Relative expression levels of immune- and non-immune-related mRNAs in chicken intestinal intraepithelial lymphocytes experimentally infected with Eimeria acervulina, E. maxima, or E. tenella were measured using a 10K cDNA microarray. Based on a cutoff of >2.0-fold differential expression compared with uninfected controls, relatively equal numbers of transcripts were altered by the three Eimeria infections at 1, 2, and 3 days post-primary infection. By contrast, E. tenella elicited the greatest number of altered transcripts at 4, 5, and 6 days post-primary infection, and at all time points following secondary infection. When analyzed on the basis of up- or down-regulated transcript levels over the entire 6 day infection periods, approximately equal numbers of up-regulated transcripts were detected following E. tenella primary (1,469) and secondary (1,459) infections, with a greater number of down-regulated mRNAs following secondary (1,063) vs. primary (890) infection. On the contrary, relatively few mRNA were modulated following primary infection with E. acervulina (35 up, 160 down) or E. maxima (65 up, 148 down) compared with secondary infection (E. acervulina, 1,142 up, 1,289 down; E. maxima, 368 up, 1,349 down). With all three coccidia, biological pathway analysis identified the altered transcripts as belonging to the categories of “Disease and Disorder” and “Physiological System Development and Function”. Sixteen intracellular signaling pathways were identified from the differentially expressed transcripts following Eimeria infection, with the greatest significance observed following E. acervulina infection. Taken together, this new information will expand our understanding of host-pathogen interactions in avian coccidiosis and contribute to the development of novel disease control strategies.
Citation: Kim DK, Lillehoj H, Min W, Kim CH, Park MS, Hong YH, et al. (2011) Comparative Microarray Analysis of Intestinal Lymphocytes following Eimeria acervulina, E. maxima, or E. tenella Infection in the Chicken. PLoS ONE 6(11): e27712. https://doi.org/10.1371/journal.pone.0027712
Editor: Zhanjiang Liu, Auburn University, United States of America
Received: August 9, 2011; Accepted: October 23, 2011; Published: November 28, 2011
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This project was supported, in part, by an ARS in-house CRIS project 1265-32000-086, the WCU Program (R33-10013) through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology, Republic of Korea and a grant from the Next-Generation BioGreen 21 Program (No. PJ008084), Rural Development Administration, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Avian coccidiosis is caused by seven species of Eimeria protozoa (E. acervulina, E. maxima, E. tenella, E. mitis, E. necatrix, E. praecox, and E. brunetti) that differ in pathogenicity and immunogenicity , . The life cycles of all Eimeria species are of the monoxenous sporozoan type. Generally, infection develops following ingestion of sporulated oocysts and release of sporozoites, which subsequently invade intestinal epithelial cells. Through asexual reproduction, gametes are formed and fertilized to produce a zygote, which matures into an oocyst, ruptures the host cell, and is excreted in the feces. The entire cycle normally develops over the course of 4–6 days, depending on the species .
Eimeria infection inflicts significant economic losses to the commercial poultry industry due to decreased nutrient absorption, retarded growth rate, reduced egg production, and mortality , . Although prophylactic chemotherapy has been traditionally used for disease control, the emergence of drug-resistant parasites and legislative bans on the use of in-feed antibiotic growth promoters and non-therapeutic antimicrobial feed additives encourages the development of alternative coccidiosis control strategies . Accordingly, there has been great interest in understanding the host-pathogen interactions at the cellular and molecular levels and to identify effector molecules mediating protective immunity to Eimeria.
Functional genomics and bioinformatics technologies have recently emerged as powerful technologies for investigation of host-pathogen interactions during avian coccidiosis , , , , , . New candidate genes which influence host immune responses to Eimeria have been identified using chicken macrophage and lymphocyte cDNA microarrays , , , , , , . Host genetic alterations following Eimeria infection have been investigated employing a variety of new microarray data mining tools , , , , . However, a comparative analysis of gene expression in gut lymphocytes in response to infection by different species of Eimeria has not been reported, which would aid in the understanding of species-related differences in parasite pathogenicity and immunogenicity. Therefore, this study was conducted to compare the global gene transcripts of the three species of coccidia that most commonly infect commercial poultry, E. acervulina, E. maxima, and E. tenella.
Materials and Methods
Experimental animals and Eimeria infection
All experiments were approved by the Beltsville Agriculture Research Center Small Animal Care and Use Committee (Protocol #09-019). One week-old chickens were uninfected (negative control) or were orally inoculated with sporulated oocysts of E. acervulina, E. maxima, or E. tenella (1.0×104oocysts/bird). One week later, the infected chickens were challenged with an identical inoculum of the homologous parasite. Intestinal samples were collected daily from 5 birds in a treatment group at from 1 to 6 days post-infection (DPI) following primary and secondary infections. Cecum, duodenum, and jejunum were collected from the birds challenged with E. acervulina, E. maxima, and E. tenella, respectively.
RNA extraction and aminoallyl-labeled RNA preparation
Intestines were cut longitudinally and washed three times with ice-cold Hanks' balanced salt solution (HBSS) containing 100 U/ml of penicillin and 100 mg/ml of streptomycin (Sigma, St. Louis, MO). The mucosal layer of intestine was carefully scraped using a cell scraper and intraepithelial lymphocytes (IELs) were isolated by Percoll density gradient centrifugation as described (Min et al., 2005). Total RNA was isolated using Trizol (Invitrogen, Carlsbad, CA), and RNAs from animals in the same treatment group were pooled and purified using the RNeasy Mini RNA Purification Kit (Qiagen, Valencia, CA). Aminoallyl-labeled RNA was prepared using the Amino Allyl Message Amp II aRNA Amplification Kit (Ambion, Austin, TX) according to the manufacturer's instruction. Two of 20 µg aliquots of each aminoallyl-RNA sample were fluorescently labeled with AlexaFluor 555 or AlexaFluor 647 (Invitrogen). RNA concentrations and labeling efficiencies were determined spectrophotometrically.
The avian IEL array (AVIELA) consists of 10,162 spots representing duplicates of cDNAs from chicken IELs, immune-related cDNAs from lipopolysaccharide-activated HD11 chicken macrophages, and direct PCR clones of selected chicken cytokine and chemokine genes , , . Uninfected control samples and one of the 3 infection group samples were labeled with different fluorescent dyes and hybridized simultaneously on the same slide using a reference design with a dye swap protocol . Hybridizations were performed overnight at 50°C using HybIt hybridization buffer (TeleChem, Sunnyvale, CA) in ArrayIt reaction cassettes as described . Following hybridization, the slides were rinsed in 0.5×SSC, 0.01% SDS and washed once for 15 min at room temperature in 0.2×SSC, 0.2% SDS at 50°C, 3 times for 1 min at room temperature in 0.2×SSC, and 3 times for 1 min at room temperature in distilled water.
Microarray scanning and data analysis
Images were acquired by laser confocal scanning using a ScanArray Lite microarray analysis system (Perkin-Elmer, Boston, MA) at a resolution of 10 µm. A 16-bit TIFF image was generated for each channel corresponding to the Alexa Fluor 555 and Alexa Fluor 647 dyes. The scanned microarray images for each channel were overlaid and fluorescent intensities were quantified using ScanArray Express version 3.0 software (Perkin-Elmer). Spots were detected using an adaptive circle algorithm in the ScanArray program and all spots were visually confirmed. The MIDAS 2.19 of the TM4 package (http://www.tigr.org) was used to qualify and normalize the array data. The poor-quality channel tolerance policy was stringent and the signal-noise threshold was 2.0. Two-step normalization, total intensity, and global LOWESS (locally weighted regression and smoothing scatter) plot methods were applied followed by standard deviation (SD) regularization between blocks and slides. GeneSpring GX 11.0 (Silicon Genetics, Redwood, CA) was used to perform statistical analyses of the qualified and normalized array data. Flag information was applied to filter bad spots with genes missing more than 50% of their values because of low signal-to-noise ratio. Student's t-test and ANOVA analysis by parametric test with multiple testing correction (Benjamini and Hochberg False Discovery Rate) were applied for data normalization to control values and to compare values between different infection groups. All microarray data in this study adheres to the reporting guidelines provided by MIAME and have been submitted online into the Gene Expression Omnibus (GEO) (Series #GSE31213; Samples #GSM773800-GSM773871).
Gene expression changes observed by microarray analysis were confirmed by quantitative (q)RT-PCR as described . Equivalent amounts of the same RNA samples used for microarray hybridizations from 1–6 days post-primary or post-secondary infections with the different Eimeria species were pooled. The RNAs were reverse transcribed using the StrataScript First Strand Synthesis System (Stratagene, La Jolla, CA). Amplification and detection were carried out using the Mx3000P system and Brilliant SYBR Green qRT-PCR master mix (SABioscience, Frederick, MD). Oligonucleotide primers are listed in Table 1. Standard curves were generated using log10 diluted standard RNA and the levels of individual transcripts were normalized to those of GAPDH by the Q-gene program . The fold changes were calculated in the normalized mRNA levels between the uninfected control group and each infection groups. Each analysis was performed in triplicate and the comparisons of the mean values were performed by one-way analysis of variance (ANOVA) followed by the Duncan's multiple range test using SPSS software (SPSS 15.0 for Windows, Chicago, IL).
IEL cDNA sequences in the AVIELA were mapped to the chicken genome reference assembly (version 2.1) and to reference RNA and protein sequences using National Center for Bioinformatics Institute (NCBI) Blast (version 2.2.13). The networks and pathway information of genes which were differentially expressed were analyzed by Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, http://www.ingenuity.com). The dataset containing gene identifiers mapped to UniGene IDs in the chicken (Gallus gallus) database (Build #43) and corresponding expression values were uploaded into the application. Each identifier was mapped to its corresponding gene object in the Ingenuity knowledge base. Both up- and down-regulated identifiers were defined as value parameters for the analysis. These genes, called focus genes, were overlaid onto a global molecular network developed from information contained within IPA. Functional gene analysis was performed to identify the biological functions and canonical pathways of genes from the mapped datasets. The Fischer's exact test was used to calculate P values to assess the probability that each biological function and pathway assigned to that dataset. Biological functions and pathways of those focus genes were generated based on their connectivity.
Comparison of mRNA expression levels following infection with E. acervulina, E. maxima, or E. tenella
Using a cutoff of >2.0-fold differential expression compared with uninfected controls, relatively equal numbers of transcripts were altered at 1, 2, and 3 days post-primary infection with E. acervulina, E. maxima, or E. tenella (Figure 1). By contrast, E. tenella elicited the greatest number of altered transcripts at 4, 5, and 6 days post-primary infection, and at all time points following secondary infection. The numbers of transcripts that were significantly (P<0.0005) up- or down-regulated over the entire 6 day infection periods when comparing each infection group with uninfected controls are illustrated in Figure 2A. Approximately equal numbers of up-regulated transcripts were detected following E. tenella primary (1,469) and secondary (1,459) infections, with fewer down-regulated mRNAs following primary (890) vs. secondary (1,063) infection. On the other hand, only a small subset of mRNAs were modulated following primary infection with E. acervulina (35 up, 157 down) or E. maxima (65 up, 148 down) compared with secondary infection (E. acervulina, 1,142 up, 1,289 down; E. maxima, 368 up, 1,349 down). Figure 2B showed the number of the differentially expressed genes that were overlapped between the infections of three different Eimeria species. In the primary and secondary infections, the genes commonly changed by three species were three and three hundred sixty one, respectively. When comparing the total number of transcripts altered between 1–6 DPI following primary or secondary infection, the levels of 430 mRNAs were greater in primary vs. secondary infection with E. acervulina, and 389 transcript levels were greater in secondary vs. primary infection (Figure 3). For E. maxima, 472 transcripts were greater following primary and 449 transcripts were greater after secondary infection. Relatively equal numbers were altered following E. tenella infection (42 vs. 41). Next, we compared the numbers of transcripts between early (1–3 DPI) and late (4–6 DPI) primary or secondary infections whose levels were altered compared with uninfected controls (P<0.01). As shown in Figure 4, following primary infection with E. acervulina, E. maxima, or E. tenella, more transcripts were altered between 1–3 DPI compared with 4–6 DPI. By contrast, there were essentially no differences between these two time frames after secondary infection with any of the coccidia parasites. All of the annotated genes that were differentially expressed by three different species of Eimeria in primary or secondary infections and between early and late primary infections are shown in Table S1 and Table S2, respectively.
(A) The number of transcripts differentially expressed and (B) the number of the overlapping transcripts differentially expressed following 1st and 2nd infections by EA, EM, or ET.
Quantitative RT-PCR confirmation
The expression patterns observed by microarray hybridization were validated by qRT-PCR with 6 transcripts whose levels were significantly modulated during primary or secondary infection compared with uninfected controls. These genes were adenosine deaminase (ADA), beclin 1, autophagy related (BECN1), chemokine (C-C motif) ligand 20 (CCL20), CD8α (CD8A), Toll-like receptor 4 (TLR4), and interleukin-6 (IL-6). The levels of all 6 transcripts that were up-or down-regulated by microarray analysis also were consistently up- or down-regulated when analyzed by qRT-PCR (Table 2). As previously discussed, the differences in the magnitude of the changes observed might be due to differences in the normalization methods used and/or the different fluorescent dyes used by the two protocols .
Biological pathway analysis of differentially regulated transcripts
Biological function analysis using the IPA database was performed for the mRNAs that were differently altered (P<0.0005) following Eimeria primary or secondary infections, compared with uninfected controls. In this manner, the transcripts were classified under the categories of “Disease and Disorder” or “Physiological Development and Function”. In the “Disease and Disorder” category, 10 unique biological functions were identified, all of which were recognized following primary infection with one or more of the coccidia parasites (Table 3). A subset of five of these (“Cancer”, “Genetic Disorder”, “Gastrointestinal Disease”, “Infectious Disease”, “Infection Mechanism”) were also identified following secondary infection with all of the denoted Eimeria species. In the category of “Physiological Development and Function”, 10 unique biological functions were identified, but only those related to “Cell-mediated Immune Response” and “Hematological System Development and Function” were universally identified following primary E. acervulina, E. maxima, or E. tenella infection (Table 4).
Network analysis of differentially regulated transcripts
Sixteen signal transduction pathways were identified from the differentially expressed transcripts following infection with all three Eimeria species, compared with uninfected controls (Figure 5). With the exception of the HMGB1 (high-mobility group protein B1) pathway, the greatest statistical significance was observed between these mRNAs and E. acervulina infection for all pathways. Seven of the pathways identified (“CD28 Signaling in T Helper Cells”, “fMLP Signaling in Neutrophils”, “HMGB1 Signaling”, “IL-3 Signaling”, “IL-4 Signaling”, “Production of Nitric Oxide and Reactive Oxygen Species in Macrophages”, “Role of NFAT in Regulation of the Immune Response”) are included in the category of “Cellular Immune Response”, and four pathways are located in the category of “Humoral Immune Response” (“B Cell Receptor Signaling”, “HMGB1 Signaling”, “IL-4 Signaling”, “Role of NFAT in Regulation of the Immune Response”).
The significantly modified signaling pathways (P<0.05) for the transcripts differentially expressed following primary and secondary infections by E. acervulina (EA), E. maxima (EM), or E. tenella (ET). A1; EA, A2; EM, and A3; ET.
Because the comparison of modulated transcripts revealed that the vast majority of alterations between 1–3 DPI and 4–6 DPI were seen following primary, but not secondary, infection (Figure 4), further network analysis was performed using these two time frames. Shown in Table 5 are the associated network functions, and the number of their corresponding focus genes, that were identified by comparison of the altered transcripts at 1–3 DPI with those at 4–6 DPI following primary infection with the three coccidia parasites. Focus genes are those identified and mapped to corresponding gene objects in the IPA database, and whose expression is significantly differentially regulated in a given network. The network functions with the greatest number of focus genes that were identified were “Genetic Disorder, Metabolic Disease, Amino Acid Metabolism” and “Lipid Metabolism, Small Molecule Biochemistry, Amino Acid Metabolism” for E. acervulina infection, “Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry” for E. maxima infection, and “Carbohydrate Metabolism, Lipid Metabolism, Small Molecule Biochemistry” for E. tenella infection.
The monoxenous life cycle of Eimeria is complex and involves intracellular, extracellular, asexual, and sexual stages for invasion and reproduction. It is therefore not surprising that avian immune responses against the parasite are correspondingly complicated, involving aspects of innate vs. adaptive, humoral vs. cellular, and passive vs. active immunity , . Because the Eimeria life cycle is normally completed in 4–6 days, depending on the particular coccidia species, this study was designed to examine variations in gene expression during the first 6 days following primary or secondary infection. Our findings are summarized as follows: (a) whereas infection by all three Eimeria species altered the levels of relatively equal numbers of transcripts at 1–3 DPI following primary infection, E. tenella elicited the greatest number of altered transcripts at 4–6 DPI post-primary infection, and at all time points following secondary infection, (b) while equivalent numbers of up-regulated transcripts were detected following E. tenella primary and secondary infections, relatively fewer mRNA were modulated following primary vs. secondary infection with E. acervulina or E. maxima, (c) irrespective of the coccidia used for infection, biological pathway analysis identified the altered transcripts as belonging to the categories of “Disease and Disorder” and “Physiological System Development and Function”, and (d) 16 intracellular signal transduction pathways were identified from the differentially expressed transcripts following Eimeria infection, several of which are directly relevant to protective immunity, including those for IL-3, IL-4, CD28, nitric oxide, nuclear factor of activated T cells (NFAT), and the B cell receptor.
These results confirm and extend our prior study which identified lymphocyte transcripts that were altered following E. acervulina infection of naïve chickens . In addition to modulation of immune-related transcripts, these two reports also suggest that the expression of genes related to cellular metabolism, especially lipid metabolism, are altered during coccidia infection. Correspondingly, following primary Eimeria infection, it is the asexual replicative phase that is responsible for the majority of intestinal tissue damage, with negative consequences for nutrient absorption . Therefore, it is not unexpected to detect changes in the expression of genes related to host metabolic function during the primary infection. In particular, several mRNAs encoding proteins with known effects on lipid metabolism were altered by Eimeria infection. These include perilipin 2 (PLIN2), which was increased subsequent to primary infection by E. acervulina and E. maxima, as well as prostaglandin reductase 1 (PTGR1), CD36, and carnitine O-octanoyltransferase (CROT), which were decreased following E. acervulina primary infection. Moreover, E. maxima infection suppressed the expression of the lipid-related mRNAs for scavenger receptor class B, member 1 (SCARB1), hydroxysteroid (11-β) dehydrogenase 1 (HSD11B1), hydroxysteroid (17-β) dehydrogenase 4 (HSD17B4), solute carrier organic anion transporter family, member 2A1 (SLCO2A1), while E. tenella infection decreased that for glycerol-3-phosphate acyltransferase, mitochondrial (GPAM).
Following both primary and secondary infections, E. tenella modulated the levels of the greatest number of transcripts compared with uninfected controls (primary, 2,359; secondary, 2,522), as opposed to E. acervulina (primary, 195; secondary, 2,431) and E. maxima (primary, 213; secondary, 1,717). E. tenella is known to cause cecal or “bloody” coccidiosis, and primarily invades the intestinal ceca . Severe intestinal bleeding, eroding of the mucosal surface, and thickening of the cecal wall are all clinical signs of E. tenella infection. By 6–8 DPI, rupture of the cecal wall may occur, with associated high mortality . Relevant to this topic, our biological function analysis identified E. tenella-elicited transcripts in the category “Hematological System Development and Function” and “Hematopoiesis” of “Physiological System Development and Function”, with 126 and 80 focus genes, respectively. Among these genes related to hematological functions were adenosine deaminase (ADA), BCL2-related protein A1 (BCL2A1), caspase 1, apoptosis-related cysteine peptidase (CASP1), chemokine (C-C motif) receptor 9 (CCR9), chemokine (C-X-C motif) receptor 4 (CXCR4), CD5, CD44, CD69, cyclin D1 (CCND1), IL-6, and IL-10 receptor α (IL10RA).
Whereas E. tenella altered the expression of the greatest number of transcripts, compared with uninfected chickens, comparisons between primary and secondary infections revealed the fewest number of modified transcripts for this species. In other words, primary infection with E. tenella induced the greatest transcriptional response in intestinal lymphocytes that was maintained during secondary infection. These results imply that primary infection of E. tenella may be more likely to induce protective immunity, compared with E. acervulina or E. maxima. Although the particular immune effector cell(s) involved in protective immunity against individual Eimeria species remain to be determined, previous studies showed that depletion of CD4+ lymphocytes enhanced primary infection by E. tenella, but did not influence the course of E. acervulina infection, suggesting that this subpopulation is important in controlling primary infection by the former but not the latter . On the contrary, no differences between the two coccidia were noted following depletion of CD8+ cells. Ongoing studies in our laboratory are designed to characterize the transcriptional profiles of CD4+, CD8+ and other intestinal lymphocyte subpopulations following primary and secondary infection with the different coccidia species.
In summary, this report describes the transcriptional responses of chicken intestinal lymphocytes following in vivo experimental infection with E. acervulina, E. maxima, or E. tenella using the AVIELA microarray. Biological function and pathway analysis identified the altered transcripts being relevant to lipid metabolism, as well as cellular and humoral immunity. These new developments further enhance our understanding of the host response to Eimeria infection that may someday contribute to the development of the alternative control strategies against avian coccidiosis whose treatment has traditionally relied upon prophylactic medication and antibiotics.
The annotated transcripts (P<0.0005) that were differentially expressed over the entire 6 day infection periods when comparing each infection group with uninfected controls.
Conceived and designed the experiments: DKK CHK HSL. Performed the experiments: DKK CHK MSP. Analyzed the data: DKK. Contributed reagents/materials/analysis tools: DKK HSL WM CHK MSP YHH. Wrote the paper: DKK EPL.
- 1. Williams RB (1998) Epidemiological aspects of the use of live anticoccidial vaccines for chickens. Int J Parasitol 28: 1089–1098.
- 2. Shirley MW, Smith AL, Blake DP (2007) Challenges in the successful control of the avian coccidia. Vaccine 25: 5540–5547.
- 3. Morris GM, Gasser RB (2006) Biotechnological advances in the diagnosis of avian coccidiosis and the analysis of genetic variation in Eimeria. Biotechnol Adv 24: 590–603.
- 4. Lillehoj HS, Li G (2004) Nitric oxide production by macrophages stimulated with Coccidia sporozoites, lipopolysaccharide, or interferon-gamma, and its dynamic changes in SC and TK strains of chickens infected with Eimeria tenella. Avian Dis 48: 244–253.
- 5. Lillehoj HS, Kim CH, Keeler CL Jr, Zhang S (2007) Immunogenomic approaches to study host immunity to enteric pathogens. Poult Sci 86: 1491–1500.
- 6. Kim CH, Lillehoj HS, Hong YH, Keeler CL Jr, Lillehoj EP (2010) Comparison of global transcriptional responses to primary and secondary Eimeria acervulina infections in chickens. Dev Comp Immunol 34: 344–351.
- 7. Kim DK, Kim CH, Lamont SJ, Keeler CL Jr, Lillehoj HS (2009) Gene expression profiles of two B-complex disparate, genetically inbred Fayoumi chicken lines that differ in susceptibility to Eimeria maxima. Poult Sci 88: 1565–1579.
- 8. Min W, Lillehoj HS, Kim S, Zhu JJ, Beard H, et al. (2003) Profiling local gene expression changes associated with Eimeria maxima and Eimeria acervulina using cDNA microarray. Appl Microbiol Biotechnol 62: 392–399.
- 9. Hong YH, Lillehoj HS, Lee SH, Dalloul RA, Lillehoj EP (2006) Analysis of chicken cytokine and chemokine gene expression following Eimeria acervulina and Eimeria tenella infections. Vet Immunol Immunopathol 114: 209–223.
- 10. Hong YH, Lillehoj HS, Lillehoj EP, Lee SH (2006) Changes in immune-related gene expression and intestinal lymphocyte subpopulations following Eimeria maxima infection of chickens. Vet Immunol Immunopathol 114: 259–272.
- 11. Kim CH, Lillehoj HS, Bliss TW, Keeler CL Jr, Hong YH, et al. (2008) Construction and application of an avian intestinal intraepithelial lymphocyte cDNA microarray (AVIELA) for gene expression profiling during Eimeria maxima infection. Vet Immunol Immunopathol 124: 341–354.
- 12. Min W, Lillehoj HS, Ashwell CM, van Tassell CP, Dalloul RA, et al. (2005) Expressed sequence tag analysis of Eimeria-stimulated intestinal intraepithelial lymphocytes in chickens. Mol Biotechnol 30: 143–150.
- 13. Hong YH, Lillehoj HS, Lee SH, Park DW, Lillehoj EP (2006) Molecular cloning and characterization of chicken lipopolysaccharide-induced TNF-alpha factor (LITAF). Dev Comp Immunol 30: 919–929.
- 14. Hong YH, Lillehoj HS, Dalloul RA, Min W, Miska KB, et al. (2006) Molecular cloning and characterization of chicken NK-lysin. Vet Immunol Immunopathol 110: 339–347.
- 15. Jimenez-Marin A, Collado-Romero M, Ramirez-Boo M, Arce C, Garrido JJ (2009) Biological pathway analysis by ArrayUnlock and Ingenuity Pathway Analysis. BMC Proc 3: Suppl 4S6.
- 16. Hedegaard J, Arce C, Bicciato S, Bonnet A, Buitenhuis B, et al. (2009) Methods for interpreting lists of affected genes obtained in a DNA microarray experiment. BMC Proc 3: Suppl 4S5.
- 17. Neerincx PB, Casel P, Prickett D, Nie H, Watson M, et al. (2009) Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis. BMC Proc 3: Suppl 4S1.
- 18. Prickett D, Watson M (2009) IMAD: flexible annotation of microarray sequences. BMC Proc 3: Suppl 4S2.
- 19. Nie H, Neerincx PB, Poel J, Ferrari F, Bicciato S, et al. (2009) Microarray data mining using Bioconductor packages. BMC Proc 3: Suppl 4S9.
- 20. McShane LM, Shih JH, Michalowska AM (2003) Statistical issues in the design and analysis of gene expression microarray studies of animal models. J Mammary Gland Biol Neoplasia 8: 359–374.
- 21. Muller PY, Janovjak H, Miserez AR, Dobbie Z (2002) Processing of gene expression data generated by quantitative real-time RT-PCR. Biotechniques 32: 1372–1374, 1376, 1378–1379.
- 22. Lee PD, Sladek R, Greenwood CM, Hudson TJ (2002) Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res 12: 292–297.
- 23. McDougald LR, Hu J (2001) Blackhead disease (Histomonas meleagridis) aggravated in broiler chickens by concurrent infection with cecal coccidiosis (Eimeria tenella). Avian Dis 45: 307–312.
- 24. Motha MX, Egerton JR (1984) Influence of reticuloendotheliosis on the severity of Eimeria tenella infection in broiler chickens. Vet Microbiol 9: 121–129.
- 25. Conway DP, McKenzie ME (2007) Poultry coccidiosis: diagnostic and testing procedures. Ames, Iowa 50014, USA: Blacwell Publishing Professional. pp. 32–33.
- 26. Trout JM, Lillehoj HS (1996) T lymphocyte roles during Eimeria acervulina and Eimeria tenella infections. Vet Immunol Immunopathol 53: 163–172.