SNP diversity

10. Experimental criticisms EST SNP diversity doesn’t equate to the total SNP diversity of genomic coding sequences –Controls are needed However we should not be surprised if SNP diversity was as high as in Anopheles - biogeographically there are strong similarities High levels of heterozygosity would create annotation problems

11. What can a genome do? Recipe: A) Take one draft genome B) Add a bioinformatics pipeline to –B1) identify small tandem repeats –B2) Design primers for each tandem repeat C)Apply genome-scale microsatellite loci to field samples

12. Microsatellites 70 loci spanning 2Mb of T. cruzi genome. Resolution of population genetic structure of T. cruzi lineages in principal host species. Hardy-Weinberg recombination analysis

13. Brazil: opossum Philander, Didelphis and monkey Bolivia: opossum Philander and Didelphis Venezuela: opossum Didelphis ALLOPARY VICARIANCE Isolation not by pure geo-graphic distance Biogeographic markers

14. Sympatry and TCIIc Between species

15. Sympatry and TCI Geneflow Within species

16. The State of Play 1 X draft genome next year Funding in place to stripe out the MSATs (NBN) Some MSATs defined Evidence of genetic allopatry Leverhulme network of Chris Schofield coordinates the PATTEC Lake Victoria Basin projects in Kenya, Uganda and Tanzania Kenyan and Ugandan governments have taken development loans to control tsetse Community ecology Genetics Governments

17. Combining public health & pop. gen. Kenyan and Ugandan government Population collections Schofield network –Kenya, –Uganda –(Tanzania) Morphometrics MSATs PATTEC Proposed strategy Targeted tsetse control African development loans

18. In summary Fly collections are completed Genome is poised - could be a heterozygosity issue Governments are interested and monies are available Good geneticists in Kenya, Uganda and Tanzania Combine a high throughput, low cost technology (morphometrics) with MSATs - standardize the method …. then we have ignition

19. Acknowledgements Win Hide, SANBI, SAWin Hide, SANBI, SA Chris Schofield, LSHTM, UKChris Schofield, LSHTM, UK Mark Walmawa (SANBI pending)Mark Walmawa (SANBI pending) Christopher Maher & Lincoln Stein (Cold Spring Harbour, US) Johnson Omur (BTRC, Kenya)Johnson Omur (BTRC, Kenya) Dan Masiga (ICIPE, Kenya)Dan Masiga (ICIPE, Kenya) Funding from the Wellcome Trust, NBN, SA and RCUK fellowship to MWG Michael Miles, LSHTM Martin Llewellyn, LSHTM Tsetse fly Chagas disease