Circadian clocks in enzymes

Circadian clocks in enzymes

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I'm aware of the existence of circadian clocks, but I don't understand exactly how circadian clocks regulate other circadian mechanisms. For example, if an enzyme is found to have a circadian rythm, how is this rhythm produced? Is it because of one or more clock genes?

Molecular feedback mechanisms create rhythmic patterns. The circadian rhythm is driven by a handful of genes, which in turn can regulate a number of downstream factors.

Here is a very nice explanation of the Drosophila circadian clock, which gives you an idea how this works. The video shows the same animation adding more factors every time to guide you through how the system works on the genetic/protein level.

A brief summary: The genes period and timeless are transcribed over night. Their proteins stabilize each other and build up in the cell. At the same time they block their own transcription, limiting the amount of proteins. Other factors destabilize the proteins and evoke their degradation. One of them is the light activated protein Cryptochrome. This helps entraining the clock and results in the removal of Period and Timeless protein during the day. The negative feedback of their blockage is removed and transcription starts anew at night, when Cryptochrome is inactive.

These clock genes do not only regulate themselves but can also activate or deactivate other factors resulting in daytime-dependent transcription of genes or protein activity.

The whole picture is of course a bit more complicated and involves a lot of additional factors. But also in humans and other mammals you find very similar mechanisms.

The interactions between the circadian clock and primary metabolism

Primary metabolism in plants is tightly regulated by environmental factors such as light and nutrient availability at multiple levels. The circadian clock is a self-sustained endogenous oscillator that enables organisms to predict daily and seasonal changes. The regulation of primary metabolism by the circadian clock has been proposed to explain the importance of circadian rhythms in plant growth and survival. Recent transcriptomic and metabolomic analyses indicate a wide spread circadian regulation of different metabolic processes. We review evidence of circadian regulation of pathways in primary metabolism, discuss the challenges faced for discerning the mechanisms regulating circadian metabolic oscillations and present recent evidence of regulation of the circadian clock by metabolites.


► The circadian clock regulates the transcript levels of many metabolic enzymes. ► The extent of circadian regulation of enzyme activities is unknown. ► The levels of some metabolites oscillate in a circadian manner. ► Some metabolites influence circadian rhythms.

The Nobel Prize in Medicine Goes to Your Body's Circadian Clock

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Professor Michael Rosbash, one of the Nobel Prize recipients in medicine. Scott Eisen/Getty Images

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Today, the Nobel committee kicked off its 2017 season by awarding the Nobel Prize in Physiology or Medicine to three scientists for their discoveries of the molecular mechanisms that control circadian rhythms. The Americans—Jeffrey C. Hall, Michael Rosbash, and Michael W. Young—used fruit flies to isolate a gene that dictates the biological clock ticking away inside all living organisms. Their work, though decades-old, has been crucial to understanding how the light emanating from screens can affect humans’ well-being, as it takes people further and further out of sync with their internal timekeepers.

Newer, flashier science had dominated predictions going into Monday’s announcement. Crispr, the transformational gene-editing system being harnessed to make climate-resistant crops, more bountiful biofuels, and cutting edge therapies, was passed over this year. So was pioneering work in the promising field of immuno-oncology, in which the body’s immune system is jump-started to fight off cancer without the need for toxic chemotherapy or radiation.

In the end, the Nobel Prize committee recognized the less-trendy but increasingly relevant work of Hall, Rosbash, and Young for explaining something as fundamental as “how plants, animals, and humans adapt their biological rhythm so that it is synchronized with the Earth’s revolutions.”

That’s right: Moon cycles aren’t just for astrologists and crystal-wielding spiritualists. All organisms operate on a 24-hour circuit that governs critical functions such as blood pressure, heart rate, body temperature, hormone levels, metabolism, sleep, and even behavior—all in time with shifts between day and night. But it wasn't until 1984 that Hall, Rosbash, and Young identified a gene that seemed to control this circadian rhythm. Working with fruit flies, they discovered that insects lacking this gene also lost the ability to self-regulate these biological functions. Replacing the gene’s function gave them their groove back.


Each morning we wake up from a night of sleep, and each day we eat our regularly timed meals, go through our normal routines, and fall asleep again for another night. This rhythm, so-called circadian—after the Latin words circa diem (“about a day”) —underlies a wide variety of human physiological functions, including sleep-wake cycles, body temperature, hormone secretion, locomotor activity, and feeding behavior.

The Cyclic AMP Pathway, CREM and Melatonin Synthesis

A key question in the field of transcription is how extracellular signals, elicited for example by hormones and growth factors, lead to modulation in gene expression. The second messenger cAMP is critical in a large variety of neuronal, endocrine and metabolic responses. We discovered and characterized the CREM gene whose complex structure allows remarkable flexibility so as to generate a variety of transcription factors with multiple functions (Foulkes et al. 1991 de Groot et al. 1994). Among these, the CREM gene leads to the dynamic synthesis of an inducible cAMP early repressor (ICER) that is responsible for the transcriptional attenuation of cAMP-dependent early activation of many genes (Molina et al. 1993 Stehle et al. 1993). ICER protein stability is timed so to allow for a new transcriptional activation cycle, generating a transcriptional-translational feedback loop that controls several genes, with implications to neuronal, hormonal and endocrine responses (Foulkes et al. 1996). Among the genes controlled by the CREM-ICER couple, there is the one encoding the 5HT N-acetyltransferase, the rate-limiting enzyme in the melatonin synthesis pathway in the pineal gland (Foulkes et al. 1996a 1996b). Thus, the CREM transcription factor modulates the oscillatory levels of the circadian hormone melatonin.

A network of clocks and their interplay
The central clock in the suprachiasmatic nucleus (SCN) controls various endocrine and metabolic functions. Neurons of the SCN undergo oscillations in depolarization and gene expression. The SCN indirectly controls oscillations of humoral factors coming from other tissues, such as the pineal and adrenal cortex. Other tissues also maintain circadian output through positive and negative feedback loops within cells that make up different compartments of the tissue. Oscillations in humoral factors control the circadian release of factors from the periphery, such as ghrelin, leptin, insulin and glucose, and these in turn provide positive and negative feedback to the brain. Melatonin, which is released in a circadian fashion from the pineal gland, is involved in feed- back regulation of the SCN, where melatonin receptors are abundantly expressed. Thus, the periphery may influence brain functions, and specifically SCN neurons, through yet undefined feedback mechanisms. Additional elements, such as food intake and exercise, may uncouple peripheral tissues from the central clock. Indeed, time-restricted feeding (TRF) experiments, as well as time-specific exercise, have been shown to have fundamentally distinct metabolic effects on peripheral clocks.

Peripheral Circadian Oscillators

Our studies on cAMP-dependent transcription lead us to discover the mechanism controlling circadian melatonin synthesis and introduced us to the field of circadian biology. Various contributions include the elucidation of signaling and transcriptional pathways and the discovery of key clock proteins post-translational modifications. By establishing the zebrafish as a valuable model for the study of circadian rhythms, we discovered the presence of peripheral oscillators that operate by using an intrinsic clock that is independent from the neuronal pacemaker (Whitmore et al. 1998). We went on to discover that peripheral organs and cells directly sense light and to decipher the signaling pathways involved in light transduction. These features were recapitulated in a cell-based system in culture (Whitmore et al. 2000 Pando et al. 2001). Based on these concepts, we explored whether in mammals the presence of independent clocks may be illustrated by applying cell grafting. Using a tissue implant methodology we uncovered the hierarchical organization of the circadian system in the mouse (Pando et al. 2002). These studies challenged the common knowledge of the field that for decades had accepted the view of a unique brain-based clock (Schibler and Sassone-Corsi 2002). The impact of these findings is extensive because of its implications in systems biology, endocrine control and physiology.

Molecular Features of the Circadian Clock
The transcriptional-translational feedback loop (TTFL) of the circadian clock is elaborate, with external loops and posttranslational modifications, such as phosphorylation contributing to maintenance of the core oscillatory players. The CLOCK:BMAL1 activators bind E-box elements on promoters of clock-controlled genes (CCGs). The E-box is most common promoter element on the genome. Post-translational modifications of CRY and PER proteins play a critical role in their stability and degradation. Phosphorylation has been shown to be regulating the negative-feedback potential of these proteins on the CLOCK:BMAL1 complex. The transcription of Bmal1 is negatively regulated by the product of one of its own gene targets, Rev-erba, the process of which controls the amount of BMAL1 protein available for CLOCK binding. These interlocked loops control the expression of thousand genes, leading to clock outputs that govern a large array of physiological, metabolic and behavioral functions.

The Circadian Clock Links Metabolism to Epigenetics

The circadian system transcriptionally controls a significant fraction of the genome, suggesting a role for the clock in chromatin remodeling. One features of clock proteins is their capacity of being dynamically modified post-translationally. Our laboratory has contributed to the field by illustrating that BMAL1 is for example phosphorylated (Tamaru et al. 2009), acetylated (Hirayama et al. 2007) and SUMOylated (Cardone et al. 2005).

We first uncovered that indeed clock function is associated with chromatin remodeling (Crosio et al. 2000), and then went on to elucidate the molecular mechanisms by revealing the histone acetyltransferase function of the regulator CLOCK (Doi et al. Cell 2006). We then discovered the implication of the NAD + -dependent deacetylase SIRT1, which established a direct link of the clock with cellular metabolism and histone modifications (Nakahata et al 2008). This led to the discovery that SIRT1 enzymatic activity is cyclic because the NAD + levels oscillate through a direct control by the clock of the Namptgene. This gene encodes the nicotinamide (NAM) phosphoribosyltransferaseenzyme, which controls rate-limiting spet of the NAD + salvage pathway. Thus, the circadian clock links transcriptional regulation to enzymatic control (Nakahata et al. 2009). The implications of this finding are multiple as NAD + operates as a central element in energy metabolism and functions as coenzyme for several NAD + -consuming enzymes. Further studies demonstrated that SIRT1 is involved also in the control of Acetyl-CoA synthesis through the cyclic acetylation of the acetyl-coenzyme A synthase I enzyme (Sahar et al. 2014) and that another sirtuin, SIRT6, contributes to the partitioning of the circadian epigenome in a manner that is distinct from SIRT1 (Masri et al. 2014).

These findings prompted further studies by MS metabolomics to determine the oscillating metabolome in various tissues and to generate, in collaboration with the Institute for Genomics and Bioinformatics at UCI ( a Biocomputing resource that integrates and links circadian metabolomics to transcriptomics (CircadiOmics (Patel et al. 2012 Eckel-Mahan et al. 2012). This high-throughput metabolome approach, coupled with transcriptomics and epigenomics, has been instrumental to uncover the impact of nutrition, and other physiological regimes. Our findings have revealed that the clock can be reprogrammed through regulatory circuits that link cellular metabolism to epigenetic control.

Reprogramming of the Circadian Clock

We have revealed previously unforeseen pathways of circadian control that connect to nutrition, cancer and aging. Accumulating evidence shows that time of food is critical in the control of circadian metabolism (Asher and Sassone-Corsi 2005). These studies provide new leads towards therapeutic strategies for metabolic disorders.

We have revealed how nutritional challenges reprogram circadian homeostasis whether these are in the form of high-fat diet (Eckel-Mahan et al. 2013), ketogenic diet (Tognini et al. 2017) and fasting (Kinouchi et al. 2018). These studies have particular relevance with aging as we have demonstrated that claroic restriction connects directly with acetylation pathways in the control of liver metabolism (Sato et al. 2017). Importantly, analysis of the circadian acetylome had already pointed to unique metabolic pathways in energy metabolism (Masri et al. 2013). Reprogramming is also evident in response to time-specific exercise in both liver and muscle tissues (Sato et al. 2019). These studies provide new leads towards therapeutic strategies for metabolic disorders.

Linking the Clock to the NAD + Salvage Pathway
The sirtuin SIRT1 links circadian rhythmicity to metabolism. The SIRT1:CLOCK:BMAL1 complex drives expression of Nampt, the rate-limiting enzyme in the salvage pathway for SIRT1’s own cofactor, NAD + . The two loops depicted in the figure demonstrate the mechanisms of both the transcriptional feedback circuit as well as the enzymatic feedback circuit.

Communications of Circadian Clocks

The mammalian circadian clock system orchestrates daily rhythms in behavior and physiology, allowing animals to anticipate environmental changes and synchronize their internal processes accordingly. The discovery of peripheral oscillators nearly 20 years ago changed the perspective of the field until then, the central oscillator, localized in suprachiasmatic nucleus (SCN) neurons, was considered the sole pacemaker directing all cyclic physiology and behavior. Subsequently, the concept of a ‘web of pacemakers’ has been largely adopted, where the SCN controls the tempo of oscillators in peripheral tissues and organs (Schibler and Sassone-Corsi, 2002). Additional work has also demonstrated the presence in the ventromedial hypothalamus of clock cells that operate independently of the SCN to control energy expenditure (Orozco-Solis et al. 2016).

Peripheral clocks respond to signals emanating from the SCN, which responds to light, through yet ill-defined pathways. By regulating the sleep-wake cycle, the SCN clock also indirectly evokes the feeding-fasting cycle, a synchronizer and driver of cycling transcripts in the periphery.These notions prompted the question of whether peripheral clocks may communicate with each other. Recent findings demonstrated system-wide metabolic coordination between mammalian clocks (Dyar et al., 2018).Dysregulation of clock function may also be a hallmark of various pathological states, as in the case of lung adenocarcinoma, which distally rewires circadian function in the liver (Masri et al., 2016). Identifying how peripheral clocks interplay will provide critical information on circadian physiology as well as clock disruption and disease.

Among peripheral clocks, the liver is central in controlling cyclic metabolism and adapts remarkably to changes in nutritional regimes. Liver-specific clock deficiency causes loss of circadian transcription and ultimately loss of oscillation in key glucose, lipid and oxidative pathways. Nevertheless, some cyclic transcripts persist in the absence of a functioning clock, suggesting alternative mechanisms contributing to fluctuations in the liver.

The presence of a circadian clock in virtually all cells begs the question of its dependence on external cyclic signals. The molecular clock consists of an oscillator, based on interlocked transcriptional-translational feedback loops. Rhythmic post-translational modification of clock proteins and daily chromatin remodeling also contribute to this canonical feedback loop. The extent to which this complex regulatory circuit is tissue-autonomous or requires external cues from other clocks remains unclear.

Tissue-specific clock ablation has been instrumental in identifying the functions of peripheral clocks, however they have not allowed assessment of their degree of autonomy. In collaboration with the laboratory of Salvador Aznar Benitah (Barcelona) (Welz et al. 2019) we have developed a mouse model in which the liver clock is reconstituted in an otherwise BMAL1-deficient animal (Liver-RE) (Koronowski et al. 2019). This model is a tool to study whether and to what extent a peripheral clock operates independently from all other clocks. We demonstrate that the liver is intrinsically capable of clock function even in absence of functioning clocks in all other tissues. The independence of the liver clock illustrates a degree of autonomy at the tissue level, limited to a specific set of genes and metabolic pathways. Remarkably, the genome-wide capacity and specificity of reconstituted BMAL1 to bind chromatin is comparable to that of wild type mice, demonstrating that external clock-dependent inputs are required to elicit a full circadian program. Lastly, lack of circadian rhythms in liver-RE mice maintained under constant darkness reveals a potentially critical regulatory role of the light-dark cycle on tissue-autonomous function.

Cardone, L., Hirayama, J., Giordano, F., Tamaru, T., Palvimo, J. J., Sassone-Corsi, P. (2005) Circadian clock control by SUMOylation of BMAL1. Science 309: 1390-1394.

Crosio C, Cermakian N, Allis CD, Sassone-Corsi P. (2000) Light induces chromatin modification in cells of the mammalian circadian clock. Nature Neurosci. 3: 1241-7.

Dyar KA, Lutter D, Artati A, Ceglia NJ, Liu Y, Armenta D, Jastroch M, Schneider S, de Mateo S, Cervantes M, Abbondante S, Tognini P, Orozco-Solis R, Kinouchi K, Wang C, Swerdloff R, Nadeef S, Masri S, Magistretti P, Orlando V, Borrelli E, Uhlenhaut NH, Baldi P, Adamski J, Tschöp MH, Eckel-Mahan K, Sassone-Corsi P. (2018) Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell 174: 1571-1585

Eckel-Mahan, K. L., Patel, V. R., de Mateo, S., Orozco-Solis, R., Ceglia, N. J., Sahar, S., Dilag-Penilla, S. A., Dyar, K. A., Baldi, P., Sassone-Corsi, P. (2013) Reprogramming of the circadian clock by nutritional challenge. Cell 155: 1464-78.

Eckel-Mahan KL, Patel VR, Mohney RP, Vignola KS, Baldi P, Sassone-Corsi P. (2012) Coordination of the transcriptome and metabolome by the circadian clock.Proc. Natl. Acad. Sci. USA 109: 5541-6

Foulkes NS, Borjigin J, Snyder SH, Sassone-Corsi P. (1997) Rhythmic transcription: the molecular basis of circadian melatonin synthesis. Trends Neurosci. 20: 487-92.

Foulkes NS, Borjigin J, Snyder SH, Sassone-Corsi P. (1996) Transcriptional control of circadian hormone synthesis via the CREM feedback loop. Proc. Natl. Acad. Sci. USA 93: 14140-5.

Hirayama J, Sahar S, Grimaldi B, Tamaru T, Takamatsu K, Nakahata Y, Sassone-Corsi P. (2007) CLOCK-mediated acetylation of BMAL1 controls circadian function. Nature 450:1086-90.

Kinouchi K, Magnan C, Ceglia N, Liu Y, Cervantes M, Pastore N, Huynh T, Ballabio A, Baldi P, Masri S, Sassone-Corsi P. (2018) Fasting Imparts a Switch to Alternative Daily Pathways in Liver and Muscle. Cell Rep. 25: 3299-3314.

Koronowski KB, Kinouchi K, Welz PS, Smith JG, Zinna VM, Shi J, Samad M, Chen S, Magnan CN, Kinchen JM, Li W, Baldi P, Benitah SA, Sassone-Corsi P. (2019) Defining the Independence of the Liver Circadian Clock. Cell 177: 1448-1462

Masri S., Papagiannakopoulos T., Kinouchi K., Liu Y., Cervantes M., Baldi P., Jacks T. and Sassone-Corsi P. (2016) Lung adenocarcinoma distally rewires hepatic circadian homeostasis. Cell 165: 896-909.

Masri, S., Rigor, P., Cervantes, M., Ceglia, N., Sebastian, C., Xiao, C., Roqueta-Rivera, M., Deng, C., Osborne, T. F., Mostoslavsky, R., Baldi, P., Sassone-Corsi, P. (2014) Partitioning circadian transcription by SIRT6 leads to segregated control of cellular metabolism. Cell 158: 659-672.

Masri S, Patel VR, Eckel-Mahan KL, Peleg S, Forne I, Ladurner AG, Baldi P, Imhof A, Sassone-CorsiP. (2013) Circadian acetylome reveals regulation of mitochondrial metabolic pathways. Proc. Natl. Acad. Sci. USA 110: 3339-44.

Nakahata, Y., Sahar, S., Astarita, G., Kaluzova, M., Sassone-Corsi, P. (2009) Circadian control of the NAD+ salvage pathway by CLOCK-SIRT1. Science 324: 654-657.

Orozco-Solis R., Aguilar-Arnal L., Murakami M., Peruquetti R., Ramadori G., Coppari R. and Sassone-Corsi P. (2016) The circadian clock in the ventromedial hypothalamus controls cyclic energy expenditure. Cell Metab. 23: 467-478.

Sahar S, Masubuchi S, Eckel-Mahan K, Vollmer S, Galla L, Ceglia N, Masri S, Barth TK, Grimaldi B, Oluyemi O, Astarita G, Hallows WC, Piomelli D, Imhof A, Baldi P, Denu JM, Sassone-Corsi P. (2014) Circadian control of fatty acid elongation by SIRT1 protein-mediated deacetylation of acetyl-coenzyme A synthetase 1. J. Biol. Chem. 289: 6091-7.

Sato S, Solanas G, Peixoto FO, Bee L, Symeonidi A, Schmidt MS, Brenner C, Masri S, Benitah SA, Sassone-Corsi P. (2017) Circadian Reprogramming in the Liver Identifies Metabolic Pathways of Aging. Cell 170: 664-677

Sato S, Basse AL, Schönke M, Chen S, Samad M, Altıntaş A, Laker RC, Dalbram E, Barrès R, Baldi P, Treebak JT, Zierath JR, Sassone-Corsi P. (2019) Time of Exercise Specifies the Impact on Muscle Metabolic Pathways and Systemic Energy Homeostasis.Cell Metab. 30: 92-110

Schibler U and Sassone-Corsi, P. (2002). A web of circadian pacemakers. Cell 111: 919-922

Tamaru T, Hirayama J, Isojima Y, Nagai K, Norioka S, Takamatsu K, Sassone-CorsiP. (2009) CK2alpha phosphorylates BMAL1 to regulate the mammalian clock. Nat. Struct. Mol. Biol. 16: 446-8.

Tognini P, Murakami M, Liu Y, Eckel-Mahan KL, Newman JC, Verdin E, Baldi P, Sassone-Corsi P. (2017) Distinct Circadian Signatures in Liver and Gut Clocks Revealed by Ketogenic Diet. Cell Metab. 26: 523-5

Welz PS, Zinna VM, Symeonidi A, Koronowski KB, Kinouchi K, Smith JG, Guillén IM, Castellanos A, Crainiciuc G, Prats N, Caballero JM, Hidalgo A, Sassone-Corsi P, Benitah SA. (2019) BMAL1-Driven Tissue Clocks Respond Independently to Light to Maintain Homeostasis. Cell 177: 1436-1447

Group finds circadian clock common to almost all life forms

The peroxiredoxin active site is highly conserved in all domains of life. Image: Nature (2012) doi:10.1038/nature11088

( -- A group of biology researchers, led by Akhilesh Reddy from Cambridge University have found an enzyme that they believe serves as a circadian clock that operates in virtually all forms of life. In a paper published in the journal Nature, they describe a class of enzymes known as peroxiredoxins which are present in almost all plants and other organisms and which appear to serve as a basic ingredient in non-feedback loop biological clocks.

Up till now, researchers have not been able to find any kind of common biorhythmic clock among the Earth’s varied organisms, each class seemed to have its own. They did find though that one common feature of most was a feedback loop, which is where genes are transcribed before being translated into proteins which then build up until they reach a tipping point. Once that happens, transcription is turned off and the enzyme goes dormant. This cycle, for most organisms occurs on a twenty four hour basis, and is responsible for such things as the feelings of sleepiness or hunger in people that occur at roughly the same time each day.

But now, this new research suggests that the true clock controlling behavior in virtually every imaginable plant, animal, fungus, etc. has its roots in an enzyme whose purpose is to help clean up residue left over from the ravages of antioxidants.

Peroxiredoxins, which exist in virtually all life forms, are enzymes that cycle between two states depending on whether they have reacted recently with hydrogen peroxide, or not. The researchers found that this cycle occurs on a roughly twenty four hour cycle in all of the organisms they’ve tested to date. What’s more, the cycle continued even in the absence of light, proving that it’s not part of a feedback loop. Unfortunately, the team has not yet been able to show how or if the enzyme controls other clock mechanisms that are a part of feedback loops.

The team suggests that peroxiredoxins developed their cyclical behavior just after organisms began to develop some two and half billion years ago that were able to handle the increased amounts of oxygen that had begun to appear in the atmosphere in a time period known as the Great Oxidation Event the time when bacteria developed photosynthesis and began pumping out oxygen. Those organisms that managed to survive had to develop a means of dealing with the damage caused by antioxidants, and thus was born the role of peroxiredoxins. And because oxygen levels rose and fell on a regular daily schedule, the enzymes developed a clock over time to help predict when to go to work, and when to remain dormant, thus paving the way for the first circadian clock.

Cellular life emerged

3.7 billion years ago. With scant exception, terrestrial organisms have evolved under predictable daily cycles owing to the Earth’s rotation. The advantage conferred on organisms that anticipate such environmental cycles has driven the evolution of endogenous circadian rhythms that tune internal physiology to external conditions. The molecular phylogeny of mechanisms driving these rhythms has been difficult to dissect because identified clock genes and proteins are not conserved across the domains of life: Bacteria, Archaea and Eukaryota. Here we show that oxidation–reduction cycles of peroxiredoxin proteins constitute a universal marker for circadian rhythms in all domains of life, by characterizing their oscillations in a variety of model organisms. Furthermore, we explore the interconnectivity between these metabolic cycles and transcription–translation feedback loops of the clockwork in each system. Our results suggest an intimate co-evolution of cellular timekeeping with redox homeostatic mechanisms after the Great Oxidation Event

Chronotherapeutics & Parkinson’s

It would seem that chronotherapeutics should serve as a core component to Parkinsonian therapeutics, given that the circadian system is dysregulated. Chronotherapy is not a new idea. Traditional medical systems such as TCM (Traditional Chinese Medicine) and Ayurvedic Medicine have long recognized the times of the day and night as key interval phases, of organ, system and dosha functions. Future applications for chronotherapy in Parkinson’s disease envisions precise timing of food, fasting, supplements, drugs, exercise, sleep, wake and rest, with the end goal of re-synchronizing the internal circadian clock.

Moreover, future research studies should consider metabolomic and lipidomic patient testing to be conducted at different intervals throughout the day and night, in order to determine which biological and physiological processes are de-synchronized to the clock. This research is limited by cost at present time, but underscores a need for precision-focussed, circadian-based, biomarker analyses, and treatment.


Research in S. Panda’s laboratory is supported by National Institutes of Health grants DK091618 and EY016807, the Leona M. and Harry B. Helmsley Charitable Trust grant 2012-PG-MED002, the Glenn Foundation for Medical Research, and the American Federation for Aging Research grant M14322. A. Chaix is supported by an American Diabetes Association mentor-based Postdoctoral Fellowship (7–12-MN-64). A. Zarrinpar received support from National Institutes of Health grant K08 DK102902 and the American Association for the Study of Liver Diseases Emerging Liver Scholars Award.

CLOCK Acetylates ASS1 to Drive Circadian Rhythm of Ureagenesis

In addition to responding to environmental entrainment with diurnal variation, metabolism is also tightly controlled by cell-autonomous circadian clock. Extensive studies have revealed key roles of transcription in circadian control. Post-transcriptional regulation for the rhythmic gating of metabolic enzymes remains elusive. Here, we show that arginine biosynthesis and subsequent ureagenesis are collectively regulated by CLOCK (circadian locomotor output cycles kaput) in circadian rhythms. Facilitated by BMAL1 (brain and muscle Arnt-like protein), CLOCK directly acetylates K165 and K176 of argininosuccinate synthase (ASS1) to inactivate ASS1, which catalyzes the rate-limiting step of arginine biosynthesis. ASS1 acetylation by CLOCK exhibits circadian oscillation in human cells and mouse liver, possibly caused by rhythmic interaction between CLOCK and ASS1, leading to the circadian regulation of ASS1 and ureagenesis. Furthermore, we also identified NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9 (NDUFA9) and inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) as acetylation substrates of CLOCK. Taken together, CLOCK modulates metabolic rhythmicity by acting as a rhythmic acetyl-transferase for metabolic enzymes.

Keywords: acetylation circadian metabolism ureagenesis.

Part 2: Clock Genes, Clock Cells and Clock Circuits Continued

00:00:07.28 So, in this second section,
00:00:10.10 what I'd like to do is to really
00:00:12.21 look in more detail
00:00:14.15 at the differences between
00:00:16.26 central and peripheral oscillators
00:00:20.16 using both genetic and non-genetic methods
00:00:25.17 of perturbing the circadian system.
00:00:28.02 So, one way that we have looked at this
00:00:31.22 is to go back and examine
00:00:34.25 some of what we would call
00:00:36.19 the classic mutants
00:00:39.05 of either Period or Cryptochrome,
00:00:42.09 which are shown here
00:00:44.11 for Cryptochrome 1 and 2.
00:00:45.29 These are loss of function or knockout mice,
00:00:49.04 and in this case what we found
00:00:51.20 is that if you delete Cry1,
00:00:54.11 the mouse still has a rhythm,
00:00:56.14 but it's one hour short.
00:00:58.22 If you delete Cry2,
00:01:00.23 the mouse still has a rhythm,
00:01:03.20 but in this case it's long.
00:01:08.12 And then if you delete both genes,
00:01:10.15 Cry1 and Cry2,
00:01:12.15 the mouse then loses its rhythm,
00:01:14.06 and this is really the reason that we called
00:01:19.02 Cry1 and Cry2
00:01:21.09 part of the central clock gene network.
00:01:23.06 And so Cry1 and 2 mice
00:01:26.10 have no rhythm they're arrhythmic.
00:01:28.25 And so what we've done is to then ask,
00:01:31.15 what are the effects of these mutations,
00:01:33.22 such as Cry1 and 2,
00:01:35.16 on the SCN clock and a peripheral clock,
00:01:40.16 in this case this example shows the lung.
00:01:44.02 And so this is using this PER::LUC imaging
00:01:48.10 in a wild type mouse
00:01:51.00 for the SCN and for lung,
00:01:53.23 and what you can see is
00:01:58.00 both tissues have very nice rhythms of PER::LUCIFERASE,
00:02:01.08 but if we knock out either Per1 or Cry1,
00:02:06.06 this leads to a strong reduction
00:02:10.06 in the rhythm in the lung,
00:02:12.28 but has very little effect
00:02:15.08 in the suprachiasmatic nucleus.
00:02:17.23 In the suprachiasmatic nucleus,
00:02:20.03 we have to do the double knockout,
00:02:21.14 as we did for behavior for Cry1 and Cry2.
00:02:24.14 This of course works in the lung as well,
00:02:27.21 but in peripheral tissues
00:02:30.26 we see a clear difference.
00:02:33.19 It's not just any Cry gene
00:02:35.20 that has this effect,
00:02:37.04 so for example Cry1
00:02:39.12 leads to this loss of rhythm phenotype,
00:02:41.05 shown here,
00:02:42.17 but Cry2 doesn't.
00:02:44.08 The same is true for Per1 and Per3.
00:02:47.02 So, there is clearly some difference
00:02:49.19 in the Per and Cry genes,
00:02:51.15 and some specificity in their role in the clock system.
00:02:56.24 So, to look into this further,
00:03:00.05 we then asked,
00:03:02.16 what effect do these mutations have
00:03:04.25 on single-cell rhythm?
00:03:07.19 So these are now single-cell recordings
00:03:09.29 from either fibroblasts or
00:03:14.18 dissociated, isolated SCN neurons.
00:03:17.10 Okay?
00:03:19.02 And what we find is a very interesting result,
00:03:21.15 and that is that the gene mutations Cry1 and Per1
00:03:26.10 have the same effect in a fibroblast
00:03:30.08 as they do in the SCN neuron,
00:03:33.13 and this is surprising because we thought before
00:03:36.05 that perhaps the SCN might be different,
00:03:38.09 it might be more robust.
00:03:40.17 And as you remember,
00:03:42.09 in the previous slide
00:03:44.11 I showed you that the SCN
00:03:46.04 was resistant to these mutations,
00:03:47.28 but that's because
00:03:50.14 in that experiment the SCN itself
00:03:54.07 was somewhat intact,
00:03:56.16 it was in an organotypic slice,
00:04:00.04 where the organization of the SCN it still intact,
00:04:04.04 as compared to
00:04:06.27 physically dissociated SCN neurons.
00:04:08.13 So, here's an experiment
00:04:12.16 in which the SCN in a slice
00:04:17.03 is compared to SCN dissociated neurons,
00:04:21.00 looking at the effect of the Cry2 knockout.
00:04:24.10 So, on the bottom are shown
00:04:26.23 heat map representations
00:04:28.12 of single-cell recordings from SCN neurons,
00:04:32.29 about 20 cells in each case,
00:04:34.28 and what you can see is in Cry2 knockout SCN neurons,
00:04:40.09 the cells are coherent and synchronized,
00:04:45.18 as indicated by the red and green stripes,
00:04:50.15 but in dissociated SCN neurons,
00:04:52.23 each of the cells can generate
00:04:54.22 intact circadian rhythms,
00:04:56.17 but they are no longer coupled,
00:04:58.16 and so the pattern becomes fragmented.
00:05:02.06 In contrast, in Cry1 knockout SCN neurons,
00:05:07.12 we see that in the intact SCN,
00:05:09.24 rhythms are generated and are coherent,
00:05:14.07 but when we dissociate the cells
00:05:16.17 the SCN cells can no longer
00:05:19.05 generate strong circadian rhythms,
00:05:21.14 and at the cell-autonomous level
00:05:23.13 the rhythms are disrupted.
00:05:25.13 So, these genetic experiments
00:05:27.20 have really uncovered
00:05:30.07 a new role for the suprachiasmatic nucleus,
00:05:35.28 and that is to be able to integrate the information
00:05:39.21 from many cells.
00:05:40.26 And so what we saw in these genetic experiments
00:05:45.25 is that the Cry1 mutation
00:05:48.14 could actually lead to a loss of rhythm
00:05:50.23 in the cell-autonomous level,
00:05:53.19 which was then reflected in peripheral tissues,
00:05:58.04 but in contrast the Cry2 neurons,
00:06:02.17 which have intact rhythms,
00:06:05.04 then did not have any effect
00:06:08.14 on peripheral tissues.
00:06:10.20 In contrast, in suprachiasmatic nucleus tissue,
00:06:14.18 we found a very interesting result,
00:06:17.10 where the cell-autonomous defect
00:06:20.07 can actually be rescued
00:06:22.25 by the SCN network.
00:06:24.10 Interestingly, because the SCN
00:06:26.29 then regulates circadian behavior,
00:06:29.26 we can see that at the behavioral level
00:06:33.21 the Cry1 mutant is also rescued.
00:06:38.27 And so I think these experiments
00:06:40.21 are important for a number of reasons.
00:06:42.07 One is that it shows that
00:06:47.00 circadian behavior is really
00:06:50.14 not a direct reflection of the cell-autonomous oscillator
00:06:55.26 information at the cell-autonomous level
00:06:57.18 can be transformed by the SCN network
00:07:01.20 to rescue that function,
00:07:04.22 which then in turn rescues circadian behavior.
00:07:08.17 On the other hand,
00:07:10.09 at another level,
00:07:11.21 if we were interested in the specific role
00:07:14.01 of, say, Cry1 or Cry2,
00:07:15.29 then trying to interpret
00:07:19.11 the role of Cry1 and Cry2
00:07:21.07 purely on the basis of behavior
00:07:23.19 might be misleading,
00:07:25.20 because we see this very
00:07:28.12 different cell-autonomous defect
00:07:30.16 at the level of Cry1 and Cry2.
00:07:33.07 And so if we're trying to understand
00:07:35.26 the biochemical function of Cry1,
00:07:38.08 then it might make more sense, for example,
00:07:43.00 to study the cell-autonomous clock,
00:07:45.08 rather than the SCN or behavioral clock.
00:07:50.26 So, going back to the organization
00:07:53.13 of circadian rhythms,
00:07:55.17 how is it that rhythms
00:07:58.16 are really synchronized and orchestrated
00:08:01.21 throughout the entire organism?
00:08:04.26 So, we know that the SCN
00:08:07.01 is really still in charge.
00:08:08.19 So for example,
00:08:10.01 in these experiments shown on the left.
00:08:12.10 these are records of control mice,
00:08:15.00 and then at the bottom
00:08:16.29 are records of SCN lesion mice.
00:08:19.26 What SCN lesion does
00:08:21.29 is to disrupt the behavioral rhythm,
00:08:25.17 and with PER::LUC recording of peripheral tissues,
00:08:28.29 we can then ask,
00:08:30.24 what is the effect of SCN lesioning
00:08:33.08 of the central clock
00:08:35.00 on peripheral rhythms?
00:08:36.26 And so shown here
00:08:40.27 are PER::LUCIFERASE tracings from the pituitary,
00:08:43.19 a peripheral oscillator,
00:08:46.28 and in intact mice
00:08:51.06 the pituitary gland rhythms
00:08:53.03 are actually very normal
00:08:56.11 in either light-dark cycles or in constant darkness.
00:09:01.06 But when we lesion the suprachiasmatic nucleus,
00:09:03.28 what we find is that
00:09:07.22 peripheral tissues become desynchronized,
00:09:11.14 so when we compare the peripheral rhythms
00:09:13.25 from different mice,
00:09:15.10 we see that they have adopted different phases.
00:09:18.07 Each mouse has a slightly different phase
00:09:21.22 for its pituitary and other peripheral tissues.
00:09:27.00 So, interestingly,
00:09:29.12 the SCN is not necessary for maintaining rhythms
00:09:32.23 in peripheral tissues,
00:09:34.12 but it plays a role
00:09:36.28 in synchronizing or coordinating those rhythms.
00:09:41.01 So, how is it that the SCN
00:09:44.14 really communicates this information?
00:09:47.20 So, we know that light
00:09:49.07 is one of the major
00:09:51.15 inputs to the brain and the SCN,
00:09:54.05 which then controls many behaviors,
00:09:56.18 such as feeding and sleep-wake cycles,
00:09:59.07 but recent work has also shown
00:10:01.24 a very important role for
00:10:05.08 nutritional cycles and signals,
00:10:07.08 as well as feeding behavior,
00:10:10.03 particularly for regulating peripheral tissues
00:10:14.16 such as the liver.
00:10:18.27 Now, to really address this,
00:10:22.05 we've gone back and examined
00:10:24.14 a second environmental signal,
00:10:26.09 and that is temperature.
00:10:28.08 So, in almost every organism
00:10:32.06 living in the free world,
00:10:34.26 light and temperature both synchronize clocks,
00:10:39.20 and temperature
00:10:42.22 is involved both in entrainment,
00:10:44.19 or synchronization of rhythms,
00:10:46.00 but there's also an interesting feature of rhythms
00:10:50.03 called temperature compensation,
00:10:51.23 and that is that the period of the rhythm
00:10:54.15 is resistant to dramatic changes in temperature,
00:10:59.06 so the period is actually compensated
00:11:02.07 against temperature fluctuations.
00:11:06.24 Now, mammals are actually a little bit unusual.
00:11:09.12 So, this is a record of a mouse,
00:11:11.16 it's a very long activity record,
00:11:14.20 and at the top the mouse
00:11:17.18 is in a constant temperature,
00:11:19.02 but it's exposed to a light cycle
00:11:20.13 which synchronizes its rhythm, shown here.
00:11:23.06 It goes into darkness at this point
00:11:25.02 and then, at the bottom of this record,
00:11:27.15 shown in the gray bar,
00:11:29.24 is a temperature cycle
00:11:32.20 of about 24-32°C,
00:11:37.06 and what you can see is that
00:11:39.15 this temperature cycle
00:11:42.12 can synchronize the rhythm transiently,
00:11:45.13 but it's not very strong,
00:11:47.01 so over time the activity rhythm
00:11:50.29 breaks away and free runs.
00:11:52.14 So, in mammals, temperature is
00:11:56.12 kind of a weak entraining signal
00:11:58.19 for circadian rhythms
00:12:00.26 at the whole-organismal level.
00:12:03.01 But interestingly, mice, as in humans,
00:12:07.14 have a very dramatic circadian body temperature rhythm,
00:12:10.23 and so this is a temperature recording
00:12:13.14 from a mouse over a ten day period,
00:12:16.16 and what you can see is the body temperature
00:12:19.02 fluctuates from about 36°C
00:12:21.18 at the lowest
00:12:23.08 to about 38.5°C at the peak,
00:12:25.25 each day.
00:12:27.25 And so Ethan Buhr asked,
00:12:31.13 can this subtle change in temperature, 2.5°C,
00:12:36.22 actually perturb or entrain
00:12:39.16 the phase of clocks in the periphery?
00:12:42.07 So, this is a PER::LUC recording
00:12:45.11 from liver tissue samples,
00:12:48.07 and at this point they were given
00:12:52.03 a temperature pulse of just 2.5°C
00:12:55.07 for six hours to the liver,
00:12:58.09 shown in the red trace,
00:13:00.09 and in the blue trace is another liver sample
00:13:04.00 that was handled the same,
00:13:05.26 but did not receive the temperature change,
00:13:08.24 and what you can see is, after this treatment,
00:13:12.07 the liver exposed to this temperature pulse
00:13:15.26 is delayed.
00:13:17.24 The phase is changed.
00:13:20.10 And if we do this experiment systematically,
00:13:23.04 we give a temperature pulse
00:13:26.05 at all times of the cycle,
00:13:28.02 shown on the x-axis of this graph.
00:13:30.26 this is a graph called a phase transition curve,
00:13:33.27 it plots the phase of the rhythm on the x-axis
00:13:38.26 and then the new phase of the rhythm on the y-axis.
00:13:43.01 Okay?
00:13:45.15 So, if you were to give
00:13:50.22 a stimulus that had no effect,
00:13:53.08 then the old phase and the new phase
00:13:56.07 would be the same,
00:13:58.19 and all the data points would lie on this 45° line,
00:14:03.20 where the blue points are.
00:14:05.06 Those are the handling controls.
00:14:07.26 You can see that they have no effect.
00:14:10.10 But temperature has a very strong resetting effect,
00:14:13.17 those data are shown in red dots.
00:14:15.26 They reset at almost any time of day
00:14:20.03 to a new set of phases,
00:14:22.27 okay?
00:14:24.00 And these data have a horizontal slope,
00:14:29.04 a slope of 0.
00:14:31.02 This is called strong resetting.
00:14:34.02 It's also called type 0 resetting,
00:14:36.08 because the slope is 0,
00:14:38.04 as opposed to type 1 resetting,
00:14:40.06 a slope of 1,
00:14:41.16 which is weak resetting.
00:14:43.10 So, temperature turns out to be
00:14:46.00 a very strong signal to peripheral clocks
00:14:49.28 such as those found in the liver.
00:14:53.04 And so, this is another set of experiments,
00:14:56.26 in this case, the pituitary gland.
00:15:00.11 The blue and red dots now
00:15:02.17 indicate different duration temperature pulses.
00:15:05.12 The blue dots are 1 hour temperature pulses
00:15:08.08 and the red dots are six hour temperature pulses,
00:15:11.01 as we saw before.
00:15:12.25 And as we can see here,
00:15:15.05 the pituitary shows strong resetting
00:15:18.15 the slope of these data are 0.
00:15:21.03 Okay?
00:15:22.08 But surprisingly,
00:15:24.06 when we look at the suprachiasmatic nucleus
00:15:26.29 in the same kind of conditions,
00:15:30.04 those data are all type 1,
00:15:34.04 or very weak resetting,
00:15:36.07 so the SCN is resistant
00:15:38.26 to temperature resetting pulses.
00:15:43.25 So, we then asked,
00:15:48.09 can the body temperature profile in a mouse
00:15:51.08 act to synchronize rhythms in peripheral tissues?
00:15:55.00 So, this shows you the average profile
00:15:58.25 measured from a mouse over one day,
00:16:03.28 and what Ethan Buhr then did
00:16:06.15 was to program this temperature profile
00:16:09.15 into an incubator
00:16:13.21 and expose different peripheral tissues
00:16:16.03 to these cycles.
00:16:17.15 So, the blue cycles
00:16:19.21 indicate one phase
00:16:21.18 and the red cycles indicate
00:16:23.20 a temperature cycle that's shifted
00:16:25.22 to the opposite phase.
00:16:27.27 And in these two examples shown here,
00:16:29.23 these are pituitary glands
00:16:31.25 that were exposed to three cycles
00:16:33.19 at these temperature cycles.
00:16:35.24 The red trace indicates
00:16:38.07 the phase of the pituitary rhythm
00:16:40.08 exposed to the red temperature cycles,
00:16:43.00 and the blue trace
00:16:44.29 indicates the phase of the rhythm
00:16:47.26 in a pituitary gland exposed
00:16:50.08 to the blue temperature cycles.
00:16:51.10 And what you can see is
00:16:53.29 the two sets of pituitaries
00:16:56.17 are out of phase,
00:16:58.20 and they match the phase of the temperature cycle.
00:17:01.03 That means that the temperature cycle
00:17:03.20 reset the phase,
00:17:05.25 within three days,
00:17:07.26 of both the pituitary gland and the lung,
00:17:10.24 in this case at that bottom.
00:17:13.01 So, the very subtle body temperature variation
00:17:16.19 in the mouse
00:17:18.13 is a very strong signal
00:17:20.07 and can completely reset the oscillators
00:17:23.06 in different organs.
00:17:25.07 Okay.
00:17:26.22 So, what I'd like to do now is
00:17:29.02 to go back to the SCN and ask,
00:17:31.09 why is it that the SCN
00:17:33.16 is different from a peripheral tissue?
00:17:36.24 Why is it resistant to temperature?
00:17:40.01 And as we saw in the case of
00:17:42.00 those genetic experiment before,
00:17:43.14 coupling in the SCN
00:17:46.13 might be an important factor.
00:17:48.25 And so we can use a drug
00:17:51.27 called tetrodotoxin, or TTX,
00:17:54.21 which blocks sodium-dependent action potentials
00:17:58.12 in the suprachiasmatic nucleus,
00:18:00.26 and can uncouple or desynchronize
00:18:03.21 the neurons in the SCN.
00:18:05.17 So, this panel on the left
00:18:08.10 shows single cell recordings of SCN neurons,
00:18:12.14 indicated in [red/green] heatmaps,
00:18:16.15 which were treated with tetrodotoxin,
00:18:18.28 and what happens is, at the single cell level,
00:18:21.09 those neurons start desynchronizing.
00:18:24.12 And when we give a temperature pulse,
00:18:26.13 incredibly,
00:18:28.22 now the SCN becomes sensitive to temperature.
00:18:31.11 So, at the top this is showing
00:18:34.18 SCN slices not treated with tetrodotoxin
00:18:37.21 -- they're resistant, they have type 1 resetting --
00:18:40.22 and at the bottom
00:18:43.01 are SCN slices treated with tetrodotoxin.
00:18:46.05 Just that single manipulation alone
00:18:48.13 then converts the temperature sensitivity
00:18:51.02 to type 0 resetting, or very strong resetting,
00:18:55.01 just like a peripheral tissue.
00:18:57.08 So, this suggests that it is really the coupling
00:19:00.09 within the SCN
00:19:02.15 that is making it more robust
00:19:04.11 and more resistant to temperature resetting,
00:19:06.22 and also making it different from a peripheral tissue.
00:19:12.23 Now, interestingly,
00:19:14.09 the SCN has two major subdivisions.
00:19:17.19 One is called the ventrolateral or VL
00:19:20.01 and the other is called dorsomedial (DM),
00:19:23.10 and you can do a very simple experiment
00:19:26.01 and transect the SCN
00:19:29.12 to separate the dorsal and ventral regions of the nucleus,
00:19:33.29 as shown here.
00:19:35.12 When you culture those two
00:19:38.05 halves of the SCN, they both have rhythms,
00:19:41.12 but incredibly they now have strong,
00:19:45.01 or type 0 resetting.
00:19:47.07 In contrast, if we were to cut the SCN
00:19:50.15 down the midline,
00:19:52.26 both the right and the left SCN, of course,
00:19:54.23 still have rhythms,
00:19:56.22 but in this case they remain robust,
00:20:00.29 or resistant to temperature.
00:20:03.22 So, this very simple experiment
00:20:05.21 suggests that there's a pathway
00:20:07.28 between the ventrolateral and dorsomedial SCN
00:20:11.05 that confers this kind of temperature resistance,
00:20:15.24 again suggesting that coupling
00:20:18.04 is actually important within the nucleus
00:20:21.02 to make it robust.
00:20:24.15 So, what is it that senses temperature?
00:20:27.20 And so, in experiments from Ueli Schibler's lab,
00:20:32.13 where they screened
00:20:35.07 different transcription factors in the liver
00:20:37.19 for circadian expression patterns,
00:20:40.09 one of the most robust transcription factors that they found
00:20:44.17 was HSF1.
00:20:46.15 So, this is a western blot
00:20:48.19 showing the amount of HSF protein
00:20:53.16 in the nucleus of liver cells
00:20:56.06 over the time of day,
00:20:57.20 and what you can see is that in the daytime
00:21:00.08 there's virtually no HSF in the nucleus,
00:21:02.29 and then at night HSF1 is very abundant,
00:21:06.25 so this leads to a very strong pattern of HSF1
00:21:10.21 in the nucleus of liver cells.
00:21:16.28 And so, to test
00:21:20.04 whether HSF1 might be involved
00:21:22.20 in temperature sensing for resetting the clock,
00:21:27.25 we used an inhibitor of HSF1 called KNK437.
00:21:34.01 This inhibitor can very strongly
00:21:37.01 block the heatshock response in cells.
00:21:40.23 This is the HSP72 response to temperature.
00:21:44.17 In the presence of drug,
00:21:46.20 this is very strongly blocked.
00:21:49.11 And when we apply this inhibitor for HSF1
00:21:53.15 to different peripheral tissues,
00:21:55.12 such as the lung,
00:21:57.11 as a pulse for one hour,
00:22:00.04 we find that it causes
00:22:02.04 very strong resetting of the clock,
00:22:05.26 but interestingly the phase of that resetting curve
00:22:09.21 is slightly different from what we saw with temperature.
00:22:12.26 So, in the gray
00:22:16.11 are shown the temperature pulses
00:22:18.17 that we saw before for temperature increases.
00:22:23.01 In light blue are shown
00:22:26.16 resetting curves for "cool" pulses,
00:22:30.21 a reduction in temperature.
00:22:33.23 This also shifts the clock very effectively
00:22:35.25 and, interestingly,
00:22:37.19 KNK and cool pulses
00:22:40.07 have the same kind of effect on the clock.
00:22:43.07 So this suggests that inhibition of HSF1
00:22:46.23 mimics a temperature reduction,
00:22:49.00 and this is consistent with the idea,
00:22:51.17 because temperature normally increases HSF1.
00:22:55.12 A lowering of temperature would reduce HSF1,
00:22:59.18 as would inhibition of HSF1.
00:23:02.09 And so we think that this is evidence
00:23:07.01 that HSF1, in part,
00:23:08.27 can mediate the effects of both
00:23:11.06 cool and warm pulses
00:23:14.06 in resetting peripheral tissues.
00:23:16.11 Now, does HSF1
00:23:20.05 mediate temperature pulses?
00:23:21.16 And we can ask that question
00:23:23.13 by doing a blocking experiment.
00:23:25.00 We can ask, if we block
00:23:27.07 the increase in HSF1 with KNK437,
00:23:31.08 will this block the temperature shift,
00:23:34.02 and so this is an experiment shown on the top here.
00:23:37.08 The gray bar shows the effect of temperature
00:23:40.21 using a vehicle control,
00:23:42.25 so temperature is giving a very large reset.
00:23:46.19 At this same phase,
00:23:48.23 we can give the drug alone,
00:23:50.05 it causes no shift at this phase,
00:23:52.19 and then the third condition
00:23:55.05 is the drug plus the temperature pulse,
00:23:56.17 and you can see that there's no shift,
00:23:59.01 showing that KNK can completely block
00:24:02.00 temperature resetting.
00:24:03.12 So this is very strong evidence that
00:24:05.23 HSF1 elevations
00:24:08.20 are required for temperature resetting
00:24:10.10 in peripheral tissues.
00:24:13.14 And we can also do this experiment
00:24:15.20 in a more complex manner
00:24:17.06 by testing all phases of the cycle,
00:24:19.16 and that's shown in these resetting curves.
00:24:23.14 And what's important to see in these curves
00:24:25.22 is the gray dots
00:24:27.26 show the effect of temperature by itself,
00:24:29.21 and then the orange and red dots
00:24:31.22 show the effect of either drug,
00:24:33.29 or drug plus temperature,
00:24:35.10 which are indistinguishable.
00:24:37.06 And this shows the drug is
00:24:40.03 blocking the effect of temperature
00:24:41.22 at all phases of the cycle.
00:24:44.00 This is of course in a peripheral tissue.
00:24:47.20 And then finally,
00:24:49.26 interestingly, the SCN,
00:24:51.13 which was resistant to temperature,
00:24:54.14 is also resistant to the inhibitor of HSF1, KNK
00:24:59.17 -- it has a type 1 resetting curve
00:25:02.04 to the drug --
00:25:04.01 further indicating that
00:25:07.13 this drug is working on the same pathway,
00:25:10.02 and that the SCN coupling network
00:25:13.02 can interfere with not only temperature pulses,
00:25:16.11 but also HSF1 interference.
00:25:21.27 Finally, the other feature of temperature
00:25:25.12 was this phenomenon that I mentioned before,
00:25:29.09 which is called temperature compensation.
00:25:33.07 And so this is an illustration
00:25:35.12 of temperature compensation in the SCN
00:25:37.06 and in the pituitary.
00:25:39.22 If you measure the period length
00:25:43.01 of the rhythm, shown here,
00:25:45.13 at different temperatures,
00:25:47.18 what we see is the period
00:25:50.20 is very similar.
00:25:51.25 And when we calculate the temperature coefficient,
00:25:54.14 or Q10,
00:25:56.03 we see that that coefficient is very close to 1
00:25:58.25 -- 0.97 in the case of pituitary
00:26:02.07 and 1.04 in the case of the SCN --
00:26:05.18 almost perfect temperature compensation.
00:26:09.10 But if we expose these tissues
00:26:12.14 to the HSF1 inhibitor, KNK437,
00:26:16.10 we see that the Q10s now
00:26:20.08 are taken out of the circadian range
00:26:21.23 and become much bigger,
00:26:23.16 and you can see the orange curves here
00:26:25.21 are kind of slanted.
00:26:28.03 Finally, in blue, in the SCN,
00:26:31.22 we can ask,
00:26:33.23 what is the effect of treatment
00:26:37.16 with tetrodotoxin
00:26:39.22 and uncoupling the network?
00:26:42.06 And what we find is that
00:26:44.18 the Q10 is still the same, 1.06.
00:26:47.28 So, this is a very interesting difference.
00:26:49.20 Temperature compensation of period
00:26:51.27 does not depend on the SCN network.
00:26:54.27 It is a cell-autonomous property,
00:26:57.02 not only of SCN cells,
00:26:59.28 but the pituitary, peripheral tissues, and fibroblasts.
00:27:03.09 But temperature resistance
00:27:06.22 is a network phenomenon
00:27:09.08 that's characteristic of the SCN
00:27:11.16 and not peripheral tissues.
00:27:15.03 Okay.
00:27:17.10 So this is kind of an overall summary
00:27:19.23 of our understanding
00:27:21.27 of the role of temperature
00:27:24.18 as a signal for resetting peripheral clocks.
00:27:28.00 The suprachiasmatic nucleus
00:27:30.08 generates a circadian rhythm of body temperature,
00:27:34.28 this signal is propagated throughout the organism,
00:27:39.23 and can be used by
00:27:41.25 many different peripheral clocks,
00:27:44.00 and we believe that in these peripheral clocks
00:27:46.29 HSF1 is one of the signaling pathways
00:27:50.25 for mediating this temperature information
00:27:53.05 to reset those clocks.
00:27:56.04 Now, the SCN itself
00:27:58.22 is resistant to this body temperature signal
00:28:02.15 and in retrospect that kind of makes sense.
00:28:05.01 If the SCN is setting out a resetting signal,
00:28:10.01 then it might not be a good idea
00:28:12.15 for it to be sensitive
00:28:14.01 to its own resetting signal.
00:28:15.12 That might cause some kind of feedback problems.
00:28:19.01 And so we think that that could be the reason,
00:28:22.06 or one of the reasons,
00:28:24.12 that the SCN is really resistant to temperature,
00:28:27.09 because it wouldn't make sense
00:28:30.10 to be paying attention to its own signal
00:28:33.14 that it's trying to propagate out.
00:28:36.10 So, I've tried to give you
00:28:40.27 a sort of an introduction to clock genes,
00:28:44.13 clock cells,
00:28:46.02 and clock circuits
00:28:48.19 in the circadian system,
00:28:50.24 and I think in the field of neuroscience
00:28:55.07 we're really at a very exciting time today,
00:28:58.25 because the tools of both genetics and genomics
00:29:02.11 are really enabling us
00:29:05.07 to understand how
00:29:08.01 behavior and physiology are really regulated.
00:29:10.14 And we can very easily
00:29:14.28 go all the way from genes,
00:29:17.03 cells, circuits, to behavior,
00:29:19.27 in the circadian system,
00:29:22.28 where we have,
00:29:24.15 correspondingly at these many levels of organization,
00:29:26.19 clock genes, clock cells, clock circuits
00:29:29.19 in the SCN,
00:29:31.21 which then can regulate both physiology and behavior.
00:29:35.01 And it's a very exciting time
00:29:37.24 because both normal behavior
00:29:40.06 as well as pathological conditions
00:29:43.10 might be regulated by this system.
00:29:47.11 So, I'd like to end here
00:29:49.07 and acknowledge all of my colleagues
00:29:51.24 over the many years
00:29:53.26 who contributed to all of this work.
00:29:55.21 Thank you very much.

Watch the video: How to Spot a Fake Rolex - Comparing a Real to a Fake (September 2022).


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