Tag Archives: Long term potentiation

A new kid on the Alzheimer’s block

There’s a new kid on the Alzheimer’s block, and it may explain why the huge sums thrown at beta-secretase inhibitors by big pharma has been such an abject failure. First, a lot of technical background.

The APP (for amyloid precursor protein) contains anywhere from 563 to 770 amino acids in 5 distinct transcripts made by alternate splicing of the single gene. The 3 main forms contain 695, 751 and 770 amino acids. The 695 amino acid form is found only in brain and peripheral nerve where it predominates, while the transcripts containing 751 and 770 amino acids are found everywhere but predominate in other tissues. The A4 peptides (Abeta peptides) which are the major components of the Alzheimer senile plaque are derived from from the carboxy terminal end of APP (beginning at amino acid #597 ) and contain only 39 – 43 amino acids. About 1/3 of the 39 – 43 amino acid amyloid beta peptide (A beta peptide) is found within the transmembrane segment of APP the other two thirds being found just outside the membrane.  So to get A beta peptides the APP must be cut (more than once) at its carboy terminal end.

For Abetaxx (xx between 39 and 43) to be formed, cleavage must occur outside the membrane in which APP is embedded by beta secretase. This produces a soluble extracellular fragment, with the rest embedded in the membrane (this is called C99). Then gamma secretase (another enzyme) cleaves C99 within the membrane forming the Abeta peptides, which constitute much of the senile plaque of Alzheimer’s disease.

Alpha secretase (yet another enzyme) also cleaves the APP in its carboxy terminal extramembranous part, but does so closer to the membrane, so that part of the protein which would form the aBeta peptide is removed.

R. Scheckman personal communication (2012) — The Abeta peptide is appears to be cleaved by gamma secretase from the fragment generated by beta secretase. However, this happens well inside the cell in the last station of the Golgi apparatus. Then Abeta is swept out of the cell by the secretory pathway. So all this happens INSIDE the cell, rather than at the neuron’s extracellular membrane (which is what I thought).

Remarkably it is very difficult (for me at least) to find out just at what amino acids of the amyloid precursor protein(s) the 3 secretases (alpha, beta, gamma) cleave.

[ Nature vol. 526 pp. 443 – 447 ’15 ] describes a totally new kid on the block, which (if replicated) should make us rethink everything we thought we knew about the amyloid precursor protein and the Abeta peptide. Another set of carboxy terminal fragments (CTFs) called CTFneta is formed from the amyloid precurosr protein (APP). Formation is mediated (in part) by MT5-MMP, a matrix metalloprotease. (In grad school neta is how we pronounced the Greek letter eta, which looks like a script N). The authors call the enzymatic activity forming them neta-secretase (clearly not all the enzymes which do this have been identified at this point). At least the authors tell you where the neta secretases cleave APP695 (between amino acids #504 – #505) . This is amino terminal to the beta and alpha sites (which are at higher amino acid numbers and the gamma site which is at a higher number still).  Alpha and beta secretase then work on CTFneta to produce shorter peptides, called Aneta-alpha, and Aneta-beta.

This isn’t idle chatter as Aneta-alpha, and Aneta-beta are found in the dystrophic neurites in an Alzheimer mouse model (human work is sure to follow). Inhibition of beta secretase activity results in accumulation of CTFneta and Aneta-alpha.

Aneta-alpha itself lowers long term potentiation (LTP) in hippocampal slices (LTP is considered by most to be the best molecular and physiological model we have of learning). As judged by intracellular calcium levels, hippocampal neuronal activity is also inhibited by Aneta-alpha.

What’s fascinating about all this, is that the work possibly explains why the huge amount of money big pharma has spend on beta secretase inhibitors has been such a failure.

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The neuron as motherboard

Back in the day when transistors were fairly large and the techniques for putting them together on silicon were primitive by today’s standards, each functionality was put on a separate component which was then placed on a substrate called the motherboard. Memory was one component, the central processing unit (CPU) another, each about the size of a small cellphone today. Later on as more and more transistors could be packed on a chip, functionality such as memory could be embedded in the CPU chip. We still have motherboards today as functionality undreamed of back then (graphic processors, disc drives) can be placed on them.

It’s time to look at individual neurons as motherboards rather than as CPUs which sum outputs and then fire. The old model was to have a neuron look like an oak tree, with each leaf functioning as an input device (dendritic spine). If enough of them were stimulated at once, a nerve impulse would occur at the trunk (the axon). To pursue the analogy a bit further, the axon has zillions of side branches (e.g,. the underground roots) which than contact other neurons. Probably the best example of this are the mangrove trees I saw in China, where the roots are above ground.

How would a contraption like this learn anything? If an impulse arrives at an axonal branch touching a leaf (dendritic spine) — e.g. a synapse, the spine doesn’t always respond. The more times impulses hit the leaf when it is responding to something else, the more likely the spine is to respond (this is called long term potentiation aka LTP).

We’ve always thought that different parts of the dendritic tree (leaves and branches) receive different sorts of information, and can remember (by LTP). Only recently have we been able to study different leaves and branches of the same neuron and record from them in a living intact animal. Well we can, and what the following rather technical description says, its that different areas of a single neuron are ‘trained’ for different tasks. So a single neuron is far more than a transistor or even a collection of switches. It’s an entire motherboard (full fledged computer to you).

Presently Intel can put billions of transistors on a chip. But we have billions of neurons, each of which has tends of thousands of leaves (synapses) impinging on it, along with memory of what happened at each leaf.

That’s a metaphorical way of describing the results of the following paper (given in full jargon mode).

[ Nature vol. 520 pp. 180 – 185 ’15 ] Different motor learning tasks induce dendritic calcium spikes on different apical tuft branches of individual layer V pyramidal neurons in mouse motor cortex. These branch specific calcium spikes cause long lasting potentiation of postsynaptic dendritic spines active at the time of spike generation.