1 00:00:04,750 --> 00:00:06,606 Welcome to the 2 00:00:06,628 --> 00:00:08,846 Proteomics in Proximity podcast, where 3 00:00:08,868 --> 00:00:10,986 your co-hosts, Dale Yuzuki, Cindy 4 00:00:11,018 --> 00:00:13,338 Lawley, and Sarantis Chlamydus from Olink 5 00:00:13,354 --> 00:00:15,694 Proteomics talk about the intersection of 6 00:00:15,732 --> 00:00:17,914 proteomics with genomics for drug target 7 00:00:17,962 --> 00:00:19,646 discovery, the application of 8 00:00:19,668 --> 00:00:21,194 proteomics to reveal disease 9 00:00:21,242 --> 00:00:23,822 biomarkers, and current trends in using 10 00:00:23,876 --> 00:00:25,346 proteomics to unlock 11 00:00:25,378 --> 00:00:27,814 biological mechanisms. Here we have 12 00:00:27,852 --> 00:00:29,894 your hosts, Dale, Cindy, and 13 00:00:29,932 --> 00:00:31,670 Sarantis. Welcome to another 14 00:00:31,740 --> 00:00:33,874 episode of Proteomics in Proximity. 15 00:00:33,922 --> 00:00:35,814 I'm your host, Dale Yusuki, with my 16 00:00:35,852 --> 00:00:37,174 co-hosts, Cindy and 17 00:00:37,212 --> 00:00:39,666 Sarantis. And this morning - Hey 18 00:00:39,708 --> 00:00:41,418 there - and, Sarantis, what are we going to 19 00:00:41,424 --> 00:00:43,818 be talking about today? Today, it's a 20 00:00:43,824 --> 00:00:45,674 great paper, actually. There's a great paper 21 00:00:45,792 --> 00:00:47,430 where it describes 22 00:00:47,590 --> 00:00:49,686 ways of using biomarkers - protein 23 00:00:49,718 --> 00:00:51,774 biomarkers - in early discovery of 24 00:00:51,812 --> 00:00:53,530 inflammatory bowel disease (IBD), 25 00:00:53,690 --> 00:00:55,994 and it is a great example of a crosstalk 26 00:00:56,042 --> 00:00:57,822 between gut microbioma and 27 00:00:57,876 --> 00:00:59,950 immunohomeostasis. And 28 00:01:00,020 --> 00:01:02,174 it's really great because it combines a 29 00:01:02,212 --> 00:01:04,722 multiomics approach where they have 30 00:01:04,776 --> 00:01:06,638 investigated genetics 31 00:01:06,814 --> 00:01:08,514 and proteomics, and 32 00:01:08,552 --> 00:01:10,840 metabolomics, and also 33 00:01:10,840 --> 00:01:12,718 they have done sequencing 34 00:01:12,814 --> 00:01:14,786 of microbiota in order to 35 00:01:14,808 --> 00:01:16,790 identify this crosstalk. 36 00:01:16,790 --> 00:01:18,998 Cindy, what do 37 00:01:19,004 --> 00:01:21,158 we know about the DUOX2? What is the 38 00:01:21,164 --> 00:01:23,014 importance of this protein? 39 00:01:23,052 --> 00:01:25,910 So the name, DUOX, stands for 40 00:01:25,980 --> 00:01:27,374 Double Oxygen. 41 00:01:27,522 --> 00:01:29,594 So it's a 42 00:01:29,632 --> 00:01:31,962 marker within the genome. And, 43 00:01:32,016 --> 00:01:34,214 in fact, this paper highlights 44 00:01:34,342 --> 00:01:36,506 some variants in the 45 00:01:36,528 --> 00:01:38,950 DUOX2 gene 46 00:01:39,030 --> 00:01:41,850 that code for something that will 47 00:01:41,850 --> 00:01:43,962 produce hydrogen peroxide, 48 00:01:44,026 --> 00:01:46,634 H2O2, in the epithelial 49 00:01:46,682 --> 00:01:48,846 lining. So in what is 50 00:01:48,868 --> 00:01:50,610 the apical layer, or 51 00:01:50,610 --> 00:01:52,734 the layer that's right next to 52 00:01:52,772 --> 00:01:54,978 where essentially the results of 53 00:01:54,984 --> 00:01:56,834 our food go by. And so 54 00:01:56,872 --> 00:01:58,658 that production has an effect on the 55 00:01:58,664 --> 00:02:00,630 microbiome. So this 56 00:02:00,630 --> 00:02:02,638 idea that we might have a proteomic 57 00:02:02,654 --> 00:02:04,878 biomarker that 58 00:02:04,984 --> 00:02:06,582 is indicating something about 59 00:02:06,636 --> 00:02:08,754 the host microbiome 60 00:02:08,802 --> 00:02:10,866 interaction, that gut bacteria 61 00:02:10,898 --> 00:02:12,854 that we all are so curious about and 62 00:02:12,892 --> 00:02:14,886 has been the topic of so 63 00:02:14,908 --> 00:02:16,330 many interesting 64 00:02:16,330 --> 00:02:18,646 publications that we might have a protein 65 00:02:18,678 --> 00:02:20,890 biomarker that's indicating something 66 00:02:21,040 --> 00:02:23,818 about the dysbiosis or the 67 00:02:23,984 --> 00:02:25,782 shift in that microbiome 68 00:02:25,846 --> 00:02:27,926 that precedes inflammatory 69 00:02:27,958 --> 00:02:29,958 bowel disease, is super exciting. 70 00:02:30,054 --> 00:02:32,670 So that's what I see here. 71 00:02:32,670 --> 00:02:34,474 Inflammatory bowel disease is 72 00:02:34,512 --> 00:02:36,750 devastating. I mean, I personally know 73 00:02:36,820 --> 00:02:38,814 three individuals who are affected. You 74 00:02:38,852 --> 00:02:40,814 may know others, right? 75 00:02:40,852 --> 00:02:42,778 Their digestion, their 76 00:02:42,884 --> 00:02:44,658 immune system, and 77 00:02:44,744 --> 00:02:46,706 it's really, really difficult because 78 00:02:46,808 --> 00:02:48,382 you talk about the interplay 79 00:02:48,446 --> 00:02:50,990 between the person, 80 00:02:50,990 --> 00:02:52,254 their gut 81 00:02:52,302 --> 00:02:54,930 microbiota, which is off. 82 00:02:55,080 --> 00:02:57,686 It is different than regular people and 83 00:02:57,708 --> 00:02:59,766 it's a complex organ, right? The 84 00:02:59,788 --> 00:03:01,958 gut microbiome is essentially, I 85 00:03:01,964 --> 00:03:03,446 mean, a lot of people talk about it as an 86 00:03:03,468 --> 00:03:05,734 organ. This 87 00:03:05,772 --> 00:03:07,846 group, including a group out 88 00:03:07,868 --> 00:03:09,702 of Institute for Systems Biology, 89 00:03:09,846 --> 00:03:11,846 does a fair amount of work on gut 90 00:03:11,878 --> 00:03:13,494 microbiome, particularly 91 00:03:13,542 --> 00:03:15,622 in their wellness 92 00:03:15,686 --> 00:03:17,466 cohort. So, Cindy, why don't you tell 93 00:03:17,488 --> 00:03:19,386 us more about this wellness cohort? I 94 00:03:19,408 --> 00:03:21,760 understand this is Arivale. 95 00:03:21,760 --> 00:03:23,770 That's right. So Arivale, 96 00:03:23,850 --> 00:03:25,670 was a 97 00:03:25,670 --> 00:03:27,962 company that spun 98 00:03:28,026 --> 00:03:30,366 out of the Institute for Systems Biology. It 99 00:03:30,388 --> 00:03:32,500 was based in Seattle. 100 00:03:32,500 --> 00:03:34,434 And the 101 00:03:34,472 --> 00:03:36,820 team focused on 102 00:03:36,820 --> 00:03:38,386 collecting data 103 00:03:38,488 --> 00:03:40,660 longitudinally 104 00:03:40,660 --> 00:03:42,882 for a bunch of people 105 00:03:42,936 --> 00:03:44,386 that just opted in for 106 00:03:44,488 --> 00:03:46,566 reconsenting, so opted in 107 00:03:46,588 --> 00:03:48,562 for updating their consent 108 00:03:48,626 --> 00:03:50,806 and recontact. And 109 00:03:50,828 --> 00:03:52,774 the great thing about that is they're coming 110 00:03:52,812 --> 00:03:54,370 in regularly. They're 111 00:03:54,530 --> 00:03:56,978 "well" when they start. And we'll 112 00:03:56,994 --> 00:03:58,806 be talking about some other papers 113 00:03:58,838 --> 00:04:00,528 that are coming out of this 114 00:04:00,528 --> 00:04:02,522 exciting group. 115 00:04:02,656 --> 00:04:04,522 But it offers an opportunity 116 00:04:04,656 --> 00:04:06,954 to look retrospectively at 117 00:04:06,992 --> 00:04:08,934 samples where individuals 118 00:04:08,982 --> 00:04:10,830 who were healthy 119 00:04:10,830 --> 00:04:12,734 actually will develop 120 00:04:12,852 --> 00:04:14,922 some diseases. 121 00:04:14,986 --> 00:04:16,862 As we age, we tend to 122 00:04:16,996 --> 00:04:18,840 do that, unfortunately. 123 00:04:18,840 --> 00:04:20,826 So it's 124 00:04:20,858 --> 00:04:22,830 a powerful demonstration 125 00:04:22,830 --> 00:04:24,874 of a wellness cohort 126 00:04:24,922 --> 00:04:26,970 and having longitudinal sampling. 127 00:04:26,970 --> 00:04:28,898 Like I 128 00:04:28,904 --> 00:04:30,946 said, we'll double-click on that in this 129 00:04:30,968 --> 00:04:32,882 podcast I think quite a bit. This paper 130 00:04:32,936 --> 00:04:34,578 talks about over 2800 131 00:04:34,664 --> 00:04:36,834 individuals from this cohort, but it was 132 00:04:36,872 --> 00:04:38,822 larger. Was it almost 5000, something 133 00:04:38,876 --> 00:04:40,918 like that? Yeah, that's right. 134 00:04:41,084 --> 00:04:42,806 And of course, over time, you're going to 135 00:04:42,828 --> 00:04:44,950 have people dropping out 136 00:04:44,950 --> 00:04:46,950 inevitably. And so this is a really 137 00:04:47,020 --> 00:04:49,366 great sample number. And the longitudinal 138 00:04:49,398 --> 00:04:51,946 data that they have collected for 139 00:04:52,128 --> 00:04:54,666 this group of individuals is stunning. So 140 00:04:54,688 --> 00:04:56,710 they already had their whole genome 141 00:04:56,790 --> 00:04:58,986 sequence. And then what? Did they 142 00:04:59,008 --> 00:05:01,718 also sample their microbiome, 143 00:05:01,814 --> 00:05:03,770 like from feces? Yeah, 144 00:05:03,770 --> 00:05:05,946 exactly. But then they don't 145 00:05:05,978 --> 00:05:07,982 need to necessarily collect data 146 00:05:08,036 --> 00:05:10,554 on all the samples in all the modalities. 147 00:05:10,682 --> 00:05:12,566 What they've done is they've 148 00:05:12,698 --> 00:05:14,898 seen interesting things evolve in 149 00:05:14,904 --> 00:05:16,754 this cohort and 150 00:05:16,792 --> 00:05:18,660 then do specific, 151 00:05:18,800 --> 00:05:20,820 work within 152 00:05:20,820 --> 00:05:22,526 a subset. And that's 153 00:05:22,558 --> 00:05:24,834 exactly what happened here. 154 00:05:24,872 --> 00:05:26,674 So a subset of the individuals 155 00:05:26,722 --> 00:05:28,822 from this 156 00:05:28,956 --> 00:05:30,950 2800 157 00:05:30,950 --> 00:05:32,626 subset of the Arivale 158 00:05:32,658 --> 00:05:34,914 cohort had these 159 00:05:34,962 --> 00:05:36,720 DUOX2 variants 160 00:05:36,720 --> 00:05:38,266 and those DUOX2 161 00:05:38,288 --> 00:05:40,080 variants had some, 162 00:05:40,080 --> 00:05:42,554 I would 163 00:05:42,592 --> 00:05:44,794 say this sort of 164 00:05:44,832 --> 00:05:46,666 brings me back to this 165 00:05:46,768 --> 00:05:48,698 idea that if we had - 166 00:05:48,864 --> 00:05:50,646 what was the total number, Dale? 167 00:05:50,758 --> 00:05:52,398 You pulled it up for me right before the 168 00:05:52,404 --> 00:05:54,940 podcast. 300 and some 169 00:05:55,012 --> 00:05:57,454 odd individuals? It was like 357 170 00:05:57,492 --> 00:05:59,810 individuals. 171 00:05:59,810 --> 00:06:01,050 With rare variants. 172 00:06:01,130 --> 00:06:03,959 12.4%. Yeah, if we were going 173 00:06:03,959 --> 00:06:05,394 to do a GWAS on 174 00:06:05,432 --> 00:06:07,880 357 individuals 175 00:06:07,880 --> 00:06:09,298 and compare that to control 176 00:06:09,384 --> 00:06:11,522 samples, that's not a very 177 00:06:11,576 --> 00:06:13,746 powerful GWAS by our 178 00:06:13,768 --> 00:06:15,926 standards, in the genetic space. And 179 00:06:15,948 --> 00:06:17,782 so making the association between 180 00:06:17,836 --> 00:06:19,920 these variants and 181 00:06:20,044 --> 00:06:21,906 inflammatory bowel disease, or Crohn's 182 00:06:21,938 --> 00:06:23,730 disease, or this IBD 183 00:06:23,810 --> 00:06:25,974 phenotype, might have been very 184 00:06:26,012 --> 00:06:28,582 challenging. And so this is a novel 185 00:06:28,646 --> 00:06:30,282 discovery, this DUOX2 186 00:06:30,336 --> 00:06:32,778 association with IBD. And it 187 00:06:32,784 --> 00:06:34,874 was made through an association that 188 00:06:34,912 --> 00:06:36,420 includes 189 00:06:36,420 --> 00:06:38,934 phenotypic data that is closer 190 00:06:38,982 --> 00:06:40,634 to the disease, which includes 191 00:06:40,682 --> 00:06:42,750 proteomics. 192 00:06:42,750 --> 00:06:44,446 That, as well as the 193 00:06:44,468 --> 00:06:46,926 microbiome. So the ability to 194 00:06:46,948 --> 00:06:48,366 have this multiomics as 195 00:06:48,388 --> 00:06:50,702 Sarantis highlighted and 196 00:06:50,836 --> 00:06:52,842 amplify the power to detect 197 00:06:52,906 --> 00:06:54,926 the relationship between genetics and 198 00:06:54,948 --> 00:06:56,606 disease is something I think is 199 00:06:56,628 --> 00:06:58,782 exciting. We end up with whole genome 200 00:06:58,846 --> 00:07:00,802 data, right? And we also 201 00:07:00,856 --> 00:07:02,654 have then all the clinical 202 00:07:02,782 --> 00:07:04,626 laboratory measurements. You 203 00:07:04,648 --> 00:07:06,806 have metabolites, where apparently they 204 00:07:06,828 --> 00:07:07,862 measured some over 205 00:07:07,916 --> 00:07:09,470 950 206 00:07:09,470 --> 00:07:11,782 metabolites from these patients or 207 00:07:11,836 --> 00:07:13,266 these volunteers, healthy 208 00:07:13,298 --> 00:07:15,734 individuals, it looks like they used 209 00:07:15,772 --> 00:07:17,794 three Olink Target 96 panels 210 00:07:17,842 --> 00:07:19,346 for a total of 266 211 00:07:19,388 --> 00:07:21,866 proteins. And then the microbiome, which 212 00:07:21,888 --> 00:07:23,594 is a really important part, they're doing 213 00:07:23,632 --> 00:07:25,334 16S ribosomal RNA 214 00:07:25,382 --> 00:07:27,850 sequencing, which gives you this idea of 215 00:07:27,920 --> 00:07:29,786 what the constellation of 216 00:07:29,808 --> 00:07:31,758 bacterial species looks like 217 00:07:31,924 --> 00:07:32,954 just from sampling 218 00:07:33,002 --> 00:07:35,546 rsRNA. But then they do a 219 00:07:35,578 --> 00:07:37,886 PheWAS. And maybe 220 00:07:37,988 --> 00:07:39,454 we can walk through that a little bit? 221 00:07:39,492 --> 00:07:41,470 What is a PheWAS 222 00:07:41,540 --> 00:07:43,678 again? I don't know. Sarantis, you want to 223 00:07:43,684 --> 00:07:45,978 talk about it, or cindy? I guess I'm 224 00:07:45,994 --> 00:07:47,434 not so familiar with a PheWAS, 225 00:07:47,492 --> 00:07:49,906 actually, to be honest. 226 00:07:49,928 --> 00:07:51,826 I mean, it's one of the few 227 00:07:51,848 --> 00:07:53,474 times I have seen that or come across that. 228 00:07:53,512 --> 00:07:55,854 But I checked a little bit in the literature. 229 00:07:55,902 --> 00:07:57,986 It has to do with phenotypic associations 230 00:07:58,098 --> 00:08:00,326 and how these are correlating with the 231 00:08:00,348 --> 00:08:02,566 changes that happens in the genome, for 232 00:08:02,588 --> 00:08:04,310 example. And that's how 233 00:08:04,380 --> 00:08:06,534 phenotype is like the correlation. 234 00:08:06,572 --> 00:08:08,746 And the phenotype, their phenotype is 235 00:08:08,848 --> 00:08:10,506 IBD. So they 236 00:08:10,528 --> 00:08:12,826 have individuals with IBD from 237 00:08:12,848 --> 00:08:14,886 these 2800 people, and they're 238 00:08:14,918 --> 00:08:16,990 saying: What's associating 239 00:08:16,990 --> 00:08:18,426 out of all this 240 00:08:18,528 --> 00:08:20,986 multiomic data? You just say, 241 00:08:21,008 --> 00:08:23,706 "Well, Dale, there's how many different kinds 242 00:08:23,738 --> 00:08:25,946 of microbes in the stool?" We're 243 00:08:25,978 --> 00:08:27,966 looking at 266 proteins and 244 00:08:27,988 --> 00:08:29,854 950 metabolites and all 245 00:08:29,892 --> 00:08:31,706 these other clinical measurements. 246 00:08:31,898 --> 00:08:33,874 But then they zero in on this 247 00:08:33,912 --> 00:08:35,598 one particular enzyme. 248 00:08:35,694 --> 00:08:37,842 Or is it an enzyme? 249 00:08:37,896 --> 00:08:39,422 This DUOX2? 250 00:08:39,486 --> 00:08:41,742 Yeah, it's an enzyme. Enzyme. 251 00:08:41,806 --> 00:08:43,778 And what is it doing? 252 00:08:43,864 --> 00:08:45,518 What is the function of 253 00:08:45,534 --> 00:08:47,820 DUOX2? Producing hydrogen peroxide 254 00:08:47,820 --> 00:08:49,670 actually 255 00:08:49,670 --> 00:08:51,930 kills bacteria. 256 00:08:51,930 --> 00:08:53,894 And that is 257 00:08:53,932 --> 00:08:54,806 adjusting the 258 00:08:54,828 --> 00:08:56,534 microbiome, it 259 00:08:56,572 --> 00:08:58,746 appears, right? I like to use the 260 00:08:58,768 --> 00:09:00,886 language that we're 261 00:09:00,998 --> 00:09:02,762 unveiling here in this paper - 262 00:09:02,816 --> 00:09:04,986 these authors are unveiling - a little bit 263 00:09:05,008 --> 00:09:07,222 about the mechanism. Super exciting. 264 00:09:07,286 --> 00:09:09,826 But there could also be complexity 265 00:09:09,958 --> 00:09:11,790 that they're yet to uncover 266 00:09:11,790 --> 00:09:13,886 here. 267 00:09:13,908 --> 00:09:15,646 As far as that particular 268 00:09:15,748 --> 00:09:17,726 function, we have this idea 269 00:09:17,828 --> 00:09:19,034 of hydrogen 270 00:09:19,082 --> 00:09:21,866 peroxide, and we normally use hydrogen 271 00:09:21,898 --> 00:09:23,438 peroxide to clean 272 00:09:23,524 --> 00:09:25,514 up. It's antibacterial. 273 00:09:25,642 --> 00:09:27,354 It oxidizes. 274 00:09:27,482 --> 00:09:29,966 Exactly. What is 275 00:09:29,988 --> 00:09:31,794 it then? DUOX2 is 276 00:09:31,832 --> 00:09:33,906 normally doing what in the 277 00:09:33,928 --> 00:09:35,846 body? I mean, DUOX2 in 278 00:09:35,868 --> 00:09:37,622 the context of these 279 00:09:37,676 --> 00:09:39,702 individuals is keeping a certain 280 00:09:39,756 --> 00:09:41,282 type of gram-negative 281 00:09:41,346 --> 00:09:43,974 bacteria away. We're talking 282 00:09:44,012 --> 00:09:46,182 about homeostasis at the end. Whatever happens 283 00:09:46,236 --> 00:09:48,754 in biology is homeostasis. 284 00:09:48,802 --> 00:09:50,854 It's like the equilibrium. And 285 00:09:50,892 --> 00:09:52,586 when you have the shifting of 286 00:09:52,608 --> 00:09:53,946 equilibrium, then you have all of these 287 00:09:53,968 --> 00:09:55,402 pathologies that's at the end, 288 00:09:55,536 --> 00:09:57,466 according to Greek philosophy also, I 289 00:09:57,488 --> 00:09:59,222 thought, let's talk about the equilibrium. 290 00:09:59,286 --> 00:10:00,838 And keeping the equilibrium at the 291 00:10:00,864 --> 00:10:02,894 end. That's pretty much what we have in our 292 00:10:02,932 --> 00:10:04,770 body. 293 00:10:04,770 --> 00:10:06,974 Looking there, yeah, 294 00:10:07,012 --> 00:10:09,198 that's the thing. And looking a little bit 295 00:10:09,364 --> 00:10:11,646 at the rare mutations, they have mutations of 296 00:10:11,668 --> 00:10:13,546 frameships. Let's 297 00:10:13,578 --> 00:10:15,794 say mis-sense. We have 298 00:10:15,832 --> 00:10:17,746 different types of rare mutations in 299 00:10:17,768 --> 00:10:19,874 the DUOX2 gene that 300 00:10:19,912 --> 00:10:21,860 leads to loss of function. 301 00:10:21,860 --> 00:10:23,378 They have 302 00:10:23,544 --> 00:10:25,806 heterozygous individuals 303 00:10:25,838 --> 00:10:27,678 with loss-of-function allele. 304 00:10:27,774 --> 00:10:29,394 And, of course, this affects the level 305 00:10:29,432 --> 00:10:31,974 of DUOX2. And, as a consequence, they have 306 00:10:32,012 --> 00:10:34,840 seen this increase of IL-17C. 307 00:10:38,840 --> 00:10:40,978 But just to 308 00:10:41,084 --> 00:10:43,306 repeat what Sarantis said in terms of 309 00:10:43,328 --> 00:10:45,926 homeostasis in the normal, healthy 310 00:10:45,958 --> 00:10:47,494 gut with a normal, 311 00:10:47,542 --> 00:10:49,642 functioning DUOX2 312 00:10:49,776 --> 00:10:51,866 sort of enzyme right at the surface 313 00:10:51,898 --> 00:10:53,902 of the gut lining, you've got 314 00:10:53,956 --> 00:10:55,726 gram-negative bacteria held at 315 00:10:55,748 --> 00:10:57,546 bay because of the hydrogen 316 00:10:57,578 --> 00:10:59,486 peroxide. Apparently there's some kind of 317 00:10:59,508 --> 00:11:01,946 segmented filamentous bacteria that's 318 00:11:01,978 --> 00:11:03,678 also a healthy bacteria 319 00:11:03,774 --> 00:11:05,586 that's associated 320 00:11:05,768 --> 00:11:07,554 in that side of the 321 00:11:07,592 --> 00:11:09,454 gut that's facing 322 00:11:09,582 --> 00:11:11,678 the external environment. 323 00:11:11,854 --> 00:11:13,906 But we have these individuals with these 324 00:11:13,928 --> 00:11:15,966 DUOX2 mutations, where DUOX2 325 00:11:15,998 --> 00:11:17,590 is not doing its job 326 00:11:17,660 --> 00:11:19,586 correctly. This 327 00:11:19,618 --> 00:11:21,426 PheWAS, this genetic 328 00:11:21,538 --> 00:11:23,942 association. And then we have 329 00:11:23,996 --> 00:11:25,714 the gram-negative bacteria 330 00:11:25,762 --> 00:11:27,870 disrupting, right? 331 00:11:27,870 --> 00:11:29,414 No more segmented 332 00:11:29,462 --> 00:11:31,830 filamentous bacteria, gram-negative 333 00:11:31,910 --> 00:11:33,994 bacteria invade. And then we have 334 00:11:34,032 --> 00:11:36,934 this IL-17C being activated. 335 00:11:37,062 --> 00:11:39,482 And so now we're talking 336 00:11:39,536 --> 00:11:41,182 about no more 337 00:11:41,236 --> 00:11:43,454 homeostasis. 338 00:11:43,492 --> 00:11:45,018 It's dysbiosis. 339 00:11:45,034 --> 00:11:47,940 Dysbiosis. 340 00:11:49,410 --> 00:11:51,330 IBD, 341 00:11:51,330 --> 00:11:53,878 right? The Greek 342 00:11:53,914 --> 00:11:55,780 word of the day. There you go. 343 00:11:55,780 --> 00:11:57,810 And then we have the same 344 00:11:57,880 --> 00:11:59,954 cells that are being affected by 345 00:11:59,992 --> 00:12:01,726 these gram-negative bacteria 346 00:12:01,918 --> 00:12:03,630 producing IL-17C 347 00:12:03,710 --> 00:12:05,422 in the 348 00:12:05,576 --> 00:12:07,734 system, in the body that can be 349 00:12:07,772 --> 00:12:09,930 picked up. 350 00:12:09,930 --> 00:12:11,890 In mice, this 351 00:12:11,890 --> 00:12:13,846 DUOX2 deficiency and 352 00:12:13,868 --> 00:12:15,602 this IL-17 elevation 353 00:12:15,746 --> 00:12:17,474 are associated with this 354 00:12:17,532 --> 00:12:19,590 expansion of proteobacteria, 355 00:12:19,590 --> 00:12:21,850 sorry, pathogenic 356 00:12:21,850 --> 00:12:23,950 bacteria. 357 00:12:23,950 --> 00:12:25,834 That was pretty 358 00:12:25,872 --> 00:12:27,514 fascinating, right? Where they're able to 359 00:12:27,552 --> 00:12:29,894 take the same system and draw these 360 00:12:29,952 --> 00:12:31,294 conclusions even down to 361 00:12:31,332 --> 00:12:33,646 invertebrates. I'm like, wait, 362 00:12:33,668 --> 00:12:35,534 we're talking about DUO function in 363 00:12:35,572 --> 00:12:37,882 invertebrates. Primordial, 364 00:12:37,946 --> 00:12:39,806 they say, right? So you've got 365 00:12:39,828 --> 00:12:40,986 your primordial 366 00:12:41,098 --> 00:12:43,726 DUOX2 producing hydrogen peroxide. 367 00:12:43,748 --> 00:12:45,394 You've got, in mice, this DUOX2 368 00:12:45,432 --> 00:12:47,858 deficiency increasing IL-17C levels in the 369 00:12:47,864 --> 00:12:48,754 intestine and 370 00:12:48,792 --> 00:12:50,286 proteobacteria 371 00:12:50,478 --> 00:12:52,994 elevation. And then you've got 372 00:12:53,032 --> 00:12:55,494 in IBD patients this evidence. I 373 00:12:55,532 --> 00:12:57,610 love 374 00:12:57,610 --> 00:12:59,622 this approach to see 375 00:12:59,676 --> 00:13:01,494 multiple lines of evidence in different 376 00:13:01,532 --> 00:13:03,366 systems, especially in such a 377 00:13:03,468 --> 00:13:05,990 historic pathway. 378 00:13:06,060 --> 00:13:08,394 Historic meaning going back to 379 00:13:08,432 --> 00:13:10,182 invertebrates in evolution. Evolution, 380 00:13:10,246 --> 00:13:12,986 exactly. In C. elegans, 381 00:13:13,008 --> 00:13:15,994 in worms. And here, even this whole 382 00:13:16,032 --> 00:13:18,950 idea of, we've got this mechanism 383 00:13:19,030 --> 00:13:21,210 that they're able to look at via - what 384 00:13:21,280 --> 00:13:23,902 did they use - knockout mice? Yeah. And so 385 00:13:23,956 --> 00:13:25,866 these mice were deficient in 386 00:13:25,898 --> 00:13:27,806 DUOX2. They can go ahead and do a lot of 387 00:13:27,828 --> 00:13:29,614 experimentation on those 388 00:13:29,652 --> 00:13:31,534 tissues. And then there is something called 389 00:13:31,572 --> 00:13:33,410 a colonoid. And I'm like, 390 00:13:33,480 --> 00:13:35,810 colonoid? What's a colonoid? 391 00:13:35,810 --> 00:13:37,986 Sarantis, what is 392 00:13:38,008 --> 00:13:40,946 a colonoid? Again, I'm taking 393 00:13:40,968 --> 00:13:42,840 the difficult questions now. 394 00:13:44,840 --> 00:13:46,978 I try to be creative also in 395 00:13:46,984 --> 00:13:48,678 the way that I'm answering. 396 00:13:48,844 --> 00:13:50,006 Organoids. A lot of 397 00:13:50,028 --> 00:13:52,374 buzzwords, different things. They called 398 00:13:52,412 --> 00:13:54,700 organoids, I'm guessing, 399 00:13:54,700 --> 00:13:56,982 that what they mean here 400 00:13:57,036 --> 00:13:59,606 is just a culture. They take a 401 00:13:59,628 --> 00:14:01,734 tissue and they culture this tissue, like 402 00:14:01,852 --> 00:14:03,754 primary cell culture, in a way, but 403 00:14:03,792 --> 00:14:05,466 keeping the integrity of the tissue. And 404 00:14:05,488 --> 00:14:07,254 they can do measurements of the proteome 405 00:14:07,302 --> 00:14:09,066 based on this tissue, I'm guessing this is 406 00:14:09,088 --> 00:14:10,438 pretty much what they are doing there. I 407 00:14:10,464 --> 00:14:12,860 see. And this is what? Colonoid. But 408 00:14:12,860 --> 00:14:14,970 mini colons 409 00:14:15,050 --> 00:14:17,660 from primary tissue. Mini tissues in 410 00:14:17,748 --> 00:14:19,582 vitro or something, or 3D 411 00:14:19,636 --> 00:14:21,566 tissue culture or something. 412 00:14:21,588 --> 00:14:23,794 To take then 413 00:14:23,832 --> 00:14:25,794 that finding in mice and then 414 00:14:25,832 --> 00:14:27,682 being able to translate it 415 00:14:27,736 --> 00:14:29,810 to humans, to say 416 00:14:29,960 --> 00:14:31,938 that they believe that these 417 00:14:32,024 --> 00:14:34,954 sort of deficient individuals. 418 00:14:35,102 --> 00:14:37,394 And I thought one of the interesting figures 419 00:14:37,442 --> 00:14:39,794 was just how rare these variants 420 00:14:39,842 --> 00:14:41,878 were, but yet it 421 00:14:41,884 --> 00:14:43,574 was in 422 00:14:43,612 --> 00:14:45,926 certain populations it was 423 00:14:45,948 --> 00:14:47,850 like the odds ratio was 424 00:14:47,920 --> 00:14:49,946 really high. Was 425 00:14:49,968 --> 00:14:51,926 it Ashkenazi Jews 426 00:14:52,118 --> 00:14:54,378 as a population? Yeah, that's right. And 427 00:14:54,384 --> 00:14:56,122 then there was another 428 00:14:56,176 --> 00:14:58,566 cohort that it seemed 429 00:14:58,598 --> 00:15:00,934 like these odds ratio 430 00:15:00,982 --> 00:15:02,774 estimates were pretty high 431 00:15:02,832 --> 00:15:04,494 because they were enriched. Right. These 432 00:15:04,532 --> 00:15:06,462 populations have a lot of these 433 00:15:06,516 --> 00:15:08,526 DUOX2 mutations in the 434 00:15:08,548 --> 00:15:10,382 population. I just thought that was really 435 00:15:10,436 --> 00:15:12,946 interesting, where you can have then maybe a 436 00:15:12,968 --> 00:15:14,834 genetic test where 437 00:15:14,872 --> 00:15:16,994 somebody's susceptible. 438 00:15:17,192 --> 00:15:19,566 I will say, though, the 439 00:15:19,598 --> 00:15:21,900 IL-17C levels 440 00:15:21,900 --> 00:15:23,986 were high in 441 00:15:24,008 --> 00:15:26,046 other subjects that didn't have that 442 00:15:26,078 --> 00:15:28,674 DUOX2 variant enrichment. Didn't have 443 00:15:28,712 --> 00:15:30,518 that DUOX2. So you have the 444 00:15:30,604 --> 00:15:32,934 option, a possibility 445 00:15:32,972 --> 00:15:34,706 suggested here, that you could do a genetic 446 00:15:34,738 --> 00:15:36,546 test to see about those rare 447 00:15:36,578 --> 00:15:38,860 variants. But in fact, the 448 00:15:38,860 --> 00:15:40,394 IL-17C 449 00:15:40,432 --> 00:15:42,922 levels were 450 00:15:42,976 --> 00:15:44,410 elevated. There was a common 451 00:15:44,480 --> 00:15:46,410 response, sort of a cascade, 452 00:15:46,660 --> 00:15:48,860 that suggested this. 453 00:15:48,860 --> 00:15:50,726 I don't know if they use the word 454 00:15:50,768 --> 00:15:52,974 inflammatory, but in my mind that's what I 455 00:15:53,012 --> 00:15:55,960 picture. And I think 456 00:15:55,960 --> 00:15:57,774 this is 457 00:15:57,812 --> 00:15:59,358 where I think the excitement around 458 00:15:59,364 --> 00:16:01,946 proteomics is, but also with the 459 00:16:01,978 --> 00:16:03,806 DUOX2, just going back to the DUOX2 460 00:16:03,828 --> 00:16:05,874 variants, this is kind of how 461 00:16:05,912 --> 00:16:07,934 I think about proteomics in the context 462 00:16:07,982 --> 00:16:09,666 of genomics. If you 463 00:16:09,768 --> 00:16:11,680 have a DUOX2 464 00:16:11,740 --> 00:16:13,960 rare variant that suggests 465 00:16:13,960 --> 00:16:15,574 an increased IL-17C 466 00:16:15,612 --> 00:16:17,414 level - Dale, we'll just pick 467 00:16:17,452 --> 00:16:19,840 you - 468 00:16:19,840 --> 00:16:21,974 and I don't you see how 469 00:16:22,012 --> 00:16:24,886 I end up in the good category? And 470 00:16:24,908 --> 00:16:26,802 then you have an exposure 471 00:16:26,866 --> 00:16:28,810 to a protein level over 472 00:16:28,880 --> 00:16:30,510 your lifetime, 473 00:16:30,510 --> 00:16:32,910 perhaps that I don't, 474 00:16:32,910 --> 00:16:34,902 that exposure internally 475 00:16:35,046 --> 00:16:37,514 in our body is a way that I sometimes think 476 00:16:37,552 --> 00:16:39,834 about the relevance of 477 00:16:39,872 --> 00:16:41,902 how we make these connections between 478 00:16:41,956 --> 00:16:43,920 genetics and protein levels. 479 00:16:43,920 --> 00:16:45,998 But anyway, that's just a 480 00:16:46,164 --> 00:16:48,542 way of thinking about it that I think. 481 00:16:48,596 --> 00:16:50,910 No, that was great. And then 482 00:16:50,980 --> 00:16:52,874 I will add also the macrobiome factor 483 00:16:52,922 --> 00:16:54,962 here because they have done a really crazy 484 00:16:55,016 --> 00:16:56,926 experiment and they really like it in mice. 485 00:16:56,958 --> 00:16:58,498 They use antibiotics to 486 00:16:58,504 --> 00:17:00,958 eliminate actually the negative, the gram- 487 00:17:00,974 --> 00:17:02,920 negative and they see that 488 00:17:04,920 --> 00:17:06,706 IL-17C levels, they drop 489 00:17:06,738 --> 00:17:08,834 down again. That means there's 490 00:17:08,882 --> 00:17:10,134 really close talk with 491 00:17:10,172 --> 00:17:12,886 microbiome. 492 00:17:12,908 --> 00:17:14,982 One of the 493 00:17:15,036 --> 00:17:17,426 applications of this paper is: could 494 00:17:17,548 --> 00:17:19,460 fecal microbiota 495 00:17:19,542 --> 00:17:21,494 transplantation, FMT, 496 00:17:21,622 --> 00:17:23,782 or antibiotic treatment 497 00:17:23,926 --> 00:17:25,550 for people 498 00:17:25,550 --> 00:17:27,958 with this dysbiosis 499 00:17:28,054 --> 00:17:30,900 where the gram-negative bacteria 500 00:17:30,900 --> 00:17:32,666 are where they shouldn't 501 00:17:32,698 --> 00:17:34,890 be is pretty, 502 00:17:34,890 --> 00:17:36,942 how do I say, remarkable. 503 00:17:36,996 --> 00:17:38,766 To think this is 504 00:17:38,868 --> 00:17:40,960 a potential sort of mechanism, 505 00:17:40,960 --> 00:17:42,474 by which, 506 00:17:42,612 --> 00:17:44,430 again, knowing the mechanism 507 00:17:44,590 --> 00:17:46,930 suggests therapeutics, 508 00:17:46,930 --> 00:17:48,974 and even though the complexity 509 00:17:49,022 --> 00:17:51,950 of DUOX2, and then the IL-17C, 510 00:17:52,030 --> 00:17:54,530 and then there's a whole 511 00:17:54,600 --> 00:17:56,860 bacterial story, 512 00:17:56,860 --> 00:17:58,934 and then the immune system ... 513 00:17:59,052 --> 00:18:01,718 There's T-helper cells. 514 00:18:01,724 --> 00:18:03,634 CCR6, the chemo 515 00:18:03,682 --> 00:18:05,850 attractant for lymphocytes, 516 00:18:05,850 --> 00:18:07,710 there's FGF23 517 00:18:07,810 --> 00:18:09,494 There's 518 00:18:09,542 --> 00:18:11,622 just a cascading 519 00:18:11,686 --> 00:18:13,606 mucosal 520 00:18:13,638 --> 00:18:15,782 immunity, intestinal mucosal immunity 521 00:18:15,846 --> 00:18:17,790 that has a protein signature 522 00:18:17,790 --> 00:18:19,910 that I think can be 523 00:18:20,000 --> 00:18:22,810 shifted and has that potential to 524 00:18:22,810 --> 00:18:24,686 provide value. Well, 525 00:18:24,788 --> 00:18:26,558 we should probably highlight the Crohn's and 526 00:18:26,564 --> 00:18:28,926 Colitis Foundation, actually, in this 527 00:18:29,028 --> 00:18:31,354 discussion. Right. So Crohn's and Colitis 528 00:18:31,402 --> 00:18:33,822 Foundation, of course, has developed 529 00:18:33,966 --> 00:18:35,394 a subset of 530 00:18:35,432 --> 00:18:37,186 proteins that are 531 00:18:37,288 --> 00:18:39,278 useful in helping 532 00:18:39,374 --> 00:18:41,582 identify in a pediatric 533 00:18:41,646 --> 00:18:43,954 cohort which of 534 00:18:43,992 --> 00:18:45,782 those kids is 535 00:18:45,836 --> 00:18:47,830 likely to develop complications 536 00:18:47,830 --> 00:18:49,958 from their diagnosis of 537 00:18:49,964 --> 00:18:51,980 IBD. So, again, 538 00:18:51,980 --> 00:18:53,586 a longitudinal 539 00:18:53,618 --> 00:18:55,926 cohort that they followed, and they came up 540 00:18:55,948 --> 00:18:57,800 with this signature that helps them 541 00:18:57,800 --> 00:18:59,402 have the potential to 542 00:18:59,456 --> 00:19:01,750 score and insert a pause 543 00:19:01,830 --> 00:19:03,834 between taking out a kid's belly, which 544 00:19:03,872 --> 00:19:05,994 seems like a pretty good 545 00:19:06,032 --> 00:19:08,890 use of a proteomic signature. So, 546 00:19:08,890 --> 00:19:10,810 that's an important 547 00:19:10,810 --> 00:19:12,826 story. We've got several 548 00:19:12,858 --> 00:19:14,686 webinars from the team there. 549 00:19:14,868 --> 00:19:16,810 Yes. 550 00:19:16,810 --> 00:19:18,814 One of the interesting take homes, getting 551 00:19:18,852 --> 00:19:20,590 back to the IL-17C 552 00:19:20,660 --> 00:19:22,790 story ... 553 00:19:22,790 --> 00:19:24,862 I'm reading from, 554 00:19:24,916 --> 00:19:26,746 right before the discussion, high 555 00:19:26,778 --> 00:19:28,882 IL-17C in carriers of DUOX2 556 00:19:28,936 --> 00:19:30,882 loss-of-function variants is 557 00:19:30,936 --> 00:19:32,766 not only a potential biomarker 558 00:19:32,798 --> 00:19:34,190 for disturbed gut 559 00:19:34,270 --> 00:19:36,994 microbe immune homeostasis, 560 00:19:37,122 --> 00:19:39,782 but appears to reflect an early stage of 561 00:19:39,836 --> 00:19:40,578 IBD 562 00:19:40,674 --> 00:19:42,786 pathogenogenesis. 563 00:19:42,978 --> 00:19:44,854 So here now we're talking about 564 00:19:44,892 --> 00:19:46,534 a biomarker that could 565 00:19:46,572 --> 00:19:48,590 be an early 566 00:19:48,590 --> 00:19:50,682 predictive marker of 567 00:19:50,736 --> 00:19:52,842 disease. And you think, 568 00:19:52,976 --> 00:19:54,410 wow, by 569 00:19:54,480 --> 00:19:56,762 studying genetics, by 570 00:19:56,816 --> 00:19:58,726 studying phenotype, 571 00:19:58,838 --> 00:20:00,978 by looking at multiomics 572 00:20:01,094 --> 00:20:03,102 here it is: we come up with a 573 00:20:03,156 --> 00:20:04,770 plasma 574 00:20:04,770 --> 00:20:06,930 biomarker. 575 00:20:06,930 --> 00:20:08,974 And you're doing 576 00:20:09,012 --> 00:20:11,134 it simultaneously, or they're doing it 577 00:20:11,172 --> 00:20:13,406 simultaneously. So I don't know if 578 00:20:13,428 --> 00:20:15,800 you remember the Alnylum story, where 579 00:20:15,800 --> 00:20:17,582 hereditary 580 00:20:17,646 --> 00:20:19,458 amyloidosis has 581 00:20:19,544 --> 00:20:21,822 this diagnosis 582 00:20:21,886 --> 00:20:23,774 that's based upon a Gait 583 00:20:23,822 --> 00:20:25,746 test, like your walking test in your 584 00:20:25,768 --> 00:20:27,830 doctor's office. 585 00:20:27,830 --> 00:20:29,846 So that is sort of a 586 00:20:29,868 --> 00:20:31,986 difficult thing to diagnose, 587 00:20:32,098 --> 00:20:34,102 but you can have the genetic test very 588 00:20:34,156 --> 00:20:36,806 and know you've got a predisposition for it. 589 00:20:36,828 --> 00:20:38,102 You just don't know if it will ever 590 00:20:38,156 --> 00:20:40,794 penetrate and if you'll ever actually 591 00:20:40,832 --> 00:20:42,874 be diagnosed with it. It 592 00:20:42,912 --> 00:20:44,966 took time for them to identify a protein 593 00:20:44,998 --> 00:20:46,634 biomarker that had promise for that 594 00:20:46,672 --> 00:20:48,938 diagnosis. Here they're doing both at the 595 00:20:48,944 --> 00:20:50,918 same time. So here you've 596 00:20:50,934 --> 00:20:52,782 got a potential for a genetic test, 597 00:20:52,916 --> 00:20:54,430 but maybe these people never 598 00:20:54,500 --> 00:20:56,794 develop IBD. 599 00:20:56,794 --> 00:20:58,686 And so you can do the 600 00:20:58,708 --> 00:21:00,958 genetic test, and then you know who you will 601 00:21:01,044 --> 00:21:03,938 have to monitor over time and again. This is 602 00:21:03,944 --> 00:21:05,842 all research use only, right? But 603 00:21:05,896 --> 00:21:07,906 we're talking about the potential for the 604 00:21:07,928 --> 00:21:09,830 future. 605 00:21:09,830 --> 00:21:11,406 What would be the clinical 606 00:21:11,518 --> 00:21:13,870 utility of something like this? And 607 00:21:13,870 --> 00:21:15,682 that 608 00:21:15,736 --> 00:21:17,858 seems like -- A comment on that. Probably 609 00:21:17,864 --> 00:21:19,778 more to a philosophical point-of-view, it's 610 00:21:19,794 --> 00:21:21,814 like, at the end, 611 00:21:21,932 --> 00:21:23,670 having a genetic test will 612 00:21:23,740 --> 00:21:25,526 help for a prediction because there are so 613 00:21:25,548 --> 00:21:27,810 many rare, let's say, mutations, 614 00:21:27,890 --> 00:21:29,894 that at the end, you will never know the 615 00:21:29,932 --> 00:21:31,834 real levels of your protein. 616 00:21:31,872 --> 00:21:33,786 Because at the end, what people care about is the 617 00:21:33,808 --> 00:21:35,894 proper levels of DUOX2, independent 618 00:21:35,942 --> 00:21:37,830 of the mutation or not. 619 00:21:37,830 --> 00:21:39,706 That means probably you will 620 00:21:39,728 --> 00:21:41,814 need the protein biomarker, 621 00:21:41,862 --> 00:21:43,546 the IL-17C, and all the 622 00:21:43,568 --> 00:21:45,742 cascades that follows to be more 623 00:21:45,796 --> 00:21:47,502 sure and more concrete in what we are seeing. 624 00:21:47,556 --> 00:21:49,294 That's the way that they see, because so 625 00:21:49,332 --> 00:21:51,306 many rare mutations, difficult to predict 626 00:21:51,338 --> 00:21:53,594 the levels of the protein. I think that's 627 00:21:53,642 --> 00:21:55,358 really important to have a plasma 628 00:21:55,374 --> 00:21:57,154 biomarker to follow at this point. 629 00:21:57,192 --> 00:21:59,750 Yeah. And 630 00:21:59,750 --> 00:22:01,842 everybody's not going to use our 3K, 631 00:22:01,896 --> 00:22:03,910 our Explore panel, 632 00:22:03,910 --> 00:22:05,586 right? We're talking about something that's 633 00:22:05,618 --> 00:22:07,760 developed for the clinic 634 00:22:07,760 --> 00:22:09,462 that's very 635 00:22:09,516 --> 00:22:11,558 specific. That would need to go through 636 00:22:11,644 --> 00:22:13,980 regulatory approval to 637 00:22:13,980 --> 00:22:15,942 get into clinical utility. But 638 00:22:15,996 --> 00:22:17,774 yeah, very exciting. 639 00:22:17,842 --> 00:22:19,434 And you think this could be 640 00:22:19,552 --> 00:22:21,914 ... well, in the 641 00:22:21,952 --> 00:22:23,734 commentary, the researchers 642 00:22:23,782 --> 00:22:25,926 saying patients and physicians 643 00:22:25,958 --> 00:22:27,530 and scientists are looking 644 00:22:27,600 --> 00:22:29,814 for how to unlock 645 00:22:29,862 --> 00:22:31,898 this microbiome host 646 00:22:31,994 --> 00:22:33,678 and then immune system 647 00:22:33,844 --> 00:22:35,982 as like a Holy Grail 648 00:22:36,036 --> 00:22:38,750 within IBD. And I thought that was 649 00:22:38,820 --> 00:22:40,766 so interesting, that here it 650 00:22:40,788 --> 00:22:42,702 is. They're looking at so 651 00:22:42,756 --> 00:22:44,926 many different variables. When you 652 00:22:44,948 --> 00:22:46,866 think about whole genome data and all the 653 00:22:46,888 --> 00:22:48,786 genetics that could be evolved, and then you 654 00:22:48,808 --> 00:22:50,482 think about all the different other 655 00:22:50,536 --> 00:22:52,962 measures that they did, and then to 656 00:22:53,016 --> 00:22:55,862 settle on a model, 657 00:22:55,996 --> 00:22:57,530 a gene, a 658 00:22:57,612 --> 00:22:59,318 particular plasma-based 659 00:22:59,404 --> 00:23:01,782 biomarker, and then looking at how they all 660 00:23:01,836 --> 00:23:03,542 interact, it's just 661 00:23:03,596 --> 00:23:05,830 really interesting. And 662 00:23:05,980 --> 00:23:07,894 down to the 663 00:23:07,932 --> 00:23:09,940 types of microbiota 664 00:23:09,940 --> 00:23:11,190 that are being 665 00:23:11,340 --> 00:23:13,502 affected. It's 666 00:23:13,666 --> 00:23:15,898 so cool. I mean, it's a 667 00:23:15,904 --> 00:23:17,926 big data story, right? It's 668 00:23:17,958 --> 00:23:19,926 how big data and a cohort that's 669 00:23:19,958 --> 00:23:21,658 collected and consented longitudinally - I 670 00:23:21,664 --> 00:23:23,838 know I've already said this - can drive 671 00:23:23,924 --> 00:23:25,970 mechanistic discovery 672 00:23:25,970 --> 00:23:27,790 to help define 673 00:23:27,790 --> 00:23:29,518 disease biomarkers. And I think having a 674 00:23:29,524 --> 00:23:31,694 biomarker is great, but having it where you 675 00:23:31,732 --> 00:23:33,666 actually kind of have a sense of the, 676 00:23:33,768 --> 00:23:35,534 like these multiple lines of evidence 677 00:23:35,582 --> 00:23:37,690 - a mouse model - 678 00:23:37,690 --> 00:23:39,886 having multiple 679 00:23:39,918 --> 00:23:41,694 lines of evidence and some mechanistic 680 00:23:41,742 --> 00:23:43,618 understanding of it, makes 681 00:23:43,784 --> 00:23:45,830 it so much ... 682 00:23:45,830 --> 00:23:47,938 I'm much more comforted in 683 00:23:48,024 --> 00:23:50,706 seeing it implement in the clinic. And I 684 00:23:50,728 --> 00:23:52,918 would hope it has the potential to move to 685 00:23:52,924 --> 00:23:54,406 the clinic a little more quickly when we 686 00:23:54,428 --> 00:23:55,874 actually have that mechanistic insight. 687 00:23:55,922 --> 00:23:57,910 Sorry, go ahead. I was going to say that 688 00:23:58,060 --> 00:24:00,506 after reading a paper like this, because it 689 00:24:00,528 --> 00:24:02,358 was pretty dense, it's pretty intense, 690 00:24:02,454 --> 00:24:04,954 right? It was, 691 00:24:04,992 --> 00:24:06,662 I don't know, maybe 12 or 14 692 00:24:06,726 --> 00:24:08,646 pages of heavy duty reading 693 00:24:08,678 --> 00:24:10,918 and lots and lots of immunology, 694 00:24:11,014 --> 00:24:12,974 which can be difficult to grasp. We had 695 00:24:13,012 --> 00:24:15,326 mouse models in terms of dual knockouts. We 696 00:24:15,348 --> 00:24:17,466 have all these 697 00:24:17,498 --> 00:24:19,806 multiomics, and yet it's almost as 698 00:24:19,828 --> 00:24:21,950 if, man, this is like the final word, 699 00:24:22,100 --> 00:24:24,350 right? This is one 700 00:24:24,420 --> 00:24:26,194 conclusive particular 701 00:24:26,312 --> 00:24:28,978 avenue. And then it makes me think, wow, it 702 00:24:28,984 --> 00:24:30,770 was only 250 703 00:24:30,840 --> 00:24:32,498 or so proteins in the 704 00:24:32,504 --> 00:24:34,546 plasma [that were measured]. What if they did an 705 00:24:34,568 --> 00:24:36,726 Explore 3K on it, right? 706 00:24:36,828 --> 00:24:38,966 And then I think, wow, I don't 707 00:24:38,988 --> 00:24:40,838 know. Cindy, maybe you can shed light on 708 00:24:40,844 --> 00:24:42,980 this. Does the UK Biobank collect, 709 00:24:42,980 --> 00:24:44,880 710 00:24:44,880 --> 00:24:46,738 the microbiome 711 00:24:46,914 --> 00:24:48,880 samples from individuals? 712 00:24:48,880 --> 00:24:50,886 Not to my knowledge. I haven't 713 00:24:50,918 --> 00:24:52,880 seen any studies on it, which 714 00:24:52,880 --> 00:24:54,810 I would expect. I know they're doing 715 00:24:54,880 --> 00:24:56,602 metabolomics on 716 00:24:56,656 --> 00:24:58,426 samples in the UK Biobank, but my 717 00:24:58,448 --> 00:25:00,486 understanding is it's on plasma. Don't quote 718 00:25:00,518 --> 00:25:02,830 me on that. But I just haven't seen 719 00:25:02,900 --> 00:25:04,334 any data come out on the 720 00:25:04,372 --> 00:25:06,590 microbiome on those patients. Because that 721 00:25:06,660 --> 00:25:08,894 data set, that 16S 722 00:25:09,012 --> 00:25:11,662 ribosomal data is super 723 00:25:11,716 --> 00:25:13,826 important, because that's one-third of the 724 00:25:13,848 --> 00:25:15,826 whole story here in terms 725 00:25:15,848 --> 00:25:17,920 of how that microbiome 726 00:25:17,920 --> 00:25:19,778 is interacting with the 727 00:25:19,784 --> 00:25:21,778 host. Any 728 00:25:21,864 --> 00:25:23,666 final comments on this? I mean, this is 729 00:25:23,688 --> 00:25:25,958 such a cool paper, right? Even though it 730 00:25:25,964 --> 00:25:26,646 was published in 731 00:25:26,668 --> 00:25:28,410 2021. 732 00:25:28,410 --> 00:25:30,546 This association 733 00:25:30,546 --> 00:25:32,966 with inflammatory bowel disease and 734 00:25:32,988 --> 00:25:34,966 its practical application, and a 735 00:25:34,988 --> 00:25:36,790 wonderful multiomic story. 736 00:25:36,940 --> 00:25:38,774 I mean, it'll be really 737 00:25:38,812 --> 00:25:40,750 interesting to follow. 738 00:25:40,750 --> 00:25:42,938 Yeah, it's a great story. And 739 00:25:42,944 --> 00:25:44,986 so I'm thinking in the show notes, we 740 00:25:45,008 --> 00:25:47,386 can maybe even put some of these proteins in 741 00:25:47,408 --> 00:25:49,882 our Insight app and provide a link 742 00:25:49,936 --> 00:25:51,862 to the ability to browse 743 00:25:51,926 --> 00:25:53,398 through those, because I think looking at 744 00:25:53,424 --> 00:25:54,830 these pathways might be really interesting. 745 00:25:54,900 --> 00:25:56,782 And just to remind folks around our Insight 746 00:25:56,836 --> 00:25:58,714 app, we've had a podcast 747 00:25:58,762 --> 00:26:00,874 about this before, but essentially 748 00:26:00,922 --> 00:26:02,814 it's a browser where you can very 749 00:26:02,852 --> 00:26:04,946 quickly what I love about it is 750 00:26:04,968 --> 00:26:06,770 that you can actually convert gene names to 751 00:26:06,840 --> 00:26:07,874 UniProt IDs. And with 752 00:26:07,912 --> 00:26:09,966 UniProt IDs, there's 753 00:26:09,998 --> 00:26:11,966 only one UniProt ID per protein, whereas 754 00:26:11,998 --> 00:26:13,586 we've got multiple gene names for 755 00:26:13,608 --> 00:26:15,794 them. So that's really handy. But 756 00:26:15,832 --> 00:26:17,926 also being able to just look at the 757 00:26:17,948 --> 00:26:19,734 pathways and see which 758 00:26:19,772 --> 00:26:21,254 proteins we have in our 759 00:26:21,292 --> 00:26:23,974 panels, that you can get at that 760 00:26:24,012 --> 00:26:26,646 pathway through multiple proteins versus the 761 00:26:26,668 --> 00:26:28,198 ones that we don't have in our panels. And 762 00:26:28,204 --> 00:26:30,226 that also helps people to identify the ones 763 00:26:30,268 --> 00:26:32,666 they want us to put in the panels. And we 764 00:26:32,688 --> 00:26:34,902 have a mechanism by which you can request 765 00:26:34,966 --> 00:26:36,970 proteins to be included in our future 766 00:26:37,040 --> 00:26:39,786 product development efforts. And 767 00:26:39,808 --> 00:26:41,754 so we've talked 768 00:26:41,792 --> 00:26:43,406 about how we're trying to cover the 769 00:26:43,428 --> 00:26:45,630 proteome. We've got 3000 [proteins that our platforms measure] 770 00:26:45,700 --> 00:26:47,822 today, but certainly our R&D team 771 00:26:47,876 --> 00:26:49,982 is working hard on 772 00:26:50,036 --> 00:26:52,346 covering more. Yeah, thank you for bringing 773 00:26:52,378 --> 00:26:53,934 that up, Cindy. 774 00:26:54,062 --> 00:26:56,722 Insight.olink.com is how to access this free 775 00:26:56,776 --> 00:26:58,770 resource. There's some really great 776 00:26:58,840 --> 00:27:00,834 tools inside it. Even if people wanted 777 00:27:00,872 --> 00:27:02,610 to browse publications by 778 00:27:02,680 --> 00:27:04,920 biomarker, right? 779 00:27:04,920 --> 00:27:06,980 Very good point. Wouldn't that be 780 00:27:06,980 --> 00:27:08,474 interesting to punch in 781 00:27:08,542 --> 00:27:10,854 IL17-C and see what other 782 00:27:10,892 --> 00:27:12,770 publications come up? 783 00:27:12,770 --> 00:27:14,806 Which, by the way, is a low, 784 00:27:14,828 --> 00:27:16,518 abundant protein, right? So if you look in 785 00:27:16,524 --> 00:27:18,586 our validation data, also available on our 786 00:27:18,608 --> 00:27:20,886 website that you can freely download, you'll 787 00:27:20,918 --> 00:27:22,810 see that IL-17C is 788 00:27:22,880 --> 00:27:24,986 in the dilution category of one-to- 789 00:27:25,008 --> 00:27:27,910 one. So it's neat, right? 790 00:27:27,910 --> 00:27:29,674 You're not 791 00:27:29,712 --> 00:27:31,694 adding any dilution factor to it 792 00:27:31,732 --> 00:27:33,970 to 793 00:27:33,970 --> 00:27:35,374 manage how much 794 00:27:35,492 --> 00:27:37,758 reagent might be needed to count 795 00:27:37,924 --> 00:27:39,966 when our NGS readout or our 796 00:27:39,988 --> 00:27:41,934 qPCR readout is used. And so 797 00:27:41,972 --> 00:27:43,886 that one-to-one ratio suggests that it's in 798 00:27:43,908 --> 00:27:45,986 that area that we've talked about before, 799 00:27:46,088 --> 00:27:48,434 where Olink has really shined a light that 800 00:27:48,472 --> 00:27:50,594 makes it much easier to see these low, 801 00:27:50,632 --> 00:27:52,630 abundant proteins 802 00:27:52,630 --> 00:27:54,786 than some traditional methods that we, of 803 00:27:54,808 --> 00:27:56,934 course, have already made lots of great 804 00:27:56,972 --> 00:27:58,854 discoveries using mass spectrometry, for 805 00:27:58,892 --> 00:28:00,834 example. But this low abundant 806 00:28:00,882 --> 00:28:02,966 area, I think the 807 00:28:02,988 --> 00:28:04,934 number of publications we have in our 808 00:28:05,052 --> 00:28:07,074 1100-plus publication 809 00:28:07,122 --> 00:28:09,434 database, many of those are 810 00:28:09,472 --> 00:28:11,950 focused on those one-to-one 811 00:28:11,950 --> 00:28:13,866 neat proteins that are in that 812 00:28:13,888 --> 00:28:15,686 low, abundant range. Speaking of show notes, 813 00:28:15,718 --> 00:28:17,882 I'll be sure to include Dr. 814 00:28:17,970 --> 00:28:19,366 Uh, Hurtada-Lorenzo's 815 00:28:19,398 --> 00:28:21,946 talk from the Crohn's and Colitis 816 00:28:21,978 --> 00:28:23,790 Foundation. This is where 817 00:28:23,940 --> 00:28:25,102 they were looking at 818 00:28:25,156 --> 00:28:27,626 pediatric Crohn's and colitis. 819 00:28:27,738 --> 00:28:29,966 And it's a remarkable story in 820 00:28:29,988 --> 00:28:31,790 terms of was it maybe 70 821 00:28:31,860 --> 00:28:33,570 or 80 real-time 822 00:28:33,570 --> 00:28:35,314 PCR markers that they had 823 00:28:35,352 --> 00:28:37,986 developed from biopsy tissue, but 824 00:28:38,008 --> 00:28:40,226 then they went to plasma and they found a 825 00:28:40,248 --> 00:28:42,514 much smaller signature that's much more 826 00:28:42,552 --> 00:28:44,482 practical. Instead of using 827 00:28:44,536 --> 00:28:46,946 biopsy tissue and real-time PCR, here 828 00:28:46,968 --> 00:28:48,882 it is. They're able to look at circulating 829 00:28:48,946 --> 00:28:50,822 biomarkers. Some really exciting work. 830 00:28:50,850 --> 00:28:52,470 And that team 831 00:28:52,540 --> 00:28:54,786 has created, of course, it's a nonprofit 832 00:28:54,818 --> 00:28:56,546 Crohn's and Colitis Foundation, but they've 833 00:28:56,578 --> 00:28:58,966 created a ventures arm for that 834 00:28:59,070 --> 00:29:01,494 entity in order to bring investment in 835 00:29:01,532 --> 00:29:03,366 to take things like this to the clinic. 836 00:29:03,398 --> 00:29:05,706 And they're funding the majority of 837 00:29:05,728 --> 00:29:07,354 Crohn's and Colitis research in the world. 838 00:29:07,392 --> 00:29:09,686 In fact, I was at a meeting in South 839 00:29:09,718 --> 00:29:11,710 Africa, in Cape Town a couple of weeks ago, 840 00:29:11,780 --> 00:29:13,918 and there was a poster on 841 00:29:14,084 --> 00:29:16,734 IBD, and I talked to the 842 00:29:16,772 --> 00:29:18,958 authors, and in fact, they were also 843 00:29:19,044 --> 00:29:20,922 funded by the Crohn's and Colitis Foundation. 844 00:29:20,986 --> 00:29:22,926 So it's just a 845 00:29:22,948 --> 00:29:24,622 small world in some of these disease 846 00:29:24,686 --> 00:29:26,674 areas where the movers and shakers are 847 00:29:26,712 --> 00:29:28,610 really making a difference. 848 00:29:28,760 --> 00:29:30,718 Well, thank you for joining 849 00:29:30,814 --> 00:29:32,882 us this afternoon or this morning, 850 00:29:32,936 --> 00:29:34,722 wherever you may be, and, 851 00:29:34,776 --> 00:29:36,800 Cindy and Sarantis, 852 00:29:38,890 --> 00:29:40,760 till next time, so long. 853 00:29:40,760 --> 00:29:42,806 Thank you 854 00:29:42,828 --> 00:29:44,834 for listening to the Proteomics in Proximity 855 00:29:44,882 --> 00:29:46,786 podcast brought to you by Olink 856 00:29:46,818 --> 00:29:48,866 Proteomics. To contact the hosts 857 00:29:48,898 --> 00:29:50,754 or for further information, simply 858 00:29:50,802 --> 00:29:52,900 email info@olink.com.