The Compatibility Gene, page 13
A warning: this is research in progress. And presentation of particular peptides by B*57, for example, may not be the sole way in which HIV Controllers avoid AIDS but it is (almost certainly) a major factor. B*27 is also one of the HLA types frequent in HIV Controllers, and yet this same genetic variation is part of the problem in ankylosing spondylitis – discovered by Brewerton and Terasaki. That is, the very same HLA that is good for us in dealing with one disease, HIV, is bad for us by contributing to another disease, ankylosing spondylitis. The role that B*27 has in the auto-immune disease ankylosing spondylitis is (almost certainly) also a result of its job of presenting peptides to T cells. But in this case, normal self-peptides – chopped-up pieces of protein found in healthy cells – are wrongly identified as non-self and trigger an immune response. This gives rise to the auto-immune disease – because our immune system makes a mistake and attacks healthy tissue.
But why would one particular type of HLA gene underlie this kind of disease? The short answer is that nobody really knows. It could be that B*27 binds an abundant self-peptide extremely well and this leads to problems. Or alternatively, there could be some real non-self peptide, say from a virus, presented by B*27 to activate T cells, which then accidentally attack uninfected cells by responding to a self-peptide that just happens to be similar to the one from the virus.44 Even though we don’t understand exactly how B*27 is involved in ankylosing spondylitis, there’s plenty of evidence that, in general, T cells and HLA genes are important in many auto-immune diseases. Class II compatibility genes designated HLA-DR*03 and -DR*04 are found in the vast majority of people with type I diabetes, for example. In fact, virtually the whole gamut of possible illnesses that could ever affect us are known to be influenced by our compatibility genes – including cancer, infections, auto-immune diseases and even some neurological disorders. HLA-B*53 can protect against severe malaria, for example, by binding a particular peptide made by the parasite.45 And other compatibility genes are linked to multiple sclerosis, Parkinson’s, Hodgkin’s lymphoma, inflammatory bowel disease, leprosy, narcolepsy and so on.
By controlling our response to disease, our compatibility genes influence how we live, when we die – and from what we die. Even so, it’s important to keep in mind that, even though many Elite Controllers of HIV have HLA-B*27, having this type of HLA is not sufficient to survive AIDS. There is still much to be learned about our immune response to HIV and other infections. And, whatever HLA type you have inherited, by far the best way to stay safe is to avoid being infected in the first place.
The compatibility gene system doesn’t only explain how any one of us might fare better or worse against a particular disease like AIDS. It is a far bigger system than that. It also works between us all, across the whole human species, because our immune system has evolved in defence of humanity as a whole – to protect all of us as a species from anything dangerous that could arise.
As we’ve seen, these genes vary from person to person, and across the human population there are billions of combinations of HLA types possible. In fact, it’s theoretically possible for everyone on the planet to have a different set of HLA genes. This doesn’t quite happen because some HLA types are more common than others, but there is still enormous diversity in the population. This means that a virus escaping detection by the HLA proteins in one individual will face different HLA types in someone else. If we were all infected with one especially deadly virus, for example, some of us may survive by having a particularly potent HLA type for dealing with that virus. An outcome of this is that the distribution of HLA types among us evolves over time, as waves of different infections influence who reproduces.
But if some HLA genes are better at fighting a particular infection – because they can pick up appropriate peptides – then why do we each have only six class I HLA genes, for example? Why don’t we each have hundreds or thousands of HLAs like the whole population does? If a virus has managed to avoid detection via one HLA type, it’s useful to have another that may work. So hundreds or thousands of HLA genes in each person would surely be better at catching all possible infections, wouldn’t they?
It’s hard to do an experiment to answer this, but the generally accepted theory is that the system has a limit because of the way our body discriminates self and non-self. Recall that any T cell capable of reacting to ourselves is killed off in the thymus. That implies that, for each HLA type, all the T cells reacting to self peptides clasped by that type of HLA protein have to be killed off. Too many HLA types and it would be hard to have a big enough pool of T cells left. So there is a balance between maximizing the number of non-self peptides that can be grabbed by HLA, while still allowing enough T cells to exist for the detection of all possible non-self peptides. Or, in broader terms, there is delicate balance in making sure the immune system cannot attack our own bodies yet can respond to all kinds of potential infections. The outcome of this balancing act is that our HLA type makes each of us more susceptible or resistant to different diseases – not like the individual genetic mutations which cause cystic fibrosis or Huntington’s; compatibility genes influence our response to all kinds of diseases.
From this depth of understanding, the next question is pragmatic: how do we get to new cures?
6
A Path to New Medicine
The urgent debate in universities and pharmaceutical companies alike is about how to get the best out of the knowledge we’ve accumulated, how to translate revelations in our understanding of genetics and disease into actual medical benefit. Many of our best medicines so far have been vaccinations, but development of a vaccine for HIV, for example, has proved to be a long and bumpy ride ever since the US health secretary suggested in 1984 that it would take a couple more years. The discovery of HIV Controllers is encouraging for vaccine development, because these people show us that immune responses at least have the potential to control HIV in the right circumstances. If we could get other HLA types to be as potent as, say, HLA-B*57, then more people might join those who inherited superpowers.
There’s no shortage of scientists trying to translate our knowledge into practical outcomes; conferences about HIV nowadays gather about 20,000 professionals and 2,000 journalists. Such meetings are not unlike Star Trek conventions; the passion is the same and the heroes are equally revered. Both are ignited by imagination and wonder. But the paramount fact distinguishing scientists from their science-fiction counterparts is that they are also driven by an important real cause: to create new medicines.
Some – Nobel laureate Rolf Zinkernagel is one – think the key issue in getting to new medicines is to perform experiments in which everything is as close as possible to being physiologically right: using animals, real viruses and doses that would occur naturally.1 Others – such as Ron Germain, a leading scientist at the NIH – agrees that this is important but also advocates other approaches, such as computer simulations of immune responses.2 The difficulty is that it’s relatively easy to do something new; very hard to do something important, because, as Einstein put it, ‘Not everything that can be counted counts.’ My view is that, since the very essence of discovery is that nobody predicted it, who’s to know what’s best to do next?
In truth, many of the drugs that we use today were found by chance – or at least serendipitously. The discovery of antibiotics is a well-known example: on 28 September 1928, Alexander Fleming noticed that a fungus had contaminated one of his experiments and killed off the bacteria he was studying. A more recent example is Viagra, developed as a drug for high blood pressure and then later discovered to be of use in preventing erectile dysfunction. It has proved difficult, and all too rare, to systematically translate our knowledge into direct medical benefit. Sport commentators call something ‘academic’ when it’s not important, but this issue is anything but ‘academic’; our well-being and even our survival depend on us choosing the right path to new ways of conquering disease. So are there radically different approaches we could take?
Eric Schadt is one leading scientist who says there is. Renowned for turning up to meetings in shorts whatever the weather or formality of the occasion, he argues that molecular biology has been great for uncovering individual genes important for human traits but that we’ve largely failed to fulfil the medical promises that have been touted – and it’s enough already. He argues that the main problem is that we haven’t adequately tackled the complexity of genes and disease.
In general, many genes – not one – contribute to a disease risk or human trait. As we’ve seen, compatibility genes influence our susceptibility and resistance to all manner of diseases, but they don’t fully protect against, or absolutely cause, any one. There are exceptions such as Huntington’s disease, which is caused by a single genetic variant, but by and large things are more complicated than one gene causing one disease or trait. Studies of how frequently twins share an illness are one way in which we can estimate the total effect that genes have. And in comparison to the sum of the individual genes known to be important, we can account for only about 10 per cent of the total genetic risk for many human traits and diseases.3 So, Schadt says, something big is missing in our understanding of genes and disease.
Genome-wide associations studies have worked well in identifying many important genes. But even these huge studies – scanning the genes of thousands of people – are not perfect. Things that aren’t easily picked up include rare genetic variants and modifications to our DNA made after birth (so-called epigenetic changes) and differences we can sometimes have in the number of copies of a given gene. But most important of all is that genes interact – the status of one influences another, and so on – like computer, social and financial networks. And variation in groups of genes is hard to analyse – only the effect of individual genes is easily studied.
So, Schadt, and others like him, suggests that a seismic shift in our approach is needed because most diseases involve interactions between constellations of genes. And things are even more complex than you might think at first because the interactions between genes are affected by diet, age, gender, exposure to toxins and so on.4 Schadt’s close colleague Stephen Friend, a paediatric oncologist, says it plainly: ‘Traditional human disease research models are now archaic. The academic [grant] process is choked by favourite gene efforts that result primarily in “impactful” journal articles . . . And the patients? They’re getting more and more frustrated.’5 Yesteryear’s revolution in biology was the Human Genome Project; Schadt wants to deliver the next one.
There’s always a personal story behind the approach a scientist takes. Schadt’s almost anarchic attitude – and his ability to weather a storm – was undoubtedly shaped by a series of fights early in his life.6 His Christian parents brought him up, with his six siblings, with the attitude that secular education was worthless. Going to college was frowned upon, and Schadt joined the air force. But an accident while rappelling down a rockface left him with poor mobility in his shoulder, and he was told his role in the military would have to change. Having done well in various aptitude tests, in 1986, aged nineteen, he went to college after all. Physical exertion had been Schadt’s release, but once he was at college, ideas and academic challenges became a new source of freedom. His religious upbringing helped focus his mind on big issues – how things are connected, underlying principles – and he was drawn to studying maths and philosophical logic. But by pursuing a college education his father considered he had become possessed by the devil; he told his son that he should never return home.7
Estranged from his family, Schadt had to fight to stay away from the military – after realizing that he had, in fact, been overwhelmingly depressed there – because they were paying for his education and expected him to return. Eventually he got a PhD from UCLA in 1999, and by that time he had already begun working at the pharmaceutical giant Roche. It was a time when large-scale analysis of genes was fairly new, and Roche was using a specific process for analysing genetic data which used machines purchased from another company, Affymetrix. Schadt wasn’t happy that Affymetrix wouldn’t let anyone see the computer codes being used to analyse the genetic data. It meant that he couldn’t fiddle with it to tailor it to his specific needs. So he wrote his own software, which won him fame within Roche. But he grew tired of corporate meetings and decided to move to a small start-up company based in Seattle. Then, in November 1999 – a week before Schadt left Roche – things got ugly.
The first trace of trouble came when Schadt tried to remotely access his office computer but couldn’t. He called his wife and asked if she could go to his office and check it. Maybe it had been turned off by mistake? She went in and found everything in his office had disappeared. Someone had taken all his stuff. Worse, someone had told the president of Roche that Schadt had directly based his own-written software on the code from the other company, Affymetrix, which was illegal and potentially a huge problem for Roche. Schadt knew he was innocent, but one of his lawyers told him, quite plainly, that the truth is irrelevant: plenty of innocent people go to jail.
On Christmas Eve 1999, Schadt was shopping for presents with his two young kids when his wife phoned to say someone had just telephoned her and needed to talk urgently. Schadt had never heard of the person so he didn’t call back and continued shopping with his kids. But the mysterious caller phoned again, this time clarifying that he was from the FBI. Schadt panicked. Why was the FBI after him? Or was this someone pretending to be from the FBI? Tired and scared, he even thought that Affymetrix might have hired a hit man to kill him.
When he got home, it was indeed FBI agents who were waiting for him in a black car outside his house. Schadt was accused of taking the allegedly illegal computer code to the new start-up company.8 His life became unbearable for months.9 He saw endless numbers of lawyers. With one, Schadt spent three days going through all the details of the computer codes. The lawyer seemed to take it all in. But at the end, the lawyer politely asked whether algorithms were like logarithms. They’re nothing like the same – though the words do rhyme.
Eventually, Schadt met lawyers who could understand computer code, and ultimately it was concluded that he had written his code independently. He became a hero to the academic community, because he had written the computer code simply to do better science and he took on a huge corporation to do so. And then, out of the blue, Schadt’s parents called and said he was welcome home again. They had been to a Christian conference where one of the speakers struck a chord with them, leading to them to see a counsellor. They now openly celebrated their son’s success.10
Schadt’s battles – with his family, the military, Affymetrix and the US judiciary – prepared him for the real fight of his life: to establish a new approach in medical research. Whether intentionally or not, Schadt’s shorts outfit reminds you of his focus. He’s got no time to waste on piffle like thinking about what to wear. Yet, when Schadt visited me on one occasion in 2008, he e-mailed ahead to check if wearing shorts would be OK, or whether he’d need other clothes – perhaps at the evening dinner. Like many successful revolutionaries, he can play at being conventional when needed – he had trained in the air force, after all.
The problem with translating our knowledge into medicine, Schadt and his colleagues argue, is that our effort has just been too simplistic: we’ve naively focused on finding a causative gene or protein and then a drug to fix it. Perhaps this approach is an inevitable consequence of our brain having evolved to think in terms of one thing leading to the next, or perhaps it’s because ‘one disease, one gene, one cure’ is a straightforward plan readily sold to investors.11 But our pipeline of new drug development is pretty blocked, because, without mastering the complexity of the system, Schadt argues, it’s almost impossible to know what effect a drug will have on something as intricate as the human body.
The side-effects of any new drug, for example, usually only become apparent during a clinical trial because they are so hard to predict in advance. In fact, it’s been estimated that about 90 per cent of drugs fail to reach the marketplace because of unexpected side-effects, a direct consequence of the complexity and inter-connectedness of human biology. The thought that most life processes don’t work in a straightforward linear way drives most scientists to despair. But here’s Schadt’s punchline: breathe it all in, embrace complexity, and let’s just establish a whole new way of doing things.
Of course, Schadt’s advocacy that genes interact in complex ways is not a new idea in itself. In his 1959 BBC Radio lectures, Peter Medawar had said that ‘the forms of heredity that can be seen to obey fairly simple rules are not a representative sample of heredity as a whole’.12 But what Schadt and his like-minded peers are really doing that’s new is to establish a way to tackle the complexity; the time is ripe, they say, to navigate us through the fog of genetic interactions and reach medically useful ideas. Science has reduced humanity to a list of genes and components; now to figure out how those elements give rise to the beast itself.
Schadt, and his kindred spirits, argue that what’s needed is to reconstruct the underlying networks of interactions between genes causal to traits associated with disease. He thinks that to do this we can sequence DNA, check the functions of cells, measure disease markers – such as levels of sugar and insulin in our blood – and in short obtain an enormous set of information for a huge number of people, to work out which set of genes influences each disease or human trait.
The problem is that, even with just ten genes, the number of possible interactions between them is about 1018 (a one followed by eighteen zeroes – or a billion billion). And, of course, we don’t each have ten genes; we have 25,000. The raw data alone from the sequence of genes in 1,000 individuals amounts to over 1012 bytes.13 Clearly, computing must take centre stage for analysing all the information. The wild-eyed anarchist in a lab coat mixing cells and chemicals is a poor reflection of what’s needed for this kind of science. Here, there needs to be multi-disciplinary multi-talented teams that include scientists clicking at computer screens and thinking about abstract algorithms seemingly far removed from the biological processes they’re studying. Astronomy, climate change and particle physics have all embraced computationally intensive science long ago; the approach must now be used for studying human health.14 But before we wholeheartedly nod in agreement, let us recall an allegory with an appropriate cautionary message – a parable from Argentine storyteller Jorge Luis Borges.
