Abstract
Current pharmacological treatments for major depressive disorder (MDD) are ineffective in a significant proportion of patients, and the identification of new antidepressant compounds has been difficult. ‘Connectivity mapping’ is a method that can be used to identify drugs that elicit similar downstream effects on mRNA levels when compared to current treatments, and thus may point towards possible repositioning opportunities. We investigated genome-wide transcriptomic changes to human hippocampal progenitor cells treated with therapeutically relevant concentrations of a tricyclic antidepressant (nortriptyline) and a selective serotonin reuptake inhibitor (escitalopram). We identified mRNA changes common to both drugs to create an ‘antidepressant mRNA signature’. We used this signature to probe the Library of Integrated Network-based Cellular Signatures (LINCS) and to identify other compounds that elicit similar changes to mRNA in neural progenitor cells. Results from LINCS revealed that the tricyclic antidepressant clomipramine elicited mRNA changes most similar to our mRNA signature, and we identified W-7 and vorinostat as functionally relevant drug candidates, which may have repositioning potential. Our results are encouraging and represent the first attempt to use connectivity mapping for drug repositioning in MDD.
Highlights
Major depressive disorder (MDD) is a complex disorder characterised by a pathological distortion of affect (Jones et al, 2002)
Our results revealed no false discovery rate (FDR)-significant transcripts associated with escitalopram dose, but 324 were nominally affected by dose
Our results revealed that two antidepressants with differing upstream mechanisms activate a set of both distinct and common downstream molecular pathways in hippocampal progenitor cells
Summary
Major depressive disorder (MDD) is a complex disorder characterised by a pathological distortion of affect (Jones et al, 2002). Evidence suggests that antidepressant drugs of different classes may evoke some convergent downstream effects at the messenger RNA (mRNA) level (Malki et al, 2012). Genetic factors can further moderate how initial upstream targets within each drug class relay their effects on to downstream transcriptional pathways (Malki et al, 2011; Powell et al, 2013). There has been some promising evidence of drug repositioning in other fields of medicine using ‘connectivity mapping’, facilitated by the use of the Library of Integrated Network-based Cellular Signatures (LINCS; Lamb et al, 2006; Vempati et al, 2014). LINCS is essentially a mRNA library characterising the effects of over 20,000 small-molecule compounds, including over 1300 FDA approved drugs, in cell lines (Qu and Rajpal, 2012). LINCS allows users to identify compounds that elicit similar or opposite mRNA profiles to an experimentally-derived query mRNA signature (Qu and Rajpal, 2012)
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