Podcasts about qtl

In his weekly clinical update, Dr. Griffin updates us about polio SIA campaign in Gaza, the durability of protection following administration of GSK's recombinant antigen RSV vaccine, mpox in an Ugandan jail, the Marburg virus outbreak in Rwanda, before reviewing the recent statistics on SARS-CoV-2 infection, the WasterwaterScan dashboard, immunogenicity, reactogenicity and safety of different COVID-19 vaccine platforms, disease severity in those not previously vaccinated with those vaccinated but not infected, where to find PEMGARDA, a reminder of how and when to use steroids to treat COVID-19, what do when healthcare workers succumb to SARS-CoV-2 infection, if COVID-19 doubles the risk for heart attacks, stroke and death and if COVID-19 affects the regions of the brainstem. Subscribe (free): Apple Podcasts, RSS, email Become a patron of TWiV! Links for this episode Polio: second round of vaccination in Gaza (Reuters) Season 3 for GSK RSV vaccine protection (CIDRAP) Positive data for Arexvy, the respiratory syncytial virus vaccine (GSK) Mpox convicted felon: in Ugandan prison (Reuters) How to stop Marburg virus (Rwanda Biomedical Centre) Marburg virus fact sheet (Rwanda Biomedical Centre) CDC summary of the “Rwanda Marburg Situation” (CDC) 700 doses of a Marburg vaccine from the Sabin Vaccine Institute to Rwanda  (CIDRAP) USAID mobilizes Marburg outbreak response in Rwanda (United States Agency International Development) COVID-19 deaths (CDC) COVID-19 national and regional trends (CDC) Waste water scan for 11 pathogens (WastewaterSCan) COVID-19 variant tracker (CDC) SARS-CoV-2 genomes galore (Nextstrain) PICOBOO: immunogenicity, reactogenicity and safety of different COVID-19 vaccine (Journal of Infection) COVID-19 in previously unvaccinated compared with previously uninfected vaccinated (JID) New preventive COVID-19 agent (ISDA) IDSA Guidelines on the Treatment and Management of Patients with COVID-19 (IDSA) Where to get pemgarda (Pemgarda) EUA for the pre-exposure prophylaxis of COVID-19 (INVIYD) CDC Quarantine guidelines (CDC) NIH COVID-19 treatment guidelines (NIH) Infectious Disease Society guidelines for treatment and management (ID Society) COVID Data tracker (CDC Differences in COVID-19 outpatient antiviral treatment (MMWR) Drug interaction checker (University of Liverpool) Molnupiravir safety and efficacy (JMV) Convalescent plasma recommendation for immunocompromised (ID Society) What to do when sick with a respiratory virus (CDC) When your healthcare provider is infected/exposed with SARS-CoV-2 (CDC) Managing healthcare staffing shortages (CDC) Steroids,dexamethasone at the right time (OFID) Anticoagulation guidelines (hematology.org) COVID-19 is a coronary artery disease with genetic interaction with ABO blood type (AHAIASA Journals) COVID-19 doubles risk of heart attack, stroke and death (EurekaAlert!) QTL mapping at 7T in COVID-19: brainstem and outcomes (Brain) Letters read on TWiV 1156 Dr. Griffin's COVID treatment summary (pdf) Timestamps by Jolene. Thanks! Intro music is by Ronald Jenkees Send your questions for Dr. Griffin to daniel@microbe.tv

Erratic weather like deluge rain, longer falls, and patches of drought disrupt vinifera's adaptation to long-sustained winters. Jason Londo, Associate Professor of Horticulture in the School of Integrative Plant Sciences at Cornell AgriTech explains how big weather changes in the Pacific North East can cause vines to wake up earlier posing a risk to freeze or frost damage. By researching acclimation and deacclimation, Jason is working to breed and select varieties for enhanced cold resistance, drought resistance, pest resistance, plus good fruit quality. In the future, to reduce inputs in vineyards and increase economic sustainability we need to put the right grape in the right climate.  Resources:         135: Cold Hardiness of Grapevines Cold Hardiness prediction model and monitoring website for the Eastern US Foliar Applied Abscisic Acid Increases ‘Chardonnay' Grapevine Bud Freezing Tolerance during Autumn Cold Acclimation Jason Londo Jason Londo's Recent Publications Vitis Underground: NSF-PGRP project looking at rootstock-scion interaction across multiple environments. Vineyard Team Programs: Juan Nevarez Memorial Scholarship - Donate SIP Certified – Show your care for the people and planet   Sustainable Ag Expo – The premiere winegrowing event of the year Sustainable Winegrowing On-Demand (Western SARE) – Learn at your own pace Vineyard Team – Become a Member Get More Subscribe wherever you listen so you never miss an episode on the latest science and research with the Sustainable Winegrowing Podcast. Since 1994, Vineyard Team has been your resource for workshops and field demonstrations, research, and events dedicated to the stewardship of our natural resources. Learn more at www.vineyardteam.org.   Transcript Craig Macmillan  0:00  Our guest today is Jason Londo. He is Associate Professor of horticulture in the School of integrative Plant Sciences at Cornell agritech. We're gonna talk about some pretty cool stuff today. Thanks for coming on the show. Jason,   Jason Londo  0:11  Thank you for having me.   Craig Macmillan  0:12  Your work tends to center around identifying things like climate induced disorders, developing medication methods, improving resiliency and sustainability of crops like apples and grapes. How did you become interested in that that's a pretty interesting area.   Unknown Speaker  0:26  Originally, I was mostly interested in how plants adapt to stress just in general plants, because they're stuck to the ground that the seed lands on they are forced with so many complicated life's challenges, that it's really amazing what a plant can do in the face of stress. And so my curiosity has always been trying to figure out those strategies. But climate induced part of it is sort of reality striking into my passion, right? We know the climate is shifting, and it is shifting those stresses in a way that our plants can't necessarily respond in the same way that they used to, particularly because of the rate of climate change. So that's how I got interested in this topic, just trying to figure out how plants work when they're stressed out.   Craig Macmillan  1:13  And you're interested in plants in general. And then now you're focusing on specific crops, right?   Jason Londo  1:18  Yes, indeed, I started out originally working on endangered mints. If you can imagine that. Then I worked on rice. Then I worked on canola and I landed and fruit crops. And so yeah, lots of lots of diversity in those systems. All those plants have different stresses.   Craig Macmillan  1:35  They're all different families. I mean, he really jumped around.   Jason Londo  1:37  Oh, yeah. One of the coolest things about working in plant stress is plants across different clades evolved different ways of handling maybe the same stress. And you can learn a lot about sort of the limitations of stress response and the advantages and opportunities when you work across a lot of different systems. And so it makes for a tricky CV, because my publications kind of snake all over the place. But from trying to figure out the next strategy or figure out the next experiment, I feel like it's a real positive to have that background.   Craig Macmillan  2:13  I want to go back for a second because I think this is an important topic. And you mentioned clade. What is a clade? And how does that apply to looking at plant stress?   Jason Londo  2:24  And its most basic a clade is a group of plants that belong to the same sort of evolutionary history, and without getting into the real jargony. And the fights between what makes a species and what doesn't make a species. The basic concept is an evolutionary group. And so when I talk about plant stress strategies and differences between clades if we think about rice, it's a monocot. And so it has a completely different evolutionary lineage from most of our dicot fruit crops. Canola is a dicot it's a mustard. Both rice and canola are typically annualized, maybe sometimes there's a perennial version, when we talk about fruit crops, we're talking about, in my case, grapes and apples, Woody perennials, so dicot species that persists for many, many years. And so the strategies that are successful for for getting through a stressful situation can vary very much by those different life histories.   Craig Macmillan  3:24  We're kind of talking about stresses in general, what are particular stresses on things like apples and grapes that you're looking at.   Jason Londo  3:29  So in my program, it has a climate adaptation focus. And we all know that the main drivers behind climate change are temperature and precipitation. And here in the northeast, we do have a benefit in that we've got some room to get warm before it gets uncomfortable. And we have plenty of rain. But what we're seeing here is big changes in our winter weather shifts in our phonology. So the spring is coming earlier, the fall is coming later. And then we're also having big changes in precipitation. So little patches of drought, deluge, rain, and so very different from California, where things may be drying out. We're drying out, but in a very episodic sort of pattern. And the systems here are not built on drought management. They're not built so much on water logging either, although we do use tiling in the fields to Drain off excess water. And so when we're talking about climate impacts, here are primarily talking about temperature and shifts in precipitation.    I know that you've been looking at cold hardiness. What has been the pattern? What's the change that's happening in the Northeast as far as cold goes?   Yeah, so most of my career, as a as a PI has been in cold hardiness and cold stress response in grapes. I spent 10 years at the USDA as a geneticist, particularly diving into this topic, and even in those 10 years years I've seen a major shift in the intensity of our winters they are getting much more mild, but they're also coming very erratic. And so we're having large swings in temperature. I'm sure your listeners are familiar with the concept of a polar vortex we've had enough of them. Now, that is pretty common. When you take a perennial crops like grape, and you put it through winter, it's it's adapted to a long, sustained winter, not a real chaotic, episodic type winter where it gets warm and cold and warm than cold. The the complex molecular components of what tells the grape that it's safe to wake up don't function as well when you have those erratic temperatures. And so we're seeing, in general more mild, which is good for baseline cold hardiness, but also an uptick in sort of chaos. And that's not good for for any form of cold hardiness. And it particularly affects late winter, because the the plants wake up. As they're coming into spring, they respond to heat. And when you have weird weather in that really late winter, early spring, they can wake up too early and then suffer a lot of freeze damage or frost damage if they happen to break bud.   Craig Macmillan  6:11  What is the mechanism around freeze damage? I've interviewed some folks from like Michigan and Iowa and Ohio, we don't have freeze damage in California very much Washington, obviously. What are the parameters there? How cold for how long? And what's the actual mechanism of damage to the volume?   Jason Londo  6:29  Yeah, great questions. Very complicated questions.   Craig Macmillan  6:35  That's why we're here.   Jason Londo  6:35  Yeah, yeah. All grapes gain cold hardiness in the winter, regardless of where they are, it's a part of going dormant and making it through winter. The biggest changes that we see in the vine is that the buds will isolate from the vasculature. And so the little connections that come from the xylem and the phloem, into the bud, they actually get clogged up with pectins. And so you have to think of the bud is sort of like a little island tissue, it's not connected to the cane during winter. Once the bud does that it's able to gain cold hardiness and traverse winter. And that process is called acclamation. And so the buds gain a greater and greater ability to survive lower and lower temperatures. We don't know exactly how all of it works. But it's a mixture of making more sugars and making more Ozma protectant inside the buds so that water freezes at lower temperatures and also controlled dehydration. So the more you can dehydrate a tissue, the less likely ice crystals will form in pure water. But and we don't know how they do this. And it's quite magical if you think about it, but they're able to suck out all of this internal water so that it is less and less likely for water to freeze inside the cell. If they can keep the ice crystals from forming inside the cell. We call that cold hardiness that they they are surviving freeze damage, we can measure the temperature that reaches that defense. And you've had other speakers on your show that have talked about cold hardiness. It's called differential thermal analysis. And we basically measure the precise temperature where the water freezes through some tricks of thermodynamics, that cold hardiness failure point changes throughout the whole winter, and it changes by the location that the grape is growing in. What we do know about the system is that it takes oscillating temperatures to gain cold hardiness. So it has to get warm than cold warm than cold, warm than cold and progressively colder in order to ramp down and gain cold hardiness, then it has to stay cold for the cold hardiness to sort of hang out at the maximum cold hardiness. And that maximum cold heartedness is going to differ by region. So here in New York, something like Chardonnay will reach a maximum cold hardiness of maybe negative 27 Celsius. I cannot do the Fahrenheit conversion,.   Craig Macmillan  9:00  That's fine. That's fine.   Jason Londo  9:03  But say, say negative 27 Fahrenheit, whereas in California, it may not gain more than negative 20. And that's because it just doesn't get pushed. As you go through winter. You go through a whole bunch of other stuff with dormancy chilling our requirement, and that changes the way that the bud responds to temperature. And you enter a phase called Eco dormancy, which is now resistance to freezing based on how cold the vineyard is. And so when you get warm spikes in late winter, when the buds are eco dormant. They think those are a little preview that it's springtime and so they lose their cold heartedness really quickly they start reabsorbing that water, and they'll freeze that warmer and warmer temperatures. And so that's really the most dangerous time in this sort of climate chaos. When you think about winter that late winter period is when the vines are reacting with their adaptive complex for 1000s of years. When it started to warm up. It meant it was spring and now they're starting To think, okay, spring is coming. But we're still in February in New York, maybe in. In California. It's more like it's January and you're getting a warming event. And they all move right towards bud break. And then of course, they can get hit pretty hard by a leak freeze or a frost.   Craig Macmillan  10:15  Yeah, exactly. I'm guessing this varies by variety.   Jason Londo  10:19  Yes, very much. So, vinifera varieties are typically less hardy than the North American adapted varieties, the, the hybrid varieties is often gets used. I don't particularly like the word hybrid. But these cold climate grapes that have been bred by University of Minnesota and Cornell, they tend to have greater maximum cold hardiness. But they also tend to wake up in the spring much faster. And that's partly because of the genetic background that those hybrids were made from. When you breed with species that come from the far north, like Vitis riparia, those species are adapted to a very short growing season, which means as soon as it's warm enough to start growing, they go for it to try to get through their entire cycle. So now we're starting to see that there are some potential issues with climate change when we think about hybrid varieties that use those those northern species, and that they may be more prone to frost damage in the future.   Craig Macmillan  11:15  Oh, really, that's I wouldn't have thought that I would have thought the opposite. So obviously, we have different species. So we have some genetic differences between what I'll call wild grapes or native grapes, the Oh, invasive plant itis vinifera that has been  thrown around. What can we learn by looking at the genetics of native North American varieties?   Jason Londo  11:38  from a cold hardiness perspective,   Craig Macmillan  11:40  cold hardness, just in general drought resistance, pest resistance?   Jason Londo  11:44  Well, in general, they're a massive resource for improvement, which depends on who is who's calling a species species. But there may be up to 20 Different wild species in North America. And each of those wild species has a different evolutionary trajectory that has given it the ability to create adaptive gene complexes, that could be useful in viticulture, as we have shifting climate, away from what maybe vinifera likes, hot and dry into further northern latitudes, you know, that if the California industry has to start moving up in latitude or up in altitude, we start integrating different stresses that maybe those vines haven't been exposed to in their evolutionary history, you know, from Europe. And so these wild species just have these potentially novel genes, potentially novel pathways where genes are interacting with one another, that give vines a greater plasticity. And so this concept of plasticity is if you take an individual and you put it in environment a, and it grows to size 10, but you put it in environment B and it grows to size 20. The difference there is the plasticity between those two environments. And we really, if we want sustainable viticulture, what we want to encourage is using cultivars that have maximal plasticity. So as the environment shifts around them, they're still able to give you the same yield the same sugars, the same quality, you know, within a within an error bar anyway, they're the most resilient over time. And incorporating traits and pathways that come from wild grapes can help build that plasticity in the genetic background coming from the European great.   Craig Macmillan  13:23  So we're talking about crosses, we're talking about taking a native plant and then vinifera crossing to create something new. You had said that you don't know you don't care for the word hybrid. Why not? That's interesting to me.   Jason Londo  13:35  Because it has a negative connotation in the wine drinker. realm, right people think of hybrids as lower quality as not vinifera, so lesser. And I think I'm not an enologists. I'm not a viticulturalists. So I want to be careful on whose toes I mash. But if we're talking about sustainability of a crop through an erratic climate, we can do a lot with vinifera we can we can mitigate climate change a lot with vinifera, but at some point, the inputs may become too much to make it a sustainable crop and then we need to be able to move to adapted varieties. And we can adapt the wine quality from vinifera to climate chaos, by breeding and and selecting for enhanced cold resistance, enhanced drought resistance, enhance pest resistance, and good fruit quality. That's a little bit of a soapbox. But when people say hybrid, it's like lesser, but it's, in my opinion, it's more we're taking something great. And we are increasing its plasticity across the the country across the growing zones. We are giving it a chance to grow in more regions reach more local communities create a bigger fan base. So I get really my hackles got up because there is amazing hybrid based on Climate adapted based wines, and winemakers. And when we use the word hybrid people just automatically in their mind shifted into lesser. And I think that's unfortunate. I think it's something that we need to work actively as an industry against, because a lot of those particular disease resistance traits are coming from wild germ plasm. That is not in the European grape. And we just can't solve all our problems with that one species.   Craig Macmillan  15:30  So the kinds of traits that we're talking about these environmental adaptations, or acclamations, these will be polygenic trades, how do you find these? I'm assuming that you're looking for those specific genetic information to say, Yeah, this is the plant that I want to use in my my breeding program. What does that look like? How do you do that?   Jason Londo  15:49  So the approaches are very similar to when you're working on single locus traits. And so disease resistance and fruit color are good examples of traits that often can be found in single locus examples, again, would be fruit color, or sort of run one disease resistance, there's a whole bunch of different disease resistance was like polygenic traits can be found the same way, you have to make a cross between two different grapes that have different phenotypes. And so that might be a drug sensitive, and a drought tolerant individual. And you plant out a whole lot of baby grapes 200, 300 progeny from that cross, and then you score them with phenotypes. And with polygenic traits, it's a lot harder to find them sometimes, because in that group of, say, 300, babies, you're not looking for the movement of one gene. In that background, you're looking for maybe the movement of five to 10 different genes. And that means instead of getting a light switch kind of trait, red or white fruit, you're getting a little bit more drought resistant, a whole lot more drought resistant, but there is a gradient, right? When you have a gradient for a phenotype, you need a lot more grape babies in order to get the statistical support to say, hey, this piece of the genome right here, this makes a grape, a little bit more drought resistant. And over here, this piece of the genome does the same thing. And when you put them together, they either add up one plus one, or sometimes they multiply two times two, you use the same approaches, it's typically a little trickier. And you got to kind of do a couple extra years of screening. But it's the same basic playbook to track down those different traits. And we have to do a lot of different phenotypes for drought response, you might be looking for the ability to root deeper, have bigger root masses, you might be looking at bigger hydraulic conductance in the trunk, you might be looking at betters to model control. You might be looking at pyres to model density or lowers to model density, you could be looking at thicker or thinner leaves. So you can imagine if there's lots of ways to be more drought resistant. There's lots of genes that help you in that pursuit. You need a lot of baby grapes in order to find all those little pockets where those genes come together and give you a statistical shift and in the phenotype.   Craig Macmillan  18:10  So you're able to identify these are you using something like qualitative trait?   Jason Londo  18:13  Exactly. Quantitative trait loci?   Craig Macmillan  18:16  Yes, exactly. So that helps speed the process up a little bit. Maybe.   Unknown Speaker  18:20  Yeah, so so QTL mapping, quantitative trait loci mapping is the probably the dominant way that we map traits. There's another way called GWAS, genome wide association studies, is built on the same concept where you have a big enough population of either grape babies or in the case of GWAS its diversity. So you'd say, let's say you had 200 Different Vitis riparias instead of 200. Babies, the principle is the same. You are looking for across all of those vines, statistical association between a specific part of the genome and a phenotype to like make it really simple. In 200 babies, grape babies, you want to have enhanced drought resistance. You let's say we take a measurement of carbon isotope concentration and so that carbon isotopes tell you how often the stomates are open, right? So you do an experiment. And you drought stress your plants, and you use carbon isotopes as the phenotype and you say, Okay, this group of 75 individuals, they all shut their stomates right away, and this other group of 125, they kept their stomates open. So then in those two groups, you look at all the genetic markers that are in the background, right, which are like little signposts across the genome. And you say, in this group of 75, which genetic markers do we see over and over and over again, outside of statistical randomness, right? And what that will give you a peek a QTL peak, if you're lucky, right, I'll give you a cue to help you can say hey, right here on chromosome four, every single baby in that pool has a has this set of markers, these five Mark occurs. So there must be a gene, somewhere near these five markers that contribute to closing your stomates. And so then extrapolate that out whatever trait you want to look at how whatever phenotype method you're using, maybe it's not carbon isotope, maybe it's leaf mass, maybe it's node number, I don't know, whatever that screening process is, the concept is the same. You have big enough population, a good genetic marker background, and a phenotype that you can measure. And you can find the statistical associations.   Craig Macmillan  20:32  And actually, that reminds me of something, how many chromosomes do grapes have?   Jason Londo  20:36  Well, bunch grapes have 19 muscadine. grapes have 20.   Craig Macmillan  20:39  That's a lot. Which means that there's a lot of genetic variation in the genome of these plants, then.   Jason Londo  20:47  Yeah, if you think about, I mean, grape is sort of a funky beast, because a lot of these varieties that we grow, they're all They're all of the arrays, we grow our clonal. And some of them are 1000s of years old, the same genetic individual from 7000 to 10,000 years ago, we still have around today, in that process, it's it's changed, right? There's mutations that happen in the field all the time. And so even thinking about genetic clones and thinking the idea of Chardonnay being around that long, it's changed in those 7000 years, just naturally. So when you think about comparing two different clones, or two different cultivars, or clones, there's something like 43,000 Different recognized genes in vitis vinifera, that number I can give you in the different wild species, because it varies by species, but roughly 40,000 at those 40,000 genes in a in a single individual, you can have up to two different copies, right. So you could have essentially 80,000 different alleles, then you go across, I don't know, what do we have 12,000 recognized cultivars or something like that? There are something like 60 Grape species. And so now imagine the amount of potential variation you have across that entire gene pool. And so yeah, the genetic diversity within the crop as a whole is incredible. There's a lot of room for improvement. And there's a lot of room for climate adaptation. Just takes a lot of grape babies to figure it out.   Craig Macmillan  22:12  And that brings us something else. And that is the the idea of mutation. One of the issues, I think that is a stumbling block, and you mentioned it, there is the consumer, if it's not Cabernet Sauvignon, can't call it Cabernet Sauvignon. I'm not as interesting, which is something that I think we need some help from the marketing world with. Because I agree with you very much. I think if we're going to have wine in the future, we're going to have to start thinking about things other than just the cultivars that we have. Now, can you do the same kind of work with but mutation? Can you take a cane grew from a button, plant that out and look for differences between the same plant?   Jason Londo  22:53  Yeah, so you're basically talking about clonal selection clonal selection is practice worldwide by different regions, always with this eye towards making something that we currently have a little bit better or a little bit more unique, right, somatic mutations, random mutations occur in the genetic background all the time. And they often occur in response to stress, which is a really interesting angle, if you think about climate stress. So these mutations happen all the time in the background. Frequently, they will land on pieces of DNA that don't do anything that we know up. I don't want to say that no DNA is unimportant, that there are sections that we don't believe are that important. We call these non coding regions are sometimes introns. When you have a mutation in that area, sometimes there's no effect on the vine at all. And that's happening all the time in the fields. Right now. If you think about all the 1000s to millions of cab sauv vines that are growing in the world, we like to think of them even if you pick a single clone as the same genetic individual. And that is, that's simply not possible. There's so much background mutation going on in those parts of the DNA that don't give us any change in phenotype. There's no way it's all the same. We'd like to simplify it. We'd like to simplify it for our drinking behavior, as well as you know, like our sanity. But yes, you can select for clonal variation. And clonal variation happens all the time when those changes happen to land in a gene producing region, exon or perhaps a promoter or, or even in a transposable element to make a piece of DNA jump around the genome, we get a new clone, you can purposely create clones as well. So it happens naturally, but you can create clones on your own and mutational breeding is something that gets used in a lot of crop species in grapes it doesn't get used as often because it's modifying the base plant, right? So if you take Chardonnay and you want to increase his disease resistance, if it doesn't have a gene that you can break or change that would give it more disease resistance, then you can't create a clone with more disease resistance, right? You're working with a big a base plant that has limitations, but we have So we have a population where this was done it was it was done actually by the USDA by Dr. Amanda Garis. She no longer works for the USDA, but she worked here in Geneva. And they did a project where they took the variety of vignoles, which has a very compact cluster and tends to get a lot of rot. And they took a bunch of dormant canes with the buds, and they put it in a high powered X ray machine at the hospital and blasted it with X rays. What X ray damage does to DNA is it causes breaks between the double strands so all of our DNA and all our genes are wrapped up in in double stranded DNA. And when you do DNA damage with X ray mutagenesis, you break the two strands. And then when they heal themselves back together, it's often imperfect. And so they'll often lose a couple base pairs like there'll be a little piece get that gets nipped out. When you put those two pieces back together and repair, if that landed in exon, you can sometimes change the protein that would have been made by that exon or completely knocked the gene out in its entirety. Creating a clone, you're just doing it faster than nature is doing it on its own. We do it with a hospital X ray machine. And so with this method, they created about 1000 clones of vignoles. And they've made I think 10 selections out of that group that have bigger, looser clusters, so the berries are further spaced out. So they don't get damaged, they don't get as much rot. And I think those are now starting to make their way out into trials. There's an example of a very directed approach to creating a clone to fit fit a very specific viticultural problem that may or may not work for climate adaptation because of the polygenic aspect that you brought up before. Because if you break one gene and a poly genic, adaptive complex, it may not be enough to shift the entire physiology into a recognizably different pattern, it could work to make them less resilient, because you could break something that's really important. But breaking something that's important, but works out for you in the long run is just playing that randomizer lottery a little bit further. So it's doable. It can happen in nature, it can happen on purpose in our hands, but it is trickier for certain traits.   Craig Macmillan  27:21  So we're not going to X ray our way out of climate problems, basically, or diseases problems, right? Well, there may not be the right genetic information in the background of vinifera that even if we tried that, we'd have that set of genes that we would need, whereas we would have it in a native, native vine North American vine.   Jason Londo  27:42  And just a sheer a sheer number of breaks that you might have to make in order to shift the physiology enough to matter. These climate adaptation pathways are highly networked. They involve hormones, they involve sugar metabolism. And so if you really break something important, it's going to cause a really bad phenotype of death phenotype, you have to nudge the system enough in a specific direction to make a meaningful change. And so, given the complexity of the trade, it makes it harder. I don't want to say anything is impossible. I do think that there would be ways to make vinifera better, more plastic in the environment. I think the potential is there for vinifera to do better in a lot of climates. I don't know if directed mutagenesis is the most efficient way to do it. I mentioned is that random, right, you're breaking double stranded DNA at random, and then it's really healing and there's so many things have to work out for you to hit the right gene, have the right repair, you know, all of that sort of stuff that it's a method, but I don't I wouldn't say it's the most efficient method breeding with wild germ plasm is also a method, the key weakness there is then it's no longer Chardonnay, right from our wine drinking sort of our own personal biases on that situation. We outcross Chardonnay to make it more climate resilient. It's no longer Chardonnay. So it can't be sold as Chardonnay. And that itself creates a market pressure against changing it to something that's more resilient. And I think until the climate imparts an equal level of pain as consumer pressure, we won't get there. I don't think it's a question of if it will happen. It's a question of when.   Craig Macmillan  29:23  What kind of projects are you working on currently? You've mentioned experiments and breeding and it's now what do you what do you up to?   Jason Londo  29:29  So I have a pretty diverse program climate impacts is all season so we have a lot of winter projects. And we've covered some of that now trying to understand how Acclimation and deaacclimation work and if we can enhance it, we're working with but birth control. So if we could slow down deacclimation and delay by break, we could get around frost damage. And then I'm also working on a really big project is actually coming to an end where we've been looking at what the role of a rootstock is our mapping population concept that we talked about for QTL Mapping, we were talking about the scion, I have a project where we did that with the rootstock. And so we created a mapping population. The only part that is the grape babies is the roots. And we've grafted the same variety onto those roots. And then we're looking at how the different grape baby roots change the scions behavior. A really cool thing about this project is that we've replicated it clonally replicated it and grafted it in three different locations. So we have a vineyard in Missouri, a vineyard in South Dakota and a vineyard here in New York. And so across those three different environments, which are quite different, both in maximum temperature, minimum temperature and precipitation, we're learning so many cool things about what the roots can do to the same scion for your listeners, of course, they know grapes, so they know hopefully enough about grafting and that the rootstock and the scion are two different individuals. And they're mechanically grafted together. From a climate adaptation point of view, what you've done is you've taken an intact and adapted individual, and you've cut its head off, and then you've taken another climate adapted individual, and you've cut its legs off, and you've glued them together, and ask them to perform in the environment, which is just a wild, wild communication question. When the roots are experiencing one environment, and the shoot is experiencing another, how do they communicate? And then how does that affect our grape quality and wine quality? And so we're looking at drought response, can we increase the drought resistance of the Scion, based on the type of root it's on? Can we change the leaf nutrient profile, so the different ions that are taken up from the soil and how they're concentrated in the leaves. And of course, we don't really care about the leaves as much as we care about the fruit, the leaves are easy to work with. And we're even started working on wine quality. And so it looks like across our experiments, we might be able to optimize the rootstock and scion combinations we grow in different climates. To produce specific wind quality attributes, which is really cool.   Craig Macmillan  32:00  That is really cool. That is really cool. We're just about out of time. But I want to is there one thing on the on these topics that you would like or recommend to our listeners, or you'd like our listeners to know?   Jason Londo  32:11  Oh, well, I think their take home is is that we should all appreciate the new cultivars that come on the scene, whether they be from early regions like the the Eastern caucuses, something that we are not used to having in this country, or its climate adapted varieties that are bred in this country, and grown in these different regions. We need to do our best to open our minds not to does this grape or that grape tastes like cab sauv, or tastes like Chardonnay. But isn't it amazing what this grape tastes like period, because a lot of the the advances in resilience and sustainability that we can get out of either adopting new cultivars, shifting cultivars from climate to climate, or by using hybrid varieties in different regions, all of the benefits that we can get out of growing the right kind of grapes in the right climate, reduces inputs in the vineyard reduces inputs on the ecology. It increases the economic stability of rural communities. And it gives you pride in what the local region can produce. And I guess my take home would be is drink more adapted wines, enjoy them, figure out the nuances. Some of them are not great, but some of them are really great. drink more wine.   Craig Macmillan  33:33  Where can people find out more about you and your work?   Jason Londo  33:36  So the easiest way is just to Google my name and Cornell and that will take you right to my Cornell page. There's not a lot of information on my Cornell page, and I'm a big procrastinator on my personal website. But you can find my contact information there and certainly get a hold of me directly. If there's anything of interest. I will also send you some links that you can use to take listeners to the Vitis underground project, which is the NSF rootstock project I talked about, I can send you a link to we have a cold hardiness website where we post prediction models that we've built about cold hardiness across most of the Eastern US. We hope to expand that to be nationwide once once I get a stronger computer, but I can send you some links there. Yeah, I would say that that's probably the best places to find information on me and the program here. And if people are in town to come and see Cornell Agrotech and see some of the stuff in the field.   Craig Macmillan  34:30  I would love to pay a visit. I've interviewed a number of your colleagues there and there's so much cool stuff going on. really innovative and really groundbreaking feel like we're on the leading edge of a wave that some point is going to break again. Maybe we'll be drinking wines other than the ones we've been drinking. I can see that happening. Anyway. So our guest today was Jason Londo. He's Associate Professor of horticulture in the School of integrative Plant Sciences at Cornell agritech. Thank you.   Jason Londo  34:55  Thanks   Nearly perfect transcription by https://otter.ai

Among other things, UK-headquartered company Phenotypeca has been working with the Bill & Melinda Gates Foundation to produce albumin for low and middle-income countries to reduce the costs of vaccines.The company's CEO, Johnny Cordiner, and research and development director, Professor Ed Louis, tell us about the work, as well as the company and its other projects.01:01-11:57: About Phenotypeca 11:57-14:04: What is recombinant protein technology? 14:04-16:39: What are the issues around the cost of albumin? 16:39-20:48: How can you help improve affordability? 20:48-26:55: What is QTL technology? 26:55-30:18: What are the benefits of this technology? 30:19-34:22: Partnering with the Bill & Melinda Gates Foundation 34:22-36:37: What's the timeline? 36:37-39:45: What else are you working on?Interested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletter

In this week's Episode Ed interviews Dr. Sean Toporek of South Dakota State University. They discuss the work done by Sean for his Ph.D. Research with cucurbit downey mildew. Additional Resources: https://www.researchgate.net/publication/362300801_QTL_Mapping_of_Resistance_to_Pseudoperonospora_Cubensis_Clade_2_Mating_Type_A1_in_Cucumis_Melo_and_Dual-clade_Marker_Development https://www.researchgate.net/publication/369758519_QTL_mapping_of_resistance_to_Pseudoperonospora_cubensis_clade_2_mating_type_A1_in_Cucumis_melo_and_dual-clade_marker_development How to cite the podcast: Zaworski, E. (Host) and Toporek, S. (Interviewee). S2:E18 (Podcast). Kombating Fungi on Melon with KASP: Cucurbit Downey Mildew (CDM). 5/10/23. In I See Dead Plants. Crop Protection Network. https://sites.libsyn.com/416264/s2e18-kombating-fungi-on-melon-with-kasp-cucurbit-downey-mildew-cdm

Please join authors Loren Field and Sean Reuter, as well as Associate Editor Thomas Eschenhagen as they discuss the article "Cardiac Troponin I-Interacting Kinase Affects Cardiomyocyte S-Phase Activity But Not Cardiomyocyte Proliferation." Dr. Greg Hundley: Welcome listeners, to this January 10th issue of Circulation on the Run, and I am Dr. Greg Hundley, associate editor, director of the Pauley Heart Center at VCU Health in Richmond, Virginia. Dr. Peder Myhre: I am Dr. Peder Myhre from Akershus University Hospital and University of Oslo in Norway. Dr. Greg Hundley: Well, listeners, this week's feature discussion delves into the world of preclinical science and evaluates cardiac troponin I and its impact on S phase activity in cardiomyocytes, and does that relate to cardiomyocyte proliferation. But before we get to that, how about we grab a cup of coffee and Peder and I will work through some of the other articles in the issue. Peder, how about this week I go first? Dr. Peder Myhre: Go ahead, Greg. Dr. Greg Hundley: Right. So Peder, this first study evaluated whether the burden of positive coronary artery calcification on cardiovascular disease differed by multidimensional individual characteristics, and so the investigators led by Dr. Kosuke Inoue from Kyoto University sought to investigate the heterogeneity in the association between positive coronary artery calcium and incident cardiovascular disease. And so Peder, to examine this question, the authors implemented a cohort study design that included adults aged greater than 45 years, free of cardiovascular disease, from the Multi-Ethnic Study of Atherosclerosis, or MESA, and after propensity score matching in a one-to-one ratio, they applied a machine learning causal forest model to, first, evaluate the heterogeneity in the association between positive coronary artery calcium and incident cardiovascular disease and then, second, to predict the increase in cardiovascular disease risk at 10 years when the coronary artery calcium score was greater than zero, so versus is it zero at all at the individual level? Dr. Peder Myhre: Oh, Greg, that is so cool, so using machine learning for coronary artery calcium and risk prediction, I'm very excited. What did they find? Dr. Greg Hundley: Right, Peder, so the expected increases in cardiovascular disease risk when the coronary artery calcium score was greater than zero were heterogeneous across individuals. Moreover, nearly 70% of people with low atherosclerotic cardiovascular disease risk showed a large increase in cardiovascular disease risk when the coronary calcium score was greater than zero, highlighting the need for coronary artery calcium screening among such low-risk individuals. And Peder, future studies are really needed to assess whether targeting individuals for coronary artery calcium measurements based on not only the absolute ASCVD risk, but also the expected increase in CVD risk when a CAC score is greater than zero and whether that improves overall assessment of cardiovascular outcomes. Dr. Peder Myhre: Wow, that is so clinically relevant and very interesting. And we're actually going to stay clinically relevant with the next paper which is about anti-platelet therapy after PCI. And this paper describes the long-term results of the HOST-EXAM trial. To remind you, Greg, the HOST-EXAM trial was an investigator-initiated prospective, randomized, open label, multicenter trial done at 37 sites in Korea. They enrolled patients who had undergone PCI with DES and maintained dual anti-platelet therapy without any clinical event for a mean 12 months and then they were randomized one to-one to either clopidogrel, 75 milligrams once daily, or aspirin, 100 milligram once daily. The primary results of this trial was published in Lancet in 2021 and showed superiority of clopidogrel over aspirin in prevention of the composite of MACE and major bleeding during 24 months of followup. And then, through the current paper, this describes the results of the post trial extended followup of about five years. Dr. Greg Hundley: Very nice, Peder, so aspirin versus clopidogrel and looking at the maintenance of that monotherapy and cardiovascular outcomes. Wow, so what did they find? Dr. Peder Myhre: Yeah, Greg. They, in this extended followup study, had a total of 5.8 years median followup, and the primary endpoint occurred in 12.8% in the clopidogrel group versus 16.9% in the aspirin group, and that has a range of 0.74 with a 95% conference interval ranging from 0.63 to 0.86. So also the clopidogrel group had lower risk of the secondary thrombotic endpoint and the secondary bleeding endpoint while there was no significant difference in the incident on all caused death. So Greg, to conclude, these very interesting results from the primary analysis of the HOST-EXAM trial was consistent through the longer followup, and this support the use of clopidogrel over aspirin monotherapy from 12 months onwards after PCI. Dr. Greg Hundley: Very nice Peder, beautiful description and sounds like long-term clopidogrel use over aspirin was quite beneficial. Well, the next study comes to us from the world of preclinical science, and it is from the investigative group led by Dr. Yunzeng Zou from Shanghai Institute of Cardiovascular Diseases and the Zhongshan Hospital and Fudan University. Peder, the study pertains to diabetes. So diabetic heart dysfunction is a common complication of diabetes mellitus and cell death is a core event that leads to diabetic heart dysfunction. However, the time sequence of cell death pathways and the precise intervening time of particular cell death type remained largely unknown in diabetic hearts. And so, Peder, this study aimed to identify the particular cell death type that is responsible for diabetic heart dysfunction and propose a promising therapeutic strategy by intervening in this cell death pathway. Dr. Peder Myhre: Wow, Greg, that is really interesting. Heart dysfunction in diabetes is something that we really have to learn more about and I'm so excited to hear what these authors found, Greg. Dr. Greg Hundley: Right. So first, Peder, the authors identified necroptosis as the predominant cell death type at later stages in the diabetic heart. And then second, Peder, the CB2 receptor, and we'll call that CB2-R, recruits transcription factor Bach2 to repress necroptosis and protects against diabetic heart injury while hyperglycemia and MLKL in turn phosphorylates CB2-R to promote ubiquitous dependent degradation of CB2-R, thus forming a CB2-R centric feedback loop of necroptosis. And finally, Peder, cardiac CB2-R or Bach2 expression negatively correlates with both MLKL 10 expression and the extent of diabetic heart injuries in humans. And so the clinical implications of these findings, Peder, are that the CB2-R centric necrotic loop represents a promising target for the clinical treatment of diabetic heart injuries. Dr. Peder Myhre: So Greg, this paper that comes to us from corresponding author Amanda Paluch from University of Massachusetts Amherst, is a meta-analysis of eight prospective studies with device measured steps including more than 20,000 adults who were followed for CVD events. And the mean age of participants in this study was 63 years and 52% were women. And the participants were followed for a median of 6.2 years and 1,523 cardiovascular events occurred. So first, Greg, there was a significant difference in the association of steps per day in cardiovascular disease between older, that is greater or equal to 60 years, and younger, that is less than 60 years adults. So for older adults that has the ratio for cardiovascular disease using Q1 as reference was 0.80 for Q2, 0.62 for Q3, and 0.51 for Q4. And for younger adults that has ratio for cardiovascular disease using Q1 as reference was 0.79 for Q2, 0.90 for Q3, and 0.95 for Q4. And in the paper, Greg, there are some beautiful, restricted cubic lines that really illustrate the association between daily steps and the risk of cardiovascular disease among older adults and in younger adults. So the authors conclude that for older adults taking more daily steps is associated with a progressively lower risk of cardiovascular disease. And monitoring and promoting steps per day is a simple metric for clinician patient communication and population health to reduce the risk of cardiovascular disease. Dr. Greg Hundley: Well, Peder, we've got some other very interesting articles in this issue and how about we dive into that mail bag and discuss a few of those. So I'll go first. The first is a Perspective piece by Professor Powell-Wiley entitled “Centering Patient Voices through Community Engagement in Cardiovascular Research.” A very important topic where can those in the community actually help us design meaningful outcomes for our research initiatives? And next Peder, there is a Research Letter from Professor Evans entitled “Increasing Mononuclear deployed Cardiomyocytes by Loss of E2F7/8, and does that fail to improve cardiac regeneration post myocardial infarction?” Dr. Peder Myhre: Thanks, Greg. We also have an ECG Challenge by Dr. Li entitled, “What Is The Truth Behind Abnormal ECG Changes?” And this is describing a very rare and interesting cause of ST segment elevation. I recommend everyone to read that case. We also have our own Nick Murphy who gives us the Highlights from the Circulation Family of Journals where he summarizes five papers from the Circulation subspecialty journals. First, the experience with a novel visually assisted ablation catheter is reported in circulation A and E. The impact of various exercise training approaches on skeletal muscle in heart failure with preserved the F is presented in circulation heart failure. Gaps in heart failure treatment over a decade are reported in circulation cardiovascular quality and outcomes, and the associations of machine learning approaches to plaque morphology from coronary CTA with ischemia are reported in circulation cardiovascular imaging. And finally, Greg, an observational study of left main PCI at sites with and without surgical backup is reported in circulation cardiovascular interventions. Let's go on to the feature paper today describing the cardiac troponin I interacting kinase and the impact on cardiomyocyte S phase activity. Dr. Greg Hundley: Great, let's go. Welcome listeners to this January 10th feature discussion. Very interesting today as we are going to delve into the world of preclinical science. And we have with us today Dr. Loren Field and Dr. Sean Reuter from University of Indiana in Indianapolis, Indiana. And our own associate editor, Dr. Thomas Eschenhagen from University Medical Center of Hamburg in Hamburg, Germany. Welcome gentlemen. Well, Loren, we're going to start with you. Can you describe for us some of the background information that went into the preparation of your study, and what was the hypothesis that you wanted to address? Dr. Loren Field: Sure. This study actually came about in a rather roundabout fashion. We were doing a study with Kai Wollert in Hanover, Germany, where we were looking at the impact of a CXCR4 antagonist, which is used to mobilize stem cells from the bone marrow. And we had sent our mice over to Kai's lab and we have a mouse model that allows us to track S phase activity in cardiac myocytes, so these are cells are starting to replicate. And Kai crossed them into a different genetic background. And when he sent the mice back to us to analyze the hearts, we observed that we saw things that we never saw before in our experiments here. His injury model was different than ours and now the mouse also had a genetic background, so we had to spend about a year to figure out if it was the injury model or the background. It turned out to be the genetic background, and the phenotype was these mice had about a 15-fold elevated level of cell cycle reentry. So then it became a relatively simple genetics game where we took the progenitor mice, made F1 animals, looked for the phenotype, did backcross animals, and basically identified the gene responsible for the phenotype. Dr. Greg Hundley: Very nice. And so in this study moving forward, what hypothesis did you want to address? Dr. Loren Field: Well, the main hypothesis was to figure out what the gene was and then secondarily to figure out the degree of cell cycle progression. When the cell is proliferating, the first task is to replicate its genome, which is S phase activity that's followed by the nuclei dividing and then finally by the cell itself becoming two cells. So our task was to identify, first, the gene and secondly, how far through the cell cycles the cells progressed. Dr. Greg Hundley: Very nice. And how did you construct your experiment? Dr. Loren Field: It was, again, very straightforward. It was simply setting up the appropriate genetic crosses to produce the animals. For the past 10, 15 years, we've been developing a computer assisted assay that allows us to identify the anatomical position of S phase positive cardiac myocytes in sections of the heart. And basically, we apply that program to the different genetic backgrounds and after that it's a ball of mapping studies, QTL mapping. Dr. Greg Hundley: So really mechanistic understanding. Well listeners, we're next going to turn to Sean, and Sean, can you describe for us your study results? Dr. Sean Reuter: Yes, as Loren stated, we saw a 15-fold increase in the S phase activity within the remote zone. Now we partition the heart in three different zones after injury, so the scar, the border zone, and then the remote zone or injury. And as Loren stated, we saw a 15-fold increase in the S phase activity, cell cycle activity, in the remote zone. And it's only because we have this system in hand that we can anatomically map the S phase activity within the heart that we were able to detect and also quantify this. And I think that's the reason we discovered this particular phenotype. But in addition to that, we performed RNA-seq or Exome sequencing and discovered that TNNI3K was the responsible gene for elevated S phase activity within the remote zone and border zone, but interestingly not in the scar. Dr. Greg Hundley: Very interesting, Sean, and so describe for us the importance of the TNNI3K and its relationship to this S phase. Dr. Sean Reuter: Sure. This particular gene was first discovered around 2000, and it's been studied for a while now, but the targets of this kinase specifically expressed in the heart, and it does get elevated after injury, but the actual targets are not well described or well known. It's believed that it phosphorylates some mild filament fibers and structural proteins, but the actual mechanism and the consequence of this is not known. So when we saw this in the remote zone, the elevated S phase, our current theory is that we believe that it's probably increasing oxidative stress that would basically further out from the at-risk zone or the border zone and then it now is in the remote zone. So we think it's just causing the heart, a pathological area of the heart, basically to expand. And so that's our current theory. Other groups have published on the oxidative stress in over expression of TNNI3K as well. Dr. Greg Hundley: Very nice. Well listeners, next we are going to turn to our associate editor, Thomas many articles come your way and come across your desk. What attracted you to this particular article, and how do we put its results really in the context of cardiac regeneration? Dr. Thomas Eschenhagen: Indeed, there were several arguments. It's a cool paper and the whole field is still very important. As probably most of you know, the field have a rough ride over the last 20 years, went up and down, lots of bad findings. And in the end it turns out that we are there where we have been 20 years ago, the mammalian heart essentially doesn't regenerate. So anything which would improve that would be of very major importance. Why is it a good paper? Because it starts from a very clear finding, one mouse, which looks like strongly regenerating after MI, another mouse line, which doesn't. And so by applying, let's say, classical genetic, very stringent methodology, Loren Field and his group identified this troponin I kinase to be the culprit. And they also proved it, because putting it back in the strain with a low, so-called, regeneration brought it back to the other level. So it's a very clear, nice methodology. And finally, it's also a bit provocative because others in a very prominent paper, actually, have shown that this kinase... Or they concluded more or less just the opposite. The reason for the discrepancy is not quite clear and I was very happy to learn that the two groups actually discussed about it. So it's not just a bad controversy, but something which brings forward science. And finally, I think something we didn't talk about yet today, what I particularly liked, maybe the most, on this paper is that this group didn't stop at the point of DNA synthesis. Everybody else would've probably said, "Okay, here we are, one regenerate the other doesn't." But in the very important extra finding of this paper is that this is just increased DNA synthesis and not more myocytes. And this distinction is so critical to the field because people forget that adult mammalian cardiomyocytes often have several nuclei and individual nuclei have more than one set of chromosomes, so this polyploid. And so if you see DNA synthesis like in this paper, it doesn't necessarily mean more myocytes. And actually here it was shown that it is not more myocytes but more polyploidization and making this difference so clear, I think it's a very important contribution to the field. Dr. Greg Hundley: Very nice. Well, listeners, we're going to turn back to each of our guests today and we'll start with you Loren. Based on your results, what do you see as the next study moving forward in this sphere of research? Dr. Loren Field: I think these results made me appreciate for the first time that the intrinsic level of cell cycle reentry, that's just the S phase, not the cell division, is actually much higher than I had thought previously. And this was because we just fortuitously, or I guess anti-fortuitously, we're using a strain that had low levels of S phase induction. If you calculate the turnover, if every nucleus that it synthesized DNA actually went on to have that cell divide, you could replace a 50% loss of myocytes over the course of about 550 days, give or take. And to me, that's actually telling me that if we could push those cells from just being polypoid, as Thomas was saying, to actually go through cytokinesis, there would be enough intrinsic activity to go forward. So this really tells me that what we should be focusing on is now not trying to induce cell cycle, but to allow the cells that are entering the cell cycle to actually progress through it. Dr. Greg Hundley: Very nice. And Sean? Dr. Sean Reuter: Yes, well, echoing Loren's point there, it's really not necessarily cell cycle induction, it's cell cycle completion to the cytokinetic fate. And that's the key. If we can get to that point, if we can figure out the mechanism to get to that point, then we have a wonderful discovery. However, we're not quite there yet, but we hope to be. Dr. Greg Hundley: And Thomas. Dr. Thomas Eschenhagen: Well, nothing to add really from my side, except that I would like to know what this Troponin I kinase does, because that is somehow still a missing link. How does this kinase lead to more DNA synthesis or the initiation of cell cycling? That would be an important finding and I'm sure there will be more research going on. Particularly also, to solve this discrepancy, I mean, there must be something in it and we don't quite yet know how, but I think we are in a good way. I'm sure there will be papers showing that soon. So I think that's, again, a very good start for this discussion. Dr. Greg Hundley: Well, listeners, we want to thank Dr. Loren Field, Dr. Sean Reuter and Dr. Thomas Eschenhagen for bringing us this really informative study in mammalian myocellular regeneration, highlighting that the level of cardiomyocyte cell cycle reentry in hearts expressing TNNI3 kinase would lead to significant regenerative growth if each cardiomyocyte exhibiting S phase activity was able to progress through cytokinesis. And this in turn suggests that identification of factors which facilitate cardiomyocyte cell cycle progression beyond S phase will be key to unlocking the intrinsic regenerative capacity of the heart. Well, on behalf of Carolyn, Peder and myself, we want to wish you a great week and we will catch you next week on the run. This program is copyright of the American Heart Association 2023. The opinions expressed by speakers in this podcast are their own and not necessarily those of the editors or of the American Heart Association. For more, please visit ahajournals.org.

In the final part of our three-part series on the value of Quality Tolerance Limits (QTLs), WCG Senior Advisor Linda Sullivan talks about methods for early detection of risk with Steve Young and Keith Dorricott. Young is the Chief Scientific Officer at CluePoints, who oversees the research and development of advanced methods for data analytics, data surveillance, and risk. Dorricott is a Master Black Belt who has spent more than 15 years applying process improvement techniques to address clinical research problems. Young and Dorricott co-led a groundbreaking, WCG Metrics Champion Consortium QTL working group, which developed a guidance on QTL emerging best practices for Consortium members. In April 2022, a three-part series summarizing those discussions and recommendations was published on the Applied Clinical Trials Journal website: https://www.appliedclinicaltrialsonline.com/view/defining-quality-tolerance-limits-and-key-risk-indicators-that-detect-risks-in-a-timely-manner-reflections-from-early-adopters-on-emerging-best-practices-part-1 The three-part podcast series is designed to be a valuable podcast companion resource to the website article. In this episode, Young, Dorricott, and Sullivan discuss how to set the threshold and secondary tolerance limits to be proactive in dealing with risks, noting that it is sometimes difficult to determine. The group also talks about when to consider using leading indicator metrics as QTL parameters and/or companion KRI metrics to provide an earlier signal of that a risk will exceed the threshold before the end of the study. The first part of the series focused on the relationship between QTLs and KRIs. The second part of the series discussed the process of defining QTLs. If you subscribe to CTO, you will automatically receive those episodes. Want to suggest a topic for CTO? Just email Linda Sullivan at lsullivan@wcgclinical.com

In the second installment of our three-part series on defining and using Quality Tolerance Limits (QTLs), WCG Senior Advisor Linda Sullivan talks about the process of defining QTLs with Steve Young and Keith Dorricott. Young is the Chief Scientific Officer at CluePoints, who oversees the research and development of advanced methods for data analytics, data surveillance, and risk. Dorricott is a Master Black Belt who has spent more than 15 years applying process improvement techniques to address clinical research problems. Young and Dorricott co-led a groundbreaking, WCG Metrics Champion Consortium QTL working group, which developed a guidance on QTL emerging best practices for Consortium members. In April 2022, a three-part series summarizing those discussions and recommendations was published on the Applied Clinical Trials Journal website: https://www.appliedclinicaltrialsonline.com/view/defining-quality-tolerance-limits-and-key-risk-indicators-that-detect-risks-in-a-timely-manner-reflections-from-early-adopters-on-emerging-best-practices-part-1 The three-part podcast series is designed to be a valuable podcast companion resource to the website article. In this episode, Young, Dorricott, and Sullivan discuss what factors go into the decision of whether to establish a QTL; the importance of key potential study failure points in developing QTLs; and how to determine the number of QTLs that should be implemented. The first part of this series discussed the relationship between QTLs and KRIs and the final installment of the series will focus on methods for early detection of risk. If you subscribe to CTO, you will automatically receive those episodes. Want to suggest a topic for CTO? Just email Linda Sullivan at lsullivan@wcgclinical.com

In the first of this three-part series on defining and using  Quality Tolerance Limits (QTLs), WCG Senior Advisor discusses the relationship between QTLs & KRIs  with Steve Young and Keith Dorricott. Young is the Chief Scientific Officer at CluePoints, who oversees the research and development of advanced data analytics, data surveillance, and risk methods. Dorricott is a Master Black Belt who has spent more than 15 years applying process improvement techniques to address clinical research problems. Young and Dorricott co-led a groundbreaking, MCC QTL working group, which developed a guidance on QTL emerging best practices for Consortium members. In April 2022, a three-part series summarizing those discussions and recommendations was published on the Applied Clinical Trials Journal website here. The three-part podcast series is designed to be a valuable podcast companion resource to the website article. In this episode, Young, Dorricott, and Sullivan discuss parameters and thresholds as they apply to QTL and KRIs, concluding QTLs should be considered as a designated subset of KRIs. The group also discusses the working group's mission; its process for determining best practices; the vendor perspective on QTLs; the importance of tracking risk; and the relationship of QTLs to centralized monitoring. The second part of the series will concentrate on defining QTLs and the final part of the series will focus on methods for early detection of risk. If you subscribe to CTO, you will automatically receive those episodes. Want to suggest a topic for CTO? Just email Linda Sullivan at lsullivan@wcgclinical.com

As we get older, every moment should count. Its called QTL or quality time left. How can we make the most of every day and live our remaining days exactly the way we want to? Dr. B. and host Linda Corley talk about their personal roadmap in order to achieve that life you've always been dreaming about.———Welcome to The Breakdown with Doctor B., a psychologically healing conversation with well-known psychiatrist Arthur Bregman MD.  Every week Dr. B. and host Linda Corley break down issues and problems from a mental health perspective. From the incessant stresses of the pandemic to untangling relationship problems, Dr. B's years of experience help piece together the messiest of life's problems.

This is a rare opportunity for us to get a glimpse into the brain of one of the key executives behind the Qualcomm Technology Licensing business.   Our guest today, is John Han, Senior Vice President & General Manager of Qualcomm Technology Licensing (QTL), a division of Qualcomm Incorporated.   Here are the questions we covered during our conversation:   John, could you please introduce yourself and your role within Qualcomm to our audience? Your bio says you work for “QTL” and in IP. I thought Qualcomm made wireless chips? Who are your customers? How does it work?   Oh, so you’re one of those companies that buys patents and sues people when they use them?   Qualcomm doesn’t have the most patents. How can it say it has the most valuable portfolio of patents?   There are sone key terms that are important in the licensing game. FRAND is one of them. I always am thinking, “can’t we just be FRANDS?”   is the wild west, but there are also the terms “hold up” and “hold out”. What on earth are these and why should we care?   your recent earnings call, it sounded like you had signed up every single smart phone maker with 5G licenses. Is this a sweep?   had previously been under intense scrutiny for how you treated your customers and IP. But none of these jurisdictions asked you to change anything about your program, how it’s structured. So what’s the secret to the success?   We’re hearing about 5G in cars, in buildings, in PCs. Are we done with 5G?   Disclaimer: This show is for information and entertainment purposes only. While we will discuss publicly traded companies on this show. The contents of this show should not be taken as investment advice. 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.16.385633v1?rss=1 Authors: Chen, L., Jiang, Y., Yao, B., Huang, K., Liu, Y., Wang, Y., Qin, X., Saykin, A. J., Wang, Y. Abstract: Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies (GWAS) or quantitative trait locus (QTL) analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for for prioritizing non- coding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first "context-free" mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict noncoding variants of unknown and diverse context. The second "context-dependent" mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.22.351221v1?rss=1 Authors: Schilder, B. M., Humphrey, J., Raj, T. Abstract: echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium (LD) panels, quantitative trait loci (QTL) datasets, genome-wide annotations, cell type-specific epigenomics, thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.19.253989v1?rss=1 Authors: Lin, C., Inoue, M., Li, X., Bosak, N., Ishiwatari, Y., Tordoff, M., Beauchamp, G. K., Bachmanov, A., Reed, D. R. Abstract: Mice of the C57BL/6ByJ (B6) strain have a higher consumption of, and stronger peripheral neural responses to, sucrose solution than do mice of the 129P3/J (129) strain. To identify quantitative trait loci (QTLs) responsible for this strain difference and evaluate the contribution of peripheral taste responsiveness to individual differences in sucrose intake, we produced an intercross (F2) of 627 mice, measured their sucrose consumption in two-bottle choice tests, recorded the electrophysiological activity of the chorda tympani nerve elicited by sucrose in a subset of F2 mice, and genotyped the mice with DNA markers distributed in every mouse chromosome. We confirmed a sucrose consumption QTL (Scon2, or Sac) on mouse chromosome (Chr) 4, harboring the Tas1r3 gene, which encodes the sweet taste receptor subunit T1R3 and affects both behavioral and neural responses to sucrose. For sucrose consumption, we also detected five new main-effect QTLs Scon6 (Chr2), Scon7 (Chr5), Scon8 (Chr8), Scon3 (Chr9) and a sex-specific QTL Scon9 (Chr15), and an interacting QTL pair Scon4 (Chr1) and Scon3 (Chr9). No additional QTLs for the taste nerve responses to sucrose were detected besides the previously known one on Chr4 (Scon2). Identification of the causal genes and variants for these sucrose consumption QTLs may point to novel mechanisms beyond peripheral taste sensitivity that could be harnessed to control obesity and diabetes. Copy rights belong to original authors. Visit the link for more info

On this special episode of The Six Five - Insiders Edition, hosts Patrick Moorhead and Daniel Newman are joined byQualcomm EVP and QTL President Alex Rogers to discuss Qualcomm’s recent win in the Court of Appeals, its licensing business, 5G intellectual property, and how Qualcomm helping spread the technology around the world.   In our conversation, Alex, Patrick and I explored Qualcomm’s recent victory in the Ninth Circuit Court of Appeals which overturned a 2019 ruling that Qualcomm had violated antitrust law. Alex explained that Qualcomm has a unique business model with technology development on one side and licensing and patents on the other side. The court ruled that Qualcomm is allowed to license patents separately from chip sales without violating laws. This is a huge win for business model innovation.   Qualcomm’s two sides of business. Alex shared more about the different sides of Qualcomm’s business model. First, the Qualcomm Technology Licensing (QTL) business takes the intellectual property that Qualcomm develops and seeks to get a fair return by way of royalty payments. Essentially, QTL is licensing other companies to use its technology. The royalty payments in turn, continue to drive and fund the innovation that is happening in Qualcomm’s semiconductor business. The semiconductor business makes the chipsets that drive the implementation of the technology.   Qualcomm’s role in 5G technology. Alex explored all the vital roles that Qualcomm fills in the development and production of 5G technology including research, standards, and implementation. They are a leader in all aspects of 5G, but most importantly, Qualcomm helps OEMs all over the world bring the technology to market. Essentially, Qualcomm is enabling cellular and the mobile industry to move forward.   Getting the technology to succeed. We also discussed how QTL utilizes engineers to offer services to licensees to ensure the technology is implemented successfully. The company offers training modules, consultations, and testing. They even built labs around the world so licensees can test implementations before releasing to the public. Qualcomm’s interest isn’t for self-gain, but rather to spread 5G technology around the world.   5G will be a gamechanger. Finally, we explored what the future holds for 5G and how our lives will forever be altered by widespread adoption. In the past, technologies like GPS have altered the way we live, work, and connect. 5G will be no different. Alex believes — as do we — that we are on the cusp of something revolutionary and it’s only a matter of time before we will start to see the impact on the implementations.   If you’d like to read more about Qualcomm, QTL, and the advancements in 5G, be sure to check out their website. While you’re at it, be sure to subscribe so you never miss an episode of The Six Five Podcast.   Disclaimer: This show is for information and entertainment purposes only. While we will discuss publicly traded companies on this show. The contents of this show should not be taken as investment advice. 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.13.249342v1?rss=1 Authors: Kao, C.-H., Wu, P.-Y., Yang, M.-H. Abstract: Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These detection methods attempt to address some of the concerns, including the correlation structure among traits, the magnitude of LOD scores within a hotspot and computational cost, that arise during the process of QTL hotspot detection. In this article, we describe a statistical framework that can handle both types of data as well as address all the concerns at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computation cost, and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top profile, are introduced into the framework. The trait grouping attempts to group the closely linked or pleiotropic traits together to take care of the true linkages and cope with the underestimation of hotspot thresholds due to non-genetic correlations (arising from ignoring the correlation structure among traits), so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top profile is designed to outline the LOD-score pattern of a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD score distributions. Real examples, numerical analysis and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.07.241554v1?rss=1 Authors: Methorst, R., de Borst, G. J., Pasterkamp, G., van der Laan, S. W. Abstract: Background and aims: Atherosclerosis is a lipid-driven inflammatory disease presumably initiated by endothelial activation. Low vascular shear stress is known for its ability to activate endothelial cells. Differential DNA methylation (DNAm) is a relatively unexplored player in atherosclerotic disease development and endothelial dysfunction. Literature search revealed that expression of 11 genes have been found to be associated with differential DNAm due to low shear stress in endothelial cells. We hypothesized a causal relationship between DNAm of shear stress associated genes in human carotid plaque and increased risk of cardiovascular disease. Methods: Using Mendelian randomisation (MR) analysis, we explored the potential causal role of DNAm of shear stress associated genes on cardiovascular disease risk. We used genetic and DNAm data of 442 carotid endarterectomy derived advanced plaques from the Athero-Express Biobank Study for quantitative trait loci (QTL) discovery and performed MR analysis using these QTLs and GWAS summary statistics of coronary artery disease (CAD) and ischemic stroke (IS). Results: We discovered 9 methylation QTLs in plaque for differentially methylated shear stress associated genes. We found no significant effect of shear stress gene promotor methylation and increased risk of CAD and IS. Conclusions: Differential methylation of shear stress associated genes in advanced atherosclerotic plaques in unlikely to increase cardiovascular risk. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.28.176206v1?rss=1 Authors: Buttini, M., Thomas, M. H., Gui, Y., Garcia, P., Karout, M., Jaeger, C., Hodak, Z., Michelucci, A., Kollmus, H., Centeno, A., Schughart, K., Balling, R., Mittelbronn, M., Nadeau, J., Williams, R. W., Sauter, T., Sinkkonen, L. Abstract: The features of dopaminergic neurons (DAns) of nigrostriatal circuitry are orchestrated by a multitude of yet unknown factors, many of them genetic. Genetic variation between individuals at baseline can lead to differential susceptibility to and severity of diseases. As decline of DAns, a characteristic of Parkinson disease, heralds a significant decrease in dopamine level, measuring dopamine can reflect the integrity of DAns. To identify novel genetic regulators of the integrity of DAns, we used the Collaborative Cross (CC) mouse strains as model system to search for quantitative trait loci (QTLs) related to dopamine levels in the dorsal striatum. The dopamine levels in dorsal striatum varied greatly in the eight CC founder strains, and the differences were inheritable in 32 derived CC strains. QTL mapping in these CC strains identified a QTL associated with dopamine level on chromosome X containing 393 genes. RNA-seq analysis of the ventral midbrain of two of the founder strains with large striatal dopamine difference (C57BL/6J and A/J) revealed 24 differentially expressed genes within the QTL. The protein-coding gene with the highest expression difference was Col4a6, which exhibited a 9-fold reduction in A/J compared to C57BL/6J, consistent with decreased dopamine levels in A/J. Publicly available single cell RNA-seq data from developing human midbrain suggests that Col4a6 is highly expressed in radial glia-like cells and neuronal progenitors, indicating possible involvement in neurogenesis. Interestingly, the lowered dopamine levels were accompanied by reduced striatal axonal branching of striatal DAns in A/J compared to C57BL/6J. Because Col4a6 is known to control axogenesis in non-mammal model organisms, we hypothesize that different dopamine levels in mouse dorsal striatum are due to differences in axogenesis induced by varying COL4A6 levels during neural development. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.05.137190v1?rss=1 Authors: Borrelli, K. N., Dubinsky, K. R., Szumlinski, K. K., Carlezon, W. A., Chartoff, E. H., Bryant, C. D. Abstract: Rationale: Addiction to methamphetamine (MA) is a public health issue in the United States. While psychostimulant use disorders are heritable, their genetic basis remains poorly understood. We previously identified heterogeneous nuclear ribonucleoprotein H1 (Hnrnph1; H1) as a quantitative trait gene underlying sensitivity to MA-induced locomotor activity. Mice heterozygous for a frameshift deletion in the first coding exon of H1 (H1+/-) showed reduced MA-induced locomotor activity, dopamine release, and dose-dependent differences in MA conditioned place preference. However, the effects of H1+/- on innate and MA-modulated reward sensitivity are not known. Objectives: We examined innate reward sensitivity and modulation by MA in H1+/- mice via intracranial self-stimulation (ICSS). Methods: We used intracranial self-stimulation (ICSS) of the medial forebrain bundle to assess shifts in reward sensitivity following acute, ascending doses of MA (0.5-4.0 mg/kg, i.p.) at 10 min or 2 h post-MA. We also assessed video-recorded behaviors during ICSS testing sessions. Results: Ten min post-MA, H1+/- mice displayed reduced maximum response rates, H1+/- females had lower M50 values than wild-type females, and H1+/- influenced ICSS responding relative to maximum control rates (MCR). Two h post-MA, higher response rates were observed in females, irrespective of genotype. There was a dose-dependent reduction in distance to the response wheel 10 min post-MA and reduced immobility time in the perimeter corners both 10 min and 2 h post-MA. Conclusions: H1+/- mice displayed altered MA-induced reward modulation in a time-, sex-, and dose-dependent manner. This expands the set of MA-induced phenotypes observed in H1+/- mice. Keywords: Intracranial Self-Stimulation (ICSS), methamphetamine, genetics, addiction, behavioral genetics, psychostimulants, mouse, forward genetics, QTL, sensitization Copy rights belong to original authors. Visit the link for more info

Transcribed highlights of the show can be found in our episode summaries. Last week, John MacArthur celebrated 50 years in the pastorate at a conference at his congregation Grace Community Church. During the event, MacArthur accused the Southern Baptist Convention of taking a “headlong plunge” toward allowing women preachers after women spoke at the SBC’s 2019 annual meeting. That, he said, was a sign the denomination no longer believed in biblical authority.“When you literally overturn the teaching of Scripture to empower people who want power, you have given up biblical authority,” said MacArthur, as a Religious News Service story reported. A moderator also asked MacArthur and his fellow panelists to offer their gut reactions to one- or two-word phrases. When the moderator said “Beth Moore,” MacArthur replied, “Go home.” MacArthur has never shied away from controversy. Last year, he helped organize a controversial statement responding to social justice. He has frequently spoken out against the modern Charismatic movement. Part of the impetus behind MacArthur’s tendency to speak out comes from how he understands his belief in a high authority of Scripture, says Jonathan Holmes, a pastor and counselor who graduated from the Master’s College and worked there for several years. “There are a lot of things many evangelicals would say are non-essentials, for instance, a woman's role in the church, or drinking, or dancing, or creation or the end times,” said Holmes. “But those all become major touchpoints for MacArthur because his view of Scripture is such that if you budge on the grammatical, literal interpretation of the Bible in any of these areas, the whole thing begins to fall apart.” Holmes joined digital media producer Morgan Lee and editor in chief Mark Galli to discuss whether MacArthur is a fundamentalist or evangelical, whether he has ever changed his mind with regards to his own theological convictions, and what to make of a Master’s Seminary grad preaching at one of Kanye West’s Sunday Services. Today's episode of Quick to Listen is brought to you in part by Baylor University’s Truett Seminary, where Kingdom-minded women and men are equipped to follow their callings. By learning to think theologically, developing ministry skills, cultivating a community of support, and engaging in spiritual formation, Truett students are uniquely prepared to make an impact in the Church and the world. Learn more at baylor.edu/truett. This episode of Quick to Listen is also brought to you by Christianbook.com. With over 500,000 products to choose from, Christianbook.com brings everything Christian right to your fingertips. Go to Christianbook.com. This episode is also brought to you by the Wheaton College Graduate School. Respected and represented the world over, the Master of Arts in Marriage and Family Therapy at the Wheaton College Graduate School will inspire, challenge, and equip you to be a servant scholar for Christ and His Kingdom. Learn more at wheaton.edu/QTL.

W dzisiejszym podcascie dowiesz się następujących rzeczy:- co zainspirowało mnie do zgłębienia tego tematu,- poznasz niezwykłe historie bliźniąt rozdzielonych po narodzinach, które spotkały się po latach,- dowiesz się, dlaczego badania bliźniąt są tak ważne w genetyce behawioralnej, - czy bylibyśmy w stanie stworzyć superczłowieka, modyfikując jego geny. - oraz wstępnie dowiesz się, czym jest hipoteza QTL i genów generalistów, wstępnie ponieważ te tematy będą rozwijane głównie w kolejnych odcinkach.

Jane Ferguson: Hi everyone. Welcome to episode 20 of Getting Personal Omics of the Heart, the podcast brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Council on Genomic and Precision Medicine. I'm Jane Ferguson from Vanderbilt University. It's September 2018 and let's dive straight into the papers from this month's issue of Circulation: Genomic and Precision Medicine.                                 We're starting off with some pharmacogenomics. Bruce Peyser, Deepak Voora and colleagues from Duke University published an article entitled, "Effects of Delivering SLCO1B1 Pharmacogenetic Information in Randomized Trial and Observational Settings."                                 Although statins are generally well tolerated, 5-15% of patients taking statins for LDL lowering and cardiovascular protection end up developing statin associated muscular symptoms. Because onset of muscular symptoms associated with discontinuing statin use, as well as increased cardiovascular morbidity, there is a clear need to identify ways to prevent or reduce symptoms in these people. Variants affecting statin related myopathy have previously been discovered through GWAS, including a variant in the SLCO1B1 gene, which also has been shown to relate to statin myalgia and discontinuation of statin use. The risks appear to be greatest with simvastatin, indicating the people at risk of muscle complications may do better on either low-dose Simvastatin or another statin. However, there's still some uncertainty surrounding the risks and benefits of various statins as they pertain to risk of muscular symptoms.                                 The authors have previously shown that pharmacogenetics testing led to increased number of people reporting statin use, but effects of pharmacogenetic testing on adherence, prescribing, and LDL cholesterol had never been tested in a randomized control trial. In this study, they randomized 159 participants to either genotype informed statin therapy or usual care, and then followed them for months to eight months.                                 25% of participants were carriers of the SLCO1B1 star five genotype. The authors found that statin adherence was similar in both groups, but gene type guided therapy resulted in more new statin prescriptions and significantly lower LDL cholesterol at three months, and levels that were lower but no longer significantly different at eight months.                                 In individual's randomized to usual care who then crossed over to genotype informed therapy after the trial period ended, there was an additional decrease in LDL cholesterol. Overall, genotype informed statin therapy led to an increase in re-initiation of statins and decreases in LDL cholesterol, but did not appear to affect adherence.                                 The authors also examined the effects of commercial genetic testing for SLCO1B1 variants in an observational setting by looking at over 92000 individuals with data available in the EHR. They found the people who receive genetic testing results had a larger drop in LDL cholesterol compared to untested controls. Overall, the study indicates that carriers of the SLCO1B1 risk variant may benefit from genotype informed statin therapy, while for non-carriers receiving their results may has limited effects.                                 If you want to read more on this, Sony Tuteja and Dan Rader from UPenn wrote an editorial to accompany this article, which was published in the same issue.                                 We're staying on the topic of statins and LDL for our next paper. This article comes from Akinyemi Oni-Orisan, and Neil Risch and colleagues from the University of California and is entitled, "Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Utilizing Electronic Health Records in a Large Population-Based Cohort."                                 They were interested in understanding what determines variation in statin induced LDL reduction, particularly the genetic component, and they used a large EHR derived data set, the Kaiser Permanente Genetic Epidemiology research on adult health and aging cohort to address this important question. An EHR dataset does have intrinsic limitations, but also has some clear strengths, not only as a readily available and cost-effective data source for large sample sizes, but also because it reflects real world clinical care in diverse individuals, which is not always well represented within the selective constraints of a randomized trial.                                 There were over 33000 individuals who met their inclusion criteria. To account for differences in potency between different statins and doses, the authors generated a defined daily dose value, with one defined daily dose equal to 40 milligrams per day of Lovastatin. The slope of the dose response was similar across statin types and across different sex and race or ethnicity groups. But there were differences by statin type in the response independent of dose, as well as differences in absolute responses by sex, age, race, smoking, and diabetes.                                 Based on these differences, the authors revised the defined daily doses and they highlight how previously defined equivalencies between different statins may not be accurate. They found that individuals with East Asian ancestry had an enhanced response to therapy compared with individuals of European ancestry.                                 The authors identified related individuals within the data set and the estimated heritability of statin response using parent-offspring and sibling pairs. They found only modest heritability, indicating that non-genetic factors may be more important in determining variability in statin response. Overall, this large single cohort study adds to our knowledge on determinants of statin response and raises further questions on the relative effects of different statins and doses within patient subgroups.                                 Okay, so now let's talk about GWAS and Athero. Sander van der Lann, Paul de Bakker, Gerard Pasterkamp and coauthors from University Medical Center Utrecht published a paper entitled, "Genetic Susceptibility Loci for Cardiovascular Disease and Their Impact on Atherosclerotic Plaques."                                 Over the past decade, genome-wide association studies in large cohorts have been very successful in identifying cardiovascular risk loci. However, relating these to subclinical disease or two mechanisms has been more challenging. The authors were interested in understanding whether established GWAS loci for stroke and coronary disease are associated with characteristics of atherosclerotic plaque, the idea being that some of the risk loci may alter disease risk by determining the development and stability of plaque. They identified seven plaque characteristics to study and histological samples, including intraplaque fat, collagen content, smooth muscle cell percentage, macrophage percentage, calcification, intraplaque hemorrhage, and intraplaque vessel density.                                 They selected 61 known loci and examined association of those SNiPA with black phenotypes in over 1400 specimens from the athero express biobank study. Out of the 61 loci, 21 were associated with some black phenotype compared with zero of five negative control loci, which were chosen as established GWAS loci for bipolar disorder, which, presumably, should share limited mechanistic etiology with plaque. They used the software package VEGAS to run gene-based analyses. They also assessed SNiPA relationships with gene expression and methylation in multiple tissues derived from two independence Swedish biobanks, which included atherosclerotic arterial wall, internal mammary artery, liver, subcutaneous fat, skeletal muscle, visceral fat, and fasting whole blood.                                 One CAD locus on chromosome 7q22 that survived correction for multiple testing was associated with intraplaque fat, and was also an EQTL for expression of several genes across multiple tissues. In addition, it was also a methylation QTL.                                 The authors focused on this locus and looked at correlation of expression within the LDL receptor and noted associations with HDL and LDL cholesterol in the global lipids genetics contortion data, which suggests that this locus may have a role in the metabolism. At this locus, the HBP1 gene expressed foam cells may be an interesting candidate as a causal gene in determining plaque-lipid accumulation and cardiovascular risk.                                 So next up, we have a paper that is also about athero and is coauthored by many of the same group as did that previous study. So yeah, this group's productivity is kind of making the rest of us look bad this month. So Martin Siemelink, Sander van de Lann, and Gerard Pasterkamp and their colleagues published, "Smoking is Associated to DNA Methylation in Atherosclerotic Carotid Lesions."                                 Okay. So I think one of the few things we can all definitely agree on is that smoking is bad. But, does smoking exert any of its cardiovascular damage by altering within atherosclerotic plaques? That's the question this group set out to answer.                                 They carried out a two-stage epigenome-wide association study, or EWAS, with discovery and replication of differentially methylated loci with tobacco smoking within carotid arteriosclerotic plaques of a total of 664 patients undergoing carotid endarterectomy and enrolled in the arthero-expressed biobanks study. In discovery, they found 10 CpG loci within six genes that associated with smoking. Four of the CpG loci replicated. These four loci mapping within six genes showed reduced methylation in current smokers compared with former or never smokers.                                 However, there was no difference in specific plaque characteristics based on methylation at any of the four loci. There was also no significant difference in plaque gene expression at these loci based on smoking status. However, a SNiPA at a nearby locus located in the 3' UTR of the PLEKHGB4 Gene was associated with methylation at AHRR, and was a [inaudible 00:09:58] QTL for PLEKHGB4 of expression but not a AHRR expression. The authors speculate that PLEKHGB4 may co-regulate AHRR expression. The authors also examined blood methylation in a subset of the same subjects, and they were able to replicate previously identified CPG sites associated with smoking.                                 This is a really complex area, and it's hard to identify mechanisms and causality from these multiple layers of data, but the authors demonstrate the importance of using disease relevant tissues to start to understand how environmental factors interact with genetics and other underlying physiology to modify methylation and function within the vasculature.                                 Our final full-length research paper this issue from Brian Byrd and colleagues Michigan, is actually the subject of our interview today. So I won't go into too much detail on it right now, but keep listening for an interview with Brian about their paper, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension."                                 Our review article this month is about the "Dawn of Epitranscriptomic Medicine" from Konstantinos Stellos from Newcastle University and Aikaterini Gatsiou from Goethe-Universität Frankfurt. In this paper, they're taking us to the next level beyond just RNA, but towards RNA epigenetics. Given the large number of possible modifications that can and are made to RNA during RNA name metabolism, there's huge potential to gain a new biological and mechanistic understanding by studying the RNA epitranscriptome. I think we'll ignore this at our peril. So if you need to catch up on this new field, this comprehensive review will get you right up to speed.                                 Moving on, our research letters are short format papers that allow authors to present focused results. These are also a great avenue to submit findings from replication studies that might not necessitate a full-length paper. So if you have some data from a replication study that you've been procrastinating writing up, a short research letter is a great format to consider.                                 This month, Bertrand Favre, Luca Borradori and coauthors from Bern University Hospital published a letter entitled, "Desmoplakin Gene Variants and Risk for Arrhythmogenic Cardiomyopathy: Usefulness of a Functional Biochemical Assay." The desmoplakin is essential for the cell-cell adhesion complex's desmosomes. Mutations in this gene have been associated with a wide range of phenotypes, including some in skin and hair, but also in heart, which can manifest arrhythmogenic or dilated cardiomyopathy. This protein anchors intermediate filaments, so mutations that alter binding to intermediate filaments may pathogenicity.                                 The author selected seven reported amino acid altering mutations in desmoplakin, and they screened for effects on binding using a novel fluorescence binding assay. They found that three of the seven mutations had a clear impact on binding. This assay is a novel way to assess functional impact of desmoplakin variants, and may be useful to inform the severity of future phenotypes in individuals carrying a desmoplakin mutation.                                 Finally, if you want to stay up-to-date on the genetics of aortic disease and Marfan syndrome, you can find a letter from Christian Groth and colleagues and an author response from Norifumi Takeda and colleagues regarding their previously published paper on impact of pathogenic FBN1 variant types on the progression of aortic disease in patients with Marfan syndrome.                                 I am joined today by Dr. Brian Byrd from the University of Michigan, who is the senior author on a Manuscript published in this month's issue, entitled, "Human Urinary mRNA as a Biomarker of Cardiovascular Disease: A Proof-of-Principle Study of Sodium-Loading in Prehypertension."                                 So welcome Brian. Thanks so much for coming on the podcast. Brian Byrd:          Thank you for having me. Jane Ferguson: So before we get started, could you give a brief introduction of yourself to the listeners and maybe tell us a little bit about how you got into the field? Brian Byrd:          Absolutely. So I am a cardiologist and a physician scientist. I'm an assistant professor at the University of Michigan, where I have a laboratory engaged in clinical investigation. My background is that I did my Internal Medicine Residency at Vanderbilt University. And after I finished residency, I entered Nancy Brown's lab. She's the Chair of Medicine at Vanderbilt, as I know you're aware. And she had a laboratory focused, and still does have a laboratory focused, on the investigation of high blood pressure, with a lot of focus on understanding high blood pressure as it occurs in humans. And I got a Master of Science degree in clinical investigation while I was in her lab, and we did some work on a number of topics related to the renin-angiotensin-aldosterone system, which has been a long-standing interest of mine ever since then.                                 So, at the same time, I was learning how to take care of patients with very complex blood pressure problems, who required three, or four, or five, or six blood pressure medications, in some cases, to control. And it's with that background that I became very interested in the science that underlies treatment-resistant high blood pressure in people and what we might be able to do about that. Jane Ferguson: Wow. Nice. Yeah and I think that background of sort of the combination of clinical and experimental is really nice and really important. I think your paper actually exemplifies that really nicely, so using humans but also some basic science techniques and combining them to really have a very patient focused instead of mechanistic interrogation.                                 So as I mentioned, you just published this really nice manuscript using urine as a source of mRNA biomarkers, which has relevance to hypertension and potentially also to other diseases. But before we get sort of too much into the weeds on the specific details, for any of our listeners who didn't get a chance to read your paper yet, maybe you could briefly summarize what you did? Brian Byrd:          Okay, so the general overview of what we were interested in was that the patients who have treatment resistant high blood pressure tend to have a lot of activation of a receptor in the kidney called the mineralocorticoid receptor. And this receptor helps control salt in bladder in the body. Obviously the amount of salt in the blood stays very, very homeostatic, but we if eat more salt one day then the next and there needs to be a system to help regulate the homeostasis. And so, you waste more or less salt in the urine depending upon how much sodium you're taking in.                                 And one of the functions of the mineralocorticoid receptor is to control this salt and bladder regulation or to fine tune it anyway. And the reason we know that that's an important receptor in patients with treatment-resistant high blood pressure is because of a series of studies done by David Calhoun and Brian Williams and others, showing that mineralocorticoid receptor blockers, or antagonists, are very effective in the treatment of tough to control high blood pressure.                                 And so, we had some insight that there would be something interesting to study there, and one of the things that we knew was that the mineralocorticoid receptor is a ligand activated transcription factor. So when it gets activated by it's ligan which canonically is a steroid hormone from the adrenal gland aldosterone, the receptor, which is in the cytoplasm, ordinarily dimerizes and translocates to the nucleus, where it controls the regulation of a number of genes. We also were aware that cells secrete RNA, and others had the idea that it might be inside vesicles because there's a lot of ribonuclease and biofluids. And you would think it might be difficult to pass the RNA if it were sort of naked as it were.                                 And it turns out that that's right. If you, for example, introduced synthetic RNAs into biofluids, the RNAs will be gone very quickly in a matter of seconds. So, we had this idea that we might be able to look at RNA that was being secreted by cells probably in vesicles, and assay the activity of the receptor potentially. We weren't sure if that was going to be possible or not.                                 One of the things we did was we used part of the available data to look at the transcriptome of vesicles in the urine that had been isolated from 3300 milliliters of urine by ultracentrifugation [inaudible 00:18:57]. Jane Ferguson: So it's a big centrifusion. Brian Byrd:          Exactly. Jane Ferguson: Like you [inaudible 00:19:00] Brian Byrd:          It must have been some project. So that was the work of Kevin Miranda and colleagues, and we were able to compare that transcriptome to the transcriptome of human kidney cortex samples from the GTEx project, which a large consortium focused on human transcriptomics.                                 And that was sort of the first part of what we presented in this paper, and the second thing that we did was we looked within a crossover study in a collaboration with Scott Hummel, one of my close collaborators here at the University of Michigan. We looked at individuals who had been put on a low salt diet activating renin-angiotensin-aldosterone system and causing more activation of the mineralocorticoid receptor. And then, those same individuals underwent saline infusion, so salt loading, and we knew that that would suppress the renin-angiotensin-aldosterone system. And we measured the [inaudible 00:20:02] measures of the renin-angiotensin-aldosterone system, but we also took the urine samples that had been recently banked from that experiment and we centrifuged them to try to palette the cells. We took the supernatant and we extracted RNA after trying to enrich for extracellular vesicles.                                 And with that approach, we measure targets that we thought would be regulated my the mineralocorticoid receptor, as well as some things that we did not think would be regulated by mineralocorticoid receptor. So that's the general overview of what we undertook. Jane Ferguson: Great. Right. So it's very cool. I guess we can break it down into sort of the two different parts, because I think it was a really nice examples of using public data to sort of start addressing your question and then actually doing a human experiment. But so for the GTEx data and the urinary data, you looked at few different tissues, right? And was kidney the one that you were thinking upfront would sort of most likely to correlate, or were you also looking at bladder and other sort of tissues that could potentially be of relevance to urine? But what sort of the ... I guess sort of tell me more about those different tissues that you looked at and what you found and what surprised you or not. Brian Byrd:          Great question. So, the kidney was on our minds from the outset. We knew that Mark Knepper at the National Institute of Health had published in the [inaudible 00:21:25] National Academy of Sciences back in 2004 that there are urinary extracellular vesicles. And he had found proteins that are very characteristic of the aldosterone sensitive distal nephron, that part of the kidney that we're interested in, embedded in the vesicles.                                 So we became quite interested in the idea that it seemed that there was likely a population of vesicles in the urine that is of kidney origin. And that's not to say that there weren't also plenty of vesicles from other origins as well, and there could very well be RNA that is not vesicle enclosed, but is rather ribonucleic protein bound or even bound to other carriers potentially. That could be there as well, and it's possible that the origin of those things could be any number of tissues. I don't really think that we know yet where the possible tissue origins are.                                 But we were curious to know ... I guess the direct answer to your question is we thought from the outset that we probably would find some sort of signal related to the kidney. But we wanted to also consider the possibility that our findings were not very specific to the kidney. And so we thought that the brain would be an interesting negative control. If we say very high correlation with the brain, it would suggest that maybe what we're looking for is a signal that's not really coming from the kidney.                                 And we also wanted to look at the bladder just to try to understand whether or not the signals that we're detecting could be coming from the bladder. It's certainly true that some aspects of the system that we're interested in are present in the bladder, so I wondered whether that might even serve as a signal amplifier for what we were looking for since there's, presumably, quite a bit of bladder tissue right around the urine. It might be contributing vesicles.                                 So that's sort of the rationale for why we looked at those things. Jane Ferguson: And you found mostly enrichment for kidneys. So sort of I guess what you were hoping to find came true? That actually there was sort of evidence that even though there may be contribution from other tissues, that really kidney seem to be the predominant contributor to the expression of the genes in the urine. Brian Byrd:          I think there's a lot of truth to that. One of the things I would say is we found high correlation looking across all genes. But it occurred to us ... As soon as we thought that, we realized, wait a second, that could be driven by ubiquitously expressed genes. Housekeeping genes.                                 So we really wanted to stratify our analysis by things we thought would be expressed in the kidney as well as things that we thought would be ubiquitously expressed to make sure that we could see signals that correlate ... That the transcriptome of the kidney, per se, had a good correlation with those same in terms of the abundance of the gene counts or recounts. They said it was similar to what was in the vesicles.                                 And so, we looked in the literature, and we found that a group had already established a number, 55 genes actually, that were highly kidney enriched as well as over 8000 genes that were ubiquitously expressed. And so we started the analysis from this perspective of the stratification. We thought that was a very important aspect of the analysis. And it's definitely true that if you look at our findings with respect to the kidney enriched genes, as you might expect, they correlate quite well with what is in the urinary extracellular vesicles compared to the kidney cortex.                                 You look at the brain as you might expect the expression of those kidney enriched genes is not well correlated with what's happening in the urine. And then, with respect to the bladder, it's sort of somewhere in between. Jane Ferguson: Okay. Interesting. So I know that some people look at small non-coding RNAs in urine, but you were mostly focused on mRNAs. Is that right? Brian Byrd:          That's right. I thought of this as sort of frontier, something that I knew from some early publications was probably measurable. But I didn't know what it would signify, if anything, with respect to physiology. And I knew that there were quite a few papers about micro RNAs and I wanted to do something a little bit innovated, partly.                                 But the main reason that I was interested in the RNAs was because I could relatively easily tie those to the existing literature from animal models. Preclinical animal models and cell culture studies showing what happens when the mineralocorticoid receptor's activated. That was really the driving reason that I was interested in the RNA. Because if you think about what is the approximate event that might be a readout for activation of a new growth hormone receptor like the mineralocorticoid receptor, it's really the transcriptional events that happen when the receptors translocates to the nucleus and serves its ligan activated transcription factor role. Jane Ferguson: Right. So, [inaudible 00:26:43] sort of the first part of analysis, you saw these really nice correlations between expression and kidney and in urine. And then, a lot of the times when you tried to publish that kind of thing, people are like, "Okay, so what? So you didn't do any intervention. We don't really know what that means."                                 But I like that you took it to the next step and you did sort of a human intervention experimental model. So tell me more about that model and how that worked. Brian Byrd:          Right. Well, I'll just mention also that the work that was done in terms of RNA [inaudible 00:27:14] was done in collaboration with Mark Bertini in Italy as well as Dr. [inaudible 00:27:19]. They were fundamental to getting that work done.                                 With respect to the collaboration with Scott Hummel, one of my colleagues here at the University of Michigan, what we did in that setting was to look at whether or not we could identify within these urinary mRNA signals that are in the supernatant in the urine, whether we could identify changes in physiology. That was the question that was of greatest interest scientifically.                                 And for a very practical or blind perspective, the question was could we detect the activation of the receptor that might determine whether or not people should get a certain medication. Of course, we're not saying that that's an established fact yet, but this is sort of concept, that there's something here to explore further.                                 And so, what we found was that a number of genes that are regulated by the mineralocorticoid receptor, including genes encoding the subunits of the amiloride-sensitive epithelial sodium channel that regulates the salt that I was talking about earlier. We found that those genes changed with sodium loading in terms of their abundance in the expected direction.                                 We also found that several of the assays that we made changed ... I'm sorry. That they correlated with the serum aldosterone concentration. So the concentration of the ligan for the receptor whose readout we were looking for. And we also noticed an inverse correlation with urinary sodium excretion, which is what we would expect if we really identified a readout of the mineralocorticoid receptor's activity.                                 So this study supported the idea that we have identified a way to measure this nuclear hormone receptors activity in living humans. Jane Ferguson: Right. Which is really nice. So there's probably a huge amount of extra things you could do with this, some sort of different ways you could look at it. So how did you pick the time point? So, I suppose when you think about it, I mean the genes, they're transcribed and then that takes a little bit of time, and then it takes a little bit of time for that to sort of make its way into the urine and to be excreted.                                 So how did you decide on sort of what time points to use, and do you think you would see the same things or different things [inaudible 00:29:39] if you did repeated sampling or if you looked at different time points? Brian Byrd:          That's a fantastic question. So this was a study that had already been completed, and I had mentioned to Scott what we were working on. And he said, "You know, we have these samples from this study and it might be possible for us to collaborate."                                 So, we didn't get to pick the timeframes. Jane Ferguson: Right. Brian Byrd:          So, that's a great point. And what I would say is that, as you can imagine, we're very focused on exactly the questions you're asking now. What about sort of signal refinement? What about the chrono-biology of these signals, and how do we understand when we see what in the urine?                                 So, I'm actively pursuing those questions. Jane Ferguson: Right. So, I know as well, there was quite a lot of sort of technical challenges I think to doing this work. Sort of getting to be even able to amplify and get a signal from these RNAs that are really present, sort of pretty low abundance in urine compared to tissues or biofluids that we're used to working with.                                 So tell me maybe a little bit about that process and sort of how much optimization was required to get these essays to work? Brian Byrd:          Great question. So, I had known [inaudible 00:30:58] since 2014 when I took a course on isolation of extracellular vesicles in Heidelberg, Germany. And I had talked to him at a meeting in Washington DC, and I had mentioned what we were trying to do. And he said, "You know, if you were trying to do that, you might want to consider preamplification." You know, using something like 15 cycles of preamplification. And he was willing to share that protocol that he had with me, because they were interested in similar issues. So, I was able to use that protocol to evaluate these gene targets in the urine. And so that was immensely helpful.                                 And the other thing that we did was we used locked nucleic acid probes to try to increase the sensitivity and specificity of our assays. Finally, we just tried to use good logic in the design of the assays. So we were concerned that the RNA might be fragmented, so where it was possible to do so within the design constraints that I'll mention in a second, we made multiple assays per gene target just in case this was fragmented. Which makes the analysis a little more complicated, but I think it was probably the right thing to do, given the state of knowledge that we had then.                                 And one of the other things we did was we made sure that the primers either ... Within a primer, there was an intron or between the primers there was an intron, so that if we actually did try to amplify DNA, abundant amounts of DNA, with those primers just to make sure that our theorizing about the inability to amplify things was actually factual. And that turned out that we couldn't amplify anything at 40 cycles with those.                                 So, we spent a lot of time thinking about how not to get fooled, but also to have adequate signal detection. And have included in the supplement quite a bit of information about the technical merits of the assays and showing how close the technical replicates were. They tended to be very, very similar to one another. We didn't see a signal in every urine sample for every participant at both time points, and I think that was interesting to me about that there tended to be a very binary result, so that you'd either see three technical replicants for the QPCR assays, our QPCR assays that were extremely similar to each other, or you would see no CT value detected.                                 [inaudible 00:33:47] That these were valid assessments of very low copy numbers. Jane Ferguson: Right. And that's probably related to up front of what happens to urine right after it's collected and stored, or during that RNA extraction. But it seems like once you've got RNA, then downstream assays were sort of ... They held through, but I guess ... I mean, and you obviously didn't have necessarily a huge amount of control over how these urine samples were collected. So it's kind of nice that you were able to see something even though these were collected possibly in a way that was not optimized for preserving RNAs.                                 But do you think those ... Are there ways that you could make this even sort of more streamlined and better as far from the get go of how you collect the urine, whether you could be extracting stuff right away? Is that anything you sort of looked into of how this could be improved? Brian Byrd:          That's really been the focus of the labs work since we completed that project, is sort of understanding how would we do this in a prospective study in the best possible way so that the results are highly repeatable, that we get a CT value in everybody so that we're really ... I mean, as you can imagine, that actually has something to do with the input volume of urine that you use. So if you have too little input volume, then you won't be able to detect the targets that you might be interested in every person.                                 However, if you have more, then you can do more with that. But then you have to think about how you're going to deal with the larger volumes of urine. There are lots of questions that we've been interested in related to extract the RNA and the stability of the RNA. And so we have done some experiments of that type, and we continue to work in that area. And I do think that those questions you're asking are the right questions with respect to next steps. Jane Ferguson: All right. So you looked at sort of specific targets, which I think made a lot of sense. Sort of this proof of principle. But do you think this would work on a transcriptome wide level? I mean, could you look at all the genes, or do you think that's just sort beyond the possibility right now given sort of the RNA fragmentation and how you have to sort of amplify it before being able to detect anything? Brian Byrd:          I think it's possible. So the group that had preceded our work with 3300 mils of urine, isolating the vesicles from there, eight have showed that that's something that can be done. The question that's of interest to me is does it actually require such large volumes of urine? And I think the answer to that question is going to be no from what we're overseeing so far.                                 And so, we're thinking along exactly the lines that you are. And certainly some of the feedback we've gotten as we've discussed this project with people is, "Hey, could you look at everything rather than picking targets at [inaudible 00:36:41]."                                 I think there's advantages and disadvantages. I think we chose based on prior knowledge in a way that was rational. But at the same time, it may turn out that there are many things about activation of the mineralocorticoid receptor in humans, especially in the living in-tact human, that don't exactly mirror what's found in rabbits, rats, mice or cells, which are really the systems that have been evaluated the most thoroughly in the past.                                 So I'm very interested in exactly what you're proposing. Jane Ferguson: Yeah. I mean, I think it's exciting because it's obviously relevant for hypertension, but potentially a lot of other conditions, to be able to look at that sort of dynamic change. So I think it's really exciting. It's very cool. Brian Byrd:          And I appreciate your asking about this study. We were excited to do this work and very, very excited to see where we can in the future with this. And I agree with the point you were making, that here we've gone from a rather specific application driven question and we've, I think, made some insights that are probably useful outside the application that we had in mind. And it may turn out that the application where this is the most important is not even the one that we considered in the first place at all.                                 And so I'm pleased by that. I'm pleased by the fact that I think in a sense we're working in what Donald Stokes described as pasture's quadrant, which is a sense that the work is driven both by curiosity and by an intent to use the results. Jane Ferguson: Right. Brian Byrd:          And so that's really what gets me out of bed in the morning, is working that exact space. So that's what we were glad to have done and continue to do. Jane Ferguson: Yeah. No, I think it's grea.t and I feel like a lot of people will read this paper and be like, "Hey, I have urine stored in the freezer. What can I do with this now?" Brian Byrd:          Contact me. Let's talk. We'll see what we can do. But we certainly tried to describe the methods in such a way that people could easily follow in our footsteps if they want to apply these methods. Jane Ferguson: Yeah. Now having read through them, I think that ... Really thorough. I really liked the sort of attention to detail. It was definitely one of those ones where I was like, "Oh yeah. I can see exactly how I could do this if I wanted to. So I think that was great. Brian Byrd:          Thank you. Jane Ferguson: So yeah. Congratulations on the paper. Really nice work and thanks so much for talking to me. Brian Byrd:          Thank you. It was a delight. Jane Ferguson: That's it from me for September. If you haven't had enough yet, you can access all the papers online and you can choose to digest the papers in video format. Available on our website or the Circulation YouTube channel. Thank you for listening and subscribing. I look forward to bringing you more next month.  

Jane Ferguson:                 Hi, everyone. Welcome to Getting Personal: Omics of the Heart. This is podcast episode 16 from May 2018. I'm Jane Ferguson from Vanderbilt University Medical Center and this podcast is brought to you by Circulation Genomic and Precision Medicine and the AHA Council on Genomic and Precision Medicine. Jane Ferguson:                 This month we talked to Dr. Caitrin McDonough from the University of Florida. We briefly mentioned her paper in last month's episode Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study, but we wanted to go into it in more depth this month. Caitrin shared with us that this manuscript actually resulted from student course work and was a collaborative effort between students and instructors. The manuscript highlights has successful as approach can be both in increasing student engagement and as an effective way to do high quality research. You can hear her talk more about her innovative approach to student learning and the study findings later in this episode. Jane Ferguson:                 Of course, we have a great lineup of papers in Circulation Genomic and Precision Medicine this month. First up, a paper entitled, "SCN5A Variant Functional Perturbation and Clinical Presentation Variants of a Certain Significance" by Brett Kroncke, Andrew Glazer, and Dan M. Roden and colleagues from Vanderbilt University Medical Center. They were interested in investigating the functional significance of variants in the cardiac sodium channel in particular to see if they could explain why some variant carriers present with cardiac arrhythmias while others remain asymptomatic. Through a comprehensive literature search, they identified 1712 SCN5A variants and characterized the carriers by disease presentation. Variants associated with disease were more likely to fall in transmembrane domains consistent with the importance of these domains for channel function. Jane Ferguson:                 Using American College of Medical Genetics Criteria for variant classification, they found that variants classified as more pathogenic were also more penetrant. Penetrance was also associated with electrophysiological parameters. This approach highlights how modeling the penetrance of different variants can help define disease risk for individuals who carry potentially pathogenic variants. Jane Ferguson:                 Next we have a paper from Vincenzo Macri, Jennifer Brody, Patrick Ellinor, Nona Sotoodehnia and colleagues from the University of Washington and Massachusets General Hospital. This is also related to sodium channels and the paper is entitled, "Common Coding Variants in SCN10A Are Associated With the Nav1.8 Late Current and Cardiac Conduction". They were interested in SCN10A and sequenced this gene in over 3600 individuals from the CHARGE consortium to identify variants associated with cardiac conduction. They were able to replicate associations between variants and PR and the QRS intervals in a sample of almost 21,000 individuals from the CHARGE Exome sample. They identified several missense variants have clustered into distinct haplotypes and they showed that these haplotypes were associated with late sodium current. Jane Ferguson:                 Continuing the cardiac conduction theme, Honghuang Lin,  Aaron Isaacs and colleagues published a manuscript entitled, "Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval". They conducted a meta-analyses of PR interval in over 93000 individuals which included over 9000 individuals of African ancestry. They identified 31 loci, 11 of which have not been reported before. We see SCN5A come up again as a gene of interest in this study but their analyses also implicated a novel locus, MYH6. Jane Ferguson:                 Next up moving from the heart to the vasculature, Janne Pott, Markus Scholz and colleagues from the University of Leipzig published a manuscript entitled, "Genetic Regulation of PCSK9 Plasma Levels and Its Impact on Atherosclerotic Vascular Disease Phenotypes". They were interested in whether circulating PCSK9 can be used as a diagnostic or predictive biomarker. To address this, they conducted a GWAS of plasma PCSK9 in over 3000 individuals from the LIFE-Heart study. They found that several independent variants within the PCSK9 gene were associated with plasma PCSK9 as well as some suggestive variants in another gene locus FBXL18. They used Mendelian randomization to probe causality and the data suggest that PCSK9 variants have a causal role in the presence and severity of atherosclerosis. Jane Ferguson:                 Moving on to another biomarker, Lisanne Blauw, Ko Willems van Dijk and colleagues from the  Einthoven Laboratory for Experimental Vascular Medicine report on CETP in their manuscript Cholesteryl Ester Transfer Protein Concentration A Genome-Wide Association Study Followed by Mendelian Randomization on Coronary Artery Disease. They aimed to assess potential causal effects of circulating CDP on cardiovascular disease through GWAS and Mendelian randomization. Jane Ferguson:                 In a study of over 4000 individuals from the Netherlands Epidemiology of Obesity Study, they identified three variants in CTP that associated with plasma levels of CETP and explained over 16% in the total variation in CDP levels. Genetically predicted in CETP was associated with reduced HDL and LDL cholesterol suggesting that CETP may be causally associated with coronary disease. Jane Ferguson:                 Rounding out the table of contents we also have a clinical case perspective from Nosheen Reza, Anjali The Importance of Timely Genetic Evaluation in family members in cases of cardiac disfunction and cardiomyopathy. We have a report from Adrianna Vlachos, Jeffrey Lipton and colleagues on the Diamond Blackfan Anemia Registry and we have a clinical case from Yukihiro Saito, Hiroshi Ito and colleagues on TRP and poor mutations in patients with ventricular non-compaction and cardiac conduction disease. Jane Ferguson:                 To read all of these papers and the accompanying commentaries, log on to circgenetics.aha.journals.org and if you're a visual learner or you need a work related excuse to spend time on YouTube, you can also access video summaries of all our articles from the CircGen website or directly from our YouTube channel Circulation Journal. Lastly, follow us on Twitter at circ_gen or on Facebook to get new content directly in your feed. Jane Ferguson:                 I'm joined today by Caitrin McDonough from the University of Florida and Caitrin is an Assistant Professor in the Department of Pharmacotherapy and Translational research in the College of Pharmacy and she's the first author on a recently published manuscript entitled, "Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study". This was published in the April 2018 issue of Circulation Genomic and Precision Medicine. Welcome, Caitrin. Caitrin M.:                           Thank you. Jane Ferguson:                 For listeners who haven't had a chance to read the paper yet, I wonder could you give us a brief overview of what prompted you to do this study? Caitrin M.:                           Sure so this looks at plasma renin activity and just initially a GWAS but it was done in a hypertensive population from the pharmogenomic evaluation of antihypertensive responses study. Particularly, since our group here at the University of Florida is more interested in pharmacogenomics we wanted to address plasma renin since it can influence blood pressure response to antihypertensive medication particularly if you use it as something to predict but also to correlate it with that as there have been also prior data from our group that shows if you have different levels of plasma renin that would predict if you would respond better to certain types of antihypertensive medications such as a beta-blocker or a diuretic. Caitrin M.:                           We used both a GWAS approach as well as a prioritization through blood pressure response to focus in on signals and then furthered by using prioritization using data from RNA seq and looking at eQTLs and then finally looking at more of just a traditional net replication of the original plasma renin activity signal. Caitrin M.:                           Overall, one of the interesting things and why we were initially doing this study was really in connection with a graduate course that myself and another faculty member here who's also an author on the paper, Yan Gong [inaudible 00:09:12]. We often have the students analyze data from the PEAR study as we have a lot of data from that study and it helped us analyze additional papers but we didn't necessarily know if this was going to be an interesting phenotype but through that course work which turned out that it really did have some interesting signals so we wanted to follow up more on. Jane Ferguson:                 Yeah, I love that approach so I think that's a really smart way to do it. To actually get your students to analyze your data and get them really involved in the process. How much then did the students ... how much were then they able to get involved when it started transpiring that their results would actually be something that could be put together for a manuscript? Caitrin M.:                           Overall, they are fairly involved. During the course work, what we usually do is give them just directly types data since a lot of them have not done this type of genetic analysis before and we split it up where each student gets about four to five chromosomes of data and then different phenotypes in the different race groups as we have both whites and African Americans. They get a certain race group, certain number of chromosomes and so they're able to conduct the analysis just using the Uplink software which is fairly user-friendly and straightforward. Then they get experience making Manhattan plots and using LocusZoom. Caitrin M.:                           After they have the basic techniques, then we teach them how to start following up top signals and determine what is a good signal. They're looking at the LD or SNP function or possibly gene function or looking at their genotype, phenotype relationships and making sure that it's not just one person who's driving the whole signal. Then selecting what top reasons and top SNPs may need a follow up. That part they all do there in the class and learn more of the basics. Caitrin M.:                           After the class, the students who want to continue to participate we get together and redistribute data where they would then move on to working on the imputed data sets and we teach them how to do that. Then we give them ... operate it somewhat similar to a consortia level meta-analysis type thing. I write up an analysis plan, each student does some part of the analysis. They have to bring it all back to me. I sort through it. We meet and go through it. Then we set our next steps to follow up. Then different students get different SNPs to investigate the function of or different subanalyses to do. Caitrin M.:                           One of our graduate students who is on this particular project, her dissertation project was very focused on our RNA seq data so that was how we were able to bring in the eQTL analysis using the RNA seq data as she had done a lot of the groundwork with that already. In one of our discussions that was one of the ways that we were able to incorporate the prioritization since she was intimately familiar with that data set. Jane Ferguson:                 Yeah and I think that's great. I can imagine that, that's a much more compelling way for students to learn about how to analyze data when they see the natural follow through. Do you find that some of the students maybe get more excited about research or are more likely to pursue future research opportunities by having had this hands on experience with the publication process and completing a project really did to this very end? Caitrin M.:                           They do, yeah. I see some of the students that end up sticking with it more are the students who I work more closely with and see more closely some of the students who are from other departments still stay involved but sometimes don't stay quite as involved. But, all of them really do continue to follow up and ask if they can still help or if there's anything they need to do until we get it to publication which is really nice. Jane Ferguson:                 Yeah, right. I think that's fantastic and I'm sure every study has its challenges. I'm interested what were the challenges you encountered in doing this study and which one of them may be unique to the way you have a lot of different people analyzing different aspects of the data versus the regular challenges that would come up in a study like this. Caitrin M.:                           Yeah so some of it I think is just keeping everyone on track and keeping it organized, making sure I think some of our challenges with this study was just me making I think on a lot of other studies, while I had certainly hands on the data it was more of an oversight rule for some pieces of it and just making sure everything looked the way that I thought it did, double checking. Some of it I think the teaching aspect of it just making sure everything was also done correctly and then keeping everything organized made the study a little bit more challenging. Caitrin M.:                           I think part of it too was with the PEAR study, it is a very rich data set. Determining what we wanted for our prioritization scheme and how to work through the different types of data sets that we had and put it all together as initially we just assign each student a different piece and we had a vague plan but it was a little bit more tricky as to work through how it was all coming together then when everyone came back together since a lot of people were doing as opposed to just one person doing it. Jane Ferguson:                 Right, so yeah and I think you're touching on the part that all of us have when we're writing papers that you sometimes end up with a lot of data at the beginning, you're trying to sift through it and then sometimes at a certain point you see something and you're like, "Okay, yes. This is interesting." Then you start following it up. Jane Ferguson:                 I wonder at what point did that happen? I suppose you probably ... You ran the GWAS for plasma renin activity and then find a number of suggested SNPs that were significant you associated but then ... Describe your strategy and you did so the second screening stuff to look at the pharmacological aspect defining [crosstalk 00:15:12]? Caitrin M.:                           Yeah, our initial plan going in was the first two steps, to do the GWAS for plasma renin activity and then to do the prioritization through blood pressure responses. I was very familiar with what our lab was familiar with but then after we got there, I think we were then troubled with what we did next and where to go. When we decided to bring in the RNA seq data, I think that was when it really started coming together as our top signal, the SNN-TXNDC11 gene region really stuck out then and it showed up. That seemed like a much stronger signal and it gave us a little bit more focus and also brought it much more of a functional aspect where we would maybe start to believe that signal more. That I think was really when we did that more of a turning point for the study and helped us focus more on where then to go with the results. Jane Ferguson:                 As far as the data you had I think over 700 people for your GWAS. Then you had a pretty large number of ... Was it the same subjects or different subjects where you also had the RNA seq data to do the QTL analysis? Caitrin M.:                           The same subject so not everyone has RNA seq. We have RNA seq data on 50 individuals and they were selected from whites and at the extremes of the blood pressure response so that it has a slightly interesting selection process. It's the main data analysis there was a best responder, worse responder to thiazide diuretics. Caitrin M.:                           When we do the eQTL analysis, we aren't always sure what we're going to get since we're missing the middle of blood pressure response. But, when we're just looking strictly at eQTL analysis sometimes we get lucky and sometimes it looks weird. Jane Ferguson:                 In your case as well you had the added issue of subjects were randomized to drug treatment so it was some where responders were ... I guess some people got the drug that worked for them and some people did not get the drug that worked for them. Caitrin M.:                           Yeah. Jane Ferguson:                 Did you I guess were incorporating both groups so good responders to either and some of that was because of their gene. They got the right drug for their genotype. Caitrin M.:                           Exactly, yeah. Jane Ferguson:                 It's good and then you were able to replicate this. After you were able to prioritize your gene region based on the GWAS and the drug response and the eQTL data, you actually ended up being able to go to a second sample to replicate the association right? Caitrin M.:                           Yes, it was in a lot of the same investigators, we have a second study PEAR-2, which has a very similar design to the PEAR study but used different drugs but also collected baseline plasma renin activity. We were able to use that phenotype again. We did have slight differences in GWAS to imputation panels at that point in time for when we were conducting this study so we ended up using a proxy to replicate but we did see the same signal in the second population which was very nice to see. Jane Ferguson:                 What is known about this gene region or either of these two genes? Caitrin M.:                           Overall, that was I think one of our harder points when we started trying to make the connections back to our phenotype. This was one of the areas where we did also have help from our students and the students as that was part of their initial training where they really looked to see what function was of various different genes and how to follow them up. That was one area where they came in, was to help look up some of the function of these ... there have been some connections with the various genes, the other phenotypes and with SNN and to atherosclerosis and other inflammatory cytokines such as TNF-alpha. Then there have been data also from [inaudible 00:19:37] that really show that there is an eQTL in this region that which supports what we saw in our own data. However, there really wasn't any direct connections with renin and the renin angiotensin aldosterone system and blood pressure regulation that we could find in the literature. Caitrin M.:                           We're not exactly sure how it connects but based off of our functional data and levels of evidence and then we saw some of that in publicly available data, we're still very interested in the region. Jane Ferguson:                 I think the data is compelling enough that it looks like you've identified the new region that probably is the mechanistically related that will require a whole bunch of basic mechanistic research to figure out what exactly the genes in this region are doing and how this ultimately connect back to blood pressure and response to drug therapy. Caitrin M.:                           Exactly. Jane Ferguson:                 I could see this ... I mean obviously there's a whole lot of potential functional work there and then probably also the clinical work, I wonder what you think about how this would affect any pharmacogenetic therapeutic ... You know at present I think you can look at plasma renin activity and use that as a predictor of drug response to help guide therapies. Would you think that a genotype guided therapy may end up being more effective than the plasma renin activity measurement in this case? Caitrin M.:                           In this case since this is so connected with a phenotype that you could use with plasma renin, I think if you're able to draw a plasma renin you may just want to do that. I think our overall goal would be if someone had preexisting genetic data and you weren't wanting to do an additional test or if you're contemplating response to a lot of different drugs that perhaps you could use a genetic data. One of the issues that was brought up on review and that are a lot of group considers quite a lot is that we have a lot of signals and that our group has certainly published a lot in this area and there's a lot of signals that we have to a lot of different drugs and how do you incorporate all of them together, is there overlap between them or where do they all fall? Caitrin M.:                           That is certainly something that we're still working on as more I think ultimate goal would be more to delve more of a SNP score or gene score and some type of risk score that would help you determine what drug you would best respond to. We've done that a little bit in some of our prior publications but we haven't yet taken all of our data together and help to build something that would if you had a lot of data on an individual and various different alleles at various different genes, how that would respond. Caitrin M.:                           Overall, when we look at blood pressure response as a pharmacogenetic signal, certainly we see larger affect sizes than you would in disease genetics but we're not seeing affect sizes like you would with more of an adverse drug event. We're in between there and we're often times it's not necessarily just going to be one SNP or one gene that would tell your whole story but a combination of quite a few of them. Jane Ferguson:                 I wonder are there more similar stories like this from the same data set? You know you've been through this process from start to finish and building in the functional work and do you think that next year's class will be able to do this again with the same data set? That maybe pick one of the next priority candidate down the list and maybe find another interesting story like this? Caitrin M.:                           Yeah, so we actually just finished our class this year and they looked at potassium. We just got done grading final papers and submitting grades so we will over the summer be working with them a little bit more. I think some of our new graduate students too are starting to work on trying to make more connections between a lot of our different phenotypes and as you start to layer those together what it exactly means for a patient or implementation perspective. Jane Ferguson:                 Yeah, interesting. We'll have to look for that story whenever you guys get done with it. Otherwise, are you planning on following up this specific SNP region in any other way or any other studies? Where's next for you guys overall? Caitrin M.:                           I think one of the things we would like to do is look at this more in PEAR-2. We really just brought the PEAR-2 data set in here as replication of the top region in that last stage but we have that data set and we can certainly look at that data set. Caitrin M.:                           The other thing that I would like to do is as we started this project in conjunction with the class that was a couple of years ago at this point in time, we used [TAP/MAP 3 00:24:35] imputed data since that was what we had in the lab and what we were using at that point in time. At this point in time, we have now imputed both PEAR and PEAR-2 2000 genomes phase three data. It'd be interesting to see if we are able to see any additional signals or if these regions become stronger or exactly what would happen using a more imputation panel that has more coverage and where we would have the same panel between both PEAR and PEAR-2. Jane Ferguson:                 Right because you may or may not have identified the causal SNPs in the previous access but- Caitrin M.:                           Yeah. Jane Ferguson:                 -yeah so it'd be nice to see if you can actually get that out. That potentially could end being a drugable target maybe suitable for something more specific but who knows. Is there anything else that we haven't covered yet that you'd like to mention? Caitrin M.:                           Overall, I think that just this type of model of utilizing more of a real world analysis and data in a class project really certainly engages our students a lot and I think they all enjoy actually being able to work with data that came out of this study and have a lot more hands-on experience and really project-based analysis experience. We've been very happy with this model and have used it multiple times. We have an HDL paper, the renin paper, our glucose response paper and now we're working on the potassium project. It's been a good model for us here with our pharmacogenomics class. Jane Ferguson:                 Yeah, I mean I think it's a really smart and intuitive way to think about education. It's mutually beneficial it sounds like, so it's helping you guys get your data analyzed. It's really helping the students learn so I think it's a win-win situation. I think it's a model that a lot of other people would really be interested in adopting. Caitrin M.:                           Yeah. Jane Ferguson:                 Okay well thanks so much for talking to me and talking about your model and your research. It's been great. Caitrin M.:                           Yes, thank you very much for having me. Jane Ferguson:                 That's it from us for May. Thank you for listening and come back for more next month.  

Jane Ferguson:                Hi, everyone. Welcome to Episode 08 of Getting Personal: -Omics of the Heart. I'm Jane Ferguson, and this podcast is brought Circulation Cardiovascular Genetics and the AJ Functional Genomics and Translational Biology Council. This is the September 2017 episode, and this month we delve into some of the newest research coming out in the October 2017 issue of CircGenetics. If you go on to the CircGenetics website at circgenetics.ahajournals.org, you can see the table of contents for the latest issue, and see sneak previews of upcoming papers that are published online in advance of the next issue. You can also more in-depth materials for each paper, like editorials and other resources, so it's a really nice way to keep up with the newest cardiovascular genomics research.                                            One particularly interesting paper included in the October 2017 issue is entitled "Diminished PRRX1 Expression Is Associated With Increased Risk of Atrial Fibrillation and Shortening of the Cardiac Action Potential," from Elena Dolmatova, Nathan Tucker, Patrick Ellinor, and colleagues. This is a really nice paper which highlights some beautiful approaches used to go from a GWAS hit to functional understanding. This type of research is challenging but really crucial as we move on from the GWAS discovery era, and I recommend you go online to read the whole paper. I talked to the first authors, Elena and Nathan, to find out more about their work.                                            So I'm here with Doctor Nathan Tucker and Doctor Elena Dolmatova, they're the first authors on a recently published paper. So, welcome and thank you for joining us. Nathan Tucker:                Thank you. Jane Ferguson:                So, for the benefit of our listeners, could you tell us a little bit about yourselves? Nathan Tucker:                Sure, so my name is Nathan Tucker, PhD, researcher, instructor of medicine at Mass General Hospital and the Broad Institute in Boston. Elena Dolmatova:           And my name is Elena Dolmatova, if you could probably tell, I'm Russian by origin, currently I'm a internal medicine resident at Rutgers University and I'm in process of applying for a cardio research fellowship. Jane Ferguson:                And so the two of you co-led a really interesting publication that came out this month, so congratulations on that. Nathan Tucker:                Thank you. Jane Ferguson:                So, some of our listeners may not have had the time yet to read your paper, so I was hoping you could give us just a brief summary of what this publication was about. Nathan Tucker:                Sure, I'd be happy to start. So the focus of this paper, and a lot of the other work that goes on in our group, is genetics of what's the most common cardiac arrhythmia, which, atrial fibrillation. So, really over the past decade or so, once these large Genome-Wide Association Studies have been performed, in order to identify regions that are associated with disease, and then we followed up on that, to try to determine some of the mechanism that underlies those loci. So this is an example of that type of study. So, I think for the vast majority of these regions, and this is not exclusive to our disease at all, but the loci that are associated reside in what we used to refer to as "junk DNA" or intergenic DNA, that we now know is regulatory DNA. But the important point is, we have no ... for the majority of these loci, we have no idea of the mechanism through which they confer risk. So the point of this study was to examine a single locus for atrial fibrillation, which we'll call AF for the rest of this, and try to determine the mechanisms through which is might confer that risk.                                            So, kind of the start, the study started back in an era where we were using, you know, genotyping chips, and large cohorts of cases and controls to identify variation then impute variants to see what's associated. But we wanted to go into this study with a comprehensive understanding of what's at that locus. So to do that, we performed sequencing and a pretty modest cohort of 500 cases and 500 reference from Framingham Heart Study. And although it didn't really change what we knew about the landscape of that region, we were able to go in with a confident understand of what variants might be associated with disease risk at that locus.                                            So then Elena really spearheaded a lot of the work to identify which of those variants might be important at the locus, so I'll let her take over from here. Elena Dolmatova:           So, as Nathan mentioned earlier, many of those intergenic regions contained enhancers of regulatory elements and a lot of data was coming up about the genetic loci in the genome. And we wanted maybe to narrow down that region, down to some of the pieces that could be active, or could be functional. So we used the activity markers that [inaudible 00:05:20] modifications and DNA hypersensitivity, to identify those potentially active elements. And then we tested them on zebra fish [inaudible 00:05:31] to see if they're actually active in the heart. When we realized that they are active in the heart, we were able to then do a little bit more targeted [inaudible 00:05:42] after that, identify the ones that are actually differential between the risk and non-risk allele. So in that some of the SNPs can be actually changing the enhancer function. So this is how we actually identified the SNP that was actually functional.                                            Then next what we wanted to do is to link this enhancer to the gene. And initially we performed a Hi-C analysis, which is a chromatin conformation capture. Which is actually captures a 3D structure of the DNA and shows what regions are interacting with what regions. And we were able to see that this SNP was within the same block as the PRRX promoter. To maybe narrow down and to identify the interaction a little bit better, we performed 3C analysis. That allowed us actually to link the enhancer directly to to PRRX promoter. So, we have the SNP that would change the activity of the enhancer, we have the enhancer linked to the promoter, we wanted to see if the change in the SNP would have any functional consequences on gene expression. And we performed a QTL study.                                            So what it was, is we looked at the genotype of the SNP and related it to the expression of the genes within that region. And among all the genes that we actually tested, only PRRX1 expression was affected, with the risk allele conferring decreased expression of the gene. However, the consequences of gene decreased PRRX expression were yet to be revealed, and that was part of the critical experiment that Nate focused a lot of his efforts on. Nathan Tucker:                So, we found the gene that was important, we knew the directionality, but a lot of times, with these type of functional genomics where you, which I hope we can elaborate a little bit more later, is that the results given, like, what gene you identify and the direction, aren't as clear as you would sometimes think for a given disease or trait. So, for example, a lot of the coding variation for AF is identified in ion channel genes. It's thought to be an electrophysiological disease. But here we identified a transcription factor, which is what we actually thought to be a developmental transcription factor. So, you kind of went in from a functional angle and say, "Alright, what are the consequences of this alteration?"                                            So we used two different models, the first was zebra fish, which I had reasonably strong background in. And we knocked the gene down, examined the development of the heart, everything seemed reasonably normal, and then we actually examined the electrophysiology of that heart by optical mapping, and we looked at the action potential duration. Which is basically the cellular phenotype for ... that governs depolarization, re-polarization and thus contraction of any given myosin. And found that that action potential duration in the zebra fish was shorter. We wanted to follow that up and confirm it in a different model, we actually created a CRISPR/Cas9 media knockout of the gene, and embryonic stem cells, and differentiated those into cardiomyocytes, and then saw that similar decrease in action potential duration.                                            So, kind of altogether, I mean, a paper that spans a lot of different techniques, but what we did, we took associates in locus for a human disease, we found a variant at that locus that seems to drive differential expression of a nearby gene, and then modeled that gene effect in order to give a physiological phenotype that matches with the disease of interest. Jane Ferguson:                Something that struck me, I think you sort of touched on this a little bit earlier, is, you know, the SNP that you end up showing to be causal, are S577676. It's not necessarily the one that you would have picked sort of a priori, by going through the GWAS strength of association, and you know, I know we sort of all know that we shouldn't place too much weight on the specific P value of an association when we're doing GWAS, but I think a lot of the time, that sort of ends up being a screening mechanism, and people look at sort of the strongest SNP and think that's probably going to be the most biologically relevant. But do you think that we're sort of, you know, by relying on this relative strength association the GWAS to pick targets, we're really missing a lot of the potential biology that's underlying these diseases? Nathan Tucker:                The way you look at a normal GWAS locus is, we've always traditionally marked them with what we call a sentinel SNP which is a SNP that's most associated, and then other times, act as though that one might be mediating the function? Whereas in reality, you'll see a block of roughly equivalently associated SNPS that rely or lie within the same linkage to [inaudible 00:10:42] block. And, at least for our cases, when we move forward we really wanted to treat all of those SNPS to be equivalent. And in this one, the SNP that turned out to be functionally active was actually below, a little below that, what we would call that sentinel SNP.                                            So I think there are a couple different explanations for that. One is, there could be more than one functional variant at a locus, and the LD structure kind of heightens that. The other could be that the sample you're using in order to identify the SNPs of interest or the SNPs that are functionally associated may be biasing you a little bit, particularly with a smaller cohort like this. But I will say, for our SNP, when you look at it in the larger GWAS studies, it's again roughly equivalently associated, is what we'd call a top SNP. So, to answer your question briefly, we always look at all of them. We have to be inclusive when we're trying to find functional variants. Jane Ferguson:                Yeah, no, absolutely agree. And that's one thing I absolutely loved about your paper, was how you, you know, pulled together all these different data types and used as many different resources as you had access to to really tackle this question. So I wonder, out of all of the different things you did, what was the most challenging aspect of this study? Elena Dolmatova:           Well, that was something that nobody's really done before. It was something that there were few studies out by when, the time we started, that would tie some of the GWAS hits with the mechanism of the disease development in [inaudible 00:12:16] in other conditions, but there was really no paved road to take to get an answer to our question. Nathan Tucker:                For me, personally, I mean, I really started this project, which, you know, this project took a considerable amount of time, and I started as a cell biologist, and modeling gene function in zebra fish, and by the end, we ended up using so many different techniques, and integrating so many new types of data into this study, that I don't even know what I would define myself as anymore. So I think it's a, it's challenging to learn how to use all of these new data, and to generate these new data both. That's the kind of, I don't know, that's why we got into this business. That's why people want to do research. So that's, it's challenging, but it's rewarding too. Jane Ferguson:                Absolutely. And so, to look at the converse aspect, then, was there anything that was easier than you expected? You know, did you have a eureka moment where you sort of said, "Yes, now everything is falling into place."? Nathan Tucker:                So, I think, yeah. I've been part of studies where I've really felt that that's happened. And given all of the kind of independent moving parts that were in this study, it was, it's really hard to think of one thing that clicked. You know, every sub-component had its own individual moment where it may have clicked, but really, until they all started, all the pieces of data started to come together, you never really felt that eureka moment. And, you know, I think that's part of what science is in normal ... I mean, this paper was a lot of sweat. And not only mine and Elena's, you have all of our collaborators as well. But I will say, you know, at least using the genetics as a basis, and the GWAS data as a basis, we knew that something was there, going in. We knew that we weren't on some wild goose chase, but really we're filling in a gap knowing that we have a strong basis to build on. Jane Ferguson:                Yeah. It's good to hear from you, sort of that, you know, you had to do all of those experiments, they were all necessary, because I think, a lot of the time, when people are trying to follow up GWAS findings, they're really, I don't know, they sort of have a preconceived idea maybe of what path they want to go down, and I think that's not the answer. I think we have a lot of GWAS hits now, and I think the sort of approach that you did to do all of these different experiments and to just do the hard work that's required to figure this out, I think is really necessary and very laudable. Nathan Tucker:                Thank you. Jane Ferguson:                So, was there anything that surprised you along the way? Elena Dolmatova:           Well, Nathan touched a little bit on that. It was nice to see all the electrophysiological phenotype, that was quite amazing. And the fact that the directionality of the effect was ... fit with what we expected to be, with the risk allele, and how we were able to demonstrate it both in zebra fish and human cells, and they were, again, matching. Seeing how those results could tie to the genetic data and what we know about atrial fibrillation susceptibility, was great and rewarding. I wouldn't call it surprising. More like rewarding. Honestly, we were concerned that we wouldn't be able to observe any physiological phenotype. Because, I mean, we didn't even have a good reason why PRRX would be involved in atrial fibrillation, that was a transcription factor, not an ion channel, like everybody thinks about, everything is an ion channel, by the way, not the same. So it was great that we were actually tie the transcription factor to the disease when we not even quite sure that it would happen. Jane Ferguson:                Yeah. Yeah, and I suppose, you mention the ion channels, and of course there has been several other loci that have been identified for AF, and from your work, how important do you think PRRX1 is, compared to these other loci, and, you know, do you think that this sort of study has to be done for every single one of these loci to really understand what's causing the disease in different people? Nathan Tucker:                First of all, I think the answer to that question depends a little bit on what the person asking it would deem to be important. So, if we're looking at GWAS signals for effect size, generally the effects of each given locus are pretty modest, and PRRX1 locus isn't even at the top for AF. So if you were looking for, like, clinical risk stratification, then it's not going to be the most important locus for AF. But I think what looking at these types of stories does, is identifies novel mechanisms for disease pathogenesis. I think they're often unexpected, it steps outside of the pathway analysis, and candidate gene approaches that have been used in the past. And a really unbiased way to look at, you know, why the disease risk has changed. I think if our ultimate goal is to develop new therapeutics, you know, we don't know which one of these loci might give us that hook into developing the new therapeutic.                                            So the second part of the question, I guess you'd say, does it need to be done for each locus? So, yes, I guess, given what I just previously said. I think we've invested a lot of time and effort and resources into identifying all these loci, to really, really large discovery efforts, but if we want to really maximize what we've done there, with that discovery effort, then I think we owe it to ourselves as a field to identify mechanism, and see which one of these are going to give us that hook to make that next big clinical therapeutic discovery.                                            But, that being said, you know, this study, as much as we love it, it was really laborious. And it was a lot of moving parts. And it was a lot of work from a lot of people for a lot of time. And if we're going to have to do this for every locus, not only for AF but for all of the other GWAS that have been performed ... it's just an unacceptably slow rate of discovery, so ... What we've been doing since this one has been completed is, you know, trying to find some higher throughput ways to screen through what might be functional variants, to integrating or generating new transcriptional data sets, so we can better predict what might be the chain at a given locus, and working on our models as well for when we want to look at physiology. So we hope that we can talk more about these briefs soon, a lot of them are in the works, so we'll update soon. Jane Ferguson:                Oh, that's exciting, yeah. And I think you've laid out a really nice blueprint, how you can do these kind of experiments, and how to follow up a locus, and, you know, I'm sure you learned a ton a long the way, and you both mentioned some of these, you probably can't talk about everything you're working on, but I suppose with the benefit of hindsight now, is there anything specific about sort of the study design or the methods along the way that you would change for future studies? Elena Dolmatova:           One of the things that when we started, we started having one toolkit. And when we're finished, we had a completely different toolkit. And it's all because the science is developing every day, so every moment, something new comes up, it's ... In the beginning, there wasn't enough epigenetic data to, for us to guess about the enhancers. And it was coming in almost on a weekly basis, and we were trying, and pretty successful, implementing it, all the knowledge that was acquired and published, almost immediately. We almost had to implement CRISPR/Cas to knockout PRRX in the embryonic stem cells, and they were the five cardiomyocytes, after that it's from them. So all of that knowledge was not there when we started the study. So we actually implemented them almost immediately. But in hindsight, if we had all these tricks up our sleeves back then, of course it would be much more efficient and finish it much faster. Nathan Tucker:                I'll follow up on that, too. It's like, is one thing we learned too, which Elena mentioned, all of epigenomic data sets that were updating, and all the techniques that were updating, I mean I really think one thing that we learned was, our prediction is really only good as the data that we put into it. And I think our plan to learn, particularly for all the other loci, is we really need to understand the epigenomic landscape in relevant to [inaudible 00:21:23] and cells, so, you know, moving towards that first, before screening on what variant or what transcript might be important is a really important step for us, and one that we've used as we've moved forward. Elena Dolmatova:           Mm-hmm (affirmative). Jane Ferguson:                So, what do you think would be the ideal follow-on study to this paper? Elena Dolmatova:           Well, we know that diminished PRRX expression shortens the action potential, but we have little idea about how it is happening. Is it acting through the changing cardiomyocyte state? Is it altering maturation or development of cardiomyocytes? Is it governing ion channel expression? Or maybe changing something with intracellular calcium regulation. Transcription factors can have many targets, and we're not quite, quite sure what the targets are in this particular case, so that would be a nice study, thought that I, to follow up on this study. Jane Ferguson:                So I suppose just to wrap this up, is there any message that you're hoping that readers will be able to take away from your paper? Nathan Tucker:                Sure, I think from, if we're going to look from a disease standpoint, I think the finding regarding the relationship between the gene and atrial fibrillation is important, but I think, I hope we've also illustrated somewhat through this study how complex the genetics of the disease are. I mean, it's ... so much of the focus in the past has been, really, on ion channel regulation, but there's so much more to this condition that can really, is yet to be discovered. So I hope we shed a little bit of light on a path forward for how to uncover some of this other, these other mechanisms, over the next few years.                                            And then I think, hopefully the other thing, well, at least, that we hope gets relayed through this and other similar studies from other groups, is the importance to fill in this knowledge gap between the population genetics stories, the GWAS studies, and that basic biology. And I think there's a lot of potential for making important discoveries, for human health and clinical intervention, in that space. So hopefully, us and other groups can use some of the things that we did in this paper. And hopefully improve on them, to address this in other GWAS loci, to keep the field moving forward. Jane Ferguson:                Yeah, I couldn't agree more. I think that's a really important message, and I think you've done a fantastic job on sort of starting us down that path, to really translating these GWAS findings into more meaningful biology. So, Elena, Nathan, thank you so much for taking the time to talk to me. Nathan Tucker:                Thanks a lot for having us. Elena Dolmatova:           Thank you. Jane Ferguson:  And that's all for this month. Thank you for listening, and we look forward to getting up close and personal with -Omics of the Heart, and with you, next month.

Hintergrund dieser Arbeit ist die in den letzten Jahrzehnten stetig abnehmende Fruchtbarkeit der Rinderrasse Holstein-Friesian (SILVIA, 1998, PRYCE et al., 2004). Dem Titel entsprechend hat es sich diese Studie zum Ziel gesetzt, Genom-regionen mit Einfluss auf die Fruchtbarkeit durch einen Kartierungsansatz ausfin-dig zu machen. Aufgrund ihres Einflusses auf quantitative Merkmale werden sol-che Regionen auch als Quantitative Trait Loci (QTL) bezeichnet. Bei den in dieser Arbeit untersuchten zehn Fruchtbarkeits- und Kalbemerkmale handelt es sich um die folgenden vom VIT übermittelten Zuchtwerte: Verzögerungszeit für Rinder (VZR) und Kühe (VZK); der Anteil der nicht erneut brünstigen Tiere 56 Tage nach Besamung bei Rindern (NR56R) und Kühen (NR56K); Rastzeit (RZ); Güstzeit (DO); paternaler (direkter) Kalbeverlauf (pKV) für Rinder; maternaler (indirekter) Kalbeverlauf (mKV) für Rinder; paternale Totgeburt (pTG) für Rin-der und maternale Totgeburt (mTG) für Rinder. Die für diese Studie verwendeten 2527 HF Bullen wurden auf BOVINE SNP50 BEADCHIPS (Illumina) genotypisiert. Die eigentliche Kartierung der 29 bovinen Autosomen erfolgte anschließend mit einer kombinierten Kopplungsungleichge-wichts- und Kopplungsanalyse (cLDLA). Hierzu wurde das Genom in 40 SNP umfassende Gleitfenster unterteilt und in jeder Fenstermitte eine Varianzkompo-nentenanalyse durchgeführt. Basierend auf dem cLDLA-Kartierungsansatz wurden insgesamt 90 signifikante lokale Maxima gefunden, die anhand mehrerer Kriterien 50 verschiedenen QTL zugeordnet werden konnten. Einige dieser kartierten Loci bestätigten bereits zuvor publizierte QTL, andere QTL waren neu. Der signifikanteste QTL wurde auf BTA18 in einer Region gefunden, welche bereits durch zahlreiche Autoren als besonders signifikant deklariert wurde. Allerdings befand sich das hier kartierte Maximum (59.179.424 bp) etwa 1,59 Megabasen von der Stelle entfernt, an welcher aufgrund der Ergebnisse früherer Studien aktuell geforscht wird, dem SNP ARS-BFGL-NGS-109285 bei 57.589.121 bp. Nach genauerer Analyse des signifikantesten 40-SNP Fensters konnte ein ‚ursächlicher‘ Haplotyp identifiziert werden, der im weiteren Verlauf als Haplotyp Q1 bezeichnet wird. Dieser Haplotyp ist mit hoher Wahrscheinlichkeit ursächlich für den QTL, welcher für pKV und pTG im Bereich 55.282.968 ­ 60.119.636 bp kartiert wurde. Um den Haplotyp Q1 weiter zu verfeinern, wurden 86 Bullen zusätzlich auf BOVINEHD BEADCHIPS (Illumina) mit wesentlich mehr Markern und dadurch höherer Markerdichte genotypisiert. So konnte der auf BTA18 identifizierte Haplotyp Q1 schließlich auf einen Bereich von 58.280.048 bis 58.819.413 bp eingegrenzt werden. In weiterführenden Analysen, unter anderem in zwei genomweiten Assoziations-studien (GWAS), die keine SNP-Fenster sondern einzelne SNP betrachteten (MLMA#1 und MLMA#2), sowie vier weiteren cLDLAs (MODELL#2 - MODELL#5) auf Chromosom 18, konnte gezeigt werden, dass die gewählte Methode (cLDLA) als Hauptursache für die oben genannte Positionsabweichung gesehen werden kann. Die Ergebnisse der Modelle zeigten, dass der Einfluss des Haplotyps Q1 in der Lage ist, die Effekte bezüglich der Kalbemerkmale im Bereich von 50 bis 60 Megabasenpaaren weitestgehend auszulöschen und nicht der von vielen Forschern für paternalen Kalbeverlauf kartierte SNP ARS-BFGL-NGS-109285 für den Effekt als ursächlich angesehen werden kann. Trotz reger bisheriger Forschung im Bereich dieses SNP konnten erst jüngst vier mögliche kausale Varianten im Bereich um den SNP ARS-BFGL-NGS-109285 identifiziert werden. Da aber in vier sequenzierten HF Bullen mit Haplotyp Q1 keine der Varianten von PURFIELD et al. (2015) entsprechend der Genotypen nachvollzogen werden konnte, können diese nicht als gegenseitige Bestätigung verstanden werden. Unsere Ergebnisse sprechen deutlich für eine weiterhin unbe-kannte Mutation innerhalb des Haplotyps Q1. Zusammenfassend lässt sich feststellen, dass die hier in dieser Arbeit dargelegten Ergebnisse wichtige neue Fakten zum aktuellen Wissensstand beitragen und sich somit für die erfolgreiche Identifizierung kausaler Variante(n) als hilfreich erwei-sen werden. In der Zwischenzeit steht der für direkten Kalbeverlauf und Totgeburt als ‚schädlich‘ identifizierte Haplotyp Q1 zur Verfügung, so dass unverzüglich begonnen werden kann, indirekt gegen die kausale Variante zu selektieren, um sie langfristig aus der Holstein-Friesian Population zu entfernen.

The genetic basis underlying adaptive evolution is still largely unknown. Adaptive evolution is facilitated by natural selection that acts on the genetic variation present in a population. Favoring some genetic variants over others, natural selection eventually produces adaptations that allow populations to survive in changing or new environments. Populations colonizing new habitats that differ from their original habitat are often confronted with a multitude of novel ecological constraints to which they need to adapt. A well-annotated genome and a diverse genetic toolkit make the fruit fly Drosophila melanogaster an ideal model system for studying the genetics underlying adaptation. As a cosmopolitan species, D. melanogaster has adapted to a wide range of thermal environments. Despite having a tropical origin in southern-central Africa, it has successfully settled in temperate environments around the world. Thermal adaptations that have helped to deal with the greater range and variability in temperature as well as low-temperature extremes have been required to prosper in temperate environments. Chromatin-based gene regulation is known to be disrupted by varying temperatures. Variation in the temperature, at which flies live, result in varying expression levels of Polycomb group (PcG) regulated genes with higher expression at lower temperatures. Chapter 1 and 2 of this thesis aim to answer the question whether this thermosensitivity of PcG regulation has been detrimental for colonizing temperate environments and thus needed to be buffered by natural selection. Thermosensitivity of PcG regulation was observed in different natural populations of D. melanogaster. A lower degree of thermosensitive expression was consistently found for populations from temperate climates when compared to those from the tropics. In Chapter 1, evidence is presented for positive selection acting on the polyhomeotic (ph) gene region to reduce thermosensitivity of PcG regulation in temperate populations from Europe. The targets of selection appear to be single nucleotide polymorphisms (SNPs) in a relatively small cis-regulatory region between the two PcG target genes polyhomeotic proximal (ph-p) and CG3835 that are highly differentiated between European and African populations. Using reporter gene assays, it was demonstrated that these SNPs influence gene expression and that the European alleles confer reduced thermosensitivity of expression in contrast to the African alleles. In Chapter 2, thermosensitivity of another PcG target gene, vestigial (vg), was investigated in six natural populations including four temperate populations from high-altitude Africa and central to high-latitude Europe, and two tropical populations from the ancestral species range. All four temperate populations exhibited a lower degree of thermosensitive expression than the two tropical populations. The underlying mechanisms of increased buffering, however, seem to differ between these temperate populations. Thermal adaptation to temperate environments also includes dealing with low-temperature extremes. Severe cold stress is a main limiting factor imposed on D. melanogaster by temperate climates. Increased cold tolerance in temperate populations is thought to have evolved by natural selection. Cold tolerance is a quantitative trait that appears to be highly polygenic and has been mapped to different quantitative trait loci (QTL) in the genome. In Chapter 3, such a QTL region was fine-mapped to localize causal genes for increased cold tolerance in temperate flies. As a result, brinker (brk) was identified as a new candidate gene putatively involved in cold stress adaptation.

The fixation of beneficial variants leaves genomic footprints characterized by a reduction of genetic variation at linked neutral sites and strong, localized allele frequency differentiation among subpopulations. In contrast, for phenotypic evolution the effect of adaptation on the genes controlling the trait is little understood. Theoretical work on polygenic selection suggests that fixations of beneficial alleles (causing selective sweeps) are less likely than small-to-moderate allele frequency shifts among subpopulations. This thesis encompasses three projects in which we have experimentally addressed the issue of selective sweeps vs. allele frequency shifts in the context of polygenic adaptation. We studied three X-linked QTL underlying variation in chill coma recovery time (CCRT), a proxy for cold tolerance, in Drosophila melanogaster from temperate (European) and tropical (African) environments. The analysis of these QTL was performed by means of selective sweep mapping and quantitative complementation tests coupled with expression assays. While the results of the selective sweep mapping approach identified a gene (CG4491) that is unlikely to be affecting CCRT, quantitative and gene expression analyses revealed two linked candidate genes (brk and CG1677) that appear to differ in their evolutionary histories. We found that the difference in expression of the gene brk between populations affects CCRT variation. Cold tolerant flies from the temperate zone have a lower expression of this gene than cold sensitive flies from the tropics. We found that a likely cause of this difference is variation in a cis-regulatory element in the brk 5’ enhancer region. Sequence variants in this element exhibit moderate frequency differences between populations from temperate and tropical environments, forming two latitudinal clines: one from the equator to the north and another one in opposite direction to the south. In contrast, the other gene within the same QTL (CG1677), which is linked to brk, showed no measurable effect on cold tolerance but is a likely target of strong positive selection leading to a selective sweep in the European population. These results are consistent with the aforementioned theoretical predictions about footprints of selection in polygenic adaptation. They are also proof of the conceptual bias incurred when identifying candidate genes within a QTL via selective sweep mapping, at least in naturally evolving populations. The challenge for the evolutionary genetics community in the coming years is to develop statistical tools that are as powerful and robust as those already available to map selective sweeps to identify sites in the genome where allele frequency shifts have occurred due to adaptive evolution at the phenotypic level. Finally, the last section of the results is a report of a new population genetics dataset. It consists of a collection of 80 inbred lines from a natural D. melanogaster population in Sweden and 19 full genome sequences derived from this sample. We hope this material will provide us with further insight into the processes underlying adaptation to novel and stressful environments.

Background: Since the pig is one of the most important livestock animals worldwide, mapping loci that are associated with economically important traits and/or traits that influence animal welfare is extremely relevant for efficient future pig breeding. Therefore, the purpose of this study was a genome-wide mapping of quantitative trait loci (QTL) associated with nine body composition and bone mineral traits: absolute (Fat, Lean) and percentage (FatPC, LeanPC) fat and lean mass, live weight (Weight), soft tissue X-ray attenuation coefficient (R), absolute (BMC) and percentage (BMCPC) bone mineral content and bone mineral density (BMD). Methods: Data on the nine traits investigated were obtained by Dual-energy X-ray absorptiometry for 551 pigs that were between 160 and 200 days old. In addition, all pigs were genotyped using Illumina's PorcineSNP60 Genotyping BeadChip. Based on these data, a genome-wide combined linkage and linkage disequilibrium analysis was conducted. Thus, we used 44 611 sliding windows that each consisted of 20 adjacent single nucleotide polymorphisms (SNPs). For the middle of each sliding window a variance component analysis was carried out using ASReml. The underlying mixed linear model included random QTL and polygenic effects, with fixed effects of sex, housing, season and age. Results: Using a Bonferroni-corrected genome-wide significance threshold of P < 0.001, significant peaks were identified for all traits except BMCPC. Overall, we identified 72 QTL on 16 chromosomes, of which 24 were significantly associated with one trait only and the remaining with more than one trait. For example, a QTL on chromosome 2 included the highest peak across the genome for four traits (Fat, FatPC, LeanPC and R). The nearby gene, ZNF608, is known to be associated with body mass index in humans and involved in starvation in Drosophila, which makes it an extremely good candidate gene for this QTL. Conclusions: Our QTL mapping approach identified 72 QTL, some of which confirmed results of previous studies in pigs. However, we also detected significant associations that have not been published before and were able to identify a number of new and promising candidate genes, such as ZNF608.

Caractérisation génétique de l’effort reproducteur de l' huître creuse, Crassostrea gigas, dans le cadre des mortalités estivales juvéniles : approche QTL

Background: Since the times of domestication, cattle have been continually shaped by the influence of humans. Relatively recent history, including breed formation and the still enduring enormous improvement of economically important traits, is expected to have left distinctive footprints of selection within the genome. The purpose of this study was to map genome-wide selection signatures in ten cattle breeds and thus improve the understanding of the genome response to strong artificial selection and support the identification of the underlying genetic variants of favoured phenotypes. We analysed 47,651 single nucleotide polymorphisms (SNP) using Cross Population Extended Haplotype Homozygosity (XP-EHH). Results: We set the significance thresholds using the maximum XP-EHH values of two essentially artificially unselected breeds and found up to 229 selection signatures per breed. Through a confirmation process we verified selection for three distinct phenotypes typical for one breed (polledness in Galloway, double muscling in Blanc-Bleu Belge and red coat colour in Red Holstein cattle). Moreover, we detected six genes strongly associated with known QTL for beef or dairy traits (TG, ABCG2, DGAT1, GH1, GHR and the Casein Cluster) within selection signatures of at least one breed. A literature search for genes lying in outstanding signatures revealed further promising candidate genes. However, in concordance with previous genome-wide studies, we also detected a substantial number of signatures without any yet known gene content. Conclusions: These results show the power of XP-EHH analyses in cattle to discover promising candidate genes and raise the hope of identifying phenotypically important variants in the near future. The finding of plausible functional candidates in some short signatures supports this hope. For instance, MAP2K6 is the only annotated gene of two signatures detected in Galloway and Gelbvieh cattle and is already known to be associated with carcass weight, back fat thickness and marbling score in Korean beef cattle. Based on the confirmation process and literature search we deduce that XP-EHH is able to uncover numerous artificial selection targets in subpopulations of domesticated animals.

Summary The present work is focused on the identification of positively selected genes that are involved in local adaptation in European Drosophila melanogaster. This species is globally distributed as human commensal and occupies almost every ecozone. The ancestral range, however, is afrotropical and the questions arise how and when the fruit fly managed to invade new environments that differed in environmental parameters. One phenotype that might have been of crucial importance for the cosmopolitan distribution of an insect species is cold tolerance. Using fly samples from the ancestral range of tropical Africa and the derived range of temperate Europe we compared their cold tolerance by measuring the time they need to recover from a cold induced chill coma. We picked the most divergent African and European fly lines as parental lines and created a huge population of X chromosomal recombinants. We searched their X chromosome for quantitative trait loci (QTL) that caused phenotypic divergence between the parental lines and identified several loci that were associated with chill coma recovery time. Subsequently, we went back to the original population samples from Africa and Europe and characterized a European selective sweep that was co-localized with one QTL. We established a novel colonization model to tackle the question when D. melanogaster spread from Africa and invaded new environments, such as Europe and Asia. We sequenced ~280 fragments of the X and third chromosome of an Asian population sample and aligned them with the corresponding fragments of the African and European sample that were already sequenced before. By means of Approximate Bayesian Computation (ABC) we found one common ancestor of European and Asian D. melanogaster that left Africa around 16,800 years ago. We reject an ancient colonization event from Africa to Asia, which could have led to the strongly divergent Asian phenotype of the ‘Far East Race’. A formerly performed genome scan of X-linked genetic variation of the European and African sample revealed interesting candidates of European-specific adaptation. To analyze one candidate region more closely we conducted a follow-up study and sequenced the entire candidate in both population samples. We found multiple European specific genetic variants one of which was an insertion/deletion polymorphism that generates a new transcript of the flotillin-2 gene. This transcript (Flo-2-C) is unique to D. melanogaster and encodes a truncated version of flotillin, a membrane-anchoring scaffolding protein. An expression analysis revealed that the Flo-2-C transcript is expressed in most fly lines independent of the gene structure in third instar larvae. Thus, a disordered gene structure does not prevent the process of transcription and might reflect a young gene variant.

Background: Leg weakness issues are a great concern for the pig breeding industry, especially with regard to animal welfare. Traits associated with leg weakness are partly influenced by the genetic background of the animals but the genetic basis of these traits is not yet fully understood. The aim of this study was to identify quantitative trait loci (QTL) affecting leg weakness in pigs. Methods: Three hundred and ten F(2) pigs from a Duroc x Pietrain resource population were genotyped using 82 genetic markers. Front and rear legs and feet scores were based on the standard scoring system. Osteochondrosis lesions were examined histologically at the head and the condylus medialis of the left femur and humerus. Bone mineral density, bone mineral content and bone mineral area were measured in the whole ulna and radius bones using dual energy X-ray absorptiometry. A line-cross model was applied to determine QTL regions associated with leg weakness using the QTL Express software. Results: Eleven QTL affecting leg weakness were identified on eight autosomes. All QTL reached the 5% chromosome-wide significance level. Three QTL were associated with osteochondrosis on the humerus end, two with the fore feet score and two with the rear leg score. QTL on SSC2 and SSC3 influencing bone mineral content and bone mineral density, respectively, reached the 5% genome-wide significance level. Conclusions: Our results confirm previous studies and provide information on new QTL associated with leg weakness in pigs. These results contribute towards a better understanding of the genetic background of leg weakness in pigs.

Background: In a previous study in the Fleckvieh dual purpose cattle breed, we mapped a quantitative trait locus (QTL) affecting milk yield (MY1), milk protein yield (PY1) and milk fat yield (FY1) during first lactation to the distal part of bovine chromosome 5 (BTA5), but the confidence interval was too large for positional cloning of the causal gene. Our objective here was to refine the position of this QTL and to define the candidate region for high-throughput sequencing. Methods: In addition to those previously studied, new Fleckvieh families were genotyped, in order to increase the number of recombination events. Twelve new microsatellites and 240 SNP markers covering the most likely QTL region on BTA5 were analysed. Based on haplotype analysis performed in this complex pedigree, families segregating for the low frequency allele of this QTL (minor allele) were selected. Single-and multiple-QTL analyses using combined linkage and linkage disequilibrium methods were performed. Results: Single nucleotide polymorphism haplotype analyses on representative family sires and their ancestors revealed that the haplotype carrying the minor QTL allele is rare and most probably originates from a unique ancestor in the mapping population. Analyses of different subsets of families, created according to the results of haplotype analysis and availability of SNP and microsatellite data, refined the previously detected QTL affecting MY1 and PY1 to a region ranging from 117.962 Mb to 119.018 Mb (1.056 Mb) on BTA5. However, the possibility of a second QTL affecting only PY1 at 122.115 Mb was not ruled out. Conclusion: This study demonstrates that targeting families segregating for a less frequent QTL allele is a useful method. It improves the mapping resolution of the QTL, which is due to the division of the mapping population based on the results of the haplotype analysis and to the increased frequency of the minor allele in the families. Consequently, we succeeded in refining the region containing the previously detected QTL to 1 Mb on BTA5. This candidate region contains 27 genes with unknown or partially known function(s) and is small enough for high-throughput sequencing, which will allow future detailed analyses of candidate genes.

The aim of the present work was to identify the genes that played a role in ecological adaptation in D. melanogaster. This species, which originated in Africa, successfully adapted to a broad range of climates during the last 100.000 years. To find the genes involved, I used two different approaches: (1) a genomic region containing several ecologically relevant candidate genes and putatively carrying footprints of selection was investigated using selective sweep mapping, and (2) cold tolerance that might have been an important phenotype for the adaptation to the temperate climates was investigated using a QTL analysis. Using the technique of selective sweep mapping pioneered in the Stephan’s group, I detected evidence that recent strong positive selection has been acting on a small DNA region of 2.7 kb overlapping with the 3’ end of the HDAC6 gene in the ancestral African population. This gene codes for a newly characterized cell stress surveillance factor. HDAC6 is an unusual histone-deacetylase. It is localized in the cytoplasm and has a ubiquitin-binding and a tubulin-deacetylase activity. These properties make HDAC6 a key regulator of cytotoxic stress resistance. The phenotypic analyses show that the African and the European populations have very strong cold tolerance differences. By removing the effects of the autosomes, I showed that a significant amount of the phenotypic variance is due to genetic factors carried by the X chromosome. These factors were then more precisely mapped to two genomic regions of the X chromosome. By comparing the present results with other association studies and the Gene Ontology database, it was possible to determine a list of candidate genes influencing cold tolerance in D. melanogaster. As this list is limited to a very small number of genes, additional investigations for footprints of selection in these regions may be used to confirm their role in ecological adaptation.

The aim of this study was to identify quantitative trait loci (QTL) affecting milk production traits in one advanced backcross Fleckvieh x Red Holstein population, that are identical by descent, according to both origin and effect.Genome wide scan and consequent QTL mapping by means of “selective DNA pooling” in the daughter design revealed two advanced backcross (Fleckvieh x Red Holstein) families segregating for QTL affecting protein percent (PP-QTL) on bovine chromosome 19 (BTA19). Identical by descent analysis indicated a 20 cM region as the most possible location of this QTL. After initial interval mapping in 20 granddaughter design families, six families segregating for PP-QTL in the same region were detected. PP-QTL segregating and related families were combined into a set of 11 granddaughter design families for intensive study performed with 21 microsatellite markers. Final interval mapping was able to confirm but not to refine the PP-QTL position. Combined linkage disequilibrium and linkage mapping refined the PP-QTL position. By this method we were able to locate highly significant PP-QTL in the region of 4-9 cM. Analyses performed on all available traits showed effects of the mapped QTL on milkability also. The analysis of estimated sires’ effects suggests rather one QTL with effect on both traits.

Das Ziel dieser Arbeit war die Kartierung eines QTL mit Effekt auf paternalen Kalbeverlauf und paternale Totgeburt auf Bos Taurus Autosom 9 (BTA09) in einer fortgeschrittenen Fleckvieh x Red-Holstein Rückkreuzungspopulation mit positioneller und funktioneller Kandidatengenanalyse. Dazu wurden Untersuchungen mit verschiedenen Kartierungsdesigns in Granddaughter und Daughter Designs durchgeführt. Intervallkartierung und Linkage / Linkage-Disequilibrium-Kartierung wurden verwendet um den QTL feinkartieren zu können. Die LDL-Kartierung wurde in Ansätzen mit MCMC-geschätzten (durchschnittlichen) und wahrscheinlichsten Haplotypen durchgeführt. Mit der Intervallkartierung konnten zwei signifikante QTL für paternalen Kalbeverlauf und paternale Totgeburt auf BTA09 lokalisiert werden: ein QTL im proximalen Bereich und ein QTL im distalen Bereich des Chromosoms. Die Ergebnisse der LDL-Kartierung weisen auf nur einen signifikanten QTL im distalen Bereich von BTA09 hin. Als mögliches funktionelles und positionelles Kandidatengen für den distalen QTL mit Effekt auf paternalen Kalbeverlauf und Totgeburt konnte IGF2R als Rezeptor des insuline-like growth factor 2 evaluiert werden. Einflüsse von IGF2R auf das fetale und embryonale Wachstum wurden beschrieben. Die Intervallkartierung auf BTA29 - das IGF2-Hormon codierende Chromosom - ließ auf keinen QTL mit Effekt auf Kalbeverlauf oder Totgeburt schließen. Auf BTA09 wurde neben den QTL für paternalen Kalbeverlauf und paternale Totgeburt mit Intervallkartierung und Approximativem Interval-Mapping zwei QTL mit Effekt auf Proteinprozent kartiert. Diese QTL sind ebenfalls im proximalen und distalen Bereich des Chromosoms lokalisiert. Die LDL-Kartierung konnte nur einen QTL mit Effekt auf Proteinprozent im distalen Bereich bestätigen. Ein mögliches funktionelles und positionelles Kandidatengen im distalen Bereich des Chromosoms stellt der Östrogenrezeptor ESR1 (ERα) dar. ESR1 nimmt nachweislich großen Einfluss in der Entwicklung des alveolären Gewebes in der bovinen Milchdrüse.

Nasonia vitripennis is a small parasitic hymenopteran with a 50-year history of genetic work including linkage mapping with mutant and molecular markers. For the first time we are now able to anchor linkage groups to specific chromosomes. Two linkage maps based on a hybrid cross (N. vitripennis x N. longicornis) were constructed using STS, RAPID and microsatellite markers, where 17 of the linked STS markers were developed from single microdissected banded chromosomes. Based on these microdissections we anchored all linkage groups to the five chromosomes of N. vitripennis. We also verified the chromosomal specificity of the microdissection through in situ hybridization and linkage analyses. This information and technique will allow us in the future to locate genes or QTL detected in different mapping populations efficiently and fast on homologous chromosomes or even chromosomal regions. To test this approach we asked whether QTL responsible for the wing size in two different hybrid crosses (N. vitripennis x N. longicornis and N. vitripennis x N. giraulti) map to the same location. One QTL with a major effect was found to map to the centromere region of chromosome 3 in both crosses. This could indicate that indeed the same gene/s is involved in the reduction of wing in N. vitripennis and N. longicornis. Copyright (C) 2003 S. Karger AG, Basel.