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Published as doi: 10.1096/fj.07-8271com.
 (The FASEB Journal. 2007;21:3262-3271.)
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Long oligonucleotide microarrays for African green monkey gene expression profile analysis

Béatrice Jacquelin*,{dagger}, Véronique Mayau*, Guillaume Brysbaert, Béatrice Regnault{dagger}, Ousmane M. Diop§, Fernando Arenzana-Seisdedos{ddagger}, Lars Rogge||, Jean-Yves Coppée{dagger}, Françoise Barré-Sinoussi*, Arndt Benecke and Michaela C. Müller-Trutwin*,1

* Unité de Régulation des Infections Rétrovirales,

{dagger} Plateforme Puces à ADN-Genopole,

{ddagger} Laboratoire de Pathologie Virale Moléculaire,

|| Groupe à 5 ans Immunorégulation, Institut Pasteur, Paris, France;

§ Laboratoire de Rétrovirologie, Institut Pasteur, Dakar, Senegal; and

Institut de Recherche Interdisciplinaire-CNRS and Institut des Hautes Études Scientifiques, Bures sur Yvette, France

1Correspondence: Unité de Régulations des Infections Rétrovirales, Institut Pasteur, 25, rue du Docteur Roux, 75724 Paris cedex 15, France. E-mail: mmuller{at}pasteur.fr


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Nonhuman primates, including African green monkey (AGM), are important models for biomedical research. The information on monkey genomes is still limited and no versatile gene expression screening tool is available. We tested human whole genome microarrays for cross-species reactivity with AGM transcripts using both long oligonucleotide arrays (60-mer probes) and short oligonucleotide arrays (25-mer). Using the long oligonucleotide arrays, we detected 4-fold more AGM transcripts than with the short oligonucleotide technology. The number of detected transcripts was comparable to that detected using human RNA, with 87% of the detected genes being shared between both species. The specificity of the signals obtained with the long oligonucleotide arrays was determined by analyzing the transcriptome of concanavalin A-activated CD4+ T cells vs. nonactivated T cells of two monkey species AGM and macaque. For both species, the genes showing the most significant changes in expression, such as IL-2R, were those known to be regulated in human CD4+ T cell activation. Finally, tissue specificity of the signals was established by comparing the transcription profiles of AGM brain and tonsil cells. In conclusion, the ABI human microarray platform provides a highly valuable tool for the assessment of AGM gene expression profiles. —Jacquelin, B., Mayau, V., Brysbaert, G., Regnault, B., Diop, O. M., Arenzana-Seisdedos, F., Rogge, L., Coppée, J-Y., Barré-Sinoussi, F., Benecke, A., Müller-Trutwin, M. C. Long oligonucleotide microarrays for African green monkey gene expression profile analysis.


Key Words: nonhuman primate • macaque • CD4+ T cell • brain • tonsil


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
BECAUSE OF THEIR CLOSE RELATIONSHIP TO HUMANS, nonhuman primates (NHP) are used in many areas of biomedical research (1) . Rhesus macaques, cynomolgus macaques, and African green monkeys (AGM) are among the most widely used NHP models. The advent of microarrays made possible the use of large-scale gene expression screening to define related cellular pathways and networks. Three kinds of microarray platforms are currently available based on cDNA or on short or long oligonucleotides (2) . Most of the commercial microarrays are now covering the whole human genome, except for the cDNA approach. Several studies have shown that commercial platforms performed better than in-house platforms and that the performance of one-dye platforms was consistently among the best (3 4 5 6 7) . Nevertheless, gene expression profiling of nonhuman primate is hampered by the fact that most simian genomes have not been sequenced, and only a few nonhuman primate-specific arrays are available. Affymetrix (Santa Clara, CA, USA) (8 , 9) and Agilent (Santa Clara, CA, USA) (10) have recently commercialized rhesus macaque-specific oligonucleotide arrays, and a marmoset-specific cDNA array is available through the European Consortium. However, these arrays do not yet cover the entire genome.

Several groups have reported successful use of the human Affymetrix GeneChip to study simian gene expression (11 12 13 14 15) . On these chips, 11 to 20 different 25-mer probe pairs are designed for each transcript or gene sequence as perfect match and mismatch pairs. Probably due to the short length of the probe, however, between 15 and 40% of macaque RNA is not detected (12 , 15 16 17) . Similarly, one of these studies used the U133 Plus 2.0 Affymetrix GeneChip with human and AGM whole blood samples and detected with Boolean analysis only 2303 and 2643 probe sets respectively over the 54,000 probe sets on the chip (15) . Another kind of human microarray based on 60-mer oligonucleotides was recently developed by Applied Biosystems (ABI, Foster City, CA, USA). With these arrays one might expect a better cross-hybridization between species due to the utilization of longer oligonucleotides. In fact, a comparative study has already shown this platform to outperform the Affymetrix one with human and cynomolgus macaque RNA (17 , 18) in terms of the number of genes detected. The performance of this platform for RNA from other nonhuman primate species is unknown. Our aim was to develop a tool for gene expression profiling of AGM. AGM are more distant from human than macaques. They are used for research in immunology (19) (vaccine, allograft), infectious diseases (20 21 22) (i.e., AIDS; refs. 23 24 25 26 27 ), neuroscience (28 , 29) , such as Parkinson’s disease (30 31 32) , cardiovascular disease (atherosclerosis (33) , heart disease (34) , cell biology (AGM kidney cells; refs. 35 , 36 ), and pharmacology (37) . We evaluated the sensitivity and specificity of the two human whole genome platforms (ABI and Affymetrix) for AGM RNA from CD4+ T lymphocytes relative to human and rhesus samples. We chose as a model system CD4+ T cell lymphocytes, which are well known and characterized. Moreover, these cells are present in blood and many tissues, and are important for orchestrating the adaptive immune response. In addition, they play a key role in AIDS. We also chose to analyze the cross-specificity of the human long oligonucleotide platform for AGM brain and tonsils, since these correspond to frequently analyzed tissues in the literature.

This report provides the first data on the performance of the ABI human microarray platform for analysis of gene expression profiles in AGM.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Five Chinese rhesus macaques (Macaca mulatta) and five AGM (Chlorocebus sabaeus from Senegal) were used in this study. The Central Committee for Animals at Institut Pasteur, Paris, France, and the Committee for Ethics and Animal Experimentation at the International School of Science and Veterinary Medicine in Dakar, Senegal, reviewed and approved the use and care of animals.

Cell and tissue processing
Whole blood was collected from monkeys under anesthesia in heparinized tubes. Simian peripheral blood mononuclear cells (PBMC) were isolated by Ficoll-Hypaque (Pharmacia Biotech, Piscataway, NJ, USA) density gradient centrifugation and activated or not with concanavalin A (ConA) from Canavalia ensiformis (Sigma-Aldrich, St. Louis, MO, USA). For the activation, 20 x 106 cells were plated in RPMI 1640 with 5% fetal calf serum (FCS). ConA was added at a final concentration of 10 µg/ml. The cells were harvested after 48 h. The CD4+ cell subset was further isolated using the MACS magnetic labeling system (Miltenyi Biotec, Cologne, Germany) according to instructions from the manufacturer. Briefly, cells were incubated with monoclonal anti-human CD4 antibody-conjugated microbeads (isotype: mouse IgG1; clone: M-T466) in buffer containing PBS supplemented with 0.5% FCS and 2 mM EDTA, and separated on an LS column placed on a magnetic separator. The isolated cells had routinely a purity > 95% as determined by fluorescence-activated cell sorting (FACS) staining. The CD4+-enriched fraction consists of > 95% of CD4+ T lymphocytes (CD3+ CD4+ cells). The remainder of the isolated subset contained between 0.5 and 5% of CD8+ cells, which is not surprising knowing that CD4+ T cells from AGMs can coexpress CD8 (23 , 26 , 38) , other CD4+ cells such as 0.5 to 3% of monocytes (CD14+ cells), and between 0.1 and 1% of B lymphocytes (CD20+ cells).

Brain and tonsil tissues were collected from one sacrificed AGM of a former experiment (39) and kept in OCT buffer at –80°C until use.

RNA extraction
Total RNA was extracted from 3 to 10 x 106 cells using the RNeasy® Mini extraction Kit (Qiagen, Courtaboeuf, France) following the manufacturer’s instructions. Cells were lysed in 350 µl of RLT buffer, run over a QiaShredder column (Qiagen) to ensure homogeneous lysis, and resuspended in 30 µl of sterile water. The tissues were mechanically homogenized (X620 CAT; M. Zipperer GmbH, Staufen, Germany) in RLT buffer before extraction (RNeasy® Mini Kit, Qiagen). We added a DNase-RNase free (Qiagen) treatment on the column to eliminate any potential DNA contamination of RNA preparations. To assess the quality and concentration of the total RNA obtained, we analyzed 1 µl (25 to 300 ng) on a RNA Nano LabChip (Agilent Technologies) following the manufacturer’s instructions. RNA extracted from the same species, the same tissue or cell compartment, and the same activation type was pooled in identical quantities for each animal.

Microarray procedures
RNA was analyzed on the ABI Human Whole Genome Arrays v1.0 and Affymetrix U133 Plus 2.0 Arrays. The ABI Human Genome Survey Array contains 31,700 60-mer oligonucleotide probes representing a set of 27,868 individual human genes and > 1000 control probes. It uses the highly sensitive chemiluminescence detection technology that allows detection of as little as a femtomole of expressed mRNA (40 , 41) . The Affymetrix GeneChip contains > 54,000 probes, which also cover nearly all human genes. Fluorescence is used as detection signal (42) .

To minimize the bias encountered with amplified RNA, a single round of linear amplification was performed from total RNA according to Affymetrix or ABI protocol. We used the RNA quantities recommended by the manufacturers: 1 to 15 µg for Affymetrix and 0.2 to 2 µg for ABI. For both, we chose 4-fold more than the minimal quantities recommended (i.e., 4 µg and 0.8 µg for Affymetrix and ABI, respectively). In a second part of the study, we tested on the ABI platform distinct quantities ranging from 0.5 to 2 µg of RNA. cDNA synthesis, in vitro transcription, fragmentation, hybridization, staining, and scanning were performed as directed by the suppliers.

Data analysis
For ABI arrays, a robust signal is defined as a signal-to-noise ratio (S/N) superior to 3. The S/N is a metric that captures the confidence of the measurement "detectability" above all known sources of noise. This metric is used to bin genes or probes as "present" or "absent" at the desired level of confidence. Since the S/N expresses the number of SD, the associated confidence can be looked up from a probability table for a normal distribution. For example, signals with S/N ≥ 3 have > 99.9% confidence that the measurement is real, whereas an S/N of 2 would have a higher false positive rate with a confidence of 97.7%. Furthermore, the overall flag set by the analysis software was inferior to 212 (4096) as recommended by Applied Biosystems. Indeed, the 24 different flags the software can set are organized in an ascending manner with respect to their potential impact on interpretation and are expressed as binaries. Therefore, flags between 21 and 211 all indicate minor problems with the signal estimate, flags > 212 refer to quality issues that are considered failures. To compute a single number for all flags set per probe, the factorization theorem is used. The overall flag value is computed as {Sigma} 2x. As 212 = (20 + 21 + 22... +211) + 1, an overall flag of < 212 indicates that, at most, all 11 minor but none of the more serious flags have been set. For Affymetrix arrays, the detection threshold was set as the "present" call output from Affymetrix MAS software (P<0.05), again according to the manufacturer’s guidelines. Calculation of subtraction profiles was performed according to standard procedures with the following modifications: data from different biological conditions were compared in an "everyone-against-everyone" scheme and log2 quotients ("logQ", "L") were then determined as averages of weighted individual logQ values. The weights were antiproportional to the variance over the individual logQ values. For these interassay comparisons, the NeONORM method was used for normalization (43) . P values were determined based on a normal distribution hypothesis of signal intensities using standard methods. In the case of ABI data, multiple probes for a single-gene cross-reactivity of a single probe to several genes, as well as the resolution of probe-ID annotations, were done according to defined standards (44) . Heat-maps were created according to standard methods. To underline the single-color chemiluminescence detection character of the ABI system, we used a black (signal=0) to bright green (signal>10) gradient to represent signal intensities. Differential gene expression analysis with the ABI platform was based on two criteria: a > 99% probability (P<0.01) to be differentially expressed and an expression level change of 2-fold or greater (|logQ|>1.00). Gene Ontology (GO) annotations were analyzed using the Panther Protein Classification System (45) (http://www.pantherdb.org) to identify functional annotations that were significantly enriched in the different gene sets compared with the whole set of genes present on the ABI microarray.

The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession no. GSE6982.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Number of AGM genes detected using short and long oligonucleotide human microarrays
Using AGM transcripts, we first established which human long vs. short oligonucleotide arrays detect more genes. We prepared a pool of total RNA from enriched blood CD4+ T lymphocytes isolated from 5 AGM. The cells were previously activated with ConA to increase the number and diversity of expressed genes. Two technical replicates were analyzed on each array platform. For the two methods, the average Pearson correlation R2 value between replicas was determined to be 0.99 for ABI and 0.97 for Affymetrix (data not shown). These values show that both platforms are highly reproducible with the AGM RNA, also reported for human RNA (18 , 46) . For the ABI microarray, 47% of the probes or 13558 genes were detected; for the Affymetrix GeneChip, 9.5% of the probe sets representing 4196 genes were detected. We cannot exclude the possibility that, by using higher RNA quantities, we would have detected more genes on the Affymetrix platform. With respect to the RNA concentration tested, the ABI platform showed almost 4-fold as many positive signals as the Affymetrix platform when using one-fifth of the RNA as starting material. Furthermore, the total number of AGM genes detected with the ABI system closely approaches the total number of genes detected with human RNA. Indeed, using human RNA, typically ~45–55% of the spotted probes return robust signals for both platforms (18 , 47) . The major difference between the two technologies regarding analysis of AGM transcripts thus was the percentage of probes returning robust signals and, consequently, the number of genes detected.

Interspecies comparison of signals detected with the ABI long oligonucleotide human microarray
Due to the 4-fold higher number of probes detected with the ABI long oligonucleotide platform, we focused further assessment of AGM gene expression on this platform. To determine optimal quantities of AGM RNA to use with this platform, we tested four distinct RNA input quantities (0.5, 0.8, 1.2, and 2 µg of RNA). We used a pool of total RNA from unstimulated CD4+ T cells isolated from 4 AGM. With 500 ng, the signals were less intense, but no major difference was observed between the four distinct RNA inputs regarding the number of detected genes (Fig. 1 A).


Figure 1
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  		Figure 1. Heat-maps of significantly regulated genes in AGM and rhesus macaque stimulated and unstimulated CD4+ T cells. For ABI arrays, the detection threshold was set as S/N > 3 and quality flag < 212, as recommended by the manufacturer. For Affymetrix arrays, the detection threshold was set as the "present" call output from Affymetrix MAS software (P<0.05), according to the manufacturer’s guidelines. A) The probes found to be differentially expressed (|logQ|>1.00, P<0.01) when comparing pairwise AGM stimulated and unstimulated CD4+ T cells are shown in descending order with respect to the log2 difference in mean signal (logQ). A legend with the color scheme can be found in panel C. AGM_stim_1 and _2 stand for two microarray technical replicates that were hybridized with RNA amplified from 0.8 µg of AGM total RNA extracted from ConA-activated CD4+ T cells as starting material. The microarrays AGM_unstim_1, _2, _3, and _4 were hybridized with RNA amplified from respectively 0.5, 0.8, 1.2, and 2 µg of AGM total RNA extracted from unstimulated CD4+ T cells. Supplemental data file 1 "AGM.txt" contains a tab-delimited list of all probes and associated measures for this experiment. B) The probes found to be differentially expressed (|logQ|>1.00, P<0.01) when comparing pairwise MAC stimulated and unstimulated CD4+ T cells are shown in descending order with respect to the log2 difference in mean signal (logQ). MAC_stim_1 and _2 stand for two microarray technical replicates that were hybridized with RNA amplified from 1.2 µg of macaque total RNA extracted from ConA-activated CD4+ T cells. MAC_unstim_1 was hybridized with RNA amplified from 1.2 µg of macaque total RNA extracted from unstimulated CD4+ T cells. The supplemental data file 2 "MAC.txt" contains a tab-delimited list of all probes and associated measures for this experiment. C) A color legend for the heat-maps shown in panels A and B, as well as Fig. 3 . LogQ is the log2 difference in mean signal, S the signal value, and P the probability.

To assess the specificity of the signals obtained for AGM RNA with the human ABI microarray, we first compared the genes detected with AGM RNA isolated from unstimulated CD4+ T cells with those from another simian species, rhesus macaque, and with that of human RNA. Figure 2 shows a Venn diagram of the number of genes common to the three species. Of the 16,289 probes returning a robust signal with AGM RNA, 76% were common to humans and 91% to macaque. As many as 11,919 probes (or 83% with respect to the 14,301 probes returning a robust signal with human RNA) were common to the three species, suggesting that the signals obtained with AGM RNA are specific.


Figure 2
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  		Figure 2. Venn diagram of the number of detected genes in the three species assessed with the ABI technology. Human, AGM, and macaque total RNA were extracted from unstimulated CD4+ T cells isolated from four healthy donors/animals for each species. The RNA samples extracted from the same species were pooled in identical quantities for each donor/animal before using ABI technology and the Human Whole Genome Array v1.0. Probes with a signal-to-noise ratio of >3 were counted for each species, then compared with each other. The number of genes detected in rhesus macaque, African green monkey, and human unstimulated CD4+ T cells is depicted.

To further analyze the specificity of the signals, we then determined the biological pathways represented by these 11,919 common probes using the PANTHERTM database (45) . The 10 most significantly over-represented pathways are listed in Table 1 . Several pathways were related to the immune system (T cell activation, B cell activation) among more general pathways (ubiquitin proteasome pathway, ras pathway). The presence of gene transcripts (listed in Table 2 ) belonging to the pathways of the immune system further indicates the cell specificity of the human ABI platform for AGM RNA.


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  		Table 1. The 10 most significantly over-represented pathways in the list of 11,919 probes common to human, macaque, and AGM CD4+ T cellsa


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  		Table 2. Gene transcripts common to the three species and belonging to pathways found in the immune system

Specificity of the gene expression profiles in activated and nonactivated CD4+ T cells from AGM and macaques with the long oligonucleotide platform
To more deeply analyze whether the signals are specific, we investigated the differential gene expression in nonactivated and ConA-activated CD4+ T cells from macaque and AGM (Fig. 1) . Significantly changed genes were identified using stringent parameters: 2-fold change and P < 0.01. The heat-map of those genes for the two species is given in Fig. 1 . The figure shows that the gene signals are similar for the same type of stimulation in an intra- and interspecies comparison. This analysis also revealed 955 genes commonly up-regulated in activated CD4+ T cells of the two species compared with unactivated cells and 727 down-regulated genes. Among the genes the most significantly up-regulated, we found several genes known to be induced in human and mouse CD4+ T cells upon activation, such as CTLA-4, LLT1, CXCL13, granzyme A, interleukin 1 receptor, CXCR6, and CD2 antigen (48) . The results are summarized in Table 3 ; each value is the log2 of the fold change between activated and nonactivated cells. We also looked at genes involved in TH1 and TH2 profiles. We found a predominant TH1 gene expression profile, with up-regulation of genes coding for IFN-{gamma}, T-Bet, CD25 and the down-regulation of GATA-3 gene expression (Table 4 ), as expected, since according to the literature the in vitro stimulation of human and mice CD4+ T cells with a potent mitogen like ConA induces a preferential secretion of TH1 cytokines (49 50 51) . We also looked at IL-2 gene expression, as it is known to be generally increased in activated CD4+ T cells (52) . Unfortunately, its transcript signal was too noisy to allow a significant estimate. We observed slight differences between the different species regarding the gene expression profile in ConA-activated CD4+ cells. Several explanations are possible. One issue is the effect of interspecies sequence divergence on probe affinity. Another nonexclusive possible source of variation could be differences in CD4+ T cell reactivity to ConA between species.


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  		Table 3. Genes differentially expressed for both species and known in the literature to be involved in CD4+ T cell activationa


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  		Table 4. Expression pattern of genes involved in TH1 and TH2 profiles expressed in AGM and Macaque CD4+ T cellsa

Altogether, these data nevertheless demonstrate the specificity of the hybridization signals with AGM and macaque RNA.

Detection of AGM tissue-specific transcripts using the long oligonucleotide platform
As a final validation of the ABI platform’s usefulness in studying AGM gene expression, we evaluated the detection of tissue-specific genes. We used two distinct tissues from a single animal: 1) the brain was chosen since AGM are widely used as models of human neurology (53 , 54) and 2) tonsil as a secondary lymphoid organ, where immune responses are initiated. Each tissue was hybridized on one microarray. The most differentially expressed genes between the two tissues were extracted and represented as a heat-map image based on signal values (Fig. 3 ). As shown on the right of the figure, the differences were striking. Most of the differentially expressed genes gave a signal in only one of the two tissues. Among the 30 genes in brain that are the most induced, 19 have been reported in studies of human tissues to be expressed only in brain, such as GABRD (55) ; Table 5 lists the top 10 of these. Genes induced in the tonsil are ones that are most were related to the salivary and immune systems, and were absent in the brain (data not shown). Thus, the signals obtained with AGM RNA on the ABI platform are not only specific for the lymphoid compartment, but also for other tissues, such as the brain.


Figure 3
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  		Figure 3. Heat-map of AGM brain vs. tonsil transcriptomes. On the left, the probes found to be differentially expressed (|logQ|>8.00, P<0.01) when comparing AGM brain and tonsil RNA are shown in descending order with respect to the log2 difference in mean signal (logQ). An example of 4 genes is reported on the right. Each histogram indicates the difference of the signal between the two tissues for the same gene. A legend with the color scheme can be found in panel C of Fig. 1 . For the most and least differentially expressed probes (|logQ| >8.00, thus fold change of >256), the non-normalized signal and signal variance measurements for the brain and tonsil RNA are also depicted. The supplemental data file 4 "BrainTonsil.pdf" contains all original measures for each probe as well as complete annotation information associated with each probe.


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  		Table 5. Identification of genes absent in tonsil but induced in the brain of AGMa


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Transcriptome profiling with microarrays has opened up new possibilities for the analysis of gene expression in a massively parallel format (56) , taking advantage of the vast amount of sequence information. But applying this technology in primate research is still limited by the lack of sufficient gene sequence information. Whereas the chimpanzee genome is closely related to the human one (> 99% identity) (57) , DNA sequences are less conserved between Old World monkeys and humans (15 , 58 , 59) . Overall, the rhesus macaque genome shares ~93% of its sequence with humans (15 , 59) and ~95% with the few AGM cDNA sequenced, whereas no > 90% homology is observed between AGM and human cDNA (60 61 62) . The absence of a high-throughput screening tool for AGM is a significant problem since AGM are valuable models in cell biology research and for several human diseases (19 20 21 22 , 28 29 30 31 32 33 34 35 36) . AGM is also one of the two major nonhuman primate models of resistance to AIDS (23) . Our first objective was to find a microarray platform to study the AGM transcriptome. We therefore compared a short (Affymetrix) and long (ABI) oligonucleotide microarray platform to study the degree of cross-species hybridization between AGM RNA and the human probe sets. The second objective was an analysis of sensitivity for AGM RNA using a low amount of RNA as starting material. This last point is a major concern when few quantities of material are available for in vivo and/or cellular subpopulation studies. Because in vivo samples usually generate very small quantities of RNA, techniques for RNA amplification have been developed. But gene expression patterns of amplified samples can be biased when compared with samples processed by the standard protocol due to selective amplification of only some transcripts (63) . To avoid such a potential bias, we limited our approach to a single amplification round. Despite that limitation, 500 ng of total RNA was sufficient for AGM gene detection on the long oligonucleotide platform.

We show here that human long oligonucleotide probes, in contrast to short oligonucleotide probes, reveal a higher efficiency in detecting AGM gene transcripts. The ability to detect gene expression in AGM cells on ABI long oligonucleotide and Affymetrix short oligonucleotide platforms was respectively 47% and 9.5%. Similar results have been shown with cynomolgus macaque cerebellum, with which 28% of the probes were detected with the Affymetrix platform and 56% with ABI (17) . These higher values with macaque compared with AGM can be due to the use of distinct tissue and/or to the fact that AGM sequences are more distant from humans than macaque. The fact that the ABI platform uses long oligonucleotides compared with the short oligonucleotides such as those used in Affymetrix likely enables more specificity and sensitivity. The ABI human microarrays have also been shown to identify >3.5-fold more genes with human RNA than with Affymetrix (18) . These authors proposed three reasons: 1) the nature of chemiluminescence used as a detection signal in the ABI platform, with higher signal range and lower background, 2) the sequence sensitivity and specificity of the 60-mer oligonucleotide compared with the 25-mer oligonucleotide (41) , and 3) the particularly large number of vendor-annotated probes (17 , 18) .

We also show that 83% of the AGM-detected genes were the same as those detected with macaque or human RNA. This value is high knowing that a large proportion of genes (from 5% to 39% for PBMC) can exhibit statistically significant differences between individuals of the same species (64 , 65) . The specificity of the ABI platform for the AGM transcriptome profiles was further confirmed by an investigation of gene regulatory patterns in two distinct tissues (brain and tonsil) and two distinct cell subpopulations (activated vs. nonactivated CD4+ T cells). For AGM and macaques, genes showing the most significant changes in expression in in vitro activated CD4+ T cells were those known to also be regulated in human CD4+ T cell activation. This last point is important to the field of HIV/AIDS research, as T CD4+ cell dysfunction is the major characteristic of this infection. Moreover, AGM, unlike macaques, do not develop AIDS and do not display abnormal CD4+ T cell activation after SIV infection even though they do show similar SIV tissue propagation rates and viral replication levels. A comparison of the two species offers a model where induction of abnormal T cell activation and dysfunction can be studied in the context of similar amounts of viral antigen in the blood. We showed in this study that the ABI microarray platform can be used efficiently to identify host factors of susceptibility and protection to CD4+ T cell dysfunction by comparing CD4+ T cell gene expression patterns in the two primate models.

Taken together, the results presented in this study demonstrate that expression profiling of AGM cells and tissues can be done efficiently using the long oligonucleotide microarray platform developed by ABI with the human probe set. The number of detected genes is higher than what is observed with other technologies, even with a macaque dedicated array (9) . Moreover, we confirm that AGM is a relevant model for studies related to the immune system. Hence, AGM gene expression profiling can now be achieved even in absence of a completed genome sequence and a dedicated microarray technology. Given AGM’s primordial role as model organism for many different biomedical applications, these results open new avenues of research.


   ACKNOWLEDGMENTS
 
We are grateful to Dr. Brendan Bell for his help in editing this manuscript. This study was supported by grants from Institut Pasteur (GPH no. 2) and ANRS (no. 03/172). Work in the Benecke group is funded by the European Hematology Association José Carreras Foundation, the Institut des Hautes Études Scientifiques, the Institut de Recherche Interdisciplinaire, the Centre National de la Recherche Scientifique (CNRS), the Région Nord, and the French Ministry of Research through the "Complexité du Vivant Action STICS-Santé" program.

Received for publication February 8, 2007. Accepted for publication April 19, 2007.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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