Deciphering the Insect Olfactory Code | Dr. Thomas M. Dykstra | Part 3 of 4

All right. Hello and welcome to another AEA seminar. This one is actually going to be part three of a four part series. Hopefully many of you have viewed the first two already. This will give you a background as to what I plan on doing for part three. And we'll also give you a great window into part four. Part four will be live, that is right now planned to be August 30th, and this will be the third which will introduce. It will tie in some of the what the first two presentations did and then we'll be able to give you enough background in order to follow along with four in a couple of weeks. The title of this talk is towards the Deciphering of the Insect Olfactory Code. My name is Dr. Thomas Dykstra. I will be presenting on this topic today. All right, just a quick review before I go into the main topic today. I just wanted you to be aware there are two YouTube videos that are already out already. Presentation one, which is the first of the four part series, was problems with the current theory of insect olfaction. And I felt that was necessary in order to present as such, because oftentimes when I am presenting, people will ask me why I'm presenting a new theory, what's wrong with the old one. And so that became more imperative for me in order to cover that. So that's what occurred on presentation one. So if you, you are having those questions and you haven't seen that first presentation, I would direct you to that. On presentation two, I needed to introduce the new theory of insect olfaction that I have put forward for your information. And I realize that I've mentioned it already on the other presentations, but in 2016, we felt we had sufficiently uncovered and decipher the insect olfactory code. And so it has been a journey of these past six years for me to understand how insects smell a little bit better. And during presentation two, what I needed to do was I needed to introduce the evidence that is out there so that you can understand if there's a new theory. How does this work? You need to understand the biophysics and whether or not something like that is possible. Because for those of you who remember, in presentation one, I made the point that, that this was not an unlikely theory, it was actually an impossible theory. So if I left you with an impossible theory, I needed to be able to express to you a theory that was possible. And that's why I chose to talk a little bit about the biophysics in presentation two. So in presentation three, this is now towards the deciphering of the insect olfactory code. And it is this that I want to focus on today. And so, as I alluded to before, there are odorants in the air. They are picked up by the antenna of the insect. And even though there is an antenna nerve on the insect, and the antenna nerve does pass to the main brain, specifically the dudal cerebrum, and specifically the antenna lobe, which is off to the right, right there. And they specifically go to glomeruli, based upon what type of sensilla they are. All of that is relatively worked out. I will not be discussing that today specifically. Instead, I'm going to be focusing not so much on the cns, the central nervous system. I'm going to be focusing on the pns, the peripheral nervous system, and how this is initially detected by the insect once it is detected. Everything is very similar to what is being discussed in the literature, with just a few exceptions. I've just got this diagram right here to show you a little bit of how insects do smell. And then I've got one of my spectrums. Now, we've done over 10,000 of these spectrums, and this just happens to be one of them up there, just to kind of give you an idea. And I'll talk more about these during the presentation. All right, I left you hanging on presentation number two. I was talking about pheromone detection from a distance. I had mentioned that the pheromone was being detected from a distance. It is a very small distance, probably less than 3 millimeters and probably even less than 1 millimeter, but it's still from a distance. And the pheromone is going to be giving off frequencies. And is these frequencies which I proposed are being picked up by the antenna. And I talked about a little bit of the possible biophysics. And two, so we have a pheromone. It is a certain distance away. I'll keep it a large distance right now for the purpose of this slide. And we're looking for resonance. This is really the key. I alluded to this in the second presentation. Do we have resonance? Because if an odorant such as a pheromone, is going to be detected, it is going to be absorbing and giving off frequencies. All that is fine and good, but the insect needs to be attuned to these specific frequencies associated with the odorant, whether it be a pheromone or whether it be a general odorant, it doesn't really matter too much. And so because of this, we need resonance. If we have resonance, then we have evidence for the new Theory which I am proposing. If we do not have resonance, then we do not have evidence for the new theory that I am proposing. So therefore there need to be frequencies that are mediating between the odorants and the antenna. And that will be. And I don't want to focus on that evidence and the resonant effect during this presentation. So this is a familiar slide for those of you who has seen 1 and 2. This is a cross section of a sensilla and insects in silla, and they have pheromone molecules around it. And they are going to be detected by the sensilla. But it's not the final detector, the sensilla, the part that is actually sticking up, but the little spaghetti strands inside of it, these are the dendritic extensions, are not the final detectors. It's actually going to be a membrane protein receptor. These membrane protein receptors are found on the dendritic membrane, these spaghetti type things that look, that look like on your screen. And these membrane proteins are found all along this particular dendritic membrane. And these are considered to be the final detectors of the particular odorant, again, whatever it may be. And so these membrane receptors need to have a type of resonance with the odorant, otherwise we are not able to successfully proceed. And so we have a pheromone, it needs to be detected by these orange membrane receptors that I have right there. And this needs to occur, and it needs to occur quickly and efficiently. And if so, what is the evidence for it? Well, in order to find the evidence for it, we're going to need, with resonance, you're going to need for peaks to line up. If you understand how resonance works, peaks do need to line up in order for resonance to occur. Let's just say in my imaginary graph right here that the pheromone molecule is the red line and they have a number of peaks. Let's just say that the absorption frequencies of the membrane protein receptors are the blue line. You can see that they line up and they line up very well with one another. They may not have exactly the same amplitude as one another, but you can see that they line up. And therefore we have what is known as a resident effect between the odorant and the membrane receptors. But it's never that way in a perfect world. Unfortunately, these are perfectly overlapping peaks. I set them up perfectly on purpose because I wanted you to see, you know, what true resonance is all about. But in truth, and with the insects, we do not have perfect resonance. Instead, we often have offsetting or overlapping peaks that are offset with one another. So even though you can see that the red line and the green line and the blue line all have peaks that are similar and that they even are resonant to one another, they. They are offset and they don't line up perfectly. This is kind of important for you to know because when calculations are made, we need to take into account the offset overlapping peaks, which exist more often than not. So if I have a given peak amplitude, the. The top of the pink amplitude, let's just say I'll give it an arbitrary figure of 10, is the amplitude of the peak at its highest point. But if it wasn't at the highest point, and it was, let's say, halfway up, it might only have an amplitude of 5. And if it was even a little bit lower, it might only have an amplitude of 1. And if the peak did not coincide with this imaginary peak that I have on the screen right now, I would of course, give it a zero. So we need to have overlapping peaks, but we are able to grade this stuff rather efficiently so that we can get some reliable numbers. So this is not going to be a digital all or none response. This is not about zeros and ones. Whether or not it's within the peak or it's not within the peak, it's going to be more analog and it will be a graded response. And a lot of these slides that I'm going to be giving to you are going to reflect this graded response. So for any of you who are suffering from insomnia and you wish to read 37 pages of pure boredom, you may consult how determining odor detection and arthropods is done. And hopefully this will cure your insomnia. But the information is there if you want to take a look at that at some later date. So if we were going to begin to take a look at resonance, where do you begin? Well, I think that the easiest thing for insects is going to be carbon dioxide. It is a known odorant. It is known to be detected by a large number of insects. And so because of that, it would be, I think, a good place to start because of the simplicity of the molecule. Simplicity, because it only contains two atoms. It's CO2. There are two oxygen atoms designated in red. The carbon atom is designated in black. And it's a linear molecule. If it were bent like water, which is more of a V shape, this is going to give you a lot more complex peaks, a lot more complex frequencies, and it's just a more complex molecule with CO2, we don't have to deal with that complexity and so it just seems that logically that would be a good place for me to start. So what are the peaks associated with CO2? Lao was able to locate about 22 of them. These 22 peaks are peaks that you would find. You can find them on the Internet, you can find them on ftir. I grabbed any peak that I could find that was associated with CO2. It doesn't matter whether it's a CO2 laser. It doesn't matter whether they're trying to detect CO2 and some planetary moon. All, all associated peaks with CO2 I have collected and put forward. Why did I do this? Because I'm not sure what the insects are going to be able to smell, but I just want to be able to have this all out so I can use this as a starting point in order to determine specifically CO2 reception in insects. So how does this process work? Let's start with an example. So I'm going to start with Lygus hesperus, which is the western tarnished plant bug. I think in the past I often tend to talk about more eastern insects. So why don't you people on the west coast to know I haven't forgotten about you. So we'll just do the western tarnished plant bug. Right now. It's a big pest out west and it's attacking many plants and it has certain characteristics and we know a lot about it. Well, one of the things we know about it is that it contains SNMPs, which are sensory neuron membrane proteins. These sensory neural membrane proteins were determined at one time to be the membrane proteins in question. These were thought to be the actual receptors for odorants. We have since determined that they are not. But originally they have different purposes. But originally they were the the original membrane receptors. So I wanted to focus on them first as kind of like a nod to a little bit of history. So There is a SNMP2B that happens to be the particular name of this particular membrane protein for Ligus hesperus. It's a rather large molecule. It's got 597amino acids there. They all are, if you want to count them. And each one of those letters corresponds to an individual amino acid. Many of you have probably seen this amino acid code before. And what I'm able to do is I'm able to take this amino acid code, which is often in a FASTA file on such sites as the NCBI website, among others, and insert them into the software which I use. There's lots of software out there to take a look at absorption frequencies and proteins. I happen to use RRM software for my analysis. So this is analysis of SNMP2B and Ligus hesperus. It includes the top 20 peaks. I have my RRM software set up for 20 peaks and here they are located in order. There are two things that I really like to focus on when I get these first 20 peaks. The first column that I like is the signal to noise ratio, which is for my purposes synonymous to amplitude. And then I also have the frequency in question which is over here. So and they give you a range for frequency. So the middle of the frequency I just treat as the middle of the, of the highest point of the amplitude and therefore everything on the side of it is going to be graded. So that's the range, for example, the first one in 1779-1833 nanometers and it has an amplitude of 79 point. And here is a readout. This is the RRM software which is now displaying what you just saw. So the 20 peaks that you just saw and a little bit more. This spectrum lists all of the pertinent peaks associated with the SNMP2B of Lycus hesperus. As you can see there, there are a lot of frequencies. We get this all the time. Some of them are relevant, some of them are not. It's good for us to know which ones are relevant and which ones may not be important, at least for our. And so as I told you, I want to analyze CO2, and if I'm going to analyze CO2, I want to be able to know what the CO2 frequencies are. I've got plenty of frequencies here for SNMP2. And if I want take a look at the peaks that are associated with carbon dioxide on the top, I've put these blue lines which correspond more or less to the CO2 frequencies that I had just mentioned on the previous slide. And they're all up there. And you could see that there seems to be quite a lot of overlap between the CO2 peaks and the SNMP2 peaks as well. But that's not exactly true. There's really only one peak that lines up. All the rest of them do not line up perfectly or at least as well as they should. And so there's only one that I can scientifically count and that is that relatively small peak or a medium sized peak down there. And because it is a graded response, I then go ahead and grade the SNMP2B on A, on a scale as far as carbon dioxide, I want to Measure the amplitude of the peak and how well it corresponds to the CO2 peak above it. And this then gives me a number where I sum all of the peaks together, and they all together end up to be 1.8, which is a very, very low number for a sum of intensities. And that's partially because there's only one peak. So my conclusion is that SNMP2B is not a carbon dioxide receptor. It's an easy conclusion to make. We've got 22 frequencies, it's only got one, and it's not even a major. It's not even a major peak. And so, to cut to the chase, to save a little bit of time, we've analyzed almost all of the SNMPs for almost all of the insects. And virtually all of the SNMPs do not have a very strong CO2 response. So this is quite common among the SNMPs. They have different purposes. And so the SNMPs are not known to be carbon dioxide receptors. And I can certainly verify that upon early analysis. But there's obviously more to it than snmp. So what about an insect that we know has carbon dioxide reception, such as the mosquito? And not just knowing that it has carbon dioxide reception, but even knowing which receptors are carbon dioxide receptors is also good to know. And there are lots of studies out there to analyze this. And so this probably is a good place to take a look at it. Since the SNMP turns out to be a dead end, let's go ahead and take a look at bonafide carbon dioxide receptors in Aedes aegypti, which is the yellow fever mosquito. So we know CO2 detection in Aedes aegypti is mediated by three receptors. They are conveniently named GR1, GR2, and GR3. GR simply refers to gustatory receptor. Many of the CO2 receptors are found in the gustatory receptor group that is a type of GR. And so to simply label them GR1, GR2 and GR3 is easy to remember, but there's a little bit more to it than that. Even though they are found on the maxillary pulps of the yellow fever mosquito, which is kind of different because some insects will have receptors on the antenna, as the mosquito does, but the mosquito also has even additional ones, specifically the CO2 receptors on the mouth parts. But these GRs 1, 2, and 3 are located in the same syncyllium, and they're also located on the same dendrite. Well, this is important because if you're dealing with an electromagnetic theory, you can now use these as a composite. So instead of using the point effect, whereas if you had a lock and key mechanism, you would have to have a CO2 molecule binding with GR1 and then a CO2 molecule binding with GR2 and then a CO2 molecule binding With GR3. But you wouldn't have to do that if detection was occurring from a distance, because all three would light up simultaneously, as I outlined during the second presentation, some of you may remember. And so there's also a composite analysis that I can do. In other words, how does GR1 work with GR2 and how do they work with GR3? This is also a question which can be looked at when you're dealing dealing with an electromagnetic theory. So if I was going to take a look at them there, I can do an individual analysis and I can also do a composite analysis first. What I'm going to do is I'm going to do an individual analysis and I want to start with, with gr2 because it's similar to some of the gr20s which I, I'm also interested in too, and I won't be discussing. And so here is one of the the grs and I've got not as many peaks as I had with the SNMPs, but there's certainly a few to work with. So now I want to be able to correlate them, if possible, with the CO2 peaks. So first, the largest peak on the graph, does that correspond to a CO2 frequency? It does. The second highest peak on the graph, does that correspond to a CO2 frequency? It does. The third highest peak on this graph, does that correspond to a CO2 frequency? It does. The fourth highest on the graph, does that one correspond to a CO2? That one does too. So we've got an interesting development right now, where the top four peaks on GR2 for 80s aegypti all correspond to the CO2 frequencies that I had mentioned before. So this is interesting, but the story doesn't end there because this peak also corresponds to CO2 as well. And so now my top six peaks on the graph of them, five of those six top peaks correspond to CO2. So the evidence is starting to grow, becomes a little more insignificant now because that peak corresponds to CO2 and there's a tiny peak over here too, which also corresponds to CO2 as well. So this is really interesting because now we have seven peaks of interest, seven peaks of interest, some of them huge, the biggest peaks in the graph, others of them little on the small side, but seven peaks of interest that correspond with CO2. Now if I were to analyze them, and as I mentioned before, it's to take a look at the amplitude with a graded response to see how well they line up. What are the sum of intensities for this particular GR for Aedes aegypti? Well, the sum of intensities is 136.9, obviously significantly higher than 1.8. This kind of gives you an idea of, of how much easier it is in order to identify some odorants more than others. So that one is very, very high. But there are two others to take a look at. GR1 is yet another one. In this particular one, that one is a CO2 peak. This second, smaller one is corresponding to a CO2 peak. So does this third one. And finally the fourth one over here, this one only has four. Four peaks correspond to CO2, and they're not major peaks either. But this is the analysis of GR1. And so if we take a look at the sum of intensities for GR1, we can see that it comes in at a modest 22.2, nowhere near the GR2, but still certainly higher than many of the SNMPs that I've had a chance to look at. Now we move on to GR3. If we take a look at GR3, we're dealing with a different graph again. We've now got a major peak off to the right. Does that one correspond to a CO2? Indeed it does. This one also corresponds to a CO2. This smaller peak also corresponds to a CO2 peak. And this small one over here also corresponds. So this one also has four peaks that correspond to CO2. The other ones do not and were therefore not included. And then go take a look at the various amplitudes using a graded response in order to determine the sum of intensities for GR3, and it comes up to 83.0. So it's certainly a hefty amount. Maybe not as high as 136, but it's still up there. So if I take the average of Gr1, Gr2 and Gr3, the sum of intensities averages to 80.7. That seems high, but it would be good to know, you know, what kind of controls do we have? Is this happening all of the time? Do the other GRs also exhibit these, these capabilities? And so There are other GRs to look at in Aedes aegypti, and there's a fair number of them too. And so if I take a look at all the remaining GRs of Aedes aegypti, and there are 30 of them, the sum of intensities average to 31.1. So clearly GR1, GR2 and GR3 have an edge over the remaining GRs. They do seem to be more attuned to CO2 than the other remaining GRs. And we seem to have some evidence for residents right now, at least as far as Aedes aegypti and the CO2 receptors are concerned. But remember what I said before, this is not just about an individual analysis. I do have the RRM software, which is enabled to give a composite. So I can take the GR1 spectrum, which has four peaks, the GR2 spectrum, which has seven peaks, and the GR3 spectrum, which has four peaks in common with CO2. I can combine them into a separate spectrum. And what this spectrum does is it amplifies all of the common frequencies and de emphasizes, if not erases, all of the peaks that are not common among the three. And so this combined spectrum is also important for me to know, because I want to know, as I mentioned before, how do they work together? So let's zoom in on this combined spectrum. And here's the combined spectrum. Again, this is not an individual spectrum. It's combined. And so we've got these new peaks that have appeared, which through computer analysis are telling me are the common peaks among these three. So now I want to go ahead and reanalyze this combined composite spectrum. So does do these peaks correspond to CO2? As it turns out, the two major peaks do correspond to CO2, which means they have some element of commonality associated with them. And so this is big because it's the two biggest peaks. But that's not the only thing. We've also got five other peaks. They're not huge, but there are five other peaks. So I've got a total of seven peaks in a composite spectrum which also associate with CO2. So this is also good to know, and it's also critical as well. The sum of intensities for this composite spectrum is 147.6. So I'm interested in the individual spectrums and I'm also interested in the composite. And I want to be able to add the sum of intensities in order to give me some idea of how well aedes aegypti smells CO2. And so if I combine them, I come up with a number of 389.7. That just gives me something to work with. It's all a relative intensity. And down below you can see the representation of CO2 2 receptors in regards to the 22 that I had originally Chosen as everybody can see, not all 22 peaks from CO2, any association I said that I can find are found in Aedes aegypti. Some of them aren't there at all and some of them are really, really tiny. But we do have some major peaks that are representative. And so this is kind of what we call a CO2 olfactory cord. In other words this is in a sense know what the insect sees when it is smelling carbon dioxide and you can see the relative intensity of these particular frequencies, I think. So if I were to do this same analysis on two other mosquitoes, the big ones, because we're dealing with the top three, dealing with attacking man includes aides Aegypti which spreads yellow fever and Zika as well Anopheles gambia which spreads malaria and then Culex quinque fasciatus which spreads encephalitis. So these are the big three. And if we take a look at their CO2 olfactory cords you can see that there, there are some similarities between them but they do not overlap perfectly. The take home message is that not all mosquitoes smell CO2 exactly the same way. And this has important repercussions with control, as you might imagine. I'm not going to get into that right now because I do want to focus on the theory. But you can see that there are some differences and don't be f down here in Culex quinque fasciitis, this looks like that not a lot is going on but the 4270 peak is actually at about 130. It's actually the highest peak of all three mosquitoes. So there are some gigantic peaks that are here associated with the mosquitoes and CO2 detection. So if I take these three mosquitoes and I put them in this nice neat table, I'm looking at the sum of intensities for various insects and CO2 reception. You can see Aedes aegypti coming in at 389.7. Anopheles gambia coming in at a respectable 273.2. Culex quincy fasciatis coming in at a very respectable 289. So we've got good evidence right now that they can detect CO2 according to the new theory. And so we're now getting some additional evidence as such with Helicoverpa armigera which is the cotton bollworm. This is a little bit different. It's interesting because they do have CO2 reception and the this, I mean it was a decades old paper but they had mentioned in the paper that they felt that it was the most sensitive to CO2 that they've ever tested. And I found that funny, especially when I calculated at 405.5. So I'm not the biggest fan of Lepidoptera but you know, look, that's just the way it is and that's the way the numbers came up. But the take home message is that Helical Verpa does seem to be able to smell CO2 pretty well. And Tribolium castaneum, which is the red flower beetle, is also known to be attuned to carbon dioxide receptors. So we go ahead and analyze them in the same way that I just showed you with Aedes aegypti as well as the ones above it. And that comes in at a very respectable 305.3. So you can see that those are all relatively high and these are all known CO2 receptors. And so we've now got association associative evidence between them. But we do have an issue and that is Diabrotica. Diabrotica came in and we were told, you know, if you take a look at the literature, the Diabrotica has got known CO2 receptors. They have to smell the CO2 from the corn roots because this is the corn root worm. And so they need to be able to smell the corn roots because the the female will lay the eggs, they need to find the corn roots and they use that as a guide. So it's a matter of life and death. And if it's a matter of life and death you them to have really good CO2 receptors. So I was surprised to see 215.2. I guess that's just the way it is. Sometimes we all can't be as good as mosquitoes at smelling CO2. But I had to content myself with the fact that there are different orders of insect out there and they all don't smell exactly the same way. Plus if there's anything I need to refine in the process then I will do so. So I pretty much let that go. Well just a few months later I attended the Entomological Society of America meeting and lo and behold there was someone giving a presentation on the CO2 receptors in diabrotica. So I thought I need to show up for this. I did. Very similar to aedes aegypti, the three receptors for CO2 reception are GR1, GR2 and GR3. Just as we've discussed before, nothing really earth shattering with that information. And that's how I calculated 215.2 during the presentation, and it was a short presentation. They're all about 10 minutes. At the ESA meeting, the individual mentioned that gr1 is found. It's expressed in the first instar and gr2 is expressed in the first instar, but gr3 isn't expressed until the final instar. So the first instar is the hatchling. So as soon as it hatches, it needs to find a food source and therefore it needs to cue in on the carbon dioxide of the corn roots. And it does it apparently very well. But GR3 isn't there until the final instar. Well, that's the last larval stage for the insect. Just before the pupal stage, I realized that it's not using GR3 in order to smell CO2 when it's a hatchling, it's only using GR1 and GR2. This requires recalculation. I immediately left that presentation within minutes, went outside, pulled up my laptop computer and recalculated everything according to Diabrotica to see whether or not there was a change when I calculated only GR1 and GR2. And after I recalculated everything, the new figure came up at 300.3. And so I was very relieved to see this. Obviously, I was still bothered by the 215.2. And I realized that sometimes mistakes are made and I had mistakenly calculated gr3 when I should not have calculated gr3. So it also is good to know what is being expressed and what's not being expressed so that I'm not making any major mistakes in the future. Although I'm sure we'll continue to do so. Now I'm much more comfortable with that. That does seem to make a lot more sense. And so I've just moved it in and there are many others, but this is now at 300.3 diabrotica, which means that it's very good at detecting CO2, just like the others are. So everything is all fine and good. Let's switch gears. Now we'll go to the Indian Meal moth. The Indian Meal Moth is something we've been working with all of our laboratory lives. And here's the Indian Meal Moth pheromone, as displayed on an FTIR for your transformed infrared spectrometer. And these are the peaks that are associated with this particular pheromone, or at least the peaks on an FTIR, which are within a very specific range of 2.5 micrometers to 25 micrometers. And so we got a lot of peaks to work with. They do need to be transferred over These are all in wave numbers. So in subsequent slides I'm going to be transferring them to nanometers so that we can actually make some good comparisons with the RM software. But these are the peaks that we begin that I'm able to start to have knowledge of and to be able to work with. And I'm able to make these graphs. I'm able to take all of the odorant receptors of the India meal moth, all 42 of them, and combine them in the RRM software. I'm able to do the same thing with all of the GRs, there's only nine of them. And I'm also able to do that with all 14 of the IRS. Once I'm. I've got these composite graphs set up, I can make a far IR graph, I can make a near IR graph and I can make a visible graph. And then I can have spectrums that are associated with these. So you can see I've got a total of nine graphs which I have just associated with the India Mail mods. It's important for you to kind of see a little bit of this process because I'm going to be talking. This is kind of what I'm doing with the fourth presentation to be coming up in two weeks. So I'm able to do a comprehensive analysis of the India meal moth and get a good idea of what it's able to smell. But I only want to focus on the first three, the odorant receptors, because that is what we know they they smell the pheromone with. So I'm going to focus on those 42. So here is a composite graph, the or absorption peaks in the far infrared spectrum for the India meal moth. So a lot of these peaks will be brought up again. But I just wanted you to show what this spectrum looks like after we go through the tedious process of combining all 42 of them. And this is what the graph looks like, the spectrum looks like in the near infrared spectrum. And then finally we have what the graph looks like in the visible spectrum for the Indian meal moss. So these are the three graphs that I come up with just in regards to the order receptors. I'm going to leave out the GRS and the IRS because of time. So I have a number of peaks associated, as I mentioned, with the FTIR associated with the India meal moth pheromone. And these peaks are listed to the left. They've been converted into nanometers right now. And of these peaks, there's a lot of them right now. But I just want to focus Right now on the purple ones. The purple ones are the general peaks. The general peaks are essentially just a general peak that you would find in any organic molecule because it's essentially all about the C H bonds, the carbon hydrogen bonds which are found in all organic molecules. And because you have carbon hydrogen bonds in all organic molecules, by definition you are going to have a lot of these peaks that are going to be synonymous and you're going to see them over and over again. So because these are general peaks, I really didn't quite know what we're going to be able to find in the Indian meal moth because they're so general, they're not really indicative of much. But I did take a look to see in the IMM genome based upon the annotation of the chemoreceptors and the analysis that I just showed you on the former slides. And I found out that only two of the peaks correspond to the Indian meal moth genome. So two of them do not. I was not able to find a clear association between two of them, but two of them were certainly close enough in order to warrant a hit. So the take home message here is that two out of the four general peaks in the India meal moth are represented in the in the analysis which I've shown, which is okay. It's not fantastic, but it seems to be okay. The next analysis to take a look at the the other peaks, all the other peaks are specific. A lot of them have to do with the acetate part of the molecule, CoC bonds, also part of the acetate molecule, the carbonyl compound also part of the acetate molecule, acetyl groups, part of the acetate part of the molecule. So they've all got these very specific bonds or peaks that are associated with them. Do I have any association with the Indian meal MOF genome? And I and I do. So I'm able to find some associations between them. Not all, but for these specific peaks I find that I'm looking at about 7 out of 10 of the specific peaks correspond to the India meal moth pheromones. So this is good circumstantial evidence to show how the Indian male moth can actually perceive its pheromone. But of those seven peaks which corresponds six of them, them specifically have to correspond to the acetate portion of the pheromone, which is generally considered to be the most important and indicative part of the molecule, certainly as far as the fingerprint region is concerned. So the evidence continues to mount as we're able to analyze pheromone detection and Pheromone detection in the Indian meal moth is huge. This is really the only molecule that it can detect with absolute certainty. You could usually do so within less than a tenth of a second because it will respond with wing fanning immediately. So the association is pretty strong here, the Indian meal moth. But I'm going to switch gears again this time now, now that we've got a working theory, and I'm being kind of selective about what I do in the interest of time because I did talk about CO2. So I do want to talk about CO2 again, but I also want to do it in the context of hopefully answering some questions that have not been able to have been answered thus far. One of these is the problem of the bedbug. The problem with the bedbug is that, well, one, they bite. We know that when we're sleeping. So that's problem number one. But that's not the one I'm talking about. The problem I'm talking about is that they haven't been able to find the CO2 receptors. So I was looking at a paper just a few years ago and, you know, they said, we haven't been able to find the CO2 receptors yet. We don't know how it works. And yet everybody and their dog knows that Cymex Luxurylaria smells CO2. We've done plenty of studies, plenty of behavioral studies to know that it can detect CO2. So by this time I had analyzed a huge number of mosquitoes and a number of other insects with CO2. So I looked at this problem as a challenge and I thought, thought, you know, it's feeling kind of high and mighty. I think I could do this. This shouldn't be that hard. I understand they're having some difficulties, but I'll step in and see if I can't save the day. So I knew in advance that Simax was going to have its CO2 receptors on the GRS, because they're always on the GRS. And so it was a matter of taking a look at them. And I'm going to be able to get my answers that way. So here's a chemoreceptor analysis regarding CO2 detection in Cimex lectularius. Just for your information, there are 49 ors in SIMEX, 45 grs, and then 30 irs. So there's a. Well, they're well represented as far as ors, GRS and irs. So the sum of intensities I knew was going to be higher in the grs, and I knew it was Going to be a little lower in the ORs and the IRS, because I know that this is where they're located. And so it was just a matter of starting with that and then getting specific. So I analyzed the sum of intensities for each of these three. The ORs. The average of 49 of them came in at 36.7. The average of 30 IRS came in at 34.7, and the average of 45 GRS came in at a surprisingly low value of 23.8. And so I remember scratching my head thinking maybe this is why they're having trouble. I'm having trouble. I don't understand why this is not as easy as I thought because clearly I've got a problem right from the get go where it doesn't look like we're getting a strong representation of CO2 from the GRS. So where do I go from here? Well, I've got 45 GRs, and I know from taking a look at the literature that GRS are not just about CO2 detection, they're also about water detection. So I thought, okay, maybe there's a lot of water receptors here and that there aren't that many GR receptors associated with CO2, and this is why things are a little bit confusing. So I just need to focus on finding the carbon dioxide receptors. So based upon what we know from Diabrotica, based upon what we know about Aedes aegypti, We know that GR1, GR2 and GR3 is where I need to be, and that's where the CO2 receptors will be. So I analyze GR1, GR2 and GR3 and the average of the sum of intensities for each of those three, it was 9.1. So I was going in exactly the wrong direction of where I needed to be. So I ran into a brick wall with the GRS. I decided to go the GR1, GR2, GR3 route, ran into another brick wall. Things were not going well and so I figured I had to get innovative. I got to start looking to see what I can find based upon the analysis that I've learned over the past few years and taking a look at CO2. So I decided to take a look at the predominant peak for bedbug GRS. And that is taking all 45, subjecting them to the RRM software, looking to see if there's a common predominant peak associated with these GRs. So I can zero in on that because right now I'm having a lot of difficulty. And so if you, if you combine all 45 together we get two peaks and only two peaks that are considered to be in common enough in order to register on this particular spectrum. So there's a small peak off to the left. I happen to know that's a water peak, but it's a very small peak. We're not worried about that right now. Obviously, I'm sure everyone's focusing on the big peak. So there's only one big predominant peak for GRS that is common among the 45, and that one happens to correspond with the 4270 nanometer region. So this is one of the CO2 peaks, and it is one of the major peaks that we found, if you recall, on Aedes aegypti. And also it was that, that big peak on Culex Quinque fasciatis. So I've now got a situation here where the GRS are difficult to determine, but I've got evidence right now that the 4270 peak, May King, and I need to focus on that, so I do. So now what I want to do is I want to take a look at the chemoreceptors and I want to see how many of them have the 4270 peak. Is it going to be like three, like you may find in some insects? Is it going to be a little bit more? And it turns out it's going to be a lot more. So I found 47% of the 45 grs had a 4270 peak. This is huge. I've never seen this before on any of the analyses that I've done thus far. Not to say that there isn't something out there right now, but that's what I found. And so to have the 4270 peak almost seems to suggest that maybe there aren't three peaks dealing with CO2 reception. Maybe they're all collectively working with CO2 detection. I also want to know, is it all about the 4270 peak? Are the other 21 peaks even relevant on this insect? What is going on? But I need again to run a control because right now 47% is extremely high. I don't expect to see this. So I got to do an analysis, and I do, of the other 49 IRS and the other 30 IRS. And I found out that 12% of them have the 4270 peak, albeit usually small, and that 17% of the IRS have the 4270 peak as well. So there's a big difference between the IRS and the ORS in terms of that that particular predominant peak, you find them, I wouldn't say almost exclusively, but certainly predominantly among the grs. So this is where we're at right now. Chemoreceptor analysis. All of my GRs are at 23.8. I've already pointed that already. I already pointed out to you that the GR1, GR2 and GR3 is coming in at 9.1. Now, I want to analyze the difference between those with a 4270 peak and those without a 4270 peak. So all of the GRS, 24 of them that do not have a 4270 peak, have a sum of intensities of 15.6. Those that do have a 4270 peak, no matter how small, and there are 21 of them have a sum of intensities of 33.3. So right now, those with the 4270 peak are showing, it looks like carbon dioxide detection abilities twice as good as those that do not have the 4270 peak. But I need to be able to start splitting hairs right now. I want to know a little bit more. And as I said, these 4270 peaks are not all synonymous. They're not all the same. So I want first want to look at those with a 4270 peak that is below 1, and you can't get much lower than that. These are very, very tiny peaks, but they're there. And there are eight of them. And of those eight with the 4270 peak, very, very tiny peak, the sum of intensities came to 30.3, so that's not bad. But if you take a look at those that are larger than 1, even if it's 1.1 or 1.2, and there are 13 of those, the sum of intensities rises to 35.1. If we raise our standard and say, I only want to deal with 4270 peaks above 5, this is just arbitrary. And there are eight of them. Now the sum of intensities rises to 51.3. Well, we're really getting high now as far as possible CO2 detection. And remember, this sum of intensity includes the 4270 peak, but it also includes all other CO2 peaks as well, which are contributing to the 51.3. And then I want to go one step further, and I want to analyze only the 4270 peaks that are above 10. And at that point there's only four that are left. And the sum of intensities for those particular GRs come in at 86.9. So this is competitive with the receptors that we saw in Aedes aegypti. They were averaging in the 80s. I've now got four receptors on Cymex lectularius which are averaging 86.9. And so we are starting to zero in and things are starting to make sense. I was able to start making a little bit more sense of what was going on with the bed bug. Yes, they've got a lot more, it seems, that are tuned to CO2, but they still have a relatively few that are really maximally tuned to CO2. So if you take a look at the cumulative peaks for bedbugs, you can see the 4270 peaks is king by a long shot. And the rest of them are all pretty small, with the exception of 2800. So the 2800 peak is kind of big. It doesn't look that small. As a matter of fact, it's not as big as the 4270P, but it certainly rises above the others. And this warrants another quick look to try and understand why is this 2800 peak as high as it is? So if we take a look at the absorption of CO2 and this is something we just pull right off the Internet, there's absorption of the 4270 peak off to the right, right there. And you can see it's a pretty big. It's the biggest detection. And then off to the left, we've got a dual peak right there. It's a 2700, 2800 peak. And that 2800 peak is also pretty big. It's not as big as the 4270, but on this particular graph it's the second largest peak. And so it is interesting. I think you'll agree that the two largest peaks exhibited by Cymax are also the two largest peaks on this particular graph. So if we take a look at this graph just one more time, we can all see the biggest peak and we can all see the second biggest peak. But let's take one more look at this. We do have one more peak. All the rest of them are small, including the 15,000 peak. I get it, it's not that big. But you do have to give it its due because it is officially third. It's not much better than fourth, but it is third. So if we take a look at these three peaks right now and we then take a look at the CO2 graph and I now stretch it out beyond 5500nm and I go all the way to 20000nm, we get a different story. So here is another spectrum of CO2. The big one very close to the four is the 4270 nanometer peak. Over here to the left we have the 2800 and 2700 peak which are also very big and obviously the two that we saw on the other graph. But over here we have another peak. We didn't see it on the first one because we didn't extend out as far as we should have. But on this particular spectrum we can see the 15 micrometer peak is also very big and it's also very sharp and it's a little bit wider than the 4270 peak, but it is there. So isn't it interesting that the top three peaks for SIMAX are the top three peaks that we see on this particular graph of, of CO2, which again is something you could just easily pull off of the Internet. I find that all very interesting. So what I wanted to do is I just wanted to focus on some specific examples in the interest of time. But now I just want to summarize and I also want to tell you a little bit of what we've been able to do because with this capability which I've very briefly outlined, you should know that we've been able to go far beyond what I've just mentioned already. And so we can analyze any, or, and we can analyze it with CO2 detection and with any type of, whether it be a pheromone or whether it be a water. We could do the same thing with the GRS and the IRS. We've certainly done it with the SNMPs. OBPs are a different story. They're free floating proteins found in the, in the hemolymph, as are the CSPs. We've also taken a look and analyzed those as well. They're all very interesting on their own. Right. And these are the six main proteins. They're not all membrane proteins. The first three, four. The first four are membrane proteins, but the last two are not. But we've been able to take a look at them in depth. As I said, we've, we're well into the five figures as far as spectrums. We have a very healthy knowledge of what is going on in insect olfaction. So some of the things that we've been able to do as we can see how an insect will identify a given odorant and then give it an olfactory cord so that we can visualize how it smells a particular odorant. We can take a look at olfactory cords and see how they differ between closely related species. I briefly did that with the mosquitoes. So you saw that we can do that with really any group of insects. We are interested to see how the membrane proteins may interact with one another based upon the new theory, which is electromagnetically in base. Therefore there will be an interaction and that needs to be taken into account and we're able to do that as well. We can rank the chemoreceptors for a given odorant. So let's say it's CO2. We can rank them and show which one has the highest ability to detect it and down to the lowest ability. And this all has incredible relevance too because it's a relatively brand new technology called RNA interference technology, where these insecticides are used in order to knock out particular proteins in order to prevent an insect from doing A, B or C. And so there are ways of preventing an insect from smelling and therefore preventing it from doing its job. And so RNA interference technology certainly would have great relevance to that particular aspect. So I just mentioned that over briefly because that is kind of the new craze. Right now we are able to look at pheromone reception at both attractants and deterrence because there are a number of pheromones that are deterrence for certain insects. We analyze water. This is by far the most number of peaks are the water peaks and everything that we look at at. So the water analysis we look at. We can analyze liquid water. We can also analyze water vapor because they have different frequencies. And we can get an idea as to how the insect, how well the insect is smelling water and differentiate between water vapor and liquid, which is kind of cool. We can differentiate between optical isomers such as the cysts and trans, and we can identify the predominant odorants in a given species. That's going to be kind of a prelude to talk number four, coming up into two weeks. But one of the things we can do is we can differentiate between the alcohols, the aldehydes and the acetates, just to give you an example, and be able to differentiate and let and find out what the insect is really keying in on. And we can also do an analysis of SNMPs across insect groups. Now that's interesting because we could take a look at all the Lepidopteran SNMPs and compare them with all the Coleopter and SNMPs to see what the similarities are and what the difference is. We could do the same thing with the orcos. I haven't mentioned anything about the orcos, but the ORCOS are the odorant receptor CO receptors that have been talked about a lot in the literature. We've analyzed most of the orcos with insects and we can also analyze them across insect groups as well. And we can also analyze the OBPs across insect groups because there are certain trends that are associated with these interesting free floating proteins. So this just gives you a little bit of a window on what would have been able to do. I wanted to be able to give you that first presentation and the second presentation in order to prepare you for a little bit of what you saw. I wanted to be as brief as possible and I realize this may be long for some. Or for those of you who've gotten through all three presentations, I think you're going to enjoy the fourth presentation a lot. The fourth presentation will focus on one group of insects. I'm not going to be skipping around to hemipterous insects like the bedbug and lepidopteran insects like the Indian meal moth or dipteran insects, the mosquitoes. I'm just going to focus on the aphids. And then we're going to focus really on two species, one, and it's going to be a comprehensive analysis of exactly what the aphids are smelling and how they work. And I think you're going to be quite blown away with what we're able to do. So I just wanted to give you a little bit of a window into what we've been able to do. A lot of these things that I've mentioned, as well as some of the examples that I talked about during this presentation will be manifest. You will see them again during the fourth presentation. But I don't have time to go into the background for that fourth presentation. And that's why AEA and thank you, aea, very gracious to allow me to give these three presentations. So for anyone who wanted to go through these three presentations, you would hopefully have enough background in order to get through the fourth one so that you can see exactly how a particular insect smells or tastes, as the case may be. It's going to be more tasting with the aphid and I'm really looking forward to giving that presentation. So I want to thank you very much for getting through this presentation. We are obviously here at the end. That's the thank you slide. This is just another picture I took in Kauai. I do like Kauai, I think a lot of people do. And I just wanted to conclude on this very pretty slide for those of you who have gotten through all three. I wanted to also thank you for getting through all three because you've done what I hoped you would do so that you can understand what I'm about to say for the fourth presentation coming up in two weeks. So feel free to get on YouTube. If you haven't reviewed the first two, please do. And then this one will be posted, I'm sure in short order, and all three of them will be posted before the fourth one is, is done live. And I can't tell you how much I'm looking forward to it. I'm looking forward to completing the cycle, the Circle, so to speak. Speak about insect olfaction. I hope you've enjoyed the ride so far and I'm looking forward to more concluding with you. I shouldn't say conclude, because AEA actually has me analyzing other insects as well. And so we will not be concluding per se though, but we are going to conclude our series with the aphids. So I think that'll do it for me. Now, once again, thank you for putting up with me for this long and I look forward to seeing you all on the fourth presentation. All right, have a good night and a good weekend. Bye bye now.