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Invited Report
Stochastic versus stable transcriptional differences on Plasmodiumfalciparum DNA microarrays
Karthikeyan Ganesana, Lei Jianga, Pradipsinh K. Rathoda,b,*
aDepartment of Chemistry, University of Washington, Seattle, WA 98105, USAbThe Seattle Biomedical Research Institute, Seattle, WA 98109, USA
Received 24 July 2002; received in revised form 13 August 2002; accepted 15 August 2002
Abstract
The recent availability of the Plasmodium falciparum genome sequence has opened up convenient, large-scale analysis of transcriptional
products in malaria. Protocols for cDNA labelling, cDNA hybridisation, and fluorescent signal detection developed for other organisms can
be applied directly to malaria. However, P. falciparum offers unique challenges in data analysis due to stochastic variability in expression of
some gene products, such as variable erythrocyte surface proteins. Careful comparison of global transcriptional patterns in two well-studied
clones of P. falciparum (Dd2 and HB3) indicates that reliable, stable transcriptional alterations in malaria can be readily distinguished from
stochastic processes. To do this, we utilised a complex experimental design which involves a combination of self-hybridisations and cross-
hybridisations between two independently grown parasite populations for each clone being examined (for short, we call this a ‘2 £ 2
CombiScan’). While even a simple 2 £ 2 CombiScan required 12 microarray hybridisations, the effort generated output that was highly
interpretable. Reliable RNA transcriptional differences between Dd2 and HB3 could be readily visualised using public algorithms for data
normalisation and clustering. q 2002 Published by Elsevier Science Ltd. on behalf of Australian Society for Parasitology Inc.
Keywords: Malaria; Transcription; Clustering; Data analysis; Validation
1. Introduction
1.1. RNA changes in malaria
With the recent availability of the malaria genome
sequences, the next pressing question is to determine the
role of gene products, particularly for genes that have
malaria-specific functions and have no obvious homologues
in other organisms. One approach is to co-relate specific
gene expression changes with precise physiological or
genetic changes. Many previous studies on individual
malarial transcripts have shown differential expression as
a part of normal malaria development (Waters et al.,
1989; Zhu et al., 1990; Li et al., 1994; Feagin and Drew,
1995; Smith et al., 1995; Lin et al., 1996; Dechering et al.,
1997; Scherf et al., 1998; Lobo and Kumar, 1999; Dyer and
Day, 2000; Blair et al., 2002; Preiser et al., 2002). While
transcriptional changes in response to specific external
signals are not well documented (Goldberg, 2002), heat
shock does induce specific transcripts in a reversible manner
(Kumar et al., 1991).
Recently a variety of methods have emerged to identify
global gene expression changes in malaria in an open-ended
manner. They include differential display (Cui et al., 2001),
serial analysis of gene expression (SAGE) (Munasinghe et
al., 2001; Patankar et al., 2001), suppression subtractive
hybridisation (Dessens et al., 2000), and DNA microarrays
(Hayward et al., 2000; Ben Mamoun et al., 2001; Kappe et
al., 2001; Rathod et al., 2002).
1.2. DNA microarrays
In its most common application, DNA microarrays deter-
mine global transcript abundance in cells (DeRisi et al., 1997;
Brown and Botstein, 1999). Analysis of steady-state levels of
various transcripts can be useful for validating activity of
metabolic pathways in differentiated cells (Miki et al.,
2001). Additionally, changes in transcript levels during differ-
entiation or through external perturbation can help identify
sets of genes that undergo common co-ordinated control of
transcription (Chu et al., 1998; Gasch et al., 2000).
Before the Plasmodium falciparum genome sequence
was available, microarrays were prepared through amplifi-
International Journal for Parasitology 32 (2002) 1543–1550
0020-7519/02/$20.00 q 2002 Published by Elsevier Science Ltd. on behalf of Australian Society for Parasitology Inc.
PII: S0020-7519(02)00182-0
www.parasitology-online.com
* Corresponding author. Department of Chemistry, Box 351700, Univer-
sity of Washington, Seattle, WA 98195-1700, USA. Tel.: 11-206-543-
1653; fax: 11-206-685-8665.
E-mail address: [email protected] (P.K. Rathod).
cation of malaria DNA inserts using plasmid-specific flank-
ing primers (Hayward et al., 2000). Such arrays were instru-
mental in showing that standard microarray techniques
could be readily transferred to the study of differential
gene expression in malaria. The shotgun microarray also
identified a large number of transcriptional changes between
the blood stage form of P. falciparum and the sexual stage
gametocytes (Hayward et al., 2000). In alternate
approaches, stage-specific cDNA libraries were used to
generate DNA microarrays for P. falciparum and Plasmo-
dium yoelii (Ben Mamoun et al., 2001; Kappe et al., 2001).
Such arrays for P. falciparum helped identify genes that are
differentially expressed during the asexual developmental
program in red blood cells (Ben Mamoun et al., 2001).
Microarrays generated from P. yoelii were used to identify
differential gene expression in sporozoites (Kappe et al.,
2001). Recently, the availability of the comprehensive data-
base on P. falciparum genome sequences (Bahl et al., 2002)
and the success of long oligonucleotides on microarrays
(Kane et al., 2000; Hughes et al., 2001) have permitted
the development of a new generation of malaria microar-
rays: 70 bp single-stranded oligonucleotides (long oligos)
that are deposited directly on polylysine-coated slides with-
out a need for PCR amplification or product cleanup (J.L.
DeRisi, unpublished data). The oligonucleotides for printing
such slides are available through a commercial source
(Qiagen Operon; http://www.operon.com/arrays/oligosets_-
malaria.php). In addition, the public resource centre MR4,
sponsored by the US National Institute of Health, has taken
the lead in distributing DNA microarrays for those without
access to printing facilities (http://www.malaria.mr4.org/
Linked_files/MR4_ARRAY.html).
1.3. Potential sources of confounding variability in malaria
transcripts
With the DNA microarray tools in place, before one
contemplates extensive studies on genome-wide transcrip-
tional differences in parasites, it must be noted that malarial
parasite populations have large families of proteins that they
express on the surface of infected erythrocytes (Baruch et
al., 1995; Smith et al., 1995; Su et al., 1995; Cheng et al.,
1998; Kyes et al., 1999). In a small population of parasites
many, but not all, of these potential gene products are
expressed (Craig and Scherf, 2001; Fernandez et al., 2002;
Noviyanti et al., 2002). A single trophozoite in an infected
erythrocyte may express only one of 50 possible var genes
and switch frequently between alleles (Roberts et al., 1992).
The switching patterns are not entirely predictable. There is
rapid switching between some expressed var genes (Scherf
et al., 1998), but not others (Deitsch et al., 2001; Vazquez-
Macias et al., 2002). The switching of expression amongst
members of other surface protein families is less well under-
stood. To compound matters, the characteristics of surface
protein expression in different Plasmodium clones and
isolates is expected to be different. All this has the potential
to confound causal relationships between gene expression
and malaria physiology.
1.4. Statement of goals
After reviewing the current state of DNA microarrays in
malaria and having pointed out the common occurrence of
‘unpredictable’ transcript abundance in malaria, the follow-
ing study shows a simple experimental design strategy to
identify stable, reliable transcriptional differences between
two populations of P. falciparum and accompanying data
analysis schemes to readily visualise the results.
2. Materials and methods
2.1. Parasite clones
The present studies examine stable, high-level expression
differences between two well characterised clones of P.
falciparum: Dd2 and HB3. Clone Dd2 was derived from
clone W2 from Southeast Asia and clone HB3 originates
from Honduras in Central America (Oduola et al., 1988;
Rathod et al., 1997). In addition to a significant higher
level of drug resistance, clone Dd2 is known to harbour
specific deletions in genes coding for knob-associated
proteins (Pologe and Ravetch, 1986; Rathod, unpublished
data). Such differences serve as internal controls for estab-
lishing stable transcriptional differences between strains. In
the present example, we propagated two independent popu-
lations of Dd2 clones and two independent populations of
HB3 clones, starting with about 100 infected erythrocytes in
each pool. RNA from these four parasite populations was
hybridised to each other in every possible combination
including dye-swaps (2 £ 2 CombiScan which involves 12
hybridisations; see Fig. 2).
2.2. Isolation of RNA
Plasmodium falciparum was cultured by established
methods (Trager and Jensen, 1976). For each ‘batch’,
about 30 ml of culture at 4% haematocrit was grown to 3–
6% parasitaemia. Parasites were released with Saponin
treatment (Rathod et al., 1992). After two washes in PBS,
the parasites were resuspended in 350 ml of lysis buffer
(Ambion RNAqueouse Kit (Cat #1912)) and RNA was
isolated according to the manufacturer’s instructions.
RNA was quantitated by UV spectroscopy. Typically, 20–
30 mg of total RNA was obtained from 30 ml of parasite
culture.
2.3. Labelling of cDNA
For each hybridisation, fluorescently labelled cDNA was
generated from 10 mg total RNA using protocols described
by Joe DeRisi and colleagues (www.microarrays.org). Of
all the options available, the present experiments used
pd(N)6 random hexamer primers (Amersham Biosciences
K. Ganesan et al. / International Journal for Parasitology 32 (2002) 1543–15501544
Corp.), allylamine-dUTP (Sigma-Aldrich Corp.) incorpora-
tion during reverse transcription, Microcon YM-30 (Milli-
pore, Bedford, MA) cleanup of labelled cDNA, N-
hydroxysuccinimide-activated Cy5 and Cy3 (Amersham
Biosciences Corp.) for labelling cDNA, hydroxylamine-
based quenching of excess label, and Qia-quick (Qiagen,
Inc.) column-based purification of final hybridisation mate-
rial.
2.4. Hybridisation to DNA microarrays
Oligonucleotides for printing the array were from Qiagen
Operon (http://www.operon.com/arrays/oligosets_malar-
ia.php). The 6,375 element arrays, each representing the
majority of malarial open reading frames, were printed on
polylysine-coated slides using a new generation ultra fast,
linear servo driven DeRisi microarrayer. Slides were post-
processed and hybridised in 3 £ SSC at 63 8C for 12 h as
previously described (www.microarrays.org).
2.5. Data acquisition and analysis
Slides were scanned in an Axon GenePix 4000B micro-
array scanner (Axon Instruments, Inc.) with 532 nm (17
mW) and 635 nm (10 mW) lasers. Data were collected as
an image file, grided, and converted into a text file using
Genepix 3.0 software (Axon Instruments, Inc.). Experi-
ments from each hybridisation were normalised for print-
tip variability and for slide to slide variability using the
publicly available software from Lund University (http://
www.braju.com/R/com.braju.sma/) run in R project envir-
onment (http://cran.r-project.org/). Meaningful, stable
K. Ganesan et al. / International Journal for Parasitology 32 (2002) 1543–1550 1545
Fig. 1. Illustration of unexpected hybridisation patterns in raw microarray images. (A) Schematic representation of dye-labelling patterns for Dd2 and HB3
cDNA. To generate the image on the left, cDNA corresponding to P. falciparum clone Dd2 was labelled with Cy5 and cDNA from clone HB3 was labelled with
Cy3. The panel on the right had the dyes swapped. (B) To the first approximation, colour images from the dye-swap experiments show the predictable
switching of red and green fluorescence (e.g. see yellow arrows). However, some genes fluoresce green regardless of the origin of the RNA (see blue arrow). In
such direct displays of fluorescence (as opposed to the clustered data in Fig. 2), the intensity of red or green fluorescence usually reflects the abundance of
transcripts corresponding to the genes on the arrayed DNA spot.
K. Ganesan et al. / International Journal for Parasitology 32 (2002) 1543–15501546
Fig. 2. A two-sample complex hybridisation scheme to extract information on differences in gene expression that are stable and reliable. (A) Details of the
‘2 £ 2 CombiScan experiment’. Each pair of red and green arrows represents a different hybridisation experiment: red arrows represent labelling with Cy5 and
green arrows with Cy3. (B) Clustering of a 2 £ 2 CombiScan experiment showing genes that show at least an eight-fold change in red/green fluorescence ratios
in at least eight of 12 hybridisations. The upper cluster represents eight genes that show no changes in self-hybridisations (columns 1–4) and consistent changes
in cross-hybridisations (columns 5–12). Experimental data labels with an ‘s’ represent dye-swap experiments (columns 2, 4, 6, 8, 10, and 12). The lower cluster
represents genes that were preferentially labelled with Cy3 in some hybridisations. These genes are not expected to be differentially expressed between Dd2
and HB3. Unlike Fig. 1, in the present representation of clustered data from multiple hybridisations, the colours represent relative changes in transcript level at
a given spot between experiments. They do not represent absolute levels of fluorescence at a DNA spot. In a given row, redness represents an increase in
expression and greenness represents a decrease in expression. Thus, two sets of black boxes for two different genes (representing no change in fluorescence
between hybridisation experiments) may actually have large differences in absolute fluorescence between each other. (C) An averaged, normalised scatter plot
from a CombiScan experiment showing red/green fluorescence ratios for all oligos on the array. Again, the data have been transformed so that all Dd2 signals
are represented in red and all HB3 signals are represented in green regardless of the actual use of Cy5 and Cy3 dyes. The genes that were in the upper cluster in
(B) are shown as filled red or filled green circles. Note that some genes that had a high red to green (or high green to red) fluorescence between Dd2 and HB3
were not included in the cluster in (B) because they did not match the required pattern for reliability.
differences in transcript levels were extracted using the
publicly available Cluster and Treeview programs (http://
rana.lbl.gov/EisenSoftware.htm) developed by Michael
Eisen (Eisen et al., 1998). Generally, the default data
show Dd2 cDNA labelled with red fluorescence (Cy5) and
HB3 labelled with green fluorescence (Cy3). During dye-
swap experiments, the numeric red/green fluorescence
ratios were inverted to facilitate direct side by side compar-
isons between different hybridisations in a cluster.
3. Results and discussion
3.1. Unexpected and unstable observations
Based on standard description of DNA microarray tech-
nology, identification of true transcriptional differences
between two clones of P. falciparum on a microarray
ought to be straightforward. While that is the case for
many genes on the array, as shown in an example in Fig.
1, there are genes that present anomalous behaviour that, if
not carefully excluded, can be misleading. A simple dye-
swap between Dd2 and HB3 cDNA is expected to turn
previously red fluorescent genes green and make green
fluorescent genes glow red (e.g. yellow arrows; Fig. 1A).
Not surprisingly, some genes show slight irregularities
between arrays in the dye-swap experiment. However, in a
few extreme cases, gene sequences hybridise preferentially
with green fluorescent cDNA regardless of which parasite
RNA sample was labelled with Cy3 (see blue arrow in Fig.
1). These kinds of dye effects have previously been docu-
mented (Kerr and Churchill, 2001; also see http://www.jax.-
org/research/churchill/pubs/index.html).
3.2. Sources of variability
When comparing cDNA hybridisations from two sets of
P. falciparum cells, variability is expected to arise from (i)
technical challenges of performing microarray experiments,
(ii) biological variations not directly related to traits under
investigation, and (iii) causal transcriptional changes
directly related to the physiological or genetic traits being
compared.
The technical challenges of producing good array data are
substantial. The reliability and usefulness of a data set
generated during a microarray experiment depends on
many factors, including the quality of the DNA solutions,
the quality of the slides used to generate the arrays, the
quality of the print-tips, the efficiency of cDNA labelling
reagents, and the expertise of the operators. To avoid
substantial problems, many checks are built into the DNA
microarray technology itself. This includes the use of
competing labelled cDNA in every hybridisation and the
reliance on red/green fluorescence ratios for identifying
changes in transcript level rather than absolute fluorescence
levels. It is not uncommon to have some variations from
slide to slide and in the quality of the array from one end of a
slide to another; these can be overcome by performing
multiple hybridisations on different slides using the same
batch of RNA samples. Variations in printing quality from
tip sector to tip sector and variation in overall signal from
two slides can be overcome through normalisation of data
(see Section 2). Unequal labelling of RNA by Cy3 versus
Cy5 can be handled by changing laser intensity during
scans, by mathematical corrections of final text data, dye-
swapping, or by shifting to an entirely different cDNA label-
ling strategy. The potential problems discussed in this para-
graph are shared by all microarray users, regardless of
which organism they work on.
Some problems, however, are unique to the biological
system one works with. The case of random expression of
surface proteins in malaria was discussed in detail in Section
1. This cannot be corrected, or anticipated a priori, for any
single hybridisation because regulation of expression of
surface protein in different types of parasite clones and
isolates remains poorly understood. Malaria cultures may
also be sensitive to batches of red blood cells, media, time
outside the incubator, atmospheric oxygen pressures, etc.
3.3. Complex experimental designs
In an effort to minimise contributions from non-specific
biological effects on transcript levels, we use complex
hybridisation schemes centred around independent growth
of more than one parasite population. We start with near-
clonal P. falciparum parasites and grow each of them to
about 109 infected erythrocytes. cDNA from these different
populations is hybridised in every possible combination
(Fig. 2A). Such schemes are an extension of orthogonal
hybridisation schemes advocated by Kerr and Churchill
(2001). They simultaneously incorporate redundancy, dye-
swap effects, and adjust for stochastic switching of tempor-
ary expression differences.
As shown in Fig. 2A, a comparison between clone Dd2
and clone HB3 can be set up as a hybridisation between all
combinations of two populations of Dd2 and two popula-
tions of HB3 (we call this a 2 £ 2 CombiScan). Including
dye-swaps, this analysis requires 12 microarray hybridisa-
tions. To facilitate side by side comparisons during cluster-
ing, red/green ratio data from dye-swap experiments are
inverted. For true, stable transcriptional differences between
Dd2 and HB3, we expect the expression of a given gene not
to change in four of the 12 hybridisations (Fig. 2, self-hybri-
disations 1–4: Dd2 to Dd2 and HB3 to HB3) and we expect
eight of the 12 hybridisations to show changes (Fig. 2, cross-
hybridisations 5–12: Dd2 to HB3). When these ‘pattern
criteria’ are met, one can be confident that one is dealing
with authentic, clone-specific stable transcript differences
among the clones. A more forgiving clustering regimen
may ask for, say, six of the 12 hybridisations to show an
eight-fold difference (this prevents a bad data point on one
of the arrays from eliminating a gene from a cluster). Our
laboratory has verified through independent analysis that the
K. Ganesan et al. / International Journal for Parasitology 32 (2002) 1543–1550 1547
genes showing these patterns of differential hybridisations
can be trusted (data not shown).
3.4. Sample clustering outcome
If we set up highly stringent inclusion criteria for the
clustering analysis (at least an eight-fold change in red/
green fluorescence in at least eight of 12 hybridisations),
31 genes out of 6,375 meet the criteria (as shown in Fig.
2B). However, only eight of 31 oligos show the required
pattern: no changes in the self hybridisations (first four
columns on the left in Fig. 2B) and consistent alterations
in cross-hybridisations (columns 5–12 on the right side of
Fig. 2B). In the present example, we use the eight-fold
change threshold for balance in data volume: we see enough
genes in the cluster that some, whose biology is known, can
be used as anchors to interpret the data. If several arrays had
a deformation or poor quality image in an important gene in
two or more of the eight hybridisations, this would cause the
gene to be left out of the cluster. If the stringency of cluster-
ing is relaxed to three observations with eight-fold change
or eight observations with four-fold change, then as
expected it is possible to identify scores of genes that
show differential transcription between Dd2 and HB3
(data not shown). Lowering the stringency generates far
more data but interpretations require more care because a
significant part of this data set is of lower quality.
One of the recognisable genes that show a difference in
cDNA hybridisation between Dd2 and HB3 is the knob-asso-
ciated histidine-rich-protein (KAHRP; PFB0100c) which is
deleted in Dd2 but not HB3. Two different oligos on the
array for KAHRP shows the pattern. A neighbouring gene
(PfEMP3; PFB0095c) does not show up in the very high strin-
gency data but appears when the clustering criteria are relaxed
to six out of 12 genes showing an eight-fold variation. The
appearance of such known differences between Dd2 and HB3
adds confidence to this data analysis strategy. Of course, one of
the purposes of doing open-ended functional genomic experi-
ments is to discover unanticipated biological questions or
develop new hypotheses based on new data. Many of the
genes in the cluster have no known function; it will be inter-
esting if they can eventually be tied to phenotypic differences
between Dd2 and HB3. Along these lines, a specific oligonu-
cleotide from a vacuolar pyrophosphatase I, whose function is
currently under investigation (McIntosh and Vaidya, 2002),
consistently showed higher hybridisation with Dd2 cDNA.
The molecular basis for this is not clear. It is noteworthy that
the present very high stringency scheme for identifying stable
differences in transcription between two clones does not pick
up a single member of the common multi-gene erythrocyte
surface protein families, other than the deleted genes.
Most of the remaining genes in this cluster meet the
criteria of having at least eight out of 12 hybridisations
showing an eight-fold change, however, there is no distinc-
tion between self-hybridisations and cross-hybridisation
(Fig. 2C). Considering that all the dye-swap data had been
inverted prior to clustering, the alternating red/green
columns in Fig. 2C reveal that this set of genes simply
preferred to be labelled by Cy3 regardless of the origin of
the RNA. The origin of this technical problem is not entirely
clear and the problem sometimes ‘disappears’ with fresh
reagents.
3.5. Summary
DNA microarray technology promises to be a powerful
tool for analysis of nucleic acid sequences in parasites of
different physiological or genetic backgrounds. However,
optimum use of the technology can benefit from meaningful
design of experiments to capture the most relevant data. We
expect CombiScan designs to play an important role in
analysis of physiologically important transcriptional
changes in P. falciparum.
Acknowledgements
We are most grateful to Joe DeRisi and his colleagues at
UCSF for assisting us in the fabrication of malaria micro-
arrays and for guidance in all aspects of microarray work, to
John White for technical assistance at UW, and to Tom
Wellems (NIH) for parasite clones. This work was
supported by NIH grants AI 26912 and AI 40956. P.K.R.
also received startup support in Seattle from the Keck Foun-
dation (UW) and the Bill and Malinda Gates Foundation
(SBRI).
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