genomic approaches to trypanosome resistance

BIG DIFFERENCES BETWEEN GENOTYPES AND OVER TIME. Between 600 and 750 probes were differently expressed between infected and uninfected cattle. Principle component analysis of the expression data clearly shows genome-wide differences between the transcriptomes of tolerant () and susceptible ( ) cattle (Top Right, PCA component 3) and some of these differences are associated with the presence and progression of trypanosome infection (Top left, PCA component 1).

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Genomics approaches to trypanosomiasis resistance

Breeds of cattle which are resistant and susceptible to pathology following Trypanosoma congolense infection, show differences in gene expression following challenge. Examination of the networks of genes responding most differently between these genotypes provides insights into the biology of the response to infection.

Where differentially used networks include genes within QTL for resistance, those genes become functional candidates irrespective of whether they are differentially expressed themselves. The diversity of cattle and disease challenge across Africa provides a unique natural experiment which can be used to evaluate these genes for evidence of a functional role in trypanosome resistance.

The responding networks provide new ways of examining the pathology, raising hypotheses and suggesting novel interventions which may be unrelated to the present diversity represented by the QTL.

GeneGo was used to identify networks amongst the genes that were differentially expressed. The figure (right) shows the largest network of connected genes that were differently expressed by trypanosome infected resistant (NDama) and susceptible (Boran) cattle.

STAT3 and c-Fos have the most connectivity. STAT3 is a transcription factor which is activated in response to the IL-6 family of cytokines and is involved in the acute phase response in the liver

Interestingly, STAT3 is modulated by RAC1 which is in turn controlled by VAV1 and ARHGAP15 which are both located in the QTLs controlling trypanotolerance.

Different cattle genotypes show differences in gene expression before and during trypanosome infection. Trypanosome infection induces profound changes in the steady state level of many genes. Network-based analysis reveals some highly concordantly responding networks Genes that are within QTL and also within highly connected networks are strong candidates for QTL genes Some differentially expressed genes are highly connected to many other differentially expressed genes. These are key points for intervention.

Twenty NDama (tolerant) cattle and 20 Boran (susceptible) cattle were challenged with a lethal dose of T. congolense. Liver biopsy samples were taken from each individual in specified days prior to and post infection such that at each time point there were samples from at least 5 Boran and 5 NDama.

The mRNA profiles were assayed using the Affymetrix 24K probe sets (B). The gene data were fed into an analysis workflow (C) that integrates the expression measures, gene ontology, QTL information, and gene pathways data.

Agaba M1

Anderson S4

Archibald A4

Brass A2

Gibson J5

Hall L4

Hanotte O1

Hulme H2

Kemp SJ1,3

Mwakaya J1

Noyes, HA3

Ogugo M1

Rennie C3

Addresses 1International Livestock Research Institute,Box 30709 - 00100, Nairobi Kenya

2The University of ManchesterLF8 Kilburn Bldg, Oxford RdManchester M13 9PLUK

3School of Biological Sciences,University of Liverpool,Liverpool, L69 7ZB, UK

4Roslin Institute,Roslin,Midlothian, EH25 9PS, UK

5The Institute for Genetics & Bioinformatics, Hawkins Homestead, University of New England, Armidale, NSW 2351, Australia

Acknowledgements:We thank all the staff at the ILRI large animal facility and all colleagues in the Welcome Trust Consortium

This work was supported by the Wellcome Trust.

QTL with p