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An engram found? Evaluating the evidence from fruit flies
An engram found? Evaluating the evidence from fruit flies Bertram Gerber1, Hiromu Tanimoto2 and Martin Heisenberg3 Is it possible to localize a memory trace to a subset of cells in the brain? If so, it should be possible to show: first, that neuronal plasticity occurs in these cells. Second, that neuronal plasticity in these cells is sufficient for memory. Third, that neuronal plasticity in these cells is necessary for memory. Fourth, that memory is abolished if these cells cannot provide output during testing. And fifth, that memory is abolished if these cells cannot receive input during training. With regard to olfactory learning in flies, we argue that the notion of the olfactory memory trace being localized to the Kenyon cells of the mushroom bodies is a reasonable working hypothesis. Addresses Universita¨t Wu¨rzburg, Lehrstuhl fu¨r Genetik und Neurobiologie, Biozentrum Am Hubland, D 970 74 Wu¨rzburg, Germany 1 e-mail: [email protected] 2 e-mail: [email protected] 3 e-mail: [email protected]
Current Opinion in Neurobiology 2004, 14:737–744 This review comes from a themed issue on Neurobiology of behaviour Edited by Alexander Borst and Wolfram Schultz Available online 5th November 2004 0959-4388/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.conb.2004.10.014
Abbreviations GFP green fluorescent protein PKA protein kinase A
‘‘There is nothing like future and past (. . .). There is only the presence of the past, the presence of the presence, and the presence of the future. These three I see in the soul, but I cannot see them independent of it: present is the memory of the past, present is the perception of the presence, and present is the expectation of the future.’’ Augustine, Confessions, Book 11, Chapter 20. At any given moment, animals need to organize their behavior; whether to stay or to go, where to move to, what to do. It is the function of brains to provide on-line solutions to these problems. In this sense, brain function is about ongoing behavior — and memory is as well. Thus, unlike technical storage devices, biological memory does not seem primarily designed to replay the past, but to integrate selected aspects of it into present behavior. www.sciencedirect.com
It seems clear that associative learning episodes, to which we restrict this discussion, induce plasticity across ensembles of neurons, and it is believed that memoryguided behavior requires such neuronal plasticity to have taken place [1,2]. Thus, the past is present in the form of a pattern of changed neuronal efficiencies. Here, we discuss whether it is possible to localize these changes to specific cells in the brain and suggest criteria for such an endeavor. If a certain set of cells were said to be the site of a memory trace, one should be able to show: first, that neuronal plasticity occurs in these cells. Second, that neuronal plasticity in these cells is sufficient for memory. Third, that neuronal plasticity in these cells is necessary for memory. Fourth, that memory is abolished if these cells cannot provide output during test. And fifth, that memory is abolished if these cells cannot receive input during training. If all of these criteria are met, there is no escape from concluding that the group of cells in question is the one and only site of the memory trace under investigation. Clearly, if criteria one to three are met, criteria four and five have to be met as well. Criteria four and five, thus, can serve as validations, which is important, as for them no knowledge of the underlying molecular mechanism(s) is required. However, retention might rely on several redundant memory traces [3]. If each one could be experimentally manipulated separately, criteria one and two, but not three, could be met. With regards to criteria four and five, different scenarios would result depending on whether memory traces are organized in series or in parallel. If two or more memory traces could act additively, the manipulations with respect to criteria two to five would yield only partial effects. Finally, memory traces might be widely distributed across the brain, with each site of plasticity contributing an essential component. In this case, criterion two could not be met, or only for an unsatisfyingly large group of cells (such as ‘the brain’). Criteria two to five rely on the possibility to manipulate neurons and criterion one on the ability to measure neuronal activity at the suspect sites; clearly, neither of these approaches alone is sufficient to localize a memory. Using olfactory learning in fruit flies as a study case, we go through the available evidence for these criteria. Current Opinion in Neurobiology 2004, 14:737–744
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Figure 1
The reciprocal design for Drosophila olfactory discrimination learning. (a) Training and test tubes are aspirated to produce a constant flow. A group of flies (black dots) are put into the training tube covered by a copper grid (orange) for electrification. During training, flies receive one odor together with an electric shock, and the control odor without shock. Subsequently, flies are transferred into the elevator compartment (E) and shuttled to a choice point where they can distribute between the previously punished and the control odor. Typically, flies avoid the previously punished odor. (b) To measure associative learning, a reciprocal experimental design is essential: two groups of flies receive either odor A with shock and B without or odor B with shock and A without. For both groups, the preference between odor A and B is measured after training. The learning index is then calculated by taking the mean preference of the two reciprocally trained groups, and thus purely represents associative memory, excluding any non-associative effects (e.g. sensitization by shock, habituation to the odors). Note that the learning index is unaffected by any overall bias for either of the two odors.
The behavioral paradigm(s) The learning paradigm used in most studies on fly olfactory learning is shown in Figure 1 [4]. During training, flies receive pairings of one door and an electric shock, whereas another odor is presented without a shock. If in a subsequent test they are given the choice between the two odors, flies show conditioned avoidance of the previously punished odor. A similar paradigm can be used with sugar instead of electric shock, leading to conditioned approach during testing [5,6]. We restrict our discussion to the short-term (approximately 30 min) memory component established in these kinds of task.
The fly brain, and the minimal model of olfactory learning Odors are detected by broadly tuned receptor neurons that project to the antennal lobes, the functional equivalent of the olfactory bulb in vertebrates (Figure 2b,c; [7]). Each receptor neuron expresses one functional receptor gene, and those receptor neurons expressing a common receptor gene converge to one glomerulus in the antennal lobe [8,9]. For each odor, this will entail a specific combinatorial activity pattern of glomeruli. From there, uniglomerular projection neurons relay to the lateral horn, a presumed premotor center, as well as to the mushroom body calyx [10–15]. Output from the mushroom bodies Current Opinion in Neurobiology 2004, 14:737–744
then projects to a variety of target regions including premotor areas [16]. Thus, the mushroom bodies constitute a side loop of the olfactory pathway. In the mushroom bodies, the activation pattern of the sensory and the projection neurons [17–20] is transformed into an activation pattern of the mushroom body — intrinsic Kenyon cells [21,22]. It has been proposed that a memory trace for the association between odor and shock is localized within the Kenyon cells: when the activation of a pattern of Kenyon cells representing an odor occurs simultaneously with a modulatory reinforcement signal, output from these activated Kenyon cells onto mushroom body output neurons is suggested to be strengthened [23]. This strengthened output is thought to mediate conditioned behavior towards the odor when it is encountered during testing.
Criterion one: neuronal plasticity in the mushroom bodies? In Drosophila, no method is available to observe directly neuronal, and in particular synaptic, plasticity in vivo in adult central brain neurons, for example Kenyon cells — despite impressive advances in physiological techniques [17–21,24]. In a strict sense, this leaves criterion one unmet. Presently, the only way to observe neuronal www.sciencedirect.com
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plasticity directly in flies is to study the larval neuromuscular junction [24,25]. Being left with inferences from the larval neuromuscular junction to central brain neurons in adults, however, is unsatisfying. Two novel learning assays for larval Drosophila might have the potential to study both kinds of plasticity at the same developmental stage [26,27]. In adult flies, a second best approach was taken. Genetic intervention was used to manipulate molecular components underlying neuronal plasticity locally. The process chosen was the cAMP–protein kinase A (PKA) signaling cascade [28]. It has been suggested that the type I adenylate cyclase (encoded in flies by the rutabaga gene, rut) acts as a timing-specific molecular coincidence detector for the to-be-associated stimuli [29]. Hence, one is inclined to attribute an impaired or restored capacity to learn, caused by a genetic manipulation of the cAMP cascade in the mushroom bodies, to an impaired or restored neuronal plasticity in the mushroom bodies (see below). It is this indirect argument that suggests the existence of neuronal plasticity in Drosophila mushroom bodies. Thus, although not demonstrated directly, it seems likely that criterion one for the localization of a memory trace to the mushroom bodies is met.
Criterion two: sufficiency of neuronal plasticity in the mushroom bodies? Mutations in rut lead to impaired plasticity at the larval neuromuscular junction [30], and in the adult to defects in associative learning [31]. In such mutants, local transgenic expression of an intact rut gene can rescue this learning impairment completely [32]: six GAL4 driver lines with expression in a certain substructure of the mushroom bodies, the gamma lobe, are effective for rescue (247, c772, 30y, 238y, H24, 201y). By contrast, three drivers not expressed in the gamma lobes (c232, 189y, 17d) are not [32] (with regards to H24, also see supplementary material provided with [33]). Using the 247 driver line, a similar rescue was recently found for the reward-learning version of this paradigm [6]. Furthermore, to test for the temporal sufficiency of rut expression, Davis and coworkers developed two new tools for the temporal control of gene expression using either a temperature sensitive form of Gal80 [33] or a 247-Gal4 driver, the activity of which can be induced by progesterone [34]. Both studies showed that for rescue it is sufficient to provide the intact rut gene during adulthood. This is an important factor in the argument that the restoration of memory is indeed because of the rescue of an acute rut function such as neuronal plasticity, rather than a developmental process. This kind of experiment has not yet been performed for appetitive learning. In any event, these results suggest that the mushroom bodies are sites at which restoring neuronal plasticity is sufficient to restore aversive olfactory learning. www.sciencedirect.com
Thus, criterion two for the localization of a memory trace to the mushroom bodies is met.
Criterion three: necessity of neuronal plasticity in the mushroom bodies? Connolly and co-workers [35] have shown that transgenic mushroom body expression of a dominant negative Gas protein subunit that constitutively activates the adenylate cyclase cascade can completely abolish associative learning. From the six GAL4 driver lines tested, four show expression in the mushroom bodies. Of those, three showed a complete (238y, c309, c747), and one (201y) a partial impairment. The two lines with expression outside of the mushroom bodies (c232, ok348) were normal. Under the plausible assumption that a constitutively activated cyclase prevents regulation of cAMP levels and hence regulation of neuronal efficacy [36], this suggests that plasticity within the mushroom bodies is necessary for associative learning. Thus, criterion three for the localization of a memory trace to the mushroom bodies is met.
Interplay: kinds of plasticity Clearly, the scope of the conclusions so far is limited to those kinds of neuronal plasticity that involve the cAMP– PKA cascade; this includes the main mechanisms of plasticity investigated to date, namely spike broadening in Aplysia, paired-pulse facilitation at the Drosophila neuromuscular junction, and N-methyl-D-aspartate (NMDA) receptor-dependent plasticity [2,37–39]. Still, the learning defects in rut mutants, in particular in the most widely used rut2080 allele, are partial. That is, in the first 30 min, rut2080 typically shows a 50% reduction as compared with wild type, but no abolishment of memory. This suggests that either rut2080 is not a total loss of function allele or that there are rut2080-independent forms of memory, using different adenylate cyclases and/or an altogether different biochemistry. Still, as the discussion below will show, even a potential rut2080-independent form of plasticity is probably localized to the mushroom bodies.
Criterion four: blocking mushroom body output during test In flies it is possible to within minutes reversibly turn off the output of chemical synapses in defined small groups of neurons by the spatially restricted expression of a dominant negative dynamin transgene shits that has temperature-sensitive properties [40]. If shits is expressed in the mushroom bodies and temperature is raised, the temperature sensitive dynamin can no longer fulfill its role in vesicle housekeeping. This effectively blocks output from chemical synapses. Thus, flies can be trained with mushroom body output enabled, but tested with mushroom body output blocked. Using five different mushroom-body-expressing GAL4 driver lines (c739, c747, c309, 247, c772), three different laboratories have Current Opinion in Neurobiology 2004, 14:737–744
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(a)
(b)
(c) AL
Training
PN
‘Good!’
Conditioned approach
Motor programs
MB ORN
Odor
‘Bad!’
Conditioned avoidance
Food Unconditioned, consumatory behavior
Shock Unconditioned, escape behavior
(d)
‘Good!’
Conditioned approach
Motor programs
Test
Odor
‘Bad!’
Conditioned avoidance
Food Unconditioned, consumatory behavior
Shock Unconditioned, escape behavior
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found independently that under these conditions flies cannot express any memory [41–43] (for long-term memory [44,45,46]). A similar conclusion is reached with respect to appetitive learning, using the 247 GAL4 driver [6]. Thus, criterion four for the localization of a memory trace to the mushroom bodies is met.
Criterion five: blocking input to the mushroom body during training Olfactory input to the mushroom bodies is carried by the uniglomerular projection neurons; if shits is expressed in approximately 60% of the antennal lobe projection neurons during training (using the GAL4 driver line GH146 [10,12–15]), these projection neurons cannot provide input to the mushroom bodies. In such an experiment, flies do not show any sign of memory during subsequent test; however, odor responses are normal at the relatively high odor concentrations used [47]. Thus, criterion five for the localization of a memory trace to the mushroom bodies seems to be met. However, the projection neurons provide input not only to the mushroom bodies but also to the lateral protocerebrum [11] (and possibly even the antennal lobe [19]). Therefore, this conclusion needs to be taken with care as we are currently lacking the appropriate tools to block specifically only mushroom body input, for example, by a dominant-negative acetylcholine receptor transgene with temperature sensitivity expressed in the mushroom body. Thus, it is likely that olfactory input to the mushroom bodies during training is required for the flies to express memory during test, so that criterion five is probably met. Concerning reinforcer-related input to the mushroom bodies, Schwaerzel et al. found that for appetitive learning
octopamine but not dopamine signaling is required, whereas for aversive learning dopamine but not octopamine signaling is necessary. Both dopamine and octopamine signaling were shown to be specifically required during training, not during test [6]. The Kenyon cells clearly express octopamine and dopamine receptors and are targets of dopamine- and octopamine immunoreactive neurons [22,48–51]; however, whether specifically those octopaminergic and/or dopaminergic neurons that carry the reinforcing information do indeed directly impinge onto the mushroom bodies is at present unknown. Mushroom body input via the amnesiac peptide released by the dorsal paired medial neurons (DPM) is unlikely to carry reinforcement-related information, as short-term retention is unaffected if these neurons are blocked during training [52,53].
A memory trace downstream of the mushroom bodies? For the time being, suppose that the mushroom bodies house an olfactory memory trace. Can we be sure that there is no additional associative memory trace outside the mushroom bodies? For example, a subset of mushroom body cells is glutamatergic [22], and there are predicted NMDA receptors in the fly genome. However, three different laboratories found that blocking mushroom body output during training in the GAL4 driver lines c739, c747, c309, 247, c772 leaves performance during test intact [41–43]. Thus, in order for learning to occur, olfactory information needs to enter, but does not have to leave, the mushroom bodies. This suggests that whatever kind of plasticity is underlying conditioned behavior, be it rut2080-dependent or rut2080-independent, it is happening between mushroom body input and mushroom body output. Consistent with this view, memory can be effectively extinguished even during blockade of
(Figure 2 Legend) The Drosophila brain, shown as a histological preparation (a), a schematic of major brain regions (b), and as highly simplified diagram of the essential olfactory-learning circuitry (c and d). (a) Frontal section of a Drosophila brain. The reduced silver technique labels nuclear and cytoskeletal proteins and Nissl granules. This enables the visualization of fiber tracts and neuropil regions in the central brain, as well as the cell body rind. The calyx neuropil with the very thin Kenyon cell dendrites in the mushroom bodies can be discerned by the light stain (abbreviations: lo, lobula; p op t, posterior optic tract, x and z indicate coordinates). Grid intersections are at 50 microns. Taken as section number 22, y = 7.5 from http://www.flybrain.org, where further details can be found. (b) Frontal view of a Drosophila brain with the major brain regions reconstructed on the basis of nc82-immunreactivity labeling presynaptic proteins. The 3-D representation was obtained from 1 micron confocal serial sections using AMIRA visualization software (Indeed Visual Concepts, Berlin). Depicted are medulla (red, foreground), lobula (orange, background), the central complex (green), the antennal lobes (blue), and the mushroom bodies (brown). The image is slightly tilted to show the calyx region of the mushroom bodies (background), together with the peduncles (back-to-front), and lobes (foreground). The light pink shade sketches the rest of the brain. (c and d) A minimal model for Drosophila olfactory learning. A highly simplified diagram showing the olfactory pathways. Olfactory receptor neurons (ORN) project to the antennal lobe (AL), leading to a specific combinatorial activity pattern. From there, uniglomerular projection neurons (PN) relay to the lateral horn and to premotor centers (box labeled ‘Motor output’), as well as to the mushroom body (MB) calyx. Output from the mushroom bodies then projects to a variety of target regions including premotor areas. In the model, we assume that a Kenyon cell needs input from at least three projection neurons to fire. In the mushroom bodies, the activation pattern of the sensory and the projection neurons is therefore transformed into an activation pattern of the mushroom body — intrinsic Kenyon cells. A memory trace for the association between odor and reinforcement is proposed to be localized within the Kenyon cells: during training, when the activation of a pattern of Kenyon cells representing an odor occurs simultaneously with a modulatory reinforcement signal (labeled ‘Good!’ and ‘Bad!’; potentially octopaminergic and dopaminergic neurons concerning reward and punishment, respectively), output from these activated Kenyon cells onto mushroom body output neurons is suggested to be strengthened. This strengthened output is thought to mediate conditioned behavior towards the odor when encountered during test, during which no reinforcer is present. Activated cells or synapses and motor programs are represented by filled symbols and bold lettering, respectively. www.sciencedirect.com
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mushroom body output [43]. Thus, we are forced to conclude that there is no memory trace downstream of the mushroom bodies — and leave it to future research to disprove this conclusion.
A memory trace upstream of the mushroom bodies? Another scenario is that there might be an additional memory trace upstream of the mushroom bodies, for example in the projection neurons and/or the antennal lobes, as suggested by the work on appetitive olfactory learning in bees [3,54] and by recent imaging studies in flies [55]. This seems unlikely because Connolly and coworkers [35] have shown that if neuronal plasticity in the mushroom bodies is prevented by expression of a constitutively activated Gas protein subunit, there is a total abolishment of memory. Thus, any associative plasticity outside of the mushroom bodies, be it up- or downstream, does in itself not seem sufficient to support memory. We therefore have to conclude that there is no memory trace upstream of the mushroom bodies. This claim could be tested by trying to rescue the rut-dependent learning defect by expressing the intact rut gene in the projection neurons by means of the GAL4 driver line GH146, or using the same driver line to express the constitutively active Gas protein subunit.
A memory trace ‘lateral’ to the mushroom bodies? Finally, a memory trace could be localized postsynaptic to the projection neurons, but in the lateral protocerebrum, that is, ‘lateral’ to the mushroom bodies. Such a memory trace would obviously not be sufficient to support memory, as argued by the total abolishment of learning when preventing neuronal plasticity within the mushroom bodies (see previous paragraph). Furthermore, such a trace could be read out even when mushroom body output is blocked during retrieval. As, however, under such conditions no retention is possible (see above), we again are inclined to conclude that there is no such memory trace. Obviously, criterion three is central for the argument that there is only one associative olfactory memory trace in the fly brain. Thus, an independent approach to this issue seems warranted, for example, using a knock-down of the rut-cyclase specifically in the mushroom bodies with an RNA interference approach.
there is at present no means to detect directly the transgenically expressed effector proteins. This leaves us with inferences from the expression patterns of reporter genes like green fluorescent protein (GFP). Using the same GAL4 driver line, however, the expression patterns of different effectors (shits, the rut-cyclase and GFP), and even the expression pattern for the same effector construct at different insertion sites (e.g. UAS–GFP on different chromosomes; A. Jenett, M. Heisenberg, pers comm), might differ considerably and lead conclusions astray, in particular if too few GAL4 lines are used (see discussion by Kido and Ito [59]). Thus, the use of tagged transgenes, tests of multiple GAL4 lines, and appropriate immunocytochemistry are necessary to deal with these concerns. Finally, as mentioned above, there is great incentive to develop physiological methods to be able to observe directly and in vivo neuronal and/or synaptic plasticity in central brain neurons of flies. This will also be important to elucidate the mechanism(s) of plasticity and its subcellular localization, particularly with respect to the substrates of PKA and their ultimate effects.
Conclusions We have suggested a set of criteria that need to be met to localize a site of plasticity as the associative memory trace. Three of these criteria are clearly met by the joint efforts of the Drosophila research community; one is only partially met, and one only indirectly. This last criterion requires a demonstration that the Kenyon cells undergo synaptic and/or neuronal plasticity during associative training. Taken together, we conclude that the working hypothesis that the short-term olfactory memory trace is localized to the mushroom bodies has so far held up well, and will continue to provide a basis for further research.
Acknowledgements We thank B Gru¨ newald and R Menzel, Freie Universita¨ t Berlin, M Giurfa, Universite´ Toulouse, T Preat, CNRS Gif sur Yvette, and M Schwa¨ rzel, University of Missouri for discussion and/or comments on the manuscript; E Rudolph, Universita¨ t Luzern for advice concerning the quotation of Augustin; A Jenett, Universita¨ t Wu¨ rzburg for providing Figure 2b. Current support comes from the Deutsche Forschungsgemeinschaft (SFB 554) (to M Heisenberg) and the Human Frontiers Science Program (to H Tanimoto).
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest
Caveats, problems and future directions There are two important caveats for all of these conclusions. First, the spatial expression patterns of GAL4 lines are in many cases distressingly unspecific and hard to assess reliably [56,57]. It thus seems important to ‘sharpen’ expression patterns and to pursue a digital quantitative approach to the neuroanatomy of these expression patterns [58]. Second, both for shits expression and for the rescue expression of the wild type rut-cyclase Current Opinion in Neurobiology 2004, 14:737–744
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43. Schwaerzel M, Heisenberg M, Zars T: Extinction antagonizes olfactory memory at the subcellular level. Neuron 2002, 35:951-960.
52. Waddell S, Armstrong JD, Kitamoto T, Kaiser K, Quinn WG: The amnesiac gene product is expressed in two neurons in the Drosophila brain that are critical for memory. Cell 2000, 103:805-813.
44. Pascual A, Preat T: Localization of long-term memory within the Drosophila mushroom body. Science 2001, 294:1115-1117. 45. Isabel G, Pascual A, Preat T: Exclusive consolidated memory phases in Drosophila. Science 2004, 304:1024-1027. This study and that by Pascual and Preat [44] attempt to target the localization of longer-term memory. In Pascual and Preat [44], the structural brain mutant a lobes absent is shown to be specifically impaired in long-term memory. In Isabel et al. [45], output from the a or b lobes of the mushroom bodies is demonstrated to be necessary during retrieval 24 h after training; this finding meets criterion four concerning longer-tem memory. 46. Chiang AS, Blum A, Barditch J, Chen YH, Chiu SL, Regulski M, Armstrong JD, Tully T, Dubnau J: radish encodes a phospholipase-A2 and defines a neural circuit involved in anesthesia-resistant memory. Curr Biol 2004, 14:263-272. The authors identify an enhancer trap line defining a set of non-mushroom body cells. These cells need to be able to output during the period of consolidation into long-term memory. The gene affected might be radish, provided that a rescue of the original radish1 phenotype can be demonstrated. 47. Schwaerzel M: Localizing engrams of olfactory memories in Drosophila. [PhD Thesis]. Wu¨ rzburg: Universita¨ t Wu¨ rzburg; 2003. 48. Han KA, Millar NS, Grotewiel MS, Davis RL: DAMB, a novel dopamine receptor expressed specifically in
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53. Tamura T, Chiang AS, Ito N, Liu HP, Horuichi J, Tully T, Saitoe M: Aging specifically impairs amnesiac-dependent memory in Drosophila. Neuron 2003, 40:1003-1101. 54. Farooqui T, Robinson K, Vaessin H, Smith BH: Modulation of early olfactory processing by an octopaminergic reinforcement pathway in the honeybee. J Neurosci 2003, 23:5370-5380. 55. Yu D, Ponomarev A, Davis RL: Altered representation of the spatial code for odors after olfactory classical conditioning; memory trace formation by synaptic recruitment. Neuron 2004, 42:437-449. 56. Ito K, Okada R, Tanaka NK, Awasaki T: Cautionary observations on preparing and interpreting brain images using molecular biology-based staining techniques. Microsc Res Tech 2003, 62:170-186. 57. Saper CB, Sawchenko PE: Magic peptides, magic antibodies: guidelines for appropriate controls for immunohistochemistry. J Comp Neurol 2003, 465:161-163. 58. Rein K, Zockler M, Mader MT, Grubel C, Heisenberg M: The Drosophila standard brain. Curr Biol 2002, 12:227-231. 59. Kido A, Ito K: Mushroom bodies are not required for courtship behavior by normal and sexually mosaic Drosophila. J Neurobiol 2002, 52:302-311.
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Current Opinion in Neurobiology 2004, 14:737–744 This review comes from a themed issue on Neurobiology of behaviour Edited by Alexander Borst and Wolfram Schultz Available online 5th November 2004 0959-4388/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.conb.2004.10.014
Abbreviations GFP green fluorescent protein PKA protein kinase A
‘‘There is nothing like future and past (. . .). There is only the presence of the past, the presence of the presence, and the presence of the future. These three I see in the soul, but I cannot see them independent of it: present is the memory of the past, present is the perception of the presence, and present is the expectation of the future.’’ Augustine, Confessions, Book 11, Chapter 20. At any given moment, animals need to organize their behavior; whether to stay or to go, where to move to, what to do. It is the function of brains to provide on-line solutions to these problems. In this sense, brain function is about ongoing behavior — and memory is as well. Thus, unlike technical storage devices, biological memory does not seem primarily designed to replay the past, but to integrate selected aspects of it into present behavior. www.sciencedirect.com
It seems clear that associative learning episodes, to which we restrict this discussion, induce plasticity across ensembles of neurons, and it is believed that memoryguided behavior requires such neuronal plasticity to have taken place [1,2]. Thus, the past is present in the form of a pattern of changed neuronal efficiencies. Here, we discuss whether it is possible to localize these changes to specific cells in the brain and suggest criteria for such an endeavor. If a certain set of cells were said to be the site of a memory trace, one should be able to show: first, that neuronal plasticity occurs in these cells. Second, that neuronal plasticity in these cells is sufficient for memory. Third, that neuronal plasticity in these cells is necessary for memory. Fourth, that memory is abolished if these cells cannot provide output during test. And fifth, that memory is abolished if these cells cannot receive input during training. If all of these criteria are met, there is no escape from concluding that the group of cells in question is the one and only site of the memory trace under investigation. Clearly, if criteria one to three are met, criteria four and five have to be met as well. Criteria four and five, thus, can serve as validations, which is important, as for them no knowledge of the underlying molecular mechanism(s) is required. However, retention might rely on several redundant memory traces [3]. If each one could be experimentally manipulated separately, criteria one and two, but not three, could be met. With regards to criteria four and five, different scenarios would result depending on whether memory traces are organized in series or in parallel. If two or more memory traces could act additively, the manipulations with respect to criteria two to five would yield only partial effects. Finally, memory traces might be widely distributed across the brain, with each site of plasticity contributing an essential component. In this case, criterion two could not be met, or only for an unsatisfyingly large group of cells (such as ‘the brain’). Criteria two to five rely on the possibility to manipulate neurons and criterion one on the ability to measure neuronal activity at the suspect sites; clearly, neither of these approaches alone is sufficient to localize a memory. Using olfactory learning in fruit flies as a study case, we go through the available evidence for these criteria. Current Opinion in Neurobiology 2004, 14:737–744
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Figure 1
The reciprocal design for Drosophila olfactory discrimination learning. (a) Training and test tubes are aspirated to produce a constant flow. A group of flies (black dots) are put into the training tube covered by a copper grid (orange) for electrification. During training, flies receive one odor together with an electric shock, and the control odor without shock. Subsequently, flies are transferred into the elevator compartment (E) and shuttled to a choice point where they can distribute between the previously punished and the control odor. Typically, flies avoid the previously punished odor. (b) To measure associative learning, a reciprocal experimental design is essential: two groups of flies receive either odor A with shock and B without or odor B with shock and A without. For both groups, the preference between odor A and B is measured after training. The learning index is then calculated by taking the mean preference of the two reciprocally trained groups, and thus purely represents associative memory, excluding any non-associative effects (e.g. sensitization by shock, habituation to the odors). Note that the learning index is unaffected by any overall bias for either of the two odors.
The behavioral paradigm(s) The learning paradigm used in most studies on fly olfactory learning is shown in Figure 1 [4]. During training, flies receive pairings of one door and an electric shock, whereas another odor is presented without a shock. If in a subsequent test they are given the choice between the two odors, flies show conditioned avoidance of the previously punished odor. A similar paradigm can be used with sugar instead of electric shock, leading to conditioned approach during testing [5,6]. We restrict our discussion to the short-term (approximately 30 min) memory component established in these kinds of task.
The fly brain, and the minimal model of olfactory learning Odors are detected by broadly tuned receptor neurons that project to the antennal lobes, the functional equivalent of the olfactory bulb in vertebrates (Figure 2b,c; [7]). Each receptor neuron expresses one functional receptor gene, and those receptor neurons expressing a common receptor gene converge to one glomerulus in the antennal lobe [8,9]. For each odor, this will entail a specific combinatorial activity pattern of glomeruli. From there, uniglomerular projection neurons relay to the lateral horn, a presumed premotor center, as well as to the mushroom body calyx [10–15]. Output from the mushroom bodies Current Opinion in Neurobiology 2004, 14:737–744
then projects to a variety of target regions including premotor areas [16]. Thus, the mushroom bodies constitute a side loop of the olfactory pathway. In the mushroom bodies, the activation pattern of the sensory and the projection neurons [17–20] is transformed into an activation pattern of the mushroom body — intrinsic Kenyon cells [21,22]. It has been proposed that a memory trace for the association between odor and shock is localized within the Kenyon cells: when the activation of a pattern of Kenyon cells representing an odor occurs simultaneously with a modulatory reinforcement signal, output from these activated Kenyon cells onto mushroom body output neurons is suggested to be strengthened [23]. This strengthened output is thought to mediate conditioned behavior towards the odor when it is encountered during testing.
Criterion one: neuronal plasticity in the mushroom bodies? In Drosophila, no method is available to observe directly neuronal, and in particular synaptic, plasticity in vivo in adult central brain neurons, for example Kenyon cells — despite impressive advances in physiological techniques [17–21,24]. In a strict sense, this leaves criterion one unmet. Presently, the only way to observe neuronal www.sciencedirect.com
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plasticity directly in flies is to study the larval neuromuscular junction [24,25]. Being left with inferences from the larval neuromuscular junction to central brain neurons in adults, however, is unsatisfying. Two novel learning assays for larval Drosophila might have the potential to study both kinds of plasticity at the same developmental stage [26,27]. In adult flies, a second best approach was taken. Genetic intervention was used to manipulate molecular components underlying neuronal plasticity locally. The process chosen was the cAMP–protein kinase A (PKA) signaling cascade [28]. It has been suggested that the type I adenylate cyclase (encoded in flies by the rutabaga gene, rut) acts as a timing-specific molecular coincidence detector for the to-be-associated stimuli [29]. Hence, one is inclined to attribute an impaired or restored capacity to learn, caused by a genetic manipulation of the cAMP cascade in the mushroom bodies, to an impaired or restored neuronal plasticity in the mushroom bodies (see below). It is this indirect argument that suggests the existence of neuronal plasticity in Drosophila mushroom bodies. Thus, although not demonstrated directly, it seems likely that criterion one for the localization of a memory trace to the mushroom bodies is met.
Criterion two: sufficiency of neuronal plasticity in the mushroom bodies? Mutations in rut lead to impaired plasticity at the larval neuromuscular junction [30], and in the adult to defects in associative learning [31]. In such mutants, local transgenic expression of an intact rut gene can rescue this learning impairment completely [32]: six GAL4 driver lines with expression in a certain substructure of the mushroom bodies, the gamma lobe, are effective for rescue (247, c772, 30y, 238y, H24, 201y). By contrast, three drivers not expressed in the gamma lobes (c232, 189y, 17d) are not [32] (with regards to H24, also see supplementary material provided with [33]). Using the 247 driver line, a similar rescue was recently found for the reward-learning version of this paradigm [6]. Furthermore, to test for the temporal sufficiency of rut expression, Davis and coworkers developed two new tools for the temporal control of gene expression using either a temperature sensitive form of Gal80 [33] or a 247-Gal4 driver, the activity of which can be induced by progesterone [34]. Both studies showed that for rescue it is sufficient to provide the intact rut gene during adulthood. This is an important factor in the argument that the restoration of memory is indeed because of the rescue of an acute rut function such as neuronal plasticity, rather than a developmental process. This kind of experiment has not yet been performed for appetitive learning. In any event, these results suggest that the mushroom bodies are sites at which restoring neuronal plasticity is sufficient to restore aversive olfactory learning. www.sciencedirect.com
Thus, criterion two for the localization of a memory trace to the mushroom bodies is met.
Criterion three: necessity of neuronal plasticity in the mushroom bodies? Connolly and co-workers [35] have shown that transgenic mushroom body expression of a dominant negative Gas protein subunit that constitutively activates the adenylate cyclase cascade can completely abolish associative learning. From the six GAL4 driver lines tested, four show expression in the mushroom bodies. Of those, three showed a complete (238y, c309, c747), and one (201y) a partial impairment. The two lines with expression outside of the mushroom bodies (c232, ok348) were normal. Under the plausible assumption that a constitutively activated cyclase prevents regulation of cAMP levels and hence regulation of neuronal efficacy [36], this suggests that plasticity within the mushroom bodies is necessary for associative learning. Thus, criterion three for the localization of a memory trace to the mushroom bodies is met.
Interplay: kinds of plasticity Clearly, the scope of the conclusions so far is limited to those kinds of neuronal plasticity that involve the cAMP– PKA cascade; this includes the main mechanisms of plasticity investigated to date, namely spike broadening in Aplysia, paired-pulse facilitation at the Drosophila neuromuscular junction, and N-methyl-D-aspartate (NMDA) receptor-dependent plasticity [2,37–39]. Still, the learning defects in rut mutants, in particular in the most widely used rut2080 allele, are partial. That is, in the first 30 min, rut2080 typically shows a 50% reduction as compared with wild type, but no abolishment of memory. This suggests that either rut2080 is not a total loss of function allele or that there are rut2080-independent forms of memory, using different adenylate cyclases and/or an altogether different biochemistry. Still, as the discussion below will show, even a potential rut2080-independent form of plasticity is probably localized to the mushroom bodies.
Criterion four: blocking mushroom body output during test In flies it is possible to within minutes reversibly turn off the output of chemical synapses in defined small groups of neurons by the spatially restricted expression of a dominant negative dynamin transgene shits that has temperature-sensitive properties [40]. If shits is expressed in the mushroom bodies and temperature is raised, the temperature sensitive dynamin can no longer fulfill its role in vesicle housekeeping. This effectively blocks output from chemical synapses. Thus, flies can be trained with mushroom body output enabled, but tested with mushroom body output blocked. Using five different mushroom-body-expressing GAL4 driver lines (c739, c747, c309, 247, c772), three different laboratories have Current Opinion in Neurobiology 2004, 14:737–744
740 Neurobiology of behaviour Figure 2
(a)
(b)
(c) AL
Training
PN
‘Good!’
Conditioned approach
Motor programs
MB ORN
Odor
‘Bad!’
Conditioned avoidance
Food Unconditioned, consumatory behavior
Shock Unconditioned, escape behavior
(d)
‘Good!’
Conditioned approach
Motor programs
Test
Odor
‘Bad!’
Conditioned avoidance
Food Unconditioned, consumatory behavior
Shock Unconditioned, escape behavior
Current Opinion in Neurobiology 2004, 14:737–744
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found independently that under these conditions flies cannot express any memory [41–43] (for long-term memory [44,45,46]). A similar conclusion is reached with respect to appetitive learning, using the 247 GAL4 driver [6]. Thus, criterion four for the localization of a memory trace to the mushroom bodies is met.
Criterion five: blocking input to the mushroom body during training Olfactory input to the mushroom bodies is carried by the uniglomerular projection neurons; if shits is expressed in approximately 60% of the antennal lobe projection neurons during training (using the GAL4 driver line GH146 [10,12–15]), these projection neurons cannot provide input to the mushroom bodies. In such an experiment, flies do not show any sign of memory during subsequent test; however, odor responses are normal at the relatively high odor concentrations used [47]. Thus, criterion five for the localization of a memory trace to the mushroom bodies seems to be met. However, the projection neurons provide input not only to the mushroom bodies but also to the lateral protocerebrum [11] (and possibly even the antennal lobe [19]). Therefore, this conclusion needs to be taken with care as we are currently lacking the appropriate tools to block specifically only mushroom body input, for example, by a dominant-negative acetylcholine receptor transgene with temperature sensitivity expressed in the mushroom body. Thus, it is likely that olfactory input to the mushroom bodies during training is required for the flies to express memory during test, so that criterion five is probably met. Concerning reinforcer-related input to the mushroom bodies, Schwaerzel et al. found that for appetitive learning
octopamine but not dopamine signaling is required, whereas for aversive learning dopamine but not octopamine signaling is necessary. Both dopamine and octopamine signaling were shown to be specifically required during training, not during test [6]. The Kenyon cells clearly express octopamine and dopamine receptors and are targets of dopamine- and octopamine immunoreactive neurons [22,48–51]; however, whether specifically those octopaminergic and/or dopaminergic neurons that carry the reinforcing information do indeed directly impinge onto the mushroom bodies is at present unknown. Mushroom body input via the amnesiac peptide released by the dorsal paired medial neurons (DPM) is unlikely to carry reinforcement-related information, as short-term retention is unaffected if these neurons are blocked during training [52,53].
A memory trace downstream of the mushroom bodies? For the time being, suppose that the mushroom bodies house an olfactory memory trace. Can we be sure that there is no additional associative memory trace outside the mushroom bodies? For example, a subset of mushroom body cells is glutamatergic [22], and there are predicted NMDA receptors in the fly genome. However, three different laboratories found that blocking mushroom body output during training in the GAL4 driver lines c739, c747, c309, 247, c772 leaves performance during test intact [41–43]. Thus, in order for learning to occur, olfactory information needs to enter, but does not have to leave, the mushroom bodies. This suggests that whatever kind of plasticity is underlying conditioned behavior, be it rut2080-dependent or rut2080-independent, it is happening between mushroom body input and mushroom body output. Consistent with this view, memory can be effectively extinguished even during blockade of
(Figure 2 Legend) The Drosophila brain, shown as a histological preparation (a), a schematic of major brain regions (b), and as highly simplified diagram of the essential olfactory-learning circuitry (c and d). (a) Frontal section of a Drosophila brain. The reduced silver technique labels nuclear and cytoskeletal proteins and Nissl granules. This enables the visualization of fiber tracts and neuropil regions in the central brain, as well as the cell body rind. The calyx neuropil with the very thin Kenyon cell dendrites in the mushroom bodies can be discerned by the light stain (abbreviations: lo, lobula; p op t, posterior optic tract, x and z indicate coordinates). Grid intersections are at 50 microns. Taken as section number 22, y = 7.5 from http://www.flybrain.org, where further details can be found. (b) Frontal view of a Drosophila brain with the major brain regions reconstructed on the basis of nc82-immunreactivity labeling presynaptic proteins. The 3-D representation was obtained from 1 micron confocal serial sections using AMIRA visualization software (Indeed Visual Concepts, Berlin). Depicted are medulla (red, foreground), lobula (orange, background), the central complex (green), the antennal lobes (blue), and the mushroom bodies (brown). The image is slightly tilted to show the calyx region of the mushroom bodies (background), together with the peduncles (back-to-front), and lobes (foreground). The light pink shade sketches the rest of the brain. (c and d) A minimal model for Drosophila olfactory learning. A highly simplified diagram showing the olfactory pathways. Olfactory receptor neurons (ORN) project to the antennal lobe (AL), leading to a specific combinatorial activity pattern. From there, uniglomerular projection neurons (PN) relay to the lateral horn and to premotor centers (box labeled ‘Motor output’), as well as to the mushroom body (MB) calyx. Output from the mushroom bodies then projects to a variety of target regions including premotor areas. In the model, we assume that a Kenyon cell needs input from at least three projection neurons to fire. In the mushroom bodies, the activation pattern of the sensory and the projection neurons is therefore transformed into an activation pattern of the mushroom body — intrinsic Kenyon cells. A memory trace for the association between odor and reinforcement is proposed to be localized within the Kenyon cells: during training, when the activation of a pattern of Kenyon cells representing an odor occurs simultaneously with a modulatory reinforcement signal (labeled ‘Good!’ and ‘Bad!’; potentially octopaminergic and dopaminergic neurons concerning reward and punishment, respectively), output from these activated Kenyon cells onto mushroom body output neurons is suggested to be strengthened. This strengthened output is thought to mediate conditioned behavior towards the odor when encountered during test, during which no reinforcer is present. Activated cells or synapses and motor programs are represented by filled symbols and bold lettering, respectively. www.sciencedirect.com
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mushroom body output [43]. Thus, we are forced to conclude that there is no memory trace downstream of the mushroom bodies — and leave it to future research to disprove this conclusion.
A memory trace upstream of the mushroom bodies? Another scenario is that there might be an additional memory trace upstream of the mushroom bodies, for example in the projection neurons and/or the antennal lobes, as suggested by the work on appetitive olfactory learning in bees [3,54] and by recent imaging studies in flies [55]. This seems unlikely because Connolly and coworkers [35] have shown that if neuronal plasticity in the mushroom bodies is prevented by expression of a constitutively activated Gas protein subunit, there is a total abolishment of memory. Thus, any associative plasticity outside of the mushroom bodies, be it up- or downstream, does in itself not seem sufficient to support memory. We therefore have to conclude that there is no memory trace upstream of the mushroom bodies. This claim could be tested by trying to rescue the rut-dependent learning defect by expressing the intact rut gene in the projection neurons by means of the GAL4 driver line GH146, or using the same driver line to express the constitutively active Gas protein subunit.
A memory trace ‘lateral’ to the mushroom bodies? Finally, a memory trace could be localized postsynaptic to the projection neurons, but in the lateral protocerebrum, that is, ‘lateral’ to the mushroom bodies. Such a memory trace would obviously not be sufficient to support memory, as argued by the total abolishment of learning when preventing neuronal plasticity within the mushroom bodies (see previous paragraph). Furthermore, such a trace could be read out even when mushroom body output is blocked during retrieval. As, however, under such conditions no retention is possible (see above), we again are inclined to conclude that there is no such memory trace. Obviously, criterion three is central for the argument that there is only one associative olfactory memory trace in the fly brain. Thus, an independent approach to this issue seems warranted, for example, using a knock-down of the rut-cyclase specifically in the mushroom bodies with an RNA interference approach.
there is at present no means to detect directly the transgenically expressed effector proteins. This leaves us with inferences from the expression patterns of reporter genes like green fluorescent protein (GFP). Using the same GAL4 driver line, however, the expression patterns of different effectors (shits, the rut-cyclase and GFP), and even the expression pattern for the same effector construct at different insertion sites (e.g. UAS–GFP on different chromosomes; A. Jenett, M. Heisenberg, pers comm), might differ considerably and lead conclusions astray, in particular if too few GAL4 lines are used (see discussion by Kido and Ito [59]). Thus, the use of tagged transgenes, tests of multiple GAL4 lines, and appropriate immunocytochemistry are necessary to deal with these concerns. Finally, as mentioned above, there is great incentive to develop physiological methods to be able to observe directly and in vivo neuronal and/or synaptic plasticity in central brain neurons of flies. This will also be important to elucidate the mechanism(s) of plasticity and its subcellular localization, particularly with respect to the substrates of PKA and their ultimate effects.
Conclusions We have suggested a set of criteria that need to be met to localize a site of plasticity as the associative memory trace. Three of these criteria are clearly met by the joint efforts of the Drosophila research community; one is only partially met, and one only indirectly. This last criterion requires a demonstration that the Kenyon cells undergo synaptic and/or neuronal plasticity during associative training. Taken together, we conclude that the working hypothesis that the short-term olfactory memory trace is localized to the mushroom bodies has so far held up well, and will continue to provide a basis for further research.
Acknowledgements We thank B Gru¨ newald and R Menzel, Freie Universita¨ t Berlin, M Giurfa, Universite´ Toulouse, T Preat, CNRS Gif sur Yvette, and M Schwa¨ rzel, University of Missouri for discussion and/or comments on the manuscript; E Rudolph, Universita¨ t Luzern for advice concerning the quotation of Augustin; A Jenett, Universita¨ t Wu¨ rzburg for providing Figure 2b. Current support comes from the Deutsche Forschungsgemeinschaft (SFB 554) (to M Heisenberg) and the Human Frontiers Science Program (to H Tanimoto).
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest
Caveats, problems and future directions There are two important caveats for all of these conclusions. First, the spatial expression patterns of GAL4 lines are in many cases distressingly unspecific and hard to assess reliably [56,57]. It thus seems important to ‘sharpen’ expression patterns and to pursue a digital quantitative approach to the neuroanatomy of these expression patterns [58]. Second, both for shits expression and for the rescue expression of the wild type rut-cyclase Current Opinion in Neurobiology 2004, 14:737–744
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