Center for Gene Therapy, Tulane University Health Sciences Center, Center for Infectious Diseases and Department of Tropical Medicine
Tulane University Health Sciences Center, New Orleans
Tulane National Primate Research Center, Covington, Louisiana
We used human microarrays to examine gene expression in a rhesus monkey model of human Plasmodium vivax malaria (P. cynomolgi in Macaca mulatta). Whole-blood cells were collected for extraction of RNA before infection, during both the initial liver phase of infection and bloodstream infection, and during the course of 2 bloodstream relapses. Clustering analysis showed that similarities in gene expression were greater at similar stages of the protocol for the 2 different monkeys than for the same monkey at different stages of the protocol. Interestingly, a large number of genes involved in RNA processing showed distinct down-regulation during the initial liver phase of infection. When only up-regulated genes were examined, there was evidence of an increasing number of "defense response" genes as the infection evolved but not of "cytoskeleton" genes (P .001). These results demonstrate the value of microarrays for studying the response of the primate transcriptome to malaria infection; they suggest that the host response is modulated by groups of genes.
High-density oligonucleotide microarrays make it possible to examine the mRNA transcripts for most genes simultaneously (the transcriptome) in a way that has not been possible previously [1, 2]. This capacity is of potential value for examining the host response to a broad spectrum of diseases, including infectious diseases such as malaria. However, most of the microarray results reported previously have focused on gene expression by an invading microorganism or by malignant cells within a tumor. Previous studies have typically compared gene expression by microorganisms at 1 or 2 time points [37] or by malignant cells at 1 time point [810]. The recent study by Sexton et al. is arguably one of the few studies to approach malaria infection by use of microarray analysis of the host, albeit in a murine model [11].
We infected 2 rhesus monkeys with sporozoites of Plasmodium cynomolgi, a monkey malaria parasite, and followed the changes in levels of gene expression of the host cells during the course of a malaria infection, including relapses. A major challenge in performing the present study was that microarrays for nonhuman primates are not yet available. Several investigators have shown that microarrays designed for humans (Affymetrix) can be used to follow changes in gene expression by nonhuman primates [1215]. Cross-species mismatches may result in underestimation of the abundance of a transcript; however, relative changes in signal during the course of an infection should be reliable (R. Norgren, personal communication).
One rationale for using a monkey model is that such models permit controlled infection by sporozoite inoculation at defined times with a specific isolate, which is not possible with humans, who acquire malaria by natural transmission. Using a monkey model, we can also allow infections to continue through several relapses, which is not ethical in human studies. Confounding factors such as nutritional status and coinfections are not a consideration in monkey studies.
P. cynomolgi, the putative ancestor of P. vivax [16], readily infects humans and is used frequently as a model of human P. vivax infection, which is widespread in Asia, Latin America, the Middle East, North Africa, and the South Pacific. Both parasites have 48-h asexual erythrocytic cycles in their respective hosts [16] and produce relapses from persistent liver stages (hypnozoites) [17]. Phylogenetic trees based on small-subunit rRNA group the 2 parasites closely together [18], primers based on P. vivax sequences readily amplify homologous sequences from P. cynomolgi (J. Alger, personal communication), and proteins from P. cynomolgi are fundamentally similar in peptide sequence and function to those from P. vivax (e.g., merozoite surface protein 1) [18, 19]. For these reasons, P. cynomolgi infection of the rhesus monkey has been used as a model of human P. vivax infection to study the biology of the hypnozoite stage responsible for relapse [17], to test molecular markers for relapse, and to test for drug efficacy [20].
The present study demonstrates that microarrays can be used effectively to study the host gene expression in nonhuman primates in response to infection. This strategy should also be useful for examination of host responses in other nonhuman primate models of human disease and in humans with infectious diseases such as malaria. We show that similar patterns of expression have genes with similar biological processes in both of the recipient monkeys in the present study and that 1 of the patterns with the most-significant functions consists of RNA processing genes that were down-regulated during the initial liver phase of infection.
MATERIALS AND METHODS
Monkey infections.
A splenectomized donor monkey (Macaca mulatta) was inoculated intravenously (iv) with 106 asexual parasites of P. cynomolgi bastianellii. After the first detection of gametocytes on blood film (Giemsa-stained thick and thin), 300 female Anopheles stephensi mosquitoes/day were allowed to feed on the monkey, for 3 days. The mosquitoes were maintained for 2 weeks at 27°C, and infective-stage sporozoites were harvested, suspended in medium 199 with 10% rhesus monkey serum, and injected iv into the recipient monkeys.
The 2 recipient monkeys, with spleens intact, received 106 infective sporozoites iv via the saphenous vein. Parasitemias were monitored as above, and the monkeys were treated with chloroquine (7 mg base/kg/day intramuscularly for 5 days) and with primaquine (4 mg base/kg/day orally for 7 days) after the second relapse. One of the recipient monkeys (CP80) was given interferon (IFN) (5 g/kg/day subcutaneously for 3 days) beginning on day 1 of parasitemia (before sample 3 was obtained), in an effort to increase systemic (blood) levels of tumor necrosis factor (TNF). The effect of treatment with IFN- on levels of TNF was monitored by measuring blood levels of TNF by use of an ELISA-based kit (Biosource International), using both blood levels of TNF before treatment and those in the untreated monkey (CL61) as controls.
RNA samples.
Blood samples were obtained before infection (baseline; sample 1), during the initial liver phase of infection (8 days after iv infection; sample 2), when the parasitemia reached 10% (1 × 106 parasites/L) during the first bloodstream infection (sample 3), and during the first and second relapses (samples 4 and 5, respectively) (figure 1). Total RNA was isolated from whole blood by use of the PAXGene kit (Qiagen) and was purified by use of the RNAeasy Mini kit (Qiagen).
Microarray.
Eight micrograms of total RNA was used to synthesize double-stranded cDNA (Superscript Choice System; GIBCO BRL Life Technologies). After synthesis, the cDNA was purified by phenol/chloroform extraction (Phase Lock Gel; Eppendorf Scientific) and concentrated by ethanol precipitation. In vitro transcription was used to produce biotin-labeled cRNA (BioArray HighYield RNA Transcription Labeling Kit; Enzo Diagnostics). The biotinylated cRNA was cleaned (RNAeasy Mini Kit; Qiagen), fragmented, and hybridized on microarray chips (HG-U133A; Affymetrix) containing 22,215 probes representing 15,003 genes. After they had been washed, individual microarray chips were stained with streptavidin-phycoerythrin (Molecular Probes), amplified by use of biotinylated anti-streptavidin (Vector Laboratories), and scanned for fluorescence (GeneArray Scanner; Hewlett Packard), by use of Microarray Suite 5.0 software (MAS 5.0; Affymetrix)
The scanned images, together with absolute calls for each gene (present [P], marginal, or absent), were transferred to the dChip program (version 1.3+; available at: http://biosun1.harvard.edu/complab/dchip/) [21]. Chips were normalized against an array with a median overall signal intensity (SI) value of 172. Expression values were calculated on the basis of both perfect matches and mismatches, and negative values were assigned a value of 1. Differentially expressed genes were obtained in both experiments (monkeys) separately by searching for genes that (1) were scored P in at least 1 sample and (2) had a coefficient of variation (CV; SD of SIs divided by mean SI across all time points) >0.5. This criterion (rather than a CV >0.3) was chosen because it reduced the number of genes required for clustering to a more manageable number. After Affymetrix control genes and redundant genes were removed, the number of genes was reduced from 15,003 to 3278 (CL61) and 3532 (CP80).
Hierarchical clustering and gene ontologies (GOs).
Before hierarchical clustering, the dChip program was used to standardize the SIs for each gene by linearly adjusting their values across all time points to a mean of zero, with an SD of 1. The program was also used to perform hierarchical clustering of the samples.
Eighteen distinct patterns of gene expression were identified from the hierarchical clustering picture, representing 99% of the differentially expressed genes. The dChip program was used to calculate P values for each GO term by use of an exact hypergeometric distribution, to compare the frequencies of individual GO terms within the pattern with the frequencies of those terms on the entire microarray (P .01 was considered to be significant) [22]. GO terms provide information on cellular components, molecular function, and biological processes (9518 of the 15,003 genes on the chip have 1 GO term).
Data were searched for genes with the "transcription factor activity" GO term (n = 634). A gene was retained for further analysis if it (1) was scored P in at least 1 of the 10 samples from the 2 monkeys and (2) had significant variation in its expression across the samples (CV, >0.3). After Affymetrix control genes and redundant genes were removed, the number of transcription factors for hierarchical clustering was reduced to 208.
Next, the data were searched for genes with either a "defense response" (n = 632) or a "cytoskeleton" (n = 640) GO term. These genes were then used to perform pairwise comparisons between all the samples and the baseline sample within an experiment (monkey). Genes retained for further analysis met 3 criteria: (1) they were up-regulated to a value at least 2-fold greater than the baseline level, (2) they were scored P in at least 1 sample, and (3) their SIs were at least 30 points higher than the baseline level.
Reverse-transcriptase polymerase chain reaction (RT-PCR).
Previously isolated RNA from samples 1 and 3, from both monkeys, were used to perform RT-PCR, in accordance with the manufacturer's instructions (iScript cDNA Synthesis Kit and iQ Supermix; BioRad), and the products were separated by an agarose gel electrophoresis. The following primer pairs were used in the RT-PCR: -actin (100 bp), 5-AGAAAATCTGGCACCACACC-3 and 5-GGGGTGTTGAAGGTCTCAAA-3; TNF (155 bp), 5-AACCTCCTCTCTGCCATCAA-3 and 5-TCGAGATAGTCGGGCAGATT-3; a disintegrin and metalloproteinase domain 17 (ADAM17) (144 bp), 5-GGTGGTGGATGGTAAAAACG-3 and 5-GCCCCATCTGTGTTGATTCT-3; v-rel reticuloendotheliosis viral oncogene homolog B (RELB) (110 bp), 5-ATCTGCTTCCAGGCCTCATA-3 and 5-CGCAGCTCTGATGTGTTTGT-3; and signal transducer and activator of transcription 3 (STAT3) (123 bp), 5-CTGGCCTTTGGTGTTGAAAT-3 and 5-CTCTGCCCAGCCTTACTCAC-3.
RESULTS
Time course of P. cynomolgi infection.
After the iv inoculation of infectious sporozoites (after sample 1 was obtained on day 0), parasites entered hepatocytes in the liver within minutes, where they matured into preerythrocytic schizonts or became dormant hypnozoites within 8 days (sample 2). Between days 10 and 12, merozoites released from mature preerythrocytic schizonts in the liver entered the bloodstream, where they produced detectable parasitemias, which peaked on day 14 (sample 3). After treatment with chloroquine to clear asexual parasites from the bloodstream, the first relapse occurred on day 27 (sample 4). After an additional course of chloroquine to clear the first relapse, blood smears remained negative until the second relapse, on day 46, which was treated with chloroquine beginning on day 47 (sample 5). After treatment with primaquine to eradicate hypnozoites remaining in the liver, there were no further relapses. Blood samples for extraction of RNA were collected on days 0, 8, 14, 27, and 47 (figure 1). Neither monkey showed detectable amounts of circulating TNF- during the initial peak parasitemia.
Hierarchical clustering of samples and genes.
Samples were clustered by use of differentially expressed genes (3278 for CL61 and 3532 for CP80), and the results are presented in dendrograms in which the lengths of the branches are proportional to differences in gene expression between samples from the same recipient monkey (figure 3A) or between samples from both monkeys (figure 3B). As shown in the first dendrogram (figure 3A), gene expression in the baseline sample (sample 1) was the most different. Conversely, the most similar gene expression was observed in samples from the first and the second relapses (samples 4 and 5, respectively).
When samples from both monkeys were clustered together (4350 genes), the corresponding samples from the 2 recipient monkeys clustered pairwise. Thus, the second dendrogram (figure 3B) demonstrates that similarities in gene expression were greater at similar stages of the protocol for the 2 different monkeys than for the same monkey at different stages of the protocol. The single exception to this generalization was the second relapse in monkey CL61 (sample 5), during which gene expression was different from that in sample 5 from CP80 (figure 3B).
Next, differentially expressed genes were clustered hierarchically, and, when the results were inspected visually, 18 (CL61) and 19 (CP80) distinct patterns of expression, with narrow confidence intervals, were identified (figure 4). Patterns revealed marked changes in gene expression, including the down-regulation of multiple genes during the initial liver phase of infection (sample 2).
From gene expression to gene function.
GO terms assigned to genes clarify the cellular location, molecular function, and biological processes related to the protein product of each gene. GO terms in a pattern were considered to be significant if P .01. The most-significant GO terms in each pattern are shown in tables 1 (CL61) and 2 (CP80). The patterns with the greatest significance were 16 (CL61) and H (CP80). These patterns were for genes that, compared with the baseline level, were markedly down-regulated during the initial liver phase of infection and thereafter (figure 4). The genes in these patterns were involved in nucleic acid binding, RNA splicing, and related functions (tables 1 and 2). Other patterns with highly significant GO terms were 10 (CL61) and L (CP80). The genes in these patterns had their highest levels of expression during the second relapse (figure 3). Not surprisingly, many of these genes had defense and immune response functions (tables 1 and 2). Defense and immunity genes were also significantly represented in patterns 9 (CL61) and K and N (CP80). The genes in these patterns had their highest levels of expression during either the initial peak parasitemia or the subsequent relapses (figure 4).
RT-PCR.
RT-PCR assays were used to confirm the expression patterns of TNF, ADAM17, RELB, and STAT3. TNF is a cytokine that is important for the defense response, and ADAM17 functions as a TNF-converting enzyme, whereas RELB and STAT3 are transcription factors. The results demonstrate that the expression patterns obtained by use of RT-PCR are similar to the patterns obtained by use of the microarray (figure 7).
DISCUSSION
As demonstrated by the present study and by previous studies [1215], human microarrays can be used to study gene expression in nonhuman primates. However, some differences are inevitable when chips designed for another species are used. For instance, some investigators have suggested that up to 40% of genes in nonhuman primates may not be detected by human microarrays [23] and that the percentage of undetected genes may vary unpredictably across the genome (R. Norgren, personal communication). The percentage of genes detected (called P) in the present study (monkey RNA hybridized to human arrays) ranged from 16% to 36%, compared with 40%50% detected in human studies (human RNA hybridized to human arrays) by use of the same microarrays and software (J.Y. and J. Manges, unpublished observations).
Although interspecies differences are important, the most challenging aspect of gene expression studies is the question of how to move from the overwhelming amount of data generated by microarrays on the entire transcriptome to groups of genes, individual genes, and biological function. In the present study, we have addressed these questions by developing a stepwise protocol, which began by grouping genes on the basis of their expression over time (hierarchical clustering of samples and genes). We then used GO terms and the expression of transcription factors to aid in the conceptual transition from the transcriptome to groups of genes, individual genes, and biological function.
The most striking result of the hierarchical clustering studies is the complex response, on the level of gene expression, of the host to malaria (P. cynomolgi infection). With a single exception (sample 5), the gene expression patterns in the recipient monkeys were clustered primarily according to the stage of the infection (figure 3B). This suggests that the study of gene expression by use of microarrays should permit the development and testing of hypotheses about the response of the host transcriptome to infection.
Graphic representation of gene expression (figure 4) demonstrated that the response to infection was remarkably heterogeneous. Although previous studies (before microarrays were available) invariably emphasized the up-regulation of individual genes, the results presented here indicate that the host response to infection is a complex mix of both up- and down-regulation of groups of genes.
Subsequently, at times of bloodstream infection by asexual parasites (samples 35), one of the interesting patterns observed was the up-regulation of defense-response and immune-response genes in both monkeys (pattern 10, CL61; pattern L, CP80). These patterns are consistent with the development of immune responses to the parasite by the host during the course of bloodstream infection. However, conclusions drawn from these results are observational results (descriptive), rather than analytical (not hypothesis based).
To address this limitation, we compared the expression of genes with the defense response GO term with that of genes with the cytoskeleton GO term. The results of this comparison (figure 6) demonstrate an increase in the number of up-regulated defense response genes (P .001), but not of the cytoskeleton genes (P = .8 and P = .2, 2 test for trend), during P. cynomolgi infection. These results are consistent with the hypothesis that the number of up-regulated genes related to the host defense increases in response to malaria infection and also increases during the course of 2 relapses.
Because transcription factors may be responsible for the coordinated up- and down-regulation of groups of genes, we examined the expression of genes encoding transcription factors, to provide an alternative perspective on the role of gene expression in the response to infection (figure 5). Transcription factors up-regulated during the initial liver phase of infection included genes involved in NF-B activity (RELB), a thyroid hormone receptor (THRA), and a gene involved in pituitary organogenesis and motor neuron development (LHX3). Transcription factors down-regulated during the initial liver phase of infection included the gene for enolase (ENO1), the enhancer binding protein that helps regulate the inflammatory response (CEBPB), and STAT3, which is involved in cytokine release. Transcription factors up-regulated at the time of the initial peak parasitemia included a gene involved in transforming growth factor signaling (MADH4), a gene that encodes a nuclear protein with regulatory functions (HOXC6), and a gene that encodes a nuclear factor (NFE2L3) that regulates erythroid-specific genes.
In the present study, we used hierarchical clustering of microarray data to evaluate the effect of infection on the expression of groups of genes. Also, the expression of transcription factors was used to provide a second perspective on coordination of the host response, and, finally, GO terms were used to link gene expression to biological function (i.e., to test whether groups of defense response genes are up-regulated in response to malaria).
These results provide a logical point of reference for further studies of malaria in nonhuman primates, with the potential to compare the effects of relatively benign infections such as P. cynomolgi with those of more-severe infections, to evaluate the role of circulating TNF and cytokine receptor levels (A.C.R., J.Y., and F.B.C., unpublished data), and to develop strategies for the study of humans with P. vivax and P. falciparum infections.
Acknowledgments
We thank the late Chris Kirijan, for veterinary support; Justin Manges, for technical assistance; and Leena Ala-Kokko, James Colborn, Mark James, Robert Norgren, Jr., Bruce Bunnell, and Darwin J. Prockop, for their thoughtful and constructive reviews of the manuscript.
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