Category Archives: Philosophical issues raised

A Troublesome Inheritance – IV — Chapter 3

Chapter III of “A Troublesome Inheritance” contains a lot of very solid molecular genetics, and a lot of unfounded speculation. I can see why the book has driven some otherwise rational people bonkers. Just because Wade knows what he’s talking about in one field, doesn’t imply he’s competent in another.

Several examples: p. 41 “”Nonethless, it is reasonable to assume that if traits like skin color have evolved in a population, the same may be true of its social behavior.” Consider yes, assume no.

p. 42 “The society of living chimps can thus with reasonable accuracy stand as a surrogate for the joint ancester” (of humans and chimps — thought to be about 7 megaYears ago) and hence describe the baseline from which human social behavior evolved.” I doubt this.

The chapter contains many just so stories about the evolution of chimp and human societies (post hoc propter hoc). Plausible, but not testable.

Then follows some very solid stuff about the effects of the hormone oxytocin (which causes lactation in nursing women) on human social interaction. Then some speculation on the ways natural selection could work on the oxytocin system to make people more or less trusting. He lists several potential mechanisms for this (1) changes in the amount of oxytocin made (2) increasing the number of protein receptors for oxytocin (3) making each receptor bind oxytocin more tightly. This shows that Wade has solid molecular biological (and biological) chops.

He quotes a Dutch psychologist on his results with oxytocin and sociality — unfortunately, there have been too many scandals involving Dutch psychologists and sociologists to believe what he says until its replicated (Google Diederik Stapel, Don Poldermans, Jens Forster, Markus Denzler if you don’t believe me). It’s sad that this probably honest individual is tarred with that brush but he is.

p. 59 — He notes that the idea that human behavior is solely the result of social conditions with no genetic influence is appealing to Marxists, who hoped to make humanity behave better by designing better social conditions. Certainly, much of the vitriol heaped on the book has come from the left. A communist uncle would always say ‘it’s the system’ to which my father would reply ‘people will corrupt any system’.

p. 61 — the effect of mutations of lactose tolerance on survival on society are noted — people herding cattle and drinking milk, survive better if their gene to digest lactose (the main sugar in milk) isn’t turned off after childhood. If your society doesn’t herd animals, there is no reason for anyone to digest milk after weaning from the breast. The mutations aren’t in the enzyme digesting lactose, but in the DNA that turns on expression of the gene for the enzyme (e.g. the promoter). Interestingly, 3 separate mutations in African herders have been found to do this, and different from the one that arose in the Funnel Beaker Culture of Scandinavia 6,000 yers ago. This is a classic example of natural selection producing the same phenotypic effect by separate mutations.

There is a much bigger biological fish to be fried here, which Wade doesn’t discuss. It takes energy to make any protein, and there is no reason to make a protein to help you digest milk if you aren’t nursing, and one very good reason not to — it wastes metabolic energy, something in short supply in humans as they lived until about 15,000 years ago. So humans evolved a way not to make the protein in adult life. The genetic change is in the DNA controlling protein production not the protein itself.

You may have heard it said that we are 98% Chimpanzee. This is true in the sense that our 20,000 or so proteins are that similar to the chimp. That’s far from the whole story. This is like saying Monticello and Independence Hall are just the same because they’re both made out of bricks. One could chemically identify Monticello bricks as coming from the Virginia piedmont, and Independence Hall bricks coming from the red clay of New Jersey, but the real difference between the buildings is the plan.

It’s not the proteins, but where and when and how much of them are made. The control for this (plan if you will) lies outside the genes for the proteins themselves, in the rest of the genome. The control elements have as much right to be called genes, as the parts of the genome coding for amino acids. Granted, it’s easier to study genes coding for proteins, because we’ve identified them and know so much about them. It’s like the drunk looking for his keys under the lamppost because that’s where the light is.

p. 62 — There follows some description of the changes of human society from hunter gathering, to agrarian, to the rise of city states, is chronicled. Whether adaptation to different social organizations produced genetic changes permitting social adaptation or were the cause of it isn’t clear. Wade says “changes in social behavior, has most probably been molded by evolution, through the underlying genetic changes have yet to be identified.” This assumes a lot, e.g. that genetic changes are involved. I’m far from sure, but the idea is not far fetched. Stating that genetic changes have never, and will never shape society, is without any scientific basis, and just as fanciful as many of Wade’s statements in this chapter. It’s an open question, which is really all Wade is saying.

In defense of Wade’s idea, think about animal breeding as Darwin did extensively. The Origin of Species (worth a read if you haven’t already read it) is full of interchanges with all sorts of breeders (pigeons, cattle). The best example we have presently are the breeds of dogs. They have very different personalities — and have been bred for them, sheep dogs mastifs etc. etc. Have a look at [ Science vol. 306 p. 2172 '04, Proc. Natl. Acad. Sci. vol. 101 pp. 18058 - 18063 '04 ] where the DNA of variety of dog breeds was studied to determine which changes determined the way they look. The length of a breed’s snout correlated directly with the number of repeats in a particular protein (Runx-2). The paper is a decade old and I’m sure that they’re starting to look at behavior.

More to the point about selection for behavioral characteristics, consider the domestication of the modern dog from the wolf. Contrast the dog with the chimp (which hasn’t been bred).

[ Science vol. 298 pp. 1634 - 1636 '02 ] Chimps are terrible at picking up human cues as to where food is hidden. Cues would be something as obvious as looking at the containing, pointing at the container or even touching it. Even those who eventually perform well, take dozens of trials or more to learn it. When tested in more difficult tests requiring them to show flexible use of social cues they don’t

This paper shows that puppies (raised with no contact with humans) do much better at reading humans than chimps. However wolf cubs do not do better than the chimps. Even more impressively, wolf cubs raised by humans don’t show the same skills. This implies that during the process of domestication, dogs have been selected for a set of social cognitive abilities that allow them to communicate with humans in unique ways. Dogs and wolves do not perform differently in a non-social memory task, ruling out the possibility that dogs outperform wolves in all human guided tasks.

All in all, a fascinating book with lots to think about, argue with, propose counterarguments, propose other arguments in support (as I’ve just done), etc. etc. Definitely a book for those who like to think, whether you agree with it all or not.

Old dog does new(ly discovered) tricks

One of the evolutionarily oldest enzyme classes is aaRS (for amino acyl tRNA synthetase). Every cell has them including bacteria. Life as we know it wouldn’t exist without them. Briefly they load tRNA with the appropriate amino acid. If this Greek to you, look at the first 3 articles in https://luysii.wordpress.com/category/molecular-biology-survival-guide/.

Amino acyl tRNA syntheses are enzymes of exquisite specificity, having to correctly match up 20 amino acids to some 61 different types of tRNAs. Mistakes in the selection of the correct amino acid occurs every 1/10,000 to 1/100,000, and in the selection of the correct tRNA every 1/1,000,000. The lower tRNA error rate is due to the fact that tRNAs are much larger than amino acids, and so more contacts between enzyme and tRNA are possible.

As the tree of life was ascended from bacteria over billions of years, 13 new protein domains which have no obvious association with aminoacylation have been added to AARS genes. More importantly, the additions have been maintained over the course of evolution (with no change in the primary function of the synthetase). Some of the new domains are appended to each of several synthetases, while others are specific to a single synthetase. The fact that they’ve been retained implies they are doing something that natural selection wants (teleology inevitably raises its ugly head with any serious discussion of molecular biology or cellular physiology — it’s impossible to avoid).

[ Science vol.345 pp 328 - 332 '14 ] looked at what mRNAs some 37 different AARS genes were transcribed into. Six different human tissues were studied this way. Amazingly, 79% of the 66 in-frame splice variants removed or disrupted the aaRS catalytic domain. . The AARS for histidine had 8 inframe splice variants all of which removed the catalytic domain. 60/70 variants losing the catalytic domain (they call these catalytic nulls) retained at least one of the 13 added domains in higher eukaryotes. Some of the transcripts were tissue specific (e.g. present in some of the 6 tissues but not all).

Recent work has shown roles for specific AARSs in a variety of pathways — blood vessel formation, inflammation, immune response, apoptosis, tumor formation, p53 signaling. The process of producing a completely different function for a molecule is called exaptation — to contrast it with adaptation.

Up to now, when a given protein was found to have enzymatic activity, the book on what that protein did was closed (with the exception of the small GTPases). End of story. Yet here we have cells spending the metabolic energy to make an enzymatically dead protein (aaRSs are big — the one for alanine has nearly 1,000 amino acids). Teleology screams — what is it used for? It must be used for something! This is exactly where chemistry is silent. It can explain the incredible selectivity and sensitivity of the enzyme but not what it is ‘for’. We have crossed the Cartesian dualism between flesh and spirit.

Could this sort of thing be the tip of the iceberg? We know that splice variants of many proteins are common. Could other enzymes whose function was essentially settled once substrates were found, be doing the same thing? We may have only 20,000 or so protein coding genes, but 40,000, 60,000, . . . or more protein products of them, each with a different biological function.

So aaRSs are very old molecular biological dogs, who’ve been doing new tricks all along. We just weren’t smart enough to see them (’till now).

Novels may have only 7 basic plots, but molecular biology continues to surprise and enthrall.

Trouble in River City (aka Brain City)

300 European neuroscientists are unhappy. If 50,000,000 Frenchmen can’t be wrong, can the neuroscientists be off base? They don’t like that way things are going in a Billion Euro project to computationally model the brain (Science vol. 345 p. 127 ’14 11 July, Nature vol. 511 pp. 133 – 134 ’14 10 July). What has them particularly unhappy is that one of the sections involving cognitive neuroscience has been eliminated.

A very intelligent Op-Ed in the New York Times 12 July by psychology professor, notes that we have no theory of how to go from neurons, their connections and their firing of impulses, to how the brain produces thought. Even better, he notes that we have no idea of what such a theory would look like, or what we would accept as an explanation of brain function in terms of neurons.

While going from the gene structure in our DNA to cellular function, from there to function at the level of the organ, and from the organ to the function of the organism is more than hard (see https://luysii.wordpress.com/2014/07/09/heres-a-drug-target-for-schizophrenia-and-other-psychiatric-diseases/) at least we have a road map to guide us. None is available to take us from neurons to thought, and the 300 argue that concentrating only on neurons, while producing knowledge, won’t give us the explanation we seek. The 300 argue that we should progress on all fronts, which the people running the project reject as too diffuse.

I’ve posted on this problem before — I don’t think a wiring diagram of the brain (while interesting) will tell us what we want to know. Here’s part of an earlier post — with a few additions and subtractions.

Would a wiring diagram of the brain help you understand it?

Every budding chemist sits through a statistical mechanics course, in which the insanity and inutility of knowing the position and velocity of each and every of the 10^23 molecules of a mole or so of gas in a container is brought home. Instead we need to know the average energy of the molecules and the volume they are confined in, to get the pressure and the temperature.

However, people are taking the first approach in an attempt to understand the brain. They want a ‘wiring diagram’ of the brain. e. g. a list of every neuron and for each neuron a list of the other neurons connected to it, and a third list for each neuron of the neurons it is connected to. For the non-neuroscientist — the connections are called synapses, and they essentially communicate in one direction only (true to a first approximation but no further as there is strong evidence that communication goes both ways, with one of the ‘other way’ transmitters being endogenous marihuana). This is why you need the second and third lists.

Clearly a monumental undertaking and one which grows more monumental with the passage of time. Starting out in the 60s, it was estimated that we had about a billion neurons (no one could possibly count each of them). This is where the neurological urban myth of the loss of 10,000 neurons each day came from. For details see http://luysii.wordpress.com/2011/03/13/neurological-urban-legends/.

The latest estimate [ Science vol. 331 p. 708 '11 ] is that we have 80 billion neurons connected to each other by 150 trillion synapses. Well, that’s not a mole of synapses but it is a nanoMole of them. People are nonetheless trying to see which areas of the brain are connected to each other to at least get a schematic diagram.

Even if you had the complete wiring diagram, nobody’s brain is strong enough to comprehend it. I strongly recommend looking at the pictures found in Nature vol. 471 pp. 177 – 182 ’11 to get a sense of the complexity of the interconnection between neurons and just how many there are. Figure 2 (p. 179) is particularly revealing showing a 3 dimensional reconstruction using the high resolutions obtainable by the electron microscope. Stare at figure 2.f. a while and try to figure out what’s going on. It’s both amazing and humbling.

But even assuming that someone or something could, you still wouldn’t have enough information to figure out how the brain is doing what it clearly is doing. There are at least 6 reasons.

l. Synapses, to a first approximation, are excitatory (turn on the neuron to which they are attached, making it fire an impulse) or inhibitory (preventing the neuron to which they are attached from firing in response to impulses from other synapses). A wiring diagram alone won’t tell you this.

2. When I was starting out, the following statement would have seemed impossible. It is now possible to watch synapses in the living brain of awake animal for extended periods of time. But we now know that synapses come and go in the brain. The various papers don’t all agree on just what fraction of synapses last more than a few months, but it’s early times. Here are a few references [ Neuron vol. 69 pp. 1039 - 1041 '11, ibid vol. 49 pp. 780 - 783, 877 - 887 '06 ]. So the wiring diagram would have to be updated constantly.

3. Not all communication between neurons occurs at synapses. Certain neurotransmitters are generally released into the higher brain elements (cerebral cortex) where they bathe neurons and affecting their activity without any synapses for them (it’s called volume neurotransmission) Their importance in psychiatry and drug addiction is unparalleled. Examples of such volume transmitters include serotonin, dopamine and norepinephrine. Drugs of abuse affecting their action include cocaine, amphetamine. Drugs treating psychiatric disease affecting them include the antipsychotics, the antidepressants and probably the antimanics.

4. (new addition) A given neuron doesn’t contact another neuron just once as far as we know. So how do you account for this by a graph (which I think allows only one connection between any two nodes).

5. (new addition) All connections (synapses) aren’t created equal. Some are far, far away from the part of the neuron (the axon) which actually fires impulses, so many of them have to be turned on at once for firing to occur. So in addition to the excitatory/inhibitory dichotomy, you’d have to put another number on each link in the graph, about the probability of a given synapse producing and effect. In general this isn’t known for most synapses.

6. (new addition) Some connections never directly cause a neuron to fire or not fire. They just increase or decrease the probability that a neuron will fire or not fire with impulses at other synapses.These are called neuromodulators, and the brain has tons of different ones.

Statistical mechanics works because one molecule is pretty much like another. This certainly isn’t true for neurons. Have a look at http://faculties.sbu.ac.ir/~rajabi/Histo-labo-photos_files/kora-b-p-03-l.jpg. This is of the cerebral cortex — neurons are fairly creepy looking things, and no two shown are carbon copies.

The mere existence of 80 billion neurons and their 150 trillion connections (if the numbers are in fact correct) poses a series of puzzles. There is simply no way that the 3.2 billion nucleotides of out genome can code for each and every neuron, each and every synapse. The construction of the brain from the fertilized egg must be in some sense statistical. Remarkable that it happens at all. Embryologists are intensively working on how this happens — thousands of papers on the subject appear each year.

(Addendum 17 July ’14) I’m fortunate enough to have a family member who worked at Bell labs (when it existed) and who knows much more about graph theory than I do. Here are his points and a few comments back

Seventh paragraph: Still don’t understand the purpose of the three lists, or what that buys that you don’t get with a graph model. See my comments later in this email.

“nobody’s brain is strong enough to comprehend it”: At some level, this is true of virtually every phenomenon of Nature or science. We only begin to believe that we “comprehend” something when some clever person devises a model for the phenomenon that is phrased in terms of things we think we already understand, and then provides evidence (through analysis and perhaps simulation) that the model gives good predictions of observed data. As an example, nobody comprehended what caused the motion of the planets in the sky until science developed the heliocentric model of the solar system and Newton et al. developed calculus, with which he was able to show (assuming an inverse-square behavior of the force of gravity) that the heliocentric model explained observed data. On a more pedestrian level, if a stone-age human was handed a personal computer, his brain couldn’t even begin to comprehend how the thing does what it does — and he probably would not even understand exactly what it is doing anyway, or why. Yet we modern humans, at least us engineers and computer scientists, think we have a pretty good understanding of what the personal computer does, how it does it, and where it fits in the scheme of things that modern humans want to do.

Of course we do, that’s because we built it.

On another level, though, even computer scientists and engineers don’t “really” understand how a personal computer works, since many of the components depend for their operation on quantum mechanics, and even Feynman supposedly said that nobody understands quantum mechanics: “If you think you understand quantum mechanics, you don’t understand quantum mechanics.”

Penrose actually did think the brain worked by quantum mechanics, because what it does is nonAlgorithmic. That’s been pretty much shot down.

As for your six points:

Point 1 I disagree with. It is quite easy to express excitatory or inhibitory behavior in a wiring diagram (graph). In fact, this is done all the time in neural network research!

Point 2: Updating a graph is not necessarily a big deal. In fact, many systems that we analyze with graph theory require constant updating of the graph. For example, those who analyze, monitor, and control the Internet have to deal with graphs that are constantly changing.

Point 3: Can be handled with a graph model, too. You will have to throw in additional edges that don’t represent synapses, but instead represent the effects of neurotransmitters.Will this get to be complicated graph? You bet. But nobody ever promised an uncomplicated model. (Although uncomplicated — simple — models are certainly to be preferred over complicated ones.)

Point 4: This can be easily accounted for in a graph. Simply place additional edges in the graph to account for the additional connections. This adds complexity, but nobody ever promised the model would be simple. Another alternative, as I mentioned in an earlier email, is to use a hypergraph.

Point 5: Not sure what your point is here. Certainly there is a huge body of research literature on probabilistic graphs (e.g., Markov Chain models), so there is nothing you are saying here that is alien to what graph theory researchers have been doing for generations. If you are unhappy that we don’t know some probabilistic parameters for synapses, you can’t be implying that scientists must not even attempt to discover what these parameters might be. Finding these parameters certainly sounds like a challenge, but nobody ever claimed this was an un-challenging line of research.

In addition to not knowing the parameters, you’d need a ton of them, as it’s been stated frequency in the literature that the ‘average’ neuron has 10,000 synapses impinging on it. I’ve never been able to track this one down. It may be neuromythology, like the 10,000 synapses we’re said to lose every day. With 10,000 adjustable parameters you could make a neuron sing the Star Spangled Banner. Perhaps this is why we can sing the Star Spangled Banner.

Point 6: See my comments on Point 5. Probabilistic graph models have been well-studied for generations. Nothing new or frightening in this concept.

“A Troublesome Inheritance” – II – Four Anthropological disasters of the past 100 years

Page 5 of Wade’s book contains two recent pronouncements from the American Anthropological Association stating that “Race is about culture not biology”. It’s time to look at anthropology’s record of the past 100 years. It isn’t pretty.

Start with the influential Franz Boas (1858 – 1942) who taught at Columbia for decades. His most famous student was Margaret Mead.

He, along with his students, felt that the environment was everything for human culture and that heredity had minimal influence. Here’s what Boas did over 100 years ago.

[ Proc. Natl. Acad. Sci. vol. 99 pp. 14622 - 14623, 14436 - 14439 '02 ] Retzius invented the the cephalic index in the 1890s. It is just the widest breadth of the skull divided by the front to back length. One can be mesocephalic, dolichocephalic or brachycephalic. From this index one could differentiate Europeans by location. Anthropologists continue to take such measurements. Franz Boas in 1910 – 1913 said that the USA born offspring of immigrants showed a ‘significant’ difference from their immigrant parents in their cephalic index. This was used to reinforce the idea that environment was everything.

Boas made some 13,000 measurements. This is a reanalysis of his data showing that he seriously misinterpreted it. The genetic component of the variability was far stronger than the environmental. Some 8500 of his 13,000 cases were reanalyzed. In a later paper Boas stated that he never claimed that there were NO genetic components to head shape, but his students and colleagues took the ball and ran with it, and Boas never (publicly) corrected them. The heritability was high in the family data and between ethnic groups, which remains in the American environment.

One of Boas’ students wrote that “Heredity cannot be allowed to have acted any part in history.” The chain of events shaping a people “involves the absolute conditioning of historical events by other historical events.” Hardly scientific statements.

On to his most famous student Margaret Mead (1901 -1978) who later became the head of the American Association for the Advancement of Science (1960). In 1928 she published “Coming of Age in Samoa” about the sexual freedom of Samoan adolescents. It had a big play, and I was very interested in such matters as a pimply adolescent. It fit into the idea that ” “We are forced to conclude that human nature is almost unbelievably malleable, responding accurately and contrastingly to contrasting cultural conditions.”. This certainly fit nicely with the idea that mankind could be reshaped by changing the system — see http://en.wikipedia.org/wiki/New_Soviet_man one of the many fantasies of the left promoted by academia.

Subsequently, an anthropologist (Freeman) went back to Samoa and concluded that Mead had been hoaxed. He found that Samoans may beat or kill their daughters if they are not virgins on their wedding night. A young man who cannot woo a virgin may rape one to extort her into eloping. The family of a cuckolded husband may attack and kill the adulterer. For more details see Pinker “The Blank Slate” pp. 56 –>

The older among you may remember reading about “the gentle Tasaday” of the Philippines, a Stone age people who had no word for war. It was featured in the NY times in the 70s. They were the noble savages of Rousseau in the 20th century. The 1970 ”discovery” of the Tasaday as a ”Stone Age” tribe was widely heralded in newspapers, shown on national television in a National Geographic Society program and an NBC special documentary, and further publicized in ”The Gentle Tasaday: A Stone Age People in the American journalist, John Nance.

In all, Manuel Elizalde Jr., the son of a rich Filipino family, was depicted as the savior of the Tasaday through his creation of Panamin (from presidential assistant for national minorities), a cabinet-level office to protect the Tasaday and other ”minorities” from corrosive modern influences and from environmentally destructive logging companies.It appears that Manuel Elizalde hoodwinked almost everybody by paying neighboring T’boli people to take off their clothes and pose as a ”Stone Age” tribe living in a cave. Mr. Elizalde then used the avalanche of international interest and concern for his Tasaday creation to create the Panimin organization for control over ”tribal minority” lands and resources and ultimately deals with logging and mining companies.

Last but not least is “The Mismeasure of Man” (1981) in which Steven Gould tore apart the work of Samuel Morton, a 19th century Anthropologist who measured skulls. He accused Morton of (consciously or unconsciously) manipulating the data to come up with the conclusions he desired.

Well, guess what. Someone went back and looked at Morton’s figures, and remeasured some of his skulls (which are still at Penn) and found that the manipulation was all Gould’s not Morton’s. I posted about this when it came out 3 years ago — here’s the link http://luysii.wordpress.com/2011/06/26/hoisting-steven-j-gould-by-his-own-petard/.

Here is the relevant part of that post — An anthropologist [ PLoS Biol. doi:10.1371/journal.pbio.1001071;2011 ] went back to Penn (where the skulls in question reside), and remeasured some 300 of them, blinding themselves to their ethnic origins as they did. Morton’s measurements were correct. They also had the temerity to actually look at Morton’s papers. They found that, contrary to Gould, Morton did report average cranial capacities for subgroups of both populations, sometimes on the same page or on pages near to figures that Gould quotes, and therefore must have seen. Even worse (see Nature vol. 474 p. 419 ’11 ) they claim that “Gould misidentified the Native American samples, falsely inflating the average he calculated for that population”. Gould had claimed that Morton’s averages were incorrect.

Perhaps anthropology has gotten its act together now, but given this history, any pronouncements they make should be taken with a lot of salt. In fairness to the field, it should be noted that the debunkers of Boas, Mead and Gould were all anthropologists. They have a heavy load to carry.

“A Troublesome Inheritance” – I

One of the joys of a deep understanding of chemistry, is the appreciation of the ways in which life is constructed from the most transient of materials. Presumably the characteristics of living things that we can see (the phenotype) will someday be traceable back to the proteins, nucleic acids,and small metabolites (lipids, sugars, etc..) making us up.

For the time being we must content ourselves with understanding the code (our genes) and how it instructs the development of a trillion celled organism from a fertilized egg. This brings us to Wade’s book, which has been attacked as racist, by anthropologists, sociologists and other lower forms of animal life.

Their position is that races are a social, not a biological construct and that differences between societies are due to the way they are structured, not by differences in the relative frequency of the gene variants (alleles) in the populations making them up. Essentially they are saying that evolution and its mechanism descent with modification under natural selection, does not apply to humanity in the last 50,000 years when the first modern humans left Africa.

Wade disagrees. His book is very rich in biologic detail and one post about it discussing it all would try anyone’s attention span. So I’m going to go through it, page by page, commenting on the material within (the way I’ve done for some chemistry textbooks), breaking it up in digestible chunks.

As might be expected, there will be a lot of molecular biology involved. For some background see the posts in https://luysii.wordpress.com/category/molecular-biology-survival-guide/. Start with http://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/ and follow the links forward.

Wade won me over very quickly (on page 3), by his accurate and current citations to the current literature. He talks about how selection on a mitochondrial protein helped Tibetans to live at high altitude (while the same mutation those living at low altitudes leads to blindness). Some 25% Tibetans have the mutation while it is rare among those living at low altitudes.
Here’s my post of 10 June 2012 ago on the matter. That’s all for now

Have Tibetans illuminated a path to the dark matter (of the genome)?

I speak not of the Dalai Lama’s path to enlightenment (despite the title). Tall people tend to have tall kids. Eye color and hair color is also hereditary to some extent. Pitched battles have been fought over just how much of intelligence (assuming one can measure it) is heritable. Now that genome sequencing is approaching a price of $1,000/genome, people have started to look at variants in the genome to help them find the genetic contribution to various diseases, in the hopes of understanding andtreating them better.

Frankly, it’s been pretty much of a bust. Height is something which is 80% heritable, yet the 20 leading candidate variants picked up by genome wide association studies (GWAS) account for 3% of the variance [ Nature vol. 461 pp. 458 - 459 '09 ]. This has happened again and again particularly with diseases. A candidate gene (or region of the genome), say for schizophrenia, or autism, is described in one study, only to be shot down by the next. This is likely due to the fact that many different genetic defects can be associated with schizophrenia — there are a lot of ways the brain cannot work well. For details — see http://luysii.wordpress.com/2010/04/25/tolstoy-was-right-about-hereditary-diseases-imagine-that/. or see http://luysii.wordpress.com/2010/07/29/tolstoy-rides-again-autism-spectrum-disorder/.

Typically, even when an association of a disease with a genetic variant is found, the variant only increases the risk of the disorder by 2% or less. The bad thing is that when you lump them all of the variants you’ve discovered together (for something like height) and add up the risk, you never account for over 50% of the heredity. It isn’t for want of looking as by 2010 some 600 human GWAS studies had been published [ Neuron vol. 68 p. 182 '10 ]. Yet lots of the studies have shown various disease to have a degree of heritability (particularly schizophrenia). The fact that we’ve been unable to find the DNA variants causing the heritability was totally unexpected. Like the dark matter in galaxies, which we know is there by the way the stars spin around the galactic center, this missing heritability has been called the dark matter of the genome.

Which brings us to Proc. Natl. Acad. Sci. vol. 109 pp. 7391 – 7396 ’12. It concerns an awful disease causing blindness in kids called Leber’s hereditary optic neuropathy. The ’cause’ has been found. It is a change of 1 base from thymine to cytosine in the gene for a protein (NADH dehydrogenase subunit 1) causing a change at amino acid #30 from tyrosine to histidine. The mutation is found in mitochondrial DNA not nuclear DNA, making it easier to find (it occurs at position 3394 of the 16,569 nucleotide mitochondrial DNA).

Mitochondria in animal cells, and chloroplasts in plant cells, are remnants of bacteria which moved inside cells as we know them today (rest in peace Lynn Margulis).

Some 25% of Tibetans have the 3394 T–>C mutations, but they see just fine. It appears to be an adaptation to altitude, because the same mutation is found in nonTibetans on the Indian subcontinent living about 1500 meters (about as high as Denver). However, if you have the same genetic change living below this altitude you get Lebers.

This is a spectacular demonstration of the influence of environment on heredity. Granted that the altitude you live at is a fairly impressive environmental change, but it’s at least possible that more subtle changes (temperature, humidity, air conditions etc. etc.) might also influence disease susceptibility to the same genetic variant. This certainly is one possible explanation for the failure of GWAS to turn up much. The authors make no mention of this in their paper, so these ideas may actually be (drumroll please) original.

If such environmental influences on the phenotypic expression of genetic changes are common, it might be yet another explanation for why drug discovery is so hard. Consider CETP (Cholesterol Ester Transfer Protein) and the very expensive failure of drugs inhibiting it. Torcetrapib was associated with increased deaths in a trial of 15,000 people for 18 – 20 months. Perhaps those dying somehow lived in a different environment. Perhaps others were actually helped by the drug

Never stop thinking, never stop looking for an angle

Derek Lowe may soon be a very rich man if he owns some Vertex stock. An incredible pair of papers in the current Nature (vol. 505 pp. 492 – 493, 509 – 514 ’14, Science (vol 343 pp. 38 – 384, 428 – 432 ’14) has come up with a completely new way of possibly treating AIDs. Instead of attacking the virus, attack the cells it infects, and let them live (or at least die differently).

Now for some background. Cells within us are dying all the time. Red cells die within half a year, the cells in the lining of your gut die within a week and are replaced. None of this causes inflammation, and the cells die very quietly and are munched up by white cells. They even send out a signal to the white cells called an ‘eat me’ signal. The process is called apoptosis. It occurs big time during embryonic development, particularly in the nervous system. Neurons failing to make strong enough contacts effectively kill themselves.

Apoptosis is also called programmed cell death — the cell literally kills itself using enzymes called caspases to break down proteins, and other proteins to break down DNA.

We have evolved other ways for cell death to occur. Consider a cell infected by a bacterium or a virus. We don’t want it to go quietly. We want a lot of inflammatory white cells to get near it and mop up any organisms around. This type of cell death is called pyroptosis. It also uses caspases, but a different set.

You just can’t get away from teleological thinking in biology. We are always asking ‘what’s it for?’ Chemistry and physics can never answer questions like this. We’re back at the Cartesian dichotomy.

Which brings us to an unprecedented way to treat AIDS (or even prevent it).

As anyone conscious for the past 30 years knows, the AIDS virus (aka Human Immunodeficiency Virus 1 aka HIV1) destroys the immune system. It does so in many ways, but the major brunt of the disease falls on a type of white cell called a helper T cell. These cells carry a protein called CD4 on their surface, so for years docs have been counting their number as a prognostic sign, and, in earlier days, to tell them when to start treatment.

We know HIV1 infects CD4 positive (CD4+) T cells and kills them. What the papers show, is that this isn’t the way that most CD4+ cells die. Most (the papers estimate 95%) CD4+ cells die of an abortive HIV1 infection — the virus gets into the cell, starts making some of its DNA, and then the pyroptosis response occurs, causing inflammation, attracting more and more immune cells, which then get infected.

This provides a rather satisfying explanation of the chronic inflammation seen in AIDS in lymph nodes.

Vertex has a drug VX-765 which inhibits the caspase responsible for pyroptosis, but not those responsible for apoptosis. The structure is available (http://www.medkoo.com/Anticancer-trials/VX-765.html), and it looks like a protease inhibitor. Even better, VX-765 been used in humans (in phase II trials for something entirely different). It was well tolerated for 6 weeks anyway. Clearly, a lot more needs to be done before it’s brought to the FDA — how safe is it after a year, what are the long term side effects. But imagine that you could give this to someone newly infected with essentially normal CD4+ count to literally prevent the immunodeficiency, even if you weren’t getting rid of the virus.

Possibly a great advance. I love the deviousness of it all. Don’t attack the virus, but prevent cells it infects from dying in a particular way.

Never stop thinking. Hats off to those who thought of it.

The death of the synonymous codon – III

The coding capacity of our genome continues to amaze. The redundancy of the genetic code has been put to yet another use. Depending on how much you know, skip the following two links and read on. Otherwise all the background to understand the following is in them.

http://luysii.wordpress.com/2011/05/03/the-death-of-the-synonymous-codon/

http://luysii.wordpress.com/2011/05/09/the-death-of-the-synonymous-codon-ii/

There really was no way around it. If you want to code for 20 different amino acids with only four choices at each position, two positions (4^2) won’t do. You need three positions, which gives you 64 possibilities (61 after the three stop codons are taken into account) and the redundancy that comes with it. The previous links show how the redundant codons for some amino acids aren’t redundant at all but used to code for the speed of translation, or for exonic splicing enhancers and inhibitors. Different codons for the same amino acid can produce wildly different effects leaving the amino acid sequence of a given protein alone.

The following recent work [ Science vol. 342 pp. 1325 - 1326, 1367 - 1367 '13 ] showed that transcription factors bind to the coding sequences of proteins, not just the promoters and enhancers found outside them as we had thought.

The principle behind the DNAaseI protection assay is pretty simple. Any protein binding to DNA protects it against DNAase I which chops it up. Then clone and sequence what’s left to see where proteins have bound to DNA. These are called footprints. They must have removed histones first, I imagine.

The work performed DNAaseI protection assays on a truly massive scale. They looked at 81 different cell types at nucleotide resolution. They found 11,000,000 footprints all together, about 1,000,000 per cell type. In a given cell type 25,000 were completely localized within exons (the parts of the gene actually specifying amino acids). When all the codons of the genome are looked at as a group, 1/7 of them are found in a footprint in one of the cell types.

The results wouldn’t have been that spectacular had they just looked at a few cell types. How do we know the binding sites contain transcription factors? Because the footprints match transcription factor recognition sequences.

We know that sequences around splice sites are used to code for splicing enhancers and inhibitors. Interestingly, the splice sites are generally depleted of DNAaseI footprints. Remember that splicing occurs after the gene has been transcribed.

At this point it isn’t clear how binding of a transcription factor in a protein coding region influences gene expression.

Just like a work of art, there is more than one way that DNA can mean. Remarkable !

A new parameter for ladies to measure before choosing a mate — testicular volume

I’m amazed that they actually did this work [ Proc. Natl. Acad. Sci. vol. 110 pp. 15746 - 15751 '13 ] but they did. From Atlanta Georgia, the home of the Southern Gentleman. You do have to wonder what sort of wimps would permit this type of work. 70 such individuals were found, still cohabiting with the mother. Clearly a skewed distribution as 65/70 were actually married. No mention of any effect of the sex of the offspring on what they found.

Here’s what they did

Testosterone levels and testicular volume predicted how much parenting a male actually did (based on self-reports from the two parents). Functional MRI on viewing a picture of the offspring also predicted the degree of male parenting.

So which way do you think it went?

The bigger the testicles and the higher the testosterone, the less parenting the father did. Similarly the less activation of one area of the brain in response to a picture of their chile, the less parenting.

So ladies, you may get a macho dude for a mate, but don’t expect much help.

Your fetus can hear you

There have been intimations of this. 10 years ago [ Proc. Natl. Acad. Sci. vol. 100 pp. 11702 - 11705 '13 ] Full term infants were shown to respond more to human speech played forwards than when the tape was reversed. A clever technique called optical topography was used — it is noninvasive, and relies on the thinness of the neonatal skull.

Now comes [ Proc. Natl. Acad. Sci. vol. 110 pp. 15145 - 15150 '13 ] The authors presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. (What this means is that there is (again a noninvasive) way of measuring brain activity — and there is greater activity when an unexpected variant is presented. We are novelty seekers from the get go.

Also there was a significant correlation between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Even more impressive, the learning effect was generalized to other types of similar speech sounds not included in the training material.

We know know that the infant brain gets tuned to the language they hear. Japanese infants can hear the r sound at birth (again brain electrical activity responds to it) but after 6 – 9 months of listening to a language without the sound, their brain becomes tuned so they can’t.

So maybe pregnant ladies should listen to Mozart

The most interesting paper I’ve read in the past 5 years — finale

Recall from https://luysii.wordpress.com/2013/06/13/the-most-interesting-paper-ive-read-in-the-past-5-years-introduction-and-allegro/ that if you knew the ones and zeroes coding for the instruction your computer was currently working on you’d know exactly what it would do. Similarly, it has long been thought that, if you knew the sequence of the 4 letters of the genetic code (A, T, G, C) coding for a protein, you’d know exactly what would happen. The cellular machinery (the ribosome) producing output (a protein in this case) was thought to be an automaton similar to a computer blindly carrying out instructions. Assuming the machinery is intact, the cellular environment should have nothing to do with the protein produced. Not so. In what follows, I attempt to provide an abbreviated summary of the background you need to understand what goes wrong, and how, even here, environment rears its head.

If you find the following a bit terse, have a look at the https://luysii.wordpress.com/category/molecular-biology-survival-guide/ . In particular the earliest 3 articles (Roman numerals I, II and III) should be all you need).

We’ve learned that our DNA codes for lots of stuff that isn’t protein. In fact only 2% of it codes for the amino acids comprising our 20,000 proteins. Proteins are made of sequences of 20 different amino acids. Each amino acid is coded for by a sequence of 3 genetic code letters. However there are 64 possibilities for these sequences (4 * 4 * 4). 3 possibilities tell the machinery to quit (they don’t code for an amino acid). So some amino acids have as many as 6 codons (sequences of 3 letters) for them — e.g. Leucine (L) has 6 different codons (synonymous codons) for it while Methionine (M) has but 1. The other 18 amino acids fall somewhere between.

The cellular machine making proteins (the ribosome) uses the transcribed genetic code (mRNA) and a (relatively small) adapter, called transfer RNA (tRNA). There are 64 different tRNAs (61 for each codon specifying an amino acid and 3 telling the machine to stop). Each tRNA contains a sequence of 3 letters (the antiCodon) which exactly pairs with the codon sequence in the mRNA, the same way the letters (bases if you’re a chemist) in the two strands of DNA pair with each other. Hanging off the opposite end of each tRNA is the amino acid the antiCodon refers to. The ribosome basically stitches two amino acids from adjacent tRNAs together and then gets rid of one tRNA.

So which particular synonymous codon is found in the mRNA shouldn’t make any difference to the final product of the ribosome. That’s what the computer model of the cell tells us.

Since most cells are making protein all the time. There is lots of tRNA around. We need so much tRNA that instead of 64 genes (one for each tRNA) we have some 500 in our genome. So we have multiple different genes coding for each tRNA. I can’t find out how many of each we have (which would be very nice to know in what follows). The amount of tRNA of each of the 64 types is roughly proportional to the number of genes coding for it (the gene copy number) according to the papers cited below.

This brings us to codon usage. You have 6 different codons (synonymous codons) for leucine. Are they all used equally (when you look at every codon in the genome which codes for leucine)? They are not. Here are the percentages for the usages of the 6 distinct leucine codons in human DNA: 7, 7, 13, 13, 20, 40. For random use they should all be around 16. The most frequently appearing codon occurs as often as the least frequently used 4.

It turns out the the most used synonymous codons are the ones with the highest number of genes for the corresponding tRNA. Makes sense as there should be more of that synonymous tRNA around (at least in most cases) This is called codon bias, but I can’t seem to find the actual numbers.

This brings us (at last) to the actual paper [ Nature vol. 495 pp. 111 - 115 '13 ] and the accompanying editorial (ibid. pp. 57 – 58). The paper says “codon-usage bias has been observed in almost all genomes and is thought to result from selection for efficient and accurate translation (into protein) of highly expressed genes” — 3 references given. Essentially this says that the more tRNA around matching a particular codon, the faster the mRNA will find it (le Chatelier’s principle in action).

An analogy at this point might help. When I was a kid, I hung around a print shop. In addition to high speed printing, there was also a printing press, where individual characters were selected from boxes of characters, placed on a line (this is where the font term leading comes from), and baked into place using some scary smelling stuff. This was so the same constellation of characters could be used over and over. For details see http://en.wikipedia.org/wiki/Printing_press. You can regard the 6 different tRNAs for leucine as 6 different fonts for the letter L. To make things right, the correct font must be chosen (by the printer or the ribosome). Obviously if a rare font is used, the printer will have to fumble more in the L box to come up with the right one. This is exactly le Chatelier’s principle.

The papers concern a protein (FRQ) used in the circadian clock of a fungus — evolutionarily far from us to be sure, but hang in there. Paradoxically, the FRQ gene uses a lot of ‘rare’ synonymous codons. Given the technology we have presently, the authors were able to switch the ‘rare’ synonymous codons to the most common ones. As expected, the organism made a lot more FRQ using the modified gene.

The fascinating point (to me at least) is that the protein, with exactly the same amino acids did not fulfill its function in the circadian clock. As expected there was more of the protein around (it was easier for the ribosome machinery to make).

Now I’ve always been amazed that the proteins making us up have just a few shapes, something I’d guess happens extremely rarely. For details see http://luysii.wordpress.com/2010/10/24/the-essential-strangeness-of-the-proteins-that-make-us-up/.

Well, as we know, proteins are just a linear string of amino acids, and they have to fold to their final shape. The protein made by codon optimization must not have had the proper shape. Why? For one thing the protein is broken down faster. For another it is less stable after freeze thaw cycles. For yet another, it just didn’t work correctly in the cell.

What does this mean? Most likely it means that the protein made from codon optimized mRNA has a different shape. The organism must make it more slowly so that it folds into the correct shape. Recall that the amino acid chain is extruded from one by one from the ribosome, like sausage from a sausage making machine. As it’s extruded the chain (often with help from other proteins called chaperones) flops around and finds its final shape.

Why is this so fascinating (to me at least)? Because here,in the very uterus of biologic determinism, the environment (how much of each type of synonymous tRNA is around) rears its head. Forests have been felled for papers on the heredity vs. environment question. Just as American GIs wrote “Kilroy was here” everywhere they went in WWII, here’s the environment popping up where no one thought it would.

In addition the implications for protein function, if this is a widespread phenomenon, are simply staggering.

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