hypothesis for energy

Hypotheses in scientific research: definition and types

Hypothesis definition, importance in scientific research, types of hypotheses, step by step formulation, practical example.

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AP®︎/College Biology

Course: ap®︎/college biology   >   unit 7.

• Earth formation
• Beginnings of life
• Origins of life

Hypotheses about the origins of life

• The RNA origin of life
• Origins of life on Earth

Key points:

• The Earth formed roughly 4.5 ‍   billion years ago, and life probably began between 3.5 ‍   and 3.9 ‍   billion years ago.
• The Oparin-Haldane hypothesis suggests that life arose gradually from inorganic molecules, with “building blocks” like amino acids forming first and then combining to make complex polymers.
• The Miller-Urey experiment provided the first evidence that organic molecules needed for life could be formed from inorganic components.
• Some scientists support the RNA world hypothesis , which suggests that the first life was self-replicating RNA. Others favor the metabolism-first hypothesis , placing metabolic networks before DNA or RNA.
• Simple organic compounds might have come to early Earth on meteorites.

Introduction

When did life appear on earth, the earliest fossil evidence of life, how might life have arisen.

• Simple inorganic molecules could have reacted (with energy from lightning or the sun) to form building blocks like amino acids and nucleotides, which could have accumulated in the oceans, making a "primordial soup." 3 ‍  
• The building blocks could have combined in further reactions, forming larger, more complex molecules (polymers) like proteins and nucleic acids, perhaps in pools at the water's edge.
• The polymers could have assembled into units or structures that were capable of sustaining and replicating themselves. Oparin thought these might have been “colonies” of proteins clustered together to carry out metabolism, while Haldane suggested that macromolecules became enclosed in membranes to make cell-like structures 4 , 5 ‍   .

From inorganic compounds to building blocks

Were miller and urey's results meaningful, from building blocks to polymers, what was the nature of the earliest life, the "genes-first" hypothesis, the "metabolism-first" hypothesis, what might early cells have looked like, another possibility: organic molecules from outer space.

• Miller, Urey, and others showed that simple inorganic molecules could combine to form the organic building blocks required for life as we know it.
• Once formed, these building blocks could have come together to form polymers such as proteins or RNA.
• Many scientists favor the RNA world hypothesis, in which RNA, not DNA, was the first genetic molecule of life on Earth. Other ideas include the pre-RNA world hypothesis and the metabolism-first hypothesis.
• Organic compounds could have been delivered to early Earth by meteorites and other celestial objects.

Works cited:

• Harwood, R. (2012). Patterns in palaeontology: The first 3 billion years of evolution. Palaeontology , 2(11), 1-22. Retrieved from http://www.palaeontologyonline.com/articles/2012/patterns-in-palaeontology-the-first-3-billion-years-of-evolution/ .
• Wacey, D., Kilburn, M. R., Saunders, M., Cliff, J., and Brasier, M. D. (2011). Microfossils of sulphur-metabolizing cells in 3.4-billion-year-old rocks of Western Australia. Nature Geoscience , 4 , 698-702. http://dx.doi.org/10.1038/ngeo1238 .
• Primordial soup. (2016, January 20). Retrieved May 22, 2016 from Wikipedia: https://en.wikipedia.org/wiki/Primordial_soup .
• Gordon-Smith, C. (2003). The Oparin-Haldane hypothesis. In Origin of life: Twentieth century landmarks . Retrieved from http://www.simsoup.info/Origin_Landmarks_Oparin_Haldane.html .
• The Oparin-Haldane hypothesis. (2015, June 14). In Structural biochemistry . Retrieved May 22, 2016 from Wikibooks: https://en.wikibooks.org/wiki/Structural_Biochemistry/The_Oparin-Haldane_Hypothesis .
• Kimball, J. W. (2015, May 17). Miller's experiment. In Kimball's biology pages . Retrieved from http://www.biology-pages.info/A/AbioticSynthesis.html#Miller's_Experiment .
• Earth’s early atmosphere. (Dec 2, 2011). In Astrobiology Magazine . Retrieved from http://www.astrobio.net/topic/solar-system/earth/geology/earths-early-atmosphere/ .
• McCollom, T. M. (2013). Miller-Urey and beyond: What have learned about prebiotic organic synthesis reactions in the past 60 years? Annual Review of Earth and Planetary Sciences , 41_, 207-229. http://dx.doi.org/10.1146/annurev-earth-040610-133457 .
• Powner, M. W., Gerland, B., and Sutherland, J. D. (2009). Synthesis of activated pyrimidine ribonucleotides in prebiotically plausible conditions. Nature , 459 , 239-242. http://dx.doi.org/10.1038/nature08013 .
• Lurquin, P. F. (June 5, 2003). Proteins and metabolism first: The iron-sulfur world. In The origins of life and the universe (pp. 110-111). New York, NY: Columbia University Press.
• Ferris, J. P. (2006). Montmorillonite-catalysed formation of RNA oligomers: The possible role of catalysis in the origins of life. Philos. Trans. R. Soc. Lond. B. Bio.l Sci ., 361 (1474), 1777–1786. http://dx.doi.org/10.1098/rstb.2006.1903 .
• Kimball, J. W. (2015, May 17). Assembling polymers. In Kimball's biology pages . Retrieved from
• Montmorillonite. (2016, 28 March). Retrieved May 22, 2016 from Wikipedia: https://en.wikipedia.org/wiki/Montmorillonite .
• Wilkin, D. and Akre, B. (2016, March 23). First organic molecules - Advanced. In CK-12 biology advanced concepts . Retrieved from http://www.ck12.org/book/CK-12-Biology-Advanced-Concepts/section/10.8/ .
• Hollenstein, M. (2015). DNA catalysis: The chemical repertoire of DNAzymes. Molecules , 20 (11), 20777–20804. http://dx.doi.org/10.3390/molecules201119730 .
• Breaker, R. R. and Joyce, G. F. (2014). The expanding view of RNA and DNA function. Chemistry & biology , 21 (9), 1059–1065. http://dx.doi.org/10.1016/j.chembiol.2014.07.008 .
• Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter, P. (2002). A pre-RNA world probably predates the RNA world. In Molecular biology of the cell (4th ed.). New York, NY: Garland Science. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK26876/#_A1124_ .
• Moran, L. A. (2009, May 15). Metabolism first and the origin of life. In Sandwalk: Strolling with a skeptical biochemist . Retrieved from http://www.simsoup.info/Origin_Landmarks_Oparin_Haldane.html .
• Kimball, J. W. (2015, May 17). The first cell? In Kimball's biology pages . Retrieved from http://www.biology-pages.info/A/AbioticSynthesis.html#TheFirstCell?
• Kimball, J. W. (2015, May 17). Molecules from outer space? In Kimball's biology pages . Retrieved from http://www.biology-pages.info/A/AbioticSynthesis.html#Molecules_from_outer_space? .
• Jeffs, W. (2006, November 30). NASA scientists find primordial organic matter in meteorite. In NASA news . Retrieved from http://www.nasa.gov/centers/johnson/news/releases/2006/J06-103.html .

Additional references:

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scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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July 17, 2024

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Physicists develop new theorems to describe the energy landscape formed when quantum particles gather together

by Trinity College Dublin

An international team of physicists, centered at Trinity, has proven new theorems in quantum mechanics that describe the "energy landscapes" of collections of quantum particles.

Their work addresses decades-old questions, opening up new routes to make computer simulation of materials much more accurate. This, in turn, may help scientists design a suite of materials that could revolutionize green technologies.

The new theorems have just been published in the journal Physical Review Letters . The results describe how the energy of systems of particles (such as atoms, molecules and more exotic matter) changes when their magnetism and particle count change. Solving an open problem important to the simulation of matter using computers, this extends a series of landmark works commencing from the early 1980s.

The combined pen-and-paper and computational work was done by Andrew Burgess, Ph.D. Candidate in Trinity's School of Physics, together with Dr. Edward Linscott, from the Paul Scherrer Institute in Switzerland, and Dr. David O'Regan, Associate Professor in Physics at Trinity.

The exploration and understanding of molecules and materials using computer simulation is a mature and thriving research area. It has a long track record of success over several decades, and several materials in current use were developed with the help of such simulations. When studying systems at the atomic level , the equations that describe the particles and their interactions are those of quantum mechanics .

These equations are very demanding, and must be approximated for practical simulations. The art of making such approximations more reliable, while keeping the computational costs manageable, is approaching its 100th year. This work is increasingly guided by the handful of known "exact conditions," i.e., definite rules from quantum theory , such as those found here.

Explaining how to visualize what the team discovered, Dr. O'Regan says, "Imagine a steep-sided valley , where the ground is not curved but instead made up of angular tiles, like you might see in an old arcade game where the images were made using polygons.

"We have found that the height profile in fractured valleys like this represents the exact energy of isolated collections of particles, like molecules. Heading straight up the valley corresponds to changing the number of electrons that hold together the molecule, while moving to each side increases its magnetism. This work completes the mapping of this valley up to high magnetic states, finding that the valley walls are steep and tilted."

Burgess, lead author, describes in more detail how the discovery came about, saying, "While working on a different problem, I needed to know the shape of this energy valley for simple systems. Hunting through published research, I could find lots of nice graphs but to my surprise they stopped short of mapping the entire valley.

"I realized that existing quantum mechanical theorems could be used for systems with one electron, such as the hydrogen atom. However, for systems with two electrons such as the helium atom, these theorems could tell me little about the sides of the valley. Specifically, a quantum mechanical theorem known as the spin constancy condition was incomplete."

Dr. Linscott, from the Laboratory for Materials Simulations at PSI, adds, "Understanding the geography of this energy landscape may seem quite abstract and esoteric—but actually, this knowledge can help solve all sorts of real-world problems.

"When colleagues of ours use computer simulations to try to find next-generation materials for more efficient solar panels, or catalysts for more energy efficient industrial chemistry, our knowledge of the energy landscape can be baked into the calculations that they perform, making their predictions more accurate and reliable."

Dr. O'Regan added, "The energy differences and slopes of this valley landscape underpin the stability of matter, interactions between materials and light, chemical reactions, and magnetic effects. Knowing what the entire valley surface looks, including at high magnetization, is already helping us to build better tools for simulating complex materials, even when they are not magnetic.

"Motivating this work is the need to provide improved simulation theory and methods for developing materials for renewable energy and chemistry applications. When a battery is discharging, for example, there are metal atoms that change their particle count and magnetism.

"Here we see that we're moving in that same valley landscape and it's the drop in height, so to speak, that gives the energy that the battery provides. This is an example of applied simulation and abstract quantum theory being practiced side by side, each motivating and improving the other."

Reflecting on the nature of this kind of research, Burgess added, "This interplay between theory and practical simulation is what I love most about this area of research. We have already developed a new method for modeling materials based on these theorems and are testing it out on battery cathode materials, so there is plenty of exciting work in the pipeline."

Journal information: Physical Review Letters , arXiv

Provided by Trinity College Dublin

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The energy hypothesis of sleep revisited

Matthew t. scharf.

a Center for Sleep and Respiratory Neurobiology, University of Pennsylvania School of Medicine, Translational Research Building, Suite 2100, 125 S. 31st Street, Philadelphia, PA 19104, USA

b Neuroscience Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA

Nirinjini Naidoo

c Division of Sleep Medicine, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA

John E. Zimmerman

Allan i. pack.

One of the proposed functions of sleep is to replenish energy stores in the brain that have been depleted during wakefulness. Benington and Heller formulated a version of the energy hypothesis of sleep in terms of the metabolites adenosine and glycogen. They postulated that during wakefulness, adenosine increases and astrocytic glycogen decreases reflecting the increased energetic demand of wakefulness. We review recent studies on adenosine and glycogen stimulated by this hypothesis. We also discuss other evidence that wakefulness is an energetic challenge to the brain including the unfolded protein response, the electron transport chain, NPAS2, AMP-activated protein kinase, the astrocyte-neuron lactate shuttle, production of reactive oxygen species and uncoupling proteins. We believe the available evidence supports the notion that wakefulness is an energetic challenge to the brain, and that sleep restores energy balance in the brain, although the mechanisms by which this is accomplished are considerably more complex than envisaged by Benington and Heller.

Introduction

Sleep has been investigated for over a century. Yet, the fundamental question of why we need to sleep remains unanswered. In particular, what physiological functions are fulfilled by sleep? One hypothesis is that sleep is necessary to replenish energy stores in the brain that are depleted during wakefulness. This theory posits that during waking, a relatively active metabolic period in the brain, energy stores become progressively diminished, thereby promoting sleep. During sleep, there is recovery of energy stores and thus restoration of energy balance.

Based on this concept, in 1995, Benington and Heller proposed that the energy-related substrates glycogen and adenosine are key sleep regulators ( Benington and Heller, 1995 ). They suggested that alterations in astrocytic glycogen and extracellular adenosine in the brain both reflect metabolic alterations that occur during wakefulness and sleep and can influence the amount and quality of subsequent sleep. In particular, this model proposes that glycogen depletion during wakefulness leads to an increase in extracellular adenosine, which facilitates sleepiness and influences delta power during sleep, a measure of sleep homeostasis ( Borbely and Achermann, 1999 ).

This hypothesis led to extensive studies and, over the ensuing years, numerous studies have examined, in relation to sleep and wakefulness, the role of glycogen and adenosine in addition to other aspects of regulation of brain energetics. We review the recent studies on adenosine and glycogen and place them in the context of earlier studies and Benington and Heller’s formulation of the energy hypothesis of sleep. We also discuss changes in other aspects of energy regulation in the brain with wakefulness and sleep. In particular, we discuss new information on the electron transport chain, AMP-activated protein kinase, NPAS2 and clock, uncoupling proteins, reactive oxygen species, and the unfolded protein response. We believe that available evidence does support the notion that wakefulness provides an energetic challenge to the brain, and one of the functions of sleep is to allow for recovery from this energy-challenged state, thereby allowing needed synthetic processes in the brain to occur during sleep. The Benington-Heller hypothesis has been fruitful in terms of stimulating research. The situation is, however, more complex than they proposed.

A. Adenosine

The production of adenosine has been linked to energy depletion. As the major cellular energy molecule ATP is depleted, the byproduct, AMP, is produced. AMP can be further metabolized to adenosine. Therefore, an increase in adenosine may reflect increased degradation of ATP, and a net decrease in the availability of cellular energy stores ( Figure 1 ).

Adenosine metabolic pathways. In the cell, ATP is metabolized to AMP and then to adenosine by cytosolic 5’-nucleotidase. Adenosine can be converted back to AMP by adenosine kinase or metabolized to inosine by adenosine deaminase. Extracellularly, ATP is metabolized to AMP and then to adenosine by ecto 5’-nucleotidase. Adenosine can be converted to inosine by exo adenosine deaminase.

One of the proposed functions of sleep is to replenish energy stores in the brain that are depleted during wake. This theory would, therefore, predict that as energy stores diminish, adenosine would increase progressively during wake, thereby increasing sleepiness and then subsequently decrease during resultant sleep ( Benington and Heller, 1995 ). Indeed, alterations in adenosine levels during normal sleep/wake have been demonstrated. Extracellular adenosine in the cat, as measured by microdialysis, is lower during NREM and REM sleep compared to wakefulness in the basal forebrain, cortex, thalamus and preoptic area of the hypothalamus ( Porkka-Heiskanen et al., 2000 ). However, with 6 hours of sleep deprivation, extracellular adenosine increases only in the basal forebrain and cortex but in the other areas measured (thalamus, preoptic area of the hypothalamus, dorsal raphe nucleus, pedunculopontine tegmental area) it either remains the same or decreases ( Figure 2 ). The reason why adenosine increases with sleep deprivation in certain brain regions but not others is unknown. It likely reflects that unlike cortex, the metabolism of these areas shows little difference between sleep and wake. Nonetheless, these findings indicate that adenosine may act, at least in the basal forebrain or the cortex, to alter aspects of sleep ( Porkka-Heiskanen et al., 2000 ).

Alterations in extracellular adenosine measured by microdialysis following sleep deprivation and in subsequent recovery sleep in cats. Cats were sleep deprived for 6 hours and then allowed to sleep for 3 hours. Extracellular adenosine was measured at the beginning of the experiment by microdialysis and then in 1 hour intervals for the duration of the experiment. Adenosine is presented as a percentage of the baseline value. Extracellular adenosine increases during sleep deprivation only in the basal forebrain and cortex. In the other areas studied, adenosine levels progressively decline. Adenosine stays elevated during recovery sleep only in the basal forebrain. BF, basal forebrain; POA, preoptic area of the hypothalamus; DRN, dorsal raphe nucleus; PPT, pedunculopontine tegmental area. ( Porkka-Heiskanen et al. 2000 ; reprinted with permission).

Early studies showed that intracerebroventricular infusion of adenosine leads to a decrease in wake and an increase in sleep including slow wave sleep ( Virus et al., 1983 ). Also, caffeine, a non-specific antagonist of adenosine receptors, increases wake, and decreases both NREM and REM sleep including slow wave sleep ( Yanik et al., 1987 ). These and other early studies led Radulovacki to initially propose that adenosine is a sleep-promoting molecule ( Radulovacki, 1985 ). Several early studies using different pharmacologic agents demonstrated that adenosine in the brain increases the homeostatic drive to sleep reflected in both the amount of sleep as well as in the quality of sleep as indicated by the increase in slow wave sleep during NREM sleep (reviewed in Radulovacki, 1985 ). Furthermore, systemic or intracerebroventricular administration of the A 1 receptor agonist CPA increases EEG delta power during NREM sleep ( Benington et al., 1995 ). The demonstration that activation of the A 1 receptor directly leads to increases in delta power in sleep suggested a putative mechanism for how changes in cellular metabolism could alter sleep homeostasis since increases in EEG delta power are thought to be a measure of the homeostatic response to sleep deprivation ( Borbely and Achermann, 1999 ). This evidence connecting adenosine to EEG delta power led Benington and Heller to propose that adenosine increases during wakefulness in cortex increase sleepiness and that when sleep occurs there is an increase in delta power ( Benington and Heller, 1995 ). They did not envisage that adenosine played a role in the switch from wakefulness to sleep although others have proposed this (see further below).

A1. Adenosine Receptors

The effects of adenosine are mediated by G-protein coupled adenosine receptors of which there are 4 subtypes (reviewed in Dunwiddie and Masino, 2001 ). The A 1 , A 2A and A 3 receptors have a widespread distribution in the brain while the A 2B has a more limited distribution with high expression primarily in the striatum, olfactory tubercle and nucleus accumbens (reviewed in Dunwiddie and Masino, 2001 ). The A 1 and A 3 receptors couple negatively to adenylyl cyclase and positively to phospholipase C. The latter may be an indirect effect. The A 2A and A 2B receptors couple positively to adenylyl cyclase (reviewed in Dunwiddie and Masino, 2001 ). Adenosine receptors can act on a number of ion channels including potassium and calcium channels (reviewed in Dunwiddie and Masino, 2001 ). The A 1 receptor is primarily inhibitory while the A 2A receptor is primarily excitatory (reviewed in Dunwiddie and Masino, 2001 ).

Both the A 1 and A 2A receptors have been implicated in the regulation of sleep. Intraperitoneal administration of the A 1 receptor agonist CHA leads to an increase in slow wave and REM sleep ( Radulovacki et al., 1984 ). Similarly, stimulation of the A 1 receptor by either intraperitoneal or intracerebroventricular administration of the A 1 receptor agonist CPA increases slow wave activity during NREM sleep ( Benington et al., 1995 ). Consistent with these results implicating the A 1 receptor, antagonizing the A 1 receptor by intraperitoneal administration of CPT causes the opposite effect, namely, an increase in wake and a decrease in slow wave and REM sleep ( Virus et al., 1990 ). The A 2A receptor is also involved in the regulation of sleep since infusion into the subarachnoid space or the ventricular system of the A 2A selective agonist {"type":"entrez-protein","attrs":{"text":"CGS21680","term_id":"878113053","term_text":"CGS21680"}} CGS21680 increases NREM and REM sleep ( Gerashchenko et al., 2000 ; Satoh et al., 1998 ; Satoh et al., 1999 ). These effects of the A 1 receptor and A 2A are likely mediated by actions of adenosine in different brain regions (see further below).

However, mice with genetic deletion of the A 1 receptor have the same amounts of NREM and REM sleep as wild-type mice ( Stenberg et al., 2003 ). Furthermore, both the wild-type and knockout mice show the same rebound in NREM and delta power following 6 hours of sleep deprivation indicating that sleep homeostasis is normal in the A 1 receptor knockout mice ( Stenberg et al., 2003 ). Thus, these results would argue that A 1 receptor is not essential for sleep homeostasis. A 2A receptor knockout mice have not been as well characterized but are reported to have impairments in NREM rebound following sleep deprivation ( Urade et al., 2003 ). A 2A receptor knockout mice do not respond to intraperitoneal administration of caffeine but A 1 receptor knockout mice display a normal response to caffeine. This suggests that the A 2A receptor is likely the more important mediator of the effects of caffeine on sleep ( Huang et al., 2005 ). Although these results from knockout mice suggest that the A 2A receptor is the key receptor for sleep/wake control and the effects of caffeine, constitutive lack of a gene can lead to compensatory changes, which may confound interpretation of the data. Genetic deletion of the A 1 and A 2A receptors in a spatially and temporally controlled manner would help elucidate the particular functions of A 1 and A 2A receptors in sleep homeostasis. Local administration of antisense oligonucleotides to selectively reduce expression of adenosine receptors ( Thakkar et al., 2003b ) has led to additional insights as we discuss more fully below, where we consider the action of adenosine in different neuronal groups.

A2. Adenosine Metabolism

Studies have also assessed whether adenosine metabolism is altered during sleep. Adenosine can be metabolized either intracellularly or extracellularly. A schematic of the adenosine metabolic pathways is presented in Figure 1 . Within the cell, adenosine can be formed from AMP in a reaction catalyzed by cytosolic 5’-nucleotidase ( Meghji, 1991 ). Also within a cell, adenosine can be converted back to AMP via adenylate kinase or converted to inosine via adenosine deaminase ( Meghji, 1991 ). In the extracellular space, ATP, ADP and AMP can be converted to adenosine by ecto-5’-nucleotidases ( Cunha et al., 1998 ; Dunwiddie et al., 1997 ).

In a study from our laboratory, we assessed activities of both the cytosolic and extracellular forms of 5’-nucleotidase using a new method to assess their activities ( Mackiewicz et al., 2000 ), as well as activity of adenosine deaminase and adenosine kinase. We found that while activity of these enzymes did exhibit diurnal variation in a number of different brain regions, sleep deprivation did not alter the activity of any of these enzymes ( Mackiewicz et al., 2003 ). Similarly, others have reported that activity of 5’-nucleotidase is altered following only 4 days of REM sleep deprivation but not following 2 days of REM sleep deprivation, suggesting that 5’-nucleotidase activity is not likely altered during spontaneous wake ( Thakkar and Mallick, 1996 ). These studies suggest that modulation of the activities of adenosine enzymes is not a likely mechanism to explain the increase in adenosine that occurs during wakefulness. The activity of extracellular 5’-nucleotidase is, however, increased with age in cortex ( Mackiewicz et al., 2006 ). This likely explains the increase in extracellular adenosine with age in basal forebrain that has been found using microdialysis in rat ( Murillo-Rodriguez et al., 2004 ).

Adenosine has been extensively studied because increases in extracellular adenosine may reflect depletion of ATP in the cell concomitant with energy depletion. However, the connection between cellular metabolism and extracellular adenosine levels is not straightforward since there are numerous sources for extracellular adenosine. Adenosine can be produced in the cell and transported across the cell membrane by a number of transporters (reviewed in Cass et al., 1998 ). But adenosine can also be produced in the extracellular space by conversion of ATP, ADP and AMP ( Cunha et al., 1998 ; Dunwiddie et al., 1997 ). ATP is co-localized with neurotransmitters such as acetylcholine, norepinephrine, serotonin and dopamine and can be released upon stimulation (reviewed in Dunwiddie and Masino, 2001 ). Impairment of synaptic transmission selectively in astrocytes by spatially and temporally restricted expression of a dominant negative SNARE domain leads to decreased ATP release, which decreases extracellular adenosine implicating astrocytes as a source of extracellular adenosine in the brain ( Pascual et al., 2005 ). This is particularly interesting since mutant mice with the dominant negative SNARE have impaired sleep homeostasis, i.e., an attenuated increase in NREM sleep and delta power following sleep deprivation ( Halassa et al., 2007 ). In particular, in these mice the increase in delta power following sleep deprivation is short and not sustained ( Halassa et al., 2007 ). Furthermore, in basal forebrain, impairing adenosine transport with NBTI increases extracellular adenosine ( Porkka-Heiskanen et al., 1997 ) suggesting that, at least in basal conditions in this region, the net flow of adenosine is from the extracellular space into the cell. Therefore, adenosine in the extracellular space may be produced within the cell or outside it, and the observed increase in extracellular adenosine induced by sleep deprivation does not necessarily reflect cellular energy depletion occurring with prolonged wakefulness. However, lesions of the cholinergic cells in the basal forebrain (see section A3a) completely negate the increase in adenosine with sleep deprivation in this region suggesting that during sleep deprivation adenosine is coming into the extracellular space from these neurons, i.e., it has an intracellular source. Thus, the source of adenosine in baseline conditions and during sleep deprivation may be different. It is also possible that the lesioned animals could have abnormalities in astrocytes, which may be responsible for the decrease in adenosine. Damage to the central nervous system is accompanied by changes to astrocytes, a process known as reactive gliosis (reviewed in Pekny et al., 2007 ). It is possible, therefore, that lesioning of the cholinergic neurons of the basal forebrain leads to alterations in astrocytes, which results in alteration in extracellular adenosine. Indeed, the enzyme adenosine kinase, which metabolizes adenosine to AMP, is increased in astrocytes during the process of reactive gliosis ( Boison, 2006 ), which could contribute to the decreased extracellular adenosine observed in the lesioned animals. Nonetheless, the increase in adenosine following sleep deprivation is correlated with the restorative processes occurring during NREM sleep and may be caused by the synthesis of glycogen.

A3. Action of Adenosine in Different Brain Regions

Manipulation of the adenosine system in a number of different brain regions alters sleep/wake states. The basal forebrain, laterodorsal tegmental area, pontine reticular formation and ventrolateral preoptic area have all been implicated as important regions mediating the effects of adenosine on sleep. Following is an examination of the role of adenosine in each of these systems that are involved in sleep regulation and the adenosine receptor subtypes involved.

A3a. Basal Forebrain

The basal forebrain is involved in sleep/wake regulation. The term “basal forebrain” has become synonymous with the magnocellular cholinergic system in the medial septum, vertical and horizontal limbs of the diagonal band of Broca, magnocellular preoptic area, the substantia innominata and the nucleus basalis of Meynert ( Szymusiak, 1995 ). Other cell types, however, are contained in the basal forebrain including GABA-ergic and various peptidergic neurons and these may be of functional significance ( Szymusiak, 1995 ). In this review, we will refer to the basal forebrain as the anatomical region containing both the cholinergic neurons and noncholinergic neurons. The cholinergic neurons of the basal forebrain project diffusely and play a role in the cortical activation associated with wake and REM sleep ( Jones, 2005 ; Szymusiak, 1995 ). Given the role of the cholinergic system of the basal forebrain in sleep/wake regulation, a number of studies have examined whether the basal forebrain is the “adenosine sensor” of the brain that is responsible for the adenosinergic modulation of sleep/wake.

If the adenosine sensor is in the basal forebrain, adenosine should increase with wakefulness in the basal forebrain. Indeed, extracellular adenosine increases during wake in a number of brain regions including the basal forebrain ( Porkka-Heiskanen et al., 2000 ; Porkka-Heiskanen et al., 1997 ). Of these areas, extracellular adenosine increases only in the basal forebrain and cortex during sleep deprivation. During 3 hours of sleep recovery, adenosine declines in the cortex below pre-sleep deprivation levels. In basal forebrain it also decreases during recovery sleep but is still elevated above pre-sleep deprivation levels even after 3 hours of recovery sleep ( Porkka-Heiskanen et al., 2000 ) ( Figure 2 ). Therefore, McCarley and colleagues have suggested that the basal forebrain may be uniquely situated to detect changes in adenosine occurring with sleep and wake and be able to modulate sleep accordingly ( Basheer et al., 2004 ; Porkka-Heiskanen et al., 2000 ). It should be noted, however, that a single location of action of adenosine is unlikely to explain the many effects that are observed with sleep deprivation and the recovery from sleep deprivation. In particular, it cannot explain the local increase in slow wave activity during sleep in brain areas specifically activated during wakefulness ( Vyazovskiy and Tobler, 2008 ).

As further proof of the role of the basal forebrain in mediating the effects of adenosine on sleep/wake, modulation of the adenosinergic system in the basal forebrain can alter sleep. Administration of the adenosine transport inhibitor NBTI increases extracellular adenosine and concomitantly decreases wake and increases slow wave and REM sleep as well as delta power during NREM sleep ( Methippara et al., 2005 ; Porkka-Heiskanen et al., 1997 ). Similarly, perfusion of adenosine into the basal forebrain by microdialysis decreases wake and increases NREM, REM and delta power ( Basheer et al., 1999 ; Portas et al., 1997 ). These studies demonstrate that increasing adenosine levels in the basal forebrain can increase both the amount of sleep and the quality of sleep as reflected in the delta power, which is a sensitive indicator of the homeostatic component of sleep ( Borbely and Achermann, 1999 ).

A 1 and A 2A receptors mediate the effects of adenosine on sleep/wake (see section A1 ). In the basal forebrain, A 1 receptor mRNA increases following sleep deprivation but A 2A receptor expression is undetectable ( Basheer et al., 2001a ). Reduction of A 1 receptor in the basal forebrain by antisense oligonucleotides increases wake and decreases NREM and REM sleep ( Thakkar et al., 2003b ), although one needs to be aware of the potential for non-specific effects of antisense oligonucleotides (reviewed in Lebedeva and Stein, 2000 ). Since this intervention has the opposite effect to activating the A 1 receptor, the results are compatible with the concept that adenosine promotes sleep in this region through the effects of its action on the A 1 receptor. Furthermore, animals with their A 1 receptors reduced in basal forebrain by antisense oligonucleotides show decreases in NREM and delta power following sleep deprivation, suggesting that it is the A 1 receptor in the basal forebrain that mediates sleep homeostasis ( Thakkar et al., 2003b ). This result is incongruent with that from studies of mice with knockout of specific adenosine receptors that we described above.

A number of studies have examined the electrophysiological mechanisms by which adenosine can modulate the neuronal activity responsible for sleep/wake alteration. In the basal forebrain, microdialysis perfusion of adenosine, the adenosine transport inhibitor NBTI (which leads to an increase in extracellular adenosine) (see above), or the A 1 receptor agonist CHA, decreases the neuronal activity of wake-active neurons ( Alam et al., 1999 ; Thakkar et al., 2003a ). Consistent with this result, the A 1 receptor antagonists CPDX or CPT, also delivered by microdialysis perfusion into the basal forebrain, increase the discharge of wake-active neurons ( Alam et al., 1999 ; Thakkar et al., 2003a ). Administration of the A 2A receptor agonist CGS had no effect on these wake-active neurons ( Thakkar et al., 2003a ). Therefore, adenosine decreases the activity of wake-active neurons in the basal forebrain by acting on the A 1 receptor. One study reported a decrease in the firing rate of sleep-active neurons in this region with adenosine activation by NBTI ( Alam et al., 1999 ), and one study with adenosine activation by the A 1 receptor agonist CHA did not observe a change in the firing rate of these sleep-active neurons ( Thakkar et al., 2003a ).

The A 1 receptor decreases neuronal activity by acting on inwardly rectifying potassium channels or the hyperpolarization activated cation current ( Rainnie et al., 1994 ). In in vitro brain slices, adenosine inhibits the cholinergic neurons of the basal forebrain by activating an inwardly rectifying potassium channel ( Arrigoni et al., 2006 ). This effect is blocked by the A 1 receptor antagonist CPT ( Arrigoni et al., 2006 ). Some of the non-cholinergic neurons of the basal forebrain are also inhibited by adenosine ( Arrigoni et al., 2006 ). In contrast to the cholinergic neurons, however, the non-cholinergic neurons decrease activity in response to adenosine by inhibiting the hyperpolarization-activated cation current ( Arrigoni et al., 2006 ). Therefore, although both cholinergic and non-cholinergic neurons are inhibited by adenosine, this inhibition is mediated by different ion currents; for the former it is an inwardly rectifying potassium current and the latter it is the hyperpolarization-activated cation current.

The A 1 receptor mediates intracellular events by acting on cell signaling pathways, in particular by increasing the activity of phospholipase C (reviewed in Dunwiddie and Masino, 2001 ). Phospholipase C catalyzes the production of inositol triphosphate, which leads to activation of calcium channels and increases in intracellular calcium. Consistent with phospholipase C activation, adenosine causes an increase in intracellular calcium in cholinergic neurons of the basal forebrain by acting primarily through the A 1 receptor and to a lesser extent, the A 3 receptor ( Basheer et al., 2002 ). This calcium is released from internal stores via inositol triphosphate receptors ( Basheer et al., 2002 ).

Phospholipase C activation leads to activation of protein kinase C ( Alberts et al., 2002 ). Protein kinase C alters transcription by acting on a number of downstream targets (reviewed in Ventura and Maioli, 2001 ) including NF-κB (reviewed in Weil and Israel, 2006 ). NF-κB translocates to the nucleus when one of its binding partners, I-κB, is phosphorylated and detaches from it ( Alberts et al., 2002 ). A number of different kinases can phosphorylate I-κB including protein kinase C (reviewed in Siebenlist et al., 1994 ). In basal forebrain brain slices, adenosine increases NF-κB DNA binding activity in nuclear extracts compatible with it translocating to the nucleus and pretreatment with the A 1 receptor selective antagonist CPT decreases NF-κB DNA binding activity ( Basheer et al., 2001b ). NF-κB translocation to the nucleus following sleep deprivation as well as following microinjection into the basal forebrain of either adenosine or the A 1 receptor agonist CHA occurs almost exclusively in the cholinergic cells of the basal forebrain ( Ramesh et al., 2007 ). Furthermore, blocking the nuclear translocation of NF-κB with an inhibitor peptide, SN50, reduces delta power following sleep deprivation ( Ramesh et al., 2007 ). These observations are consistent with an A 1 receptor-mediated activation of the phospholipase C signaling cascade in the basal forebrain and suggest that NF-κB may be an important mediator of both the molecular and behavioral effects of adenosine in this region.

The behavioral, electrophysiological and molecular studies of McCarley and colleagues lead to the hypothesis that the basal forebrain is a critical site for the effects of adenosine on sleep homeostasis (reviewed in Basheer et al., 2004 ). Benington and Heller posited that adenosine produced sleepiness and subsequently increased delta power during sleep by its action in cortex ( Benington and Heller, 1995 ). McCarley and colleagues redefined the Benington-Heller hypothesis by positing that adenosine mediates it effect on sleep by acting focally in the basal forebrain. They specifically proposed that the wake-promoting cholinergic neurons of the basal forebrain are mediators of the action of adenosine on wakefulness and sleep ( Porkka-Heiskanen et al., 1997 ). Although McCarley and coworkers provided much evidence for their hypothesis, they did not test it directly. In particular, is the increase in adenosine in the basal forebrain required for the homoeostatic response to sleep deprivation? Furthermore, are the cholinergic neurons of the basal forebrain required for the increases in sleep and EEG delta power following sleep deprivation?

A recent study ( Blanco-Centurion et al., 2006 ) addressed these critical questions by destroying the cholinergic neurons of the basal forebrain by intracerebroventricular injection of the neurotoxin saporin conjugated to an antibody that is taken up by the p75 nerve growth factor receptor, which is selectively expressed in cholinergic neurons of the basal forebrain ( Heckers et al., 1994 ). Lesioning of the cholinergic neurons of the basal forebrain eliminates the increase in extracellular adenosine induced by sleep deprivation in this region. However, surprisingly, loss of the cholinergic neurons of the basal forebrain does not alter baseline sleep/wake or recovery sleep following 6 or 12 hours of sleep deprivation. The increase in EEG delta power following sleep deprivation was also normal indicating an intact sleep homeostatic response ( Blanco-Centurion et al., 2006 ). The lack of an increase in adenosine following sleep deprivation while the homeostatic response stays intact indicates that the increase in adenosine in the basal forebrain that occurs during sleep deprivation is not required for sleep homeostasis. Furthermore, this provocative study indicates that the cholinergic neurons of the basal forebrain are not necessary for sleep/wake control or for sleep homeostasis.

There is, however, some controversy surrounding this study and the role of the cholinergic neurons of the basal forebrain in mediating sleep homeostasis (for commentaries, see Heller, 2006 ; Kalinchuk et al., 2006 ; Noor Alam et al., 2006 ; Radulovacki, 2006 ). It is possible that since there is redundancy in the processes regulating sleep homeostasis, the loss of one component, i.e., the cholinergic cells of the basal forebrain, may not alter overall sleep homeostasis ( Noor Alam et al., 2006 ). However, lesioning of the histamine neurons in the tuberomammillary nucleus and the noradrenergic locus ceruleus neurons in addition to the cholinergic neurons of the basal forebrain decreases wake and increases NREM sleep during only the first 4 hours of the dark period but over a full 24 hour period produces no significant change in wake or NREM ( Blanco-Centurion et al., 2007 ). This result challenges the notion that the lack of an effect on total sleep in the animals with a lesion of the cholinergic basal forebrain is due to compensatory changes in other wake-promoting systems since lesioning of other wake-promoting neurons also fails to alter overall sleep. Since the triple lesioned animals had increased bout lengths for both wake and NREM, the role of these systems may be to alter the architecture of sleep rather than the total amount of NREM and wake ( BlancoCenturion et al., 2007 ).

It is also possible that when the conjugated saporin is administered intracerebroventricularly, as was done in the study of Blanco-Centurion et al. ( Blanco-Centurion et al., 2006 ), there are different effects than would be found if the saporin was delivered locally to the basal forebrain ( Kalinchuk et al., 2006 ). This could be the result of the toxin acting in regions other than basal forebrain. Indeed, injection of saporin conjugated to an antibody that is taken up by the p75 nerve growth factor receptor as was used in the study of Blanco-Centurion et al. directly into the basal forebrain causes a decrease in recovery NREM sleep and delta power following sleep deprivation, indicating impaired sleep homeostasis ( Kaur et al., 2008 ). Adenosine was not measured, however, in this study ( Kaur et al., 2008 ) and the cause for the different results from local microinjection and ICV injection is currently not known. While much remains to be learned, the notion of the exclusivity of the cholinergic neurons of the basal forebrain as the adenosine sensor and mediator of the effects of adenosine on sleep homeostasis is seriously challenged, and can no longer be considered as a tenable hypothesis.

A3b. Laterodorsal Tegmental Nucleus

The laterodorsal tegmental nucleus is a sleep-promoting region with a particular role in REM sleep generation ( Monti and Monti, 2000 ). The laterodorsal tegmental nucleus includes cholinergic and glutamatergic neurons ( Lee et al., 2003 ; Monti and Monti, 2000 ). Microdialysis perfusion of adenosine into the laterodorsal tegmental nucleus in cats decreases wake and increases slow wave sleep and REM sleep ( Portas et al., 1997 ). In vitro in brain slices, adenosine and the A 1 receptor agonist CHA inhibit the firing of both cholinergic and non-cholinergic neurons in the laterodorsal tegmental nucleus by activating an inwardly rectifying potassium current and inhibiting the hyperpolarization-activated cation current ( Rainnie et al., 1994 ). This study did not identify whether these two currents were active in both the cholinergic and noncholinergic cells or whether the effect of adenosine on cholinergic and non-cholinergic neurons was specifically mediated by one of the channels as was observed in the basal forebrain (see above). Adenosine also inhibits glutamatergic transmission in the laterodorsal tegmental nucleus by activating presynaptic A 1 receptors ( Arrigoni et al., 2001 ). Therefore, adenosine may modulate sleep/wake by acting on the neurons of the laterodorsal tegmental nucleus. If so, the precise role of adenosine in this region in sleep/wake control in vivo is currently unknown.

A3c. Pontine Reticular Formation

Another region to consider is the pontine reticular formation. The pontine reticular formation has a well-established role in the modulation of REM sleep ( Monti and Monti, 2000 ). Microinjection of the A 1 receptor agonist CHA into the pontine reticular formation of the rat decreases wake and increases REM ( Marks and Birabil, 1998 ; Marks et al., 2003 ) and to a lesser extent NREM ( Marks and Birabil, 1998 ). Microinjection of the A 2A receptor agonist CGS into the pontine reticular formation also increases REM sleep ( Coleman et al., 2006 ; Marks et al., 2003 ) and NREM sleep ( Coleman et al., 2006 ), and decreases wake ( Marks et al., 2003 ). The pontine reticular formation receives cholinergic projections from the laterodorsal and pedunculopontine tegmental nuclei, which are important for REM sleep generation ( Lydic and Baghdoyan, 2005 ). Interestingly, CGS injected into the pontine reticular formation increases acetylcholine release in the pontine reticular formation ( Coleman et al., 2006 ). These data suggest that adenosine may also act in the pontine reticular formation to increase acetylcholine release and thereby increase REM sleep.

A3d. Preoptic Area of the Hypothalamus

The preoptic area of the hypothalamus is an important sleep-promoting region. In particular, a population of neurons in the ventrolateral preoptic area shows increased fos levels ( Sherin et al., 1996 ), and increased neuronal firing activity during sleep consistent with a sleep-promoting function ( McGinty and Szymusiak, 2001 ; reviewed in Saper et al., 2005 ). However, despite this key difference that neurons in the ventrolateral preoptic area are sleep-active, microinjection of adenosine into the preoptic area increases total sleep, slow wave sleep and REM sleep as it does in the basal forebrain, pontine reticular formation and laterodorsal tegmental nucleus ( Ticho and Radulovacki, 1991 ). This suggests that adenosine is excitatory to sleep-active neurons in the ventrolateral preoptic area in contrast to the inhibitory action on the wake-active cells in the basal forebrain. Local administration of the adenosine transport inhibitor NBTI into the lateral preoptic area, which in the basal forebrain has been shown to increase extracellular adenosine levels and sleep ( Porkka-Heiskanen et al., 1997 ), decreases NREM and increases wake ( Methippara et al., 2005 ). Thus, administration of adenosine ( Ticho and Radulovacki, 1991 ) or NBTI ( Methippara et al., 2005 ) produces opposite effects. But we do not know the source of adenosine in the preoptic area, i.e., extracellular or intracellular (see above). If in this region the net flux of adenosine through the transporter is from intracellular to extracellular space, NBTI could lead to a decline in extracellular adenosine. In comparison to studies described above for basal forebrain, the effect of local administration of NBTI on extracellular adenosine levels in the preoptic area has not been assessed. There may, moreover, be issues about the precise location of injection sites in this small region that could affect results. This seems likely since studies report opposite results for the administration of the A 1 receptor agonist CPA into the preoptic area of the hypothalamus, which in one study decreases NREM and increases wake ( Methippara et al., 2005 ) and in the other study increases sleep ( Ticho and Radulovacki, 1991 ). The discrepancy in these results may reflect that although the ventrolateral preoptic area has a population of sleep-active neurons, injection of drugs cannot specifically target these neurons and may act on other populations of cells.

Different studies have also led to different conclusions about the role of the A 2A receptor in the lateral preoptic area. Administration of the less selective A 2 receptor agonist CV-1808 does not alter sleep or wake ( Ticho and Radulovacki, 1991 ), but the relatively selective A 2A receptor agonist CGS increases NREM and decreases wake ( Methippara et al., 2005 ). The specificities of these drugs for the adenosine receptor subtypes are different ( Hutchison et al., 1989 ), and therefore the results of these studies need to be interpreted with caution. Nonetheless, the latter study suggests that A 2A receptor activation, which is excitatory in the lateral preoptic area, increases sleep. Neuronal excitation of sleep-active neurons mediated by the A 2A receptor is a plausible explanation for the sleep-promoting role of adenosine in the ventrolateral preoptic area.

Adenosine reduces spontaneous GABA release onto neurons of the ventrolateral preoptic area suggesting a presynaptic mechanism for ventrolateral preoptic area activation, whereby adenosine disinhibits sleep-promoting neurons of the ventrolateral preoptic area ( Chamberlin et al., 2003 ; Morairty et al., 2004 ). In a separate study, two groups of GABA-ergic sleep-promoting neurons in the ventrolateral preoptic area have been identified that are both inhibited by adenosine and the A 1 receptor agonist CPA via a post-synaptic mechanism ( Gallopin et al., 2005 ). The discrepancy between the former and latter studies may have to do with method of recording (patch clamp in whole cell recording versus loose cell-attached configuration). However, the latter study went further by also assessing the role of the A 2A receptor in the two types of neurons that are inhibited by adenosine. The A 2A receptor agonist CGS excites only one type ( Gallopin et al., 2005 ) demonstrating that different neurons in the ventrolateral preoptic area have the capacity to respond differently to adenosine. There may be a subgroup of neurons in this region that are responsible for the sleep-promoting role of adenosine.

The A 2A receptor may influence ventrolateral preoptic area neurons indirectly as well. Infusion of the A 2A receptor agonist CGS into the subarachnoid space increases fos expression in the ventrolateral preoptic area suggesting that activation of A 2A receptors in the leptomeninges or nucleus accumbens may also increase NREM sleep by increasing the activity of sleep-active ventrolateral preoptic area neurons ( Scammell et al., 2001 ). Therefore, adenosine likely acts on the A 2A receptor both directly in the preoptic area and indirectly to excite the ventrolateral preoptic area sleep-promoting neurons thereby increasing sleep.

A4. Synthesis of Studies of Adenosine

While the idea that adenosine might be a sleep-promoting molecule did not originate with Benington-Heller ( Benington and Heller, 1995 ), but rather as a result of important earlier studies ( Feldberg and Sherwood, 1954 ; Haulica et al., 1973 ; Marley and Nistico, 1972 ; Radulovacki, 1985 ; Virus et al., 1983 ), their manuscript did provide a cogent synthesis that stimulated research in this area. Benington et al. observed that activation of the A 1 receptor leads to increases in EEG delta power during NREM sleep ( Benington et al., 1995 ). This finding was a stimulus for Benington and Heller (1995) to propose that changes in adenosine in the cortex could promote sleepiness and subsequently, when sleep occurred, alter delta power, the main electrophysiologic correlate of sleep homeostasis ( Borbely and Achermann, 1999 ). They did not envisage that adenosine played a role in the wakefulness to sleep switch. However, other investigators have examined whether changes in adenosine in other brain regions could alter not only sleep homeostasis as indicated by delta power, but also the total amounts of sleep and wake, i.e., could adenosine also play a role in the initiation of sleep or wake? There is now evidence that at least in some brain areas, e.g., the basal forebrain, extracellular adenosine increases in relationship to the duration of prior wakefulness ( Basheer et al., 1999 ; Porkka-Heiskanen et al., 2000 ; Porkka-Heiskanen et al., 1997 ). However, this does not occur uniformly throughout the brain and it appears that in some wake-active brain regions such as the dorsal raphe nucleus, adenosine levels actually decline as wakefulness is prolonged ( Porkka-Heiskanen et al., 2000 ). The basis for this result is unknown but likely reflects local differences in the metabolic changes between sleep and wakefulness. Moreover, it may be somewhat artifactual, since determining extracellular adenosine by microdialysis in these small neuronal populations is challenging.

There is extensive evidence that adenosine can alter sleep/wake states. To date, the basal forebrain, the laterodorsal tegmental nucleus, the pontine reticular formation and the preoptic area have been implicated as sites of adenosine action on sleep/wake control. The role of adenosine in sleep/wake control may result from inhibition of basal forebrain wake-active neurons ( Thakkar et al., 2003a ) or activation of preoptic area sleep-active neurons, the latter effect being mediated by A 2A receptors ( Gallopin et al., 2005 ). The role of the basal forebrain adenosinergic system in altering sleep has been the most studied. McCarley and co-workers have suggested that cholinergic neurons in the basal forebrain sense alterations of adenosine that occur during wake or sleep and alter the quantity and quality of sleep appropriately (reviewed in Basheer et al., 2004 ).

This modification of the Benington-Heller hypothesis, i.e., that for its role in control of sleep/wake, adenosine is specifically “sensed” by cholinergic neurons in the basal forebrain, while intriguing, now seems disproven ( Blanco-Centurion et al., 2006 ). A challenge to this hypothesis from the beginning is that it has never been shown in what way cholinergic neurons of the basal forebrain had any special or unique properties with respect to the effects of adenosine on their function, i.e., what was special about the basal forebrain neurons that allowed them to play this role? There is no evidence, for example, that the regulation of adenosine enzymes controlling adenosine metabolism across the day or with sleep deprivation is different in the basal forebrain compared to other brain regions ( Mackiewicz et al., 2003 ).

Other cell types in the basal forebrain or in other areas of the brain are likely also to be important for the mechanisms of action of adenosine on sleep/wake control. As discussed earlier, the laterodorsal tegmental nucleus, the pontine reticular formation and the preoptic area have all been shown to alter sleep upon modulation of their intrinsic adenosinergic system. These areas may work independently or, more likely, in concert with other regions of the brain to alter sleep. One intriguing possibility is that adenosine acts in the cortex to increase slow wave activity ( Benington and Heller, 1995 ) while adenosine acts in other regions such as the basal forebrain to initiate sleep.

A recent exciting discovery is the role of astrocytes in response to sleep deprivation. These cells are involved in modulating effects of synapses- the tripartite synapse (reviewed in Fellin et al., 2006 ). Since a single astrocyte can contain greater than 100,000 synapses ( Bushong et al., 2002 ), they provide the ability to coordinate responses over a large numbers of synapses. Astrocytes release neurochemicals that can affect synaptic transmission (reviewed in Fellin et al., 2006 ). One particular neurochemical that is released is ATP that is metabolized extracellularly to adenosine ( Pascual et al., 2005 ). Thus, astrocytes can modulate adenosine “tone” of synapses and are likely to play a key role in the coupling of adenosine to the regulation of sleep. This discovery as to the role of astrocytes is likely to lead to more intensive investigation of the biology of astrocytes in relation to sleep/wake control and the response to sleep deprivation.

Pharmacologic studies implicate adenosine as a modulator of sleep/wake with the effects mediated by the A 1 and A 2A receptors. Antisense oligonucleotide reduction of the A 1 receptor produces alterations in sleep homeostasis consistent with the pharmacologic data ( Thakkar et al., 2003b ). However, genetic deletion of the A 1 receptor produces no alterations in sleep or in the response to sleep deprivation ( Stenberg et al., 2003 ), while genetic deletion of the A 2A receptor has been reported to produce sleep alterations in mice such as unresponsiveness to the wake-promoting effects of caffeine ( Huang et al., 2005 ; Urade et al., 2003 ). Pharmacologic and genetic tools each have, however, their limitations. Pharmacologic agents are confounded by issues of specificity, delivery and dosage. However, given the number of drugs tested and the number of different experimental models used, it is extremely unlikely that non-specific effects can explain the sleep/wake alterations observed in these studies. These pharmacologic experiments implicate the adenosine system as being a modulator of both the quantity and quality of sleep.

Global deletion of a gene may produce developmental defects (for example see Lee et al., 2007 ) or lead to compensatory changes that may mask the physiological functions of the deleted gene when compared to temporally and/or spatially restricted deletion (for example see Balschun et al., 2003 ). Genetic tools are now available that allow for spatial and temporal control of gene expression (for reviews, see Mallo, 2006 ; Miyoshi and Fishell, 2006 ). These elegant genetic tools avoid many of the problems associated with pharmacologic agents and deletion of a target gene prior to development. Given the recent controversy of the role of the basal forebrain in mediating the effects of adenosine ( Blanco-Centurion et al., 2006 ; Heller, 2006 ; Kalinchuk et al., 2006 ; Noor Alam et al., 2006 ; Radulovacki, 2006 ), future studies utilizing these genetic tools will allow for more precisely controlled experiments and should prove illuminating.

There is, however, no doubt that adenosine does play some role in sleep/wake control, and likely more prominently in the behavioral response to sleep deprivation. Adenosine is, however, not likely to be the major player but rather one of many mechanisms used to promote sleep when wakefulness is prolonged. Microarray studies conducted by us in both Drosophila ( Zimmerman et al., 2006 ) and mice ( Mackiewicz et al., 2007 ) show that the most common changes in gene expression with sleep and wakefulness are downregulation of expression of many genes during sleep deprivation, supporting the notion that multiple mechanisms are used to limit wakefulness. The adenosine studies to date are consistent with a role of adenosine in regulation of sleep but are insufficient on their own to explain how changes in brain energy are linked to control of sleep.

B. Glycogen

The other component of the Benington-Heller hypothesis is alteration in glycogen. In addition to being stored and produced in the liver, there is a relatively small amount of glycogen that is synthesized and stored in astrocytes of the brain. Benington and Heller proposed that glycogen stores in brain are depleted during wakefulness and restored during sleep ( Benington and Heller, 1995 ). We first describe how glycogen is regulated and then review the evidence for changes in glycogen in brain with sleep/wake and sleep deprivation.

B1. Metabolic Regulation of Glycogen

There are many recent in-depth reviews of glycogen metabolism and the regulation of glycogen synthesis and degradation ( Brown, 2004 ; Brown et al., 2003 ; Brushia and Walsh, 1999 ; Greenberg et al., 2006 ; Gruetter, 2003 ; Newgard et al., 2000 ; Oikonomakos, 2002 ). Briefly follows a summary of glycogen regulation in the brain. Glycogen is present in astrocytes in the brain. The synthesis of glycogen chains from glucose molecules is catalyzed by glycogen synthase and the release of glucose-1-phosphate from glycogen is catalyzed by glycogen phosphorylase. The activities of both of these enzymes are dependent on their state of phosphorylation, but in opposite directions. Glycogen phosphorylase is activated when phosphorylated and glycogen synthase is activated when dephosphorylated. Therefore, dephosphorylation of these 2 enzymes favors the synthesis of glycogen removing glucose from the cell energy pool and phosphorylation favors the release of glucose and availability of energy for the cell.

Both glycogen phosphorylase and glycogen synthase are dephosphorylated by the same enzyme, protein phosphatase 1. In the brain, complexes are formed that place all three of these enzymes in close proximity to glycogen by protein targeting to glycogen (PTG), a scaffolding molecule. PTG is a targeting subunit of protein phosphatase 1, which directs it to its substrates ( Brady et al., 1997 ; Ou et al., 2005 ; Printen et al., 1997 ). Unlike other targeting subunits of protein phosphatase 1, the binding of PTG to protein phosphatase 1 is not regulated by phosphorylation ( Brady et al., 1997 ). The concentration of PTG alters the magnitude of the increase in glycogen synthesis ( Green et al., 2004 ).

Glycogen phosphorylase is phosphorylated by phosphorylase kinase ( Brushia and Walsh, 1999 ), while glycogen synthase is a substrate of multiple enzymes that lead to its phosphorylation ( Larner et al., 1979 ; Lawrence et al., 1997 ). The change in phosphorylation state is rapid and can lead to degradation of glycogen within minutes ( Cruz and Dienel, 2002 ; Hutchins and Rogers, 1970 ; Karnovsky et al., 1983 ). This makes accurately measuring the glycogen content of any tissue problematic, and especially difficult in the brain as glycogen levels are lower than in other tissues, although estimates of glycogen in brain may be artifactually low as a result of tissue isolation techniques during which time glycogen is degraded ( Cruz and Dienel, 2002 ). Regardless of the basal amount of glycogen in brain tissue, measuring glycogen levels in the brain requires methods that inactivate kinase and phosphatase pathways instantly.

B2. Changes in Brain Glycogen with Sleep/Wake and Sleep Deprivation

Two general strategies have been used to preserve the glycogen content in brain tissue, thereby permitting assessment of its amount. One strategy is extreme heat, generated by exposure to high-energy focused microwave irradiation in mammalian studies ( Franken et al., 2003 , 2006b ; Gip et al., 2002 ; Gip et al., 2004 ; Kong et al., 2002 ; Swanson et al., 1992 ) or immersion in boiling water in a Drosophila study ( Zimmerman et al., 2004 ). The other strategy is extreme cold achieved by immersion in liquid nitrogen ( Cruz and Dienel, 2002 ; Hutchins and Rogers, 1970 ; Karnovsky et al., 1983 ). These strategies have been used to assess changes in brain glycogen occurring with short durations of wakefulness or sleep, diurnal variations and changes following sleep deprivation.

Glycogen stores can be depleted and repleted rapidly during wake or sleep, respectively. In one study, sleep/wake state was assessed in rats and then whole animals were dropped into liquid nitrogen after a specified duration of sleep or wake. A significant decrease in whole brain glycogen is seen after 2 to 5 minutes of spontaneous wake compared to sleep levels. In contrast, 5 to 11 minutes of spontaneous sleep is sufficient to restore glycogen levels ( Karnovsky et al., 1983 ). These studies, which have not been repeated, imply that rapid changes in glycogen take place in the brain in wake or sleep, and likely result from changes in the phosphorylation state of glycogen synthase and glycogen phosphorylase. However, since freezing in liquid nitrogen is not sufficient to prevent post-sacrifice changes in glycogen ( Kong et al., 2002 ), these results need to be interpreted with caution.

Other studies have indicated longer-term alterations in brain glycogen. There is a diurnal variation in total brain glycogen content, which correlates with the activity of animals, both in mice ( Hutchins and Rogers, 1970 ) and flies ( Zimmerman et al., 2004 ). In the fly, brain glycogen content increases during the consolidated rest period to the highest level at ZT 18 and then significantly declines between ZT 18 to ZT 22; this is the beginning of the early active period for the fly ( Zimmerman et al., 2004 ). In flies, the glycogen content of the entire head (including brain) and the rest of the body stores do not demonstrate any significant diurnal variation; only the brain exhibits diurnal changes ( Zimmerman et al., 2004 ). In male albino mouse brains, glycogen content also rises during a period of low locomotor activity, ZT 18 (6 hours after lights off) to ZT 22, and the greatest drop in glycogen levels is from ZT 10 to ZT 14 (2 hours after the beginning of their locomotor active period) ( Hutchins and Rogers, 1970 ). That diurnal variation in brain glycogen correlates with overall activity, both in mouse and fly, is in agreement with studies that show stimulation of facial vibrissae in the rat leads to glycogen depletion in the lateral forebrain structures, which are known to be activated by such stimulation ( Swanson, 1992 ; Swanson et al., 1992 ). Thus, it is hypothesized that increased neuronal activity results in reduced glycogen levels ( Swanson, 1992 ; Swanson et al., 1992 ).

However, the studies of the effects of sleep deprivation on brain glycogen have produced seemingly contradictory results both in rats and mice. One study examined the effects of prolonged sleep deprivation on 1-month-old Sprague-Dawley rats sacrificed using high-energy focused microwave irradiation. Whole brains minus cerebellum and brainstem from rats sleep deprived for 6, 12 and 24 hours were examined for glycogen content and compared to that in rats left undisturbed, allowed to sleep normally, and sacrificed at the same diurnal time ( Kong et al., 2002 ). No differences in brain glycogen were seen between groups after 6 hours of sleep deprivation but significant decreases were observed after 12 and 24 hours of sleep deprivation ( Kong et al., 2002 ). A separate study using 24-day-old Long-Evans rats sacrificed with focused microwave irradiation also found no changes in brain glycogen after 6 hours of sleep deprivation in the cerebral cortex, but did find a significant decrease in the cerebellum ( Gip et al., 2002 ). (The cerebellum was not examined in the study of Kong et al. (2002) .) However, after 12 hours of sleep deprivation in 59-day-old rats, glycogen had actually increased in the cortex but was unchanged in the cerebellum ( Gip et al., 2002 ). Thus, in rats, there are regional differences in the brain with respect to changes in glycogen following sleep deprivation. Both decreases and increases have been reported and the changes observed depend on rat strain and age. Changes in glycogen in one brain region, i.e., cerebral cortex, are summarized in Table 1 .

Effect of sleep deprivation on glycogen in cerebral cortex. Various studies assessing glycogen in the cortex of mouse and rat. Glycogen increases, decreases or stays the same depending on species, strain and length of sleep deprivation.

Note: Kong et al. 2002 assessed tissue consisting mostly of cortex but other brain regions were present as well.

Several mouse strains have also been examined for the effects of sleep deprivation on glycogen content. In C57BL/6J mice sacrificed by focused microwave irradiation, there is no change in glycogen content in cerebral cortex after 1 hour of sleep deprivation ( Franken et al., 2006b ), a significant increase after 3 hours of sleep deprivation, and a further increase after 6 hours of sleep deprivation ( Franken et al., 2003 , 2006b ). No significant changes were observed in the glycogen content of the cerebellum or brainstem after 6 hours of sleep deprivation ( Franken et al., 2003 ). Two other strains, AKR/J and DBA/2J, demonstrated brain glycogen changes very similar to those observed in Long-Evans rats, i.e., no change in glycogen in the cortex and a decrease in the cerebellum and brainstem following 6 hours of sleep deprivation ( Franken et al., 2003 ). Therefore, the changes in glycogen are dependent on the strain of mouse and the region of the brain ( Franken et al., 2003 ).

In Drosophila, the effects of sleep deprivation on glycogen content are also not straightforward. Brain glycogen levels are decreased slightly but significantly after 3 hours of sleep deprivation, but, after 6 hours of sleep deprivation, glycogen levels are unaltered compared to sleeping controls sacrificed at the same diurnal time point ( Zimmerman et al., 2004 ). Regional differences in glycogen content in the brain were not assessed in Drosophila so the extent to which regional differences contribute to these effects remains unknown.

The majority of studies to date examining the effects of sleep deprivation on glycogen in the brain have in common a study involving 6 hours of sleep deprivation (see Table 1 ). Following 6 hours of sleep deprivation, the changes in glycogen levels in vivo are different in rats and mice, different strains of rats and mice and different regions of the brain (see Table 1 ).

In vitro studies of glycogen utilization in primary cultures of astrocytes may hold the key to understanding these seemingly contradictory in vivo results. Exposure of primary cell cultures of mouse cerebral cortical astrocytes to wake-active neurotransmitters such as vasoactive intestinal peptide and noradrenaline depletes their glycogen content in minutes ( Sorg and Magistretti, 1991 ), similar to the in vivo studies ( Karnovsky et al., 1983 ) (see Figure 3 ). Within 30 minutes, the glycogen content drops to 25% of starting levels. However, thereafter there is synthesis of glycogen such that after 2 hours from application of the wake-active neurotransmitter, glycogen has returned to baseline levels (i.e., no change) (see Figure 3 ). At 8 hours following administration of the neurotransmitter, there is synthesis of glycogen and the glycogen content of the cells actually exceeds baseline levels by a substantial amount. This synthesis of glycogen is dependent upon cAMP signaling and protein synthesis ( Allaman et al., 2000 ; Sorg and Magistretti, 1992 ). Finally, at 48 hours, glycogen levels return to baseline ( Sorg and Magistretti, 1992 ) ( Figure 3 ). Therefore, under constant exposure to wake-active neurotransmitters simulating the effects of wakefulness, there is a three-phase dynamic response: an initial glycogen degradation followed by synthesis to levels eventually above the starting levels followed by depletion once again. A similar temporal pattern of glycogen levels is found in vitro after brief exposures to wake-active neurotransmitters, although the changes are not as large ( Sorg and Magistretti, 1992 ).

Changes in glycogen in astrocytic cell culture following application of vasoactive intestinal peptide. Primary cultures of cerebral cortical astrocytes were treated with vasoactive intestinal peptide. Glycogen (in nmol/mg protein) was measured at 0.5, 1, 2, 4, 8, 24 and 48 hours after treatment. There is an initial decrease in glycogen (see inset) followed by an increase, which peaks at 8 hours. Levels of glycogen then decline again at 24 hours and 48 hours post-treatment. ( Sorg and Magistretti, 1992 ; reprinted with permission, copyright 1992 by the Society for Neuroscience.)

The synthesis of glycogen some time after exposure to wake-active neurotransmitters (see above) is likely mediated by the scaffolding protein PTG. PTG is abundant in astrocytes and transcription of this gene is induced in primary cell cultures of astrocytes after exposure to wake-active neurotransmitters ( Allaman et al., 2000 ). Interestingly, in vivo , PTG mRNA increases in the cerebral cortex of mice following 6 hours of sleep deprivation ( Petit et al., 2002 ). Therefore, there is evidence in vivo that extended wakefulness, similar to the in vitro exposure of wake-active neurotransmitters, leads to an increase in PTG, which would promote glycogen synthesis.

An extension of the in vitro observations to the in vivo situation would predict that the change in glycogen in vivo , i.e., increase, decrease, or no change, depends on the temporal dynamics of two processes: glycogen depletion and synthesis ( Gip et al., 2002 ). These two processes are predicted to occur at different rates in different species, strains of the same species or brain regions within an animal. Therefore, measurement of glycogen at any given time is a measurement of the current balance of the two opposite processes of glycogen depletion and synthesis. Differing rates of glycogen depletion and synthesis could explain the species, strain and regional variations in glycogen content observed after sleep deprivation.

B3. Conclusions about Brain Glycogen and Sleep/Wake Control

The hypothesis proposed by Benington-Heller ( Benington and Heller, 1995 ) had the advantage that it was testable. It led to a series of studies primarily by Heller and collaborators ( Franken et al., 2003 , 2006b ; Gip et al., 2002 ; Gip et al., 2004 ), and by our laboratory and collaborators ( Kong et al., 2002 ; Zimmerman et al., 2004 ), to address whether glycogen was depleted with prolonged wakefulness as the hypothesis suggested.

While at first sight it may seem that disparate data arose from these studies, the results of the various studies can be rationalized when one considers the complex time course that has been demonstrated in vitro in response to administration of wake-active neurotransmitters such as noradrenaline ( Sorg and Magistretti, 1991 ) that we have just described. In addition, the different levels of glycogen depletion and synthesis in vivo between strains of the same rodent at a single duration of sleep deprivation may be due to differential glucocorticoid response to the stress of sleep deprivation. Indeed, 34-day-old adrenalectomized Long-Evans rats have increased glycogen levels in the cerebral cortex after 6 hours of sleep deprivation compared to no significant change in intact animals ( Gip et al., 2004 ). This effect of adrenalectomy may be explained by in vitro data that shows that the addition of a synthetic glucocorticoid to primary astrocyte cultures suppresses the synthesis of glycogen normally induced by noradrenaline ( Allaman et al., 2004 ).

Studies of the time course of glycogen change in vivo are not as complete as the in vitro data, but the results are compatible with the major aspects: early depletion of glycogen, later return to baseline, still later repletion of glycogen with increased activity of glycogen synthase, and much later depletion again ( Figure 3 ). That the in vivo data are not as complete is not surprising since, as discussed above, measurement of glycogen in vivo is challenging due to the rapid decline in its level post-sacrifice, making detailed time course measurements difficult.

The early depletion of glycogen on awakening was demonstrated in a seminal study ( Karnovsky et al., 1983 ). This is compatible with sudden activation through phosphorylation of the degradative enzyme glycogen phosphorylase, mediated by increased cyclic AMP and activation of PKA. This suggests that early depletion of glycogen is likely part of the arousal mechanism. On sudden awakening, there is a rapid increase in neuronal firing, which will require immediately available ATP. Glycogen depletion is likely part of this as increased neuronal activity leads to increased glycogen degradation ( Swanson, 1992 ; Swanson et al., 1992 ).

After this early depletion, it appears that glycogen returns to control levels as it does in vitro . Studies in rodents ( Franken et al., 2003 ; Gip et al., 2002 ; Gip et al., 2004 ; Kong et al., 2002 ) and Drosophila ( Zimmerman et al., 2004 ) show no alterations in glycogen from baseline in the cortex of most strains of rodents or whole brain of the fly after 6 hours of sleep deprivation ( Table 1 ). Other brain areas were examined in some but not all of these studies. However, in two studies a significant increase in glycogen was observed in cortex after 3 and 6 hours of deprivation in C57BL/6J mice ( Franken et al., 2003 ) and 12 hours in the cortex of 59-day-old Long-Evans rats ( Gip et al., 2002 ).

The concept that glycogen is repleted even as wakefulness continues is supported by the C57BL/6J data ( Franken et al., 2006b ). This glycogen repletion is likely mediated by the increase in PTG ( Allaman et al., 2000 ; Petit et al., 2002 ) that enhances the function of glycogen synthase ( Green et al., 2004 ). It is surprising that given the increased energy demand of wakefulness that there would be increased synthesis of glycogen when there is continued need for increased ATP. Therefore, could this repletion of glycogen play a role in reducing ATP levels and increasing adenosine? One possible mechanism would be that synthesizing glycogen would utilize ATP and lead to increased adenosine that would promote sleepiness as predicted by Benington and Heller. If so, this would argue that timed repletion of glycogen is one of the mechanisms serving to promote sleepiness , rather than depletion of glycogen as envisaged by Benington-Heller ( Benington and Heller, 1995 ). But this is likely to be only one of many mechanisms since multiple mechanisms seem to be used to limit wakefulness when it is prolonged (see Mackiewicz et al., 2007 ; Zimmerman et al., 2006 ; and above).

Finally, it seems that with even longer-term sleep deprivation glycogen is again depleted, i.e., after 24 hours of sleep deprivation in rat. This duration of sleep deprivation has, however, been evaluated in only one study and this needs to be replicated ( Kong et al., 2002 ). Moreover, these data need to be interpreted with caution since longer term sleep deprivation may lead to a greater stress response, mediated by the hypothalamic-pituitary-adrenal axis, since keeping rodents awake for this long period of time is challenging due to the increasing pressure for sleep.

Thus, some data about changes in brain glycogen are compatible with the Benington-Heller hypothesis but control of glycogen in relationship to sleep/wake and sleep deprivation is more complex than they envisaged. Our analyses lead to two additional concepts about changes in brain glycogen. First, sudden depletion of glycogen is part of the arousal mechanism. Second, repletion of glycogen, which occurs as wakefulness is prolonged, is, we propose, part of the mechanism used to contribute to sleepiness.

C. Other Aspects of Energy Regulation in the Brain

While the Benington-Heller hypothesis specifically focused on changes in adenosine and glycogen, regulation of cellular energetics in the brain is more complex and likely involves many other mediators. In this section, we discuss, albeit briefly, other energy-related mechanisms that may play a role in sleep/wake control.

C1. Unfolded Protein Response

Folding of proteins in the endoplasmic reticulum is an energy-dependent process ( Braakman et al., 1992 ; Dorner et al., 1990 ). When cells are energetically-challenged, there may be stress in the endoplasmic reticulum with protein misfolding ensuing ( Kaufman, 2002 ; Lee, 2001 ; Schroder and Kaufman, 2005b ). One of the defense mechanisms cells have to respond to misfolded proteins is to activate the unfolded protein response (reviewed in Harding et al., 2002 ; Ma and Hendershot, 2002 ; Schroder and Kaufman, 2005a ; 2005b , 2006 ; Zhang and Kaufman, 2004 ). The unfolded protein response, acting through a master regulator, BiP (also known as Glucose Regulated Protein 78), and three transducers, PERK, IRE1, and ATF6, serves to reduce energy-costly protein translation, increase degradation of misfolded proteins, and to bolster the protein-folding capacity by increasing the expression of specific chaperones. BiP is an endoplasmic reticulum specific chaperone, which in addition to chaperoning misfolded proteins, plays an essential role in activating IRE1, PERK, and ATF6 in response to endoplasmic reticulum stress ( Shen et al., 2002 ). Under conditions of endoplasmic reticulum stress, PERK phosphorylates the translation initiation factor eIF2α. When eIF2α is phosphorylated, the formation of the ternary translation initiation complex eIF2-GTP-tRNA Met i is prevented, leading to inhibition of protein translation.

Upregulation of BiP mRNA with acute short-term sleep deprivation has been described in rat cortex ( Cirelli et al., 2004a ; Terao et al., 2003 ), mouse cortex and hypothalamus ( Mackiewicz et al., 2007 ), fly head ( Shaw et al., 2000 ) and bird brain ( Jones et al., 2008 ). BiP protein levels have also been shown to increase with short term sleep deprivation in mouse cortex and in fly brain ( Naidoo et al., 2007 ; Naidoo et al., 2005 ). Following 6 hours of sleep deprivation, the increased BiP protein levels in fly brain decline slowly over the next 24 hours if the flies are left to sleep undisturbed ( Naidoo et al., 2007 ). Furthermore, with 6 hours of sleep deprivation in mouse, there is dissociation of PERK from BiP, PERK activation through autophosphorylation and phosphorylation of eIF2α in the cerebral cortex ( Naidoo et al., 2005 ) ( Figure 4 ). These observations demonstrate activation of the unfolded protein response with sleep deprivation.

Unfolded protein response during sleep deprivation. Conditions of endoplasmic reticulum stress such as sleep deprivation lead to increases in unfolded proteins. When proteins are misfolded, the chaperone BiP binds to them. BiP dissociates from ATF6, PERK, and IRE1. PERK autophosphorylates and then phosphorylates eIF2α leading to decreases in protein translation. ATF6 initiates transcription of BiP, which is translated independent of eIF2α.

While activation of the unfolded protein response is one mechanism to defend against the endoplasmic reticulum stress that occurs with extended wakefulness, the ultimate defense is to sleep so that the endoplasmic reticulum is no longer under stress. Sleep deprivation leads to increased sleep amounts in rodents ( Franken et al., 1999 ) and in flies ( Hendricks et al., 2000 ), i.e., there is recovery sleep. The unfolded protein response itself plays a role in determining the amount of recovery sleep. Flies overexpressing BiP show no alteration in baseline sleep/wake amounts but do show increased recovery sleep following 6 hours of sleep deprivation compared to wild-type control flies ( Naidoo et al., 2007 ). Flies expressing a dominant negative of BiP, with reduced BiP activity, have reduced amounts of recovery sleep following sleep deprivation ( Naidoo et al., 2007 ). These results may imply that increased BiP itself can promote sleep, although more likely is the effect of altered BiP on the unfolded protein response. Overexpression of BiP leads to a delay in the unfolded protein response since it delays the kinase PERK becoming free ( Dorner et al., 1992 ). Thus, if the unfolded protein response is delayed, recovery sleep increases. The converse occurs with loss of function of BiP, i.e., earlier activation of the unfolded protein response and less subsequent recovery sleep.

These results indicate that the unfolded protein response is induced with sleep deprivation. The mechanism by which the unfolded protein response is activated during sleep deprivation is not known but an attractive explanation is that the unfolded protein response is activated by depletion of cellular energy occurring with extended wakefulness. We believe that the unfolded protein response is a mechanism by which the brain responds to the energetic challenge of extended wakefulness.

C2. Electron Transport Chain

Oxidative phosphorylation occurring in mitochondria is the main process by which eukaryotic cells produce ATP. In mammals, the oxidative phosphorylation system comprises five tightly regulated multi-subunit enzyme complexes, complexes I to V, transcripts of which originate from both nuclear and mitochondrial genomes and are expressed in a tissue-specific fashion ( Lenka et al., 1998 ). Addition of electrons to complex I or II allows for the establishment of a proton gradient, which ultimately leads to ATP synthesis.

There is evidence of alterations in mitochondrial ATP production with sleep/wake. Increased transcription of components of the oxidative phosphorylation machinery from the mitochondrial genome (subunit I of cytochrome C oxidase, subunit 2 of NADH dehydrogenase, and 12S rRNA) has been shown in cortex after 3 hours of spontaneous wakefulness or sleep deprivation ( Cirelli and Tononi, 1998 ). An early up-regulation of the mRNA of the mitochondrial encoded subunit 1 of cytochrome C oxidase with 3 hours of sleep deprivation has also been seen in fly heads ( Shaw et al., 2000 ). In addition to transcriptional upregulation, increased activity of cytochrome C oxidase enzyme, a key enzyme in respiratory control, has been described after 3 hours of sleep deprivation in multiple brain regions in rat, including cortex ( Nikonova et al., 2005b ). Further studies extend that finding by demonstrating that cytochrome C oxidase enzyme activity in mouse cerebral cortex remains elevated even after 12 hours of sleep deprivation ( Nikonova et al., 2005a ). Both transcript and protein levels of mitochondrial-encoded cytochrome C oxidase subunit I and nuclear-encoded cytochrome C oxidase subunit IV are increased after 12 hours of sleep deprivation ( Nikonova et al., 2005a ). Somewhat surprisingly, an extensive microarray study in mouse found that many nuclear encoded components for the enzyme subunits of the various complexes in the electron transport chain are upregulated during sleep, not wakefulness ( Mackiewicz et al., 2007 ). It is possible that mitochondrial and nuclear proteins are synthesized in temporally distinct profiles, which may simply reflect the fact that mitochondrial proteins can be made in areas of the cell that are very metabolically active such as the nerve terminal ( Wong-Riley et al., 1997 ), whereas nuclear proteins need to be transported longer distances to their sites of action. Nonetheless, the increase in cytochrome C oxidase enzyme activity and the upregulation of subunits I and IV suggests that mitochondria increase energy production to meet the increased energy demand occurring during wakefulness.

C3. Transcriptional Factors of the Molecular Clock

The CLOCK protein is a key component of the circadian clock in the suprachiasmatic nucleus, the master circadian pacemaker. It is a transcription factor that is part of the negative feedback loop producing the circadian oscillation in gene expression (reviewed in Lowrey and Takahashi, 2004 ). Mice with a mutation of clock have increased activity during the normally quiescent light period and decreased activity during the active dark period ( Turek et al., 2005 ). Furthermore, clock mutant mice have increased food intake and metabolic rate during the light period and decreased food intake and metabolic rate during the dark period ( Turek et al., 2005 ). Overall, the clock mutant mice are hyperphagic and obese ( Turek et al., 2005 ). These data suggest a link between circadian and energy-regulating processes. However, it should be noted, that the mechanism by which clock alters energy regulation is unknown, it may be a central or a peripheral process, e.g., in kidney or liver, since clock is expressed in many tissues ( Chilov et al., 2001 ).

Another transcription factor involved in the clock mechanism is NPAS2. NPAS2 has been proposed as a specific energy sensor for control of rest/activity (sleep/wake) ( Rutter et al., 2002 ). NPAS2 is a transcription factor that is involved in regulation of expression of circadian clock genes. Transcription of these genes is controlled by a transcription complex of CLOCK/BMAL or NPAS2/BMAL, i.e., NPAS2 acts in a similar fashion to CLOCK ( Reick et al., 2001 ). The main transcription factor complex in the master clock, the suprachiasmatic nucleus, is CLOCK/BMAL, while NPAS2/BMAL functions outside the suprachiasmatic nucleus, including in other brain regions ( Rutter et al., 2002 ).

The concept that NPAS2 may act as an energy sensor came from in vitro observations that the dimerization and the DNA binding of NPAS2:BMAL heterodimers are markedly affected by the energy status as assessed by the redox state of NAD cofactors ( Rutter et al., 2001 ). NPAS2 alters sleep/wake amounts and entrainment of sleep/wake cycles to stimuli other than light as revealed by studies of the NPAS2 knockout mice ( Dudley et al., 2003 ; Franken et al., 2006a ). The NPAS2 knockout mouse has less sleep, specifically NREM sleep, and more wakefulness than wild-type controls during the night, which is the active period of the mouse ( Franken et al., 2006a ). During the night, wild-type mice have "siesta sleep" that is not found in NPAS2 knockout mice ( Dudley et al., 2003 ; Franken et al., 2006a ).

NPAS2 knockout mice also have markedly impaired entrainment to altered feeding patterns, i.e., restricted feeding ( Dudley et al., 2003 ). In restricted feeding, mice are fed only during the day when they are normally inactive. Over a short period of days their rest/activity patterns shift such that mice become active during the day in anticipation of food. This phenomenon is described in both mice ( Marchant and Mistlberger, 1997 ) and rats ( Mistlberger et al., 1990 ). It occurs in suprachiasmatic nucleus lesioned animals and hence does not involve the suprachiasmatic nucleus ( Marchant and Mistlberger, 1997 ). After entrainment to this new pattern of feeding, the diurnal rhythm of clock gene expression is altered in, for example, liver and heart, but not in the suprachiasmatic nucleus ( Damiola et al., 2000 ; Stokkan et al., 2001 ; Watanabe et al., 2006 ). NPAS2 is very relevant to this process. Mice lacking NPAS2 have very delayed entrainment to restricted feeding; such knockout mice lose weight due to lack of food and some die before being entrained ( Dudley et al., 2003 ). In NPAS2 knockout mice, entrainment to light (mediated by the suprachiasmatic nucleus) is normal, or indeed, enhanced ( Dudley et al., 2003 ). Thus, there is a clock mechanism that involves NPAS2 outside the suprachiasmatic nucleus, thought to be in forebrain ( Dudley et al., 2003 ) that can alter both the timing of rest/activity (sleep/wake) cycles as well as the amount of sleep. This mechanism may be affected by energy status and hence provides a direct mechanism to link the energy state of brain to sleep/wake control.

C4. AMP-activated protein kinase

Another mechanism by which a cell can respond to an energetic challenge is by the AMP-activated protein kinase (AMPK), which is a cellular adaptor to alterations in energy state. AMPK is activated by phosphorylation when energy consumed exceeds energy produced and the ratio of AMP to ATP increases. Activated AMPK preserves ATP by decreasing energy consuming processes and increasing energy producing processes (reviewed in Hardie et al., 2006 ).

Assessing the phosphorylation status of AMPK in vivo is challenging ( Scharf et al., 2008 ). Preliminary results from our laboratory have shown that AMPK phosphorylation is greater following 6 hours of sleep deprivation than sleep in whole brain, cortex and basal forebrain consistent with the brain being energetically challenged in wakefulness ( Scharf et al., 2006 ), and we are currently assessing changes in AMPK phosphorylation with sleep deprivation utilizing a methodology we developed to preserve in vivo AMPK phosphorylation ( Scharf et al., 2008 ). Thus, AMPK may serve as a molecular sensor to shift the brain from energy-consuming synthetic processes that occur during sleep to catabolic energy-producing processes that occur during extended wakefulness.

C5. Astrocyte-to-Neuron Lactate Shuttle

Another postulated metabolic role for astrocytes is the astrocyte-to-neuron lactate shuttle although this idea has been controversial since it was first proposed ( Pellerin and Magistretti, 1994 ). Recent reviews have either supported ( Bergersen, 2007 ; Pellerin et al., 2007 ) or contested ( Fillenz, 2005 ; Simpson et al., 2007 ) the hypothesis. The central tenet of the hypothesis is that during neuronal activation, astrocytes take up glucose from blood in adjacent capillaries and metabolize the glucose into lactate. The astrocytes release lactate, which is transported into neurons, where it serves as an energy source for neurons to meet increased energy demand occurring with neuronal activation ( Pellerin and Magistretti, 1994 ).

Components that could mediate the lactate shuttle have been found to be upregulated during sleep deprivation. Lactate dehydrogenase 2 B protein, which is associated with astrocytes ( Pellerin et al., 1998 ) and catalyzes the conversion of pyruvate to lactate ( Bishop et al., 1972 ), is significantly increased in mouse cerebral cortex after 6 hours of sleep deprivation ( Pawlyk et al., 2007 ). Furthermore, glucose transporter 1, associated with epithelial cells of the blood-brain barrier and astrocytes (reviewed in Duelli and Kuschinsky, 2001 ), shows increased gene expression in rat cerebral cortex following sleep deprivation, and is also increased in spontaneous wakefulness compared to sleep ( Cirelli et al., 2004b ; Cirelli and Tononi, 2000 ). Presumably, increased glucose transporter 1 would facilitate glucose transport into astrocytes. The observations of increased lactate dehydrogenase 2 B protein and glucose transporter 1 mRNA are consistent with increased activity of the astrocyte-neuron lactate shuttle during wakefulness, and suggest that this may be another mechanism by which the brain adapts to the increased metabolic demand of wakefulness.

C6. Reactive Oxygen Species

One of the consequences of increased metabolic activity and, hence, an increase in oxygen consumption, is an increase in production of reactive oxygen species. Ikeda et al. proposed that reactive oxygen species are produced during wakefulness and that increased antioxidant enzyme activity during sleep reduces their levels in preparation for subsequent wakefulness ( Ikeda et al., 2005 ). Reactive oxygen species are difficult to measure in vivo since they are so labile. Hence, evidence that they change in the brain with sleep/wake is indirect.

The evidence for alteration in reactive oxygen species production with sleep/wake is limited. Activity of a key antioxidant enzyme, superoxide dismutase, decreases in hippocampus and brainstem following sleep deprivation ( Ramanathan et al., 2002 ). Moreover, mRNA for a number of antioxidant enzymes, including superoxide dismutase, glutathione S-transferase, glutathione peroxidase, glutathione reductase, catalase, methionine sulfoxide reductase, thioredoxin reductase and the transcription factor Nfe2, which is involved in induction of genes encoding antioxidant proteins and certain detoxifying enzymes, are all upregulated during sleep in cerebral cortex as revealed by a microarray study in mouse ( Mackiewicz et al., 2007 ).

There is also evidence that reactive oxygen species may also directly alter sleep. Intracerebroventricular infusion of oxidized glutathione increases slow wave and REM sleep in rats ( Ikeda et al., 2005 ) although the mechanism by which it does so is unknown. Intracerebroventricular administration of an inhibitor of glutathione peroxidase, tert-butyl hydroperoxide (TBHP), which would attenuate metabolism of reactive oxygen species thereby increasing their level, also increases the amount of NREM and REM sleep ( Ikeda et al., 2005 ). Although the data are limited, these studies are consistent with the notion that there are alterations in reactive oxygen species with sleep/wake.

C6. Uncoupling Proteins

The first described uncoupling protein, UCP1, was shown to increase the proton conductance of the inner mitochondrial membrane and, therefore, uncouple oxidative phosphorylation. Consistent with this uncoupling mechanism, UCP1 in brown adipose tissue was shown to catalyze adaptive thermogenesis by increasing heat production ( Cannon and Nedergaard, 2004 ). Additional UCPs found in other tissues without a clear thermogenic function include UCP2 ( Fleury et al., 1997 ), UCP3 ( Boss et al., 1997 ), UCP4 ( Mao et al., 1999 ) and UCP5 (BMCP1) ( Sanchis et al., 1998 ). Interestingly, UCP2, UCP4 and UCP5 are expressed in brain (see Andrews et al., 2005 ). The role of these new UCPs, in particular in the brain, is a subject of debate, i.e., do they act to uncouple oxidative phosphorylation (see Kim-Han and Dugan, 2005 )?

There are no differences in UCP2 or UCP5 mRNA expression in the cerebral cortex between spontaneous wakefulness and sleep in rats ( Cirelli and Tononi, 2004 ) (mRNA for UCP4 could not be detected). However, short-term sleep deprivation of 8 hours in rats leads to a 40–50% increase in mRNA for UCP2 and UCP5 in cerebral cortex ( Cirelli and Tononi, 2004 ). Thus, increased expression of UCPs in brain may be, like the unfolded protein response, part of the mechanism to protect against the effects of extending wakefulness. Uncoupling of oxidative phosphorylation will likely result in decreased production of reactive oxygen species.

The Benington-Heller hypothesis stimulated a lot of useful research. While it has turned out to be an oversimplification, this does not detract from its value in providing a focus to investigate the role of energy utilization in determining sleep/wake states. As proposed, there is increasing evidence that adenosine does act to promote sleep. There are, however, questions about whether changes in adenosine with wakefulness reflect altered cellular energy states. The elaboration of the Benington-Heller hypothesis that adenosine changes are specifically “sensed” by cholinergic neurons of the basal forebrain to effect sleep/wake control now seems disproven. Adenosine is likely to act in different brain regions to alter both the amount of sleep as well as delta power. How adenosine regulates the quantity and quality of sleep and how adenosine exerts its effects in specific brain regions are questions that are being actively investigated. The emerging role of astrocytes in sleep homeostasis is likely to be important since astrocytes contribute to adenosinergic tone.

The predictions of Benington and Heller about glycogen changes with sleep/wake and sleep deprivation have been supported, at least in part. However, the dynamic changes are more complex than they proposed. A reassessment of data obtained in experiments stimulated by their hypothesis leads to new hypotheses: a) depletion of glycogen is part of the arousal response providing ATP for the sudden increase in energy required to support enhanced neuronal firing on sudden awakening; and b) it is repletion, not depletion, of glycogen that is likely the signal to promote sleepiness.

Control of energy utilization in the brain is, however, more complex than is simply reflected by changes in adenosine and glycogen. There are growing data that many aspects of the regulation of cellular energetics in the brain are altered with sleep/wake and sleep deprivation: the unfolded protein response; components of the electron transport chain; the astrocyte-to-neuron lactate shuttle; antioxidant enzymes; uncoupling proteins; clock transcription factors and the degree of activation of the cellular energy regulator AMPK. Available data suggest that there may be regulation of many components to provide the necessary amounts of ATP that are required during wakefulness and to provide responses to the energetic challenge of wakefulness. In turn, these pathways likely mediate the quantity and quality of sleep.

Altogether, the evidence reviewed here supports the fundamental tenet that extended wakefulness poses an energetic challenge to the brain. It would therefore appear likely that sleep is a stage of synthesis necessary to respond to the “insult” of wakefulness as we have proposed ( Mackiewicz et al., 2007 ). We believe that energy-related pathways in addition to glycogen and adenosine are involved in transitioning the brain from the metabolically-deplete catabolic state of wakefulness to the metabolically-replete anabolic state of sleep. This elaboration of the energy hypothesis of sleep is illustrated in Figure 5 . Hopefully, this new view of the energy hypothesis of sleep-wake control will stimulate as much helpful research as did the provocative review of Benington and Heller ( Benington and Heller, 1995 ).

Proposed model of alterations in energy homeostasis during extended wakefulness. In astrocytes, glycogen breakdown and synthesis occur concomitantly. Lactate is transported out of astrocytes and taken up by neurons where it enters the mitochondria and the krebs cycle to produce ATP. Also within the mitochondria, there is an increase in the activity of the electron transport chain to increase ATP production. Reactive oxygen species are produced as a byproduct of this increased activity of the electron transport chain. UCP-2 increases to attenuate production of reactive oxygen species. In the endoplasmic reticulum, the unfolded protein response occurs. This ultimately leads to transcriptional changes as well as decreases in energy-consuming synthetic processes. AMPK is activated, which also attenuates energy-consuming synthetic processes. NPAS2 most likely also acts to preserve energy. Multiple pathways lead to changes in gene expression. Adenosine increases in the extracellular space either by transport out of the cell and/or by conversion of ATP released from glia or neurons into adenosine.

Acknowledgements

We would like to thank Daniel Barrett and Jennifer Montoya for help in preparation of this manuscript. This research was supported by the National Institute of Aging Grant AG-17628 and National Heart, Lung, and Blood Institute Grants HL-60287 and HL-07953.

Abbreviations Used

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• Alam MN, Szymusiak R, Gong H, King J, McGinty D. Adenosinergic modulation of rat basal forebrain neurons during sleep and waking: neuronal recording with microdialysis. J Physiol. 1999; 521 (Pt 3):679–690. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular Biology of the Cell. 2002 [ Google Scholar ]
• Allaman I, Pellerin L, Magistretti PJ. Protein targeting to glycogen mRNA expression is stimulated by noradrenaline in mouse cortical astrocytes. Glia. 2000; 30 :382–391. [ PubMed ] [ Google Scholar ]
• Allaman I, Pellerin L, Magistretti PJ. Glucocorticoids modulate neurotransmitter-induced glycogen metabolism in cultured cortical astrocytes. J Neurochem. 2004; 88 :900–908. [ PubMed ] [ Google Scholar ]
• Andrews ZB, Diano S, Horvath TL. Mitochondrial uncoupling proteins in the CNS: in support of function and survival. Nat Rev Neurosci. 2005; 6 :829–840. [ PubMed ] [ Google Scholar ]
• Arrigoni E, Chamberlin NL, Saper CB, McCarley RW. Adenosine inhibits basal forebrain cholinergic and noncholinergic neurons in vitro. Neuroscience. 2006; 140 :403–413. [ PubMed ] [ Google Scholar ]
• Arrigoni E, Rainnie DG, McCarley RW, Greene RW. Adenosine-mediated presynaptic modulation of glutamatergic transmission in the laterodorsal tegmentum. J Neurosci. 2001; 21 :1076–1085. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Balschun D, Wolfer DP, Gass P, Mantamadiotis T, Welzl H, Schutz G, Frey JU, Lipp HP. Does cAMP response element-binding protein have a pivotal role in hippocampal synaptic plasticity and hippocampus-dependent memory? J Neurosci. 2003; 23 :6304–6314. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Basheer R, Arrigoni E, Thatte HS, Greene RW, Ambudkar IS, McCarley RW. Adenosine induces inositol 1,4,5-trisphosphate receptor-mediated mobilization of intracellular calcium stores in basal forebrain cholinergic neurons. J Neurosci. 2002; 22 :7680–7686. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Basheer R, Halldner L, Alanko L, McCarley RW, Fredholm BB, Porkka-Heiskanen T. Opposite changes in adenosine A1 and A2A receptor mRNA in the rat following sleep deprivation. Neuroreport. 2001a; 12 :1577–1580. [ PubMed ] [ Google Scholar ]
• Basheer R, Porkka-Heiskanen T, Stenberg D, McCarley RW. Adenosine and behavioral state control: adenosine increases c-Fos protein and AP1 binding in basal forebrain of rats. Brain Res Mol Brain Res. 1999; 73 :1–10. [ PubMed ] [ Google Scholar ]
• Basheer R, Rainnie DG, Porkka-Heiskanen T, Ramesh V, McCarley RW. Adenosine, prolonged wakefulness, and A1-activated NF-kappaB DNA binding in the basal forebrain of the rat. Neuroscience. 2001b; 104 :731–739. [ PubMed ] [ Google Scholar ]
• Basheer R, Strecker RE, Thakkar MM, McCarley RW. Adenosine and sleep-wake regulation. Prog Neurobiol. 2004; 73 :379–396. [ PubMed ] [ Google Scholar ]
• Benington JH, Heller HC. Restoration of brain energy metabolism as the function of sleep. Prog Neurobiol. 1995; 45 :347–360. [ PubMed ] [ Google Scholar ]
• Benington JH, Kodali SK, Heller HC. Stimulation of A1 adenosine receptors mimics the electroencephalographic effects of sleep deprivation. Brain Res. 1995; 692 :79–85. [ PubMed ] [ Google Scholar ]
• Bergersen LH. Is lactate food for neurons? Comparison of monocarboxylate transporter subtypes in brain and muscle. Neuroscience. 2007; 145 :11–19. [ PubMed ] [ Google Scholar ]
• Bishop MJ, Everse J, Kaplan NO. Identification of lactate dehydrogenase isoenzymes by rapid kinetics. Proc Natl Acad Sci U S A. 1972; 69 :1761–1765. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Blanco-Centurion C, Gerashchenko D, Shiromani PJ. Effects of saporin-induced lesions of three arousal populations on daily levels of sleep and wake. J Neurosci. 2007; 27 :14041–14048. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Blanco-Centurion C, Xu M, Murillo-Rodriguez E, Gerashchenko D, Shiromani AM, Salin-Pascual RJ, Hof PR, Shiromani PJ. Adenosine and sleep homeostasis in the Basal forebrain. J Neurosci. 2006; 26 :8092–8100. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Boison D. Adenosine kinase, epilepsy and stroke: mechanisms and therapies. Trends Pharmacol Sci. 2006; 27 :652–658. [ PubMed ] [ Google Scholar ]
• Borbely AA, Achermann P. Sleep homeostasis and models of sleep regulation. J Biol Rhythms. 1999; 14 :557–568. [ PubMed ] [ Google Scholar ]
• Boss O, Samec S, Paoloni-Giacobino A, Rossier C, Dulloo A, Seydoux J, Muzzin P, Giacobino JP. Uncoupling protein-3: a new member of the mitochondrial carrier family with tissue-specific expression. FEBS Lett. 1997; 408 :39–42. [ PubMed ] [ Google Scholar ]
• Braakman I, Helenius J, Helenius A. Role of ATP and disulphide bonds during protein folding in the endoplasmic reticulum. Nature. 1992; 356 :260–262. [ PubMed ] [ Google Scholar ]
• Brady MJ, Printen JA, Mastick CC, Saltiel AR. Role of protein targeting to glycogen (PTG) in the regulation of protein phosphatase-1 activity. J Biol Chem. 1997; 272 :20198–20204. [ PubMed ] [ Google Scholar ]
• Brown AM. Brain glycogen re-awakened. J Neurochem. 2004; 89 :537–552. [ PubMed ] [ Google Scholar ]
• Brown AM, Tekkok SB, Ransom BR. Glycogen regulation and functional role in mouse white matter. J Physiol. 2003; 549 :501–512. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brushia RJ, Walsh DA. Phosphorylase kinase: the complexity of its regulation is reflected in the complexity of its structure. Front Biosci. 1999; 4 :D618–D641. [ PubMed ] [ Google Scholar ]
• Bushong EA, Martone ME, Jones YZ, Ellisman MH. Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains. J Neurosci. 2002; 22 :183–192. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cannon B, Nedergaard J. Brown adipose tissue: function and physiological significance. Physiol Rev. 2004; 84 :277–359. [ PubMed ] [ Google Scholar ]
• Cass CE, Young JD, Baldwin SA. Recent advances in the molecular biology of nucleoside transporters of mammalian cells. Biochem Cell Biol. 1998; 76 :761–770. [ PubMed ] [ Google Scholar ]
• Chamberlin NL, Arrigoni E, Chou TC, Scammell TE, Greene RW, Saper CB. Effects of adenosine on gabaergic synaptic inputs to identified ventrolateral preoptic neurons. Neuroscience. 2003; 119 :913–918. [ PubMed ] [ Google Scholar ]
• Chilov D, Hofer T, Bauer C, Wenger RH, Gassmann M. Hypoxia affects expression of circadian genes PER1 and CLOCK in mouse brain. Faseb J. 2001; 15 :2613–2622. [ PubMed ] [ Google Scholar ]
• Cirelli C, Gutierrez CM, Tononi G. Extensive and Divergent Effects of Sleep and Wakefulness on Brain Gene Expression. Neuron. 2004a; 41 :35–43. [ PubMed ] [ Google Scholar ]
• Cirelli C, Gutierrez CM, Tononi G. Extensive and divergent effects of sleep and wakefulness on brain gene expression. Neuron. 2004b; 41 :35–43. [ PubMed ] [ Google Scholar ]
• Cirelli C, Tononi G. Differences in gene expression between sleep and waking as revealed by mRNA differential display. Brain Res Mol Brain Res. 1998; 56 :293–305. [ PubMed ] [ Google Scholar ]
• Cirelli C, Tononi G. Gene expression in the brain across the sleep-waking cycle. Brain Res. 2000; 885 :303–321. [ PubMed ] [ Google Scholar ]
• Cirelli C, Tononi G. Uncoupling proteins and sleep deprivation. Arch Ital Biol. 2004; 142 :541–549. [ PubMed ] [ Google Scholar ]
• Coleman CG, Baghdoyan HA, Lydic R. Dialysis delivery of an adenosine A2A agonist into the pontine reticular formation of C57BL/6J mouse increases pontine acetylcholine release and sleep. J Neurochem. 2006; 96 :1750–1759. [ PubMed ] [ Google Scholar ]
• Cruz NF, Dienel GA. High glycogen levels in brains of rats with minimal environmental stimuli: implications for metabolic contributions of working astrocytes. J Cereb Blood Flow Metab. 2002; 22 :1476–1489. [ PubMed ] [ Google Scholar ]
• Cunha RA, Sebastiao AM, Ribeiro JA. Inhibition by ATP of hippocampal synaptic transmission requires localized extracellular catabolism by ecto-nucleotidases into adenosine and channeling to adenosine A1 receptors. J Neurosci. 1998; 18 :1987–1995. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Damiola F, Le Minh N, Preitner N, Kornmann B, Fleury-Olela F, Schibler U. Restricted feeding uncouples circadian oscillators in peripheral tissues from the central pacemaker in the suprachiasmatic nucleus. Genes Dev. 2000; 14 :2950–2961. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dorner AJ, Wasley LC, Kaufman RJ. Protein dissociation from GRP78 and secretion are blocked by depletion of cellular ATP levels. Proc Natl Acad Sci U S A. 1990; 87 :7429–7432. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dorner AJ, Wasley LC, Kaufman RJ. Overexpression of GRP78 mitigates stress induction of glucose regulated proteins and blocks secretion of selective proteins in Chinese hamster ovary cells. Embo J. 1992; 11 :1563–1571. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dudley CA, Erbel-Sieler C, Estill SJ, Reick M, Franken P, Pitts S, McKnight SL. Altered patterns of sleep and behavioral adaptability in NPAS2-deficient mice. Science. 2003; 301 :379–383. [ PubMed ] [ Google Scholar ]
• Duelli R, Kuschinsky W. Brain glucose transporters: relationship to local energy demand. News Physiol Sci. 2001; 16 :71–76. [ PubMed ] [ Google Scholar ]
• Dunwiddie TV, Diao L, Proctor WR. Adenine nucleotides undergo rapid, quantitative conversion to adenosine in the extracellular space in rat hippocampus. J Neurosci. 1997; 17 :7673–7682. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dunwiddie TV, Masino SA. The role and regulation of adenosine in the central nervous system. Annu Rev Neurosci. 2001; 24 :31–55. [ PubMed ] [ Google Scholar ]
• Feldberg W, Sherwood SL. Injections of Drugs Into the Lateral Ventricle of the Cat. J Physiol. 1954; 123 :148–167. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fellin T, Pascual O, Haydon PG. Astrocytes coordinate synaptic networks: balanced excitation and inhibition. Physiology (Bethesda) 2006; 21 :208–215. [ PubMed ] [ Google Scholar ]
• Fillenz M. The role of lactate in brain metabolism. Neurochem Int. 2005; 47 :413–417. [ PubMed ] [ Google Scholar ]
• Fleury C, Neverova M, Collins S, Raimbault S, Champigny O, Levi-Meyrueis C, Bouillaud F, Seldin MF, Surwit RS, Ricquier D, Warden CH. Uncoupling protein-2: a novel gene linked to obesity and hyperinsulinemia. Nat Genet. 1997; 15 :269–272. [ PubMed ] [ Google Scholar ]
• Franken P, Dudley CA, Estill SJ, Barakat M, Thomason R, O'hara BF, McKnight SL. NPAS2 as a transcriptional regulator of non-rapid eye movement sleep: genotype and sex interactions. Proc Natl Acad Sci U S A. 2006a; 103 :7118–7123. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Franken P, Gip P, Hagiwara G, Ruby NF, Heller HC. Changes in brain glycogen after sleep deprivation vary with genotype. Am J Physiol Regul Integr Comp Physiol. 2003; 285 :R413–R419. [ PubMed ] [ Google Scholar ]
• Franken P, Gip P, Hagiwara G, Ruby NF, Heller HC. Glycogen content in the cerebral cortex increases with sleep loss in C57BL/6J mice. Neurosci Lett. 2006b. [ PubMed ]
• Franken P, Malafosse A, Tafti M. Genetic determinants of sleep regulation in inbred mice. Sleep. 1999; 22 :155–169. [ PubMed ] [ Google Scholar ]
• Gallopin T, Luppi PH, Cauli B, Urade Y, Rossier J, Hayaishi O, Lambolez B, Fort P. The endogenous somnogen adenosine excites a subset of sleep-promoting neurons via A2A receptors in the ventrolateral preoptic nucleus. Neuroscience. 2005; 134 :1377–1390. [ PubMed ] [ Google Scholar ]
• Gerashchenko D, Okano Y, Urade Y, Inoue S, Hayaishi O. Strong rebound of wakefulness follows prostaglandin D2- or adenosine A2a receptor agonist-induced sleep. J Sleep Res. 2000; 9 :81–87. [ PubMed ] [ Google Scholar ]
• Gip P, Hagiwara G, Ruby NF, Heller HC. Sleep deprivation decreases glycogen in the cerebellum but not in the cortex of young rats. Am J Physiol Regul Integr Comp Physiol. 2002; 283 :R54–R59. [ PubMed ] [ Google Scholar ]
• Gip P, Hagiwara G, Sapolsky RM, Cao VH, Heller HC, Ruby NF. Glucocorticoids influence brain glycogen levels during sleep deprivation. Am J Physiol Regul Integr Comp Physiol. 2004; 286 :R1057–R1062. [ PubMed ] [ Google Scholar ]
• Green AR, Aiston S, Greenberg CC, Freeman S, Poucher SM, Brady MJ, Agius L. The glycogenic action of protein targeting to glycogen in hepatocytes involves multiple mechanisms including phosphorylase inactivation and glycogen synthase translocation. J Biol Chem. 2004; 279 :46474–46482. [ PubMed ] [ Google Scholar ]
• Greenberg CC, Jurczak MJ, Danos AM, Brady MJ. Glycogen branches out: new perspectives on the role of glycogen metabolism in the integration of metabolic pathways. Am J Physiol Endocrinol Metab. 2006; 291 :E1–E8. [ PubMed ] [ Google Scholar ]
• Gruetter R. Glycogen: the forgotten cerebral energy store. J Neurosci Res. 2003; 74 :179–183. [ PubMed ] [ Google Scholar ]
• Halassa MM, Fellin T, Munoz JR, Haydon PG, Frank MG. Astrocytes regulate sleep homeostasis. Soc Neurosci Abs. 2007:631.16. [ Google Scholar ]
• Hardie DG, Hawley SA, Scott JW. AMP-activated protein kinase--development of the energy sensor concept. J Physiol. 2006; 574 :7–15. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Harding HP, Calfon M, Urano F, Novoa I, Ron D. Transcriptional and translational control in the Mammalian unfolded protein response. Annu Rev Cell Dev Biol. 2002; 18 :575–599. [ PubMed ] [ Google Scholar ]
• Haulica I, Ababei L, Branisteanu D, Topoliceanu F. Letter: Preliminary data on the possible hypnogenic role of adenosine. J Neurochem. 1973; 21 :1019–1020. [ PubMed ] [ Google Scholar ]
• Heckers S, Ohtake T, Wiley RG, Lappi DA, Geula C, Mesulam MM. Complete and selective cholinergic denervation of rat neocortex and hippocampus but not amygdala by an immunotoxin against the p75 NGF receptor. J Neurosci. 1994; 14 :1271–1289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Heller HC. A global rather than local role for adenosine in sleep homeostasis. Sleep. 2006; 29 :1382–1383. discussion 1387–9. [ PubMed ] [ Google Scholar ]
• Hendricks JC, Finn SM, Panckeri KA, Chavkin J, Williams JA, Sehgal A, Pack AI. Rest in Drosophila is a sleep-like state. Neuron. 2000; 25 :129–138. [ PubMed ] [ Google Scholar ]
• Huang ZL, Qu WM, Eguchi N, Chen JF, Schwarzschild MA, Fredholm BB, Urade Y, Hayaishi O. Adenosine A2A, but not A1, receptors mediate the arousal effect of caffeine. Nat Neurosci. 2005; 8 :858–859. [ PubMed ] [ Google Scholar ]
• Hutchins DA, Rogers KJ. Physiological and drug-induced changes in the glycogen content of mouse brain. Br J Pharmacol. 1970; 39 :9–25. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hutchison AJ, Webb RL, Oei HH, Ghai GR, Zimmerman MB, Williams M. CGS 21680C, an A2 selective adenosine receptor agonist with preferential hypotensive activity. J Pharmacol Exp Ther. 1989; 251 :47–55. [ PubMed ] [ Google Scholar ]
• Ikeda M, Ikeda-Sagara M, Okada T, Clement P, Urade Y, Nagai T, Sugiyama T, Yoshioka T, Honda K, Inoue S. Brain oxidation is an initial process in sleep induction. Neuroscience. 2005; 130 :1029–1040. [ PubMed ] [ Google Scholar ]
• Jones BE. Basic Mechanisms of Sleep-Wake States. In: Kryger MH, Roth T, Dement WC, editors. Principles and Practice of Sleep Medicine. 2005. pp. 136–153. [ Google Scholar ]
• Jones S, Pfister-Genskow M, Benca RM, Cirelli C. Molecular correlates of sleep and wakefulness in the brain of the white-crowned sparrow. J Neurochem. 2008; 105 :46–62. [ PubMed ] [ Google Scholar ]
• Kalinchuk AV, Porkka-Heiskanen T, McCarley RW. Basal forebrain and saporin cholinergic lesions: the devil dwells in delivery details. Sleep. 2006; 29 :1385–1387. discussion 1387–9. [ PubMed ] [ Google Scholar ]
• Karnovsky ML, Reich P, Anchors JM, Burrows BL. Changes in brain glycogen during slow-wave sleep in the rat. J Neurochem. 1983; 41 :1498–1501. [ PubMed ] [ Google Scholar ]
• Kaufman RJ. Orchestrating the unfolded protein response in health and disease. J Clin Invest. 2002; 110 :1389–1398. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kaur S, Junek A, Black MA, Semba K. Effects of ibotenate and 192IgG-saporin lesions of the nucleus basalis magnocellularis/substantia innominata on spontaneous sleep and wake states and on recovery sleep after sleep deprivation in rats. J Neurosci. 2008; 28 :491–504. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kim-Han JS, Dugan LL. Mitochondrial uncoupling proteins in the central nervous system. Antioxid Redox Signal. 2005; 7 :1173–1181. [ PubMed ] [ Google Scholar ]
• Kong J, Shepel PN, Holden CP, Mackiewicz M, Pack AI, Geiger JD. Brain glycogen decreases with increased periods of wakefulness: implications for homeostatic drive to sleep. J Neurosci. 2002; 22 :5581–5587. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Larner J, Roach PJ, Huang LC, Brooker G, Murad F, Hazen R. Hormonal control of glycogen metabolism. Adv Exp Med Biol. 1979; 111 :103–123. [ PubMed ] [ Google Scholar ]
• Lawrence JC, Jr, Skurat AV, Roach PJ, Azpiazu I, Manchester J. Glycogen synthase: activation by insulin and effect of transgenic overexpression in skeletal muscle. Biochem Soc Trans. 1997; 25 :14–19. [ PubMed ] [ Google Scholar ]
• Lebedeva IV, Stein CA. Antisense oligonucleotides in cancer: recent advances. BioDrugs. 2000; 13 :195–216. [ PubMed ] [ Google Scholar ]
• Lee AS. The glucose-regulated proteins: stress induction and clinical applications. Trends Biochem Sci. 2001; 26 :504–510. [ PubMed ] [ Google Scholar ]
• Lee HS, Kim MA, Valentino RJ, Waterhouse BD. Glutamatergic afferent projections to the dorsal raphe nucleus of the rat. Brain Res. 2003; 963 :57–71. [ PubMed ] [ Google Scholar ]
• Lee JH, Koh H, Kim M, Kim Y, Lee SY, Karess RE, Lee SH, Shong M, Kim JM, Kim J, Chung J. Energy-dependent regulation of cell structure by AMP-activated protein kinase. Nature. 2007; 447 :1017–1020. [ PubMed ] [ Google Scholar ]
• Lenka N, Vijayasarathy C, Mullick J, Avadhani NG. Structural organization and transcription regulation of nuclear genes encoding the mammalian cytochrome c oxidase complex. Prog Nucleic Acid Res Mol Biol. 1998; 61 :309–344. [ PubMed ] [ Google Scholar ]
• Lowrey PL, Takahashi JS. Mammalian circadian biology: elucidating genome-wide levels of temporal organization. Annu Rev Genomics Hum Genet. 2004; 5 :407–441. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lydic R, Baghdoyan HA. Sleep, anesthesiology, and the neurobiology of arousal state control. Anesthesiology. 2005; 103 :1268–1295. [ PubMed ] [ Google Scholar ]
• Ma Y, Hendershot LM. The mammalian endoplasmic reticulum as a sensor for cellular stress. Cell Stress & Chaperones. 2002; 7 :222–229. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mackiewicz M, Geiger JD, Pack AI. Simultaneous assessment of ecto- and cytosolic-5'-nucleotidase activities in brain micropunches. J Neurosci Methods. 2000; 104 :9–18. [ PubMed ] [ Google Scholar ]
• Mackiewicz M, Nikonova EV, Zimmerman JE, Galante RJ, Zhang L, Cater JR, Geiger JD, Pack AI. Enzymes of adenosine metabolism in the brain: diurnal rhythm and the effect of sleep deprivation. J Neurochem. 2003; 85 :348–357. [ PubMed ] [ Google Scholar ]
• Mackiewicz M, Nikonova EV, Zimmermann JE, Romer MA, Cater J, Galante RJ, Pack AI. Age-related changes in adenosine metabolic enzymes in sleep/wake regulatory areas of the brain. Neurobiol Aging. 2006; 27 :351–360. [ PubMed ] [ Google Scholar ]
• Mackiewicz M, Shockley KR, Romer MA, Galante RJ, Zimmerman JE, Naidoo N, Baldwin DA, Jensen ST, Churchill GA, Pack AI. Macromolecule biosynthesis - a key function of sleep. Physiol Genomics. 2007 [ PubMed ] [ Google Scholar ]
• Mallo M. Controlled gene activation and inactivation in the mouse. Front Biosci. 2006; 11 :313–327. [ PubMed ] [ Google Scholar ]
• Mao W, Yu XX, Zhong A, Li W, Brush J, Sherwood SW, Adams SH, Pan G. UCP4, a novel brain-specific mitochondrial protein that reduces membrane potential in mammalian cells. FEBS Lett. 1999; 443 :326–330. [ PubMed ] [ Google Scholar ]
• Marchant EG, Mistlberger RE. Anticipation and entrainment to feeding time in intact and SCN-ablated C57BL/6j mice. Brain Res. 1997; 765 :273–282. [ PubMed ] [ Google Scholar ]
• Marks GA, Birabil CG. Enhancement of rapid eye movement sleep in the rat by cholinergic and adenosinergic agonists infused into the pontine reticular formation. Neuroscience. 1998; 86 :29–37. [ PubMed ] [ Google Scholar ]
• Marks GA, Shaffery JP, Speciale SG, Birabil CG. Enhancement of rapid eye movement sleep in the rat by actions at A1 and A2a adenosine receptor subtypes with a differential sensitivity to atropine. Neuroscience. 2003; 116 :913–920. [ PubMed ] [ Google Scholar ]
• Marley E, Nistico G. Effects of catecholamines and adenosine derivatives given into the brain of fowls. Br J Pharmacol. 1972; 46 :619–636. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• McGinty D, Szymusiak R. Brain structures and mechanisms involved in the generation of NREM sleep: focus on the preoptic hypothalamus. Sleep Med Rev. 2001; 5 :323–342. [ PubMed ] [ Google Scholar ]
• Meghji P. Adenosine Production and Metabolism. In: Stone T, editor. Adenosine in the Nervous System Vol. 1. San Diego, CA: Academic Press Inc.; 1991. p. 278. [ Google Scholar ]
• Methippara MM, Kumar S, Alam MN, Szymusiak R, McGinty D. Effects on sleep of microdialysis of adenosine A1 and A2a receptor analogs into the lateral preoptic area of rats. Am J Physiol Regul Integr Comp Physiol. 2005; 289 :R1715–R1723. [ PubMed ] [ Google Scholar ]
• Mistlberger RE, Houpt TA, Moore-Ede MC. Food-anticipatory rhythms under 24-hour schedules of limited access to single macronutrients. J Biol Rhythms. 1990; 5 :35–46. [ PubMed ] [ Google Scholar ]
• Miyoshi G, Fishell G. Directing neuron-specific transgene expression in the mouse CNS. Curr Opin Neurobiol. 2006; 16 :577–584. [ PubMed ] [ Google Scholar ]
• Monti JM, Monti D. Role of dorsal raphe nucleus serotonin 5-HT1A receptor in the regulation of REM sleep. Life Sci. 2000; 66 :1999–2012. [ PubMed ] [ Google Scholar ]
• Morairty S, Rainnie D, McCarley R, Greene R. Disinhibition of ventrolateral preoptic area sleep-active neurons by adenosine: a new mechanism for sleep promotion. Neuroscience. 2004; 123 :451–457. [ PubMed ] [ Google Scholar ]
• Murillo-Rodriguez E, Blanco-Centurion C, Gerashchenko D, Salin-Pascual RJ, Shiromani PJ. The diurnal rhythm of adenosine levels in the basal forebrain of young and old rats. Neuroscience. 2004; 123 :361–370. [ PubMed ] [ Google Scholar ]
• Naidoo N, Casiano V, Cater J, Zimmerman J, Pack AI. A role for the molecular chaperone protein BiP/GRP78 in Drosophila sleep homeostasis. Sleep. 2007; 30 :557–565. [ PubMed ] [ Google Scholar ]
• Naidoo N, Giang W, Galante RJ, Pack AI. Sleep deprivation induces the unfolded protein response in mouse cerebral cortex. J Neurochem. 2005; 92 :1150–1157. [ PubMed ] [ Google Scholar ]
• Newgard CB, Brady MJ, O'Doherty RM, Saltiel AR. Organizing glucose disposal: emerging roles of the glycogen targeting subunits of protein phosphatase-1. Diabetes. 2000; 49 :1967–1977. [ PubMed ] [ Google Scholar ]
• Nikonova EV, Frank BC, Mackiewicz M, Quackenbush J, Naidoo N, Pack AI. Changes in components of the electron transport chain in mouse cortex with increases in wakefulness. Soc Neurosci Abs. 2005a [ Google Scholar ]
• Nikonova EV, Vijayasarathy C, Zhang L, Cater JR, Galante RJ, Ward SE, Avadhani NG, Pack AI. Differences in activity of cytochrome C oxidase in brain between sleep and wakefulness. Sleep. 2005b; 28 :21–27. [ PubMed ] [ Google Scholar ]
• Noor Alam MD, Szymusiak R, McGinty D. Adenosinergic regulation of sleep: multiple sites of action in the brain. Sleep. 2006; 29 :1384–1385. discussion 1387–9. [ PubMed ] [ Google Scholar ]
• Oikonomakos NG. Glycogen phosphorylase as a molecular target for type 2 diabetes therapy. Curr Protein Pept Sci. 2002; 3 :561–586. [ PubMed ] [ Google Scholar ]
• Ou H, Yan L, Osmanovic S, Greenberg CC, Brady MJ. Spatial reorganization of glycogen synthase upon activation in 3T3-L1 adipocytes. Endocrinology. 2005; 146 :494–502. [ PubMed ] [ Google Scholar ]
• Pascual O, Casper KB, Kubera C, Zhang J, Revilla-Sanchez R, Sul JY, Takano H, Moss SJ, McCarthy K, Haydon PG. Astrocytic purinergic signaling coordinates synaptic networks. Science. 2005; 310 :113–116. [ PubMed ] [ Google Scholar ]
• Pawlyk AC, Ferber M, Shah A, Pack AI, Naidoo N. Proteomic analysis of the effects and interactions of sleep deprivation and aging in mouse cerebral cortex. J Neurochem. 2007; 103 :2301–2313. [ PubMed ] [ Google Scholar ]
• Pekny M, Wilhelmsson U, Bogestal YR, Pekna M. The role of astrocytes and complement system in neural plasticity. Int Rev Neurobiol. 2007; 82 :95–111. [ PubMed ] [ Google Scholar ]
• Pellerin L, Bouzier-Sore AK, Aubert A, Serres S, Merle M, Costalat R, Magistretti PJ. Activity-dependent regulation of energy metabolism by astrocytes: an update. Glia. 2007; 55 :1251–1262. [ PubMed ] [ Google Scholar ]
• Pellerin L, Magistretti PJ. Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc Natl Acad Sci U S A. 1994; 91 :10625–10629. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pellerin L, Pellegri G, Bittar PG, Charnay Y, Bouras C, Martin JL, Stella N, Magistretti PJ. Evidence supporting the existence of an activity-dependent astrocyte-neuron lactate shuttle. Dev Neurosci. 1998; 20 :291–299. [ PubMed ] [ Google Scholar ]
• Petit JM, Tobler I, Allaman I, Borbely AA, Magistretti PJ. Sleep deprivation modulates brain mRNAs encoding genes of glycogen metabolism. Eur J Neurosci. 2002; 16 :1163–1167. [ PubMed ] [ Google Scholar ]
• Porkka-Heiskanen T, Strecker RE, McCarley RW. Brain site-specificity of extracellular adenosine concentration changes during sleep deprivation and spontaneous sleep: an in vivo microdialysis study. Neuroscience. 2000; 99 :507–517. [ PubMed ] [ Google Scholar ]
• Porkka-Heiskanen T, Strecker RE, Thakkar M, Bjorkum AA, Greene RW, McCarley RW. Adenosine: a mediator of the sleep-inducing effects of prolonged wakefulness. Science. 1997; 276 :1265–1268. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Portas CM, Thakkar M, Rainnie DG, Greene RW, McCarley RW. Role of adenosine in behavioral state modulation: a microdialysis study in the freely moving cat. Neuroscience. 1997; 79 :225–235. [ PubMed ] [ Google Scholar ]
• Printen JA, Brady MJ, Saltiel AR. PTG, a protein phosphatase 1-binding protein with a role in glycogen metabolism. Science. 1997; 275 :1475–1478. [ PubMed ] [ Google Scholar ]
• Radulovacki M. Role of adenosine in sleep in rats. Rev Clin Basic Pharm. 1985; 5 :327–339. [ PubMed ] [ Google Scholar ]
• Radulovacki M. Adenosine and sleep homeostasis in the basal forebrain: commentary on Blanco-Centurion et al. Sleep. 2006; 29 :1381–1382. discussion 1387–9. [ PubMed ] [ Google Scholar ]
• Radulovacki M, Virus RM, Djuricic-Nedelson M, Green RD. Adenosine analogs and sleep in rats. J Pharmacol Exp Ther. 1984; 228 :268–274. [ PubMed ] [ Google Scholar ]
• Rainnie DG, Grunze HC, McCarley RW, Greene RW. Adenosine inhibition of mesopontine cholinergic neurons: implications for EEG arousal. Science. 1994; 263 :689–692. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ramanathan L, Gulyani S, Nienhuis R, Siegel JM. Sleep deprivation decreases superoxide dismutase activity in rat hippocampus and brainstem. Neuroreport. 2002; 13 :1387–1390. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ramesh V, Thatte HS, McCarley RW, Basheer R. Adenosine and sleep deprivation promote NF-kappaB nuclear translocation in cholinergic basal forebrain. J Neurochem. 2007; 100 :1351–1363. [ PubMed ] [ Google Scholar ]
• Reick M, Garcia JA, Dudley C, McKnight SL. NPAS2: an analog of clock operative in the mammalian forebrain. Science. 2001; 293 :506–569. [ PubMed ] [ Google Scholar ]
• Rutter J, Reick M, McKnight SL. Metabolism and the control of circadian rhythms. Annu Rev Biochem. 2002; 71 :307–331. [ PubMed ] [ Google Scholar ]
• Rutter J, Reick M, Wu LC, McKnight SL. Regulation of clock and NPAS2 DNA binding by the redox state of NAD cofactors. Science. 2001; 293 :510–514. [ PubMed ] [ Google Scholar ]
• Sanchis D, Fleury C, Chomiki N, Goubern M, Huang Q, Neverova M, Gregoire F, Easlick J, Raimbault S, Levi-Meyrueis C, Miroux B, Collins S, Seldin M, Richard D, Warden C, Bouillaud F, Ricquier D. BMCP1, a novel mitochondrial carrier with high expression in the central nervous system of humans and rodents, and respiration uncoupling activity in recombinant yeast. J Biol Chem. 1998; 273 :34611–34615. [ PubMed ] [ Google Scholar ]
• Saper CB, Cano G, Scammell TE. Homeostatic, circadian, and emotional regulation of sleep. J Comp Neurol. 2005; 493 :92–98. [ PubMed ] [ Google Scholar ]
• Satoh S, Matsumura H, Hayaishi O. Involvement of adenosine A2A receptor in sleep promotion. Eur J Pharmacol. 1998; 351 :155–162. [ PubMed ] [ Google Scholar ]
• Satoh S, Matsumura H, Koike N, Tokunaga Y, Maeda T, Hayaishi O. Region-dependent difference in the sleep-promoting potency of an adenosine A2A receptor agonist. Eur J Neurosci. 1999; 11 :1587–1597. [ PubMed ] [ Google Scholar ]
• Scammell TE, Gerashchenko DY, Mochizuki T, McCarthy MT, Estabrooke IV, Sears CA, Saper CB, Urade Y, Hayaishi O. An adenosine A2a agonist increases sleep and induces Fos in ventrolateral preoptic neurons. Neuroscience. 2001; 107 :653–163. [ PubMed ] [ Google Scholar ]
• Scharf MT, Mackiewicz M, Naidoo N, O'callaghan JP, Pack AI. AMP-activated protein kinase phosphorylation in brain is dependent on method of killing and tissue preparation. J Neurochem. 2008 In Press. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Scharf MT, Romer MA, Mackiewicz M, Pack AI. The role of AMP-activated protein kinase in sleep-wake state. Soc Neurosci Abs. 2006:458.11. [ Google Scholar ]
• Schroder M, Kaufman RJ. ER stress and the unfolded protein response. Mutat Res. 2005a; 569 :29–63. [ PubMed ] [ Google Scholar ]
• Schroder M, Kaufman RJ. The mammalian unfolded protein response. Annu Rev Biochem. 2005b; 74 :739–789. [ PubMed ] [ Google Scholar ]
• Schroder M, Kaufman RJ. Divergent roles of IRE1alpha and PERK in the unfolded protein response. Curr Mol Med. 2006; 6 :5–36. [ PubMed ] [ Google Scholar ]
• Shaw PJ, Cirelli C, Greenspan RJ, Tononi G. Correlates of sleep and waking in Drosophila melanogaster. Science. 2000; 287 :1834–1837. [ PubMed ] [ Google Scholar ]
• Shen Y, Meunier L, Hendershot LM. Identification and characterization of a novel endoplasmic reticulum (ER) DnaJ homologue, which stimulates ATPase activity of BiP in vitro and is induced by ER stress. J Biol Chem. 2002; 277 :15947–15956. [ PubMed ] [ Google Scholar ]
• Sherin JE, Shiromani PJ, McCarley RW, Saper CB. Activation of ventrolateral preoptic neurons during sleep. Science. 1996; 271 :216–219. [ PubMed ] [ Google Scholar ]
• Siebenlist U, Franzoso G, Brown K. Structure, regulation and function of NF-kappa B. Annu Rev Cell Biol. 1994; 10 :405–455. [ PubMed ] [ Google Scholar ]
• Simpson IA, Carruthers A, Vannucci SJ. Supply and demand in cerebral energy metabolism: the role of nutrient transporters. J Cereb Blood Flow Metab. 2007; 27 :1766–1791. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sorg O, Magistretti PJ. Characterization of the glycogenolysis elicited by vasoactive intestinal peptide, noradrenaline and adenosine in primary cultures of mouse cerebral cortical astrocytes. Brain Res. 1991; 563 :227–233. [ PubMed ] [ Google Scholar ]
• Sorg O, Magistretti PJ. Vasoactive intestinal peptide and noradrenaline exert long-term control on glycogen levels in astrocytes: blockade by protein synthesis inhibition. J Neurosci. 1992; 12 :4923–4931. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stenberg D, Litonius E, Halldner L, Johansson B, Fredholm BB, Porkka-Heiskanen T. Sleep and its homeostatic regulation in mice lacking the adenosine A1 receptor. J Sleep Res. 2003; 12 :283–290. [ PubMed ] [ Google Scholar ]
• Stokkan KA, Yamazaki S, Tei H, Sakaki Y, Menaker M. Entrainment of the circadian clock in the liver by feeding. Science. 2001; 291 :490–493. [ PubMed ] [ Google Scholar ]
• Swanson RA. Physiologic coupling of glial glycogen metabolism to neuronal activity in brain. Can J Physiol Pharmacol. 1992;(70 Suppl):S138–S144. [ PubMed ] [ Google Scholar ]
• Swanson RA, Morton MM, Sagar SM, Sharp FR. Sensory stimulation induces local cerebral glycogenolysis: demonstration by autoradiography. Neuroscience. 1992; 51 :451–461. [ PubMed ] [ Google Scholar ]
• Szymusiak R. Magnocellular nuclei of the basal forebrain: substrates of sleep and arousal regulation. Sleep. 1995; 18 :478–500. [ PubMed ] [ Google Scholar ]
• Terao A, Steininger TL, Hyder K, Apte-Deshpande A, Ding J, Rishipathak D, Davis RW, Heller HC, Kilduff TS. Differential increase in the expression of heat shock protein family members during sleep deprivation and during sleep. Neuroscience. 2003; 116 :187–200. [ PubMed ] [ Google Scholar ]
• Thakkar M, Mallick BN. Effect of rapid eye movement sleep deprivation on 5'-nucleotidase activity in the rat brain. Neurosci Lett. 1996; 206 :177–180. [ PubMed ] [ Google Scholar ]
• Thakkar MM, Delgiacco RA, Strecker RE, McCarley RW. Adenosinergic inhibition of basal forebrain wakefulness-active neurons: a simultaneous unit recording and microdialysis study in freely behaving cats. Neuroscience. 2003a; 122 :1107–1113. [ PubMed ] [ Google Scholar ]
• Thakkar MM, Winston S, McCarley RW. A1 receptor and adenosinergic homeostatic regulation of sleep-wakefulness: effects of antisense to the A1 receptor in the cholinergic basal forebrain. J Neurosci. 2003b; 23 :4278–4287. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ticho SR, Radulovacki M. Role of adenosine in sleep and temperature regulation in the preoptic area of rats. Pharmacol Biochem Behav. 1991; 40 :33–40. [ PubMed ] [ Google Scholar ]
• Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, Laposky A, Losee-Olson S, Easton A, Jensen DR, Eckel RH, Takahashi JS, Bass J. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005; 308 :1043–1045. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Urade Y, Eguchi N, Qu WM, Sakata M, Huang ZL, Chen JF, Schwarzschild MA, Fink JS, Hayaishi O. Sleep regulation in adenosine A2A receptor-deficient mice. Neurology. 2003; 61 :S94–S96. [ PubMed ] [ Google Scholar ]
• Ventura C, Maioli M. Protein kinase C control of gene expression. Crit Rev Eukaryot Gene Expr. 2001; 11 :243–267. [ PubMed ] [ Google Scholar ]
• Virus RM, Djuricic-Nedelson M, Radulovacki M, Green RD. The effects of adenosine and 2'-deoxycoformycin on sleep and wakefulness in rats. Neuropharmacology. 1983; 22 :1401–1404. [ PubMed ] [ Google Scholar ]
• Virus RM, Ticho S, Pilditch M, Radulovacki M. A comparison of the effects of caffeine, 8-cyclopentyltheophylline, and alloxazine on sleep in rats. Possible roles of central nervous system adenosine receptors. Neuropsychopharmacology. 1990; 3 :243–249. [ PubMed ] [ Google Scholar ]
• Vyazovskiy VV, Tobler I. Handedness leads to interhemispheric EEG asymmetry during sleep in the rat. J Neurophysiol. 2008; 99 :969–975. [ PubMed ] [ Google Scholar ]
• Watanabe T, Kojima M, Tomida S, Nakamura TJ, Yamamura T, Nakao N, Yasuo S, Yoshimura T, Ebihara S. Peripheral clock gene expression in CS mice with bimodal locomotor rhythms. Neurosci Res. 2006; 54 :295–301. [ PubMed ] [ Google Scholar ]
• Weil R, Israel A. Deciphering the pathway from the TCR to NF-kappaB. Cell Death Differ. 2006; 13 :826–833. [ PubMed ] [ Google Scholar ]
• Wong-Riley MT, Mullen MA, Huang Z, Guyer C. Brain cytochrome oxidase subunit complementary DNAs: isolation, subcloning, sequencing, light and electron microscopic in situ hybridization of transcripts, and regulation by neuronal activity. Neuroscience. 1997; 76 :1035–1055. [ PubMed ] [ Google Scholar ]
• Yanik G, Glaum S, Radulovacki M. The dose-response effects of caffeine on sleep in rats. Brain Res. 1987; 403 :177–180. [ PubMed ] [ Google Scholar ]
• Zhang K, Kaufman RJ. Signaling the unfolded protein response from the endoplasmic reticulum. J Biol Chem. 2004; 279 :25935–25938. [ PubMed ] [ Google Scholar ]
• Zimmerman JE, Mackiewicz M, Galante RJ, Zhang L, Cater J, Zoh C, Rizzo W, Pack AI. Glycogen in the brain of Drosophila melanogaster: diurnal rhythm and the effect of rest deprivation. J Neurochem. 2004; 88 :32–40. [ PubMed ] [ Google Scholar ]
• Zimmerman JE, Rizzo W, Shockley KR, Raizen DM, Naidoo N, Mackiewicz M, Churchill GA, Pack AI. Multiple mechanisms limit the duration of wakefulness in Drosophila brain. Physiol Genomics. 2006; 27 :337–350. [ PubMed ] [ Google Scholar ]

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• Published: 05 March 1998

The hydrogen hypothesis for the first eukaryote

• William Martin 1 &
• Miklós Müller 2  

Nature volume  392 ,  pages 37–41 ( 1998 ) Cite this article

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A new hypothesis for the origin of eukaryotic cells is proposed, based on the comparative biochemistry of energy metabolism. Eukaryotes are suggested to have arisen through symbiotic association of an anaerobic, strictly hydrogen-dependent, strictly autotrophic archaebacterium (the host) with a eubacterium (the symbiont) that was able to respire, but generated molecular hydrogen as a waste product of anaerobic heterotrophic metabolism. The host's dependence upon molecular hydrogen produced by the symbiont is put forward as the selective principle that forged the common ancestor of eukaryotic cells.

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The role of mitochondrial energetics in the origin and diversification of eukaryotes

Müller, M. The hydrogenosome. J. Gen. Microbiol. 139 , 2879–2889 (1993).

Article   Google Scholar  

Cavalier-Smith, T. Eukaryotes with no mitochondria. Nature 326 , 332–333 (1987).

Article   ADS   CAS   Google Scholar  

Sogin, M. L., Silberman, J. D., Hinkle, G. & Morrison, H. G. Problems with molecular diversity in the Eukarya. Symp. Soc. Gen. Microbiol. 54 , 167–184 (1996).

Google Scholar  

Whatley, J. M., John, P. & Whatley, F. R. From extracellular to intracellular: the establishment of mitochondria and chloroplasts. Proc. R. Soc. Lond. B 204 , 165–187 (1979).

Gray, M. W. & Doolittle, W. F. Has the endosymbiont hypothesis been proven? Microbiol. Rev. 46 , 1–42 (1982).

CAS   PubMed   PubMed Central   Google Scholar  

Cavalier-Smith, T. The origin of eukaryote and archaebacterial cells. Ann. NY Acad. Sci. 503 , 7–54 (1987).

ADS   Google Scholar  

Cavalier-Smith, T. & Chao, E. E. Molecular phylogeny of the free-living archaezoan Trepomonas agilis and the nature of the first eukaryote. J. Mol. Evol. 43 , 551–562 (1996).

Zillig, W. et al . Did eukaryotes originate by a fusion event? Endocytobiosis Cell Res. 6 , 1–25 (1989).

Gupta, R. S. & Golding, G. B. The origin of the eukaryotic cell. Trends Biochem. Sci. 21 , 166–171 (1996).

Article   CAS   Google Scholar  

Lake, J. A. & Rivera, M. C. Was the nucleus the first endosymbiont? Proc. Natl Acad. Sci. USA 91 , 2880–2881 (1994).

Clark, C. G. & Roger, A. J. Direct evidence for secondary loss of mitochondria in Entamoeba histolytica . Proc. Natl Acad. Sci. USA 92 , 6518–6521 (1995).

Henze, K. et al . Anuclear gene of eubacterial origin in Euglena reflects cryptic endosymbioses during protist evolution. Proc. Natl Acad. Sci. USA 92 , 9122–9126 (1995).

Keeling, P. W. & Doolittle, W. F. Evidence that eukaryotic triosephosphate isomerase is of alpha-proteobacterial origin. Proc. Natl Acad. Sci. USA 94 , 1270–1275 (1997).

Doolittle, W. F. Some aspects of the biology of cells and their possible evolutionary significance. Symp. Soc. Gen. Microbiol. 54 , 1–21 (1996).

Rosenthal, B. et al . Evidence for the bacterial origin of genes encoding fermentation enzymes of the amitochondriate protozoan parasite Entamoeba histolytica . J. Bacteriol. 179 , 3736–3745 (1997).

Searcy, D. G. in The Origin and Evolution of the Cell (eds Hartman, H. & Matsuno, K.) 47–78 (World Scientific, Singapore, (1992)).

de Duve, C. Blueprint for a Cell: the Nature and Origin of Life (Patterson, Burlington, NC, (1991)).

Coombs, G. H. & Müller, M. in Biochemistry and Molecular Biology of Parasites (eds Marr, J. J. & Müller, M.) 33–47 (Academic, London, (1995)).

Book   Google Scholar  

Müller, M. in Evolutionary Relationships Among Protozoa (eds Coombs, G. H., Vickermann, K., Sleigh, M. A. & Warren, A.) 109–132 (Chapman Hall, London, (1998)).

Müller, M. Energy metabolism of protozoa without mitochondria. Annu. Rev. Microbiol. 42 , 465–488 (1988).

Müller, M. in Christian Gottfried Ehrenberg-Festschrift anla¨ßlich der 14. Wissenschaftlichen Jahrestagung der Deutschen Gesellschaft für Protozoologie, 9.-11. Marz 1995 in Delitzsch (Sachsen) (eds Schlegel, M. & Hausmann, K.) 63–76 (Leipziger Universitätsverlag, Leipzig, (1996)).

Iwabe, N., Kuma, K.-I., Hasegawa, M., Osawa, S. & Miyata, T. Evolutionary relationship of archaebacteria, eubacteria and eukaryotes inferred from phylogenetic trees of duplicated genes. Proc. Natl Acad. Sci. USA 86 , 9355–9359 (1989).

Woese, C., Kandler, O. & Wheelis, M. L. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria and Eukarya. Proc. Natl Acad. Sci. USA 87 , 4576–4579 (1990).

Langer, D., Hain, J., Thuriaux, P. & Zillig, W. Transcription in Archaea: similarity to that in Eukarya. Proc. Natl Acad. Sci. USA 92 , 5768–5772 (1995).

Yamamoto, A., Hashimoto, T., Asaga, E., Hasegawa, M. & Goto, N. Phylogenetic position of the mitochondrion-lacking protozoan Trichomonas tenax , based on amino acid sequences of elongation factors 1-α and 2. J. Mol. Evol. 44 , 98–105 (1997).

Horner, D. S., Hirt, R. P., Kilvington, S., Lloyd, D. & Embley, T. M. Molecular data suggest an early acquisition of the mitochondrion endosymbiont. Proc. R. Soc. Lond. B 263 , 1053–1059 (1996).

Bui, E. T. N., Bradley, P. J. & Johnson, P. J. Acommon evolutionary origin for mitochondria and hydrogenosomes. Proc. Natl Acad. Sci. USA 93 , 9651–9656 (1996).

Germot, A., Philippe, H. & Le Guyader, H. Presence of a mitochondrial-type 70-kDa heat shock protein in Trichomonas vaginalis suggests a very early mitochondrial endosymbiosis in eukaryotes. Proc. Natl Acad. Sci. USA 93 , 14614–14617 (1996).

Roger, A. J., Clark, C. G. & Doolittle, W. F. Apossible mitochondrial gene in the early-branching amitochondriate protist Trichomonas vaginalis . Proc. Natl Acad. Sci. USA 93 , 14618–14622 (1996).

Hrdý, I. & Müller, M. Primary structure and eubacterial relationships of the pyruvate : ferredoxin oxidoreductase of the amitochondriate eukaryote, Trichomonas vaginalis . J. Mol. Evol. 41 , 388–396 (1995).

Article   ADS   Google Scholar  

Blattner, F. R. et al . The complete genome sequence of Escherichia coli K-12. Science 277 , 1453–1474 (1997).

Kaneko, T. et al . Sequence analysis of the genome of the unicellular cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions. DNA Res. 3 , 109–136 (1996).

Sánchez, L. B. & Müller, M. Purification and characterization of the acetate forming enzyme, acetyl-CoA synthetase (ADP-forming) from the amitochondriate protist, Giardia lamblia . FEBS Lett. 378 , 240–244 (1996).

Schönheit, P. & Schäfer, T. Metabolism of hyperthermophiles. World. J. Microbiol. Biotechnol. 11 , 26–57 (1995).

Markoŝ, A., Miretsky, A. & Müller, M. Aglyceraldehyde-3-phosphate dehydrogenase with eubacterial features in the amitochondriate eukaryote Trichomonas vaginalis . J. Mol. Evol. 37 , 631–643 (1993).

Martin, W. & Schnarrenberger, C. The evolution of the Calvin cycle from prokaryotic to eukaryotic chromosomes: a case study of functional redundancy in ancient pathways through endosymbiosis. Curr. Genet. 32 , 1–18 (1997).

Fenchel, T. & Finlay, B. J. Ecology and Evolution in Anoxic Worlds (Oxford Univ. Press, Oxford, (1995)).

Gibson, J. L. & Tabita, F. R. The molecular regulation of the reductive pentose phosphate pathway in proteobacteria and cyanobacteria. Arch. Microbiol. 166 , 141–150 (1996).

Murrel, J. C. Genetics and molecular biology of methanotrophs. FEMS Microbiol. Lett. 88 , 233–248 (1992).

Thauer, R. K., Hedderich, R. & Fischer, R. in Methanogenesis: Ecology, Physiology, Biochemistry and Genetics (ed. Ferry, J. G.) 209–252 (Chapman & Hall, New York, (1993)).

Conrad, R. Soil microorganisms as controllers of atmospheric trace gases (H2, CO, CH4, OCS, N2, and NO). Microbiol. Rev. 60 , 609–640 (1996).

Bryant, M. P., Wolin, E. A., Wolin, M. J. & Wolfe, R. S. Methanobacillus omelianskii , a symbiotic association of two species of bacteria. Arch. Microbiol. 59 , 20–31 (1967).

CAS   Google Scholar  

Broers, C. A. M., Stumm, C. K., Vogels, G. D. & Brugerolle, G. Psalteriomonas lanterna gen. nov., sp. nov., a free living amoboflagellate isolated from freshwater anaerobic sediments. Eur. J. Protistol. 25 , 369–380 (1990).

Embley, T. M. et al . Multiple origins of anaerobic ciliates with hydrogenosomes within the radiation of aerobic ciliates. Proc. R. Soc. Lond. B 262 , 87–93 (1995).

Finlay, kB. J., Embley, T. M. & Fenchel, T. Anew polymorphic methanogen, closely related to Methanocorpusculum parvum , living in stable symbiosis within the anaerobic ciliate Trimyema sp. J. Gen. Microbiol. 139 , 371–378 (1993).

Stevens, T. O. & McKinley, J. P. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science 270 , 450–454 (1995).

Brinkmann, H. & Martin, W. Higher plant chloroplast and cytosolic 3-phosphoglycerate kinases: a case of endosymbiotic gene replacement. Plant. Mol. Biol. 30 , 65–75 (1996).

Kasting, J. F. Earth's early atmosphere. Science 259 , 920–926 (1993).

Poole, A. M., Jeffares, D. C. & Penny, D. The path from the RNA world. J. Mol. Evol. 46 , 1–17 (1998).

Rospert, S. et al . Methyl-coenzyme M reductase and other enzymes involved in methanogenesis from CO2and H2in the extreme thermophile Methanopyrus kandleri . Arch. Microbiol. 156 , 49–55 (1991).

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Acknowledgements

We thank H. Brinkmann, M. Embley, K. Henze, R. Herrmann, R. Hensel, D.Oesterheld and L. Sánchez for critical comments on the manuscript and gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (W.M.) and the National Institutes of Health (M.M.).

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Miklós Müller

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Martin, W., Müller, M. The hydrogen hypothesis for the first eukaryote. Nature 392 , 37–41 (1998). https://doi.org/10.1038/32096

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism. Run a free check.

Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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August 1, 2013

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New Hypothesis Explains Why We Sleep

During sleep, the brain weakens the connections among nerve cells, apparently conserving energy and, paradoxically, aiding memory

By Giulio Tononi & Chiara Cirelli

Every night, while we lie asleep, blind, dumb and almost paralyzed, our brains are hard at work. Neurons in the sleeping brain fire nearly as often as they do in a waking state, and they consume almost as much energy. What is the point of this unceasing activity at a time when we are supposedly resting? Why does the conscious mind disconnect so completely from the external environment while the brain keeps nattering on?

The brain's activity during rest likely serves some essential function. The evidence for this importance starts with sleep's ubiquity. All animals apparently sleep even though being unconscious and unresponsive greatly raises the risk of becoming another creature's lunch. Birds do it, bees do it, iguanas and cockroaches do it, even fruit flies do it, as we and others demonstrated more than a decade ago.

Furthermore, evolution has devised a few extraordinary adaptations to accommodate sleep: dolphins and some other marine mammals that must surface often to breathe, for example, sleep by alternately switching off one hemisphere of their brain while the other remains in a waking state.

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Like many scientists and nonscientists, the two of us have long wondered what benefit sleep provides that makes it so crucial to living creatures. More than 20 years ago, when we worked together at the Sant'Anna School of Advanced Studies in Pisa, Italy, we began to suspect that the brain's activity during slumber may somehow restore to a baseline state the billions of neural connections that get modified every day by the events of waking life. Sleep, in this telling, would preserve the ability of the brain's circuitry to form new memories continually over the course of an individual's lifetime without becoming oversaturated or obliterating older memories.

We also have an idea of why awareness of the external environment must be shut off during sleep. It seems to us that conscious experience of the here and now has to be interrupted for the brain to gain the chance to integrate new and old memories; sleep provides that respite.

Our hypothesis is somewhat controversial among our fellow neuroscientists who study sleep's role in learning and memory because we suggest that the return to baseline results from a weakening of the links among the neurons that fire during sleep. Conventional wisdom holds, instead, that brain activity during sleep strengthens the neural connections involved in storing newly formed memories. Yet years of research with organisms ranging from flies to people lend support to our notions.

School of Nod

Scientists first proposed the idea that sleep is important to memory nearly a century ago, and plenty of experiments since then have shown that after a night of sleep, and sometimes just a nap, newly formed memories “stick” better than they would if one had spent the same amount of time awake. This pattern holds for declarative memories, such as lists of words and associations between pictures and places, as well as for procedural memories, which underlie perceptual and motor skills, such as playing a musical instrument.

The evidence that sleep benefits memory led scientists to look for signs that the brain rehashes newly learned material at night. They found them: studies performed over the past 20 years, first in rodents and then in humans, show that patterns of neural activity during sleep sometimes do resemble those recorded while subjects are awake. For example, when a rat learns to navigate a maze, certain neurons in a part of the brain called the hippocampus fire in specific sequences. During subsequent sleep, rats “replay” these sequences more often than predicted by chance.

Because of such findings, many researchers came to assume that sleep “replay” consolidates memories by further reinforcing synapses—the contact points between neurons—that have been strengthened when an individual is awake. The idea is that, as linked neurons fire repeatedly, the synapses connecting them more readily convey signals from one neuron to another, helping neuronal circuits to encode memories in the brain. This process of selective strengthening is known as synaptic potentiation, and it is the favored mechanism by which the brain is thought to accomplish learning and remembering.

Yet while replay and potentiation are known to occur during waking activities, scientists have so far found no direct evidence that the synapses in replayed circuits get strengthened during sleep. This lack of evidence hardly surprises us. It is consistent with our suspicion that while the sleeper lies unaware, all that brain activity—the “replay” as well as other, seemingly random firings—might actually be weakening neural connections, not strengthening them.

The Price of Plasticity

There are many good reasons to propose that synapses must become weakened as well as strengthened for the brain to function properly. For one thing, strong synapses consume more energy than weak ones, and the brain does not have infinite stores of energy. In humans the brain accounts for almost 20 percent of the body's energy budget—more than any other organ by weight—and at least two thirds of that portion goes to supporting synaptic activity. Building and bolstering synapses is also a major source of cellular stress, requiring cells to synthesize and deliver components ranging from mitochondria (the cell's power plants), to synaptic vesicles (which ferry signaling molecules), to various proteins and lipids that are needed for communication across synapses.

It seems clear to us that this strain on resources is unsustainable. The brain cannot go on strengthening and maintaining revved-up synapses both day and night for the whole of an individual's lifetime. We do not doubt that learning occurs mainly through synaptic potentiation. We simply doubt that strengthening continues to happen during sleep.

In contrast, synaptic weakening during sleep would restore brain circuitry to a baseline level of strength, thereby avoiding excessive energy consumption and cellular stress. We refer to this baseline-restoring function of sleep as preserving synaptic homeostasis, and we call our overall hypothesis about the role of sleep the synaptic homeostasis hypothesis, or SHY. In principle, SHY explains the essential, universal purpose of sleep for all organisms that do it: sleep restores the brain to a state where it can learn and adapt when we are awake. The risk we take by becoming disconnected from the environment for hours at a time is the price we pay for this neural recalibration. Most generally, sleep is the price we pay for the brain's plasticity—its ability to modify its wiring in response to experience.

But how does SHY explain sleep's salutary effects on learning and memory? How can weakened synapses improve the overall retention of skills and facts? Consider that, over the course of a typical day, almost everything you experience leaves a neural trace in the brain and that the significant events, like meeting a new person or learning a piece of music on the guitar, make up just a trifling portion of that neural encoding. To improve memory, the sleeping brain must somehow distinguish the “noise” of irrelevant information from the “signal” of significant happenings.

We suggest that in sleep, the spontaneous firing of neurons in the brain activates many different circuits in many different combinations, encompassing both new memory traces and old networks of learned associations. (You get a glimpse of this neural free-for-all in dreams.) The spontaneous activity lets the brain try out which new memories fit better with stored memories of proved significance and weakens those synapses that do not fit well in the grand scheme of memory. We and other investigators are exploring possible mechanisms by which brain activity could selectively weaken synapses that encode the “noise” while preserving those that correspond to the “signal.”

While the brain tries out these imaginary scenarios and enacts weakening where appropriate, we had best be unaware of the surrounding environment and be incapable of acting in it; that is, we had best be asleep. Likewise, restoring synaptic homeostasis should not take place while we are awake because the events of the day would dominate the process, giving salience to them rather than to all the knowledge the brain has accumulated over a lifetime. The profound disconnection of sleep frees our brain from the tyranny of the present, creating an ideal circumstance for integrating and consolidating memories.

A Weak Connection

Our proposal that the brain uses neuronal firing during sleep to weaken rather than strengthen synapses is supported in part by close analyses of data from a standard workhorse of sleep research: the electroencephalogram, or EEG. EEGs record patterns of electrical activity in the cerebral cortex via electrodes attached to the scalp. Decades ago EEG recordings of the sleeping brain revealed two main categories of sleep, called rapid eye movement (REM) and non-REM (NREM), that alternate throughout the night. Each has distinctive brain-wave patterns. In addition to the jittering of eyeballs underneath closed lids that gives REM sleep its name, that stage is dominated by relatively fast oscillations—quick ups and downs in the curves of the EEG readout, resembling EEG recordings of the waking state. In contrast, slow oscillations—with frequencies of about one cycle per second—are the most prominent feature of NREM sleep.

A decade ago the late Mircea Steriade of Laval University in Quebec discovered that the slow oscillations of NREM sleep arise when groups of neurons fire together for a little while (so-called on periods), then fall silent for about a fraction of a second (off periods) and then resume their synchronized firing. This was one of the fundamental discoveries in sleep research. Since then, scientists have also discovered that in birds and mammals, the slow waves are large if preceded by a long period of wakefulness and become smaller as sleep goes on.

We reasoned that if synapses are strong, neurons will synchronize their firing more, producing larger slow waves. If synapses are weak, neurons will be less synchronized and the resulting slow waves will be smaller. Results of computer simulations and experiments in humans and animals led us to conclude that the big, steep slow waves early in the night indicate that synapses have been strengthened by prior wakefulness, whereas the small, shallow slow waves early in the morning indicate that synapses have become weaker during sleep.

Direct support for the idea that synapses become weaker during sleep, and may even be pruned away, comes from studies in animals. In fruit flies, for instance, we find that sleep reverses a progressive increase in the number and size of synapses that occurs during the day, especially when the flies are exposed to stimulating environments. Synaptic spines are specialized protrusions on a neuron's signal-detecting arm. When fruit flies spend the day interacting with other flies, neurons throughout their brain sprout more synaptic spines by evening than were present in the morning.

Just as remarkably, the number of spines goes back to the baseline level by the following morning if—and only if—the flies are allowed to sleep. We saw a similar phenomenon in the cerebral cortex of adolescent mice: the number of synaptic spines tended to rise when the animals were awake and to fall when they slept. In adult rodents, the upshot is the same, although it is not the number of synaptic spines that changes with wakefulness and sleep but rather the abundance of certain spine molecules, known as AMPA receptors, that determine the strength of a synapse. When we monitored these AMPA receptors, we found that their number per synapse increases after wakefulness and decreases after sleep. More receptors make for stronger synapses; fewer mean the synapses have weakened.

Synaptic strength can be gauged directly by using an electrical probe to stimulate neural fibers in the cortex. The neurons respond with an induced electrical discharge that is larger when synapses are strong and smaller when the connections are weak. We showed that in rats, stimulated neurons fire more strongly after a few hours of wakefulness and less strongly after sleep. Marcello Massimini of the University of Milan in Italy and Reto Huber, now at the University of Zurich, performed a similar experiment in humans. Instead of an electrical probe, they turned to transcranial magnetic stimulation—a short magnetic pulse applied to the scalp—to stimulate the underlying neurons. They then recorded the strength of the cortical responses with high-density EEG. The results were clear: the longer a subject was awake, the larger the EEG responses. It took a night of sleep for cortical responses to return to the baseline.

Less Is More

The common conclusion of these experiments, which we performed over two decades, is that spontaneous cortical activity in sleep does indeed weaken the synaptic connections in neural circuits, whether by damping their ability to send electrical impulses or by erasing them outright.

This process, which we call down selection, would ensure the survival of the circuits that are “fittest,” either because they were activated strongly and consistently during wakefulness (say, by playing the right notes on a guitar while trying to master a new piece) or because they were better integrated with previous, older memories (as would be the case for a new word encountered in a known language). Meanwhile synapses in circuits that were only mildly enhanced during wakefulness (such as fumbled notes on the guitar) or that fit less with old memories (such as a new word presented in an unknown language) would be depressed.

Down selection would ensure that insignificant events would leave no lasting trace in our neural circuitry, whereas memories of note would be preserved. As an additional bonus, down selection would also make room for another cycle of synaptic strengthening during wakefulness. Indeed, some findings imply that among its many other benefits for learning and memory, sleep aids the subsequent acquisition of new memories (material encountered before the next bout of sleep). Quite a few studies have shown that after a night of sleep, you can learn new material much better than you can after having been awake all day. (Students, take note.)

Although we have no direct evidence for a mechanism that would produce selective weakening of activated synapses as yet, we have a notion of how synaptic weakening could occur. We suspect the slow waves of mammalian NREM sleep somehow play a role. In lab studies of rat brain tissue, nerve cells became less effective at passing signals to one another when stimulated in ways that mimic the synchronized on/off cycles of slow-wave sleep.

The chemistry of the brain also changes in NREM sleep in a way that could lead to synaptic weakening. In the awake individual, a concentrated soup of signaling chemicals, or neuromodulators—including acetylcholine, norepinephrine, dopamine, serotonin, histamine and hypocretin—bathe the brain and bias synapses toward strengthening when signals pass through them. During sleep—especially NREM sleep—the soup becomes much less concentrated. This diluted milieu of neuromodulators may bias the neural circuitry so that synapses become weakened, rather than strengthened, when signals flow across them. The process might also involve a substance called brain-derived neurotrophic factor (BDNF), which is known to promote synaptic strengthening and to be involved in memory acquisition. BDNF levels are high in neurons during wakefulness and minimal during sleep.

Local Sleep

Regardless of specific mechanisms and selective processes, the evidence is strong in several species that overall synaptic strength goes up during wakefulness and down during sleep: the core prediction of SHY. We can test SHY further by examining some of its intriguing corollaries.

For example, if the hypothesis is correct, then the more plasticity a part of the brain undergoes during wakefulness, the more that part should need to sleep. “Sleep need” can, in turn, be indicated by an increase in the size and duration of NREM slow waves. To explore this prediction, we asked human subjects to learn a novel task: how to reach a target on a computer screen while the cursor (controlled by a mouse) is systematically rotated. The part of the brain that engages in this kind of learning is the right parietal cortex. Sure enough, when our subjects slept, the slow waves over their right parietal cortex were larger, relative to waves from the same area on the night before learning occurred. These large waves did flatten out in the course of the night, as such oscillations do. But those large, localized waves at the start of the night tell us that particular part of the brain had been exhausted by the task we assigned.

Many other experiments by the two of us and others have since confirmed that learning, and more generally the activation of synapses in circuits, produces a local increase in sleep need. Recently we have even found that prolonged or intense use of certain circuits can make local groups of neurons “fall asleep” even though the rest of the brain (and the organism itself) remains awake. Thus, if a rat stays awake longer than usual, some cortical neurons show brief periods of silence that are basically indistinguishable from the off periods observed during slow-wave sleep. Meanwhile the rat is running around, its eyes open, tending to its business, as any awake rat would do.

This phenomenon is called local sleep, and it is attracting scrutiny from other investigators. Our latest studies indicate that localized off periods also occur in the brains of sleep-deprived humans and that those periods become more frequent after intense learning. It seems that when we have been awake for too long or have overexerted certain circuits, small chunks of the brain may take quick naps without giving notice. One wonders how many errors of judgment, silly mistakes, irritable responses and foul moods result from local sleep in the brains of exhausted people who believe they are fully awake and in complete control.

SHY also predicts that sleep is especially important in childhood and adolescence, times of concentrated learning and of intense synaptic remodeling, as many studies have shown. In youth, synapses are formed, strengthened and pruned at an explosive rate never approached in adulthood. It makes sense that down selection during sleep would be crucial to minimize the energy costs of this frenzied synaptic remodeling and to favor the survival of adaptive neural circuits in these stages of life. One can only wonder what happens when sleep is disrupted or insufficient during critical periods in development. Might the deficit corrupt the proper refinement of neural circuits? In that case, the effect of sleep loss would not merely be occasional forgetfulness or misjudgment but a lasting change in the way the brain is wired.

We look forward to testing SHY's predictions and exploring its implications further. For example, we hope to discover whether sleep deprivation during neural development leads to changes in the organization of brain circuitry. We would also like to learn more about the effect of sleep on deep-brain areas, such as the thalamus, cerebellum, hypothalamus and brain stem, and about the role of REM sleep in synaptic homeostasis. Perhaps we would then learn if sleep is indeed the price of waking plasticity, a price that every brain and every neuron must pay.

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Energy content in foods

In association with Nuffield Foundation

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Try this class experiment to investigate how much energy different foods contain

In this practical, students burn a sample of a foodstuff of known mass, heating a known volume of water. From the measured temperature change, students calculate the energy transferred to the water, and hence estimate the energy present per unit mass of food.

This is a class experiment in which different groups can investigate different foodstuffs. If each group investigates two foodstuffs – one in common with the rest of class to provide a common baseline, and the other a different foodstuff from the rest – a comparative table of energy in different foods can be drawn up from the class results. This should be possible to achieve in 45–60 minutes.

• Eye protection
• Thermometer (–10 to 110 °C), short, stirring type
• Boiling tube, or metal calorimeter (or similar metal container) (see note 4 below)
• Measuring cylinder, 25 cm 3
• Bunsen burner
• Heat resistant mat
• Mounted needle
• Stand and clamp
• Balance, weighing to 0.1 g

Use of a variety of dry foodstuffs, such as:

• Mini-marshmallows
• Popcorn (already popped)
• Broad beans (dried)

See notes 5, 6 and 7 below for additional guidance.

Health, safety and technical notes

• Read our standard health and safety guidance.
• Wear eye protection throughout.
• Students must be instructed NOT to taste or eat any of the foods used in the experiment.
• While boiling tubes are easy to use for this experiment, the poor thermal conductivity of glass may be a major cause of error. Metal containers, such as copper calorimeters or tin cans of similar dimensions that can be held in a clamp, provide more effective heat transfer to the water. Note that this benefit is lost if the can is stood on a tripod, as the latter will also be heated.
• Check in advance for common allergy problems, eg peanuts.
• Some foodstuffs can be burned safely and easily using a mounted needle. Others may melt and drop off the needle, so burning on an old metal teaspoon is an alternative method – this can also be used for liquid foodstuffs, such as olive oil. High protein foodstuffs may produce pungent fumes, and should be burned in a fume cupboard. Each foodstuff provided should be tested beforehand to check that it is capable of sustained combustion without having to be relit repeatedly.
• One foodstuff needs to be selected as suitable for the standard experiment used by each group. Enough samples of approximately the same mass of this foodstuff need to be provided for the class. A trial run before the lesson should be carried out to establish that it will burn readily, sustain combustion and leave little unburnt residue, and that the mass of this foodstuff that will cause a temperature rise in the water used of around 20–30 °C.

Using a test tube

• Measure 10 cm 3 of water into the test tube.
• Clamp the test tube in the retort stand at an angle as shown in the diagram, and over a heat resistant mat.

Source: Royal Society of Chemistry

The equipment required to investigate the energy content of different foodstuffs

• Weigh a small piece of food and record the mass.
• Take the temperature of the water and record it in the table.
• Fix the food on the end of the mounted needle. If the food is likely to melt when heated put it on a teaspoon instead of on the needle.
• Ignite the food using a Bunsen burner, and immediately hold it about 1 cm below the test tube and above a heat resistant mat. If the flame goes out, quickly relight it.
• When the food stops burning, stir the water with the thermometer and record the temperature.
• If there is a significant amount of unburnt food left on the needle, reweigh this and record the mass remaining.
• Empty the test tube and refill it with another 10 cm 3 of cold water. Repeat the experiment using a different food.

Using a metal container

Instructions as for a test tube, except:

• Use a larger volume of water, eg 25 or 50 cm 3 , and a larger food sample.
• Clamp the container in a level position above a heat resistant mat.

Teaching notes

This experiment provides an opportunity to use a temperature sensor linked to a data logger instead of a thermometer.

One foodstuff should be preselected to be the one used by all groups to standardise their experiments with each other (see Health, safety and technical notes ), while each group needs to be allocated a different foodstuff for the second run.

As the same amount of water is heated each time, the temperature rise can be used to compare the amount of heat energy given off per gram of each foodstuff by dividing the rise by the mass of foodstuff burnt.

For classes familar with the equation q = m  ×  C  × Δ T , where q is the heat energy, m the mass of water heated, C the specific heat of water (4.2 J g –1 deg – 1 ) and Δ T the temperature rise, this can be used to calculate the energy (in J) absorbed by the water each time. Dividing this by the mass of foodstuff burnt gives the heat energy absorbed by the water in J per g, as shown in the table below.

Each group needs to prepare a results table along the lines of the example below:

Class results for the heat absorbed by water per gram of food may then be collected and compared on a class spreadsheet prepared by the teacher. After this has been done, a class discussion of ‘fair test’ problems will be appropriate, identifying sources of error and ideas for improving the technique used.

More resources

Add context and inspire your learners with our short career videos showing how chemistry is making a difference .

Additional information

This is a resource from the  Practical Chemistry project , developed by the Nuffield Foundation and the Royal Society of Chemistry. This collection of over 200 practical activities demonstrates a wide range of chemical concepts and processes. Each activity contains comprehensive information for teachers and technicians, including full technical notes and step-by-step procedures. Practical Chemistry activities accompany  Practical Physics  and  Practical Biology .

© Nuffield Foundation and the Royal Society of Chemistry

• 11-14 years
• 14-16 years
• 16-18 years
• Practical experiments
• Thermodynamics
• Quantitative chemistry and stoichiometry
• Analytical chemistry

Specification

• The quantity of heat energy released can be determined experimentally and calculated using Eₕ=cmΔT.
• Fats and oils are: a concentrated source of energy; essential for the transport and storage of fat-soluble vitamins in the body.
• 2.8.7 calculate enthalpy changes from experimental data using the equation q = mcΔT;
• determine the enthalpy changes for combustion and neutralisation using simple apparatus; and
• 2. Develop and use models to describe the nature of matter; demonstrate how they provide a simple way to to account for the conservation of mass, changes of state, physical change, chemical change, mixtures, and their separation.
• 9. Consider chemical reactions in terms of energy, using the terms exothermic, endothermic and activation energy, and use simple energy profile diagrams to illustrate energy changes.

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Condensed Matter > Strongly Correlated Electrons

Title: novel low-energy peak in the one-particle spectral function of the electron gas: breakdown of the landau's hypothesis as to the fermi liquids in simple metals.

Abstract: Based on a nonperturbative scheme to determine the self-energy \Sigma(k,iw_n) with automatically satisfying the Ward identity and the total momentum conservation law, a fully self-consistent calculation is done in the electron gas at various temperatures T to obtain G(k,iw_n) the one-particle Green's function with fulfilling all known conservation laws, sum rules, and correct asymptotic behaviors; here, T is taken unprecedentedly low, namely, T/E_F down to 10^{-4} with E_F the Fermi energy, and tiny mesh as small as 10^{-4}k_F is chosen near the Fermi surface in k space with k_F the Fermi momentum. By analytically continuing G(k,iw_n) to the retarded function G^R(k,w), we find a novel low-energy peak, in addition to the quasiparticle (QP) peak and one- and two-plasmon high-energy satellites, in the spectral function A(k,w)[= -Im G^R(k,w)/\pi] for T less than about 10^{-3}E_F in the simple-metal density region (2<r_s<6 with r_s the dimensionless density parameter). This new peak is attributed to the effect of excitonic attraction on \Sigma(k,iw_n) arising from multiple excitations of tightly bound electron-hole pairs in the polarization function \Pi(q,iw_q) for |q| equal to about 2k_F and |w_q| << E_F and thus it is dubbed ``excitron''. Although this excitron peak height is only about a one-hundredth of that of QP, its excitation energy is about a half of that of QP for |k| equal to about k_F, seemingly in contradiction to the Landau's hypothesis as to the one-to-one correspondence of low-energy excitations between a free Fermi gas and an interacting normal Fermi liquid. As for the QP properties, our results of both the effective mass m^* and the renormalization factor z^* are in good agreement with those provided by recent quantum Monte Carlo simulations and available experiments.

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This Tiny Particle Could Upend Everything We Know About Gravity—And the Universe—Scientists Say

A scientific breakthrough on the tiniest scale could soon help us answer the universe’s greatest mysteries.

EVERYWHERE YOU LOOK , you can see gravity’s fingerprint. It’s in the path the moon takes around Earth each night and the humbling thump when you wipe out on an icy patch of sidewalk.

For decades, scientists have dreamed of finding a way to reconcile both gravity’s effects on the classical and quantum scale through complex ideas like string theory or loop quantum gravity. A unified theory of gravity could be the key to solving other big questions in the universe as well—like how the Big Bang began or what makes up dark matter. Yet, while both ideas have their own merit in theory, actually being able to detect the small effects of gravity on the quantum level is another matter entirely.

That’s where new research published earlier this year in Science Advances comes into play. In this work, a research group from the U.K., Netherlands, and Italy designed an experiment so sensitive that it can measure a gravitational force equal to one-quintillionth of a Newton (on the scale of 1 attoNewton) on a particle weighing only 0.43 milligrams. For reference, the gravitational force of one Newton is roughly equivalent to the force of gravity pushing down on an apple sitting on a table.

Tjerk Oosterkamp, Ph.D, is a senior author on the paper and a professor of theoretical physics at Leiden University in the Netherlands. He says that even though the gravitational force his team measured was on a very tiny particle—in fact, the tiniest particle to date to have such a force measured—he stresses that this measurement is still “a million miles away” from demonstrating quantum gravity.

“What we’re saying is that this is a step on the way towards measuring quantum gravity effects,” Oosterkamp explains.

Being able to measure these effects could be an important first step toward a clearer understanding of quantum gravity —which could unlock secrets about the very origin of the universe itself.

YOU CAN THINK ABOUT gravitational effects like a sound wave. To detect a quieter noise, an audio recorder needs to be more sensitive and it needs to filter out background noise. Similarly, the smaller an object, the “quieter” its gravitational force.

To “hear” the gravitational force on their 0.43-milligram particle, Oosterkamp and his colleagues needed to design an experiment to listen very closely while filtering out non-gravitational vibrations, like the random motion of particles buzzing and colliding that creates thermal energy. The cooler the experiment, the fewer stray vibrations to remove.

To do this, the team relied on a combination of tools to increase sensitivity, including: a dilution refrigerator (similar to the kind used to cool down quantum computers ) to minimize thermal energy, a mass-spring system to absorb environmental vibrations, and a superconducting “trap” to levitate the small particle to isolate it from any lingering vibrations. A second 2.4-kilogram source mass was placed nearby to create a gravitational force for the levitating particle; two objects with mass are required in such an experiment so that one source’s gravitational force can act upon the other, much like Earth and the moon.

According to Oosterkamp, building this contraption to operate under such extremely cold conditions—very close to absolute zero , or -273.15 degrees Celsius—is what sets this result apart. It’s also why he thought the experiment might never take place to begin with.

“It was unexpected that this actually works,” Oosterkamp says. “I showed my efforts to a retired colleague when he revisited the lab, and he saw all these masses and springs suspended from this very cold plate in our dilution refrigerator, and he asked ‘Why do you expect you can even cool this Christmas tree?’”

Because of these precautions to eliminate excess vibrations, the team was able to measure a 30-attoNewton gravitational force on the levitating test particle.

Yasunori Nomura, Ph.D., is a professor of theoretical physics at UC Berkeley whose work focuses on quantum theory and quantum gravity. Nomura says that while this experimental design could play a role in isolating gravitational forces on even smaller particles, it may still have limitations when attempting to measure quantum gravity itself.

“This measurement is a step toward directly observing gravitational forces in a truly quantum regime,” Nomura says. However, one sticking point, he says, is that the effects of quantum gravity are thought to only become significant at extremely small scales. “Reaching these scales with current measurement techniques, including levitating a small mass in superconducting traps, is impossible,” Nomura says.

Nomura says there may also be other approaches to measuring quantum gravity that avoid directly measuring small particles at all.

WHILE OOSTERKAMP’S GRAVITY DETECTOR may not be measuring quantum gravity effects anytime soon, he hopes that it could soon play a role in detecting large gravity effects instead. In particular, he hopes to use it as a tool to increase the sensitivity of experiments looking for gravitational waves —the ripple effects in spacetime left behind by large gravitational events like colliding black holes. Experiments like the U.S.-based Laser Interferometer Gravitational-wave Observatory (LIGO) and Italy-based Virgo gravitational wave observatory (VIRGO) are already detecting these ripples by measuring very small changes in the path of a laser across multiple kilometers.

“We’re hoping to build the successor to LIGO/VIRGO, which is called the Einstein Telescope ,” says Oosterkamp. This telescope is planned to be built in Europe in the mid-2030s and would be a next-generation gravitational wave detector. “They [the LIGO/VIRGO team] can teach us about even lower vibrations, and we tell them what we know about cooling things.”

Rana Adhikari, Ph.D., is a professor of physics at CalTech who has contributed to LIGO. He agrees that learning how to limit vibrations through cooling will play an important role in future gravitational wave detectors.

“The most interesting part [of this work] is how they are able to get the temperature so low and maintain such exquisitely low acceleration noises,” Adhikari says. “Future gravitational wave detectors operated [under cold conditions] will need to build on the foundation of this work. Being able to operate at such a low temperature would eliminate nearly all of the thermodynamic noise sources that we struggle with.”

And while Oosterkamp’s work may not yet pave a clear path toward measuring quantum gravity, Adhikari says that it’s likely one of many puzzle pieces that will unlock this world-changing scientific discovery.

“This [work] is a great example of how experimental ingenuity can lead to making measurements of the universe in a new way,” Adhikari says. “The road towards quantum gravity will be decorated with experiments of ever increasing sensitivity.”

Sarah is a science and technology journalist based in Boston interested in how innovation and research intersect with our daily lives. She has written for a number of national publications and covers innovation news at Inverse .

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Exploring off-grid energy choices: household decisions in upper blinkwater, south africa.

1. Introduction

2. theoretical literature of household energy decision, 3. materials and methods, 3.1. study area, 3.2. research methodology and data collection, 3.3. sampling, 3.4. data analysis, 4.1. demographics and energy consumption patterns of respondents, 4.2. factors influencing household energy choices, 5. discussion, 6. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Sarkodie, S.A.; Adams, S. Electricity Access and Income Inequality in South Africa: Evidence from Bayesian and NARDL Analyses. Energy Strategy Rev. 2020 , 29 , 100480. [ Google Scholar ] [ CrossRef ]
• Pandyaswargo, A.H.; Ruan, M.; Htwe, E.; Hiratsuka, M.; Wibowo, A.D.; Nagai, Y.; Onoda, H. Estimating the Energy Demand and Growth in Off-Grid Villages: Case Studies from Myanmar, Indonesia, and Laos. Energies 2020 , 13 , 5313. [ Google Scholar ] [ CrossRef ]
• Muhumuza, R.; Zacharopoulos, A.; Mondol, J.D.; Smyth, M.; Pugsley, A. Energy Consumption Levels and Technical Approaches for Supporting Development of Alternative Energy Technologies for Rural Sectors of Developing Countries. Renew. Sustain. Energy Rev. 2018 , 97 , 90–102. [ Google Scholar ] [ CrossRef ]
• Meried, E.W. Rural Household Preferences in Transition from Traditional to Renewable Energy Sources: The Applicability of the Energy Ladder Hypothesis in North Gondar Zone. Heliyon 2021 , 7 , e08418. [ Google Scholar ] [ CrossRef ]
• General Household Survey. Statistical Release P0318 ; Stats SA: Pretoria, South Africa, 2022.
• Shackleton, C.; Sinasson, G.; Adeyemi, O.; Martins, V. Fuelwood in South Africa Revisited: Widespread Use in a Policy Vacuum. Sustainability 2022 , 14 , 11018. [ Google Scholar ] [ CrossRef ]
• Masuku, B. Rethinking South Africa’s Household Energy Poverty through the Lens of off-Grid Energy Transition. Dev. South. Afr. 2024 , 41 , 467–489. [ Google Scholar ] [ CrossRef ]
• Meyer, E.L.; Overen, O.K. Towards a Sustainable Rural Electrification Scheme in South Africa: Analysis of the Status Quo. Energy Rep. 2021 , 7 , 4273–4287. [ Google Scholar ] [ CrossRef ]
• Ravanbach, B.; Makaka, G.; Shambira, N.; Ernest Van Dyk, E.; Schultz, R.D.; Vorster, F.J.; Agert, C.; Von Maydell, K.; Hanke, B.; Weigel, O.; et al. A Systematic Approach to Development of Key Performance Indicators for PV Hybrid Mini-Grids. Available online: https://www.sasec.org.za/papers2019/9.pdf (accessed on 14 August 2022).
• Ravanbach, B.; Kühnel, M.; Hanke, B.; von Maydell, K.; Van Dyk, E.E.; Vumbugwa, M.; Makaka, G.; Lesala, M.E.; Shambira, N.; Roro, K. Development of a Smart Monitoring and Evaluation Framework for Hybrid Renewable Mini-Grids. In Proceedings of the 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 10–12 September 2020. [ Google Scholar ]
• Mensah, J.T.; Adu, G. An Empirical Analysis of Household Energy Choice in Ghana. Renew. Sustain. Energy Rev. 2015 , 51 , 1402–1411. [ Google Scholar ] [ CrossRef ]
• Verma, P.; Kumari, T.; Raghubanshi, A.S. Energy Emissions, Consumption and Impact of Urban Households: A Review. Renew. Sustain. Energy Rev. 2021 , 147 , 111210. [ Google Scholar ] [ CrossRef ]
• Steg, L.; Shwom, R.; Dietz, T. What Drives Energy Consumers?: Engaging People in a Sustainable Energy Transition. IEEE Power Energy Mag. 2018 , 16 , 20–28. [ Google Scholar ] [ CrossRef ]
• Jan, I.; Khan, H.; Hayat, S. Determinants of Rural Household Energy Choices: An Example from Pakistan. Pol. J. Environ. Stud. 2012 , 21 , 635–641. [ Google Scholar ]
• Longe, O.M. An Assessment of the Energy Poverty and Gender Nexus towards Clean Energy Adoption in Rural South Africa. Energies 2021 , 14 , 3708. [ Google Scholar ] [ CrossRef ]
• Ngarava, S.; Zhou, L.; Ningi, T.; Chari, M.M.; Mdiya, L. Gender and Ethnic Disparities in Energy Poverty: The Case of South Africa. Energy Policy 2022 , 161 , 112755. [ Google Scholar ] [ CrossRef ]
• O’Brien, J.; Do, P.; Edelson, M. The Effects of Fuelwood on Children’s Schooling in Rural Vietnam. J. Asian Econ. 2021 , 72 , 101266. [ Google Scholar ] [ CrossRef ]
• Sule, I.K.; Yusuf, A.M.; Salihu, M.K. Impact of Energy Poverty on Education Inequality and Infant Mortality in Some Selected African Countries. Energy Nexus 2022 , 5 , 100034. [ Google Scholar ] [ CrossRef ]
• Kaygusuz, K. Energy Services and Energy Poverty for Sustainable Rural Development. Renew. Sustain. Energy Rev. 2011 , 15 , 936–947. [ Google Scholar ] [ CrossRef ]
• Ye, Y.; Koch, S.F. Measuring Energy Poverty in South Africa Based on Household Required Energy Consumption. Energy Econ. 2021 , 103 , 105553. [ Google Scholar ] [ CrossRef ]
• Israel-Akinbo, S.; Snowball, J.; Fraser, G. The Energy Transition Patterns of Low-Income Households in South Africa: An Evaluation of Energy Programme and Policy. J. Energy South. Afr. 2018 , 29 , 75–85. [ Google Scholar ] [ CrossRef ]
• Wernecke, B.; Langerman, K.E.; Howard, A.I.; Wright, C.Y. Fuel Switching and Energy Stacking in Low-Income Households in South Africa: A Review with Recommendations for Household Air Pollution Exposure Research. Energy Res. Soc. Sci. 2024 , 109 , 103415. [ Google Scholar ] [ CrossRef ]
• Todd, I.; McCauley, D. An Inter-Disciplinary Approach to the Energy Transition in South Africa. Discov. Sustain. 2021 , 2 , 33. [ Google Scholar ] [ CrossRef ]
• Dolšak, J.; Hrovatin, N.; Zorić, J. Analysing Consumer Preferences, Characteristics, and Behaviour to Identify Energy-Efficient Consumers. Sustainability 2020 , 12 , 9870. [ Google Scholar ] [ CrossRef ]
• Brown, A.; Hampton, H.; Foley, A.; Furszyfer Del Rio, D.; Lowans, C.; Caulfield, B. Understanding Domestic Consumer Attitude and Behaviour towards Energy: A Study on the Island of Ireland. Energy Policy 2023 , 181 , 113693. [ Google Scholar ] [ CrossRef ]
• Makonese, T.; Meyer, J. Household energisation in rural South Africa: A systems approach towards energy access. In Proceedings of the 2018 International Conference on the Domestic Use of Energy (DUE), Cape Town, South Africa, 3–5 April 2018. [ Google Scholar ]
• Uhunamure, S.E.; Nethengwe, N.S.; Musyoki, A. Driving Forces for Fuelwood Use in Households in the Thulamela Municipality, South Africa. J. Energy South. Afr. 2017 , 28 , 25–34. [ Google Scholar ] [ CrossRef ]
• Shrestha, B.; Tiwari, S.; Bajracharya, S.; Keitsch, M. Role of Gender Participation in Urban Household Energy Technology for Sustainability: A Case of Kathmandu. Discov. Sustain. 2021 , 2 , 19. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Johnson, O.W.; Han, J.Y.C.; Knight, A.L.; Mortensen, S.; Aung, M.T.; Boyland, M.; Resurrección, B.P. Intersectionality and Energy Transitions: A Review of Gender, Social Equity and Low-Carbon Energy. Energy Res. Soc. Sci. 2020 , 70 , 101774. [ Google Scholar ] [ CrossRef ]
• Steg, L.; Perlaviciute, G.; Sovacool, B.K.; Bonaiuto, M.; Diekmann, A.; Filippini, M.; Hindriks, F.; Bergstad, C.J.; Matthies, E.; Matti, S.; et al. A Research Agenda to Better Understand the Human Dimensions of Energy Transitions. Front. Psychol. 2021 , 12 , 672776. [ Google Scholar ] [ CrossRef ]
• Muazu, N.B.; Ogujiuba, K.; Tukur, H.R. Biomass Energy Dependence in South Africa: Are the Western Cape Province Households Descending the Energy Ladder after Improvement in Electricity Access? Energy Rep. 2020 , 6 , 207–213. [ Google Scholar ] [ CrossRef ]
• Quinn, A.K.; Bruce, N.; Puzzolo, E.; Dickinson, K.; Sturke, R.; Jack, D.W.; Mehta, S.; Shankar, A.; Sherr, K.; Rosenthal, J.P. An Analysis of Efforts to Scale up Clean Household Energy for Cooking around the World. Energy Sustain. Dev. 2018 , 46 , 1–10. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nyaga, P.M.; Sang, A.; Bw’Obuya, M.; Mundia, S.M. Consumer Preferences for Cooking and Lighting Fuels and Domestic Energy Transition: A Nyeri Town, Kenya, Perspective. J. Energy Res. Rev. 2020 , 5 , 14–25. [ Google Scholar ] [ CrossRef ]
• Ateba, B.B.; Prinsloo, J.J.; Fourie, E. The Impact of Energy Fuel Choice Determinants on Sustainable Energy Consumption of Selected South African Households. J. Energy South. Afr. 2018 , 29 , 51–65. [ Google Scholar ] [ CrossRef ]
• Heltberg, R. Household Fuel and Energy Use in Developing Countries—A Multi Country Study ; The World Bank: Washington, DC, USA, 2003; pp. 1–87. [ Google Scholar ]
• Ruiz-Mercado, I.; Masera, O. Patterns of Stove Use in the Context of Fuel–Device Stacking: Rationale and Implications. Ecohealth 2015 , 12 , 42–56. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lesala, M.E.; Shambira, N.; Makaka, G.; Mukumba, P. The Energy Poverty Status of Off-Grid Rural Households: A Case of the Upper Blinkwater Community in the Eastern Cape Province, South Africa. Energies 2023 , 16 , 7772. [ Google Scholar ] [ CrossRef ]
• Bardazzi, R.; Pazienza, M.G. Vulnerable Households in the Energy Transition ; Bardazzi, R., Pazienza, M.G., Eds.; Studies in Energy, Resource and Environmental Economics; Springer International Publishing: Cham, Switzerland, 2023; ISBN 978-3-031-35683-4. [ Google Scholar ]
• Masekela, M.E.; Semenya, K. Factors Influencing the Use of Firewood Post-Electrification in Rural South Africa: The Case of Ga-Malahlela Village. J. Energy South. Afr. 2021 , 32 , 24–40. [ Google Scholar ] [ CrossRef ]
• Kasangana, K.K.; Masekameni, D.; Saliwa, S. Exploring Access to Clean Energy for Various Domestic Uses in Mpumalanga Province, South Africa. In Proceedings of the 2017 International Conference on the Domestic Use of Energy (DUE), Cape Town, South Africa, 4–5 April 2017; IEEE: Piscataway, NJ, USA, 2017. [ Google Scholar ]
• Lesala, M.E.; Shambira, N.; Makaka, G.; Mukumba, P. Exploring Energy Poverty among Off-Grid Households in the Upper Blinkwater Community, South Africa. Sustainability 2024 , 16 , 4627. [ Google Scholar ] [ CrossRef ]
• Shambira, N.; Makaka, G.; Mukumba, P.; Lesala, M.; Roro, K.; Julies, J.; Tazvinga, H. Wind Resource Assessment in the Upper Blinkwater Area in the Province of Eastern Cape, South Africa. Int. J. Eng. Res. Technol. 2020 , 9 , 387–402. [ Google Scholar ]
• Kühnel, S.; Ahmed, T.; Mehta, K.; Ehrenwirth, M.; Besner, R.; Knepper, K.; Ileka, H.; Trinkl, C.; Zörner, W. Holistic Approach to Develop Electricity Load Profiles for Rural Off-Grid Communities in Sub-Saharan Africa. In Proceedings of the Proceedings-ISES Solar World Congress 2021, Online, 25–29 October 2021; International Solar Energy Society: Freiburg im Breisgau, Germany, 2021; pp. 348–361. [ Google Scholar ]
• Nombakuse, Z. Assessment of Wind Power Integration to Solar Photovoltaic Mini-Grid. Doctoral Dissertation, University College Dublin, Dublin, Ireland, 2019. [ Google Scholar ]
• Korngiebel, D.M.; Taualii, M.; Forquera, R.; Harris, R.; Buchwald, D. Addressing the Challenges of Research with Small Populations. Am. J. Public Health 2015 , 105 , 1744–1747. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cochran, W.G. Sampling Techniques , 3rd ed.; John Wiley & Sons Inc.: New York, NY, USA, 1977. [ Google Scholar ]
• Danlami, A.H. Assessment of Factors Influencing Firewood Consumption in Bauchi State, Nigeria. J. Sustain. Sci. Manag. 2019 , 14 , 99–109. [ Google Scholar ]
• Alexopoulos, E.C. Introduction to Multivariate Regression Analysis. Hippokratia 2010 , 14 , 23–28. [ Google Scholar ]
• Mitchell, H.; Katz, M. Multivariable Analysis: A Primer for Readers of Medical Research. Ann. Intern. Med. 2003 , 138 , 644–650. [ Google Scholar ]
• Makonese, T.; Ifegbesan, A.P.; Rampedi, I.T. Household Cooking Fuel Use Patterns and Determinants across Southern Africa: Evidence from the Demographic and Health Survey Data. Energy Environ. 2018 , 29 , 29–48. [ Google Scholar ] [ CrossRef ]
• Netshipise, L.F.; Semenya, K. Evaluating Factors Influencing Firewood Consumption in Households at the Thulamela Local Municipality, South Africa. J. Energy South. Afr. 2022 , 33 , 48–62. [ Google Scholar ] [ CrossRef ]
• Rangel, M.; Thomas, D. Decision-Making in Complex Households ; National Bureau of Economic Research: Cambridge, UK, 2019. [ Google Scholar ]
• Aminu, R.O.; Dzudzor, M.A.; Oyawole, F.P.; Afolayan, S.O. Understanding the Determinants of Household Cooking-Fuel Choice in Sub-Saharan Africa: Evidence from Nigeria. Clean Energy 2024 , 8 , 144–155. [ Google Scholar ] [ CrossRef ]
• Osei-Gyebi, S. Does Inward Remittance Influence the Choice of Households’ Cooking Fuel? Int. J. Sustain. Energy 2023 , 42 , 1226–1240. [ Google Scholar ] [ CrossRef ]
• Hamid Danlami, A.; Islam, R.; Dewi Applanaidu, S. An Analysis of the Determinants of Households’ Energy Choice: A Search for Conceptual Framework. Int. J. Energy Econ. Policy 2015 , 5 , 197–205. [ Google Scholar ]
• Longe, O.M.; Rao, N.D.; Omowole, F.; Oluwalami, A.S.; Oni, O.T. A Case Study on Off-Grid Microgrid for Universal Electricity Access in the Eastern Cape of South Africa. Int. J. Energy Eng. 2017 , 2017 , 55–63. [ Google Scholar ] [ CrossRef ]
• Sovacool, B.K.; Griffiths, S. Culture and Low-Carbon Energy Transitions. Nat. Sustain. 2020 , 3 , 685–693. [ Google Scholar ] [ CrossRef ]
• Shackleton, S.; Campbell, B.M.; Wollenberg, E.; Campbell, B.; Edmunds, D. Devolution and Community-Based Natural Resource Management: Creating Space for Local People to Participate and Benefit? Nat. Resour. Perspect. 2012 , 76 , 1–6. [ Google Scholar ]

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Lesala, M.E.; Makaka, G.; Mukumba, P. Exploring Off-Grid Energy Choices: Household Decisions in Upper Blinkwater, South Africa. Energies 2024 , 17 , 3556. https://doi.org/10.3390/en17143556

Lesala ME, Makaka G, Mukumba P. Exploring Off-Grid Energy Choices: Household Decisions in Upper Blinkwater, South Africa. Energies . 2024; 17(14):3556. https://doi.org/10.3390/en17143556

Lesala, Mahali Elizabeth, Golden Makaka, and Patrick Mukumba. 2024. "Exploring Off-Grid Energy Choices: Household Decisions in Upper Blinkwater, South Africa" Energies 17, no. 14: 3556. https://doi.org/10.3390/en17143556

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A minimum energy cost hypothesis for human arm trajectories

Affiliation.

• 1 Department of Biology, University of Leeds, UK.
• PMID: 9116080
• DOI: 10.1007/s004220050324

Many tasks require the arm to move from its initial position to a specified target position, but leave us free to choose the trajectory between them. This paper presents and tests the hypothesis that trajectories are chosen to minimize metabolic energy costs. Costs are calculated for the range of possible trajectories, for movements between the end points used in previously published experiments. Calculated energy minimizing trajectories for a model with biarticular elbow muscles agree well with observed trajectories for fast movements. Good agreement is also obtained for slow movements if they are assumed to be performed by slower muscles. A model in which all muscles are uniarticular is less successful in predicting observed trajectories. The effects of loads and of reversing the direction of movement are investigated.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Teach yourself statistics

Hypothesis Test for a Mean

This lesson explains how to conduct a hypothesis test of a mean, when the following conditions are met:

• The sampling method is simple random sampling .
• The sampling distribution is normal or nearly normal.

Generally, the sampling distribution will be approximately normally distributed if any of the following conditions apply.

• The population distribution is normal.
• The population distribution is symmetric , unimodal , without outliers , and the sample size is 15 or less.
• The population distribution is moderately skewed , unimodal, without outliers, and the sample size is between 16 and 40.
• The sample size is greater than 40, without outliers.

This approach consists of four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results.

State the Hypotheses

Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis . The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa.

The table below shows three sets of hypotheses. Each makes a statement about how the population mean μ is related to a specified value M . (In the table, the symbol ≠ means " not equal to ".)

The first set of hypotheses (Set 1) is an example of a two-tailed test , since an extreme value on either side of the sampling distribution would cause a researcher to reject the null hypothesis. The other two sets of hypotheses (Sets 2 and 3) are one-tailed tests , since an extreme value on only one side of the sampling distribution would cause a researcher to reject the null hypothesis.

Formulate an Analysis Plan

The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements.

• Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used.
• Test method. Use the one-sample t-test to determine whether the hypothesized mean differs significantly from the observed sample mean.

Analyze Sample Data

Using sample data, conduct a one-sample t-test. This involves finding the standard error, degrees of freedom, test statistic, and the P-value associated with the test statistic.

SE = s * sqrt{ ( 1/n ) * [ ( N - n ) / ( N - 1 ) ] }

SE = s / sqrt( n )

• Degrees of freedom. The degrees of freedom (DF) is equal to the sample size (n) minus one. Thus, DF = n - 1.

t = ( x - μ) / SE

• P-value. The P-value is the probability of observing a sample statistic as extreme as the test statistic. Since the test statistic is a t statistic, use the t Distribution Calculator to assess the probability associated with the t statistic, given the degrees of freedom computed above. (See sample problems at the end of this lesson for examples of how this is done.)

Sample Size Calculator

As you probably noticed, the process of hypothesis testing can be complex. When you need to test a hypothesis about a mean score, consider using the Sample Size Calculator. The calculator is fairly easy to use, and it is free. You can find the Sample Size Calculator in Stat Trek's main menu under the Stat Tools tab. Or you can tap the button below.

Interpret Results

If the sample findings are unlikely, given the null hypothesis, the researcher rejects the null hypothesis. Typically, this involves comparing the P-value to the significance level , and rejecting the null hypothesis when the P-value is less than the significance level.

Test Your Understanding

In this section, two sample problems illustrate how to conduct a hypothesis test of a mean score. The first problem involves a two-tailed test; the second problem, a one-tailed test.

Problem 1: Two-Tailed Test

An inventor has developed a new, energy-efficient lawn mower engine. He claims that the engine will run continuously for 5 hours (300 minutes) on a single gallon of regular gasoline. From his stock of 2000 engines, the inventor selects a simple random sample of 50 engines for testing. The engines run for an average of 295 minutes, with a standard deviation of 20 minutes. Test the null hypothesis that the mean run time is 300 minutes against the alternative hypothesis that the mean run time is not 300 minutes. Use a 0.05 level of significance. (Assume that run times for the population of engines are normally distributed.)

Solution: The solution to this problem takes four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results. We work through those steps below:

Null hypothesis: μ = 300

Alternative hypothesis: μ ≠ 300

• Formulate an analysis plan . For this analysis, the significance level is 0.05. The test method is a one-sample t-test .

SE = s / sqrt(n) = 20 / sqrt(50) = 20/7.07 = 2.83

DF = n - 1 = 50 - 1 = 49

t = ( x - μ) / SE = (295 - 300)/2.83 = -1.77

where s is the standard deviation of the sample, x is the sample mean, μ is the hypothesized population mean, and n is the sample size.

Since we have a two-tailed test , the P-value is the probability that the t statistic having 49 degrees of freedom is less than -1.77 or greater than 1.77. We use the t Distribution Calculator to find P(t < -1.77) is about 0.04.

• If you enter 1.77 as the sample mean in the t Distribution Calculator, you will find the that the P(t < 1.77) is about 0.04. Therefore, P(t >  1.77) is 1 minus 0.96 or 0.04. Thus, the P-value = 0.04 + 0.04 = 0.08.
• Interpret results . Since the P-value (0.08) is greater than the significance level (0.05), we cannot reject the null hypothesis.

Note: If you use this approach on an exam, you may also want to mention why this approach is appropriate. Specifically, the approach is appropriate because the sampling method was simple random sampling, the population was normally distributed, and the sample size was small relative to the population size (less than 5%).

Problem 2: One-Tailed Test

Bon Air Elementary School has 1000 students. The principal of the school thinks that the average IQ of students at Bon Air is at least 110. To prove her point, she administers an IQ test to 20 randomly selected students. Among the sampled students, the average IQ is 108 with a standard deviation of 10. Based on these results, should the principal accept or reject her original hypothesis? Assume a significance level of 0.01. (Assume that test scores in the population of engines are normally distributed.)

Null hypothesis: μ >= 110

Alternative hypothesis: μ < 110

• Formulate an analysis plan . For this analysis, the significance level is 0.01. The test method is a one-sample t-test .

SE = s / sqrt(n) = 10 / sqrt(20) = 10/4.472 = 2.236

DF = n - 1 = 20 - 1 = 19

t = ( x - μ) / SE = (108 - 110)/2.236 = -0.894

Here is the logic of the analysis: Given the alternative hypothesis (μ < 110), we want to know whether the observed sample mean is small enough to cause us to reject the null hypothesis.

The observed sample mean produced a t statistic test statistic of -0.894. We use the t Distribution Calculator to find P(t < -0.894) is about 0.19.

• This means we would expect to find a sample mean of 108 or smaller in 19 percent of our samples, if the true population IQ were 110. Thus the P-value in this analysis is 0.19.
• Interpret results . Since the P-value (0.19) is greater than the significance level (0.01), we cannot reject the null hypothesis.

What is the hypothesis of energy resources?

Energy resources include fossil fuel (as coal, natural gas, and oil), renewable energy resources (as solar energy, wind energy, biomass, geothermal energy, ...), and nuclear energy (based on fission and/or fusion).

The hypothesis of energy resources is that sustainable and renewable energy sources, such as solar and wind power, are essential for reducing greenhouse gas emissions and addressing climate change. This hypothesis suggests that transitioning from fossil fuels to cleaner energy sources will help to create a more environmentally friendly and sustainable energy system.

Add your answer:

Which is not a way to conserve existing energy resources?

Wasting energy, by leaving lights on or appliances running when not in use, is not a way to conserve existing energy resources. It is important to be mindful of our energy usage in order to preserve resources for the future.

The hypothesis states that when rocks are deformed they bend and then break releasing stored energy?

This is a statement, not a hypothesis. A hypothesis is a testable explanation for a phenomenon based on observations and prior knowledge. A hypothesis for this scenario could be: &quot;If rocks are subjected to stress beyond their elastic limit, then they will deform by bending until they reach a breaking point, releasing stored energy in the form of seismic waves.&quot;

How are inexhaustible and renewable energy resources different?

Inexhaustible energy resources are those that are naturally replenished and limitless in supply, such as solar and wind energy. Renewable energy resources are also naturally replenished, but they may have limitations in terms of available quantity or generation capacity, such as geothermal energy.

What is a good hypothesis for new energy source?

A possible hypothesis could be: &quot;A new energy source utilizing advanced solar technology will be able to efficiently and sustainably meet global energy demands, reducing reliance on fossil fuels and mitigating the impact of climate change.&quot;

Which energy resources are found above the Earth surface?

solar wind biomass

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Novel low-energy peak in the one-particle spectral function of the electron gas: Breakdown of the Landau's hypothesis as to the Fermi liquids in simple metals

• Takada, Yasutami

Based on a nonperturbative scheme to determine the self-energy \Sigma(k,iw_n) with automatically satisfying the Ward identity and the total momentum conservation law, a fully self-consistent calculation is done in the electron gas at various temperatures T to obtain G(k,iw_n) the one-particle Green's function with fulfilling all known conservation laws, sum rules, and correct asymptotic behaviors; here, T is taken unprecedentedly low, namely, T/E_F down to 10^{-4} with E_F the Fermi energy, and tiny mesh as small as 10^{-4}k_F is chosen near the Fermi surface in k space with k_F the Fermi momentum. By analytically continuing G(k,iw_n) to the retarded function G^R(k,w), we find a novel low-energy peak, in addition to the quasiparticle (QP) peak and one- and two-plasmon high-energy satellites, in the spectral function A(k,w)[= -Im G^R(k,w)/\pi] for T less than about 10^{-3}E_F in the simple-metal density region (2<r_s<6 with r_s the dimensionless density parameter). This new peak is attributed to the effect of excitonic attraction on \Sigma(k,iw_n) arising from multiple excitations of tightly bound electron-hole pairs in the polarization function \Pi(q,iw_q) for |q| equal to about 2k_F and |w_q| << E_F and thus it is dubbed ``excitron''. Although this excitron peak height is only about a one-hundredth of that of QP, its excitation energy is about a half of that of QP for |k| equal to about k_F, seemingly in contradiction to the Landau's hypothesis as to the one-to-one correspondence of low-energy excitations between a free Fermi gas and an interacting normal Fermi liquid. As for the QP properties, our results of both the effective mass m^* and the renormalization factor z^* are in good agreement with those provided by recent quantum Monte Carlo simulations and available experiments.

• Condensed Matter - Strongly Correlated Electrons