EBioMedicineNeurosciencePeer-reviewed journal article
Region-specific microRNA alterations in marmosets carrying SLC6A4 polymorphisms are associated with anxiety-like behavior.
Natalia PopaiD, Dipankar Bachar, Angela RobertsiD, Andrea M. SantangeloiD, Eduardo GasconiD
- Citations
- 14
- FWCI
- 1.32
- Field percentile
- top 22%
Abstract
Psychiatric diseases such as depression and anxiety are multifactorial conditions, highly prevalent in western societies. Human studies have identified a number of high-risk genetic variants for these diseases. Among them, polymorphisms in the promoter region of the serotonin transporter gene (SLC6A4) have attracted much attention. However, due to the paucity of experimental models, molecular alterations induced by these genetic variants and how they correlate to behavioral deficits have not been examined. Marmosets have emerged as a powerful model in translational neuroscience to investigate molecular underpinnings of complex behaviors. Here, we took advantage of naturally occurring genetic polymorphisms in marmoset SLC6A4 gene that have been linked to anxiety-like behaviors. Using FACS-sorted cells from different brain regions, we revealed that marmosets bearing different SLC6A4 variants exhibit distinct microRNAs signatures in a region of the prefrontal cortex whose activity has been consistently altered in patients with depression/anxiety. We also identified DCC, a gene previously linked to these diseases, as a downstream target of the dysregulated microRNAs. Significantly, we showed that levels of both microRNAs and DCC in this region were highly correlated to anxiety-like behaviors as well as to the response to citalopram, a selective serotonin re-uptake inhibitor and widely prescribed anti-depressant. Our findings establish links between genetic variants, molecular modifications in specific cortical regions and complex behavioral/pharmacological responses, providing new insights into gene-behavior relationships underlying human psychopathology.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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