alone or in concert with previously identiﬁed polymorphisms to favor parasite survival under drug pressure. The completion of the full genome sequence of the 3D7 strain of P.falciparum (8) has ushered in exciting new possib- ilities in malaria research using whole-genome gene expres- sion proﬁling methods (9). We recently demonstrated for the HB3 strain of P.falciparum that a continuous cascade of gene expression guides Plasmodium development throughout the IDC, from erythrocyte invasion to merozoite release. This cascade is highly streamlined for efﬁcient progression toward parasite replication, with most genes being turned on once and only once with peak expression correlating with the function of the gene products (10) similar to a ‘just in time’ production process. The apparent lack of complexity in this progression, coupled with the paucity of predicted transcription factors found in the genome (8) suggests that the IDC gene expression program in P.falciparum may be governed by a relatively small set of factors that ensure its ﬁdelity and precision over many cycles. This is further supported by the identiﬁca- tion of only 156 transcription-associated proteins in the P.falciparum genome (two-thirds less by genome size than in other eukaryotes), with an over-representation of proteins containing a CCCH-type zinc ﬁnger found in single strand RNA binding proteins involved in posttranscriptional control mechanisms (11). Given the high degree of conservation in both the number and functional similarities of genes present in the genomes of related species, the observed differences in protein coding regions alone are not sufﬁcient to explain the degree of divers- ity seen among species. Clearly, phenotypic diversity relies on variation in gene expression as well (12). Gene expression- based variation has been well demonstrated in organisms from yeast to humans using whole-genome approaches (13–16). In some cases, this variation has been shown to be heritable and has been exploited for use as quantitative trait loci in genetic association studies. In the budding yeast Saccharomyces cer- evisiae, for example, it is remarkable that two strains grown under identical culture conditions result in variable gene expression for nearly 50% of the measurable genes (16). Sim- ilarly, we were interested in determining the likelihood that changes in gene expression may play a role in phenotypic variation among strains of P.falciparum, including the evolu- tion of drug resistance. We hypothesized that by characterizing the IDC transcriptome of several P.falciparum strains with different drug resistances, we could identify strain-speciﬁc changes in gene expression and gain insight into the underly- ing phenotypic strain differences. To investigate the basis for strain-speciﬁc phenotypes and regulatory mechanisms, we analyzed the complete IDC tran- scriptomes of the 3D7 and Dd2 strains of P.falciparum by DNA microarray analysis. The results are compared to our prior analysis of the HB3 strain transcriptome (10). The three isolates we chose originate from distant geographical regions and represent distinct drug sensitivity phenotypes. 3D7, HB3 and Dd2 were isolated from The Netherlands, Honduras and Indochina, respectively. These strains are resistant and/or sensitive to various common anti-malarial chemotherapeutics. HB3 and 3D7 are both sensitive to chloroquine, although 3D7 is sulfadoxine resistant and HB3 is pyrimethamine resistant (17), while Dd2 is chloroquine and meﬂoquine resistant. Des- pite these geographical and phenotypic differences, our results Nucleic Acids Research, 2006, Vol. 34, No. 4 1167 indicate a remarkable conservation of the transcriptional program between these three strains of P.falciparum. Signi- ﬁcant transcriptional differences are detected mostly for genes encoding surface antigens and suggest either chromo- somal deletions or signiﬁcant polymorphism between strains. In contrast to other microorganisms, our results are consistent with a simple, highly conserved streamlined cascade of gene expression. MATERIALS AND METHODS Culturing, DNA microarrays and northern blotting P.falciparum 5 l fermentor preparation and culturing, RNA isolation, amino-allyl labeled cDNA preparation and DNA microarray printing were performed as described previously (10). In brief, a highly synchronized population of late-stage schizonts was attained by culturing parasites in 150 cm2 tissue culture ﬂasks and by standard synchronization with 5% sor- bitol. Invasion of fresh red blood cells was initiated within the fermentor by adding cells harvested from 25–30 ﬂasks at 15% parasitaemia and allowing these parasites to reinvade at high hematocrit ( 16%) followed by dilution to a total volume of 4.5 l after 2–3 h. The end of invasion established the time for timepoint one and thereafter, hourly samples were harvested and frozen for RNA extraction. A pool of amino- allyl labeled cDNAs from each of the timepoints used in the experiment was assembled for each strain and used as a ref- erence. For DNA microarray hybridization, the pool cDNA was always coupled to Cy3 dye as reference, while cDNA from an individual timepoint was coupled to Cy5 dye. Northern blots were performed as described (18). To gen- erate our 597 bp DNA probe for the MAL13P1.344 northern blot, we used the following two primers for PCR: 50- GAATTGTCCTATAGTAAAAACAGG-30 and 50-GCAAA- TCTTTGTAATTCTCCTCCAC-30. The PCR product was labeled with [a-32P]dCTP using the Invitrogen Random Primers DNA Labeling System as per the manufacturer’s instructions. RNA used for the northern blot was puriﬁed from sorbitol-synchronized parasites isolated at the ring and schizont stages of development. Data analysis For strains 3D7 and Dd2, 60 and 58 DNA microarray hybrid- izations were performed, respectively. For each strain we col- lected as many timepoints as possible. The 3D7 dataset were comprised of 53 hourly timepoints with hours 3, 15, 27, 30, 32, 35 and 39 represented by more than one array hybridization. The Dd2 dataset were comprised of 50 timepoints and is missing timepoint 8 with hours 4, 7, 12, 14, 20, 25, 28, 30 and 47 represented by more than one array hybridization. DNA microarrays were scanned using an Axon 4000B scanner and images analyzed using Axon GenePix software (Axon Instruments, Union City, CA, USA). Microarray data were stored using our NOMAD database (http://ucsf-nomad. sourceforge.net/) and data for individual arrays were normal- ized by a global normalization using unﬂagged features with a regression correlation coefﬁcient greater than or equal to 0.75 and median intensity value greater than zero. All unﬂagged spots were selected and extracted for further