Mitochondria-related Transcriptomic Biomarkers for Differential Diagnosis of Major Depressive Disorder and Bipolar Disorder

Abstract

High rates of misdiagnosis in patients with bipolar disorder (BD) and major depressive disorder (MDD) can be attributed to similarities in the clinical presentation of these conditions and the fact that their diagnosis relies solely on behavioral observation. To address the need for an objective method of diagnosis, we examined transcriptomic biomarkers that could be used for differential diagnosis of BD and MDD. Our hypothesis was that some of the candidate transcriptomic biomarkers we identify would be related to the mitochondria due to its dysfunction being implicated in the development of mood disorders. First, we conducted differential expression analysis of RNA-seq data to identify candidate transcriptomic biomarkers as well as global transcriptomic differences between controls and BD and MDD cases. Then, we created and evaluated a range of predictive machine learning models to investigate the feasibility of determining a patient’s condition from their transcript abundance data and identify the most significant transcripts in these predictive models. Our findings suggest global transcriptomic downregulation in BD compared with controls and upregulation of GPX1, a gene essential for mitochondrial function, in BD and MDD compared with other transcripts. We also found a pseudogene of MRPL10 to be a potential biomarker for differential diagnosis of BD and MDD in our ML analyses. In summary, we found promising transcriptomic biomarker candidates for differential diagnosis of BD and MDD that can be investigated in future studies.