Send to printer »

Feature Articles : Sep 15, 2012 (Vol. 32, No. 16)

The Search for Depression Biomarkers

Predicting Drug Responses More Accurately Is Driving Efforts
  • Patricia Fitzpatrick Dimond, Ph.D.

Antidepressants effectively treat only about 50% of patients, and current drug treatment is mostly a trial-and-error process, often taking months to find a helpful drug.

Scientists continue to search for biomarkers to help guide therapy and, potentially, improve chances of discovering new drugs.

Clinicians treating depression say that one reason for the lack of predictive biomarkers is that little is known with absolute certainty about how antidepressants improve mood. All currently approved medications for depression act in a similar way, increasing the availability of monoamine neurotransmitters like serotonin in the brain. According to scientists, genetic variation accounts for at least part of why some individuals, but not others, may develop depression.

Genetic variations can, for example, explain why some medications work better than others in an individual. If a genetic mutation affects the target of a particular drug in the cell, it’s unlikely to work.

And as much as clinicians would like a reliable set of relatively easily accessible biological markers for depression, finding them remains challenging. Writing in the May 2012 DANA foundation publication Cerebrum, Madhukar H. Trivedi, M.D., and Marisa Toups, M.D., said “But despite all the enthusiasm, we have yet to see biomarkers used in doctors’ offices. The single biggest hurdle is that many of the recent discoveries have been in animals, and translating them to humans has been very difficult. First, there are no direct models of mental illness in animals—what does it mean for a mouse to be depressed?”

Mice though, like humans, show discernable anxiety. In 2008, Chen Xy and colleagues working at the Weill Medical College of Cornell University reported that they had developed a variant brain derived neurotrophic factor (BDNF) mouse that reproduces the phenotypic characteristics of humans with the variant allele. Either BDNF expression or signaling have been associated with the development of some human neuropsychiatric disorders, including major depression.

Variant BDNF(Met) mice expressed the gene at normal levels the investigators reported, but its secretion from neurons was defective. In this context, the BDNF(Met/Met) mouse represents a unique model that directly links altered activity-dependent release of BDNF to a defined set of in vivo consequences.

When placed in conflict settings, BDNF (Met/Met) mice display increased anxiety that the antidepressant fluoxetine failed to normalize. A genetic variant BDNF, they concluded, may thus play a key role in genetic predispositions to anxiety and depressive disorders.

Last April, researchers at Dalhousie University Faculty of Medicine, Northwestern University, Ohio State University College of Medicine, and The Jackson Laboratory, Bar Harbor reported that they had identified a group of genetic biomarkers that they say is associated with early-onset major depression, suggesting the possibility of an objective blood test in the future.

The researchers reported that they carried out genome-wide transcriptomic profiles in the blood of two animal models of depression that represented the genetic and the environmental, stress-related, etiology of major depressive disorder (MDD). They analyzed this combined set of 26 candidate blood transcriptomic markers in a sample of fourteen 15–19-year-old teenagers with MDD (N=14) and 14 with no disorder (ND).

A panel of 11 blood markers differentiated study participants with early-onset MDD from the ND group. Four transcripts, discovered from the chronic stress animal model, correlated with maltreatment scores in youths.

This pilot data, the investigators said, suggest that their approach may lead to clinically valid diagnostic panels of blood transcripts for early-onset MDD to reduce diagnostic heterogeneity in this population as well as advance individualized treatment strategies.

A second larger study will involve using hundreds of blood samples from Nova Scotian teenagers.

MDD Gene

Scientists at Texas Biomedical Research Institute (TxBiomed) and Yale University say they may have identified a single gene that could help identify individuals at risk for major depression.

Led by John Blangero of TxBiomed and David Glahn, Ph.D., of Yale the scientists used blood samples from 1,122 people enrolled in the Genetics of Brain Structure and Function Study, a large family study involving people from 40 extended Mexican American. The scientists analyzed about 11,000 endophenotypes, or heritable factors, and searched for those linked with the risk of major depression.

They found that disease risk correlated most strongly with expression levels of a gene, RNF123 encoding the enzyme E3 ubiquitin-protein ligase, which among its other functions, helps regulate neuron growth. Having found this risk factor, further analysis directed scientists to an area on chromosome 4 containing genes that appear to regulate RNF123.

GEN asked Dr. Blangero how his and his colleagues’ approach differed from other genetic studies of depression markers. “Our study is the first to formally screen large numbers of potential endophenotypes using an objective statistical measure, the endophenotype ranking value, which optimally requires family-based information to estimate,” he said. “Our study is also the first study on the genetics of major depression to employ large-scale transcriptional profiling to search for genes that are involved in the depression pathway.”

Dr. Blangero told GEN that to accomplish the study’s goals, the team used microarrays for large-scale genome-wide transcriptional profiling and conventional genotyping approaches to query the whole genome in its linkage-based search for quantitative trait loci influencing risk of major depression.

“While we did not employ next-generation sequencing for this article, we now have complete whole-genome sequence (WGS) on more than 500 of these subjects and are in the process of obtaining WGS on all of them. “This exhaustive assessment of all existing genetic variation in these families should allow us to rapidly identify the underlying functional variants and causal genes involved in this disease whose biology is so poorly understood.”

Of particular note, he said, was “To our surprise that the resulting gene expression phenotypes (derived from white blood cells) exhibited greater evidence for shared genetic control with depression than did our other more standard endophenotypes derived from neurocognitive assessments and structural brain imaging.”

GEN asked Dr. Glahn what next steps in this research will be. “Now that we have data on upwards of 500 people, we are looking at the genes associated with the RNF123 transcript to determine what variations influence transcript levels. “We have also identified families with a high prevalence of depression, and we plan to study those families in depth with transcriptional profiling to discern a pattern associated with the depression.”