Maintaining Memories, Changing Transcription

Under the right circumstances, a memory can last a lifetime.  Yet at the molecular level the brain is constantly in flux: the typical protein has a half-life of only a few hours to days; for mRNA a half-life of 2 days is considered extraordinarily long.   If the important biological molecules in the brain are constantly undergoing decay and renewal, how can memories persist?

The Slug Lab has a bit of new light to shed on this issue today.  We’ve just published the next in our series of studies elucidating the transcriptional changes that accompany long-term memory for sensitization in Aplysia.  In a previous paper, we looked at transcription 1 hour after a memory was induced, a point at which the nervous system is first encoding the memory.  We found that there is rapid up-regulation of about 80 transcripts, many of which function as transcription factors (Herdegen, Holmes, Cyriac, Calin-Jageman, & Calin-Jageman, 2014).

For the latest paper (Conte et al., 2017), we examined changes 1 day after training, a point when the memory is now being maintained (and will last for another 5 days or so).  What we found is pretty amazing.  We found that the transcriptional response during maintenance is very complex, involving up-regulation of >700 transcripts and down-regulation of <400 transcripts.  Given that there are currently 21,000 gene models in the draft of the Aplysia genome, this means more than 5% of all genes are affected (probably more due to the likelihood of some false negatives and the fact that our microarray doesn’t cover the entire Aplysia genome).   That’s a lot of upheaval… what exactly is changing?  It was daunting to make sense of such a long list of transcripts, but we noticed some very clear patterns.  First, there is regulation influencing growth: an overall up-regulation of transcripts related to producing, packaging, and transporting proteins and a down-regulation of transcripts related to catabolism.  Second, we observed lots of changes which could be related to meta-plasticity.  Specifically, we observed down regulation in isoforms of PKA, in some serotonin receptors, and in a phosphodiesterase.  All of these changes might be expected to limit the ability to induce sensitization, which would be consistent with the BCM rule (once synapses are facilitated, raise the threshold for further facilitation).  (Bienenstock, Cooper, & Munro, 1982).

One of the very intriguing findings to come out of this study is that the transcriptional changes occuring during encoding are very distinct from those occuring during maintenance.  We found only about 20 transcripts regulated during both time points.  We think those transcripts might be especially important, as they could play a key regulatory/organizing role that spans from induction through maintenance.  One of these transcripts encoded a peptide transmitter called FMRF-amide.  This is an inhibitory transmitter, which raises the possibility that as the memory is encoded, inhibitory processes are simultaneously working to limit or even erode the expression of the memory (a form of active forgetting).

There are lots of exciting pathways for us to explore from this intriguing data set.  We feel confident heading down these paths because a) we used a reasonable sample size for the microarray, and b) we found incredibly strong convergent validity in an independent set of samples using qPCR.

This is a big day for the Slug Lab, and a wonderful moment of celebration for the many students who helped bring this project to fruition: Catherine Conte (applying to PT schools), Samantha Herdegen (in pharmacy school), Saman Kamal (in medical school), Jency Patel (about to graduate), Ushma Patel (about to graduate), Leticia Perez (about to graduate), and Marissa Rivota (just graduated).  We’re so proud of these students and so fortunate to work with such a talented and fun group.

Bienenstock, E., Cooper, L., & Munro, P. (1982). Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 2(1), 32–48. [PubMed]
Conte, C., Herdegen, S., Kamal, S., Patel, J., Patel, U., Perez, L., … Calin-Jageman, I. E. (2017). Transcriptional correlates of memory maintenance following long-term sensitization of Aplysia californica. Learning and Memory, 24, 502–515. doi: 10.1101/lm.045450117 [Source]
Herdegen, S., Holmes, G., Cyriac, A., Calin-Jageman, I. E., & Calin-Jageman, R. J. (2014). Characterization of the rapid transcriptional response to long-term sensitization training in Aplysia californica. Neurobiology of Learning and Memory, 116, 27–35. doi: 10.1016/j.nlm.2014.07009

Red, Romance, and Replication – Cross posted with thenewstatistics.com

I (Bob) have a new replication paper out today, a collaboration with DU student Elle Lehmann (Lehmann & Calin-Jageman, 2017).  The OSF page for the paper with all the materials and data is here: https://osf.io/j3fyq/ (Calin-Jageman & Lehmann, 2015).

The paper replicates a set of previous findings showing that the color red dramatically increases romantic attraction for both women rating men (A. J. Elliot et al., 2010) and men rating women (A. Elliot & Niesta, 2008).  Elle and I conducted two replications: one in-person with a standard psychology participant pool, the other online with MTurk participants.  In each case we planned for an informative sample, used original materials, pre-registered our design and analysis plan, and used extensive exclusion criteria to ensure suitable participants (e.g. testing for color-blindness).  In both cases, we are sad to report that there was little-to-no effect of red on perceived attractiveness or desired sexual behavior.

Example of the types of stimuli used in red-romance studies (not the actual stimuli we used, though)

There were a few weaknesses: 1) for the in-person study we didn’t obtain nearly enough men to make a good test of the hypothesis, 2) for the online study we couldn’t control the exact parameters for the color red.  Still, we found no strong evidence that incidental red influences perceived attractiveness.

Beyond the (disappointing) replication results, there are some really interesting developments to this story:

  • Our replication work drew the attention of science journalist Dalmeet Singh who wrote a cool article summarizing the field and our contribution for Slate.  Dalmeet has made covering negative results a part of his beat–how great is that!
  • There have been some questions about these studies almost from the start.  Greg Francis highlighted the fact that the original study of women rating men by Elliot & Niesta (2008) is just too good to be true–every study was statistically significant despite very low power, something that ought not to regularly happen (Francis, 2013).
  • Although there have been some studies showing red effects (though often in subgroups or only with some DVs), there is a growing number of studies reporting little-to-no effect of red manipulations on attraction: (Hesslinger, Goldbach, & Carbon, 2015)(Peperkoorn, Roberts, & Pollet, 2016)(Seibt, 2015)(Lynn, Giebelhausen, Garcia, Li, & Patumanon, 2013)(Kirsch, 2015) plus a whole raft of student-led precise replications that were part of the CREP project (Grahe et al., 2012): https://osf.io/ictud/
  • To help make sense of the data, Elle and I embarked on conducting a meta-analysis.  It has turned out to be a very big project.  We hope we’re nearly ready for submission.
  • Andrew Elliot, the original investigator, was extremely helpful in assisting with this replication.  Then, as the meta-analysis progressed, he became even more involved and has now joined the project as a co-author.  The project’s still not complete yet, but I’ve really enjoyed working with him, and I’m proud that this will (hopefully) become an example of how collegial and productive replication work can be towards better and more cumulative science.

References

Calin-Jageman, R., & Lehmann, G. (2015). Romantic Red – Registered Replications of effect of Red on Attractiveness (Elliot & Niesta, 2008; Elliot et al. 2010). Open Science Framework. https://doi.org/10.17605/osf.io/j3fyq [Source]
Elliot, A. J., Niesta Kayser, D., Greitemeyer, T., Lichtenfeld, S., Gramzow, R. H., Maier, M. A., & Liu, H. (2010). Red, rank, and romance in women viewing men. Journal of Experimental Psychology: General, 139(3), 399–417. https://doi.org/10.1037/a0019689
Elliot, A., & Niesta, D. (2008). Romantic red: red enhances men’s attraction to women. Journal of Personality and Social Psychology, 95(5), 1150–64. [PubMed]
Francis, G. (2013). Publication bias in “Red, rank, and romance in women viewing men,” by Elliot et al. (2010). Journal of Experimental Psychology. General, 142(1), 292–6. [PubMed]
Grahe, J. E., Reifman, A., Hermann, A. D., Walker, M., Oleson, K. C., Nario-Redmond, M., & Wiebe, R. P. (2012). Harnessing the Undiscovered Resource of Student Research Projects. Perspectives on Psychological Science, 7(6), 605–607. https://doi.org/10.1177/1745691612459057
Hesslinger, V. M., Goldbach, L., & Carbon, C.-C. (2015). Men in red: A reexamination of the red-attractiveness effect. Psychonomic Bulletin & Review, 22(4), 1142–1148. https://doi.org/10.3758/s13423-015-0866-8
Kirsch, F. (2015). Wahrgenommene Attraktivität und sexuelle Orientierung. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-08405-9
Lehmann, G. K., & Calin-Jageman, R. J. (2017). Is Red Really Romantic? Social Psychology, 48(3), 174–183. https://doi.org/10.1027/1864-9335/a000296
Lynn, M., Giebelhausen, M., Garcia, S., Li, Y., & Patumanon, I. (2013). Clothing Color and Tipping. Journal of Hospitality & Tourism Research, 40(4), 516–524. https://doi.org/10.1177/1096348013504001
Peperkoorn, L. S., Roberts, S. C., & Pollet, T. V. (2016). Revisiting the Red Effect on Attractiveness and Sexual Receptivity. Evolutionary Psychology, 14(4), 147470491667384. https://doi.org/10.1177/1474704916673841
Seibt, T. (2015). Romantic Red Effect in the Attractiveness Perception. In Proceedings of The 3rd Human and Social Sciences at the Common Conference. Publishing Society. https://doi.org/10.18638/hassacc.2015.3.1.186

Pre-Order Now: Introduction to the New Statistics

Can a statistics textbook change the world? Maybe yes! At least that’s the aspiration behind Introduction to the New Statistics: Estimation, Open Science and Beyond, a new textbook by Geoff Cumming and yours truly (978-1138825529)ITNS

How can a statistics textbook change the world? By teaching the estimation approach to data analysis–one that emphasizes confidence intervals, replication, and meta-analysis. The estimation approach is vast improvement over Null Hypothesis Significance Testing. Students find estimation easier to learn and it supports better inference. Hopefully, this book will be an important step in the ongoing battle to abolish p values.

Switching to estimation is not enough, though. Students of research also need to learn the new Open Science practices that are evolving to enhance research rigor: pre-registration, Open Data, and Open Materials. So the textbook is also the first to teach these essential practices from the start.

To sum it up, Geoff and I believe that this is a unique book in a large sea of statistics texts–one which we hope will inspire the next generation of researchers who will be raised from the start on good statistical and methodological practices. Perhaps the replication crisis will fade into the

The book is now available for pre-order on Amazon. It should be published by August, 2016–which is running just a bit late for fall adoptions. If you’d like a desk copy, shoot me an email or leave a comment.

Bibliography

Rest easy — organic food probably does not make you into a jerk…

My student Eileen Moery and I have a new paper out today in Social Psychology and Personality Science. It’s a replication paper that I’m quite proud of (10.1177/1948550616639649). It represents some evolution in how I’m supervising replication projects.

The new paper replicates a study purporting to show that being exposed to images of organic food produces a strong decrease in prosocial behavior and a strong up-tick in being morally judgmental (10.1177/1948550612447114). This is a potentially fascinating phenomenon–something like ‘moral licensing’, the ironic effect of good behavior fostering subsequent bad behavior.

The original paper caught fire and the media covered these findings extensively. Rush Limbaugh even crowed about them as evidence of liberal hypocrisy. I noticed the media coverage, and this is how the original study made it onto my ‘possible replication’ list. Eileen found it there, read the paper, and developed a fantastic honors project to put the initial study to the test.

For her project, Eileen contacted the original author to obtain the original materials. She planned and executed a large pre-registered replication attempt. She included a positive control (Retrospective Gambler’s task) so that if the main study ‘failed’ we would have a way to check if it was somehow her fault. She also devised a nice memory manipulation check to be sure that participants were attending to the study materials. She conducted the study and found little to no impact of organic food exposure on moral reasoning and little to no impact on prosocial behavior. She did find the expected outcome on the positive control, though–so sorry, doubters, this was not an example of researcher incompetence.

One of the things I don’t like about the current replication craze is the obsessive emphasis on sample size (this paper is not helping: (10.1177/0956797614567341)). Sure, it’s important to have good power to detect the effect of interest. But power is not the only reason a study can fail. And meta-analysis allows multiple low-power studies to be combined. So why be so darned focused on the informativeness of a single study? The key, it seems to me, is not to put all your eggs in one basket but rather to conduct a series of replications–trying different conditions, participant pools, etc. The pattern of effects across multiple smaller studies is, to my mind, far more informative than the effect found in a single but much larger study. I’m talking about you, verbal overshadowing (10.1177/1745691614545653)

Anyways, based on this philsophy, Eileen didn’t stop with 1 study. She conducted another larger study using Mechanical Turk. There are lots of legitimate concerns about MTurk, so we used the quality controls developed in Meg Cusack’s project (10.1371/journal.pone.0140806 )–screening out participants who don’t speak English natively, who take way too long or too short of a time to complete the study, etc. Despite all this care (and another successful positive control), Eileen still found that organic food produced about 0 change in moral judgments and prosocial behavior.

Still not finished, Eileen obtained permission to conduct her study at an organic food market in Oak Park. Her and I spent two very hot Saturday mornings measuring moral judgments in those arriving at or leaving from the market. We reasoned those leaving from had just bought organic food and should feel much more smug than those merely arriving or passing by. Yes, there are some problems of making this assumption–but again, it was the overall pattern across multiple studies we cared about. And the pattern was once again consistent but disappointing–only a very small difference in the expected direction.

Although Eileen and I were ready to call it quits at this point, our reviewers did not agree. They asked for one additional study with a regular participant pool. Eileen had graduated already, but I rolled up my sleeves and got it done. Fourth time, though, was not the charm–again there was little to no effect of organic food exposure.

With all that said and done, Eileen and I conducted a final meta-anlysis integrating our results. The journal would not actually allow us to report on the field study (too different!?), but across the other three studies we found that organic food exposure has little to no effect on moral judgments (d = 0.06, 95% CI [0.14, 0.26],N=377) and prosocial behavior (d=0.03, 95% CI [?0.17, 0.23],N=377).

So–what’s our major contribution to science? Well, I suppose we have now dispelled what in retrospect is a somewhat silly notion that organic food exposure could have a substantial impact on moral behavior. We are also contributing to the ongoing meta-science examining the reliability of our published research literature–it gives me no joy to say that this ongoing work is largely painting a relatively bleak picture. Finally, I hope that we have now gained enough experience with replication work to be (modestly) showing the way a bit. I hope the practices that are now becoming routine for my honors students (pre-registration, multiple studies, positive controls, careful quality controls, and synthesis through meta-analysis) will become routine in the rest of replication land. No, strike that–these are practices that should really be routine in psychology. Holding my breath.

Oh – an one other important thing about this paper–it was published in the same journal that published the original study. I think that’s exactly as it should be (journals should have to eat their own dog food). Obviously, though, this is exceptionally rare. I think it was quite daring for the journal to have published this replication, and I hope the good behavior of its editors are a model for others and a sign that things really are changing for the better.

Bibliography

Grant Awarded to study the mechanisms of sensitization maintenance and decay

Woot! The Slug Lab has just been awarded a 3-year R15 grant from NIH to study the transcriptional mechanisms of sensitization memory and decay. What does that mean? It means that Irina and I will continue to be working our a**’ off trying to understand what genes are activated as an animal stores a long term memory, and even more importantly, as a long-term memory is forgotten.

Here’s the screen grab from ERA commons:
grant*

Big thanks to our dedicated and amazing students, and to the incredibly supportive colleagues and administrators we have here at Dominican University. We’re looking forward to crushing it with this project.

*Technically, that’s not the actual notice of the award, but of our priority score from a peer review of our grant proposal by a panel of esteemed scientists in the field. We got the award letter via email last week.

2014×3 – Transcriptional correlates of long-term habituation

Third paper of the year for the lab (gasp!) is now out in Learning and Memory (10.1101/lm.036970.114).

The focus of the project is habituation, considered the simplest and most ancient form of memory. Long-term habituation requires changes in gene expression, but to date there is almost nothing known about what specific changes are required to encode and store a long-term habituation memory.

We’re not the first to try to tackle this issue, but it turns out to be a very difficult topic for study. Habituation is typically very site specific, occurring only at the site of training. This implies a relatively discrete set of neurons encode the memory, and that presents a real problem for qPCR and microarray analysis, because the signal from memory-encoding neurons could easily be washed out from signal from non-encoding neurons, glia, etc.

Our strategy was to develop a new, automated protocol for inducing long-term habituation over the entire body of an Aplysia. With the help of a tinker-toy set, a windshield-wiper motor, a relay box, an old computer with a parallel port, and some qBASIC programming (blast from the bast), we developed a slug car wash–an apparatus we could place over the tanks to repeatedly (though gently) brush Aplysia without any need for human intervention during training. We made a video to show off the system, which you can see here.

The slug car wash turns out to work great. We tracked the development of habituation over repeated rounds of training and saw a classic pattern of behavior–robust decreases in behavior at the end of each round of training, substantial overnight recovery (forgetting), but a progressive development of a persistently decreased response within 3 days of training. Importantly, we could observe habituated responding when stimulating the animal at the head, the siphon, or the tail. Moreover, the effect sizes were huge. So it was pretty clear that the slug car wash was producing the high impact we were looking for. In addition, we found that pattern of training really does matter–when training has breaks between sessions and is spaced out over 3 days it is extremely effective; massing all the same stimulation together into a single one-day session (at a slightly higher rate to squeeze it all in) produced neither long-term nor short-term habituation. This is a useful finding because it gave us an additional no-memory control, one which could ensure any molecular correlates identified are specific to memory formation, not just to the activity induced by brushing.

So what’s changing transcriptionally? We decided to focus on the pleural ganglia containing the VC nociceptors. These are relatively high-threshold neurons, and are probably not carrying the bulk of the activity induced by the brush. Unfortunately, though, no one yet knows *where* in the Aplysia nervous system to find the cell bodies of the low-threshold neurons that mediate light touch (probably in the periphery). Not to worry, though–we did record from the VCs in reduced preps and found that they do actually get some activation from the brush: about 1/4 fired APs, and most of the rest got lots of IPSPs from off-center stimulation.

To track transcriptional changes, we used the custom-designed microarray we recently developed in the lab (25117657). Some quick words about methods: We again used a large-ish sample size (n=8/group; can you believe that n=3/group is still common in microarray!?). We also used very high statistical standards by adopting the ‘treat’ function in limma which allows you to specify a reasonable null hypothesis (e.g. at least 10% regulation in either direction, rather than the standard practice of testing against a null of no regulation). Adopting a more reasonable null enables you to test for statistical and practical significance at the same time, and we’ve found that transcripts which pass such a rigorous test generalize very well to new samples. We’ve been finding R and limma surprisingly easy to use, which is pretty fantastic for free software.

Anyways, back to the data. The microarray results were a bit of a bummer. Out of over 20,000 transcripts tested, only *one* came up as strongly regulated. Bummer. Another 20 transcripts came up as regulated if you use a standard null hypothesis, but, as expected, none of these validated.

Although the microarray results were not what we hoped, we did further explore the one regulated transcript, and it turns out to be quite interesting. From sequence alignment, it seems to be an Aplysia homolog of cornichon, an auxiliary subunit for AMPA receptors. In invertebrates, cornichon seems to limit trafficking to AMPA receptors to the membrane and therefore reduces glugatmate-induced currents(24094107). Note that this is precisely the type of effect that could produce behavioral habituation. Moreover, one of the few known molecular correlates of long-term habituation is a decrease in surface expression of glutamate receptors (14573539). Fits perfectly!

To ensure that cornichon is truly regulated in our paradigm, we did some additional follow-ups. First, we used qPCR to check cornichon levels not only in the microarray samples but in an additional, independent set of samples. Sure enough, we confirmed up-regulation of cornichon in the pleural ganglia 1 day after training. In addition, we checked levels in massed animals, who display no memory after training. In this case, cornichon was actually slightly down, and was significantly different than in the regularly trained animals. So, cornichon is quite specifically and consistently up-regulated after long-term habituation training. As far as we know, this is the first specific transcriptional correlate of long-term habituation to be identified.

Needless to say, we’re quite proud of this work. It wouldn’t have been possible without two of the most talented undergrads we’ve had in the lab: Geraldine Holmes and Samantha (Sami) Herdegen. Geraldine was the most diligent slug trainer in the history of the lab. For this paper alone she ran over 48 animals, testing each 8 times a day for 3-5 days–that’s a whole lot of behavior to monitor! Sami, of course, has been the qPCR wizard in the lab, testing lots and lots and lots and lots of transcripts for regulation. It’s no surprise that both are on to bigger and better things, Geraldine is now in a PhD program in Canada and Sami is soon to start pharmacy school. We also had contributions from John Schuon (when he could fight his way in for some qPCR; now off to medical school), Ashly Cyriac (who helped start the project before heading off to pharmacy school), Jamie Lass and Catherine Conte. Congrats!

As has now become the norm for the lab, all the raw data from this study been posted online at the Open Science Framework: https://osf.io/6ew4i/.

Bibliography

Sluglab Strikes Again – New paper tracing dynamics of learning-induced changes in transcription

A nice way to wrap up 2014–we have a new paper out (25486125) where we trace learning-induced changes in transcription over time and over different location in the CNS. We think it’s a nice follow-up to the microarray paper, because:

  • We show that some transcriptional changes are likely occuring in interneurons and motor neurons, not just in the VC nociceptive sensory neurons.
  • We found some transcripts which, like Egr, are rapidly *and* persistently up-regulated by sensitization training (GlyT2, VPS36, and an uncharacterized protein known for now as LOC101862095). We’re interested in such transcripts because they could be related to memory maintenance
  • We were able to better test the notion that CREB supports memory maintenance. So far, our evidence continues to go against this hypothesis, with no long-lasting changes detected in the VC sensory neurons nor in the pedal ganglia.
  • As a methodological point, we found that microdissecting out the VC cluster really really improves signal:noise for identifying transcriptional changes induced by learning. This is exciting–most work on the molecular mechanisms of memory uses tissue samples representing homogenous cell types. Zooming in on a single cell type of known relevance for storing the memory really enhances the power of the analysis.
  • We re-rested the four novel transcripts identified in our microarray paper from earlier this year (25117657). All four validated again! Moreover, all 4 were specifically up-regulated in the VC nociceptors (and some elsewhere as well). Another good indication that we’re on the right track with our microarray approach.
  • Another 3 student co-authors on this paper! We’re especially proud of Sami, Catherine, and Saman.
  • The paper is free on PLOSE ONE: http://dx.plos.org/10.1371/journal.pone.0114481. Also, you can download our raw data to examine for yourself at the Open Science Framework: https://osf.io/ts9ea/.

    Bibliography

    New Publication – Microarray analysis of sensitization

    We’ve got a new paper out (25117657) with the first of what we hope will be a series of studies using microarray to track the transcriptional changes following long-term sensitization training. This paper looks at the changes that occur immediately (1 hour) after training. It provides lots of details and data to validate the microarray design we developed, but also identifies a set of 81 transcripts that are strongly regulated after learning. Best of all, for a microarray paper, we use a large sample size (n = 8) and show using a subset of transcripts that most generalize to a completely independent sample. Among the changes we fully validated are up-regulation of a c/ebp-gamma (what the what!?), a glycine transporter, and a subunit of ESCRTII. The rest of the gene list that we’re working on has some exciting possibilities, too.

    Another thing to be proud of, is our three student co-authors on the paper.

    The paper is free for the next 50 days via this link, then it goes behind a paywall for 305 days, then it will be in PubMedCentral for free again (strange, right?). All the raw data is available on the Open Science Framework: https://osf.io/8pgfh/.

    Bibliography