Thrust and Parry about memory storage outside neurons.

First the post of 23 Feb ’14 discussing the paper (between *** and &&& in case you’ve read it already)

Then some of the rather severe criticism of the paper.

Then some of the reply to the criticisms

Then a few comments of my own, followed by yet another old post about the chemical insanity neuroscience gets into when they apply concepts like concentration to very small volumes.

Enjoy
***
Are memories stored outside of neurons?

This may turn out to be a banner year for neuroscience. Work discussed in the following older post is the first convincing explanation of why we need sleep that I’ve seen.https://luysii.wordpress.com/2013/10/21/is-sleep-deprivation-like-alzheimers-and-why-we-need-sleep-in-the-first-place/

An article in Science (vol. 343 pp. 670 – 675 ’14) on some fairly obscure neurophysiology at the end throws out (almost as an afterthought) an interesting idea of just how chemically and where memories are stored in the brain. I find the idea plausible and extremely surprising.

You won’t find the background material to understand everything that follows in this blog. Hopefully you already know some of it. The subject is simply too vast, but plug away. Here a few, seriously flawed in my opinion, theories of how and where memory is stored in the brain of the past half century.

#1 Reverberating circuits. The early computers had memories made of something called delay lines (http://en.wikipedia.org/wiki/Delay_line_memory) where the same impulse would constantly ricochet around a circuit. The idea was used to explain memory as neuron #1 exciting neuron #2 which excited neuron . … which excited neuron #n which excited #1 again. Plausible in that the nerve impulse is basically electrical. Very implausible, because you can practically shut the whole brain down using general anesthesia without erasing memory.

#2 CaMKII — more plausible. There’s lots of it in brain (2% of all proteins in an area of the brain called the hippocampus — an area known to be important in memory). It’s an enzyme which can add phosphate groups to other proteins. To first start doing so calcium levels inside the neuron must rise. The enzyme is complicated, being comprised of 12 identical subunits. Interestingly, CaMKII can add phosphates to itself (phosphorylate itself) — 2 or 3 for each of the 12 subunits. Once a few phosphates have been added, the enzyme no longer needs calcium to phosphorylate itself, so it becomes essentially a molecular switch existing in two states. One problem is that there are other enzymes which remove the phosphate, and reset the switch (actually there must be). Also proteins are inevitably broken down and new ones made, so it’s hard to see the switch persisting for a lifetime (or even a day).

#3 Synaptic membrane proteins. This is where electrical nerve impulses begin. Synapses contain lots of different proteins in their membranes. They can be chemically modified to make the neuron more or less likely to fire to a given stimulus. Recent work has shown that their number and composition can be changed by experience. The problem is that after a while the synaptic membrane has begun to resemble Grand Central Station — lots of proteins coming and going, but always a number present. It’s hard (for me) to see how memory can be maintained for long periods with such flux continually occurring.

This brings us to the Science paper. We know that about 80% of the neurons in the brain are excitatory — in that when excitatory neuron #1 talks to neuron #2, neuron #2 is more likely to fire an impulse. 20% of the rest are inhibitory. Obviously both are important. While there are lots of other neurotransmitters and neuromodulators in the brains (with probably even more we don’t know about — who would have put carbon monoxide on the list 20 years ago), the major inhibitory neurotransmitter of our brains is something called GABA. At least in adult brains this is true, but in the developing brain it’s excitatory.

So the authors of the paper worked on why this should be. GABA opens channels in the brain to the chloride ion. When it flows into a neuron, the neuron is less likely to fire (in the adult). This work shows that this effect depends on the negative ions (proteins mostly) inside the cell and outside the cell (the extracellular matrix). It’s the balance of the two sets of ions on either side of the largely impermeable neuronal membrane that determines whether GABA is excitatory or inhibitory (chloride flows in either event), and just how excitatory or inhibitory it is. The response is graded.

For the chemists: the negative ions outside the neurons are sulfated proteoglycans. These are much more stable than the proteins inside the neuron or on its membranes. Even better, it has been shown that the concentration of chloride varies locally throughout the neuron. The big negative ions (e.g. proteins) inside the neuron move about but slowly, and their concentration varies from point to point.

Here’s what the authors say (in passing) “the variance in extracellular sulfated proteoglycans composes a potential locus of analog information storage” — translation — that’s where memories might be hiding. Fascinating stuff. A lot of work needs to be done on how fast the extracellular matrix in the brain turns over, and what are the local variations in the concentration of its components, and whether sulfate is added or removed from them and if so by what and how quickly.

We’ve concentrated so much on neurons, that we may have missed something big. In a similar vein, the function of sleep may be to wash neurons free of stuff built up during the day outside of them.

&&&

In the 5 September ’14 Science (vol. 345 p. 1130) 6 researchers from Finland, Case Western Reserve and U. California (Davis) basically say the the paper conflicts with fundamental thermodynamics so severely that “Given these theoretical objections to their interpretations, we choose not to comment here on the experimental results”.

In more detail “If Cl− were initially in equilibrium across a membrane, then the mere introduction of im- mobile negative charges (a passive element) at one side of the membrane would, according to their line of thinking, cause a permanent change in the local electrochemical potential of Cl−, there- by leading to a persistent driving force for Cl− fluxes with no input of energy.” This essentially accuses the authors of inventing a perpetual motion machine.

Then in a second letter, two more researchers weigh in (same page) — “The experimental procedures and results in this study are insufficient to support these conclusions. Contradictory results previously published by these authors and other laboratories are not referred to.”

The authors of the original paper don’t take this lying down. On the same page they discuss the notion of the Donnan equilibrium and say they were misinterpreted.

The paper, and the 3 letters all discuss the chloride concentration inside neurons which they call [Cl-]i. The problem with this sort of thinking (if you can call it that) is that it extrapolates the notion of concentration to very small volumes (such as a dendritic spine) where it isn’t meaningful. It goes on all the time in neuroscience. While between any two small rational numbers there is another, matter can be sliced only so thinly without getting down to the discrete atomic level. At this level concentration (which is basically a ratio between two very large numbers of molecules e.g. solute and solvent) simply doesn’t apply.

Here’s a post on the topic from a few months ago. It contains a link to another post showing that even Nobelists have chemical feet of clay.

More chemical insanity from neuroscience

The current issue of PNAS contains a paper (vol. 111 pp. 8961 – 8966, 17 June ’14) which uncritically quotes some work done back in the 80’s and flatly states that synaptic vesicles http://en.wikipedia.org/wiki/Synaptic_vesicle have a pH of 5.2 – 5.7. Such a value is meaningless. Here’s why.

A pH of 5 means that there are 10^-5 Moles of H+ per liter or 6 x 10^18 actual ions/liter.

Synaptic vesicles have an ‘average diameter’ of 40 nanoMeters (400 Angstroms to the chemist). Most of them are nearly spherical. So each has a volume of

4/3 * pi * (20 * 10^-9)^3 = 33,510 * 10^-27 = 3.4 * 10^-23 liters. 20 rather than 40 because volume involves the radius.

So each vesicle contains 6 * 10^18 * 3.4 * 10^-23 = 20 * 10^-5 = .0002 ions.

This is similar to the chemical blunders on concentration in the nano domain committed by a Nobelist. For details please see — http://luysii.wordpress.com/2013/10/09/is-concentration-meaningful-in-a-nanodomain-a-nobel-is-no-guarantee-against-chemical-idiocy/

Didn’t these guys ever take Freshman Chemistry?

Addendum 24 June ’14

Didn’t I ever take it ? John wrote the following this AM

Please check the units in your volume calculation. With r = 10^-9 m, then V is in m^3, and m^3 is not equal to L. There’s 1000 L in a m^3.
Happy Anniversary by the way.

To which I responded

Ouch ! You’re correct of course. However even with the correction, the results come out to .2 free protons (or H30+) per vesicle, a result that still makes no chemical sense. There are many more protons in the vesicle, but they are buffered by the proteins and the transmitters contained within.

Taking a break

No posts for a while. Off to Maine for some R & R after an intense two months of our daughter in law’s pregnancy complicated by pre-eclampsia followed by an emergency delivery at 34 weeks gestation of a 3.5 pound infant who had to spend 3 weeks in the neonatal ICU. Mother and daughter doing well presently. Sometimes you can really know too much. As a neurologist I saw everything which could go wrong in this situation (and plenty did).

There is a lot of very interesting material to post about which I’ve not had time for
l. A thermodynamic (rather than a chemical) explanation of temperature sensitivity of ion channels
2. The importance of a long terminal repeat of an endogenous retrovirus in our genome for the production of induced pluripotent stem cells (IPSCs)
3. A serious attack on the validity of some work which I posted on earlier http://luysii.wordpress.com/2014/02/23/are-memories-stored-outside-of-neurons/

Perhaps when we get back

Breaking benzene

Industrially to break benzene aromaticity in order to add an alkyl group using the Friedel Crafts reaction requires fairly hairy conditions — http://www.chemguide.co.uk/organicprops/arenes/fc.html e.g. pressure to keep everything liquid and temperatures of 130 – 160 Centigrade.

A remarkable paper [ Nature vol. 512 pp. 413 - 415 '14 ] uses a Titanium hydride catalyst and mild conditions (22 C — room temperature) for little over a day to form a titanium methylcyclopentenyl complex from benzene (which could be isolated) and studied spectroscopically.

The catalyst itself is rather beautiful. 3 titaniums, 6 hydrides and 3 C5Me4SiMe3 groups.

Benzene is the aromaticity workhorse of introductory organic chemistry. If you hydrogenate cyclohexene 120 kiloJoules is given off. Hydrogenating benzene should give off 360 kiloJoules, but because of aromatic stabilization only 208 is given off — implying that aromaticity lowers the energy of benzene by 152 kiloJoules. Clayden uses kiloJoules. I’m used to kiloCalories. To get them divide kiloJoules by 4.19.

What other magic does transition metal catalysis have in store?

A very UNtheoretical approach to cancer diagnosis

We have tons of different antibodies in our blood. Without even taking mutation into account we have 65 heavy chain genes, 27 diversity segments, and 6 joining regions for them (making 10,530) possibilities — then there are 40 genes for the kappa light chains and 30 for the lambda light chains or over 1,200 * 10,530. That’s without the mutations we know that do occur to increase antibody affinity. So the number of antibodies probably ramming around in our blood is over a billion (I doubt that anyone has counted then, just has no one has ever counted the neurons in our brain). Antibodies can bind to anything — sugars, fats, but we think of them as mostly binding to protein fragments.

We also know that cancer is characterized by mutations, particularly in the genes coding for proteins. Many of the these mutations have never been seen by the immune system, so they act as neoantigens. So what [ Proc. Natl. Acad. Sci. vo. 111 pp. E3072 - E3080 '14 ] did was make a chip containing 10,000 peptides, and saw which of them were bound by antibodies in the blood.

The peptides were 20 amino acids long, with 17 randomly chosen amino acids, and a common 3 amino acid linker to the chip. While 10,000 seems like a lot of peptides, it is a tiny fraction (actually 10^-18
of the 2^17 * 10^17 = 1.3 * 10^22 possible 17 amino acid peptides).

The blood was first diluted 500x so blood proteins other than antibodies don’t bind significantly to the arrays. The assay is disease agnostic. The pattern of binding of a given person’s blood to the chip is called an immunosignature.

What did they measure? 20 samples from each of five cancer cohorts collected from multiple geographic sites and 20 noncancer samples. A reference immunosignature was generated. Then 120 blinded samples from the same diseases gave 95$% classification accuracy. To investigate the breadth of the approach and test sensitivity, the immunosignatures 75% of over 1,500 historical samples (some over 10 years old) comprising 14 different diseases were used as training, then the other 25% were read blind with an accuracy of over 98% — not too impressive, they need to get another 1,500 samples. Once you’ve trained on 75% of the sample space, you’d pretty much expect the other 25% to look the same.

The immunosignature of a given individual consists of an overlay of the patterns from the binding signals of many of the most prominent circulating antibodies. Some are present in everyone, some are unique.

A 2002 reference (Molecular Biology of the Cell 4th Edition) states that there are 10^9 antibodies circulating in the blood. How can you pick up a signature on 10K peptides from this. Presumably neoAntigens from cancer cells elicit higher afifnity antibodies then self-antigens. High affiity monoclonals can be diluted hundreds of times without diminishing the signal.

The next version of the immunosignature peptide microArray under development contains over 300,000 peptides.

The implication is that each cancer and each disease produces either different antigens and or different B cell responses to common antigens.

Since the peptides are random, you can’t align the peptides in the signature to the natural proteomic space to find out what the antibody is reacing to.

It’s a completely atheoretical approach to diagnosis, but intriguing. I’m amazed that such a small sample of protein space can produce a significant binding pattern diagnostic of anything.

It’s worth considering just what a random peptide of 17 amino acids actually is. How would you make one up? Would you choose randomly giving all 20 amino acids equal weight, or would you weight the probability of a choice by the percentage of that amino acid in the proteome of the tissue you are interested in. Do we have such numbers? My guess is that proline, glycine and alanine would the most common amino acids — there is so much collagen around, and these 3 make up a high percentage of the amino acids in the various collagens we have (over 15 at least).

Physics to the rescue

It’s enough to drive a medicinal chemist nuts. General anesthetics are an extremely wide ranging class of chemicals, ranging from Xenon (which has essentially no chemistry) to the steroid alfaxalone which has 56 carbons. How can they possibly have a similar mechanism of action? It’s long been noted that anesthetic potency is proportional to lipid solubility, so that’s at least something to hang your hat on.

Other work has noted that enantiomers of some anesthetics vary in potency implying that they are interacting with something optically active (like proteins). However, you should note sphingosine which is part of many cell membrane lipids (gangliosides, sulfatides etc. etc.) contains two optically active carbons.

A great paper [ Proc. Natl. Acad. Sci. vol. 111 pp. E3524 - E3533 '14 ] notes that although Xenon has no chemistry it does have physics. It facilitates electron transfer between conductors (clearly a physical effect). The present work does some quantum mechanical calculations purporting to show that Xenon can extend the highest occupied molecular orbital (HOMO) of an alpha helix so as to bridge the gap to another helix.

This paper shows that Xe, SF6, NO and chloroform cause rapid increases in the electron spin content of Drosophila (probably another physical effect). The changes are reversible. Anesthetic resistant mutant strains (in what protein) show a different pattern of spin responses to anesthetic.

So they think general anesthetics might work by perturbing the electronic structure of proteins. It’s certainly a fresh idea.

What is carrying the anesthetic induced increase in spin? Speculations are bruited about. They don’t think the spin changes are due to free radicals. They favor changes in the redox state of metals. Could it be due to electrons in melanin (the prevalent stable free radical in flies). Could it be changes in spin polarization? Electrons traversing chiral materials can become spin polarized.

Why this should affect neurons isn’t known, and further speculations are given (1) electron currents in mitochondria, (2) redox reactions where electrons are used to break a disulfide bond.

Fascinating paper, and Mark Twain said it the best “There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.”

Physics to the rescue

It’s enough to drive a medicinal chemist nuts. General anesthetics are an extremely wide ranging class of chemicals, ranging from Xenon (which has essentially no chemistry) to a steroid alfaxalone which has 56 carbons. How can they possibly have a similar mechanism of action? It’s long been noted that anesthetic potency is proportional to lipid solubility, so that’s at least something. Other work has noted that enantiomers of some anesthetics vary in potency implying that they are interacting with something optically active (like proteins). However, you should note sphingosine which is part of many cell membrane lipids (gangliosides, sulfatides etc. etc.) contains two optically active carbons.

A great paper [ Proc. Natl. Acad. Sci. vol. 111 pp. E3524 - E3533 '14 ] notes that although Xenon has no chemistry it does have physics. It facilitates electron transfer between conductors. The present work does some quantum mechanical calculations purporting to show that
Xenon can extend the highest occupied molecular orbital (HOMO) of an alpha helix so as to bridge the gap to another helix.

This paper shows that Xe, SF6, NO and chloroform cause rapid increases in the electron spin content of Drosophila. The changes are reversible. Anesthetic resistant mutant strains (in what protein) show a different pattern of spin responses to anesthetic.

So they think general anesthetics might work by perturbing the electronic structure of proteins. It’s certainly a fresh idea.

What is carrying the anesthetic induced increase in spin? Speculations are bruited about. They don’t think the spin changes are due to free radicals. They favor changes in the redox state of metals. Could it be due to electrons in melanin (the prevalent stable free radical in flies). Could it be changes in spin polarization? Electrons traversing chiral materials can become spin polarized.

Why this should affect neurons isn’t known, and further speculations are given (1) electron currents in mitochondria, (2) redox reactions where electrons are used to break a disulfide bond.

The article notes that spin changes due to general anesthetics differ in anesthesia resistant fly mutants.

Fascinating paper, and Mark Twain said it the best “There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.”

Watch this space

I know far more about head trauma than any neurologist should. For three and a half years I worked with two active neurosurgeons covering a huge area of an eastern state. Our drawing radius ranged from 35 to 125 miles depending on direction. I was on first call every other night and weekend, and covered all the patients (including the neurosurgical ones) during those times. It’s amazing what you’ll do to get your kids through college. I was the first to see any head trauma cases that came in whether our service admitted them or not (multiple trauma cases usually went to general surgery and/or orthopedics, with out group following them as consultants).

So it’s time to talk about orbital (eye socket) fractures. This has great relevance for the case against Darren Wilson, the cop who killed Michael Brown. As far as I can tell, whether Wilson did or not sustain an orbital fracture is extremely controversial, with statements and denials all over the internet (most of them 5 -6 days old).

The truth of the matter will be very easy to establish once his X-rays (and CAT scans) are available. If Wilson sustained any head trauma at all, it is inconceivable to me that he didn’t have X-rays and CAT scans out the gazoo (technical term).

Some orbital fractures are very easy to see with a CAT scan, which shows bone beautifully. The orbit is adjacent to sinuses (air filled spaces) below and toward the nose. Fractures bleed. Normally the sinuses are filled with air which doesn’t stop X-rays, so they normally look black. Bone stops X-rays so they look white on CAT scan. Blood (or mucus) is very easy to see in a sinus on a CAT scan.

There is always a question about how old a fracture is, but if blood is found in a sinus adjacent to the fracture, you can conclude that the fracture is new.

Sometimes there is a sinus (the frontal sinus) above the orbit, but not always. The side of the orbit toward the ear is just bone.

So the data is out there somewhere. Watch this space for more interpretation should Wilson actually have sustained one.

The only other data available for all to see, are the convenience store videos, which show how Brown was acting shortly before he was killed. It isn’t pretty. I’m sure there are better links to it, so ignore the right wing chatter, and just look at the data. http://www.breitbart.com/Big-Government/2014/08/18/Michael-Brown-Allegedly-Bum-Rushed-Officer-Punched-Him-in-Face-Grabbed-Gun-Taunted-Him

Brown was big (reportedly 6′ 4” and 300 pounds), and the video shows him pushing a clerk who doesn’t even come up to his shoulder, when the clerk (who also appears to be a person of color) confronts him.

Tolstoy rides again — Schizophrenia

“A field plagued by inconsistency, and perhaps even a degree of charlatanism” — strong stuff indeed [ Neuron vol. 83 pp. 760 - 763 '14 ]. They are talking about studies attempting to find the genetic causes of schizophrenia.

This was the state of play four and a half years ago (in a post of April 2010)

Happy families are all alike.; every unhappy family is unhappy in its own way”. Thus beginneth Anna Karenina. That wasn’t supposed to happen with hereditary disease. The examples we had before large scale DNA sequencing became cheap were basically one gene causing one disease. Two of the best known cases were sickle cell anemia and cystic fibrosis. In the former, a change in a single position (nucleotide) of DNA caused a switch of one amino acid (valine for glutamic) acid at position #6 in beta hemoglobin. In the latter, all mutations have been found in a single gene called CFTR. 85% of known mutations involve the loss of 1 amino acids. But by 2003 over 600 different mutations accounted for only part of the other 15%. There is plenty of room for mutation as CFTR has 1480 amino acids. The kids I took care of in the muscular dystrophy clinic all turned out to have mutations in the genes for proteins found in muscle.

Why not look for the gene causing schizophrenia? It’s a terrible disease (the post “What is Schizophrenia really Like?” is included after the **** ) with a strong hereditary component. There was an awful era in psychiatry when it was thought to be environmental (e.g. the family was blamed). Deciding what is hereditary and what is environmental can be tricky. TB was thought to be hereditary (for a time) because it also ran in families. So why couldn’t schizophrenia be environmental? Well, if you are an identical twin and the other twin has it, your chances of having schizophrenia are 45%. If you are a fraternal twin your chance of having it are 3 times less (15%). This couldn’t be due to the environment.

It’s time to speak of SNPs (single nucleotide polymorphisms). Our genome has 3.2 gigaBases of DNA. With sequencing being what it is, each position has a standard nucleotide at each position (one of A, T, G, or C). If 5% of the population have one of the other 3 at this position you have a SNP. By 2004 some 7 MILLION SNPs had been found and mapped to the human genome. So to find ‘the gene’ for schizophrenia, just take schizophrenics as a group (there are lots of them — about 1% of the population) look at their SNPs, and see if they have any SNPs in common.

Study after study found suspect SNPs (which can be localized exactly in the genome) for schizophrenia. The area of the genome containing the SNP was then searched for protein coding genes to find the cause of the disease. Unfortunately each study implicated a different bunch of SNPs (in different areas of the genome). A study of 750 schizophrenics and an equal number of controls from North Carolina used 500,000 SNPs. None of the previous candidate genes held up. Not a single one [ Nature vol. 454 pp., 154 - 157 '08 ]

As of 2009 here are 3,000 diseases showing simple inheritance in which a causative gene hasn’t been found. This is the ‘dark matter’ of the genome. We are sure it exists (because the diseases are hereditary) but we simply can’t see it.

There is presently a large (and expensive) industry called GWAS (Genome Wide Association Studies) which uses SNPs to look for genetic causes of diseases with a known hereditary component. One study on coronary heart disease had 23,000 participants. In 2007 the Wellcome Trust committed 45 million (pounds? dollars?) for studies of 27 diseases in 120,000 people. This is big time science. GWAS studies have found areas of the genome associated with various disorders. However, in all GWAS studies so far, what they’ve picked up explains less than 5% of the heritability. An example is height (not a disease). Its heritability is 80% yet the top 20 candidate genetic variants identified explain only 3% of the variance. People have called for larger and larger samples to improve matters.

What’s going on?

It’s time for you to read “Genetic Heterogeneity in Human Disease” [ Cell vol. 141 pp. 210 - 217 '10 ]. It may destroy GWAS. Basically, they argue that most SNPs are irrelevant, don’t produce any functional change, and have arisen by random mutation. They are evolutionary chaff if you will. A 12 year followup study of 19,000 women looked at the 101 SNPs found by GWAS as risk variants for cardiovascular disease — not one of them predicted outcome [ J. Am. Med. Assoc. vol. 303 pp. 631 - 637 '10 ]. The SNPs haven’t been eliminated by natural selection, because they aren’t causing trouble and because the human population has grown exponentially.

There’s a lot more in this article, which is worth reading carefully. It looks like what we’re calling a given disease with a known hereditary component (schizophrenia for example) is the result of a large number of different (and rather rare) mutations. A given SNP study may pick up one or two rare mutations, but they won’t be found in the next. It certainly has been disheartening to follow this literature over the years, in the hopes that the cause of disease X, Y or Z would finally be found, and that we would have a logical point of attack (but see an old post titled “Some Humility is in Order”).

Is there an analogy?

200 years ago (before Pasteur) physicians classified a variety of diseases called fevers. They knew they were somewhat different from each other (quotidian fever, puerperal fever, etc. etc.). But fever was the common denominator and clinically they looked pretty much the same (just as dogs look pretty much the same). Now we know that infectious fever has hundreds of different causes. The Cell article argues that, given what GWAS has turned up so far, this is likely to be the case for many hereditary disorders.

Tolstoy was right.

Fast forward to the present [ Nature vol. 511 pp. 412 - 413, 421 - 427 '14 ] This is a paper from the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) which analyzed some 36,989 and 113,075 controls. They found 128 independent associations spanning 108 conservatively defined loci, meeting genome-wide significance. 83/128 hadn’t been previously reported. The associations were enriched among genes expressed in brain. Prior to this work, some 30 schizophrenia associated loci had been found through genome wide association studies (GWAS).

Interestingly 3/4 of the 108 loci include protein coding genes (of which 40% represent a single gene and another 8% are within 20 kiloBases of a gene).

The editorial noted that there have been 800 associations ‘of dubious value’

The present risk variants are common, and contribute in most (if not all) cases. One such association is with the D2 dopamine receptor, but not with COMT (which metabolizes dopamine). The most significant association is within the major histocompatibility complex (MHC).

The paper in Neuron cited above notes that schizophrenia genetics is a “field plagued by inconsistency, and perhaps even a degree of charlatanism” and that that there have been 800 associations ‘of dubious value’. The statistical sins of earlier work are described resulting in many HUNDREDS of variant associations with schizophrenia, and scores of falsely implicated genes. Standards have been developed to eliminate them [ Nat. Rev. Genet. vol. 9 pp. 356 - 369 '08 ].

Here is their description of the statistical sins prior to GWAS “Before GWAS, the standard practice for investigating schizophrenia genetics (as well as many other areas) was to pick a candidate gene (usually based on dopamine or glutamate pathways or linkage studies) and compare the frequency of genetic variants in cases and controls. Any difference with a p value 0.05) p values, and associations seen in partitions of a data set. Beyond all of these obvious statistical transgressions, these studies often entirely ignored well-established causes of spurious associations such as population stratification. Labs would churn out separate papers for gene after gene with no correction for multiple testing, and, on top of all of that, there was a publication bias against negative findings. “

There is a hockey stick model in which few real associations aren’t found until a particular sample size is breached. This works for hypertension.

GWAS identifies genomic regions, not precise risk factors. It is estimated the the 108 loci implicate 350 genes. However the Major Histocompatibility Complex (MHC) counts as one locus and it has tones of genes.

The NHGRI website tracks independent GWAS signals for common diseases and traits, and currently records 7,300 associations with a p value under 5 * 10^-8. Only 20 have been tracked to causal variants (depending on criteria.

The number of genes implicated will only grow as the PGC continues to increase the sample size to capture smaller and smaller effect sizes — how long will it be until the whole genome is involved? There are some significant philosophical issues involved, but this post is long enough already.

*****
What Schizophrenia is really Like

I feel that writing to you there I am writing to the source of a ray of light from within a pit of semi-darkness. It is a strange place where you live, where administration is heaped upon administration, and all tremble with fear or abhorrence (in spite of pious phrases) at symptoms of actual non-local thinking. Up the river, slightly better, but still very strange in a certain area with which we are both familiar. And yet, to see this strangeness, the viewer must be strange.”

“I observed the local Romans show a considerable interest in getting into telephone booths and talking on the telephone and one of their favorite words was pronto. So it’s like ping-pong, pinging back again the bell pinged to me.”

Could you paraphrase this? Neither can I, and when, as a neurologist I had occasion to see schizophrenics, the only way to capture their speech was to transcribe it verbatim. It can’t be paraphrased, because it makes no sense, even though it’s reasonably gramatical.

What is a neurologist doing seeing schizophrenics? That’s for shrinks isn’t it? Sometimes in the early stages, the symptoms suggest something neurological. Epilepsy for example. One lady with funny spells was sent to me with her husband. Family history is important in just about all neurological disorders, particularly epilepsy. I asked if anyone in her family had epilepsy. She thought her nephew might have it. Her husband looked puzzled and asked her why. She said she thought so because they had the same birthday.

It’s time for a little history. The board which certifies neurologists, is called the American Board of Psychiatry and Neurology. This is not an accident as the two fields are joined at the hip. Freud himself started out as a neurologist, wrote papers on cerebral palsy, and studied with a great neurologist of the time, Charcot at la Salpetriere in Paris. 6 months of my 3 year residency were spent in Psychiatry, just as psychiatrists spend time learning neurology (and are tested on it when they take their Boards).

Once a month, a psychiatrist friend and I would go to lunch, discussing cases that were neither psychiatric nor neurologic but a mixture of both. We never lacked for new material.

Mental illness is scary as hell. Society deals with it the same way that kids deal with their fears, by romanticizing it, making it somehow more human and less horrible in the process. My kids were always talking about good monsters and bad monsters when they were little. Look at Sesame street. There are some fairly horrible looking characters on it which turn out actually to be pretty nice. Adults have books like “One flew over the Cuckoo’s nest” etc. etc.

The first quote above is from a letter John Nash wrote to Norbert Weiner in 1959. All this, and much much more, can be found in “A Beatiful Mind” by Sylvia Nasar. It is absolutely the best description of schizophrenia I’ve ever come across. No, I haven’t seen the movie, but there’s no way it can be more accurate than the book.

Unfortunately, the book is about a mathematician, which immediately turns off 95% of the populace. But that is exactly its strength. Nash became ill much later than most schizophrenics — around 30 when he had already done great work. So people saved what he wrote, and could describe what went on decades later. Even better, the mathematicians had no theoretical axe to grind (Freudian or otherwise). So there’s no ego, id, superego or penis envy in the book, just page after page of description from well over 100 people interviewed for the book, who just talked about what they saw. The description of Nash at his sickest covers 120 pages or so in the middle of the book. It’s extremely depressing reading, but you’ll never find a better description of what schizophrenia is actually like — e.g. (p. 242) She recalled that “he kept shifting from station to station. We thought he was just being pesky. But he thought that they were broadcasting messages to him. The things he did were mad, but we didn’t really know it.”

Because of his previous mathematical achievments, people saved what he wrote — the second quote above being from a letter written in 1971 and kept by the recipient for decades, the first quote from a letter written in 12 years before that.

There are a few heartening aspects of the book. His wife Alicia is a true saint, and stood by him and tried to help as best she could. The mathematicians also come off very well, in their attempts to shelter him and to get him treatment (they even took up a collection for this at one point).

I was also very pleased to see rather sympathetic portraits of the docs who took care of him. No 20/20 hindsight is to be found. They are described as doing the best for him that they could given the limited knowledge (and therapies) of the time. This is the way medicine has been and always will be practiced — we never really know enough about the diseases we’re treating, and the therapies are almost never optimal. We just try to do our best with what we know and what we have.

I actually ran into Nash shortly after the book came out. The Princeton University Store had a fabulous collection of math books back then — several hundred at least, most of them over $50, so it was a great place to browse, which I did whenever I was in the area. Afterwards, I stopped in a coffee shop in Nassau Square and there he was, carrying a large disheveled bunch of papers with what appeared to be scribbling on them. I couldn’t bring myself to speak to him. He had the eyes of a hunted animal.

The climate gods have a sardonic sense of humor

Things haven’t been going too well for Global Warming. First, there has been essentially no change in global mean temperature for 14 – 17 years (depending on which of 4 measures you use). So Global Warming was rebranded as Climate Change. Then, we’ve been told that climate change would lead to more and more ‘extreme weather events’ (translation hurricanes, tornadoes etc. etc.) So in one of the coolest New England summers within memory and with nearly half of the 6 month hurricane season gone, we have a very quiet, not to say comatose, hurricane season.

At the onset of the 2014 season NOAA (National Oceanic and Atmospheric Administration) predicted a 70% chance of a below average season. The numbers they expected were

8 – 13 Named storms (top winds over 39 mph — not very impressive)

3 – 6 Hurricanes (top winds over 74 mph)

1 – 2 Category 3 storms (sustained winds over 110 mph)

This was updated 7 August to a 70% probability of an even less exciting season

7 – 12 Named storms (top winds over 39 mph — not very impressive)

3 – 6 Hurricanes (top winds over 74 mph)

0 – 2 Category 3 storms (sustained winds over 110 mph)

So instead of extreme weather events, we have extremely boring (but pleasant) weather and just 2 named storms which turned into hurricanes. No category 3 events, and as of this writing, the Atlantic is extremely quiet. This has been blamed on dry air from Africa and (amazingly enough) unusually cool water temperatures in the Atlantic. Recall that it has been argued that the stability of global temperature over the past decade is due to heat going into the deep ocean where we can’t see it.

To be noted if you look at the graph in http://www.accuweather.com/en/weather-news/atlantic-tropical-threat-possible-gulf-coast/32555368, which is of hurricane frequency vs. date, and try to mentally integrate the area under the curve in your head, that only about 20% of the hurricanes have occurred by this time. Peak frequency is 2 weeks from now (11 September) and the frequency of 20 August is half maximal.

Would anyone like to guess when (not if) this will be blamed on Global Warming/Climate Change? I’d be very surprised if it weren’t, and if it is, remember that a theory which can explain anything explains nothing.

Bad news on the cancer front

[ Nature vol. 512 pp. 143 - 144, 155 - 160 '14 ] Nuc-seq is an innovative sequencing method which achieves almost complete sequencing of whole genomes in single cells. It sequences DNA from cells about to divide (the G2/M stage of the cell cycle which has twice the DNA content of the usual cell). Genomes of multiple single cells from two types of human breast cancer (estrogen receptor positive and triple negative — the latter much more aggressive) and found that no two genomes of individual tumor cells were identical. Many cells had new mutations unique to them.

This brings into question what we actually mean by a cancer cell clone. They validated some of the single cell mutations by deep sequencing of a single molecule (not really sure what this is).

Large scale structural changes in DNA (amplification and deletion of large blocks of DNA) occurred early in tumor development. THey remain stable as clonal expansion of the tumor occur (e.g. they were found in all the cancer cells whose genome was sequenced). Point mutations accumulated more gradually generating extensive subclonal diversity. Many of the mutations occur in less than 10% of the tumor mass. Triple negative breast cancers (aggressive) have mutation rates 13 times greater than the slower growing estrogen receptor positive breast cancer cells.

This implies that the mutations are there BEFORE chemotherapy. This has always been a question as most types of chemotherapy attack DNA replication and are inherently mutagenic. It also implies that slamming cancer with chemotherapy early before it has extensively mutated is locking the barn door after the horse has been stolen. It still might help in preventing metastasis, so the approach remains viable.

However nuc-seq may only be useful for cancer cells without aneuploidy http://en.wikipedia.org/wiki/Aneuploidy which is extremely common in cancer cells.

Why is this such bad news? It means that before chemotherapy even starts there is a high degree of genetic diversity present in the tumor cell population. This means that natural selection (in the form of chemotherapy) has a diverse population to work on at the get go, making resistance far more likely to occur.

Had enough? Here’s more — [ Nature vol. 511 pp. 543 - 550 '14 ] A report of 230 resected lung adenocarcinomas using mRNA, microRNA and DNA sequencing found an incredible 8.8 mutations/megaBase — e.g. 3.2 * 3.8 * 1,000 == 28,000 mutations. Aberrations in NF1, MET. ERBB2 and RIT1 occured in 13% and were enriched in samples otherwise lacking an activated oncogene. Even when not mutated, mRNA splicing was different in tumors. As far as oncogenic pathways, multiple pathways were involved — p53in 63%, PI3K mTOR in 25%, Receptor Tyrosine Kinase in 76%, cell cycle regulators 64%.

This is the opposite side of the coin from the first paper, where the genomes of single tumor cells were sequenced. It is doubtful that all cells have the 28,000 mutations, which probably result from each cell having a subset. The first paper didn’t count how many mutations a single cell had (as far as i could see).

So oncologists are attacking a hydra-headed monster.

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