Abstract

There is a growing emphasis in the field of psychiatry on the need to identify candidate biomarkers to aid in diagnosis and clinical management of depression, particularly with respect to predicting response to specific therapeutic strategies. MicroRNAs are small nucleotide sequences with the ability to regulate gene expression at the transcriptomic level and emerging evidence from a range of studies has highlighted their biomarker potential. Here we compared healthy controls (n=20) with patients diagnosed with major depression (n=40) and who were treatment-resistant to identify peripheral microRNA biomarkers, which could be used for diagnosis and to predict response to electroconvulsive therapy (ECT) and ketamine (KET) infusions, treatments that have previously shown to be effective in treatment-resistant depression (TRD). At baseline and after treatment, blood samples were taken and symptom severity scores rated using the Hamilton Depression Rating Scale (HDRS). Samples were analyzed for microRNA expression using microarray and validated using quantitative PCR. As expected, both treatments reduced HDRS scores. Compared with controls, the baseline expression of the microRNA let-7b was less by ~40% in TRD patients compared with controls. The baseline expression of let-7c was also lower by ~50% in TRD patients who received ECT. Bioinformatic analysis revealed that let-7b and let-7c regulates the expression of 27 genes in the PI3k-Akt-mTOR signaling pathway, which has previously been reported to be dysfunctional in depression. The expression of miR-16, miR-182, miR-451 and miR-223 were similar to that in controls. Baseline microRNA expression could not predict treatment response and microRNAs were unaffected by treatment. Taken together, we have identified let-7b and let-7c as candidate biomarkers of major depression.

Highlights

  • Depression is the most prevalent psychiatric disorder and current projections indicate that it will be the leading cause of disability by the year 2030.1 Subjective diagnostic schemes such as DSM-IV, ICD-10 are constrained in their ability to accurately diagnose depression[2] and its subtypes such as treatment-resistant depression (TRD), which affects a significant proportion of patients.[3,4] As such, the potential benefits of using biomarkers to improve diagnostic precision and refine therapeutic strategies are significant.[5]

  • There were no correlations between age, gender or medication profile and the electroconvulsive therapy (ECT)/KET-induced reduction in Hamilton Depression Rating Scale (HDRS) scores

  • No microRNAs identified at baseline in TRD patients were predictive of response to ECT or KET and there were no other microRNAs that were affected by treatments

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Summary

Introduction

Depression is the most prevalent psychiatric disorder and current projections indicate that it will be the leading cause of disability by the year 2030.1 Subjective diagnostic schemes such as DSM-IV, ICD-10 are constrained in their ability to accurately diagnose depression[2] and its subtypes such as treatment-resistant depression (TRD), which affects a significant proportion of patients.[3,4] As such, the potential benefits of using biomarkers to improve diagnostic precision and refine therapeutic strategies are significant.[5]. MicroRNAs are small non-coding nucleotide sequences (18– 24 nt), which regulate the expression of ~ 60% of the mammalian genome.[8] Each microRNA can alter the translation of multiple messenger RNAs (mRNA) into proteins and each mRNA is the target of multiple microRNAs. MicroRNAs are small non-coding nucleotide sequences (18– 24 nt), which regulate the expression of ~ 60% of the mammalian genome.[8] Each microRNA can alter the translation of multiple messenger RNAs (mRNA) into proteins and each mRNA is the target of multiple microRNAs This has led microRNAs to being known as ‘master-regulators’ of cellular processes.[9] The existence of microRNAs in bodily fluids such as blood and saliva[10] has provided the impetus to evaluate their potential as biomarkers of illnesses and to predict response to different therapeutic strategies. In the context of depression, several microRNAs have shown biomarker potential including miR-16,11 miR-182,12

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