So much work, so little progress

Two years ago, I found going to a memorial service for a friend and classmate who died of Alzheimer’s curiously uplifting  (see the link at the end). The disease is far from ignored. A monster review in Neuron vol. 97 pp. 32 – 58 ’18 —  contained references to over 400 research articles half of them published since January 2013.

Still I found it quite depressing.  Tons of work and tons findings, and yet no coherent path to the cause (or causes); something absolutely necessary for a rational treatment, unless we somehow stumble into a therapy.

In a way it’s like cancer.  The cancer genome atlas intensively studied the genome of various cancers, looking for ‘the’ or ‘the set of’ causative mutations.  They found way too much.  The average colon and breast cancer had an average of 93 mutated genes, of which 11 were thought to be cancer promoting.  Not only that, but the same 11 were not consistent from tumor to tumor.

So it is with this epic review.  Which of the myriad findings described are causative of the disease and which are responses of the nervous system to the ’cause’ (or causes).

In the review the authors posit that Alzheimer’s disease is due to failure of ‘homeostatic systems’ that maintain a ‘set point’ of neuronal firing.  Unfortunately what is measured to determine the set point isn’t known. This seems to be an example of redefining a question into an answer.  Clearly if you juice up neuronal firing rates by stimulation they come back down, or if you inhibit them, they come back up.  So you can operationally define set point without defining it mechanistically.   It must be due to some sort of feedback on whatever it is that is sensing ‘the set point’ , but what is it that is being sensed?

The following is from an earlier post but is quite relevant to homeostasis and set points.

The whole notion of control in the presence of feedback is far from clear cut.  Here’s the story of the first inklings of feedback in endocrinology.  I watched it happen.

Endocrinology was pretty simple in med school back in the 60s. All the target endocrine glands (ovary, adrenal, thyroid, etc.) were controlled by the pituitary; a gland about the size of a marble sitting an inch or so directly behind the bridge of your nose. The pituitary released a variety of hormones into the blood (one or more for each target gland) telling the target glands to secrete, and secrete they did. That’s why the pituitary was called the master gland back then.  The master gland ruled.

Things became a bit more complicated when it was found that a small (4 grams or so out of 1500) part of the brain called the hypothalamus sitting just above the pituitary was really in control, telling the pituitary what and when to secrete. Subsequently it was found that the hormones secreted by the target glands (thyroid, ovary, etc.) were getting into the hypothalamus and altering its effects on the pituitary. Estrogen is one example. Any notion of simple control vanished into an ambiguous miasma of setpoints, influences and equilibria. Goodbye linearity and simple notions of causation.

As soon as feedback (or simultaneous influence) enters the picture it becomes like the three body problem in physics, where 3 objects influence each other’s motion at the same time by the gravitational force. As John Gribbin (former science writer at Natureand now prolific author) said in his book ‘Deep Simplicity’, “It’s important to appreciate, though, that the lack of solutions to the three-body problem is not caused by our human deficiencies as mathematicians; it is built into the laws of mathematics.” The physics problem is actually much easier than endocrinology, because we know the exact strength and form of the gravitational force.


Body Mass Index (BMI): mine, yours, and Trump’s

The ‘investigative press’ is quite concerned about President Trump’s height.  If he is 6 foot 3 and 239 pounds, he is overweight but not obese, if he is 6 foot 2 he is obese.  All this is a matter of definition by a single number — the body mass index (BMI).

So let’s all calm down and find out what ours actually is.  There is a website which will do it for you. Here’s the link — — have at it. You can use pounds feet and inches as well as kilograms and meters.

The current definition of obese is a BMI over 30, overweight between 25 and 30, and normal weight under 25.

Who cares?  Well, you should if BMI’s correlate with mortality and they do.

A great paper 5 years ago by Katherine Flegal analyzed nearly 3 million people with 270, 000 deaths reported in a variety of studies —

The problem is that the lowest mortality didn’t occur in those with normal weight (BMI < 25) but was lowest in the overweight group — not by much (6%), and second lowest in the mildly obese (BMI 30 – 35), over 35 it was 20% higher.

Naturally this did not sit well people who'd staked their research careers on telling people to lose weight.  There is a truly hilarious article describing a meeting at Harvard discussing the paper.  Here's a link   It's worth reading in its entirety, particularly for a graph it contains.

One study by a Harvard guy removed 900,000 people from the study leading to the following great comment — “It's hard to argue with data,” says Robert Eckel, an endocrinologist at University of Colorado in Denver. “We're scientists. We pay attention to data, we don't try to un-explain them.”

The Nature paper contains a terrific graph from the following paper — Source: Childers, D.K. & Allison, D.B. Int. J. obesity 34, 1231–1238 (2010).

Look at it carefully.  Mortality vs. BMI is plotted in several curves one for people between 20 – 30, one for 30 – 40, etc. etc.  Under 50 the best BMI to have in terms of mortality is under 25, but over 50 it rises, so that at 70 the low point is around 27 (in the overweight range) and not far from Trump at 6' 3" (29.9) or even at 6' 2" (30.7).

In a way this data fits with the fact that for a long time Americans were getting fatter and fatter, yet living longer and longer.  For details see —

Why should the best BMI for you to have rise after age 50?  I've not seen this explanation anywhere else.

The BMI is far from perfect, but to calculate it all you need are two simple measurements that anyone can make — height and weight. It doesn't rely on what people remember.  However the calculation is not a simple ratio of weight divided by height but weight divided by height squared.

People lose height as they age, so the BMI is quite sensitive to it (remember the denominator has height squared).  Well as a high school basketball player my height was 6′ 1”+, now (at age 75) its 6’0″ (God knows what it is several years later). So even with constant weight my BMI goes up.

Well it’s time to do the calculation to see what a fairly common shrinkage from 73.5 inches to 72 would to to the BMI (at a constant weight). Surprisingly it is not trivial — (72/73.5) * (72/73.5) = .9596. So the divisor is 4% less meaning the BMI is 4% more, which is almost exactly what the low point on the curve does with each passing decade after 50 ! ! ! This might even be an original observation, and it would explain a lot.

As long as I’m on the subject of the ‘investigative press’  here is how they glossed over Hillary’s fainting spell during the presidential campaign, calling it a stumble.  Remarkable discipline that they all used the same word.  So take their worries about Trump’s weight with a grain of salt.

“Like the Michigan poll I started out with, most of the latest campaign surveys were carried out during last weekend and earlier this week, when the news was dominated by Clinton’s stumble outside Ground Zero, which prompted the campaign to reveal that she had pneumonia.” John Cassiday New Yorker

“For Orient—and the many media organizations that have recently been circulating her work—Clinton’s stumble looked like proof that they were right.” Wired —

The Boston Globe had a similar echo of the meme that all Hillary did when leaving the 9/11 ceremony was stumble.

As Richard Pryor famously said when his wife caught him with another woman. He denies anything is going on, and asks his wife, “Who you gonna believe, me or your lying eyes?”

See for yourself —


Why drug development is hard #31: retroviruses at the synapse

What if I told you that a very important neuronal synaptic protein Arc (Arg3.1) is acting like like a virus, sending copies of itself (and its messenger RNA) across the synapse?  Would a team of shrinks, who’ve never examined me, tell you that I was crazy and unfit to blog?  Well there is very good evidence that exactly this occurs in one situation and probably many more [ Cell vol. 172 pp. 8 – 10, 262 – 274, 275 – 288 ’18] —

Arc stands for Activity Regulated Cytoskeleton associated protein.  It’s messenger RNA (mRNA) is transcribed from the gene in response to neuronal activity.  More importantly, the mRNA for  Arc is rapidly distributed to active synapses through the cell body and dendrites, where it is translated into protein. It is locally and rapidly stimulated during the induction of long term depression and plays a critical role in removing a class of glutamic acid receptors (AMPA receptors) from the synapse.  To whet the interest of drug developers, Arc regulates the activity dependent cleavage of the Amyloid Precursor Protein (APP) and beta amyloid production by its interaction with presenilin

Several posts could easily be filled with what Arc does, but that’s not what is so amazing about these papers.  Parts of the Arc protein arose from one of the many transcriptionally dead retroviruses found in our genome.  Our species literally wouldn’t exist without other retroviral gifts.  For instance syncytin1 is a protein expressed a high levels in the placenta.  It is produced from the envelope gene of an endogenous retrovirus (HERV-W) which has undergon inactivating mutations in its other major genes (gag and pol).  Mutant mice in which the gene has been knocked out die in utero due to failure of placenta formation.

Part of the arc gene arose from the Gag gene (Group specific antigen gene) of a retrovirus.  Recall most viruses have proteins coating their genetic material when they’re on the move (e. g. a capsid).  In the case of retroviruses, the genetic material is RNA rather than DNA.  Well the gag elements of the Arc protein form a capsid containing the mRNA for Arc (just like a virus).  In some way or other the capsid containing mRNA gets outside the neuron at the nerve muscle junction and gets into muscle.  The evidence is good that this happens, but in a system somewhat removed from us — the fruitfly (Drosophila).  Fruitfly neuromuscular junctions lacking this mechanism are weaker.

Well that’s pretty far from us.  However one of the papers (275 – 288) showed that the Arc protein and its mRNA was found in extracellular vesicles released from mouse neurons cultured from their cerebral cortex.  Could viral-like particles be crossing the synapses in our brains (which are already pretty chockfull of stuff — see  It’s very early times (in fact the Cell issue came out 3 days ago) but people are sure to look.  There are at least 100 Gag derived genes in the human genome (Campillos, M., Doerks, T., Shah, P.K., and Bork, P. (2006). Computational characterization of multiple Gag-like human proteins. Trends Genet. 22, 585–589.).

Remarkable.  Remember CRISPR was hiding in plain sight for half a century.  We have a lot to learn.  No wonder drugs have unexpected side effects.

What are prions for?

Prions existed in yeast billions of years before humanity came on the scene. Why are they still there? What are they for?  Immediately we are back in the Aristotelian world of teleology, where everything had a reason for existence and a purpose.  Teleology is simply impossible to avoid in biology. “Nothing in Biology Makes Sense Except in the Light of Evolution” is a famous quote from the magnificently named Theodosius Dobzhansky, which clothes naked teleology with respectable scientific garments.

Here’s an example of this sort of thing from back in the day.  When I was back in the Denver VA as a neurology resident dealing with the complications of immunosuppressants in Starzl’s early work on transplantation, we wondered what on earth the transplantation antigens were for.  All we knew then, is that they were important in transplant rejection. Surely they were not there to prevent cells of the same or another species from finding a new home in us.  Only later did we figure out that they were involved in antigen presentation.

A fascinating article from the first Science of the new year — describes how the yeast organism might be using one of them (Sup35) — e.g. what the prion domains are for.  Normally the Sup35 protein functions to terminate messenger RNA (mRNA) translation into protein. However the first 123 amino acids of Sup35 can aggregate forming amyloid fibrils.  It contains a series of 9 amino acid repeats with consensus sequence PQGGYQQYN (single letter amino acid code — which is similar to the human prion protein repeats (PHGGGWGQ).

This work showed that under a variety of stesses (energy depletion, lowering of intracellular pH) Sup35 doesn’t form amyloid-like prions, but something rather different — liquidlike spherical condensates, which subsequently solidify to form a protein gel.  Next to the prion domain is a very acidic region, important in formation of the condensate.  Low pH is seen in energy depletion, and protonates the acidic amino acids in the acidic region leading to condensate formation.   A mutated Sup35 containing only the prion domain and the acidic region will form the condensates as well in a pH dependent manner.  The condensates are far from irreversible (like prions) as they quickly disappear when the pH is raised.

If you take out the prion domain from Sup35, the catalytic region (a GTPase) in the carboxy terminal part forms irreversible aggregates — so the prion domain is in some way preventing this.

So basically the two other parts of Sup35 function to protect the business end of Sup35 from being totally put out of commission by irreversible aggregation.

The authors found that yeast cells containing Sup35 lacking the prion domain, after recovering from stress, showed impaired translational activity and a growth defect presumably because there was less functional Sup35 around. This may be what the prion domain is doing.

My guess is that the aggregation of Sup35 into actual prions has a function in yeast that we just haven’t figured out yet.

It will be interesting to see if other yeast prions (there are many) show the similar behavior (condensate formation under stress).

Are the inclusions found in neurologic disease attempts at defense rather then the cause?

Thinking about pathologic changes in neurologic disease has been simplistic in the extreme.  Intially both senile plaques and neurofibrillary tangles were assumed to be causative for Alzheimer’s.  However there are 3 possible explanations for any microscopic change seen in any disease.  The first is that they are causative (the initial assumption).  The second is that they are a pile of spent bullets, which the neuron uses to defend itself against the real killer.  The third is they are tombstones, the final emanations of a dying cell.

A fascinating recent paper [ Neuron vol. 97 pp. 3 – 4, 108 – 124 ’18 ] gives strong evidence that some inclusions can be defensive rather than toxic.  It contains the following;

“In these studies, we found that formation of large inclusions was correlated with protection from a-synuclein toxicity”

The paper is likely to be a landmark because it ties two neurologic diseases (Parkinsonism and Alzheimer’s) together by showing that they may due to toxicity produced by single mechanism — inhibition of mitochondrial function.

Basically, the paper says that overproduction of alpha synuclein (the major component of the Lewy body inclusion of Parkinsonism) and tau (the major component of the neurofibrillary tangle of Alzheimer’s disease) produce death and destruction by interfering with mitochondria.  The mechanism is mislocalization of a protein called Drp1 which is important in mitochondrial function (it’s required for mitochondrial fission).

Actin isn’t just found in muscle, but is part of the cytoskeleton of every cell.  Alpha-synuclein is held to alter actin dynamics by binding to another protein called spectrin (which also binds to actin).  The net effect is to mislocalize Drp1 so it doesn’t bind to mitochondria where it is needed.  It isn’t clear to me from reading the paper, just where the Drp1 actually goes.

In any event overexpressing spectrin causes the alpha-synuclein to bind to it forming inclusions and protecting the cells.

There is a similar mechanism proposed for tau, and co-expressing alpha synuclein with Tau significantly enhances the toxicity of both models of tau toxicity which implies that they work by a common mechanism.

Grains of salt are required because the organism used for the model is the humble fruitfly (Drosophila).

Not a great way to end 2017

Not a great way to end 2017

2017 ended with a rejection of the following letter to PNAS.

As a clinical neurologist with a long standing interest in muscular dystrophy(1), I was referred many patients who turned out to have chronic fatigue syndrome (CFS) . Medicine, then and now, has no effective treatment for CFS.

A paper (2) cited In an excellent review of cellular senescence (3) was able to correlate an intracellular marker of senescence (p16^INK4a) with the degree of fatigue experienced by patients undergoing chemotherapy for breast cancer. Chemotherapy induces cellular senescence, and the fatigue was thought to come from the various cytokines secreted by senescent cells (Senescence Associated Secretory Phenotype—SASP) It seems logical to me to test CFS patients for p16^INK4a (4).
I suggested this to the senior author; however, he was nominated as head of the National Cancer Institute just 9 days later. There the matter rested until the paper of Montoya et al. (5) appeared in July. I looked up the 74 individual elements of the SASP and found that 9 were among the 17 cytokines whose levels correlated with the degree of fatigue in CFS. However, this is not statistically significant as Montoya looked at 51 cytokines altogether.

In October, an article(6) on the possibility of killing senescent cells to prevent aging contained a statement that Judith Campisi’s group (which has done much of the work on SASP) had identified “hundreds of proteins involved in SASPs”. (These results have not yet been published.) It is certainly possible that many more of Montoya’s 17 cytokines are among them.

If this is the case, a rational therapy for CFS is immediately apparent; namely, the senolytics, a class of drugs which kills senescent cells. A few senolytics are currently available clinically and many more are under development as a way to attack the aging process (6).

If Montoya still has cells from the patients in the study, measuring p16^INK4a could prove or disprove the idea. However, any oncology service could do the test. If the idea proves correct, then there would be a way to treat the debilitating fatigue of both chemotherapy and CFS—not to mention the many more medical conditions in which severe fatigue is found.
Chemotherapy is a systemic process, producing senescent cells everywhere, which is why DeMaria (2) was able to use circulating blood cells to measure p16^INK4a. It is possible that the senescent cells producing SASP in CFS are confined to one tissue; in which case testing blood for p16^INK4a would fail. (That would be similar to pheochromocytoma cells, in which a few localized cells produce major systemic effects.)

Although senolytics might provide symptomatic treatment (something worthwhile having since medicine presently has nothing for the CFS patient), we’d still be in the dark about what initially caused the cells to become senescent. But this would be research well worth pursuing.

Anyone intrigued by the idea should feel free to go ahead and test it. I am a retired neurologist with no academic affiliation, lacking the means to test it.

1 Robinson, L (1979) Split genes and musclar dystrophy. Muscle Nerve 2: 458 – 464

2. He S, Sharpless N (2017) Senescence in Health and Disease. Cell 170: 1000 – 1011

3. Demaria M, et al. (2014) Cellular senescence promotes adverse effects of chemotherapy and cancer relapse. Cancer Discov. 7: 165 – 176


5. Montoya JG, et al., (2017) Cytokine signature associated with disease severity in chronic fatigue syndrome patients, Proc Natl Acad Sci USA 114: E7150-E7158

6. Scudellari M, (2017) To stay young, kill zombie cells Nature 551: 448 – 450

Merry Christmas

From a friend to me.  Enjoy ! Back in business after the New Year. She’s 7.

Why drug development is hard #30 — more new interactions we had no idea existed

We’re full of proteins which bind RNA wrangling it into a desired conformation.  The ribosome (whose enzymatic business end is pure RNA) has a mere 80 proteins doing this.  Its mass is 4,300,000 times that of a hydrogen atom.  However the idea that RNA could return the favor was pretty much unheard of until [ Science vol. 358 pp. 993 – 994, 1051 – 1055 ’17 — ].

As is often the case, viruses and the RNA world continue to instruct us.  In order to survive, some viruses induce cells to express a long (2,200+ nucleotides) nonCoding (for protein that is) RNA called lncRNA-ACOD1.   It binds to a protein enzyme (called GOT2, for Glutamic acid OxaloAcetic Transaminase 2) increasing its catalytic efficiency.  This shifts cellular metabolism around making it more favorable for virus proliferation, as GOT2 is found in mitochondria being used to replenish tricarboxylic cycle intermediates — e.g. making more energy available to the virus.

lncRNA-ACOD1 is induced by a variety of viruses, most importantly influenza virus in man, and vaccinia, herpes simplex 1, vesicular stomatitis virus in mice.  Exactly how viruses induce it isn’t clear, but the transcription factor NFkappaB is involved.

Viruses continue to teach us.  The amino acids of GOT2 (#15 – #68) and the interacting sequence of nucleotides in lncRNA-ACOD1 (#165 – #390) are well conserved across species.  This might be a primordial mechanism from the RNA world (forgotten but not gone) to produce ATP production to compe with metabolic stress.   The RNA/protein binding site is close (4.2 Angstroms) to the substrate binding site.

The fun is just starting as several other lncRNAs are induced by viruses.  You can only imagine what they will tell us.  Another set of drug targets perhaps, or worse, the cause of peculiar side effects from drugs already in use.

The flying Wallendas of the synapse

Is anything similar to the flying Wallendas ( going on in the synapse? The first electron micrographs of the synaptic cleft back in the day showed a clear space about 400 Angstroms (40 nanoMeters) thick.  Well we now know that there are tons of proteins occupying this space — a copy of a previous post

The bouillabaisse of the synaptic cleft

appears after the **** at the end of this post.  It shows just how many proteins occupy that clear space. Could a presynaptic protein directly bond to a postsynaptic protein across the cleft (perhaps with the help of a third or fourth Wallenda protein between the two?  A nice review [ Neuron vol. 96 pp. 680 – 696 ’17b ] sets out what is known.

We know that neurexins (presynaptic) bind to neuroligins (postsynaptic) across the cleft.  This is the best studied pair, and most of the earlier post discusses what is known about them.

Figure 1c p. 682 is particularly fascinating as it shows that there are many more molecules which shake hands across the cleft.  Even more interesting is the fact that just where they are relative to the center/periphery of  the synapses isn’t shown for the neurexin/neuroligin pair and the LAR/Strk pair (e.g. one of the best studied pairs) because apparently this isn’t known.   The ephrins/ephrin pair and the syncam pair are in the center, while N-cadherin is shown at the edge.

One of the crucial elements of the post-synaptic membrane, the AMPAR receptor for glutamic protrudes its amino terminal domain 1/3 of the way across the cleft (assuming it is 40 nanoMeters thick).

Postsynaptic receptors are said to be clustered in nanoDomains 80 – 100 nanoMeters in diameter, Similarly, presynaptic RIM nanoClusters are the same size and are said to be aligned with postSynaptic nanoClusters of PSD95 as measured by 3D-STORM, the current most cutting edge technique we have for visualizing these things [ Nature vol. 536 pp. 210 – 214 ’17 ].

So, all in all, the paper is fascinating and shows how much more there is to know.

Unfortunately the paper contains one statement which raises my chemical hackles;  “A consistent prediction across models is that the glutamate concentration profile reaches a very high peak (over 1 milliMolar), but only for a brief time period (100 microSeconds) and over a small distance (100 nanoMeters).” Glutamate is the major excitatory neurotransmitter in brain and is what binds to AMPAR.

Models are lovely, but how many molecules of glutamic acid are they talking about?  It’s easy (but tedious) to figure this out.

We know the volume they are talking about: a cylinder 100 nanoMeters in diameter and 40 nanoMeters tall (the width of the synaptic cleft).   So it contains pi * 100 * 40 = 12,566 cubic nanometers –round this down to 10^4 cubic nanoMeters. A liter is a cube .1 meters (10 centimeters) on a side. So 10 centimeters is 10^8 nanoMeters, meaning that a liter contains (10^8)^3 = 10^24 cubic nanoMeters.

A 1 molar solution of anything contains 6 * 10^23 molecules per liter (Avogadro’s number), so a 1 milliMolar solution (of glutamate in this case) contains 6 * 10^20 molecules/liter or  6 * 10^-4 molecules per cubic nanoMeter. Multiply this by the volume of the cylinder and you get a grand total of 6 molecules of glutamic acid in the cylinder.

If I’ve done the calculations correctly (and I think I have), “a very high peak (over 1 milliMolar)” is basically scientific garbage, the concept of concentration being stretched far beyond its range of meaningful applicability.

I’d love to stand corrected if my calculations are incorrect. Just make a comment.

Addendum 12 Dec — well my calculation is wrong. Here’s the dialog

APAJ — “We know the volume they are talking about: a cylinder 100 nanoMeters in diameter and 40 nanoMeters tall (the width of the synaptic cleft). So it contains pi * 100 * 40 = 12,566 cubic nanometers –round this down to 10^4 cubic nanoMeters.”
Just one err0r in the maths: the volume is r^2*pi*h so it’s closer to 3^5 cubic nm. This leads to ~188 glumate molecules following your further calculations. A more significant number, but I agree concentrations should not be used in these kind of volumes.

APAJ — Thanks — you’re correct and I’m embarrassed — pi * diameter is circumference not volume. so its pi * 50^2 * 40 = 314,259 cubic microns == 25 x more than 12,566 bringing the number of glutamic acids up to 150 (when 12,566 is rounded down to 10^4).

The criticism still stands. Concentration is meaningless in such small volumes.



The bouillabaisse of the synaptic cleft

The synaptic cleft is so small ( under 400 Angstroms — 40 nanoMeters ) that it can’t be seen with the light microscope ( the smallest wavelength of visible light 3,900 Angstroms — 390 nanoMeters).  This led to a bruising battle between Cajal and Golgi a just over a century ago over whether the brain was actually made of cells.  Even though Golgi’s work led to the delineation of single neurons he thought the brain was a continuous network.  They both won the Nobel in 1906.

Semifast forward to the mid 60s when I was in medical school.  We finally had the electron microscope, so we could see synapses. They showed up as a small CLEAR spaces (e.g. electrons passed through it easily leaving it white) between neurons.  Neurotransmitters were being discovered at the same time and the synapse was to be the analogy to vacuum tubes, which could pass electricity in just one direction (yes, the transistor although invented hadn’t been used to make anything resembling a computer — the Intel 4004 wasn’t until the 70s).  Of course now we know that information flows back and forth across the synapse, with endocannabinoids (e. g. natural marihuana) being the major retrograde neurotransmitter.

Since there didn’t seem to be anything in the synaptic cleft, neurotransmitters were thought to freely diffuse across it to being to receptors on the other (postsynaptic) side e.g. a free fly zone.

Fast forward to the present to a marvelous (and grueling to read because of the complexity of the subject not the way it’s written) review of just what is in the synaptic cleft [ Cell vol. 171 pp. 745 – 769 ’17 ] (It is likely behind a paywall).  There are over 120 references, and rather than being just a catalogue, the single author Thomas Sudhof extensively discusseswhich experimental work is to be believed (not that Sudhof  is saying the work is fraudulent, but that it can’t be used to extrapolate to the living human brain).  The review is a staggering piece of work for one individual.

The stuff in the synaptic cleft is so diverse, and so intimately involved with itself and the membranes on either side what what is needed for comprehension is not a chemist but a sociologist.  Probably most of the molecules to be discussed are present in such small numbers that the law of mass action doesn’t apply, nor do binding constants which rely on large numbers of ligands and receptors. Not only that, but the binding constants haven’t been been determined for many of the players.

Now for some anatomic detail and numbers.  It is remarkably hard to find just how far laterally the synaptic cleft extends.  Molecular Biology of the Cell ed. 5 p. 1149 has a fairly typical picture with a size marker and it looks to be about 2 microns (20,000 Angstroms, 2,000 nanoMeters) — that’s 314,159,265 square Angstroms (3.14 square microns).  So let’s assume each protein takes up a square 50 Angstroms on a side (2,500 square Angstroms).  That’s room for 125,600 proteins on each side assuming extremely dense packing.  However the density of acetyl choline receptors at the neuromuscular junction is 8,700/square micron, a packing also thought to be extremely dense which would give only 26,100 such proteins in a similarly distributed CNS synapse. So the numbers are at least in the right ball park (meaning they’re within an order of magnitude e.g. within a power of 10) of being correct.

What’s the point?

When you see how many different proteins and different varieties of the same protein reside in the cleft, the numbers for  each individual element is likely to be small, meaning that you can’t use statistical mechanics but must use sociology instead.

The review focuses on the neurExins (I capitalize the E  to help me remember that they are prEsynaptic).  Why?  Because they are the best studied of all the players.  What a piece of work they are.  Humans have 3 genes for them. One of the 3 contains 1,477 amino acids, spread over 1,112,187 basepairs (1.1 megaBases) along with 74 exons.  This means that just over 1/10 of a percent of the gene is actually coding for for the amino acids making it up.  I think it takes energy for RNA polymerase II to stitch the ribonucleotides into the 1.1 megabase pre-mRNA, but I couldn’t (quickly) find out how much per ribonucleotide.  It seems quite wasteful of energy, unless there is some other function to the process which we haven’t figured out yet.

Most of the molecule resides in the synaptic cleft.  There are 6 LNS domains with 3 interspersed EGFlike repeats, a cysteine loop domain, a transmembrane region and a cytoplasmic sequence of 55 amino acids. There are 6 sites for alternative splicing, and because there are two promoters for each of the 3 genes, there is a shorter form (beta neurexin) with less extracellular stuff than the long form (alpha-neurexin).  When all is said and done there are over 1,000 possible variants of the 3 genes.

Unlike olfactory neurons which only express one or two of the nearly 1,000 olfactory receptors, neurons express mutiple isoforms of each, increasing the complexity.

The LNS regions of the neurexins are like immunoglobulins and fill at 60 x 60 x 60 Angstrom box.  Since the synaptic cleft is at most 400 Angstroms long, the alpha -neurexins (if extended) reach all the way across.

Here the neurexins bind to the neuroligins which are always postsynaptic — sorry no mnemonic.  They are simpler in structure, but they are the product of 4 genes, and only about 40 isoforms (due to alternative splicing) are possible. Neuroligns 1, 3 and 4 are found at excitatory synapses, neuroligin 2 is found at inhibitory synapses.  The intracleft part of the neuroligins resembles an important enzyme (acetylcholinesterase) but which is catalytically inactive.  This is where the neurexins.

This is complex enough, but Sudhof notes that the neurexins are hubs interacting with multiple classes of post-synaptic molecules, in addition to the neuroligins — dystroglycan, GABA[A] receptors, calsystenins, latrophilins (of which there are 4).   There are at least 50 post-synaptic cell adhesion molecules — “Few are well understood, although many are described.”

The neurexins have 3 major sites where other things bind, and all sites may be occupied at once.  Just to give you a taste of he complexity involved (before I go on to  larger issues).

The second LNS domain (LNS2)is found only in the alpha-neurexins, and binds to neuroexophilin (of which there are 4) and dystroglycan .

The 6th LNS domain (LNS6) binds to neuroligins, LRRTMs, GABA[A] receptors, cerebellins and latrophilins (of which there are 4)_

The juxtamembrane sequence of the neurexins binds to CA10, CA11 and C1ql.

The cerebellins (of which there are 4) bind to all the neurexins (of a particular splice variety) and interestingly to some postsynaptic glutamic acid receptors.  So there is a direct chain across the synapse from neurexin to cerebellin to ion channel (GLuD1, GLuD2).

There is far more to the review. But here is something I didn’t see there.  People have talked about proton wires — sites on proteins that allow protons to jump from one site to another, and move much faster than they would if they had to bump into everything in solution.  Remember that molecules are moving quite rapidly — water is moving at 590 meters a second at room temperature. Since the synaptic cleft is 40 nanoMeters (40 x 10^-9 meters, it should take only 40 * 10^-9 meters/ 590 meters/second   60 trillionths of a second (60 picoSeconds) to cross, assuming the synapse is a free fly zone — but it isn’t as the review exhaustively shows.

It it possible that the various neurotransmitters at the synapse (glutamic acid, gamma amino butyric acid, etc) bind to the various proteins crossing the cleft to get their target in the postsynaptic membrane (e.g. neurotransmitter wires).  I didn’t see any mention of neurotransmitter binding to  the various proteins in the review.  This may actually be an original idea.

I’d like to put more numbers on many of these things, but they are devilishly hard to find.  Both the neuroligins and neurexins are said to have stalks pushing them out from the membrane, but I can’t find how many amino acids they contain.  It can’t find how much energy it takes to copy the 1.1 megabase neurexin gene in to mRNA (or even how much energy it takes to add one ribonucleotide to an existing mRNA chain).

Another point– proteins have a finite lifetime.  How are they replenished?  We know that there is some synaptic protein synthesis — does the cell body send packages of mRNAs to the synapse to be translated there.  There are at least 50 different proteins mentioned in the review, and don’t forget the thousands of possible isoforms, each of which requires a separate mRNA.

Old Chinese saying — the mountains are high and the emperor is far away. Protein synthesis at the synaptic cleft is probably local.  How what gets made and when is an entirely different problem.

A large part of the review concerns mutations in all these proteins associated with neurologic disease (particularly autism).  This whole area has a long and checkered history.  A high degree of cynicism is needed before believing that any of these mutations are causative.  As a neurologist dealing with epilepsy I saw the whole idea of ion channel mutations causing epilepsy crash and burn — here’s a link —’ve-found-the-mutation-causing-your-disease-not-so-fast-says-this-paper/

Once again, hats off to Dr. Sudhof for what must have been a tremendous amount of work

Been busy

I haven’t posted for a while because I’ve been writing a letter to PNAS concerning my idea that chronic fatigue syndrome symptoms are a manifestation of an excess of senescent cells pumping out all sorts of inflammatory proteins into the systemic circulation.  The way to prove or disprove the idea is to measure p15^INK4a in circulating white cells.  The letter is now written and my wife is attempting to put it into English.  For details about the idea please see  Wish me luck that the letter is accepted.