Am J Physiol Cell Physiol 283: C2–C28, 2002;
10.1152/ajpcell.00581.2001.
invited review
Cross signaling, cell specificity, and physiology
J. E. DUMONT, S. DREMIER, I. PIRSON, AND C. MAENHAUT
Institute of Interdisciplinary Research, Free University of Brussels,
Campus Erasme, B-1070 Brussels, Belgium
signal transduction; effect of hormone modulations; isoforms; combinatorial logic
CELL SIGNAL TRANSDUCTION ENCOMPASSES all the biological
and biochemical phenomena that lead from the perception of a signal by a cell to the response of the cell. The
signal transduction machinery of a cell integrates all
the signals it recognizes and translates them in a
coordinated behavior. A signal for a cell is whatever is
recognized as such by a receptor that itself initiates a
response to this signal. A receptor is the structure that
recognizes and reacts to the signal and interprets the
specificity of the signal. These are circular definitions.
Our physiology, which integrates the requirements of a
living organism (for metabolism, growth, reproduction)
and its responses to the outside world, uses thousands
of signals. For each cell, these include hormones and
neurotransmitters, signals from neighboring cells, soluble such as paracrine factors, or membrane bound
such as ephrins, and signals from the inert substratum
Address for reprint requests and other correspondence: J. E. Dumont, Inst. of Interdisciplinary Research (I.R.I.B.H.N.), School of
Medicine, Univ. of Brussels, 808 route de Lennik, B-1070 Brussels,
Belgium (E-mail: [email protected]).
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such as fibronectin. Because each signal may be recognized by different receptors (e.g., for norepinephrine or
serotonin), the number of receptors is a multiple of the
number of signals; hence the tremendous complexity
and specificity of signals and their receptors. One category of receptors, the seven transmembrane receptors, are coded by ⬃700 genes. i.e., ⬃2% of the number
of genes in the human genome. This contrasts with the
rather limited repertoire of known signal transduction
pathways that are modulated by such receptors, with
only a few ubiquitous intracellular signal molecules
(cAMP, cGMP, Ca2⫹, etc.), phosphorylation cascades,
and other pathways [nuclear factor (NF)-␬B, etc.].
The cross signalings (anthropomorphically called
cross talks) between the various cascades further simplify the picture in appearance: everything seems to
modulate everything (Fig. 1). See, for example, a recent
title: “Signaling networks— do all roads lead to the
same genes?” (261). Furthermore, the opposite cross
signalings between cascades, as reported, give a totally
incoherent picture (141). It is striking that excellent
reviews on known signal transduction proteins or cas-
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Dumont, J. E., S. Dremier, I. Pirson, and C. Maenhaut. Cross
signaling, cell specificity, and physiology. Am J Physiol Cell Physiol 283:
C2–C28, 2002; 10.1152/ajpcell.00581.2001.—The literature on intracellular signal transduction presents a confusing picture: every regulatory
factor appears to be regulated by all signal transduction cascades and to
regulate all cell processes. This contrasts with the known exquisite
specificity of action of extracellular signals in different cell types in vivo.
The confusion of the in vitro literature is shown to arise from several
causes: the inevitable artifacts inherent in reductionism, the arguments
used to establish causal effect relationships, the use of less than adequate models (cell lines, transfections, acellular systems, etc.), and the
implicit assumption that networks of regulations are universal whereas
they are in fact cell and stage specific. Cell specificity results from the
existence in any cell type of a unique set of proteins and their isoforms at
each level of signal transduction cascades, from the space structure of
their components, from their combinatorial logic at each level, from the
presence of modulators of signal transduction proteins and of modulators
of modulators, from the time structure of extracellular signals and of
their transduction, and from quantitative differences of expression of
similar sets of factors.
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INVITED REVIEW
cades mention so many demonstrated effects obtained
in different cell models that the authors have great
difficulties in drawing a coherent picture or end up by
concluding for each cascade, or even enzyme: “X is
regulated by all signal transduction cascades and regulates almost all cellular processes, from gene expression to cell death” or “these results suggest that the
pathway linking A to B involves the integration of
numerous signal transduction steps by a highly complex network” (6, 18, 19, 25, 26, 28, 42, 77, 92, 93, 121,
125, 179, 260, 271, 272, 275, 298–300, 359, 360). The
very restricted phenotypes of knockout mice models in
which a supposedly essential protein is absent do not
fit with such statements. The impression left was recently summarized: “elegant complexity coupled with
hopeless confusion better defines our current state of
knowledge” (309). In fact, such reviews give a comprehensive picture of all the interactions that may exist in
mammalian cells, i.e., of the toolkit available for differentiation. On the other hand, attempts to give a
unifying picture lead to unwarranted generalizations
and/or selective consideration of the literature and
scientific myths (e.g., “Ca2⫹ causes cell growth”). Thus
a first paradox, which has been spelled out for a few
AJP-Cell Physiol • VOL
MANY RELATIONS DESCRIBED IN SIGNAL
TRANSDUCTION PATHWAYS MAY NOT BE
PHYSIOLOGICALLY RELEVANT
Attempts to synthesize any field of the signal transduction literature these days either simplify it in a
personal, selective, and therefore distorted way or end
up giving a very confusing picture. Part of the confusion may be clarified by considering the systems used
to obtain the data and their possible artifacts (188,
309).
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Fig. 1. Insulin action: from 1977 to 2000. GH, growth hormone;
IGF-I, insulin-like growth factor-I; IL, interleukin.
cascades (226, 227, 230), opposes on the one hand the
multiplicity and specificity of signals, their receptors,
and their effects and on the other hand the nonspecificity or promiscuity of the few signaling cascades (78,
167, 205).
Another paradox arises from the use of simplified
models for the study of normal physiology, namely, the
behavior of the cell within the whole organism, preferably human. Because of the limitations of clinical investigation and ever-increasing restrictions of animal
experimentation, one has to rely on models that, from
experimental animals to reconstituted systems, may
lose in physiological relevance what they gain in simplicity and in definition (Table 1). In the 1960s hundreds of articles, published in the best journals, on the
direct action of thyroid hormone on mitochondria or on
various enzymes were undoubtedly true but physiologically irrelevant. There is no doubt that work on cell
lines has tremendously enriched our knowledge of signal transduction, of the actors involved, and of their
interactions. However, the literature on signal transduction shows that similar pathways may have different, sometimes opposite, effects in different cells. This
suggests that, if we are interested in the human thyroid cell, it is this cell type that we must study. In fact,
the choice of a model dictates our concepts, of which we
become prisoners. If enough researchers use a model,
they tend to forget about the caveats and to reject in
their research or as referees the ugly facts that may
question their way of life. They become a constituency
of the model. Furthermore, it is easier to publish clean
data and mechanisms on cell lines than more disperse
and less clear-cut in vivo results. But then, as one
mentor asked after a seminar on some peculiar properties of a much-used cell line model, “So what?” Of
course, this should not detract from work on cell lines
because this allows us to identify new partners and
interactions and defines what is possible in signal
transduction. On the other hand, work on physiological
models defines what is relevant in the physiology of a
given cell type at a given stage.
In this review we analyze the physiological relevance of
signal transduction data and discuss the mechanisms
that, despite the apparently generalized “textbook”
schemes, account for the exquisite specificity of in vivo
cell signaling. Furthermore, we explore the possible biological consequences of a loosening of this specificity. For
graphic representation of signal transduction pathways,
we use a recently proposed system (267).
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In silico
In vitro
Molecular modeling
Tissue slices
Primary cultures
Reconstituted
Monolayers
Cell lines
Homogenate fractions
Pure subcellular fractions
Enzymes reconstituted
systems
The physiological character of the systems decreases from top to bottom, whereas their simplicity and definition increase.
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Arteriovenous differences
Ex vivo organ perfusion
Metabolites
and Hormones
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Simplification of the systems used for the study of
signal transduction is a double-edged sword. At each
level from the in vivo study of humans to the precise
molecular definition of proteins by X-ray crystallography, what we gain in precision, rigor, and definition we
may lose in relevant biology (Table 1).
There are numerous examples of apparent discrepancies between findings in simplified systems, e.g., in
vitro and the situation in vivo. For instance, expression
of the “cell proliferation signal” epidermal growth factor (EGF) in transgenic mice causes growth retardation (41). Lack of phosphoinositol-3-kinase (PI3K)-␥, an
essential element of proliferation cascades, leads to
colorectal carcinomas (292). There are great differences, not commented on by the authors, between the
specificities of G proteins and their ␤␥-subunits for
their controlling receptors in membrane preparations
and in whole cells (122). Similarly, the history of
knockout mice is rich in genes whose protein product is
essential for a cell process in vitro but not in vivo.
The crucial fact that teratocarcinoma cells aggregated with normal morulas give rise to normal cells in
adult animals (265, 148) or that bone marrow cells
injected in heart regenerate myocardium (253) testifies
to the importance of the proper tissue environment in
the behavior of the cell. Similarly, the interrelations
between cancer cells and their neighboring cells are
fundamental to their biology (89). Normal ovarian stromal cells promote the growth of normal ovarian epithelial cells but inhibit the corresponding tumor cells in
vivo (258). These observations led to the development
of new transgenic models in which oncogenes can be
activated by a spontaneous recombination event rather
than systematically in all cells of a given type (161).
Autocrine and paracrine effects may differ in culture
and in vivo because of the dilution of factors in vitro,
the washout of factors in vivo, or the absence of a
necessary factor in the medium. In COS7 cells, for
instance, the stimulatory effect of insulin-like growth
factor (IGF)-I on the mitogen-activated protein (MAP)
kinase (MAPK) cascade is secondary to the autocrine
release of EGF (287). The simple manipulation of cells
in culture introduces new variables: change of medium
or even minor mechanical stress causes ATP release
and activation of the ubiquitous purinergic receptors
(254, 255). Because many biological effects require the
conjunction of several factors, simplified systems may
lose properties, including the specificity of interactions.
For instance, direct interactions of transcription factors with DNA in acellular systems are much more
promiscuous than in vivo (43).
Yet, to define mechanisms, simplified systems and
even pure molecular species are necessary. To fully
understand the reaction of a pharmacophore with its
target one must define the precise structures involved.
Still, the biological relevance of the findings needs to be
validated up to the level of the human organism. Animal models, transgenics or knockouts, general or local,
Table 1. Experimental systems and physiological characteristics they retain
Loss of Biologically Important Information in
Simplified Systems
Tissue
Architecture,
NonHomeostasis
Cell-Cell,
Physiological
Physiological
SupraGenetic and
Control
perturbed
and
Cell-Matrix Extracellular Cellular Intracellular
Organelle
molecular
Epigenetic
System
Feedbacks Hormonal Nervous Local Supply Removal
Relations
Medium
Structure
Fluid
Architecture Assemblies
Integrity
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permanent, permanently or transiently inducible, allow us to test precisely the role of a given gene in vivo.
Defined human genetic diseases, when they exist, allow extension of the conclusions to humans.
Arguments In Favor of a Causal Relationship May
Not Be Proofs
Fig. 2. Causal sequences and their effects. A: suppression of an effect
by a specific inhibitor (in this case the inhibition of B or D), even if
specific, does not imply that the target of the inhibitor is in the causal
sequence (e.g., D is not in the sequence). B: and/and control ⫽
coincidence detection. A, B, and C are necessary to cause D; either/or
control: A, B, or C is sufficient to elicit D. C: biphasic control. A
causes C. If the concentration effect curve of A vs. C is biphasic
excess A will not cause C. (A), concentration of A; (C), concentration
of C.
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Validity of Reported Relations May Be Restricted to
Artificial Systems
Many interrelations within and between elements of
the signal transduction pathways are protein-protein
or protein-DNA interactions. The methods used to define such interactions have provided a tremendous
yield of new information. However, their results should
be properly assessed and their physiological relevance
validated. Protein-protein interactions are difficult to
study at the low concentrations that prevail in normal
cells. For instance, coimmunoprecipitations depend on
the affinity and specificity of the antibodies used, on
the dissociation rate of the interaction, and on the
concentrations of the targets to be demonstrated in
Western blots. Of course, the investigator can diversify
the antibodies and modulate their concentrations, the
washings, etc. To overcome such difficulties the overexpression of one or several proteins in transfected
systems has become very general. Factors of overexpressions up to 100-fold are common. At these concentrations, weak, nonphysiological interactions and effects may well take place and the specificity of action of
isoforms may disappear (see, for example, pRb and
p107; Ref. 158, Fig. 3, A and B). For example, proteins
that associate with the GABA receptors and cluster
them in transfected cells may not do so in their native
neurons (171, 172). Moreover, interactions that are
normally impaired by compartmentation may occur in
such systems. If an interaction is constrained by stoichiometric binding in a protein scaffold, even a doubling of the concentration of one of the locked proteins
may be sufficient to allow spillover outside (Fig. 3C).
On the other hand, overexpression of a scaffold protein
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The best argument in favor of the hypothesis that a
biological event is necessary for a signal transduction
pathway is to show that its suppression inhibits the
downstream events. Suppression can be achieved by
pharmacological inhibitors, antibodies, dominant-negative or competing peptides or proteins, deletion of the
protein by inhibition of its synthesis (e.g., antisense,
RNA interference), or gene knockout. For inhibitors,
the postulated suppression must take place in the
system studied under the conditions used and it must
be specific. Such controls are often missing. Many hormonal effects have been related to protein kinase A
(PKA) because they are inhibited by the supposedly
specific H89, which, in the same concentration range,
inhibits MAP kinase-stimulated kinase (MSK1), a kinase downstream of MAPK (332).
In addition, the fact that an event is necessary does
not necessarily imply that it is a required step in the
causal relationship sequence (Fig. 2A). Metabolites or
an O2 supply are necessary for the survival of many
cells and therefore for the operation of their signal
transduction pathways, but they are not part of these
pathways!
Demonstration that experimental induction of what is
supposed to be the primary event causes the downstream
steps of the pathway is an argument but no more. Con-
stitutively active forms of signal transduction proteins
are often used for such purpose, although their specificity
may also have been altered, as shown for EGF receptors
(EGFRs) (210). Failure to induce the consequences may
just indicate that parallel events are also necessary (Fig.
2B). Overexpression of the primary event will be ineffective or inhibitory if the effect is biphasic vs. concentration
(182), i.e., in hormesis (36). This is the case for cAMP
induction of proliferation in granulosa cells (282) (281) or
for p53 and apoptosis (187) (Fig. 2C). Constant expression of the primary event will be ineffective or inhibitory
in a sequential process in which each successive step
requires the arrest of the previous one (e.g., in phagocytosis with extension of the membrane, engulfment, scission of the vesicle from the membrane, etc.; Refs. 12, 228).
Induction per se indicates that the initial event may
cause the downstream consequences, but the fact that
this event actually takes place in the pathway remains to
be proven. Preferably, as laid out by Robison et al. (280)
in their rules for cAMP causal relationships, activation/
inhibition of each of the actors of a cascade should be
shown in the intact cell, direct activation/inhibition of
each actor by its upstream modulator should be demonstrated, and the kinetics and concentration effect relationships should be compatible with the proposed
scheme.
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will segregate its binding proteins in separate complexes and thus have an inhibitory effect (Refs. 197,
252; Fig. 3D). Expression constructs can interfere with
nuclear receptors or transcription factors. From such
artificial interactions authors infer effects on the corresponding endogenous proteins. In this case, the cell
is no more than a glorified test tube.
Other confounding factors in transfection studies are
the use of constitutively activated mutants whose persistent activity does not reproduce the temporally organized activation of the natural proteins or, conversely, the use of transient transfections that do not
reproduce the sustained activity of oncogenes, or the
use of dominant-negative mutants whose actions may
be much more diverse than foreseen (309). Similarly,
the whole field of gene regulation, which has relied on
cotransfections of plasmids expressing transcription
factors and promoters with reporter genes, is undergoing a reappraisal now that it is realized that genes in
normal chromatin may behave differently, or at least
in a more sophisticated way, than genes in the naked
or poorly “chromatinized” plasmids. In Drosophila, for
instance, there is little correlation between binding in
vitro of transcription factors to DNA fragments and
DNA binding in vivo (21).
In vitro acellular systems with proteins, purified or
not, are also used and thus have little relation to
physiology. They may allow us to estimate thermodynamic properties or to pinpoint possible interactions.
However, the concentrations used may be much higher
than in the cell and may generate many false positives.
Moreover, the artificial system used may lack components that would confer specificity. Interactions of Hox
transcription factors with naked DNA are much less
specific than they are in vivo (150, 151). G␤␥ complexes
lose their target specificity in acellular systems (73).
Similar reservations hold for yeast double-hybrid
systems. For instance, steroid receptors in double-hybrid systems respond to all ligands by activating the
downstream promoters, whereas in vivo some ligands
AJP-Cell Physiol • VOL
act as agonists and others as antagonists. To reproduce
the in vivo situation, Yamamoto et al. (371) had to
introduce also in yeast the needed coupling factors.
Still, as a first step to define the repertoire of possible
interactions, this method has allowed gigantic steps
(150, 151).
All interactions proposed on the basis of transfection,
double-hybrid, or acellular experiments should therefore be validated at the normal concentrations in the
cells in which they are supposed to take place, which is
easier said than done (247). In each case, of course, the
investigator can overexpress to detect unfavorable interactions (for example, interactions that would require the nonexisting phosphorylation of the protein)
or underexpress to indicate specificity. Conviction
about the validity of interactions demonstrated in vitro
arises from the convergence of independent arguments.
Possible Relations May Not Apply Because the Proteins
Involved Are Not Expressed in the Same Cells in the
Tissue or in the Same Compartment in the Cell
To interact, proteins must be expressed in the same
cell. Evidence for this assumption may be invalid for
several reasons. The detection by PCR of the corresponding mRNA in the cell may be due to minor “illegitimate transcription” as, with enough cycles of PCR
reaction, every mRNA can be detected in every tissue.
With such evidence, in the thyroid field, eye muscles
would express thyroglobulin, thyroperoxidase, and
thyrotropin (TSH) receptor and could be considered as
pseudo-thyroid follicular cells! On the other hand, tissue distribution studies (for proteins or mRNA) do not
indicate which cells contain the protein studied unless
one relies on immunohistochemical or in situ hybridization evidence. Human thyroids contain many muscarinic receptors, but, in contrast to the dog thyroid,
the receptors are not in their follicular cells.
For an effect to take place (e.g., DNA synthesis after
growth factor action), all the elements of the corre-
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Fig. 3. Effects of overexpression on
protein-protein interactions. A: forcing
a nonphysiological interaction (A and
B). At normal concentration, A does
not interact with B, which is on the
membrane; when it is overexpressed,
interaction takes place. B: forcing new
interactions (A and C). A and B are on
the membrane and interact. A does not
interact with cytosolic C. If overexpressed, A spills over in the cytosol and
interacts with C. C: interaction in the
presence of a scaffold protein. A, B, and
C are sequestrated in a scaffold protein
so that B cannot interact with free D. A
slight excess of B will saturate the scaffold binding site; some B will spill over
and interact with C. D: absence of interactions in the presence of excess
scaffold protein. A, B, and C are sequestrated but on different scaffold
proteins.
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Demonstrated Interactions May Only Occur in Some
Cell Types, in Some Species, or in Model Cells, Which
Explains Why a Cascade May Have Different and
Even Opposite Effects in Different Cells
Differentiation in about 200 different cell types implies a specific program of protein expression for each
of them. A clear indication of specificity is given by the
numerous examples of opposite results of the same
cascade in different cells. The same Ras oncogene product blocks proliferation in human fibroblasts and induces it in human thyrocytes and immortalized fibroblasts (57, 112, 113, 250). It induces cyclin D in an
intestinal cell line, while causing cyclin D1 phosphorylation and degradation in Rat1 fibroblasts (303). The
same cAMP cascade inhibits cell proliferation in many
cells (e.g., fibroblasts and other cells of mesodermal
origin) but triggers it in some others (thyrocytes, somatotrophs, etc.); depending on the cell type, it activates, inhibits, or does not modulate MAPKs (285). In
tadpoles the same hormone, triiodothyronine (T3), acting on the same T3 receptor, induces cell death in the
tail and cell proliferation in the rest of the body (308,
328). NF-␬B inhibits apoptosis in most normal cells but
induces it in some cancer cells (289). Even supposedly
identical cells (e.g., endothelial or mesenchymal cells)
are different in different tissues (2, 319). Blood vessels
in endocrine tissues have a specific angiogenic mitogen
(190).
Activation of the same pathway in the same cell type
in different species may also lead to opposite results
(209). The TSH receptor in human thyroid cells activates both the cAMP and phospholipase C cascades
and accordingly activates thyroid secretion by the
former and thyroid hormone synthesis by the latter,
whereas in dog thyrocytes it only activates the cAMP
pathway, which activates both functions (348). The
same end result of TSH is obtained by different pathways in dog and human. In vertebrates as in viruses,
evolution often conserves the function but may achieve
it by different mechanisms.
Effects May Not Occur at All Times in a Given Cell
Fig. 4. The STE5 scaffolding protein routes the mitogen-activated
protein kinase (MAPK) module in yeast (90). The pheromone and its
receptor release ␣- and ␤␥-subunits from the G protein, which will
activate the first element (STE20) of the MAPK cascade, which by a
linear path leads to mating behavior. STE11 corresponds to MAPK
kinase kinase (MEKK), STE7 to MAPK kinase (MEK), and FUSS3 to
MAPK in vertebrates.
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Receptors and signal transduction proteins are differentially expressed during embryogenesis, growth,
and even under different physiological conditions. Effects may therefore differ at different stages. In fact,
because signals during embryogenesis act through a
few cascades, embryogenic development could not occur if these cascades were not interpreted differently
by the target cells at different stages (62, 102). The
whole early tissue organization in Xenopus development depends on the well-defined, orderly time sequence of the action of four inducing factors on cells,
which becomes different at each stage (314). The same
Ras activation applied to Drosophila imaginal tissue at
different stages leads to proliferation, apoptosis, or
suppression of apoptosis (162). The evolution of cells in
embryogenesis is now mimicked in vitro in embryonic
stem (ES) cells (212). A maturating dendritic cell or T
lymphocyte changes its panel of secreted cytokines and
cytokine receptors, i.e., its signaling systems, within a
few hours (27, 249).
The same stimulus may achieve the same result by
different mechanisms at different times of the life of a
cell, e.g., protein kinase C (PKC)-␪ is required for T cell
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sponding signal transduction cascade should be
present in the cell. In fact, illicit expression of protooncogenes in cells in which they are not normally expressed is a major cause of cancer (274). The finding
that EGFR activation by G protein-coupled receptors
(GPCRs) and the consequent cell division may require
the processing of cell-bound pro-EGF by a metalloprotease, itself activated by the cascade downstream of the
GPCR (269), explains why such a mechanism operates
only in some cases (58). In addition, quite a few different mechanisms have been proposed in different cell
types to explain GPCR activation of the growth factor
receptor and MAPK cascades, with no attempt to disprove other hypotheses (120, 125, 170, 214, 215, 231,
294, 354, 382). Most of these articles refer to “the
mechanism of EGFR activation by GPCRs.”
Similarly, proteins may be restricted to different cell
compartments or even to distinct macromolecular assemblies (scaffolds) that insulate them from others and thus
prevent interaction, even though all are present in the
same cell. This compartmentation generates functional
modules, i.e., discrete entities whose function is separable from that of other molecules (133). By generating
such a module with the elements of the MAPK cascade,
the yeast scaffold protein Ste 5 confers to nonspecific
enzymes the specificity of action of mating hormones
(Fig. 4; Refs. 61, 95).
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Cell Lines May Not Be Good Models of Their
In Vivo Counterparts
Most articles on cell lines extrapolate their findings
to the in vivo cell counterpart, e.g., extrapolating to the
human thyroid cell what has been found in FRTL5 rat
thyroid cell line. An ever greater part of the literature
on cell signaling bears on cell lines as models in part
because of the ease of working with them. In fact, cell
lines are poor models of their in vivo counterparts.
First, by definition, contrary to normal somatic cells,
they are immortal, i.e., they reproduce indefinitely.
The process by which they are obtained implies a
selection over several generations of genetically altered cells having the desired properties, very different
from those of the cells of origin. For instance, whereas
tyrosine kinases of the Src family are necessary for the
proliferating effect of platelet-derived growth factor
(PDGF) in normal fibroblasts, they are not in NIH 3T3
cells or other cell lines (32). It is striking that similar
rat thyroid cell lines selected by different criteria, the
PCCl3 and the FRTL5 cell lines, exhibit quite different
properties. For example, they require one (FRTL5) or
two (PCCl3) oncogenes to become transformed. Finally,
cell lines may evolve: the FRTL5 cells described at
their origin required insulin and TSH to grow. Presently available samples require only one of the two or,
in some laboratories, only insulin, TSH having just a
complementary role. Some FRTL5 cells used in different laboratories are stimulated by EGF, some not; in
some, serum enhances TSH mitogenic action, in some
not. What is the interest for physiologists of the differences between two thyroid cell lines or between the
same cell line having evolved in different laboratories?
Another example of a molecular evolution of cell lines
is the progressive accumulation and selection of cells
with larger alleles due to unstable triplet repeats (117).
Similarly, cancer cell lines are often studied as representative of in vivo cancer cells even though they
have developed new characteristics: p53 is mutated in
thyroid and esophageal carcinoma cell lines but not in
the corresponding primary tumors (327, 368). p16INK4
is inactivated in thyroid tumor cell lines but not in
thyroid tumors (37). PTEN mutations are detected in
AJP-Cell Physiol • VOL
melanoma cell lines but little in melanomas (379–381).
Even in cancer cells the characteristics of disseminated
cells can be very different from those of the tumor that
releases them (177). How relevant to metastasis are
human cancer cell lines injected in animals (318)?
Intuitively, one would guess that the variations between similar cells (e.g., thyrocytes of different species)
would be much greater at the initial steps of the cascades than at their core mechanisms: there are hundreds of receptors modulating cAMP levels but only six
isoenzymes of cAMP-dependent kinase, and these have
the same substrates. However, this is not always true,
as variations may also occur at the core of signal
transduction pathways: in dog thyrocytes cAMP activates cyclin D/cyclin-dependent kinase (CDK)4 complexes without inducing cyclin Ds, whereas in FRTL5
cells it does so by inducing cyclin D1 (65).
Conclusions on Physiological Relevance
of Reported Effects
It is therefore dangerous to extrapolate to other
systems the data obtained in one species. One sees
articles in which data on human, dog, or pig thyroid
cells in primary cultures or rat thyroid cell lines are
combined in reasoning about the nonexistent paradigmatic “the thyroid cell.” Although the existence of a
given mechanism is often demonstrated only in one
system, articles imply, implicitly or explicitly, that the
mechanism described is general (e.g., “activation of the
EGFR by seven transmembrane receptor,” “cAMP activates MAPK”). The specificity of cell signaling in
different cells, even for the same or similar extracellular signals, and even through the same initial receptor,
is demonstrated a contrario in reviews that attempt to
synthesize our present knowledge. In a recent article
on seven transmembrane receptors and cell proliferation, no two of the systems described work in the same
way, and even when one receptor is considered, its
mitogenic cascade differs from one model to another
(125). The buzzwords of such reviews are “complex,
pivotal, subtle. . .” (260, 261). This explains the wrong
impression of confusion emerging from such reviews.
The map of all possible interactions and causal relations in signal transduction should therefore be considered as a map of possibilities, only few of which really
take place at a given time in a given cell type. The
exquisite cell- and stage specificity in signal transduction is fortunate for the pharmacologist (364) who aims
at such a specificity for his drugs.
HOW IS CELL SPECIFICITY OF ACTION OF SIGNAL
TRANSDUCTION CASCADES ACHIEVED?
Cell Responses Depend on the Pattern of
Their Protein and Isoform Expression
The specificity of response to one cascade in different
cell types depends on its differentiation, i.e., on its
protein composition and, therefore, on the genes whose
promoters are accessible. For example, in kinase cascades the response to the same cAMP and cAMP-
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receptor-induced NF-␬B activation in mature but not
immature T lymphocytes (323).
Many effects depend on the cellular environment.
Vasopressin, acting through its V1a receptor, activates
Gq in proliferating Swiss 3T3 cells but Gq and G13 in
cells in the G0/G1 phase of the cell cycle (1). In hepatocytes in culture, norepinephrine stimulates DNA synthesis through ␤-receptors at low density and through
␣-receptors at high density (166). Muscarinic receptors
stimulate growth of quiescent NIH 3T3 cells but inhibit
it when the cells are growing (245). The same increase
in cytosolic Ca2⫹ may push or retract the growth cone
of a nerve, depending on preexisting Ca2⫹ or cAMP
level (378). Malignant mammary epithelial cells may
revert to a normal phenotype in a specific intracellular
matrix environment (23).
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allowing specific regulations (236–238, 333). The same
Ca2⫹ signal will enhance or decrease cAMP accumulation depending on the type of adenylate cyclase present
(147). Whereas the ␤- and ␥-subunits of the G proteins
transducing the action of seven transmembrane receptors were long thought to be interchangeable, it appears more and more that the response to a given
receptor requires the presence of a defined set of ␣-, ␤-,
and ␥-subunits (140, 160, 278). E2F2 and E2F4 have
opposite roles on cell differentiation (257). With only
some of the possible isoforms present in a given cell, of
all the possible controls only the few permitted by this
selection will operate in this cell. This has been well
demonstrated, e.g., for G protein ␣-subunits in the
different cells of human fetal adrenal gland (30) or for
AU-rich element (ARE) binding proteins that regulate
the stability and translation of mRNA in embryos
(142).
The nature of the expressed isoforms may itself depend on the physiological state of the cell: depolarization induces, through Ca2⫹ and Ca2⫹/calmodulin-dependent protein kinase, a different splicing of the premRNA of the Slo channels and therefore the expression
of different proteins with different allosteric properties
(369).
The relatively low number of genes in the human
genome seems to put a lid over the number of possible
isoforms of any protein. In fact, any gene coding for an
isoform of a signal transduction protein may also code
for several isoforms by mRNA alternative splicing (24,
119), e.g., each cyclic nucleotide phosphodiesterase
gene for 3–10 alternatives (24). In these, the presence
or absence of one motif of protein-protein interaction in
an alternative form of a protein may channel a pathway in a given direction or not (24, 119) according to
the protein recognition code (321). The presence of one
or another intraprotein signaling module such as a
protein phosphorylation motif may confer positive or
negative regulation by PKA (Ref. 66; Fig. 5) or by
MAPK (340). A splice variant of phospholipase C behaves as a negative regulator of phospholipase C␦
(242). Truncation by alternative splicing of fosB into
Fig. 5. Control of the dual enzymes phosphofructokinase/fructose 2–6 biphosphatase (PFK2-FBPase2).
The enzymes have 2 opposite exclusive functions:
the phosphorylation and dephosphorylation in 2 of
fructose-6-phosphate. In the case of the liver, enzyme phosphorylation by cAMP-dependent protein
kinase (PKA) in the NH2 terminus leads to activation of the phosphatase activity and inhibition of the
kinase activity. In the case of the heart, phosphorylation by PKA of the COOH terminus of the enzyme leads to the opposite result. The isoforms are
coded by different genes. Liver PFK2 has a PKA
module in the NH2 terminal part. In the heart PFK2
has a PKA module in the COOH terminus.
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dependent kinase depends on the population of phosphorylable proteins in a specific cell type, i.e., on its
differentiation. Similarly, the response to a similar
transcription factor in different cells depends on the
nature of the gene promoters that are accessible.
At each step of most cascades, several isoforms of
proteins perform overlapping functions. They may be
encoded by different genes or result from different
mRNA splicing of the same gene or from postranslational processing. In mammalian cells there are at the
present time 10 adenylate cyclases, more than 40 cyclic
nucleotide phosphodiesterases (315), 70 A-kinase-anchoring proteins (AKAPs) (74), and 11 families of nonreceptor tyrosine kinases (279). Evolution multiplies
the varieties of possible isoforms at each stage, from
one kinase at each step of the MAPK STE pathway in
yeast, Caenorhabditis elegans, and Drosophila to several kinases in mammalian cells (61). This explains
why work on such simple model organisms is fundamental to demonstrate basic mechanisms and schemes,
their actors, and their interactions and thus give the
foundation of signal transduction. It also explains why
direct extrapolation to mammalian cells is risky.
Because the isoforms have relatively similar properties, they are often, when discovered, lumped together
as though interchangeable. In fact, more detailed investigations reveal different (sometimes opposite) and
overlapping regulatory properties, e.g., the positive or
negative effect of phosphorylation by MAPK on phosphodiesterase 4D isoforms (217) and the opposite and
qualitatively different effects of p53 isoforms (239,
240). They also present cell type-specific expressions
(30), intracellular localizations (206) as determined by
specific docking domains (305), effectors (e.g., for receptors or kinases; Ref. 126), and controls in expression at
the transcriptional, translational, and posttranslational level (70, 123). These differences may give the
isoforms entirely different physiological roles. They
may also respond differently to direct and cross signalings. Isoforms of glucose transporters are usually tissue specific, with a conserved transmembrane catalyzing transport domain and different cytoplasmic tails
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Operation of a Pathway May Depend on Spatial
Structure of Its Constitutive Elements: Subcellular,
Membrane Localizations, Multiprotein Complexes
Spatial structure in the cell refers to cell compartments and to supramolecular complexes. Nuclear or
cytoplasm compartmentation prevents many possible
interactions, sequestrating active molecules from each
other. For example, sequestration of MDM2 in the
nucleolus by p19ARF blocks its inhibition of p53. The
regulation of the cell cycle and of transcription represents a ballet of nuclear to cytosol import-export dynamics (110, 232, 266). Similar dynamic controls of
protein localization operate even in bacteria (154, 155,
304). Thus the existence or not of a translocation mechanism or of its regulation may greatly differentiate the
effects of a signal transduction pathway in different
cell types. Loss of spatial structure of signal transduction pathways is a cause of several diseases (243).
The targeting or nontargeting of a protein at the
membrane may change the whole pattern of its interactions. Such targeting may involve protein-lipid or
protein-protein interactions (193). It may require specified mRNA localization and protein production (313).
Nonprenylated Ras or, in some cells, nonmyristoylated
cGMP-activated kinase does not activate its cascade
(136, 219). Insulin and WNT both inhibit glycogen
synthase kinase 3␤, but this leads exclusively to increased glycogen synthesis for insulin and exclusively
to increased availability of ␤-catenin for WNT (71).
Integrin necessarily stimulates at defined contacts
(298–300). Compartmentalization goes further with
the segregation of some membrane proteins within or
without lipid rafts and caveolae (107). GPCRs may
have only access to their compatible G proteins in “raft”
subdomains of the membrane (254). This could explain
the discrepancy between TSH promiscuous effects on G
proteins in isolated membranes and its more restricted
effects in intact cells (5). The numerous proteins whose
main function is to anchor signal transduction proteins
(e.g., the AKAPs for cAMP-dependent protein kinase)
to definite structures show the importance of such
localizations (86).
AJP-Cell Physiol • VOL
Similarly, scaffold proteins also have the role of
assembling supramolecular complexes, bringing together signal transduction proteins in one permanent
or transitory functional unit, module, or “signalosome”
(35, 133, 260, 261). Such complex functional multiprotein assemblies were first described for metabolic enzymes, accounting for metabolic channeling (256).
Their properties are more than the sum of the properties of their individual constituents (106). For instance,
by associating tightly phospholipase C and its G protein, the INAD scaffold protein allows the former to
activate the GTPase activity of the latter and thus to
shorten the signaling of the photoreceptor (51, 95). For
cAMP, inositol 1,4,5-trisphosphate (IP3), and even
phosphatidylinositol-3,4,5-trisphosphate (PIP3), colocalization of the signal generation effector and remover
allows highly localized effects in dendritic spines (169).
The synapse or the neuromuscular junction is a permanent multimeric complex constituted sequentially
(84, 329–331) and dependent on specific targeting (44–
46, 132, 171, 172). In yeast the Ste5 scaffold protein
channels the activation of the MAPK pathway by mating factor to mating-specific genes (Refs. 61, 108; Fig.
4). 14–3-3 Proteins have a similar role in vertebrates
(241, 370). Activation of the T cell or B cell receptor
requires the constitution of a large integrated multimeric complex in membrane rafts (181, 326, 336, 358).
The stability of such complexes varies from very transient to quasi-permanent (97).
Specificity of Response of Different Cell Types May
Result from a Combinatorial Logic
From specific combination of elements involved in
parallel. In this type of regulation, a few factors may
combine to specify many different instructions. A signal can be compared to a letter in a word: it has no
meaning per se, only the combination of several letters
has a meaning and the same letter used in a different
word or combination may have a different or opposite
meaning. Such regulations have been demonstrated,
for example, for gene expression and for odor discrimination by the olfactory system (34, 109, 220). In the
former, it is generally the combination of several complementary DNA regulatory elements and their specific transcription factors that confers specificity and
strength to a promoter (204). Thus even broadly overlapping sets of regulated transcription factors may
have very different end effects (Refs. 94, 371; Figs. 6
and 7). Assuming that in humans, as in other species,
transcription factors may represent 5% of the 100,000
expressed mRNAs, the number of possible combinations of three (with repetitions) is 2 ⫻ 1010! The distribution of the 30 Ets transcription factors in different
cell types can be considered as a fingerprint of each
type (223). The expression of thyroid-specific genes
depends on three transcription factors [thyroid transcription factor (TTF)1, TTF2, Pax8], each of which is
expressed in the thyrocyte and in at least one other cell
type, but all three are only coexpressed in the thyrocyte
(60). Similarly different cascades with partially over-
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⌬fosB leads to a different transcriptional repertoire
(290). Splice forms of an Eph receptor inverse the
adhesion/repulsion response caused by this receptor
(137). Variations in the upstream open reading frames
of mRNAs also greatly change the life of the mRNA and
its translocation efficiency (236–238).
Finally, the repertoire of possible effectors of each
signal transduction protein, as presently known, will
probably expand in the future. When an action of such
a protein is discovered, it is often assumed to be the
only one until we are shown otherwise. Thus we restrict the role of GPCRs to their effects on G proteins
(129, 134) and the role of PTEN to its protein phosphatase activity (68) to discover later that other effects
exist. The specificity of isoform expression thus goes a
long way in explaining cell specificity of responses.
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lapping sets of induced genes may also exhibit the
same combinatorial logic (Ref. 94; Fig. 8).
This combinatorial logic and the various possibilities
that it offers apply at other levels of signal transduction cascades. Even at the level of the few intracellular
signals [cAMP, cGMP, Ca2⫹, diacylglycerol (DAG), ceramide, etc.] controlled in a binary mode (⫹ or ⫺), 5
signals could allow 25 ⫽ 32 combinations. Examples
abound in physiology and pathology. Different receptors acting on the Janus kinase (JAK) system act on
different combinations of Jaks that will activate different signal transducer and activator of transcription
(STAT) transcription factors (275). In macrophages,
inflammation results from the activation of different
Fig. 7. Signals or receptors (A, B, C) acting on 3 cascades or genes (␣,
␤, ␥) positively, each cascade or gene resulting in a specific effect and
each combination of cascades or genes resulting in the sum of
individual effects and in new effects elicited by the combination.
Even though there are only 3 primary actors and 3 intermediates
and only positive effects are considered, the regulation has 7 outcomes. 0, None.
AJP-Cell Physiol • VOL
Toll-like receptors, each triggering secretion of specific
sets of chemokines with partially overlapping target
receptors differentially expressed in target cells (Ref.
33; Fig. 9). Heterodimerization of EGFR-like receptors
generates many different receptors that discriminate
between ligands and between effectors (251, 324). Control of cyclin-CDKs in different tissues results from
various combinations of the CDK inhibitors, which
explains why the different combinations of knockouts
generate different types of tumors (101).
The simplest case of combinatorial logic is the case of
coincidence detection, whereby two factors acting in
parallel are necessary for an effect (Fig. 2B). Permissivity, in which one factor is necessary for the action of
another, is analogous. The requirement of the conjunction of several stimuli to cause an effect (and/and
control) is a general feature of signal transduction. The
classic example is the requirement of activation of both
the T cell receptor and CD28 for T cell stimulation (11,
181) or the necessity of both ATP and tumor necrosis
factor (TNF)-␣ to induce maturation of human dendritic cells (295). Another example is the necessary
complementarity of PI 3-kinase and Ras activation in
the induction of metastasis by MET-hepatocyte growth
factor (HGF) receptor (13). Such a requirement explains why general overexpression of only one protooncogene leads only to a few types of tumors. An example
at the level of protein direct activation is the necessary
coincidence of RacGTP and p67phox to activate O2⫺ generation by phagocyte p91phox (69) or at the level of
protein phosphorylation, the necessary coincidence of
phosphorylation by target of rapamycin (TOR) and
3-phosphoinositide-dependent protein kinase (PDK)-1
of p70 S6 kinase for full activation of the enzyme (149,
154, 155).
An alternative to the coincidence detection or and/
and control is the either/or type of control by which any
of the various parallel upstream cascades can elicit the
cell response. This type of control obeys a different
physiological logic: the cell response is so important
that its occurrence is ensured by the redundancy of
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Fig. 6. Venn diagram depicting genes specifically dependent on
components of the transcription machinery (small circles) in relation
to the transcriptome. I, II, III, IV, components of the transcription
machinery; N, sum of genes dependent on each component (I, II, III,
IV) for transcription; n, sum of genes dependent on more than 1
component n1 (I, II), n2 (II, IV), n3 (II, III), n4 (I, IV), n5 (I, II, IV),
n6 (II, III, IV). All the genes in set I (N1) depend on transcription
factor I, but n1 gene expression requires both I and II and n5
requires I, II, and IV.
Fig. 8. Overlapping and nonoverlapping inductions (⫹}) of genes by
the cAMP cascade and the diacylglycerol (DAG) protein kinase C
cascade. Both cascades activate (arrowheads) the cAMP response
element-binding protein (CREB) transcription factors. Each cascade
also activates distinct transcription factors X for cAMP and Y for
DAG. Both cascades induce B through CREB, but cAMP also induces
A, which requires both X and CREB, and DAG specifically induces C,
which requires both Y and CREB.
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controls. The absence of phenotype of many gene knockout models testifies to the frequency of such controls.
From combination of unregulated parallel factors
and of one regulated factor in each cell type: the triggering reaction or “switch.” Many different signals operate in their target cell by inducing one or several
rather ubiquitous transcription factors, i.e., early-immediate genes. C-Fos and Egr1, which are induced in
many cells in response to all sorts of signals, are an
obvious example (372). Cell-specific response is given
by the other cell-specific transcription factors that are
also necessary for induction of a specific gene in the
cell. The nonspecific Egr1, in conjunction with SF1,
leads in gonadotroph cells to the subsequent induction
of LH␤ gene (165, 338) and, in conjunction with WT1,
induces Mullerian inhibiting substance in Sertoli cells
(Ref. 310; Fig. 10). The interleukin (IL)-6 promoter in
monocytes requires cAMP response element-binding
protein (CREB), AP1, and cellular enhancer-binding
protein (CEBP) but is activated by the newly released
NF-␬B (344). Just as in an electric circuit the response
to an electric switch corresponds to the system downstream (light, air conditioning, heating, etc.), the tran-
Fig. 10. The triggering reaction or switch. The induction (⫹}) of the
same early-immediate transcription factor egr1 induces LH␤ in
gonadotrophs in conjunction with SF1, and MIS in Sertoli cells in
conjunction with WT1 (310, 338).
AJP-Cell Physiol • VOL
scription response to a signaling pathway and its general triggering transcription factor depends on the
existing tissue-specific transcription factors and their
regulatory elements. This concept would account for
synexpression, i.e., the expression with a similar pattern of a set of genes in a cell type in response to a
stimulus (124, 246), and in some cases for cell differentiation (124). It goes a long way in explaining the
puzzling question of how a promiscuous signaling cascade can achieve unique effects in a given cell type. The
phosphorylation of histones H1 by cAMP-dependent
kinases in different cell types presumably causes a
general loosening of the chromatin structure, which
might facilitate later, more promoter-specific transcription effects, fits with the same concept. This concept of a single switch whose meaning is only determined by the specific existing elements of the cell also
applies to early steps of signal transduction cascades.
The effect of a general signal such as Ca2⫹ or cAMP in
a cell depends on its complement of existing protein
substrates of calmodulin or PKA (Fig. 11). Similarly,
the pattern of gene expression induced by EGF in a
single cell line depends on the composition of the cell
matrix (373).
From specific combination of sequential factors. In
the specificity of nuclear receptors action at least six
different factors are involved, each of which can switch
the sign (⫹ or ⫺) of the response: the regulatory element in the promoter DNA, the nuclear receptor or the
transcription factor binding to the regulatory element,
the hormone binding to the receptor, the phosphorylation of the receptor, the other transcription factors
present on the promoters, the coactivator or corepressor binding to the receptor, and their modulators and
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Fig. 9. Combinatorial combinations of chemokines, their receptors (CCR1–6), and the target cells expressing the
receptors (all positive controls). Each chemokine activates some receptors and therefore the target cells that
express the receptors. MCP, monocyte chemoattractant protein; MIP, macrophage inflammatory protein.
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adaptors (157, 192, 221, 283, 371). Other independent or
dependent factors regulating the outcome are the various
feedbacks between transcription factors (276), the methylation of the promoter and enhancer, and the state of
the chromatin (histone acetylation and phosphorylation,
high-mobility group (HMG) protein binding, polycomb
group protein binding, etc.), DNA methylation and chromatin structure being generally linked (40, 47, 152, 277,
297, 339, 353). “Coordination of large sets of genes could
be accomplished by affecting the function of specific components of the transcriptional machinery” itself (139).
Moreover, some genes like that of the human fibroblast
growth factor (FGF)-1 have four different promoters, differently regulated, directing the expression of four alternatively spliced transcript variants (48).
Thus each different combination of factors in a multimeric complex with two different possibilities for
each factor (a binary switch) will lead to a specific
result (Fig. 7) (one of the two possibilities can be the
absence of a factor). For a complex of n factors with 2
possibilities for each factor, the number of possible
combinations is 2n and with 3 possibilities 3n. To understand such a system the matrix of all possible combinations should be analyzed. As long as one factor is
missing from the analysis, its results are doubtful.
The nature of the regulatory elements and their
necessary transcription factors, coactivators, corepressors, etc. reflects the genetic background; the opening
of the gene (methylation of the DNA, chromatin structure) and the presence of the receptor and transcription factors and especially of the modulators depend on
the differentiation of the cell, and the presence of the
hormone and the phosphorylation of the receptor transcription factors and modulators reflect the state of
this cell, i.e., the physiological context. Similar regulations operate on enhancers and silencers (105, 152,
221, 284). The physical basis of the regulation by the
nuclear receptor is its multifaceted character with different domains and functional surfaces, binding to
different agents in the multiprotein transcription comAJP-Cell Physiol • VOL
Specificity of Response of a Cell to Different Cascades
Having Apparently Similar Consequences May Result
From Specific Combination of Biochemical Effects of
Each Cascade: Common “Awakening” Reaction and
Specific Effects
Some cell responses are common to many if not all
signaling pathways in a given cell type. At the post-
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Fig. 11. The same cascade has different effects depending on the
protein substrates (P) of protein kinase A (PKA) which are expressed
in different cells. The phosphorylation of P1, P2, and P3 in cell I will
cause effect I; the phosphorylation of P3 and P4 in cell II will cause
effect II; and the phosphorylation of P4 and P5 in cell III will cause
effect III.
plex and signaling to each other and to the domain,
which finally selects either a coactivator or a corepressor (143, 144). “Implicit in the concept of combinatorial
regulation is the dynamic nature of regulatory complexes. The requirements for complex formation are
that the component parts must be sufficiently flexible
in their interactions to allow mixed assembly, yet must
be sufficiently precise to insure decisiveness in their
regulatory effects” (Ref. 371; see also Ref. 192). Within
one nuclear receptor diversity may result from a flexible construct leading to diverse conformations. Decisiveness will result from precise assembly leading to
specific conformations and supramolecular assemblies
(31). Such fragile flexible structure may be protected by
chaperones like heat shock protein (HSP)90 (288). At
the level of mRNA stability and reading, a plethora of
ARE binding proteins with either positive or negative
effects also allows for numerous possibilities (363).
Examples of such logic also exist at other levels:
given the occurrence of 2 A, 2 C, 4 B, at least 8 B⬘, 4 B⬙,
and 2 B“⬘ isoforms, a total of about 75 different dimeric
and trimeric protein phosphatase (PP)2A holoenzymes
can be generated (153). Also, the qualitatively different
effectors and effects of c-Src depending on the nature of
its activator (114, 115) reflect such sequential combinatorial logic. Multiple possible phosphorylation sites
with distinct meanings, each with a binary choice
(phosphorylated or not), may provide the same type of
combinatorial logic at the level of one protein. For
example, the same p53, depending on which of its five
phosphorylation sites are phosphorylated, will activate
different but partially overlapping sets of genes (146,
356). A similar phosphorylation code applies to ribosomal S6 kinase or N-formyl peptide receptor (80, 218).
Similarly, histone modifications could also act in combination to form a histone code (320). The flexible
multimodular protein model also applies to many signal transduction proteins (64, 174, 260, 261).
The complexity of the regulations reviewed in this
section involves the triggering or extinction of one
gene, of one regulatory step. A further level of complexity involves the use of combinatorial regulations at
several successive levels: for example, combinations of
transcription factors activating a set of genes, the products of which are transcription factors that in defined
combinations with newly generated or existing factors
will govern other genes, etc. (184). Another example
would be possible combinations of splicing factors regulating qualitatively and quantitatively which RNA
species are exported and combinations of ARE binding
proteins regulating the life and translation of messenger RNAs, etc. (70, 123).
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Specificity of Response to a Cascade May Depend
on Timing of Stimulus
Timing may also explain specificity. A signal may be
short or long, immediate or delayed, continuous or
Fig. 12. Overlapping and nonoverlapping biochemical effects of different cascades in the dog thyroid. An example of convergence and
divergence of cascades (⫹arrowhead, stimulation; //, ⫹arrowhead
with 1 or more steps omitted). The 3 cascades activate Rap1, an
awakening reaction, but each one also activates its specific protein
phosphorylation cascade(s), which leads to specific effects. ACh,
acetylcholine; PKC, protein kinase C; CmCaPK, calmodulin calciumdependent protein kinase; PI3K, phosphatidylinositol-3-kinase; GF,
growth factors (80, 348).
AJP-Cell Physiol • VOL
oscillatory. The multiple qualitative differences in effect that such modalities confer have been well studied
in the case of the intracellular signal Ca2⫹ (18, 19, 75,
82, 83, 99, 202–204, 233). For example, the frequency
of calcium transients dictates NF-␬B transcriptional
activity and neuronal differentiation (143, 144, 317).
Calcium influx and consequent pituitary hormone secretion depend on the step duration of spontaneous
depolarization (346). Insulin stimulation of mitogenesis requires a longer occupancy of the receptor than its
metabolic action (311). In dog thyroid cells IGF-I,
which activates MAPK kinase for a short time and
PI3K for longer, has a permissive effect on TSH mitogenic action, whereas EGF, which activates PI3K for a
short time and MAPK for longer, has a mitogenic effect
in the presence of IGF-I (55). Short-term stimulation of
the MAPK pathway by EGF causes proliferation,
whereas longer stimulation by nerve growth factor
(NGF) causes differentiation in PC12 cells (337). Similarly, short-term activation of MAPK in Drosophila
ommatidia causes growth and survival, whereas
longer activation causes differentiation (128) and
sometimes growth arrest (362). In regenerating liver,
two waves of MAPK activation are necessary for the
division of hepatocytes: an early wave for the awakening competence acquisition and a late wave for progression and cyclin D induction (325). In T cells long-term
stimulation of Rap1 enhances the activation of Elk by
the Ras cascade, whereas short-term stimulation inhibits it (59). The main mechanisms involved in the
timing of effects in a signaling cascade are the positive
and negative feedbacks and feedforward regulations
(103). The presence of one such regulation may completely modify the timing of a cascade. For example,
negative feedbacks and feedforward controls translate
a constant stimulus in a sequence of wavelike causally
related events (Fig. 13). In a biphasic effect the relative
strength of a negative feedback on an initial step may
transform a positive to a globally negative effect and
conversely. Constant infusion of the hypothalamic
stimulatory hormone gonadotropin-releasing hormone
(GnRH) leads, through desensitization of its cascade,
to an overall inhibition of gonadotropic pituitary cells.
Many cell processes imply an orderly causal sequence
of events, some of which require the extinction of previous steps and the absence of subsequent events. This
is evident for cell motion or phagocytosis (176). In a
sequential process by which ␤-adrenergic receptor first
activates Gs, then through arrestin Gi and Src (213–
215), because Gs and Gi have opposite effects, differences in the kinetics of the sequence will lead to opposite results (231). The presence of a single positive
feedback can confer bistability to a system (i.e., it is
either on or off) and thus permanence to an effect (334).
Similarly, the hysteresis of calmodulin-dependent protein kinase converts a short Ca2⫹ signal in a much
longer protein phosphorylation (18, 19, 104). Thus differences in timing of a response to a signal result from
the characteristics of the proteins involved, i.e., from
the pattern of the proteins present.
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translational level CREB and cAMP-responsive element modulator (CREM) phosphorylation and activation are caused by the cAMP cascade, intracellular
Ca2⫹, and growth factors acting through MAPK, stress,
and p38 and MAPK-activated protein (MAPKAP) kinase,
etc. (63). Glycogen synthase kinase 3␤ is phosphorylated on the same serine and inactivated by protein
kinase B (PKB) and PKA (202–204). At the transcription level the early-immediate gene c-Fos is induced by
almost any cell stimulant from the growth factors inducing mitogenesis in fibroblasts to repetitive activation of nondividing neurons (145, 235). Such common
responses to cascades having very different effects imply by definition that these responses are general and
nonspecific. In fact, they suggest a sort of undifferentiated “awakening” reaction. Other more specific effects of the cascades or the specific complement of
transcription factors (see From combination of unregulated parallel factors and of one regulated factor in
each cell type: the triggering reaction or “switch”) will
determine the direction or response the “awakened
cell” will choose. For instance in the thyroid, as in other
cells (25, 26), each cascade, besides its common activation of Rap1, also activates specific kinases with more
or less specific substrates (PKA for cAMP, PKC for
diacylglycerol, Ca2⫹/calmodulin-dependent protein kinases for Ca2⫹ and Ras MAPK and PKB for insulin and
growth factors), which confers specificity to its action
(Fig. 12; Refs. 55, 79, 81). Each cascade also induces a
more or less specific panel of early-immediate genes
besides c-Fos. At the level of chromatin, similar general rapid modifications suggested that “diverse pathways can then contribute to the modification of these
proteins without the necessity of targeting these pathways to a particular chromosomal site” (365).
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Fig. 13. Feedback controls can transform the simple
kinetics of action of a stimulus on a cascade. In a linear
cascade, the signal A should elicit D with similar kinetics and a small delay. In A, the negative feedback of D
on B shuts off the generation of D despite the constancy
of stimulus A [like protein tyrosine phosphatase induction by tyrosine kinase (379) or MAPK phosphatase by
MAPK]. In B, the positive feedback of C on B transforms a pulse of A in a continuous stimulation of B and
C. In C, the negative feedback of B on A and the
negative feedforward controls of A on B to C effect and
of B on C to D relation generate a wavelike response of
B, C, and D to A. A: initial stimulus. ⫹arrowhead,
activation; ⫺//, inhibition.
Modulators are proteins not involved in the signal
transduction cascade themselves but which positively
or negatively modulate the proteins of this cascade
(Table 2). When a given protein has opposite actions,
differential modulations of them will lead to one result
or the opposite, e.g., cMyc, which induces cell proliferation or apoptosis (50). Examples of such inhibitory
modulating proteins abound: starting from natural antagonists of extracellular signals, soluble receptors
that compete with extracellular signals for membranebound receptors, kinase inhibitors, G protein inhibitors
[regulator of G protein signaling (RGS) proteins; Ref.
312], and Bin1 or Groucho inhibitors of MYC or LLT1/
TCF⫺ transactivating action (85), among others. An
excess of scaffold proteins can also cause inhibition
(35). Other modulators enhance the activation of a
cascade, e.g., DARPP32, which after phosphorylation
by PKA inhibits PP1 and thus enhances and prolongs
the phosphorylation and thus the activity of CREB
(306). Tyrosine phosphorylation of calmodulin increases many of its activations (53). Targeting of a
signal transduction protein, for example to the membrane, may also greatly modify its activity.
Some modulators even change the receptivity of a
protein from one signal to another. Receptor activitymodifying protein (RAMP) proteins switch the specificity of the cGRP receptor to one or the other hormone,
RAMP1 to CGRP, RAMP2 to adrenomodulin (196,
236–238, 302). For hormone nuclear receptors, besides
the controls mentioned above, there are modulators of
coactivators [e.g., pCIP for CREB binding protein
Fig. 14. Opposite effects of a cascade.
AJP-Cell Physiol • VOL
(CBP)] and competitors. There are even modulators of
modulators such as p34SEI-I, which antagonizes the
inhibition by p16INK4a of cyclin-CDK complexes (322),
and prothymosin-␣, which sequestrates the repressor
of estrogen activity (224). Finally, signal transduction
cascades may induce, depending on the cell type, the
synthesis and secretion of autocrine or paracrine factors or of proteases generating such factors, i.e., extracellular modulators from inactive precursors (183,
195). Just as the field of nuclear receptors is now
discovering all the modulators of coactivators and corepressors that provide specificity of action in different
cell types, the field of signal transduction cascades is
now identifying its modulators. The role of multifaceted proteins should also be considered at this level.
Specificity of Response to a Cascade Depends on
Quantitative Differences of Expression or Activity
Many cascades activate two or more different
branches that may have parallel, synergic, or opposite
effects. When the effects are opposite the expected
behavior of the system may be very complex (341–343)
(Fig. 14). Moreover, quantitative differences of expression may lead to opposite results: Fas receptor activates the caspase cascade, leading to apoptosis, and
the MAPK cascade which inhibits it (138). Depending
on the strength of the two effects, TNF or Fas receptor
cause apoptosis in some cells but not in others (9, 10).
The complex SMAD2/SMAD4, activated by transforming growth factor (TGF)-␤ receptor, induces expression
of the target gene when it is bound to the coactivator
p300/CBP and represses it when bound to the corepressor TGTF and histone deacetylase. Formation of one of
these mutually exclusive complexes is determined by
the relative levels of corepressors and coactivators in
the cell (366). Somatostatin receptor sst2 activates
proliferation of CHO cells through the PI3 kinase cascade and inhibits it through the p38 kinase pathway
(301). Ca2⫹, through calcineurin, both activates and
inhibits apoptosis in a myeloid cell line (211). CD45
phosphatase dephosphorylates the activating and inhibitory tyrosine phosphates of LCK (264). Similarly,
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Cell Specificity of Response May Depend on
Qualitative Difference of Protein Expression
of Modulators
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Table 2. Roles of modulators
Nos. in parentheses indicate references.
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Positive
Activators
e.g., calmodulin
PTPA for protein phosphatase 2A (153) regulating independently the stimulation of tyrosine phosphatase activity
Calcyon for dopamine D1 receptor (201)
Protectors
14-3-3 Proteins protecting B Raf from inactivation by protein kinase A (271)
Inhibition of proteolysis of signal transduction proteins
e.g., cAMP for arylalkylamine N-acetyltransferase (296)
Negative
Inhibitors, pseudosubstrates, or competitors
e.g., AGOUTI or AGRP for MSH vs. MSH or melanocortin receptor (72, 100)
ARGOS for EGF (350)
Inhibitory receptors of immune system Fc␥RIIB for FC receptors: CD22, killer inhibitory receptors, CTLA4 for antigen receptor (38)
Frzb for WNT on Frzl (54, 200)
Tsg for BMP molecule decapeptide (131)
TRID for TRAIL (307)
PKI for PKA
11 and 12 PP2A for protein phosphatase 2A (153)
SOCS for STATs (4)
TGIF, SKI, SNO, SMAD5 and -7 for SMAD1, -2, -3 (208, 366)
Par4 for PKC␨ and -␭
AP2 for MYC/MAX on its RE (135)
ALT III for phospholipase C (242)
R KIP for Raf kinase (374)
SNP competitor for coactivator on transcription factor HNF-4 (191)
Stabilizers of inactive conformation
e.g., SYN1A for CFTR (127)
Sequestrators
e.g., P14ARF sequestrating MDM2 in the nucleolus (351)
14-3-3 proteins for forkhead associated domains proteins, for regulator of G protein signaling (16, 241)
LRP5 sequestrators of ascin (248)
Kir/Gem sequestration of the ␤-subunit of Ca2⫹ channel and block of Ca2⫹ channel expression at cell membrane (15)
Protein tagging for destruction (E3 ubiquitin ligases)
e.g., VHL for HIF-1␣
SCF for cyclins (88)
Cbl for EGF receptor ubiquitinylation (159)
Cblb for Vav (181)
Smurf for Smad1 and Smad 5 (7, 227)
Power breakers
e.g., RGS for G proteins (56, 67)
Tyrosine phosphatases for kinase substrates (216)
Linkers
Adaptors (2 proteins)
e.g., Grb2 for growth factor receptors and SOS
SARA for Smads and TGF␤ receptors (226)
14-3-3 proteins for Raf1 and PKC␨ (345) or scaffolds (several proteins)
e.g., STE 5 for the yeast MAPK module (35, 168)
14-3-3 Proteins (16, 241)
JIP for the MEKK-MKK7-JNK module (116, 359)
AKAPS for PKA and other signal transduction proteins (74, 86)
Axin for the WNT pathway (173, 262)
Cas proteins for the integrin receptor complex (252)
Localizers
Proteoglycans for chemokines or activins (198)
AKAP proteins for PKA, Ras for Raf
␤-Subunit of IgE receptor (76)
SARA for SMADS (367)
Qualitative modifiers (changing specificity of proteins)
e.g., RAMP for cGRP receptors (302)
␤-Subunits of PP2A phosphatase (regulating activity and substrate specificity) (153)
PML for the transcription specificity of p53 (118)
MAE modulator of the transcription factor substrate specificity of MAPK (216)
Positive and negative
Modulators by posttranslational covalent addition mechanisms
Kinases and phosphatases for phosphorylation (17, 222)
Acetylases and deacetylases for acetylation (29, 180)
Methylases and demethylases for methylation (17)
Glycosylation for extracellular signals (270)
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M-Ras and p21 Ras positively and negatively regulate
Elk1-dependent gene induction through MAPK and
Ras pathway modulator (RPM), respectively (87). Moderate levels of cAMP promote, whereas high levels
inhibit, the bone morphogenetic protein (BMP)2-induced sympathoadrenal cell development (22) and the
proliferation of granulosa cells (281, 282). Thus, when
an agent causes opposite effects, differences in the
expression of the target proteins or of the concentration
of the agent can switch the sign of the response. Quantitative differences of even a factor of 2 can have
dramatic consequences as shown by numerous phenotypic expressions of haploid insufficiency or by the
growth advantage of neurofibromatosis 1 (NF1) heterozygosity for astrocytes (8).
Thus the mechanisms endowing cell types with an
exquisite specificity in signaling in response to similar
stimuli come down to the level and time structure of
the stimuli, to the unique qualitative and quantitative
pattern of expression of isoforms and modulators, and
to their spatial arrangement in compartments and
supramolecular complexes (293).
LOSS OF SPECIFICITY MAY BE A CHARACTERISTIC
OF DEGENERATE SYSTEMS
As discussed above, much work on signal transduction and “cross talk” is carried out on cell lines, mostly
for reasons of convenience. Cross talk, originally designating interference in radiocommunication (247),
may thus represent a loss of specificity in signaling.
The question may then be raised whether this loss is
not a characteristic of these cell lines and of the way
they have been generated. This is suggested by many
examples. Oncogenes would not have transformed NIH
3T3 cells and thus would not have been discovered if
these cells had not been already half-transformed (130,
186). Whereas PDGF action on fibroblasts has a stringent requirement for Src-type kinases, this requirement
is lost in NIH 3T3 cells or other cell lines (Ref. 32).
Our knowledge of thyroid cell lines such as the
FRTL5 cell line supports such conclusions. Generated
by continuous culture of rat thyroid primary cultures,
the cells were selected by their property of multiplying
only in the presence of serum, TSH, and insulin. In the
first report the cells did not multiply in the presence of
insulin or TSH alone. Now, as used after many generations, the cells multiply in the presence of either
insulin or TSH. This is inconsistent with in vivo work
showing that mice or human thyroid cells do not multiply in the absence of TSH or when its receptor is
inactive and that they do not respond to TSH in the
absence of IGF-I. Moreover, aging of the cell line leads
to an increase in the unstimulated proliferation rate.
At the biochemical level, whereas the pathways of
insulin and TSH are clearly distinct in primary cultures (with the cAMP-PKA response specific to TSH
and the Ras MAPK and PI3 kinase responses specific
to insulin), the pattern blurs in the FRTL5 cells, in
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Conclusion on Cell Specificity
which TSH and cAMP have been reported also to
activate the Ras-MAPK and the PI3K pathways. The
specificity of the TSH-cAMP and IGF-I-PI3K pathways
has completely disappeared (175). Similarly, Ras oncogene induces proliferation with no effect on differentiation in human thyroid cells in primary culture, but it
enlarges its action to dedifferentiation in the rat thyroid cell lines (112, 113). Whereas the proliferation
effect in human cells requires at least MAPK and PI3K
activations and another factor, in the WRT cells either
MAPK, PI3K, or Ral GDS is sufficient (112, 113). It is
obvious that such cells in which the activation of one
pathway is sufficient for mitogenesis will be more
prone to uncontrolled proliferation. Similar discrepancies have been reported for pituitary cell lines.
Thus the pattern of signaling is blurred in cell lines.
This may result from genetic or, more likely, from
epigenetic changes, from quantitative dysregulations
leading to differences in expression, or from qualitative
changes due to mutations. When specificity of an enzyme is constrained by its localization on a scaffold,
even a slight increase of expression may lead to escape,
new cross signalings, and scrambling of a network. The
development of the embryo illustrates how a relatively
shallow gradient, i.e., concentration difference, can
lead to qualitatively different differentiation. These
arguments should be related to recent findings in Drosophila. In these flies, mutations of HSP90 induce
phenotypic variations in nearly all tissues, suggesting
that HSP90 may be a part of a molecular buffering
system that keeps cryptic signaling cassette variants
silent (247). Disruption of such a system would lead to
blurring in signaling.
There are indications that, besides protooncogene
activation and antioncogene inhibition, a similar blurring is taking place in cancer cells. Illegitimate transcription leads to the synthesis and eventual secretion
of heterotypic hormones in these cells. Heterotypic
receptors also appear. Cancer in multiple endocrine
neoplasia is accompanied by a loss of substrate specificity by tyrosine protein kinase Ret (in Men 2B) (291,
379–381). A clear example of the role of blurring signal
transduction specificity in cancer is given by the loss of
specificity of androgen hormone receptors and their
corresponding abnormal responses in prostate cancers
(361, 349, 377).
The normal hypermutation process of lymphocytes,
when wrongly targeted, is at the source of many lymphomas (259). Such a blurring probably confers a selective growth advantage, as no inactivation, negative
control, or checkpoint can stop a cell from proliferating
when cross signaling ensures the bypass of any block.
As the null phenotype of a cell is to grow and multiply,
blurring of the controls should favor growth (229, 316).
Moreover, if a loss of signaling specificity is a disadvantage for the growth of a cell, it will be selected
against and disappear. If it is an advantage, the cell
will outgrow the others in what becomes a Darwinian
process (130). The same reasoning applies to random
methylation of promoters, which accounts for some
cancers (20, 194), presumably by loss of tumor suppres-
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UTILITY OF NETWORKS
Among the infinite possibilities of cross signalings
between signal transduction pathways in their superimposed layers of controls, only a restricted number
will operate in a given cell at a given time and the
pattern of these controls will be very different from one
cell type to another, reflecting the diversity of function
and constraints of the cells (133). One may wonder
about the interest of these control networks. Their
value may lie in their robustness: intuitively, we guess
that the more complex regulatory web is also the more
robust (14, 52, 207, 352). This has been proved experimentally in ecology (268) and by the Internet. However, such statements should be qualified. Communication networks display a high degree of robustness,
the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. “However, error tolerance comes at a price in that these
networks are extremely vulnerable to removal of a few
key nodes” (3). This and functional redundancies (178,
185, 376) explain why null genotypes for so many genes
have no or a very poor phenotype whereas a minority
are lethal. In fact, in yeast, the proteins that have the
largest potential repertoire of possible interactions,
i.e., the so-called “nodes” of network, or more mundanely the common bottlenecks of regulation networks,
are the only ones whose disruption is detrimental to
the cell (156). Another role for cross signaling in signal
transduction cascades is that, just as metabolic pathways, as they diverge and converge, they may be conAJP-Cell Physiol • VOL
trolled at multiple steps for efficiency (52). Finally,
complex networks may allow fine tuning in regulation,
which when applied to many cells, may become very
important at the level of the organism. Small advantages may be very important in long-term survival and
thus in evolution. On the other hand, a converse explanation for some complex signalings may be that they
have no role and are irrelevant, innocuous relics of
evolutionary history. Simulating and possibly understanding such complicated networks will require new
tools, sophisticated algorithms, perhaps similar to
those used in the study of neural networks (99).
GENERAL CONCLUSIONS
The significance of a signal on a given cell depends
on the network in which it is inserted. The response of
a cell to a signal depends on the timing and strength of
the signal, on the cooperativity of the response, on the
cell environment, on the cell population, on the subcellular localization of the signal transduction proteins,
on the nature of the isoenzymes present at each level,
on the positive and negative feedbacks and feedforward controls, and on the synergic and/and or either/or
controls, etc., that is, on its program, i.e., the regulation network, namely, the nature, quantitative importance, and localization of its constituent proteins, the
actors in the game. Proteomics, i.e., the definition of
the protein population in a cell type in a given condition, could give this information (198, 199) and thus,
with the use of a database of direct or indirect proteinprotein interactions and controls, theoretically allow
us to draw the awfully complex regulatory network of a
given cell at a given time in its history (91). There are
several caveats to be considered. First, the existence of
a regulatory pathway does not necessarily indicate an
important physiological role. We and others have delineated the mechanisms of action of neurotransmitters on the dog and rat thyroids without ever being able
to demonstrate a role for these controls. Second, given
the fact that the addition of one factor or even one motif
in a protein may change the whole result of a network,
the task may be enormous. Third, anybody who has
tried his hand at quantitative simulation knows that
by choosing the right parameters in even very simple
models almost any possible behavior can be predicted.
Fourth, the requisites of Pollard (as quoted in Ref. 39)
for successful computational prediction are rather
tough: molecular inventory, molecular structures, molecular partners, kinetic constants, genetic and pharmacological phenotypes. At each step enzymes may be
location specific or not, inhibited and/or stimulated,
controlling and/or controlled. In fact, the functioning of
a network may only be predicted when it is completely
known. As expressed in a recent review, “it ain’t over til
it’s over” (309). Moreover, even simple cross signaling
may elicit very complex behaviors (189, 341–343).
Present simulations on general schemes based on data
from different systems (163) may be useful didactic
intellectual exercises but have little predictive value.
The possibility of predicting the behaviors of a cell by
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sor gene activity (234), to the role of the conjunction of
mutations in several cascades, and to the role of aneuploidy (225) in cancer. The condition is described as
“gene addiction,” in which the full malignant phenotype depends on the continued interaction between
pathways (357).
It would be interesting to explore whether aging per
se induces such loosening of controls. Normal human
mammary epithelial cells spontaneously escape senescence and acquire genomic changes akin to those of the
earliest lesions of breast cancer (286). This would explain the multitude of benign tumors arising with age.
Extensive cross signaling may therefore represent not
only a consequence of evolution and selection, i.e., of
the carcinogenetic process, but also a factor in the
initiation or further proliferation of cancer cells or cell
lines.
The blurring of specificity and extension of cross
signaling in cell lines or cancer cells could be called
signaling entropy. Its mechanisms have been little
studied. However, one might speculate that they involve the loosening of all the controls described earlier
in this article that ensure cell specificity of signaling,
e.g., loss of space or time structure, broadening of the
substrate repertoire of enzymes, and increased illegitimate transcription, that is, an increase of entropy at
all levels of signal transduction pathways. This in turn
could result from a loosening of the very strict specificity of transcription.
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AJP-Cell Physiol • VOL
For physiological relevance the choice of the right
model is paramount. For human physiologists, this is
the human cell in vivo. Any departure from this material entails the risk of irrelevance. Who cares about the
peculiar properties of the FRTL5 cells used in our
laboratory? When we use model systems the validity of
the scheme we outline should be assessed in “real
cells,” physiological or pathological. To get our story on
the role of TSH through cAMP in the control of thyroid
function and growth accepted, we needed to generate
transgenic mice in which constitutive activation of this
cascade in the thyroid led to goiter and hyperthyroidism and to demonstrate the same mechanism and
consequences in human autonomous thyroid adenomas
(175, 285, 347)! Similarly, the positive role of cyclin D1
on proliferation and its negative control by p27 have
been definitely validated by double-knockout mice
(335). This increasingly recognized need explains the
expanding literature on gene knockouts and especially
on cell-specific and inducible gene knockouts.
One example from our group illustrates the necessity
of in vivo validation. An enzyme hydrolyzing the intracellular signal PIP3 had been cloned in our lab: SHIP2
(263). PIP3 has been involved in various cells in the
stimulation of protein synthesis, proliferation, etc., in
the prevention of apoptosis, and in the action of growth
factors IGF-I and insulin. Our prediction was therefore
that mice in which SHIP2 had been knocked out would
present growth anomalies and tumors. In fact, the
main effect of this knockout was not predicted: a
greatly increased sensitivity to insulin with rapid
death from hypoglycemia. The affected mice were born
with normal weight (49). The phenotype is extraordinarily restricted, which suggests that the enzyme
mostly deals with the PIP3 generated by the insulin
receptor! It is interesting that, whereas the 3⬘ phosphatase PTEN also inactivates PIP3 and in transfected
cells inhibits insulin action (244), its half-knockout in
mice does not, as the SHIP2 half-knockout, suffer from
hypoglycemia. Thus for the physiologist the safest approaches to signal transduction may be at the two
extremes of the experimental spectrum: on in vivo
models (transgenics and gene knockout) and on the
structure of the relevant proteins. With regard to the in
vivo models, their use in the study of specific organs in
laboratories specialized in these organs would provide
a wealth of information for both these groups and the
groups generating the models.
In fact, there is more coherence in the physiology of
regulation systems than in the mechanisms that
achieve this physiology. TSH activates both the synthesis and the secretion of thyroid hormones in all the
species studied, but in some it activates both the phospholipase C and the cAMP pathways, which stimulate
synthesis and secretion, respectively, whereas in others the hormone only stimulates the cAMP pathway,
which there enhances synthesis and secretion (348).
Similarly, somatostatin uses different cascades and
mechanisms to inhibit cell proliferation in different
systems (96). The diversity of mitogenic pathways used
by GPCRs is even more striking, making any attempt
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knowing its protein composition will be tested by the
very thorough work of the Alliance on a few cell types
as pioneered by A. Gilman (111).
The research in signal transduction therefore follows, legitimately, a three-pronged approach, each
prong of which supports the others but should not be
confused with them. The study of simple models, from
yeast to Caenorhabditis, or even Drosophila, defines
basic mechanisms, actions, and their relations (273,
355). After all, the basic steps of apoptosis and of the
EGF pathway were demonstrated first in C. elegans
and Drosophila, respectively (98). The study of various
types of mammalian cell lines defines the many possible variations that evolution and the multiplication at
each level of isoforms and new modulators and cross
signalings have provided, i.e., the available toolkit for
differentiation. It is precisely in these variations and
cross signalings that different cell types will mainly
differ. The study of a given cell type in its physiological
context defines which of the almost infinite possible
combinations of controls applies to this cell type.
Thus regulatory schemes can be presented and interpreted in two ways. The first way is as maps of all
the possible interactions like the conventional metabolic pathway maps. Reviews on signal transduction
pathways and protein belong to this category. They
should provide information about all possible interactions, the “toolkits” of cell biology, but refrain from
specific functional interpretation. Second, regulatory
schemes can be presented and interpreted as the regulating scheme applied to one cell type at a given time
in its history. In this case, the cell type, species, history, environment, and experimental condition should
be defined. The exquisite specificity of cell types is
illustrated by the exquisite cell specificity of the phenotypes of knockout models for supposedly ubiquitously important genes. Reasoning about the behavior
of a given cell type should only be applied to the second
type of scheme. In such reasoning one should never
lose what Barbara McClintock called “the feeling for
the organism,” i.e., in this case, how signal transduction in a cell fits in with physiology. One outstanding
example of success of the study in depth of one cell type
is the study of T lymphocyte signaling (181). Editors
might recommend modesty in titles and conclusions of
articles, restricting specifically the domain of application of conclusions to the systems really studied or at
least specifying that the controls described are possible
but not necessarily universal: “cAMP can activate the
MAPK pathway” is tolerable; the “tabloid”-type title
“cAMP activates the MAPK pathway” is not. Otherwise, as Bacon said, “words turn back and reflect their
power upon the understanding” (as quoted in Ref. 164),
i.e., vagueness of language leads to confused thinking.
In cases in which existing schemes do not explain the
results, modulators of the proteins in a cascade and
even modulators of modulators should be sought after.
The research on nuclear hormone receptors has shown
the way to the whole signal transduction field in this
regard.
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INVITED REVIEW
to generalize illusory. Thus physiologists, as embryologists, rightly consider what regulates their system of
interest and what the effects of these regulations are
and define the elements of the signal transduction
pathways black box later. For the physiologist, as well
as for the pharmaceutical industry, those mechanisms
operating in their cell of interest are most important.
Cell specificity in signaling, even in very basic mechanisms, is the key to therapeutic targeting (375).
The authors thank R. Beauwens, G. Rousseau, and G. Vassart for
critical reading of the manuscript.
The work of the group is supported by the “Service du Premier
Ministre Affaires Scientifiques, Techniques et Culturelles SSTC”
(PAI), the “Fonds National de la Recherche Scientifique,” “Fonds de
la Recherche Scientifique Médicale,” “Fonds Cancérologique Fortis,”
“Opération Télévie,” and “Fédération Belge contre le Cancer.”
17.
18.
19.
20.
21.
22.
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