1 Department of Molecular and Cellular Biology, Harvard University, Cambridge,
MA 02138, USA
2 Department of Genomics, Wyeth Research, Cambridge, MA 02140, USA
Author for correspondence (e-mail:
hunter{at}mcb.harvard.edu)
Accepted 3 February 2005
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SUMMARY |
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Key words: C. elegans, Regulatory network, Patterning, Embryonic, Cell fate specification
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Introduction |
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Each of the six founder blastomeres gives rise to different cell types by
characteristic patterns of cell division (lineages)
(Sulston et al., 1983).
Founder blastomere fates are specified by a variety of spatially and
temporally restricted maternal gene activities. In addition, the embryo
contains a global anteroposterior (AP) patterning system that differentially
specifies the daughters of blastomeres dividing on the AP axis
(Kaletta et al., 1997
;
Lin et al., 1998
). When
gastrulation commences at the 26-cell stage, all of the founder blastomeres
have been born and tissue identity begins to be specified, as indicated by the
initial expression of cell fate-specific genes, whose functions are required
for the development of specific cell types, and increasing resistance of the
embryo to cell fate transformations induced by ectopic expression of such
genes (Gilleard, 2001
; Zhu,
1998). It has been noted that although such fate-specific genes are expressed
in multiple lineages (most tissues being polyclonal), the cells expressing
them are born at about the same time and in regional domains as if in creation
of a tissue or organ primordium (Labouesse
and Mango, 1999
). From work on the C. elegans pharynx it
is clear that organ identity genes control autonomous organ-specific genetic
networks (Gaudet and Mango,
2002
). However, it remains to be determined how the lineage-based
mechanisms in the early embryo work together with the global AP patterning
system to pattern tissue and organ identity gene expression, thus causally
relating the maternal genetic network that patterns the early embryo with the
zygotic networks that pattern later developmental structures.
The fate of the C and D founder blastomeres, the somatic descendants of P2,
is specified by the Caudal-like homeobox gene pal-1. Maternal PAL-1
activity is temporally and spatially targeted to the C and D founder
blastomeres by first restricting translation of maternal pal-1 mRNA
to the descendants of the posterior blastomere P1 (EMS and P2) and then by
restricting the activity of the translated protein to the somatic descendants
of P2 (C and D) (Hunter and Kenyon,
1996). The KH domain protein MEX-3 is required to restrict
translation of maternal pal-1 mRNA to the posterior blastomeres at
the four-cell stage (EMS and P2) (Draper
et al., 1996
; Huang et al.,
2002
; Hunter and Kenyon,
1996
), while the bZIP transcription factor SKN-1 blocks PAL-1
function in EMS, and the zinc-finger protein PIE-1 maintains the germline
blastomeres P2 and P3 in a transcriptionally quiescent state so that PAL-1
activity is restricted to their somatic descendants, C and D
(Bowerman et al., 1993
;
Hunter and Kenyon, 1996
;
Mello et al., 1996
;
Seydoux et al., 1996
).
The C lineage gives rise primarily to muscle and epidermis but also two
neuronal cells and a cell death (Sulston
et al., 1983). In the absence of maternal PAL-1 activity, the C
and D blastomeres fail to develop in any discernible way, while ectopic PAL-1
activity causes other blastomeres to produce muscle, epidermal and neuronal
cells by a C-like lineage (Draper et al.,
1996
; Hunter and Kenyon,
1996
). Although other somatic lineages also give rise to muscle
and epidermal cells, the lack of discernable C cell fates in the absence of
maternal PAL-1 function (Hunter and
Kenyon, 1996
), indicates that PAL-1 activates cell fate
specification factors (tissue identity genes) in the C lineage.
To learn how maternal PAL-1 activity leads to the patterned specification of multiple cell fates within a single blastomere lineage, we aim to identify the genes directly and indirectly activated by PAL-1 and determine their loss-of-function phenotypes and regulatory interactions. To identify genes expressed in the C lineage by microarray we have used pie-1 and mex-3 mutations, as well as skn-1 RNAi, to produce embryos that either lack a C blastomere or that contain almost exclusively C-like blastomeres. Our results are verified by reporter gene analysis and complemented by phenotypic analysis of PAL-1 targets, and a model for the regulatory network specified by PAL is presented.
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Materials and methods |
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Microarray hybridization and data reduction
Biotinylated, amplified RNA (1 µg) was hybridized to the Affymetrix
C. elegans microarray as described
(Baugh et al., 2003). Array
data were quantile normalized and reduced by the robust multi-chip average
algorithm (RMA) (Irizarry et al.,
2003
), using the Bioconductor Affy package (version 1.0,
www.bioconductor.org)
for the R statistics software (version 1.5.0,
www.r-project.org).
All expression levels reported here were back-transformed to the linear scale,
i.e. reported values are 2(RMA). All raw data have been submitted
to the Gene Expression Omnibus database, Accession Number GSE2180, and
averaged data and analysis are available in the supplementary material.
Clustering of gene expression profiles
Clusters were generated by a modified version of the QT clustering
algorithm (Heyer et al.,
1999). This algorithm assembles a series of clusters ordered by
size with a defined limit on the largest pair-wise distance allowed between
any two profiles in a cluster. Distance between profiles is measured as 1-R,
where R is the Pearson correlation coefficient. Although we limited this
distance to 0.3, some genes are included in clusters simply by chance. To
reduce the spurious inclusion of these genes in the final clusters, we
systematically re-sampled our data (100 times) with two forms of synthetic
noise added at each reiteration to generate an Ravg. Noise was
added to log2 scale RMA expression data, and was generated by a
two-component model consisting of an additive Gaussian background with
standard deviation 0.2, and a multiplicative Gaussian sampling error with a
standard deviation of 0.05. Simulated data were floored at 1 RMA unit. Graphs
plotting average expression for each cluster, and the cluster to which each
gene belongs can be found in Fig. S1 and Data S1, S2 in supplementary
material.
ANOVA
Analysis of variance (ANOVA) was performed by using a randomization test to
assess differences in expression among genotypes at each time point. Tests
were performed to assess for each gene, overall variation among all three
genotypes at each time point, and variation between each of the three possible
pairs of genotypes at each time point. All statistical tests were performed on
the log2 scale data. Data for the pie-1(zu154) and
pie-1(zu154); pal-1(RNAi) genotypes were pooled to form a single
group, denoted `pm'. Sample labels were randomly shuffled 100 times, and for
each shuffling, at each time point, a null distribution of F-statistics were
computed among all three genotypes, and between each of the three pair-wise
combinations of genotypes at each time point [N2 versus mex-3(zu155);
skn-1(RNAi), N2 versus pm, and mex-3(zu155); skn-1(RNAi) versus
pm). P-values for differential expression among groups were
determined by referring the F-statistics from the observed data to the null
distribution arising from the random permutations. P-values are not
adjusted for multiple testing. The total number of statistical tests per gene
was 40 (10 time points with three pair-wise comparisons and 1 overall
comparison).
Target scoring
Clustering, correlation to known target genes and ANOVA were used to score
the PAL-1 target potential of each gene. Only genes with maximum expression
over time and genotype greater than the median of all genes over time and
genotype (transcript abundance of thirteen RMA units) were considered as
potential targets. Clusters 24, 50, 60, 140, 187, 141, 168, 85, 105, 130, 131,
144, 177, 195 and 88 were selected as potential target clusters. Because our
selection of pal-1 target clusters was subjective, each gene
belonging to one of these clusters was given a target score of only 1. We also
leveraged our limited prior knowledge to give genes a target score of 1 if
they were either one of the ten best correlated with vab-7 over time
and genotype or one of the 100 best correlated with cwn-1. vab-7 and
cwn-1 had been validated as target genes and are both expressed
specifically in the C lineage. The decision to include the 10 and 100 best
correlated genes for each was based on inspection of expression patterns.
The most rigorous approach was model-based and quantitative ANOVA, which
was used to assign a target score of 1-5, depending on the P-value
assigned to each gene at each time point for the observed differences in
expression between the three genotypes. For each gene at each time point it
was also determined if it was higher in mex-3(zu155); skn-1(RNAi)
than wild type and lower in pie-1(zu154) than wild type. Genes with a
P-value less than a given cut-off and appropriate differences between
genotypes were noted. With this information, a target score was generated for
each gene in two ways and the maximum was kept. The first relies on the gene
satisfying both criteria in a pair of adjacent time points. Genes with a
P-value below 10-2 for two adjacent time points were given
a score of 1, those below 10-3 were given a score of 2, those below
10-4 were given a score of 3, those below 10-5 were
given a score of 4, and those below 10-6 were given a score of 5.
The adjacency requirement excludes late genes that show differences between
genotypes in only the last time point, and so the second target score requires
the gene to satisfy both criteria in only a single time point. Genes with a
P-value below 10-4 were given a score of one, those below
10-5 were given a score of two, and those below 10-6
were given a score of three. A nominal P-value of 10-4 (or
10-2 twice) with 10,000 genes considered at ten time points
with two models (pair of adjacent time points and single time point) should
result in about 20 false positives; however, the actual number of false
positives should be less given the additional requirement that the genotypes
differ in specific ways.
To assign each gene a final target score the max of the ANOVA-based models is added to the score from cluster analysis (1 or 0) and correlation to known targets (1 or 0) producing a maximum score of 7. The target score therefore relies heavily on the ANOVA analysis, with a score of 7 indicating that the gene is in a potential target cluster, correlates well with either of the known targets, and has a P-value below 10-6 in a pair of adjacent time points. By contrast, genes with a score of 1 are either in a potential target cluster or well correlated with a known target or have a minimally sufficient P-value.
Reporter analysis
Reporter constructs were made by PCR
(Hobert, 2002). 5'
genomic sequence, either 5 kb or up to the next gene, was used as promoter.
YFP was from pPD132.112, which includes C. elegans introns, a nuclear
localization signal and the unc-54 3'UTR; for additional
information see
http://www.ciwemb.edu/pages/firelab.html
(Fire et al., 1990
). Either 4
kb from pRF4 containing a dominant rol-6
(Mello et al., 1991
) or a 2.2
kb sequence unc-119 rescue sequence from pDPMM051
(Maduro and Pilgrim, 1995
) was
included in the final PCR construct as co-transformation marker. The
vab-7 reporter (HC14) was made by ligating a 5 kb
AgeI/PstI GFP fragment from pPD104.53
(Fire et al., 1990
) to a 14 kb
AgeI/PstI digestion pJA15
(Ahringer, 1996
) to produce
pHC16. JK3363 (Mathies et al.,
2003
) was used rather than our hnd-1 reporter, as our
reporter was too dim to score. Either N2 or CB4845 [unc-119(e2498)]
was injected with PCR product diluted 10-fold in water as described
(Mello et al., 1991
);
rol-6 plasmid (pRF4) was co-injected at 50 ng/µl where
rol-6 was used as co-transformation marker, including HC14. Stable
extrachromosomal lines were used for initial reporter analysis, and select
reporters were chosen for chromosomal integration by gamma irradiation as
described (Inoue et al.,
2002
). Table S1 in the supplementary material provides strain
names and oligonucleotide sequences used.
RNAi
Hairpin RNAi feeding vectors were made as described
(Winston et al., 2002) for
pal-1, mex-3 and skn-1, and transformed into E.
coli HT115; for protocol see
http://mcb.harvard.edu/hunter/Protocols/protocols.htm.
JJ532 was grown on both OP50 and HT115 expressing double-stranded
pal-1 RNA, but no differences were detected by microarray (data not
shown) and the data were merged. JJ518 was only grown on HT115 expressing
double-stranded skn-1 RNA. For the reporter assay, in addition to
being grown on RNAi food, worms were soaked in double-stranded RNA as
described (Maeda et al., 2001
)
(Table 1).
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Results |
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Embryos from homozygous mex-3(zu155) mothers translate
pal-1 mRNA throughout the embryo, transforming the eight
great-granddaughters of the AB blastomere into C-like blastomeres
(Fig. 1)
(Draper et al., 1996). These
eight anterior blastomeres, born at approximately the same time as the C
blastomere, produce eight serially homologous lineages giving rise to the
muscle and epidermal cell types characteristic of the C lineage. This
transformation requires pal-1 function
(Hunter and Kenyon, 1996
),
thus expression of PAL-1 targets should be greater in mex-3(zu155)
than in wild type. To sensitize our ability to detect PAL-1 targets further,
skn-1 RNAi was also used in the mex-3(zu155) background,
because, in the absence of skn-1 function, PAL-1 is active in the EMS
lineage transforming its daughters into C-like blastomeres
(Bowerman et al., 1992
;
Hunter and Kenyon, 1996
).
To test the feasibility of using mutants to deconvolve lineage-specific
patterns of gene expression and to develop computational rules to enrich for
lineage-specific transcripts, we analyzed a known, skn-1-dependent
lineage-specific transcriptional cascade. In contrast to PAL-1 targets (genes
regulated directly or indirectly by PAL-1), SKN-1 targets should be more
abundant in pie-1(zu154) and less abundant in
mex-3(zu155); skn-1(RNAi)
(Fig. 1). SKN-1 directly
activates its earliest zygotic targets, the redundant GATA transcription
factors med-1 and med-2, in the EMS blastomere, initiating a
transcriptional cascade resulting in expression of new genes after each cell
division (Maduro, 2001; Maduro and
Rothman, 2002). med-1 and med-2 activate the
expression of two more GATA factors, end-1 and end-3,
specifically in the E lineage, which activate another GATA factor,
elt-2, that activates the expression of the gut esterase gene
ges-1 (Fig. 2).
med-1 and med-2 are expressed at too low abundance to detect
quantitatively in wild type (Baugh et al.,
2003
), but they show an expected increase in
pie-1(zu154) at 23 minutes (eight-cell stage).
end-1 transcripts are about 1.5-fold more abundant in
pie-1(zu154) and are not detected following skn-1
RNAi. Although end-3, elt-2 and ges-1 are not detected at
increased abundance in pie-1(zu154), all three are not
detected following skn-1 RNAi. These observations indicate that
skn-1(RNAi) appears to phenocopy a null mutant with respect
to target gene expression. Furthermore, the times of activation and maximum
expression of the skn-1 target genes are equivalent in the mutants
and wild type, suggesting that the transformed lineage is developing at the
same molecular rate as its native sister. However, the fact that only
end-1 shows elevated expression in pie-1(zu154) indicates
that few target genes will behave as expected and that we must use flexible
rules to identify candidate PAL-1 target genes.
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The muscle-specific genes hnd-1, hlh-1 and unc-120 are
expressed in muscle precursors in the MS, C and D lineages
(Krause et al., 1990;
Mathies et al., 2003
)
(Fig. 5,
Fig. 8). Although the
expressing cells are derived from three lineages, they are born in the
ventral-posterior region of the embryo and within two cell divisions form four
longitudinal stripes pre-ordaining the four quadrants of body wall muscle
present at hatching (Fig. 5, Fig. 8). To determine if the
decision to express these tissue identity genes is controlled in a
cell-autonomous fashion, we examined reporter gene expression more carefully
following RNAi of pal-1. Consistent with a lineage-based mechanism,
pal-1 function is required for expression of hnd-1, hlh-1
and unc-120 reporter genes in the C and D lineages but not MS
(Fig. 5), where we presume an
unknown lineage-specific factor is required for their expression.
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Predicting regulatory network structure from expression
To model the regulatory network specified by PAL-1, we focused on the
expression patterns of validated targets most likely to directly effect gene
expression. This includes 12 transcription factors, an uncharacterized
wingless ligand (cwn-1) and a conserved novel protein known to affect
cell fate decisions in the male tail (mab-21). Global analysis of
transcript abundance in wild-type embryos has shown that most temporally
modulated embryonic genes are expressed transiently, with abundance increasing
for one cell cycle and decreasing for one after that
(Baugh et al., 2003),
consistent with the timing of gene expression during specification of
endodermal cell fate (Maduro and Rothman,
2002
). The first three divisions of the C blastomere are
asymmetric in either cell fate or gene expression
(Ahringer, 1996
;
Sulston et al., 1983
),
suggesting that the PAL-1 network may also operate on a one cell cycle time
scale. Consistent with this expectation, transcription of these target genes
is activated in one of four temporal phases, each a cell cycle apart
(Fig. 7). Twelve out of the 14
selected targets are expressed in two waves corresponding to the 4C-cell stage
(phase II) and the 8C-cell stage (phase III). mab-21 is the only
phase IV gene among the 14, and pal-1 is considered phase I because
its zygotic transcripts are first detected by in situ hybridization at the
2C-cell stage in Ca and Cp (Hunter and
Kenyon, 1996
).
To generate a working model of the regulatory network, we began with the simple notion that targets from one phase regulate targets of the next, with transcription factors regulating targets expressed in the same cells and signaling molecules regulating targets in adjacent cells from where each is expressed. As YFP expression perdures, we compared spatial expression patterns of each target at the end of phase IV, capturing the spatial expression patterns initiated earlier. Fig. 8 shows dorsal and lateral views of volume rendered images of transcriptional reporters (see also Movies 1-13 in the supplementary material). The expression patterns are summarized in Table 4, and are consistent with published descriptions. hnd-1, hlh-1 and unc-120 are expressed in all of the embryonic myoblasts, but hnd-1 is phase II and hlh-1 and unc-120 are phase III, suggesting that hnd-1 is a positive regulator of hlh-1 and unc-120. We have extended this logic, combining spatial expression with quantitative temporal data to identify the best candidate upstream activator for each of the 14 target genes (Table 4). This set of predictions provides a first draft model of the transcriptional network specified by PAL-1 in the C lineage (Fig. 9). Expression of all fourteen targets is by definition dependent on pal-1 function, and we presume that both maternal and zygotic PAL-1 act directly on phase II targets, but we do not know if PAL-1 acts directly on phase III or IV targets. The model predicts which phase II targets activate which phase III targets, as well as the activation of the phase IV target mab-21 by the phase III target elt-3, and it can easily be extended for other validated targets.
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Discussion |
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The identification of PAL-1 targets confirms previous phenotypic analysis
(Hunter and Kenyon, 1996) by
demonstrating that it controls development of the C lineage, including
specification of multiple cell types. PAL-1 activates expression of target
genes responsible for various developmental functions such that their
disruption phenocopies different aspects of the pal-1 mutant
phenotype (Table 3). Although
many of the target genes function in the descendants of additional founder
blastomeres, their expression in the C lineage depends on PAL-1
(Table 1). We specifically show
three muscle-specific genes to require pal-1 function for C-lineage
expression (Fig. 5) and find
the same to be true for epidermis-specific expression
(Table 1). If specification of
cell fate were controlled by regionalizing influences as opposed to
lineage-based mechanisms, we would not expect expression of fate-specific
genes to depend on pal-1 function (unless pal-1 were
required to respond to regionalizing influences). Furthermore, expression of
the posterior HOX gene nob-1 is complex with respect to lineage but
simple with respect to region, yet we show that nob-1::YFP expression
in the C lineage requires PAL-1.
We propose a model for the structure of the network specified by PAL-1
based only on temporal and spatial expression patterns in wild type
(Fig. 9). The rationale behind
the model is simple: genes activated in one cell cycle affect the expression
of genes expressed in the next cell cycle. This premise is supported by both
functional analysis of the endodermal network
(Maduro and Rothman, 2002) and
global analysis of expression dynamics
(Baugh et al., 2003
).
Furthermore, whereas zygotic pal-1 transcripts are first detected at
the 2C-cell stage in Ca and Cp (phase I), loss of zygotic pal-1
function results in a detectable mutant phenotype in their daughters at the
4C-cell stage (Edgar et al.,
2001
; Hunter and Kenyon,
1996
). In addition, protein for the phase II gene elt-1
is first detected at the end of the 4C-cell stage, and transcription of its
confirmed phase III target elt-3 begins in the 8C-cell stage
(Fig. 7)
(Gilleard and McGhee, 2001
;
Page et al., 1997
).
That ectopic PAL-1 activity in early blastomeres is sufficient to cause
complete transformation of one lineage into another indicates that the
regulatory network specified by PAL-1 is modular or self-contained
(Draper et al., 1996;
Hunter and Kenyon, 1996
).
After maternal PAL-1 specifies the C lineage, embryonically expressed PAL-1 is
required for C-lineage development (Edgar
et al., 2001
). We therefore hypothesize that PAL-1 continuously
regulates target genes during patterning of the C lineage, as opposed to
simply initiating a transcriptional cascade. Although it is not known how far
into development PAL-1 function is required, phenotypically mutant
pal-1 mosaic animals were recovered corresponding to loss of
pal-1 in one Cxx cell at the 4C-cell stage and PAL-1 expression is
detectable in the C lineage until the 16C-cell stage
(Edgar et al., 2001
), leaving
open the possibility that PAL-1 directly activates each of the target genes
identified here. Combinatorial control of gene expression, where early targets
regulate late targets in combination with PAL-1, offers one possible mechanism
for the timing of gene expression within this modular network
(Mangan and Alon, 2003
;
Penn et al., 2004
).
There must be additional regulation not predicted by our model. Genes that
are not PAL-1 targets are likely to participate in transcriptional regulation
and patterning of the C lineage. For example, the Homothorax ortholog
unc-62 and the Extradenticle homologs ceh-20 and
ceh-40 have superficially similar phenotypes to nob-1 and
pal-1, suggesting that these co-factor homeodomain proteins interact
with and modify the function of PAL-1 and NOB-1
(Van Auken et al., 2002).
Likewise, the Tcf/Lef factor pop-1 is thought to mediate cell-fate
decisions associated with every cell division on the AP axis of the early
embryo (Lin et al., 1998
),
and, as has been shown for development of the E lineage
(Calvo et al., 2001
;
Maduro et al., 2002
), we
expect POP-1 to contribute to patterning of PAL-1 target expression, in
particular where targets are expressed in only the anterior or posterior
daughters following a round of C cell divisions (e.g. hlh-1, elt-1
and vab-7). Repression is completely ignored in the current model,
but is probably crucial for patterning, as indicated by the fact that very few
targets are expressed in all PAL-1-expressing cells. So there may also be
genes repressed by PAL-1. We have not allowed for genes of the same temporal
phase to regulate each other, though it is likely that there is mutual
repression between genes specifying muscle and epidermis, leading to
insulation of the two states. In addition, genes of the same temporal phase
expressed in the same cells may activate the expression of one another, and we
imagine multiple auto-regulatory positive feedbacks in addition to the one
demonstrated for pal-1. It will be interesting to compare the
structures of different developmental regulatory networks in an effort to
understand better how different topological motifs contribute to the
functional properties of the regulatory network
(Milo et al., 2002
;
Shen-Orr et al., 2002
) and
ultimately how network structure relates to body plan.
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ACKNOWLEDGMENTS |
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![]() |
Footnotes |
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Supplementary material for this article is available at http://dev.biologists.org/cgi/content/full/132/8/1843/DC1
* Present address: Biology Division, California Institute of Technology,
Pasadena, CA 91125, USA
Division of Genetics, Department of Pediatrics, University of Florida
College of Medicine, Gainesville, FL 32610, USA
Department of Computer Science, Tufts University, Medford, MA 02155,
USA
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