Category Archives: Neurology & Psychiatry

Hillary’s stroke

Hillary Clinton had a stroke toward the end of 2012. It was not due to the faint she had presumably because of the flu in mid December. The information given out at the time was extremely sketchy and confusing (see the copy of the post of 31 Dec ’12 at the end).

She fainted while giving a speech in Buffalo according to one account and at her home in Washington according to another and was not hospitalized. She is said to have suffered a concussion when she fell. Then on the 30th of December she was hospitalized because a blood clot was found (more later) and placed on blood thinners. She suffered double vision and had to wear corrective glasses (Fresnel lenses) for congressional testimony 23 January 2013.

So she had a blood clot in her head and a neurologic deficit persisting for a few weeks. That’s what a stroke is.

Could it have been due to the head trauma? This is extremely doubtful based on an intense 42 month experience managing acute head injuries.

To get my kids through college, I took a job working for two busy neurosurgeons. When I got there, I was informed that I’d be on call every other night and weekend, taking first call with one of the neurosurgeons backing me up. Neurologists rarely deal with acute head trauma although when the smoke clears we see plenty of its long term side effects (post-traumatic epilepsy, cognitive and coordination problems etc. etc.). I saw plenty of it in soldiers when I was in the service ’68 – ’70, but this was after they’d been stabilized and shipped stateside. Fortunately, my neurosurgical backup was excellent, and I learned and now know far more about acute head trauma than any neurologist should.

We admitted some of the head trauma cases to our service, but most cases had trauma to other parts of the body, so a general surgeon would run the show with our group as consultants. The initial consultant in half the cases was me. If I saw them initially, I followed the patients until discharge. On weekends I covered all our patients and all our consults, usually well over 20 people.

We are told that Hillary had a clot in one of the large draining veins in the back of her head (venous sinuses actually). In all the head trauma I saw (well over 300 I’d guess), I never saw a clot develop there. I’ve spoken to two neuroradiologists still in practice, and they can’t recall seeing such a clot without a skull fracture near the vein. Nothing like this was mentioned at any time about Hillary.

Hillary’s neurologic deficit involved a nerve going to the muscles of her left eye. These start in the brainstem, a part of the brain quite near the site where she is said to have the clot in her vein. The brainstem is crucial in maintaining consciousness, and it is far more likely that the faint in early December was a warning sign of the stroke she had subsequently.

I can’t believe that she would not have been hospitalized had she complained of double vision when she fainted in early December, so it must have come on later.

So the issue is why did she have the stroke, and how likely is it to recur. I seriously doubt that it had anything to do with the head injury she sustained when she fainted. We’ve have two presidents neurologically impaired by stroke in the past century (Woodrow Wilson after World War I and Franklin Delano Roosevelt at Yalta). The results were not happy for the USA or the World.

Certainly all this would be cleared up if her medical records were released. Only Hillary can do this, but at least she cannot destroy them, as although she ‘owns’ them, they are not in her sole possession.

The following is a post written 31 December ’12 when the news of Hillary’s illness first broke showing how fragmentary the information about it was back then (it isn’t a good deal better now).

Medical tribulations of politicians — degrees of transparency

Remarkably on the last day of the year, 3 political figures and their medical problems are in the news. Here they are in order of medical transparency (highest first).

l. George Bush Sr. — the most transparent. We are told what he has (pneumonia), when he was admitted to hospital when he was in the ICU, when he came out. Docs call pneumonia ‘the old man’s friend’ not out of cynicism, but because its a mode of death with (relatively) little suffering. The patient lapses into unconsciousness and usually dies quickly and quietly. It took my cellist’s father only a day or two to pass away this month. Clearly it isn’t invariably fatal, and Bush Sr. was now out of the ICU at last count (he’s 88).

2. Hillary Clinton — admitted to the hospital yesterday with a ‘blood clot’ somewhere, said to be a complication of the concussion she suffered a few weeks ago. Also said to be under treatment with anticoagulants. Most clots due to head trauma are inside the head and treating them with anticoagulants is a disaster. The most likely type of clot given the time from the concussion is a subdural hematoma. It is possible that she’s been so inactive since the concussion that she developed thrombophlebitis in her legs, in which case anticoagulation would be indicated.

More disturbingly, is that her passing out a few weeks ago is a sign of something more serious going on. Hopefully not.

The powers that be should have told us where the clot actually is.

Update 5:50 PM EST — Well the powers that be did open up and it is a most unusual complication of head injury (and one I’d never seen in nearly 4 decades of practice) — a venous thrombosis in the head. I’m not even sure it’s due to her head injury. It might have even caused her syncope, but presumably she had some sort of radiologic study of her head when she fainted, which should have picked it up. The venous sinuses draining the brain in the back of the head are notoriously asymmetric, so a narrowing attributable to a clot could just be a variant anatomy. One very bad complication of cerebral venous thrombosis back there (which I saw as a complication of chronic mastoid bone infection — not head trauma) is pseudotumor cerebri. I really wonder if these guys have the right diagnosis.

3. Hugo Chavez — Yesterday it was announced that he’s had a third complication since his surgery for cancer 3 weeks ago. Naturally, we’re not told just what this complication actually is. This is consistent with the information that has been released about his case. We know almost nothing about his actual tumor (except that he has one). He most assuredly is not free of cancer as he stated last fall. He is said to have have a bleeding problem and a lung infection as the first two complications.

My guess for this third complication is that it is a dehiscence of his abdominal incision, which must have been fairly large for a 6 hour operation. Dehiscence just means that the wound has spontaneously opened up exposing abdominal contents (which means that peritonitis is not far behind). Why should this happen? Two reasons — he’s had radiation to the area which inhibits wound healing, and he’s been on high dose steroids in the past (and perhaps presently) which also inhibits wound healing.

I don’t think he’s going to be able to take office 10 days hence, and doubt that he’ll come back to Venezuela alive. Transparency has been zilch. Hopefully the people of Venezuela are beginning to realize just how misleading the information they’ve been fed about his health has been.

This is the sort of thing physicians taking care of really sick people deal with daily, which may explain why your doc friends aren’t as jolly as you are at the New Year’s Eve parties you’re about to attend.

Nonetheless, Happy New Year to all ! ! ! !

One reason our brain is 3 times that of a chimpanzee

Just based on the capacity of the skull, our brain is 3 – 4 times larger than that of our closest primate relative, the chimp. Most of the increase in size occurs in the cerebral cortex (the gray matter) just under the skull. Our cortex is thrown into folds because there is so much of it. Compare the picture of the mouse brain (smooth) and ours, wrinkled like a walnut http://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=130442.

We now may have part of the explanation. A fascinating paper http://www.sciencemag.org/content/347/6229/1465.full.pdf studied genetic differences between the progenitor cells from which the cortex arises (radial glia) in man and mouse. They found 56 protein coding genes expressed in our radial glia not present in the mouse (out of 20,000 or so).

One in particular called by the awful name ARHGAP11B is particularly fascinating. Why? Because it’s the product of a gene duplication of ARHGAP11A. When did this happen — after the human line split off from the chimp 6 million years ago. Chimps have no such duplication, just the original

Put ARHGAP11B into a developing mouse and its cortex expands so much it forms folds.

There has been all sorts of work on the genetic difference between man and chimp. There almost too many — [ Nature vol. 486 pp. 481 – 482 ’12 ] — some 20,000,000. Finding the relevant ones is the problem. ARHGAP11A is by far the best we’ve found to date.

Another fascinating story is the ‘language gene’ discovered in a family suffering from a speech and language disorder. It’s called FOXP2. Since the last common ancestor of humans and mice (70 megaYears ago) there have been only 3 changes in the 715 amino acids comprising the protein. 2 of them have occurred in the human lineage since it split with the chips 6 megaYears ago. So far no one has put the human FOXP2 gene into a chimp and got it to talk. For more details see http://en.wikipedia.org/wiki/FOXP2

There is all sorts of fascinating molecular biology about what these two genes actually do in the cell, but that would make this post too long,. This is, in part, a chemistry blog and just what FOXP2 and ARHGAP11A actually do involves some beautiful and elegant chemistry — look up RhoGAP and Winged Helix transcription factors. Ferrari’s are beautiful cars, and become even more beautiful when you understand what’s going on under the hood. Chemistry gives you that for molecular, cellular and organismal biology.

Why we imperfectly understand randomness the way we do.

The cognoscenti think the average individual is pretty dumb when it comes to probability and randomness. Not so, says a fascinating recent paper [ Proc. Natl. Acad. Sci. vol. 112 pp. 3788 – 3792 ’15 ] http://www.pnas.org/content/112/12/3788.abstract. The average joe (this may mean you) when asked to draw a random series of fifty or so heads and tails never puts in enough runs of heads or runs of tails. This leads to the gambler’s fallacy, that if an honest coin gives a run of say 5 heads, the next result is more likely to be tails.

There is a surprising amount of structure lurking within purely random sequences such as the toss of a fair coin where the probability of heads is exactly 50%. Even with a series with 50% heads, the waiting time for two heads (HH) or two tails (TT) to appear is significantly longer than for an alternation (HT or TH). On average 6 tosses will be required for HH or TT to appear while only an average of 4 are needed for HT or TH.

This is why Joe SixPack never puts in enough runs of Hs or Ts.

Why should the wait be longer for HH or TT even when 50% of the time you get a H or T. The mean time for HH and TT is the same as for HT and TH. The variance is different because the occurrences of HH and TT are bunched in time, while the HT and TH are spread evenly.

It gets worse for longer repetitions — they can build on each other. HHH contains two instances of HH, while alterations do not. Repetitions bunch together as noted earlier. We are very good at perceiving waiting times, and this is probably why we think repetitions are less likely and soon to break up.

The paper goes a lot farther constructing a neural model, based on the way our brains integrate information over time when processing sequences of events. It takes into consideration our perceptions of mean time AND waiting times. We average the two. This produces the best fitting bias gain parameter for an existing Bayesian model of randomness.

See, you’re not as dumb as they thought you were.

Another reason for our behavior comes from neuropsychology and physiological psychology. We have ways to watch the electrical activity of your brain and find out when you perceive something as different. It’s called mismatch negativity (see http://en.wikipedia.org/wiki/Mismatch_negativity for more detail). It a brain potential (called P300) peaking .1 -.25 seconds after a deviant tone or syllable.

Play 5 middle c’s in a row followed by a d than c’s again. The potential doesn’t occur after any of the c’s just after the d. This has been applied to the study of infant perception long before they can speak.

It has shown us that asian and western newborn infants both hear ‘r’ and ‘l’ quite well (showing mismatch negativity to a sudden ‘r’ or ‘l’ in a sequence of other sounds). If the asian infant never hears people speaking words with r and l in them for 6 months, it loses mismatch negativity to them (and clinical perception of them). So our brains are literally ‘tuned’ to understand the language we hear.

So we are more likely to notice the T after a run of H’s, or an H after a run of T’s. We are also likely to notice just how long it has been since it last occurred.

This is part of a more general phenomenon — the ability of our brains to pick up and focus on changes in stimuli. Exactly the same phenomenon explains why we see edges of objects so well — at least here we have a solid physiologic explanation — surround inhibition (for details see — http://en.wikipedia.org/wiki/Lateral_inhibition). It happens in the complicated circuitry of the retina, before the brain is involved.

Philosophers should note that this destroys the concept of the pure (e.g. uninterpreted) sensory percept — information is being processed within our eyes before it ever gets to the brain.

Update 31 Mar — I wrote the following to the lead author

” Dr. Sun:

Fascinating paper. I greatly enjoyed it.

You might be interested in a post from my blog (particularly the last few paragraphs). I didn’t read your paper carefully enough to see if you mention mismatch negativity, P300 and surround inhibition. if not, you should find this quite interesting.

Luysii

And received the following back in an hour or two

“Hi, Luysii- Thanks for your interest in our paper. I read your post, and find it very interesting, and your interpretation of our findings is very accurate. I completely agree with you making connections to the phenomenon of change detection and surround inhibition. We did not spell it out in the paper, but in the supplementary material, you may find some relevant references. For example, the inhibitory competition between HH and HT detectors is a key factor for the unsupervised pattern association we found in the neural model.

Yanlong”

Nice ! ! !

Should pregnant women smoke pot?

Well, maybe this is why college board scores have declined so much in recent decades that they’ve been normed upwards. Given sequential MRI studies on brain changes throughout adolescence (with more to come), we know that it is a time of synapse elimination. (this will be the subject of another post). We also know that endocannabinoids, the stuff in the brain that marihuana is mimicking, are retrograde messengers there, setting synaptic tone for information transmission between neurons.

But there’s something far scarier in a paper that just came out [ Proc. Natl. Acad. Sci. vol. 112 pp. 3415 – 3420 ’15 ]. Hedgehog is a protein so named because its absence in fruitflies (Drosophila) causes excessive bristles to form, making them look like hedgehogs. This gives you a clue that Hedgehog signaling is crucial in embryonic development. A huge amount is known about it with more being discovered all the time — for far more details than I can provide see http://en.wikipedia.org/wiki/Hedgehog_signaling_pathway.

Unsurprisingly, embryonic development of the brain involves hedgehog, e,g, [ Neuron vol. 39 pp. 937 – 950 ’03 ] Hedgehog (Shh) signaling is essential for the establishment of the ventral pattern along the whole neuraxis (including the telencephalon). It plays a mitogenic role in the expansion of granule cell precursors during CNS development. This work shows that absence of Shh decreases the number of neural progenitors in the postnatal subventricular zone and hippocampus. Similarly conditional inactivation of smoothened results in the formation of fewer neurospheres from progenitors in the subventricular zone. Stimulation of the hedgehog pathway in the mature brain results in elevated proliferation in telencephalic progenitors. It’s a lot of unfamiliar jargon, but you get the idea.

Of interest is the fact that the protein is extensively covalently modified by lipids (cholesterol at the carboxy terminal end and palmitic acid at the amino terminal end. These allow hedgehog to bind to its receptor (smoothened). It stands to reason that other lipids might block this interaction. The PNAS work shows this is exactly the case (in Drosophila at least). One or more lipids present in Drosophila lipoprotein particles are needed in vivo to keep Hedgehog signaling turned off in wing discs (when hedgehog ligand isn’t around). The lipids destabilize Smoothtened. This work identifies endocannabinoids as the inhibitory lipids from extracts of human very low density lipoprotein (VLDL).

It certainly is a valid reason for women not to smoke pot while pregnant. The other problem with the endocannabinoids and exocannabinoids (e.g. delta 9 tetrahydrocannabinol), is that they are so lipid soluble they stick around for a long time — see https://luysii.wordpress.com/2014/05/13/why-marihuana-scares-me/

It is amusing to see regulatory agencies wrestling with ‘medical marihuana’ when it never would have gotten through the FDA given the few solid studies we have in man.

Scary stuff

While you were in your mother’s womb, endogenous viruses were moving around the genome in your developing developing brain according to [ Neuron vol. 85 pp. 49 – 59 ’15 ].

The evidence is pretty good. For a while half our genome was called ‘junk’ by those who thought they had molecular biology pretty well figured out. For instance 17% of our 3.2 gigaBase DNA genome is made of LINE1 elements. These are ‘up to’ 6 kiloBases long. Most are defective in the sense that they stay where they are in the genome. However some are able to be transcribed into RNA, the RNA translated into proteins, among which is a reverse transcriptase (just like the AIDS virus) and an integrase. The reverse transcriptase makes a DNA copy of the RNA, and the integrates puts it back into the genome in a different place.

Most LINE1 DNA transcribed into RNA has a ‘tail’ of polyAdenine (polyA) tacked onto the 3′ end. The numbers of A’s tacked on isn’t coded in the genome, so it’s variable. This allows the active LINE1’s (under 1/1,000 of the total) to be recognized when they move to a new place in the genome.

It’s unbelievable how far we’ve come since the Human Genome Project which took over a decade and over a billion dollars to sequence a single human genome (still being completed by the way filling in gaps etc. etc [ Nature vol. 517 pp. 608 – 611 ’15 ] using a haploid human tumor called a hydatidiform mole ). The Neuron paper sequenced the DNA of 16 single neurons. They found LINE1 movement in 4

Once a LINE1 element has moved (something very improbable) it stays put, but all cells derived from it have the LINE1 element in the new position.

They found multiple lineages and sublineages of cells marked by different LINE1 retrotransposition events and subsequent mutation of polyA microsatellites within L1. One clone contained thousands of cells limited to the left middle frontal gyrus, while a second clone contained millions of cells distributed over the whole left hemisphere (did they do whole genome on millions of cells).

There is one fly in the ointment. All 16 neurons were from the same ‘neurologically normal’ individual.

Mosaicism is a term used to mean that different cells in a given individual have different genomes. This is certainly true in everyone’s immune system, but we’re talking brain here.

Is there other evidence for mosaicism in the brain? Yes. Here it is

[ Science vol. 345 pp. 1438 – 1439 ’14 ] 8/158 kids with brain malformations with no genetic cause (as found by previous techniques) had disease causing mutations in only a fraction of their cells (hopefully not brain cells produced by biopsy). Some mosaicism is obvious — the cafe au lait spots of McCune Albright syndrome for example. DNA sequencing takes the average of multiple reads (of the DNA from multiple cells?). Mutations foudn in only a few reads are interpreted as part of the machine’s inherent error rate. The trick was to use sequencing of candidate gene regions to a depth of 300 (rather than the usual 50 – 60).

It is possible that some genetically ‘normal’ parents who have abnormal kids are mosaics for the genetic abnormality.

[ Science vol. 342 pp. 564 – 565, 632 -637 ’13 ] Our genomes aren’t perfect. Each human genome contains 120 protein gene inactivating variants, with 20/120 being inactivated in both copies.

The blood of ‘many’ individuals becomes increasingly clonal with age, and the expanded clones often contain large deletions and duplications, a risk factor for cancer.

Some cases of hemimegalencephaly are due to somatic mutations in AKT3.

30% of skin fibroblasts ‘may’ have somatic copy number variations in their genomes.

The genomes of 110 individual neurons from the frontal cortex of 3 people were sequenced. 45/110 of the neurons had copy number variations (CNVs) — ranging in size from 3 megaBases to a whole chromosome. 15% of the neurons accounted for 73% of of the CNVs. However, 59% of neurons showed no CNVs, while 25% showed only 1 or 2.

Big Brother is watching you and you’re telling him everything he needs to know (if you’re on Facebook)

Big Brother is watching you and you’re telling him everything he needs to know (if you’re on Facebook). Here’s why. A computer analysis of your ‘likes’ predicts the results of your completing a 100 item personality questionnaire, better than those whom you’ve friended on Facebook. [ Proc.Natl. Acad. Sci. vol. 112 pp. 1036 – 1040 ’15 ] Has the gory details.

We do know that people lie when completing such things and the MMPI (Minnesota Multiphase Personality Inventory) has a scale for lying. Apparently everyone steals from mommy’s purse at some point, and your lie score on the MMPI goes up if you say you never did.

The study used a mere 86,220 volunteers who completed the 100-item International Personality Item Pool (IPIP) Five-Factor Model of personality questionnaire, measuring traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism. The sample used in this study was obtained from the myPersonality project. myPersonality was a popular Facebook application that offered to its users psychometric tests and feedback on their scores. The data was anonymized and is in the public domain. How normal such an individual can be I leave up to you.

Human personality judgments were obtained from the participants’ Facebook friends, who were asked to describe a given participant using 10 of the 100 items of the IPIP personality measure. E.g. the friends were filling out the 10 items as they thought the subject would (or as they saw the subject).

So it’s the same questionnaire. The paper pitted a computer algorithm based on your Likes to predict your IPIP responses against those of your so-called Facebook friends who presumably know much more about you than just your Facebook Likes. The algorithm won. It didn’t win by much. Computer-based judgments (r = 0.56) correlate more strongly with participants’ self-ratings than average human judgements did (r = 0.49). Surprisingly, neither did terribly well, but then we all know that our judgement of ourselves is usually rather different than others. It’s why city people often tell you what they’re ‘really like’, while Montanans don’t. They know that there are so few people around that they’ll see you again. Your long term behavior will tell them everything they need to know.

Update 31 Jan ’15 — I told the people I play piano trios with about the paper. The cellist (a retired Actuary) had an excellent explanation of why the algorithm was more accurate than the friends individually. See if you can think of the reason.

She notes that the 3 of us interact with each other individually, e.g. we act differently for each of our friends, exposing just the parts of our personalities we choose. They aren’t the same for everyone. Obvious, now that she’s thought of it (did you?)

As usual the Poets have said it better

And would some Power the small gift give us
To see ourselves as others see us!
It would from many a blunder free us,
And foolish notion:

Robert Burns (1786)

We know how to make a mouse dream when we want

Everybody knows abut Rapid Eye Movement sleep (REM sleep) now. It wasn’t always that way. I found out about it in med school when my wife pointed me to a fascinating article in the New Yorker, concerning the work of Dement and Kleitman. Briefly, if you wake someone up during REM, they’ll tell you they’re dreaming. As a budding Neurologist, I actually got an afternoon off from my internship to hear Dement talk. I’d been up most of the previous night, and after a nice lunch they turned the lights off as Dement began showing slides and I promptly feel asleep. After it was over and the lights came back on, the guy next to me asked what I’d been dreaming.

There’s been a huge amount of progress on sleep in the past year.

1. At long last, we may actually have a clue as to why we spend a third of our lives asleep. The short answer is that it is to flush out the brain. For details please see https://luysii.wordpress.com/2013/10/21/is-sleep-deprivation-like-alzheimers-and-why-we-need-sleep-in-the-first-place/

2. A recent paper found an area in the brain, which, when stimulated, takes a sleeping mouse into REM sleep. The technique is yet another use of optogenetics (which is almost sure to win Karl Diesseroth a Nobel). For details please see https://luysii.wordpress.com/2013/05/19/a-certain-nobel-prize/.

Optogenetics gives you the ability (after a lot of molecular biological work) to turn specific sets of neurons on (or off). It was known that a very old area of the brain was involved in consciousness, wake and sleep. Just which areas were crucial for REM was controversial. Prior to optogenetics, lesions were made in various place and the animals studied. Neurologic diagnosis of what part of the brain did what was essentially done this way using the various natural disasters which befall the brain. A stroke here cause language problems, a tumor there, caused visual disturbance etc. etc. It worked well, but always contained an essential ambiguity. If you turn of a switch, a light bulb stops shining. But the switch doesn’t really produce the light although it is necessary.

However, stimulating a given nucleus and shifting an animal from regular sleep to REM sleep is far less ambiguous.

The details are quite technical and probably not comprehensible to most of the readership, but here they are for the neurophysiologists in the audience.

[ Proc. Natl. Acad. Sci. vol. 112 pp. 584 – 589 ’15 ] Cholinergic neurons in the mesopontine tegmentum have been implicated in REM sleep, but lesions of the area have had varying effects on REM. This work shows that selective optogenetic activation of cholinergic neurons in the pedunculopotine tegmentum (PPT) or the laterodorsal tegmentum (LDT) increases the number of REM sleep episodes without changing REM sleep duration. Activating them in either nucleus during NREM induces REM. The work was done in transgenic mice which have extra copies of the vesicular AcCh transporter with increased cholinergic tone.

Monamines (particularly norepinephrine) are alerting, and it has been shown that neurons in LDT are inhibited by seronin in rat and guinea pig.

How little we know

Who would have thought that a random mutagenesis experiment throwing Ethyl Nitroso Urea (ENU) at unsuspecting mice looking for genes using a mutagenesis strategy to identify novel immune regulatory genes would point to a possible treatment for muscular dystrophy? When the experimenters looked at the mutated offspring, they found that the muscles appeared unusually red.

What happened?

You need to know a bit more about muscles. On a very simplistic level there are only two types of muscle fibers, red and white. Carnivores eating chicken know about dark meat and white meat. The dark meat is composed of red fibers, which have that appearance because of large numbers of mitochondria (which are full of iron) giving them the same red appearance as blood (which is also full of iron). In both cases the iron is bound by porphyrin rings. As one might expect, these muscles consume a lot of energy, being postural for the most part. The white meat made of white fibers has muscle which can contract very quickly and strongly, for flight and fight. They don’t have nearly the endurance of red muscle, because they can’t produce energy for the long term.

Humans have the two types of muscle fibers mixed up in each of our muscles.

The ENU had produced a mutation in something called fnip1 (Folliculin INteracting Protein 1). What’s folliculin? It prevents a gene transcription factor (TFE3) from getting into the nucleus. Folliculin prevents an embryonic stem cell from differentiating. It is mutated in the Birt Hogg Dube syndrome which is characterized by many benign hair follicle tumors. What in the world does this have to do with muscular dystrophy? It’s not something someone would start investigating looking for a cure is it? Knock out both copies of folliculin and the embryo dies in utero.

It gets deeper.

What does Fnip1 do to folliculin? It, and its cousin fnip2 form complexes with folliculin. The complex binds an enzyme called AMPK (which is turned on by energy depletion in the cell. AMPK phosphorylates both fnip1 and folliculin. Folliculin binds and inhibits AMPK.

So animals lacking fnip1 have a more activated AMPK. So what? Well AMPK activates a transcriptional coactivator called PGC1alpha (you don’t want to know what the acronym stands for). This ultimately results in production of more mitochondria (recall that AMPK is an energy sensor, and one of the main functions of mitochondria is to produce energy, lots of it).

This ultimately means more red muscle fibers. There is a mouse model of Duchenne dystrophy called the mdx mouse (which has a premature termination codon in the dystrophin protein, resulting in a protein only 27% as long as it should be. That still leaves a lot, as normal dystrophin contains 3,685 amino acids. Knocking out fnip1 in the mdx mice improved muscle function. Impressive !!

I’m quite interested in this sort of work, as I ran a muscular dystrophy clinic from ’72 to ’87 and watched a lot of kids die. The major advance during that time wasn’t anything medical. It came from engineering — lighter braces using newer materials allowed the kids to stay out of wheelchairs longer.

You can read all about it in Proc. Natl. Acad. Sci. vol. 112 pp. 424 – 429 ’15 ] Clearly we know a lot (AMPK, dystrophin, PGC1alpha, fnip1, fnip2, folliculin, TFE3), but what we didn’t know was how in the world they function together in the cell. We’re sure to learn a lot more, but this whole affair was uncovered when looking for something else (immune regulators) using the bluntest instrument possible (throw a mutagen at an animal and see what happens). No one applying for a muscular dystrophy grant would dare to offer the original work as a rationale, yet here we are.

So directed research isn’t always the way to go. Although we know a lot, we still know very little.

Framingham shows us just how there is more to biology than genetics

If you have two copies of a particular variant (rs993609) of the FTO gene (FaT mass and Obesity associated gene) you are likely to weigh 7 pounds more then if you have neither. Pretty exciting stuff for the basic scientist, given the problems obesity causes (or at least is associated with). The study involved 39,000 people [ Science vol. 316 pp. 889 – 894 ’07 ]. At the end of the post, I’ll have a lot of technical stuff about just what FTO is thought to do inside the cell, but that’s not why I’m posting this.

Framingham Massachusetts is a town about 30 miles west of Boston. Thanks to the cooperation of its citizenry, it has taught us huge swaths of human biology since it began nearly 70 years ago. Briefly, The Framingham Health Study (FHS) was initiated in 1948 when 5,209 people were enrolled in the original cohort; since then, the study has come to be composed of four separate but related populations. The Framingham Offspring Study began in 1971, consisting of 5,124 individuals who represented the children of the original cohort population and their spouses. Participants in the offspring study were given physical examinations and detailed questionnaires at regular intervals starting in 1972, with a total of eight waves completed through 2008. The Body Mass Index (BMI) was calculated from measured height and weight. The offspring cohort was born over a 40-y period, with participants ranging in age from their teens to their late 50s at the time of study onset in 1971. In addition to providing survey and examination data, a large fraction of participants (73.0%, 3,742 individuals) had their DNA genotyped using the 100KAffymetrix array (43). Genotypes at the rs9939609 allele of FTO were extracted using PLINK (44) from data contained in the Framingham SHARe database.

Given the same gene, its effects should be constant through time, other things being equal. The following work [ Proc. Natl. Acad. Sci. vol. 112 pp. 354 – 359 ’15 ] mined the Framingham study to see if when you were born mattered to how fat you became if you carried the fat variant. There were 8 waves of data collection data from ’71 to ’08. Those born before ’42 showed less penetrance of the FTO gene.

Figure 1 p.356 is particularly impressive. Everyone became heavier as they got older. This is because height declines with age raising BMI even in the presence of constant weight. As far as I know, the following explanation from another post ( https://luysii.wordpress.com/2013/05/30/something-is-wrong-with-the-model-take-2/is original — “People lose height as they age, yet the BMI is quite sensitive to it (remember the denominator has height squared). The great thing about BMI is that it’s easily measured, and doesn’t rely on what people remember about their weight or their height. Well as a high school basketball player my height was 6′ 1”+, now (at age 75) its 6’0″. So even with constant weight my BMI goes up.

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

What is impressive about figure 1, is that those born before 1942 with two copies of the risk allele weren’t much heavier than those with one or no copies of the risk allele. This was true at all ages measured (remember these people were sequentially followed). Those born after 1942 carrying two copies of the high risk allele were 2 – 4 pounds heavier (again measured at all ages).

This is as good proof as one could hope for that environment affects gene expression, something we all assumed instinctively. There is no way one could repeat the experiment, except to start a new one in the future, which, as this shows, will occur in a different environment, which should make a difference. MDs gradually woke up to the fallacy of using historical rather than concurrent controls particularly in studies of therapies to prevent heart attack and stroke, as the rates of both dropped significantly in the past 50 years, and survival from individual heart attacks and strokes also improved.

So what does FTO actually do? Naturally anyone dealing with strokes wants to know as much as possible about one of the largest risk factors — obesity. What follows is a fairly undigested copy of my notes over the years on papers concerning FTO. I make no attempt to provide the relevant background, although most readers will have some. It’s interesting to see how our knowledge about FTO has grown over the years. Enjoy ! !

*****
[ Science vol. 316 p. 185, 889 – 894 ’07 ] FTO was first found in type II diabetics by looking for single nucleotide polymorphisms distinguishing 1924 UK type II diabetics from 2938 UK controls (were southeast Asians included?). Subsequently, larger populations (3757 type IIs and 5346 controls) were independently studied and the findings replicated. [ Cell vol. 134 p. 714 ’08 ] — The association hasn’t held up in the Han Chinese.

The FTO gene is found on chromosome #16. 16% of white adults have two copies of the variant (46% have one copy). They are 1.67 times more likely to be obese. At this point (13 Apr ’07) no one knows what the gene does.

FTO is a gene of unknown function in an unknown pathway that was originally cloned as a result of a fused-toe mutant mouse, that results from a 1.6 megaBase deletion of mouse chromosome #8. The deletion removes some 6 genes.

[ Cell vol. 131 p. 827 ’07 ] A blurb about something to be published in Science. This work shows that FTO codes for a nucleic acid demethylase. It has the enzymatic activity of a 2 oxo-glutaric acid oxygenase. The enzyme removes methyl groups from 3 methyl thymine (in DNA) 3 methyl uracil (in RNA). The SNPs linking FTO to obesity are in introns in the gene. In mice, the mRNA for FTO is highly enriched in the hypothalamus. Levels of FTO mRNA drop by 60% in fasting mice.

[ Science vol. 318 pp. 1469 – 1472 ’07 ] The Science paper at last. The gene produce catalyzes the Fe++ and 2-oxoglutaric acid dependent demethylation of 3 methyl thymine (which may not be the relevant substrate) in single stranded DNA with production of succinic acid, formaldehyde, and CO2. FTO is found in the nucleus in transfected cells. The mRNA for FTO is most abundant in the brain particularly in hypothalamic nuclei governing energy balance. FTO is inhibited by Krebs cycle intermediates (isn’t 2 oxoglutarate a Krebs cycle intermediate? ) particularly fumaric acid.

[ Science vol. 334 pp. 569 – 571 ’11 ] FTO removes methyl groups from 3 Methylthymine, and 3 methylUridine in single stranded DNA and RNA (ssDNA, ssRNA). The present work shows FTO converts 6 methylamino Adenine to adenine in RNA. FTO associates with speckles containing RNA splicing factors and RNA polymerase II

[ Nature vol. 457 p. 1095 ’09 ] Mice lacking FTO were normal at birth, but at 6 weeks weighed 30 – 40% less than normal mice (or haploinsufficients). This was due to loss of white fat — which was nearly completely absent at 15 months. The mutants ate more (in proportion to their body weight) than normal. On a high fat diet, both groups gained less weight than normals. Mice lacking FTO use more energy while not moving much.

[ Nature vol. 458 pp. 894 – 898 ’09 ] Loss of FTO in mice leads to postnatal growth retardation and a significant reduction both in fat and in lean body mass. The leanness is due to increased energy expenditure and sympathetic cativation, despite decreased sspontaneous motor activity and relative hyperphagia.

[ Proc. Natl. Acad. Sci. vol. 107 pp. 8404 – 8409 ’10 ] Carriers of the fat allele of FTO have smaller brains (8% smaller in the frontal lobes, 12% smaller in the occipital lobes). The brain differences weren’t due to differences in cholesterol, hypertension or white matter hyperintensities. So FTO risk isn’t a surrogate for the metabolic changes of obesity. The study was done in 206 cognitively normal adults (average age 76). Every 1 unit increase in BMI was assocaited with 1 – 1.5% reduction in brain volume in a variety of brain regions.

The highest expression of FTO is in the cerebral cortex. Whether expression in the hypothalamus changes after food deprivation is controversial.

It is known that obesity (BMI > 30) is associated with smaller brains. In this group temporal lobe atrophy was found in people with higher BMI but not in people with risk allele of FTO.

There was no effect of BMI on brain size in noncarriers of the FTO allele. So FTO status may influence the effect of BMI on the brain.

[ Cell vol. 149 pp. 1635 – 1646 ’12 ] A study of just what 6methylamino adenine (m6A) is doing and where in the genome it is doing it. m6A is the physiologically relevant target of FTO. It is found in tRNA, rRNA and mRNA. It fact m6A is found in 7,676 different mRNAs. The modification is markedly increased throughout brain development. m6A sites are enriched near stop codons and in 3′ untranslated regions (3′ UTRs). Even more interestingly, there is an association between m6A and microRNA binding sites in the 3′ UTRs ! ! ! m6A is not enriched at splice junctions. 30% of genes are said to have microRNA binding sites, but 67% of the 3′ UTRs containing m6A have microRNA binding sites. However, the two can’t overlap in the 3′ UTR. Many features of m6A localization are the same in man and mouse.

[ Nature vol. 490 pp. 267 – 272 ’12 ] In some way the SNP rs7202116 in FTO is associated with phenotypic variability per se. No other locus causes BMI variability this way.

[ Proc. Natl. Acad. Sci. vol. 110 pp. 2557 – 2562 ’13 ] FTO is widely expressed, with highest levels in brain, particularly the hypothalamus. FTO expression in the hypothalamus is decreased after a 48 hour fast, and incraeasing after a 10 week exposure to a high fat diet.

Carriers of the obesity promoting allele are hyperphagic and show altered (how?) macronutrient preference. This work shows that cells lacking FTO show decreased activation of the mTORC1 pathway, decreased rates of mRNA translation, and increased autophagy — all of which helps explain the stunted growth seen in man homozygous for FTO mutations.

FTO is rapidly degraded when cells are deprived of amino acids (this decreases TORC1 activity, making it a part of the physiological response to starvation). How this reoates to the demethylase activity of FTO isn’t known (yet). The methylase action is crucial for its ability to sustain mTORC1 activity in the face of amino acid deprivation.

[ Nature vol. 507 pp. 309 – 310, 371 – 375 ’14 ] Amazingly, the association between obesity and FTO involves another gene (IRX3) which is 500 kiloBases away. This was determined by chromosome conformation capture (CCC). The promoter of IRX3 interacts physically interacts with the first intron of FTO — this was found human cell lines, and other organisms. Obesity li9nked SNPs are associated with IRX3 expression in these samples, but not with expression of FTO. Mice lacking a functional copy of IRX3 have 25 – 30% lower body weight than controls (primarily due to loss of fat mass and increase in BMR with browning of white fat.

There is another case — an enhancer in an intron of LMBR1 reglates the developmental gene SHH found over a megaBase away. Mutations in the enhancer can cause limb malformations due to altered SHH expression.

Cancer as the telephone game

An interesting paper just out [ Science vol. 347 pp. 78 – 81 ’14 ] basically says that cancer is just bad luck due to copying errors of the 3.2 megaBase genome when cells divide. It’s a version of the telephone game in which a message is passed around a circle of people getting progressively garbled each time.

The evidence in support of the assertion is that the variation in cancer rates between tissues is strongly related to the number of divisions of the stem cells required to maintain that tissue. For instance the lifetime risk of being diagnosed with cancer is 7% for lung but .6% for brain (about this more later). Risk in the GI tract varies by a factor of 24 (.5% for the esophagus 4.8% for the colon) which is proportional to the number of stem cell divisions undergone during lifetime.

They estimate that at most 1/3 of the variation in risk among tissues is due to environmental factors or inherited predisposition. That’s certainly not to say that you should go ahead and smoke.

The idea makes a lot of sense. Even though the error rate in copying the parental genome to a child is an amazingly low 1/100,000,000 that still is 32 mutations per generation (more from the father than the mother and more from him the older he is, not so for the mother)– for details please see https://luysii.wordpress.com/2012/08/30/how-fast-is-your-biological-clock-ticking-ii-latest-results/.

There is even better evidence for this based on my clinical experience in neurology for 35+ years. The lifetime chance of a brain tumor is stated to be .6%. However in all these years I never saw a brain tumor made of neurons. They were all derived from glia (astrocytoma, glioblastoma) or the coverings of the brain (meningiomas). Why? Essentially neurons in the cerebral cortex (not the deeper parts of the brain) don’t divide. [ Cell vol. 153 pp. 1183 – 1185, 1219 – 1227 ’13, Science vol. 340 pp. 1180 – 1181 ’13 (Editorial) ] Even the parts that do divide add a trivial amount of neurons to the brain (700 neurons a day). Even if you live 100 years — that’s only 100 * 365 * 700 == 26 million neurons, a trivial amount compared to the 100 billion neurons you are estimated to have (this number grows each time I read about it).

You might be interested in how we can make statements like this about new neuron formation in the brain. It’s very clever — Carbon-14 accumulated in the atmosphere between the mid 50s and early 60s as a byproduct of above ground testing of nuclear weapons. Such testing was banned by treaty in 1963 and carbon-14 levels in the atmosphere declined in the following decades to previous low background levels. Carbon-14 is used in archeologic dating because its halflife is 5730 years.

Using postmortem tissue samples of individuals born before and after the nuclear bomb tests, the integration of carbon-14 into genomic DNA was measured. This would have occurred during the cell’s last division cycle. One can calculate the birth dates of different cell types collected from various tissues including brain. The approach is accurate to within a few years. The 5730 year half life of 14-C means that whatever is in human DNA hasn’t had a chance to decay (by much) in 50 years. The amount of carbon-14 in cellular DNA therefore reflects the amount of carbon-14 in the atmosphere when the cells underwent their last division. The amount of carbon-14 in the atmosphere was determined by measuring it in the annual growth rings of pine trees in Sweden — a surrogate for atmospheric carbon-14 levels in the past 60 years. The birthdate of cells is determined as the year the C-14 in them matches those of the pine trees.

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