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July 21, 2020
About this episode
Dr. Graciela Pineyro’s love for GPCR pharmacology started in Uruguay where she first worked on the serotonin receptors. This interest in research and pharmacology took Graciela to Canada where she stayed ever since she arrived for her Ph.D. work. Graciela has done extensive work on the molecular pharmacology of opioid receptors, exploring their signaling, trafficking, and their ability to activate different signaling pathways and signaling bias. Today, Graciela and her team’s efforts are directed towards the characterization of the pharmacological properties of cannabinoids in conjunction with terpenes for pain relief.
Dr. Graciela Pineyro on the web
Episode transcript
Dr. Yamina Berchiche 0:01
So Hello, everybody. Today I have with me Dr. Graciela Pineyro, from the University of Montreal. Hi, Graciela.
Dr. Graciela Pineyro 0:08
Hello, hello to everyone. Thank you for having me in the program. Yeah, let's do this.
Dr. Yamina Berchiche 0:16
Thank you so much for being here today. So while I was doing some research, I realized that you were a professor at the Department of physiology and
Dr. Graciela Pineyro 0:26
The Department of pharmacology and physiology of the University of Montreal, that is pharmacology but then they fused together.
Dr. Yamina Berchiche 0:35
Alright, for some reason, I was always sure that you were a professor at the University of Montreal but in the department of biochemistry for some reason.
Dr. Graciela Pineyro 0:44
No, I did my postdoc with Michel Bouvier who is in the department of biochemistry, but I have always been a pharmacologist. Like I have a career in pharmacology from the very beginning. I am MD by, you know, by choice I started. I did my career as an MD in Uruguay. But I always knew what I wanted, when I started as an MD, I started with basic sciences and I was really, really interested in that. The problem is I could never change into research. Because in Uruguay at the time I was learning and doing my degree. There was no Faculty of Science. So the only way we could do research was through trying to get doing some research in medicine. So that is the first time I've had a contact with a research question in pharmacology at the Department of pharmacology, a professor was looking for help in trying to understand how benzodiazepines and alcohol would change the sleep architecture. So that was my first question, and then I stayed working with him not with patients but with rats. And the question was about serotonin and the role of serotonin in against sleep architecture. And there is the first time that GPCRs appeared in my lab, you know, like the 5-HT1A receptor, we were looking at agonists for that receptor and presynaptic and postsynaptic receptor that was a big school in Latin America that came from Salomon Langer and the adrenergic receptors and pre and postsynaptic activities, and then you know, if we followed that big school, and then you know, I started reading papers on serotonin and fed loan papers from someone in Canada who was quite well known for serotonin and depression. And the mechanism of action of antidepressants. So his name is he's deceased now, but he was Claude de Montigny at the Department of Pharmacology at McGill. So one day I wrote to him and I said, Do you want to get in? Give me a try. And I was supposed to come to Canada for two years. And I've been here ever since I did my Ph.D. at McGill Department of pharmacology. And when I finished there, I was supposed to go on a postdoctoral fellowship again, following serotonin receptors with Bryan Roth. So I wrote a grant. I got the NIH grant to go to his lab and we go to a very high placing in the competition. We will both be very happy about that. And then I got diagnosed with depression after studying depression for so long, I got diagnosed with major depression and it really was a big change in my life. And we, my husband and I decided to stay in Canada. I could not follow my postdoctoral fellowship with Bryan, which was, you know, it was a door that closed, but then another one that opened because I went to Michel's lab here in Montreal. I needed to stay in Montreal for health reasons. And I was very, very happy still, you know, I have, I've had a wonderful time there. And this is where my GPCR career I think started Michel's lab was, I think, for me was not so much the GPCR world, but you know, it was the freedom that I had in that lab in order to explore my own questions about GPCRs. So when I got there, I went to Michel's lab because he had a project on serotonin 5-HT2 receptors, but by the time I got to the lab, the project had already been finished. So I was thrust into opioids. And well, I started with a project in opioids that was trying to get binding on detergent to solubilize receptors, which I hated profoundly.
Dr. Yamina Berchiche 5:30
It is an art in itself.
Dr. Graciela Pineyro 5:34
I don't know, it didn't come to me as art. So now it came as torture. I was not very good at doing anything on that project. But you know, in order to deal with anxiety, I was trying to do something as in parallel and that parallel thing became my real project then and which was all to do with you know, like inverse agonism, protein signaling all in the delta-opioid receptor. And also we learned a lot at that time, like, the idea is that we were seeing things very strange that an agonist will turn into an inverse agonist and vice versa. And at the beginning, we were thinking that these were artifacts. So, and
Dr. Yamina Berchiche 6:27
At that time, when you were in Michel's lab and working on the opioid receptors, what was the status of the research what was known about these receptors?
Dr. Graciela Pineyro 6:35
So, of course, they had all been cloned in the 90s. And, you know, we like inverse agonism was first described for the Delta opioid receptor. It was there it was, I think, Tommaso Costa, who published the first paper on that, and then there were the beta two adrenergic receptors that were published by Michel and Dr. Lefkowitz lab. And so I was into the inverse agonism. But by looking at inverse agonists, we found that some drugs were either agonist or inverse agonist, depending on the level of activity of your, of your system. And this is what at that point was being exactly described theoretically by Dr. Kenakin as protean agonists. And that was like the first hint that there were multiple active conformations of the GPCRs. And like this, I was really, really interested in the theoretics of this. There were papers coming out in Trends in pharmacology and sciences by Dr. Kenakin, which at the beginning, I am an MD by training. So you know, it took me years, weeks to understand the profound things that I was reading for the first time and, it seemed to me incredible, you know, like to be able to sort of to have models that could quantify and allow you to predict, and why for my project that was essential because we didn't know what it was, what was happening, and the only idea to be able to measure and predict something will transform your artifact into a real response. So that is why I got so into the in order to salvage my project, let's say, and this has been a major interest in my life, you know, like in my career, I mean, in my scientific life, I have always been from that point on interested in multiple active confirmations, measuring drug effects, the models that we can use for measuring drugs. And you know, finally, my Ph.D. ended with this. Sort of the landmark paper on the beta two adrenergic receptors, where, you know, it was a group effort, and I was not doing the experiments, I was really sort of discussing with Michel the theoretics. And he was very nice to let me sort of have a very, you know, like, supervising also moment in that paper and he gave me the authorship also, it was the PNAS paper, which is a landmark, like an agonism manifest agonism of the beta two adrenergic. And with that, I went to have my own lab back to the opioid receptor. And we have been working on that ever since. The question of interest is, what is it that makes drugs do what they do? We want to know, essentially, drug diversity. And in this diversity, how can we sort of rationalize drug effects in order to get analgesics that will produce analgesia with fewer side effects, like the mainstream it has been Delta opioid agonists that will not produce tolerance. There has been like a lot of movement in that sense and the idea about signaling and trafficking like the has been bought this idea that internalization might be a predictor of tolerance, we did not go in that sense, we think that the predictor is not internalization per se, but what happens to the receptor once it has been internalized. So my lab has been very interested in recycling and we have shown that different patterns of recycling determine either acute or chronic towards the drugs that recycle and all that allow receptor recycling are the ones that produce less tolerance, whether it is acute or chronic. And that is one of the big messages that I think we have been able to put out there. The Delta opioid receptor has always been considered as a non-recycling receptor, which is again, a mountain that we had to go up. But we found that the receptor is sent to the lysosomes, as it has been said, but it recycles from the lysosomes. So that really sorted of, you know, you ask somewhere you asked me, I had an aha moment. You know, like it recycles. It recycles from the place that we think they are being degraded, but they recycle.
Dr. Yamina Berchiche 11:47
But it's the first example of GPCR recycling from the lysosome?
Dr. Graciela Pineyro 11:52
That I know of. Yes, that I know of. It was the first one, you know, but maybe there was something that escaped my mind. But as I say, as I said to you at the beginning, I am not that interested in the first or the biggest or the I am really interested that it allowed us to answer a question that was essential to our research. So, that was very interesting for us. It was a similar molecular Life Sciences paper.
Dr. Yamina Berchiche 12:32
And I think the whole idea is to understand GPCR biology and for example, the delta opioid biology as well in order to be able to target them and have analgesics, which do what they need to do without creating that dependency.
Dr. Graciela Pineyro 12:47
Yeah, I think when I would like to say something before we leave the recycling world, I would really like to thank Mark Von Zastrow because he gave me the tools to do that project for a long time. And he has been an inspiration for that. So, you know, like Mark, really his papers really something that I have looked at, you know, for guidance. I really liked his work and that, you know, he is the one thing that they do not recycle. And we have discussed this and I, you know, I think we came to a nice conclusion that yes, they do recycle from the place where they are degraded. So, that I would like to say, and with respect to the rationality of opioid analgesics, then we moved a little bit from the opioid to the mu because, you know, of all the possibilities about the bias that was sort of coming across. So, whenever we started a project with our new opioid receptor bias in Beta-Arrestin, and inactivation, mostly G protein activation, we looked at a lot of compounds, known standard compounds, and new compounds from Pfizer company. We looked at a number of G proteins versus Beta-Arrestin, recruitment Beta-arrestin-1, Beta-arrestin-2 in presence of different GRKs. And eventually, like this was an inverse aha moment because we get into the terrible conclusion that we've had no bias, you know, like, we look at about 25 pounds, and we've had a very nice sort of dichotomy, where we have Beta-Arrestin, not being recruited, and a big sort of signal for G alpha or for Q or for cyclic MP, but those were all partial agonists. Like we never saw real evidence of bias. We saw evidence of partial agonism. And that is what our Nature Communications paper is about. We tried to look for bias and classify our drugs, new and standard drugs, according to a bias signature, but there was no bias signature, what we could do was classify our drugs, according to the relative efficacies, and from those relative efficacies. We were able to predict secondary effects in the clinic.
Dr. Yamina Berchiche 15:43
But there were recent reports that came out recently that said that having a bias in the context of the opioid receptors was not necessarily the answer to finding the perfect analgesics. So at some point, There were all these papers coming out that said, you have unique beta-arrestin and I think if I remember correctly, but you don't need G protein or vice versa. But recently it has been shown that it's not that clear. What's the status of the research on that?
Dr. Graciela Pineyro 16:14
So from like, you know, at the time that our paper came out, this was exactly what was discussed, appeared in the discussion. We do not seem to have bias drives, we seem to have a partial agonist that will produce a side effect, like respiratory depression or constipation that is proportional to the efficacy to activate that receptor. And like this is, again what the new papers that came out, were showing exactly as you know like it is a question of partial agonism. The drugs that produce more recruitment of beta-arrestin are the most effective they produce the Beta-Arrestin recruitment and side effects, the ones that are partial agonist, they produce less recruitment of Beta-Arrestin and fewer side effects because they are partial agonist, then what happened is a very sort of landmark or sobering moment for the bias movement in opioids, it was shown that you know, all the idea of the benefits of avoiding Beta-Arrestin recruitment and to avoid side effects is based on the idea that if you knock out Beta-Arrestin in mice, you will have less respiratory depression and less constipation. Now, there was a new paper that came out from groups in Australia, in Germany, and in the UK, that was clearly showing that in another model of knockout mice, there is no beta-arrestin but the respiratory depression is still there oh, you know, like, it is a sobering moment we have obviously, we have to come back to the drawing board and acknowledge that there is nothing as simple as black and white - Beta-Arrestin and the protein. So, that is something that we need to go back and try to start thinking about again because it is something that we learned the hard way you know, like, but that I think should not discourage the people from thinking about bias. Bias is something that is a pharmacological phenomenon, it is of interest and which is its basis that it is the multiplicity of active conformation. That is what I think we should be sort of concentrating on and trying to concentrate on activating some forms or confirmations of the receptor and not others, the bias is the consequence. We have to go and look at biology or at the underlying biology.
Dr. Yamina Berchiche 19:15
Definitely. So you had mentioned a while in Michel's lab, you were working on the opioid receptors, and you were seeing agonist versus inverse agonist effects depending on the system how it was tuned. Can you elaborate a little bit more? Was it dependent on the expression of the receptor or coexpression with other signaling proteins?
Dr. Graciela Pineyro 19:37
So what the way we thought we're rationalizing it is if you had more activity in your receptor, you would be able to see, drugs behave as inverse agonists. If your system was not activated, those drugs could sort of becoming partial agonists. So one thing that was really sort of changing the level of the receptor was the level of receptor expression. The more you have receptors, the more you can see the constitutive activity. So that was what we were sort of rationalizing the problem was sort of, I didn't want to work with constitutively active receptors.
So I wanted to stay a little bit more in the rear receptor. So we didn't go to measure that. And we sort of challenged our system by desensitizing and reducing activities to desensitization or producing an exposing to inverse agonist and increasing the amount of receptor so we work pharmacologically in order to change the level of receptor expression and activity. And this is what we came to the conclusion that you know, the more active that is, the more that are active receptors in the system, the more the inverse agonism will be evident and vice versa. If you desensitize your system, something that was an inverse agonist can become a partial agonist.
Dr. Yamina Berchiche 21:18
It was all about tuning the system.
Dr. Graciela Pineyro 21:20
Yeah. But more than that, what I and you know, another aha moment that because I loved your voice, you know what I really thought about them. When, at that point, the Director of the Department of pharmacology at the University of Montreal was Dr. Andre De Lean. And she had come up with a program where you could sort of predictor you could model like the level of active conformation, nonactive conformation, like a coupled or uncoupled active receptors. So that allowed me sort of to play a lot with my data and understand and come up with that that rationalization It was really is something that was for me a changing experience because it's sort of directed me to more the quantitative world and a little bit more into trying to predict a reality from the models that we saw the work in vitro. That seems so unreal.
Dr. Yamina Berchiche 22:29
Definitely. I mean, I remember one of his papers from the Lefkowitz lab that was the De Lean 1980 paper that I had went through as well. What I also wanted to ask you is so a lot of us do these experiments and these assays in HEK cells. What do you think about working in HEK cells and what do you think about, you know, the information that we gain about these receptors in this quote-unquote, artificial system
Dr. Graciela Pineyro 23:00
Well, you know, it depends, I think we get very good information from those because I say, you know, we were able to imagine the simplicity of a system that expresses that receptor, looks at how the receptor changes one conformation change, you know between two proteins beta, beta, gamma, and G alpha or recruitment of the beta-arrestin to the receptor. You look at this simplicity, and you are able to predict what has been sort of reported to a dirty database like the FDA pharmacovigilance database. So, if you can predict something like that with a system that simple, you have a very, very good system. We should not sort of crash clashes, we really need to acknowledge the value of what we have. That being said. We have also to think about how can we make it better. And obviously, you know, like, we now have tools that we can sort of go and try to produce the real somatic cells that are the target for it in which the GPCRs are being expressed. So obviously, we have to go in that sense, because I think the better the model, the better it would be the prediction. But, but at the same time, we have to bear in mind that using stem cells, transforming them into somatic sense, putting a biosensor in there in order to monitor drug, it's a very complicated process. It's a very expensive process. So we really have to be able to measure the cost-benefit, how much more information and we're going to get from this somatic cells from IPSCs as compared to what we already have with the HEK cells, that is one question, we also have to keep in mind we are trying to do this. So, you know I am very and it has to be a rational effort I think. But another thing is we have to keep in mind that the cells that we obtained through differentiation of IPSCs are not the adult cells that will be the target So, we have to remember that yes, we are getting nearer, but it's not the same. So, you know, we cannot always we will always be running for the perfect model. We always have to assume that we have a model that we do not have real and that is something to bear in mind. When we go into these stem cells because they will be a model this is what I am saying. We have a good model and very efficient model in HEK cells, we can sort of go better and predictive, but they will not be the reality they will still be a model, this is something that I think we need to keep in mind. And there is another thing is so how do can we get these cells somatic cells that we get from iPSCs cells, they are immature. So the question is, can how can we get information about mature cells that are like, what happens in the body of a patient with the mature cells that are living within a pathological environment? And I think that the way to look at that is all that has to do with the drop seq movement. You know, like the single-cell phenotyping that is something that I think is we'll be growing in in the future. But for us for the GPCR people, the idea of being able to look at what are the receptors and the effectors that are in the cell of interest in the pathology of interest, that is a new Pandora's box, but it's also a treasure trove. So I think that is what will be keeping me interested. Now, you know, for the time being,
Dr. Yamina Berchiche 27:29
yeah, definitely, definitely. It's hard because a lot of people say, Well, you know, we shouldn't be working in HEK cells, and we should be looking at receptor function in a more relevant system. But at the end of the day, as you had mentioned, it's difficult to look at a more relevant system and it's expensive, and HEK cells are so convenient at the same time, but we still need to keep in mind that you know, CRISPR hang out a G protein cells adapt, and we need to keep that in mind.
Dr. Graciela Pineyro 27:56
What I think is like everything, you know, it's a little bit of this and a little bit of that, that makes the perfect recipe. It's not one or the other. But a perfect recipe has to have in also into account take into account the costs, because you know, not because you throw everything at your recipe that is expensive. It's going to make it good. So yeah,
Dr. Yamina Berchiche 28:19
agreed, agreed. Oh, in the context of analgesics. What would the best analgesic look like? Would it be an allosteric molecule or would it be an agonist for that preferentially activates some pathways and not others? What are your thoughts on that?
Dr. Graciela Pineyro 28:40
Of course, you know, I can tell you what everyone says that an allosteric modulator would be the best. What I would also try to my message would be like I would like really to understand the endogenous ligands you know, the endogenous ligands are the endogenous analgesics. So I think trying to understand what makes them so special is something that will take us maybe a little bit further, we can like we cannot do drugs with the endogenous opiates because they are peptides and but you know, learning what makes them so special what type of signaling I think that for me is where we should be looking for the answer or have good energy think like that is also something of interest for our lab. Now we are trying to move a little bit away from opioids to cannabinoids because these also are analgesics and that is the big difference because we are trying to go to cannabinoids not as like the molecule of THC or CBD. We are really interested in the cannabinoid hypothesis, as the entourage hypothesis if you take cannabinoids as this complex pharmacological stimulus that combines many cannabinoids terpenes, you know, can you get an analgesic response is the analgesic response that you get from this mixture different from or more effective than an analgesic response that you get from a pure drug, which is the basic basis of the entourage hypothesis. This hypothesis has been a sort of theoretical until now. But the question is, can we sort of use pharmacological quantitative methods in order to answer the question? Is there a benefit of combining like cannabinoids terpenes in order to produce more analgesics is like, how does this work? If it's better, how does it work? What is the mechanism is there allosteric is how is it that they work. And one thing that is complex about the cannabinoids and much different as compared to the opiates is the complexity of the stimulus and then the complexity of the targets because you will have the GPCRs but you also have the TRPs. You have also the enzymes in the endogenous endocannabinoid system. So I think that that is another thing that analgesia is a complicated thing. And the more you are treated with complicated solutions, might be sort of finding better answers you know, like, the perfect drug for the perfect. Nothing is clean, like for me, so this sort of complexity in cannabinoids I think it's something that it's a challenge is an incredible challenge. intellectual quantitative but We want to sort of to try to address the question and try to see is an analgesia better with this type of drug. And not only that, like, there is always this idea of opiates THC, they are addictive drugs. So you know like, for me, it is true that is addictive drugs but THC is more than an addictive drug. It is a drug that is taken by teenagers. This may change the plasticity of the brain in the long run. And we know very little of how it might affect cognitive functions. Not only you know addiction is certainly but this is the only thing that is what is being taken most care of by the law. The law is saying you do not have to have THC because it is addictive, we really do not know what the THC the CBD is doing to the cognitive functions. So that is something of interest also not only analgesia but also the effect of cannabinoids on cognitive functions. We have been looking at specific effects of cannabinoids there, but that might be something for another time.
Dr. Yamina Berchiche 33:24
Definitely. So you had mentioned you're working on so many projects and you had mentioned in a previous conversation that you were building a tool, that bioinformatics tool that you were hoping to share. Can you tell us a little bit more about it?
Dr. Graciela Pineyro 33:39
Okay, like, the idea is when we did our opioid project, we were using a lot of GraphPad. And the thing with GraphPad is to analyze one curve at a time. So what we try this take, there is nothing sort of new different from what is out there. At this stage. It's like just automatizing so that you can press a button and get all your curves analyzed at the same time. At the same time, what we want to do once you have analyzed your curves, can you sort of recover your parameters and be able to do sort of simple graphs that will allow you to compare parameters in a graphical way, in a quick way, you know, because now, we are generating an enormous amount of data and how do we systematize that data in order to make it clear, what is it that we should be focusing on and we found that the radial plots are very useful in visualizing differences and similarities. So in our first step, what we are going to try to put together like this is not ready yet. I don't know whether you should have brought it up but trying to do is just automatize the analysis and the graphical representation. Also, when we were sort of analyzing the data for the opiate, new opioid and predictive analysis, we got a lot of insight from the reviewers that like, I don't know, whether you are aware there is this sort of conversation going on, on how bias should be analyzed, but what is it and essentially how you should consider like affinities, functional affinities, which is the way that Dr. Kenakin and Dr. Christopoulos use the operational model. And then there is another group of scientists like Dr. Costa. Yeah, sort of an ongoing on around that they were sort of saying no, do not let functional affinity be free and, you know, use binding affinity. We, we were caught into that discussion during the analysis of our paper and we had our reviewer that was very insightful and said, you know, maybe one thing that could be done is not a free-floating Kd, not Ka, not an actual strong Kd maybe you can do a shared Ka for all the biosensors at the same time. So we are trying to put this model into place. We are sort of trying to take the suggestion of one of the reviewers in the paper. We are trying to get it a little bit further. So we are trying some new ways of fitting curves in order to assess bias and get information about tau that has less error that what we have now, I just don't want to get into those. Yes. Right. And people are waiting, you know.
Dr. Yamina Berchiche 37:15
was definitely I mean, it's important to have a tool to automate when you acquire so many dose-response curves and you need to plot them one by one, having this ability to automate analysis helps speed up and reduces man-hour and you get more time to think about what the data means actually than having to do copy and paste operation.
Dr. Graciela Pineyro 37:40
Also, what we want to try to do is to take a little bit further that so that people sort of, you know, sort of make our paper on predictive analysis a little bit more user friendly and provide a tune that once you have your parameters, you can also try to see okay, if you have something to predict. Could you be able to use your parameters to predict it, but that would be a second stage you know like the first stage is the automatization and then is it possible that we can help people so give them something to do some predictive analysis but that now is a little bit like I need to get my lab working again after COVID.
Dr. Yamina Berchiche 38:28
Definitely, definitely. So you had mentioned at the beginning that you had different GPCR loves and you started out in Uruguay with the serotonin receptors, then moved to Canada and then you worked again on the serotonin receptors. Then in Michel's lab, you had the beta-adrenergic receptors, the delta, and you opioid receptors then in your lab, and now you're switching to the cannabinoid receptors. Any other GPCR loves or GPCR or pro D protein favorites. I remember at some point you were working heavily on the cure channels.
Dr. Graciela Pineyro 39:05
Yeah, well, we have like as an effector for GPCRs we have looked at that we are looking into you know, like cannabinoids have a node of channel effectors by themselves. So one question of interest would be to look, you know as cannabinoids work on TRPs. They have effects on calcium channels. One of the questions do they have an effect on KIR directly that would be a question of interest, but at this time we are just sort of thinking about it, you know, that we are still trying to systematize cannabinoid signatures in terms of CB1, CB2, and TRPs.
Dr. Yamina Berchiche 39:56
Okay, so what do you think? Since you're trying to systematize and you're obviously going to acquire a lot of data, what do you think about using machine learning to accelerate drugs? GPCR related drug discovery?
Dr. Graciela Pineyro 40:08
Okay, so when one of the tools that we used for looking at the opioid receptor was machine learning type of clustering analysis, I think that is we want to try, we want to continue in that sense. What we did is we use this machine learning approach in the order it applied on to the parameters. We want to go a little bit further and explore without any model just going on the data by itself. And suddenly looking at points in the space will take a lot of machine learning approaches, let's say but that is something we are looking into. Yes.
Dr. Yamina Berchiche 40:59
All right. Thank you. So much what is your advice for younger Junior scientists who want to get to work in the field?
Dr. Graciela Pineyro 41:08
I told you already that I don't know why you chose me because I do not think I have a career. Like so what I do have is I was interested in questions and I followed my questions. I was not building a career, I was simply following my interest. So what I would say to the young people follow your interest because it worked for me, then that's it I and but honestly, I think it's essential because you know, like, it is difficult out there. It's not an easy one, there is not that much money and you have to be competitive. So the best thing is that you like what you do when you're interested in what you do in order to get the strength in order to go through all the hoops that you would have to.
Dr. Yamina Berchiche 42:02
Great, great. So we also talked a little bit about your aha moments. I, you had mentioned that you really liked that question. Just to recap, on the aha moments, you had said that the inverse agonist and agonist responses of the opioid receptors in Michel's lab was, depending on how the system was tuned, was one aha moment. Can you remind us what are the other aha moments you had?
Dr. Graciela Pineyro 42:29
Yeah, like I said, like in me, for me, the aha moment was working with Dr. DeLean's program. That was the aha moment is like the ability to be able to take data, analyze it, and predict the response from your model. That was the aha moment. Like for me, it was something that turned my turn something that was an artifact into a response, a pharmacological response. For me, that was the life-changing moment as a scientist, because it gave me the love he wants. It gave me the vivid experience of how wonderful it is to do science in this way quantitative, that you can sort of transform, something that doesn't come value into something that has value. So this for me is the aha moment. Then, as I said, the other aha moment, which is the inverse organism was that we were looking for biased ligands in using the new opiate receptor as signaling as a readout. And we looked at G protein versus Beta-Arrestin. And we had an enormous amount of ligands that we thought they were biased, but in the end, they were simply partial agonists. So we realize that that time that we might not be having bias anywhere. So what we are looking at as something that is a beneficial response is simply that the analgesics that produce fewer side effects, they do so because they are partial agonists. And they will also be partial in producing the analgesic response. So, you know, it was a sort of down moment in that sense, but at the same time, you know, you have, we were, what we were very happy about, is that our analysis told us that reality that is the reality we were able to see it. It was sad for the bias movement but you know, it's something for us it was good but we were very happy about the analysis that we had come up with. So it was high in one sense, but you know, the bias when a little bit tough, and do I have any other aha moment? I think that you know, like, it's not an aha moment, but it's a realization with the stem cell, the use of stem cells for drug discovery and for GPCRs and it is a complicated system. It is probably going to be a very predictive system, but we really have to be cautious about costs and how we use them. And they might not be the only answer because the cells that we get are not essentially like the cells we are targeting and that we will need to complement that. That is my little neighbor, right. Okay. So he's getting frustrated but we should complement the IPSCs with data from patients and from mature cells. And I think we will have a better picture. I don't know whether it became clear.
Dr. Yamina Berchiche 46:09
It did. Thank you. So to sum up, as the young Junior scientists follow the question, follow the science, jump through the hoops and the aha moments will come, and with that will come also a better understanding, of biology and GPCR biology.
Dr. Graciela Pineyro 46:26
Yeah, do you know? Exactly. And everything falls into place...
Dr. Yamina Berchiche 46:36
If you have job openings in your lab where can people find you? You know, usually, people contact me from reading the papers. I have not to sort of all from the
Dr. Graciela Pineyro 46:51
Bouche an Oreille. I don't know like, what were
Dr. Yamina Berchiche 46:54
The word of mouth?
Dr. Graciela Pineyro 46:55
Yeah. So honestly, I do not put up advertising, but I will read every single people that send me emails, I always respond. So you know, I can I, that's the way I recruited people now, we have to get back on our feet. So, advertising, we are not advertising for that. But I consider every single application that I get. So that's the way my lab works.
Dr. Yamina Berchiche 47:24
It's fantastic. So on the page, where you'll find when the when where the podcast transcript will be, there will be a page where people will see your website and they can contact you directly
Dr. Graciela Pineyro 47:37
by email.
Dr. Yamina Berchiche 47:38
that is the best. Yes. And they will also have access to your publication so they can read the papers, and then send you an email and then see what you think. Okay, great, fantastic. One last question before I let you go if we weren't in the situation that we are in now, and you had to recommend a top two or three conferences for people to go to
What would those be?
Dr. Graciela Pineyro 48:02
I certainly the GPCR Retreat like I, every student in my lab goes there, whether they have read science or not, because I think this is like essential for them, they get the very good science at very good level, sort of interacting directly with the people that produce this science. So that would be my number first choice. And the Gordon conferences for me are always great. And then the ones in California and I'm sorry, the other side. One, what what happened
Dr. Yamina Berchiche 48:38
In Italy Lucca?
Dr. Graciela Pineyro 48:41
Yeah, that's it. Yes. Oh, those are the ones that but essentially the GPCR and I also recommend people I go to neuroscience when I can, because a big and a small conference is all that it takes at the same time. I do not know, we have to become much more conscious about traveling not only because of the COVID but because of what is happening with climate change. And I think that the COVID experience tells us that we can learn a lot from whatever is on side and all the incredible lectures and conferences that are available through the web and the the initiatives of doing presentations through zoom for conferences is something that will be more and more present and we should take advantage of that and not go and fly to conferences all over the place. Agreed.
Dr. Yamina Berchiche 49:41
Thank you so much for Graciela
Dr. Graciela Pineyro 49:44
Okay. Thank you. Bye-bye. Bye-bye. Thank you for having me bye-bye
* The transcript has been minimally edited to improve readability
