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Sunday, October 24, 2010

Searching for a needle in a needle-stack


Whole-genome sequencing is a game-changer for human genetics. It is now possible to deduce every base of an individual’s genome (all 6 billion of them – two copies of 3 billion each) for a couple of thousand euros, and dropping. (Yes, euros). Even Ozzy Osbourne just got his genome sequenced! For researchers searching for the causes of genetic disease (or resistance to vast quantities of drugs and alcohol), this means they no longer have to infer where a mutation is by tracking a sampling of “markers” spaced across the genome – they can directly see all of the genetic information.

The problem is, they directly see all of the genetic information. If each of us carries thousands of mutations – changes that are very rare or may even have never been seen before in any other person – then telling which one of those changes is actually causing the condition is a tough task. Researchers in psychiatric genetics are currently grappling with how to handle this glut of information.

The problem is particularly acute in this field, where there is a (very slowly) growing realisation that many so-called common disorders, such as schizophrenia and autism – are really umbrella terms for collections of very rare disorders. Each of these conditions can be caused by mutations in single genes. The reason they are so common is that there are so many genes required to wire the brain properly – mutations in any of probably hundreds of genes can lead to the kinds of neurodevelopmental defects that ultimately result in psychopathology. (At least, that is the working hypothesis - see review below for a discussion of the evidence supporting it).

Very large studies are now underway to sequence the genomes of thousands of people with schizophrenia, autism or other psychiatric disorders, along with “control” individuals from different populations. The hope is that by comparing the spectrum of mutations in patients with those in controls, it will be possible to deduce which mutations are pathogenic. The most obvious ones will be those which recur in multiple individuals with a psychiatric disorder, are not present in the control population and are predicted to affect the biochemical function of the encoded protein. Those parameters can be used to prioritise candidate mutations for further study.

So far, however, it has been far more difficult to generate the type of statistical evidence that psychiatric geneticists have been used to from genome-wide association or linkage studies. One major problem is that, while it is true that mental illness can be caused by single mutations, it is also true that the situation is likely more complicated than that in many cases. Most such mutations that have been identified to date are only partially “penetrant” – that means that not all of the people who carry the mutation have the disorder in question. Another way of describing that is to say that the mutations have “variable expressivity” – that means the phenotypes they result in vary widely across mutation-carriers. This makes it crucially important for genetic studies to very carefully define the phenotype being mapped – in many cases a particular clinical diagnosis will not be the best phenotype to choose.

One reason for such variable phenotypes due to a mutation in any single gene is that its effects may be modified by other mutations that each person carries. That situation is not unique to psychiatric disease – it’s actually true of all so-called Mendelian disorders. Even in classical examples like cystic fibrosis, which is caused by mutations in a single gene, the effects of such mutations are quite variable and are strongly affected by genetic background.

But it does pose a major problem – if you find a mutation in two or three people with disease and one person without disease, how can you assign a p-value to the likelihood of that mutation being causative? And how do you distinguish mutations in that gene from those that happen to occur in all the other genes in the genome? Hopefully, this problem will partly solve itself as larger samples of patients and control individuals are sequenced. A move back to family-based studies will also be hugely helpful as it will provide evidence based on which mutations segregate with illness (or, even better, with some more fundamental neurobiological “endophenotype”).

However, we will still likely be left with a situation where the statistical evidence we can get from considering the spectrum of mutations in single genes will run into mathematical limits. At some point it will be necessary to look for other types of evidence from outside the system. One type of evidence will come from analysing the biochemical pathways of the implicated genes – it is already becoming apparent that many such genes encode proteins that interact with each other (see review below for examples).

For example, mutations in the gene Contactin-associated protein 2 (CNTNAP2) have been found in patients with autism, schizophrenia, epilepsy, Tourette’s syndrome, ADHD and other disorders. The evidence for this gene by itself is extremely strong. Recently, mutations in genes encoding the related proteins CNTNAP4 and CNTNAP5 have also been found in patients with epilepsy and autism, respectively. By themselves, the evidence for each of these genes is not at all convincing – in fact it is not possible to even generate a p-value for how likely it is that they are causative. But taken together, the findings of mutations in each of these genes greatly strengthens the implication of the pathway in general. Findings of mutations in the genes encoding the interacting proteins Contactin-3, -4 and -5, similarly add to the weight of evidence.

These proteins are all involved in forming synaptic contacts between neurons, as are many other genes identified in patients, further implicating defects in this process as one route to mental illness.

The effects of mutations in particular genes can also be investigated in genetically modified mice. If a mutation in Gene A causes neurodevelopmental defects and physiological or behavioural phenotypes that are similar to those seen in mice with mutations in a gene known to cause psychiatric illness, then that is strong evidence that Gene A may be the culprit in individuals carrying a mutation that disrupts it.

The next few years will be tremendously exciting as the data from sequencing projects become available. To fully interpret these it will be necessary to look beyond statistical measures from the human data themselves and include evidence of biological plausibility, converging biochemical pathways and neurobiological phenotypes in both humans and animal models.


Mitchell KJ (2010). The genetics of neurodevelopmental disease. Current opinion in neurobiology PMID: 20832285

Monday, October 18, 2010

Colour my world


Colour does not exist. Not out in the world at any rate. All that exists in the world is a smooth continuum of light of different wavelengths. Colour is a construction of our brains. A lot is known about how the brain does this, beginning with complicated circuits in the retina itself. Thanks to a new paper from Greg Field and colleagues we now have an even more detailed picture of how retinal circuits are wired to enable light to be categorized into different colours. This study illustrates a dramatic and fundamental principle of brain wiring – namely that cells that fire together, wire together.

Colour discrimination begins with the absorption of light of different wavelengths. This is accomplished by photopigment proteins, called opsins, which are expressed in cone photoreceptor cells in the retina. Humans have three opsin genes, which encode proteins that preferentially absorb light of different wavelengths: short (S, in what we perceive as the blue part of the spectrum), medium (M, green) and long (L, red). Each cone expresses only one of these opsin genes and is thus particularly sensitive to light of the corresponding wavelength. However, by itself the response of a single cone cell cannot be used to determine the colour (wavelength) of incoming light. The reason is that each cone is responsive to both the wavelength and the intensity of the light – so an M-cone would respond equally to a dim green light or a strong red light.

Colour information only arises by comparing the responses of multiple cone cells. This is accomplished in two distinct channels – one which compares the inputs of L and M cones (the red-green channel) and one which compares the inputs of S cones to the combined inputs of L and M cones (the blue-yellow channel). The latter of these is the original, evolutionarily older system, dating back at least 500 million years. It is found in most mammals, in which there are only two opsin genes – an S opsin and one whose absorbance is midway between L and M.

The L/M system evolved much more recently, due to a gene duplication that occurred in the lineage of Old World primates, probably around 40 million years ago. The duplication of the primordial L/M opsin gene allowed the two resultant genes to diverge from each other in sequence, generating proteins with different absorption spectra, which could then be compared. Something similar can actually be achieved even in species with only one copy of the L/M gene. This gene is on the X chromosome, so females will carry two copies of it. Due to the random inactivation of one X chromosome in each cell in females, each cone will express only one of the two copies of this opsin gene. If the two copies differ from each other, encoding proteins with alterations in the amino acid sequence that affect their light absorbance, then what will arise is a set of L cones and a set of M cones.

All of this raises an important question – how are the inputs to these different cone cells compared? If the cells which express L and M cones are essentially the same, with the sole difference being that they express different opsin genes, then how is the wiring in the retina set up so that their inputs are distinguished, allowing their subsequent comparison? Cells in the retina are arranged in a series of layers. Cone cells connect, through bipolar and other cells, to retinal ganglion cells, which in turn convey visual information to the brain. Retinal ganglion cells integrate inputs from multiple cones, but in a very specialized way – some cones connect through ON bipolar cells (which are activated by light) and others through OFF bipolar cells (which are inactivated). Typically, one cone in the centre of an array of cells is connected to an ON bipolar cell, while surrounding cones connect to the same retinal ganglion cell target via OFF bipolar cells. The result is that the light signal hitting an array of cones is integrated – if the central cone is an L cell and the surrounding cones are M cells then the retinal ganglion cell will be most strongly activated by red light.

This has been known for quite a long time now. What has not been clear is how this system gets wired up during development. S, M and L cones are distributed randomly across the retina. S cones, which are the least frequent, are molecularly distinct from L/M cones in many ways and connect to a dedicated set of S channel bipolar and retinal ganglion cells. The development of the wiring that carries out the comparison between S and L/M cones is thus molecularly specified. This cannot be the case for the comparison between L and M cones, which differ only in the opsin gene they express.

The new study by Field and colleagues worked out in breathtaking detail the circuitry of the retina at a cellular level. Their results reveal the beauty and elegance of this circuitry but also resolve an important question relating to how L and M cone cells are wired. Each retinal ganglion cell in the centre of the retina receives ON inputs from a single cone and OFF inputs from the surrounding cones. In the periphery, however, the ON “centre” is composed of up to twelve cones. For the ganglion cell to discriminate colours there must be a bias in how many L or M cone cells wire up to it through the ON and OFF channels.

Their results reveal exactly such a bias and further show that it cannot be explained simply by random clumping of L or M cones in the photoreceptor array. What this indicates is that there is some additional mechanism whereby inputs from just one type of cone are strengthened in each of the ON and OFF channels. In effect, the L and M cones are competing for inputs in each channel, presumably through so-called “Hebbian mechanisms” whereby inputs to a cell are strengthened if they fire at the same time and asynchronous inputs are actively weakened. Despite their being no molecular differences between these cone cells, the brain is thus primed to wire them into distinct channels based on their patterns of activity.

A remarkable experiment performed a few years ago dramatically illustrates this principle. Mice are naturally dichromatic – they only have two opsin genes (S and L/M). Researchers in Jeremy Nathans’s group replaced one copy of the L/M gene with a version of the human L gene. This meant that female mice could be generated which carried one mouse opsin (L/M) and one human version (L). Cone cells could express one or the other of these genes. The result was astonishing – in visual tests, these mice could clearly distinguish between light of wavelengths which they were previously unable to discriminate. (They could now tell red from green). Despite normally having only two channels, their nervous system was clearly primed to perform this comparison.

Amazingly, this may extend to humans as well. The opsin genes in humans can also be polymorphic – each one comes in several different versions. Females who carry one version of, say, the L gene on one X chromosome, and another on the other X chromosome, can effectively have four different channels of absorption: S, M, L and L’. If the retina is primed to compare inputs based on their patterns of activity then one would predict that such females would be tetrachromatic – they should be able to distinguish between more colours than trichromatic individuals (just as trichromats can distinguish more colours than dichromats – people with a mutation in one of the L or M opsin genes, who are red-green colourblind).

This increased ability to discriminate colours is, apparently, indeed present in about 50% of females and can be revealed by a very simple test. Consider the picture of the colour spectrum shown below. If you print this out and mark on it with a pencil everywhere there seems to be a clear border between two distinct colours, then what you will find is that most trichromats mark out about 7 colour domains, while tetrachromats mark out between 9-10 (and dichromats about 5).




So, where a man may just see “green”, a woman may see chartreuse or olive. Realising that people literally see things differently (and not just colours) could avoid needless argument. (That said, the woman is clearly more right, and it is usually best to concede graciously).


Field GD, Gauthier JL, Sher A, Greschner M, Machado TA, Jepson LH, Shlens J, Gunning DE, Mathieson K, Dabrowski W, Paninski L, Litke AM, & Chichilnisky EJ (2010). Functional connectivity in the retina at the resolution of photoreceptors. Nature, 467 (7316), 673-7 PMID: 20930838

Jacobs, G., Williams, G., Cahill, H., & Nathans, J. (2007). Emergence of Novel Color Vision in Mice Engineered to Express a Human Cone Photopigment Science, 315 (5819), 1723-1725 DOI: 10.1126/science.1138838

Jameson KA, Highnote SM, & Wasserman LM (2001). Richer color experience in observers with multiple photopigment opsin genes. Psychonomic bulletin & review, 8 (2), 244-61 PMID: 11495112

Monday, October 4, 2010

Mice with fully functioning human brains

I wouldn’t usually discuss politics in a blog like this, but a recent story caught my eye, as it provides an example of the depressing and sometimes bizarre level of scientific illiteracy among elected officials or some people who hope to be elected. The example is from the United States, which is an easy target in this regard, but we have had a similar episode in Ireland recently so I don’t think we (or indeed any other non-Americans) can feel particularly smug about it. This one is especially funny, though.

Christine O’Donnell has recently won the Republican nomination in Delaware for the upcoming election to the Senate. I just love her – for comic entertainment this woman is very good value. She makes Sarah Palin look like the most reasonable, well-informed, level-headed person around. Among many clangers that she has dropped in the past, the one that really got my attention was the following assertion, made during a debate on stem cells on The O’Reilly Factor show on Fox News a few years ago:

"American scientific companies are cross-breeding humans and animals and coming up with mice with fully functioning human brains. So they're already into this experiment."

That’s right, cross-breeding humans and animals. I’m not sure how she imagines that to have taken place and would rather not know. And yes, she did say: mice with fully functioning human brains. Now, the average mouse weighs around 20 grams. The average human brain (clearly there are exceptions) weighs around 1.4 kilograms. I’m not sure Ms. O’Donnell really thought that through, even from a purely mechanical standpoint. However, she apparently had the opportunity to qualify or alter her assertion but did not, so one can assume she meant something like what she actually said.

(She also thinks evolution is a myth, because if we evolved from monkeys, then how come the monkeys are not still evolving into humans? That some people buy that kind of “argument” exemplifies the poor grasp that many people have of geological time. And of the fact that we did not evolve from monkeys – monkeys and humans evolved from a common ancestor. It reminds me of an even funnier comment I read from another creationist: if we evolved from monkeys, then how come we don’t speak monkey? There’s just no answer to that.)

What the imaginative Ms. O’Donnell may have been trying to refer to was a story that got some press coverage at the time of scientists who had transplanted a small number of human cells into a mouse brain to see if they would migrate and integrate normally. Apparently, about 100 such cells survived, in a brain that contains over 20 million cells. So, transplantation, not cross-breeding, and not fully functioning human brains, but to be fair to her she did, in an incredibly inept and confused manner, raise an interesting issue.

That is the question of whether it is ever a good idea (or morally or ethically right) to create an organism whose cells derive from two different species – a so-called chimera (named after the mythically mixed-up creature). This is especially touchy when some of the cells are of human origin. Why, you might legitimately ask, would anyone want to do such a thing?

Well, there are lots of reasons, none of which involves playing God just for fun, or actually wanting to create a hybrid organism. Most of the studies that have carried out such experiments are designed to test the potential of stem cells for regeneration of damaged parts of the brain. Stem cells can be obtained from many different sources, including early human embryos, umbilical cord blood and bone marrow. New technologies now allow fully differentiated adult cells from various tissues to be retro-differentiated into stem cells (so-called induced pluripotent stem cells). All of these cell types hold great promise for regenerative medicine, especially ones that are of the same genotype as the prospective patient.

But how to test them? Just injecting them into patient’s brains doesn’t seem like the best approach, though actually it has been done in some cases of seriously ill patients in the late stages of Parkinson’s and Huntington’s disease. Initial results seemed to suggest some clinical improvement but larger, more carefully controlled trials have been largely disappointing. These studies involved injection of primary human fetal cells into the brains of adult patients and were not particularly sophisticated in terms of how these cells were treated prior to injection.

With better defined populations of stem cells it is now possible, for example, to differentiate them into particular types of neurons (or their direct progenitors) prior to transplantation. To determine the efficacy of such treatments, animal models have of these disorders have been used. Human cells will integrate fairly happily into a rodent or even a chick brain. (No chick jokes, please). The brain is immune privileged and grafts of foreign cells are generally well tolerated by the host. Using this approach it is possible to determine how such transplanted cells survive, migrate and integrate into the brain (under the assumption that such processes would be much the same in a human brain). More importantly, it is possible to determine whether transplantation of such cells results in any improvement in the animal’s condition.

Such studies are generating promising results in models of stroke, spinal cord injury and neurodegenerative diseases such as Alzheimer’s, Huntington’s and Parkinson’s diseases (see a few recent examples below). It is still early days, however, and a lot more pre-clinical research like this will have to be carried out to characterise how these cells behave after transplantation, before they will be approved for clinical use. So, nothing sinister, no witchcraft (sorry, Christine, I know you like that), no hybrid mouse-humans scuttling into the dark corner of the lab when the lights are turned on. Just scientists and clinicans trying hard to find cures for serious diseases. Nothing sensationalist at all really. Sorry.



Snyder BR, Chiu AM, Prockop DJ, & Chan AW (2010). Human multipotent stromal cells (MSCs) increase neurogenesis and decrease atrophy of the striatum in a transgenic mouse model for Huntington's disease. PloS one, 5 (2) PMID: 20179764

Salazar DL, Uchida N, Hamers FP, Cummings BJ, & Anderson AJ (2010). Human neural stem cells differentiate and promote locomotor recovery in an early chronic spinal cord injury NOD-scid mouse model. PloS one, 5 (8) PMID: 20806064

Lee HJ, Lim IJ, Lee MC, & Kim SU (2010). Human neural stem cells genetically modified to overexpress brain-derived neurotrophic factor promote functional recovery and neuroprotection in a mouse stroke model. Journal of neuroscience research PMID: 20818776
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