During the study, the team measured whole-genome gene expression differences in blood samples from subjects with mood disorders, who had low mood vs. those that had high mood at the time of the blood draw. They also measured changes in gene expression in the brain and blood of a mouse model.
The team then integrated the human blood gene expression data with animal model gene expression data, an approach called Convergent Functional Genomics.
With the help of the approach, they were able to identify five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signalling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6, and Ptprm).
A predictive score developed on the basis of five biomarkers for high mood and five for low mood showed sensitivity and specificity as the factors responsible for the mood state.
These studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.
The findings may help in developing new treatments for the disease.