Understanding bovine trypanosomiasis and trypanotolerance: the promise of functional genomics

Veterinary Immunology and Immunopathology 105 (2005) 247–258 www.elsevier.com/locate/vetimm

Understanding bovine trypanosomiasis and trypanotolerance: the promise of functional genomics Emmeline W. Hill a, Grace M. O’Gorman a, Morris Agaba b, John P. Gibson b,1, Olivier Hanotte b, Stephen J. Kemp c, Jan Naessens b, Paul M. Coussens d, David E. MacHugh a,* a

Animal Genomics Laboratory, Department of Animal Science and Conway Institute for Biomolecular and Biomedical Research, Faculty of Agri-Food and the Environment, University College Dublin, Belfield, Dublin 4, Ireland b International Livestock Research Institute, Box 30709, Nairobi 00100, Kenya c School of Biological Sciences, University of Liverpool, Liverpool L69 7ZD, UK d Department of Animal Science and Center for Animal Functional Genomics, Michigan State University, East Lansing, MI 48824, USA

Abstract African bovine trypanosomiasis, caused by the protozoan parasite Trypanosoma congolense, is endemic throughout subSaharan Africa and is a major constraint on livestock production. A promising approach to disease control is to understand and exploit naturally evolved trypanotolerance. We describe the first attempt to investigate the transcriptional response of susceptible Boran (Bos indicus) cattle to trypanosome infection via a functional genomics approach using a bovine total leukocyte (BOTL) cDNA microarray platform. Four male Boran cattle were experimentally infected with T. congolense and peripheral blood mononuclear cells (PBMC) were collected before infection and 13, 17, 23 and 30 days post-infection (dpi). A reference experimental design was employed using a universal bovine reference RNA pool. Data were normalised to the median of a set of invariant genes (GAPDH) and BRB-Array tools was used to search for statistically significant differentially expressed genes between each time-point. Using a set of 20 microarray hybridisations, we have made a significant contribution to understand the temporal transcriptional response of bovine PBMC in vivo to a controlled trypanosome infection. The greatest changes were evident 13 dpi after parasites were first detected in the blood. Significant differences were observed in clusters of protein kinase C subunits and MHC class I/II related molecules. # 2005 Elsevier B.V. All rights reserved. Keywords: Cattle; Trypanosome; Parasitaemia; Trypanotolerance; cDNA microarray; Gene expression

1. Introduction * Corresponding author. Tel.: +353 1 7167738; fax: +353 1 7161103. E-mail address: [email protected] (D.E. MacHugh). 1 Present address: The Institute for Genomics and Bioinformatics, University of New England, Armidale, NSW 2351, Australia.

African bovine trypanosomiasis, caused by the extracellular flagellate protozoan trypanosome parasite (Trypanosoma spp.), is endemic throughout the

0165-2427/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.vetimm.2005.02.004

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humid and semi-humid zones of sub-Saharan Africa. The disease is coincident with the distribution of the tsetse fly (Glossina spp.), which acts as a vector for the parasite and infests an area of some 10 million km2 encompassing 36 countries (Black and Seed, 2002). A number of trypanosome species are important in bovine trypanosomiasis (Trypanosoma brucei brucei, Trypanosoma congolense and Trypanosoma vivax) that differ from those causing the human form of the disease, sleeping sickness (T. b. gambiense, T. b. rhodesiense). Bovine trypanosomiasis is a chronic debilitating disease causing severe cachexia and anaemia with associated intermittent fever, oedema and loss of condition (Uilenberg and Boyt, 1998). The disease is frequently fatal and is a major constraint on livestock and agricultural production in Africa (Swallow, 2000). Accordingly, trypanosomiasis is ranked among the top 10 global cattle diseases impacting on the poor (Perry et al., 2002). It has been estimated that the cost of the disease to livestock keepers and consumers exceeds US$ 1 billion annually (Kristjanson et al., 1999). In southern Africa, bovine trypanosomiasis is known locally as ‘nagana’, a Zulu term meaning ‘‘to be in low or depressed spirits’’—a fitting description for the disease. A variety of control measures have been implemented to combat bovine trypanosomiasis in subSaharan Africa, though none has been successful at eradicating the disease. At the present time, effective control of the disease is largely dependent on vector management using insecticides and tsetse traps, and the use of trypanocidal drugs (Schofield and Maudlin, 2001). Vaccine development has been hindered by the ability of the parasite to evade the host immune response via an elaborate mechanism combining antigenic variation and immunosuppression (Donelson, 2003). A particularly promising approach to disease control is to understand and exploit naturally evolved trypanotolerance—the ability of certain cattle breeds to remain productive in areas of high tsetse challenge. Certain indigenous taurine (Bos taurus) cattle populations (e.g. N’Dama), particularly in West and Central Africa, have evolved a relative tolerance to the disease, presumably due to strong natural selection over several millennia. This inherited tolerance has allowed some populations to live in the humid tsetse-infested equatorial regions of West Africa, where the more recently introduced trypano-

susceptible Bos indicus (zebu) cattle (e.g. Boran) can only be maintained with costly control measures (Murray et al., 1982, 1991). In recent decades, increasing human population pressure and consequent deforestation and land clearance for agriculture has fragmented tsetse habitats in West Africa. Coupled with the availability of veterinary prophylaxis and treatment, this has led to male-mediated hybridisation between trypanotolerant and trypanosusceptible animals, particularly in areas where zebu characteristics are perceived as more desirable (Bradley et al., 1994; MacHugh et al., 1997; Hanotte et al., 2000, 2002). This has resulted in large-scale introgression of the zebu genome—presumably at the expense of natural disease resistance. Consequently, many trypanotolerant populations are currently under threat. It has been demonstrated that trypanotolerance in African taurine cattle is a multigenic trait and crosses between trypanotolerant and trypanosusceptible animals display the expected intermediate phenotype (Murray et al., 1982). In a large-scale project spanning more than a decade, scientists at the International Livestock Research Institute (ILRI) in Kenya have developed multigenerational crosses between tolerant and susceptible breeds for genetic linkage mapping studies using bovine microsatellite markers. Sixteen phenotypic traits characterising the response to trypanosome infection were analysed and statistical analyses of these data in conjunction with microsatellite genotypes revealed 19 quantitative trait loci (QTL) contributing to the trypanotolerance trait (Hanotte et al., 2003). The physiological and genetic mechanisms underlying trypanotolerance in cattle are not well understood, though it has been established that trypanotolerant animals have a superior ability to limit parasitaemia and control anaemia, while maintaining body weight (Ellis et al., 1987; Paling et al., 1991a,b). A number of recent studies have shed some light on the requirements of these processes. By challenging haemopoietic chimeric N’Dama and Boran twin calves with T. congolense, Naessens et al. established that, although N’Dama singletons performed better than Boran singletons, measures of anaemia in N’Dama chimaeras displaying a susceptible haemopoeitic phenotype were similar to that of Borans, while the ability to control parasitaemia was maintained (Naessens et al., 2003). These results suggest two mechanisms: the ability to control

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parasitaemia is independent of haemopoietic tissue (and therefore the immune system), whereas the ability to control anaemia is dependent on a tolerant haemopoietic phenotype. Furthermore, studies have shown that depletion of CD8+ T cells does not effect parasitaemia or anaemia (Sileghem and Naessens, 1995), neither CD8+ nor gd-T cells contribute to control of parasitaemia and that control of parasitaemia is independent of CD4+ T cells and/or antibodies (Naessens et al., 2002). This suggests that innate immune responses may be more actively involved in the ability of trypanotolerant animals to control parasitaemia and may be independent of acquired responses. However, it has been observed that multiply-challenged N’Dama have a far superior ability to limit early parasitaemia and anaemia than naı¨ve animals, suggesting a role for memory-related immune responses (Paling et al., 1991a; Authie´ , personal communication). Protective immunity in cattle can be induced by the generation of variant antigen type (VAT) specific antibodies that react with the variable surface glycoprotein (VSG) of the trypanosome (Luckins, 1976; Musoke et al., 1981). VAT-specific antibodies promote trypanosome lysis, agglutination and uptake by macrophages (Ngaira et al., 1983; Crowe et al., 1984; Wei et al., 1990; Russo et al., 1994). VATspecific antibody responses show little if any difference between trypanotolerant and susceptible cattle infected with trypanosomes (Murray et al., 1982; Pinder et al., 1987; Williams et al., 1996). Unlike VAT-specific responses, antibody responses to non-variant antigens vary between trypanotolerant and susceptible animals. It has been proposed that trypanotolerance may partly be associated with higher titres of antibodies to a 33 kDa trypanosome-encoded cysteine proteinase—congopain (Authie´ et al., 1993). Antibodies to trypanosomal proteinases and other nonvariant antigens may not be involved in destruction or clearance of the parasites; they may reduce the toxic or pathogenic effects of the antigens themselves and could provide therapeutic vaccination to alleviate pathological effects. In this regard, it has been demonstrated that immunisation with recombinant trypanosome cysteine proteinases provides a protective effect to trypanosusceptible cattle (Authie´ et al., 2001). In this study, immunised animals, unlike controls maintained or gained weight during infection. In addition, their red blood cell and leukocyte counts tended to recover after

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2–3 months of infection, suggesting that the trypanosome cysteine proteinases may play a critical role in the anaemia and immunosuppression observed in susceptible animals with trypanosomiasis. Large-scale analysis of gene expression using cDNA microarrays has great potential for studying cellular responses to infection and the host–parasite interaction. In this report, we describe the first attempt to investigate the transcriptional response of susceptible Boran cattle to trypanosome infection via a functional genomics approach using a bovine total leukocyte (BOTL) cDNA microarray platform. The BOTL-5 microarray is a bovine immune-specific microarray generated from 932 expressed sequence tag (EST) clones from a bovine total leukocyte cDNA library (Yao et al., 2001). The vast majority of these EST sequences are known bovine genes or have annotated human or murine gene homologs. In addition to the BOTL-derived EST sequences, the arrays are augmented with more than 450 PCR amplicons from bovine genes known to function in immune responses and endocrine and apoptotic pathways (Coussens and Nobis, 2002).

2. Materials and methods 2.1. Animals and experimental infection Four male 14–15-month-old Boran (B. indicus, trypanosusceptible) cattle raised together at the International Livestock Research Institute farm at Kapiti Plains, Kenya, in an area free from trypanosomiasis were experimentally infected with T. congolense clone IL1180 (Geigy and Kauffmann, 1973; Nantulya et al., 1984) delivered through the bites of eight infected tsetse flies Glossina morsitans centralis (Emery and Moloo, 1981; Dwinger et al., 1987). Flies were allowed to feed on the shaved flanks of the animals, until engorgement. 2.2. Blood collection, peripheral blood mononuclear cell (PBMC) isolation and RNA extraction Blood samples (200 ml) for cDNA microarray experiments were collected in heparinised syringes before infection and 13, 17, 23 and 30 days postinfection (dpi). Clinical data were also collected throughout the course of infection: development of

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parasitaemia was monitored after 6 dpi every 2–3 days via direct microscopy using the buffy-coat darkground (DG) method (Paris et al., 1982); and anaemia, estimated by the percent packed cell volume (PCV) of peripheral blood, was monitored every 2–3 days from 1 day prior to infection. Peripheral blood mononuclear cells were isolated on a Percoll (Amersham Biosciences) gradient using a standard protocol (Ulmer et al., 1984) and stored immediately in TriReagent (Medical Research Center Inc.) at 80 8C. Total RNA was extracted using an optimised protocol combining the TriReagent, DNase treatment (RQ1 RNase-Free DNase, Promega Corporation) and Qiagen RNeasy (Qiagen Inc.) methods. The concentration and quality of total RNA was determined using the RNA 6000 Nano LabChip kit with a 2100 Bioanalyzer (Agilent Technologies) according to the manufacturer’s instructions. 2.3. cDNA conversion, labelling and hybridisation In order to facilitate comparisons among all experimental groups, a reference experimental design was employed using a universal bovine reference RNA (UBRR) pool (Kerr and Churchill, 2001; Yang and Speed, 2002). This RNA pool was constructed from RNA samples purified from a wide range of tissues collected from a euthanized Kerry cow (Hill et al., in preparation). Eight micrograms of sample RNA and UBRR were converted to cDNA using the AtlasTM Powerscript Fluorescent Labelling Kit (BD Biosciences) according to the manufacturer’s instructions. cDNA samples were subsequently labelled with Cy5 and Cy3 mono-reactive fluorescent dyes (Amersham Biosciences), such that the Boran test samples were always labelled with Cy5 (red) and the UBRR with Cy3 (green). Labelled samples were combined and co-hybridised on BOTL-5 cDNA microarrays using SlideHyb Glass Array Hybridisation Buffer #3 (Ambion Inc.). Microarray hybridisations were performed on an automated HS400 hybridisation station (Tecan Ltd.) with the following protocol—wash: 75 8C, runs 1, wash 10 s, soak 20 s; probe injection: 85 8C; denaturation: 95 8C, 2 min; hybridisation: 65 8C, agitation frequency medium, 35 s; hybridisation: 55 8C, agitation frequency medium, 35 s; hybridisation: 50 8C, agitation frequency medium, 2 min 30 s; wash: 42 8C, runs 2, wash 10 s, soak 20 s;

wash: 33 8C, runs 2, wash 15 s, soak 30 s; wash: 33 8C, runs 2, wash 20 s, soak 40 s; slide drying: 30 8C, 1 min 30 s. BOTL-5 microarrays contain 3888 features representing 1391 genes spotted in duplicate, supplemented with positive (lambda Q gene, beta actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and RPL19) and negative (blank and negative) control spots (Yao et al., 2001; Coussens et al., 2002). 2.4. Data collection, normalisation, quality control and statistical analysis Twenty microarray hybridisations were performed in total using the UBRR pool as a reference sample and the four animals at each of the five time-points (0, 13, 17, 23 and 30 dpi). Hybridised and dried slides were scanned using a GenePix 4000B scanner (Axon Instruments Inc.) and image acquisition, first-pass data analysis and filtering were carried out using the GenePix 6.0 microarray image analysis package (Axon Instruments Inc.). Competitive hybridisation was measured by determining the relative fluorescence of Cy5 (635 nm) and Cy3 (532 nm) for each spot on the array. The median fluorescence of each spot in each channel was adjusted by subtracting the median fluorescence of the local background. All spots with a consequent measure below background (<1) were given an arbitrary value of 1. This enabled calculation of expression ratios, when the test sample hybridised to a gene feature spot not represented in the UBRR (<25%) (Hill et al., in preparation). The UBRR used to provide an internal standard for each measurement, was assembled by combining mRNA from 13 diverse bovine tissues to provide a signal for as many gene features on the array as possible (Hill et al., in preparation). Employing the UBRR permitted inferences of changes in gene expression among the samples determined via an indirect ratio measure of the test signal (Cy5) relative to the reference sample (Cy3). To account for biased incorporation of dyes, the entire data set was normalised to a set of 76 glyceraldehyde-3-phosphate dehydrogenase cDNA array features. Normalisation to an invariant set of spots is considered more appropriate for targeted arrays where a large number of genes are expected to change, rather than a global normalisation strategy such as LOWESS, useful when a relatively small

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proportion of genes are expected to change (Simon et al., 2004). To normalise the data, the median of the logarithm base 2 (log2) ratio of the background corrected expression values in the set of invariant genes (correction factor) was subtracted from the log2 ratio of the background corrected expression values of the spots of interest (Simon et al., 2004). The average of the duplicate spots was used for further downstream data analysis. BRB-Array Tools Version 3.0.1 (Simon and Lam, 2003) was used to search for statistically significant differentially expressed genes between each time-point (class) in all combinations of standard paired t-tests (class comparisons: 0 dpi versus 13 dpi; 0 dpi versus 17 dpi; 0 dpi versus 23 dpi; 0 dpi versus 30 dpi; 13 dpi versus 17 dpi; 13 dpi versus 23 dpi; 13 dpi versus 30 dpi; 17 dpi versus 23 dpi; 17 dpi versus 30 dpi; 23 dpi versus 30 dpi). Univariate significance tests (probability threshold p < 0.05) produced permutation p-values (statistical significance) for changes in expression for each gene in each class comparison. Input data were mean of the normalised log2 ratios (test:UBRR) for the two spots for each gene at each time-point. To minimise the chance of detecting false positives in the gene lists, agreement between duplicate spots was examined and paired spots with a greater than two-fold difference were removed from the gene lists. All differentially expressed genes ( p < 0.05) between 13 dpi and 17, 23 and 30 dpi were selected for further analysis. To estimate relative expression values between each time-point, mean expression values for 0 dpi were set to zero, and the mean relative

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expression values for each gene were estimated as the difference between the normalised log2 ratios (test:UBRR) at 0 dpi and each post-infection time-point. The 30 genes with the most variable expression profiles across the time course were identified as those with the highest variance among expression values.

3. Results 3.1. Parasitaemia and PCV Trypanosomes were detected in the peripheral blood of all animals by 12 dpi at which stage packed cell volume began to decline. In no case did the PCV fall below 15%, the point at which chemotherapy treatment (Berenil 7 mg/kg) is recommended. Peak parasitaemia was reached by 20 dpi, with an average DG score of 4.25, corresponding to a parasite load of approximately 105 trypanosomes ml 1. 3.2. Microarray analysis and detection of significant differentially expressed genes across the time course One of the most important goals of microarray studies is to identify genes that are differentially expressed between pre-defined classes. Paired permutation t-tests between each combination of the five time-points revealed those genes differentially expressed between classes (detailed in Table 1). The greatest number of differentially expressed genes ( p < 0.05) was detected after 13 dpi (13 dpi versus 17

Table 1 Class comparisons among time-points and detection of differentially expressed genes via permuted t-tests Class comparison No. of differentially expressed genes before QCa

Total no. of Genes p < 0.001 p < 0.01 p < 0.05 differentially passing expressed QCa (%) genes

No. of differentially genes expressed in other class comparisons

Dfferentially genes expressed in other class comparisons (%)

0 dpi vs. 13 dpi 0 dpi vs. 17 dpi 0 dpi vs. 23 dpi 0 dpi vs. 30 dpi 13 dpi vs. 17 dpi 13 dpi vs. 23 dpi 13 dpi vs. 30 dpi 17 dpi vs. 23 dpi 17 dpi vs. 30 dpi 23 dpi vs. 30 dpi

17 15 13 3 52 59 54 27 15 18

13 3 10 0 30 32 36 12 9 6

76 20 77 0 58 54 67 44 60 33

a

77 84 55 65 126 147 145 66 78 60

QC, quality control filtering.

22 18 24 5 41 40 37 41 19 30

– – – – – 4 3 1 – –

5 1 1 1 7 12 9 2 1 4

12 14 12 2 45 43 42 24 14 14

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Table 2 Thirty most differentially expressed genes across time course and class comparisons between 13 dpi time-point and post-13 dpi time-points Name/blast matcha

Activin A receptor type IB Ataxia telangiectasia mutated Bos taurus beta 2-microglobulin mRNA Beta 2-microglobulin Beta type Protein kinase C Bos taurus ribosomal protein (QM) mRNA Bovine C10 protein Bovine growth hormone 2 Cadherin-11, type 2 Ephrin B1 Ephrin B4 Eukaryotic translation elongation factor 1 gamma Growth arrest and DNA damage inducible protein beta Homo sapiens carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 6 (CHST6), mRNA Homo sapiens GRB2-associated binding protein 3 (GAB3), mRNA Homo sapiens small inducible cytokine subfamily A (cys–cys), member 21 (SCYA21), mRNA Major histocompatibility complex, class II, DR alpha Mitogen-activated protein kinase 13 Protein kinase, cAMP-dependent, regulatory, type I, alpha (tissue specific extinguisher 1) Secreted apoptosis related protein 2 Thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-erb-a) oncogene homolog, avian) TLH29 protein precursor (TLH29), interferon, alpha-inducible Unclassified Unclassified Unclassified Unclassified Unclassified Unclassified V-ets avian erythroblastosis virus E26 oncogene homolog Voltage-dependent anion channel 3 a

Human gene symbol

GenBank accession

13 dpi vs. 17 dpi

13 dpi vs. 23 dpi

13 dpi vs. 30 dpi

p-Value

Fold change

p-Value

Fold change

p-Value

Fold change

ACVR1B ATM B2M B2M PRKCB1 RPL10 – GH2 CAD-11 EPHB1 EPHB4 EEF1G

BC040531 BM671057 BM251848 BM251848 AW335987 BM252037 BM251985 AW631765 AW298192 BM726719 BC052804 BF230159

– 0.006 0.026 0.025 0.009 0.016 – – 0.015 0.027 – 0.031

– 1.63 1.52 1.75 2.21 1.34 – – 1.91 1.88 – 1.57

– – 0.033 0.020 – 0.008 0.012 – – – – 0.026

– – 2.50 3.00 – 2.69 2.37 – – – – 2.33

0.014 – 0.007 – – 0.007 – 0.023 – 0.047 0.017 0.004

3.28 – 1.51 – – 1.74 – 2.87 – 1.86 1.86 1.59

GADD45B

AF087853

0.040

1.51

–

–

–

–

CHST6

BM252046

–

–

0.040

2.47

–

–

GAB3

BM252099

0.023

1.68

–

–

–

–

SCYA21

AW658027

0.017

1.70

–

–

–

–

HLA-DRA

BM251923

0.025

2.62

–

–

0.011

1.86

MAPK13 PRKAR1A

BM680925 AL542113

– 0.011

– 1.79

0.017 –

2.85 –

– 0.003

– 2.12

SARP2 THRA

AK127331 BE754163

0.047 –

1.52 –

0.038 –

2.92 –

0.023 0.033

2.56 1.97

TLH29

AA418284

–

–

–

–

0.023

1.90

– – – – – – ETS1

BM252051 BM251241 BM251520 BM251453 BE754637 BG692205 BX640634

– 0.040 – – 0.018 – 0.044

– 3.57 – – 1.63 – 2.29

0.016 – 0.012 0.043 – – –

2.23 – 2.07 2.45 – – –

0.022 – –

2.25 – –

– 0.027 –

– 1.48 –

VDAC3

BG690489

0.020

2.28

0.037

2.29

0.016

1.53

Name of bovine gene from original gene feature PCR product or BOTL clone BLAST match to bovine or other sequence in GenBank sequence repository.

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dpi, n = 126; 13 dpi versus 23 dpi, n = 147; 13 dpi versus 30 dpi, n = 145), approximately twice the number of induced or repressed genes determined for all other class comparisons. The requirement that duplicate spots show less than two-fold difference caused 39–95% of significant genes to be removed in each class comparison. An average of 63 genes were removed from each gene list. Gene lists containing the greatest numbers of differentially expressed genes before quality control (i.e., 13 dpi versus 17 dpi; 13 dpi versus 23 dpi; 13 dpi versus 30 dpi) retained a higher proportion (37– 41%). Following removal of disparate pairs, the class comparisons containing the greatest number of up- or down-regulated genes remained 13 dpi versus 17 dpi (n = 52), 13 dpi versus 23 dpi (n = 59) and 13 dpi versus 30 dpi (n = 54) dpi. On an average, more than half (54%) of the genes in these gene lists were also differentially expressed in at least one other class comparison. Throughout the time course, a total of 185 genes were significantly differentially expressed, representing more than 13% of genes on the BOTL-5 microarray. Generally, the trend observed was that differential expression manifested as up-regulation at 13 dpi. The fold changes in the comparisons across the time course varied from 3.79-fold down-regulation to 4.35-fold upregulation. Sixteen genes were 2-fold down-regulated and 38 were 2-fold up-regulated. For differentially expressed genes at 13 dpi versus 17 dpi, 13 dpi versus 23 dpi and 13 dpi versus 30 dpi, 60% were significantly

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different in at least one other comparison. A total of 126 genes were either up- or down-regulated in comparisons between 13 dpi and subsequent time-points (13 dpi versus 17 dpi, 13 dpi versus 23 dpi and 13 dpi versus 30 dpi). Most transcripts were down-regulated after 13 dpi, 32 having a 2-fold decrease in expression and only one a 2-fold increase. 3.3. Visualisation of gene expression changes Class comparisons of microarray data are limited by allowing detection of differential expression between only two time-points at once. In order to identify transcripts with the most variable expression, the 126 genes that changed after 13 dpi were ranked according to their variation in expression across the time course. Standard deviations (S.D.) ranged from 0.11 to 0.89. The 30 most variable genes (S.D. 0.46–0.89) are listed in Table 2 and their expression profiles across the timecourse are shown in Fig. 1. Eighteen (60%) of these genes were differentially expressed in more than one class comparison. A small proportion (20%) of the genes changed significantly between 0 and 13 dpi, whereas 40% changed between 13 and 17 dpi and 13 and 23 dpi, and 50% changed between 13 and 30 dpi. In cases where only BOTL clone sequences were known, 6 of the 30 genes returned no match in a BLAST sequence search (McGinnis and Madden, 2004) and were labelled ‘unclassified’. Analysis of

Fig. 1. Gene expression fold change values across the infection time course for the 30 most differentially expressed genes detailed in Table 2. All log2 fold change values are scaled from a zero baseline at day 0.

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Fig. 2. Gene expression fold change values for three protein kinase C genes that cluster tightly based on their gene expression profile across the infection time course. Also shown are the fold change values for the MAP3K10 gene. All log2 fold change values are scaled from a zero baseline at day 0.

gene function and ontology revealed that, of the remaining 24, 7 were predicted to exhibit protein kinase activity and three MHC class I/II activity. In a hierarchical cluster analysis (average linkage) performed using the TIGR Multiple Array Viewer (Saeed et al., 2003) (data not shown) on the 126 genes

differentially expressed after 13 dpi, a cluster of eight genes contained three members of the protein kinase C (PKC) superfamily (a, b, g) together with mitogenactivated protein kinase kinase kinase 10 (MAP3K10) and four unrelated genes. The temporal expression profiles of these genes are shown in Fig. 2. Similarly,

Fig. 3. Gene expression fold change values across the infection time course for two distinct microarray features that represent the same gene: (i) PCR amplicon for bovine beta-2 microglobulin (b2-m) and (ii) BOTL EST clone for bovine beta-2 microglobulin (b2-m). Fold change values are also shown for the MHC class I lymphocyte antigen gene and all log2 fold change values are scaled from a zero baseline at day 0.

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in another cluster of eight, two distinct features representing the beta-2-microglobulin (b2-m) gene (a BOTL clone and a PCR amplicon) clustered with an MHC class I lymphocyte antigen and five unrelated genes. The expression profiles of these genes across the course of infection are shown in Fig. 3.

4. Discussion Analysis of the clinical data suggests that the animals exhibited normal phenotypic responses to infection, having a prepatent period of 12 days and returning PCV values consistent with those previously reported for naı¨ve Boran (Logan et al., 1988). By extension, the molecular response may be considered normal for a susceptible group of animals. The most notable feature of the transcriptional profile is that a significant molecular response to parasite infection is detected only after parasites are apparent in the peripheral blood at 13 dpi, although no sample for transcriptional analysis was taken between 0 and 13 dpi. The response comprises a significant repression of most genes to 23 dpi followed by a return towards preinfection levels by 30 dpi. Parasites were detected in peripheral blood by 12 dpi and reached their peak density by 20 dpi. Therefore, one would expect to see some cell activation by 13 dpi although not at high levels. It is not surprising therefore that relatively few differentially expressed genes were found between 0 and 13 dpi and that the observed changes in expression were mostly less than two-fold. Most of these genes were up-regulated, probably as a consequence of the response to the infection. In contrast, the largest changes occurred between 13 dpi and 17 or 23 dpi when parasitaemic load is at its highest. Between 13 and 17 dpi the majority of genes were down-regulated, the opposite of what one would expect if an activation process was taking place. This apparent down-regulation may be explained by a dramatic change in leukocyte subpopulations during this phase of the infection. A serious decrease in the absolute number of total white blood cells per volume of blood occurs from 12 to 28 dpi after infection (Ellis et al., 1987; Naessens et al., 2003). Furthermore, the proportions of the different lymphocyte populations and subpopulations also change at that time. After the detection of trypanosomes in blood, the proportion of

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circulating B lymphocytes increases and is matched by a corresponding decrease in the proportion of T lymphocytes (Naessens and Williams, 1992), while the ratio of CD4/CD8 T cells increases (Ellis et al., 1987). It is possible that such alterations in the number and the proportions of the different blood cell types may have caused this apparent negative change in differential expression. In future studies, it may therefore be valuable to look at gene expression in purified cell populations. A number of genes returned in the significantly differentially expressed gene lists are expected to be false positives. For the BOTL-5 microarray containing 1391 genes, at significance levels of p < 0.05 and 0.01, approximately 70 and 14 significant genes, respectively, will be expected by chance. We have attempted to remove aberrant genes by applying a strict quality control to each of the genes returned in the gene lists, thereby retaining only those demonstrating real biological differences. Importantly, comparisons returning the greatest numbers of genes retained a proportionally greater number of genes than those with less differentially expressed genes in their gene list. This might suggest that for larger gene lists, relatively fewer false positives are initially returned and that application of a strict quality control to the gene lists may facilitate removal of a large proportion of false positives. Even after a strict quality control screening, more than 13% of the genes on the BOTL-5 microarray responded significantly to infection with trypanosome parasites. Although the magnitude of fold changes were not much greater than two-fold, they were similar to those reported for ConA stimulated bovine PBMC after 6 and 24 h with a smaller targeted bovine microarray (Tao et al., 2004). The number of genes significantly induced or repressed after 13 dpi is much greater than for any other temporal comparisons. These 126 genes are promising targets for closer investigation and verification by quantitative real-time reverse transcriptase PCR (qRT-PCR) (Bustin, 2000). Two examples demonstrate the quality of the data so far collected. Beta-2-microglobulin is the invariant light chain of the MHC class I molecule, which functions by presenting antigen to CD8+ T cells. Four spots representing this gene (AB021288: mRNA for beta 2-microglobulin and BOTL clone BOTL0100001XE12R) are among the 30 most variable genes significantly differentially

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expressed after 13 dpi (see Table 1). Plotting the expression of these MHC class I subunits along with the MHC class I lymphocyte antigen (HLA-A 0201), which is also significantly differentially expressed after 13 dpi, clearly demonstrates a tight coordinate regulation of transcription of MHC class I molecules (Fig. 3), providing confidence in the data filtering, quality control and analytical procedures used. For example, both b2-m gene features have identical relative expression values at 23 dpi (S.D. = 0.007), and for all three genes across the time course the standard deviations are low, ranging from 0.0308 to 0.2538. The most abundant class of transcript found among the 30 most temporally variable differentially expressed genes after 13 dpi were those that encoded various types of protein kinase. In particular, protein kinase C (PKC) is well represented among the 126 genes differentially expressed after 13 dpi. There are at least 10 members of the PKC superfamily, each enzyme involved in specific signal transduction pathways initiated by certain hormones, growth factors and neurotransmitters. Three PKC isoforms, PKC-b, PKC-g and PKC-a binding protein are among the 126 genes differentially regulated after 13 dpi. All three cluster along with mitogenactivated protein kinase kinase kinase 10 in a group of eight genes in a hierarchical cluster analysis. Fig. 2. shows coordinate regulation of the PKC genes and MAP3K10, which is particularly apparent for PKC-b and PKC-g. A comparison of Figs. 2 and 3 shows that the gene expression profile for each set of genes is different both quantitatively (gene expression fold change) and qualitatively (shape of the graph curves). The coordinate regulation at each time-point among subunits of the MHC class I molecules and isoenzymes of PKC, coupled with significant differential regulation of transcription through the time-course suggest that b2m and PKC may play pivotal roles in the first line of defence against parasite invasion. Studies have shown that during infection with Trypanosoma cruzi, a trypanosome species causing Chagas disease in South America, b2-m knockout mice suffer high parasitaemias and early death (Tarleton et al., 1992). Also it has been demonstrated that b2-m is necessary to create the MHC class I-like CD1d molecular complex required by natural killer (NK) T-cells for activation, and that during T. cruzi infection, parasite-produced glycophosphoinositol (GPI) may stimulate a protective NK T-cell response (Duthie et al., 2002). The 2.5–3-fold repression

of b2-m after parasites are apparent at 13 dpi may affect NK T-cell activation and figure in the consequential peak parasitaemia at 20 dpi. A return to normal expression levels after 23 dpi may reflect the clearing of the first wave of parasites and a consequent return to normal molecular activity. Tao et al. (2004) have also reported a significant down-regulation of MHC class I related genes following in vitro mitogen stimulation in bovine PBMCs. PKC is necessary for the regulation of phagocytosis by macrophages in a successful innate host response to infection (Greenberg, 1995; Mellor and Parker, 1998). The protozoan parasite Leishmania has been found to impair PKC-dependent processes in macrophages enabling the intracellular replication of the parasite (Descoteaux et al., 1992). A significant (>2-fold) repression of PKC isoenzymes during T. congolense infection prior to peak parasitaemia may reflect the initial evasion of macrophages by impairing PKC production in a similar manner to Leishmania. As suggested for b2-m, a return to normal transcriptional activity at 23 dpi may also be correlated with the clearance of the first wave of parasitaemia and the subsequent repression corresponding to the mounting of a second wave of heterogeneous parasites. At least two strategies are required to validate these hypotheses. Quantification of mRNA by qRT-PCR will be necessary to provide more accurate expression values at each time-point and would eliminate the possibility of non-biological variance caused by the use of a universal reference. In addition, in future experiments it will be necessary to monitor lymphocyte sub-population fluctuations through the course of infection by flow cytometry in order to determine whether alterations of mRNA levels reflect changes in transcription or changes in the relative abundance of different lymphocytes and monocytes. These experiments have demonstrated the utility of an immunospecific array platform for the investigation of a host response to an in vivo parasitic infection. Ongoing experiments at the International Livestock Research Institute hold much promise to distinguish between a susceptible and tolerant host response using a functional genomics approach. We are currently applying microarray and ancillary technologies to investigate the host response to infection with T. congolense in trypanotolerant N’Dama and susceptible Boran. A vertical microarray experimental design will

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be used, involving direct comparisons between N’Dama and Boran cDNA samples at appropriate time-points such that each time-point represents a single balanced block experiment with two classes (Dobbin and Simon, 2002). It should therefore be possible to readily detect genes that are differentially expressed between the trypanosusceptible and trypanotolerant phenotypes in response to trypanosome infection. Acknowledgements We would like to thank the following for help and advice: Kieran G. Meade, Joel R. Mwakaya, Moses Ogugo, Edith Authie´ and all the staff at ILRI who assisted with animals and laboratory work. This work was supported by an Investigator Grant from Science Foundation Ireland (grant no. 01/F.1/B028) to D.E.M.

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