These clock genes regulate 24-hour circadian rhythms affecting hormonal, body temperature, sleep and behavioural patterns.
The research findings provide the first evidence of altered circadian gene rhythms in brain tissue of people with depression and suggest a physical basis for many of the symptoms that depressed patients report.
The study involved researchers from University of California Irvine Health, University of Michigan, University of California Davis, Cornell University, the Hudson Alpha Institute for Biotechnology and Stanford University.
"Our findings involved the analysis of a large amount of data involving 12,000 gene transcripts obtained from donated brain tissue from depressed and normal people," said Dr William Bunney, the study's senior author, and Distinguished Professor of Psychiatry & Human Behaviour at UC Irvine.
"The findings provide clues for potential new classes of compounds to rapidly treat depression that may reset abnormal clock genes and normalise circadian rhythms," Bunney said.
Circadian clock genes play an important role in regulating many body rhythms over a 24-hour cycle. Although animal data provide evidence for the circadian expression of genes in brain, little has been known as to whether there is a similar rhythmicity in the human brain.
The investigators isolated multiple RNA samples from six regions of each brain and arranged the gene expression data around a 24-hour cycle based on time of death.
Several hundred genes in each of six brain regions displayed rhythmic patterns of expression over the 24-hour cycle, including many genes essential to the body's circadian machinery.
"There really was a moment of discovery when we realised that many of the genes that we saw expressed in the normal individuals were well-known circadian rhythm genes - and when we saw that the people with depression were not synchronised to the usual solar day in terms of this gene activity," said Jun Li, an assistant professor in the Department of Human Genetics at the University of Michigan who led the analysis of the massive amount of data generated by the rest of the team.
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