Current Biology Vol 25 No 6 R236
many recent clinical trials for new AD medications have been focused on ways to reduce levels of toxic Ab [1]. With respect to the latter it is notable that several Ab-induced alterations have been recapitulated in wild-type mice following seizure induction or prevented in an AD mouse model by blocking overexcitation, thus supporting a role for neuronal hyperactivity in AD symptomology [18]. Surprisingly, among various anti-epileptics that have been tested head-to-head in a mouse model of AD, only LEV reduced hyperexcitability, remodeling of hippocampal circuits, synaptic dysfunction, and cognitive deficits [19]. It will thus be important to determine why LEV is more effective than other suppressors of neuronal activity at reducing AD symptomology and how exactly this drug mediates its effects. While attempts to slow neurodegeneration in AD patients have met little success, the finding that sleep loss and neural excitability may underlie Ab accumulation in the fruit fly paves the way for new approaches to drug development. References 1. Spencer, B., and Masliah, E. (2014). Immunotherapy for Alzheimer’s disease: past, present and future. Front. Aging Neurosci. 6, 114. 2. Ju, Y.E., Lucey, B.P., and Holtzman, D.M. (2014). Sleep and Alzheimer disease pathology–a bidirectional relationship. Nat. Rev. Neurol. 10, 115–119.
3. Tabuchi, M., Lone, S.R., Liu, S., Liu, O., Zhang, J., Spira, A.P., and Wu, M.N. (2015). Sleep interacts with Abeta to modulate intrinsic neuronal excitability. Curr. Biol. 25, 702–712. 4. Bertram, L., and Tanzi, R.E. (2008). Thirty years of Alzheimer’s disease genetics: the implications of systematic meta-analyses. Nat. Rev. Neurosci. 9, 768–778. 5. Jack, C.R., Jr., and Holtzman, D.M. (2013). Biomarker modeling of Alzheimer’s disease. Neuron 80, 1347–1358. 6. Roh, J.H., Jiang, H., Finn, M.B., Stewart, F.R., Mahan, T.E., Cirrito, J.R., Heda, A., Snider, B.J., Li, M., Yanagisawa, M., et al. (2014). Potential role of orexin and sleep modulation in the pathogenesis of Alzheimer’s disease. J. Exp. Med. 211, 2487–2496. 7. Moran, M., Lynch, C.A., Walsh, C., Coen, R., Coakley, D., and Lawlor, B.A. (2005). Sleep disturbance in mild to moderate Alzheimer’s disease. Sleep Med. 6, 347–352. 8. Iijima, K., and Iijima-Ando, K. (2008). Drosophila models of Alzheimer’s amyloidosis: the challenge of dissecting the complex mechanisms of toxicity of amyloid-beta 42. J. Alzheimers Dis. 15, 523–540. 9. Sehgal, A., and Mignot, E. (2011). Genetics of sleep and sleep disorders. Cell 146, 194–207. 10. Nilsberth, C., Westlind-Danielsson, A., Eckman, C.B., Condron, M.M., Axelman, K., Forsell, C., Stenh, C., Luthman, J., Teplow, D.B., Younkin, S.G., et al. (2001). The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat. Neurosci. 4, 887–893. 11. Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan, M., O’Donnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J., et al. (2013). Sleep drives metabolite clearance from the adult brain. Science 342, 373–377. 12. Tononi, G., and Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34. 13. Yang, G., Lai, C.S., Cichon, J., Ma, L., Li, W., and Gan, W.B. (2014). Sleep promotes branch-specific formation of dendritic spines after learning. Science 344, 1173–1178.
Animal Cognition: Bumble Bees Suffer ‘False Memories’ The existence of ‘false memories’, where individuals remember events that they have never actually experienced, is well established in humans. Now a new study reports that insects similarly form illusory memories through merging of memory traces.
Judith Reinhard Memories define us as individuals. We accumulate them throughout our lives, lay some of them down as long-term memories that last a life-time, while other memories are short-lived and only last for minutes or days. Although we tend to believe that our memories are precise recollections of past events, memories are a fickle thing.
No two people have the exact same memories even if they experience the same events, which is why eye witness records are notoriously unreliable. Memories may change over time as the brain recalls and re-consolidates them, resulting in various types of ‘false memories’, where individuals remember events or objects that they have never actually been exposed to or merge different
14. Kamenetz, F., Tomita, T., Hsieh, H., Seabrook, G., Borchelt, D., Iwatsubo, T., Sisodia, S., and Malinow, R. (2003). APP processing and synaptic function. Neuron 37, 925–937. 15. Cirrito, J.R., Kang, J.E., Lee, J., Stewart, F.R., Verges, D.K., Silverio, L.M., Bu, G., Mennerick, S., and Holtzman, D.M. (2008). Endocytosis is required for synaptic activity-dependent release of amyloid-beta in vivo. Neuron 58, 42–51. 16. Cirrito, J.R., Yamada, K.A., Finn, M.B., Sloviter, R.S., Bales, K.R., May, P.C., Schoepp, D.D., Paul, S.M., Mennerick, S., and Holtzman, D.M. (2005). Synaptic activity regulates interstitial fluid amyloid-beta levels in vivo. Neuron 48, 913–922. 17. Bero, A.W., Yan, P., Roh, J.H., Cirrito, J.R., Stewart, F.R., Raichle, M.E., Lee, J.M., and Holtzman, D.M. (2011). Neuronal activity regulates the regional vulnerability to amyloid-beta deposition. Nat. Neurosci. 14, 750–756. 18. Palop, J.J., Chin, J., Roberson, E.D., Wang, J., Thwin, M.T., Bien-Ly, N., Yoo, J., Ho, K.O., Yu, G.Q., Kreitzer, A., et al. (2007). Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron 55, 697–711. 19. Sanchez, P.E., Zhu, L., Verret, L., Vossel, K.A., Orr, A.G., Cirrito, J.R., Devidze, N., Ho, K., Yu, G.Q., Palop, J.J., et al. (2012). Levetiracetam suppresses neuronal network dysfunction and reverses synaptic and cognitive deficits in an Alzheimer’s disease model. Proc. Natl. Acad. Sci. USA 109, E2895–E2903.
1Department
of Biology, University of Nevada, Reno. Reno, NV 89557, USA. 2Department of Pharmacology, Biomedical Sciences Graduate Program, Neurosciences Graduate Program, and Center for Chronobiology, University of California, San Diego, La Jolla, CA 92093-0636, USA. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2015.02.003
memory traces into an illusory memory [1–3]. For example, when presented with a list of words all pertaining to fruit, people would remember the word ‘apple’ even if it was not on the list. While the existence of such memory errors is well established in humans [2,3], it has never before been shown in animals, although many animal species from insects to vertebrates are known for their sophisticated learning and memory abilities. Now, a new study by Kate Hunt and Lars Chittka [4], published in this issue of Current Biology, has revealed that an insect with a small brain, the bumble bee, also suffers false memories. The study suggests that memory traces for different visual stimuli such as a colour and a black-and-white pattern are merged
Dispatch R237
in the bumble bee brain into a false memory of an ‘imagined’ stimulus that combines the colour and the pattern (Figure 1). Bumble bees like honeybees are excellent animal models to study visual learning and memory phenomena [5–7]. The lifestyle of foraging bees predisposes them to learn and remember the colours, shapes and patterns of different flowers, so that they can return to rewarding flowers over and over again. Importantly, flowers may bear nectar at different times of day and the selection of nectar-bearing flowers changes over the lifetime of a bee as new plants come into bloom and others stop flowering. Hence, bees form multiple complex memories of flowers throughout the days and weeks of their lives, both simultaneously and sequentially [8]. How does a small brain like a bee’s cope with multiple memories of different colours, shapes and patterns? Countless reports have demonstrated the amazing cognitive and visual abilities of bees [7,9]. But now Hunt and Chittka [4] show that a bee’s memory is not quite as reliable as we thought and memory mistakes are being made even by bees. To investigate whether bee brains merge memory traces into false memories of never encountered objects, the authors conducted a series of carefully designed visual learning experiments with free-flying bumble bees. Individual bees were trained to visit artificial flowers consisting of small disks with a drop of sugar solution in the centre as reward. The disks were either of a uniform colour or had a black-and-white pattern of concentric rings (Figure 1). One group of bumble bees was first offered yellow disks with sugar water, while the black-and-white patterned disks were empty and thus unrewarding. After learning to land on the yellow disks, the bumble bees were then presented with the reverse situation, namely empty yellow disks and rewarded black-and-white patterns. This extensive training procedure lasted several hours and created two distinct memories of rewarding food sources in the bee’s brain: of a yellow flower and of a black-and-white flower. A second group of bees was trained to the same stimuli, but this group first experienced the black-and-white disks as rewarding and the yellow disks as empty, and
Current Biology
Figure 1. Memory merging in the bee brain. After learning a yellow stimulus and a black-and-white patterned stimulus during training, bumble bees show preference for a never-encountered hybrid stimulus that combines features of both learnt stimuli.
then the reverse situation. Thus, both groups of bees had the same memories of two different flowers, only the sequence of experiencing the stimuli was reversed. Immediately after training, both groups of bumble bees were presented with three unrewarded disks: a yellow disk, a black-and-white disk (these two stimuli being identical to the ones the bees had learnt), and a stimulus they had never seen before, namely a hybrid of the two other stimuli: a yellow pattern of concentric rings (Figure 1). In this scenario, the bees had to rely on the short-term memory from their last training session to choose a rewarding flower, and bees from both groups simply picked the stimulus that they had last experienced as rewarding. This is typical for recall of short-term memories, which are often laid down in early processing centres of the brain, easily accessed and lasting only minutes [10]. However, when bees were tested 1 day and 3 days after training, a different picture emerged. Now the bees had to recall long-term memories of the training stimuli, which rely on different mechanisms [10]. Considering that bees had formed stable memories of both the yellow disk and the black-and-white disk, one would have expected them to visit both of those stimuli equally. However, over the course of the test evaluating 20 choices from each bumble bee, the bees switched from initial
preference for the most recently rewarded stimulus to preference for the hybrid, which had combined features of both known flowers, but which they had never experienced before. A series of control experiments excluded generalisation to colour or patterns as underlying mechanisms. Using other colour-pattern combinations, the study confirmed that bees switched their preference to hybrid stimuli which had merged features of previously learnt stimuli [4]. These novel findings strongly suggest that bumble bees had indeed created a false memory based on previous memory traces. Importantly, memory merging only occurred when bees were trained first to a black-and-white pattern and then to a colour, not the other way round. Possibly having a black-and-white pattern as second memory was more salient and less prone to suggestibility and memory manipulations. This tells us that memory merging in bumble bees only occurs under certain conditions and with certain stimulus combinations, similar to humans, where memory merging occurs for pictures of faces, nonsense words and simple sentences, but not necessarily with other, more salient events [11,12]. What is the point of having the mechanism of memory merging and illusory memories? The fact that it exists in insects as well as humans suggests it is common to many
Current Biology Vol 25 No 6 R238
animals. Why create a false reality? Some argue that memory errors may simply be inevitable by-products of the adaptive cognitive ability to form general concepts and categories, which is well established in both humans and bees [2,13,14]. But maybe it is a useful mechanism that has been evolutionarily conserved. At least in the case of bees one could imagine that creating a memory of an imaginary flower that combines features of known flowers gives bees a head start during foraging. The illusory memory could create a search pattern to predict and more rapidly recognise new potential food sources. Although there are countless reports of the astonishing cognitive and learning abilities of bees [7,9], studies like the one by Hunt and Chittka [4] remind us that there is still a lot that we do not know about the animal mind. It is a wake-up call to not merely discard negative results from learning and memory studies in animals such
as failure to recall stimuli. Previously undiscovered mechanisms such as false memories and memory merging might be at play, which should be taken into account as modifying factors just as much as an individual bee’s experience [7]. Because as Hunt and Chittka have shown in their exciting new study: bees are human after all; they make memory mistakes just like us. References 1. Roediger, H.L., and McDermott, K.B. (1995). Creating false memories - Remembering words not presented in lists. J. Exp. Psychol. Learn. 21, 803–814. 2. Schacter, D.L. (2001). The Seven Sins of Memory (Boston, USA: Houghton Mifflin). 3. Loftus, E.F. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learn. Memory 12, 361–366. 4. Hunt, K.L., and Chittka, L. (2015). Merging of long-term memories in an insect. Curr. Biol. 25, 741–745. 5. Riveros, A.J., and Gronenberg, W. (2009). Learning from learning and memory in bumblebees. Commun. Integr. Biol. 2, 437–440. 6. Srinivasan, M.V. (2010). Honey bees as a model for vision, perception, and cognition. Annu. Rev. Entomol. 55, 267–284.
Autophagy: Starvation Relieves Transcriptional Repression of ATG Genes Autophagy is a highly regulated process about which relatively little is known, particularly concerning the transcriptional control of autophagy regulation. A new study identifies a key regulator of the expression of autophagy-related genes, thereby providing insights into the signalling pathways modulating autophagy. Rodney J. Devenish and Mark Prescott The yeast Saccharomyces cerevisiae is a proven model organism for the investigation of many fundamental biological pathways, including autophagy. Indeed, the rapid progress and expansion of knowledge concerning autophagic processes that has occurred over the last 20 years has been driven by fundamental studies in yeast [1], which have identified important molecular components and regulators of this degradative pathway. A study published in a recent issue of Current Biology by Bernard et al. [2] reports the application of targeted
library screening to a search for new transcriptional regulators of autophagy in yeast and has yielded new insights into transcriptional regulation of ‘autophagy-related’ (ATG) genes and, by extension, autophagy. Autophagy (more strictly, macroautophagy) is a pathway by which cytoplasmic components, organelles or pathogens can be sequestered within double-membrane vesicles, or autophagosomes [3], and delivered to the degradative compartment of the cell — the vacuole in yeast, and lysosomes in mammals. The action of the resident complement of acid hydrolases in this compartment typically degrades the autophagosome
7. Dyer, A.G. (2012). The mysterious cognitive abilities of bees: why models of visual processing need to consider experience and individual differences in animal performance. J. Exp. Biol. 215, 387–395. 8. Reinhard, J., Srinivasan, M.V., and Zhang, S.W. (2006). Complex memories in honeybees: can there be more than two? J. Comp. Physiol. A 192, 409–416. 9. Avargue`s-Weber, A., and Giurfa, M. (2013). Conceptual learning by miniature brains. Proc. R. Soc. B 280, 20131907. 10. Menzel, R. (2012). The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768. 11. Reinitz, M., Lammers, W., and Cochran, B. (1992). Memory-conjunction errors: Miscombination of stored stimulus features can produce illusions of memory. Mem. Cognition 20, 1–11. 12. Reinitz, M.T., and Demb, J.B. (1994). Implicit and explicit memory for compound words. Mem. Cognition 22, 687–694. 13. Chittka, L., and Niven, J. (2009). Are bigger brains better? Curr. Biol. 19, R995–R1008. 14. Chittka, L., and Jensen, K. (2011). Animal cognition: Concepts from apes to bees. Curr. Biol. 21, R116–R119.
Queensland Brain Institute, The University of Queensland, St Lucia, QLD 4072, Australia. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2015.01.072
contents, and the molecular ‘building blocks’ that are then recovered are reused in biosynthetic pathways. Thus, autophagy is an important homeostatic adaptation allowing cells to adapt to changes in nutritional availability and physiological stresses. Autophagosome formation is a multi-step process utilizing machinery composed of many different ATG gene products. Given its important role in cellular functions, it is not surprising that autophagy is a tightly regulated process. Yet, despite the machinery of autophagy being relatively well defined, the detailed mechanisms underlying the regulation of autophagy, including at the level of transcription of the ATG genes, remain to be fully elucidated. The new study [2] reports the screening of a collection of yeast mutants lacking a single DNA-binding protein by analysing the expression of a set of ATG genes using RT-qPCR. The screen focused on a subset of ATG genes specifically chosen because they encode proteins involved in different steps of the autophagy pathway and are known to be subject to strong transcriptional induction after
many recent clinical trials for new AD medications have been focused on ways to reduce levels of toxic Ab [1]. With respect to the latter it is notable that several Ab-induced alterations have been recapitulated in wild-type mice following seizure induction or prevented in an AD mouse model by blocking overexcitation, thus supporting a role for neuronal hyperactivity in AD symptomology [18]. Surprisingly, among various anti-epileptics that have been tested head-to-head in a mouse model of AD, only LEV reduced hyperexcitability, remodeling of hippocampal circuits, synaptic dysfunction, and cognitive deficits [19]. It will thus be important to determine why LEV is more effective than other suppressors of neuronal activity at reducing AD symptomology and how exactly this drug mediates its effects. While attempts to slow neurodegeneration in AD patients have met little success, the finding that sleep loss and neural excitability may underlie Ab accumulation in the fruit fly paves the way for new approaches to drug development. References 1. Spencer, B., and Masliah, E. (2014). Immunotherapy for Alzheimer’s disease: past, present and future. Front. Aging Neurosci. 6, 114. 2. Ju, Y.E., Lucey, B.P., and Holtzman, D.M. (2014). Sleep and Alzheimer disease pathology–a bidirectional relationship. Nat. Rev. Neurol. 10, 115–119.
3. Tabuchi, M., Lone, S.R., Liu, S., Liu, O., Zhang, J., Spira, A.P., and Wu, M.N. (2015). Sleep interacts with Abeta to modulate intrinsic neuronal excitability. Curr. Biol. 25, 702–712. 4. Bertram, L., and Tanzi, R.E. (2008). Thirty years of Alzheimer’s disease genetics: the implications of systematic meta-analyses. Nat. Rev. Neurosci. 9, 768–778. 5. Jack, C.R., Jr., and Holtzman, D.M. (2013). Biomarker modeling of Alzheimer’s disease. Neuron 80, 1347–1358. 6. Roh, J.H., Jiang, H., Finn, M.B., Stewart, F.R., Mahan, T.E., Cirrito, J.R., Heda, A., Snider, B.J., Li, M., Yanagisawa, M., et al. (2014). Potential role of orexin and sleep modulation in the pathogenesis of Alzheimer’s disease. J. Exp. Med. 211, 2487–2496. 7. Moran, M., Lynch, C.A., Walsh, C., Coen, R., Coakley, D., and Lawlor, B.A. (2005). Sleep disturbance in mild to moderate Alzheimer’s disease. Sleep Med. 6, 347–352. 8. Iijima, K., and Iijima-Ando, K. (2008). Drosophila models of Alzheimer’s amyloidosis: the challenge of dissecting the complex mechanisms of toxicity of amyloid-beta 42. J. Alzheimers Dis. 15, 523–540. 9. Sehgal, A., and Mignot, E. (2011). Genetics of sleep and sleep disorders. Cell 146, 194–207. 10. Nilsberth, C., Westlind-Danielsson, A., Eckman, C.B., Condron, M.M., Axelman, K., Forsell, C., Stenh, C., Luthman, J., Teplow, D.B., Younkin, S.G., et al. (2001). The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat. Neurosci. 4, 887–893. 11. Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan, M., O’Donnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J., et al. (2013). Sleep drives metabolite clearance from the adult brain. Science 342, 373–377. 12. Tononi, G., and Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34. 13. Yang, G., Lai, C.S., Cichon, J., Ma, L., Li, W., and Gan, W.B. (2014). Sleep promotes branch-specific formation of dendritic spines after learning. Science 344, 1173–1178.
Animal Cognition: Bumble Bees Suffer ‘False Memories’ The existence of ‘false memories’, where individuals remember events that they have never actually experienced, is well established in humans. Now a new study reports that insects similarly form illusory memories through merging of memory traces.
Judith Reinhard Memories define us as individuals. We accumulate them throughout our lives, lay some of them down as long-term memories that last a life-time, while other memories are short-lived and only last for minutes or days. Although we tend to believe that our memories are precise recollections of past events, memories are a fickle thing.
No two people have the exact same memories even if they experience the same events, which is why eye witness records are notoriously unreliable. Memories may change over time as the brain recalls and re-consolidates them, resulting in various types of ‘false memories’, where individuals remember events or objects that they have never actually been exposed to or merge different
14. Kamenetz, F., Tomita, T., Hsieh, H., Seabrook, G., Borchelt, D., Iwatsubo, T., Sisodia, S., and Malinow, R. (2003). APP processing and synaptic function. Neuron 37, 925–937. 15. Cirrito, J.R., Kang, J.E., Lee, J., Stewart, F.R., Verges, D.K., Silverio, L.M., Bu, G., Mennerick, S., and Holtzman, D.M. (2008). Endocytosis is required for synaptic activity-dependent release of amyloid-beta in vivo. Neuron 58, 42–51. 16. Cirrito, J.R., Yamada, K.A., Finn, M.B., Sloviter, R.S., Bales, K.R., May, P.C., Schoepp, D.D., Paul, S.M., Mennerick, S., and Holtzman, D.M. (2005). Synaptic activity regulates interstitial fluid amyloid-beta levels in vivo. Neuron 48, 913–922. 17. Bero, A.W., Yan, P., Roh, J.H., Cirrito, J.R., Stewart, F.R., Raichle, M.E., Lee, J.M., and Holtzman, D.M. (2011). Neuronal activity regulates the regional vulnerability to amyloid-beta deposition. Nat. Neurosci. 14, 750–756. 18. Palop, J.J., Chin, J., Roberson, E.D., Wang, J., Thwin, M.T., Bien-Ly, N., Yoo, J., Ho, K.O., Yu, G.Q., Kreitzer, A., et al. (2007). Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron 55, 697–711. 19. Sanchez, P.E., Zhu, L., Verret, L., Vossel, K.A., Orr, A.G., Cirrito, J.R., Devidze, N., Ho, K., Yu, G.Q., Palop, J.J., et al. (2012). Levetiracetam suppresses neuronal network dysfunction and reverses synaptic and cognitive deficits in an Alzheimer’s disease model. Proc. Natl. Acad. Sci. USA 109, E2895–E2903.
1Department
of Biology, University of Nevada, Reno. Reno, NV 89557, USA. 2Department of Pharmacology, Biomedical Sciences Graduate Program, Neurosciences Graduate Program, and Center for Chronobiology, University of California, San Diego, La Jolla, CA 92093-0636, USA. *E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2015.02.003
memory traces into an illusory memory [1–3]. For example, when presented with a list of words all pertaining to fruit, people would remember the word ‘apple’ even if it was not on the list. While the existence of such memory errors is well established in humans [2,3], it has never before been shown in animals, although many animal species from insects to vertebrates are known for their sophisticated learning and memory abilities. Now, a new study by Kate Hunt and Lars Chittka [4], published in this issue of Current Biology, has revealed that an insect with a small brain, the bumble bee, also suffers false memories. The study suggests that memory traces for different visual stimuli such as a colour and a black-and-white pattern are merged
Dispatch R237
in the bumble bee brain into a false memory of an ‘imagined’ stimulus that combines the colour and the pattern (Figure 1). Bumble bees like honeybees are excellent animal models to study visual learning and memory phenomena [5–7]. The lifestyle of foraging bees predisposes them to learn and remember the colours, shapes and patterns of different flowers, so that they can return to rewarding flowers over and over again. Importantly, flowers may bear nectar at different times of day and the selection of nectar-bearing flowers changes over the lifetime of a bee as new plants come into bloom and others stop flowering. Hence, bees form multiple complex memories of flowers throughout the days and weeks of their lives, both simultaneously and sequentially [8]. How does a small brain like a bee’s cope with multiple memories of different colours, shapes and patterns? Countless reports have demonstrated the amazing cognitive and visual abilities of bees [7,9]. But now Hunt and Chittka [4] show that a bee’s memory is not quite as reliable as we thought and memory mistakes are being made even by bees. To investigate whether bee brains merge memory traces into false memories of never encountered objects, the authors conducted a series of carefully designed visual learning experiments with free-flying bumble bees. Individual bees were trained to visit artificial flowers consisting of small disks with a drop of sugar solution in the centre as reward. The disks were either of a uniform colour or had a black-and-white pattern of concentric rings (Figure 1). One group of bumble bees was first offered yellow disks with sugar water, while the black-and-white patterned disks were empty and thus unrewarding. After learning to land on the yellow disks, the bumble bees were then presented with the reverse situation, namely empty yellow disks and rewarded black-and-white patterns. This extensive training procedure lasted several hours and created two distinct memories of rewarding food sources in the bee’s brain: of a yellow flower and of a black-and-white flower. A second group of bees was trained to the same stimuli, but this group first experienced the black-and-white disks as rewarding and the yellow disks as empty, and
Current Biology
Figure 1. Memory merging in the bee brain. After learning a yellow stimulus and a black-and-white patterned stimulus during training, bumble bees show preference for a never-encountered hybrid stimulus that combines features of both learnt stimuli.
then the reverse situation. Thus, both groups of bees had the same memories of two different flowers, only the sequence of experiencing the stimuli was reversed. Immediately after training, both groups of bumble bees were presented with three unrewarded disks: a yellow disk, a black-and-white disk (these two stimuli being identical to the ones the bees had learnt), and a stimulus they had never seen before, namely a hybrid of the two other stimuli: a yellow pattern of concentric rings (Figure 1). In this scenario, the bees had to rely on the short-term memory from their last training session to choose a rewarding flower, and bees from both groups simply picked the stimulus that they had last experienced as rewarding. This is typical for recall of short-term memories, which are often laid down in early processing centres of the brain, easily accessed and lasting only minutes [10]. However, when bees were tested 1 day and 3 days after training, a different picture emerged. Now the bees had to recall long-term memories of the training stimuli, which rely on different mechanisms [10]. Considering that bees had formed stable memories of both the yellow disk and the black-and-white disk, one would have expected them to visit both of those stimuli equally. However, over the course of the test evaluating 20 choices from each bumble bee, the bees switched from initial
preference for the most recently rewarded stimulus to preference for the hybrid, which had combined features of both known flowers, but which they had never experienced before. A series of control experiments excluded generalisation to colour or patterns as underlying mechanisms. Using other colour-pattern combinations, the study confirmed that bees switched their preference to hybrid stimuli which had merged features of previously learnt stimuli [4]. These novel findings strongly suggest that bumble bees had indeed created a false memory based on previous memory traces. Importantly, memory merging only occurred when bees were trained first to a black-and-white pattern and then to a colour, not the other way round. Possibly having a black-and-white pattern as second memory was more salient and less prone to suggestibility and memory manipulations. This tells us that memory merging in bumble bees only occurs under certain conditions and with certain stimulus combinations, similar to humans, where memory merging occurs for pictures of faces, nonsense words and simple sentences, but not necessarily with other, more salient events [11,12]. What is the point of having the mechanism of memory merging and illusory memories? The fact that it exists in insects as well as humans suggests it is common to many
Current Biology Vol 25 No 6 R238
animals. Why create a false reality? Some argue that memory errors may simply be inevitable by-products of the adaptive cognitive ability to form general concepts and categories, which is well established in both humans and bees [2,13,14]. But maybe it is a useful mechanism that has been evolutionarily conserved. At least in the case of bees one could imagine that creating a memory of an imaginary flower that combines features of known flowers gives bees a head start during foraging. The illusory memory could create a search pattern to predict and more rapidly recognise new potential food sources. Although there are countless reports of the astonishing cognitive and learning abilities of bees [7,9], studies like the one by Hunt and Chittka [4] remind us that there is still a lot that we do not know about the animal mind. It is a wake-up call to not merely discard negative results from learning and memory studies in animals such
as failure to recall stimuli. Previously undiscovered mechanisms such as false memories and memory merging might be at play, which should be taken into account as modifying factors just as much as an individual bee’s experience [7]. Because as Hunt and Chittka have shown in their exciting new study: bees are human after all; they make memory mistakes just like us. References 1. Roediger, H.L., and McDermott, K.B. (1995). Creating false memories - Remembering words not presented in lists. J. Exp. Psychol. Learn. 21, 803–814. 2. Schacter, D.L. (2001). The Seven Sins of Memory (Boston, USA: Houghton Mifflin). 3. Loftus, E.F. (2005). Planting misinformation in the human mind: A 30-year investigation of the malleability of memory. Learn. Memory 12, 361–366. 4. Hunt, K.L., and Chittka, L. (2015). Merging of long-term memories in an insect. Curr. Biol. 25, 741–745. 5. Riveros, A.J., and Gronenberg, W. (2009). Learning from learning and memory in bumblebees. Commun. Integr. Biol. 2, 437–440. 6. Srinivasan, M.V. (2010). Honey bees as a model for vision, perception, and cognition. Annu. Rev. Entomol. 55, 267–284.
Autophagy: Starvation Relieves Transcriptional Repression of ATG Genes Autophagy is a highly regulated process about which relatively little is known, particularly concerning the transcriptional control of autophagy regulation. A new study identifies a key regulator of the expression of autophagy-related genes, thereby providing insights into the signalling pathways modulating autophagy. Rodney J. Devenish and Mark Prescott The yeast Saccharomyces cerevisiae is a proven model organism for the investigation of many fundamental biological pathways, including autophagy. Indeed, the rapid progress and expansion of knowledge concerning autophagic processes that has occurred over the last 20 years has been driven by fundamental studies in yeast [1], which have identified important molecular components and regulators of this degradative pathway. A study published in a recent issue of Current Biology by Bernard et al. [2] reports the application of targeted
library screening to a search for new transcriptional regulators of autophagy in yeast and has yielded new insights into transcriptional regulation of ‘autophagy-related’ (ATG) genes and, by extension, autophagy. Autophagy (more strictly, macroautophagy) is a pathway by which cytoplasmic components, organelles or pathogens can be sequestered within double-membrane vesicles, or autophagosomes [3], and delivered to the degradative compartment of the cell — the vacuole in yeast, and lysosomes in mammals. The action of the resident complement of acid hydrolases in this compartment typically degrades the autophagosome
7. Dyer, A.G. (2012). The mysterious cognitive abilities of bees: why models of visual processing need to consider experience and individual differences in animal performance. J. Exp. Biol. 215, 387–395. 8. Reinhard, J., Srinivasan, M.V., and Zhang, S.W. (2006). Complex memories in honeybees: can there be more than two? J. Comp. Physiol. A 192, 409–416. 9. Avargue`s-Weber, A., and Giurfa, M. (2013). Conceptual learning by miniature brains. Proc. R. Soc. B 280, 20131907. 10. Menzel, R. (2012). The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768. 11. Reinitz, M., Lammers, W., and Cochran, B. (1992). Memory-conjunction errors: Miscombination of stored stimulus features can produce illusions of memory. Mem. Cognition 20, 1–11. 12. Reinitz, M.T., and Demb, J.B. (1994). Implicit and explicit memory for compound words. Mem. Cognition 22, 687–694. 13. Chittka, L., and Niven, J. (2009). Are bigger brains better? Curr. Biol. 19, R995–R1008. 14. Chittka, L., and Jensen, K. (2011). Animal cognition: Concepts from apes to bees. Curr. Biol. 21, R116–R119.
Queensland Brain Institute, The University of Queensland, St Lucia, QLD 4072, Australia. E-mail: [email protected]
http://dx.doi.org/10.1016/j.cub.2015.01.072
contents, and the molecular ‘building blocks’ that are then recovered are reused in biosynthetic pathways. Thus, autophagy is an important homeostatic adaptation allowing cells to adapt to changes in nutritional availability and physiological stresses. Autophagosome formation is a multi-step process utilizing machinery composed of many different ATG gene products. Given its important role in cellular functions, it is not surprising that autophagy is a tightly regulated process. Yet, despite the machinery of autophagy being relatively well defined, the detailed mechanisms underlying the regulation of autophagy, including at the level of transcription of the ATG genes, remain to be fully elucidated. The new study [2] reports the screening of a collection of yeast mutants lacking a single DNA-binding protein by analysing the expression of a set of ATG genes using RT-qPCR. The screen focused on a subset of ATG genes specifically chosen because they encode proteins involved in different steps of the autophagy pathway and are known to be subject to strong transcriptional induction after
