An orderly retreat: Dedifferentiation is a regulated process

Abstract

Differentiation is a highly regulated process whereby cells become specialized to perform specific functions and lose the ability to perform others. In contrast, the question of whether dedifferentiation is a genetically determined process, or merely an unregulated loss of the differentiated state, has not been resolved. We show here that dedifferentiation in the social amoeba Dictyostelium discoideum relies on a sequence of events that is independent of the original developmental state and involves the coordinated expression of a specific set of genes. A defect in one of these genes, the histidine kinase dhkA, alters the kinetics of dedifferentiation and uncouples the progression of dedifferentiation events. These observations establish dedifferentiation as a genetically determined process and suggest the existence of a developmental checkpoint that ensures a return path to the undifferentiated state.

Full text

An orderly retreat: Dedifferentiation is a
regulated process
Mariko Katoh*
†
, Chad Shaw*, Qikai Xu*
‡
, Nancy Van Driessche*
§
, Takahiro Morio
†
, Hidekazu Kuwayama
†
,
Shinji Obara
†
, Hideko Urushihara
†
, Yoshimasa Tanaka
†¶
, and Gad Shaulsky*
‡§储
*Department of Molecular and Human Genetics,
‡
Graduate Program in Structural and Computational Biology and Molecular Biophysics, and
§
Graduate
Program in Developmental Biology, Baylor College of Medicine, Houston, TX 77030; and
†
Institute of Biological Sciences, University of Tsukuba, Tsukuba,
Ibaraki 305-8572, Japan
Edited by Igor B. Dawid, National Institutes of Health, Bethesda, MD, and approved March 15, 2004 (received for review October 28, 2003)
Differentiation is a highly regulated process whereby cells become
specialized to perform specific functions and lose the ability to
perform others. In contrast, the question of whether dedifferen-
tiation is a genetically determined process, or merely an unregu-
lated loss of the differentiated state, has not been resolved. We
show here that dedifferentiation in the social amoeba Dictyoste-
lium discoideum relies on a sequence of events that is independent
of the original developmental state and involves the coordinated
expression of a specific set of genes. A defect in one of these genes,
the histidine kinase dhkA, alters the kinetics of dedifferentiation
and uncouples the progression of dedifferentiation events. These
observations establish dedifferentiation as a genetically deter-
mined process and suggest the existence of a developmental
checkpoint that ensures a return path to the undifferentiated state.
D
edifferentiation is the progression of cells from a more
differentiated to a less differentiated state. It is observed in
a variety of processes such as cancer, organ regeneration, and
stem cell renewal, but it has been difficult to study because there
are few experimentally tractable systems that dedifferentiate
(1–4). Dedifferentiation in Dictyostelium is an experimentally
tractable process. During development, starving cells aggregate
and undergo synchronous morphological transitions until they
form fruiting bodies after 24 hr (5). If the multicellular structures
are disaggregated and incubated in nutrient medium, the cells
dedifferentiate. Dedifferentiation is characterized by a loss of
developmental markers and a subsequent gain of proliferative
capacity (6, 7, 45), but these characteristics do not prove that
dedifferentiation is a regulated process. The most convincing
evidence in support of regulation has been the observation that
a mutant strain (HI4) was defective in dedifferentiation (8, 9).
An argument against the idea that dedifferentiation is a
regulated process comes from the observation that dedifferen-
tiation occurs at different rates, depending on the developmental
stage at which the cells were disaggregated (6, 10–13). This
dependence may indicate that each developmental stage has a
dedicated dedifferentiation program that is executed at a dif-
ferent rate, or that dedifferentiation is a stochastic event
whereby cells lose developmental markers and regain growth
markers.
The purpose of this work was to test whether dedifferentiation
is a regulated process by comparing the molecular progression of
cells from different developmental stages. We propose that if we
found a common set of molecular changes, which is independent
of the initial developmental stage, that finding would support the
regulated process hypothesis. We used microarray transcrip-
tional profiling to detect changes in the pattern of global gene
expression during dedifferentiation. These transcriptional
changes reveal physiological changes without prejudice as to
what processes are involved (14–18), making them suitable for
testing whether dedifferentiation from different developmental
stages occurs in a regulated way. We followed the physiological
changes that occur in cells during dedifferentiation from three
developmental stages: aggregation, finger, and Mexican hat.
These stages are quite different from each other in physiology
and in morphology (5), yet we found that the dedifferentiating
cells exhibited common transcriptional profiles. This finding
indicates that dedifferentiation is a regulated process. Exami-
nation of the coordinately regulated transcripts revealed genes
that are also induced during development, raising the possibility
that development and dedifferentiation are regulated by
some common genes. We tested that possibility and found that
one of the genes, dhkA, regulates both development and
dedifferentiation.
Materials and Methods
Growth, Development, and Dedifferentiation. Wild-type Dictyoste-
lium discoideum strains AX2 (19) and dhkA
⫺
(20) were grown in
HL5 and developed as described (21). At each stage (aggrega-
tion, finger, or Mexican hat) structures were harvested by
filtration through 77-
␮
m nylon mesh, resuspended in 20 mM
potassium phosphate, pH 6.4 (KK2)兾 20 mM EDTA and disso-
ciated by repeated pipetting. Cells were passed through a 32-
␮
m
nylon mesh, resuspended in HL5 at 1–2 ⫻ 10
6
cells per ml, and
shaken at 200 rpm at 22°C.
Viability. Cells were counted microscopically, 200 cells were
plated in association with Klebsiella pneumoniae (also known as
Klebsiella aerogenes) on nutrient agar, and plaques were counted
after 3–5 days.
Redifferentiation. Redifferentiation was tested as described (6)
with a minor modification: after incubation in HL5, cells were
washed in KK2 and developed on filters at 22°C. Aggregation
was monitored with a dissecting microscope.
BrdUrd Incorporation. Cells were shaken in HL5 supplemented
with 0.5 mM BrdUrd, harvested, and washed in KK2. Genomic
DNA was purified as described (22). DNA (10–50 ng) was
blotted on Hybond-N⫹ membranes (Amersham Pharmacia),
incubated with a 1:5,000 dilution of anti-BrdUrd antibody
(Roche) followed by incubation with 1:40,000 dilution of horse-
radish peroxidase-conjugated goat anti-mouse IgG antibody
(Jackson ImmunoResearch) and developed with SuperSignal
West Pico Chemiluminescent Substrate (Pierce). Signals were
visualized (BAS system, Fuji) and their intensity was quantified
(
IMAGEGAUGE, Fuji).
Microarrays. Test RNA (10
␮
g) was labeled by reverse transcrip-
tion using Cy5-conjugated (dT)
18
primers, and reference RNA
This paper was submitted directly (Track II) to the PNAS office.
Abbreviation: GO, Gene Ontology.
¶
To who correspondence regarding the Japanese cDNA project should be addressed.
E-mail: ytanaka@biol.tsukuba.ac.jp.
储
To whom correspondence should be addressed at: Department of Molecular and Human
Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030. E-mail:
gadi@bcm.tmc.edu.
© 2004 by The National Academy of Sciences of the USA
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was labeled with Cy3-conjugated (dT)
18
(18). Labeled cDNA was
purified, resuspended in distilled deionized water, mixed with
PerfectHyb Plus hybridization buffer (Sigma), and hybridized to
arrays containing nearly 8,000 targets by using a GeneTAC
hybridization station (Genomic Solutions, Ann Arbor, MI).
Arrays were scanned (ScanArray5000, GSI Lumonics, Billerica,
MA) and images were processed with
GLEAMS (NuTec Sciences,
Atlanta). Data were processed as described (18) and combined
into multiexperiment sets for all subsequent analysis (23) as
detailed in the Appendix, which is published as supporting
information on the PNAS web site.
Cluster Analysis and Generation of Gene Lists. The data analysis
procedure is described in the Appendix. Briefly, an analysis of
covariance (ANCOVA) model, which contains a categorical
term for hybridization batch effects and continuous terms for
representing each gene as a time function, was fitted to each time
course. Clustering was performed on the smooth time fit coef-
ficients from the three experiments. The three sets of coeffi-
cients were concatenated into a 15-element vector of coefficients
for each gene and clustering was performed recursively by
k-means while varying k from 2 to 10.
A directed filtering method was used to find genes that have
a pattern in dedifferentiation and not in development. The filter
selected for genes that: (i) have a poor fit to the developmental
consensus pattern; (ii) follow the pattern T
max
(Mex) ⬎ T
max
(Fin-
ger) ⬎ T
max
(Agg), where T
max
is the time of maximal expression
during dedifferentiation and Mex, Finger, and Agg represent the
respective developmental stages; and (iii) whose expression level
at the T
max
(Agg) in the dedifferentiation process is greater than
their level of expression at the aggregation stage of development.
To test for nonrandomness we generated 10,000 random filters
and measured group size and variance. Our group was both
unusually large and tight (P ⬍ 0.05 for both characteristics).
Gene Annotation. To annotate the putative function of selected
genes, we used Gene Ontology (GO), which is a controlled
vocabulary for describing gene function (24). Near-full-length
cDNA sequences of each microarray target were matched to the
sequences in the GO database (www.godatabase.org兾dev兾
database). The relevant identifiers were mapped to the GO data
structure. Groups with significantly high representation were
identified by comparing the number of genes in the experimental
group with a common GO category to the total number of genes
from that category on the entire array. The data are presented
graphically, with bar lengths representing the ratio between the
list frequency (no. of genes in list兾no. of genes at GO level) and
the array group frequency (no. of genes with specific GO
annotation on array兾no. of all array genes at particular GO
level). The x axis is the scale for that ratio.
Results
Dedifferentiation from Various Developmental Stages Occurs at Dif-
ferent Rates. We first established that dedifferentiation from
different developmental stages occurs at different rates. Cells
that developed for 10 hr (aggregates), 13 hr (fingers), and 16 hr
(Mexican hats) exhibited different morphologies and physiolo-
gies (Fig. 1A). These cells were dedifferentiated by disaggrega-
tion and incubation in growth medium. Dedifferentiation was
measured by using three parameters: cell division, DNA synthe-
sis, and erasure (Fig. 1). The time of cell division after dedif-
ferentiation was directly proportional to the initial developmen-
tal time (Fig. 1B). Aggregation stage cells started to divide after
6 hr, finger cells after 12 hr, and Mexican hat cells after 20 hr.
DNA synthesis was monitored by following the incorporation of
BrdUrd into nuclear DNA [differentiating Dictyostelium cells do
not replicate nuclear DNA (22)]. The aggregation stage cells
began to synthesize DNA after 3–6 hr, finger stage cells after
12–16 hr, and Mexican hat stage cells after 16–24 hr of dedif-
ferentiation (Fig. 1C). Finally, we monitored the time required
for dedifferentiated cells to reaggregate after dissociation. Dis-
aggregated cells can reaggregate rapidly, and that property is lost
during dedifferentiation in a process called erasure (6). We
found that erasure was also directly proportional to the time the
cells had been developing: aggregation, finger, and Mexican hat
stage cells erased after 5, 10, and 16 hr of dedifferentiation,
respectively (Fig. 1D).
In summary, the three measurements of dedifferentiation
time were internally consistent and directly proportional to the
initial developmental time (Fig. 1). They confirm and extend the
observation that dedifferentiation from the three stages occurs
at different rates (6, 10–13). We used these three initial condi-
tions to introduce variability into the system. We hypothesize
that if dedifferentiation were a regulated process, it would be
accompanied by an invariant set of transcriptional changes that
is independent of the original differentiated state.
Reversal of Developmental Gene Expression Patterns in Dedifferen-
tiation.
Dedifferentiation reverses development, so we expected
that the transcriptional profile of dedifferentiating cells would be
reversed relative to that of developing cells. We dedifferentiated
cells from the three developmental stages and analyzed their
RNA with a microarray that represents nearly 6,000 genes.
Previously we showed that 2,000 Dictyostelium genes are robustly
coregulated during development. Half of them are up-regulated
and half are down-regulated between 8 and 12 hr of development
(Fig. 2, Development) (18). We found that during dedifferen-
tiation, these genes were coregulated with a reversed pattern
(Fig. 2). During the first hour of dedifferentiation, cells from the
three stages exhibited similar transcriptional profiles: develop-
mentally induced genes were expressed at a high level and the
others at a low level. We define this period as phase I of
Fig. 1. Dedifferentiation markers. Cells were developed on filters to the
indicated stage [Agg, aggregation (䉬); Finger (
■
); Mex, Mexican hat (
Œ
)],
dissociated, incubated in HL5, and sampled as indicated.(A) Developmental
morphology. Aggregates appear after 10 hr, fingers after 13 hr, and Mexican
hats after 16 hr of development. (B) Proliferation. Dedifferentiating cells were
counted and the data were plotted relative to the initial density. Data are
means and standard deviation of four experiments. (C) Nuclear DNA synthesis.
Dedifferentiating cells were labeled with BrdUrd as indicated. Nuclear DNA
was dot-blotted and BrdUrd incorporation was detected (photographs). La-
beling intensity was plotted relative to the terminal level in each experiment
(graphs). (D) Erasure. Dedifferentiating cells were starved on filters and
reaggregation was determined microscopically (right y axis). Erasure is the
fraction (percent, left y axis) of the time required for vegetative cells to
aggregate (10 hr).
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dedifferentiation (Fig. 2). By the second hour, the distinction
between developmentally up- and down-regulated genes became
blurred (phase II). The duration of phase II was directly pro-
portional to the length of time the cells had been developing. In
aggregation cells, phase II was almost undetected at the 2-hr
resolution of the experiment and the genes exhibited a fairly
sharp transition. In finger cells, phase II lasted 4 hr and in
Mexican hat cells it lasted 10 hr. The difference between the up-
and down-regulated genes became clear again during phase III,
when proliferation began. Aggregation cells entered phase III
after 4 hr, finger cells after 6 hr, and Mexican hat cells after 16
hr of dedifferentiation (Fig. 2).
The division into three phases was independent of the length
of time the cells had been developing, but the duration of phase
II was directly proportional to the initial developmental time
(Fig. 2), similar to the duration of dedifferentiation described in
Fig. 1. We propose that after disaggregation, the cells remain
differentiated for about 1 hr (phase I). They then down-regulate
the expression of the developmental genes and degrade their
mRNA (phase II), and the duration of this phase is directly
proportional to the length of time the cells had developed.
Eventually, the cells begin to express growth genes (phase III) at
the time of DNA synthesis and cell division. These results were
verified by Northern blot analysis with the prespore genes cotA
and DP87, the prestalk gene ecmA, and the vegetative genes V14
and V18 (data not shown).
Invariant Gene Expression During Dedifferentiation. Fig. 2 showed
that developmentally coregulated genes are also coregulated
during dedifferentiation. To find dedifferentiation-specific
genes, we performed cluster analysis and selected genes that
were up-regulated during phase II, regardless of the length of
time the cells had developed (Fig. 3A). Expression of these 272
genes was observed at 1–3 hr of dedifferentiation in the aggre-
gation cells, 0.5–5 hr in the finger cells, and 0–8 hr in the Mexican
hat cells. Therefore, the down-regulation time was directly
proportional to the initial developmental time, but the time of
induction was inversely proportional. This observation suggested
that the dedifferentiation genes have already been expressed
during late development. We therefore examined the expression
of these genes during development and found them to be sharply
induced after 8 hr (Fig. 3A, Development). This finding accounts
for the early expression during dedifferentiation of finger (13-hr)
and Mexican hat (16-hr) cells and suggests that some dediffer-
entiation genes have a role in development as well.
The results in Fig. 3A raised the possibility that all dediffer-
entiation genes are also developmentally regulated. To test that
possibility, we used a filtering method to find genes that are
induced only during dedifferentiation; we found 122 genes that
were coordinately regulated during dedifferentiation but not
during development (Fig. 3B). The grouping of these genes was
independent of the original developmental stage, but their time
of maximal expression was directly proportional to the original
Fig. 2. Transcriptional profiles of dedifferentiation. RNA samples were collected from dedifferentiating cells at the indicated times (hr). Color charts (Agg,
dedifferentiation from aggregates; Finger, from fingers; Mex, from Mexican hats; Development, normal development) represent the expression pattern of 2,066
developmentally regulated genes (rows) (18). The gene order is identical in all charts. Vertical lines delineate three transcriptional phases (I, II, and III). Colors
indicate lower than average (blue), average (gray), and higher than average (yellow) expression as described (18). Scale: log
2
of the ratio between sample and
standard.
Fig. 3. Dedifferentiation-specific gene expression. Data were collected as in Fig. 2. Time (hr) is indicated above and the developmental stage below each chart
(Agg, dedifferentiation from aggregates; Finger, from fingers; Mex, from Mexican hats; Development, normal development). (A) The 259 genes up-regulated
in phase II. The gene order is identical in all charts (the genes are represented by 272 targets). (B) The 122 genes coregulated during dedifferentiation but not
during normal development.
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developmental time. Aggregation cells expressed these genes as
soon as dedifferentiation began and turned them down after 3 hr
of dedifferentiation; finger cells expressed them between 2 and
6 hr; and Mexican hat cells, from 8 hr on (Fig. 3B). The gene
group was found to be highly statistically significant for its large
size and its low variance (P ⬍ 0.05 for both characteristics).
The data in Fig. 3 show that dedifferentiation is accompanied
by the regulation of an invariant group of genes. This finding
strongly supports the idea that dedifferentiation is a regulated
process, because a random process would not be expected to
induce consistent gene expression profiles from cells that begin
the process from different developmental stages.
Biological Processes Implicated by Dedifferentiation-Specific Gene
Expression. Genes that may have important roles in biological
processes can be found by annotation of gene groups discovered
by microarray experiments (15), despite the fact that there is
little correlation between function and expression of individual
genes (25, 26). Because the annotation of the Dictyostelium
genome is incomplete, we compared the gene sequences with
sequences of several well annotated genomes. We then assigned
the GO classification of the closest homologue to the respective
Dictyostelium gene and used the classifications to annotate the
coregulated genes in Fig. 3. The chart in Fig. 4A describes the
GO annotation of the group described in Fig. 3A. This group
contains genes that participate in transcriptional regulation and
RNA metabolism (Fig. 4A, white bars). Interestingly, the max-
imal expression of these RNA metabolism genes (Fig. 3A)
coincides with the time of major changes in gene expression (Fig.
2), suggesting a true functional correlation. Other significant
groups of genes suggest a role for protein transport and ribosome
biogenesis (Fig. 4A, black bars). The most significant group is the
two-component signal transduction system (Fig. 4A, bottom
black bar), suggesting the intriguing possibility that signaling is
involved in the regulation of dedifferentiation.
Fig. 4B shows a similar analysis of the genes from Fig. 3B.It
implicates genes that participate in protein modification (prob-
ably protein degradation due to the ubiquitin cycle annotation)
and amino acid metabolism (Fig. 4B, white bars) as well as
protein targeting (Fig. 4B, black bar). These findings may reflect
an adaptation of the cells to the exogenous source of nutrients
as they cease the developmental utilization of endogenous
proteins as an energy source (5).
A Dedifferentiation-Induced Gene Is Required for Dedifferentiation.
The above analysis implicated several groups of genes as being
involved in dedifferentiation, but it only provided reasonable
correlations. To begin to test whether dedifferentiation-induced
genes have a causative relationship to the process, we tested the
effect of one gene on dedifferentiation. We selected dhkA
because it is a two-component signal transduction system gene
up-regulated during phase II (Fig. 3) and because it has a known
role in signal transduction (20, 27). dhkA
⫺
cells were developed
to the finger stage, disaggregated, and incubated in nutrient
medium to induce dedifferentiation. Monitoring cell number
revealed that the mutant cells dedifferentiated more slowly than
the wild type (Fig. 5A, WT). dhkA
⫺
cells started to proliferate
after 25–30 hr, whereas stage-matched wild-type cells divided
after 12 hr. To exclude the possibility that the difference between
the wild type and the dhkA
⫺
mutant was due to cell death, we
measured viability by testing plating efficiency. We found that
the two strains were nearly indistinguishable in viability during
growth (vegetative) and at the finger stage of development (Fig.
5B). The delayed dedifferentiation of the mutant strain suggests
that dhkA has a role in dedifferentiation.
Monitoring nuclear DNA synthesis revealed that the mutant
Fig. 4. Annotation of dedifferentiation genes. Genes from Fig. 3 were
GO-annotated and the ‘‘biological process’’ annotation of significantly en-
riched groups is shown (P values are indicated below). The GO tree levels of the
‘‘biological process’’ annotation are shown as numbers inside bars (ranging
from 2 to 9). The table on the right indicates the number of genes in each
group (List), genes with that annotation on the entire array (Total), the P
value, and the annotation. Bar lengths represent the fold enrichment (scale,
x axis). Indented bars are subgroups of bars immediately above, as indicated
by the branching pattern. Bar colors represent the group annotation at GO
level 2: white, physiological process; black, cellular process. (A) Genes regu-
lated during phase II of dedifferentiation and during development as shown
in Fig. 3A (P ⬍ 0.1). (B) Genes regulated during phase II of dedifferentiation
but not during development as shown in Fig. 3B (P ⬍ 0.15).
Fig. 5. Dedifferentiation of dhkA
⫺
cells. Wild-type and mutant aggregate
(WT, solid diamonds; dhkA
⫺
, hatched diamonds) and finger cells (WT, solid
squares; dhkA
⫺
, hatched squares) were dedifferentiated and sampled as
indicated. (A) Growth. Cells were counted and the data are plotted relative to
the initial density. Data are means and standard deviation of three or four
experiments. The WT data are from Fig. 1 A.(B) Viability. Plating efficiency of
vegetative and dissociated finger cells was measured. Viability is the percent-
age of microscopically visible cells that formed plaques. Results are means and
standard deviations of three experiments. WT, gray bars; dhkA
⫺
, hatched
bars. (C) Nuclear DNA synthesis. Cells were labeled with BrdUrd; incorporation
into nuclear DNA was determined and quantified as in Fig. 1 (photographs).
WT, gray bars; dhkA
⫺
, hatched bars. (D) Erasure. Dedifferentiating cells were
starved on filters and reaggregation time was monitored microscopically.
Erasure is the fraction (percent, left x axis) of the time required for vegetative
cells to aggregate (10 hr).
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began to synthesize DNA at 12–16 hr of dedifferentiation, much
like the wild type (Fig. 5C). Moreover, in reaggregation exper-
iments, dhkA
⫺
cells erased after 8–10 hr, just like the wild type
(Fig. 5D). These results show that dhkA does not affect dedif-
ferentiation by regulating DNA replication or erasure. There-
fore, dhkA has a specific effect on dedifferentiation because it
uncouples cell division from DNA synthesis and erasure. This
finding further supports the idea that dedifferentiation is a
regulated, multistep process because the steps can be separated
by a specific mutation.
Discussion
Our data support the hypothesis that dedifferentiation is a
regulated process because cells induced to dedifferentiate from
three developmental stages underwent an identical set of tran-
scriptional changes. In the first hour of dedifferentiation, devel-
opmental genes were still abundant in the cells and some
dedifferentiation genes began to be expressed. During the
second phase of dedifferentiation, the abundance of the devel-
opmental transcripts was greatly reduced, indicating a massive
down-regulation of gene expression and a degradation of many
transcripts. This process was also accompanied by the induction
of 381 genes. Annotation of these genes revealed that some are
probably related to mRNA metabolism and regulation of gene
expression, likely relations in light of the massive changes in
transcript abundance during phase II. Finally, in phase III, the
cells began to express vegetative genes. The compelling finding
is that the sequence of events and the specific regulation of
several hundred dedifferentiation genes were independent of the
initial developmental stage and the consequential variable rates
of dedifferentiation. In addition, all of the measured parameters
were coordinately regulated: the microarray profiles, DNA
synthesis, cell division, and erasure. It is unlikely that such
coordination could result from a stochastic unregulated process.
Annotation of the dedifferentiation genes revealed the
possible involvement of several processes, including signal trans-
duction, ubiquitin-mediated proteolysis, and regulation of
transcription. The signal transduction genes are from the two-
component system family (28, 29), including the histidine kinase
gene dhkA, a hybrid histidine kinase essential for proper termi-
nal differentiation (20, 27).
It is tempting to speculate about the function of a gene in
dedifferentiation based on its annotation, but a functional test is
better. We found that the signal transduction gene dhkA was
essential for dedifferentiation, supporting the idea that dedif-
ferentiation is a genetically regulated process because it is
mutable. This finding also indicates that signal transduction is
involved, and it adds confidence to the interpretation that the
other gene groups may regulate dedifferentiation. The promise
of manipulating the process genetically is supported by previous
studies of a strain (HI4) defective in dedifferentiation (8, 9, 13).
Unfortunately, the gene mutated in HI4 could not be cloned at
the time and the strain has been lost. The dedifferentiation
characteristics of dhkA
⫺
cells also indicate that regulation of
dedifferentiation is a stepwise process because dhkA is required
only for cell division, not for DNA replication or for erasure.
Finally, our findings suggest that dedifferentiation and devel-
opment have coevolved while using common genes, because
dhkA is a regulator of both processes and several hundred genes
are regulated during both processes. We propose that develop-
ment consists of checkpoints that ensure a return path to the
undifferentiated state in case development fails. The checkpoint
could condition developmental progression on the accumulation
of a protein, such as DhkA, that is essential for dedifferentiation.
If development cannot proceed, the cells may attempt to reini-
tiate development as some strains undergo several cycles of
development and dedifferentiation due to mutations in gene of
the lagC pathway (30). However, dedifferentiation is not merely
a reversal of development because it involves the coordinate
regulation of 122 genes in patterns not seen in development.
The selective advantage of dedifferentiation is obvious, but
the need for an intricately regulated process is intriguing. In
vertebrate wound healing and regeneration, regulation may
attenuate dedifferentiation to preserve information about tissue
proportioning (31, 32). The lack of regulation may be a causative
factor in cancer and other diseases (1–3, 33–41). Dictyostelium is
a soil amoeba, and the sheath that surrounds the multicellular
organism cannot protect it from all of the mechanical insults that
the soil environment may inflict. Therefore, Dictyostelium de-
differentiation probably evolved under the pressure of occa-
sional mechanical disaggregation during development. Regulat-
ing dedifferentiation allows the cells to reaggregate rapidly and
continue to develop if starvation persists. It also provides an
alternative: in the presence of food, cells can return to the more
advantageous process of growth. Because Dictyostelium and
higher eukaryotes share many signal transduction mechanisms
(42–44), further investigation of dedifferentiation in this model
organism may help identify molecules that regulate dedifferen-
tiation in vertebrates.
We are grateful to N. Whitehouse for assistance with the GO annotation
and to A. Kuspa, V. Lundblad, P. Hastings, and C. Thompson for critique
and discussion. The Japanese cDNA project team is grateful to Prof. N.
Ogasawara of the Nara Institute of Science and Technology for supporting
the project. This work was supported by National Institute of Child Health
and Human Development Grant P01 HD39691-01. Work in Japan was
supported by the Japan Society for the Promotion of Science–Research
for the Future Program (96L00105 and 00L01412) and a Grant-in-Aid
for Scientific Research on Priority Area C (12206001).
1. Gimm, O. (2001) Cancer Lett. 163, 143–156.
2. Dominguez-Malagon, H. & Gaytan-Graham, S. (2001) Ultrastruct. Pathol. 25,
497–516.
3. Matias-Guiu, X., Catasus, L., Bussaglia, E., Lagarda, H., Garcia, A., Pons,
C., Munoz, J., Arguelles, R., Machin, P. & Prat, J. (2001) Hum. Pathol. 32,
569–577.
4. Tsonis, P. A. (2000) Dev. Biol. 221, 273–284.
5. Loomis, W. F. (1975) Dictyostelium discoideum: A Developmental System
(Academic, New York).
6. Waddell, D. R. & Soll, D. R. (1977) Dev. Biol. 60, 83–92.
7. Soll, D. R. & Waddell, D. R. (1975) Dev. Biol. 47, 292–302.
8. Soll, D. R., Mitchell, L. H., Hedberg, C. & Varnum, B. (1984) Dev. Genet. 4,
167–184.
9. Kraft, B., Chandrasekhar, A., Rotman, M., Klein, C. & Soll, D. R. (1989) Dev.
Biol. 136, 363–371.
10. Finney, R., Varnum, B. & Soll, D. R. (1979) Dev. Biol. 73, 290–303.
11. Finney, R. E., Mitchell, L. H., Soll, D. R., Murray, B. A. & Loomis, W. F. (1983)
Dev. Biol. 98, 502–509.
12. Finney, R., Ellis, M., Langtimm, C., Rosen, E., Firtel, R. & Soll, D. R. (1987)
Dev. Biol. 120, 561–576.
13. Alexander, S., Cibulsky, A. M., Mitchell, L. & Soll, D. R. (1985) Differentiation
30, 1– 6.
14. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald,
A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) Nature 403,
503–511.
15. Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R.,
Armour, C. D., Bennett, H. A., Coffey, E., Dai, H., He, Y. D., et al. (2000) Cell
102, 109–126.
16. Good, J., Cabral, M., Sharma, S., Yang, J., Van Driessche, N., Shaw, C.,
Shaulsky, G. & Kuspa, A. (2003) Development (Cambridge, U.K.) 130, 2953–
2965.
17. Kibler, K., Nguyen, T. L., Svetz, J., Van Driessche, N., Ibarra, M., Thompson,
C., Shaw, C. & Shaulsky, G. (2003) Dev. Biol. 259, 193–208.
18. Van Driessche, N., Shaw, C., Katoh, M., Morio, T., Sucgang, R., Ibarra, M.,
Kuwayama, H., Saito, T., Urushihara, H., Maeda, M., et al. (2002) Development
(Cambridge, U.K.) 129, 1543–1552.
19. Watts, D. J. & Ashworth, J. M. (1970) Biochem. J. 119, 171–174.
20. Wang, N., Shaulsky, G., Escalante, R. & Loomis, W. F. (1996) EMBO J. 15,
3890–3898.
21. Shaulsky, G. & Loomis, W. F. (1993) Dev. Biol. 160, 85–98.
Katoh et al. PNAS
兩
May 4, 2004
兩
vol. 101
兩
no. 18
兩
7009
DEVELOPMENTAL
BIOLOGY

22. Shaulsky, G. & Loomis, W. F. (1995) Proc. Natl. Acad. Sci. USA 92, 5660–5663.
23. Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. (2003) Bioinformatics
19, 185–193.
24. Asburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A.,
Dolinski, K., Dwight, S., Eppig, J., et al. (2000) Nat. Genet. 25, 25–29.
25. Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Veronneau, S., Dow,
S., Lucau-Danila, A., Anderson, K., Andre, B., et al. (2002) Nature 418,
387–391.
26. Winzeler, E. A., Shoemaker, D. D., Astromoff, A., Liang, H., Anderson, K.,
Andre, B., Bangham, R., Benito, R., Boeke, J. D., Bussey, H., et al. (1999)
Science 285, 901–906.
27. Wang, N., Soderbom, F., Anjard, C., Shaulsky, G. & Loomis, W. F. (1999) Mol.
Cell. Biol. 19, 4750 –4756.
28. Thomason, P. & Kay, R. (2000) J. Cell Sci. 113, 3141–3150.
29. Loomis, W. F., Shaulsky, G. & Wang, N. (1997) J. Cell Sci. 110, 1141–1145.
30. Kibler, K., Svetz, J., Nguyen, T. L., Shaw, C. & Shaulsky, G. (2003) Dev. Biol.
264, 506–521.
31. Echeverri, K. & Tanaka, E. M. (2002) Semin. Cell Dev. Biol. 13, 353–360.
32. Liu, Y. & Rao, M. S. (2003) J. Cell. Biochem. 88, 29–40.
33. Clerc, P., Saillan-Barreau, C., Desbois, C., Pradayrol, L., Fourmy, D. &
Dufresne, M. (2002) Pharmacol. Toxicol. 91, 321–326.
34. Aigner, T. (2002) Virchows Arch. 441, 219–230.
35. Dei Tos, A. P. (2000) Ann. Diagn. Pathol. 4, 252–266.
36. Arendt, T. (2000) Neurobiol. Aging 21, 783–796.
37. Arendt, T., Holzer, M., Stobe, A., Gartner, U., Luth, H. J., Bruckner, M. K. &
Ueberham, U. (2000) Ann. N.Y. Acad. Sci. 920, 249 –255.
38. Heusch, G. & Schulz, R. (2002) Ital. Heart J. 3, 282–284.
39. Khan, I. A. (2003) Am. Heart J. 145, 787–794.
40. Previtali, M. (2001) Ital. Heart J. 2, 93–99.
41. Vignola, A. M., Gagliardo, R., Siena, A., Chiappara, G., Bonsignore, M. R.,
Bousquet, J. & Bonsignore, G. (2001) Curr. Allergy Asthma Rep. 1, 108–115.
42. Chung, C. Y., Funamoto, S. & Firtel, R. A. (2001) Trends Biochem. Sci. 26,
557–566.
43. Parent, C. A. & Devreotes, P. N. (1999) Science 284, 765–770.
44. Coates, J. C. & Harwood, A. J. (2001) J. Cell Sci. 114, 4349– 4358.
45. Takeuchi, I. & Sakai, Y. (1971) Devel. Growth Differ. 13, 201–210.
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