Phase Shift in Grid Cells

I would like to fully understand the phase shift in the grid cell in MEC. From my understanding, since there is a hexagonal (i.e. equilateral triangle) lattice, and hence there are 3 $phi$'s, I have the following questions in regards to that:

1). If there are 3 $phi$'s, how does the phase shift happen in (I guess) 3 phases, i.e. why are there 3 shifts? I am just very confused at a fundamental level: why does the phase shift happen, is it because of the phase resettling in neurons?

2). As Wikipedia defines it: "Phase shift is any change that occurs in the phase of one quantity, or in the phase difference between two or more quantities", so my question is, what is the "quantity" in the example with grid cells or what is "the phase difference between two or more quantities" here?

In other words, why there is the 1st shift, the 2nd and the 3rd? Why do the second and the third happen? Is it because the pattern of grid cells is an equilateral triangle and the 1st type of signal produces the 1st grid field (the blob) and the 2nd type of signal produces the 2nd blob, here I edited this picture to illustrate my thought:

and we can see, if we model it with cosine, since

Phase-contrast microscopy

Phase-contrast microscopy is an optical microscopy technique that converts phase shifts in light passing through a transparent specimen to brightness changes in the image. Phase shifts themselves are invisible, but become visible when shown as brightness variations.

When light waves travel through a medium other than a vacuum, interaction with the medium causes the wave amplitude and phase to change in a manner dependent on properties of the medium. Changes in amplitude (brightness) arise from the scattering and absorption of light, which is often wavelength-dependent and may give rise to colors. Photographic equipment and the human eye are only sensitive to amplitude variations. Without special arrangements, phase changes are therefore invisible. Yet, phase changes often convey important information.

Phase-contrast microscopy is particularly important in biology. It reveals many cellular structures that are invisible with a bright-field microscope, as exemplified in the figure. These structures were made visible to earlier microscopists by staining, but this required additional preparation and death of the cells. The phase-contrast microscope made it possible for biologists to study living cells and how they proliferate through cell division. It is one of the few methods available to quantify cellular structure and components that does not use fluorescence. [1] After its invention in the early 1930s, [2] phase-contrast microscopy proved to be such an advancement in microscopy that its inventor Frits Zernike was awarded the Nobel Prize in Physics in 1953. [3]

The basic principle to making phase changes visible in phase-contrast microscopy is to separate the illuminating (background) light from the specimen-scattered light (which makes up the foreground details) and to manipulate these differently.

The ring-shaped illuminating light (green) that passes the condenser annulus is focused on the specimen by the condenser. Some of the illuminating light is scattered by the specimen (yellow). The remaining light is unaffected by the specimen and forms the background light (red). When observing an unstained biological specimen, the scattered light is weak and typically phase-shifted by −90° (due to both the typical thickness of specimens and the refractive index difference between biological tissue and the surrounding medium) relative to the background light. This leads to the foreground (blue vector) and background (red vector) having nearly the same intensity, resulting in low image contrast.

In a phase-contrast microscope, image contrast is increased in two ways: by generating constructive interference between scattered and background light rays in regions of the field of view that contain the specimen, and by reducing the amount of background light that reaches the image plane. First, the background light is phase-shifted by −90° by passing it through a phase-shift ring, which eliminates the phase difference between the background and the scattered light rays.

When the light is then focused on the image plane (where a camera or eyepiece is placed), this phase shift causes background and scattered light rays originating from regions of the field of view that contain the sample (i.e., the foreground) to constructively interfere, resulting in an increase in the brightness of these areas compared to regions that do not contain the sample. Finally, the background is dimmed

70-90% by a gray filter ring this method maximizes the amount of scattered light generated by the illumination (i.e., background) light, while minimizing the amount of illumination light that reaches the image plane. Some of the scattered light that illuminates the entire surface of the filter will be phase-shifted and dimmed by the rings, but to a much lesser extent than the background light,which only illuminates the phase-shift and gray filter rings.

The above describes negative phase contrast. In its positive form, the background light is instead phase-shifted by +90°. The background light will thus be 180° out of phase relative to the scattered light. The scattered light will then be subtracted from the background light to form an image with a darker foreground and a lighter background, as shown in the first figure. [4] [5] [6]

Press release

How do we know where we are? How can we find the way from one place to another? And how can we store this information in such a way that we can immediately find the way the next time we trace the same path? This year’s Nobel Laureates have discovered a positioning system, an “inner GPS” in the brain that makes it possible to orient ourselves in space, demonstrating a cellular basis for higher cognitive function.

In 1971, John O’Keefe discovered the first component of this positioning system. He found that a type of nerve cell in an area of the brain called the hippocampus that was always activated when a rat was at a certain place in a room. Other nerve cells were activated when the rat was at other places. O’Keefe concluded that these “place cells” formed a map of the room.

More than three decades later, in 2005, May-Britt and Edvard Moser discovered another key component of the brain’s positioning system. They identified another type of nerve cell, which they called “grid cells”, that generate a coordinate system and allow for precise positioning and pathfinding. Their subsequent research showed how place and grid cells make it possible to determine position and to navigate.

The discoveries of John O’Keefe, May-Britt Moser and Edvard Moser have solved a problem that has occupied philosophers and scientists for centuries – how does the brain create a map of the space surrounding us and how can we navigate our way through a complex environment?

How do we experience our environment?

The sense of place and the ability to navigate are fundamental to our existence. The sense of place gives a perception of position in the environment. During navigation, it is interlinked with a sense of distance that is based on motion and knowledge of previous positions.

Questions about place and navigation have engaged philosophers and scientists for a long time. More than 200 years ago, the German philosopher Immanuel Kant argued that some mental abilities exist as a priori knowledge, independent of experience. He considered the concept of space as an inbuilt principle of the mind, one through which the world is and must be perceived. With the advent of behavioural psychology in the mid-20th century, these questions could be addressed experimentally. When Edward Tolman examined rats moving through labyrinths, he found that they could learn how to navigate, and proposed that a “cognitive map” formed in the brain allowed them to find their way. But questions still lingered – how would such a map be represented in the brain?

John O’Keefe and the place in space

John O’Keefe was fascinated by the problem of how the brain controls behaviour and decided, in the late 1960s, to attack this question with neurophysiological methods. When recording signals from individual nerve cells in a part of the brain called the hippocampus, in rats moving freely in a room, O’Keefe discovered that certain nerve cells were activated when the animal assumed a particular place in the environment (Figure 1). He could demonstrate that these “place cells” were not merely registering visual input, but were building up an inner map of the environment. O’Keefe concluded that the hippocampus generates numerous maps, represented by the collective activity of place cells that are activated in different environments. Therefore, the memory of an environment can be stored as a specific combination of place cell activities in the hippocampus.

May-Britt and Edvard Moser find the coordinates

May-Britt and Edvard Moser were mapping the connections to the hippocampus in rats moving in a room when they discovered an astonishing pattern of activity in a nearby part of the brain called the entorhinal cortex. Here, certain cells were activated when the rat passed multiple locations arranged in a hexagonal grid (Figure 2). Each of these cells was activated in a unique spatial pattern and collectively these “grid cells” constitute a coordinate system that allows for spatial navigation. Together with other cells of the entorhinal cortex that recognize the direction of the head and the border of the room, they form circuits with the place cells in the hippocampus. This circuitry constitutes a comprehensive positioning system, an inner GPS, in the brain (Figure 3).

A place for maps in the human brain

Recent investigations with brain imaging techniques, as well as studies of patients undergoing neurosurgery, have provided evidence that place and grid cells exist also in humans. In patients with Alzheimer’s disease, the hippocampus and entorhinal cortex are frequently affected at an early stage, and these individuals often lose their way and cannot recognize the environment. Knowledge about the brain’s positioning system may, therefore, help us understand the mechanism underpinning the devastating spatial memory loss that affects people with this disease.

The discovery of the brain’s positioning system represents a paradigm shift in our understanding of how ensembles of specialized cells work together to execute higher cognitive functions. It has opened new avenues for understanding other cognitive processes, such as memory, thinking and planning.

Key publications:

O’Keefe, J., and Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely‐moving rat. Brain Research 34, 171-175.

O’Keefe, J. (1976). Place units in the hippocampus of the freely moving rat. Experimental Neurology 51, 78-109.

Fyhn, M., Molden, S., Witter, M.P., Moser, E.I., Moser, M.B. (2004) Spatial representation in the entorhinal cortex. Science 305, 1258-1264.

Hafting, T., Fyhn, M., Molden, S., Moser, M.B., and Moser, E.I. (2005). Microstructure of spatial map in the entorhinal cortex. Nature 436, 801-806.

Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B.L., Witter, M.P., Moser, M.B., and Moser, E.I. (2006). Conjunctive representation of position, direction, and velocity in the entorhinal cortex. Science 312, 758-762.

John O’Keefe was born in 1939 in New York City, USA, and holds both American and British citizenships. He received his doctoral degree in physiological psychology from McGill University, Canada in 1967. After that, he moved to England for postdoctoral training at University College London. He has remained at University College and was appointed Professor of Cognitive Neuroscience in 1987. John O’Keefe is currently Director of the Sainsbury Wellcome Centre in Neural Circuits and Behaviour at University College London.

May-Britt Moser was born in Fosnavåg, Norway in 1963 and is a Norwegian citizen. She studied psychology at the University of Oslo together with her future husband and co-Laureate Edvard Moser. She received her Ph.D. in neurophysiology in 1995. She was a postdoctoral fellow at the University of Edinburgh and subsequently a visiting scientist at University College London before moving to the Norwegian University of Science and Technology in Trondheim in 1996. May-Britt Moser was appointed Professor of Neuroscience in 2000 and is currently Director of the Centre for Neural Computation in Trondheim.

Edvard I. Moser was born in born 1962 in Ålesund, Norway and has Norwegian citizenship. He obtained his Ph.D. in neurophysiology from the University of Oslo in 1995. He was a postdoctoral fellow together with his wife and co‐Laureate May‐Britt Moser, first at the University of Edinburgh and later a visiting scientist in John O’Keefe’s laboratory in London. In 1996 they moved to the Norwegian University of Science and Technology in Trondheim, where Edvard Moser became Professor in 1998. He is currently Director of the Kavli Institute for Systems Neuroscience in Trondheim.

The Nobel Assembly, consisting of 50 professors at Karolinska Institutet, awards the Nobel Prize in Physiology or Medicine. Its Nobel Committee evaluates the nominations. Since 1901 the Nobel Prize has been awarded to scientists who have made the most important discoveries for the benefit of mankind.

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Nobel Prizes 2020

Twelve laureates were awarded a Nobel Prize in 2020, for achievements that have conferred the greatest benefit to humankind.

Their work and discoveries range from the formation of black holes and genetic scissors to efforts to combat hunger and develop new auction formats.

Onion Root Tips Mitosis

Most of the cells that I identified were in interphase. 20 of 36 were in interphase and 55.6% of them were in interphase. This is because most cells spend their time growing and preparing to reproduce.

2. What percent were in mitosis?

The percent of cells in mitosis were 44.5%. You don’t count the cells in interphase because they haven’t started the process of mitosis yet.

3. Which phase of mitosis takes the longest?

The phase of mitosis that takes the longest is interphase because this includes the whole amount of time that the cell is resting and then when it prepares to enter prophase.

4. During which stage is the nucleus visible as a dark spot?

During interphase, the cell is visible as a dark spot because the nucleus is still intact and prophase hasn’t started yet.

5. How can you recognize a cell in metaphase?

A cell in metaphase looks very interesting. This is when the chromosomes are starting to align and what you see looks like dark slightly wavy lines in side of the cell.

The next thing we did , was we applied the things we learned through the online projects to onion root tips. We looked at them under a microscope and collected data by looking at a group of cells, and counting up the amount of cells in each phase.

Advanced information

The 2014 Nobel Prize in Physiology or Medicine is awarded to Dr. John M. O’Keefe, Dr. May-Britt Moser and Dr. Edvard I. Moser for their discoveries of nerve cells in the brain that enable a sense of place and navigation. These discoveries are ground breaking and provide insights into how mental functions are represented in the brain and how the brain can compute complex cognitive functions and behaviour. An internal map of the environment and a sense of place are needed for recognizing and remembering our environment and for navigation. This navigational ability, which requires integration of multi-modal sensory information, movement execution and memory capacities, is one of the most complex of brain functions. The work of the 2014 Laureates has radically altered our understanding of these functions. John O’Keefe discovered place cells in the hippocampus that signal position and provide the brain with spatial memory capacity. May-Britt Moser and Edvard I. Moser discovered in the medial entorhinal cortex, a region of the brain next to hippocampus, grid cells that provide the brain with an internal coordinate system essential for navigation. Together, the hippocampal place cells and the entorhinal grid cells form interconnected nerve cell networks that are critical for the computation of spatial maps and navigational tasks. The work by John O’Keefe, May-Britt Moser and Edvard Moser has dramatically changed our understanding of how fundamental cognitive functions are performed by neural circuits in the brain and shed new light onto how spatial memory might be created.


The sense of place and the ability to navigate are some of the most fundamental brain functions. The sense of place gives a perception of the position of the body in the environment and in relation to surrounding objects. During navigation, it is interlinked with a sense of distance and direction that is based on the integration of motion and knowledge of previous positions. We depend on these spatial functions for recognizing and remembering the environment to find our way.

Questions about these fundamental brain functions have engaged philosophers and scientists for a long time. During the 18 th century the German philosopher Immanuel Kant (1724-1804) argued that some mental abilities exist independent of experience. He considered perception of place as one of these innate abilities through which the external world had to be organized and perceived.

A concept of a map-like representation of place in the brain was advocated for by the American experimental psychologist Edward Tolman, who studied how animals learn to navigate (Tolman, 1948). He proposed that animals could experience relationships between places and events and that the exploration of the environment gradually resulted in the formation of a cognitive map that enabled animals to navigate and find the optimal path through the environment. In this view, cognitive maps represent the environment as a gestalt that allows the subject to experience the room and navigate.

Tolman’s theory opposed the prevailing view among behaviourists that complex behaviours are achieved by chains of sensory-motor response relationships. But it did not address where in the brain these functions may be localized and how the brain computes such complex behaviours. The advent of techniques to record from cells in the brain of animals that were freely moving in the environment, using chronically implanted micro wires (Sturmwasser, 1958), made it possible to approach these questions.

Finding the place cells

John O’Keefe had a background in physiological psychology, working with Ronald Melzack at McGill University before he moved to the laboratory of the pain researcher Patrick Wall at University College in London, where he started his work on behaving animals in the late 1960s. There he discovered the place cells, when recording from neurons in the dorsal partition of hippocampus, called CA1, together with Dostrovsky, in rats moving freely in a bounded area (O’Keefe and Dostrovsky, 1971) (Figure 1).

Figure 1. Place cells. To the right is a schematic of the rat. The hippocampus, where the place cells are located is highlighted. The grey square depicts the open field the rat is moving over. Place cells fire when the animal reaches a particular location in the environment. The dots indicate the rat’s location in the arena when the place cell is active. Different place cells in the hippocampus fire at different places in the arena.

The firing pattern of these cells was completely unexpected. Place cells were active in a way that had not been seen for any cells in the brain before. Individual place cells were only active when the animal was in a particular place in the environment, namely their place field. By systematically changing the environment and testing different theoretical possibilities for the creation of the place fields O’Keefe showed that place cell firing did not merely reflect activity in sensory neurons, but that it represented a complex gestalt of the environment.

Different place cells could be active in different places and the combination of activity in many place cells created an internal neural map representing a particular environment (O’Keefe, 1976 O’Keefe and Conway, 1978). O’Keefe concluded together with Nadel that place cells provide the brain with a spatial reference map system, or a sense of place (O’Keefe and Nadel, 1978). He showed that the hippocampus can contain multiple maps represented by combinations of activity in different place cells that were active at different times in different environments. A specific serial combination of active place cells may therefore represent a unique environment, while other combinations represent other environments. Through O’Keefe’s discoveries, the cognitive map theory had found its representation in the brain.

A prerequisite for O’Keefe’s experiments was the development of appropriate recording techniques to be used in freely moving animals. Although O’Keefe was an early user of these techniques, he was not the first to record from hippocampal or other nerve cells in intact animals (see O’Keefe and Nadel 1978). However, researchers mostly used restricted behavioural task or strict stimulus-response protocols. In contrast, O’Keefe recorded the cellular activity during natural behaviour, which allowed him to observe the unique place fields and relate the neural activity in the place cells to represent the sense of place.

In subsequent experiments, O’Keefe showed that the place cells might have memory functions (O’Keefe and Conway, 1978 O’Keefe and Speakman, 1987). The simultaneous rearrangement in many place cells in different environments was called remapping and O’Keefe showed that remapping is learned, and once it is established, it can be stable over time (Lever et al., 2002). The place cells may therefore provide a cellular substrate for memory processes, where a memory of an environment can be stored as specific combinations of place cells.

At first, the proposition that the hippocampus was involved in spatial navigation was met with some scepticism. However, it was later appreciated that the discovery of place cells, the meticulous demonstration that these cells represent a mental map far from primary sensory input, and the proposal that hippocampus contains an inner map that can store information about the environment, were seminal. O’Keefe’s discovery sparked a large number of experimental and theoretical studies on how place cells are engaged in generating spatial information and in spatial memory processes. The general notion from these studies is that the key function of the place cells are to create a map of the environment, although they may also be involved in measuring distance under some circumstances (Ravassard et al., 2013).

From hippocampus to grid cells in the entorhinal cortex

Through the 1980s and 1990s the prevailing theory was that the formation of place fields originated within the hippocampus itself.

May-Britt Moser and Edvard Moser, who were studying the hippocampus, both during their PhD work in Per Andersen’s laboratory in Oslo and afterwards both as visiting scientists in Richard Morris’ laboratory in Edinburgh and John O’Keefe’s laboratory in London, asked whether the place cell firing can be generated from activity outside hippocampus. The major input to the hippocampus comes from a structure on the dorsal edge of the rat’s brain, the entorhinal cortex. A large part of the output from the entorhinal cortex projects to the dentate gurus in hippocampus, which in turn connect to the region in the hippocampus called CA3, and further to CA1 in the dorsal hippocampus. Interestingly, this is the same the part of the brain in which John O’Keefe first found the place cells. In 2002, the Mosers found that disconnecting projections from the entorhinal cortex through CA3 did not abolish the CA1 place fields (Brun et al., 2002). These findings, and the knowledge that medial entorhinal cortex is also directly and reciprocally connected to the CA1 region, prompted May-Britt Moser and Edvard Moser to look in the medial entorhinal cortex for place coding cells. In a first study they established, similar to what others had shown, that the medial entorhinal cortex contained cells that shared characteristics with the place cells in hippocampus (Fyhn et al., 2004). However, in a later study using larger encounters for the animals to move in, they discovered a novel cell type, the grid cells, that had unusual properties, (Hafting et al., 2005).

The grid cells showed an astonishing firing pattern. They were active in multiple places in the open box that together formed nodes of an extended hexagonal grid (Figure 2), similar to the hexagonal arrangements of holes in a beehive.

Figure 2. Grid cells. The grid cells are located in the entorhinal cortex depicted in blue. A single grid cell fires when the animal reaches particular locations in the arena. These locations are arranged in a hexagonal pattern.

Grid cells in the same area of the medial enthorinal cortex fire with the same spacing and orientation of the grid, but different phasing, so that together they cover every point in the environment.

The Mosers found that the distance of the grid fields varies in the medial entorhinal cortex with the largest fields in the ventral part of the cortex. They also showed that the grid formation did not arise out of a simple transformation of sensory or motor signals, but out of complex network activity.

The grid pattern had not been seen in any brain cells before! The Mosers concluded that the grid cells were part of a navigation or path integration system. The grid system provided a solution to measuring movement distances and added a metric to the spatial maps in hippocampus.

The Mosers further showed that grid cells were embedded in a network in the medial entorhinal cortex of head direction cells and border cells, and in many cases, cells with a combined function (Solstad et al., 2008). Head-direction cells were first described by James Ranck (1985) in another part of the brain, the subiculum. They act like a compass and are active when the head of an animal points in a certain direction. Border cells are active in reference to walls that the animal encounters when moving in a closed environment (Solstad et al., 2008 Savelli, et al. 2008). The existence of border cells was predicted by theoretical modelling by O’Keefe and colleagues (Hartley, et al. 2000). The Mosers showed that the grid cells, the head direction cells, and the border cells, projected to hippocampal place cells (Zhang et al. 2013). Using recordings from multiple grid cells in different parts of the entorhinal cortex, the Mosers also showed that the grid cells are organized in functional modules with different grid spacing ranging in distance from a few centimetres to meters, thereby covering small to large environments.

The Mosers further explored the relationship between grid cells and place cells in theoretical models Solstad et al., 2006), lesion experiments (Bonnevie et al., 2013 Hafting et al., 2008), and in remapping experiments (Fyhn et al. 2007). These and other studies by Mosers and O’Keefe, as well as by others, have shown that there is a reciprocal influence between grid cells in the medial entorhinal cortex and place cells in the hippocampus and that other spatially- tuned cells in the entorhinal cortex, in particular the border cells (Figure 3), may contribute in the generation of the firing pattern of the place cells (Brandon et al., 2011 Koenig et al., 2011 Bush, Berry and Burgess, 2014, Bjerkness et al. 2014).

Figure 3. A schematic showing grid cells (blue) and place cells (yellow) in the entorhinal cortex and hippocampus, respectively.

The Mosers’ discovery of the grid cells, a spatial metric coordination system, and their identification of the medial entorhinal cortex as a computational centre for spatial representation, is a break-through that opens up new avenues to advance the understanding of the neural mechanisms underlying spatial cognitive functions.

The grid and place cell systems are found in many mammalian species including humans

Since the initial description of place and grid cells in rat and mice, these cell types have also been found in other mammals (Killian et al., 2012 Ulanovsky et al., 2007 Yartsev et al., 2011, 2013). Humans have large hippocampal-entorhinal brain structures and these structures have long been implicated in spatial learning and episodic memory (Squire, 2004). A number of studies support the idea that the human brain has a spatial- coding system that is similar to that found in non-human mammals. Thus, researchers have found place-like cells in the hippocampus (Ekstrom et al., 2003 Jacobs et al., 2010) and grid-like cells in the entorhinal cortex (Jacobs et al., 2013) when directly recording from nerve cells in the human brain of patients with epilepsy undergoing pre-surgical investigation. Using functional imaging (fMRI). Doeller et al. (2010) have also provided support for the existence of grid cells in the human entorhinal cortex.

The similarity of the hippocampal-entorhinal structure in all mammals and the presence of hippocampal-like structures in non- mammalian vertebrates with navigational capacity suggest that the grid-place cells system is a functional and robust system that may be conserved in vertebrate evolution.

The importance of the discovery of place cells and grid cells for research in cognitive neuroscience

It is an emergent theme that place-coding cells in the hippocampal structures are involved in storing and/or retrieving spatial memories. In the 1950s Scoville and Milner (1957) published their report on the patient Henry Molaison (HM), who had his two hippocampi surgically removed for treatment of epilepsy. The loss of hippocampi caused severe memory deficits, as evident by the clinical observation that HM was unable to encode new memories, while he could still retrieve old memories. HM had lost what has later been named episodic memory (Tulving and Markowitch 1998), referring to our ability to remember self-experienced events. There is no direct evidence that place cells are coding episodic memory. However, place cells can encode not only for the current spatial location, but also where the animal has just been and where it is going next (Ferbinteanu and Shapiro, 2003). The past and present may also be overlapping in time in place cells when animals are rapidly tele-transported between two physical different environments (Jezek et al., 2011). An encoding of places in the past and present might allow the brain to remember temporally ordered representations of events, like in the episodic memory.

After a memory has been encoded, the memory undergoes further consolidation, e.g. during sleep. Ensemble recording with multi-electrodes in sleeping animals has made possible the study of how memories of spatial routes achieved during active navigation are consolidated. Groups of place cells that are activated in a particular sequence during the behaviour display the same sequence of activation in episodes during the subsequent sleep (Wilson and McNaughton, 1994). This replay of place cell activity during sleep may be a memory consolidation mechanism, where the memory is eventually stored in cortical structures.

Together the activity of place cells may be used both to define the position in the environment at any given time, and also to remember past experiences of the environment. Maybe related to this notion is the findings that the hippocampus of London taxi drivers, which undergoes extensive training to learn how to navigate between thousands of places in the city without a map, grew during the year long training period and that the taxi drivers after this training had significantly larger hippocampal volume than control subjects (Magurie et al. 2000, Woollett and Maguire, 2011).

Relevance for humans and medicine

Brain disorders are the most common cause of disability and despite the major impact on people’s life and on the society, there is no effective way to prevent or cure most of these disorders. The episodic memory is affected in several brain disorders, including dementia and Alzheimer’s disease. A better understanding of neural mechanisms underlying spatial memory is therefore important, and the discoveries of place and grid cells have been a major leap forward to advance this endeavour. O’Keefe and co-workers have showed in a mouse model of Alzheimer’s disease that the degradation of place fields correlated with the deterioration of the animals’ spatial memory (Cacucci et al., 2008). There is no immediate translation of such results to clinical research or practice. However, the hippocampal formation is one of the first structures to be affected in Alzheimer’s disease and knowledge about the brain’s navigational system might help understand the cognitive decline seen in patients with this diseases.


The discoveries of place and grid cells by John O’Keefe, May-Britt Moser and Edvard I. Moser present a paradigm shift in our understanding of how ensembles of specialized cells work together to execute higher cognitive functions. The discoveries have profoundly promoted new research with grid and place cell systems now found in many mammals, including humans. Studies of the navigation system have opened new avenues for studying how cognitive processes are computed in the brain.

Ole Kiehn and Hans Forssberg Karolinska Institutet

Ole Kiehn, MD, PhD
Professor of Neuroscience, Karolinska Institutet
Member of the Nobel Committee
Member of the Nobel Assembly

Hans Forssberg, MD, PhD
Professor of Neuroscience , Karolinska Institutet Adjunct
Member of the Nobel Committee
Member of the Nobel Assembly

Illustrations: Mattias Karlén

Cited literature

Bjerknes, T.L., Moser, E.I. and Moser, M.B. (2014). Representation of geometric borders in the developing rat. Neuron, 82(1), 71-78.

Bonnevie, T., Dunn, B., Fyhn, M., Hafting, T., Derdikman, D., Kubie, J.L., Roudi, Y., Moser, E.I., and Moser, M.B. (2013). Grid cells require excitatory drive from the hippocampus. Nature Neuroscience 16, 309-317.

Brandon, M.P., Bogaard, A.R., Libby, C.P., Connerney, M.A., Gupta, K., and Hasselmo, M.E. (2011). Reduction of theta rhythm dissociates grid cell spatial periodicity from directional tuning. Science 332, 595-599.

Brun, V.H., Otnass, M.K., Molden, S., Steffenach, H.A., Witter, M.P., Moser, M.B., and Moser,

E.I. (2002). Place cells and place recognition maintained by direct entorhinal-hippocampal circuitry. Science 296, 2243-2246.

Bush. D., Barry, C., Burgess, N. (2014). What do grid cells contribute to place cell firing? Trends in Neuroscience, 37(3), 136-145

Cacucci, F., Yi, M., Wills, T.J., Chapman, P. and O´Keefe, J. (2008) Place cell firing correlates with memory deficits and amyloid plaque burden in Tg2576 Alzheimer mouse model. PNAS, 105, 7863-7868.

De Hoz, L., and Wood, E.R. (2006). Dissociating the past from the present in the activity of place cells. Hippocampus, 16, 704-715.

Doeller, C.F., Barry, C., and Burgess, N. (2010). Evidence for grid cells in a human memory network. Nature 463, 657-661.

Ekstrom, A.D., Kahana, M.J., Caplan, J.B., Fields, T.A., Isham, E.A., Newman, E.L., and Fried, I. (2003). Cellular networks underlying human spatial navigation. Nature 425, 184-188.

Ferbinteanu, J., and Shapiro, M.L. (2003). Prospective and retrospective memory coding in the hippocampus. Neuron, 40, 1227-1239.

Fyhn, M., Hafting, T., Treves, A., Moser, M.B., and Moser, E.I. (2007). Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190-194.

Fyhn, M., Molden, S., Witter, M.P., Moser, E.I., and Moser, M.B. (2004). Spatial representation in the entorhinal cortex. Science 305, 1258-1264.

Hafting, T., Fyhn, M., Bonnevie, T., Moser, M.B., and Moser, E.I. (2008). Hippocampus- independent phase precession in entorhinal grid cells. Nature 453, 1248-1252.

Hafting, T., Fyhn, M., Molden, S., Moser, M.B., and Moser, E.I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801-806.

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Nobel Prizes 2020

Twelve laureates were awarded a Nobel Prize in 2020, for achievements that have conferred the greatest benefit to humankind.

Their work and discoveries range from the formation of black holes and genetic scissors to efforts to combat hunger and develop new auction formats.

Growth in Bacteria: 4 Main Phases

Lag phase represents a period of active growth during which bacteria prepare for reproduction, synthesizing DNA, various inducible enzymes, and other macromolecules needed for cell division. Therefore, during this phase, there may be increase in size (volume) but no increase in cell number. The lag phase may last for an hour or more, and near the end of this phase some cells may double or triple in size.

The lag phase is necessary before the initiation of cell division due to variety of reasons. If the cells are taken from an old culture or from a refrigerated culture, it might be possible that the cells may be old and depleted of ATP, essential cofactors and ribosomes.

If the medium is different from the one in which the microbial population was growing previously, new enzymes would be needed by the cells to use new nutrients in the medium.

However, these deficiencies are fulfilled by the cells during lag phase. It is, therefore, the lag phase is generally longer if the cells are taken from an old or refrigerated culture. In contrast, if the cells are taken from young, vigorously growing culture (microbial population) and inoculated to a fresh medium of the identical composition, the lag phase may be short or even absent.

2. Log or Exponential Growth Phase:

Bacterial cells prepared for cell division during lag phase now enter into the log phase or exponential growth phase during which the cells divide at a maximal rate and their generation time reaches a minimum and remains constant.

The growth in this phase is quite balanced (i.e. all cellular constituents are synthesized at constant rates relative to each other) hence, the most uniform in terms of chemical and physiological properties, the log phase cultures are usually used in biochemical and physiological studies.

Since the generation time is constant, a logarithmic plot of growth during log phase produces an almost a straight line. This phase is called log phase because the logarithm of the bacterial mass increases linearly with time, and exponential growth phase because the number of cells increases as an exponential function of 2 n (i.e. 2 1 , 2 2 , 2 3 , 2 4 ,2 5 and so on).

The log phase also represents the time when bacterial cells are most active metabolically, and in industrial production, this is the period of peak activity and efficiency.

3. Stationary Phase:

Since the bacteria are growing in a constant volume of medium of batch culture, and no fresh nutrients are added, the growth of bacterial population eventually ceases and the growth curve becomes horizontal. Such a phase of growth in bacteria is attained at a population level of around 10 9 cells per ml.

The ceasation of growth may be because of the exhaustion of available nutrients or by the accumulation of inhibitory end products of metabolism. The ceasation of growth may also be due to O2 availability particularly in case of aerobes. Oxygen is not very soluble and may be depleted so quickly that only cells on the surface of the culture may find necessary oxygen concentration for adequate growth.

Sooner or later, the bacterial cells start dying and the number of such cells balances the number of new born cells, and the bacterial population stabilizes. This state of growth, during which the total number of viable cells remains constant because of no further net-increase in cell number and the growth rate is exactly equal to the death rate, is called stationary phase.

The transition between the log and exponential and stationary phases involves a period of unbalanced growth during which the various cellular components are synthesized at unequal rates. Consequently, cells in the stationary phase have a different chemical composition from those in the exponential phase.

4. Death or Decline Phase:

After a while, the number of dying cells begins to exceed the number of new-born cells and thus the number of viable bacterial cells present in a batch culture starts declining.

This condition represents the death of decline phase which continues until the population is diminished to a tiny fraction of more resistant cells, or it may die out entirely. Like exponential growth, death is also exponential, but inverse, as the number of viable bacterial cells decreases exponentially.


Our results confirm the average yield losses of 4.9% per K warming and 5% per 10% decrease in precipitation as reviewed by Challinor et al. ( 2014 ) and show that variety adaptation can contribute to an increase in global caloric production for all investigated scenarios. For SSP5-8.5, variety adaptation could globally compensate and outweigh long-term climate change induced losses. Thereby, variety adaptation minimizes negative effects of temperature increase at global scale while crop growth benefits at the same time from elevated atmospheric CO2 concentrations. The results suggest that mitigation toward SSP1-2.6 could be beneficial for global crop productivity (Figure 1). In global average, the results show that current global production can be assured up to 2 K of growing season warming with variety adaptation at constant atmospheric CO2 concentration. Temperature increases of more than 2 K would require additional measures, such as, for example, the application of additional water for adapted varieties to compensate production losses (Figure 3).

In our experimental setup, we adopt an approach tested by Asseng et al. ( 2015 ) for wheat as the main cereal crops, and assume that adapted varieties require higher heat units for delayed maturity to regain the previous growing period that would be shortened without an adaptation in varieties. We classify regions in classes of likelihood to be able to choose adapted varieties among already existing varieties, based on today's distribution of GDDs. Although we consider possible constraints for shifting varieties into other regions, the approach may not reflect all crop-specific traits that would be required to transfer existing varieties to new regions under climate change. However, it helps illustrating how strongly the potential in growing season adaptation may depend on the need for developing new adapted varieties. The results suggest that existing crop varieties cover a broad spectrum of GDD requirements that are needed in the future. For low levels of warming such as in SSP1-2.6, 85% of global cropland areas can be supplied with adapted varieties from the pool of currently existing varieties. On the other hand, 39% of global cropland areas could require new varieties for SSP5-8.5. For these areas, regional information on long-term required GDDs can be provided to allow for targeted long-term breeding programs (Boote et al., 1996 Challinor et al., 2016 ).

Studies have shown that the overall process of breeding, delivery, and adoption of new varieties can take up to 30 years (Challinor et al., 2016 ), which could additionally restrict the availability of new varieties over time. However, new approaches and technologies, such as CRISPR-Cas9, speed breeding, or participatory plant breeding, could accelerate the development of adapted varieties in the future (Fedoroff et al., 2010 Garcia-Molina & Leister, 2020 Long et al., 2015 Tester & Langridge, 2010 ). Our results show that even important production regions could be at high risk that new varieties may not be available because their required GDD requirements are at the edge or outside the range of GDD requirements observed in varieties grown globally today. The development of locally adapted varieties to warming climatic conditions is particularly important for regions where agriculture is an important socioeconomic factor to avoid implications for local economy, employment, society, and culture.

The effectiveness for variety adaptation is generally lower in tropical and arid regions, where developing countries tend to have fewer adaptive capacities and rapidly increasing food demands. Different pathways of seed development and delivery (Challinor et al., 2016 ) could improve the provisioning of adapted and affordable seeds for developing countries and are also important measures to close yield gaps. The strong regional differences in the effectiveness of variety adaptation that show up in our results, in addition to different abilities and capacities for adaptation, could have major implications for future land-use changes, which are tightly coupled to trade flows and agricultural markets (Delzeit et al., 2018 Ewert et al., 2015 Robinson et al., 2018 ). Since market forces will likely intensify agricultural production in areas with suitable varieties and more conducive agricultural conditions (Nelson et al., 2014 ), the global importance of breadbaskets could increase.

This study focuses on long-term production changes that may obscure more acute extreme climate events. Studies also suggest increasing inter-annual yield variability (Challinor et al., 2014 ) that could reduce beneficial effects of variety adaptation, since adapted varieties might not be suitable or beneficial in extreme seasons, for example, due to heat waves or drought events.

Future variety adaptation assessments should consider ranges of adaptation rather than exploring the impacts of full adaptation versus no adaptation. In addition to variety adaptation, other adaptation measures, such as changing sowing dates (Waha et al., 2012 ), exploiting the potential of longer growing periods in regions where growing periods are currently limited by cold temperatures, or shifting to different crops could be beneficial and should be investigated jointly in further studies. The modelling exercise used here does not consider shifts in sowing dates as this was not part of the experimental protocol of the underlying dataset (Franke, Müller, Elliott, Ruane, Jägermeyr, Balkovic, et al., 2020 ). Shifting both sowing and harvest dates into cooler periods could contribute to adaptation in regions with sufficient temperature amplitude and help exploit the pool of current varieties for adaptation (Minoli et al., 2019 Waha et al., 2012 ). However, water availability and other factors like crop rotation constraints and risk of early frost events might prevent such an adaptation, but could be explored in future studies.

Switching to different crops as an adaptation measure could also be investigated, but will require considerations of regional consumer preferences and supply chain adaptations. Altering land-use patterns with warming climate could move farmland into more suitable but currently uncultivated areas, although potentially at the expense of natural ecosystems and biodiversity (Mbow et al., 2019 Zabel et al., 2014 , 2019 ). In contrast, variety adaptation offers an opportunity to increase resilience without requiring land-use change or shifting agricultural regions into previously unused areas (Morales-Castilla et al., 2020 ), thus preventing negative impacts on biodiversity.

Region-specific breeding efforts are needed to allow for successful adaptation. The identified hot spot regions for crop breeding highlight the limits and challenges of variety adaptation. They could be considered as input to economic models to make SSP- and region-specific assumptions on variety adaptation and its limitations. By bridging the gap between genotyping, phenotyping, and the parameters used in crop models (Chenu et al., 2017 Marshall-Colón & Kliebenstein, 2019 Marshall-Colon et al., 2017 Peng et al., 2020 ), farmers could benefit from digital platforms to select best suited varieties.

Supporting information

S1 Fig. Spatial phase-coding cells were theta-modulated and theta-rhythmic.

We show distributions of single-unit recordings with non-significant spatial phase information Iphase (‘non-phase-coding’, n.s., orange n = 840) or significant Iphase (‘phase-coding’, p < 0.02, blue n = 233 Methods). Violin plots show Gaussian kernel-density estimates (using Scott’s bandwidth rule) normalized by group size for each split long-dash lines, medians short-dash lines, 1st/3rd quartiles. (A) Phase-coding recordings had maximal spatial firing rates (median, 7.35 spikes/s) that were distributed higher than non-phase-coding recordings. (B) Autocorrelogram-based estimates of burst frequency (Methods) were similar (median: phase-coding, 7.66 s −1 non-phase-coding, 7.65), but phase-coding recordings were more narrowly distributed (interquartile range: 0.524) than non-phase-coding recordings (1.031). (C) Theta modulation and rhythmicity indices (Methods) show that phase-coding recordings were distributed higher, but this is likely due to the substantial low-rhythmicity subpopulation evident in non-phase-coding recordings. Jittered strip plots show every phase-coding data point. (D+E) Spatial phase-coding cells had broadly distributed rate-phase correlations. (D) Iphase for phase-coding cells (median, 0.36 bits) was positively skewed across a wide range ([0.012, 3.67]bits). (E) Circular-linear regressions of mean phase onto mean rate based on spatial map pixels. Non-phase-coding recordings were distributed around zero. Correlation coefficient (left) and total phase shift (right Methods) showed broader distributions for phase-coding than non-phase-coding cells: Compare quartiles (short-dash lines) and fatter tails reflecting excess negative and positive correlations. Total phase shift (right) was computed by rate-normalizing the regression slope (middle).

S2 Fig. Phaser cells: Moderate firing rates and stable spatial phase coding.

(A) Violin plots show distributions comparing spatial phase-coding recordings (with significant Iphase) that were not selected (‘nonphaser’ n = 233) or were selected (‘phaser’ n = 101) by the phaser cell criteria (see numbered listing of criteria preceding Fig 2 in Results). (Left) Maximal spatial firing rates for phaser cell recordings had a substantially restricted range (interquartile interval, [5.34, 9.86] s −1 ) compared to nonphaser recordings ([2.94, 20.4]). Note, a minimum firing rate of 3.5 spikes/s was one of the phaser cell criteria, and the y-axis truncates, for clarity, nonphaser data that is shown in S1 Fig, panel A. The observed range is commensurate with activity that, on average, consists of 1 or 2 spikes per theta cycle at theta frequencies from 5–12 Hz. Theoretically, having fewer spikes per theta cycle decreases the lower bound of spike-phase variance, which may enhance the effectiveness of temporal coding by oscillatory phase. (Right) Theta rhythmicity of phaser cell recordings was distributed similarly, but slightly lower than nonphaser cell recordings. (B) Phaser cells recorded across multiple days (n = 19) demonstrated substantial stability in day-to-day measurements of phase-coding quantities: spatial phase information (left) and total phase shift (right). Large jumps (or sign-changing for phase shifts) were relatively rare (3/19 cells). The phase shift data (right) is the basis for the within-cell pair-wise phase-coding histogram in Fig 2E. Only phaser-classified recordings for each cell are shown. Lines are color-coded to unique cells.

S3 Fig. Anatomical distribution and space–trajectory coding of phaser cell recordings.

(A) Counts of uniquely identified cells with at least one negative or positive phaser-classified recording. (Left) Distributions of recorded phaser cell locations across brain areas. Hipp. = hippocampus Thal. = thalamus Other includes nucleus accumbens, caudate nucleus, and putamen. (Right) Distribution across septal recording sites. IG = indusium griseum LS = lateral septum LSD = dorsal nucleus of the lateral septum LSI = intermediate nucleus of the lateral septum Ld = lambdoid septal zone SHi = septal-hippocampal nucleus UNK = unknown gcc = genu of the corpus callosum. (B) Comparison of spatial phase information Iphase with spike information content (Methods [45]) for position (‘spatial rate information’ left), direction (middle), and speed (right). Most phaser cells carried strong spatial rate information (left) and a minority carried relatively low direction (middle) or speed (right) information. Stars: hippocampal (hipp.) recordings circles: non-hippocampal (not hipp.) recordings dashed lines: parity solid lines: least-squares optimized slopes. (C) Trajectory-based firing-rate modulation indices (Methods) revealed potential source of bias in spatial recordings. Histograms: modulation indices for direction (left) and speed (right), positive data composited over negative. Gray line: kernel-density estimate (0.05 bandwidth Gaussian) of nonphaser cell recordings (arbitrary scale for visual comparison).

S4 Fig. Regularization and shrinkage curves used for training GLM models.

We trained GLMs to predict spike counts in 300-ms intervals based on spatial (L, Q, W) and/or trajectory-based (S, D) variables (Methods). For the analysis (Fig 5B+5C S6 Fig), the model was trained and tested on a 3 × 3 spatial grid (C) however, the penalty parameter used for training was derived by optimizing the model on a 2 × 2 grid (B). Both values were similar, but the 2 × 2 value (B, bottom) was used because the directional likelihood was strongly peaked and the model better captured wall responses because the center grid of the 3 × 3 model was isolated from the walls. The GLM that we used to generate spatial inputs for the realistic 2D open-field phaser simulations was trained only on the spatial variables (A, 1 × 1 grid). (Top) Absolute model weights for each variable. (Second row) Softmax normalization of absolute model weights. (Third row) Spike-count prediction errors. (Last row) Model likelihood is the softmax W (A) or D (B+C) divided by the prediction error (Eq (14) Methods). The maximum likelihood α parameter (red circle) was chosen as the 2- regularization penalty for the ridge regressions.

S5 Fig. GLM ratemap reconstructions for example directional cells.

To show that the LQW-SD 3 × 3 model could accurately reconstruct ratemaps of directional cells, we show example cells with homogeneous (A) and inhomogeneous (B) directionality. (A) The high maximal firing rates and crescent-like spatial modulation indicate that these may have been head-direction cells or cells with head-direction inputs. The GLM’s directional predictors (arrows) were consistently large and well-aligned across grid sections. (B) Recordings with inhomogeneous directionality showed minimal spatial modulation but included center-facing (left) and clockwise (middle) or anti-clockwise (right) directionality.

S6 Fig. GLM weights and contributions for every phaser cell recording.

GLM weights (A+B) and maximal contributions (C-E) for phaser cell recordings are shown in pseudocolor matrix plots. For visualization, recordings are presented in the same order in every grid section and grid average according to the expected value of the cell’s grid-averaged model weights to the left (toward L, i.e., more spatial) or right (toward D, i.e., more trajectory-related). To reveal model structure, each variable row in a grid section was sum-normalized and the paired grid plots (A+B, C+D) share color scales. (E) The contribution averages from (D) are displayed by phaser cell subtype: negative (left) and positive (right). The two subtypes demonstrated qualitatively similar inverse patterns of spatial (L, Q) vs. speed-related (S) contributions to firing.

S7 Fig. Noisy theta-bursting target neuron model: Pulse synchronization.

An intrinsic bursting model (Eq (11) [51]) was tuned with constant input (Table 4) to fire doublet bursts (A) close to the reference theta frequency, 7.5 Hz. The deviation between the reference frequency and the resulting burst rate, 7.519 bursts/s, meant that the unit’s theta phase (B) slowly drifted (precessed) over time (gray line). To test whether this unit could be phase-synchronized by periodic stimulation, we simulated an instantaneous pulse (VV+ 15mV) every other theta cycle at theta peak (0 radians). This pulse-synchronized unit (B, orange line) monotonically delayed toward theta peak and then (around 5 s into the simulation) discontinuously jumped past theta peak before slowly precessing to just before the peak. This dynamic, of jumping forward and precessing back, repeated (around 9 s) and continued stereotypically. This sawtooth pattern encapsulated the model’s theta-synchronization dynamics. For simulations with phaser network input, we added a stochastic input current to this ‘target burster’ model (Eq (11)). We chose a noise level (Table 4) that preserved theta bursting (C, same as Fig 7C, inset) but caused its burst phase to randomly drift over a 30-s simulation (D, gray dots, 36 trials). With noise, the pulse stimulation was able to reproduce the sawtooth pattern of synchronization (D, orange line).

S8 Fig. 1D phaser-target entrainment across noise and phaser input levels.

We show additional 1-hr simulations of the 1D phaser-target network shown in Fig 7F+7G. (A) With the input gain from the phasers fixed (Table 4), simulations with 0.0σ, 0.1σ, 0.3σ, and 0.5σ noise levels demonstrated that the supervised modes of the artificial phase-code remained functional across different levels of noise. (B) With the noise level fixed at 0.3σ, the effect of zero phaser input gain (top left) can be compared to weaker (top right) and stronger (bottom right) levels of phaser input. Weak phaser input (top right) entrained the target burster, but the phase trajectories were extended due to the slower development of phase locking on approaches toward positions 0 or 1.

S9 Fig. Generative samples of model LQW-phasers in open-field simulations.

(A) Ratemap/phase-map pairs are shown for 50/1,000 negative phasers from the realistic 2D open-field simulations (Fig 8). The rate and phase response of each phaser was driven by a randomly sampled spatial function from the LQW generative input model (S10 Fig, panel A). In the phase maps, note that the phasers advanced from pre-theta-peak (green see phase-vector color wheel at bottom) to theta-trough (pink) from low- to high-rate regions. Missing phase map pixels reflect insufficient numbers of nearby spikes for spatial averaging. (B) Ratemap/phase-map pairs are shown for 50/1,000 positive phasers. The rate and phase response of each phaser was driven by theta excitation and feedforward inhibition from a negative phaser with an LQW-generated spatial input (A). In the phase maps, note that the phasers delayed from theta peak (green) to halfway through the falling phase (blue/green π/2 radians). Like the 1D model (Fig 6) and phaser cell recordings (Fig 4), the positive rate-phase coupling was weaker than the negative.

S10 Fig. Bayesian decoding of target burst phase from open-field simulations.

Realistic 2D simulations of phasers and target neurons were simulated and the bursting activity of the target neurons was decoded to assess position-error correction (Methods). (A) The steps to sample spatial input functions from the generative model for negative phasers are illustrated (Methods). From left to right: Phaser cell recordings (examples from Fig 4A) were learned by the 1 × 1 LQW model (Eq (3)) and their linear predictor functions were normalized to [0, 1] with a sigmoid nonlinearity. To generate a novel spatial input, we randomly selected one of these normalized spatial functions, added 20% Gaussian noise to the LQW parameters, and randomly center-rotated the coordinate frame. (B) Target networks were simple collections of target burster units. The Ring collection of target bursters varied across phase offsets (orange) the Phase 1 and Phase 2 collections varied across preferred direction at opposing phase offsets (blue and green). (C) Normalized temporal autocorrelograms of decoding error for full-sized collections (64 units in each collection 192 units for the combination of all collections). The correlation width indicates the timescale of error correction, which was quantified as the HWHM timescale in Fig 8H (Methods).

S1 Movie. Competitive 2D open-field phaser entrainment across spatial phase offsets.

The spatial phase codes in Fig 8B differed by the reference phase offset of the VCO-like phase code. Here we show a movie in which the frames iterate through 64 units in the Ring collection of target bursters (S10 Fig, panel B, orange) that were simulated with a 600-s behavioral trajectory. The supervised phase code (top left) moves smoothly along the 45° diagonal for a complete cycle, allowing the video to be looped. The broad negative/positive (pink/blue) synchronization regions competed to encode the environment for each of the different target bursters in the collection. (top right) Space-phase distribution of the total phaser network input to the target burster. (bottom left) Burst phase map of target burster output.

S2 Movie. Competitive 2D open-field phaser entrainment across preferred direction: Phase 1.

The spatial phase codes in Fig 8B have a 45° preferred direction, which determines the angular orientation of the VCO-like phase code. Here we show a movie in which the frames iterate through 64 units in the Phase 1 collection of target bursters (S10 Fig, panel B, blue) that were simulated with a 600-s behavioral trajectory. The supervised phase code (top left) rotates smoothly for a complete cycle, allowing the video to be looped. With this phase offset (0.0, at the center of the arena), the negative phasers synchronized a boundary region (oranges/pinks) along the preferred direction. (top right) Space-phase distribution of the total phaser network input to the target burster. (bottom left) Burst phase map of target burster output.

S3 Movie. Competitive 2D open-field phaser entrainment across preferred direction: Phase 2.

The spatial phase codes in Fig 8B have a 45° preferred direction, which determines the angular orientation of the VCO-like phase code. Here we show a movie in which the frames iterate through 64 units in the Phase 2 collection of target bursters (S10 Fig, panel B, green) that were simulated with a 600-s behavioral trajectory. The supervised phase code (top left) rotates smoothly for a complete cycle, allowing the video to be looped. With this phase offset (π, at the center of the arena), the positive phasers synchronized a boundary region (blue/green) along the preferred direction. (top right) Space-phase distribution of the total phaser network input to the target burster. (bottom left) Burst phase map of target burster output.


A. Methods of Obtaining Pure Cultures (a culture that contains only 1 species of organism)

1. The Streak Plate Method – Bacteria are picked up on a sterile wire loop, and the wire is moved lightly along the agar surface, depositing streaks of bacteria on the surface. The loop is flamed and a few bacteria are picked up from the region already deposited and streaked onto a new region. Fewer and fewer bacteria are deposited as the streaking continues, and the loop is flamed after each streaking. Individual organisms (individual cells) are deposited in the region streaked last. After the plate is incubated at a suitable growth temperature for the organism, small colonies (each derived from a single bacterial cell) appear. The loop is used to pick up a portion of an isolated colony and transfer it to another medium for study. The use of aseptic technique assures that the new medium will contain organisms of a single species. We’ll do this in lab.

Results and Discussion

Overall patterns of gene expression

A total of 540 cDNA clones were identified as differentially expressed by filtering the data to include cDNA clones that changed their expression levels at least twofold compared to the appropriate reference sample in at least 1 time point (Figs 3, 4, 5). For most transcripts that were found to be responsive to temperature, shifts in gene expression were typically between two- and fourfold changes, although some transcripts (e.g. transcripts that encode for the high mobility group protein B1 and HMG-CoA synthase) experienced six- to tenfold changes in relative abundance. The use of an arbitrary twofold cut-off criterion is probably a conservative approach to identifying genes whose levels of expression change, because physiologically important effects may arise from shifts in gene expression that are less than twofold. We adopted this conservative approach because of the challenges faced in designing time series experiments of this nature that can be subjected to conventional statistical analysis. In designing these experiments, which involved sampling at many time points, we had no a priori basis for predicting the sampling times at which changes in gene expression would be greatest. Thus, we had to strike a compromise between obtaining data at a large number of time points with the need for large sample sizes at each time point. We reasoned that, even though our design precludes carrying out statistical analysis at all time points, if time-dependent trends were present, they would appear clearly in the data, as we in fact observed and discuss below. In this exploratory study, then, we discuss patterns in gene expression that reflect distinct temporal responses, in both cycling and steady state acclimation conditions, but we refrain from referring to these patterns as statistically significant. Nonetheless, the temporal changes in gene expression that we discuss (for instance, those for whose expression correlates with the daily cycling of temperature see Fig. 4) manifest a regularity that cannot easily be explained as due simply to noise in the data. These cDNA clones represent genes involved in a wide variety of cellular and organismal functions (Fig. 5). In discussing these differentially expressed genes we first provide a general overview of thermal effects on gene expression and then examine the specific physiological systems that seem most responsive to temperature under either steady state or cycling conditions.

Genes with a cyclic expression pattern in response to temperature fluctuations. Gene expression profiles are organized by phase shift relative to the temperature cycle in the fluctuating temperature acclimation. Each phase shift cluster was organized with a complete linkage algorithm (Peterson,non-centered Eisen et al.,1998). Each row represents a single cDNA clone and each column a time point in the acclimation time course. About 40% of the 540 cDNA clones that were differentially expressed have a cyclic expression pattern in response to temperature cycling as determined by cross correlation analysis. For the control daily cycles (26°C) and constant acclimation to 20°C and 37°C, expression patterns are corrected relative to t=0. For the fluctuating temperature treatment, the data are presented relative to t=0 as well as with circadian rhythms subtracted (labeled `The effect of temperature'). Cycling temperature profiles presented with daily rhythms subtracted will have an expression pattern near a 1:1 ratio with controls if there is a negligible effect of temperature on gene expression (see Materials and methods for an expanded discussion). Letters on the right margin of the figure correspond to the line graphs presented in Fig. 4. For non-cyclical gene expression patterns see supplemental Fig. 1.

Genes with a cyclic expression pattern in response to temperature fluctuations. Gene expression profiles are organized by phase shift relative to the temperature cycle in the fluctuating temperature acclimation. Each phase shift cluster was organized with a complete linkage algorithm (Peterson,non-centered Eisen et al.,1998). Each row represents a single cDNA clone and each column a time point in the acclimation time course. About 40% of the 540 cDNA clones that were differentially expressed have a cyclic expression pattern in response to temperature cycling as determined by cross correlation analysis. For the control daily cycles (26°C) and constant acclimation to 20°C and 37°C, expression patterns are corrected relative to t=0. For the fluctuating temperature treatment, the data are presented relative to t=0 as well as with circadian rhythms subtracted (labeled `The effect of temperature'). Cycling temperature profiles presented with daily rhythms subtracted will have an expression pattern near a 1:1 ratio with controls if there is a negligible effect of temperature on gene expression (see Materials and methods for an expanded discussion). Letters on the right margin of the figure correspond to the line graphs presented in Fig. 4. For non-cyclical gene expression patterns see supplemental Fig. 1.

Diverse patterns of cyclic gene expression. The dotted line in each graph represents a 1:1 ratio relative to the appropriate control. Letters on the left margin of the figure correspond to the same letters in Fig. 3. The temperature cycle is represented by the light gray line.

Diverse patterns of cyclic gene expression. The dotted line in each graph represents a 1:1 ratio relative to the appropriate control. Letters on the left margin of the figure correspond to the same letters in Fig. 3. The temperature cycle is represented by the light gray line.

Gene expression patterns grouped according to cellular function. Data are presented as explained in Fig. 3. (A) Heat-shock proteins and molecular chaperones (B)cholesterol and lipid metabolism (C) solute carriers (D)glycolysis/gluconeogenesis, blood glucose homeostasis (E) intermediary metabolism (F) nitrogen metabolism (G) cytoskeletal elements (H) protein turnover (I) acute phase response and complement proteins (J) cell growth and proliferation (K) clones with unknown function or no homology to known sequences. Probable gene homologies were determined using Blast searches of the GenBank sequence database. The most significant or relevant results of these homology searches are listed in supplemental Table 1, as are the accession numbers for the DNA sequence of the cDNA clones presented in this paper.

Gene expression patterns grouped according to cellular function. Data are presented as explained in Fig. 3. (A) Heat-shock proteins and molecular chaperones (B)cholesterol and lipid metabolism (C) solute carriers (D)glycolysis/gluconeogenesis, blood glucose homeostasis (E) intermediary metabolism (F) nitrogen metabolism (G) cytoskeletal elements (H) protein turnover (I) acute phase response and complement proteins (J) cell growth and proliferation (K) clones with unknown function or no homology to known sequences. Probable gene homologies were determined using Blast searches of the GenBank sequence database. The most significant or relevant results of these homology searches are listed in supplemental Table 1, as are the accession numbers for the DNA sequence of the cDNA clones presented in this paper.

Changes in gene expression may arise for a variety of reasons, some of which may not be specifically associated with changes in the activity of gene products (e.g. proteins). For instance, transcripts that are highly labile or have a very short half-life may require constant transcription to maintain physiological transcript levels, and this may be exacerbated by changes in the physical environment that destabilize or stabilize the mRNA transcripts. It is also possible to have large changes in gene transcription without changing the level of expressed protein if changes in protein or mRNA stability are associated with the experimental treatment, or if the RNA is the active gene product (antisense RNA is an example). For example, changes in gene expression may simply represent the attempt to maintain the current level of protein in the cell in the face of changes in protein translation or degradation. In this situation, if the coupling of protein degradation and transcription is tight,there may be a large change in transcript abundance without any change in protein abundance. Although in this situation the amount of transcript does not parallel the changes in protein level, it still indicates an important cellular process that must be closely regulated to maintain cellular function. This type of gene regulation may be just as important, possibly even more important, than regulation that results in changes in the protein levels. We,therefore, argue that most changes in transcript abundance, whether they reflect effects of mRNA and protein stability or adaptive alterations in protein concentrations, are likely to be important in the context of temperature acclimation, especially during the initial phases of the process.

Transcript levels do not change in response to temperature in a global manner that would indicate large changes in rates of synthesis or degradation of mRNAs across all genes (Fig. 3). In fact, the majority of the cDNAs (>90%) changed less than twofold, if at all, in response to temperature, which indicates a very tight regulation of steady state levels of mRNA transcripts during large-scale temperature changes. Yet rates of transcription elongation in a hibernating mammal, as measured in nuclear run-on assays, have recently been shown to have a typical temperature sensitivity (Q10≈2–3)(van Breukelen and Martin,2002). This suggests that temperature compensation in rates of transcription should occur at the level of either transcript initiation,degradation or both. We provide some evidence that global control of initiation through activities of high mobility group B1 proteins (HMGB1 see below, Fig. 3) may play an important role in the global regulation of transcription in response to temperature. However, it is important to note that changes in the abundance of mRNA transcripts in a cell can occur without alterations in rates of transcription. For instance, changes in mRNA turnover (e.g. stabilization or destabilization of a transcript) can cause changes in mRNA levels independent of rates of transcription. Thus, it is not safe to assume that changes in the relative abundance of mRNA transcripts are due to changes in rates of transcription. The nature by which specific mRNA levels are changed can only be addressed by more detailed investigation of each transcript species using conventional gene-specific approaches.

Diverse phase patterns in gene expression associated with the temperature cycle

There is a diversity of gene expression patterns associated with acclimation to cycling temperatures. To examine these patterns, the gene expression data expressed relative to t=0 were ordered according to their phase shift relative to the temperature cycle(Fig. 3). A clustering diagram of the genes with expression patterns found to have a statistically significant cross correlation with the temperature cycle is presented in Fig. 3. Representative expression patterns from each phase shift cluster are illustrated using line plots in Fig. 4. These patterns include changes in the phase pattern of gene expression as well as shifts in the amplitude and level of expression. The relative abundances of some transcripts are reduced when temperatures are high (ER membrane protein,HMGB1) while others such as hydroxymethyl glutaryl coenzymeA synthase (HMG-CoA synthase) increase. Some transcripts (myosin heavy chain) show a complex pattern with two negative peaks occurring as a result of temperature cycling. This pattern merges into a single negative peak associated with high temperatures after 2 weeks of continuous temperature cycling. Other transcripts show a clear cycling pattern with temperature and also show a shift in overall transcript abundance (complement protein C7, apolipoprotein E, the heat shock protein 70 kDa). Some transcripts show very little response during the first few temperature cycles and then develop a cycling pattern over time (unidentified clone LU07B24). Many transcripts that change in relative abundance in response to a cycling temperature regime return to the control pattern (e.g. unidentified clone LU05K02, HMG-CoA synthase) after 2 weeks of temperature cycling, while others develop new patterns (myosin heavy chain, ER membrane protein). Surprisingly few genes are simply turned off or on in response to cycling temperatures and it appears that the expression of many genes may be altered on a hourly basis under temperature cycling conditions.

Temperature and daily rhythms

A number of transcripts found to have temperature-dependent patterns of expression also have strong daily rhythms under constant temperatures (Figs 3, 4). In almost all cases, these patterns are modulated by temperature in a consistent manner. For instance,the ER membrane protein and myosin heavy chain transcripts show opposite patterns of expression in response to temperature compared to the normal daily rhythm. These data indicate that natural daily rhythms of expression are likely to be strongly modulated by the temperature cycle in A. limnaeus. Some of the rhythms observed under control conditions may be a consequence of the feeding regime used during the study (fish were fed once daily at 09:00 h). This hypothesis is supported by the number of transcripts associated with energy metabolism (e.g. lipid and glucose metabolism) that exhibit strong daily rhythmic patterns under control and temperature cycling conditions (Fig. 5). It has recently been reported that circadian rhythms observed in mammalian liver can be entrained to the feeding cycle, independent of the light cycle(Stokkan et al., 2001). Our data suggest that daily rhythms of gene expression under natural conditions are likely to be the result of multiple environmental and nutritional inputs in the liver and that variation in environmental temperature is likely to have a strong influence on this integrated response.

Daily versus long-term gene expression responses to temperature: general principles and background data

The majority of laboratory temperature acclimation experiments force constant conditions upon organisms that typically occur in thermally variable environments in which daily changes in temperature occur. The need to study such short-term thermal fluctuations is apparent based on the findings of studies that have shown that acclimations to fluctuating and constant thermal environments result in different physiological phenotypes(Lowe and Heath, 1969 Feldmeth et al., 1974 Otto, 1974 Shrode and Gerking, 1977 Woiwode and Adelman, 1992 Heath et al., 1993). Most of these studies focused on thermal tolerance or thermal preference. In almost all cases, acclimation to fluctuating environments resulted in a higher thermal tolerance or an increased range of thermal tolerance compared to acclimation to constant conditions that approximated the mean temperature of the fluctuating acclimation. These data indicate that the physiological state of a fish exposed to fluctuating temperatures is indeed unique when compared to fish acclimated to constant environments. Gene expression data presented in this paper support the general conclusion that constant and fluctuating environments elicit different transcriptional and likely physiological responses.

The different responses in gene expression during acclimation to constant and fluctuating temperatures discussed below may have broad implications for other species of aquatic organisms that live in both constant and fluctuating environments. For instance, many marine intertidal organisms spend part of their day in the relatively thermally stable conditions of the ocean, and other parts of the tidal cycle exposed to air and more variable temperatures. Much of what is known about the temperature biology of these species is from acclimations to constant conditions, and a very different perspective might be gained from looking at acclimation to daily fluctuations in temperature. Additionally, the differences in transcriptional responses observed in constant versus fluctuating temperatures may indicate differences in organismal responses to daily versus seasonal changes in temperature. While there are likely to be daily fluctuations in temperature during all seasons, the mean temperature levels will be likely to change, as will the amplitude of the fluctuation. Constant acclimation regimes may mimic seasonal changes in daily mean temperature and thus may be eliciting changes in gene expression associated with adjustments needed for long-term survival or for seasonal changes in reproduction and feeding. In contrast, large-scale fluctuation in temperature on a daily basis may require more immediate changes in gene expression that are required for short-term survival and thus the fish respond via more temporary mechanisms that are not associated with long-term adjustments in physiology.

Gene expression grouped by cellular function

In order to explore the biochemical and molecular pathways that are affected by temperature on either daily or long-term time scales we organized the data according to cellular functions(Fig. 5). Fig. 5 illustrates the diversity of cellular pathways that are affected by temperature at the transcriptional level. For each cellular function we discuss the changes in transcription that we feel are most instructive for evaluating the effects of temperature on the function in question. By placing focus on a subset of the transcriptional changes we do not intend to imply that other changes in gene expression are without significance for the process in question.

Molecular chaperones

Upon initial exposure to temperature cycling, a number of molecular chaperones are strongly induced (Fig. 5A). However, the transcript levels of most of these chaperones return to control levels after 2 weeks of temperature cycling. Transcripts of the small heat-shock proteins Hsp27 and Hsp22 are strongly induced (four- to fivefold induction) by the initial temperature cycles whereas the larger heat-shock proteins Hsc70 and Hsp90 are only mildly induced (>twofold)after several temperature cycles. However, transcripts encoding for Hsc70 and Hsp90 are strongly induced by chronically elevated temperatures. These differences in the kinetics of induction among different classes of heat-shock proteins indicate a complex transcriptional response to temperature cycling that is distinct from constant exposure to elevated temperatures. Transcripts for the other major types of molecular chaperones, such as protein disulfide isomerase (PDI) or calreticulin, which are initially induced by temperature cycling, are also induced and maintained by exposure to 37°C. These data taken together suggest that for A. limnaeus chronic high temperatures may be more `stressful', i.e. cause more protein damage, than exposure to constantly changing temperatures. This conclusion is consistent with the variable natural habitat in which the fish are found to thrive(Fig. 1A). The apparent importance of the small heat-shock proteins to the survival of A. limnaeus in its thermally variable and extreme desert habitat is in agreement with the data of Hightower et al.(Norris et al., 1997 Hightower et al., 1999) for the survival of desert fishes from Mexico. These authors suggest that Hsp27 may play a role in signal transduction to the cytoskeleton during temperature stress (Norris et al.,1997).

Cholesterol and fatty acid synthesis – membrane structure

The maintenance of membrane integrity (homeoviscous and homeophasic adaptation) is known to be a crucial part of the acclimatory response to temperature and to involve major alterations in the lipid compositions of membranes (Hazel, 1995 Hochachka and Somero, 2002). Thus, we predicted that a number of genes related to lipid biosynthesis would alter their expression in response to fluctuating or long-term acclimatory temperatures. Indeed, this prediction was fulfilled(Fig. 5B). Two transcripts, one for a Δ6-fatty acyl desaturase and the other for a polyunsaturated fatty acid elongase, were found to be induced by constant acclimation to 20°C and repressed by chronic exposure to 37°C. If these changes in transcript levels are reflected in protein levels, then these data are completely consistent with homeoviscous adaptation theory, which predicts an increase in long-chain polyunsaturated fatty acids at lower temperatures. However, these two transcripts are not strongly regulated during exposure to cycling temperatures. It appears that membrane restructuring during temperature cycling may be accomplished by alternate means.

Insertion of cholesterol into lipid bilayers has multiple effects on membrane structure, and, in general, increased levels of cholesterol are associated with increased temperatures(Robertson and Hazel, 1997). The relative abundance of a transcript for an enzyme in the cholesterol biosynthetic pathway, 3-hydroxy-3-methylglutaryl-CoA synthase (HMG-CoA synthase) (cytoplasmic form), is strongly and positively correlated with the temperature cycle (Figs 3, 4, 5). This suggests a role for cholesterol in the maintenance of membrane integrity during temperature cycling. After 2 weeks of cycling, the expression levels of the HMG-CoA synthase transcript return to control levels. This return to control levels of transcripts after 2 weeks of temperature cycling suggests that either a new steady state level of cholesterol has been achieved that is sufficient to maintain membrane integrity in the face of temperature cycling, or that other mechanisms, such as changes in the content of polyunsaturated fatty acids, may be employed during extended periods of exposure to cycling temperatures.

The pattern of HMG-CoA synthase transcript appears to have a daily rhythm that may be associated with the daily feeding pattern (see discussion above). HMG-CoA synthase transcript levels are highest about 3.5 h after feeding (at 12:30 h) under control conditions (26°C, Fig. 4). However, during temperature cycling, levels of this transcript peak strongly 8 h after feeding(4 h after peak temperature) and are highly correlated with the first four temperature cycles. It is worth noting that regulation of cholesterol biosynthesis is typically attributed not to HMG-CoA synthase but to HMG-CoA reductase. In this study transcript levels of HMG-CoA reductase are slightly induced during chronic cold acclimation (20°C) and repressed during acclimation to chronic temperature elevation (37°C), suggesting the lack of an appropriate transcriptional response to temperature that would be consistent with the role of cholesterol in current homeoviscous adaptation theory (Robertson and Hazel,1997 Zehmer and Hazel,2003). However, this enzyme is known to be regulated by allosteric and post-translational modifications and may not require a transcriptional response to effect a change in gene activity. The many biological roles of cholesterol in organismal physiology, and the complex control of sterol synthesis, suggest that the expression pattern of this gene is a result of the interplay between many converging pathways.

The expression pattern of the transcript encoding adipose differentiation-related protein (ADRP, Fig. 5B) provides additional evidence that cholesterol is probably important for the maintenance of membrane integrity during temperature cycling. When transcript levels of HMG-CoA synthase are high, ADRP transcript levels are low. Increased mRNA levels of ADRP are associated with increased storage of cholesterol and polyunsaturated fatty acids (PUFA) in lipid droplets of mammalian cells (Atshaves et al., 2001 Brown,2001). ADRP binds tightly to cholesterol and is thought to be a critical regulator of cholesterol and PUFA storage and release from lipid droplets (Brown, 2001). Thus,decreased levels of ADRP mRNA transcripts would likely lead to the mobilization of cholesterol and PUFA stores for transport to the plasma membrane.

Recent studies suggest that the plasma membrane is organized into discrete lipid and lipid/protein domains. (Ikezawa,2002 Morandat et al.,2002). The glycosylphosphatidylinositol (GPI)-anchored proteins are associated with specific plasma membrane subdomains called membrane`rafts', which are rich in cholesterol and sphingolipids(Ikezawa, 2002 Morandat et al., 2002). These rafts are thought to be critical sites for many membrane-associated functions including cellular signaling. We observed a strong induction of transcripts for the GPI-anchored membrane protein p137 during exposure to chronically elevated temperatures, and a mild induction after several temperature cycles. These data suggest that increases in membrane raft proteins may play a role in the stabilization of the plasma membrane rafts and the maintenance of membrane functions during exposures to elevated temperatures. Recent studies suggest that homeoviscous adaptation of plasma membranes may occur primarily in these rafts and that cholesterol plays an especially important role in adjusting the physical properties of the rafts (Zehmer and Hazel, 2003). Thus our data support a role for both raft proteins and lipids in the adaptation of membranes to temperature.

Solute carriers

A number of transcripts that encode solute transporters and transmembrane proteins have strong daily expression rhythms that are not strongly modulated by temperature (Fig. 5C). These genes include aquaporin 1, and solute carriers found in the plasma membrane as well as in mitochondrial membranes. These genes seem to be temperature independent in their transcriptional regulation across a wide range of temperatures. The reason for this strong daily rhythm and temperature independence is not known, but may be due to action of the proteins encoded by these transcripts in the digestion and assimilation of food. For instance,aquaporins have recently been identified as being important for proper secretion of bile in hepatocytes (Huebert et al., 2002). The protein encoded by the gene for solute carrier family 3, member 2 (SLC3A2) is thought to be an amino acid transporter with high affinity (μmol l –1 range) for dibasic and zwitterionic amino acids (Palacin et al.,1998). Anion transport function similar to that of the band 3 anion transporter from mammalian erythrocytes has been found in kidney tubules and may be important in solute transport across kidney tubules. Perhaps a similar function is required for transport and secretion of substances by hepatocytes during digestion.

Carbohydrate metabolism

Glucose is an important fuel source for many cell types in vertebrates and is supplied largely through the circulatory system. The liver plays an essential role in blood glucose homeostasis by balancing uptake and release of glucose (Nordlie et al.,1999). Changes in transcript levels for a number of genes critical for regulation of carbohydrate metabolism and blood glucose concentrations occur in response to temperature. Notably, glucokinase has a highly variable expression pattern that is nearly identical to that for another enzyme critical to gluconeogenesis, phosphoenolpyruvate carboxykinase(Fig. 5D). These genes are almost always expressed in parallel during temperature cycling and,importantly, are almost always expressed in an opposite manner to glucose-6-phosphatase, the major regulator of glucose transport from cells into the bloodstream. Glucokinase has also been shown to play a role in the regulation of glucose metabolism and is often found in the nucleus of cells,where it is thought to act as a glucose sensor. The regulation of glucokinase and glucose-6-phosphatase is very complex and includes many effectors(Nordlie et al., 1999). Gene expression data from the present study indicate that blood glucose levels are likely to be highly responsive to nutritional status and strongly affected by temperature.

Two additional transcripts that encode for enzymes that are important regulators of glucose and glycogen metabolism, glycogen synthase and pyruvate kinase, show different responses to acclimation to chronic high temperatures,but do not exhibit strong changes in transcript levels during temperature cycling. The increase in glycogen synthase transcripts and decrease in pyruvate kinase transcripts during acclimation to 37°C would indicate that glycogen synthesis should be favored during acclimation to chronic high temperatures. The absence of a strong transcriptional response in these transcripts during temperature cycling is not surprising considering the many post-translational mechanisms for regulating these proteins on short time scales.

Intermediary metabolism

The transcript levels of carbonic anhydrase and creatine kinase genes appear to be affected by temperature in a similar manner(Fig. 5E). These proteins have both been implicated in the regulation of cellular energetics through their contributions to phosphotransfer networks that can act to couple spatially separated ATP-consuming and ATP-producing metabolic pathways(Dzeja and Terzic, 2003). The transcripts for these genes are both mildly upregulated after the coldest part of the temperature cycle (near t=0, ∼26°C), when compared to the normal circadian expression pattern. This may indicate a need to increase the capacity of the phosphotransfer networks during cold periods in the face of continually changing environmental temperatures in order to maintain tight coupling of catabolic and anabolic processes.

The pentose phosphate shunt enzyme 6-phosphogluconate dehydrogenase appears to be strongly affected by temperature cycling during the first three temperature cycles. This transcript is upregulated when compared to the normal daily expression pattern at 26°C, but is downregulated during acclimation to 37°C (Fig. 5E). Previous studies indicate that the activity of this enzyme is responsive to temperature acclimation, being upregulated during exposure to reduced temperatures(Seddon and Prosser, 1997). Increased activity of the pentose phosphate shunt would be expected to support increased biosynthesis of fatty acids by providing reducing equivalents for biosynthetic reactions. Others have suggested a link between pentose phosphate shunt activity and antioxidant protection via glutathione(Winkler et al., 1986). However, in our study, expression of at least one enzyme involved in glutathione-based detoxification of oxygen radicals,glutathione-s-transferase, is not similar to that for 6-phosphogluconate dehydrogenase, which suggests that expression of the latter is more likely to be involved in control of biosynthesis and overall redox balance in the cytoplasm and not responsive to oxidative damage per se. Additional support for this conclusion comes from expression patterns of the cytosolic isoform of isocitrate dehydrogenase. This enzyme has been found to play an important role in defense against oxidative stress in cultured NIH3T3 cells(Lee et al., 2002). Transcript levels for this enzyme are downregulated during temperature cycling and during acclimation to 37°C, and upregulated during acclimation to 20°C. This transcriptional response is much more consistent with temperature compensation of metabolic function than a need to cope with oxidative damage.

Dihydropyrimidine dehydrogenase catalyzes the rate-limiting step in pyrimidine catabolism, converting uracil to 5,6-dihydrouracil (KEGG website, This conversion eventually yields β-alanine or a number of other pyrimidine-derived metabolites. The transcript level of dihydropyrimidine dehydrogenase is upregulated strongly after repeated temperature cycling(Fig. 5E). The upregulation of this transcript could indicate an increased need for the metabolism of pyrimidines due to increased turnover of RNA and DNA, perhaps even due to cell damage after repeated temperature cycling. An alternative hypothesis is that temperature cycling induces the production of β-alanine as an organic osmolyte. β-alanine is a known organic osmolyte in prokaryotes and various animal lineages, including marine elasmobranchs(Hochachka and Somero, 2002). It is possible that accumulation of organic osmolytes (see discussion below)could help to mediate temperature stress by stabilizing protein structure in the face of fluctuating temperatures.

Nitrogen metabolism

A number of transcripts that encode for proteins in nitrogen metabolism are differentially regulated during acclimation to constant and fluctuating temperatures (Fig. 5F). The transcript for betaine homocysteine methyltransferase is downregulated under constant cold temperatures and upregulated under constant warm temperatures(Fig. 5F). Levels of this transcript are highly variable under temperature cycling conditions. These data may indicate that methylamine metabolism is important for temperature acclimation, and suggest that levels of methylamines may be elevated at high temperatures and reduced at low temperatures, which would be consistent with data emerging that indicate that methylamines, especially glycine betaine, can act as `chemical chaperones' and have a stabilizing effect on proteins during exposures to combined high salt and temperature stress in E. coli(Diamant et al., 2001). These authors suggest that organic osmolytes, especially glycine betaine and, to some extent, proline, may regulate the activity of macromolecular chaperones such as the major heat-shock proteins. Interestingly, levels of transcripts for the proteins arginase and ornithine aminotransferase are also elevated during acclimation to 37°C and temperature cycling. These enzymes are both involved in proline biosynthesis (KEGG website, We predict, based on these data, that proline and glycine betaine levels are likely to increase during acclimation to chronic high temperatures and cycling temperatures. Elevated levels of organic osmolytes may help to offset the need for molecular chaperones on a long-term basis and may also explain why transcript levels of heat-shock proteins return to control values after several temperature cycles. The use of organic osmolytes to enhance protein stability would seem to be an economical way to deal with variable environmental conditions without continually mounting a heat-shock response. We note that protein-stabilizing methylamine solutes have been shown to accumulate to high concentrations in deep-sea fishes and invertebrates and to be effective in counteracting the destabilizing effects of high pressure on protein structure and function (Yancey et al., 2002).

Cytoskeletal elements and contractile proteins

A number of genes that encode cytoskeletal proteins and proteins involved in contractile functions are variably expressed in relation to temperature(Fig. 5G). The mRNA transcript for α-tubulin appears to be especially variable during temperature cycling. Transcripts for myosin heavy chain and light chain are also strongly affected by temperature and share almost identical expression patterns. The reasons for these expression patterns are not yet clear, but may be related to a need to stabilize the cytoskeleton in response to changing temperatures. This hypothesis is supported by the increased levels of expression for ficolin 1 and other actin binding proteins as well as microtubule associated proteins. These data should indicate that normalization of mRNA levels to cytoskeletal genes such as tubulin, a standard procedure, can likely result in misleading or false interpretation of mRNA levels in situations of fluctuating temperatures.

Protein turnover

Acclimation to constant low or high temperatures appears to induce alterations in transcription that may affect levels of protein synthesis and degradation (Fig. 5H). Two translation elongation factors, a tRNA synthase, and at least one ribosomal protein all have increased levels of expression, while there is a slight decrease in the amount of transcript for a regulatory subunit of the 26S proteosome (part of the ubiquitin-dependent proteolysis system) in response to chronic elevated temperatures. These data suggest an increase in protein turnover during exposure to chronic high temperatures, and an attempt by the organism to maintain protein levels by increasing the capacity for protein synthesis and decreasing the capacity for protein degradation. During acclimation to cold conditions, there is a strong upregulation of the regulatory subunit of the 26S proteosome, but transcript levels for proteins involved in the protein synthetic machinery remain unchanged or are slightly downregulated after 2 weeks of acclimation. Temperature cycling appears to elicit a very weak induction of transcripts for proteins involved in protein synthesis. In contrast, transcript levels of regulatory subunit for the 26S proteosome appear to be highly responsive to temperature cycling, and may indicate that regulation of protein degradation is critical during short-term fluctuations in temperature. These data on key components of the protein turnover machinery indicate that certain aspects of protein turnover are likely to be regulated at the transcriptional level during temperature acclimation, and the response to constant conditions is unique when compared to those for exposures to temperature cycling.

The acute phase response, complement and innate immunity

Several transcripts that encode for proteins involved in the acute phase response are differentially regulated during temperature acclimation(Fig. 5I). Many of the components of the acute phase response are initially upregulated during temperature cycling and then downregulated after 5 days of cycling. These transcripts are also strongly upregulated by exposures to chronic high temperatures and more weakly to acclimation to 20°C. While these results may appear inconsistent initially, upon closer examination they are consistent with what is known about the acute phase response and, in particular, what is known about the complement proteins in fish.

The complement proteins are a major part of the innate immune system of all vertebrates (Sunyer and Lambris,1998). Complement proteins are known effectors of the inflammation response to tissue damage and infection, and activation of the complement pathway results in the marking of target cells (opsonization) with complement proteins, activation of leucocytes, and lysis of target cells via the formation of a membrane attack complex (MAC) comprising complement proteins that self-insert into the plasma membrane(Roitt et al., 1993). The activity of complement proteins and the marking of target cells are largely regulated by the activation of the C3 complement protein through two alternative pathways. One of these pathways, the alternate pathway, is regulated by the hydrolysis of a thiol-ester bond within the C3 protein itself, by the C3 convertase enzyme. This bond is known to react with water spontaneously at a low level, so that this pathway is always slightly activated (Roitt et al.,1993). The activation of C3 and thus the complement pathway are amplified by a positive feedback loop that is regulated largely by the presence or absence of a non-self surface for opsonization. Complement proteins can bind to both self and non-self surfaces but are retarded from binding to self surfaces by specific proteins. The activation of the complement pathway has been shown to contribute to tissue damage after ischemic injury to cardiac tissues (Roitt et al., 1993), which indicates that the complement pathway must be carefully regulated to function in the immune response without causing major damage to self tissues.

The initial upregulation of transcripts encoding several complement proteins upon exposure to high or cycling temperatures may be the result of an increased activation of the complement pathway via spontaneous activation of the C3 protein due to increased thermal energy, or viasignals originating from tissue damage. In either case, the response would be counterproductive in the long-term, and is downregulated after several temperature cycles in the fluctuating acclimation regime. However, high levels of transcript are maintained during exposure to chronic high temperature,which may be a sign of continued tissue damage, or a maladaptive immune response similar to that observed following ischemia(Roitt et al., 1993). The slower upregulation kinetics of complement protein transcripts during cold acclimation is probably a temperature compensatory mechanism to boost innate immune function, which is consistent with earlier findings that the classical immune response (antibody mediated) is attenuated in fish at low temperatures(Bayne and Gerwick, 2001).

There is also evidence suggesting that gene expression patterns associated with liver regeneration and the acute phase response are similar(Milland et al., 1990). It is possible that the initial exposure to temperature cycling and chronic exposure to high temperatures induced cellular damage and cell death in a subpopulation of liver cells and the activation of complement proteins is in response to cellular damage. Other evidence supporting the possibility of conditions causing cell damage is the upregulation of haptoglobin gene expression, which is known to be important in binding and scavenging hemoglobin from ruptured red blood cells (Dobryszycka,1997).

Cell growth and proliferation

A number of transcripts that encode proteins that regulate cell growth and proliferation have changing expression patterns during acclimation to constant and cycling temperatures (Fig. 5J). For instance, two tumor suppressor genes, arginine-rich protein (ARP Shridhar et al.,1997) and quiescin Q6 (Coppock et al., 1998), are induced by high temperatures, with ARP strongly induced during the initial temperature cycles and quiescin Q6 slowly induced after several cycles. Quiescin Q6 is strongly induced during exposure to chronic high temperatures, while ARP is only weakly induced. Quiescin Q6 has been shown to be strongly induced during entry into reversible cellular quiescence in mammalian cells and is expressed at very low levels in actively proliferating cells (Coppock et al.,1998). These data suggest that cell proliferation is probably arrested during temperature cycling and during exposure to chronic high temperatures, but perhaps through different pathways. The transcript for a putative oncogene, Mig-6 (Makkinje et al.,2000 Tsunoda et al.,2002), known to be critical to stress-activated protein kinase signaling (SAPK/JNK Makkinje et al.,2000) also appears to be regulated by temperature. Mig-6 is induced during the first temperature cycle, but has a highly variable expression pattern in general. There is evidence that Mig-6 may be involved in the sustained activation of SAPK/JNK-induced changes in gene expression(Makkinje et al., 2000). This sustained activation leads to cellular hypertrophy due to increased cell growth but not cell division in some mammalian chronic diseases. The variable expression pattern of the Mig-6 transcript during temperature cycling may be a way to carefully titer the activity of the SAPK/JNK signaling pathways and thus avoid the problems associated with chronic activation of this pathway. The relative abundance of TIEG-1 mRNA, which encodes a TGF-β early response gene (Cook and Urrutia,2000 Hefferan et al.,2000) decreases during the initial temperature cycles. TIEG-1 mRNA has been shown to be rapidly induced by TGF-β signaling and is generally associated with a decrease in cell growth and proliferation in pancreas cells(Cook and Urrutia, 2000). The data presented here indicate that TGF-β signaling is probably not initially activated by temperature cycling, but may be activated by chronic exposure to cold temperatures (20°C) and during the cold parts of the daily temperature cycle (Fig. 5J). Anintegrated view of the above data suggests that temperature cycling activates the SAPK/JNK pathway and represses TIEG-1 expression,leading to increased cellular growth, while induction of ARP and quiescin Q6 indicates a cessation of cellular proliferation. These data suggest that different parts of the cell growth and proliferation cycle may be entrained to temperature. The partitioning of different parts of the cell cycle into different temperature conditions may have profound influences on the energetics and physiology of organisms exposed to cycling temperatures in nature, and may dictate when complex behaviors such as gamete synthesis or reproduction are favorable.

Unknown transcripts

A number of cDNAs that have no homology to known sequences or have unknown function were identified as responsive to temperature acclimation in this study (Fig. 5K). These cDNAs may represent critical parts of the acclimation response that have not yet been identified or studied. Further interrogation of these sequences and their expression patterns in response to temperature and other environmental factors is likely to result in the discovery of new genes and gene functions that are critical for survival of environmental variation.

Global regulation of transcription

The high mobility group protein HMGB1 transcript exhibits one of the most striking patterns of gene expression associated with cycling temperatures(Figs 3, 4, 5J). The relative abundance of this transcript is highly negatively correlated with temperature(Fig. 4). The HMGB1 transcript changes over tenfold during the daily temperature cycle and both the pattern and magnitude of the expression are consistent over the entire 2-week period of temperature cycling. Further, this transcript does not show any changes in expression on a daily basis under constant temperature conditions. The unique properties of the HMGB1 protein, coupled with the expression pattern presented in this paper, lead us to propose that the HMGB1 protein may be a critical part of a compensatory transcriptional response to temperature and may indeed be a highly sensitive temperature sensor.

The HMGB1 protein is in many ways the perfect candidate as an immediate effector of transcription in response to temperature. HMGB1 is a small (28 kDa) and abundant protein that is highly conserved and ubiquitous among the vertebrates (Wolffe, 1999 Thomas and Travers, 2001). HMGB1 can bind DNA in a structure-specific manner with a preference for single stranded, bent or supercoiled structures(Hamada and Bustin, 1985 Stros, 2001 Thomas and Travers, 2001). It has been shown to partner with many important transcription factors such as p53, HoxD9 and steroid hormone receptors through specific interaction domains(Wolffe, 1999 Thomas and Travers, 2001). The protein is also very `sticky' in nature and is able to bind to a variety of other proteins, including cytoskeletal elements and extracellular matrix proteins, and to various classes of lipids(Einck and Bustin, 1985). It is generally agreed that HMGB1 has an integral role in assembly of numerous nucleoprotein complexes that are critical to cell function such as V(D)J recombination and the formation of enhanceosome complexes(Stros and Reich, 1998 Wolffe, 1999 Ellwood et al., 2000 Thomas and Travers, 2001). HMGB1 is known to increase the affinity of the TATA binding protein TBPII for the TATA box by over 20-fold (Das and Scovell, 2001). Overexpression of HMGB1 in mammalian cell lines results in a global stimulation of transcription (gene- and polymerase-independent) that is associated with a generalized decondensation of chromatin structure (Aizawa et al.,1994 Ogawa et al.,1995). Additionally, injection of antibodies against HMGB1 inhibits transcription in Xenopus oocytes(Ogawa et al., 1995). These data taken together indicate a key role for HMGB1 in the global regulation of transcription.

The thermal stability and biochemical properties of HMGB1 protein suggest that the protein should be very sensitive to temperature in vivo. HMGB1 has a very broad thermal melting curve under dilute acidic conditions(Ramstein et al., 1999). The protein begins to melt at 20°C and is not completely denatured until 65°C, giving the protein a melting range of over 40°C. This broad melting range is likely due to the differing thermal stabilities of different domains within the protein (Ramstein et al., 1999). This point is critical because it indicates that one part of the protein may be stable and functional while the other part is denatured at physiologically relevant temperatures near 37°C. Additionally, these proteins may be post-translationally modified(Einck and Bustin, 1985) by the addition of several moieties, and the thermal stability of the protein can be modulated by as many as 5°C by these modifications(Stemmer et al., 2002). This property would allow for seasonal adjustments in the thermal stability of the HMGB1 protein. We hypothesize that the melting of this protein at physiologically relevant temperatures disrupts the ability of the HMGB1 protein to maintain the nucleoprotein complexes associated with transcription initiation and causes a global change in the rate of transcription, especially for those genes that contain a TATA box in their promoter. This mechanism is hypothesized to be important for modulating the level of transcription in a very general manner, while still allowing for specific changes in gene expression to be induced or repressed by regulatory transcription factors or enhancers and silencers. The patterns of transcript abundance presented in this report are consistent with this theory if the HMGb1 gene is autoregulated by its own activity. This is likely to be the case since the hypothesized mechanism is global and not gene specific. Additionally, in order for this model to work, HMGb1 transcripts need to have a very short half-life in the cell. Our data are consistent with a high turnover rate for this transcript in vivo, simply because a stable transcript would probably not show such large fluctuations in relative abundance on an hourly basis. Interestingly, the 3′UTR of the HMGB1 transcript is highly conserved among all vertebrates (Bustin et al., 1990), which strongly suggests a regulatory role because 3′UTR regions are classically highly variable.

We have identified the HMGB1 protein as a putative global regulator of transcription in response to temperature. If this hypothesis is supported by additional studies of protein function, this model could help resolve many unexplained phenomena associated with temperature acclimation. For example,seasonal shifts in thermal tolerance may be associated with changes in post-translational modification of the HMGB1 protein. The replacement of the linker histone H1 with HMGB1 during early development(Nightingale et al., 1996 Ner et al., 2001) may also explain the extreme temperature sensitivity of early embryos due to a loss of chromatin architecture, as HMGB1 proteins are easily denatured at biologically relevant temperatures. We believe that further investigations into the role of HMGB1 in temperature acclimation are likely to lead to a new understanding of how eukaryotic cells maintain homeostasis in the face of an ever-changing thermal environment.

In conclusion, these studies illustrate the utility of cDNA microarray approaches in both hypothesis-driven and `discovery-based' experimentation in environmental physiology (Gracey and Cossins, 2003). In the former context, microarray studies can unravel the alterations in gene expression that are conjectured to underlie known physiological responses to temperature, e.g. in expression of heat-shock proteins and in the restructuring of cellular membranes. Of equal, if not greater, importance in microarray studies is the discovery of new facets of physiological responses that are not anticipated by the investigator, for instance potential global regulators of environmentally induced gene expression and interplay between normal circadian patterns of gene expression and alterations in gene expression made in response to fluctuations in temperature. Microarray technologies thus allow a type of exploration in`molecular natural history' (Brown and Botstein, 1999) that seems certain to open up critical new areas for study in ecological and evolutionary physiology(Feder and Mitchell-Olds,2003).

Antigenic shift

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Antigenic shift, genetic alteration occurring in an infectious agent that causes a dramatic change in a protein called an antigen, which stimulates the production of antibodies by the immune systems of humans and other animals. Antigenic shift has been studied most extensively in influenza type A viruses, which experience this change about once every 10 years. The newly emerged viruses have the potential to cause epidemics or pandemics, since very few, if any, humans possess immunity against the new antigens.

Antigenic shift occurs because influenza A viruses have a large animal reservoir, consisting primarily of wild aquatic birds (e.g., ducks). It also occurs because the RNA genome of influenza A viruses is in the form of eight segments, which during viral replication are susceptible to a type of genetic exchange known as genetic reassortment. Reassortment can result in antigenic shift when an intermediate host, such as a pig, is simultaneously infected with a human and an avian influenza A virus. The new version of the virus that is produced represents a new influenza A subtype and thus is immunologically distinct from influenza A viruses that have been circulating in the human population. Influenza A subtypes are distinguished by the two major antigenic glycoproteins, hemagglutinin (H) and neuraminidase (N), that exist on their viral coats. (H1N1, H3N2, and H5N1 are examples of influenza A subtypes.)

Antigenic shift may also occur when an influenza A virus jumps directly from aquatic birds to humans or when a virus passes from aquatic birds to humans through an intermediate host without undergoing reassortment.

This article was most recently revised and updated by Kara Rogers, Senior Editor.

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