HHMI Report 2005
Cell Signaling and Morphogenesis in Drosophila
1.
Gene manipulation methods
Since the realization, half a century ago, that genes encode the
building blocks of cells, identifying their functions has become a priority in
the life sciences. Linking genotype to phenotype has been the most rewarding
approach to identify the function of genes and over the years many advances in
the field have been made possible by the development of methods that allow
precise spatial and temporal control of gene activity.
For example, a particular challenge in mosaic analyses is to
easily detect marked clones in internal tissues. Towards this goal, we
developed the "Positively Marked Mutant Lineages" (PMML) technique
(Harrison and Perrimon, 1993) that allows one to generate clones of mutant
cells that express either GFP or LacZ. More recently, we have improved this
system based on the reconstitution of an actin-Gal4 gene following an
FRT-mediated recombination event between two homologous chromosomes. It differs
from a related technology developed by Lee and Luo (1999), which is based on
the loss of the Gal80 repressor. In particular, it resolves the inherent
problem associated with perdurance of the Gal80 repressor. Our method
facilitates the detection of small marked clones of mutant cells, which is
especially powerful for the analysis of stem cell lineages.
As
we have learned, in particular from studies in yeast, temperature sensitive
(TS) mutations are a powerful tool to identify gene functions, especially in
the context of those with pleiotropic roles. Because TS mutations are rare and
difficult to recover, we designed a general method to generate TS proteins
based on Conditional Splicing Inteins (Zeidler et al., 2004). Recently, in order to
generate a collection of TS mutations that will be useful to study developmental
processes, we have initiated a large-scale screen using a "gene trap TS
Intein" Piggyback transposon. In addition, to add a different inducible
level of control, we are considering developing Inteins that are controllable
by a small molecule that could be fed to the flies.
2.
Functional genomic screens using high-throughput RNAi screens
The availability of the Drosophila genome sequence in year 2000
(Adams et al., 2000) has provided us with an unprecedented resource for
functional genomic studies. To address the issue that 75% of the genome is not
yet functionally annotated, and to systematically analyze the functions of the
~14,000 predicted genes, we established a High-Throughput Screening (HTS)
platform to conduct RNA interference (RNAi) screens in Drosophila tissue culture cells in the
384 well plate format. This approach is possible because, as first shown by
Clemens et al. (2000), the simple addition of long, double-stranded RNAs
(dsRNAs) to Drosophila cells in culture reduces or eliminates specifically the
expression of target genes, and thus efficiently phenocopies loss-of-function
mutations. Genome-wide RNAi screens allow, once an appropriate cell-based assay
has been established, to literally identify most genes involved in the process
under investigation in a matter of a few weeks. Results from the tissue culture
screens are then followed up by in vivo validation using genetic approaches that
include: existing mutations, P-element insertions and transgenic RNAi
constructs.
The development of this
platform has required the establishment of a screening infrastructure and the
development of new protocols (Armknecht et al., 2005). Following our initial
success with the technology (Kiger et al., 2003), we generated in collaboration
with Renato Paro's group (Heidelberg Germany), the first Drosophila genome-wide library of 21,300 dsRNAs directed against all
predicted open reading frames (ORFs) (Boutros et al., 2004). We then
evaluated the robustness of this screening method in a diverse set of cell-based
assays that are based on transcriptional reporter assays, protein modification
(i.e., using a phospho-specific antibody) or high-content imaging. As part of
these studies, we have developed protocols for the experimental and analytical
optimization of the screens, including normalization strategies, identification
of "off-target" effects, and statistical methods for plate analysis.
In addition, with regards to the high-content imaging screens, we have made
significant progress in developing automated image analysis algorithms and data
mining tools. Importantly, we have applied this methodology to existing cell
lines, but have also demonstrated that RNAi screens can be performed in primary
cells established from embryos, thus expanding the repertoire of assays that
can be developed and questions that can be addressed. We have also established
at Harvard Medical School a Drosophila RNAi Screening Center (hrttp//flyrnai.org) that is open to the scientific community for
performing screens based on the technology and "know how" that we
developed. The center is very successful as, in the short time that it has been
in operation, more than 28 screens have been conducted and 53 are in the
pipeline.
The screens that we have
conducted to date fall under three categories: signal transduction, cell
biology and differentiation, and host/pathogen interactions (Table 1). These
are somewhat complementary as aspects of signal transduction involve cell
biological processes (e.g., trafficking, cytoskeletal regulation), and
host/pathogen interactions rely, in part, on processes such as trafficking. In
addition, we have successfully conducted screens in primary cells. In
particular, we have completed screens for axonal outgrowth and neuron viability
(Sepp et al., in prep.) as well as muscle differentiation (Bai et al., in
preparation). Finally, in
collaboration with Tim Mitchison's laboratory (HMS), we have demonstrated the
power of comparative RNAi and compound screens to identify protein targets of
small molecules (Eggert et al., 2004).
The goals behind the
large-number of RNAi screens that we have performed were to: 1. determine how
widely this new screening methodology could be applied and evaluate its
robustness; and 2. generate a database of "RNAi signatures" of all Drosophila genes; a resource that can now be "mined" using
clustering algorithms to establish correlations between groups of genes that
allows discovery of their functions. Importantly, the diversity of the screens
conducted and their quantitative natures have allowed us to obtain functional
information for 8,500 of the 13,918 predicted ORFs (release 3.2), which is
significant since previously functional information from other approaches was
only available for 2,700 genes (W. Gelbart/Flybase, pers. comm.).
Altogether, these screens have identified hundreds of new genes
involved in the various assays. We have used combinatorial RNAi to gain insight
into the interaction and epistatic relationships between the factors
identified. In addition, we have
validated some of the candidate genes either in the fly (Baeg et al., 2005), or
with collaborators in other systems, such as in mammalian cells (Cherry et al.,
2005; Nybakken et al., 2005; Phillips et al., 2005) and in Zebrafish (Dasgupta
et al., 2005).
A number of important conclusions can be drawn from our RNAi
screens. First, the quality of the data is impressive as the levels of false
negatives and positives are very low (approx. 5 to 10%). Second, it is
extremely powerful to conduct overlapping screens to quickly filter factors
that are shared between pathways or processes from those that are unique. For
example, in a comparative screen for host factors that are required either for
entry or growth of the intracellular pathogens Listeria and Mycobacterium, we were able to identify
factors that were required for both and others that were specific to each
pathogen (Phillips et al., 2005; Agaisse et al., 2005). Third, the results from
the screens can provide exquisite quantitative information, allowing us to
probe pathways beyond the usual levels of sensitivity that can be reached using
more classic genetic approaches, such as suppressor/enhancer and synthetic
lethal screens. Thus, the RNAi methodology is emerging as a unique tool for "quantitative
genetics",
and will most likely play an important role in the identification of factors
involved in "genetic
background" and disease susceptibility.
RNAi screens in tissue culture are attractive because they can be
conducted relatively fast and also have technical features (i..e.,
multiplexing, subcellular resolution) that are not easily achievable in vivo. However, we have to be
cautious that the findings made in culture are meaningful in vivo. Thus, in the future we will
give priority to RNAi screens in primary cells, whose differentiation programs
follow closely their in vivo differentiation (Bai et al., in preparation; Sepp et al., in
preparation). In addition, to facilitate and increase the throughput of in
vivo
validation, we are currently working on transgenic RNAi methods to optimize the
generation time and robustness of the technology. A major limitation of the
technology resides in the reproducibility of the expression levels of the
hairpin, an issue that we propose to resolve using the Phage phiC31 integrase
method recently developed by Groth et al. (2004).
4. Network analysis of
signal transduction pathways
The
canonical and linear view of signal transduction pathways has been extremely
useful to identify components that biochemically interact. However, the
inherent simplicity of this organizational view of pathway integrity is now
hindering new conceptual advances in the field. For example, the concept of a
linear flow of information is simplistic in light of the complexity of feedback
loops, as we have identified for example in the context of the Tor/S6Kinase
pathway (Kockel et al., 2005). In addition, the growing number of regulatory
interactions between signaling cassettes, usually referred to as
"cross-talk", challenge the view of modularity in signaling networks.
Perhaps a more physiologically significant way to analyze signaling is to study
the flow of information through interconnected signaling networks.
The
motivation behind our RNAi screens is not simply to facilitate functional gene
discovery but to gain insights into the structure of signaling networks. Full
genome RNAi screens allow us, for the first time, to obtain a global picture of
the functions of all genes in a single process. In addition, and most
importantly, many of the screens generate quantitative information on the
contribution of each gene in a specific read out. The application of this
"quantitative genetic" approach to signal
transduction is likely to provide fundamental information on the organization
and the flow of information through protein networks.
a. Identification of shared nodes between signaling
pathways. As we have
completed RNAi screens for most signal transduction pathways (Table 1), we can
now integrate these data sets to identify points of connections or "nodes".
Validation of these connecting nodes will be accomplished in vivo either in hemocytes insect blood
cells, a system that we have extensively used (Boutros et al. 2002; Agaisse et
al., 2003; Brueckner et al., 2004), or the imaginal disc. To facilitate these
analyses, we are currently building a number of pathway sensing tools that will
be used to readily assay the functions of these nodes.
b. Organization and
flow of information through protein networks. To gain insight into the global structure of signaling
networks and specifically how information flows through these networks in
response to extracellular signals, we propose, with our computational
collaborators, to use gene expression profiles in RNAi treated cells to
construct functional interactions. We have used this approach previously to
identify a novel split and negative feedback loop downstream of the Tak kinase
in response to LPS stimulation (Boutros et al., 2002). As a proof of principle,
we will initially construct a network of the kinome by successively perturbing
each kinase and protein phosphatase (PPase) by RNAi in S2 and Kc cells and
capture gene expression with our home-made "designer chip". To
evaluate the flow of information through the Kinase/PPase network, the results
will be compared under different stimuli (LPS, Insulin) and at different time
points. Altogether, this approach should allow us to gain knowledge not only on
the organization of the signaling pathways but also on the quantitative flow of
information through the network.
5.
Spatial cues and their roles in signal transduction and morphogenesis
A major interest of our laboratory is to characterize
how subcellular localization of signal transduction pathway components
influence the output of signaling networks, and how the apico-basal and planar
polarity of the cell influences this process. Ideally, for a specific pathway,
we would like to know: 1. where the ligand is secreted and how it interacts
with the extracellular matrix; 2. whether the transducing receptor is localized,
and if it is, the biological significance of such compartmentalization; and 3.
how the localization of cytoplasmic proteins in certain parts of the cells
contribute to the overall signaling characteristics of the system, such as its
kinetics and isolation from other pathways. Finally, in the context of morphogenesis, we need to gain a
better understanding of the connections between signaling pathways and dynamic
states of cytoskeletal organization.
Despite major advances in recent years, we are still far from
having a global understanding of cellular architecture and the spatial
organization of signaling pathways. From our studies of the tumor suppressor
genes scribble, disc large and l(2)giant larvae, we and others have proposed a model for the establishment of
apico-basal polarity in epithelia (Bilder et al., 2000, 2003; Tanentzapf and Tepass, 2003). These studies have
provided a framework to further analyze the spatial subdivision of the plasma
membrane and localized reception of extracellular signals, which is an area
that we will investigate actively in the next five years.
Signaling
by the Wnt/Wingless (Wg), Hedgehog (Hh) and Decapentaplegic (Dpp) morphogen
signaling pathways are critical to establishment of pattern in many tissues. In
the past five years, we, and others, have documented the fundamental roles
of Heparan Heparan Sulfate
Proteoglycans, and in particular members of the Glypican family, in regulating the movement and distribution
of morphogens through epithelia (Baeg, et al., 2001, 2004; Selva et al., 2001;
LŸders et al., 2003; see recent review by Haecker et al., 2005). What still
remains completely uncharacterized are the mechanism(s) that link these
pathways to cytoskeletal reorganization. Towards this goal, we have recently examined
the role of Dpp in imaginal discs, and shown that, unlike previously thought,
Dpp does not regulate cell viability in the imaginal disc, but instead acts to
reorganize the cytoskeleton most likely by acting on the organization of apical
microtubules (Gibson and Perrimon,
2005). The function of Dpp in imaginal discs is relevant to its function in the
embryo where it is implicated in large-scale epithelial morphogenesis during
dorsal closure (Stronach and Perrimon, 2001, 2002; Tan et al., 2003). One of our
major effort in the next few years will be to identify the pathways that link
the reception of morphogens, in particular Dpp, to the cytoskeleton.
Finally,
we have defined the basic rules that govern the maintenance of the hexagonal
cellular arrays present in epithelia and
uncovered a novel aspect of epithelial cell mitosis that we call "cytokinetic
face formation",
wherein polarized cell growth at the interface between nascent mitotic sisters
underlies the growth and topological integrity of cell sheets. Our recent identification of two
mutations affecting cytokinetic face formation will now open up the problem of
epithelial topology to conventional genetic analysis
(Gibson et al., in preparation). With this framework of epithelial organization
defined, we can now analyze the mechanisms by which the hexagonal shapes of
epithelial cells are being modulated by extracellular signals and mechanical
tension during morphogenesis. In particular, we will examine these issues in
the context of germ band retraction that we have previously described in
details (Schšck and Perrimon, 2002, 2003).
1.
Cell morphology and adhesion Kiger
et al. (2003).
2.
Cell viability and growth Boutros
et al. (2004).
3.
Cytokinesis. Eggert
et al. (2004).
4.
General secretion Bard
et al., In Preparation.
5.
Axonal outgrowth Sepp
et al., In Preparation.
6.
Myocyte differentiation Bai
et al., In Preparation.
7.
Wingless/Wnt pathway Dasgupta
et al. (2005).
8.
LRP6 signaling Dasgupta
et al., In Preparation.
9.
Hedgehog pathway Nybakken
et al. (2005).
10.
Small GTPases signaling Bradley
et al., In Progress.
11.
GEF signaling Bakal
et al., In Progress.
12.
JAK/STAT pathway Baeg
et al. (2005).
13.
EGFR pathway Friedman
et al., In Preparation.
14. Insulin pathway Kockel et al. (2005).
15.
JNK pathway Brouzes
et al., In Progress.
16.
PVR pathway Brueckner
et al., In Progress.
17.
Host factors required for Drosophila C virus pathogenesis. Cherry et al. (2005).
18.
Host factors required for VSV virus pathogenesis. Cherry
et al., In Prep.
19.
Host factors required for growth of Mycobacterium. Phillips
et al. (2005).
20.
Host factors required for growth of Listeria. Agaisse
et al. (2005).
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