Friday, April 27, 2012

Robustness and fragility in neural development

So many things can go wrong in the development of the human brain it is amazing that it ever goes right.  The fact that it usually does – that the majority of people do not suffer from a neurodevelopmental disorder – is due to the property engineers call robustness.  This property has important implications for understanding the genetic architecture of neurodevelopmental disorders – what kinds of insults will the system be able to tolerate and what kind will it be vulnerable to?

The development of the brain involves many thousands of different gene products acting in hundreds of distinct molecular and cellular processes, all tightly coordinated in space and time – from patterning and proliferation to cell migration, axon guidance, synapse formation and many others.  Large numbers of proteins are involved in the biochemical pathways and networks underlying each cell biological process.  Each of these systems has evolved not just to do a particular job, but to do it robustly – to make sure this process happens even in the face of diverse challenges. 

Robustness is an emergent and highly adaptive property of complex systems that can be selected for in response to particular pressures.  These include extrinsic factors, such as variability in temperature, supply of nutrients, etc., but also intrinsic factors.  A major source of intrinsic variation is noise in gene expression – random fluctuations in the levels of all proteins in all cells.  These fluctuations arise due to the probabilistic nature of gene transcription – whether a messenger RNA is actively being made from a gene at any particular moment.  The system must be able to deal with these fluctuations and it can be argued that the noise in the system actually acts as a buffer.  If the system only worked within a narrow operating range for each component then it would be very vulnerable to failure of any single part. 

Natural selection will therefore favour system architectures that are more robust to environmental and intrinsic variation.  In the process, such systems also indirectly become robust to the other major source of variation – mutations. 

Many individual components can be deleted entirely with no discernible effect on the system (which is why looking exhaustively for a phenotype in mouse mutants can be so frustrating – many gene knockouts are irritatingly normal).  You could say that if the knockout of a gene does not affect a particular process, that that means the gene product is not actually involved in that process, but that is not always the case.  One can often show that a protein is involved biochemically and even that the system is sensitive to changes in the level of that protein – increased expression can often cause a phenotype even when loss-of-function manipulations do not.

Direct evidence for robustness of neurodevelopmental systems comes from examples of genetic background effects on phenotypes caused by specific mutations.  While many components of the system can be deleted without effect, others do cause a clear phenotype when mutated.  However, such phenotypes are often modified by the genetic background.  This is commonly seen in mouse experiments, for example, where the effect of a mutation may vary widely when it is crossed into various inbred strains.  The implication is that there are some genetic differences between strains that by themselves have no effect on the phenotype, but that are clearly involved in the system or process, as they strongly modify the effect of another mutation.

How is this relevant to understanding so-called complex disorders?  There are two schools of thought on the genetic architecture of these conditions.  One considers the symptoms of, say, autism or schizophrenia or epilepsy as the consequence of mutation in any one of a very large number of distinct genes.  This is the scenario for intellectual disability, for example, and also for many other conditions like inherited blindness or deafness.  There are hundreds of distinct mutations that can result in these symptoms.  The mutations in these cases are almost always ones that have a dramatic effect on the level or function of the encoded protein. 

The other model is that complex disorders arise, in many cases, due to the combined effects of a very large number of common polymorphisms – these are bases in the genome where the sequence is variable in the population (e.g., there might be an “A” in some people but a “G” in others).  The human genome contains millions of such sites and many consider the specific combination of variants that each person inherits at these sites to be the most important determinant of their phenotype.  (I disagree, especially when it comes to disease).  The idea for disorders such as schizophrenia is that at many of these sites (perhaps thousands of them), one of the variants may predispose slightly to the illness.  Each one has an almost negligible effect alone, but if you are unlucky enough to inherit a lot of them, then the system might be pushed over the level of burden that it can tolerate, into a pathogenic state. 

These are the two most extreme positions – there are also many models that incorporate effects of both rare mutations and common polymorphisms.  Models incorporating common variants as modifiers of the effects of rare mutations make a lot of biological sense.  What I want to consider here is the model that the disease is caused in some individuals purely by the combined effects of hundreds or thousands of common variants (without what I call a “proper mutation”). 

Ironically, robustness has been invoked by both proponents and opponents of this idea.  I have argued that neurodevelopmental systems should be robust to the combined effects of many variants that have only very tiny effects on protein expression or function (which is the case for most common variants).  This is precisely because the system has evolved to buffer fluctuations in many components all the time.  In addition to being an intrinsic, passive property of the architecture of developmental networks, robustness is also actively promoted through homeostatic feedback loops, which can maintain optimal performance in the face of variations, by regulating the levels of other components to compensate.  The effects of such variants should therefore NOT be cumulative – they should be absorbed by the system.  (In fact, you could argue that a certain level of noise in the system is a “design feature” because it enables this buffering).

Others have argued precisely the opposite – that robustness permits cryptic genetic variation to accumulate in populations.  Cryptic genetic variation has no effect in the context in which it arises (allowing it to escape selection) but, in another context – say in a different environment, or a different genetic background – can have a large effect.  This is exactly what robustness allows to happen – indeed, the fact that cryptic genetic variation exists provides some of the best evidence that we have that the systems are robust as it shows directly that mutations in some components are tolerated in most contexts.  But is there any evidence that such cryptic variation comprises hundreds or thousands of common variants? 

To be fair, proving that is the case would be very difficult.  You could argue from animal breeding experiments that the continuing response to selection of many traits means that there must be a vast pool of genetic variation that can affect them, which can be cumulatively enriched by selective breeding, almost ad infinitum.  However, new mutations are known to make at least some contribution to this continued response to selection.  In addition, in most cases where the genetics of such continuously distributed traits have been unpicked (by identifying the specific factors contributing to strain differences for example) they come down to perhaps tens of loci showing very strong and complex epistatic interactions (1, 2, 3).  Thus, just because variation in a trait is multigenic, does not mean it is affected by mutations of small individual effect – an effectively continuous distribution can emerge due to very complex epistatic interactions between a fairly small number of mutations which have surprisingly large effects in isolation.

(I would be keen to hear of any examples showing real polygenicity on the level of hundreds or thousands of variants). 

In the case of genetic modifiers of specific mutations – say, where a mutation causes a very different phenotype in different mouse strains – most of the effects that have been identified have been mapped to one or a small number of mutations which have no effect by themselves, but which strongly modify the phenotype caused by another mutation. 

These and other findings suggest that (i) cryptic genetic variation relevant to disease is certainly likely to exist and to have important effects on phenotype, but that (ii) such genetic background effects can most likely be ascribed to one, several, or perhaps tens of mutations, as opposed to hundreds or thousands of common polymorphisms. 

This is already too long, but it begs the question: if neurodevelopmental systems are so robust, then why do we ever get neurodevelopmental disease?  The paradox of systems that are generally robust is that they may be quite vulnerable to large variation in a specific subset of components.  Why specific types of genes are in this set, while others can be completely deleted without effect, is the big question.  More on that in a subsequent post…


  1. Awesome post.

    In the Selfish Gene, Dawkins talks about "survival of the fittest" being a special case of "survival of the stable". Same idea I guess.

    In general I think most of our theories of developmental disorders are too simple and we need to take some lessons from complexity theory. You talk about feedback loops but another critically important concept I think is redundancy, meaning that any one component of the system can fail and it doesn't have much effect on the organism. Redundancy sounds inefficient but it may actually be necessary for a complex system to achieve stability.

    It follows then that neurodevelopmental disorders arise when there are multiple hits to the same system. Dorothy Bishop said something along these lines a few years ago but I don't think she framed it in quite these terms.

    1. Thanks Jon. I think in the case of neurodevelopmental systems that distributed robustness will play a more major role than direct redundancy of specific components (where two or more components do the exact same job - though there are plenty of examples of that too, where it takes a double mutant to get a phenotype).

      What I didn't have space to develop here is the flip-side of robustness, which is fragility. If the system is so robust, then
      why does it sometimes go wrong? In fact, we know of lots of examples of single mutations that cause neurodevelopmental disease - however, I think those are the ones we know about precisely because their effects are most penetrant. It may well be that more cases are caused by two or more mutations. Even for the highly penetrant ones - say for Fragile X syndrome or velo-cardio facial syndrome - the specific phenotype that emerges is quite variable and very likely affected by genetic background.

      But here's a question: why is the system so sensitive to mutation of some genes (even to dosage in many cases) while completely insensitive to even complete removal of other ones? Are there some particular characteristics that typify each set?

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