Circadian rhythms are known to regulate homeostatic physiology including sleep:wake, hormone production and body temperature, and their dysregulation with aging is accompanied by adverse health consequences (Monk et al., 1993; Monk et al., 2000; Hood and Amir, 2017), raising the possibility that health decline with age is caused in part by circadian dysfunction. Although the mechanisms responsible for age effects on circadian rhythms are unknown, signals from the central clock in the suprachiasmatic nucleus (SCN) dampen with age (Hood and Amir, 2017; Farajnia et al., 2012; Nakamura et al., 2011) and rhythms change in peripheral tissues in different ways (Nakamura et al., 2011; Yamazaki et al., 2002). Here, we aimed to develop a cell culture model to study the effect of aging on human rhythms of peripheral tissues. Given that serum can reset the clock in peripheral fibroblasts (Gerber et al., 2013), we questioned the extent to which serum factors normally contribute to peripheral rhythms of gene expression and how they might affect rhythms with age, given that blood-borne factors can influence other aspects of aging (Katsimpardi et al., 2014).

Using an established clinical study paradigm that sampled blood at 14:00 (Pagani et al., 2011; Qin et al., 2020), we collected blood from young (age 25–30) and old (age 70–76) apparently healthy individuals with behavioral and physiological outputs quantified by wearable devices, and we tested the hypothesis that age-dependent factors in the sera affect circadian rhythms in cultured fibroblasts. In support of this hypothesis, we show here that genes associated with oxidative phosphorylation and mitochondrial functions lose rhythmicity in fibroblasts exposed to aged serum factors. We also find evidence of reduced entrainment in terms of altered expression of several molecular clock genes (PER3, NR1D1, NR1D2, CRY1, CRY2, and TEF) when synchronized with aged serum. These findings suggest that age-related changes in blood borne factors contribute to impaired circadian physiology and the associated disease risks.

We enrolled eight old and seven young human subjects (Figure 1A, Figure 1—figure supplement 1), whose demographics are listed in Supplementary file 1. Behavioral and physiological assessments confirmed entrainment to the light-dark conditions local to the East Coast of the US. This is, for example, evident in the diurnal rhythms observed for physical activity (Figure 1C top, vector magnitude two-way ANOVA for time-of-day q=3.3E-06), light exposure (lux two-way ANOVA for time-of-day q=0.01), heart rate (bpm two-way ANOVA for time-of-day q=0.016) and sympathetic and parasympathetic nervous system indices derived from the Kubios heart rate variability analysis (two-way ANOVA for time-of-day q=0.099 and q=0.063, respectively). Notably BioPatch EKG data were not obtained from all subjects (Supplementary file 2).

Figure 1 with 2 supplements see all

Download asset Open asset

Figure 1—source data 1

A flow chart of the number of subjects included in each analysis present in this study.

https://cdn.elifesciences.org/articles/88322/elife-88322-fig1-data1-v1.xlsx

Download elife-88322-fig1-data1-v1.xlsx

Age-specific differences emerged for several outputs. The standard deviation of instantaneous heart rate values was significantly lower in the old compared to young (two-way ANOVA for age group q=0.042). As expected, the peak clock time (acrophase) of physical activity (triaxial accelerometry integrated as vector magnitude) was phase-advanced among old compared to young subjects, as was the midsleep time calculated from the MCTQ (Figure 1—figure supplement 2B), though not at a statistically significant level (p=0.055). On average, a lower rhythm-adjusted mean (mesor) heart rate of 55.8±3.5 bpm was found in old compared to 69.7±5.0 bmp in young, along with a higher heart rate variability (RR intervals) of 1106.7±80.5ms compared to 893.2±61.8ms, respectively (Figure 1B). Old subjects displayed lower activity in the sympathetic nervous system (SNS), and higher activity in the parasympathetic nervous system (PNS) compared to young (Figure 1—figure supplement 2A), with no significant difference in cortisol levels (Figure 1—figure supplement 2C). The overall high degree of variability rendered the cardiovascular trends, however, statistically not significant.

As noted above, blood was collected from these old/young individuals and the serum was used to synchronize BJ-5TA fibroblasts stably transfected with a BMAL1- luciferase construct (Gerber et al., 2013; Pagani et al., 2011; Ramanathan et al., 2012; Balsalobre et al., 1998). Circadian rhythms were assessed by luciferase assay (Ramanathan et al., 2012) over 4 days (Figure 2A). We conducted this study in a fibroblast line in order to build upon age-related changes found in a previous study which used human fibroblast lines (Pagani et al., 2011). An additional rationale for using this line is that it derives from normal human tissue, providing an advantage in studying normal physiology when compared to the commonly used U2OS line which is a genomically unstable osteosarcoma with chromosomal abnormalities (Al-Romaih et al., 2003). No significant differences were observed in the period, amplitude, and phase of the BMAL1-luciferase rhythm with young versus old serum treatment (Figure 2—figure supplement 1).

Figure 2 with 4 supplements see all

Download asset Open asset

To assess serum effects on circadian gene expression, we first performed RNA-seq on fibroblasts synchronized with serum from a single old or single young individual at two hour intervals and found that, compared to the first day of synchronization, the second day (36–58 hr after synchronization) showed greater differences in MESORs between young and old serum-treated groups (Figure 2—figure supplement 2). This is not surprising because the first day includes acute responses of the fibroblasts to serum, which can mask circadian rhythms, and so the second day is expected to reveal differences in endogenous rhythms between samples. Day 2 also revealed different phases of cyclic expression between young and old subjects for a larger number of genes. We proceeded to collect fibroblasts synchronized by sera from eight different subjects (four old, four young with two male and two female per group) at 2-hr intervals, from 32 to 58 hr post serum addition. This added an extra two timepoints to the second day to facilitate the calculation of rhythmicity. Using CircaCompare Parsons et al., 2020 and a weighted Bayesian Information Criterion (BIC)>0.75 cutoff, a significant number of genes were found to lose (1519 genes) or gain (637 genes) rhythmicity with age, underscoring the impact of age on the ability of serum to synchronize circadian rhythms, while 1209 genes were rhythmic in both groups (Figures 2B and 3A). Additionally, we used CircaCompare to estimate MESORs, amplitudes, and phases of gene oscillatory patterns in young and old groups, and to compare these cosinor parameters between groups (Figure 2C–D). Of the genes that were rhythmic in both groups, many also showed changes with age. For instance, 568 cyclically expressed genes showed a change in MESOR with age (q<0.05 for MESOR differences), with MESOR values increasing for 163 genes and decreasing for 405 genes in the old serum-treated samples (Figure 2C, Figure 2—figure supplement 3, right). Using q-values provided by CircaCompare we were able to detect changes in amplitude (Figure 2D, Figure 2—figure supplement 3, left) for only a small number of genes (39 genes had decreased amplitude in old and 2 had increased amplitude). Using CircaCompare and a q<0.05 cutoff for phase differences in genes rhythmic in young and old, we detected 20 genes with advanced phase, and 34 with delayed phases (Figure 2E). However, it is important to note that in these analyses low p-values can be driven by large sample-size such as in Figure 2—figure supplement 3 where distributions of MESORs and amplitudes across genes are assessed such that each gene represents a sample.

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

Raw data from LISA transcription factor analysis based on RNA sequencing results.

https://cdn.elifesciences.org/articles/88322/elife-88322-fig3-data1-v1.xlsx

Download elife-88322-fig3-data1-v1.xlsx

Figure 3—source data 2

IPA analysis of oxidative phosphorylation genes and chromosomal replication genes that are affected by entrainment with old serum.

https://cdn.elifesciences.org/articles/88322/elife-88322-fig3-data2-v1.xlsx

Download elife-88322-fig3-data2-v1.xlsx

As mentioned, we found several examples of transcripts that cycle with young serum synchronization and lose rhythmicity (ex. TCF4), decrease in amplitude (ex. HMGB2), phase shift (ex. TEF), or change MESOR (ex. HSD17B7) with aged serum (Figure 2—figure supplement 4). Search Tool for the Retrieval of Interacting Genes (STRING) Szklarczyk et al., 2019 and Ingenuity Pathway Analysis (IPA) Krämer et al., 2014 were used for functional genomics. Both approaches indicate a maintenance of cycling of cell cycle genes with young and old serum synchronization, and a loss of rhythmicity of genes associated with oxidative phosphorylation in the aged serum (Figure 3A and Figure 3—figure supplement 1). STRING analysis revealed that the dominant pathways associated with genes rhythmic in both young and old conditions, cell cycle and DNA replication, demonstrate a decrease in MESOR with old serum. For instance, checkpoint control and chromosomal replication pathway associated genes were expressed cyclically in both young and old conditions; however, several chromosomal replication pathway genes exhibit decreased MESORs in the aged sera (Figure 3—figure supplement 1, Supplementary file 3). MESORs of steroid biosynthesis genes, particularly those relating to cholesterol biosynthesis, were increased in the old sera condition (Figure 3A, Supplementary file 4).

Loss of rhythms in oxidative phosphorylation suggests mitochondrial dysfunction with age. By STRING analysis, 24 out of the 26 genes associated with oxidative phosphorylation overlap with the Alzheimer’s Disease KEGG pathway highlighting the disease relevance of this class of genes that loses cycling in older individuals. Given that Alzheimer’s pathology is closely associated with the accumulation of oxidative stress (Holubiec et al., 2022), it is possible that loss of cycling contributes to oxidative damage. An additional 31 genes in the Alzheimer’s Disease KEGG pathway lose rhythmicity with aged serum; these include amyloid precursor protein (APP) and apolipoprotein E (APOE). APP is by definition the precursor of amyloid beta, which accumulates in AD (Sehar et al., 2022). While APOE plays an important role in lipid transport, specific variants of this gene are strongly associated with AD risk as APOE interacts with amyloid beta in amyloid plaques, a hallmark of the disease (Raulin et al., 2022).

To determine whether aged serum modifies the activity of specific transcription factors, we performed an epigenetic Landscape In Silico deletion analysis (LISA), a computational tool designed to predict changes in transcription factor activity based on gene expression. Using a comprehensive database of known transcription factor binding profiles of fibroblasts, we identified altered gene expression corresponding to 59 total transcription factors (q<0.05) in our young-versus-old cycling dataset. Potential changes in the activity of these transcription factors are associated with the following five categories of cycling phenotypes: decreased MESOR, increased MESOR, phase delay, gain of rhythmicity, and loss of rhythmicity, as were defined above (Figure 3B). Twenty-six transcription factors were represented in all groups, and included those implicated in diseases such as cancer (MYC, TP53, YAP1, RBL2, E2F7, BRD4, MXl1), inflammation (CEBPB, BHLHE40), oxidative stress (NFE2L2), and neurological conditions (NEUROG2, SUMO2).

Lastly, several clock genes showed differences in expression with the aged serum, most notably genes in the Circadian Rhythm KEGG pathway (Figure 4). In particular, expression of CRY1, CRY2, NR1D1, NR1D2, PER3, and TEF was significantly phase delayed after synchronization with old serum compared to young (Figure 4). On the other hand, although several genes in the eIF2 signaling pathway decreased cycling with age, components expressed cyclically with older serum showed a phase advance (Figure 3—figure supplement 1 and Figure 4—figure supplement 1). Importantly, the RNA-seq did not reveal a difference in the phase or amplitude of BMAL1 expression with age, supporting the validity of our BMAL1-luciferase findings, although the MESOR significantly increased with age.

Figure 4 with 1 supplement see all

Download asset Open asset

Studies of the aged SCN have revealed persistent cycling of clock gene expression but a breakdown of output signals (Farajnia et al., 2012; Nakamura et al., 2011). However, whether age-related changes in systemic signaling impact tissue/organ clocks has yet to be elucidated. We demonstrate here that while the core clock continues to cycle in cultured fibroblasts synchronized with serum from old volunteers, the circadian transcriptome is different from that seen in cells treated with serum from young individuals. Through this analysis of the role of serum in age-induced changes in circadian rhythms, we suggest a potential mechanism for observations where specific genes showed a loss, gain, or maintenance of rhythmicity with age (Chen et al., 2016; Kuintzle et al., 2017; Sato et al., 2017; Solanas et al., 2017; Blacher et al., 2022). This phenomenon, known as circadian reprogramming, may illuminate which pathways are affected by or become more impactful for changes in cellular integrity through aging (Wolff et al., 2023).

In order to study the effect of circulating factors on age-related changes of the circadian transcriptome, we utilized the well-established serum starvation-serum addition protocol (Gerber et al., 2013) to synchronize cells in culture. Cultured fibroblasts from old and young subjects have robust clocks and respond similarly to synchronization with dexamethasone (Pagani et al., 2011); however, when synchronized with dexamethasone in a media containing old serum, they reportedly exhibited shortened circadian periods (Pagani et al., 2011). This effect was reversed by heat inactivating old serum. We did not observe a significant difference in period length between young and old serum synchronization in our BMAL1-luciferase experiment, perhaps because dexamethasone was not utilized. Our use of serum to synchronize allowed us to more closely simulate in vivo conditions where blood is an important mode by which central clocks can entrain peripheral clocks to maintain synchrony with the day:night cycle. Previously, serum factors were shown to activate the immediate early transcription factor, SRF, in a diurnal manner to confer time of day signals to both cells in culture and to mouse liver (Gerber et al., 2013). We show here that effects of age are also mediated by serum.

We find that while the number of rhythmic transcripts (weighted BIC > 0.75) in the young serum condition (~18%) is higher than the old serum condition (~12%), both conditions demonstrate a much larger number of cycling transcripts in these cultured fibroblasts than in other cell culture studies (Duffield et al., 2002; Grundschober et al., 2001; Hughes et al., 2009; Jang et al., 2015). In mammals, up to 20% of transcripts can cycle in a given tissue (Patel et al., 2012; Zhang et al., 2014), and the low number of cycling transcripts in culture has been cited as a major limitation of the culture model (Hughes et al., 2009). While our analysis used a normal human fibroblast cell line (BJ-5TA) we had higher statistical power, given that we had four different biological replicates at every 2 hr timepoint. However, a major contributing factor to the high rhythmicity might be the use of human serum as the synchronization signal. In this way, our model of mimicking signaling to peripheral tissues by using serum directly from humans may more accurately recapitulate the human condition. In this study, we focused only on the effects of serum synchronization on fibroblasts, but it is likely that factors circulating in serum act on several tissues, and so their effects are relatively broad. However, we acknowledge that in order to support this claim future studies should investigate other peripheral tissue cell types. Additionally, in the future we intend to analyze the serum using a combination of fractionation and either proteomics or metabolomics to identify relevant factors for the regulation of peripheral rhythms.

Interestingly, many of the genes in the circadian transcriptome that exhibit age-related changes are independently implicated in aging. For instance, some of the genes that lose rhythmicity in the aged condition are involved in oxidative phosphorylation and mitochondrial function, both of which decrease with age (Lesnefsky and Hoppel, 2006). Previous work in the field demonstrates that synchronization of the circadian clock in culture results in cycling of mitochondrial respiratory activity (Cela et al., 2016; Scrima et al., 2020) further underscoring the different effects of old serum, which does not support oscillations of oxidative phosphorylation associated transcripts. Age-dependent decrease in oxidative phosphorylation and increase in mitochondrial dysfunction (Lesnefsky and Hoppel, 2006) is seen also in aged fibroblasts (Greco et al., 2003) and contributes to age-related diseases (Federico et al., 2012). We suggest that the age-related inefficiency of oxidative phosphorylation is conferred by serum signals to the cells such that oxidative phosphorylation cycles are mitigated. On the other hand, loss of cycling could contribute to impairments in mitochondrial function with age.

Both pathway analyses utilized here identified increased MESORs of steroid biosynthesis components in the aged sera condition. In particular, the genes identified in this pathway are associated with cholesterol biosynthesis. Since elderly individuals typically have lower levels of cholesterol biosynthesis and higher levels of circulating cholesterol (Bertolotti et al., 2014), we were surprised to see increased expression of biosynthesis transcripts. Perhaps, the cholesterol synthesis related RNA levels are high as a response to low levels of cholesterol synthesis proteins within the cell. Previous studies in cultured hepatocytes demonstrated that increased reactive oxygen species resulted in higher levels of transcripts associated with cholesterol biosynthesis (Seo et al., 2019). Given that deficits in oxidative phosphorylation are already implicated in these findings, it is possible that oxidative stress plays a role in the increase of cholesterol biosynthesis transcripts.

Although our findings are largely supported by the aging literature, the relatively small sample size of our study necessitates follow up studies to control for individual differences between subjects. We observed variations in luminescence and transcript traces across individuals and while we did not see changes that could account for the overall significant differences in transcript cycling between young and old subjects, we cannot exclude the possibility that factors other than aging contribute to these data.

Together, these findings indicate that at least some of the age-related changes in the cultured fibroblast circadian transcriptome are derived from signals circulating in the serum and not the age of the cells. This has profound implications for understanding and treating circadian disruption with age, and could also be relevant for other age-related pathology, given established links between circadian disruption and diseases of aging. Notably, many of the genes whose cycling is affected by old serum contribute to age-associated disorders.

Sequencing data have been deposited in GEO under the accession code GSE270290. All other source data is provided in source data files labeled with the corresponding figure.

1. 1. Schwarz JE
   2. Mrčela A
   3. Lahens NF
   4. Li Y
   5. Hsu CT
   6. Grant G
   7. Skarke C
   8. Zhang SL
   9. Sehgal A

   (2024) NCBI Gene Expression Omnibus

   ID GSE270290. Evidence for a role of human blood-borne factors in mediating age-associated changes in molecular circadian rhythms.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270290

1. 1. Al-Romaih K
   2. Bayani J
   3. Vorobyova J
   4. Karaskova J
   5. Park PC
   6. Zielenska M
   7. Squire JA

   (2003) Chromosomal instability in osteosarcoma and its association with centrosome abnormalities

   Cancer Genetics and Cytogenetics 144:91–99.

   https://doi.org/10.1016/s0165-4608(02)00929-9

   • PubMed
   • Google Scholar
2. 1. Balsalobre A
   2. Damiola F
   3. Schibler U

   (1998) A serum shock induces circadian gene expression in mammalian tissue culture cells

   Cell 93:929–937.

   https://doi.org/10.1016/s0092-8674(00)81199-x

   • PubMed
   • Google Scholar
3. 1. Bertolotti M
   2. Mussi C
   3. Pellegrini E
   4. Magni A
   5. Del Puppo M
   6. Ognibene S
   7. Carulli L
   8. Anzivino C
   9. Baldelli E
   10. Loria P
   11. Carulli N

   (2014) Age-associated alterations in cholesterol homeostasis: evidence from a cross-sectional study in a Northern Italy population

   Clinical Interventions in Aging 9:425–432.

   https://doi.org/10.2147/CIA.S57714

   • PubMed
   • Google Scholar
4. 1. Blacher E
   2. Tsai C
   3. Litichevskiy L
   4. Shipony Z
   5. Iweka CA
   6. Schneider KM
   7. Chuluun B
   8. Heller HC
   9. Menon V
   10. Thaiss CA
   11. Andreasson KI

   (2022) Aging disrupts circadian gene regulation and function in macrophages

   Nature Immunology 23:229–236.

   https://doi.org/10.1038/s41590-021-01083-0

   • PubMed
   • Google Scholar
5. 1. Brooks TG
   2. Mrčela A
   3. Lahens NF
   4. Paschos GK
   5. Grosser T
   6. Skarke C
   7. FitzGerald GA
   8. Grant GR

   (2022) Nitecap: An exploratory circadian analysis web application

   Journal of Biological Rhythms 37:43–52.

   https://doi.org/10.1177/07487304211054408

   • PubMed
   • Google Scholar
6. 1. Cela O
   2. Scrima R
   3. Pazienza V
   4. Merla G
   5. Benegiamo G
   6. Augello B
   7. Fugetto S
   8. Menga M
   9. Rubino R
   10. Fuhr L
   11. Relógio A
   12. Piccoli C
   13. Mazzoccoli G
   14. Capitanio N

   (2016) Clock genes-dependent acetylation of complex I sets rhythmic activity of mitochondrial OxPhos

   Biochimica et Biophysica Acta - Molecular Cell Research 1863:596–606.

   https://doi.org/10.1016/j.bbamcr.2015.12.018

   • Google Scholar
7. 1. Chen CY
   2. Logan RW
   3. Ma T
   4. Lewis DA
   5. Tseng GC
   6. Sibille E
   7. McClung CA

   (2016) Effects of aging on circadian patterns of gene expression in the human prefrontal cortex

   PNAS 113:206–211.

   https://doi.org/10.1073/pnas.1508249112

   • PubMed
   • Google Scholar
8. 1. Cornelissen G

   (2014) Cosinor-based rhythmometry

   Theoretical Biology & Medical Modelling 11:16.

   https://doi.org/10.1186/1742-4682-11-16

   • PubMed
   • Google Scholar
9. 1. Dobin A
   2. Davis CA
   3. Schlesinger F
   4. Drenkow J
   5. Zaleski C
   6. Jha S
   7. Batut P
   8. Chaisson M
   9. Gingeras TR

   (2013) STAR: ultrafast universal RNA-seq aligner

   Bioinformatics 29:15–21.

   https://doi.org/10.1093/bioinformatics/bts635

   • PubMed
   • Google Scholar
10. 1. Duffield GE
    2. Best JD
    3. Meurers BH
    4. Bittner A
    5. Loros JJ
    6. Dunlap JC

    (2002) Circadian programs of transcriptional activation, signaling, and protein turnover revealed by microarray analysis of mammalian cells

    Current Biology 12:551–557.

    https://doi.org/10.1016/s0960-9822(02)00765-0

    • PubMed
    • Google Scholar
11. 1. Farajnia S
    2. Michel S
    3. Deboer T
    4. vanderLeest HT
    5. Houben T
    6. Rohling JHT
    7. Ramkisoensing A
    8. Yasenkov R
    9. Meijer JH

    (2012) Evidence for neuronal desynchrony in the aged suprachiasmatic nucleus clock

    The Journal of Neuroscience 32:5891–5899.

    https://doi.org/10.1523/JNEUROSCI.0469-12.2012

    • PubMed
    • Google Scholar
12. 1. Federico A
    2. Cardaioli E
    3. Da Pozzo P
    4. Formichi P
    5. Gallus GN
    6. Radi E

    (2012) Mitochondria, oxidative stress and neurodegeneration

    Journal of the Neurological Sciences 322:254–262.

    https://doi.org/10.1016/j.jns.2012.05.030

    • PubMed
    • Google Scholar
13. 1. Gerber A
    2. Esnault C
    3. Aubert G
    4. Treisman R
    5. Pralong F
    6. Schibler U

    (2013) Blood-borne circadian signal stimulates daily oscillations in actin dynamics and SRF activity

    Cell 152:492–503.

    https://doi.org/10.1016/j.cell.2012.12.027

    • PubMed
    • Google Scholar
14. 1. Greco M
    2. Villani G
    3. Mazzucchelli F
    4. Bresolin N
    5. Papa S
    6. Attardi G

    (2003) Marked aging-related decline in efficiency of oxidative phosphorylation in human skin fibroblasts

    FASEB Journal 17:1706–1708.

    https://doi.org/10.1096/fj.02-1009fje

    • PubMed
    • Google Scholar
15. 1. Grundschober C
    2. Delaunay F
    3. Pühlhofer A
    4. Triqueneaux G
    5. Laudet V
    6. Bartfai T
    7. Nef P

    (2001) Circadian regulation of diverse gene products revealed by mRNA expression profiling of synchronized fibroblasts

    The Journal of Biological Chemistry 276:46751–46758.

    https://doi.org/10.1074/jbc.M107499200

    • PubMed
    • Google Scholar
16. 1. Holubiec MI
    2. Gellert M
    3. Hanschmann EM

    (2022) Redox signaling and metabolism in Alzheimer’s disease

    Frontiers in Aging Neuroscience 14:1003721.

    https://doi.org/10.3389/fnagi.2022.1003721

    • PubMed
    • Google Scholar
17. 1. Hood S
    2. Amir S

    (2017) The aging clock: circadian rhythms and later life

    The Journal of Clinical Investigation 127:437–446.

    https://doi.org/10.1172/JCI90328

    • PubMed
    • Google Scholar
18. 1. Hughes ME
    2. DiTacchio L
    3. Hayes KR
    4. Vollmers C
    5. Pulivarthy S
    6. Baggs JE
    7. Panda S
    8. Hogenesch JB

    (2009) Harmonics of circadian gene transcription in mammals

    PLOS Genetics 5:e1000442.

    https://doi.org/10.1371/journal.pgen.1000442

    • PubMed
    • Google Scholar
19. Software

    1. Itmat

    (2021) Normalization, version swh:1:rev:6988cf12583ed12ea606c41b43247322fca49ec0

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:9b3d7e0419db5aaed3bc7ffdac0f424bd6641f59;origin=https://github.com/itmat/Normalization;visit=swh:1:snp:e90f9d9dd0e190bb03eb34a079834e60be5944da;anchor=swh:1:rev:6988cf12583ed12ea606c41b43247322fca49ec0
20. 1. Jang C
    2. Lahens NF
    3. Hogenesch JB
    4. Sehgal A

    (2015) Ribosome profiling reveals an important role for translational control in circadian gene expression

    Genome Research 25:1836–1847.

    https://doi.org/10.1101/gr.191296.115

    • PubMed
    • Google Scholar
21. 1. Katsimpardi L
    2. Litterman NK
    3. Schein PA
    4. Miller CM
    5. Loffredo FS
    6. Wojtkiewicz GR
    7. Chen JW
    8. Lee RT
    9. Wagers AJ
    10. Rubin LL

    (2014) Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors

    Science 344:630–634.

    https://doi.org/10.1126/science.1251141

    • PubMed
    • Google Scholar
22. 1. Krämer A
    2. Green J
    3. Pollard J Jr
    4. Tugendreich S

    (2014) Causal analysis approaches in ingenuity pathway analysis

    Bioinformatics 30:523–530.

    https://doi.org/10.1093/bioinformatics/btt703

    • PubMed
    • Google Scholar
23. 1. Kuintzle RC
    2. Chow ES
    3. Westby TN
    4. Gvakharia BO
    5. Giebultowicz JM
    6. Hendrix DA

    (2017) Circadian deep sequencing reveals stress-response genes that adopt robust rhythmic expression during aging

    Nature Communications 8:14529.

    https://doi.org/10.1038/ncomms14529

    • PubMed
    • Google Scholar
24. 1. Lahens NF
    2. Rahman M
    3. Cohen JB
    4. Cohen DL
    5. Chen J
    6. Weir MR
    7. Feldman HI
    8. Grant GR
    9. Townsend RR
    10. Skarke C
    11. Study Investigators ATC

    (2022) Time-specific associations of wearable sensor-based cardiovascular and behavioral readouts with disease phenotypes in the outpatient setting of the Chronic Renal Insufficiency Cohort

    Digital Health 8:20552076221107903.

    https://doi.org/10.1177/20552076221107903

    • PubMed
    • Google Scholar
25. 1. Lesnefsky EJ
    2. Hoppel CL

    (2006) Oxidative phosphorylation and aging

    Ageing Research Reviews 5:402–433.

    https://doi.org/10.1016/j.arr.2006.04.001

    • PubMed
    • Google Scholar
26. 1. Monk TH
    2. Buysse DJ
    3. Reynolds CF
    4. Kupfer DJ

    (1993) Inducing jet lag in older people: adjusting to a 6-hour phase advance in routine

    Experimental Gerontology 28:119–133.

    https://doi.org/10.1016/0531-5565(93)90002-u

    • PubMed
    • Google Scholar
27. 1. Monk TH
    2. Buysse DJ
    3. Carrier J
    4. Kupfer DJ

    (2000) Inducing jet-lag in older people: directional asymmetry

    Journal of Sleep Research 9:101–116.

    https://doi.org/10.1046/j.1365-2869.2000.00184.x

    • PubMed
    • Google Scholar
28. 1. Nakamura TJ
    2. Nakamura W
    3. Yamazaki S
    4. Kudo T
    5. Cutler T
    6. Colwell CS
    7. Block GD

    (2011) Age-related decline in circadian output

    The Journal of Neuroscience 31:10201–10205.

    https://doi.org/10.1523/JNEUROSCI.0451-11.2011

    • PubMed
    • Google Scholar
29. 1. Pagani L
    2. Schmitt K
    3. Meier F
    4. Izakovic J
    5. Roemer K
    6. Viola A
    7. Cajochen C
    8. Wirz-Justice A
    9. Brown SA
    10. Eckert A

    (2011) Serum factors in older individuals change cellular clock properties

    PNAS 108:7218–7223.

    https://doi.org/10.1073/pnas.1008882108

    • PubMed
    • Google Scholar
30. 1. Parsons R
    2. Parsons R
    3. Garner N
    4. Oster H
    5. Rawashdeh O

    (2020) CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms

    Bioinformatics 36:1208–1212.

    https://doi.org/10.1093/bioinformatics/btz730

    • PubMed
    • Google Scholar
31. 1. Patel VR
    2. Eckel-Mahan K
    3. Sassone-Corsi P
    4. Baldi P

    (2012) CircadiOmics: integrating circadian genomics, transcriptomics, proteomics and metabolomics

    Nature Methods 9:772–773.

    https://doi.org/10.1038/nmeth.2111

    • PubMed
    • Google Scholar
32. 1. Qin Q
    2. Fan J
    3. Zheng R
    4. Wan C
    5. Mei S
    6. Wu Q
    7. Sun H
    8. Brown M
    9. Zhang J
    10. Meyer CA
    11. Liu XS

    (2020) Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

    Genome Biology 21:32.

    https://doi.org/10.1186/s13059-020-1934-6

    • PubMed
    • Google Scholar
33. 1. Ramanathan C
    2. Khan SK
    3. Kathale ND
    4. Xu H
    5. Liu AC

    (2012) Monitoring cell-autonomous circadian clock rhythms of gene expression using luciferase bioluminescence reporters

    Journal of Visualized Experiments e4234:4234.

    https://doi.org/10.3791/4234

    • PubMed
    • Google Scholar
34. 1. Raulin AC
    2. Doss SV
    3. Trottier ZA
    4. Ikezu TC
    5. Bu G
    6. Liu CC

    (2022) ApoE in Alzheimer’s disease: pathophysiology and therapeutic strategies

    Molecular Neurodegeneration 17:72.

    https://doi.org/10.1186/s13024-022-00574-4

    • PubMed
    • Google Scholar
35. 1. Refinetti R
    2. Lissen GC
    3. Halberg F

    (2007) Procedures for numerical analysis of circadian rhythms

    Biological Rhythm Research 38:275–325.

    https://doi.org/10.1080/09291010600903692

    • PubMed
    • Google Scholar
36. 1. Roenneberg T
    2. Wirz-Justice A
    3. Merrow M

    (2003) Life between clocks: daily temporal patterns of human chronotypes

    Journal of Biological Rhythms 18:80–90.

    https://doi.org/10.1177/0748730402239679

    • PubMed
    • Google Scholar
37. 1. Sato S
    2. Solanas G
    3. Peixoto FO
    4. Bee L
    5. Symeonidi A
    6. Schmidt MS
    7. Brenner C
    8. Masri S
    9. Benitah SA
    10. Sassone-Corsi P

    (2017) Circadian reprogramming in the liver identifies metabolic pathways of aging

    Cell 170:664–677.

    https://doi.org/10.1016/j.cell.2017.07.042

    • PubMed
    • Google Scholar
38. 1. Scrima R
    2. Cela O
    3. Agriesti F
    4. Piccoli C
    5. Tataranni T
    6. Pacelli C
    7. Mazzoccoli G
    8. Capitanio N

    (2020) Mitochondrial calcium drives clock gene-dependent activation of pyruvate dehydrogenase and of oxidative phosphorylation

    Biochimica et Biophysica Acta - Molecular Cell Research 1867:118815.

    https://doi.org/10.1016/j.bbamcr.2020.118815

    • Google Scholar
39. 1. Sehar U
    2. Rawat P
    3. Reddy AP
    4. Kopel J
    5. Reddy PH

    (2022) Amyloid beta in aging and alzheimer’s disease

    International Journal of Molecular Sciences 23:12924.

    https://doi.org/10.3390/ijms232112924

    • PubMed
    • Google Scholar
40. 1. Seo E
    2. Kang H
    3. Choi H
    4. Choi W
    5. Jun HS

    (2019) Reactive oxygen species-induced changes in glucose and lipid metabolism contribute to the accumulation of cholesterol in the liver during aging

    Aging Cell 18:e12895.

    https://doi.org/10.1111/acel.12895

    • PubMed
    • Google Scholar
41. 1. Solanas G
    2. Peixoto FO
    3. Perdiguero E
    4. Jardí M
    5. Ruiz-Bonilla V
    6. Datta D
    7. Symeonidi A
    8. Castellanos A
    9. Welz PS
    10. Caballero JM
    11. Sassone-Corsi P
    12. Muñoz-Cánoves P
    13. Benitah SA

    (2017) Aged stem cells reprogram their daily rhythmic functions to adapt to stress

    Cell 170:678–692.

    https://doi.org/10.1016/j.cell.2017.07.035

    • PubMed
    • Google Scholar
42. 1. Szklarczyk D
    2. Gable AL
    3. Lyon D
    4. Junge A
    5. Wyder S
    6. Huerta-Cepas J
    7. Simonovic M
    8. Doncheva NT
    9. Morris JH
    10. Bork P
    11. Jensen LJ
    12. von Mering C

    (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

    Nucleic Acids Research 47:D607–D613.

    https://doi.org/10.1093/nar/gky1131

    • PubMed
    • Google Scholar
43. 1. Weger BD
    2. Gobet C
    3. David FPA
    4. Atger F
    5. Martin E
    6. Phillips NE
    7. Charpagne A
    8. Weger M
    9. Naef F
    10. Gachon F

    (2021) Systematic analysis of differential rhythmic liver gene expression mediated by the circadian clock and feeding rhythms

    PNAS 118:e2015803118.

    https://doi.org/10.1073/pnas.2015803118

    • PubMed
    • Google Scholar
44. 1. Wolff CA
    2. Gutierrez-Monreal MA
    3. Meng L
    4. Zhang X
    5. Douma LG
    6. Costello HM
    7. Douglas CM
    8. Ebrahimi E
    9. Pham A
    10. Oliveira AC
    11. Fu C
    12. Nguyen A
    13. Alava BR
    14. Hesketh SJ
    15. Morris AR
    16. Endale MM
    17. Crislip GR
    18. Cheng K-Y
    19. Schroder EA
    20. Delisle BP
    21. Bryant AJ
    22. Gumz ML
    23. Huo Z
    24. Liu AC
    25. Esser KA

    (2023) Defining the age-dependent and tissue-specific circadian transcriptome in male mice

    Cell Reports 42:111982.

    https://doi.org/10.1016/j.celrep.2022.111982

    • PubMed
    • Google Scholar
45. 1. Yamazaki S
    2. Straume M
    3. Tei H
    4. Sakaki Y
    5. Menaker M
    6. Block GD

    (2002) Effects of aging on central and peripheral mammalian clocks

    PNAS 99:10801–10806.

    https://doi.org/10.1073/pnas.152318499

    • PubMed
    • Google Scholar
46. 1. Yates AD
    2. Achuthan P
    3. Akanni W
    4. Allen J
    5. Allen J
    6. Alvarez-Jarreta J
    7. Amode MR
    8. Armean IM
    9. Azov AG
    10. Bennett R
    11. Bhai J
    12. Billis K
    13. Boddu S
    14. Marugán JC
    15. Cummins C
    16. Davidson C
    17. Dodiya K
    18. Fatima R
    19. Gall A
    20. Giron CG
    21. Gil L
    22. Grego T
    23. Haggerty L
    24. Haskell E
    25. Hourlier T
    26. Izuogu OG
    27. Janacek SH
    28. Juettemann T
    29. Kay M
    30. Lavidas I
    31. Le T
    32. Lemos D
    33. Martinez JG
    34. Maurel T
    35. McDowall M
    36. McMahon A
    37. Mohanan S
    38. Moore B
    39. Nuhn M
    40. Oheh DN
    41. Parker A
    42. Parton A
    43. Patricio M
    44. Sakthivel MP
    45. Abdul Salam AI
    46. Schmitt BM
    47. Schuilenburg H
    48. Sheppard D
    49. Sycheva M
    50. Szuba M
    51. Taylor K
    52. Thormann A
    53. Threadgold G
    54. Vullo A
    55. Walts B
    56. Winterbottom A
    57. Zadissa A
    58. Chakiachvili M
    59. Flint B
    60. Frankish A
    61. Hunt SE
    62. IIsley G
    63. Kostadima M
    64. Langridge N
    65. Loveland JE
    66. Martin FJ
    67. Morales J
    68. Mudge JM
    69. Muffato M
    70. Perry E
    71. Ruffier M
    72. Trevanion SJ
    73. Cunningham F
    74. Howe KL
    75. Zerbino DR
    76. Flicek P

    (2020) Ensembl 2020

    Nucleic Acids Research 48:D682–D688.

    https://doi.org/10.1093/nar/gkz966

    • PubMed
    • Google Scholar
47. 1. Zhang R
    2. Lahens NF
    3. Ballance HI
    4. Hughes ME
    5. Hogenesch JB

    (2014) A circadian gene expression atlas in mammals: implications for biology and medicine

    PNAS 111:16219–16224.

    https://doi.org/10.1073/pnas.1408886111

    • PubMed
    • Google Scholar
48. 1. Zhang SL
    2. Lahens NF
    3. Yue Z
    4. Arnold DM
    5. Pakstis PP
    6. Schwarz JE
    7. Sehgal A

    (2021) A circadian clock regulates efflux by the blood-brain barrier in mice and human cells

    Nature Communications 12:617.

    https://doi.org/10.1038/s41467-020-20795-9

    • PubMed
    • Google Scholar
49. 1. Zielinski T
    2. Moore AM
    3. Troup E
    4. Halliday KJ
    5. Millar AJ

    (2014) Strengths and limitations of period estimation methods for circadian data

    PLOS ONE 9:e96462.

    https://doi.org/10.1371/journal.pone.0096462

    • PubMed
    • Google Scholar

Author details

1. Jessica E Schwarz

   1. Howard Hughes Medical Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4831-225X

2. Antonijo Mrčela

   Institute for Translational Medicine and Therapeutics (ITMAT), Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Formal analysis, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nicholas F Lahens

   Institute for Translational Medicine and Therapeutics (ITMAT), Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Formal analysis, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3965-5624

4. Yongjun Li

   Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Cynthia Hsu

   1. Howard Hughes Medical Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Gregory R Grant

   1. Institute for Translational Medicine and Therapeutics (ITMAT), Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0139-7658

7. Carsten Skarke

   1. Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Institute for Translational Medicine and Therapeutics (ITMAT), Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Shirley L Zhang

   1. Howard Hughes Medical Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Present address

   Department of Cell Biology, Emory University School of Medicine, Atlanta, United States

   Contribution

   Formal analysis, Supervision, Investigation, Methodology, Writing – review and editing

   For correspondence

   shirley.zhang2@emory.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6672-2044

9. Amita Sehgal

   1. Howard Hughes Medical Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
   2. Chronobiology and Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Supervision, Methodology, Writing - original draft, Writing – review and editing

   For correspondence

   amita@pennmedicine.upenn.edu

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0001-7354-9641

• 598

  views

• 40

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.