ChewintheCud Podcast

Beyond Breeding: The Epigenetic Revolution

• ChewintheCud Ltd • Season 4 • Episode 5

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What if your cows could tell you exactly what they need to thrive? That's the groundbreaking promise of epigenetics, where the frontier of dairy science meets practical farm management. 

In this fascinating deep dive, Ian Garner, Head of R&D at Antler Bio, reveals how gene expression technology is revolutionizing dairy farming by unlocking hidden potential beyond traditional genetics. While genetics provides the blueprint, epigenetics determines whether those genetic instructions are actually followed—and this can be influenced by everything from nutrition to environment.

Through real-world examples, we discover how farms across Europe are seeing remarkable results by giving their cows a voice through RNA sequencing. One Finnish farm doubled milk production after implementing targeted changes, while most see a 7:1 return on investment within months. The beauty lies in the simplicity: sometimes it's as straightforward as adjusting vitamin supplementation or improving water trough placement based on what the cows' gene expression patterns reveal they truly need.

We explore how this technology works practically on farm, from blood sampling to data analysis, and how the recommendations integrate with existing farm management systems. Beyond just boosting production, we discuss the potential for early disease detection, improved fertility, and enhanced animal welfare through preventative intervention based on expression patterns.

For forward-thinking farmers looking to maximize performance and profitability, this episode offers a compelling glimpse into dairy farming's future—where understanding the language of gene expression might be the key to unlocking your herd's full potential. Ready to hear what your cows are trying to tell you?

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Andrew Jones:

This is the Chewing the Cut podcast, a podcast for the UK dairy industry, brought to you from the southwest of England and listen to Around the World. Hello, and welcome to Tune the Cud podcast, my name's Andrew Jones, and with me today is Sarah Bolt how are you doing, sarah?

Sarah Bolt:

I'm good. Thank you, andrew, and how are you?

Andrew Jones:

Yeah, not too bad. Saw some wet stuff out sky today.

Sarah Bolt:

I know that was a bit of a luxury, wasn't it?

Andrew Jones:

Well, it was, especially when I then spoke to a client just north of Bristol and said do you get any of that rain we had today? Because I'll be honest to anyone listening, we had probably a good hour, if not more of reasonable rain, today, and he's like nope. And you're like oh, okay, okay, um, but it's a bit of a funny one at the moment, isn't there?

Sarah Bolt:

because I mean there's maize being harvested because it's either ready or it's dying off, or I can't remember maize being harvested in august ever before exactly.

Andrew Jones:

and yet I was a trial yesterday of, um, some maize being grown with some runner beans, really interested to see where that goes, uh, but that was probably three weeks away and I've seen some others today that I'd say is probably the same, to be honest with you, yeah, yeah, so I guess we've all had different amounts of rain and some went in quite early, didn't it yeah? Yeah, but anyway, I'm just thinking it must be that time of year for women in dairy again, isn't it, Sarah? The conference.

Sarah Bolt:

Do you know? We're only three weeks away. So, uh, yeah, it's all exciting. We've got a great lineup of speakers. Um, I won't start mentioning them because no doubt I'll forget a few and that will be awful. So, um, so do pop along to uh, to the website and uh, and take a look, but uh, yeah, where's it being held this year?

Andrew Jones:

so it's up at uh stonely, just at chesford grange, just outside stonely yeah, so well that that should be a fun and interesting couple of days for you when you're up, when that gets going again definitely.

Sarah Bolt:

It's always great to be surrounded by some inspirational people oh, there's plenty of them around.

Andrew Jones:

Definitely, aren't there plenty of them around anyway? Um, today is well, let's be blunt about it, I wasn't going to say it, but I nerded out a little bit with this one, as sarah, I'm sure, will completely agree. Um, we, we had a really interesting talk on this on, so we, we've got to talk about epigenetics, which I find absolutely fascinating, and I definitely learned a lot from this one.

Sarah Bolt:

Um, and I know once or twice you just had to pull it back a little bit, didn't you, sarah, to bring it rain it in I do remember having to rain you both in a couple of times, so bringing this back to the farm or you know those sorts of watch out for them so, yeah, so no, no, I'll be honest, I really enjoyed it and I guess that's one of the reasons why I love doing this podcast is things I get to learn and yeah, it was just something really good.

Andrew Jones:

So so let's go and enjoy it. So, uh, today's uh podcast is all about epigenetics. This podcast has been brought to you today by chewing the cud limited, who offer completely independent dairy and beef nutrition, our signals, advice and training, along with roms mobility scoring. More details on these and other services available, please visit our website, wwwtune the cudcom, or email us directly on nutrition at tunethecudcom. Tune the Cud Ltd now offers first aid training from a registered first aid at work trainer and experienced minor injuries practitioner. For more details, please visit our website, wwwtunethecudcom, or email us directly on training at tunethecudcom. Hello, I'm Andrew Jones and I'm Sarah Bolt, and welcome to the Tune the Cud podcast, a podcast for the UK dairy industry.

Sarah Bolt:

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Andrew Jones:

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Ian Garner:

Enjoy today's episode.

Andrew Jones:

Hello and welcome back to Tune the Cud podcast and today we're going to talk about epigenetics and our guest today is Ian Garner, who is head of R&D at Antler Bio. Good day to you, ian.

Ian Garner:

Great day to you too, andrew. Thank you for having me, oh no, thank you, thank you.

Andrew Jones:

So Ian is talking to us because one of his colleagues heard my not flippant but my little remark when we were talking about the immunogy with Connor and Rachel and I said, oh, it'd be really good to do something about epigenetics. And your colleague Andy reached out, didn't he? Via LinkedIn, and we got talking and here we are today. So, before we get into what epigenetics are, just tell us a little bit about yourself, ian, and how you got to be where you are today, sure?

Ian Garner:

um, yeah, so well, it's quite a long time ago now but I I went to um Nottingham University to study nutrition. It was never my plan. I wanted to be a badminton player. I never quite reached the levels to play for England. Human nutrition on human, yeah, and then some animal, but mainly, yeah, mainly nutrition.

Ian Garner:

Um, yeah, I got really interested to do it during around the time I was 17, 18, when I played badminton I realized my diet could be improved and as I improved that, I became a lot better at badminton, I had a lot more energy and I became fascinated by that. And then I went on to study nutrition at Nottingham for three years and then I also did a master's in immunology. I studied nutrition at Nottingham for three years and then I also did a master's in immunology. Throughout that time there was a lot of talk and lectures on gene expression, genetics and then epigenetics. I got really interested in what epigenetics is. Then I ended up doing a PhD in the subject at UCL in a rare lung disease in human called IPF idiopathic pulmonary fibrosis. I had a great time at UCL and then I moved to Imperial and studied epigenetics again, but looking at variant and pancreatic cancer. So I did that for five years.

Ian Garner:

And then, yeah, then I COVID hit, as all stories seem to start and, yeah, I was looking for the next opportunity. I got a really good offer from quite a prestigious UK genomics group and then I got a random call from a recruiter. Uh got really interested in what he had to say about antler bio. Uh, met maria, the ceo, who's a great person, great lady, and yeah, the rest is history. I I ended up taking this path and here we are today, here we are today.

Andrew Jones:

Here we are today, and we'll talk a little bit. I mean, you obviously work for antler bio, but you now have a product, I think it's just coming on the market. Is it epi herd? But we'll talk a little bit. I mean, you obviously work for antler bio, but you now have a product, I think it's just coming on the market. Is it epi herd? But we'll talk about that a little bit as as this podcast goes on.

Andrew Jones:

But I guess we ought to talk a little bit about the difference between phenotype, genotype and epigenetics. Now, please correct me if I'm wrong. I mean, obviously we, we spoke a little bit with stewart and helen, and we talked about genotype and phenotype there. So genotype is the let me get this right is the potential, whereas phenotype is actually what they express. That's correct, isn't it? Yep, yep.

Andrew Jones:

And then epigenetics, though and this is where I get really fascinated by this, and I don't know a huge amount about it at all, amount about it at all but epigenetics is how you, um, how the nutrition can potentially express those genetics and how you turn different genes on and off, isn't it? And I mean, I guess the two examples always absolutely fascinate me is, I know georgina and and laura from trowel, particularly when they're talking about calves. Talk about how you know the. The queen bee is genetically exactly the same as her female worker bees, but it's just the fact that the queens have fed the royal jelly and that's what turns them into queens. Or the other one that fascinates me is how you, if you're a woman or a cow or whatever it happens to be and I'm not trying to make comparisons, but ultimately we're all the same um, um, you, um, what, as a pregnant individual, what you eat if you are carrying a female fetus, doesn't just affect that fetus, but also the eggs that that fetus is carrying. That's correct, isn't it?

Ian Garner:

Yeah, there's many things there, but, yes, epigenetics is. If you define epigenetics, it's essentially another layer of data. It's on top of genetics. Epi means above on. So you have your genetics, your version, your potential, and you have then epigenetics. Now, both genetics and epigenetics can regulate gene expression. Genetics is more static. You can think of genetics as that's what you have, that's your version of your gene. Epigenetics can be modified by the environment diet, light stress.

Andrew Jones:

Well, I was going to say the example I think of. It's just come back to me and I cannot remember the two bulls' names but there was a couple of bulls in Canada it must be probably 20 years ago now and they were from a split embryo. So they were considered to be genetically identical and when they got their proof they were put together and they became number one in Canada and I think since then they've decided no, it wasn't't the case, because one brother was definitely better than the other and in that instance I guess it must have been epigenetics that came into it. The environment, they were a flush, they were a split embryo, obviously put into different resips, et cetera, and those genes expressed slightly differently, even though they were genetically identical. And I certainly know since then that's the only time since then, if that happens again, that there's separate proofs on those bulls.

Ian Garner:

Yeah, exactly. So you can have two very genetically similar animals but they can have very different phenotypes. They can have one could be very healthy, one can make a lot of milk, the other the opposite. And although you might think you've got the best versions of genes into those animals you may have, but they might not be expressing those genes.

Andrew Jones:

And that is where epigenetics can come in and can turn on and off genes through epigenetics how easy is it then to turn these genetics on and off, because I always thought it was what happened in utero is what did it.

Ian Garner:

But yeah, so it can. It can be, um, I mean simple things that you might not really think about, like, let's say, light that can control a bunch of genes, so circadian rhythm um, wow, have uh, stress. If you're stressed, that can activate certain pathways that then turn on and off genes. Um, if you eat certain things more than others, to set, let's say, you have more vitamin b, more folate, more choline in your diet, you'll have more methyl donors and methylation can turn on and, depending off sorry, depending on where methylation is in the gene, it can either turn it off or turn it on. Um, so there's lots of different levels of control there and there's anything in your environment or diet you can think of will have an effect on gene expression.

Andrew Jones:

Okay, because I can't be honest with my understanding was was. It was just in, like, say, in uterus. What you're saying is is us today, depending on the light, or depending on well, could argue ultra, how much the ultra processed food you eat, or whatever, could then change what genes do or don't, and therefore what you do or don't crave, or or how you then go forward Exactly.

Ian Garner:

Yeah, I mean, it can be you, you, you can link it back in utero. So what your, what your mom ate, what your grandmother ate that can, or what their environment was like, um, if they, if it was a very stressful time, very stressful area, um, you, um you know high pollution levels. Uh, just the diet not so good back then, that can have a lasting impact on your health. So it isn't just, it isn't just about what you do, it is about your history, your family history, of what they've done. Um, smoking, for example, is a classic one where, uh, you know, if you smoke during uh pregnancy, um, you can induce a lot of epigenetic changes that then go on to affect the baby born.

Andrew Jones:

I was just about to say, I guess thalidomide would probably be another one, wouldn't it? Because that, they said was oh, it's only going to affect that generation, but it's completely changed the way those genes are expressed, hasn't it? And then children with thalidomide, their children, are showing the same.

Ian Garner:

Yeah, I mean, I'm not an expert on that, but that's probably targeting genetics as well. That probably caused a lot of mutations, but yes, it probably did affect epigenetics. Yeah, it's never one or the other. There are some cases where diseases are caused by a single mutation, point mutation. It's more of a combination of epigenetics and genetics work together you can regulate gene expression. The bottom line is is a gene expressed at the right time? Is it on and off at the right time? That can be through multiple mechanisms genetics and epigenetics mechanisms, genetics and epigenetics.

Sarah Bolt:

But yeah, so, going back to basics, what does this mean to farmers and you know sort of we've got our heads around genetics, we've got our heads around you know sort of that side of things. How do we now start thinking about epigenetics? And what does that mean? What can we do? We do, what can you know, what should we be doing?

Ian Garner:

yeah, definitely, I mean so, yeah, genetics is is a big thing. Obviously in farming you, there's a lot of talk when you try and breed what you think is the best genetics into your herd. Um, now, that's, that's good, that can help, but it's not a definitive that it's going to have a positive outcome. You've potentially bred in the best or a very good version of a gene, but it doesn't mean you're expressing it. You can't tell if you're expressing it just because you've got that version. That's where epigenetics comes in.

Ian Garner:

What we do at Antelope is look at whether a gene is expressed or not, regardless of genetics or epigenetics. We're just looking at is a gene expressed, yes or no? So if you think of the analogy of, say, you've got two tractors, they're top of the range tractors, they're the best version of a tractor you can get. They look identical, but one works and one doesn't. If you think of epigenetics as like the fuel or the electronics, something's gone wrong there, doesn't. If you think of, epigenetics is like the fuel or the electronics, something's gone wrong there, um, that's that's kind of my maybe my analogies are not you've just brought to my head.

Andrew Jones:

Is it the? The image from is it season four of clarkson's farm where he gets all those tractors lined up and it came down to uh, which one could get the tow hitch down quick enough or something. And even then he still didn't buy one of them.

Ian Garner:

And yes, because it didn't say lamborghini, yeah, well, that's the thing, right, if you've got a lamborghini, you've got maybe the best version of a car that you could get, perhaps um, but if it doesn't have fuel, you might as well have a nissan micro, because it still functions as a car. No offense to nissan micro drivers, obviously very good cars. Or Lamborghini because you did say probably.

Andrew Jones:

Oh dear, anyway, yes, yeah, exactly. So, sarah's right. I mean I was getting a little bit maybe carried away, because it just absolutely fascinates me the technical side of it, but in reality, what do we do? So? So if somebody wants to do this, ultimately, how do they go about finding out if their um genetic potential has been reached or has been expressed? I should say yeah so we.

Ian Garner:

So if you want to look at gene expression, you look at. I mean there's several ways of doing it. But if you want to look at gene expression, you look at. I mean there's several ways of doing it. But if you want to look at all the genes so 27,000 genes you would look at doing something called RNA-seq, where you sequence the entire genome and you look at the expression of every gene. So that's at a point in time. But you basically get data that shows you how much that gene is expressed at that point in time so the cow's genome is completely uh, mapped now, isn't it?

Andrew Jones:

um, how, how long does it take you to do an individual cow then, once you've got the dna? Because in the past that would have taken a very long time, wouldn't it?

Ian Garner:

yeah, so we, we extract rna from blood. So essentially how it works, um you take a blood sample.

Andrew Jones:

So I was gonna say it's a blood sample, not a hair sample yeah, you could do it from hair, but it's that poses other challenges. Um, blood is more of a systemic kind of look and, I guess, potentially more risk of contamination with hair maybe yeah, you probably need a bit more hairs.

Ian Garner:

Protocols would be different. I mean, you can get RNA from lots of different places saliva, blood, blood even but yes, we take a very small blood sample and then from that we extract RNA, and then from that we do what's called library preparation, where you basically tag all those sequences of RNA and then it's sequenced and then you align those sequences to the genome, the cow genome, and you see where they, where they map, and the more that map in a certain place, um, the greater that gene is expressed.

Andrew Jones:

Essentially, yeah, so I can say if you see more of the mapping for milk, you are more like to see milk expressed from that animal.

Ian Garner:

Yeah, and the g, the genes that are involved in milk yield. Yeah, exactly. So we have we know what. Not all the genes, we don't know all of them, but we know a lot of genes that are involved, say, in milk yield. We look at good and poor cows so we can say, look, these cows are making 50 kg a day, these cows are making 20 kg a day. What's the difference in gene expression? What genes are different between those cows?

Ian Garner:

And then we can put those through tools called pathway tools, pathway analysis tools, so which cluster of genes map to certain pathways, For example vitamin A pathways, vitamin D pathways or stress pathways, immune pathways, etc. And from that then we can give actionable recommendations to farmers. So we basically take that gene level data, put it through pathway analysis. It's not just that, we add in all the literature as well. So we have some tools that we've developed that takes all the knowledge basically in existence. So anything that's ever been published, we can extract that rapidly. And then you know, we can extract that rapidly. And then we can say, right, there's, say, 60 papers that say if you supplement with vitamin D, you're likely to increase your milk yield, for example. And we do that for a bunch of different pathways, a bunch of different recommendations, and then we add all the literature support to that.

Andrew Jones:

Then we say to the farmers here's what your data shows, so you're providing effectively, am I right? Is it a meta-analysis of all the yeah, essentially, along with their own gene data.

Ian Garner:

Yeah, and the great thing about it is, the earlier you start, the more data you get you build up so you can always go back to that data because if you think, let's say, in a couple of years' time, a cow goes on to develop a problem, you've got that data from before to then say could we actually see any gene expression pathways or patterns developing or was there something there that we could have said, yeah, you could have gone on to develop that. So as we get more and more data, more longitudinal data, we can start seeing that.

Andrew Jones:

Okay, so let's say I've got a herd of 100 cows and I'm listening to this, or I've heard about EpiHerd. How many cows would you suggest I start with? Or do I start with my newly born heifers or my milking animals, or where should I target? And then I guess, from what you're saying, how often should I be maybe looking at some of this to see that, let's say, you've made a recommendation, we follow through with it. How quickly should we then go? Oh, we better follow up to make sure this is doing what it's turned on the genes that it's supposed to. And I guess ultimately, the ultimate question is what's the cost of all of this?

Ian Garner:

Sure. So what we do at the moment is roughly 10% of a herd. So we biostatistically select cows. We do that, you do that, yeah. So we would look at getting some of your best cows, uh, through different stages in their lactation and some of their worst cows, again in different stages, um, and then from that, then we, like I said, we take the rna, we extract rna, we look at the gene expression. So we've got your, let's say, for yield, we've got your good and poor cows. If a farmer's interested in yield, we say, right, what's what's turned on or off in your good cows versus your poor?

Andrew Jones:

cows taking a step back. Do you guys come and take the blood, or is that just get a vet to go and take a standard blood?

Ian Garner:

or that vet takes the blood. We, we don't you just supply whatever's exactly. We supply all the kits. Yeah, there for it. So you essentially um upon upon signing up and we give you a cow list on labels, tubes, a package that you can put the samples in and deliver back. It's all prepaid, so it's pretty simple. The instructions are all there for the vet. They'll be routinely taking blood, so it's very simple. And then that gets delivered back to our warehouse and then we send that off to sequence. So 10% of the herd.

Ian Garner:

Um, what we find is so when, when farms, let's say we do, we do a lot of work in the nordic, so a lot of work in finland, uh, sweden, um, and we with those farms, we say, uh, once, once a year, to sequence. So we, we have the first sequencing. The farmers there will then make changes based on the recommendations we give them, and what we find is quite a lot of them then want to resequence after, maybe a few months after, to see you know If a change is made. Yeah, we have lots of examples where you know where that's happened and then you know some of the what we call that impact capture. So when a farm makes a change. We will record what happens to the yield and other metrics.

Ian Garner:

And some of the you know we have some really cool data. We get across all our farms. You know a significant improvement in yield in farms that make those changes. Some of those farms I mean one farm has actually doubled their milk yield, which is a bit of an outlier, but a very I was going to ask how bigger?

Andrew Jones:

what's your biggest um change that you've seen? So one farm has doubled over, doubled.

Ian Garner:

Yeah, they were, they were. They're kind of our flagship farm, if you like.

Andrew Jones:

They're a lovely, lovely couple it doesn't happen to everybody, just so no, no, it doesn't.

Ian Garner:

No that that's an extreme case we have were they quite low yielding at the time or they were quite low yielding.

Sarah Bolt:

So there was potential there to be able to do that you know, take a 10 000 litre cow and try and double. That would be a lot harder than trying to double a 3 000 litre cow.

Ian Garner:

But we do have farms that are making 70, 80 kg and they're still seeing 5% to 10% improvements. It's all kind of we do see a bigger improvement in those lower cows. To start with there's usually probably more things going on, but the changes can be very straightforward. They don't require a lot of money. They can be very simple things. It's like the cows, uh, like I said to you before, like, uh, it's like they talk to us through their gene expression.

Ian Garner:

So if you think of, you know, a cow can't say I don't like this diet, they might not eat so much. Or you might think, oh, it's not. You know, it's not working so well that for certain cows, but their gene expression might show that they're really stressed by the something. Yeah, they might show that actually, let's say, certain genes just aren't expressed so they don't go on to make enzymes that break down certain substrates, for example vitamin d. Um, you might think you're getting enough vitamin D, you might be supplementing, you might be feeding them great food that should have plenty of vitamin D, but the data can show that actually the genes that metabolize vitamin D they're not expressed or not expressed enough. So then it doesn't really matter how much you give vitamin D If they can't actually break it down to its active form, then they're not getting enough. They might need a version of vitamin D that's actually already converted.

Andrew Jones:

It might be available. What you're saying is that vitamin D might be available but not in a bio. It might be in the rumen or whatever, but it's in the cow but it's not bioavailable. It's not being converted into a form that can be, it's just going to be expressed well out the back end, basically, and and just not get used in a way that it should be being used exactly if you think in humans.

Ian Garner:

Um, some people are great at metabolizing alcohol. They don't get drunk after 10 pints, whereas someone like me will be absolutely wasted on. Uh, you know, I've got to be careful here. But maybe, yeah, just a few bites perhaps. But yeah, it's very similar to that. Everyone's different Cows are different, so what works for some cows might not work for others. We've seen a lot of really interesting data. Some things might seem really obvious, but actually, without actually having that data to show it, you might miss it. Sorry, go on Sarah.

Sarah Bolt:

So what sort of format do the farmers get the recommendations back in? You're saying that it might come back, say like this vitamin d, that, that they not metabolizing it, etc.

Ian Garner:

So how would they, how would a farmer see that as see, yeah, a practical report, I guess yeah, so we we have an app epiherd isn't is a web-based app, um, and through that the farmer to log in, they can look at, or first they're hit with what's called a radar plot. It's basically metrics and their scores based on what we've studied, and then they'll get a page of recommendations, in kind of a traffic light system, ones that they should think about considering first versus other ones, because in certain places or environments you know it doesn't matter there's certain changes that will be more important.

Sarah Bolt:

Like ones that perhaps have bigger impact.

Ian Garner:

Exactly, or that you need to correct first before the other ones can help. If you think if there's a lot of immune inflammatory pathways going on, then changing the lighting might not be as as important as sorting out, you know, the bedding or changing the feed or whatever. Um, but yeah, they, they're given a list of recommendations that fit their farm uh, based on that gene expression and pathway analysis. Um, they'll be given uh, all, well, not all the scientific literature. They'll be given a selection of the scientific literature to show studies that say have added vitamin d, that they do increase in yield or other metrics that um, what we call maybe off-target effects, but beneficial. So, for example, vitamin a um, several farms have had that recommendation. They've supplemented with vitamin A. The yield has gone up. But also they've noted fertility has gone up, silent heat detection has gone up, the overall welfare seems to be better.

Ian Garner:

So again, they get a lot of information with that and it's never us saying you know you have to do this. It is purely this is what your data shows from your rna c, your gene expression. Here's the literature that supports that we will be adding features in the future, very near future, of you know number of farms that have made this change and what the impact has been, because I think that would be really nice to show farms. You know 20 farms, 50 farms have made this change and the average increase has been x kg per cow farmers like to learn from other farmers and know that it's worked for somebody else yeah, yeah, I mean it's a peer is so important yeah, yeah, I mean what we had, uh, very early on about three years ago, we had what we called early adopters.

Ian Garner:

So the farms that you know, really pioneering farms that were willing to take a risk with us. I guess, um, and yeah, they're, they're all still there, they're all, um, yeah, made huge improvements. Um, and it's just having that data behind it, I think, yeah, and once you've you know, the more farms that join, the more information we get it's like a positive feedback loop the more farms that make certain changes. Then we can see which ones are working really well, which ones are not working so well, which ones are really economically viable, which ones aren't so economically viable but do still work. For example, in finland, choline is quite expensive, whereas in the uk it's not as expensive. Now it might be that different regions have to, or regions might want to go certain recommendations over others because of that.

Andrew Jones:

So are your recommendations purely nutritional, or do they include some environmental recommendations, light or whatever the case may be?

Ian Garner:

Yeah, exactly, they include both environmental. So ventilation is one of them, lighting, for example, lots of nutritional ones, lots of um, not not. Also not just supplement, like it's also monitor. Have a look at, say, iron levels. You, when, when those pathways come back, we we can't say whether it's an iron, you need to add more iron or you need to add less iron, um, so things like that.

Andrew Jones:

We that's where we fall back on the literature and then ask them to then consult with their nutritionist, their vets, etc yeah but yeah yeah, it's making me think blimey, because I mean, like, as well as doing the nutrition, obviously I do chaos signals, which is trying to teach people to improve the environment.

Andrew Jones:

Because you know, the quote I always use at the beginning is the best will in the world, I can give you the best diet in the world that will only affect 25 percent of production. It's the environment, whether it's light, space, air, water, whatever. And this you're saying is is a different way of of showing that information. You know, we know that, for argument's sake, you want 200 lux of light for 16 hours a day, but you're showing, actually by making showing that information. You know, we know that, for argument's sake, you want 200 lux of light for 16 hours a day, but you're showing actually by making, showing that what the difference? Because you know, anecdotally, I hear plenty of people say oh yes, we've done it, we've added a liter or two of of milk just by getting the light right. But you're putting it in some hard data as well. That's showing this.

Ian Garner:

Yeah, exactly I mean one of the one of the, I mean one of the farms. I remember very early on we had a vitamin a or they had a pathways involved with vitamin a. So we said that here's all the literature that says supplementing vitamin a will increase the old significantly. They added vitamin a, um, and then they re-sequenced about six months after that and those pathways have completely disappeared between their good and poor cows. Their milk milk yield had gone up. So you know, things like that is, you know, while it's a correlation, not causation, but it's showing that they made a change to vitamin A. Now there's no differences between their top and bottom cows in vitamin A pathways.

Ian Garner:

Same with another example of hydration. You'd think you know cows make a lot of milk, they're going to need to drink a lot, uh, and you'd think that access to water is pretty obvious, um, but when these pathways come up, that's what the cows are saying is between egg and poor cows, that's what the genes that are different between those cows are linked to, um. And I remember one farmer saying like when I saw that result. You know these, these, these pathways in hydration were hugely significant um compared with, like, the p values or adjusted p values were, you know, very small. Yeah, as in like, there's a big difference between a good and poor cows. And, uh, when I, when I saw that, I was a little bit like this is, I'm not used to this pathway being around, because I'm come from the human side of cancer research, where you look at those pathways being different, um, and I said to them, like, well, this is, this is what the data says. It doesn't. Data doesn't lie, but you can interpret it wrong. Yeah, but I remember the farmer saying nope, this makes complete sense.

Ian Garner:

What he's observed with his cows is, as a matriarch cow, they're following that cow, they go to the trough. If they can't get to that trough within about 15 seconds, or when that cow turns around, they'll just think that they've had enough to drink, or they'll just follow that cow and they're not actually drinking. And that's the beauty of this kind of data it really does give the cows I mean, it's um. Again, it's not probably not the best analogy, but it does give them a voice um, through gene expression data. Um, yeah, so they installed more drinking troughs. Um, in a different form, like a different um, uh, how to say, like different locations. Yeah, yeah, added more water troughs, basically exactly, and then created like a in a way that didn't cause a bottleneck yes, they moved into space rather than in passageways or crossways or whatever it happens to be.

Ian Garner:

Exactly, and that might seem very logical, and it is logical, but without that data it probably wouldn't have changed anything. And they did change it and they saw improvement.

Andrew Jones:

So we were asking and you sort of mentioned four and six months how regularly would you recommend somebody does a follow-up then to what they've done? They saw improvement, so so. So we were asking and you sort of mentioned four and six months, how, how regularly would you recommend somebody does a follow-up then to to what they've done is? Is it you should monitor once a year or you should do it more regularly if you've made significant changes to start with? When you do redo it, do you do the same animals or do you do different animals?

Ian Garner:

how does that go about. Yeah, so when, when we resequence um, we do, we do. It's likely we'll do different animals um, some do overlap, but we don't always choose the same and like animals from the first time. Yeah, that's mainly because some of those poor animals just aren't around anymore. They are, they were too poor um and they couldn't be kind of rescued. But some do get rescued. They do become very good animals, but we always look at the top end of the herd versus the bottom end of the herd.

Ian Garner:

There is a lot of biostatistical selection algorithms that go on there. We don't just take the top ones on yield and the bottom ones on yield. There is other things that go on there. In regards to how often um, with the finnish farmers, we we have once per year, but a lot of them opt to do a lot more than that. Every quarter or after they make a change or if something happens with their herd that's unexpected, a lot of them will want to sequence there and then, as quickly as possible, to see if we could have picked up something. Or is there something? What's going on? What's causing that problem?

Sarah Bolt:

so it's just another set of eyes on farm, really, it's yeah, yeah, exactly.

Ian Garner:

And and you know, the beauty of gene expression again, is there's a lot that can go on with expression before a phenotype develops. So if you imagine back, in fact, related back to humans, when we get unwell, um, we display symptoms like a runny nose or a headache or a cough or whatever. But before that actually happens, our genes are changing, they're expressed at different levels in preparation, um, or in reaction to something going on. So there's things like in cows, like yoni's disease. There's no way to detect that until it's too late, um, there's no way in tv before it's too late.

Ian Garner:

Uh, you know the with gene expression, you, you know we have one farm, uh, again, where we, we saw what we believed was a signature of yoni's based on the literature. We saw genes expressed very highly only in that farm compared to all our other farms. We did do um, we, we spoke to the finnish government. They did a test to look at the, the mycobacterium that causes that um, and they did see elevated levels of it, but not to their threshold um, that would class it as yonis. But there was an elevation, just wasn't significant, but that it was rumbling in the background. Basically, we believe so. We believe so. We have good evidence for that now. Um, and it's certainly along those lines which, where gene expression could be very useful, you see that pattern going towards the direction that you might not want. That's where you can intervene before it's too late.

Andrew Jones:

Um, so yeah, looking far in the future? Do you see this as something because I believe you said that this can be done using milk samples. Is that right? Yeah, you see this as maybe something that the milk recording companies might then offer through their monthly milk recording that, like now, you can do quarterly yonis testing.

Ian Garner:

You might turn around and do quarterly rna sequencing and look at your gene expression I think that would be very interesting to do, I mean as a if I could do it, I would do it myself. Um, I'd love to. Uh, if you know it is, it is quite an expensive technology. Still, there are more high throughput ways that you can look at panels of genes, so it could certainly be done that way. I mean, you collect the milk. You'd still need to send it off to extract the RNA. There is a little bit more technical.

Andrew Jones:

But like most of these things, things usually get cheaper and quicker and easier. And I guess I'm thinking 10, 20 years down the line, maybe I don't know where your technology is. But I'm just sort of thinking is that ultimately possible? Do you think ultimately possible?

Ian Garner:

I think once the sequencing machines come down in price and they are expensive, they're expensive to get. But if you could get a tabletop machine that you could integrate into your robotics, then, yes, very much. So. If you could have something and just pass the milk on, extract the RNA sequence, that is something in the future that could be done. Yes, I mean, I like to think and this is not possible at all but maybe one day everyone's got a phone, pretty much right, everyone's glued to a phone quite a lot of the time.

Ian Garner:

One day maybe we'll be able to use our phones to just, you know, you have the glucose tests for a lot of people with diabetes right where they can monitor real-time through their phone or app on their phone, their blood. Now, could we do that for RNA-seq in the future? Perhaps Could we as humans do that, could we have devices? Or a little bit big brotherish to me, you never know, you never know. It's definitely not there yet, but if you could monitor real-time gene expression, um, often that could be huge because you'd basically be seeing all the time what certain things do to your gene expression yeah, I shouldn't have eaten that packet of crisps or whatever it is, and this is what it's done.

Andrew Jones:

Yeah, another way to beat us around the head for eating the wrong thing yes, I, I do like crisps as well oh, thank god for that.

Andrew Jones:

I'm not the only one. I do, like chris as well. Oh, thank god for that. I'm not the only one. Oh, dear, but yes, but I mean, yeah, I mean listening to you say there, it's, it's I don't know, there's point music going. I'm feeling old now. I mean that that's maybe the wrong thing to say, but it's just.

Andrew Jones:

You know, it's like we've done several other ones on new technology and and it's just the amazing, the steps that potentially this could go and where it can be, and and just shows that um, dairying farming but we're obviously concentrating on dairying is not the um stereotype that some people still have, that it's just full of yokels that are all you know, all the you know or down from the farm. And you know don't worry about things too much that the technology, the, the advancements in technology, technology, and you know it's not every farm, don't get me wrong. There are still some farms that milk with parlors are 50 years old compared to ones now putting in the latest robots. But you know the, the technology that is available and the way it can go in terms of whether it's teaching ai to detect digital dermatitis, which is one we've done in the past and I know they're looking now at using that ai to detect lameness. And you know all of these things. It's just mind-blowing what some of where this is going.

Andrew Jones:

And you think, well, by the time I end my career in 20, 20 plus years time, where are we going to be with when your likes of yourself are saying, well, actually, and you're going, you know, milk recording just used to be oh well, it was, it was. You know milk yield, fat, protein. Well, now you know I start looking at fatty acids. So look at, you know c16, c18 one. You know all of those sorts of things because they give us more information. But you're going another step again by going well, actually, we can look at the rna and that is showing how those genes are expressed and actually by that we make recommendations and you can improve things. Another step.

Ian Garner:

Yeah, I mean using that lameness example is a great one, um, you know you could look at a cow and see they're lame. That's, that's too late already. You can use ai to do. You know, you can use learning models like neural networks and that's how the ai would essentially would work. You maybe take a picture, um, and then, through machine learning methods like neural networks, they might be able to say, yep, that cow looks lame. Or a little video.

Andrew Jones:

Well, yeah, with that one on the digital termoditis, they've developed it that yeah, they walk through the footbath. There's a camera on it. It takes an eight second video of that animal as it walks through. They've taught it, whereas you and I would see digital dermatitis at about stage four, I think it can pick it up at like stage one or two, I think it is, and I know they're doing the same. Let's be blunt. I've gone and mobility scored for them on a farm where they've got this. I mobility scored them, being I'm a ROMS mobility scorer, and they are going to take the numbers I said were a two or a three and they are going to take that corresponding video of the eight-second clip and then teach it to say, well, andrew, in this instance and there's others who will be doing it has said this cow is lame and I try and say what foot it is and trying to teach it and you're just Exactly, yeah.

Ian Garner:

I mean that's a classic reinforced learning model where you've given the metadata, you've given the scores and they're using that then with the video to then teach the learning model.

Andrew Jones:

As I joked, I remember when we first recorded the podcast, with a man putting himself out of a job at this rate.

Ian Garner:

Well, yeah, maybe I doubt it. Um, you're always going to need that expertise, at least for now. But if you, if you keep with that, there's the point for now.

Andrew Jones:

You just said it is now for now it depends.

Ian Garner:

It depends how quick the ai I guess the evolves. I mean, when you combine um the power of computing and you know, as we get to quantum computing I mean this might go off topic a bit, but you get the enough qubits and enough ai, then yeah, you're going to get some very interesting well, I mean, I mean I've sort of said it before, but you know, um, everybody's afraid of the whole terminator situation with ai.

Andrew Jones:

Well, I hate to say it world, but the world has already been taken over by ai. You just don't realize it. It's doing it in things like the, the, the digital dermatitis detection, or whatever it is.

Sarah Bolt:

It is already there, it is already being used, yeah, yeah yeah, um and obviously it's it's using in your product where you're going through all of that literature. It's's. Ai, that's getting all of that data?

Ian Garner:

Yeah with how we use AI. I like to think of it as very ethical, very responsible AI. Everything we do is validated. We have built a program that you know with AI, people might think, yeah, they go on to say chat, gdp or something, and type in a question.

Ian Garner:

It's probably machine learning is probably a better way to describe yeah, there's a lot of yeah, we do a lot of machine learning. We've got a lot of samples so we can do some really cool learning models that predict yield with with the ai that we do we've actually. You know, the classic problem with ai is what's called hallucination, where it says something that it thinks is real but it isn't, or it can give a very convincing answer, but it isn't actually correct. A classic example of hallucination is when you try and cite papers. It might give you the right answer, but then cite the wrong paper the completely wrong paper.

Ian Garner:

We've actually solved that problem. We do something called RAG. It's an AI method, rag-based and with sentiment analysis, so we can take a paper, we know exactly what it says, what the sentiment of that paper is, and then we can link it so we create our own kind of knowledge base. I was trying to tell my parents bearing in mind my parents were in their 80s trying to kind of explain AI. And if you imagine the dinner table, every surface on that dinner table is everything the AI has been taught. We give it, say, a placemat and it can only look at that when it makes its recommendation. So we've already got our own knowledge base. We're not using the entire knowledge base out there. That could have increased.

Sarah Bolt:

You've already taken that peer review papers and ones that have got exactly yeah.

Ian Garner:

So we say, yeah, this is a good paper, um, let's put it in our knowledge base. Uh, then the ai can only target that when it's making its decisions or etc. But go, just go back to that lameness example. You know, if you people can look at a cow and it's lame, people could use ai and neural networks or learning models and say, yep, it's lame. Um, with you know, with genetics, you can say, yeah, it's got versions of genes that might lead to lameness, but it's gene expression which can be picked up. Before that that cow is lame and said, yeah, look, they're expressing these genes. It's not quite at a level yet where it's going to cause chronic inflammation, but they're, they're going that way.

Andrew Jones:

That's where you can intervene before that cow becomes there I suppose it's no different than I know human families, where usually the heart disease is in the family, or cancers in the family, or the likelihood is that, as you say, it's not purely genetic, it's the environmental as well. But the likelihood is they are more. They are more susceptible to heart disease or they are more susceptible, in the example you're using, to lameness exactly and if you know where, you know the example of cancer.

Ian Garner:

I mean I studied a varying cancer and breast cancer and pancreatic and um, you know the classic with, say, breast cancer. Uh, if, if patients had a brachy to meet what was called a brachy to brachy to the gene, if they had a mutation in that gene that could lead to cancer. But it also meant that they could respond well to cisplatin, a chemotherapy drug, basically platinum, um, and they treated a lot of women with this drug and it can be very successful. Successful. But they also noticed people without that mutation also responding to the drug. The question was why? Well, they, they did the studies and it turned out to be epigenetics. So mutations can turn off BRCA2, but methylation at the promoter in BRCA2 can also turn off BRCA2.

Andrew Jones:

So it's allowing for more targeted what's the word I'm looking for? Therapy, yes, thank you. That's it. Yeah, there's more targeted therapy to ensure that the right therapy is being used for the right reasons.

Ian Garner:

You could look at the gene expression and you could see whether BRCA2 was turned on or off. Now, if you did that, you might say, yeah, they might respond better, rather than if you look at genetics and it was not mutated, they might say, well, yeah, you won't respond to this, but actually it's turned off. For another layer of data, the epigenetics, and genetics is a very well-studied area. So is epigenetics. Now there's still lots we don't know about, but there are other areas that we haven't probably discovered yet, or there are other areas that are just coming out, kind of thing. Um, there's lots of layers of control.

Ian Garner:

If you think cells do anything to survive, we, we, as humans, we we would do anything to try and survive, and cancer is essentially, uh, uncontrolled cell growth. It will would do anything to try and survive, and cancer is essentially uncontrolled cell growth. It will also do anything to survive. So there's a battle there. There are loads of layers of regulation. It's like a spider web of. You have histone methylation, histone deacetylation, lysine methylation, micrornas, g4, quadruplexes, gene expression, like there's so many words.

Sarah Bolt:

My poor brain is managing to keep up.

Andrew Jones:

Apologies to anybody listening to this.

Ian Garner:

I have been to the pub today.

Sarah Bolt:

I'm going to bring it all back just one moment. So in Andrew's very long question that he asked, one of the parts of that was about the cost to farmers for doing this epigenetics. So Andrew was saying about a 100 cow herd. You were saying maybe 10% of the herd once a year. What sort of cost would we be looking at on farms?

Ian Garner:

Yeah, so it's about £4,000 for about 10, like so, not 10% of your herd. If you have a 100 herd, yeah, that would be about £4,000 for about 10, like so, not 10% of your herd. If you have a 100-head herd, yeah, that would be about £4,000.

Andrew Jones:

Yeah, what for the 100 head, as in like we'd take 10% of that 10%, so effectively to do 10 animals is going to cost you about £4,000. Yeah, so that's about £400 an animal then.

Ian Garner:

Yes, exactly.

Andrew Jones:

And what kind of return on investment are we looking at? I mean, I know, obviously it depends. You say you've had somebody that's a complete outlier and doubled their milk yield, but on an average, what sort of return on investment are we looking at?

Ian Garner:

Yeah, it's about 7% no, sorry, not 7% A 7. 7 to 1. 7 to 1 return. Yeah, yeah, so some. A seven. Uh, seven to one. Seven to one return. Yeah, yeah, so some. You know, in our finished farms the quickest to repay um is eight days. It's taken eight days to get that money back after they've made the changes. So some changes can rapidly have an effect it could be flicking that switch and it's like other things might take a little bit longer to implement but they see it over time.

Ian Garner:

So we did a study on I want to say 50, I think off the top of my head of our farms in Finland where we looked at six bumps after they got their report. How had their milk improved? And the average there was, I think it was 1.5. Off the top of my head, 1.6 liters per cow per day. Um, it was again significant. That's where we see some farms. They had 22 increase in yield up to up to um. But yeah, the most of the farms had repaid.

Andrew Jones:

Uh, that epi herd cost in a couple of months or less I was gonna say how, how quickly you're saying that seven to one return on investment is within a month.

Ian Garner:

Six months a year within within six months or so. This, this study we did within um, we, yeah, so we looked at, uh, the moment they got their epi herd report back and the next six months, how their milk yield had increased. Uh, and that that's that's basically.

Andrew Jones:

So really, you're saying for a 400 cost per cow, you're potentially uh, 7, 8, 28, you're making 2 800 pounds for the investment of the 400 pounds yes, per cow, yes, exactly yes, seven times four.

Ian Garner:

Yes, yes, yes, maths at 9pm on a, but yes, yes, exactly yeah, and yeah, we have the data to show it.

Andrew Jones:

We're happy to show that are smaller, or you'd expect to be smaller. Then, as you start to flick on certain switches and turn off different pathways, and that Is that seven to one the first time, or over an average of five, six, seven, eight, analysis.

Ian Garner:

So we've only actually been going for about four years now. So we have farms that are on their third sequencing um, they are. All I can say is they're very happy. Um, I don't know the changes from each time, yeah, when I know what I do know for our farm, this, this one outlier, um, yes, they're not doubling every time, for sure it's like most things, the big gains, the first time, yes yeah, yeah, potentially.

Ian Garner:

I mean it's kind of, if you think of it is, it can be continual marginal gains that add up to a big yeah right. So we've got some, you know, some of our top farms that are doing like. We have some of the best farms in Finland. They might see 3%, 4% increase, but to them that is a huge amount when they've been static until now. But yeah, you can improve and it's not just yield. We often countless farms will come back and report, you know their their fertility's increased. The cows seem happier, they seem healthier.

Andrew Jones:

There's less vets being called out, there's less um issues on the farm is and and those are some of the things that are less quantifiable, cost-wise is, is the improved health, exactly exactly. Most people just measure it purely on how much tank.

Sarah Bolt:

A check, that's coming back in rather than those unwritten checks that never get written in the first place.

Ian Garner:

And we have some farms where you know this doesn't work for every farm, but then the data. What I like, well, what I think maybe on some of those farms is if they hadn't had us with EpiHerd, if they hadn't made some changes, would their milk milk been far worse? You know, they might not have gone up like some of our farms, but have we prevented something by giving them, you know, those recommendations? Have they prevented something? Um, and that's where that data comes in, really useful, the early you do it, because we can then go back in a few years time and say, yeah, look, your cows actually developed this or something happened here and we were able to preempt maybe not completely stop something happening, but at least help potentially.

Sarah Bolt:

So, yes, so, going back to what andrew was saying, that you know maybe year on year the the increments aren't as much, but actually something could happen on the farm that then sets them back again. So actually it's worth doing it every year to know what that journey is looking like.

Ian Garner:

Yeah, I mean, we had a farm, one farm where we gave the results and they didn't take the advice, which is fine, you know, they went against the advice. They got in an expert that said, no, you don't need to listen to this, you do it this way. And they listened to him and their milk yield dropped massively. They got rid of him. They went back to us. They started implementing changes.

Ian Garner:

They sequenced straight after that and we saw that they had really quite low levels of uh immune. Uh, their immune scores were poor. They had high inflammatory markers. It looked like chronic stress, essentially. Um, they made changes and then they resequenced. I think it was four months after that. It was november. They did the first well, the resequence. Then february, they sequenced again and their, their information and and immune scores have gone right back up.

Ian Garner:

Um, so it's. You know, it is kind of that. You, you could be a really good nutritionist, like like we, we can help, basically, nutritionists, any stakeholders, or farmers, nutritionists, whatever, any stakeholders the farmers, nutritionists, whatever, vets, for example but it's that data. It's that data that supports what we're saying. We have that data to say, yeah, look here it is, this is what it's saying, use it to your advantage. We're not trying to compete, we're just trying to help, we can help. It's just another tool in the toolbox, isn't it? Yeah, yeah, eat. We're just trying to help, we can help. It's just another tool in the toolbox, isn't it? Yeah, yeah, yeah, exactly, it's not. It's not to replace anyone or replace genetics, it's to be work. It works with that.

Sarah Bolt:

Um, so it's, yeah, it's, it's just another tool to kind of help guide, essentially so if somebody like you andrew you're perhaps body condition scoring, you're probably done scoring you all of those things that the cow is telling you. But this is the next level, up and beyond that, the cow's telling you even more yeah, oh, undoubtedly, undoubtedly, you know.

Andrew Jones:

If, if this is um potentially showing, I don't know, let's say, let's take the example you said of an information pathways. Well then you find out why, and what can we do to then make it? You know, it might not be so obvious as to what it, what is going on. Like you used the the term give the cow another voice. I mean, I suppose when I was taught nutrition, the first thing I was taught is cows can't read, so it's you know it doesn't matter what the computer says, it's what the cows are telling you.

Andrew Jones:

So so, while the computer is telling me one thing, if the cows are telling you something else, well, listen to the cows and go and find out why it's doing what it's doing, not just go. Well, it should be right. Just because it should be right, there's something going on.

Ian Garner:

Exactly. We all know vitamin D is very important for health for a number of things for marigland development, for immunity, for increasing milk yield. We all know vitamin D is important in multiple processes. You know nutritionists will say or might say you know, yeah, this has got good levels of vitamin D in, or here's some vitamin D, you'll be fine. But if the cow doesn't have the genes expressed that can metabolize that vitamin d, giving them vitamin d might not be as beneficial as giving them a converted form of vitamin d, and that's where we can help. Yeah, they actually look. With this data you might change what you recommend. Recommend I.

Andrew Jones:

I guess what you're saying is well, you know, let's be honest, usually the reference I use and other people use would be nasim. What was nrc? So nasim? Was it 2021? Is it that might say, these are your levels. What you're saying is well, actually you might hit that level and it's still not being used enough. Actually, based on this, this, uh, epigenetic data, um, actually you need to feed that little bit more than the recommendation for that cow to fully express what she needs to do is that is like athletes, right, swimmers and runners they they don't stick to the 2000 calories that we're recommending.

Ian Garner:

Why would? Why would you have your highest?

Andrew Jones:

based on the average person, isn't it exactly?

Ian Garner:

and this is based on the average cow, so if you've got a really high performing cow, you're gonna probably need to give them a bit more. Um yeah, they're telling you that through their gene expression. If they have stress, or then you know there's other things going on there. Um yeah, why wouldn't you listen to that? Essentially?

Andrew Jones:

um, so, if I'm just trying to think the way, if, um, if we want to do this, how do we do it?

Ian Garner:

So, as in EpiHUD? Yeah, yeah, so you, you would. You can contact anyone from Antler Bio team. Um, there is a. There is a website. Um, you can reach out directly to Andy or myself or Maria. Um, uh, we have a LinkedIn page as well. Basically, you can just inquire from that website or email and you can talk to one of us. We can explain a bit more if a farmer wants to know more. But essentially it's pretty simple. Once you're happy with what we offer, we send out a kit to you, you arrange a vet to come to take the blood and then you send the package back to us and then in a few weeks to months, you'll get your results.

Ian Garner:

I was going to say how quick is turnaround, turnaround is. You know, this has kind of taken off. We expected it to take off, but it's it. It has put it back a little bit. Um, we, we're, I think, currently averaging about 35 days turnaround time from when when we take the blood. So it's just over a month. We want to get that down to two weeks. Um, ideally, but we've just had such high demand. Um, yeah, it's just, we're trying to sequence as much as we can, as quick as possible so how many cows are in your database?

Ian Garner:

now. Uh, we've got about 3 000 now. Um, I think last, yeah, it's. We're maxing out basically and we're just increasing capacity rapidly. So, uh, yeah, it's just I guess it's the teething problems of you know computers.

Andrew Jones:

And so where is this available, given that we, you know, we do get listeners from all around the world in different places? Where is this available If I don't know someone from the US or Australia, or you know different people listen? They heard this and went oh, that sounds interesting. You know what about for those sorts of guys?

Ian Garner:

I'd love to be in New Zealand. It's just getting the packages over here without them defrosting so quickly. New Zealand is definitely somewhere where we're looking At the moment. Finland, or basically all the Nordics, uk, ireland we do have, I think, an Italian farm starting, but yeah, an Italian farm starting, but yeah, basically all across Europe. Yeah, we essentially went.

Ian Garner:

You know there are logistics to that right, samples have to get basically in pristine condition as quick as possible. The kits we give out are very good at that. We use very high-end tubes. The tubes are insanely expensive, but they keep the RNA very, are very good at that. We use very high-end tubes. The tubes are insanely expensive, but they keep the rna very, very good. So we get. You know, it's always the. What you put in is what you get out. So you put good quality and you get good quality data out, um. So yeah, we can, we. We have sequencing in germany, we have sequencing in um denmark. They're the two we use mainly. So samples will get sent there and then we can rapidly return those results. But the turnaround time is improving pretty much every day.

Andrew Jones:

It's just dealing with that demand and again, as technology gets better. I mean you're doing it with dairy cows, any other livestock beef, beef cattle, more I'm thinking in particular, but I mean any. What are the next steps?

Ian Garner:

beef is the next logical one. Um, we actually started in racehorses, believe it or not, I can't believe that, actually the money that's involved, yeah, yeah, so racehorses is uh one where we started, I mean, when we pivoted to dairy cows. It was during that covid kind of period. Again, like I said, all stories kind of have a covid element to them, um, recent stories anyway. But yeah, beef would beef would make sense. I mean, there's this is species agnostic, so it could be any species. We've we've done ducks really interesting. Um, it's not much out there about ducks, but yeah, it is species agnostic, so it could be anything, but beef would be the logical one.

Andrew Jones:

Well, and I suppose going back to my comment about Big Brother, what about athletes? Is that somewhere where?

Ian Garner:

potentially this can go. Yeah, I mean, rnana sequencing, gene expression is very much and epigenetics is very much in human research, right, it's just, and there it's beginning to trickle down to other species. But the majority of studies are in human and mice mice models usually for human disease, um, but there's so much we're missing from animals, if you think. I mean, when I did my PhD the disease I looked at was IPS, idiopathic pulmonary fibrosis. You'd use a mouse model that would mimic that disease but it wouldn't replicate it perfectly, whereas in horses they commonly get idiopathic pulmonary fibrosis.

Ian Garner:

But the cost of doing studies on horses, on big animals, is prohibitive. But the cost of doing studies on horses, on big animals, is prohibitive. But if you again, if you think in like in dairy or bovine, you get a lot of, I guess, like IBS problems, say in cows, you know, upset stomachs and things like that, diarrhea for example, things that are linked to the microbiome perhaps, and gene expression and everything. And you know there might be data there that we have that could actually benefit human in the future. So there's, yeah, it's a very interesting area, with athletes for sure. You know you have again in the marginal gains in sports and athletes. I would love to look at, say, elite sports teams, especially Arsenal.

Andrew Jones:

I'd love to look at, say, elite, sports teams um especially arsenal, but I'd love to look at the gene expression of their players you just want, you just want to try and find that way for them to win. Is it right?

Ian Garner:

yeah, I do, I really do yeah, I mean, but, yeah, they, you know, a lot of sports teams use technology that's pretty advanced. Um, there's a study in cows actually, where they put vr headsets on them in turkey. I think it was well. Yes, that helped them with their stress and they found that milk yield increased. Um, I remember reading that one. Yes, yeah, it's, it's things like that are. You know, there's this interesting science there that's not understood. Yeah, um, but I think there's so much to to explore.

Andrew Jones:

Um, yeah, well, I've got to be honest. I'm looking at the time, thinking I could just keep going, and I know I have been nerding out a little bit, so apologies to anybody listening, but genetics has always been something that has been absolutely fascinating, uh, to me all my life, um, but I guess any last thoughts from yourself, ian? Uh, is there anything we haven't covered that you think we should? We should talk about with the epigenetics, or do you think we've covered most bases?

Ian Garner:

I think we've we've probably covered a lot of bases. This, I mean it's a fascinating subject. I'm still yeah, it evolves, it evolves all the time. There's more learned about different genes that have regulation of epigenetic by epigenetics.

Andrew Jones:

There's yeah, it's fascinating well, I was just gonna say you look at genomics. You know what was the original chip. It was what? Less than 1k or I can't remember what it was, and now it's well over 50K, isn't it? Whatever, just as people understand things so much more and that's only in genomics have only been what? 15 years, I think? Yeah, as I left Australia a bit more than that. Then it started to come in and yeah, so more than that.

Ian Garner:

Then it started to come in and and, um, yeah, so yeah, I mean, it's also like sequencing. It was so expensive 20 years ago it was I may be wrong, but I think it was about a million pounds the sequence, uh well, and the time as well, as you say the time, yeah, and the size of the machines and everything like every, just like computers. When the computers used to take up a massive room and now we're on them, now on our laps, kind of thing. It's like that. With sequences, you could sequence things where the chip was tiny and now it can do hundreds of samples at once. It's come down a lot in price, it is still expensive technology, but it will inevitably come down.

Andrew Jones:

And then, yeah, you say that's a bit like you talk about computers. It's a bit like I tell you to my kids to getting. I remember the first computer at school, the bbc model b, and it was one computer for the whole school and it was set up in the staff room. We went up in pairs to do educational games. One was selling ice cream, I seem to remember. And now they're just everywhere and they have ipads and and they just they're taught computing and it's just yeah, and you know that's in what 40 years, what a difference that's made.

Ian Garner:

So again, we're not even there yet yeah, yeah, my three-year-old still swipes things and things like that.

Andrew Jones:

He'll interact and he's confused when it doesn't change or yeah well, yes, when my youngest was born, the oldest was two and a half and he'd been used to netflix and mom had turned her wi-fi data off because she didn't want it all eaten while he came into hospital. So he saw his little brother and was like oh yeah, all right, within two minutes, was bored, and this just shows that he's a child of his generation. She, he, was given the phone and then next thing was my netflix.

Andrew Jones:

My netflix doesn't work and you're just like well, there's a child of your generation, isn't it so?

Ian Garner:

yeah, and that's how I mean. Yeah, the future for kids. Now I don't know what that'll be right with ai and everything like that. They'll probably. It'll probably look a very different future. Yeah, yeah yeah.

Andrew Jones:

So, sarah, last thoughts from yourself. I'm sorry we are, we are in nerding out a little, you're nerding out both of you.

Sarah Bolt:

That's all I'm gonna say. So I'm gonna try and bring it back to, uh, to to my level, which is, I guess, sort of. For many years, as advisors, we've been talking about cows not reaching their genetic potential, and I think that epigenetics really starts opening the windows on the why and actually allows farmers to look at those marginal gains exactly.

Ian Garner:

Yep, yeah, because you've got you know it can. This technology can help. Whether you've put genetics in, whether you've maybe done, you've gone that route or not, it's basically what you've got. How can we maximize that potential? Um, and it's, it really is. It really is just that you know it's given an additional insights that you wouldn't have otherwise.

Andrew Jones:

Um well, I just like to say thank you very much here, and it's it's being fantastic, say mark. I always I always thought epigenetics was purely in, in, in utero, and it's obviously not. There is more to it than that and that was the whole point of this podcast, isn't it? And whenever we do this, it's to try and um, hopefully we learn, and then the people listening can learn from it, because this kind of stuff is coming, there are people go.

Andrew Jones:

Oh, I don't want to know, but this kind of stuff is coming, and and the more you have the opportunity to learn about it from the likes of ourselves or others, then it makes you more aware of the future and be able to have a conversation a little bit more about it, because you understand that a little bit more on that subject. But, yes, as I say, I'm sure we probably have, but I know we've probably got a bit too technical at times, but you know that's what it's all about. But, ian, thank you so much. It's been an absolute joy and I know I could just keep talking on this one because, hey, genetics has always been an interest of mine. So really, I guess on that one I'd like to say thank you very much and it's a goodbye from me I'll, I'll chip in first.

Ian Garner:

Goodbye everyone. Yeah, goodbye. Thank you for having me. Thank you, it's been a pleasure. Thank you very much.

Andrew Jones:

Thank you thank you for listening to the Tune the Cut podcast podcast for the UK dairy industry, brought to you from the southwest of England and listened to around the world. Now for the really boring bit, I'm afraid the legal disclaimer. The information provided during this podcast has been prepared for general information purposes only and does not constitute advice. The information must not be relied upon for any purpose and no representation or warranty is given to its accuracy, completeness or otherwise. Any reference to other organisations, businesses or products during this podcast are not endorsements or recommendations of Tune the Cud Ltd. The views of Andrew Jones are personal and may not be the views of Tune the Cud Ltd, and the views of Sarah Bolt are personal and may not be the views of Kingshay Farming and Conservation Ltd and any affiliated companies. For more information on the podcast and details of services offered by Tune the Cud Ltd, visit wwwtunethecudcom. Thank you and goodbye.