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.
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)