How Plants in Space Might Be a Cautionary Tale for Precision Medicine

A version of this originally appeared in the Timmerman Report.

When President Obama announced the Precision Medicine Initiative (PMI) back in 2015, it was a moment not unlike when a pop star drops a new album. We know she’s working on something, but details are scarce until that midnight announcement on iTunes. Then Boom!

This was Precision Medicine’s coming out party, the moment when a sensible, but somewhat obscure biomedical research concept went mainstream. And it’s exciting! As someone who’s been on the Precision Medicine bandwagon for a while now, I’m glad to see the concept getting more attention.

And yet, I’ve found myself worrying that we haven’t learned from the past and once again we’re riding a hype rocket destined to crash and burn like the first (and the second, third, and fourth) attempt at getting that SpaceX booster stage to land on a platform in the Pacific. While we’ve learned an amazing amount about human genetics over the past few decades, there’s much more we still don’t know and understand. While genetics races ahead, we are often still stuck in neutral when it comes to our understanding, much less predicting, the effect of the environment on genes’ effects on phenotype. It’s not easy.

Think, for a second about autoimmune diseases like type 1 diabetes, celiac, or rheumatoid arthritis. We don’t know what causes most of them, or causes them to flare up, but evidence is pointing toward the complex interplay of genes responding to environment. Consider the research on autoimmunity in Finland, compared with that across the border in Russia’s Karelia territory. As Moises Velasquez-Manoff has described, despite having quite homogeneous populations, and a similar geographic environment, the Finnish side sees much higher incidences of autoimmunity and allergies. Finnish scientists are increasingly pointing to genetic variations that have allowed Russians in a less-hygienic environment to avoid some of the autoimmune conditions that have emerged in Finland.

Or consider some provocative cohort studies from Florida, which characterize the timing of diagnosis for inflammatory bowel disease (IBD) in Cuban immigrants. Over time the duration between arrival in the US and diagnosis for IBD has been decreasing. Immigrants who arrived before 1980 had an average time to IBD diagnosis of 31.77 years, whereas immigrant who arrived after 1995 had an average time to diagnosis of 8.30 years. Given that the genetic background of immigrants is expected to be similar over time, the evidence suggests environmental factors.

Changes to the environment, including subtle ones that might seem trivial, sometimes matter.

Basic research often has a hard time justifying its existence in a world that wants immediate payoffs, but this is where it helps to look to basic research for guidance. In specific, we can learn from plants in space. There’s something here for everyone who studies eukaryotes.

I used to work on Arabidopsis thaliana – a genetically complex plant model organism that non-scientists would call a roadside weed. My ears perk up whenever I hear a story about Arabidopsis in the news. A few years back, researchers sent seeds up into space and grew Arabidopsis in microgravity. According to this report, while plant morphology was generally the same, the secondary branches and seed pods grew out perpendicular to the stem. On Earth, branches and pods normally point upwards, making an acute angle with the stem.

Who knew? But more to the point, who would have predicted this? I suspect no one would have guessed. Studies like this in space may help us actually figure out ahead of time, eventually, what effect microgravity will have on the growth pattern of seed pods, not to mention the many other things going on that weren’t or couldn’t be measured. It might also help us confidently predict the phenotypes that wouldn’t be affected. But this will take a lot of time and much better predictive models for gene regulation and phenotypic expression.

Arabidopsis had its genome sequenced back in 2000. It has been the subject of much basic and applied plant biology research—at this point, it’s probably the best studied plant on the planet (sorry maize—you had a good run). Scientists took advantage of the relatively easy transformation methods of Agrobacterium-mediated gene disruption and the ability to do blanket mutagenesis screens to define dozens of developmental and metabolic pathways. Many of those findings were used to improve numerous crop species (something to consider, for those in the US Congress who scoff at spending on basic research). But even after all this investment and research, I don’t think any scientist would suggest we’ve solved Arabidopsis to the point where we can predict everything about how it would grow, develop and otherwise behave when introduced to a new environment. But it’s a simpler model and easier place to start than human biology.

Parenthetically, speaking of unexpected things that happen to organisms in space, I’d suggest you check out this great piece on sex in space by Maggie Koerth-Baker and how the birds and the bees (and the rats) don’t (ahem) function the same in space either.

The heart of Precision Medicine is giving patients tailored treatments based on the molecular fingerprint of their diseases or conditions. There will be better efficacy and fewer side effects of new drugs because the treatment will be more specific. It’s no mystery why the current forefront of Precision Medicine is in cancer, where the strong causal connection between genetics and phenotype means therapeutics tailored for specific mutations in specific oncogenes or other biological processes provide a clear, straightforward path to Precision Medicine. If you have mutation X in gene Y, then take drug Z, which was developed to target that precise pathway.

This simple path works best, however, if the genetic penetrance is nearly complete and is not affected by environmental factors. Outside of cancer there aren’t as many examples of common genetic risk factors that have strong causality, even in combination. When you don’t have that kind of tight causal relationship, Precision Medicine is harder to pull off. The recent Omnigenic model proposed by Evan Boyle, Yang Li, and Jonathan Pritchard, which suggests most genes in a given tissue influence disease susceptibility, and the resulting debate about the applicability and value of GWAS moving forward shows the ongoing evolution in how genomicists and clinicians look at the interplay between our genes and phenotypes. The implication of a model like this is that even when looking only at the genetic side of things, strong causality driven by a small, testable number of variants for many diseases may just not be how biology works.

Adding to this, the plants in space example suggests to me that even as we learn more about genetic contributions and subsequent gene-by-environment interactions, some of that knowledge has an expiration date because of how our environment is changing.

Look at China. Thanks to decades of coal-burning for energy needs, China is currently experiencing a frightening air pollution challenge in some of its larger cities. How is that environmental phenomenon affecting gene expression and penetrance of variants? How will it affect development and gene expression and development of chronic diseases in the future? The airborne irritants and continual exposure might have no effect at all. Or they may have dramatic effects that will render some potential therapeutic pathways more or less effective in that population five, 20, 50 years from now. If, let’s say in a crazy hypothetical situation, which our government is assuring us will never happen, the global temperature was on track to increase by an average of 2 degrees Celsius or more over the next century, with all the cascading environmental changes that would cause, how would our genes respond?

I guess this just reduces down to a call for some circumspection. The very fact that Precision Medicine has entered the common lexicon is a reason for biomedical researchers to be cautious. Overpromising has already happened, but it could get a lot worse. At a time when funding for science is under siege, we don’t need examples at which naysayers can point as instances where scientists promised they’d cure cancer, but didn’t.

The science is already amazing. There are terrific things coming down the biopharma pipelines. And I think we’ll get a handle on gene-by-environment interactions—enough, at least, to meet the goal of creating specific health solutions based in part on each person’s genome for some diseases (although if the late Susan Lindquist’s HSP90 hypothesis turns out to be correct, many bets are off). But health and environment are moving targets. If we want Precision Medicine to be like the successful SpaceX launches, we need to keep an eye on things outside the controlled environments of our labs and clinical trials and do everything we can to embrace and understand the conversation our genes are having with the world outside.


Premature testification is not a laughing matter

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

“These studies, once validated, open an opportunity for creating tests that an expectant mother can take to see if she is producing these autoantibodies. ”

I wrote this statement back in July as part of a post describing the report that maternal autoantibodies to specific neural proteins correlated with the appearance of autism symptoms in the children of those mothers.  Little did I suspect that plans to create a test were already in the works.  Science recently reported (paywall) that researchers behind this study are teaming up with a testing company to develop and market a diagnostic test for maternal autoantibodies.

On the one hand I am much in favor of prognostic tests that will help us anticipate health problems.  I believe in many cases early knowledge and interventions can be helpful, even when there is no “cure.”  One of the hopes for the wave of genomic biomedical research going on now is that it will allow us to better estimate who will and will not come down with specific diseases based on clues in individuals’ DNA.  But on the other hand, it can be problematic when tests are created and released before the underlying biomedical hypothesis has been strongly vetted and supported. Continue reading

Maternal versus paternal effects on autism

All opinions my own and do not necessarily reflect those of Novo Nordisk

A report just out in JAMA Pediatrics (behind a paywall, but you can see the abstract at the link) reports the intriguing finding (by the way, just for the record, in blogging I’m finding it hard to find synonyms for “interesting.”  Please bear with me) that the recurrence risk for siblings of children with autism is seen even with half-siblings, albeit at a lower rate.  And more intriguing, the risk for sequential half-siblings is higher when the siblings share a mother than when they share a father.

This strikes a chord to me because it is consistent with other recent research I’ve described before, in which the presence of maternal autoantibodies to certain sets of neural proteins was predictive for development of autism.  As the abstract for the current work says, “the significant recurrence risk in maternal half-siblings may support the role of factors associated with pregnancy and the maternal intrauterine environment in ASDs.”  Whether maternal autoantibodies are associated with recurrence risk in this cohort is unknown.  The earlier study was at UC Davis; this one is from Denmark.  I’m generally of the mind that autism is an extremely complex collection of related syndromes, with many different contributing factors (but not vaccines!), so I think it’s best to just do the experiment and test for autoantibodies in the mothers of recurrent siblings.

And the nice thing about a country like Denmark is that this is probably feasible.  Unlike some other nations with extremely fragmented and incomplete health care systems (*cough*United States*cough*), Denmark has very good, integrated medical records.  Denmark also has very high standards for ethics and consent.  So finding a reasonable cohort of mothers and recontacting them may allow a test of whether an association to autoantibodies can be found here as well.  All towards figuring things out, all good.

I wrote to the authors of the study to ask about their work and how it might relate to the autoantibody studies and received the following email response from Dr. Diana E. Schendel of the CDC, via Therese Grønborg:

“Since our paper supports a role for maternal intrauterine effects in ASD, in addition to familial factors, our results are consistent with findings such as the (sic) UC Davis of maternal-derived factors that put the fetus at risk for ASD in pregnancy. One of the pregnancy related factors investigated in ASD etiology concerns abnormal immune system function – perhaps in the mother or perhaps in the fetus – that could impact brain development.

It is important to note that ASD has many potential causes and our study supports the notion of many etiologic pathways – both through family history and prenatal fetal  environments.”

This is a great statement as it jibes with my own views on diseases like autism–that we’re still very early in our understanding of what creates the presentation of a complex phenotype like autistic behavior, and that we need to keep looking and certainly not expecting simple explanations.  Finding explanations is not going to lead directly to new drugs, but greater understanding and a more personalized and nuanced view of each child’s challenges will help maximize their chances of finding success in life.

Are Biopharma reagent companies sitting on a pile of gold (or at least poptarts)?

All opinions are my own and do not necessarily reflect those of Novo Nordisk

The recent news about the United States Government monitoring a great deal of both general and specific electronic data has had one beneficial outcome (or at least, one I feel is beneficial):  it has made more people aware of what can actually be done with data, and also that we’re leaving massive amounts of personal data out there that can be traced to the behavior of individuals or organizations.  A few months ago, the Seattle Times published an article describing the explosion of big data and how that can be leveraged in so many ways.

This led me to speculate, in a very out-there kind of way, about what kinds of data Biopharma companies produce and whether there’s any hidden value in that.  Now, certainly companies are very careful about communicating information to the outside world.  Contracts with collaborators routinely contain embargo clauses, and presentations and posters are carefully vetted by legal and communications departments.  So companies would appear to be covered there.  But what kinds of data are out there that might be available, maybe not freely, but in potentia, to an interested audience?

Let me digress for a moment about mergers.  Biopharma over the last few years has seen a flurry of merger and acquisition activity.  The big pharma deals, like Pfizer/Wyeth, and Merck/Schering-Plough, have gotten big press, but there has also been a lot of consolidation among reagent suppliers.  To take one example, I’ve shamelessly taken Life Technologies’ merger history off of Wikipedia and condensed it into this table (after the jump): Continue reading

More developments in autism prediction

All opinions are my own and do not necessarily reflect those of Novo Nordisk

A recent publication about efforts to find early indicators for autism recently came out in the journal Brain and reports an intriguing observation about brain size.  The researchers sought to identify whether Magnetic Resonance Imaging (MRI) of the brains of infants and very young children could help to predict which children would go on to develop autism.  Like many pilot studies of this sort, the experiment was done simply by looking.  The researchers identified a cohort of newborn siblings of autistic children and also a control cohort without that risk factor and began taking MRI images of their brains at the age of between 6-9 months, and again at 12-15 months and 18-24 months.  Prior research has shown that having a sibling with autism greatly increases the probability that a child will also develop autism, so in this situation the expectation was that some of the sibling group would develop autism and researchers would retrospectively be able to look at the data collected during the study and identify MRI features that correlated with development of disease, should any exist.

The impetus behind this is that previous research has not shown any definitive behavioral clues in infants (6 months or younger) that predict the development of autism.  However, the earlier a child is diagnosed, the earlier behavioral interventions can be applied to help that child and his or her family cope with future challenges. Continue reading

Maternal immune systems, autism and the value of prediction

All opinions are my own and do not necessarily reflect those of Novo Nordisk

Following on these two papers (1, 2) published in Translational Psychiatry.

When I wrote about Gene by Environment (GxE) interactions and the possible health of children, I was describing how changes in maternal health might have an effect on child health at the level of what genes are turned on and off.  In that situation, there may be the possibility that actions by potential mothers before conceiving could positively impact child health.  In many other cases, though, the actions for predicted problems can only take place after birth.  Key point: in many cases these interventions are best done early, which is why states have newborn screening programs (although surprisingly the number of tested conditions varies from state to state).  In the context it’s interesting that a couple of recent papers have identified what may be a way to predict the development of autism in children.

The papers describe findings that may, if corroborated, have a large impact on autism prediction and, eventually, possible treatment and prevention for a subset of patients.  First, the team demonstrated through study of non-human primates that these human autoantibodies, when given to pregnant rhesus monkeys, led to significant changes in both maternal and infant monkey behavior.  Mothers in the experimental group showed more protective behavior toward their infants, and those offspring more frequently approached known and unknown monkeys despite not receiving commensurate social responses.  Male offspring also showed measurable increases in brain volume.  Second, the research team discovered that autoantibodies  to combinations of fetal brain proteins are found in a significant fraction of mothers who have Autism Spectrum Disorder (ASD) children, while mothers from the control group rarely have such autoantibodies to so many of these proteins. Continue reading