Continual learning is a natural ability of animals ranging from invertebrates to vertebrates but a great challenge for artificial intelligence (Fayek et al., 2020; Kudithipudi et al., 2022; Parisi et al., 2019; Wang et al., 2023). To achieve continual learning, the mutual interference between learning tasks, including proactive and retroactive interference, needs to be properly tuned. The interference from the previous task on the learning and memory of the current task is called Pro-I, while the interference from the current task on the memory of the following task is named Retro-I (Bouton, 1993; Miller, 2021; Wixted, 2004). Although, we have much understanding of the molecular mechanisms of learning and memory for a single learning task (Johansen et al., 2011; Kandel et al., 2014), the molecular mechanisms that modulate the interferences between different tasks remain unclear.

Drosophila is a well-studied and highly tractable genetic model organism for understanding molecular mechanisms underlying a single learning task (Davis, 2005; Noyes et al., 2021; Waddell and Quinn, 2001) as well as related human diseases (Mariano et al., 2020; Pandey and Nichols, 2011; van Alphen and van Swinderen, 2013). In recent years, Drosophila has also emerged as an excellent model for studying interference mechanisms between two associative learning tasks. Several molecules, including Rac1, Foraging, Scribble, SLC22A, Fmr1, SCAR, and Dia, have been reported to regulate Retro-I (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010). Of these molecules, Scribble and SLC22A, have also been reported to regulate Pro-I (Cervantes-Sandoval et al., 2016; Gai et al., 2016). It indicates that Retro-I and Pro-I may have shared regulatory molecules. Previous studies reported that resistance to Pro-I and Retro-I is very different in patients with autism (Mottron et al., 1998), schizophrenia (Torres et al., 2001), and ADHD (Orban et al., 2022). It raises a possibility that Pro-I and Retro-I may have distinct molecular mechanisms. Of note, all reported molecules in regulating Retro-I or Pro-I also play important roles in the learning or memory of a single learning task (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010). Whether there are molecules specifically responsible for modulating memory interference without affecting a single learning task remains to be determined.

Psychological studies have shown that Pro-I is closely related to content similarity, context similarity, and the time interval between the proactive task and the target task (Kliegl and Bäuml, 2021). In Drosophila, it has been known that an aversive associative learning task produces significant Pro-I on another similar task immediately following (Cervantes-Sandoval et al., 2016; Gai et al., 2016). However, the requirements for the generation of such Pro-I have not been systematically investigated, such as content similarity, context similarity, and the time interval between tasks.

Pro-I is affected by content similarity and context similarity between tasks

Consistent with previous studies (Cervantes-Sandoval et al., 2016; Gai et al., 2016), significant Pro-I was observed between two consecutive aversive tasks (Figure 1A; Associative group). This Pro-I phenomenon was still evident 1 hr later (Figure 1—figure supplement 1A) and was not affected by changing the order of the two tasks (Figure 1—figure supplement 1B). To reduce content similarity between tasks, we changed the proactive task into non-associative stimuli (Mo et al., 2022; Tully and Quinn, 1985; Zhang et al., 2018) or an appetitive learning task (Liu et al., 2012; Perisse et al., 2013; Pribbenow et al., 2022), which was less similar to the target task. With such changes, no significant Pro-I was observed (Figure 1A and B). Then, we changed the learning context of the two aversive tasks to reduce context similarity (Figure 1C). When both tasks were learned in the same context, the Pro-I was evident. In contrast, when the two tasks were learned in different contexts, the Pro-I was no longer observed. In addition, recent studies have found that a single aversive learning task can produce two opposing memory components: avoidance memory and approach memory (Jacob and Waddell, 2020; Naganos et al., 2022; Zhao et al., 2021). Here, we found that the Pro-I only affected avoidance memory (Figure 1—figure supplement 1C). Together, these data suggest that reducing inter-task similarity by changing learning content or context can release Pro-I in Drosophila, which is consistent with psychological studies (Kliegl and Bäuml, 2021).

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Pro-I and Retro-I differ in time interval sensitivity and regulatory molecules

We next tested the relationship between the Pro-I and ITI (Figure 2A–C). Flies showed significant Pro-I when the ITI was 15 min or less (0, 5, 10, and 15 min). If the ITI was 20 min or more (20, 30, and 60 min), no significant Pro-I was observed. In contrast, when the ITI was gradually increased from 0 to 60 min (0, 20, 30, and 60 min), the flies consistently exhibited significant Retro-I (Figure 2D and E). This result is consistent with previous studies on Retro-I (Cervantes-Sandoval et al., 2016; Dong et al., 2016; Gai et al., 2016; Gao et al., 2019; Reaume et al., 2011; Shuai et al., 2010).

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Given that Scribble and SLC22A regulate both Pro-I and Retro-I (Cervantes-Sandoval et al., 2016; Gai et al., 2016), other regulators of Retro-I may also affect Pro-I. Rac1 is required to mediate Retro-I when the ITI is 1.5 hr (Shuai et al., 2010). We next tested whether Rac1 also affects Retro-I with 0 min ITI and explored whether Rac1 plays a role in Pro-I (Figure 2F). The transgene dominant-negative Rac1 (Rac1-DN) or constitutively active Rac1 (Rac1-CA) was used to inhibit or increase Rac1 activity, respectively (Luo et al., 1994). Since acute manipulation of Rac1 activity in mushroom body (MB) neurons is sufficient to regulate memory performance (Gao et al., 2019; Shuai et al., 2010), Rac1 transgenes were expressed using MB-GS, a Gene-Switch (GS) tool capable of inducing transgene expression specifically in the MB only on administration of the drug RU486 (Mao et al., 2004). Suppressing (MB-GS/UAS-Rac1-DN, RU486+) or increasing (MB-GS/UAS-Rac1-CA, RU486+) Rac1 activity in MB neurons significantly mitigated or aggravated the Retro-I, respectively. However, the same manipulations did not affect the Pro-I, indicating that there is a different molecular mechanism underlying the Pro-I.

Pro-I, but not Retro-I, is bidirectionally regulated by CSW

Since the difference between Pro-I and Retro-I lies in the sensitivity to inter-task time intervals, we speculate that molecules that modulate ‘time interval’ effects may specifically regulate Pro-I. Given that CSW, an evolutionarily conserved protein tyrosine phosphatase SHP2, has been reported to regulate the ‘time interval’ effect in long-term memory (LTM) formation in Drosophila (Pagani et al., 2009), we tested the role of CSW in Pro-I. Compared to the parental control group (MB-GS/+, RU486+), acutely knocking down CSW in MB neurons using two independent RNAi lines (MB-GS/UAS-csw-RNAi-1 and MB-GS/UAS-csw-RNAi-2; RU486+) showed more severe Pro-I, which can be better reflected by the Pro-I+/Pro-I– ratio (Figure 3A). To further determine this result, we added a comparison with uninduced control groups (Figure 3B) or genetic control groups (Figure 3—figure supplement 1A and 1D) and obtained consistent results. When the ITI was 20 min, Pro-I was no longer observed in control flies but was still present in flies with acute knockdown of CSW (Figure 3C; Figure 3—figure supplement 1B). Conversely, acute overexpression of CSW in MB neurons (MB-GS/UAS-csw, RU486+) reduced Pro-I by using a newly constructed transgenic strain (Figure 3D and E; Figure 3—figure supplement 1C and E) and a previously reported strain (Botham et al., 2008; Figure 3—figure supplement 1F). Of note, bidirectional manipulation of CSW expression in MB neurons did not affect Retro-I (Figure 3F).

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The MB contains ~2000 intrinsic neurons called Kenyon cells (KCs), which are further divided into three major types: γ neurons (~675), α/β neurons (~990), and α’/β’neurons (~350) (Aso et al., 2014). We next sought to determine whether the modulatory effects of CSW on Pro-I could be narrowed to a certain type of MB neurons. 5-HT1B-Gal4 (Gao et al., 2019; Shyu et al., 2017; Yuan et al., 2005), VT30604-Gal4 (Wu et al., 2013), and C739-Gal4 (O’Dell et al., 1995) were used to drive transgene expression in γ neurons, α’/β’ neurons, and α/β neurons, respectively. Overexpressing CSW in γ neurons or α/β neurons, but not in α’/β’ neurons showed higher performance with Pro-I when compared with their respective genetic controls (Figure 3—figure supplement 1G). To rule out possible developmental effects, as there is no available GS tool for subtypes of MB neurons, we employed another inducible expression system called TARGET, which relies on a temperature shift to induce transgene expression (McGuire et al., 2003). Acute overexpression of CSW in γ neurons (Gal80^ts/+; 5-HT1B/UAS-csw, induced), but not in the α/β neurons (C739/+; Gal80^ts/UAS-csw, induced), significantly reduced the Pro-I compared with their respective controls (Figure 3G and H). In contrast, acute knockdown of CSW in γ neurons increased the Pro-I (Figure 3I). In uninduced flies, Pro-I was not affected (Figure 3—figure supplement 1H). These data suggest that CSW regulates Pro-I in MB γ neurons. In addition, the result in Figure 1C shows that no Pro-I occurred when the two tasks were learned in different contexts. In this case, knocking down the CSW remained ineffective (Figure 3—figure supplement 1I).

The Raf/MAPK pathway acts downstream of the CSW to regulate Pro-I

CSW is generally considered as a positive regulator of Ras/MAPK signaling (Perkins et al., 1996). And the regulation of CSW on the ‘time interval’ effect in LTM formation is also thought to be via the MAPK pathway (Pagani et al., 2009). So, we further explored whether CSW affects Pro-I through the MAPK pathway. Feeding U0126, a widely used inhibitor of the MAPK pathway (Thomas and Huganir, 2004), did not affect single-task learning, but significantly exacerbated Pro-I when learning two consecutive tasks (Figure 4A). Flies with acute genetic knockdown of MAPK in MB neurons (MB-GS/UAS-MAPK-RNAi, RU486+) also exhibited more severe Pro-I than uninduced and parental control flies (Figure 4B; Figure 4—figure supplement 1A).

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Consistent with these results, Raf kinase, a classical upstream regulator of MAPK (Thomas and Huganir, 2004), was found to bidirectionally regulate the Pro-I in MB neurons like CSW (Figure 4C and D). Acutely knocking down Raf significantly exacerbated the Pro-I, while acute overexpression of Raf-GOF, which encodes a constitutively active Raf kinase (Brand and Perrimon, 1994), significantly reduced Pro-I. When Raf-GOF and CSW-RNAi were co-expressed, Raf-GOF expression dominated the effect on Pro-I, indicating that Raf acts downstream of CSW (Figure 4E). Thus, our data support that CSW regulates Pro-I through Raf/MAPK pathway.

Our previous study found that the Raf/MAPK pathway is activated by learning to protect memory retention via non-muscle myosin Ⅱ Sqh after single-task learning (Zhang et al., 2018). Although the regulatory effect of CSW on Pro-I in two consecutive task learning was also through the Raf/MAPK pathway, manipulating CSW did not affect the learning and memory of a single task (Figure 4—figure supplement 1B and C), unlike Raf (Figure 4—figure supplement 1D; Zhang et al., 2018). Interestingly, as a downstream molecule of the Raf/MAPK pathway in regulating single-task memory (Zhang et al., 2018), Sqh did not participate in the Pro-I between the two tasks (Figure 4—figure supplement 1E). These results suggest that learning two different tasks consecutively may specifically recruit CSW to activate the Raf/MAPK pathway to reduce Pro-I through downstream molecules different from Sqh, a mechanism that differs from the memory protection mechanism triggered by a single learning task.

Our findings support a model for understanding molecular mechanisms of mutual interference between two successive learning tasks in Drosophila (Figure 4F). First, in learning two different associative tasks in succession, Pro-I and Retro-I occur simultaneously when the time distance between tasks is close (<20 min), while only Retro-I could be observed when the time distance was far (>20 min). Second, we identify a molecular pathway that specifically regulates Pro-I: the CSW/Raf/MAPK pathway. The upstream signaling molecule CSW of this pathway is not involved in single-task learning and memory. In learning two different tasks close in time, although Pro-I and Retro-I occurred simultaneously, CSW was only involved in regulating Pro-I. Third, Rac1 specifically regulates Retro-I without affecting Pro-I when two different tasks are temporally close. Consistently, the Rac1/SCAR/Dia pathway has been reported to regulate Retro-I when the two tasks are temporally distant (1.5 hr interval) (Gao et al., 2019; Shuai et al., 2010).

According to our data, Pro-I is no longer evident when two aversive olfactory learning tasks are separated by more than 20 min (Figure 2B and C). After 20 min, the memory performance of the proactive task partially declines and a consolidated memory component called anesthesia-resistant memory (ARM) is largely formed (Quinn and Dudai, 1976; Tully et al., 1994; Tully and Quinn, 1985; Zhang et al., 2016). It raises a possibility that the decay or consolidation of the proactive task may release Pro-I. Based on this possibility, the reason why the CSW/Raf/MAPK pathway can bidirectionally regulate Pro-I can be explained because of the ability to modulate the decay or consolidation of proactive task memory. However, it is not supported by the following experimental results. First, acute knockdown or overexpression of CSW in MB neurons did not affect the immediate and early memory performance of a single task (Figure 4—figure supplement 1B and C). Second, in the Retro-I paradigm, the proactive task in the Pro-I paradigm became the target task and was directly examined, however, its performance was not affected by manipulating CSW (Figure 3F). Third, manipulating Raf/MAPK pathway in MB neurons during the adult stage did not affect immediate memory and ARM performance after a single aversive learning task (Zhang et al., 2018).

Our current findings suggest that inter-task similarity from different dimensions contributes to Pro-I. First, the content similarity between tasks can influence Pro-I. Two similar aversive associative tasks could produce significant Pro-I. However, Pro-I was not observed after reducing the inter-task content similarity by replacing the proactive task with non-associative stimuli or an appetitive associative task (Figure 1A and B). Due to the similarity of content, both tasks are more likely to use the same neural circuit thus increasing the possibility of Pro-I. Second, the similarity of environmental contexts between tasks plays an important role in Pro-I. By using different colors of light, we changed the environmental context similarity of the two tasks during the learning stage without affecting the content similarity. Pro-I was significantly released when the similarity of environmental contexts between tasks was reduced (Figure 1C). Third, the similarity of time contexts between tasks also contributes to Pro-I. Time is also considered as a context (Bouton, 1993). When the ITI was increased to more than 20 min, the Pro-I is no longer significant (Figure 2A–C). This phenomenon can be explained as the 20 min ITI might make the time contexts of the two tasks significantly different, thus reducing the inter-task similarity. According to this explanation, the CSW/Raf/MAPK pathway can regulate Pro-I probably by affecting time context similarity between tasks. Aversive learning can induce MAPK activation that peaks at around 20 min, which can be modulated by CSW and Raf (Pagani et al., 2009; Zhang et al., 2018). Overexpression of CSW in MB neurons is reported to accelerate MAPK activation (Pagani et al., 2009) and can reduce Pro-I (Figure 3D, E and H). Knocking down Raf in MB neurons shortens the duration of MAPK activation (Zhang et al., 2018) and can exacerbate Pro-I (Figure 4C). With these findings, an interesting idea is that MAPK activation can help distinguish the time contexts of the two tasks. Thus, the CSW/Raf/MAPK pathway can bidirectionally regulate Pro-I by affecting the similarity of time contexts between tasks. These properties of Pro-I that we obtained using the Drosophila model are consistent with psychological studies (Kliegl and Bäuml, 2021).

Although Pro-I in the current work only occurs when the interval between two tasks is less than 20 min, the phenomenon of Pro-I varies across biological learning systems and tasks (Crossley et al., 2019; Epp et al., 2016; Jonides and Nee, 2006). Future works are required to determine whether orthologues of CSW also participate in other forms of Pro-I. Interestingly, the molecules regulating Pro-I and Retro-I in Drosophila are involved in different diseases with varying levels of intellectual disability. Rac1 and Fmr1, which regulate Retro-I in Drosophila, are high-risk genes for autism (Lord et al., 2020). Mutations in PTPN11, a human orthologue of csw, account for more than half of Noonan syndrome (Roberts et al., 2013). Therefore, further studies on the molecular mechanisms underlying Pro-I and Retro-I may also contribute to the understanding of the pathogenesis of autism and Noonan syndrome. In addition, studies of Pro-I and Retro-I from the synaptic and neural circuit levels may also inspire continual learning in artificial intelligence (Wang et al., 2021; Wang et al., 2022).

All data generated or analysed during this study are included in the manuscript and supporting file.

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Decision letter after peer review:

Thank you for submitting your article "Genetic Dissection of Mutual Interference between Two Consecutively Learned Tasks in Drosophila" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and K VijayRaghavan as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Emmanuel Perisse (Reviewer #1); Pedro F Jacob (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Appropriate controls and results description: To properly measure the effect of genetic manipulation, the performances of the experimental groups need to be compared to the corresponding parental control groups (cf. point 1 and last part of reviewers 1 and 2, respectively).

2) Putting the results of proactive interference into the context of existing literature: (related to point 2 and points 1-5 in reviewers 1 and 2, respectively). To address some of these points, further behavioural characterization is necessary.

Reviewer #1 (Recommendations for the authors):

1. In all the Figures, there is no UAS-… background control that must be used to conclude the effect of the genetic tool used. Why use only one background genetic control?

2. The authors could do some experiments and discuss a bit more on how (especially Pro) interference really happens. Considering the role of MB γ neurons in the acquisition of aversive memory their results are not surprising on the effect of proactive interference during memory retrieval between the trained odour versus the untrained odour. It will be interesting to understand a bit more about the proactive interference in the acquisition of both trained and untrained odours as they have meaningful information assigned during learning (negative and positive value, respectively; see Zhao et al., 2021; Nagano et al., 2022, and Jacob and Waddell, 2020). During the test, the authors could test each trained odour against a new odour. The reason why the performance index is low (or high) during the test may have different reasons. One of them could be that there is more (or less) generalization between the CS+ and the CS- in the trained task which they could test. Another could be that only CS+ and not CS- is learned which they could test. Why choose IA and EA as odours of interference learning? Do flies learn well this odour combination? Is the effect observed due to this odour combination? Can a different type of interference task induce the same proI effect, like appetitive learning?

Reviewer #2 (Recommendations for the authors):

In the current manuscript, the authors focus on a particular task of interference that might account for forgetting. As far as I am aware this is the first paper that tries to focus only on describing this task in more detail in Drosophila. However, this description of the phenomenon of Pro-I is limited and is focused only on applying previous knowledge. Therefore, in the current format, I believe that the manuscript requires more context (and experiments) within the interference literature.

Some examples:

1) In one of the senior author's (Yi Zhong) labs it was recently suggested that after a single conditioning event, two types of associative memories are encoded; one is context-independent (in the mushroom body) and the other is context-dependent (in the lateral horn). Substantial literature in the interference field has shown that Pro-I is a context-dependent phenomenon, and changing the context is a way to release from Pro-I. The authors in this manuscript show that the mushroom body is necessary for Pro-I, so how is this integrated with the fact that this memory is context-dependent but in the previous research of the authors, context is encoded in the lateral horn?

a. Further experiments are suggested to show that the observed task is indeed a classical example of Pro-I, and by changing the context of the two tasks, learning is no longer impaired. Furthermore, how is the manipulation of the expression of Corkscrew in γ Kenyon Cells influencing these tasks, after changing context?

2) The presence of Pro-I is circuit dependent both in invertebrates and vertebrates. Release of Pro-I can occur when two memories that require different circuits are sequentially learned since they can be consolidated in parallel. In Drosophila different types of memories (appetitive and aversive) require different circuits and can be formed in parallel.

a. Is Pro-I in Drosophila, also circuit-dependent? Given that aversive and appetitive memories require different compartments of the γ lobe of the mushroom body. This circuit dependency is a relevant part of the described work.

b. A more distinct proactive task from the target task is suggested to release Pro-I, since, on a posthoc basis, the target task can be more easily filtered out from the proactive set.

3) The observed Pro-I occurs within a time window (< 20 min), where the new learning (target task) occurs during the consolidation of the old memory (proactive task).

a. Contextualization within the memory consolidation is required. How passive decay of the memory for the proactive task might influence the reduced impairment of the target task in longer time intervals.

4) Over decades an important debate is how to explain Pro-I effect, some attribute Pro-I to a problem at the retrieval stage and others to a problem at the encoding stage.

a. Further contextualization of the current findings within this debate would be interesting.

5) A more general question, which stimulus/task dimensions (e.g. lag between tasks; different lengths of memory, short, mid, long-term; similarity of stimulus/tasks) influence Pro-I in Drosophila?

The manuscript would benefit from a brief introduction for the general audience of the tasks used at the beginning of the Results section.

In line 122. It should read Figure 2G instead of Figure 3G.

The overall reporting of the statistics needs to be improved across the manuscript, for example in lines 148 to 150 it is stated:

"Flies with acute genetic knockdown of MAPK in MB neurons (MB-GS/UAS-MAPK-RNAi, RU486+) also exhibited more severe Pro-I than uninduced and parental control flies (Figure 4B)."

The description of the statistics is always mentioned in this way in the manuscript, which might lead to erroneous interpretations from the readers. For example, here the MB-GS/UAS-MAPK-RNAi induced with RU486 after Pro-I was compared to MB-GS/UAS-MAPK-RNAi induced with RU486 without Pro-I, and never to the parental controls.

In figure 3, the authors start to present the data of overexpressing Corkscrew constitutively in the different subpopulations of Kenyon cells (Figure 3a) and correctly try to rule out possible development effects by only overexpressing Corkscrew in the adult. The authors conclude that the initial effect in γ Kenyon Cells is adult-specific (Figure 3b), whereas the effect in the α/β Kenyon cells is not (Figure 3c). All the manipulations should have been done in the adult only (additionally α′/β′ Kenyon Cells possibly, for sake of completion), and the presentation of the data in Figure 3a is no longer relevant because the initial effect in the α/β Kenyon cells is not reproduced in the adult. Therefore, Figure 3a should be removed, and completed with adult-specific manipulation of α′/β′ Kenyon Cells.

Given these effects during development after overexpression of Corkscrew, appropriate sensory acuity tests (for shock and odour) should be presented. This should be done for the genotypes that show statistical differences.

The n of 6/7 in some of the experiments is below the typical in the field (n=8 to 12).

The formatting of some references is different from the majority. For example Bouton (1993); Brand and Perrimon (1994), Luo et al., (1994), and McGuire et al., (2003).

Reviewer #1 (Recommendations for the authors):

1. In all the Figures, there is no UAS-… background control that must be used to conclude the effect of the genetic tool used. Why use only one background genetic control?

For inducible systems of transgene expression (e.g. GeneSwitch), we thought that uninduced flies (e.g. RU486-) are better controls relative to genetic controls (e.g. MB-GS/+ or UAS controls) because their genetic background is not altered and the main alteration is attributed to the induced expression of the transgene (RU486 feeding). Therefore, in the initial manuscript, we used only one type of genetic control group (MB-GS/+) when investigating the role of CSW in proactive interference.

To further strengthen our data about CSW, we performed four new experiments in revision. For experiments using the GeneSwitch system, as the reviewer suggested, we added UAS control groups (+/UAS-csw and +/UAS-csw-RNAi-1) and performed new experiments (revised Figure 3—figure supplement 1D and 1E). For experiments using the TARGET system, we added new data including a CSW-knockdown experiment (revised Figure 3I) and an uninduced experiment (revised Figure 3—figure supplement 1H). All these new results further strengthened our findings in the initial manuscript.

For the experimental data related to Rac1, MAPK, and Raf, we used the same fly strains as in our previous studies (Shuai, et al., Cell, 2010; Zhang, et al., Neuron, 2018; Gao, et al., PNAS, 2019; Mo, et al., Aging Cell, 2022) and did not add additional UAS control experiments.

2. The authors could do some experiments and discuss a bit more on how (especially Pro) interference really happens. Considering the role of MB γ neurons in the acquisition of aversive memory their results are not surprising on the effect of proactive interference during memory retrieval between the trained odour versus the untrained odour. It will be interesting to understand a bit more about the proactive interference in the acquisition of both trained and untrained odours as they have meaningful information assigned during learning (negative and positive value, respectively; see Zhao et al., 2021; Nagano et al., 2022, and Jacob and Waddell, 2020). During the test, the authors could test each trained odour against a new odour. The reason why the performance index is low (or high) during the test may have different reasons. One of them could be that there is more (or less) generalization between the CS+ and the CS- in the trained task which they could test. Another could be that only CS+ and not CS- is learned which they could test. Why choose IA and EA as odours of interference learning? Do flies learn well this odour combination? Is the effect observed due to this odour combination? Can a different type of interference task induce the same proI effect, like appetitive learning?

First, as suggested by the reviewer, we tested each trained odor against a new odor to obtain CS+ and CS- memory performance, respectively (Revised Figure 1—figure supplement 1C). Consistent with previous reports (Jacob and Waddell, Neuron, 2020; Zhao et al., eLife, 2021; Nagano et al., Eur J Neurosci, 2022), CS+ memory was avoidance memory, while CS- memory was approach memory. Interestingly, Pro-I significantly affected CS+ memory performance, but not CS- memory performance. This result further enriches our understanding of the Pro-I. Second, since our previous studies (Shuai et al., Cell, 2010; Dong et al., PNAS, 2016; Zhang et al., Cell Rep, 2016; Gao et al., PNAS, 2019) have used EA and IA as odors for the interference task, they were not intentionally changed in our current work. According to our newly added experimental data (Revised Figure 1—figure supplement 1B), the use of EA and IA odors in the target task also produced high learning performance and can be significantly affected by the proactive task using odors of OCT and MCH. Therefore the Pro-I effect was not due to the use of a specific odor combination. Third, we performed the experiment suggested by the reviewer and found that there was no significant Pro-I when appetitive learning was used as a proactive task and aversive learning was used as a target task (Revised Figure 1B).

Reviewer #2 (Recommendations for the authors):

In the current manuscript, the authors focus on a particular task of interference that might account for forgetting. As far as I am aware this is the first paper that tries to focus only on describing this task in more detail in Drosophila. However, this description of the phenomenon of Pro-I is limited and is focused only on applying previous knowledge. Therefore, in the current format, I believe that the manuscript requires more context (and experiments) within the interference literature.

Some examples:

1) In one of the senior author's (Yi Zhong) labs it was recently suggested that after a single conditioning event, two types of associative memories are encoded; one is context-independent (in the mushroom body) and the other is context-dependent (in the lateral horn). Substantial literature in the interference field has shown that Pro-I is a context-dependent phenomenon, and changing the context is a way to release from Pro-I. The authors in this manuscript show that the mushroom body is necessary for Pro-I, so how is this integrated with the fact that this memory is context-dependent but in the previous research of the authors, context is encoded in the lateral horn?

a. Further experiments are suggested to show that the observed task is indeed a classical example of Pro-I, and by changing the context of the two tasks, learning is no longer impaired. Furthermore, how is the manipulation of the expression of Corkscrew in γ Kenyon Cells influencing these tasks, after changing context?

In our regular experiments to study aversive olfactory memory, the learning context of flies (tubes with copper grid) is different from the test context (tubes without copper grid). Under such conditions, the lateral horn-related, context-dependent memory discovered in the previous study (Zhao, et al., Nature Communications, 2019) cannot be detected. This context-dependent memory can be observed only when the test context is consistent with the learning context (e.g., by adding the copper grid to the test tubes to make the test and learning context consistent). Since all experiments in our current manuscript were performed using the regular method (learning and test context are different), the context-dependent memory in the lateral horn was not involved in our findings.

The suggestion to change the context of the two tasks is very constructive. Since flies are trained in a dark room in regular behavioral experiments, the proactive task and the target task in Pro-I experiments had the same dark context. We can still observe the same Pro-I phenomenon after changing the context of both tasks from dark to blue light. However, when the contexts of the two tasks become different (green light context for the proactive task and blue light context for the target task), the Pro-I could no longer be observed (revised Figure 1C). In psychological studies, Pro-I can be released by making learning contexts of target and non-target tasks more distinct (Kliegl and Bauml, Neuroscience and Biobehavioral Reviews, 2021). Our new result is in line with these findings, suggesting that the observed result is indeed a classical example of Pro-I. Interestingly, when the contexts of the two tasks become different, knocking down CSW in MB neurons did not increase the Pro-I (revised Figure 3—figure supplement 1I). In contrast, the same manipulation significantly aggravated the Pro-I when it was released by increasing the ITI to 20 min (revised Figure 3C).

2) The presence of Pro-I is circuit dependent both in invertebrates and vertebrates. Release of Pro-I can occur when two memories that require different circuits are sequentially learned since they can be consolidated in parallel. In Drosophila different types of memories (appetitive and aversive) require different circuits and can be formed in parallel.

a. Is Pro-I in Drosophila, also circuit-dependent? Given that aversive and appetitive memories require different compartments of the γ lobe of the mushroom body. This circuit dependency is a relevant part of the described work.

b. A more distinct proactive task from the target task is suggested to release Pro-I, since, on a posthoc basis, the target task can be more easily filtered out from the proactive set.

As suggested by the reviewer, we used an appetitive learning task as a significantly different proactive task from the target task (revised Figure 1B). No significant proactive interference was observed. In other words, proactive interference was released, supporting that Pro-I in Drosophila is also circuit-dependent.

3) The observed Pro-I occurs within a time window (< 20 min), where the new learning (target task) occurs during the consolidation of the old memory (proactive task).

a. Contextualization within the memory consolidation is required. How passive decay of the memory for the proactive task might influence the reduced impairment of the target task in longer time intervals.

According to our data, Pro-I is no longer evident when two aversive olfactory learning tasks are separated by more than 20 min (revised Figures 2B and 2C). After 20 min, the memory performance of the proactive task partially declines and a consolidated memory component called anesthesia-resistant memory (ARM) is largely formed (Quinn and Dudai, Nature, 1976; Tully and Quinn, Journal of Comparative Physiology a-Sensory Neural and Behavioral Physiology, 1985; Tully, et al., Cell, 1994; Zhang, et al., Cell Reports, 2016). It raises a possibility that the decay or consolidation of the proactive task may release Pro-I. Based on this possibility, the reason why the CSW/Raf/MAPK pathway can bidirectionally regulate Pro-I can be explained because of the ability to modulate the decay or consolidation of proactive task memory. However, it is not supported by the following experimental results. First, acute knockdown or overexpression of CSW in MB neurons did not affect the immediate and early memory performance of a single task (revised Figure 4—figure supplement 1B and 1C). Second, in the Retro-I paradigm, the proactive task in the Pro-I paradigm became the target task and was directly examined, however, its performance was not affected by manipulating CSW (revised Figure 3F). Third, manipulating Raf/MAPK pathway in MB neurons during the adult stage did not affect immediate memory and ARM performance after a single aversive learning task (Zhang, et al., Neuron, 2018). We have added a related discussion in the revised manuscript.

4) Over decades an important debate is how to explain Pro-I effect, some attribute Pro-I to a problem at the retrieval stage and others to a problem at the encoding stage.

a. Further contextualization of the current findings within this debate would be interesting.

Regarding whether Pro-I is attributed to a problem at the retrieval stage or a problem at the encoding stage, we currently do not have a preferential answer based on the available data. Our means of manipulating the CSW/MAPK pathway could theoretically affect both the encoding and retrieval stages. In the future, if there is a very rapid tool to manipulate CSW/MAPK pathway, more clear evidence can be obtained. Alternatively, studies of Pro-I at the neural circuit level may also yield useful evidence.

5) A more general question, which stimulus/task dimensions (e.g. lag between tasks; different lengths of memory, short, mid, long-term; similarity of stimulus/tasks) influence Pro-I in Drosophila?

The manuscript would benefit from a brief introduction for the general audience of the tasks used at the beginning of the Results section.

As suggested, we added a brief introduction at the beginning of the Results section in the revised manuscript.

In line 122. It should read Figure 2G instead of Figure 3G.

We have corrected this error.

The overall reporting of the statistics needs to be improved across the manuscript, for example in lines 148 to 150 it is stated:

"Flies with acute genetic knockdown of MAPK in MB neurons (MB-GS/UAS-MAPK-RNAi, RU486+) also exhibited more severe Pro-I than uninduced and parental control flies (Figure 4B)."

The description of the statistics is always mentioned in this way in the manuscript, which might lead to erroneous interpretations from the readers. For example, here the MB-GS/UAS-MAPK-RNAi induced with RU486 after Pro-I was compared to MB-GS/UAS-MAPK-RNAi induced with RU486 without Pro-I, and never to the parental controls.

We apologize for these misleading descriptions. We described them this way because comparisons with the parental control (MB-GS/+) were also done in the analysis of the experimental data, but were not shown in the figures. For the data in Figure 4B, we made the figure in a different way (revised Figure 4—figure supplement 1A). Acutely knocking down MAPK (MB-GS/UAS-MAPK-RNAi, RU486+) led to more severe Pro-I compared to the genetic control (MB-GS/+, RU486+).

In addition, we have made the following two changes in order to present our data more clearly. First, we presented the data of Figure 3B, Figure 3C and Figure 3E in a different way (revised Figure 3—figure supplement 1A-1C). Second, we performed new experiments with another genetic control group (revised Figure 3—figure supplement 1D and 1E). All the added results were consistent with our findings.

In figure 3, the authors start to present the data of overexpressing Corkscrew constitutively in the different subpopulations of Kenyon cells (Figure 3a) and correctly try to rule out possible development effects by only overexpressing Corkscrew in the adult. The authors conclude that the initial effect in γ Kenyon Cells is adult-specific (Figure 3b), whereas the effect in the α/β Kenyon cells is not (Figure 3c). All the manipulations should have been done in the adult only (additionally α′/β′ Kenyon Cells possibly, for sake of completion), and the presentation of the data in Figure 3a is no longer relevant because the initial effect in the α/β Kenyon cells is not reproduced in the adult. Therefore, Figure 3a should be removed, and completed with adult-specific manipulation of α′/β′ Kenyon Cells.

Given these effects during development after overexpression of Corkscrew, appropriate sensory acuity tests (for shock and odour) should be presented. This should be done for the genotypes that show statistical differences.

A previous study (Pagani, et al., Cell, 2009) has shown that overexpression of CSW in all KCs or α/β KCs throughout developmental and adult stages does not affect electric shock reactivity and odor acuity. So we did not do similar experiments again. We first examined the Pro-I effect by constitutively overexpressing CSW in different KC subpopulations (Figure 3A) mainly because of the following three reasons. First, we did not have GeneSwitch tools specific to KC subpopulations including γ, α/β, and α’/β’ KCs. Second, for TARGET tools, we only have available combined fly strains for γ (Gal80^ts; 5-HT1B) and α/β KCs (C739; Gal80^ts) but not α’/β’ KCs. Third, according to the previous study (Pagani, et al., Cell, 2009), manipulating CSW in KCs with or without bypassing the developmental stage had similar effects in regulating long-term memory formation. Considering this review suggestion together with Reviewer #1’s suggestion, we moved Figure 3A to supplemental data (revised Figure 3—figure supplement 1G). At the same time, we performed new experiments to further strengthen our previous experimental data using the TARGET system. We added new data including a CSW-knockdown experiment (revised Figure 3I) and an uninduced experiment (revised Figure 3—figure supplement 1H).

The n of 6/7 in some of the experiments is below the typical in the field (n=8 to 12).

In revision, we increased the N of Figure 1F (revised Figure 2F, from 8 to 12), (Figure 2D revised Figure 3C, from 6 to 10), and Figure 2G (revised Figure 3F, from 6 to 10), respectively. The N for all revised figures is 8 or more, except for Figure 3E (n = 7-8), Figure 3—figure supplement 1H (n = 7-8), Figure 4—figure supplement 1B (n = 7) and 1D (n = 7).

The formatting of some references is different from the majority. For example Bouton (1993); Brand and Perrimon (1994), Luo et al., (1994), and McGuire et al., (2003).

We have made revisions to make the references in the same format.

Author details

1. Jianjian Zhao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Xuchen Zhang

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Present address

   Department of Molecular and Cellular Physiology and Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Bohan Zhao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Present address

   Department of Neuroscience, Dorris Neuroscience Center, Scripps Research, San Diego, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9177-1278

4. Wantong Hu

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

5. Tongxin Diao

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Liyuan Wang

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

7. Yi Zhong

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Supervision, Funding acquisition

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7927-5976

8. Qian Li

   1. School of Life Sciences, IDG/McGovern Institute for Brain Research, MOE Key Laboratory of Protein Sciences, Tsinghua University, Beijing, China
   2. Tsinghua-Peking Center for Life Sciences, Beijing, China

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   liqian8@tsinghua.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7317-1570

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