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TRANSCRIPT
i
Immunological profiles of Bos taurus and Bos indicus cattle infested with the cattle tick, 1
Rhipicephalus (Boophilus) microplus 2
3
Emily K. Piper1, Nicholas N. Jonsson
1, Cedric Gondro
3, Ala E. Lew-Tabor
2,4, Paula 4
Moolhuijzen4, Megan E. Vance
2, Louise A. Jackson
2* 5
6
Cooperative Research Centre for Beef Genetic Technologies, Armidale, Australia, 2351; 7
1 The University of Queensland, School of Veterinary Science, Brisbane, Australia, 4072; 8
2 Queensland Primary Industries and Fisheries, Brisbane, Australia, 4105; 9
3 The University of New England, The Institute for Genetics and Bioinformatics, 10
Armidale, Australia, 2351; 11
4 Murdoch University, Centre for Comparative Genomics, Perth, Australia, 6150. 12
13
14
*Corresponding author. Mailing address: Queensland Primary Industries and Fisheries, 15
Locked Mail Bag 4, Moorooka, Australia, 4105. Phone: +61 (7) 3362 9428. Fax: +61 (7) 16
3362 9440. Email: [email protected] 17
18 18
19
Copyright © 2009, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Clin. Vaccine Immunol. doi:10.1128/CVI.00157-09 CVI Accepts, published online ahead of print on 27 May 2009
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Abstract 20
The cattle tick, Rhipicephalus (Boophilus) microplus, is a major threat to the 21
improvement of cattle production in tropical and subtropical countries worldwide. Bos 22
indicus cattle are naturally more resistant to infestation with the cattle tick than Bos 23
taurus breeds although considerable variation in resistance occurs within and between 24
breeds. It is not known which genes contribute to the resistant phenotype, nor have 25
immune parameters involved in resistance to R. microplus been fully describe for the 26
bovine host. This study was undertaken to determine whether selected cellular and 27
antibody parameters of the peripheral circulation differed between tick-resistant Bos 28
indicus and tick-susceptible Bos taurus cattle following a period of tick infestations. This 29
study demonstrated significant differences between the two breeds with respect to the 30
percentage of cellular subsets comprising the peripheral blood mononuclear cell 31
population, cytokine expression by peripheral blood leukocytes, and levels of tick-32
specific IgG1 antibodies measured in the peripheral circulation. In addition to these 33
parameters, the Affymetrix bovine genome microarray was used to analyze gene 34
expression by peripheral blood leukocytes of these animals. The results demonstrate that 35
the Bos indicus cattle developed a stabilised T cell-mediated response to tick infestation 36
evidenced by their cellular profile and leukocyte cytokine spectrum. The Bos taurus cattle 37
demonstrated cellular and gene expression profiles consistent with a sustained innate, 38
inflammatory response to infestation, although high tick-specific IgG1 titres suggest that 39
these animals have also developed a T cell response to infestation. 40
41
42
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Keywords: Tick, Rhipicephalus, Boophilus, microplus, Bos indicus, Bos taurus, gene 43
expression profile44
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Introduction 45
The cattle tick Rhipicephalus (Boophilus) microplus is a major threat to the improvement 46
of cattle production in tropical and subtropical countries worldwide. Heavy tick 47
infestation has adverse physiological effects on the host resulting in decreased live weight 48
gain (21), and anemia is a common symptom of heavy infestation (34). R. microplus is 49
also the vector of Babesia bovis, B. bigemina and Anaplasma marginale that cause tick 50
fever in Australia. Acaricide treatment is the primary method of controlling ticks; 51
however, populations of ticks have subsequently developed resistance to organochlorines, 52
organophosphates, carbamates, amidines and synthetic pyrethroids (27). Resistance to 53
multiple classes of chemicals has also been observed (27). It is probable that with current 54
usage new acaricides will encounter similar problems (10) and a more sustainable 55
solution to tick control is needed. 56
57
Naturally acquired host immunity has been proposed as a viable cattle tick control 58
method because of the potential reduction in expenditure on acaricides and husbandry 59
practices associated with chemical control (10). Bos indicus cattle breeds are more 60
resistant to R. microplus than Bos taurus breeds, although considerable variation in 61
resistance occurs between and within breed (36), (45). Although innate immunity arising 62
from genetic differences between B. indicus and B. taurus breeds form the basis of 63
whether an animal will be resistant to tick infestation, host resistance is considered to be 64
predominantly an acquired trait because the higher level of resistance seen in B. indicus 65
only becomes apparent following a period of initial susceptibility to primary infestation 66
(15, 44). Host resistance to tick infestation is heritable, estimated to be between 39% and 67
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49% for British breed animals (45), and as high as 82% in Africander and Brahman (B. 68
indicus) crossbred animals (36). Since these initial studies it has been shown that the 69
resistance status of both B. taurus and B. indicus breeds can be improved by selection for 70
increased tick resistance, as demonstrated by a breeding program that has resulted in a 71
highly tick-resistant line of Hereford × Shorthorn (B. taurus) cattle, now known as the 72
Belmont Adaptaur (9, 25). Identifying the mechanisms responsible for mediating 73
naturally acquired tick resistance in cattle is an essential step in developing predictive 74
phenotypic markers to enable rapid identification of highly resistant individuals and is 75
potentially useful in the development of a tick vaccine. It is not known, however, which 76
genes contribute to the resistant phenotype, nor have immune parameters involved in 77
resistance to R. microplus been fully described for the bovine host. 78
79
Studies of immune parameters of the peripheral circulation of tick infested cattle have 80
yielded varied and sometimes conflicting results. Cattle tick infestation has been reported 81
to reduce the number of circulating T-lymphocytes and the antibody response to 82
ovalbumin injection in susceptible B. taurus animals compared to tick-free control 83
animals (17). In another study, infestation with several species of African ticks resulted in 84
higher levels of serum gamma globulin and increased number of circulating white blood 85
cells in B. taurus animals compared with Brahman cattle managed under the same 86
conditions (32). Exposure of animals to high and low levels of tick infestation has been 87
reported to result in differential patterns of immunoglobulins specific for tick salivary 88
proteins in resistant and susceptible cattle (7, 24). Sustained heavy infestation has been 89
shown to alter host hemostatic mechanisms by inhibiting platelet aggregation and 90
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coagulation functions (33), and also by altering the level of acute phase proteins in the 91
susceptible host (4). 92
93
In vitro studies of mononuclear cell populations have shown that salivary gland proteins 94
from R. microplus can inhibit immune cell function. The proliferative response of bovine 95
peripheral blood mononuclear cells (PBMC) to stimulation with the T-lymphocyte 96
mitogen phytohemagglutinin (PHA) was inhibited by the addition of salivary gland 97
protein to the culture (17), and subsequent studies showed that sufficient prostaglandin-E2 98
(PGE2) is present in tick saliva to be responsible for this inhibition (16). Turni et al. (42) 99
found that low concentrations of R. microplus salivary gland extract (SGE) inhibited the 100
oxidative burst capacity of monocytes and neutrophils, as well as the proliferation 101
response of PBMC to concanavalin-A (ConA) in vitro, in both B. taurus and B. indicus 102
cattle. However, a higher concentration of SGE caused a significant difference in the 103
degree of inhibition observed in the proliferation assay between the B. taurus and B. 104
indicus cells; a 40.7% and 88.5% reduction, respectively. The authors suggested that the 105
disproportionate increase in inhibition at the higher concentration of SGE may be an 106
indication that the mechanisms by which the two breeds resist infestation are different. 107
108
Here we report the results of a study undertaken to define selected immune parameters in 109
tick-resistant Brahman and tick-susceptible Holstein-Friesian animals following 110
challenge infestations with R. microplus. The aim of this study was to determine whether 111
cellular and antibody components of the peripheral circulation differed between these two 112
breeds of highly divergent resistance following a period of tick infestations. 113
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114
Materials and Methods 115
Animals and treatment 116
Six Holstein-Friesian (B. taurus) and six Brahman (B. indicus) heifers aged 6 months (± 1 117
month) that had been previously vaccinated against the tick fever-causing organisms 118
Babesia bovis, B. bigemina and Anaplasma marginale were used in this trial. Both groups 119
originated from tick-infested areas of Australia and consequently all animals had 120
previously been exposed to R. microplus in the field prior to the commencement of this 121
study. Infestation and tick counting procedures performed on these animals have been 122
previously described (31). Briefly, cattle were artificially infested weekly for seven 123
weeks with approximately 10 000 (0.5 g) R. microplus larvae applied to the neck and 124
withers. Animals were simultaneously exposed to ticks under natural conditions in tick-125
infested pastures. The larvae used to artificially infest the cattle were of the Non-126
Resistant Field Strain (NRFS) (39) which is maintained free of tick fever-causing 127
organisms at Queensland Primary Industries and Fisheries in Brisbane, Australia. Larvae 128
were maintained at 28°C and approximately 95% humidity and applied to animals 7-14 129
days after hatching. Standard tick counts were undertaken weekly, for seven weeks, as 130
described by Utech et al. (43). An analysis of variance of tick side counts was performed 131
using Minitab (Student Version 14) to show that the two breeds differed in their ability to 132
resist tick infestation. All animals in the trial were managed under the same conditions in 133
the same paddock for several months prior to the commencement of the trial and for the 134
duration of the trial. Weekly blood samples were obtained via jugular venipucture for 135
three weeks during the period of artificial infestations. EDTA, lithium heparin and Z clot 136
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activator Vacuette® blood tubes (Greiner Bio-One) were used for the collection of blood. 137
138
Hematology 139
A hematology report was obtained for blood samples collected in EDTA vacutainers 140
using a VetABC animal blood cell counter (ABX Hematologie). The hematology report 141
included counts of whole white and red blood cells, hemoglobin levels, platelets, packed 142
cell volume, mean corpuscular volume and mean cell hemoglobin concentration. 143
144
Flow cytometry 145
Blood collected in EDTA (100 µl) was combined with 100 µl of either a monoclonal 146
antibody (Table 1) or isotype control (mouse IgG1, Dako, Carpinteria, USA) and 147
incubated at 4ºC for 30 min after which red blood cells were lysed with 2 ml of red blood 148
cell lysing buffer (0.19 M NH4Cl2, 0.01 M Tris; pH 7.5, containing 1% NaN3). Tubes 149
were centrifuged at 500 g for 5 min at 4ºC. The supernatant was discarded and all 150
samples washed with 2 ml of cold PBS containing 1% NaN3 before being centrifuged 151
again at 500 g for 5 min at 4°C. The supernatant was again discarded. The secondary 152
antibody (anti-mouse IgG pre-adsorbed with bovine IgG conjugated to FITC, 153
Calbiochem, San Diego, USA) was diluted 1/100 in PBS containing 5% foetal bovine 154
serum (Invitrogen, Carlsbad, USA) and 1% NaN3. 50 µl of diluted secondary antibody 155
was added to all tubes and incubated at 4ºC for 30 min. Samples were washed with PBS 156
containing 1% NaN3 as before and the supernatant discarded. Each sample was 157
resuspended in 200 µl of fixative (PBS containing 1% NaN3 and 8% formaldehyde). 158
Samples were analyzed with a FACSCalibur (Becton Dickinson Immunocytometry 159
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Systems, Franklin Lakes, USA). Data from 10 000 cells per sample was acquired using 160
an argon laser with an excitation wavelength of 488 nm. Forward scatter light data was 161
acquired using a linear amplifier and side scatter light data was acquired with a 162
logarithmic amplifier. Data analysis was performed using the commercially available 163
software Cell-Quest (Becton Dickinson Immunocytometry Systems). Gates for analysis 164
were set around the peripheral blood mononuclear cell (PBMC) population on a dot plot 165
of forward angle versus side angle light scatter. Labeled lymphocyte populations were 166
analyzed using a histogram for fluorescein fluorescence and a threshold marker was set at 167
the upper 0.5% of the isotype labeled control population for each biological sample. 168
Results are presented as the percentage of PBMC that emitted fluorescence above that of 169
the negative population. 170
171
Tick antigen extraction 172
Approximately 500 semi-engorged adult female ticks (NRFS) (39) were removed from 173
penned B. taurus cattle at Queensland Primary Industries and Fisheries for preparing tick 174
antigen extracts. Tick dissection was carried out within 12 h of removal of the tick from 175
the host. Ticks were dissected while submerged in PBS and gut and salivary glands were 176
removed into separate vials on dry ice before being stored at -70°C prior to antigen 177
extraction. To extract whole adult female and larval antigens, semi-engorged NRFS adult 178
females and unfed NRFS tick larva respectively, were ground up using a mortar and 179
pestle on dry ice, and then stored at -70°C prior to antigen extraction. EDTA was added 180
to dissected organs and ground up tissue prior to freezing to remove divalent cations that 181
contribute to proteolysis. Antigen extraction was performed using the method described 182
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previously by Jackson and Opdebeeck (20). Briefly, this method employs a series of 183
centrifugation steps to separate proteins into membrane-bound and soluble fractions, and 184
the resulting antigen extractions included: salivary gland membrane (SM), larval 185
membrane (LM), gut membrane (GM), adult membrane (AM), salivary gland soluble 186
(SS), larval soluble (LS), gut soluble (GS) and adult soluble (AS) antigen extracts. 187
188
Cellular proliferation assay 189
PBMC for the proliferation assays were isolated from 10 ml blood collected in lithium 190
heparin Vacuette® tubes. Blood was mixed with 8 ml of PBS and layered onto 8 ml of 191
Ficoll-Paque (Pharmacia, Sydney, Australia) and centrifuged at 500 g for 40 min at 22ºC. 192
Cells at the interface of the Ficoll-Paque and PBS were removed and added to 8 ml of 193
RBC lysing buffer (0.19 M NH4Cl2, 0.01 M Tris; pH 7.5) and incubated at room 194
temperature for 10 min before being centrifuged at 250 g for 10 min at 22ºC. The 195
supernatant was discarded and the cell pellet resuspended and washed twice in PBS. Cells 196
were resuspended to 8 × 106 cells/ml in Complete Medium; RPMI-1640 medium (Sigma-197
Aldrich, St Louis, USA) containing 10% foetal bovine serum (Invitrogen), 1% antibiotic-198
antimycotic solution (Gibco, Carlsbad, USA) and 2 mM L-glutamine (Gibco). The 199
proliferation assay was set up in 96-well flat-bottom cell culture plates (Greiner Bio-One, 200
Frickenhausen, Germany) and assays were performed in triplicate. Each experimental 201
well contained 4 × 105 cells with either concanavalin A (ConA), PHA, soluble fractions 202
of semi-engorged adult female ticks (AS) or larvae (LS), or membrane fractions of semi-203
engorged adult female ticks (AM) or larvae (LM). ConA and PHA were diluted in 204
Complete Medium to 5 µg/ml and 20 µg/ml respectively and dispensed at 100 µl per well. 205
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LM and AM tick antigens were diluted to 10 µg/ml in Complete Medium, while LS and 206
AS antigens were diluted to 20 µg/ml in Complete Medium and each were dispensed at 207
100 µl per well. Control wells contained either media only or cells plus media, and all 208
wells were made up to a final volume of 200 µl with Complete Medium. The plates were 209
incubated at 5% CO2 at 37°C for 5 d. Subsequently, 20 µl of BrdU was added to all wells 210
and the plates incubated at 5% CO2 at 37ºC overnight. The cellular proliferation was 211
measured using a Cell Proliferation ELISA, BrdU (colorimetric) kit (Roche Diagnostics, 212
Sydney, Australia) according to the manufacturer’s instructions. Optical densities (OD) 213
were measured using a microplate reader at 450 nm in conjunction with the SoftmaxPro 214
computer software. The mean OD of each biological sample from triplicate wells was 215
employed for statistical analyses. 216
217
Tick-specific IgG antibody levels measured by ELISA 218
Serum samples collected from cattle over the three week period were used in an indirect 219
ELISA to measure tick-specific IgG1 and IgG2 antibody levels. For the IgG1 ELISA, tick 220
antigens SM, GM and LM were diluted to 7.5 µg/ml, 3.5 µg/ml and 5 µg/ml respectively 221
in carbonate buffer (0.1 M NaHCO3 and 0.1 M Na2CO3), while SS and LS antigens were 222
diluted to 10 µg/ml. For the IgG2 ELISA, all antigens were diluted to 20 µg/ml. Microtitre 223
plates (Greiner Bio-One) were coated with 100 µl/well of diluted antigen by overnight 224
incubation at 4°C. Excess antigen was discarded and plates were blocked with 200 µl of 225
carbonate buffer containing 1% gelatin. Sera were diluted 1/400 for the IgG1 ELISA and 226
1/100 for the IgG2 ELISA in PBS containing 0.05% Tween20 (PBS-T) and added to 227
triplicate wells for each biological sample. Control wells contained either PBS-T, known 228
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positive serum or known negative serum. The monoclonal antibody (mouse anti-bovine 229
IgG1 or IgG2, AbD Serotec, Raleigh, USA) was diluted 1/100 in PBS-T and added to all 230
wells. The conjugated antibody (goat anti-mouse IgG heavy and light chains specific, 231
conjugated to horseradish peroxidase, Calbiochem) was diluted 1/2000 in PBS-T and 100 232
µl was added to each well. A TMB-Peroxidase substrate (KPL, Maryland, USA) was 233
used to develop the signal and the reaction was stopped with 50 µl 2 Μ orthophosphoric 234
acid. The absorbance was read at 450 nm. The mean OD of each biological sample from 235
triplicate wells was employed for statistical analyses. 236
237
Isolation of RNA from white blood cells 238
Five ml of blood collected into EDTA was added to 45 ml of RBC lysing buffer and 239
allowed to stand at room temperature for 10 min. Samples were then spun at 250 g for 10 240
min and the supernatant removed. The cell pellet was resuspended in 4 ml of Trizol® 241
Reagent (Invitrogen) and samples were stored at -70°C prior to RNA extraction. 242
Extraction of total RNA was carried out according to the manufacturer’s instructions for 243
Trizol® Reagent (Invitrogen). The RNA pellet was resuspended in 30 µl of RNase-free 244
water and treated with 0.75 µl of Turbo DNase (Ambion, Austin, USA) according to the 245
manufacturer’s instructions and further purified using RNeasy mini-columns (Qiagen, 246
Melbourne, Australia). RNA was stored at -80°C until required. 247
248
Quantitative real time RT-PCR analysis of selected cytokines 249
cDNA synthesis was performed using 2 µg of total RNA. The complimentary strand was 250
primed with OligoDT primers (Invitrogen) and cDNA synthesis was performed using a 251
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Superscript™ III kit (Invitrogen) according to the manufacturer’s instructions. Each 252
qPCR reaction was carried out in a final volume of 12 µl containing 20 ng cDNA, 5.8 µl 253
Sensimix PLUS Sybr Master Mix (Quantace, Sydney, Australia) and gene specific 254
primers. Most primer sets used in the real-time PCR reactions for the analysis of cytokine 255
and chemokine expression have been published elsewhere (6, 40), but for ease of 256
reference they are listed in Table 2. Those primer sets that have not previously been 257
published were designed using the Primer3 software available at http://frodo.wi.mit.edu/. 258
The specificity of the primers was checked using melting curve analysis, and standard 259
curves were generated for each primer pair to obtain the amplification efficiency. 260
Quantitative PCR for each biological sample was performed in triplicate using standard 261
cycling conditions on a Rotorgene 6000 (Corbett). For each biological sample, the mean 262
of the cycle threshold (CT) values for each gene were calculated and normalized against 263
two internal controls; glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and acidic 264
ribosomal protein large, P0 (RPLPO), using the QGene software available at 265
http://www.qgene.org/. This software expresses the result in the form of the mean 266
normalized expression (MNE) +/- standard error (Simon, 2003) and this value was 267
employed for statistical analysis. 268
269
Statistical analysis of cellular and antibody parameters 270
A one-way analysis of variance (ANOVA) was performed using Minitab (Student version 271
14) for the fixed effect of breed for each of the cellular and antibody parameters 272
measured above. The dependent variable used for the analysis was the mean of the three 273
weekly observations for each animal in each group as it was confirmed that week did not 274
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have a significant impact on any of the variables using the general linear model (Minitab, 275
Student version 14). 276
277
Microarray data 278
Transcription profiling of the RNA extracted from white blood cells of the three Brahman 279
and three Holstein-Friesian animals was conducted using the Affymetrix GeneChip 280
Bovine Genome Array platform (Affymetrix, Santa Clara, USA). This expression array 281
contains 24,128 probe sets representing 11,255 gene identities from Bos taurus build 4.0 282
and 10,775 annotated UniGene identities plus 133 control probes. The experiment was 283
designed to be compliant with Minimum Information About a Microarray Experiment 284
(MIAME) standards. Each RNA sample was processed and hybridized to individual 285
slides; target preparation and microarray processing procedures were carried out by the 286
Australian Genome Research Facility (AGRF), Melbourne, Australia, as described in the 287
Affymetrix GeneChip Expression Analysis Manual (Affymetrix), and scanning was 288
performed with an Agilent Microarray Scanner (Agilent Technologies, Santa Clara, 289
USA). 290
291
The NCBI Gene Expression Omnibus (GEO) accession number for the microarray data 292
reported in this paper is GSE13725. Available for download from this GEO accession is a 293
Supplementary Analysis File containing all pre-processing analyses, annotated lists of 294
differentially expressed genes with links to NCBI and Affymetrix, and other relevant 295
images, diagrams and analyses. 296
297
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Microarray data pre-processing 298
All quality control measures, pre-processing and analyses were performed using the 299
statistical computing language R (41) and Bioconductor (13). The quality of the arrays 300
was assessed through standard quality control measures for Affymetrix arrays: pseudo-301
images of the arrays (to detect spatial effects), MA scatter plots of the arrays versus a 302
pseudo-median reference chip, and other summary statistics including histogram and 303
boxplots of raw log intensities, boxplots of relative log expressions, boxplots of 304
normalized unscaled standard errors and RNA degradation plots (3). All arrays were 305
within normal boundaries (Supplementary Analysis File). 306
307
Transcription intensities in log2 scale were estimated from the probe level data using 308
three summarization methods: MAS5.0 (1) with the R affy package (11), RMA (18, 19) 309
and GCRMA (46). Briefly, for MAS summarization, the background was corrected and 310
each probe was adjusted using a weighted average. All arrays were scaled to the same 311
mean value for normalization (200) and were summarized by an adjusted log2 scale 312
average using 1-step Tukey biweight. For RMA, the background was corrected by 313
convolution. The data were then quantile normalized and summarized by median polish. 314
GCRMA background correction used an affinity measure model based on probe 315
sequences and mismatch intensities. 316
317
MAS generates a detection call, which flags each transcript as present, marginal or 318
absent (28, 30). Detection calls for the probes were calculated (τ = 0.015, α1 = 0.04, α2 = 319
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0.06) and used as a filtering criteria in the analyses. 320
321
Statistical analysis of microarray data 322
Statistical analyses were performed following the method using by Rowe et al. (35). Prior 323
to testing for differential expression, the data were filtered to remove Affymetrix control 324
probes (n = 133) and all non-informative probes detected as marginal or absent in all 325
arrays (n = 8,920), thus leaving 15,096 probes to be tested. Differential transcription was 326
tested for each summarization method using limma (37, 38). Only differentially expressed 327
probes detected in two out of the three summarization methods (P < 0.01) and flagged as 328
present in at least 50% of the samples were considered to be significant. No false 329
discovery rate (FDR) correction method is warranted due to the stringency of the filtering 330
criteria. 331
332
Functional profiling 333
Annotation of differentially expressed probes was performed using the Database for 334
Annotation, Visualization and Integrated Discovery (DAVID) 335
(http://david.abcc.ncifcrf.gov/home.jsp) (8) and an R annotation package derived from 336
the Bos taurus build 4.0. In subsequent text the term probe is replaced by gene. The 337
differentially expressed genes were analyzed in the context of their gene ontology (GO) 338
biological process (12) and KEGG biological pathway (22, 23). 339
340
Functional profiles for the differentially expressed genes were derived for each of the 341
Gene Ontology (2) categories: cellular component, molecular function and biological 342
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process. Differentially expressed genes were mapped from their Entrez identifier to their 343
most specific GO term and these were used to span the tree structure and test for gene 344
enriched terms. Unannotated probes were dropped from the analyses. To avoid over-345
inflated P-values the background consisted exclusively of the array probes used in the 346
analyses after removal of control probes, unexpressed probes and unannotated probes. 347
Profiles for each category were also constructed for the differentially expressed genes for 348
different tree depths. 349
350
Validation of differentially expressed genes 351
Eight genes were arbitrarily chosen to validate the microarray estimates using 352
quantitative real-time RT-PCR (qPCR). Validation was performed using RNA extracted 353
from peripheral blood leukocytes of six Holstein-Friesian and five Brahman animals. 354
Primers used for the validation of genes detected as differentially expressed by the 355
microarray analysis were designed using the Primer3 software available at 356
http://frodo.wi.mit.edu/. Real-time quantitative RT-PCR and normalization was carried 357
out using the methods described above. Primers used for the validation of differentially 358
expressed genes are listed in Supplementary Table 2a. 359
360
Results 361
Tick counts 362
The Brahman cattle carried significantly fewer ticks (P < 0.001) than the Holstein-363
Friesian cattle at all time points when tick counts were undertaken, as previously reported 364
(31). The mean number of ticks observed on the Brahman animals was 15 (±14) per side, 365
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while the mean number of ticks observed on the Holstein-Friesian animals was 151 (±36) 366
per side. 367
368
Hematology 369
A significantly higher (P = 0.002) white blood cell (WBC) count was recorded for the 370
Holstein-Friesian animals on each of the three sampling time points; average Holstein-371
Friesian WBC counts were 13.27 (±2.26) × 106/ml while average Brahman WBC counts 372
were 10.84 (±2.16) × 106/ml (Table 3). Significantly higher (P < 0.001) levels of red 373
blood cells (RBC) were recorded for the Brahman animals on each of the three sampling 374
periods; mean Holstein-Friesian RBC counts were 5.47 (±0.51) × 106/mm
3 while mean 375
Brahman RBC counts were 9.25 (±0.62) × 106/mm
3 (Table 3). The Holstein-Friesian 376
animals correspondingly had significantly lower (P < 0.001) hemoglobin levels than the 377
Brahman animals. These values and other hematological parameters are listed with the 378
respective significance levels in Table 3. 379
380
Flow Cytometry 381
The Brahman animals had significantly higher levels of CD4+
T cells (P < 0.001), 382
activated T cells (CD25+) (P < 0.001) and γδ T cells (WC1
+) (P = 0.006) in their 383
peripheral circulation compared to the Holstein-Friesian animals (Figure 1). However, the 384
Holstein-Friesian group presented significantly higher levels of monocytes (CD14+) (P < 385
0.001) and other cells expressing the MHCII complex (MHCII) (P < 0.001) (Figure 1). 386
Figures 2a-e depict flow cytometry displays of CD4+, CD14
+, CD25
+, MHCII
+ and WC1
+ 387
cell populations respectively, from a representative Holstein-Friesian animal and a 388
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representative Brahman animal. No difference was observed between the breeds for the 389
percentage of CD8+ T cells, memory T cells (CD45RO
+) or B cells (WC3
+) in circulation. 390
391
Cellular proliferation assay 392
There was no significant difference between the breeds in the ability of their PBMC to 393
respond to stimulation with ConA or PHA at any sampling time point (data not shown). 394
Proliferation of PBMC in the presence of tick antigen was not significantly different to 395
the proliferation of cells in media alone (data not shown). 396
397
Tick-specific IgG antibody levels 398
Significantly higher levels (P < 0.001) of IgG1 antibodies specific for the tick antigen 399
extracts LM, SM, GM, LS and SS was observed in sera collected from the Holstein-400
Friesian animals compared with the Brahman animals (Figure 3). There was no 401
significant difference between the breeds for the level of IgG2 antibodies specific for any 402
of the tick antigen extracts (data not shown). All animals demonstrated relatively low 403
levels of tick-specific IgG2 compared to IgG1 apart from three Holstein-Friesian animals 404
that developed moderately high IgG2 specific for all antigen extracts (data not shown). 405
406
Expression of selected cytokines and chemokines by peripheral blood leukocytes 407
Blood was collected at only one time point during the height of infestation for cytokine 408
profiling of peripheral blood leukocytes. One Brahman animal was excluded from the 409
analysis due to poor RNA quality, and thus, qPCR profiling of cytokine expression is 410
based on 6 Holstein-Friesian and 5 Brahman animals. Transcripts of interleukin-1β (IL-411
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1β), interleukin 2 (IL-2), interleukin 2 receptor alpha (IL-2Rα), interleukin-10 (IL-10), 412
interleukin-12 (IL-12), interleukin-18 (IL-18), interferon-γ (IFN-γ), tumour necrosis 413
factor alpha (TNFα), CXC motif chemokine ligand 10 (CXCL-10), chemokine receptor 1 414
(CCR-1) and chemokine receptor 3 (CCR-3) were detected for every animal. 415
Significantly higher expression of IL-2 (P = 0.026), IL-2Rα (P = 0.008), TNF-α (P = 416
0.035) and CCR-1 (P = 0.009) was detected in peripheral blood leukocytes from 417
Brahman cattle compared to that of Holstein-Friesian animals, while significantly higher 418
expression of CXCL-10 (P = 0.034) was detected in the Holstein-Friesian cattle 419
compared to Brahman animals (Figure 4). The ability to detect interleukin-4 (IL-4), 420
interleukin-6 (IL-6) and interleukin-8 (IL-8) was variable in animals of both breeds (data 421
not shown). 422
423
Microarray analysis 424
Quality control checks established that the slides were of good quality and there were no 425
outliers in the samples (Supplementary Analysis File). A study comparing 45 different 426
combinations for background correction, normalization and summarization of Affymetrix 427
microarray data found the major source of variability in the analysis is the method of 428
summarization used to transform the multiple probe intensities into one measure of 429
expression (14). To obtain maximum specificity in our studies, 3 different summarization 430
methods were used (MAS, RMA and GCRMA) and only genes with P < 0.01 in at least 431
two out of the three methods and flagged as present in at least half of the samples were 432
considered to be significant. This approach increases the stringency of the study and thus 433
no FDR correction method for multiple testing is necessary. 434
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435
A total of 497 transcripts were detected as significantly differentially expressed (P < 436
0.01) by white blood cells in the peripheral circulation of Holstein-Friesian and Brahman 437
cattle. Two-hundred and fifty-three of these were more highly expressed by cells from 438
Brahman cattle, while the remaining 244 were more highly expressed by the Holstein-439
Friesian group (Figure 5 and see Table S1 of the supplementary material for full list of 440
differentially expressed genes). Quantitative PCR undertaken on 8 arbitrarily chosen 441
differentially expressed genes reflected closely the results obtained by the microarray and 442
these are presented in Table S2b. 443
444
Gene ontology and pathway analysis 445
Differentially expressed (DE) genes were analyzed in the context of their Gene Ontology 446
(GO) biological process. Due to the incomplete annotation of the bovine genome, 273 of 447
the 497 differentially expressed probe sets were not annotated and were excluded from 448
the analysis (including duplicate probe sets that target the same gene). GO analyses 449
showed that the major differences in gene expression between the two breeds were 450
associated with cellular (64.3% of DE genes) and metabolic (48.2% of DE genes) 451
processes in the Level 2 biological process ontology categories (Figure 6). Genes 452
associated with immune system processes accounted for 6.7% of differentially expressed 453
genes. The top ranking biological process GO terms over-represented by the differentially 454
expressed genes are listed in Table 4, together with the gene descriptions for the term and 455
P-value for the test of significance. From molecular pathway analysis using DAVID, four 456
major KEGG pathways were represented by genes differentially expressed by Brahman 457
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and Holstein-Friesian WBC. Ten genes more highly expressed by Brahman WBC were 458
associated with the hemopoetic cell lineage (P = 0.0079) and cytokine-cytokine receptor 459
interaction pathways (P = 0.04), while fifteen genes more highly expressed by Holstein-460
Friesian WBC were associated with the oxidative phosphorylation pathway (P = 1.1E-09) 461
and a further three represented the citrate cycle (P = 0.037) (Table 5). 462
463
Discussion 464
Results presented here demonstrate clear differences between the breeds in the level of 465
host resistance to tick infestation and in cellular and antibody parameters measured in the 466
peripheral blood. The significantly lower red blood cell count in the Holstein-Friesians, 467
and correspondingly low hemoglobin and hematocrit levels, are typical hematological 468
parameters often observed in heavily infested animals. Although all erythron parameters 469
for animals of both breeds were within ranges considered normal for cattle (26), Holstein-470
Friesian values for RBC, hemoglobin and hematocrit were verging on those considered to 471
define anemia (5). A significantly higher WBC count was also recorded for the Holstein-472
Friesian animals that is slightly above the range considered normal for cattle; the normal 473
range for cattle is reported to be 4-12 × 103/mm
3 (26). It has been noted that WBC counts 474
can be higher in calves of 6 months to 3 years of age, however, the high WBC count in 475
these heavily infested animals is more likely a reflection of the prolonged period of 476
inflammation and stress caused by the heavy tick burden. The significantly higher white 477
blood cell count in the Holstein-Friesian animals is consistent with the work of Rechav et 478
al. (32) who reported higher WBC counts in Simmentaler (B. taurus) cattle infested with 479
African tick species than in Brahman cattle managed under the same conditions. 480
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481
The two breeds showed significant differences in the relative percentage of cellular 482
subsets comprising the PBMC population. The Brahman group had higher percentages of 483
γδ T cells, CD4+ T cells and CD25
+ T cells than the Holstein-Friesians, while the 484
Holstein-Friesians had relatively higher macrophage-type cells (monocytes and MHCII-485
expressing cells) in their circulation. The higher percentage of MHCII-expressing cells 486
recorded for the Holstein-Friesian animals can be mainly attributed to higher percentage 487
of CD14+ cells in these animals, as there was no significant difference between the breeds 488
in the percentage of B cells seen in circulation. The relatively lower percentage of T cell 489
subsets observed in the Holstein-Friesian animals may have resulted from these cells 490
moving out of the blood and into the skin at the site of tick attachment, thus reducing the 491
relative numbers observed in the peripheral circulation. Similarly, the lower percentage of 492
macrophage-type cells in the Brahman animals may reflect a similar effect. However, 493
with regards to the MHCII-expressing cells, the more likely scenario is that these cellular 494
subsets have proliferated in the Holstein-Friesians in response to the heavy tick burden, 495
as these are the cell types responsible for presenting exogenously derived antigen to the 496
immune system. The authors acknowledge, however, that it is not possible to determine 497
whether these differences are a response to tick infestation or whether they are innate 498
differences between the breeds, as pre-infestation measurements were not obtained (as 499
the animals in this study had been previously exposed to ticks in the field prior to the 500
commencement of the trial). The differences in the relative percentage of cellular subsets 501
comprising the periphery in these breeds were reflected in the qPCR analysis of cytokine 502
expression of the WBC. The significantly higher expression of IL-2Rα (CD25), IL-2, 503
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TNFα and CCR-1 by WBC of the Brahman animals suggests a more vigorous T cell 504
response in this group, whereas the higher expression of CXCL-10 by WBC of the 505
Holstein-Friesian animals is consistent with the higher levels of 506
inflammatory/macrophage-type cells observed in the peripheral circulation of these 507
animals (29). 508
509
As many differences were noted between the Brahman and Holstein-Friesian cattle with 510
regard to their cellular profiles and gene expression of WBC, analysis of WBC global 511
gene expression of three Brahman and three Holstein-Friesian animals was undertaken to 512
examine more closely the processes taking place in the blood during tick infestation. To 513
our knowledge, this is the first study to undertake global gene expression analysis of 514
WBC in tick-infested cattle. Many genes that were detected as differentially expressed 515
between the two breeds fell into two general categories; those involved with adaptive 516
immune responses and those involved in metabolic processes. Gene ontology analysis 517
demonstrated that several genes more highly expressed by WBC of Brahman cattle over-518
represented processes involved in adaptive immunity such as T cell proliferation and 519
selection, and mononuclear cell proliferation. This was also reflected in the KEGG 520
pathway analysis which generated two major pathways for genes more highly expressed 521
by Brahman WBC; the hemopoietic cell lineage and cytokine-cytokine receptor 522
interactions. Conversely, genes that were more highly expressed by WBC of Holstein-523
Friesian cattle belonged to metabolic ontologies such as electron transport and glycolysis, 524
which represented two KEGG pathways; oxidative phosphorylation and the citrate cycle. 525
The results of the microarray analysis, in combination with other cellular parameters 526
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measured in these animals suggest that the Brahman cattle have developed a 527
predominantly T cell-mediated response to tick infestation. It should not, however, be 528
discounted that any T cell response elicited by the Holstein-Friesian animals may be 529
predominantly active in the skin at the site of tick attachment or in the lymph organs 530
draining the skin. 531
532
The higher tick-specific IgG1 detected in the Holstein-Friesian animals compared with the 533
Brahman group in the present study is in contrast to the results obtained by Kashino et al. 534
(23) who reported that tick saliva-specific IgG1 and IgG2 antibodies decreased in 535
susceptible animals compared with resistant animals following periods of heavy 536
infestation. The discrepancy in results between our study and that of Kashino et al. (23) 537
may be due to the length of time over which the studies were conducted. The study by 538
Kashino et al. (2005) collected serum intermittently over a period of 12-14 months during 539
which animals were exposed to natural infestations, whereas in the present study, serum 540
was collected over a three week period during the height of artificial infestations. Cruz et 541
al. (7) has reported differences in the level of IgG against different tick antigens during 542
heavy as opposed to light infestations, as well as individual variation in humoral 543
responses to tick antigens. Similarly, our results demonstrated individual variation in 544
IgG2 responses to tick antigen extracts, as three Holstein-Friesian animals developed 545
moderate to high IgG2 titers to several antigen extracts, while titers of other animals were 546
not significantly higher than that of the uninfested control sera. Preliminary western blot 547
analysis (data not shown) has demonstrated that the Holstein-Friesian animals produce 548
antibodies to different tick antigens compared to the Brahman animals. More extensive 549
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immunoblotting will be undertaken in the future to determine whether differential 550
recognition of tick antigens plays a role in resistance or susceptibility to ticks. 551
552
No proliferation was detected above background levels from PBMC stimulated with tick 553
antigen extracts in vitro in either breed. This result could be due to an inhibitory action of 554
the tick antigen extracts, as previously reported by Turni et al. (42), who showed that 555
addition of tick salivary gland extracts could inhibit the proliferative response of cells 556
from B. taurus and B. indicus animals to ConA stimulation. The extracts of whole adult 557
female ticks used in the present study may have contained sufficient quantities of salivary 558
gland extracts (and other proteins) capable of causing inhibition. However, a more 559
probable reason for the lack of response to these antigens may simply be that the 560
immunogenic proteins were not present in sufficient quantities to stimulate the cells in 561
vitro. It could be assumed that any antigen passed from the tick to the host during the 562
larval stage would be in such small quantities compared to other proteins present in the 563
whole larval extract, that any immunogenic protein may not be present in sufficient 564
concentrations to produce a detectable proliferation response. As an acquired T cell 565
response is critical to the development of tick-specific IgG and most probably to host 566
resistance to infestation, further investigation is required concerning the tick salivary 567
antigens presented to the host immune system during feeding and their role in initiating 568
adaptive immunity. 569
570
Conclusion 571
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In conclusion, we have shown that cellular, humoral, and gene expression profiles in the 572
peripheral circulation differ significantly between tick-resistant B. indicus and tick-573
susceptible B. taurus cattle infested with R. microplus. Brahman cattle demonstrated 574
PBMC profiles and WBC gene expression profiles consistent with a T cell-mediated 575
response to tick infestation, while Holstein-Friesian cattle demonstrated cellular profiles 576
consistent with an innate, inflammatory-type response to infestation. Future experiments 577
will be designed to include preinfestation measurements to track the development of the 578
immune response throughout the development and stabilization of tick 579
resistance/susceptibility. 580
581
Acknowledgements 582
The authors gratefully acknowledge funding from the Cooperative Research Centre for 583
Beef Genetic Technologies (BeefCRC). The authors would like to thank Tom Connolly 584
(University of Queensland) for his care of the animals in this project and assistance with 585
sample collection, and Ralph Stutchbury (QDPI&F) for preparation of tick larvae. Thanks 586
are also extended to the University of Queensland’s Animal Genetics Laboratory in the 587
School of Veterinary Science for their technical assistance, and also to Helle Bielefeldt-588
Ohmann for critical reading of the manuscript. 589
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Figure and Table Legends 784
785
Figure 1: Percentage of each cellular subset comprising the peripheral PBMC population 786
of Holstein-Friesian (black) and Brahman (white) cattle. Results are presented as the 787
breed mean of three time points with standard deviation from the group mean. * denotes a 788
significant difference (P < 0.01) between the Holstein-Friesian and Brahman cattle. 789
790
Figures 2a-2e: Flow cytometric displays of peripheral blood lymphocytes from a 791
representative Holstein-Friesian animal (A-C) and a representative Brahman animal (D-792
F). Figure 2a: CD4+ cells; 2b: CD14
+ cells, 2c: CD25
+ cells, 2d: MHCII
+ cells and 2e: 793
WC1+ cells. Dot plots A and D depict forward scatter vs side scatter light data. The 794
mononuclear lymphocyte population is gated in red. B and E depict the isotype-labelled 795
lymphocyte population on a dot plot of FITC fluorescence vs side scatter light data. C 796
and F depict the gated lymphocyte population labelled with the respective antibody 797
specific for the cell surface antigen. 798
799
Figure 3: IgG1 antibody levels specific for tick antigen extracts of Holstein-Friesian 800
(black) and Brahman (white) cattle. Results are presented as breed means of three time 801
points with standard deviation from the group mean. Holstein-Friesian animals had 802
significantly (P < 0.001) higher levels of IgG1 antibodies specific for all tick antigen 803
extracts compared with Brahman animals. 804
805
Figure 4: Cytokine / chemokine receptor expression by white blood cells of Holstein-806
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37
Friesian (black) and Brahman (white) cattle. Results are presented as breed mean 807
normalised expression values with standard deviation from the group mean; * denotes 808
significant difference between the breeds. 809
810
Figure 5: MAS5 heatmap plot of the top 100 (most significant) differentially expressed 811
genes clustered using hierarchical clustering. Affymetrix IDs are listed with their 812
corresponding gene symbol or NA if no gene assignment is available. For further 813
information on gene names, fold changes and significance values, consult Supplementary 814
Table 1 or the Supplementary Analysis File available at GSE13725. 815
816
Figure 6: Ontology analysis of 224 annotated differentially expressed genes in white 817
blood cells of Brahman and Holstein-Friesian cattle. The y-axis lists the major biological 818
processes represented by the 224 differentially expressed genes, and the x-axis indicates 819
the percentage of differentially expressed genes involved in the respective GO biological 820
process. (Note that a gene may be involved in more than one gene ontology category, and 821
thus, the total percentage is more than 100). 822
823
Supplementary Analysis File: This zipped file available for download from GSE13725 824
contains links to all microarray pre-processing and statistical analysis performed on 825
microarray data reported in this paper. Also contained in this file is the list of 826
differentially expressed probe sets with links to relevant external databases including 827
NCBI and Affymetrix.828
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Table 1: Monoclonal antibodies (mAb) used in flow cytometric analysis of cellular 829
subsets. Monoclonal antibodies obtained from cell culture were derived from hybridomas 830
sourced from the International Livestock Research Institute (ILRI) in Kenya. 831
832
Table 2: Genes analysed via quantitative real-time RT-PCR. Gene names, abbreviations 833
and NCBI Accessions are listed with forward and reverse primer sequences (5’ to 3’). 834
835
Table 3: Haematological parameters measured for Holstein-Friesian and Brahman cattle. 836
Results are presented as the breed mean of three time points. Means are presented ± 837
standard deviation from the group mean together with the p-value for test of significant 838
difference between breeds using the one-way ANOVA. Parameter abbreviations and 839
measurement units are as follows: White blood cells ×103/mm
3; Red blood cells × 840
106/mm
3; Haemoglobin g/dl; Haematocrit %; Platelets ×10
3/mm
3; Mean corpuscular 841
volume /µm3; Mean cell haemoglobin concentration g/dl. 842
843
Table 4: Top ranking biological process GO terms for genes differentially expressed 844
between Brahman and Holstein-Friesian cattle. The GO term ID is listed with the term 845
description and the significance value for the term, along with the differentially expressed 846
genes significant for the term, their Affymetrix probe identifier, Entrez Gene ID, gene 847
symbol and gene name. 848
849
Table 5: KEGG pathways represented by differentially expressed genes. KEGG pathways 850
are listed together with P-value for the test of significance and genes significant for the 851
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pathway in Brahman and Holstein-Friesian cattle with the Affymetrix probe identifier, 852
Entrez Gene ID and gene description. 853
854
Supplementary Table 1 (S1): Genes detected as significantly (P<0.01) differentially 855
expressed in at least two out of the three summarization methods (MAS5, RMA and 856
GCRMA) between Holstein-Friesian and Brahman cattle during tick infestation. The 857
significance levels (P-value) and log2 fold change for Brahman samples compared to 858
Holstein-Friesian samples for each method is listed together with the mean for the three 859
methods. Genes are ranked by their Entrez Identifier. 860
861
Supplementary Table 2a (S2a): Eight primer pairs used to validate microarray estimates 862
using qPCR. Differentially expressed probe sets are listed with the target gene name (if 863
available) and primer sequences. 864
865
Supplementary Table 2b (S2b): Breed mean normalised expression values ± standard 866
deviation from the group mean for each gene analysed via qPCR for validation of 867
microarray estimates. Listed are the fold change values as calculated from the qPCR 868
results (qPCR fold change) and the fold change estimate of the microarray summarization 869
method RMA (RMA fold change). 870
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Table 1
Specificity Identity Source Isotype
Isotype control IgG1 Dako IgG1
CD4 IL-A11 Cell culture IgG2a
CD8 IL-A51 Cell culture IgG1
CD14 MM61A VMRD IgG1
CD25 (IL-2Rα) IL-A111 Cell culture IgG1
CD45RO IL-A150 Cell culture IgG3
MHCII IL-A21 Cell culture IgG2a
WC3 CC37 Cell culture IgG1
WC1 IL-A29 Cell culture IgG1
Goat anti-mouse IgG-FITC Calbiochem IgG1
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Table 2
Gene name Abr NCBI Accession Forward primer Reverse primer
Acidic Ribosomal Protein P0 RPLP0 BT021080 5’caaccctgaagtgcttgacat3’ 5’aggcagatggatcagcca3’
Glyceraldehyde 3 Phosphate Dehydrogenase GAPDH NM_001034034 5’cctggagaaacctgccaagt3’ 5’gccaaattcattgtcgtacca3’
Interleukin 1 beta IL-1β NM_174093 5’aaatgaaccgagaagtggtgtt3’ 5’ttccatattcctcttggggtaga3’
Interleukin 2 IL-2 NM_180997 5’gtggaagtcattgctgctgga3’ 5’ggttcaggtttttgcttgga3’
Interleukin 2 receptor alpha IL-2Rα NM_174358 5’tgctaagagcatcccgactt3’ 5’tagcttggaggactgggcta3’
Interleukin 4 IL-4 NM_173921 5’cattgttagcgtctcctggta3’ 5’gctcgtcttggcttcattc3’
Interleukin 6 IL-6 NM_173923 5’ctgggttcaatcaggcgat3’ 5’cagcaggtcagtgtttgtgg3’
Interleukin 8 IL-8 NM_173925 5’ctgtgtgaagctgcagttct3’ 5’atggaaacgaggtctgccta3’
Interleukin 10 IL-10 NM_174088 5’cttgtcggaaatgatccagt3’ 5’tctcttggagctcactgaag3’
Interleukin 12 IL-12 EU276075 5’aacacgccccattgtagaag3’ 5’aagccaggcaactctcattc3’
Interleukin 18 IL-18 NM_174091 5’agcacaggcataaagatggc3’ 5’tggggtgcattatctgaaca3’
Tumour Necrosis Factor alpha TNFα EU276079 5’ctggttcagacactcaggtcct3’ 5’gaggtaaagcccgtcagca3’
Interferon gamma IFNγ FJ263670 5’gtgggcctctcttctcagaa3’ 5’gatcatccaccggaatttga3’
Chemokine (C-X-C motif) ligand 10 CXCL-10 NM_001046551 5'agtggaagcccctgcagtaaa3' 5'agtcccagccttgctactgaca3'
Chemokine C-C motif receptor 1 CCR-1 NM_001077839 5’ctgctggtgatgattgtctg3’ 5’tgctctgctcacacttacgg3’
Chemokine C-C motif receptor 3 CCR-3 AY574996 5’gatgggattgaaactgtggg3’ 5’ggcagcgtgaataggaagag3’
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Table 3
Parameter Holstein-Friesian Brahman P-value
White blood cell count 13.27 ± 1.66 10.84 ± 1.88 0.002
Red blood cell count 5.47 ± 0.51 9.25 ± 0.62 <0.001
Hemoglobin 9.31 ± 0.80 12.99 ± 0.74 <0.001
Hematocrit / Packed Cell
Volume 24.98 ± 2.30 34.77 ± 2.29 <0.001
Platelets 445.00 ± 116.53 581.39 ± 154.58 0.007
Mean corpuscular volume 45.78 ± 2.28 37.67 ± 2.85 <0.001
Mean cell hemoglobin
concentration 37.32 ± 0.47 37.48 ± 1.92 0.723
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Table 4
Top GO terms associated with genes more highly expressed by Brahman WBC
GO Term / Affymetrix
probe identifier
Entrez Gene
Accession Gene Symbol Gene Description P-Value
Positive regulation of T cell proliferation GO:0042102 5.40E-05
Bt.3941.1.S1_at 281861 IL2RA interleukin 2 receptor, alpha
Bt.4624.1.S1_at 282376 STAT5B signal transducer and activator of transcription 5B
Bt.48.1.S1_a_at 281050 CD28 CD28 molecule
Bt.5368.1.S1_at 281054 CD3E CD3e molecule, epsilon (CD3-TCR complex)
Negative thymic T cell selection GO: 0045060 0.00383
Bt.48.1.S1_a_at 281050 CD28 CD28 molecule
Bt.5368.1.S1_at 281054 CD3E CD3e molecule, epsilon (CD3-TCR complex)
Positive regulation of interleukin-2 biosynthetic process GO: 0045086 0.00748
Bt.4624.1.S1_at 282376 STAT5B signal transducer and activator of transcription 5B
Bt.48.1.S1_a_at 281050 CD28 CD28 molecule
Mononuclear cell proliferation 0.03039
Bt.3941.1.S1_at 281861 IL2RA interleukin 2 receptor, alpha
Bt.4624.1.S1_at 282376 STAT5B signal transducer and activator of transcription 5B
Bt.48.1.S1_a_at 281050 CD28 CD28 molecule
Bt.49.1.S1_at 282387 CD40LG CD40 ligand
Bt.5368.1.S1_at 281054 CD3E CD3e molecule, epsilon (CD3-TCR complex)
Bt.8957.1.S1_at 281736 CXCR4 chemokine (C-X-C motif) receptor 4
Top GO terms associated with genes more highly expressed by Holstein-Friesian WBC
GO Term / Affymetrix
probe identifier
Entrez Gene
Accession Gene Symbol Gene Description P-Value
Organelle ATP Synthesis Coupled Electron Transport GO:0042775 0.00092
Bt.21.1.S1_at 338046 NDUFC2 NADH dehydrogenase (ubiquinone) 1,
Bt.23361.1.S1_at 616871 UQCRB ubiquinol-cytochrome c reductase binding protein
Bt.4072.1.S1_at 287014 NDUFV1 NADH dehydrogenase (ubiquinone) flavoprotein 1,
Bt.5040.1.S1_at 281570 UQCR ubiquinol-cytochrome c reductase (6.4kD) subunit
Cobalt ion transport / Cobalamin transport GO:0006824 / GO:0015889 0.00131
Bt.3704.1.S1_at 281518 TCN2 transcobalamin II; macrocytic anemia
Bt.3704.2.S1_a_at 281518 TCN2 transcobalamin II; macrocytic anemia
Malate metabolic process GO:0006108 0.00748
Bt.5345.1.S1_at 535182 MDH1 malate dehydrogenase 1, NAD (soluble)
Bt.7915.1.S1_at 281306 MDH2 malate dehydrogenase 2, NAD (mitochondrial)
Pyridoxine biosynthetic process GO:0008615 0.00748
Bt.20893.1.A1_at 512573 PNPO pyridoxamine 5'-phosphate oxidase
Bt.20893.2.S1_at 512573 PNPO pyridoxamine 5'-phosphate oxidase
Chaperone cofactor-dependent protein folding GO:0051085 0.01217
Bt.4095.1.A1_at 533928 TOR1B torsin family 1, member B (torsin B)
Bt.4095.2.S1_at 533928 TOR1B torsin family 1, member B (torsin B)
Glycolysis 0.01526
Bt.22783.1.S1_at 281141 ENO1 enolase 1, (alpha)
Bt.3809.1.S1_at 281274 LDHA lactate dehydrogenase A
Bt.5345.1.S1_at 535182 MDH1 malate dehydrogenase 1, NAD (soluble)
Bt.7915.1.S1_at 281306 MDH2 malate dehydrogenase 2, NAD (mitochondrial)
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Table 5
Genes with higher expression in Brahman WBC
KEGG Pathway /
Affymetrix Probe Identifier
Entrez
Gene ID Gene Description P-Value
Hematopoietic cell lineage 0.0079
Bt.15905.1.S1_at 404154 Interleukin 4 receptor alpha chain
Bt.27981.1.S1_at 407126 Complement receptor type 2
Bt.3941.1.S1_at 281861 Interleukin 2 receptor alpha
Bt.26922.1.S1_at 510073 Similar to T cell antigen CD7 precursor
Bt.5368.1.S1_at 281054 Antigen CD3e, epsilon polypeptide
Cytokine-cytokine receptor interaction 0.04
Bt.12241.1.S1_at 510668 Chemokine receptor 7
Bt.15905.1.S1_at 404154 Interleukin-4 receptor alpha chain
Bt.49.1.S1_at 282387 Tumor necrosis factor (ligand) superfamily, member 5
Bt.8957.1.S1_at 281736 Chemokine (c-x-c motif) receptor 4
Bt.3941.1.S1_at 281861 Interleukin 2 receptor, alpha
Genes with higher expression in Holstein-Friesian WBC
KEGG Pathway /
Affymetrix Probe Identifier
Entrez
Gene ID Gene Description P-Value
Oxidative phosphorylation 1.10E-09
Bt.13128.1.S1_at 286840 Succinate dehydrogenase complex, subunit b, iron sulfur (ip)
Bt.21.1.S1_at 338046 NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 2, 14.5kda
Bt.4072.1.S1_at 287014 NADH dehydrogenase (ubiquinone) flavoprotein 1, 51kda
Bt.442.1.S1_at 281640 ATP synthase, h+ transporting, mitochondrial f1 complex
Bt.4431.1.S1_a_at 327675 ATP synthase, h+ transporting, mitochondrial f1 complex, beta polypeptide
Bt.4540.1.S1_at 327668 ATP synthase, h+ transporting, mitochondrial f1 complex, gamma polypeptide 1
Bt.4704.1.S1_at 327714 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13kda
Bt.5040.1.S1_at 281570 Ubiquinol-cytochrome c reductase (6.4kd) subunit
Bt.61.1.S1_at 338073 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 3, 12kda
Bt.66.1.S1_at 338064 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3, 9kda
Bt.67.1.S1_at 282289 NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 1 (6kd, kfyi)
Bt.7193.1.S1_at 282199 Cytochrome c oxidase subunit via polypeptide 1
Bt.848.1.S1_at 282290 NADH dehydrogenase flavoprotein 2 (24kd)
Bt.893.1.S1_at 327665 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 6, 17kda
Bt.8950.1.S1_at 338084 Cell death-regulatory protein grim19
Citrate cycle 0.037
Bt.7915.1.S1_at 281306 Malate dehydrogenase 2, mitochondrial
Bt.1311.1.S1_at 511090 Similar to succinyl-coa ligase [adp-forming] beta-chain
Bt.13128.1.S1_at 286840 Succinate dehydrogenase complex, subunit b, iron sulfur
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