Genetic dissection of mutual interference between two consecutive learning tasks in Drosophila
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eLife assessment
This fundamental study substantially advances our understanding of interactions of consecutive memory tasks by identifying responsible molecules and neurons. The evidence supporting the claims of the authors is generally solid, although further contextualization of the interferences in memory consolidation and more rigorous measurements of the effects of genetic manipulation would have strengthened the study. The work will be of broad interest to neuroscientists working on learning and memory as well as learning psychologists.
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Abstract
Animals can continuously learn different tasks to adapt to changing environments and, therefore, have strategies to effectively cope with inter-task interference, including both proactive interference (Pro-I) and retroactive interference (Retro-I). Many biological mechanisms are known to contribute to learning, memory, and forgetting for a single task, however, mechanisms involved only when learning sequential different tasks are relatively poorly understood. Here, we dissect the respective molecular mechanisms of Pro-I and Retro-I between two consecutive associative learning tasks in Drosophila . Pro-I is more sensitive to an inter-task interval (ITI) than Retro-I. They occur together at short ITI (<20 min), while only Retro-I remains significant at ITI beyond 20 min. Acutely overexpressing Corkscrew (CSW), an evolutionarily conserved protein tyrosine phosphatase SHP2, in mushroom body (MB) neurons reduces Pro-I, whereas acute knockdown of CSW exacerbates Pro-I. Such function of CSW is further found to rely on the γ subset of MB neurons and the downstream Raf/MAPK pathway. In contrast, manipulating CSW does not affect Retro-I as well as a single learning task. Interestingly, manipulation of Rac1, a molecule that regulates Retro-I, does not affect Pro-I. Thus, our findings suggest that learning different tasks consecutively triggers distinct molecular mechanisms to tune proactive and retroactive interference.
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eLife assessment
This fundamental study substantially advances our understanding of interactions of consecutive memory tasks by identifying responsible molecules and neurons. The evidence supporting the claims of the authors is generally solid, although further contextualization of the interferences in memory consolidation and more rigorous measurements of the effects of genetic manipulation would have strengthened the study. The work will be of broad interest to neuroscientists working on learning and memory as well as learning psychologists.
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Reviewer #1 (Public Review):
Zhao et al., first show that learning that one odour is paired with a shock and another one is not (a differential associative learning), is impeded by another differential associative learning happening less than 20 min before, the proactive interference. However, the effect of differential associative learning on a previous one (retroactive interference) lasts for at least 60 min (even 1.5h in previous studies, Shuai et al., 2010; Cervantes-Sandoval et al., 2016; Gai et al., 2016). Consistent with previous studies (Shuai et al., 2010), they demonstrate that retroactive interference is dependent on the expression of the small G protein Rac1 which is involved in actin cytoskeleton dynamics in the mushroom body (MB), the insect learning and memory center (Heisenberg et al., 2003). While a reduced expression …
Reviewer #1 (Public Review):
Zhao et al., first show that learning that one odour is paired with a shock and another one is not (a differential associative learning), is impeded by another differential associative learning happening less than 20 min before, the proactive interference. However, the effect of differential associative learning on a previous one (retroactive interference) lasts for at least 60 min (even 1.5h in previous studies, Shuai et al., 2010; Cervantes-Sandoval et al., 2016; Gai et al., 2016). Consistent with previous studies (Shuai et al., 2010), they demonstrate that retroactive interference is dependent on the expression of the small G protein Rac1 which is involved in actin cytoskeleton dynamics in the mushroom body (MB), the insect learning and memory center (Heisenberg et al., 2003). While a reduced expression of Rac1 in the MB only at the adult stage induces less retroactive interference effect, adult expression of a constitutively active Rac1 protein increases retroactive interference. Interestingly, this Rac1 expression manipulation did not alter proactive interference. By interacting with the MAPK pathway, Corkskrew, a tyrosine phosphatase SHP2, regulates the spacing effect during repeated associative learning (Pagani et al., 2009). Given this function, the authors then investigated the function Corkskrew and found that proactive but not retroactive interference involves Corkskrew. They found that reducing adult expression of Corkskrew enhances and prolongs proactive interferences while overexpression of Corkskrew in MB γ neurons reduces proactive interference. The authors finally showed that Corskrew regulates proactive interference upstream of Raf and MAPK in the MB. Overall this work demonstrates with solid evidence that proactive and retroactive interference have different temporal dynamics and molecular bases, as summarized in Figure 4F. A more complete cellular and molecular model of their findings could help the reader to understand how proactive and retroactive interference works.
The strength of this manuscript relies on the clear bidirectional effect of opposite genetic manipulations (over- and decreased gene expression) on proactive or retroactive interference analysed at the behavioural level.
A weakness of this work is that the authors did not pursue the last part of their investigation (on the Raf/MAPK) in the MB neurons but in the overall MB. In addition, Corkskrew seems to regulate the duration of proactive interference but they have not tested such a thing in the downstream Raf/MAPK. Another weakness of this manuscript relies on the absence of some background genetic controls that the field usually use to conclude the effect of a genetic tool (e.g the UAS tools). More explanation in the text is also needed to understand a bit more about the tools used and the brain structure targeted to tackle the cellular and molecular bases of proactive and retroactive interference.
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Reviewer #2 (Public Review):
Zhao et al., set out to investigate the molecular mechanisms controlling the timing between training tasks that leads to proactive interference (Pro-I) buildup (i.e. formation) and consequently interference in the retrieval of the newly learned memory.
During the time-dependent stabilization of newly acquired memory (i.e. memory consolidation), the memory traces are vulnerable to disruption by a variety of amnestic influences. When multiple learning events occur in rapid succession, competition occurs between consolidating memories. However, the factors that regulate what memory is remembered or forgotten are unknown. Two interference models of forgetting are proposed in the literature: events occurring prior to learning cause forgetting through proactive interference (Pro-I), or events occurring after …
Reviewer #2 (Public Review):
Zhao et al., set out to investigate the molecular mechanisms controlling the timing between training tasks that leads to proactive interference (Pro-I) buildup (i.e. formation) and consequently interference in the retrieval of the newly learned memory.
During the time-dependent stabilization of newly acquired memory (i.e. memory consolidation), the memory traces are vulnerable to disruption by a variety of amnestic influences. When multiple learning events occur in rapid succession, competition occurs between consolidating memories. However, the factors that regulate what memory is remembered or forgotten are unknown. Two interference models of forgetting are proposed in the literature: events occurring prior to learning cause forgetting through proactive interference (Pro-I), or events occurring after learning cause forgetting through retroactive interference (Retro-I).
The most common explanation for Pro-I and its buildup is that this phenomenon emerges due to the competition between two differing task memories (e.g. aversive memories) for storage in overlapping brain areas. The behavioural consequence of this Pro-I buildup is that the recall of newly learned information is impaired when is preceded by a similar learning task. On the other hand, several accounts are used to describe the release from Pro-I (i.e. the reduction of proactive interference): a) having a more distinctive target task compared to the interference task (either different material or relying on different neural circuits), b) prolonging the lag between training tasks and c) contextual changes between the two learnings. Drosophila is an ideal model for the study of these questions, given the detailed knowledge base of how different types of memories are encoded, consolidated, and retrieved and the effects of context changes in these memories.
In this study, the authors use the classic aversive conditioning paradigm, where flies learn to associate an odor A with shock (in the target task), this task is preceded by a proactive task or is followed by a retroactive task, where odor X is paired with a shock. Taking advantage of the known molecular pathways for the more well-characterized model of interference (Retro-I), the authors extended the knowledge for these types of interference models. To uncover the mechanisms underlying the timing of Pro-I, the authors genetically manipulated the activity of a key phosphatase (Corkscrew) and its downstream pathway (Raf/MAPk). This phosphatase was chosen given its known role in controlling the appropriate training intervals for the induction of long-term memory in flies.
A strength of the manuscript is that the authors showed the unique and exclusive role of Corkscrew in regulating Pro-I and its temporal dynamics. Furthermore, the authors described that Corkscrew regulates Pro-I via a single subset (γ) of the intrinsic cells (i.e. Kenyon cells) of the mushroom body, which is the centre of learning and memory in Drosophila. However, the manuscript would have been improved had they characterized the Pro-I task more thoroughly. This is because behaviourally what the authors are observing might look like Pro-I buildup, but other scenarios can also explain the data (e.g. passive decay of the first memory, memory storage limitations, attentional deficits). This would be solved by applying known Pro-I release protocols and, in this way, would be more comparable to the known Pro-I literature.
Interestingly in the mammalian field, phosphatase activity is also known as a key regulator of long-term depression and memory formation. Taken together, this data implies the conserved role of phosphatase activity and its subsequent plasticity during the process of learning a new task. Furthermore, this work shows the importance of phosphatase activity in facilitating memory consolidation of newly learned information, which might occur by suppressing any potential interference from old memories in the same neuronal circuits.
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