readings> molecular turnover
How do you
persist when your molecules don’t? Holism says the whole
shapes the parts. And here is how the mind does indeed act to
form the brain, reversing the usual reductionist story.
If the structure of the brain is constantly under
construction, why is the mind such
Do you know the half-life of a microtubule, the protein filaments that
form the internal scaffolding a cell? Just ten minutes.
That’s an average of ten minutes between assembly and
destruction.
Now the brain is supposed to be some sort of computer. It is an
intricate network of some 1,000 trillion synaptic connections, each of
these synapses having been lovingly crafted by experience to have a
particular shape, a particular neurochemistry. It is of course the
information represented at these junctions that makes us who we are.
But how the heck do these synapses retain a stable identity when the
chemistry of cells is almost on the boil, with large molecules falling
apart nearly as soon as they are made?
The issue of molecular turnover is starting to hit home in
neuroscience, especially now that the latest research techniques such
as fluorescent tagging are revealing a far more frantic pace of
activity than ever suspected. For instance, the actin filaments in
dendrites can need replacing within 40 seconds, making microtubules
look like positive greybeards (Star et al, 2002).
A turnover time of
five days for NMDA receptors seemed pretty steep when it was reported a
few years back. (Shimizu et al, 2000). But recently Michael Ehlers at
Duke University Medical Center in Durham, North Carolina, reported that
the entire post-synaptic density (PSD) - the protein-packed zone that
powers synaptic activity - is replaced, molecule for molecule, almost
by the hour. Ehlers had expected the turnover to take days and when he
found no labelled protein on his first 24 hour assay, he thought he
must have mucked up the experiment
Myelin and RNA molecules seem to last months. And DNA is of course
fairly hardy, though it still needs continual repair. But on the kinds
of figures that are coming out now, it seems like the whole brain must
get recycled about every other month. And certainly everything points
to the synapses as being about the most dynamic part of the whole
system.
Clearly the shape of the synapses IS somehow maintained despite the
molecular turmoil. But there is an issue here that demands some
specific theory. The stability of brain circuits cannot simply be taken
for granted.
Princeton University's Joe Tsien – famous for making mice
smarter by splicing in slower-closing NMDA receptors – is one
of a number of researchers pursuing the idea that synaptic structure
may be stabilised by pressure from both above and below.
Many people know about the emerging "below" picture of how shifts in
gene expression patterns could be necessary to underpin neural
learning. Put simply, the genes remember what kind of state a junction
ought to be in and so keep rebuilding the same old structure. As a
relative oasis of calm in the thermodynamic bustle of a cell, the genes
could anchor the homeostatic network needed to allow a given synaptic
pattern to persist.
Of course, this story is complicated by evidence
that RNA actually in the dendrites may do the same job. But it seems to
be a "loops within loops" mechanism with short-loop local feedback
nested in long-loop feedback between synapses and genes (Lisman and
Fallon, 1999).
But Tsien says that as well as this shape-maintaining pressure from
within, synapses may be just as dependent on pressures from without -
the old "jangling trace" hypothesis. Back in the early 1990s it was
discovered that there is a kind of compressed replay of the day's
accumulated memories during slow wave sleep. The networks of cells
active during learning would burst to life again. This led to the
theory that the hippocampus consolidates new learning to the cortex
when the brain is off-line.
But Tsien feels this spontaneous jangling
of neural traces is probably a much more general homeostatic mechanism
that helps to keep labile synapses stabilised. And the jangling
probably goes on around the clock, in all areas of the brain, at
regular intervals to remind each synaptic connection of its place in
the great scheme of things (Wittenberg et al, 2002).
All this Byzantine complexity does matter. To make sense of the brain
as an information processing system, clearly we must be physically able
to locate its information. And it’s long been an almost
unquestioned tenet of neuroscience that neurons with their weighted
junctions and crisp connection patterns are devices for trapping
information. The hardwired network is the solid foundation for all the
pretty patterns that play across it.
Yet when we zero in on these
synapses, suddenly their “information” appears to
scatter. The synapses turn out to be merely reflecting a living
confluence of top-down and bottom-up pressures. The information is now
out there in the system and it is making the synaptic patterns we
observe.
This kind of topsy-turvey picture can only be resolved by taking a more
holistic view of the brain as the organ of consciousness. The whole
shapes the parts as much as the parts shape the whole. No component of
the system is itself stable but the entire production locks together to
have stable existence. This is how you can manage to persist even
though much of you is being recycled by day if not the hour.
References
Star EN, Kwiatkowski DJ and Murthy VN. Rapid turnover of actin in
dendritic
spines and its regulation by activity, Nature Neuroscience 5:239-246
(2002)
Ehlers MD. Activity-dependent regulation of postsynaptic composition
and signaling by the ubiquitin-proteasome system. Nature Neuroscience
6:231-242 (2003)
Shimizu E, Tang YP, Rampon C and Tsien JZ. NMDA receptor dependent
synaptic reinforcement as a crucial process for memory consolidation.
Science 290:1170–1174 (2000)
Lisman JE and Fallon JR. What maintains memories? Science 283:339-340
(1999)
Wittenberg GM, Sullivan MR and Tsien JZ. Synaptic Reentry Reinforcement
Based Network Model for Long-Term Memory Consolidation Hippocampus
12:637–647 (2002)