What can the timing of human urination tell about the human's physical condition and circadian rhythms?

What can the timing of human urination tell about the human's physical condition and circadian rhythms?

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I've noticed a peculiar phenomenon. A subject drinks 400 ml of water, then observes time until the urge to urinate is felt. The time is 15 minutes. The subject releases water. 14 minutes later another urge to urinate is felt. The subject releases water again.

I'm particularly interested in what kinds of biological systems are involved in timing of such events. Does the time depend on how full the subject's stomach is? Does caffeine and other diuretics play a part? Is it time of day (circadian rhythm) sensitive? Does that predict anything about the suppression/release of diuretic hormones?

What I'm trying to understand is if the timing between human urges to urinate after water consumption can be used to make predictions about the human biological clock and the state of various systems within the body (for example the digestive system).

I will be conducting this experiment at different times of the day. My hypothesis is that at night, when diuretic hormones are suppressed, the timing would be longer for the same amount of water consumed. This is based on my limited research in the area. Update: I did perform the same experiment at night, the time was 75 minutes for the same amount of water. The experiment was performed at the end of one of sleep cycles, which makes me think that 75 minutes was the duration of the subsequent sleep cycle.

I appreciate your input on the subject, along with any keywords that can help me advance my research in this area. Thank you!

The fact that urine output (enuresis) and other kidney functions are circadian is well known, just search on pubmed 'circadian urine kidney'. This can be due both to the fact that liquid consumption is less during the night and to the fact that hormones regulating kidney function like aldosterone and plasma angiotensin II are circadian.

I would suggest a couple of papers

Circadian rhythms in urinary functions: possible roles of circadian clocks? - Noh et al., Int Neurourol J., 2011

Circadian regulation of renal function - Firsov and Bonny, 2010

Neural regulation of the circadian vasopressin rhythm in cerebrospinal fluid: a pre-eminent role for the suprachiasmatic nuclei - Schwartz and Reppert, J Neurosci, 1985

Delayed sleep phase disorder

Delayed sleep phase disorder (DSPD), more often known as delayed sleep phase syndrome and also as delayed sleep–wake phase disorder, is a chronic dysregulation of a person's circadian rhythm (biological clock), compared to those of the general population and societal norms. The disorder affects the timing of sleep, peak period of alertness, the core body temperature, rhythm, hormonal as well as other daily cycles. People with DSPD generally fall asleep some hours after midnight and have difficulty waking up in the morning. [1] People with DSPD probably have a circadian period significantly longer than 24 hours. [2] Depending on the severity, the symptoms can be managed to a greater or lesser degree, but no cure is known, and research suggests a genetic origin for the disorder. [3]

Delayed sleep phase disorder
Other namesDelayed sleep–wake phase disorder, delayed sleep phase syndrome, delayed sleep phase type
Comparison of standard (green) and DSPD (blue) circadian rhythms
SpecialtyPsychiatry, sleep medicine

Affected people often report that while they do not get to sleep until the early morning, they do fall asleep around the same time every day. Unless they have another sleep disorder such as sleep apnea in addition to DSPD, patients can sleep well and have a normal need for sleep. However, they find it very difficult to wake up in time for a typical school or work day. If they are allowed to follow their own schedules, e.g. sleeping from 4:00 am to 1:00 pm, their sleep is improved and they may not experience excessive daytime sleepiness. [4] Attempting to force oneself onto daytime society's schedule with DSPD has been compared to constantly living with jet lag DSPD has been called "social jet lag". [5]

Researchers in 2017 linked DSPD to at least one genetic mutation. [3] The syndrome usually develops in early childhood or adolescence. [6] An adolescent version may disappear in late adolescence or early adulthood otherwise, DSPD is a lifelong condition. The best estimate of prevalence among adults is 0.13–0.17% (1 in 600). [7] [8] Prevalence among adolescents is as much as 7–16%. [4]

DSPD was first formally described in 1981 by Elliot D. Weitzman and others at Montefiore Medical Center. [9] It is responsible for 7–13% of patient complaints of chronic insomnia. [10] However, since many doctors are unfamiliar with the condition, it often goes untreated or is treated inappropriately DSPD is often misdiagnosed as primary insomnia or as a psychiatric condition. [11] DSPD can be treated or helped in some cases by careful daily sleep practices, morning light therapy, evening dark therapy, earlier exercise and meal times, and medications such as aripiprazole, melatonin, and modafinil melatonin is a natural neurohormone partly responsible for the human body clock. At its most severe and inflexible, DSPD is a disability. A chief difficulty of treating DSPD is in maintaining an earlier schedule after it has been established, as the patient's body has a strong tendency to reset the sleeping schedule to its intrinsic late times. People with DSPD may improve their quality of life by choosing careers that allow late sleeping times, rather than forcing themselves to follow a conventional 9-to-5 work schedule.

Symptoms and Causes

What causes circadian rhythm sleep disorders?

Circadian rhythm sleep disorders are caused by continuous or occasional disruption of sleep patterns. The disruption results from either a malfunction in your “internal body clock” or a mismatch between your “internal body clock” and the external environment (for example, social and work requirements), which affects the timing and duration of sleep. This circadian mismatch causes problems functioning at work, school and in social activities.

Situations that can trigger a circadian rhythm sleep disorder include:

  • Frequent changes in work shift.
  • Jet lag.
  • Frequent changes in time to go to bed and time to wake up.
  • Brain damage resulting from such medical conditions as stroke, dementia, head injury intellectual disabilities.
  • Blindness or lack of exposure to sunlight for long periods of time.
  • Certain drugs.
  • Poor sleep hygiene (lack of practices, habits and other factors that promote good quality sleep).
  • Older age.

What are the symptoms of circadian rhythm sleep disorders?

Symptoms of circadian rhythm sleep disorders include:

    (difficulty falling asleep or staying asleep).
  • Excessive daytime sleepiness.
  • Difficulty waking up in the morning.
  • Sleep loss. .
  • Stress in relationships.
  • Poor work/school performance.
  • Inability to meet social obligations.

Part 3: Genetics of Mammalian Clocks

00:00:07.17 Hello.
00:00:09.02 I'm Joe Takahashi
00:00:10.14 at the University of Texas Southwestern
00:00:12.13 and the Howard Hughes Medical Institute,
00:00:16.02 and for this second lecture what I'd like to do
00:00:18.26 is to give you three examples
00:00:21.27 of using genetics in the mouse
00:00:25.06 to really discover genes
00:00:27.21 that are involved in controlling circadian behavior.
00:00:31.03 And so, the way a genetic screen in a mouse
00:00:36.08 is performed
00:00:38.26 is to use a chemical mutagen,
00:00:40.23 such as ENU, or ethylnitrosourea,
00:00:44.11 which is used to treat a male mouse.
00:00:47.17 That causes mutations
00:00:50.12 in the germline of that mouse,
00:00:53.15 and then that treated mouse
00:00:56.05 is crossed to normal female mice
00:00:59.22 in what we call the G0 generation.
00:01:04.17 And these mice
00:01:08.13 then produce a number of offspring
00:01:10.17 that we call G1, or generation 1, mice.
00:01:13.27 These mice are carrying mutations
00:01:17.08 that they inherit from this treated mouse
00:01:20.10 up here.
00:01:22.14 In a recessive screen,
00:01:24.03 which is a little bit complicated
00:01:26.19 because it requires three more generations
00:01:29.22 of crossing,
00:01:31.26 a male mouse is then crossed
00:01:34.06 to another normal female mouse
00:01:35.25 to produce a second generation of progeny,
00:01:40.09 and then the female progeny
00:01:42.27 in this second generation, or G2 generation,
00:01:45.29 are actually backcrossed back to the G1 male.
00:01:50.00 And that produces the third generation of mice,
00:01:53.01 which are called G3s.
00:01:55.02 And the reason we do this
00:01:57.19 is to make any mutation
00:01:59.29 that was heterozygous, or single-copy,
00:02:02.21 in the G1 mouse homozygous,
00:02:06.16 and in this particular procedure
00:02:10.01 we try to screen about 20 G3 progeny
00:02:14.26 for each G1 mouse.
00:02:16.13 That gives us about an 85% chance
00:02:18.17 of detecting a homozygous mutant mouse
00:02:21.21 in the G3 generation.
00:02:24.15 So, in this kind of screen,
00:02:27.14 shown here,
00:02:29.14 where we use circadian rhythms.
00:02:31.24 this particular screen,
00:02:33.23 we screened over 3200 mice,
00:02:35.16 and this histogram here
00:02:37.25 shows you the distribution
00:02:40.11 of period values,
00:02:42.10 similar to what I discussed in the first lecture,
00:02:44.11 for these 3000 mice.
00:02:47.27 What's remarkable about this behavior
00:02:50.26 is how similar
00:02:53.03 each of the mice are to each other.
00:02:55.06 So, the average in this experiment
00:02:57.28 was about 23.2 hours
00:03:00.11 for the whole population of 3000 mice,
00:03:03.12 but the variation within that population
00:03:07.10 was only two-tenths of an hour
00:03:09.16 -- that's only 12 minutes between mice.
00:03:13.23 And so, in this screen.
00:03:17.02 this is again an activity record
00:03:19.21 of a wild type or typically normal mouse,
00:03:22.24 first on a light cycle
00:03:25.02 then transferred to darkness,
00:03:26.19 so we can see its circadian period.
00:03:28.27 we found this mouse, shown in the middle panel,
00:03:32.25 which has a 26 hour clock.
00:03:36.13 And this mutant we named Overtime.
00:03:40.01 It turned out that
00:03:42.11 this mouse didn't breed,
00:03:43.22 but we were able to recover the mutation
00:03:46.12 from a sib, this mouse below,
00:03:48.05 which turned out to be a heterozygous carrier.
00:03:51.24 And using this mouse,
00:03:53.17 we were able to map the mutation
00:03:56.20 to this region of the genome,
00:03:59.21 and once we were able
00:04:01.22 to reduce this interval,
00:04:03.13 we found that there was only
00:04:05.28 a single mutation in this gene, here,
00:04:08.06 which is called Fbxl3,
00:04:11.08 which is an F box protein.
00:04:14.25 Now, an F box protein
00:04:17.10 turns out to be involved
00:04:20.18 in a complex of proteins
00:04:22.16 that are involved in modifying other proteins
00:04:25.14 with ubiquitin chains,
00:04:28.19 and these ubiquitin chains
00:04:31.08 then target that protein
00:04:34.00 for degradation in the proteasome.
00:04:36.29 So, to make a long story short,
00:04:40.11 FBXL3
00:04:43.03 uncovered a new F box protein
00:04:47.00 that targeted the Cryptochrome protein
00:04:51.06 for ubiquitination,
00:04:52.16 and that mark then tags Cryptochrome
00:04:56.04 for degradation.
00:04:58.09 And in the absence of this enzyme,
00:05:00.26 or loss of function of FBXL3,
00:05:03.14 the degradation of Cryptochrome protein
00:05:06.03 is slowed down tremendously,
00:05:09.04 and what that does is to
00:05:11.07 prolong the circadian period,
00:05:15.00 and lengthen the period
00:05:18.01 from 24 to 26 hours.
00:05:20.03 So we can see that this particular step
00:05:23.20 accounts for a few hours
00:05:26.13 out of 24
00:05:28.10 in the feedback circle.
00:05:31.19 So, in a second screen,
00:05:34.22 shown here,
00:05:36.23 using a slightly different strain of mouse,
00:05:39.03 so the period of the rhythm is a little longer
00:05:41.23 -- it's 23.7 hours in this case --
00:05:46.12 we found a short mutant,
00:05:49.08 and that's shown here.
00:05:51.29 It's only subtly short,
00:05:54.07 about a half an hour short,
00:05:55.29 and we named that mutation Past-time,
00:05:58.28 and when we mapped this gene
00:06:01.25 it turned out to be in a new position,
00:06:05.18 and incredibly this gene
00:06:08.14 turned out to be another F box protein.
00:06:11.12 This time it was called Fbxl21,
00:06:15.11 which is called the paralog of Fbxl3.
00:06:19.24 So, a paralog
00:06:22.10 is a gene that has very similar sequence
00:06:25.12 that arose from gene duplication
00:06:28.06 in the same organism,
00:06:30.09 and so we assumed the Fbxl21
00:06:32.18 should have a related function to Fbxl3,
00:06:37.08 but the mutant is surprising
00:06:39.17 because it has an opposite phenotype.
00:06:41.06 Instead of lengthening the period,
00:06:43.08 as we saw for Overtime,
00:06:45.06 this mutant shortens.
00:06:48.14 And so we looked for
00:06:53.23 the interaction of Overtime,
00:06:56.01 the long period mutant,
00:06:57.18 with Past-time,
00:06:59.29 the short period mutant,
00:07:02.00 by crossing those two strains of mice,
00:07:05.08 and the bottom shows the double mutant
00:07:08.09 and these histograms show
00:07:10.18 the average period, or the period distribution,
00:07:12.21 of these four genotypes of mice:
00:07:16.19 wild type
00:07:18.05 Overtime mutant, which are 26 hours long
00:07:20.27 Past-time mutants which are half an hour short
00:07:23.18 and then the double mutant,
00:07:25.25 Overtime and Past-time.
00:07:27.13 And what you can see is that
00:07:30.11 the Past-time mutant
00:07:32.17 actually neutralizes the period lengthening effect
00:07:35.29 of Overtime
00:07:37.17 and normalizes the period.
00:07:40.06 So this kind of genetic interaction
00:07:42.08 is non-additive,
00:07:44.17 and is interesting because
00:07:46.28 it suggested that Past-time
00:07:49.01 was somehow counterbalancing
00:07:51.15 the effect of Overtime.
00:07:53.22 Now, I'm not going to go into all the details,
00:07:57.18 but I'll just jump to
00:08:00.09 the end of this story,
00:08:03.04 and that was that we found,
00:08:06.20 to our surprise,
00:08:08.25 that FBXL3 is really
00:08:11.28 only found in the nucleus,
00:08:13.22 but FBXL21 is found
00:08:16.28 in both the nucleus and in the cytoplasm.
00:08:20.13 And that in the nucleus
00:08:24.03 there's a very fine balance
00:08:26.29 between FBXL3 and FBXL21
00:08:31.16 interaction for the Cryptochrome protein,
00:08:35.07 and what happens is that FBXL3
00:08:37.12 is normally promoting the degradation
00:08:40.08 of Cryptochrome,
00:08:42.21 but FBXL21 actually competes for that
00:08:46.26 and protects Cryptochrome
00:08:49.05 from degradation,
00:08:51.00 and so that's why we think
00:08:52.25 the two genes have opposite effects.
00:08:55.10 FBXL21 protects CRY
00:08:58.26 FBXL3 promotes CRY degradation in the nucleus.
00:09:02.18 So they're counterbalancing there,
00:09:05.09 but the surprise is, in the cytoplasm,
00:09:08.21 FBXL3 still degrades CRY,
00:09:10.24 but it does it more weakly.
00:09:13.25 FBXL21 still degrades CRY,
00:09:17.02 but it does so more weakly than FBXL3.
00:09:20.23 So, the discovery of these two mutations
00:09:24.22 in related F box proteins
00:09:27.27 has, first of all,
00:09:30.00 told us that the nucleus is very important
00:09:32.15 in controlling the degradation of CRY,
00:09:36.04 and that the primary effect of these mutations
00:09:39.26 on period length
00:09:41.18 appears to act
00:09:43.21 at the level of the nucleus, not they cytoplasm,
00:09:45.24 so that was a little bit surprising.
00:09:48.12 And the second is that
00:09:51.24 related F box proteins
00:09:55.05 might have very different functions in the cell,
00:09:59.01 both in location but also on their effects on the clock system,
00:10:04.00 even though they look very similar
00:10:05.21 at the sequence level.
00:10:09.19 Okay, so
00:10:13.14 this has really led to a revision
00:10:15.09 of our clock gene network,
00:10:17.10 shown here.
00:10:19.03 This is the core
00:10:21.04 CLOCK/BMAL1/PER/CRY feedback loop,
00:10:25.05 and now we have modified the diagram
00:10:29.13 so that FBXL21
00:10:33.08 is really the primary E3 ligase
00:10:35.20 in the cytoplasm.
00:10:36.24 There is no FBXL3,
00:10:38.07 as we thought before,
00:10:40.03 in the cytoplasm.
00:10:41.18 Instead, most the action
00:10:43.10 is actually occurring in the nucleus,
00:10:46.04 where FBXL21 and FBXL3
00:10:49.28 are competing for CRY
00:10:52.13 in a very fine balance
00:10:54.20 to regulate the stability of the Cryptochrome protein.
00:10:58.13 Okay. so, what I'd like to do now
00:11:02.01 for the third example
00:11:03.23 is to go back to the Clock gene
00:11:08.17 and to tell you about
00:11:10.07 another kind of genetic interaction
00:11:12.19 that we've discovered
00:11:14.27 that involves a much more
00:11:19.08 general background effect
00:11:23.22 of mouse strains
00:11:27.28 on the phenotype of circadian rhythms.
00:11:30.01 So, a number of years ago,
00:11:33.18 we looked at the phenotype of different inbred strains,
00:11:36.17 and this is just giving you an example
00:11:38.21 of two inbred strains.
00:11:40.04 C 57 Black 6J (C57BL/6J),
00:11:42.14 which is the very common inbred strain that we use.
00:11:45.14 it has a beautiful circadian rhythm.
00:11:48.11 Two records are shown here for you,
00:11:51.21 a record from a female on the left
00:11:53.19 and a male mouse on the right.
00:11:55.06 Both of them have very precise, robust rhythms
00:11:57.28 that you can just see visually.
00:11:59.23 And then on the bottom
00:12:01.21 are two records from
00:12:03.20 another inbred strain
00:12:05.21 called BALB/c,
00:12:07.06 and this mouse is very commonly used in the laboratory,
00:12:10.10 but as you can see,
00:12:12.21 its circadian behavior is not so
00:12:15.24 clear-cut as Black 6.
00:12:19.06 The pattern is much more fragmented,
00:12:22.01 the period is shorter,
00:12:23.20 and the rhythm is much less stable.
00:12:28.21 And so, using these two strains,
00:12:32.03 we can cross the strains
00:12:35.10 and then look for genetic features
00:12:38.25 that are inherited from these two strains
00:12:41.14 to affect circadian phenotypes.
00:12:44.07 And again, I'm not going to
00:12:46.27 take you through the details,
00:12:48.10 but in this analysis of these two strains,
00:12:50.12 we found about 14 different
00:12:54.25 regions of the genome
00:12:56.05 that are responsible
00:12:58.00 for controlling different phenotypic aspects
00:13:00.28 of the rhythm
00:13:02.20 between these two strains
00:13:04.10 -- their period, their amplitude,
00:13:07.01 how robust they are,
00:13:09.00 things like that --
00:13:10.10 and these are all indicated
00:13:12.02 in these oval circles with different colors.
00:13:15.18 And what you can see is they're scattered across the genome
00:13:18.25 and what's interesting is
00:13:21.24 their locations are essentially
00:13:27.10 in regions of the genome
00:13:29.12 that don't contain known clock genes.
00:13:31.09 There's only one case
00:13:35.02 where there's an overlap between a known clock gene,
00:13:38.13 casein kinase 1 epsilon,
00:13:40.17 on chromosome 15,
00:13:41.24 and one of these quantitative trait loci,
00:13:44.08 or QTLs,
00:13:45.29 for rhythms.
00:13:48.19 All the rest of them defined new genes.
00:13:51.12 So, what does that tell us?
00:13:52.20 That tells us that even though
00:13:54.07 we have this clock gene network
00:13:57.10 with a core set of genes,
00:13:59.01 there's still many additional genes
00:14:01.21 that can affect
00:14:05.00 different parameters of the circadian system,
00:14:06.17 and that we don't really know about
00:14:11.05 at the molecular level.
00:14:12.09 So, the final part of my talk.
00:14:14.24 I'm going to focus on
00:14:17.05 trying to identify
00:14:19.05 one of these types of quantitative genes
00:14:24.04 in the mouse genome,
00:14:26.06 using a very specific case.
00:14:31.01 So, when we were
00:14:34.16 mapping the Clock mutation,
00:14:36.29 we used different strains of mice
00:14:41.19 to cross the Clock mutation with in order to have
00:14:45.08 genetic markers that we can follow in these crosses,
00:14:48.20 and in one of the crosses that we made
00:14:51.04 we crossed the Clock mutant mouse
00:14:54.21 to the BALB/c mouse that I just discussed.
00:14:58.15 So, what we have here
00:15:00.28 are records from wild type mice
00:15:04.09 or Clock heterozygous mice,
00:15:07.07 but they're in different genetic backgrounds.
00:15:09.28 The top two are from C 57 Black 6 mice
00:15:12.29 and you can see what I showed you before:
00:15:15.10 the Clock mutation
00:15:18.01 lengthens the period by about one hour.
00:15:19.23 But when we cross to BALB/c,
00:15:23.19 even though this mouse on the right
00:15:26.13 is the Clock mutant,
00:15:28.17 its period looks normal,
00:15:30.26 so we call this genetic suppression.
00:15:33.08 The BALB/c background
00:15:36.07 is somehow suppressing
00:15:38.16 the period-lengthening effect of the Clock mutation.
00:15:40.18 And if we were to make
00:15:42.17 four additional crosses to BALB/c,
00:15:45.16 shown here,
00:15:47.03 we get complete suppression of the Clock mutant,
00:15:50.23 and this is shown here on this graph on the right.
00:15:53.28 In the Black 6 background,
00:15:57.11 we see this period difference,
00:16:00.10 in the F1 we see suppression
00:16:02.24 of about 50%,
00:16:05.14 and then in the four-generation cross to BALB/c
00:16:09.22 we see complete suppression of period.
00:16:13.10 So, that's at the behavioral level.
00:16:16.16 This suppression also occurs
00:16:18.17 at the tissue level.
00:16:20.05 So, shown down here
00:16:22.17 are PER::LUCIFERASE recordings
00:16:25.03 of the suprachiasmatic nucleus
00:16:26.13 and the pituitary gland,
00:16:28.08 and we see that even at the tissue level
00:16:31.02 there is suppression
00:16:34.03 of the period lengthening of Clock
00:16:36.05 in these two tissues,
00:16:38.00 so this is a global effect,
00:16:40.07 not just the behavior.
00:16:43.20 So, how do we find this suppressor gene?
00:16:47.27 So, we can approach this
00:16:50.06 by trying to genetically map the suppressor,
00:16:53.09 and this is done
00:16:56.02 by comparing different types of crosses.
00:16:58.26 So, here's the Clock mutation
00:17:02.14 on a pure B6
00:17:05.23 or C57 Black 6 background,
00:17:07.12 this is the F1 that we saw before,
00:17:10.01 and then these are two crosses
00:17:11.26 that are called backcrosses
00:17:14.20 -- back to either Black 6
00:17:18.11 or BALB/c, shown in green --
00:17:20.23 and then this lower panel is an F2 cross,
00:17:25.10 which is a cross of two F1 mice
00:17:29.04 of this type here, okay?
00:17:31.24 And what you can see is the period distribution
00:17:35.13 is much broader
00:17:38.15 in the BALB/cN2
00:17:41.04 and this F2 generation,
00:17:43.18 and this is really, we think,
00:17:46.00 due to this genetic diversity.
00:17:47.27 And so what we can do is
00:17:49.22 we can take these F2 mice
00:17:51.14 and type each of the mice,
00:17:54.00 because each mouse is
00:17:56.15 a unique combination
00:17:58.24 of Black 6 and BALB/c
00:18:02.21 DNA in their genome,
00:18:04.17 and we can scan each of their genomes
00:18:07.22 with DNA markers
00:18:09.11 to type what their genotype is
00:18:12.04 across the genome,
00:18:14.05 and then see if there's
00:18:16.22 any particular kind of association
00:18:19.18 between a BALB/c piece of DNA
00:18:22.24 and this period suppression of Clock.
00:18:26.09 And so that's what's done here in this graph,
00:18:29.10 which is scanning the genome
00:18:31.17 with different markers.
00:18:32.19 Each of these numbers
00:18:34.19 represents a mouse chromosome, from 1-19,
00:18:37.21 and then the X chromosome, here.
00:18:40.18 And what this shows you is that
00:18:44.10 on chromosome 1 we find a peak,
00:18:47.10 highly significant,
00:18:49.10 which shows an association with the suppression,
00:18:53.12 on chromosome 1,
00:18:55.16 but really nowhere else in the whole genome.
00:18:58.03 Okay?
00:18:59.14 On the bottom,
00:19:01.01 if we blow up chromosome 1,
00:19:02.21 which is the largest mouse chromosome,
00:19:04.20 it's 200 megabases in size,
00:19:07.19 the red line shows this initial peak.
00:19:09.23 It's very broad,
00:19:11.07 it covers almost half the chromosome,
00:19:14.09 which is 100 megabases
00:19:16.09 it's impossible to find the gene
00:19:19.01 in such a big region.
00:19:21.08 And then in blue and green
00:19:23.17 are shown additional crosses
00:19:27.24 that try to isolate this region of DNA
00:19:30.18 for many generations,
00:19:31.25 up to 8 generations of crossing,
00:19:34.17 and what we see is that
00:19:37.23 the distal tip of chromosome 1,
00:19:41.17 now,
00:19:42.28 is the region that carries the suppressor.
00:19:44.26 Okay?
00:19:46.01 But unfortunately this region is still huge.
00:19:49.07 And so it's really difficult to find a gene
00:19:56.03 in such a big region.
00:19:57.14 But to prove that that region of chromosome 1
00:19:59.07 really carries the suppressor,
00:20:01.09 what we do is we isolate that piece of DNA
00:20:04.27 on a Black 6 background,
00:20:07.11 so only the BALB/c DNA
00:20:11.01 on chromosome 1,
00:20:12.28 this segment shown in green, here,
00:20:15.03 on this representation of chromosome 1,
00:20:17.17 which is now 25 megabases.
00:20:21.18 this piece of BALB/c DNA by itself
00:20:26.06 can suppress the Clock mutation,
00:20:29.03 as shown in these activity records, here,
00:20:33.02 and in this graph here,
00:20:36.00 the red line,
00:20:37.28 which is showing suppression.
00:20:39.13 So this tells us the BALB/c suppressor of Clock
00:20:43.01 is within this region,
00:20:44.08 because we've completely isolated it
00:20:46.06 on that piece of DNA,
00:20:49.29 but again there are way too many genes
00:20:53.00 to find the gene in 25 megabases.
00:20:55.15 So, how do we go on from this point?
00:21:01.11 It turned out, in 2002,
00:21:03.16 the mouse genome was sequenced,
00:21:07.02 and one of the interesting discoveries
00:21:10.16 in the sequencing of the mouse genome
00:21:12.23 was that when we look at
00:21:17.13 different inbred strains of mice,
00:21:18.27 we find different blocks
00:21:22.13 within the genome
00:21:24.27 that are either similar or very different
00:21:27.24 to C57 Black 6,
00:21:30.02 and that's indicated in these bars, here,
00:21:32.25 where this is a comparison of the strain
00:21:36.05 129 with Black 6.
00:21:37.23 Red shows the differences
00:21:40.15 and light blue show the similarities
00:21:42.15 in those two strains.
00:21:44.03 This is another strain comparison,
00:21:46.02 C3H with Black 6.
00:21:48.14 And then this is BALB/c with B6,
00:21:51.21 the comparison we've been talking about.
00:21:54.03 And what you can see is that
00:21:56.14 there are little blocks of DNA
00:22:00.00 that are interspersed throughout the genome,
00:22:03.22 and so we now know
00:22:06.20 from more recent complete sequencing information
00:22:09.15 that classic laboratory inbred strains of mouse
00:22:13.24 are really hybrids
00:22:18.23 of many different kinds of mice,
00:22:22.01 primarily three wild species of mice:
00:22:25.07 Mus domesticus, shown in blue
00:22:27.29 Mus castaneus, shown in green
00:22:29.17 and Mus musculus, shown in red.
00:22:33.01 And, in general,
00:22:36.07 the laboratory mice that we use today
00:22:38.21 are really derived from mouse breeders
00:22:42.20 that domesticated mice
00:22:45.13 both in Asia and in Europe.
00:22:49.17 And these mice were ultimately derived
00:22:53.02 from these different,
00:22:55.09 natural wild progenitor strains.
00:22:57.29 So, inbred strains
00:23:00.23 are really not pure species
00:23:02.24 they're really mixtures
00:23:05.10 of different species of mice.
00:23:07.22 And so this led to the idea
00:23:12.14 that perhaps the suppressor of Clock
00:23:15.18 might be an ancestral allele
00:23:20.21 that was inherited from one of these
00:23:23.14 different species
00:23:24.29 that contributed to inbred mice.
00:23:27.10 And so, to test this idea,
00:23:30.00 what we decided to do was to
00:23:32.23 cross the mouse Clock mutant
00:23:34.22 to different inbred strains of mice,
00:23:37.03 shown here,
00:23:39.02 and ask,
00:23:41.00 does a different strain of mouse
00:23:43.08 suppress Clock or not?
00:23:45.13 Okay?
00:23:47.11 And the data would look like this.
00:23:48.26 this is a suppressed mouse
00:23:50.07 and this is a non-suppressed mouse.
00:23:52.25 So, Shimomura,
00:23:55.25 who did this experiment,
00:23:57.29 crossed to 15 additional strains of mice
00:24:01.11 and he was able to find that
00:24:06.02 8 strains carried the suppressor for [Clock],
00:24:08.26 shown at the top here,
00:24:10.15 and 7 additional strains,
00:24:13.00 in addition to C57 Black 6,
00:24:14.24 failed to suppress.
00:24:16.28 These strains are shown in the bottom, here.
00:24:19.06 You can see the red lines
00:24:21.03 are horizontal and parallel, okay?
00:24:24.04 So, this experiment
00:24:28.17 confirmed our hypothesis
00:24:32.03 that it was likely that the suppressor
00:24:35.02 in the genetic background of BALB/c
00:24:38.02 was an ancestral allele
00:24:40.18 that was carried by many other
00:24:42.28 inbred strains of mice.
00:24:45.13 And so we could then
00:24:47.15 take advantage of that
00:24:49.29 by looking within the
00:24:52.29 25 megabase suppressor region
00:24:55.23 of chromosome 1
00:24:57.12 and look at all the sequence differences
00:25:00.10 in these 15 additional strains of mice,
00:25:04.14 in comparison to Black 6.
00:25:06.10 So, these histograms show
00:25:07.24 all the sequence differences
00:25:10.02 with Black 6.
00:25:11.08 The top strains, shown in green,
00:25:13.15 are the suppressor strains,
00:25:15.04 and the bottom strains, shown in blue,
00:25:17.28 are the non-suppressor strains,
00:25:20.14 and within this 30 megabase region
00:25:23.20 we could find only one very small interval,
00:25:26.24 shown here in green,
00:25:29.18 that matched perfectly
00:25:31.14 the pattern of suppression
00:25:34.10 and non-suppression
00:25:35.27 among these strains.
00:25:38.04 And so that suggested
00:25:40.29 that the suppressor was restricted
00:25:43.01 to this narrow region,
00:25:45.05 which is about 900 [kilobases] in size,
00:25:49.02 and when we look at this region, here,
00:25:51.09 blown up,
00:25:52.26 we see that there are actually 22 genes there.
00:25:56.23 And so 22 genes
00:25:58.25 is actually a number that we can deal with.
00:26:01.11 We were able to sequence all 22.
00:26:05.05 We could find no obvious mutations
00:26:07.18 that could explain the suppression,
00:26:11.29 and so we had to use different methods.
00:26:14.24 And so, one method that we did,
00:26:17.09 shown here on the left,
00:26:19.03 is to look at the expression pattern
00:26:22.00 of these 22 genes.
00:26:24.04 And because the suppressor of Clock
00:26:27.04 is ubiquitously distributed,
00:26:30.03 like a clock gene,
00:26:31.29 we asked,
00:26:33.12 how many of the 22 suppressor genes
00:26:36.27 have an expression pattern that's common,
00:26:39.16 or similar,
00:26:41.10 to the clock genes?
00:26:42.12 So, the clock genes are shown in yellow,
00:26:47.20 and the candidate genes
00:26:50.11 are shown in light blue and gray
00:26:51.19 in this heatmap representation
00:26:55.06 of gene expression,
00:26:56.12 and we profiled 10 different tissues in the mouse.
00:26:59.15 And what you can see is the clock genes
00:27:01.16 are clustered at the top of this heatmap,
00:27:06.02 because their expression is high and ubiquitous,
00:27:09.08 and seven of the candidate genes,
00:27:11.27 shown in blue,
00:27:14.10 had expression patterns
00:27:16.22 similar to clock genes
00:27:18.17 and clustered with them.
00:27:20.14 The other candidate genes
00:27:23.05 had very different expression patterns
00:27:25.03 and that suggested that they
00:27:28.00 might not be involved.
00:27:29.06 So we focused on these seven genes,
00:27:31.01 they're shown here.
00:27:33.19 This shows their RNA expression
00:27:36.07 over time of day
00:27:39.26 in either an F1 mouse
00:27:41.27 that's a suppressor strain
00:27:43.11 or a Black 6 mouse.
00:27:44.20 Okay?
00:27:46.08 And out of these seven genes,
00:27:48.07 only one of them showed an expression difference.
00:27:50.14 That's this one here, Usf1,
00:27:53.18 or upstream factor 1 gene.
00:27:57.07 And when we looked at the levels
00:28:00.00 of the Per1/2 and Cry1/2 genes,
00:28:03.11 they are also elevated
00:28:05.12 in the suppressor strain.
00:28:09.21 Okay?
00:28:11.09 When we then look at
00:28:13.15 the expression of the USF1 protein,
00:28:17.10 we find that there's
00:28:19.22 a very subtle difference,
00:28:20.27 it's a 40% increase in USF1 protein.
00:28:23.19 very small, but significant.
00:28:26.23 So that focused our attention on Usf1.
00:28:30.10 Usf1 is interesting
00:28:32.20 because it's a transcription factor,
00:28:35.09 and because the Per genes and Cry gene
00:28:39.01 RNAs were elevated,
00:28:40.04 that suggested that
00:28:42.06 maybe their transcription was elevated,
00:28:44.14 and so we tested the
00:28:46.11 seven candidate genes
00:28:48.20 for activation of the Per1 and Per2 genes
00:28:51.15 using promoter assays.
00:28:55.07 And these two graphs show that,
00:28:57.10 of these seven genes,
00:28:59.03 Usf1 strongly activates
00:29:01.24 both Per2 and Per1
00:29:04.19 in a dose-dependent manner,
00:29:07.20 but unlike Clock and BMAL,
00:29:10.24 which also activate Per1 and Per2,
00:29:13.10 Usf1 is not suppressed
00:29:16.25 by Cryptochrome or Period,
00:29:20.10 as shown here,
00:29:22.01 where Clock activation
00:29:24.21 is suppressed by Cry1 or Cry2.
00:29:27.05 This does not occur with Usf1, here.
00:29:30.11 So, this suggests that
00:29:33.03 Usf1 might be acting
00:29:36.01 on the same pathway as Clock and BMAL.
00:29:40.19 But to prove that Usf1 is really the gene,
00:29:45.29 we had to make a transgenic mouse,
00:29:49.04 which overexpressed Usf1
00:29:52.04 just a little bit
00:29:53.25 -- this is the expression level of Usf1, here,
00:29:57.09 in the transgenic mice --
00:29:59.08 and what we found is that,
00:30:01.19 with this subtle increase
00:30:04.12 in Usf1 expression,
00:30:06.17 these transgenic mice
00:30:09.02 could also suppress the Clock mutation,
00:30:10.29 as shown in these activity records
00:30:12.28 and this bar graph here.
00:30:15.00 So, this kind of experiment
00:30:18.04 shows that Usf1
00:30:20.20 is capable of suppressing Clock
00:30:23.20 and identifies it as the suppressor of Clock.
00:30:28.28 So, how does Usf1 do this?
00:30:33.10 Because we sequenced the Usf1 gene.
00:30:36.18 there are no mutations in Usf1.
00:30:39.13 The only difference is the expression level of Usf1,
00:30:43.02 and so this suggested a regulatory mutation.
00:30:46.13 And so, in mice,
00:30:48.20 you can do a very nice experiment:
00:30:50.07 you can take an F1 mouse,
00:30:52.22 an F1 of Black 6 and BALB/c,
00:30:56.03 and if the RNA has a sequence difference
00:30:59.16 between those two strains,
00:31:01.26 you can actually measure
00:31:04.29 the BALB/c transcript
00:31:06.25 and the Black 6 transcript separately,
00:31:09.02 and so we've done that in three different ways,
00:31:11.14 illustrated in this graph.
00:31:14.25 And all three methods,
00:31:17.04 I won't go through the details,
00:31:19.03 show that in an F1 mouse,
00:31:22.07 the BALB/c allele
00:31:24.22 is preferentially increased in its expression,
00:31:27.07 over the Black 6 allele.
00:31:29.26 And that's really a beautiful experiment
00:31:32.00 because it's the same mouse
00:31:34.21 with two different alleles,
00:31:36.17 and one allele is up and the other is not.
00:31:39.10 So that shows that
00:31:42.05 the regulation of Usf1
00:31:45.04 is cis regulation
00:31:47.20 as opposed to trans regulation.
00:31:52.02 So, how might that work?
00:31:53.29 So, to look at this further,
00:31:56.03 what we did was to isolate
00:31:58.09 the upstream regulatory regions of Usf1
00:32:01.15 and, using reporter gene assays,
00:32:05.05 we could show that
00:32:08.11 the Black 6 upstream region,
00:32:10.02 which is shown in blue,
00:32:12.13 as compared to the BALB/c upstream region,
00:32:15.02 shown in green.
00:32:16.27 there's a slight increase
00:32:19.16 in the transcription from the BALB/c promoter.
00:32:23.17 And within this region,
00:32:25.20 which is about 1 kilobase of the BALB/c promoter,
00:32:29.07 there are only 7 sequence differences
00:32:33.01 in this regulatory region,
00:32:35.04 shown in green here,
00:32:37.02 these 7 SNPs,
00:32:38.27 or single nucleotide polymorphisms.
00:32:41.04 And so we tested them
00:32:43.22 and two of them,
00:32:45.26 SNP3 and SNP7,
00:32:48.18 show a difference in transcription
00:32:50.15 between the two strains,
00:32:53.03 with the BALB/c strain being higher,
00:32:54.29 and when we put SNP3 and SNP7
00:32:58.11 of BALB/c, in green,
00:33:01.00 into the Black 6 background,
00:33:04.01 so there is only one difference
00:33:06.13 in these two constructs, here,
00:33:08.05 we see that SNP7, shown here,
00:33:11.05 can completely account for the difference
00:33:14.02 between BALB/c and Black 6.
00:33:17.09 So this suggests very strongly
00:33:20.24 that the difference between Black 6 and BALB/c
00:33:24.03 is due to a single regulatory change
00:33:27.14 in the promoter of the Usf1 gene
00:33:30.03 that increases the expression of Usf1
00:33:33.16 in the BALB/c mice.
00:33:35.26 Okay.
00:33:37.29 So, how does this subtle increase
00:33:42.10 in Usf1
00:33:44.22 then suppress Clock in a mouse?
00:33:48.14 So, to examine this question,
00:33:50.23 what we did was to
00:33:53.09 go back and try to study
00:33:55.23 the binding of CLOCK/BMAL onto DNA,
00:34:01.23 and to see if USF1
00:34:06.11 might bind the same site.
00:34:08.29 And so for a number of genes
00:34:12.24 that we know are regulated by CLOCK,
00:34:15.10 called Dbp, Per1, and Per2,
00:34:19.01 we could find that
00:34:23.14 CLOCK/BMAL bound
00:34:26.14 to specific regulatory sites
00:34:28.16 that are called E-boxes,
00:34:30.09 which we can detect in this assay
00:34:32.17 which is called a gel mobility shift assay,
00:34:34.20 okay?
00:34:36.11 And so the way this experiment works is,
00:34:38.25 here in wild type mice,
00:34:41.17 we have liver extract
00:34:44.04 that contains many different proteins
00:34:46.07 from the nuclei of liver of mice,
00:34:49.28 and we bind them to
00:34:53.17 an oligonucleotide that contains
00:34:56.07 the E-box sequences from the Dbp gene,
00:34:58.08 and what you see is that
00:34:59.20 we get two major bands.
00:35:02.29 The top band turns out to be CLOCK/BMAL,
00:35:06.08 and this lower band turns out to be USF1.
00:35:09.27 So, indeed, USF1
00:35:13.01 can bind the same site
00:35:15.26 as CLOCK/BMAL and, interestingly,
00:35:19.10 in the Clock mutant,
00:35:21.06 what we see, which is shown on the right-hand side here,
00:35:23.28 the Clock mutant binding changes.
00:35:28.17 So, not only does the
00:35:31.16 affinity of binding change,
00:35:33.01 which I'll show you in a minute,
00:35:35.14 but the nature of binding,
00:35:37.02 whether it binds as a single complex, CB1,
00:35:39.26 or a double complex (CB2), also changes.
00:35:42.15 So, the wild type proteins binds as a double complex,
00:35:46.11 CB2, shown here.
00:35:48.21 The mutant protein tends to bind
00:35:51.15 more as a single complex, shown here, CB1.
00:35:55.14 And then, on the right-hand side,
00:35:57.20 when we quantitate the actual affinity of binding,
00:36:00.20 what we see is, in blue,
00:36:03.01 the wild type CLOCK/BMAL complex
00:36:05.06 has much higher affinity than USF1,
00:36:08.20 which is shown in orange and green,
00:36:12.02 but, interestingly,
00:36:14.20 in the Clock mutant,
00:36:17.01 the binding affinity, which is shown in red,
00:36:19.13 actually goes down to match USF1.
00:36:24.06 So there are a couple things going on here.
00:36:27.23 The first is that the CLOCK mutant
00:36:30.18 is actually reducing the binding affinity
00:36:33.16 of CLOCK/BMAL
00:36:36.21 such that the affinity is much closer to USF1,
00:36:41.16 and under these conditions
00:36:44.06 we think that USF1 might be able to then
00:36:47.05 compete with CLOCK/BMAL.
00:36:48.27 To examine the binding
00:36:52.03 of USF1 and CLOCK/BMAL in more detail,
00:36:55.09 we've used a different method
00:36:58.10 which allows us to look
00:37:00.29 across the entire genome
00:37:03.07 for where USF1 and CLOCK/BMAL
00:37:05.20 might bind.
00:37:07.12 And so, these are UCSC Genome Browser views
00:37:12.03 of USF1, CLOCK, and BMAL binding
00:37:16.20 to the Period1 gene,
00:37:18.26 and out here you can see
00:37:21.15 there's very strong binding at these sites here.
00:37:25.18 Red indicates wild type mice
00:37:27.19 and blue indicates Clock mutant mice,
00:37:30.11 and what you can see is that
00:37:32.21 in the Clock mutant mice
00:37:35.12 USF1 binding is much higher
00:37:37.28 than it is in wild type
00:37:41.09 to this site.
00:37:42.26 This is also true for a different gene,
00:37:45.09 this is Dbp shown in the second.
00:37:47.15 Again, at the top, here,
00:37:50.11 USF1 is much higher in the mutant
00:37:53.14 than in the wild type.
00:37:57.04 And over here for Rev-erbα,
00:37:59.06 you can also see a very big difference
00:38:01.18 in these two samples, here,
00:38:04.06 for USF1.
00:38:06.20 So, if we look genome wide,
00:38:09.21 what we see is that there is significant overlap
00:38:12.25 in the binding in wild type
00:38:16.08 and Clock mutant mice,
00:38:18.00 between USF1 and CLOCK/BMAL,
00:38:20.13 but interestingly in the mutant
00:38:23.03 the amount of binding sites go up.
00:38:25.18 And in particular, if we look at this plot, here,
00:38:29.13 which represents USF1 binding
00:38:32.21 in red, the intensity,
00:38:34.16 we see that the binding intensity
00:38:36.23 is much higher at sites across the genome
00:38:41.04 in the Clock mutant than in the wild type,
00:38:46.09 but there isn't so much different
00:38:49.00 in CLOCK or BMAL occupancy.
00:38:53.09 So this suggests that, indeed,
00:38:56.09 USF1 and CLOCK
00:38:58.20 do interact on a very large scale,
00:39:01.07 across the entire genome,
00:39:03.26 and this could explain
00:39:08.06 how USF1 is actually suppressing
00:39:10.19 the effects of Clock.
00:39:13.05 So, here's an overall summary
00:39:15.21 of what we think is going on.
00:39:19.02 In the BALB/c strain,
00:39:22.07 there are regulatory sequence differences
00:39:26.04 in the USF1 promoter
00:39:29.21 that lead to an increase in USF1 expression.
00:39:34.03 In wild type mice,
00:39:36.26 this doesn't really make much difference
00:39:39.27 because the affinity of CLOCK/BMAL
00:39:42.08 is so high that USF1
00:39:44.21 doesn't compete very well with CLOCK/BMAL.
00:39:47.18 But in the Clock mutant, shown here,
00:39:51.05 CLOCK tends to bind as a single complex,
00:39:54.15 the affinity is lower,
00:39:56.21 and so USF1 can occupy
00:39:59.11 or compete for the same E-box sequences.
00:40:02.20 Now, the Clock mutant protein
00:40:04.22 is deficient in transcription,
00:40:07.11 so USF1 actually
00:40:10.18 can rescue that function
00:40:12.29 because USF1 can actually activate transcription
00:40:16.04 from the same regulatory sequences
00:40:18.16 as CLOCK/BMAL.
00:40:20.19 The only difference is, of course,
00:40:23.11 USF1 is not subject to negative feedback,
00:40:27.00 and in fact that promotes
00:40:29.06 the activation potential of USF1 in addition.
00:40:32.18 And so, we can see in
00:40:35.26 this very complex genetic interaction analysis
00:40:39.17 that additional factors
00:40:45.21 can interact with CLOCK/BMAL
00:40:48.16 at a transcriptional level,
00:40:50.28 compete for binding,
00:40:53.05 and actually replace the function of a mutant CLOCK protein
00:40:56.23 to rescue or suppress that behavior.
00:41:02.28 So, I'll stop there
00:41:05.16 and acknowledge all of the people
00:41:07.24 who contributed to these three different stories
00:41:10.27 shown here.
00:41:12.26 Thank you very much.

Body Routine Based on The Biological Clock or Circadian Rhythms

3 AM to 5 AM in the Morning:

It is the time of Brahma muhurta . At this time, the vital-power (Prana) is exclusively in the Lungs . Therefore, lungs are most active at this time. At this time, taking a little lukewarm water and Roaming in the open air and doing Pranayama is very beneficial for the lungs.

At this time, doing long-term Shavasana (Savasana / Corpse pose / Mrtasana / death pose) develops a lot of lung function. The body gets healthy and energized by getting a lot of Pure air (oxygen) and Negative ions (N. ions are beneficial for the human body). The people who get up in the Brahma Muhurta are Intelligent and Enthusiastic, and the life of those who fall asleep becomes languid (vigourless).

At 4:30 am to 5 am, Body temperature is lowest. At this time there is a lot of chance of getting cold, so keep the AC (Air Conditioner) set to close for this time.

5 AM to 7 AM in the Morning:

At this time, vital-power (vitality) is especially in the Large intestines . Therefore, the large intestine is active at this time. Excretion and Bathing should be done between morning awakening to 7 am. Their intestine dries the faeces by exploiting the castaway fluid of the faeces. This causes arise Constipation and many other diseases.

At 6 am, The secretion of Cortisol Hormone is the highest in our body. The reason for this is that at this time the body is getting ready to wake up. At this time, it is better to do Yoga than jogging or heavy exercise.

7 AM to 9 AM in the Morning:

At this time, vital-power (vitality) is especially in the Stomach . This time is suitable for Food , because Digestive juice is produced more at this time. When you eat, drink lukewarm water (as per compatibility) between the meals.

At 7 am, your body’s Blood pressure changes rapidly. This is the reason that accidents like Stroke or Heart-Attack occur more in the morning. The BP patient should not do any kind of BP enhancing activity at this time.

9 AM to 11 AM in the Morning:

At 9 am, Testosterone starts forming more in your body. your body is most ready for any Athletic activity at this time. If you want, you can go to the Gym and do a High-level workout. This is the most favorable time to work out.

At 10 am, the body is most Alert . This is the reason that usually the work-time of Offices is of the 10 am.

At 11 AM to 1 PM in the Morning:

At this time, vital-power (vitality) or energy flow is especially in the Heart . In Indian culture, there is a practice of doing midday-evening ( Rest ) around 12 noon to develop and nurture the heart’s sensations like Compassion, Kindness, Love etc. Therefore, eating food is prohibited at this time. At this time, you can take liquids like drink whey (Buttermilk / Mattha / chhaachh) and Eat curd (yogurt).

At 1 PM to 3 PM in the Afternoon:

At this time, vital-power (vitality) is in the Small Intestine . Therefore, the small intestine is particularly active at this time. Its function is to absorb nutrients from the diet and push waste materials to the large intestine. Instead of drinking more or less water after a meal, should drink as much Water as you are thirsty, so that the large intestine can be helped to push the waste. At this time, eating or sleeping inhibits the exploitation of nutritious food and juices and the body becomes Sick and Weak.

At this time, the body is in the state of Excellent Coordination and Fastest Reaction , it would be better to reduce physical activity at this time.

At 3 PM to 5 PM in the Afternoon:

At this time, vital-power (vitality) is especially occurring in the Urinary Bladder . At this time there is a special activity in the bladder, due to which there is a tendency to pass Urine during this time.

At 5 PM to 7 PM in the Evening:

At this time vital-power (vitality) is especially in the Kidneys . Light meal should be taken at this time. Eating 40 minutes before sunset in the evening is best. Do not eat 10 minutes before sunset to 10 minutes after sunset. In the evening, Milk can be drunk three hours after the meal. Eating food at late night brings lethargy (laziness).

5 pm is the best time in terms of Muscle strength and Cardiovascular activities . Exercise at this time if you want to build muscle and Lose weight.

At 6:30 pm, Blood pressure is highest in the body. BP or heart patients should be cautious at this time.

At 7 PM to 9 PM in the Night:

At this time, Vital-power (vitality) is especially in the Brain . At this time the brain remains particularly active. Therefore, in addition to the morning, the text Read ( Study ) in this period can be remembered quickly. This has also been confirmed by modern exploration.

At 7 Pm, the body temperature is highest. So keep yourself cool then it will be better and at this time you should avoid any Debate or discussion.

Drink milk with at night provides good rest as it induces sleep.

At 9 PM to 11 PM in the Night:

At this time, vita-power (vitality) occur especially in the Spinal cord . At this time, resting by the Backside or on the Left side , helps the spinal cord to gain the strength. Sleep at this time provides the most relaxation. The awakening of this time exhausts the body and intellect. If food is taken at this time, it remains in the stomach till morning, and it is not digested even harmful substances are produced by its rot, which causes disease by going into the intestines with acid. So eating at this time is dangerous.

At 9 pm, Melatonin hormones begin to form, which naturally cause sleep in the body. This is a great time to go to bed.

At 11 PM to 1 AM in the Night:

At this time, vital-power (vitality) is particularly active in the Gall bladder . Bile storage is the main function of the gall bladder. Awakening of this time causing insomnia, headache etc. bile disorders and ophthalmology and brings old age quickly. At this time, new cells are formed, that is, after 12 o’clock in the night, new cells are produced in exchange for the damaged cells of the body by the food taken in the day. Therefore, the awakening of this time brings Old age quickly.

At 1 AM to 3 AM in the Night:

At this time, vital-power (vitality) is especially in the Liver . Micro-digestion of food is the function of the liver. Awakening of this time spoils the liver and digestive system. At this time the body needs deep sleep and if you keep awake at this time, the body starts to subjugate of sleep, the vision is slow and the body’s reactions are slow. Therefore, road accidents are more at this time.

At this time the liver destroys the Toxins (poisons) that made in your body throughout the day. As Late as you sleep, the less time you have with your liver to eliminate toxins from your body. Because of which the liver will not be able to completely destroy the toxins from your body. And if you keep sleeping late in the same way, then these toxins will accumulate (collect) in your body, and you know very well what the result will be.

At 2 Am, the body is in the deepest sleep. At this time, you should avoid waking the body from sleep or any kind of activity. But keep in mind that it is not necessary that everyone’s biological clock remains the same. In such a situation, according to the physical Rhythm for which work for which time is beneficial, you have to pay attention.

The time stated in this is an Ideal situation. It is possible that due to any compulsion, your clock cannot match this ideal clock. But try to keep your biological clock according to this ideal clock.

Circadian clock's inner gears

Scientists have long known that circadian clocks -- biochemical oscillators that control physiology, metabolism and behavior on a roughly 24-hour cycle -- are present in all forms of life, including animals, plants, fungi and some types of bacteria. However, the molecular mechanisms that "run" these systems remain largely unknown.

In a study published Sept. 7 in Molecular Cell, a team led by Harvard Medical School researcher Charles Weitz shows that a set of core clock proteins organize themselves into a handful of molecular machines that control the precise workings of circadian rhythms.

Providing the first structural glimpse of the clock's machinery, the results offer a starting point for explaining how circadian clocks run and an understanding of the variety of conditions that can develop -- including sleep disorders, metabolic aberrations and cancer -- when something in the clock machinery goes awry.

In the late 1990s, Weitz, the Robert Henry Pfeiffer Professor of Neurobiology at Harvard Medical School, and researchers from other labs discovered several key proteins involved in the clock system. These include three different period proteins (PER), two different cryptochrome proteins (CRY), and casein kinase-1 (CK1). When these proteins accumulate inside cells and enter the cell nucleus, they bind to a protein called CLOCK-BMAL1 that is attached to DNA responsible for making more PER and CRY. The influx and accumulation of these proteins inside the nucleus effectively shut down the production of PER and CRY. However, when the levels of PER and CRY drop, the CLOCK-BMAL1 can once again resume work unhindered so that the DNA responsible for making PER and CRY can do its job.

The completion of this feedback loop -- production of PER and CRY, their attachment to CLOCK-BMAL1, shutting down PER and CRY production so that it can start over again -- takes about 24 hours, Weitz explains. The traditional view, he adds, is that these proteins enter the cell nucleus independently or in small groups to do different jobs. The Weitz team findings revealed otherwise.

To figure out precisely how these proteins might run the clock, Weitz and colleagues used a laboratory technique that selectively pulled out proteins from the nuclei of mouse cells at the peak of PER and CRY negative feedback. Their findings turned up a single large protein complex that incorporated each of the six important clock proteins: the three PERs, two CRYs, and CK1, along with about thirty other accessory proteins. Additionally, the protein complex, which electron microscopy showed is quasi-spherical, was associated with CLOCK-BMAL1, the experiments showed.

Although their initial experiments were done in mouse livers -- large organs with a strong concentration of different proteins -- experiments in other tissues, including kidney and brain, detected the presence of the same large protein complex. The results suggest that this complex, which the researchers named the PER complex, is universal in tissues throughout the body. They also suggest that the six key clock proteins probably don't operate individually instead, they seem to organize themselves to work in concert to run the circadian clock's negative feedback loop.

To determine when this organization happens, the researchers looked for the presence of the six main clock proteins in the cytoplasm, the gooey liquid inside a cell that surrounds the nucleus and other organelles. There, they found four other complexes composed of different groups of the six proteins -- one with all six, named the upper complex -- and three others missing one or more of these key proteins. The researchers hypothesized that these complexes were in various states of assembly, but that the six key proteins entered the nucleus as a group.

The upper complex also had a seventh protein called GAPVD1, known from other studies to help shepherd chemicals to different locations inside cells. Although the role of GAPVD1 in the circadian clock remains somewhat unclear, Weitz said, experiments in which he and his colleagues trimmed this protein out of the upper complex caused disruption in circadian cycle -- an observation that suggests GAPVD1 plays a key role in the clock.

Weitz cautions that the precise orchestration performed by this constellation of proteins in running the body's clock remains yet to be teased out. However, he said, learning more about how these proteins interact has given researchers a clearer clue into inner workings of the system overall.

"The circadian clock is a very deep timing system that controls a large part of the physiology and behavior in all cells in the body to shape multiple processes," Weitz said. "The more we learn about it, the more links we'll get to certain kinds of disease states that aren't easily amenable to treatment today. Now that we understand how these molecular machines are built, we can start asking questions about how they work."


There are compelling reasons to study the role of steroids and sex differences in the circadian timing system. A solid history of research demonstrates the ubiquity of circadian changes that impact virtually all behavioral and biological responses. Furthermore, steroid hormones can modulate every attribute of circadian responses including the period, amplitude and phase. Finally, desynchronization of circadian rhythmicity, and either enhancing or damping amplitude of various circadian responses can produce different effects in the sexes.

Studies of the neuroendocrine underpinnings of circadian timing systems and underlying sex differences have paralleled the overall development of the field as a whole. Early experimental studies established the ubiquity of circadian rhythms by cataloging daily and seasonal changes in whole organism responses. The next generation of experiments demonstrated that daily changes are not a result of environmental synchronizing cues, and are internally orchestrated, and that these differ in the sexes. This work was followed by the revelation of molecular circadian rhythms within individual cells. At present, there is a proliferation of work on the consequences of these daily oscillations in health and in disease, and awareness that these may differ in the sexes.

In the present discourse we describe the paradigms used to examine circadian oscillation, to characterize how these internal timing signals are synchronized to local environmental conditions, and how hormones of gonadal and/or adrenal origin modulate circadian responses. Evidence pointing to endocrinologically and genetically mediated sex differences in circadian timing systems can be seen at many levels of the neuroendocrine and endocrine systems, from the cell, the gland and organ, and to whole animal behavior, including sleep/wake or rest/activity cycles, responses to external stimuli, and responses to drugs. We review evidence indicating that the analysis of the circadian timing system is amenable to experimental analysis at many levels of the neuraxis, and on several different time scales, rendering it especially useful for the exploration of mechanisms associated with sex differences.

Blue Light Photoreceptor - Important Gene For Circadian Rhythm

To understand how blue light and melatonin affect your body clock, it is important to know how light and darkness affect the human circadian rhythm. Human circadian rhythm is a complex network of genes that control the timing of our biological clocks which are closely associated with the sleep-wake and food-draining cycles. It helps us understand how blue light can affect the biological clock of our bodies.

Basically, light and darkness are the main drivers of our internal biological clocks. These clocks are made of small protein structures called 'cryptochromes' or in simpler terms, light-sensitive cells (photo-sensory neurons). Each cell in our body has a set of doublets of DNA (genetic instructions) programmed into it and these doublets are coupled together in a highly complicated series of interactions. Every day, the DNA binding to each cryptochrome is switched on and off making the whole process of genetic instruction run like clockwork.

The circadian rhythm is set to be synchronized 24 hours per day by the proteins, N-type calcium channels in the retinohypothalamic tract. However, this system is activated by the light at different times of the day. Most significantly, melatonin and other proteolytic enzymes that generate the biological response in the cryptochromes are produced during the night as a result of a disruption in the darkness. The irregularities caused by the disruption of the circadian rhythm are called 'disruption' and they have an impact on the protein levels and their stability in the human body.

The ability of the cells to make and release melatonin is compromised in the presence of light. The disruption of the circadian rhythm by light can cause a disruption of the sleep-wake patterns of the animal. When the animal is sleeping, the cryptochromes play a crucial role in the generation of the proteins that regulate the biological clock. In the case of humans, the dysfunctional or abnormal function of cryptochrome can cause serious medical conditions, including sleep disorders, psychiatric disorders, and certain types of cancer. As a consequence of the dysfunctional protein structure, the quality of sleep is altered.

The circadian light/cry protein interaction has a profound effect on the transcription of various genes in the animal body. This process regulates the genes that control the strength and duration of the regeneration of the cells. Some of these genes include the genes that are involved in the development of the neuroendocrine system and those that control the behavior of the central nervous system. The regulation of these genes is highly influenced by the effects of the disrupting light/cry protein interactions. The researchers showed that the enrichment of the transcripts of the genes that control neuroendocrine system processes was highly affected by the disrupting light/cry protein interactions.

By Justin W. Sanders | Photography by David Gresham/UT Southwestern

Takahashi in a UT Southwestern lab.

Circadian rhythms, and the disruptions imposed on them, play an increasingly vital role in human health. This role is evident in sleep medicine, and in fields as far-flung as metabolic disorders, mental health, and even oncology. Awareness of the influence of circadian rhythms is rapidly rising, a significant development for a characteristic of the body that even today seems mysterious to many: the built-in 24-hour cycle that monitors everything from our sleep patterns to our digestion.

As with most medical advancements, there was a tipping point in the mystery factor surrounding circadian rhythms, when our vague knowledge of the body’s innate ability to regulate its own timing gave way to a precise understanding of where that ability resides and how it functions at the molecular and genetic levels. It happened in the mid-1990s, at Northwestern University’s department of neurobiology and physiology. There, a laboratory research team led by Joseph S. Takahashi, PhD, isolated and cloned the first mammalian circadian rhythm gene—also known as Clock.

Mapping with Mutant Mice

The long-term goals of the Takahashi UT Southwestern laboratory are to understand the molecular and genetic basis of circadian rhythms in mammals and to utilize forward genetic approaches in the mouse as a tool for gene discovery for complex behavior.

Now chair of the Department of Neuroscience and an investigator of the Howard Hughes Medical Institute at University of Texas (UT) Southwestern Medical Center, Takahashi was then an up-and-coming researcher who had been working to understand circadian rhythms through pharmacology and cell biology. But those efforts, largely pursued through work with laboratory birds, had “hit a brick wall,” Takahashi told the Proceedings of the National Academy of Sciences, “so it became apparent that we had to move to genetics and molecular biology to continue the search.”

In the 1970s, geneticists Seymour Benzer and Ron Konopka had published seminal circadian rhythm research involving Drosophila, or fruit flies. In Benzer’s lab at the California Institute of Technology, the duo had discovered what’s known as the period (or per) locus in the tiny insects, a gene that, as the name suggests, controls the period of their circadian rhythms. Their colossally important work was “the inspiration for why we went into mouse genetics to find clock genes in mice,” Takahashi tells Sleep Review. But even with a precedent set by two of the field’s greatest figures behind him, the way forward was fraught with challenges.

“Back when we did this, people thought we were crazy that you could actually look for mutants in mice for behavior,” Takahashi says. “At the time, people thought that would be impossible to do.” There were many reasons for his peers’ skepticism, beginning with an almost existential derision of the idea that human behavior, or at least one major facet of it, could somehow be dictated by a single gene. “People don’t like to think our behavior can be reduced to something so simple,” Takahashi says.

But the naysayers did not deter Takahashi and his colleagues Martha Vitaterna, Lawrence Pinto, Fred Turek at Northwestern, and William Dove at the University of Wisconsin, Madison. His team pushed forward, and in the early 󈨞s, began screening mutant laboratory mice, looking for altered circadian rhythms. Placed in perfect, constant darkness, the mice mostly proved to have very regular circadian rhythms, as evidenced by their penchant for running on a wheel at the same time every 23.7 hours on average, or very close to 24 hours. But one mouse behaved differently, running its wheel approximately every 25 hours, and this anomaly was significant enough to move to the next testing stage.

“At that point, you’re not sure whether it’s real or not—whether it’s genetic,” says Takahashi. “So the next test is: Will it transmit that altered circadian rhythm to its offspring when you test-cross it?” Ultimately, the mouse’s offspring did exhibit the 25-hour cycle, and “that was really very exciting because that confirmed it was a mutation that could be transmitted,” Takahashi says. But his work had only just begun. Confirming a genetic mutation exists is one thing mapping its location on the genome is entirely another.

“Already at that stage we could tell that [the mutation] was likely to be caused by a single gene, just because of the way the phenotypes segregated in that very first cross,” says Takahashi. Pinpointing the location of that gene, however, was complicated by the fact that “the size of the genome of the mouse is the same as a human,” says Takahashi, “about 3 billion base pairs. And this mutation that causes the [altered circadian rhythms] is a single base-pair change. It’s one change out of 3 billion. That kind of gives you a feeling about what it’s like to try and find that: a needle in a haystack.”

Today’s scientists have access to the fully sequenced genomes of both a mouse and a human, a groundbreaking development that materialized in the early 2000s, years after Takahashi’s team succeeded in isolating the Clock gene. But “in the mid-󈨞s,” says Takahashi, “we couldn’t just go to the computer and try to look at the genome sequence where the mutation was. You had to actually isolate physically the DNA from that region. It’s kind of like a jigsaw puzzle, because all the DNA fragments that you isolate are short pieces, so you have to put together a mosaic of all those short clones or fragments of DNA to reconstruct the region.”

The process of piecing together those disparate sections of genome into a cohesive region of it is called “physical mapping,” and it is so labor-intensive it took Takahashi’s 10-person team 3 years of focused collective toil, from 1994 to 1997, to isolate and identify the area in which the Clock gene resides. Even then, the results were far from perfect. “We could have made a mistake,” Takahashi says, “and we could have been somewhere else than where we thought we were.”

Fortunately, another method was available of verifying that the section of DNA they had mapped contained the Clock gene. “We had some genetic evidence that the mutation [causing the altered circadian rhythms] could be reversed if we were able to put a normal copy of the gene into a mouse,” Takahashi says. By then, his team had created a strain of Clock mutant mice that carried two copies of the mutant gene and whose circadian periods were off by a whopping 4 hours—28 hours compared to the normal mouse’s 24 hours. Now, they took the region of the genome that they thought contained the gene, cloned it, and put it in the mutant mouse “to see if that would rescue the mutation,” Takahashi says. And sure enough, doing so “completely rescued that behavior. The mouse became completely normal….I can remember it. It was in August of 1996 that we got the first result where we could show that putting this big piece of DNA into a mouse actually repaired it.”

Partnering with Charles Weitz, MD, PhD, in Harvard’s Department of Neurobiology, Takahashi’s team followed up their breakthrough by locating Clock’s partner gene, called BMAL1, which paved the way to describing the molecular mechanism by which the two genes function together, as a unified protein. Acting as a “transcriptional activator” from their position inside the nucleus of cells, the protein CLOCK/BMAL1 directs other genes to transcribe and produce proteins in the cell outside the nucleus at the start of each day. At night, these proteins re-enter the nucleus to meet up again with their regulators and turn the tables, inhibiting CLOCK/BMAL1 and turning the protein off for the night. This transcriptional feedback loop is the “essence of how the clock works,” says Takahashi, and it happens every day, in nearly every cell in the body.

Expressions in Human Health

Takahashi examining purified protein samples with research specialist Yoga Chelliah.

Before beginning work with Takahashi in 2000, Joseph Bass, MD, PhD, a professor at the Feinberg School of Medicine and chief of Northwestern’s Division of Endocrinology, Metabolism and Molecular Medicine, approached his metabolic research from an “insulin-centric universe,” Bass tells Sleep Review. But “pretty soon after we began systematically looking at the animals that [Takahashi] had characterized at a molecular level, it became apparent that there was this opportunity to bring these two [fields] together, and that was a hugely fortuitous opening for me, and my intellectual focus and excitement was redirected as a result of it, entirely.”

The merging of the disciplines of Bass, an endocrinologist by trade, and Takahashi (which included key contributions from Fred Turek, PhD, as well, director of Northwestern’s Center for Sleep & Circadian Biology) produced yet another extremely fruitful period in Takahashi’s career. Bass’ work in the pathology of glucose regulation led them to try putting the circadian-mutated mice on a Western, high-fat diet, which resulted in two observations that “were foundational for everything we did over the next 15 years,” says Bass. The first was a propensity the circadian-altered mice had for gaining weight when fed high concentrations of fat, which their normal counterparts did not possess, even while placed on the same diet. The second was even more surprising: Even though the mutated animals were susceptible to weight gain, “they never developed what is classically seen in humans with type 2 diabetes,” says Bass. “They always had insulin deficiency. So that told us that [clock] genes are of fundamental importance in the production of insulin.”

As a direct result of Takahashi’s influence, Bass has come to focus “on understanding how clock genes may give us an insight into the molecular effects of sleep loss. And one of our ideas is that the disruption of the clock may be an element that manifests as diabetes and obesity in conditions where people are restricted in their ability to sleep under shift work and so forth.”

Bass’ research is just one development of many to advance the notion that “one of the important functions of the clock is really to fine-tune metabolism in individual cells,” says Takahashi. “We think that may be one reason why the clock is in every cell: If you want to control the metabolism of every cell, then it’s better to do it locally than to have a brain clock, which it has to signal for the body, and do it in an indirect way.”

The implications of a regulatory mechanism that exists in every cell are as far-reaching in medicine as the Clock gene is in the body itself. At the University of California, San Francisco’s Department of Neurology, to cite one example, Louis Ptacek, MD, has directed the research toward familial advanced sleep phase disorder, a circadian condition that causes its victims to have to go to sleep extremely early, about 3 to 4 hours on average earlier than a normal person, and then wake up 3 to 4 hours earlier. “It turns out it’s genetically caused,” says Takahashi, “and the first gene that [Ptacek] found that caused that was a mutation in the PERIOD2 gene—a circadian clock gene. The work from [Ptacek] really showed that in humans, the same clock gene pathway is operating [as in mice] and that it has a very strong effect on the timing of sleep in this particular disorder.”

Additionally, Takahashi explains, common sleep disorders such as shift work disorder and jet lag are “disruptions to our circadian system caused by changing the phase or timing of the clock in an abnormal way. So as we understand the mechanism of the clock better, we’re going to have better ways of either resetting our clocks faster, or trying to correct some of the problems that are caused by misalignment….There are already some medications on the market for that.”

Takahashi also pointed to the work of Eva Schernhammer, MD, DrPH, MSc, MPH, as another particularly promising application of clock-based research to human health. Schernhammer, a professor at both UCLA and Harvard, focuses on linking circadian rhythms with chronic disease, and has uncovered substantial epidemiological evidence for increased cancer risk in people who undergo shift work.

Bass says, “The discovery of the mammalian clock gene…and the full mechanism that then emerged of how the clock works enabled us and positioned us to understand many different processors, ranging from behavioral disorders all the way to peripheral disorders and diseases….Now that we have that ability, which in part relates to [Takahashi’s] work, we can understand many different processes ranging from metabolic disease, inflammation, and even cancer, ultimately.”

One day, it may even help us better understand sleep itself, whose genetic underpinnings largely “remain a mystery,” says Takahashi, “much like the circadian clock field in the early 󈨞s….Finding ‘sleep genes’ that are analogous to ‘clock genes’ is an important goal for cracking open the molecular mechanism of sleep.”

Freelance journalist Justin W. Sanders wrote the article “Better Nutrition for Better Sleep During Menopause” for our July/August 2014 issue.


Numerous investigators over the last 40 yr have endeavored to characterize the influence of GSH on circadian rhythms of mammalian physiology and behavior [2, 3, 11�, 28, 46, 47]. In humans, evidence suggests that fluctuating GSH during the menstrual cycle can alter the timing of physiology and behavior [48, 49]. While it has been shown that GSH can alter circadian rhythms of activity, their impact on the molecular clock in central and peripheral tissues is poorly understood. A limited number of reports describe the effects of ovarian steroid hormones on gene expression in the SCN [25, 27]. There is also very limited evidence for an effect of GSH on the timing of the molecular clock in peripheral oscillators [31, 36, 50]. To date there have been no attempts to determine the effects of estrous cycle stage or steroid hormone depletion after OVX on internal circadian organization. In the present study, we have determined the impact of estrous cycle stage on internal circadian organization.

The most surprising𠅊nd perhaps most significant—result in the current study is the robust effect of ovarian factors on the mean phase and synchrony of the female SCN. The existing literature, based almost exclusively on data from males [44], leads one to expect tightly clustered rhythms peaking in the second half of the light portion of the light-dark cycle (i.e., ZT6–ZT12). This expectation is fulfilled in our study by data from males. Furthermore, in our experiments SCN data from OVX females are not different from the data from males, confirming that the very different distribution of SCN phases seen in all estrus cycle stages of cycling females is the result of ovarian influence. These influences could well be ovarian steroids, though the fact that they do not vary systematically across the cycle indicates that other factors may be involved. That is, we cannot ignore the possible influence of other ovarian outputs, including the circulating peptide hormones activin and inhibin. Moreover, it is possible that the intensity of these effects may be linearly dependent on the duration of steroid depletion. While 10 days has certainly been deemed adequate for steroid levels to reach a nadir in circulation, it may not be long enough for residual effects of steroid receptor activation to dissipate [37, 38]. Nonetheless, these data point to the existence of robust sex differences in basic parameters of the central circadian pacemaker and require further exploration. Though early reports suggested that SCN neurons did not contain the primary estrogen receptor ERα [51], more recent work has refuted that claim, revealing both ERα and ERβ expression in SCN neurons [29]. Further, relatively sparse expression of PRs has been observed in SCN pacemaker cells [52, 53]. Our current findings suggest that some of the effects of steroid hormones on circadian function are mediated indirectly through brain areas outside of the SCN. This suggestion is supported by the fact that steroid-responsive brain regions, including the basal forebrain, can influence SCN-driven behavioral rhythms [30]. There are significant synaptic connections between the SCN and several nuclei in these brain regions [54, 55]. Of particular interest are the results of Perrin et al. [30[, which suggest that the changing steroid hormone levels during the estrous cycle alter the rhythm of PER2 expression in the central nucleus of the medial amygdala and the oval nucleus of the bed nucleus of the stria terminalis, but not the SCN. In these regions, the amplitude of the PER2 rhythm significantly increased on proestrus, followed by a gradual decline in amplitude and delay in peak phase between proestrus and diestrus. A similar increase in amplitude was seen in OVX rats treated with estradiol benzoate [30]. These data suggest that the rising titer of ovarian E2 and/or P4 on the day of proestrus adjusts the timing of PER2 expression in frontal brain regions, which could in turn affect the timing of activity in SCN pacemaker neurons.

Recently we reported that the phase and amplitude of PER2 expression in the mouse SCN was not affected by the transition from proestrus to metestrus, suggesting that ovarian steroid hormones do not directly affect the SCN [31]. Those experiments did not examine the phase of clock gene expression in the SCN across the entire reproductive cycle, nor did they determine the impact of steroid hormone depletion by OVX. To ascertain the direct influence of ovarian steroids on the SCN and by extension on rhythms of behavior, we measured the response of per1-luc expression rhythms in SCN explants to ovarian steroid hormones. In vitro exposure of SCN tissue from OVX rats to E2, P4, or E2+P4 at high physiological levels (200 nM𠄱 μM) did not significantly alter the period of per1-luc expression, although higher concentrations (10 μM or greater) did lengthen the period of per1-luc expression in the SCN. Thus, at physiological levels ovarian steroids do not directly affect the timing of per1-luc expression in SCN neurons. These data are somewhat surprising, given the presence of E2 and P4 receptors on SCN pacemaker neurons and the established effects of ovarian hormones on behavior [29]. It is possible that the pulsatile nature of hormone secretion, which is not replicated in our experiments, is responsible for the influence of steroid hormones on the SCN in vivo. Given the dynamic nature of ovarian steroid levels, it is possible that, though they did not affect the period of per1-luc expression when applied chronically, acute fluctuation or pulses of ovarian steroids may have considerable influence on the phase or amplitude of clock gene expression in the SCN. That said, ovarian steroid hormone levels in the circulation change gradually, over several hours and even across several days. Our approach was intended to ascertain if this “sustained” (㸒� h) period of elevated steroid hormone levels could effectively alter the timing of the circadian clock in the SCN. One would need a more concrete understanding of steroid hormone concentrations over time (days, hours) at the level of the SCN to truly address this issue. Future examination of phase-dependent exposure to acute ovarian steroid pulses, in the presence of steroid receptor antagonists, is warranted and could more completely define the impact of steroids on SCN pacemaker neurons.

Our results suggest that dynamic changes in ovarian steroid hormone levels associated with the cycle can affect the timing of the molecular clock in some peripheral tissues but not in others. Rhythms of per1-luc expression in peripheral oscillators involved in metabolic function, including the liver, kidney, lung, and WAT, were minimally affected by the reproductive cycle. However, in liver and WAT tissue, OVX resulted in a significant phase shift of per1-luc expression (liver) and/or a precipitous decline in phase synchrony (liver and WAT). Ovarian steroid hormones are known to regulate lipid metabolism, insulin sensitivity, and glucose tolerance in hepatocytes and adipose tissue [56 �]. Our data suggest that, in addition to their established impacts on metabolism, ovarian steroids may also modulate the timing of the clock. This notion is best supported by the effects of OVX we observed on the phase of per1-luc expression in these tissues. The lack of significant effects of estrous cycle stage or OVX on the mean phase of per1-luc expression and the synchrony among kidney and lung tissues suggest that the clock in these tissues is less responsive to changes in hormone levels across the cycle.

In our previous study, we did not see a significant effect of cycle stage on mean PER2::LUC expression in mouse ovarian tissue explants, though the phase distribution of isolated follicles was not reported [31]. As in that study, we did not observe a significant effect of cycle stage on the mean phase of per1-luc expression in isolated rat follicles. It is possible that the influence of cycle stage on phase synchrony among ovarian follicles reported here can be attributed to the development or maturation of the follicle. In the present study, efforts were made to collect only antral or preovulatory (Graafian) follicles [59]). Smaller preantral (or primary) follicles do not show significant rhythms of clock gene expression (our unpublished observation and [60, 61]). That said, it is possible that slight variation in follicular development (e.g., small antral to Graafian) between metestrus and proestrus could affect the phase of per1-luc expression. In fact, our results suggest that early in follicular development (late metestrus to diestrus) the follicles are synchronized, but the process of maturation increases phase distribution. It is possible that increased phase distribution among follicles is an indicator of variation in development that is normalized following stimulation with gonadotropins. This speculation is supported by the recent finding that stimulation of granulosa cells from gonadotropin-primed mice with luteinizing hormone (LH) increases expression of the gap junction protein Connexin 43 in conjunction with an increase in the amplitude of PER2 expression [62]. This effect may also depend on ovarian steroid hormones, since estrogen has been shown to stimulate Connexin gene expression in other cells, including SCN neurons [63]. Thus, increased robustness of circadian oscillators in individual granulosa (or theca) cells may lead to increased coordination among developing follicles at the onset of the preovulatory LH surge. Phase synchrony of per1-luc expression in isolated oviduct explants was modestly reduced from proestrus to estrus. It may be that the precipitous decline in circulating ovarian steroids following ovulation leads to a decline in phase coordination within the oviduct. That would not be surprising, given the density of steroid hormone receptors in the oviduct and its role in the process of ovulation [64]. It is also possible that various cell populations in the wall of the oviduct or cells at different locations along its length may have functionally disparate phases, although it is unclear how this phase variation might impact the process of ovulation, oocyte maturation, and fertilization [65, 66].

Of all the peripheral tissues we examined, the pituitary showed the most robust and persistent phase coherence. Interestingly, there was no effect of estrous cycle stage or OVX on phase synchrony among pituitary explants. However, there was a significant effect on the mean phase of per1-luc expression there was a significant advance of peak per1-luc expression between diestrus and proestrus, followed by a significant delay on estrus. It is possible that steroid hormones indirectly affect the pituitary clock by regulating neuroendocrine-releasing factors, which facilitate the timing of pituitary hormone secretion [67 �].

We measured the phase of per1-luc expression in the cornea to determine the impact of estrous cycle stage and OVX on a particularly robust peripheral oscillator outside of the reproductive axis. As with the ovarian follicles, oviduct, liver, and kidney, the phase distribution of per1-luc expression in corneal cultures was only modestly affected by estrous cycle stage. However, steroid depletion following OVX significantly reduced phase synchrony among corneal explants. ER and PR are expressed at high density in corneal epithelial cells from rats, mice, rabbit, and humans [70 �]. Given the density of these receptors, it is reasonable to hypothesize that ovarian steroid hormones directly affect clock gene expression in the cornea. It is also possible that ovarian steroids affect the cornea indirectly, by modulating the level of serum glucocorticoids [73, 74]. Glucocorticoids, including corticosterone (CORT), are known to affect the timing of clock gene expression in the cornea [44], and their level is significantly affected by the estrous cycle [73]. Changes in CORT secretion could contribute to the increase in phase synchrony among cultures collected on estrus [73]. Elimination of this steroid-dependent effect on serum CORT may also explain the decline in phase coherence among corneal explants following OVX.