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Identification of gene networks associated with lipid response to infection with Trypanosoma congolense Brass A3; Broadhead, A2; Gibson, JP1; Iraqi, FA1,

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Presentation on theme: "Identification of gene networks associated with lipid response to infection with Trypanosoma congolense Brass A3; Broadhead, A2; Gibson, JP1; Iraqi, FA1,"— Presentation transcript:

1 Identification of gene networks associated with lipid response to infection with Trypanosoma congolense Brass A3; Broadhead, A2; Gibson, JP1; Iraqi, FA1, Kemp, SJ2; Musa, H1; Naessens, J1; Noyes, HA2; 1International Livestock Research Institute, P. O. Box 30709, Nairobi, Kenya 2School of Biological Sciences, University of Liverpool, L69 7ZB, UK 3Department of Computer Science, University of Manchester, UK The innate immune response controls infection African trypanosomes are best known for their extreme antigenic variation. They generate a new surface coat every seven to 14 days. Large numbers of antibodies are generated to the surface antigens, and control each wave of parasitaemia. However, the control of the disease appears to be T cell independent since the disease in T cell deficient mice is no worse than in wild type mice. Survival time after infection varies substantially between different inbred strains of mice and between different breeds of African cattle. We have mapped QTL associated with survival time in mice and with parasitaemia, anaemia and growth rate in cattle. Introduction Human African Trypanosomiasis (HAT) or sleeping sickness is caused by subspecies of the protozoan parasite Trypanosoma brucei. A very similar disease of cattle (nagana) is caused by Trypanosoma congolense. Both these parasites live in the blood stream and are fatal unless treated. Estimates of the number of humans infected vary widely but over 2 million are believed to be infected in The Democratic Republic of the Congo alone. The cattle disease has been estimated to cause economic losses of over 4 billion USD per year and effectively restricts cattle production to areas of Africa where numbers of the tsetse files that transmit the disease are low. The symptoms of infection are generally non-specific and include cachexia, fever, anaemia in cattle and neurological symptoms in humans. 20 40 60 80 100 120 1 10 19 28 37 46 55 64 73 82 91 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 Days Post Challenge % Survival C57BL6 AJ F2 AJ x C57BL6 X Percentage survival of AJ, C57/BL6 mice and F2 AJxC57/BL6 by number of days post challenge. A common mechanism controls HDL levels and Trypanosomiasis? QTL for survival post infection with T. congolense and for HDL levels have been fine mapped to chromosomes 1,5,16 and 17 (Mammalian Genome : and Genome Research : ). Two of these QTL on chromosomes One and Five overlap. Whilst there are many genes under these QTL it is possible that a common regulatory mechanism controls both phenotypes. Cholesterol levels correlate with survival after infection. Total cholesterol, HDL and LDL cholesterol and Triglycerides were all measured in infected mice which were maintained on high and low fat diets. HDL cholesterol and LDL cholesterol levels both tracked total cholesterol which is shown. Cholesterol levels declined after infection, and absolute levels correlated with susceptibility to infection. C57BL/6 mice which survive longest after infection had highest cholesterol levels and AJ mice which have the shortest survival time had the lowest cholesterol levels on both diets. There was an indication that the Balb/c mice on the high fat diet survived longer than the same mice on a low fat diet, but numbers were not sufficient to determine if this was significant. A further experiment is currently underway to specifically test the effect of high fat isocaloric diets on survival time. HDL QTL Trypanosomiasis QTL Cyp7a1 and SRB1 may regulate the differences in cholesterol levels. C57Bl/6 mice are known to have relatively high cholesterol levels and are a common model for atherosclerosis. Gene expression was measured on Affymetrix microarrays and a key difference in gene expression in cholesterol metabolism pathways was found in Cyp7a1 (cholesterol 7 alpha hydroxylase). Cyp7a1 is the rate limiting step in bile acid synthesis and cholesterol secretion. Relative expression of this enzyme was inversely correlated with cholesterol levels at four time points post infection. SRB1 is a selective cholesterol receptor. Over-expression of SRBI is correlated with to a reduction in plasma cholesterol and an increase in biliary cholesterol (Nature : ). Plasma cholesterol increased in infected C57BL6 but not other strains at day 7 post infection (the peak of parasitaemia) and this correlated with a two fold drop in SRBI expression in C57BL/6 at this time, consistent with SRBI contributing to the increase in cholesterol in C57BL/6. SRBI is under the chromosome 5 QTL, as is ATP10d which may be involved in cholesterol export from macrophages and has a premature stop codon in C57BL6. Lipids and inflammation Cholesterol synthesis and inflammation are known to be linked by HmgCoA which is the key step in cholesterol and isoprenoid synthesis. Isoprenoids can regulate inflammatory / anti-inflammatory switch (Journal of Experimental Medicine : ). AJ mice which have the lowest cholesterol levels and the weakest inflammatory response also had approximately two fold lower levels of HmgCoAr at all time points post infection (not shown). Overlay of gene expression post infection on macrophage gene networks indicates that the RXR/LXR transcription factors are down regulated in susceptible AJ mice at day 9 post infection (E on map). These are known to also be regulators of inflammation as well as lipid metabolism suggesting that these pathways may also be involved. E Conclusion Lipids are known to be involved in the control of trypanosomiasis in humans. There is a difference in lipid responses between susceptible and resistant mouse strains. There are also differences in inflammatory responses between mouse strains. The overlap of QTL for HDL and survival time post infection suggests that the lipids may be the key regulator of inflammation. This may be via PPAR, LXR and RXR transcription factors or via another as yet unidentified mechanism.


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