Category Archives: Neurology & Psychiatry

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 ] http://www.cell.com/cell/fulltext/S0092-8674(17)31246-1 (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 — https://luysii.wordpress.com/2011/07/17/we’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

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We don’t understand amyloid very well

I must admit I was feeling pretty snarky about our understanding of amyloid and Alzheimer’s after the structure of Abeta42 was published.  In particular the structure explained why the alanine 42–> threonine 42 mutation was protective against Alzheimer’s disease while the alanine 42 –> valine 42 mutation increases the risk.  That’s all explained in the last post — https://luysii.wordpress.com/2017/10/12/abeta42-at-last/ — but a copy will appear at the end.

In that post I breathlessly hoped for the structure of aBeta40 which is known to be less toxic to neurons.  Well it’s here and it shows how little we understand about what does and what doesn’t form amyloid.  The structure appears in a paper about the amyloid formed by another protein (FUS) to be described later — Cell 171, 615–627, October 19, 2017 — figure 7 p. 624.

Now all Abeta40 lacks are the last 2 amino acids of Abeta42 — isoleucine at 41 and alanine at 42.  So solve the Schrodinger equation for it, and stack it up so it forms amyloid, or use your favorite molecular dynamics or other modeling tool.  Take a guess what it looks like.

Abeta42 is a dimer, a beta40 is a trimer, even though the first 40 amino acids of both are identical.

It gets worse. FUS (FUsed in Sarcoma) is a 526 amino acid protein which binds to RNA and is mostly found in the nucleus.  Neurologists are interested in it because over 50 mutations in have been found in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD).   FUS contains a low complexity domain (LCD) of 214 amino acids, 80% of which are one of 4 amino acids (glycine, serine, glutamine and tyrosine).  At high protein concentrations this domain of FUS forms long unbundled fibrils with the characteristic crossBeta structure of amyloid.  Only 57/214 of the LCD amino acids are part of the structured core of the amyloid — the rest are disordered.

Even worse the amino acids forming the amyloid core (#39 -#95) are NOT predicted by a variety of computational methods predicting amyloid formation (Agrescan, FISH, FOLDamyloid, Metamyl, PASTA 2.0).  The percentages of gly, ser, gln and tyr in the core forming region are pretty much the same as in the whole protein.  The core forming region has no repeats longer than 4 amino acids.

The same figure 7 has the structure of the amyloid formed by alpha-synuclein, which accumulates in the Lewy bodies of Parkinson’s disease.  It just has one peptide per layer of amyloid.

When you really understand something you can predict things, not just describe them as they are revealed.

 

Abeta42 at last

It’s easy to see why cryoEM got the latest chemistry Nobel.  It is telling us so much.  Particularly fascinating to me as a retired neurologist is the structure of the Abeta42 fibril reported in last Friday’s Science (vol. 358 pp. 116 – 119 ’17).

Caveats first.  The materials were prepared using an aqueous solution at low pH containing an organic cosolvent — so how physiologic could the structure actually be?  It probably is physiologic as the neurotoxicity of the fibrils to neurons in culture was the same as fibrils grown at neutral pH.  This still isn’t the same as fibrils grown in the messy concentrated chemical soup known as the cytoplasm.  Tending to confirm their findings is the fact that NMR and Xray diffraction on the crystals produced the same result.

The fibrils were unbranched and microns long (implying at least 2,000 layers of the beta sheets to be described).  The beta sheets stack in parallel and in register giving the classic crossBeta sheet structure.  They were made of two protofilaments winding around each other.  Each protofilament contains all 42 amino acids of Abeta42 and all of them form a completely flat beta sheet structure.

Feast your eyes on figure 2 p. 117.  In addition to showing the two beta sheets of the two protofilaments, it shows how they bind to each other.  Aspartic acid #1 of one sheet binds to lysine #28 of the other.  Otherwise the interface is quite hydrophobic.  Alanine2 of one sheet binds to alanine42 of the other, valine39 of one sheet binds to valine 39 of the other.  Most importantly isoLeucine 41 of one sheet binds to glycine38 of the other.

This is important since the difference between the less toxic Abeta40 and the toxic Abeta 42 are two hydrophobic amino acids Isoleucine 41 and Alanine 42.  This makes for a tighter, longer, more hydrophobic interface between the protofilaments stabilizing them.

That’s just a guess.  I can’t wait for work on Abeta40 to be reported at this resolution.

A few other points.  The beta sheet of each protomer is quite planar, but the planes of the two protomers are tilted by 10 degrees accounting for the helicity of the fibril. The fibril is a rhombus whose longest edge is about 70 Angstroms.

Even better the structure explains a mutation which is protective against Alzheimer’s.  This remains the strongest evidence (to me at least) that Abeta peptides are significantly involved in Alzheimer’s disease, therapeutic failures based on this idea notwithstanding.  The mutation is a change of alanine2 to threonine which can’t possibly snuggle up hydrophobically to isoleucine nearly as well as alanine did. This should significantly weaken the link between the two protofilaments and make fibril formation more difficult.

The Abeta structure of the paper also explains another mutation. This one increases the risk of Alzheimer’s disease (like many others which have been discovered).  It involves the same amino acid (alanine2) but this time it is changed to the morehydrophobic valine, probably resulting in a stronger hydrophobic interaction with isoLeucine41 (assuming that valine’s greater bulk doesn’t get in the way sterically).

Wonderful stuff to think and speculate about, now that we actually have some solid data to chew on.

Abeta raises its head again

Billions have been spent (and lost) by big Pharma on attempts to decrease Abeta peptide in the brain as a therapy for Alzheimer’s. Yet the theory that Abeta has something to do with Alzheimer’s won’t die because it is so compelling.

Here’s another example [Neuron vol. 96 pp. 355 – 372 ’17 ] Neurons in hippocampal slices stop forming new synapses when exposed to Abeta.  We think that synapse formation and elimination is going on all the time in our brains — it certainly is in mice.  For details see an excellent review [ Neuron vol. 96 pp. 43 – 55 ’17 ].  This is thought to be important in learning, something lost in Alzheimer’s as well as old memories. Two Alzheimer mouse models have shown defects in new synaptic spine formation.

Even better the authors found what Abeta is binding to — a well known brain protein — Nogo receptor 1 (Ngr1).  When it was knocked down in the slice (by bolistic short hairpin RNA infererence — shRNAi), spines started reforming.

So the work may explain some of the problems in Alzheimer’s disease but it says nothing about the neuronal loss which is also found.

Also, there is something fishy about the results.  The Abeta preparation used in the experiment was mostly oligomers of about 100 monomers (with a molecular mass of 500 kiloDaltons).  Monomers had no effect.  It is much easier to conceptualize a monomer binding to a receptor than an oligomer.  However, oligomer binding would tend to cluster receptors, something important in immune responses.

The strongest evidence for Abeta in my opinion is the fact that certain mutations PROTECT against Alzheimer’s — and given the structure just worked out we have a plausible explanation of just how this works — for details see — https://luysii.wordpress.com/2017/10/12/abeta42-at-last/

 

Abeta42 at last

It’s easy to see why cryoEM got the latest chemistry Nobel.  It is telling us so much.  Particularly fascinating to me as a retired neurologist is the structure of the Abeta42 fibril reported in last Friday’s Science (vol. 358 pp. 116 – 119 ’17).  

Caveats first.  The materials were prepared using an aqueous solution at low pH containing an organic cosolvent — so how physiologic could the structure actually be?  It probably is physiologic as the neurotoxicity of the fibrils to neurons in culture was the same as fibrils grown at neutral pH.  This still isn’t the same as fibrils grown in the messy concentrated chemical soup known as the cytoplasm.  Tending to confirm their findings is the fact that NMR and Xray diffraction on the crystals produced the same result.

The fibrils were unbranched and microns long (implying at least 2,000 layers of the beta sheets to be described).  The beta sheets stack in parallel and in register giving the classic crossBeta sheet structure.  They were made of two protofilaments winding around each other.  Each protofilament contains all 42 amino acids of Abeta42 and all of them form a completely flat beta sheet structure.

Feast your eyes on figure 2 p. 117.  In addition to showing the two beta sheets of the two protofilaments, it shows how they bind to each other.  Aspartic acid #1 of one sheet binds to lysine #28 of the other.  Otherwise the interface is quite hydrophobic.  Alanine2 of one sheet binds to alanine42 of the other, valine39 of one sheet binds to valine 39 of the other.  Most importantly isoLeucine 41 of one sheet binds to glycine38 of the other.

This is important since the difference between the less toxic Abeta40 and the toxic Abeta 42 are two hydrophobic amino acids Isoleucine 41 and Alanine 42.  This makes for a tighter, longer, more hydrophobic interface between the protofilaments stabilizing them.

That’s just a guess.  I can’t wait for work on Abeta40 to be reported at this resolution.

A few other points.  The beta sheet of each protomer is quite planar, but the planes of the two protomers are tilted by 10 degrees accounting for the helicity of the fibril. The fibril is a rhombus whose longest edge is about 70 Angstroms.

Even better the structure explains a mutation which is protective against Alzheimer’s.  This remains the strongest evidence (to me at least) that Abeta peptides are significantly involved in Alzheimer’s disease, therapeutic failures based on this idea notwithstanding.  The mutation is a change of alanine2 to threonine which can’t possibly snuggle up hydrophobically to isoleucine nearly as well as alanine did. This should significantly weaken the link between the two protofilaments and make fibril formation more difficult.

The Abeta structure of the paper also explains another mutation. This one increases the risk of Alzheimer’s disease (like many others which have been discovered).  It involves the same amino acid (alanine2) but this time it is changed to the more hydrophobic valine, probably resulting in a stronger hydrophobic interaction with isoLeucine41 (assuming that valine’s greater bulk doesn’t get in the way sterically).

Wonderful stuff to think and speculate about, now that we actually have some solid data to chew on.

The worst name for a drug I’ve ever heard of

It is simply impossible for me to think of a worse name for a drug which might help people with Down syndrome than ALGERNON.   The authors can be excused as they’re all from Japan, but the editor of the paper Fred Gage should have known about ‘Flowers for Algernon’– https://en.wikipedia.org/wiki/Flowers_for_Algernon.  Briefly, it’s a story about a drug which tripled the intelligence of Algernon a laboratory mouse which was then given to a retarded individual (Charlie Gordon) whose intelligence similarly tripled, only to decline like Algernon’s.  It was originally a short story, then a book, then a play etc. etc.

The drug is potentially quite exciting — ALGERNON is an acronym forALtered GenERatioN Of Neurons).  It increases the number of neurons form by mice with a model of trisomy 21.  The brain is bigger, and the animals do better on tests.  It is thought to work by inhibiting an enzyme (DYRK1A) which adds phosphate to serine, threonine and tyrosine, making it a dual specificity kinase.  It phosphorylates a variety of proteins known to have significant effects on brain development (tau, cyclin D1, caspase9, Notch, gli1, etc). The net effect of DYRK1A inhibition is to increase neural stem cell proliferation during fetal life.

Chemists will be interested in just how simple the structure of ALGERNON is — it’s an all aromatic compound made of a pyridine linked to a fused 6:5 ring system in which the 5 membered ring contains 2 nitrogens.  That’s it.  No alcohols, methyls, ethyls, ..  amines, amides, ethers etc., etc.

The authors blue-sky a bit at the end.  They note that mice show neural proliferation during adult life (we do as well, but to a much lesser extent).  It might be useful to improve function in living Down syndrome individuals, and just about any other neurological problem in which neural proliferation would be beneficial.  It might also be offered to women carrying a Down fetus who object to abortion on moral grounds.  Exciting stuff, but for god’s sake change the name.

I’ve done what I can

Here is the current state of play on my idea that chronic fatigue syndrome might be due to an excess of senescent cells releasing inflammatory mediators. The idea is explained in a copy of the post below, which should be read before proceeding.

The best way to test the idea would be to look for p16^INK4a, a transcription factor elevated in senescent cells.  This is relatively specific for senescence, far more so than the 74 or so inflammatory mediators which are part of the Senescence Associated Secretory Phenotype (SASP).

The best place to do this would be the Mayo clinic which is currently vigorously investigating the role of senescent cells in aging, cancer and what have you.  When I was in practice, the Mayo clinic linkage system for medical records was legendary.  When Elsewhere General had 10 patients in a series of disease X, Mayo would have 100.  I’m sure they have seen tons of chronic fatigue patients and could easily measure p16^INK4a levels on them.  I’ve corresponded with one worker there (Bennett Childs) and urged him to try the idea — e.g. measure white blood cell levels of p16^INK4a in CFS patients and compare it to controls.  This is cleaner than measuring SASP, because inflammatory mediators can be released by almost any type of infection or pathologic condition.  I noted today, that one researcher is working on developing senolytics, which would be the therapy of choice should my idea pan out.

I”ve written the authors of the PNAS editorial (Komaroff) and the paper (Davis) in the past week but have heard nothing back.

I’ve also contacted the Open Medicine Foundation® (OMF) which appears to be a patient organization founded by a woman whose daughter came down with it.  They apparently even fund research.  They said that they’d share it with their research team.

I wrote the research director of another patient organization but have heard nothing back.

The  post below has been read 250 times.

I am a 79 year old retired neurologist with no academic affiliation, and no way to test the idea.  Hopefully some of those I’ve been in contact with will do so.   The patients are waiting.

Is the era of precision medicine for chronic fatigue syndrome at hand?

If an idea of mine is correct, it is possible that some patients with chronic fatigue syndrome (CFS) can be treated with specific medications based on the results of a few blood tests. This is precision medicine at its finest.  The data to test this idea has already been acquired, and nothing further needs to be done except to analyze it.

Athough the initial impetus for the idea happened only 3 months ago, there have been enough twists and turns that the best way explanation is by a timeline.

First some background:

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some of these patients were undoubtedly neurotic, but there was little question in my mind that many others had something wrong that medicine just hadn’t figured out yet — not that it hasn’t been trying.

Infections of almost any sort are associated with fatigue, most probably caused by components of the inflammatory response.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, diagnosed as depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

The 1 June 2017 Cell had a long and interesting review of cellular senescence by Norman Sharpless [ vol. 169 pp. 1000 – 1011 ].  Here is some background about the entity.  If you are familiar with senescent cell biology skip to the paragraph marked **** below

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and — most importantly — they secrete a variety of proinflammatory molecules collectively called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence of which mutation is but one.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

****

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently invasive. Fortunately, p16^INK4a can also be measured in circulating blood cells.

What caught my eye in the Cell paper was a reference to a paper about cancer [ Cancer Discov. vol. 7 pp. 165 – 176 ’17 ] by M. Demaria, in which the levels of p16^INK4a correlated with the degree of fatigue after chemotherapy.  The more p16^INK4a in the blood cells the greater the fatigue.

I may have been the only reader of both papers with clinical experience wth chronic fatigue syndrome.  It is extremely difficult to objectively measure a subjective complaint such as fatigue.

As an example of the difficulty in correlating subjective complaints with objective findings, consider the nearly uniform complaint of difficulty thinking in depression, with how such patients actually perform on cognitive tests — e. g. there is  little if any correlation between complaints and actual performance — here’s a current reference — Scientific Reports 7, Article number: 3901(2017) —  doi:10.1038/s41598-017-04353.

If the results of the Cancer paper could be replicated, p16^INK4 would be the first objective measure of a patient’s individual sense of fatigue.

So I wrote both authors, suggesting that the p16^INK4a test be run on a collection of chronic fatigue syndrome (CFS) patients. Both authors replied quickly, but thought the problem would be acquiring patients.  Demaria said that Sharpless had a lab all set up to do the test.

Then fate (in the form of Donald Trump) supervened.  A mere 9 days after the Cell issue appeared, Sharpless was nominated to be the head of the National Cancer Institute by President Trump.  This meant Dr. Sharpless had far bigger fish to fry, and he would have to sever all connection with his lab because of conflict of interest considerations.

I also contacted a patient organization for chronic fatigue syndrome without much success.  Their science advisor never responded.

There matters stood until 22 August when a paper and an editorial about it came out [ Proc. Natl. Acad. Sci. vol. 114 pp. 8914 – 8916, E7150 – E7158 ’17 ].  The paper represented a tremendous amount of data (and work).  The blood levels of 51 cytokines (measures of inflammation) and adipokines (hormones released by fat) were measured in both 192 patients with CFS (which can only be defined by symptoms) and 293 healthy controls matched for age and gender.

In this paper, levels of 17 of the 51 cytokines correlated with severity of CFS. This is a striking similarity with the way the p16^INK4 levels correlated with the degree of fatigue after chemotherapy).  So I looked up the individual elements of the SASP (which can be found in Annu Rev Pathol. 21010; 5: 99–118.)  There are 74 of them. I wondered how many of the 51 cytokines measured in the PNAS paper were in the SASP.  This is trickier than it sounds as many cytokines have far more than one name.  The bottom line is that 20 SASPs are in the 51 cytokines measured in the paper.

If the fatigue of CFS is due to senescent cells and the SASPs  they release, then they should be over-represented in the 17 of the 51 cytokines correlating with symptom severity.  Well they are; 9 out of the 17 are SASP.  However although suggestive, this increase is not statistically significant (according to my consultants on Math Stack Exchange).

After wrote I him about the new work, Dr. Sharpless noted that CFS is almost certainly a heterogeneous condition. As a clinician with decades of experience, I’ve certainly did see some of the more larcenous members of our society who used any subjective diagnosis to be compensated, as well as a variety of individuals who just wanted to withdraw from society, for whatever reason. They are undoubtedly contaminating the sample in the paper. Dr. Sharpless thought the idea, while interesting, would be very difficult to test.

But it wouldn’t at all.  Not with the immense amount of data in the PNAS paper.

Here’s how. Take each of the 9 SASPs and see how their levels correlate with the other 16 (in each of the 192 CSF patients). If they correlate better with SASPs than with nonSASPs, than this would be evidence for senescent cells being the cause some cases of CFS. In particular, patients with a high level of any of the 9 SASPs should be studied for such correlations.  Doing so should weed out some of the heterogeneity of the 192 patients in the sample.

This is why the idea is testable and, even better, falsifiable, making it a scientific hypothesis (a la Karl Popper).  The data to refute it is in the possession of the authors of the paper.

Suppose the idea turns out to be correct and that some patients with CFS are in fact that way because, for whatever reason, they have a lot of senescent cells releasing SASPs.

This would mean that it would be time to start trials of senolyic drugs which destroy senescent cells on the group with elevated SASPs. Fortunately, a few senolytics are currently inc linical use.  This would be precision medicine at its finest.

Being able to alleviate the symptoms of CFS would be worthwhile in itself, but SASP levels could also be run on all sorts of conditions associated with fatigue, most notably infection. This might lead to symptomatic treatment at least.  Having gone through mono in med school, I would have loved to have been able to take something to keep me from falling asleep all the time.

Is the era of precision medicine for chronic fatigue syndrome at hand?

If an idea of mine is correct, it is possible that some patients with chronic fatigue syndrome (CFS) can be treated with specific medications based on the results of a few blood tests. This is precision medicine at its finest.  The data to test this idea has already been acquired, and nothing further needs to be done except to analyze it.

Athough the initial impetus for the idea happened only 3 months ago, there have been enough twists and turns that the best way explanation is by a timeline.

First some background:

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some of these patients were undoubtedly neurotic, but there was little question in my mind that many others had something wrong that medicine just hadn’t figured out yet — not that it hasn’t been trying.

Infections of almost any sort are associated with fatigue, most probably caused by components of the inflammatory response.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, diagnosed as depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

The 1 June 2017 Cell had a long and interesting review of cellular senescence by Norman Sharpless [ vol. 169 pp. 1000 – 1011 ].  Here is some background about the entity.  If you are familiar with senescent cell biology skip to the paragraph marked **** below

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and — most importantly — they secrete a variety of proinflammatory molecules collectively called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence of which mutation is but one.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

****

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently invasive. Fortunately, p16^INK4a can also be measured in circulating blood cells.

What caught my eye in the Cell paper was a reference to a paper about cancer [ Cancer Discov. vol. 7 pp. 165 – 176 ’17 ] by M. Demaria, in which the levels of p16^INK4a correlated with the degree of fatigue after chemotherapy.  The more p16^INK4a in the blood cells the greater the fatigue.

I may have been the only reader of both papers with clinical experience wth chronic fatigue syndrome.  It is extremely difficult to objectively measure a subjective complaint such as fatigue.

As an example of the difficulty in correlating subjective complaints with objective findings, consider the nearly uniform complaint of difficulty thinking in depression, with how such patients actually perform on cognitive tests — e. g. there is  little if any correlation between complaints and actual performance — here’s a current reference — Scientific Reports 7, Article number: 3901(2017) —  doi:10.1038/s41598-017-04353.

If the results of the Cancer paper could be replicated, p16^INK4 would be the first objective measure of a patient’s individual sense of fatigue.

So I wrote both authors, suggesting that the p16^INK4a test be run on a collection of chronic fatigue syndrome (CFS) patients. Both authors replied quickly, but thought the problem would be acquiring patients.  Demaria said that Sharpless had a lab all set up to do the test.

Then fate (in the form of Donald Trump) supervened.  A mere 9 days after the Cell issue appeared, Sharpless was nominated to be the head of the National Cancer Institute by President Trump.  This meant Dr. Sharpless had far bigger fish to fry, and he would have to sever all connection with his lab because of conflict of interest considerations.

I also contacted a patient organization for chronic fatigue syndrome without much success.  Their science advisor never responded.

There matters stood until 22 August when a paper and an editorial about it came out [ Proc. Natl. Acad. Sci. vol. 114 pp. 8914 – 8916, E7150 – E7158 ’17 ].  The paper represented a tremendous amount of data (and work).  The blood levels of 51 cytokines (measures of inflammation) and adipokines (hormones released by fat) were measured in both 192 patients with CFS (which can only be defined by symptoms) and 293 healthy controls matched for age and gender.

In this paper, levels of 17 of the 51 cytokines correlated with severity of CFS. This is a striking similarity with the way the p16^INK4 levels correlated with the degree of fatigue after chemotherapy).  So I looked up the individual elements of the SASP (which can be found in Annu Rev Pathol. 21010; 5: 99–118.)  There are 74 of them. I wondered how many of the 51 cytokines measured in the PNAS paper were in the SASP.  This is trickier than it sounds as many cytokines have far more than one name.  The bottom line is that 20 SASPs are in the 51 cytokines measured in the paper.

If the fatigue of CFS is due to senescent cells and the SASPs  they release, then they should be over-represented in the 17 of the 51 cytokines correlating with symptom severity.  Well they are; 9 out of the 17 are SASP.  However although suggestive, this increase is not statistically significant (according to my consultants on Math Stack Exchange).

After wrote I him about the new work, Dr. Sharpless noted that CFS is almost certainly a heterogeneous condition. As a clinician with decades of experience, I’ve certainly did see some of the more larcenous members of our society who used any subjective diagnosis to be compensated, as well as a variety of individuals who just wanted to withdraw from society, for whatever reason. They are undoubtedly contaminating the sample in the paper. Dr. Sharpless thought the idea, while interesting, would be very difficult to test.

But it wouldn’t at all.  Not with the immense amount of data in the PNAS paper.

Here’s how. Take each of the 9 SASPs and see how their levels correlate with the other 16 (in each of the 192 CSF patients). If they correlate better with SASPs than with nonSASPs, than this would be evidence for senescent cells being the cause some cases of CFS. In particular, patients with a high level of any of the 9 SASPs should be studied for such correlations.  Doing so should weed out some of the heterogeneity of the 192 patients in the sample.

This is why the idea is testable and, even better, falsifiable, making it a scientific hypothesis (a la Karl Popper).  The data to refute it is in the possession of the authors of the paper.

Suppose the idea turns out to be correct and that some patients with CFS are in fact that way because, for whatever reason, they have a lot of senescent cells releasing SASPs.

This would mean that it would be time to start trials of senolyic drugs which destroy senescent cells on the group with elevated SASPs. Fortunately, a few senolytics are currently inc linical use.  This would be precision medicine at its finest.

Being able to alleviate the symptoms of CFS would be worthwhile in itself, but SASP levels could also be run on all sorts of conditions associated with fatigue, most notably infection. This might lead to symptomatic treatment at least.  Having gone through mono in med school, I would have loved to have been able to take something to keep me from falling asleep all the time.

 

How do neural nets do what they do?

Isn’t it enough that neural nets beat humans at chess, go and checkers, drive cars, recognize faces, find out what plays Shakespeare did and didn’t write?  Not at all.  Figuring out how they do what they do may allow us to figure out how the brain does what it does.

Science recently had a great bunch of articles on neural nets, deep learning [ Science vol. 356 pp. 16 – 30 ’17 ].  Chemists will be interested in p. 27 “Neural networks learn the art of chemical synthesis”.  The articles are quite accessible to the scientific layman.

To this retired neurologist, the most interesting of the bunch was the article (pp. 22 – 270 describing attempts to figure out how neural nets do what they do. Welcome to the world of the neuroscientist where a similar problem has engaged us for centuries.  DARPA is spending 70 million on exactly this according to the article.

If you are a little shaky on all this — I’ve copied a previous post on the subject (along with a few comments it inspired) below the ****

Here are four techniques currently in use:

  1. Counterfactual probes — the classic black box technique — vary the input (text, images, sound, ..  )and watch how it affects output.  It goes by the fancy name of Local Interpretable Model agnostic Explanations (LIME).  This allows the parts of the input most important in the net’s original judgement.
  2. Start with a black image or a zeroed out array of text and transition step by step toward the example being tested.  Then you watch the jumps in certainty the net makes, and you can figure out what it thinks is important.
  3. General Additive Model (GAM) is a statistical technique based on linear regression.  It operates on data to massage it.  The net is then presented with a variety of operations of GAM and studied to see which are the best at data massage so the machine can make a correct decision.
  4. Glass Box wires monotonic relationships (e.g the price of a house goes up with the number of square feet) INTO the neural net — allowing better control of what it does

The articles don’t appear to be behind a paywall, so have at it.

***

NonAlgorithmic Intelligence

Penrose was right. Human intelligence is nonAlgorithmic. But that doesn’t mean that our physical brains produce consciousness and intelligence using quantum mechanics (although all matter is what it is because of quantum mechanics). The parts (even small ones like neurotubules) contain so much mass that their associated wavefunction is too small to exhibit quantum mechanical effects. Here Penrose got roped in by Kauffman thinking that neurotubules were the carriers of the quantum mechanical indeterminacy. They aren’t, they are just too big. The dimer of alpha and beta tubulin contains 900 amino acids — a mass of around 90,000 Daltons (or 90,000 hydrogen atoms — which are small enough to show quantum mechanical effects).

So why was Penrose right? Because neural nets which are inherently nonAlgorithmic are showing intelligent behavior. AlphaGo which beat the world champion is the most recent example, but others include facial recognition and image classification [ Nature vol. 529 pp. 484 – 489 ’16 ].

Nets are trained on real world images and told whether they are right or wrong. I suppose this is programming of a sort, but it is certainly nonAlgorithmic. As the net learns from experience it adjusts the strength of the connections between its neurons (synapses if you will).

So it should be a simple matter to find out just how AlphaGo did it — just get a list of the neurons it contains, and the number and strengths of the synapses between them. I can’t find out just how many neurons and connections there are, but I do know that thousands of CPUs and graphics processors were used. I doubt that there were 80 billion neurons or a trillion connections between them (which is what our brains are currently thought to have).

Just print out the above list (assuming you have enough paper) and look at it. Will you understand how AlphaGo won? I seriously doubt it. You will understand it less well than looking at a list of the positions and momenta of 80 billion gas molecules will tell you its pressure and temperature. Why? Because in statistical mechanics you assume that the particles making up an ideal gas are featureless, identical and do not interact with each other. This isn’t true for neural nets.

It also isn’t true for the brain. Efforts are underway to find a wiring diagram of a small area of the cerebral cortex. The following will get you started — https://www.quantamagazine.org/20160406-brain-maps-micron-program-iarpa/

Here’s a quote from the article to whet your appetite.

“By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. That tiny portion houses about 100,000 neurons, 3 to 15 million neuronal connections, or synapses, and enough neural wiring to span the width of Manhattan, were it all untangled and laid end-to-end.”

I don’t think this will help us understand how the brain works any more than the above list of neurons and connections from AlphaGo. There are even more problems with such a list. Connections (synapses) between neurons come and go (and they increase and decrease in strength as in the neural net). Some connections turn on the receiving neuron, some turn it off. I don’t think there is a good way to tell what a given connection is doing just by looking a a slice of it under the electron microscope. Lastly, some of our most human attributes (emotion) are due not to connections between neurons but due to release of neurotransmitters generally into the brain, not at the very localized synapse, so it won’t show up on a wiring diagram. This is called volume neurotransmission, and the transmitters are serotonin, norepinephrine and dopamine. Not convinced? Among agents modifying volume neurotransmission are cocaine, amphetamine, antidepressants, antipsychotics. Fairly important.

So I don’t think we’ll ever truly understand how the neural net inside our head does what it does.

 Here are a few of the comments

“So why was Penrose right? Because neural nets which are inherently nonAlgorithmic are showing intelligent behavior. ”

I picked up a re-post of this comment on Quanta and thought it best to reply to you directly. Though this appears to be your private blog I can’t seem to find a biography, otherwise I’d address you by name.

My background is computer science generally and neural networks (with the requisite exposure to statistical mechanics) in particular and I must correct the assertion you’ve made here; neural nets are in fact both algorithmic and even repeatable in their performance.

I think what you’re trying to say is the structure of a trained network isn’t known by the programmer in advance; rather than build a trained intelligence, the programmer builds an intelligence that may be trained. The method is entirely mathematical though, mostly based on the early work of Boltzmann and explorations of the Monte-Carlo algorithms used to model non-linear thermodynamic systems.

For a good overview of the foundations, I suggest J.J. Hopfield’s “Neural networks and physical systems with emergent collective computational abilities”, http://www.pnas.org/content/79/8/2554.abstract

Regards,
Scott.

Scott — thanks for your reply. I blog anonymously because of the craziness on the internet. I’m a retired neurologist, long interested in brain function both professionally and esthetically. I’ve been following AI for 50 years.

I even played around with LISP, which was the language of AI in the early days, read Minsky on Perceptrons, worried about the big Japanese push in AI in the 80’s when they were going to eat our lunch etc. etc.

I think you’d agree that compared to LISP and other AI of that era, neural nets are nonAlgorithmic. Of course setting up virtual neurons DOES involve programming.

The analogy with the brain is near perfect. You can regard our brains as the output of the embryologic programming with DNA as the tape.

But that’s hardly a place to stop. How the brain and neural nets do what they do remains to be determined. A wiring diagram of the net is available but really doesn’t tell us much.

Again thanks for responding.

Scott

Honestly it would be hard for me to accept that the nets I worked on weren’t algorithmic since they were literally based on formal algorithms derived directly from statistical mechanics, most of which was based on Boltzmann’s work back in the 19th century. Hopfield, who I truly consider the “father” of modern neural computing, is a physicist (now at Princeton I believe). Most think he was a computer scientists back on the 70’s when he did his basic work at Cal Tech, but that’s not really the case.

I understand what you’re trying to say, that the actual training portion of NN development isn’t algorithmic, but the NN software itself is and it’s extremely precise in its structure, much more so than say, for instance, a bubble sort. It’s pretty edgy stuff even now.

I began working on NN’s in ’82 after reading Hopfield’s seminal paper, I was developing an AI aimed at self-diagnosing computer systems for a large computer manufacturer now known as Hewlett-Packard (at the time we were a much smaller R&D company who were later aquired). We also explored expert systems and ultimately deployed a solution based on KRL, which is a LISP development environment built by a small Stanford AI spinoff. It ended up being a dead end; that was an argument I lost (I advocated the NN direction as much more promising but lost mostly for political reasons). Now I take great pleasure in gloating 🙂 even though I’m no longer commercially involved with either the technology or that particular company.

Luysii

I thought we were probably in agreement about what I said. Any idea on how to find out just how many ‘neurons’ (any how many levels) there are in AlphaGo? It would be interesting to compare the numbers with our current thinking about the numbers of cortical neurons and synapses (which grows ever larger year by year).

Who is the other Scott — Aronson? 100 years ago he would have been a Talmudic scholar (as he implies by the title of his blog).

Yes neural nets are still edgy, and my son is currently biting his fingernails in a startup hoping to be acquired which is heavily into an application of neural nets (multiple patents etc. etc.)

A possible new player

Drug development is very hard because we don’t know all the players inside the cell. A recent paper describes an entirely new class of player — circular DNA derived from an ancient virus.  The authoress is Laura Manuelidis, who would have been a med school classmate had I chosen to go to Yale med instead of Penn.   She is the last scientist standing who doesn’t believe Prusiner’s prion hypothesis.  She didn’t marry the boss’s daughter being female, so she married the boss instead;  Elias Manuelidis a Yale neuropathologist who would be 99 today had he not passed away at 72 in 1992.

The circular DNAs go by the name of SPHINX  an acronym  for  Slow Progressive Hidden INfections of X origin.  They have no sequences in common with bacterial or eukaryotic DNA, but there some homology to a virus infecting Acinebacter, a wound pathogen common in soil and water.

How did she find them?  By doggedly pursuing the idea the neurodegenerative diseases such as Cruetzfeldt Jakob Disease (CJD) and scrapie were due to an infectious agent triggering aggregation of the prion protein.

As she says:  “The cytoplasm of CJD and scrapie-infected cells, but not control cells, also contains virus-like particle arrays and because we were able to isolate these nuclease-protected particles with quantitative recovery of infectivity, but with little or no detectable PrP (Prion Protein), we began to analyze protected nucleic acids. Using Φ29 rolling circle amplification, several circular DNA sequences of <5 kb (kilobases) with ORFs (Open Reading Frames) were thereby discovered in brain and cultured neuronal cell lines. These circular DNA sequences were named SPHINX elements for their initial association with slow progressive hidden infections of X origin."

SPHINX itself codes for a 324 amino acid protein, which is found in human brain, concentrated in synaptic boutons.  Strangely, even though the DNAs are presumably viral derived, they contain intervening sequences which don't code for protein.

The use of rolling circle amplification is quite clever, as it will copy only circular DNA.

Stanley Prusiner is sure to weigh in.  Remarkably, Prusiner was at Penn Med when I was and was even in my med school fraternity (Nu Sigma Nu)  primarily a place to eat lunch and dinner.  I probably ate with him, but have no recollection of him whatsoever.

Circular DNAs outside chromosomes are called plasmids. Bacteria are full of them. The best known eukaryote containing plasmids is yeast. Perhaps we have them as well. Manuelidis may be the first person to look.

The best laid plans of mice and men

I sent a copy of the previous post (reprinted below) about an idea to diagnose and treat chronic fatigue syndrome to Dr. Norman Sharpless, the author of the Cell review on cellular senescence.  He thought the idea was “great”; and, even better, he ran the lab which did the test I wanted to try.  I also sent a copy to a patient group.  “Solve ME/CFS Initiative”, and they want to use the post on their website.

Sharpless noted that the problem with ideas like this is accumulating patients, something the patient group could probably provide.  So all went well until 8 days ago when Dr. Sharpless was named to be the head of the National Cancer Institute, with its 4.5 billion dollar  budget by President Trump.  Being a full prof at the University of North Carolina Medical School, he would have been the ideal individual to run the study (or find someone to do it), but he now has far bigger fish to fry.

After I wrote to congratulate him, he wrote back reiterating that the idea was good, but he said he had to sever all connections with the lab he founded due to conflict of interest considerations.  He did give me the name of someone to contact there, which is where the matter stands presently.

Since the idea is based on the correlation between the amount of fatigue after chemotherapy with the level of a white cell protein (p16^INK4a), he would have had no problem accumulating chemotherapy patients as head of NCI, but again the spectre of conflict of interest rears its ugly head.  Repeating the chemotherapy study to make sure the results are in fact real is the first order of business.

So there you have a research idea, endorsed by the new head of the NCI.  I am a retired neurologist, who no longer has a license to practice medicine (but who doesn’t need a license to think).

If you’re an academic out there, looking for something to do, write up a grant proposal.  The current treatments do help people live with chronic fatigue syndrome, but they are in no sense treatments of the underlying problem.

Here is the original post

How to (possibly) diagnose and treat chronic fatigue syndrome (myalgic encephalomyelitis)

As a neurologist I saw a lot of people who were chronically tired and fatigued, because neurologists deal with muscle weakness and diseases like myasthenia gravis which are associated with fatigue.  Once I ruled out neuromuscular disease as a cause, I had nothing to offer then (nor did medicine).  Some were undoubtedly neurotic, but there was little question in my mind that some of them had something wrong that medicine just hadn’t figured out.  Not it hasn’t been trying.

Infections of almost any sort are associated with fatigue, probably because components of the inflammatory response cause it.  Anyone who’s gone through mononucleosis knows this.    The long search for an infectious cause of chronic fatigue syndrome (CFS) has had its ups and downs — particularly downs — see https://luysii.wordpress.com/2011/03/25/evil-scientists-create-virus-causing-chronic-fatigue-syndrome-in-lab/

At worst many people with these symptoms are written off as crazy; at best, depressed  and given antidepressants.  The fact that many of those given antidepressants feel better is far from conclusive, since most patients with chronic illnesses are somewhat depressed.

Even if we didn’t have a treatment, just having a test which separated sufferers from normal people would at least be of some psychological help, by telling them that they weren’t nuts.

Two recent papers may actually have the answer. Although neither paper dealt with chronic fatigue syndrome directly, and I can find no studies in the literature linking what I’m about to describe to CFS they at least imply that there could be a diagnostic test for CFS, and a possible treatment as well.

Because I expect that many people with minimal biological background will be reading this, I’ll start by describing the basic biology of cellular senescence and death

Background:  Most cells in our bodies are destined to die long before we do. Neurons are the longest lasting (essentially as long as we do).  The lining of the intestines is renewed weekly.  No circulating blood cell lasts more than half a year.

Cells die in a variety of ways.  Some are killed (by infections, heat, toxins).  This is called necrosis. Others voluntarily commit suicide (this is called apoptosis).   Sometimes a cell under stress undergoes cellular senescence, a state in which it doesn’t die, but doesn’t reproduce either.  Such cells have a variety of biochemical characteristics — they are resistant to apoptosis, they express molecules which prevent them from proliferating and most importantly, they secrete proinflammatory molecules (this is called the Senescence Associated Secretory Phenotype — SASP).

At first the very existence of the senescent state was questioned, but exist it does.  What is it good for?  Theories abound, one being that mutation is one cause of stress, and stopping mutated cells from proliferating prevents cancer. However, senescent cells are found during fetal life; and they are almost certainly important in wound healing.  They are known to accumulate the older you get and some think they cause aging.

Many stresses induce cellular senescence.  The one of interest to us is chemotherapy for cancer, something obviously good as a cancer cell turned senescent has stopped proliferating.   If you know anyone who has undergone chemotherapy, you know that fatigue is almost invariable.

One biochemical characteristic of the senescent cell is increased levels of a protein called p16^INK4a, which helps stop cellular proliferation.  While p16^INK4a can easily be measured in tissue biopsies, tissue biopsies are inherently not easy. Fortunately it can also be measured in circulating blood cells.

The following study — Cancer Discov. vol. 7 pp. 165 – 176 ’17 looked at 89 women with breast cancer undergoing chemotherapy. They correlated the amount of fatigue experienced with the levels of p16^INK4a in a type of circulating white blood cell (T lymphocyte).  There was a 44% incidence of fatigue in the highest quartile of  p16^INK4a levels, vs. a 5% incidence of fatigue in the lowest. The cited paper didn’t mention CFS nor did the highly technical but excellent review on which much of the above is based [ Cell vol. 169 pp. 1000 -1011 ’17 ]

But it is definitely time to measure p16^INK4a levels in patients with chronic fatigue and compare them to people without it.  This may be the definitive diagnostic test, if people with CFS show higher levels of p16^INK4a.

If this turns out to be the case, then there is a logical therapy for chronic fatigue syndrome.  As mentioned above, senescent cells are resistant to apoptosis (voluntary suicide).  What stops these cells from suicide? Naturally occurring cellular suicide inhibitors (with names like BCL2, BCL-XL, BCL-W) do so .  Drugs called sensolytics already exist to target the inhibitors causing senescent cells to commit suicide.

So if excessive senescent cells are the cause of CFS, then killing them should make things better. Sensolytics do exist but there are problems; one couldn’t be used because of side effects.  Others do exist (one such is Venetoclax) and have been approved by the FDA for leukemia — but it isn’t as potent .

So there is a potentially both a diagnostic test and a treatment for CFS.

The initial experiment should be fairly easy for research to do — just corral some CSF patients and controls and run a test for p16^INK4a levels in their blood cells. Also easy on the patients as only a blood draw is involved.

This, in itself, would be great, but there is far more to think about.

If CFS patients have too many senescent cells, getting rid of them — although (hopefully) symptomatically beneficial — will not get rid of what caused the senescent cells to accumulate in the first place. In addition, getting rid of all of them at once would probably cause huge problems causing something similar to the tumor lysis syndrome – https://en.wikipedia.org/wiki/Tumor_lysis_syndrome.

But these are problems CFS patients and