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.

An interesting way to study the hydrophobic effect between protein surfaces

Protein interaction domains haven’t been studied to nearly the extent they need to be, and we know far less about them than we should. All the large molecular machines of the cell (ribosome, mediator, spliceosome, mitochondrial respiratory chain) involve large numbers of proteins interacting with each other not by the covalent bonds beloved by organic chemists, but by much weaker forces (van der Waals,charge attraction, hydrophobic entropic forces etc. etc.).

Designing drugs to interfere (or promote) such interactions will be tricky, yet they should have profound effects on cellular and organismal physiology. Off target effects are almost certain to occur (particularly since we know so little about the partners of a given motif). Showing how potentially useful such a drug can be, a small molecule inhibitor of the interaction of the AIDs virus capsid protein with two cellular proteins (CPSF6, TNPO3) the capsid protein must interact with to get into the nucleus has been developed. (Unfortunately I’ve lost the reference). For more about the host of new protein interaction domains (and potential durable targets) just discovered please see https://luysii.wordpress.com/2015/01/04/microexons-great-new-drugable-targets/

Hydrophobic ‘forces’ are certain to be important in protein protein interactions. A very interesting paper figured out a way to measure them using atomic force microscopy (AFM). [ Nature vol. 517 pp. 277 – 279, 347 – 350 ’15 ]. This is particularly interesting to me because entropy has nothing to do with the force as measured. I’ve always assumed that the the hydrophobic force was entropic, similar to the force exerted by rubber when you stretch it. It’s what pushes hydrophobic side chains into the interior of proteins (e.g water doesn’t have to decrease its entropy by organizing itself to solvate hydrophobic side chains). Not so in this case.

The authors prepared self-assembled monolayers using dodecyl thiol (CH3 (CH2) 10 CH2 SH) bound to gold. Every now and then an amino group or a guanido group was placed at the other end of the thiol. This allowed them to produce a mixture of hydrophobic groups (60%) and ionic species (NH4+ or guanidinium ions) within nanoMeters of the hydrophobic regions. The amine and the guanidino groups were the same distance as the hydrocarbon ends from the gold surface. A gold atomic force microscope (AFM) with a hydrophobic tip (the same C(12) moiety), was then used to measure the adhesive force between the tip and the surface in aqueous solution.

This is important because it is a measurement not a theoretical calculation (apologies Ashutosh). This is particularly useful since water is so complex that we don’t have a good understanding (potential function) for it.

Methanol was added (which eliminated most of the hydrophobic interactions). Sensitivity to methanol was taken as a signature of the hydrophobic component of the force. The pH could be manipulated, so the R – NH2 could be charged to R -NH3+, ditto for guanidinium to the uncharged species.

So guess what the effect of amino and guanidine groups were on the hydrophobic interaction. I was rather surprised.

The strength of hydrophobic interactions between the mixed monolayers and the tip doubled when neutral amino groups found within nanoMeters of hydrophobic regions are charged to form R -NH3+ ions by lowering the pH. A similarly placed guanidinium ion eliminates the hydrophobic interactions at all pHs. So the effect of the two side chains (NH2 for lysine, guanidinium for arginine) is opposite.

They note that the ammonium ion is well hydrated, but guanidinium is hydrated only at the edges of the plane (where the electrons are) but not above it. This allows guanidinium an amphipathic behavior, which is why it can be a denaturant (did you know this? I didn’t).

I’m sure that the effect of negative ions (e.g. carboxyl groups) and every other conceivable side chain will be studied in the future.

Thus hydrophobicity is not an intrinsic property of any given nonPolar domain. It can be changed by functional groups within 10 Angstroms.. So placing a charged group near a hydrophobic domain, should allow tuning of the hydrophobic driving force. I’d be amazed if this isn’t found to be the case evolutionarily.

They also studied some wierd looking stuff resembling proteins (beta peptides { e.g. the amino and carboxyl groups on adjacent carbons rather than the same one as with alpha amino acids) with weird side chains which are known to adopt an amphipathic helical conformation. THe nonpolar side chains were trans 2 aminocyclohexanecarboxylic acid (ACHC), and the cationic side chains were beta3 homolysine. Why didn’t they use something more natural. The peptide forms an ACHC rich nonPolar square domain 10 Angstroms on a side with a polar patch on the other side of the helix.

So it’s a fascinating piece of work with large implications for the design of drugs attacking protein protein interfaces.

What a difference a change of administration makes

This is not a scientific post. Having a son who majored in journalism educated me to the various and sundry ways news is slanted. Here in Massachusetts, the administration changed from 8 years of Democratic governance to Republican. Liberals shouldn’t fret as the legislature remains 90% Democratic.

For the past 8 years the local press has been carrying water for increased spending and taxes. We have been regaled with headlines decrying “Draconian cuts” and budget gaps. Such was the case with the outgoing administration, where stories began appearing last December about budget gaps on the order of 700 million. I wrote the reporter asking what this represented in terms of the total budget and never got anything back, ditto for the response from one of the few Republican senators still standing. Throughout the decade I could never get a straight answer as to the actual amount of the budget and the year to year changes in same.

Now we have the following http://www.masslive.com/politics/index.ssf/2015/01/gov_charlie_baker_massachusetts_765_million_budget_gap.html#incart_m-rpt-1, and from the same reporter who never responded last month. Here’s what the reporter was forced to report.

“tax revenues are coming in on target, with an approximately 4.5 percent increase over last year. However, state spending is on target to increase by 7.3 percent“. It will be amusing to see if ‘Draconian cut” stories appear as they have in the past. Mr. Micawber always had a budget gap and so do we.

Along the same lines here’s a heartwarming headline, to disguise an appeal for higher taxes. http://finance.yahoo.com/news/obama-channels-inner-robin-hood-as-rich-get-richer-154533477.html.

Derek Lowe always regrets posting anything remotely political on his blog “In the Pipeline”. Hopefully I won’t. If you must respond, please be civil.

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.

The New York Times and NOAA flunk Chem 101

As soon as budding freshman chemists get into their first lab they are taught about significant figures. Thus 3/7 = .4 (not .428571 which is true numerically but not experimentally) Data should never be numerically reported with more significant figures than given by the actual measurement.

This brings us to yesterday’s front page story (with the map colored in red) “2014 Breaks Heat Record, Challenging Global Warming Skeptics“. Well it did if you believe that a .02 degree centigrade difference in global mean temperature is significant. The inconvenient fact that the change was this small was not mentioned until the 10th paragraph. It was also noted there that .02 C is within experimental error. Do you have a thermometer that measures temperatures that exactly? Most don’t, and I doubt that NOAA does either. Amusingly, the eastern USA was the one area which didn’t show the rise. Do you think that measurements here are less accurate than in Africa, South America Eurasia? Could it be the other way around?

It is far more correct to say that Global warming has essentially stopped for the past 14 years, as mean global temperature has been basically the same during that time. This is not to say that we aren’t in a warm spell. Global warming skeptics (myself included) are not saying that CO2 isn’t a greenhouse gas, and they are not denying that it has been warm. However, I am extremely skeptical of models predicting a steady rise in temperature that have failed to predict the current decade and a half stasis in global mean temperature. Why should such models be trusted to predict the future when they haven’t successfully predicted the present.

It reminds me of the central dogma of molecular biology years ago “DNA makes RNA makes Protein”, and the statements that man and chimpanzee would be regarded as the same species given the similarity of their proteins. We were far from knowing all the players in the cell and the organism back then, and we may be equally far from knowing all the climate players and how they interact now.

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.

Do enzymes chase their prey?

Do enzymes chase their prey? At first thought, this seems ridiculous. However people have been measuring diffusion of substances in water for over a century. Even Einstein worked on it (his paper on Brownian motion). So it’s fairly easy to measure the diffusion of an enzyme in water. Several enzymes (catalase — one of the most efficient enzymes known, and urease) diffuse faster when their substrate is present. [ Nature vol. 517 pp. 149 – 150, 227 – 230 ’15 ] The hydrolysis of urea by urease and the conversion of H2O2 to O2 and water by catalase enhances the molecular diffusion of the enzymes (this is called anomlous diffusion).If you inhibit catalase enzymatic activity using azide the anomalous diffusion disappears (even though there’s still plenty of H2O2 around). This work also showed that the rate of diffusion of catalase, urease and 2 more ezymes correlates with the heat produced by the reaction catalyzed.

Heating the catalytic center of catalase (using a short laser pulse) produces the same anomalous diffusion. Proteins exist in a world in which Brownian motion is governed by viscous forces rather than by inertia, so coasting (a la Galileo and Newton’s law of inertia) isn’t an option — continuous force generation is required.

Heat generated from each catalytic cycle could be transmitted through the enzyme as a pressure wave. For this to happen the catalytic center must be NOT at the center of mass of the enzyme, so the pressure wave will create differential stress at the enzyme solvent interface (which should propel the enzyme). They call this the chemoacoustic effect.

Molecular dynamics simulations suggest that the transmission of energy through a protein can be quite fast (5 Angstroms/picoSecond) and nonuniformly distributed.

Some enzymes have a near perfect catalytic efficiency. Every time a substrate hits them, the substrate is converted to product. Examples include catalase, acetyl cholinesterase, fumarase, and carbonic anhydrase. There are 100 million to a billion collisions per mole per second in solution.

Could this be a product of evolution (to make enzymes actively search out substrates?). Note, this won’t work if the catalytic center of the enzyme is in the center of mass.

I doubt that much catalytic efficiency is gained by having a huge protein molecule sluggishly move through the cytoplasm. Why? The molecular mass of H2O2 is 19 Daltons (vs. 18 for water), so it moves slightly more slowly but water moves at 20C in water at 590 meters/second. Of course it doesn’t get very far before it bumps into another water molecule and gets deflected.

Is there an ace physical chemist out there who can put numbers on this. I couldn’t believe that I couldn’t find a simple expression for the relation between the diffusion coefficient and the mass of the diffuser, ditto for the atomic volume of a water molecule, although I’m guessing that it’s pretty close to the length of the H – O bond (.95 Angstroms) giving a mass of 3.6 cubic Angstroms. I wanted this so I could see how much room to roam a water molecule has.

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.

Microexons, great new drugable targets

Some very serious new players in cellular and tissue molecular biology have just been found. They are very juicy drugable targets, not that targeting them will be easy. If you don’t know what introns, exons and alternate splicing are, it’s time to learn. Go to https://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/ read and follow the links forward. It should be all you need to comprehend the following.

The work came out at the tail end of 2014 [ Cell vol. 159 pp. 1488 – 1489, 1511 -1523 ’14 ]. Microexons are defined as exons containing 50 nucleotides or less (the paper says 3 – 27 nucleotides). They have been overlooked, partially because their short length makes them computationally difficult to find. Also few bothered to look for them as they were thought to be unfavorable for splicing because they were too short to contain exonic splicing enhancers. They are so short that it was thought that the splicing machinery (which is huge) couldn’t physically assemble at both the 3′ and 5′ splice sites. So much for theory, they’re out there.

What is a cell and tissue differentially regulated alternative splicing event? It’s the way a given mRNA can be spliced together one way in tissue/cell #1 and another in tissue/cell #2 producing different proteins in each. Exons subject to tissue specific alternative splicing are significantly UNDERrepresented in well folded domains in proteins. Instead they are found in regions of protein disorder more frequently than one would expect by chance. Typically these regions are on the protein surface. The paper found that the microexons code for short amino acid motifs which typically interact with other proteins and ligands. 3 – 27 nucleotides lets you only code for 1 – 9 amino acids.

One well known example of a short interaction motif is RGD (for Arginine Glycine Aspartic acid in the single letter amino acid code). The sequence is found in a family of surface proteins (the integrins) with at least 26 known members. These 3 amino acids are all that is needed for the interns to bind to a variety of extracellular molecules — collagen, fibrin, glycosaminoglycans, proteoglycans. So a 3 amino acid sequence on the surface of a protein can do quite a bit.

Among a set of analyzed neural specific exons (e. g. they were only spliced that way in the brain) found in known disordered regions of the parent protein, 1/3 promoted or disrupted interactions with partner proteins. So regulated exon splicing might specify tissue and cell type specific protein interaction networks (Translation: they might explain why tissues look different even when they express the same genes). The authors regard microExon inclusion/exclusion as protein surface microsurgery.

The paper has found HUNDREDS of evolutionarily highly conserved microexons from RNA-Seq data sets (http://en.wikipedia.org/wiki/RNA-Seq) in various species. Many of them impact neurogenesis and brain function. Regulation of microExons changes significantly during neuronal differentiation. Although microexons represent only 1% of the alternate splice sites seen, they constitute ‘up to’ 1/3 of all evolutionarily conserved neural-regulated alternative splicing between man and mouse.

The inclusion in the final transcript of most identified neural microExons is regulated by a brain specific factor nSR100 (neural specific SR related protein of 100 kiloDaltons)/SRRM4 which binds to intronic enhancer UGC motifs close to the 3′ splice sites, resulting in their inclusion. They are ‘enhanced’ by tissue specific RBFox proteins. nSR100 is reduced in Autism Spectrum DIsorder (really? all? some?). nSR100 is strongly coexpressed in the developing human brain in a gene network module M2 which is enriched for rare de novo ASD assciated mutations.

MicroExons are enriched for lengths which are multiples of 3 nucleotides (implying strong selection pressure to preserve reading frames). The microExons are also enriched in charged amino acids. Most microExons show high inclusion at late stages of neuronal differentiation in genes associated with axon and synapse function. A neural specific microExon in Protrudin/Zfyve27 increases its interaction with Vesicle Associated membrane protein associated Protein VAP) and to promote neurite outgrowth. A 6 nucleotide neural microExon in Apbb1/Fe65 promotes an interaction with Kat5/Tip60. Apbb1 is an adaptor protein functioning in neurite outgrowth.

So inclusion/exclusion of microExons can alter the interactions of proteins involved in neurogenesis. Misregulation of neural specific microexons has been found in autism spectrum disorder (what hasn’t? Pardon the cynicism).

Protein interaction domains haven’t been studied to nearly the extent they need to be, and we know far less about them than we should. All the large molecular machines of the cell (ribosome, mediator, spliceosome, mitochondrial respiratory chain) involve large numbers of proteins interacting with each other not by the covalent bonds beloved by organic chemists, but by much weaker forces (van der Waals,charge attraction, hydrophobic entropic forces etc. etc.).

Designing drugs to interfere (or promote) such interactions will be tricky, yet they should have profound effects on cellular and organismal physiology. Off target effects are almost certain to occur (particularly since we know so little about the partners of a given motif). Showing how potentially useful such a drug can be, a small molecule inhibitor of the interaction of the AIDs virus capsid protein with two cellular proteins (CPSF6, TNPO3) it must interact with to get into the nucleus has been developed. (Unfortunately I’ve lost the reference)

My cousin married a high school dropout a few years ago. Not to worry — he dropped out of high school to go to college, and has a PhD in Electrical Engineering from Berkeley and has worked at Bell labs. He was very interested in combining his math and modeling skills with my knowledge of neurology to make some models of CNS function. I demurred, as I thought we knew too little about the brain to come up with models (which I generally distrust anyway). The basic problem was that I felt we didn’t know all the players in the brain and how they fit together.

MicroExons show this in spades.

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