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Effects of Aging on Circadian Patterns of Gene Expression in the Human Prefrontal Cortex

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Effects of Aging on Circadian Patterns of Gene Expression in the Human Prefrontal Cortex Effects of aging on circadian patterns of gene expression in the human prefrontal cortex Cho-Yi Chena, Ryan W. Loganb, Tianzhou Maa, David A. Lewisb, George C. Tsenga, Etienne Sibilleb,c,d,e, and Colleen A. McClungb,1 aDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261; bDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213; cCampbell Family Mental Health Research Institute of Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada M6J 1H4; dDepartment of Psychiatry, University of Toronto, Toronto, ON, Canada M5T 1R8; and eDepartment of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada M5S 1A8 Edited by Joseph S. Takahashi, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, and approved October 6, 2015 (received for review April 27, 2015) With aging, significant changes in circadian rhythms occur, in- cognitive performance and executive function. Several studies in cluding a shift in phase toward a “morning” chronotype and a loss humans have reported diurnal differences in cognitive perfor- of rhythmicity in circulating hormones. However, the effects of mance and a significant decrease in performance following cir- aging on molecular rhythms in the human brain have remained cadian rhythm disruption (10–12). Interestingly, these measures elusive. Here, we used a previously described time-of-death anal- vary by age with older adults performing better on cognitive tasks ysis to identify transcripts throughout the genome that have a in the morning and getting worse throughout the day (12, 13). In significant circadian rhythm in expression in the human prefrontal older women, there is a direct correlation between weak circadian cortex [Brodmann’s area 11 (BA11) and BA47]. Expression levels activity rhythms and poorer executive function (14). In preclinical were determined by microarray analysis in 146 individuals. Rhyth- studies, mice trained in cortical-driven cognitive tasks show pro- – micity in expression was found in ∼10% of detected transcripts nounced diurnal differences in performance (15 17). Moreover, – (P < 0.05). Using a metaanalysis across the two brain areas, we mice housed under a shortened day (20-h light dark cycle of 10-h identified a core set of 235 genes (q < 0.05) with significant circa- light, 10-h dark) displayed reduced cognitive flexibility and a loss dian rhythms of expression. These 235 genes showed 92% concor- of dendritic length and complexity in the PFC (18). dance in the phase of expression between the two areas. In Physiological and activity rhythms are generally known to addition to the canonical core circadian genes, a number of other deteriorate with aging and show a phase advance toward early genes were found to exhibit rhythmic expression in the brain. morning wakening (19, 20). Daily rhythms in hormones like mel- atonin and cortisol are decreased as are sleep and body temper- Notably, we identified more than 1,000 genes (1,186 in BA11; 1,591 ature rhythms in older individuals (21). Interestingly, the recognized in BA47) that exhibited age-dependent rhythmicity or alterations in stimulation of alertness and cognition by blue light seen in young rhythmicity patterns with aging. Interestingly, a set of transcripts people is diminished in older people, suggesting decreased input to gained rhythmicity in older individuals, which may represent a com- the clock (22). Moreover, the addition of serum from older people pensatory mechanism due to a loss of canonical clock function. Thus, to cultured cells that express a circadian reporter construct (Bmal1- we confirm that rhythmic gene expression can be reliably measured luciferase) leads to a shortening of molecular rhythms and a phase in human brain and identified for the first time (to our knowledge) advance that is not seen with serum from younger people (23). significant changes in molecular rhythms with aging that may con- This finding suggests the presence of circulating factors that alter tribute to altered cognition, sleep, and mood in later life. molecular rhythms with age. aging | postmortem | circadian rhythms | gene expression | Significance prefrontal cortex Circadian rhythms are important in nearly all processes in early all processes in the brain and body are controlled by the brain. Changes in rhythms that come with aging are Na 24-h circadian rhythm. These rhythms are important in associated with sleep problems, problems with cognition, regulating the sleep/wake cycle, metabolism, alertness, cognition, and nighttime agitation in elderly people. In this manuscript, and other processes (1). Environmental or genetic disruptions to we identified transcripts genome-wide that have a circadian circadian rhythms are strongly associated with chronic sleep prob- rhythm in expression in human prefrontal cortex. Moreover, lems, increased rates of cancer, lowered immune function, metabolic we describe how these rhythms are changed during normal disorders, and psychiatric disorders (2, 3). The molecular clock is human aging. Interestingly, we also identified a set of pre- controlled by a transcriptional/translational feedback loop with the viously unidentified transcripts that become rhythmic only in circadian locomotor output cycles kaput (CLOCK) and brain and older individuals. This may represent a compensatory clock muscle Arnt-like protein 1 (BMAL1; also known as ARNTL) pro- that becomes active with the loss of canonical clock function. teins acting as the major transcriptional activators, and the Period These studies can help us to develop therapies in the future (PER1, PER2, PER3) and Cryptochrome (CRY1, CRY2) proteins for older people who suffer from cognitive problems associated acting as the major repressors (4). This core circadian feedback loop with a loss of normal rhythmicity. regulates the diurnal expression patterns of many different genes as – it is estimated that 10 20% of all transcripts have a circadian rhythm Author contributions: G.C.T. and C.A.M. designed research; C.-Y.C., T.M., and E.S. per- (1, 5). Although the master circadian pacemaker in the supra- formed research; D.A.L. and E.S. contributed new reagents/analytic tools; C.-Y.C., R.W.L., chiasmatic nucleus (SCN) of the hypothalamus synchronizes rhythms T.M., and G.C.T. analyzed data; and C.-Y.C., R.W.L., D.A.L., and C.A.M. wrote the paper. throughout the brain and body, the genes that control circadian The authors declare no conflict of interest. rhythms are expressed in nearly every cell (6). In recent years, it has This article is a PNAS Direct Submission. become apparent that these genes serve important functions in Data deposition: The data reported in this paper have been deposited in the Gene Ex- specific brain regions, including the control of daily rhythms in pression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE71620). neuronal activity and the response to environmental stimuli (7–9). 1To whom correspondence should be addressed. Email: [email protected]. Evidence from preclinical and clinical studies suggests rhythms This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. in the prefrontal cortex (PFC) are particularly important for 1073/pnas.1508249112/-/DCSupplemental. 206–211 | PNAS | January 5, 2016 | vol. 113 | no. 1 www.pnas.org/cgi/doi/10.1073/pnas.1508249112 Downloaded by guest on September 28, 2021 In the human brain, the investigation of brain region-specific the date and the longitude and latitude of the place of death. Subject’s TOD transcriptional rhythms across the life span has been challenging was set as ZT = t hours after previous sunrise (if t < 18) or before next sunrise due to the lack of control of environmental factors, such as time (if t ≥−6). of death, sleep–wake cycles, and exposure to other factors known to influence rhythms. A recent study overcame some of these Microarray Data Preprocessing. All samples were analyzed using the Affy- obstacles by ordering postmortem samples around a 24-h clock metrix Human Gene 1.1 ST microarray platform. Gene expression values were based on their time of death, essentially reconstructing a pseudo- corrected, quantile-normalized, and log2-transformed via Affymetrix Ex- time series by treating each individual sample as an indepen- pression Console software (build 1.2.1.20) using RMA algorithm. A total of dently sampled data point over one 24-h cycle (24). In 55 healthy 33,297 probe sets were available on each array; only those probe sets with annotated gene symbols were selected (20,237 gene symbols). If a gene was controls and 34 patients with major depressive disorder (MDD), represented by multiple probe sets, the one with the largest intensity inter- they found robust expression rhythms of hundreds of genes in quartile region was selected to represent that gene. Microarray data for each several brain regions in control subjects, with many of these brain region were analyzed separately. The raw and processed microarray genes displaying significantly disrupted or altered expression data were deposited in the National Center for Biotechnology Information rhythms in subjects with MDD. Here, we used a similar analytical Gene Expression Omnibus database (GSE71620). approach to identify rhythmic transcripts in two areas of the PFC [Brodmann’s area 11 (BA11) and BA47] in a large sample of 146 Detection of Circadian Gene Expression Patterns. Nonlinear regression was subjects from across the life span with no history of psychiatric used to detect circadian gene expression patterns. All individual samples were illness. This large sample allowed us to investigate the effects of ordered by their TODs. Temporal expression was fitted to a sinusoidal curve normal aging on molecular rhythms in the human PFC and to using the nonlinear least-squares method. The coefficient of determination test the prediction that older individuals display altered or even a (R2) was used as a proxy of goodness-of-fit. The empirical P value was esti- loss of molecular rhythms in this brain region.
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