At Western University, scientist Ben Laufer is making strides in demonstrating the rampant genome-wide changes related to fetal alcohol syndrome and related disorders, a group of diseases that have been difficult to study due to heterogeneity in exposure and subsequent symptoms. By using QIAGEN’s Ingenuity Pathway Analysis (IPA), he has proven that widespread epigenetic changes once written off as biological noise are in fact effects of alcohol exposure during fetal development.
Laufer says the real power of IPA is in its systems approach — the tool lets him analyze a constellation of data that would be daunting to study manually. Because that data encompasses such a broad spectrum of biological changes, even careful inspection would likely miss the common threads. But IPA can process and make sense of these large data sets, offering the narrative that explains how these changes relate to each other. In a recent experiment, Laufer fed IPA several data sets that seemed to show little biological significance. “After just 10 minutes in IPA, we had our eureka,” he says. “It was great.”
Studying fetal alcohol spectrum disorders (FASD) offers Laufer the opportunity to do what he always wanted to: make a difference in medicine. Current diagnostics for children suspected to have FASD are rudimentary — measuring distance between the eyes, for example, to see if there’s a deviation from normal. If Laufer can determine a telltale epigenetic code indicative of FASD, it would be a major step toward improved diagnostics in the field.
The severity of FASD symptoms may correspond to the fetal development stage at the time of alcohol exposure, as well as the level and duration of exposure. These confounding variables make studying the disorders very difficult. “A lot of past research has been bogged down by heterogeneity and the challenge of not being able to get past it statistically,” Laufer says. “This area has been underexplored because of that.”
Much of Laufer’s current work involves using microarrays to study mice modeling fetal alcohol exposure, with a specific focus on neurological changes. In a recent research project, he used gene expression arrays, DNA methylation arrays, and microRNA expression arrays. “It was an overwhelming amount of data,” Laufer says. “I needed to incorporate three different technologies to get a picture of what’s going on in fetal alcohol exposure in the brain.”
That data storm prompted Laufer to use IPA to make sense of the data, which showed many small changes across the genome. “IPA helped us pick out these buried expression modules that we were seeing,” he says. “A lot of standard analysis approaches can’t tackle that type of data.”
What IPA found was that the changes were not due to noise, as the team had feared since a full third of the epigenome showed alterations even to moderate alcohol exposure. “We didn’t expect to see that broad of a change,” Laufer says. “Ingenuity not only confirmed the results but gave them a degree of power beyond just statistics by incorporating the biology into it.” IPA results indicated that the changes were not random, as they had appeared in the raw microarray data. In fact, they were associated with a number of biological functions that had previously been linked to FASD — and even some novel ones that have been implicated in schizophrenia and autism.
To learn more about Laufer’s work, including future direction of his work and how IPA saves time in his workflow, check out this case study.