ISIS Report 28/09/11
Genes Don’t Generate Body Patterns
Time to end the obsession with genes and
pay more attention to dynamic processes that generate patterns and forms Dr. Mae-Wan Ho
illustrated and referenced version of this report
is posted on ISIS members website and is available for download here
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Gene expression patterns reflect hidden
An impressive army of genes is involved in
laying down the body pattern of organisms during early development, and the fruit
fly Drosophila is the archetypal example [1, 2]. The genes are expressed
in an otherwise featureless embryo, forming remarkable patterns that establish
the anterior-posterior body axis and anticipate the subdivision of the body into
segments that appear much later in development. The major pathways of gene
interactions necessary for determining body pattern in Drosophila were discovered, and Nobel prizes awarded, long
before genome sequences were available. Since then, the genomes of some 800
organisms including Drosophila have been sequenced , and the gene
interactions are being elaborated to the finest detail based on available
genomics information .
But do we really
understand pattern formation in development? I would say no. It remains the
greatest unsolved mystery in science.
The project to
sequence the human and other genomes has failed to deliver here as in the
promises to identify the genes for all our human diseases, failings, and
talents (see  Ten
years of the Human Genome, SiS 48).
Many of the genes involved in
pattern determination in Drosophila turn out to have closely similar
counterparts in higher animals including the human species. While this may
impress some people, others are asking why closely similar genes should give
such widely different body patterns. And in any case, the similarity has been
exaggerated. For even within the Arthropod phylum, which includes insects like Drosophila,
groups differ substantially in the way they form segments and in the genes expressed
There is no doubt that knocking
out or mutating genes can interfere with pattern formation, but there is
nothing in the action of genes that generate patterns. It is obvious
that the genes are responding to and reflecting hidden dynamic processes generating
the patterns [6, 7] (Development and
and Evolution Revisited, ISIS scientific publications), beginning with the
initial symmetry-breaking that establishes the major body axis; and these
processes deserve attention at least as much as genes. It is time to end the slavish obsession with genes as the answer to everything,
and see them as one toolkit among many in the study of living organisms.
Fortunately, some scientists have been doing just that.
Physicochemical forces and flows in
growth and form
Patterns are generated everywhere in the
physical world where no genes are involved, and many of the patterns closely
resemble those found in the living world. It is the dynamics of physical and
chemical forces and flows that generate patterns and forms, much as Scottish
biologist and mathematician D’Arcy Thompson (1829-1902) so beautifully argued in
his classic book, On Growth and Form, first published in 1917 . Closer
to our time, Alan Turing (1912-1954), English mathematician, logician, code breaker and computer pioneer,
is also well-known for his work on morphogenesis. Turing’s reaction-diffusion
model published in 1952  showed, for the first time, how patterns can arise spontaneously
in an initially homogeneous domain, precisely the problem of how patterns can form
in a featureless egg in development [5, 9-11].
Turing model inspired much work on pattern formation in biological systems before
the human genome project got underway, and it was obscured in the
proliferating thicket of genes said to ‘control pattern formation’.
Back to basics with
Lately, there has been a
Turing revival in developmental biology, as scientists despair of trying to
explain pattern formation with complicated computational networks of genes that
pass for ‘systems biology’. Shigeru Kondo and Takashi Miura at Osaka University in Japan are part of this movement back to
Turing, using a combination of experiments, modelling, and computer simulation.
In a review article, Kondo and
Miura stated why they did not concentrate on computational
networks of genes , because “the behaviour of such systems often defies immediate or
intuitive understanding.” And, “it becomes almost impossible to make a
The Turing model on the other
hand can explain how spatial patterns can arise “autonomously”, and in nearly
limitless variety; not only major body axes, segments and other repeated
structures, but also intricate markings on seashells, and exotic pigmentation
patterns of fish (see Figure 1).
1 Turing markings on seashells and pigmentation patterns on popper fish;
computer generated patterns on right, biological specimen on left of each frame
Moreover it can also explain, if not
predict, why experimental manipulations result in very specific novel regenerated patterns. In Figure 2, the zebra fish
dark pigment stripes were cut out with a laser. Instead of replacing the
original pattern, a new, unexpected pattern was regenerated after 23 days. This
was simulated on computer according to a Turing model (bottom of Fig. 2) in
which black and yellow pigment cells activated each other at short range while
yellow pigment cells inhibited the black pigment cells at long range.
2 Turing model explains novel regenerated pigment pattern in the zebra fish
Short-range activation and
long range inhibition is a necessary condition for pattern formation in
Turing’s reaction diffusion model, an important result derived by Alfred Gierer
and Hans Meinhardt in the 1970s .
Although Turing’s original
model involved reaction and diffusion, it has become clear that the model is
independent of mechanisms. Any mechanism that produces short range activation
and long range inhibition would do just as well [12, 14].
The overriding significance of the Turing
model, and indeed a more general mathematical theorem of symmetry-breaking,
is that the system, whether physical or biological, spontaneously breaks or
reduces existing symmetry . For example, a spherical featureless blob with
an infinite number of axes of symmetry can become polarised to radial symmetry.
Should it elongate, it loses radial symmetry to become bilaterally symmetric,
and so on.
symmetrical system can lose its symmetry if an asymmetrical state has a lower
energy. The initial symmetrical state can be unstable or metastable. An
external trigger can push the system from its symmetrical to its asymmetrical
state, but simple noise can accomplish the same thing.
Symmetry breaking in biology invariably involves active dynamical
processes. Turing showed that patterns can be generated by simple chemical
reactions if the reactants have different diffusion rates.
Cells can polarize in response
to external signals, such as chemical gradients, cell-cell contacts, and
electromagnetic fields. However, cells can also polarize spontaneously in the
absence of external influence from internal dynamical processes.
example, spontaneous polarization can be driven by a mechanical
instability of the actin-myosin cortex (layer just beneath the cell
membrane) of cells. The cortical actin network is a thin shell of cross-linked
actin filaments, myosin motors, and actin-binding proteins between100 nm and1 mm thick, and
supports the plasma membrane. The myosin motors generate contractile forces
resulting in tensile stress in the actin network. (A stress is force per unit
area whereas tension is force per unit length). The tension in the cortex is
roughly equal to the stress multiplied by the thickness of the cortex. The
elastic energy stored in the stress actin shell can be released by rupture of
the network or by the membrane detaching from the cortex, as seen in
fibroblasts and lymphoblasts. The relaxed region produces cortical flows or
membrane protrusions called blebs.
Flows of the actin-myosin cortex take place in various cell lines at
the onset of cell division, which presumably contribute to the cleavage furrow where
the daughter cells separate. In some cells, polarization by cortex relaxation
may precede cell migration.
Spontaneous polarization of the egg cell
Among the first signs of symmetry-breaking
in development is an electrical polarization of the egg cell membrane, and a
flow of ionic currents through the cell and into the surrounding medium. This
may well be universal to all developing systems (see ).
eggs of the brown alga Fucus are released into the seawater as uniform spherical
cells. The point of sperm entry potentially breaks this symmetry, but the
fertilized egg can still be repolarized spontaneously or by environmental
stimuli, such as exposure to light in one direction. Fertilization triggers
events that break the initial symmetry. Within some minutes, transcellular ion
currents can be detected, with Ca2+ entering the pole that will become
the rhizoid (root), circulating around the cell, and leaving at the opposite
thallus (shoot) pole, thus closing the loop . Associated with this pattern
of ionic current is a calcium ion gradient - high at the pole of Ca2+
entry and low at the opposite pole  - and an electric field, which is
depolarized (less negative) at the pole of Ca2+ entry. So how
does this pattern arise spontaneously?
Fabrice Homblé at Free University of
Brussels in Belgium, and Marc Léonetti at University of Aix-Marseille in France proposed that the pattern arises from the dynamics of ion conduction through
membrane channels – the voltage-dependent calcium channels and a potassium leak
– and outside the membrane, diffusion of slower calcium ions relative to
Sperm entry triggers rapid membrane depolarization and ion channel
activation in less than 0.1 ms. Before fertilization, the electric membrane
potential difference is about -50 mV. Fertilization by sperm instantaneously
gives rise to a fertilization potential, which consists of a fast membrane
depolarization from -50 mV to -10 mV, followed by a slow recovery that lasts
several minutes. The membrane depolarization provides a rapid electrical block
to polyspermy (fertilization by more than one sperm) in the absence of a cell
depolarization activates both calcium and potassium channels and gives rise to
a calcium influx (negative current) and a potassium efflux (positive current). As
unfertilized fucoid eggs are excitable, an action potential is triggered when
the plasma membrane is depolarized below a threshold (usually at 10 mV more
positive than the resting potential). The action potential consists of a
transient depolarizing wave lasting ~100 ms with a minimum of -10 mV. The
depolarizing phase is due to activation of the voltage-dependent Ca2+
channels, giving rise to Ca2+ influx; the repolarizing phase arises
from the activation of the voltage-dependent K+ channels, which let
K+ out. This action potential occurs in the presence of
voltage-dependent ion channels with nonlinear current-voltage characteristics
and when the net conductance of the plasma membrane due to influx of Ca2+
and efflux of K+ (Gca + GK) is negative
(<0), because GCa is negative and greater in magnitude than GK.
This is the situation in unfertilized focus eggs.
fertilization, an influx of Na+ probably initiates the membrane
depolarization, which triggers the activation of voltage-dependent Ca2+
channels, leading to Ca2+ influx and the amplification of the
depolarizing phase. The long-lasting recovery period following is accompanied
by an increase of both membrane conductance and K+ permeability.
Thus, after fertilization, GK is larger than GCa, and (GCa+GK)
>0. This not only blocks polyspermy, but is an essential condition for the
formation of stationary spatial patterns of transcellular currents around the
zygote. The crucial additional factor is that Ca2+ diffusion is
significantly slower than K+. The process is explained in Figure 3.
Starting from a uniform distribution of ion channels and ion
concentrations, a local membrane depolarization (dV
> 0) gives rise to a Ca2+ influx
and a K+ efflux (Fig. 3a). A positive net
charge is set up on the cytoplasmic side and a negative one outside giving rise
to a lateral electric field (E). K+ flows outside and Ca2+
inside along their electrochemical potential gradient (Fig. 3b). Because K+ diffuse faster than Ca2+,
a lateral charge separation (d-, d+) and an electrical potential difference (DE diffusion) is established that amplifies and widens the
initial membrane depolarization. This phenomenon will spread along the membrane
as channels are progressively activated. Outside the self-organizing (self-amplified)
region, the ion electrochemical potential gradients will be dissipated through
the membrane forming transcellular current loops. The time constant of this process
is given by R2/DCa, where R is the cell radius and DCa
is the effective diffusion coefficient of calcium ions.
In Figure 3c, a global picture of the egg is
presented. (i) The local perturbation (purple) consists of a depolarization (red
inside the cell) surrounded by a hyperpolarization (blue inside the cell) so
that the global electric neutrality is preserved. (ii) As the electrical
perturbation spreads over the surface, both the central depolarization (red)
and peripheral hyperpolarization (blue) zones of the membrane grow, with the
central depolarization pushing out the surrounding hyperpolarization. (iii) At
the end of the process, a steady state is established where the depolarized
area covers half of the zygote surface; while the other half becomes the
hyperpolarized area. The red arrow indicates the direction of the electric
field inside the zygote, from relatively positive to relatively negative.
Thus, symmetry breaking occurs in some 102 seconds, in
agreement with experimental findings that a single but still labile stationary
pattern of transcellular currents is established within 30 minutes after
fertilization. Membrane, cell wall and cytoplasm reorganization and actin
polymerization are essential to sustain, orient and fix the pattern (which is
presumably further stabilized by other genes that become expressed).
Figure 3 Symmetry breaking in Fucus egg after fertilization and
establishment of transcellular ionic current (see text) 
The process of
establishing transcellular currents and the electric field in the Fucus egg
is another example of the Turing model involving local activation
(amplification of depolarization) and long range inhibition
(hyperpolarization), but the mechanism is electrodynamics, not passive
currents are associated with spore germination and directional growth and in
symmetry breaking in many other plant systems and animal systems, where
membrane potential changes are found to determine anterior-posterior axis in
development and regeneration, as well as cell proliferation, differentiation
and cancer (see  Membrane Potential
Rules, SiS 52).
These findings highlight the
electrodynamical nature of pattern formation in living systems, which are
liquid crystalline through and through (see  The Rainbow and the Worm, The
Physics of Organisms, ISIS publication). Living
liquid crystals are dielectric and intimately associated with water dipoles,
and hence particularly prone to electrodynamical patterning, which I shall deal
with in the next article  (Liquid
Crystalline Morphogenetic Field, SiS 52).