Friday, October 23, 2015

Why is the human vagina so big?

We are obsessed with penis and testicle size. Yet, we can barely say "vagina" and when we do we're usually talking about the vulva.

Everyone's come across some article somewhere on-line that is thrilled to share how big human penises really are, for primates, and to explain why they evolved to be so big. It's not really the length, but the girth. Alan Dixson is your go-to on this. He's conservative in his assessment of the literature on penis size and even he concedes that human penis "circumference is unusual when compared to the penes of other hominoids (apes)" (p. 65 in Sexual Selection and the Origins of Human Mating Systems).

A favorite explanation for the big phallus is female mate choice, that females selectively make babies with males who have larger and, presumably, more pleasurable semen delivery devices. This is backed up by studies. When life size projections of naked men are shown to female subjects, they say they find the ones with bigger ones to be more attractive. [This is exactly how mate choice works where I live, how about you?]

Other explanations include male competition. If you can deliver your package to the front yard but the other guy can deliver to the front door, his is more likely to be carried inside the house first. Or, if he can steal away what you just delivered, then, again, his package has yours beat. Thanks to his big penis he's more likely to pass on his winning penis genes than you are to pass on your loser penis genes. Loser.

All this is just terribly fun to write about and I'm not even going nuts (gah) like they do. And they do. They really do. And all over the Internet they do: "Evolution of human penis" gets 53,000 hits just on alone, and about 832,000 on Google.

But doesn't it make sense that for a penis to be somewhat useful it has to be somewhat correlated to vagina size?

I'm talking about all penises in the universe and all vaginas too. Sure there's variation, but a penis can't be too wide. It helps to be long, probably, but it can't be too long.

So neither pleasure nor psychology need matter at all, just function associated with some sort of fit. Pleasure and psychology are never invoked to explain penis morphology in other animals. If anything, it's the cornucopia of horrifying, not pleasing, animal penises that begs for evolutionary explanations.

Wouldn't you explain the size and shape of the key by the size and shape of the lock? So wouldn't it be a little more scientifically sound to hypothesize that the human penis is sized and shaped like that because it fits well into the human vagina?

Sure, it gets chicken-and-eggy or turtles-all-the-way-downy, but c'mon. Isn't it a bit obvious that the privates that fit inside the other privates are probably correlated? You'd think that even the people who have never had intercourse would default to this explanation for the evolution of the human penis.

Figure 2.  Examples of genital covariation in waterfowl.
Figure 2. Examples of genital covariation in waterfowl.
(A) Harlequin duck (Histrionicus histrionicus) and (B) African goose (Anser cygnoides), two species with a short phallus and no forced copulations, in which females have simple vaginas as in Fig 1a. (C) Long-tailed duck (Clangula hyemalis), and (D) MallardAnas platyrhynchos two species with a long phallus and high levels of forced copulations, in which females have very elaborate vaginas (size bars = 2 cm). ] = Phallus, * = Testis, ★ = Muscular base of the male phallus, ▹ = upper and lower limits of the vagina.

But we're rarely, if ever, told that human penises are relatively girthy because human vaginas are. It's always about male competition or female preference.

Sure, we may be a little weird compared to our close relatives for not having a baculum (penis bone), and maybe that's the sort of thing you want to explain for whatever reason, but does human penis size and shape need a uniquely human story?

Assuming it's correlated to the vagina like it probably is in many other species,* then no it doesn't... unless the size and shape of the human vagina has an exceptional story.

Does it? We wouldn't know. There are zero (look!) articles titled "Why is the human vagina so big?"

Until right now.

Here we go. If we were going to answer it the same way we've long explained the human penis, and other animal penis shapes, then we've got a few ideas...

Because walking upright made the vagina conspicuous and males thought a bigger vagina was better. Because big vaginas outcompete small ones at catching sperm. Because of male pleasure from coitus with a big vagina. Because of heat dissipation or thermoregulation. Because of a tradeoff with brain size.

And of course, we'd need to demonstrate that the human vagina is in fact larger, relative to body size, than the vaginas of other primates. Regardless, a sound answer to the question of vagina size and shape focuses on childbirth, wouldn't you say? She's got to be big enough to push out a baby and, for humans, it's a great big baby. 

So if there's an exceptionally human story for the great big human penis, that exceptional story originates not in a woman's orgasms, not in her pornographic thoughts or her lustful eyes, but in her decidedly unsexy "birth canal."

And I dug up a nice little note to explain this to us all written by Dr. Bowman, a gynecologist, back in 2008 for the Archives of Sexual Behavior

That note is magnificent. It starts out giving the only vagina-size-based, not to mention childbirth-based, explanation for human penises that I can find in the literature (which is thankfully cited by Dixson in his book mentioned above). But it still manages to bring the explanation beyond the vagina and onto another proud triumph: "In sum, man’s larger penis is a consequence of his larger brain."

After you clean up the coffee you just spat onto your computer screen, you can read it all for yourself up there in the figure.

Guess who didn't read it? That study in PNAS, mentioned above, that showed women naked penises, got a high attractive score for the big ones, and thinks that's evidence for mate choice now, today, let alone back when (I'm going to speculate that) women had a tiny bit less of it.

Point is, the literature rages on with the special explanations for the big penis with nary a big vagina in sight.

But you heard it here, at least.

Childbirth is why the human vagina is so big and, consequently, why the male penis is so big. It's pretty straightforward. Yet we're still left scratching our heads as to why the penis question endures.

Is evolutionary science averse to big vaginas?

Does nobody love a big vagina?

Because that's just ridiculous. Everybody came from one.

*Unfortunately a few searches led me to find no cross-species comparisons of mammalian vagina lengths or any vaginal measures. It may be out there, but I haven' t found it. I found some measures for bitches... DOGS! And some heifers... COWS! So I've got to compile some data if I'm to do this properly. Baby size might be a way to do this.

**UPDATE. p. 73 in Dixson has Figure 4.3 with nine primate species' penile and vaginal lengths plotted. Thanks Patrick C for reminding me where I'd seen something like this and where to point readers!

Thursday, October 22, 2015

My grandmother's dementia and me

My father's mother had Alzheimer's disease, or dementia of some sort, as did her sister.  Both lived with us at different times when I was a child, my great-aunt until she died in the bedroom upstairs, and my grandmother until she was impossible for my parents to care for, at which time they found a very kind, very patient woman with a big house in the country, and she went to live there.

These two sisters, the only children in their family, were always close.  They both worked all their lives, and were extremely competent and very kind.  My great-aunt never married; her fiancé had gone off to fight in the Spanish-American war, but died during an outbreak of yellow fever in Florida before he ever got to Cuba.  But, she lived with a cousin for many years.  When my parents finally cleaned out the apartment after my great aunt died, one of the things they found in the attic was a skull that must have once been used for teaching anatomy.  No one had any clue how it ended up in that attic.  My parents have displayed in their living room for most of my life.  My mother's theory, after years of living with it, is that this is the skull of a poor man who was suffering from an abscessed tooth, and he shot himself in the head because he couldn't stand the pain.  Here's a sketch.

Sketch by A Buchanan

My grandmother married and had one child, my father.  My grandparents, my great-aunt and her cousin all lived perhaps half an hour from us, in the town where my father had grown up, and my grandfather drove them all to visit us on Sunday afternoons.  He loved driving -- he enjoyed taking my sisters and me for drives in the country. What I remember most about these drives was the overwhelming odor of his strong cigars.  (He used to enjoy shooting woodchucks, too, happy to be doing farmers such a favor.  I remember going with him and my grandmother once on such an outing, but I refused to take a shot, which disappointed him.  He would steady his gun on the roof of the car, aim and shoot.  He draped the one woodchuck he killed the day I was with him over the gate into the field he'd shot it in, so that the farmer would take note.  One Sunday when they came to visit, there was a bullet hole in the roof of the car, over the passenger side -- I don't remember that that was ever explained.)

Dementia does unpredictable things to people.  My great-aunt -- Aunt, we called her, as my father had -- was always cheerful and sweet, if a bit confused.  Every morning she would ask where she was, but she was still able to play cribbage with us, she loved having us comb her long thin hair, past grey, now yellowed, and pin it into a bun.  I don't remember that she ever fussed about anything.

My grandmother, on the other hand, was distraught with worry from the moment she woke, to the moment she went to bed, and probably long after that.  She would sit at the kitchen table all day every day, every few minutes asking the same worried questions in the same frantic way.  She was miserable.  Occasionally she was able to access a part of her brain that reminded her that she was confused, and that made things even worse.

Apart from being two different versions of the same heart wrenching story that could be told by so many people, this raises several questions.  Was this two sisters with very different forms of the same disease?  Or, did they have two different diseases?

And, did the fact that both his mother and his aunt had dementia mean that my father was at higher risk of dementia himself?  Apparently not, as he is now in his late 80's, still very active, very engaged, mentally and even physically.  In turn, does this mean that my sisters and I don't have to worry about dementia ourselves?

Or is it secular trends in Alzheimer's disease that we should pay attention to?
One measure of a condition's impact is its prevalence.  That is the fraction of the population at a given point in time that is affected.  A recent BBC Radio 4 program, More or Less, discussed changes in Alzheimer's prevalence over time, after a paper reporting (among many other things) decreased prevalence of dementia in the UK was published in The Lancet ("Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition," Murray et al.). According to the study, prevalence of dementia in British people over age 65 has declined by more than 20% in the last 20 years; it's currently about 7 percent of that segment of the population.

This is in striking contrast to a recent report in the UK that estimates that 1/3 -- 33%!-- of the British children born in 2015 will have dementia in later life.  Tim Harford, presenter of More or Less, pointed out, though, that it's odd that this number was taken seriously by anyone, given that it is equivalent to thinking that predictions made 100 years ago, when AIDS wasn't known, antibiotics not yet discovered, and so on, would have any credibility. And, the 1/3 estimate was based on 20 year old data.  (A quick check of prevalence of dementia in the UK is a bit confusing -- many sites caution that the number of people with Alzheimer's disease is rising rapidly.  It's an Alzheimer's time bomb, they warn.  But, given that the population is both aging and increasing, this isn't, in itself, a surprise, or very meaningful in relation to individual biological risk because, again, it's the fraction of the population that is affected that is the significant statistic.  To be clearer, if more people live longer, even the same age-specific risk of getting a disease will lead to more people with the disease, that is, higher prevalence in the population.  Of course, the number of affected individuals is relevant to the health care burden.)

How predictable is dementia?
Carol Brayne, one of hundreds of authors on the Lancet report and interviewed for More or Less, speculates that the reported fall in prevalence has to do with changes in 'vascular health', as incidence of heart attacks and stroke have fallen as well.  She suggests that it seems as though the things we have been doing in western countries to prevent cardiovascular disease have been working.

But of course this assumes we know the cause of dementia, and that it's in some sense a cardiovascular disease.  But, we don't understand the cause nearly well enough to say this, and in fact, like most chronic diseases, dementia is many different conditions, with many different causes.

The genetic causal factors related to Alzheimer's disease include mutations in a few genes, but these account for only a fraction of cases.  Mutations in the two presenillin genes can lead to early onset Alzheimer's. The most commonly discussed genetic risk factor has to do with the E4 allele in the ApoE gene, whose physiology is related to fat transport in the blood.  It seems to be associated with the development of plaque in brains of people with late onset (60s and over) Alzheimer's, but the association is complex, people without the E4 allele also develop plaque, and people with plaque may not have dementia, and the causal mechanisms are unclear.  Risk seems to depend on whether one carries one or two copies of the E4 allele, and seems to be higher for women than for men, and is apparently affected by environmental factors, but it does seem to raise risk from something like 10-15% in people over 80 to 30-50%.

What this means, even if the statistics were reliable, the risk estimates stable, and environmental contributions minimal, is that it is obvious that even having two copies of the risk allele is not a guarantee of Alzheimer's disease. And, in some populations having two copies isn't associated with Alzheimer's at all (Nigeria, e.g.).  In addition, while the association with increased risk has long been described, the physiology is still not understood. GWAS have reported other genetic risk factors, but not nearly as consistently as ApoE4, nor as strong.

The reported decline in dementia prevalence is not new; we blogged in 2013 about dramatically decreasing rates in the UK, as well as in Denmark, as reported by Gina Kolata then.  So, how can it be declining rapidly, but the strongest risk factor we know of is genetic -- and the frequency of this variant is not changing enough to even begin to account for the data?  Or, is Carol Brayne right that dementia is a vascular disease, and vascular diseases are on the decline, so Alzheimer's is, too?

Indeed, even the definition of whether you 'have' Alzheimer's or not is changeable and not precise, and researchers don't even agree on what an Alzheimer's brain looks like.  A good discussion of these various factors, including social and economic aspects and the history of studies of Alzheimer's, is a book The Alzheimer Conundrum, by Margaret Lock, a fine medical anthropologist at McGill in Canada (and friend of ours).  

Can Alzheimer's be prevented?
The causes of Alzheimer's disease are so poorly understood that it's said that the best prevention is to exercise, quit smoking and maintain a social life.  Very generic advice that could apply to a lot of things!  If we don't know what causes it, and there are probably environmental risk factors, which we don't really understand, relevant past environmental agents are unknown, future environments impossible to predict, and genetic risk factors not good predictors, then we certainly don't know how to predict population prevalence rates, not to mention who is most likely to develop the disease.  (NB: this is pertinent to late-onset dementia; early-onset is more likely to have a genetic cause, and is thus more likely to be predictable.)

Given the experience of two generations in my family, should I or shouldn't I worry about developing dementia?  If my grandmother and great-aunt had the ApoE4 risk allele, my father may or may not, and my sisters and I may or may not.  If they did and my father does, it's a good example of an allele with "incomplete penetrance," for which either genetic background or environmental risk factors or both are also necessary.  Which makes predicting dementia difficult, whether or not we were to have the risk allele. If they didn't have it, something else caused their dementia, and we have no idea what that was.  Indeed, they were both social, never smoked, and walked to work for decades.

To me, as to most people, dementia is frightening.  But, obviously, my family history is useless in terms of determining my risk -- my grandmother had it, my father doesn't.

Still, every time I forget someone's name, I think of my grandmother.

Tuesday, October 20, 2015

Unknowns, yes, but are there unknowables in biology?

The old Rumsfeld jokes about the knowns and unknowns are pretty stale by now, so we won't really indulge in beating that dead horse.  But in fact his statement made a lot of sense.  There are things we think we know (like our age), things we think we don't know but might know (like whether there will be a new message in our inbox when we sign onto email), and things we don't know but don't know we don't know (such as how many undiscovered marine species there are). Rumsfeld is the subject of ridicule not for this pronouncement per se (at least to those who think about it), because it is actually reasonable, but for other things that he is said to have done or said (or failed to say) in regard to American politics.

Explaining what we don't know is a problem!  Source: Google images

The unknowns may be problems, but they are not Big problems.  What we don't know but might know are at least within the realm of learning.  We may eventually stumble across facts we don't know but don't yet even know are there.  The job of science is to learn what we know we don't know and even to discover what we don't yet know that we don't know.  We think there is nothing 'inside' an electron or photon, but there may be if we some day realize that possibility.  Then the guts of a photon will become a known unknown.

However, there's another, even more problematic, one may say truly problematic kind of mystery: things that are actually unknowable.  They present a Really Big problem.  For example, based on our understanding of the current understanding of cosmology, there are parts of the universe that are so far away that energy (light etc.) from them simply has not, and can never, reach us.  We know that the details of this part of space are literally unknowable, but because we have reasonably rigorous physical theory we think we can at least reliably extrapolate from what we can see to the general contents (density of matter and galaxies etc.) of what we know must exist but cannot see.  That is, it's literally unknowable but theoretically known.

However, things like whether life exists out there are in principle unknowable.  But at least we know very specifically why that is so.  In the future, most of what we can see in the sky today is, according to current cosmological theories, going to become invisible as the universe expands so that the light from these visible but distant parts will no longer be able to reach us.  If there are any living descendants, they will know what was there to see and its dynamics and we will at least be able to make reasonable extrapolations of what it's like out there even though it can no longer be seen.

There are also 'multiverse' theories of various sorts (a book discussing these ideas is Our Mathematical Universe, by Mark Tegmark).  At present, the various sorts of parallel universes are simply inaccessible, even in principle, so we can't really know anything about them (or, perhaps, even whether they exist).  Not only is electromagnetic radiation not able to reach us so we can't observe, even indirectly, what was going on when that light was emitted from these objects, but our universe is self-contained relative to these other universes (if they exist).

Again, all of this is because of the kind of rigorous theory that we have, and the belief that if that theory is wrong, there is at least a correct theory to be discovered--Nature does work by fixed 'laws', and while our current understanding may have flaws the regularities we are finding are not imaginary even if they are approximations to something deeper (but comparably regular). In that sense, the theory we have tells us quite a lot about what seems likely to be the case even if unobserved. It was on such a basis that the Higgs boson was discovered (assuming the inferences from the LHC experiments are correct).

What about biology?
Biology has been rather incredibly successful in the last century and more.  The discoveries of evolution and genetics are as great as those in any other science.  But there remain plenty of unknowns about biological evolution and its genomic basis that are far deeper than questions about undiscovered species.  We know that these things are unknown, but we presume they are knowable and will be understood some day.

One example is the way that homologous chromosomes (one inherited each of a person's parents) line up with each other in the first stage of meiosis (formation of sperm and egg cells).  How do they find each other?  We know they do line up when sex cells are produced, and there are some hypotheses and bits of relevant information about the process, but we're aware of the fact that we don't yet really know how it works.

Homologous chromosomes pair up...somehow.  Wikimedia, public domain.

Chromosomes also are arranged in a very different 3-dimensional way during the normal life of every cell.  They form a spaghetti-like ball in the nucleus, with different parts of our 23 pairs of chromosomes very near to each other.  This 'chromosome conformation', the specific spaghetti ball, shown schematically in the figure, varies among cell types, and even within a cell as it does different things.  The reason seems to be at least in part that the juxtaposed bits of chromosomes contain DNA that is being transcribed (such as into messenger RNA to be translated into protein) in that particular cell under its particular circumstances.
Chromosomes arrange themselves systematically in the nucleus.  Source: image by Cutkosky, Tarazi, and Lieberman-Aiden from Manoharan, BioTechniques, 2011
It is easy to discuss what we don't know in evolution and genetics and we do that a lot here on MT. Often we critique current practice for claiming to know far more than is actually known, or, equally seriously, making promises to the supporting public that suggest we know things that in truth (and in private) we know very well that we don't know.  In fact, we even know why some things that we promise are either unknown or known not to be correct (for example, causation of biological and behavioral traits is far more complex than is widely claimed).

There are pragmatic reasons why our current system of science does this, which we and many others have often discussed, but here we want to ask a different sort of question:  Are there things in biology that are unknowable, even in principle, and if so how do we know that?  The answer at least in part is 'yes', though that fact is routinely conveniently ignored.

Biological causation involves genetic and environmental factors.  That is clearly known, in part because DNA is largely an inert molecule so any given bit of DNA 'does' something only in a particular context in the cell and related to whatever external factors affect the cell.  But we know that the future environmental exposures are unknown, and we know that they are unknowable.  What we will eat or do cannot be predicted even in principle, and indeed will be affected by what science learns but hasn't yet learned (if we find that some dietary factor is harmful, we will stop eating it and eat something else).  There is no way to predict such knowledge or the response to it.

What else may there be of this sort?
A human has hundreds of billions of cells, a number which changes and varies among and within each of us.  Each cell has a slightly different genotype and is exposed to slightly different aspects of the physical environment as well.   One thing we know that we cannot now know is the genotype and environment of every cell at every time.  We can make some statistical approximations, based on guessing about the countless unknowns of these details, but the numbers of variables will exceed that of stars on the universe and even in theory cannot be known with knowable precision.

Unlike much of physics, the use of statistical analytic techniques is inapt, also to an unknowable degree.  We know that not all cells are identical observational units, for example, so that aggregate statistics that are used for decision-making (e.g., significance tests) are simply guesses or gross assumptions whose accuracy is unknowable.  This is in principle because each cell, each individual is always changing.  We might call these 'numerical unknowables', because they are a matter of practicality rather than theoretical limits about the phenomena themselves.

So are there theoretical aspects of biology that in some way we know are unknowable and not just unknown?  We have no reason, based on current biological theory, to suspect the kinds of truly unknowables, analogous to cosmology's parallel universes.  One can speculate about all sorts of things, such as parallel yous, and we can make up stories about how quantum uncertainty may affect us. But these are far from having the kind of cogency found in current physics.

Our lack of comparably rigorous theory relative to what physics and chemistry enjoy leaves open the possibility that life has its own knowably unknowables. If so, we would like at least to know what those limits may be, because much of biology relates to practical prediction (e.g., causes of disease). The state of knowledge in biology, no matter how advanced it has become, is still far from adequate to address the question of the levels of knowable things that may eventually be knowable, but also what the limits to knowability are.  In a sense, unlike physics and cosmology, in biology we have no theory that tells us what we cannot know.

And unlike physics and cosmology, where some of these sorts of issues really are philosophical rather than of any practical relevance to daily life, we in biology have very strong reasons to want to know what we can know, and what we can promise....but perhaps also unlike physics, because people expect benefits from biological research, strong incentives not to acknowledge limits to our knowledge.

Friday, October 9, 2015

The Elephant (not) in the Cancer Ward

Recently, Tomasetti and Vogelstein (the latter a senior and highly regarded cancer geneticist) suggested that most cancer is due just to bad luck.  We discussed that study here.  When cells divide, DNA is copied, but that is a molecular process that isn't perfect (see discussion of Wednesday's Nobel Prize in Chemistry, e.g., for the discovery of DNA repair mechanisms and their association with cancer).  There are mutation detection mechanisms of various sorts (the BRCA1 gene whose mutations are associated with breast and some other cancers, is one with that sort of function).  The more at-risk cell divisions, the more mutations, and the higher the likelihood that one cell will experience a combination of mutations that (along with inherited variation) transforms the cell into the founder of a cancer.  T and V's assertion based on statistical analysis of numbers of cells at risk, their division rate for given tissues, and age of onset patterns, was that random mutation was a major contributor to cancer, rather than inherited genotype or environmental exposures, which they argue would account for this substantial fraction of cases.

Naturally, those whose grant fortunes depending on the idea that cancer is 'genetic' and/or 'environmental' roared in opposition to an idea that could threaten their perspective (and empires). Some of the T and V paper's statistical methods were questioned, and perhaps their paper was over-stated or less definitive than claimed.  Nobody can doubt that genetic variation and environmental exposures that could cause cells to be more likely to experience mutations, play a role in cancer.  But in any practical sense, it is hard to deny that luck plays a role (even with environmental exposures, because if they cause mutations, they basically strew them randomly across the genome, rather than causing them in any particular gene, etc.).

But we mentioned an important issue then that had been raised 40 years ago by epidemiologist Richard Peto.  Essentially it is that other mammals, like mice, experience a similar array of cancer types, with similarly increasing risk with age....but that increase is roughly calibrated with their life span. In fact, mice have far fewer stem cells in, say, their intestine or blood than humans, but their risk of cancer in these tissues increases far more rapidly (in years) than does human risk, though we have orders of magnitude more at risk cells and cell divisions.  This became known as Peto's Paradox.  It has not really been answered though there are some attempts to determine how it is that different species, of different sizes, calibrate their cancer risk in relation to their observed typical lifespan.

"Elephas maximus (Bandipur)" by Yathin S Krishnappa - Own work. Licensed under CC BY-SA 3.0 via Commons - 

For example a 2014 paper in Nature Reviews Genetics by Gorbunova et al. documents the very different typical lifespans of rodent species, and suggests some plausible genetic mechanisms that may protect the longer-lived species from cancer.  There must be some such mechanism, or else we misunderstand something very important in the carcinogenesis process.

Now a new commentary has been discussed in the NY Times of a JAMA paper, that makes similar genetic arguments for the very out-of-line cancer-free longevity of elephants.  Based on their numbers of at-risk cells, elephants should drop over with cancer at a very young age, but instead they typically live for a very long time.  How can this be?

The JAMA authors, Abegglen et al., found that a gene, called TP53, that is clearly related (when mutated) to cancer susceptibility in humans and in experimental assays, at least in part because it detects and effectively kills misbehaving mutated cells.  The study included humans with Li Fraumeni syndrome (LFS), a genetic disorder that greatly increases the risk of developing cancer, susceptibility to which has long been known to be associated with variants in TP53, and blood samples from Asian and African elephants.  

The study needs close scrutiny for methodological issues, but the authors make what they feel, reasonably, is a relevant finding.  There is only one copy of the TP53 gene in humans, but in elephants there are 20.  In blood cell assays this gene's activity was higher than in humans.  The inference is that elephants' longevity relative to cancer is due to this gene. If that is indeed the (or at least, an) explanation for the elephants' cancer-related longevity, it raises some other important questions, which should at least raise eyebrows and the need for ever-present skepticism.

Questions raised by the results

As in the rodent paper cited above, single-gene mechanisms for complex traits are appealing and publication-worthy, but in a sense such claims raise questions about themselves.  Elephants live long lives relative to other diseases that essentially have little if anything to do with cancer.  One can think of heart disease, dementia, stroke, kidney failure, liver disease, neuromuscular and joint disease, and waning immune systems.  Are these traits all due to having more TP53?  That seems unlikely.  

Alternatively, apparently whales are known not to have multiple TP53 duplicates, and I don't know about other very large animals like rhinos, giraffes, and so on.  A standard argument would be that in ecological circumstances when natural selection favors longer lives for some species, it uses whatever mechanism happens to be available--that is, selection has no foresight and can't just choose genes to duplicate.  Each species will have experienced the longevity advantage in its own local time, place, and ecosystem.  Just as the genes whose mutation yields resistance to malaria in humans vary from continent to continent, so will longevity-related genes favored by selection

So, Peto's Paradox remains curious.  If each species has its own protective mechanism (and perhaps several for its different organ and physiological systems), then we can account in a reasonable way for longevity patterns.  There is no need to find, or even to expect the same thing in all species' evolution: variation in response to selection can vary by organ system, species, and location even among species.  This is exactly the sort of thing that we should expect when we think of the complexity of genomic mechanisms--and what has consistently been found by genome mapping studies (GWAS) of late onset traits (and, for that matter, even early onset ones).

In turn, that means that each paper that claims subtly or overtly to have found 'the' or even a widespread important mechanism related to aging needs to be taken circumspectly.  Aging and lifespans are complex phenomena.  We will learn from each example we document, as with GWAS results, that a simple anti-aging strategy can't be inferred.  It's not likely to be a single magic bullet.

Tuesday, October 6, 2015

The Blind Men and the Elephant -- a post-modern parable

It's an ancient parable; a group of blind men are lead to an elephant and asked to describe what they feel.  One feels a tusk, another a foot, a third the tail, and so on, and of course they disagree entirely about what it is they are feeling. This tale is usually used as an illustration of the subjectivity of our view of reality, but I think it's more than that.

I heard a talk by Anthropologist Agustin Fuentes here at Penn State the other day, on blurring the boundaries between science and the humanities.  He used the parable to illustrate why science needs the humanities and vice versa; each restricted view of the world is enhanced by the other to become complete.

But, this assumes that the tales that science tells, and the tales that the humanities tell are separate but equally true -- scientists feel the tail, humanities feel the tusk and accurately report what they feel.  Once they listen to each other's tales, they can describe the whole elephant.

"Blind monks examining an elephant" by Hanabusa Itchō (Wikipedia)

But I don't think so.  I don't think that all that scientists are missing is a humanities perspective, and vice versa.  I think in a very real sense we're all blind all of the time, and there's no way to know what we're missing and when it matters.  You feel the tusk, and you might be able to describe it, but you have no clue what it's made of.  Or, you feel the tail but you have no idea what the elephant uses it for, if anything.

Here's my own personal version of the same parable -- some years ago we purchased a new landline with answering machine.  Oddly, we have a lot of power outages here, and it seemed that every time I set the time and day on the answering machine, we'd have another outage and the time and day would disappear, having to be set once again.  I decided that was a nuisance, and I stopped setting time and day.

The next time the machine said we had a message, I listened to it, but it was blank. There was no message!  Naturally enough (I thought), I concluded that the time and day had to be set for the machine to record a message.  Unhappy consumers, we contacted the maker, and they said no, the machine should record the message anyway.  Which of course it would have if the caller had left a message, as was proven the next time someone called on unknown day at unknown time and ... left a message.

My conclusion was reasonable enough for the data I had, right? It just happened not to be based on adequate data (aka reality).  But, we always think we've got enough data to draw a conclusion, no matter how much we're in fact missing.  This is true in epidemiology, genetics, medical testing, the humanities, interpersonal relationships; we think we know enough about our partner to commit to marrying him or her, but half of us turn out to be wrong.  Indeed, if all you've seen are white swans, you'll conclude that all swans are white -- until you see your first black one.

No, you say, we did power tests and we know we've got enough subjects to conclude that gene X causes disease Y.  But, it's possible that all your subjects are from western Europe, or even better, England, say, and what you've done is identify a gene everyone shares because they share a demographic history.  You won't know that until you look at people with the same disease from a different part of the world -- until you collect more data.  Until you see your first black swan.

But, you say, no one would make such an elementary mistake now -- you've drawn you controls from the same population, and they will share the same population-specific allele, so differences between cases and controls will be disease-specific.  But, western Europe is a big area, and even England is heterogeneous, and it's possible that everyone with your disease is more closely related than people without.  So, you really might have identified population structure rather than a disease allele but you can't know, until you collect more data -- you look at additional populations, or more people in the same population.

Even then, say you look at additional populations and you don't find the same supposedly causal allele.  You can't know why -- is it causal in one population and not another?  Is it not causal in any population, and your initial finding merely an artifact of ill-conceived study design?

Without belaboring this particular example any further, I hope the point is clear.  You feel the tail, but that doesn't tell you everything about the tail.  But you can't know what you're missing until you ask more questions, and gather more data.

Darwin explained inheritance with his idea of gemmules.  He was wrong, of course, but he had no way to know how or why, and it wasn't until Mendel's work was rediscovered in 1900 that people could move on.  Everything we know about genetics we've learned since then, but that doesn't mean we know everything about genetics.  But theories of inheritance (and much else) don't include acknowledgement of glaring holes: "My theory is obviously inadequate because, as always, there is a lot we don't yet understand but we don't know what that is so I'm leaving gaps, but I don't know how big or how many."  And, in a related issue that we write about frequently here, it's also true that instead of coming clean, we often claim more than we know (and often we know what we're doing in doing so).

Even very sophisticated theories just 15 or 20 years ago had no way to include, say, epigenetics, or the importance of transcribed but untranslated RNAs (that is, RNA not coding for genes but doing a variety of other things, some of them still unknown), or interfering RNAs, and so on, and we have no idea today what we'll learn tomorrow.  But, like the blind men, we act as though we can draw adequate conclusions from the data we've got.

Science is about pushing into the unknown.  But, because it's unknown, we have no idea how far we need to push.  I think in most cases, there's always further, we're never done, but we often labor under the illusion that we are.  Or, that we're close.

But, should ductal cancer in situ, a form of breast cancer, be treated?  And how will we know for sure?  Systems biology sounds like a great idea, but how will we ever know we've taken enough of a given system into account to explain what we're trying to explain?  Will physicists ever know whether the multiverse, or the symmetry theory is correct (whatever those elusive ideas actually mean!)?

Phlogiston was once real, as were miasma and phrenology, the four humors, and the health benefits of smoking.  It's not that we don't make progress -- we do now know that smoking is bad for our health (even if only 10% of smokers get lung cancer; ok, smoking is associated with a lot of other diseases as well, so better not to smoke) -- but we've always got the modern equivalent of phlogiston and phrenology.  We just don't know which they are.  We're still groping the elephant in the dark.

Monday, October 5, 2015

Life in 'trans'-it: Why genomic causation is often so elusive

We are in a time when genes are in the daily news, with reports of how this gene or that gene is related to disease, evolution, race, ancestry, and even social behavior.  But what are 'genes', and what do they do?  This is so often presented--in classes, even at higher levels of education--as a simple story presenting genes as bits of DNA that code for a protein, and proteins the molecules that do the functions of life.  We are still heavily influenced by the pioneering work of Gregor Mendel, who did his famous experiments with peas more than 150 years ago.  So, we still think of genes as elements with one or more variant states in a population, transmitted from parents to offspring, which cause some trait (he studied traits like size, shape, or color in his pea plants to try use this fact to breed better agricultural crops).

Mendel's intentionally focused, single-cause approach opened the way for an understanding of the mechanisms of inheritance and enabled one of the most powerful research strategies in all of science. But the idea of one gene and one function is a 19th century legacy that has put a conceptual cage around our thinking ever since.  Mendelian inheritance and its terms (like dominance and recessiveness, and even some of his notation) are still around, and indeed it all is rather ubiquitous even at the university level.  But we now know better, and can do better, and the many discoveries of the last century in biology and genetics present us with many 'mysterious' facts, basically unanticipated by the long, persistent shadow of Mendel's well-chosen simplifications.  It requires some thinking outside the Mendelian box to understand what they might mean.  

The cis image of the world
DNA is located in the nucleus of our cells, but where does genetic function take place?  The usual Mendelian way of thinking is that the action occurs in a particular place in our DNA where a 'gene' is. The gene codes for protein and (usually) has nearby DNA sequences that regulate the gene's usage---turning on its expression by transcribing the gene into messengerRNA.  That is, the gene itself determines how it's used.  It's in a given place in our DNA, and the presence of a complex of regulatory proteins that attach to nearby sequence cause the gene to be transcribed into messenger RNA, which exits the nucleus and is in turn translated into an amino acid chain specified by the sequence.  The amino acid chain is then folded up into a functional protein.

This local, focal view of gene action is what is called a cis perspective.  The Latin origin has a meaning like 'right here', or 'on this side'.  The specifics of this process differ depending on the gene, as no two genes work exactly alike, but the variation in the details is not central to the main point here,  the widespread perception of genes  as modular, chromosomally local self-standing functional units.

But this common idea of how genes work is inaccurate--it's a fundamentally inaccurate way to understand genes and genomic function.

The fundamental nature of life in trans-it
DNA is itself essentially an inert molecule.  It doesn't do anything by itself.  In turn that means that each nucleotide, and that means each new mutational change, cannot be said to have a function or effect, or effect size, on its own.  It only has an effect in terms of its interactions with other aspects of the genome in the same cell, other materials in that cell, that cell in its respective organ and that organ in the organism as a whole, and indeed all of this in relation to environmental factors. While some gene-regulatory regions are near a coding gene, and act in cis, most function involves things elsewhere, on the same chromosome or on others.  This is the trans causal world of life, and it means we cannot really understand what's 'here' without knowing what's elsewhere.

Indeed even Darwinian evolution is fundamentally an ecological phenomenon--it's about organisms' resources, threats, mates, and so on, at any given time.  As well as luck, there may be many levels and aspects of life that are about competition for resources and so on, that are important to survival and reproduction.  But cooperating, in the sense of appropriate interaction, is by far the most prevalent, immediate, and vital aspect of life (Richard Dawkins' ideological 'selfish gene' excessive assertions notwithstanding).

Trans means cooperation in life and evolution
Trans interactions are just that: interactions.  That means multiple components working together, which involves the 'right' combinations in the 'right' time and the 'right' cellular place.  By 'right' I mean functionally viable.  During development and subsequent live, organisms require suitable expression patterns of genes and the dispersion and processing pattern of gene products.  If this combinatorial action--this cooperation--doesn't occur to a suitable degree, the organism fails and its reproduction is reduced.  The extent of this failure depends on the nature of the combinatorial action.

In this sense, trans interactions may be reproductively better or worse and that can be a form of natural selection, whose result is the 'better' (more viably successful) patterns proliferate.  But this does not require Darwinian selection among organisms competing for limited resource.  Genomic variants whose cooperative interactions do not function can lead to embryonic lethality, for example, which need have nothing whatever to do with competition, and certainly not with other organisms seeking mates, food, or safety.  Ineffective cooperation is an evolutionary factor not identical to natural selection in its mechanism, but with similarly 'adaptive' effects.

In our view, cooperation based on trans interactions is more important, more prevalent, and more fundamental than Darwinian natural selection (as we write in our book The Mermaid's Tale).  Interactions that are successful become increasingly installed in the life history of organisms ('canalized' to use CH Waddington's venerable term for it), and this constrains the way and perhaps the rate at which evolution can occur.  This is neither heresy nor surprise.  For example, genes present today are the descendants of 4 billion years of evolutionary history, and most are used in multiple ways in the organism (at least in complex multicellular organisms; we don't know how true this is of simple or single-celled species).  They are less likely to suffer mutational change without serious effect, mainly negative. This is a very long-established idea, and is clearly supported by the high degree of sequence conservation of genes in genomes.

Genomewide mapping of most traits identifies many different genome regions that can statistically affect a trait's presence or measure.  But mapping rarely identifies coding regions.  Most 'hits' are in regulatory regions or regions with other (usually unknown) function.

This should surprise no one.  First, as noted above, 'genes' (protein coding regions) are largely of evolutionary long standing and embedded in interaction patterns usually in multiple contexts (they are 'pleiotropic'), so the coding parts are harder than regulatory parts to modify viably by mutation. It is empirically much more likely that their expression patterns can be varied.  Second, every gene is a complex of many different components (protein code, splice and polyadenylation signals--where the required AAAAA... tail of a mRNA molecule is attached--promoter sites, enhancer sites, and so on). Each of these is mutable in principle, and ample evidence shows that regulatory regions are especially so.  And each transcription factor or other gene product that is needed to activate a given gene (that is, the tens of proteins and their DNA binding sites that must assemble to cause a nearby gene  to be expressed) is itself a gene with all the same sort of complex modular structures.  RNA has to be processed, transported and translated by factors that, again, are potentially mutable.  And so on.  And then most final functions, physiological, developmental, metabolic, or physical are the result of complex processes over time, involving many genes and systems.

In fact, in recognition of biological complexity, many investigators suggest that the proper level of analysis should be of systems, that is, organized pathways of interaction that bring about some end result.  Gene regulation, physiology and metabolism, and so on, represent such entities.  The 'emergence' of the result cannot be predicted by listing the individual contributing elements, in the same sense that the effect of a new mutational change cannot be understood without considering its context.  However, systems themselves have overlap, redundancy, and elements that contributed in different systems at different times, and many systems may themselves interact in what one might call hyper-systems for a result--like you--to come about.  Analyzing emergent systems is at present an active but in many ways immature endeavor, because we still probably don't have adequate understanding, or perhaps not even adequate technology for the job.  But it's important that people are considering the trans world in this and other ways.

Causal complexity is predictable, and what we expect is what we see
Causation in life is fundamentally about cooperation which is about trans interactions.  Since cells are isolated from each other, so they can sense their own environments and respond to them, they actively signal to each other and a major way gene expression is regulated is through complex signal sending and receiving mechanisms.  'Signals' can mean gene-coded proteins secreted from cells, or the detection by cells of ions or other chemicals in their environment, and so on.  Signaling and responding to environmental conditions involves large numbers of genes and their regulation in time and space.  Most genes, in fact, have such cooperative, communicative function.

In turn, this implies that traits have many contributing genes, and their modular coding and regulatory sequences (and other forms of genome function, such as packaging and many different types of RNA), and each of these is potentially mutable and potentially variable within and between samples, populations, and species.  The result is the high level of causal complexity that is being so clearly documented.  A very large amount of viable contributing variation can be expected, if the individual variants have small effect.  The trait itself must be viable, but viability can coexist with large amounts of variation in the hundreds of contributing components.  This is what GWAS consistently finds, and is wholly consistent with how evolution works.

Life is complex in these ways in very understandable (and predictable) ways.  Enumeration of causes or even defining 'causes' are often  fool's errands because different variants in different genome regions in different samples and populations are to be expected.

It's a highly cooperative trans world out there!