Background
The instinct to stay alive and maintain well-being is fundamental to all of us, yet nearly a million lives are lost to suicide each year around the world, presenting a significant public health challenge. Despite ongoing efforts, effectively identifying the risk and preventing suicide remains a complex issue. Biologically, suicide often involves disruptions in brain structure, function, and chemistry, particularly linked with mood disorders like depression, which contribute to over 70% of suicides globally. Alarmingly, despite our efforts, rates of depression and suicide have been increasing worldwide for the past two decades. The increasing prevalence of suicide deaths underscores the pressing need for better strategies to comprehend and address the multifaceted factors contributing to suicide.
Technology overview
Researchers at The University of Texas at Austin utilized next-generation RNA sequencing to analyze how genes in specific parts of the brain relate to mood disorders like depression and bipolar disorder, as well as to suicidal thoughts and actions. They found patterns in brain gene activity that were translated in blood gene activity linked to both negative outcomes, like mood and psychiatric symptoms and suicide. Furthermore, brain and blood gene activity were also discovered for positive life outcomes, like living longer. They discovered that certain gene activity patterns were associated with factors like severity of major depressive disorder and suicide risk. The research team then created a diagnostic panel to predict major depression severity and suicide risk.
Benefits
- First in class brain-derived blood diagnostics for depressive disorder and related suicide risk
- Benefits millions worldwide given ~10% of adults suffer from depressive symptoms at some point during their lifetime and over 700,000 people die by suicide annually (https://www.iasp.info/wspd/references/)
- Will inform novel biomarker-guided pharmacotherapeutics, potentially saving countless lives
Applications
- Mood disorder understanding: Identifying gene expression patterns associated with mood disorders deepens understanding of their biological basis.
- Biomarker development: Gene expression patterns can serve as biomarkers for diagnosis, prognosis, and treatment response prediction.
- Drug development: Targeting molecular pathways identified may lead to the development of novel therapeutics.
- Precision medicine: Tailoring treatment based on individual molecular profiles may improve efficacy.
- Suicide prevention: Identifying gene expression patterns associated with suicide risk informs prevention efforts.
Opportunity
UT Austin is seeking funding for clinical trials and a Sponsored Research Agreement with the potential to commercialize a pipeline of novel biomarker-guided diagnostics, and pharmacotherapeutics.
