1 00:00:00,080 --> 00:00:02,160 I don't think aging is one thing. 2 00:00:02,520 --> 00:00:04,960 I think aging is multiple different things. 3 00:00:04,960 --> 00:00:07,280 And therefore I think it's highly likely that there are 4 00:00:07,280 --> 00:00:10,560 different mechanisms that are driving the evolution of this 5 00:00:10,560 --> 00:00:13,400 phenomena. So actually my own pets favorite 6 00:00:13,400 --> 00:00:17,000 theory for what drives aging is that I'm a big fan of this 7 00:00:17,120 --> 00:00:20,760 entropy model of aging. And basically what this relates 8 00:00:20,760 --> 00:00:25,520 to is this idea that a cell or a system goes from an ordered to a 9 00:00:25,520 --> 00:00:28,360 disordered state. And we believe that one of the 10 00:00:28,360 --> 00:00:30,680 major drivers of this is cell division. 11 00:00:31,280 --> 00:00:34,720 Welcome to EHA on Plot, the official podcast channel of the 12 00:00:34,760 --> 00:00:37,400 European Hematology Association EHA. 13 00:00:37,920 --> 00:00:42,320 I'm your host Isabella Rivera. Today we're joined by Doctor 14 00:00:42,320 --> 00:00:45,520 Michael Nielsen. Dr. Nielsen is a division head 15 00:00:45,600 --> 00:00:50,120 at the German Cancer Research Center and group leader at the 16 00:00:50,120 --> 00:00:54,080 Heidelberg Institute for Stem Cell Technology and Experimental 17 00:00:54,080 --> 00:00:56,800 Medicine in Germany. Doctor Mason is one of the 18 00:00:56,800 --> 00:01:02,960 organizers of EHS Research Conference in 2026, Recon 2026. 19 00:01:02,960 --> 00:01:07,200 And as an appetizer today, we're going to have a discussion about 20 00:01:07,200 --> 00:01:10,200 hematopoiesis and how it changes during aging. 21 00:01:10,320 --> 00:01:11,800 Thank you for being with us today. 22 00:01:11,800 --> 00:01:12,600 Thank. You for having me? 23 00:01:13,200 --> 00:01:16,840 So to start off, what do you mean by the traditional HSC 24 00:01:16,840 --> 00:01:19,920 centric view of hematopoietic aging? 25 00:01:20,000 --> 00:01:23,040 Why do you think everything is centre in HSC? 26 00:01:23,160 --> 00:01:26,920 Well, when we think about how the hematopoietic system changes 27 00:01:26,920 --> 00:01:29,920 and evolves during the process of aging, it's a really 28 00:01:29,920 --> 00:01:33,160 attractive concept that the hematopoietic stem cells, these 29 00:01:33,160 --> 00:01:36,720 cells that we think last throughout your lifetime and 30 00:01:36,720 --> 00:01:40,160 they're essential for the ongoing production of blood 31 00:01:40,160 --> 00:01:43,960 cells and responding to stress when you get depleted of mature 32 00:01:43,960 --> 00:01:47,320 cells that these alterations that would be permanent and 33 00:01:47,320 --> 00:01:51,440 progressive should be enacted at the level of hematopoietic stem 34 00:01:51,440 --> 00:01:54,000 cells. And there are various kind of 35 00:01:54,000 --> 00:01:58,040 conditions where we know that the hematopoietic stem cells, 36 00:01:58,040 --> 00:02:01,800 their functionality is altered often detrimentally during 37 00:02:01,800 --> 00:02:05,040 aging. So it kind of makes sense 38 00:02:05,040 --> 00:02:09,280 logically that these permanent changes would be as a result of 39 00:02:09,280 --> 00:02:12,000 the stem cells getting functionally compromised during 40 00:02:12,000 --> 00:02:15,800 the ageing process. What is the main evidence that 41 00:02:15,800 --> 00:02:17,920 supports this? CSC centric. 42 00:02:18,360 --> 00:02:21,760 So I think that there are many lines of evidence that show that 43 00:02:21,760 --> 00:02:25,840 hematopoietic stem cells lose their functional potency during 44 00:02:25,840 --> 00:02:30,480 the process of ageing both in humans and in experimental model 45 00:02:30,480 --> 00:02:33,840 systems like the mouse. So for example, in humans, if 46 00:02:33,840 --> 00:02:38,960 you think back to criteria where we look to employee in terms of 47 00:02:38,960 --> 00:02:43,000 looking at donors for stem cell transplant, we're typically not 48 00:02:43,000 --> 00:02:46,200 using older donors in that setting because there's a higher 49 00:02:46,200 --> 00:02:49,320 proportion of a graft failure in that setting. 50 00:02:49,360 --> 00:02:51,920 And that's thought to result from this loss of functional 51 00:02:51,920 --> 00:02:54,160 potency of the hematopoietic stem cells. 52 00:02:54,200 --> 00:02:57,280 You can see this really nicely in in the mouse setting as well 53 00:02:57,440 --> 00:03:01,880 and where paradoxically the number of immunophenotypic stem 54 00:03:01,880 --> 00:03:04,480 cells increases with time. However, the number of 55 00:03:04,480 --> 00:03:07,200 functional stem cells declines with time. 56 00:03:08,200 --> 00:03:10,760 There also were the lines of evidence, and I think we'll talk 57 00:03:10,760 --> 00:03:14,320 a little bit later about clonal hematopoiesis, but I think this 58 00:03:14,320 --> 00:03:17,080 is another kind of excellent line of evidence that something 59 00:03:17,080 --> 00:03:20,280 happens at the stem cells level where there's a clonal 60 00:03:20,280 --> 00:03:24,280 contraction in the stem cell pool that now impacts on blood 61 00:03:24,280 --> 00:03:27,640 cell production and the evolution of a wide range of age 62 00:03:27,640 --> 00:03:30,800 associated diseases. So I think there's lots of 63 00:03:30,800 --> 00:03:34,480 evidence that the stem cells alter with aging, but where I 64 00:03:34,480 --> 00:03:37,840 think there's a big gap in knowledge is exactly how that 65 00:03:37,840 --> 00:03:41,840 might impact on a lot of the clinically relevant features of 66 00:03:41,880 --> 00:03:44,560 aging, the predisposed towards disease. 67 00:03:44,600 --> 00:03:47,560 So for example, immune dysfunction, is that really 68 00:03:47,560 --> 00:03:51,200 driven by altered biology of the hematopoietic stem cells or 69 00:03:51,200 --> 00:03:55,680 other cell types, Anemia of aging, is that really driven by 70 00:03:55,680 --> 00:03:59,680 alterations in the stem cells progenitor cell compartment or 71 00:03:59,920 --> 00:04:02,240 something further downstream? And I think that that's 72 00:04:02,240 --> 00:04:05,800 something that's very difficult to address mechanistically, but 73 00:04:05,800 --> 00:04:07,520 I think it's a really important question. 74 00:04:08,240 --> 00:04:11,280 But I think up until now we focused a lot on the 75 00:04:11,280 --> 00:04:13,240 hematopoietic stem cell compartment, so. 76 00:04:13,720 --> 00:04:18,320 You argue that this view is too narrow to focus on HSCS is too 77 00:04:18,320 --> 00:04:20,399 narrow. What are we missing? 78 00:04:20,440 --> 00:04:23,360 If we focus only on HSCS, what else is there? 79 00:04:24,160 --> 00:04:27,480 OK. So I think that before I jump 80 00:04:27,480 --> 00:04:31,080 into why we should look outside HSCS, the hematopoietic stem 81 00:04:31,080 --> 00:04:34,360 cell compartment, it's probably important to kind of take a step 82 00:04:34,360 --> 00:04:38,320 back and think about broadly what is aging and what does that 83 00:04:38,320 --> 00:04:40,600 look like in different individuals. 84 00:04:41,360 --> 00:04:45,280 And often times the way that we study this, we'll kind of use 85 00:04:45,280 --> 00:04:49,600 something like an experimental mouse cohort, young and aged, 86 00:04:50,160 --> 00:04:53,160 and basically look at the things that change on average in those 87 00:04:53,160 --> 00:04:56,480 populations in terms of the composition of the blood, the 88 00:04:56,480 --> 00:05:01,440 bone marrow, the functionality. And clearly one of the the 89 00:05:01,440 --> 00:05:07,200 problems with that is that aging isn't just a kind of a process 90 00:05:07,200 --> 00:05:09,920 where you go from A to B directly. 91 00:05:09,920 --> 00:05:13,440 I think that actually aging is going to multiple different 92 00:05:13,440 --> 00:05:17,040 destinations and potentially also at different rates. 93 00:05:17,600 --> 00:05:20,680 So if you think about humans, not everybody looks the same in 94 00:05:20,680 --> 00:05:24,920 terms of how they elicit age associated phenotypes and not 95 00:05:24,920 --> 00:05:29,400 everybody develops exactly the same kind of malignant or non 96 00:05:29,400 --> 00:05:33,400 malignant age associated hematopoietic pathologies and 97 00:05:33,400 --> 00:05:36,240 people develop those at different time points. 98 00:05:36,600 --> 00:05:40,360 I think historically we've written that off as alterations 99 00:05:40,360 --> 00:05:43,240 in the environment or genetic predisposition. 100 00:05:43,400 --> 00:05:45,760 But I can tell you in our experimental mice that are 101 00:05:45,760 --> 00:05:49,160 inbred or mice look very different in terms of their 102 00:05:49,360 --> 00:05:52,240 hematopoietic composition, old age. 103 00:05:52,240 --> 00:05:56,440 So I think that it's kind of problematic to think of aging as 104 00:05:56,440 --> 00:06:01,280 a single thing, like we, for example, think about acute 105 00:06:01,280 --> 00:06:05,040 myeloid leukemia. In reality, that's a spectrum of 106 00:06:05,040 --> 00:06:08,080 different diseases with different drivers that have 107 00:06:08,080 --> 00:06:12,200 different aggressiveness and I think aging is the same. 108 00:06:13,320 --> 00:06:17,320 Now if we kind of pivot back in a very long winded way back to 109 00:06:17,560 --> 00:06:20,600 the question about hematopoietic stem cells and what we're 110 00:06:20,600 --> 00:06:25,560 missing, I think that if we just focus in on that population, we 111 00:06:25,560 --> 00:06:27,800 probably miss a lot of important information. 112 00:06:27,800 --> 00:06:31,000 So I think it's possible that a lot of the phenotypes that 113 00:06:31,000 --> 00:06:35,040 develop during aging aren't necessarily as a result of the 114 00:06:35,040 --> 00:06:37,120 hematopoietic stem cells being altered. 115 00:06:37,600 --> 00:06:40,960 So if our focus is predominantly on the stem cells, we missed 116 00:06:40,960 --> 00:06:43,280 that and we're looking in the wrong place for a mechanism 117 00:06:43,280 --> 00:06:46,840 that's driving the phenotype. Now I think there are certain 118 00:06:46,840 --> 00:06:49,880 things that are driven predominantly by hematopoietic 119 00:06:49,880 --> 00:06:52,920 stem cell dysfunction. So I think in a range of 120 00:06:52,920 --> 00:06:56,120 different hematologic malignancies, it's quite clear 121 00:06:56,120 --> 00:06:59,520 that the hematopoietic stem cell was likely the cell of origin 122 00:06:59,680 --> 00:07:03,200 that became dysfunctional and then later on evolved into a 123 00:07:03,200 --> 00:07:07,560 full blown leukemia. So I'm kind of proposing a more 124 00:07:07,560 --> 00:07:10,240 holistic view where we maybe take a step back, don't just 125 00:07:10,240 --> 00:07:14,240 focus on one cell compartment. And actually, even if we're 126 00:07:14,240 --> 00:07:18,240 interested in hematopoietic stem cells, try to really assess 127 00:07:18,240 --> 00:07:21,480 whether in the more mature cell types or for example, in the 128 00:07:21,480 --> 00:07:24,440 niche, whether this is the thing that's really driving the 129 00:07:24,440 --> 00:07:28,960 disease phenotype. So the more mature immune cells 130 00:07:30,560 --> 00:07:33,920 show ageing phenotypes and that are independent of age. 131 00:07:34,720 --> 00:07:38,520 Yeah, I think that there are a number of clear examples of 132 00:07:38,560 --> 00:07:40,800 that. So if you think about defects in 133 00:07:40,800 --> 00:07:45,640 the adaptive immune system that evolve during aging, these are a 134 00:07:45,640 --> 00:07:50,320 major cause of a range of age associated predispositions to 135 00:07:50,400 --> 00:07:54,200 developing disease. So if you look at for example, 136 00:07:54,200 --> 00:07:59,040 by people generally have a poor response to viral infection like 137 00:07:59,040 --> 00:08:02,840 we saw during the COVID pandemic or how people respond really 138 00:08:02,840 --> 00:08:07,040 poorly to influenza infection. These are generally elicited 139 00:08:07,040 --> 00:08:10,400 through defects in in T cell production and potentially also 140 00:08:10,400 --> 00:08:12,600 B cell production and functionality. 141 00:08:13,160 --> 00:08:16,480 And I think it's highly likely that the defects there are not 142 00:08:16,880 --> 00:08:19,080 related to hematopoietic stem cells. 143 00:08:20,520 --> 00:08:23,960 I think that also if we think about some age associated 144 00:08:23,960 --> 00:08:27,240 hematologic malignancies that relate more to the lymphoid 145 00:08:27,640 --> 00:08:31,240 branch of hematopoiesis, they're the likely cell of origin 146 00:08:31,240 --> 00:08:33,320 probably isn't a hematopoietic stem cell. 147 00:08:33,320 --> 00:08:36,039 So these are kind of examples of things that have evolved 148 00:08:36,480 --> 00:08:38,600 independent of the hematopoietic system. 149 00:08:39,600 --> 00:08:44,720 So actually we've been carrying out a number of studies in the 150 00:08:44,720 --> 00:08:48,160 laboratory mouse where it's relatively straightforward to 151 00:08:48,160 --> 00:08:51,640 measure multiple different hematopoietic metrics from the 152 00:08:51,640 --> 00:08:54,240 same mouse. So for example, we would 153 00:08:54,240 --> 00:08:57,760 transplant hematopoietic stem cells, but also in the same 154 00:08:57,760 --> 00:09:02,360 individual, look at anemia, look at the level of peripheral blood 155 00:09:02,360 --> 00:09:07,000 cells, look at the, the, the, the gene expression programs, 156 00:09:07,000 --> 00:09:10,640 the different cell types. And the amazing thing to me is 157 00:09:10,640 --> 00:09:13,880 that actually we see huge diversity there, but we see a 158 00:09:13,880 --> 00:09:18,440 very poor correlation between stem cell dysfunction in that 159 00:09:18,440 --> 00:09:21,600 individual as measured by what the stem cell does when we 160 00:09:21,600 --> 00:09:26,400 transplant it into a recipient and the actual heap 161 00:09:26,400 --> 00:09:29,040 hematopoietic phenotypes in the primary animal. 162 00:09:29,120 --> 00:09:31,840 That doesn't seem to be a good correlation which would be 163 00:09:31,840 --> 00:09:35,040 predicted in the literature. And in fact, after we've 164 00:09:35,040 --> 00:09:37,960 transplanted the stem cells and these cells have regenerated 165 00:09:37,960 --> 00:09:41,040 hematopoiesis in the recipient animal, we see a poor 166 00:09:41,040 --> 00:09:44,920 correlation in blood cell parameters between the donor and 167 00:09:44,920 --> 00:09:48,160 the recipient, which you wouldn't predict if the stem 168 00:09:48,160 --> 00:09:50,640 cells were actually driving what was happening in the primary 169 00:09:50,640 --> 00:09:53,480 mouse. Why is is there this difference? 170 00:09:53,480 --> 00:09:55,960 What else is driving aging in this mice? 171 00:09:56,200 --> 00:09:57,880 Yeah, So this is the $1,000,000 question. 172 00:09:57,880 --> 00:10:01,080 What's the mechanistic driver? So again, I'd I'd revert back to 173 00:10:01,320 --> 00:10:05,160 one of my original comments that I don't think aging is one 174 00:10:05,160 --> 00:10:07,560 thing. I think aging is multiple 175 00:10:07,560 --> 00:10:09,960 different things. And therefore I think it's 176 00:10:09,960 --> 00:10:12,720 highly likely that there are different mechanisms that are 177 00:10:12,720 --> 00:10:14,920 driving the evolution of this phenomena. 178 00:10:15,720 --> 00:10:20,680 So we can see alterations in niche, niche composition. 179 00:10:20,680 --> 00:10:23,160 This is also something that we've studied and found a kind 180 00:10:23,160 --> 00:10:26,440 of lack of correlation between this and what's happening in 181 00:10:26,440 --> 00:10:29,200 terms of the blood cell compartmentalization. 182 00:10:29,320 --> 00:10:33,120 And, and actually at the moment, we're really struggling to find 183 00:10:33,720 --> 00:10:36,800 anything that correlates particularly well with the 184 00:10:36,800 --> 00:10:39,720 phenotypes that we're observing in the mice, even when we look 185 00:10:39,720 --> 00:10:41,760 at things that have been previously published in the 186 00:10:41,760 --> 00:10:45,440 literature. So actually my own pets favorite 187 00:10:45,440 --> 00:10:49,040 theory for for what drives aging is that I'm a big fan of this 188 00:10:49,120 --> 00:10:53,360 entropy model of aging. And basically what this relates 189 00:10:53,360 --> 00:10:58,480 to is this idea that a cell or a system goes from an ordered to a 190 00:10:58,480 --> 00:11:02,320 disordered state. And we believe that one of the 191 00:11:02,320 --> 00:11:04,640 major drivers of this is cell division. 192 00:11:05,360 --> 00:11:10,000 So for example, on a very basic level, we know that when you 193 00:11:10,000 --> 00:11:13,080 replicate your genome, it's very challenging to replicate it 194 00:11:13,080 --> 00:11:15,320 perfectly. I think the same thing holds 195 00:11:15,320 --> 00:11:19,320 true, for example, when we try to replicate the epigenome 196 00:11:19,320 --> 00:11:22,560 status of a daughter cell compared to the parent cell. 197 00:11:23,000 --> 00:11:26,600 So you end up with a diversification and the more 198 00:11:26,600 --> 00:11:29,720 cell divisions, the more you differ from the original cell. 199 00:11:30,760 --> 00:11:33,760 So this we think is potentially A mechanism through which 200 00:11:33,760 --> 00:11:37,360 hematopoietic stem cells achieve diversification. 201 00:11:38,120 --> 00:11:41,680 And this diversification can also drive an evolutionary 202 00:11:41,680 --> 00:11:44,960 process. So this kind of creates a system 203 00:11:44,960 --> 00:11:48,680 of clonal competition, outgrowth of clones with, for example, 204 00:11:48,680 --> 00:11:53,240 favorable growth characteristics and then potentially also 205 00:11:53,240 --> 00:11:55,640 disordered functionality afterwards. 206 00:11:56,000 --> 00:11:59,760 So this is attractive to me, but that's extremely difficult to 207 00:11:59,760 --> 00:12:03,160 study because most of our computational analysis methods 208 00:12:03,160 --> 00:12:07,160 tend to focus on what's coherently changing rather than 209 00:12:07,560 --> 00:12:10,320 to what degree do we see diversification. 210 00:12:10,840 --> 00:12:15,120 So your work has shown that inflammatory stress, even early 211 00:12:15,120 --> 00:12:17,720 in life can accelerate HSE ageing. 212 00:12:17,760 --> 00:12:20,120 What can you tell us about this? Yeah. 213 00:12:20,120 --> 00:12:23,800 So we were interested in inflammation and aging for quite 214 00:12:23,800 --> 00:12:26,640 a long period of time. So actually I used to work in 215 00:12:26,640 --> 00:12:29,560 the the field of inherited bone marrow failure, specifically 216 00:12:29,560 --> 00:12:32,320 looking at Franconi anemia. And it was very clear from the 217 00:12:32,320 --> 00:12:35,960 literature that there was this link between inflammation and 218 00:12:36,240 --> 00:12:38,560 bone marrow failure in that setting. 219 00:12:39,080 --> 00:12:41,840 And so this kind of piqued my interest and I was working on 220 00:12:41,840 --> 00:12:43,880 this in the setting of Franconi anemia. 221 00:12:43,880 --> 00:12:48,480 And it really was kind of one of these Eureka moments when I 222 00:12:48,480 --> 00:12:51,320 started working in Heidelberg and saw the work of Andreas 223 00:12:51,320 --> 00:12:55,680 Trump, who showed that these pro inflammatory cytokines can push 224 00:12:55,680 --> 00:13:01,240 stem cells from this long term quiescent or dormant state into 225 00:13:01,240 --> 00:13:05,000 active cell cycle. So now this idea that cell 226 00:13:05,000 --> 00:13:08,880 division is something that drives loss of functionality, it 227 00:13:08,880 --> 00:13:12,120 kind of made sense to me that potentially the inflammation was 228 00:13:12,120 --> 00:13:15,440 making the stem cells divide. And the more inflammation you 229 00:13:15,440 --> 00:13:19,600 would be exposed to, the more those cells would diversify away 230 00:13:19,600 --> 00:13:22,000 from the original cells that you'd got in there. 231 00:13:23,040 --> 00:13:26,560 So in that context, we start to carry out studies. 232 00:13:26,560 --> 00:13:29,440 We had some very elegant mouse models that have been developed 233 00:13:29,440 --> 00:13:33,760 by Andreas Trump and and her and Marika S, as a former postdoc in 234 00:13:33,760 --> 00:13:36,160 his laboratory, is now also a group leader and works in the 235 00:13:36,160 --> 00:13:38,760 next office to me. And these mouse models allowed 236 00:13:38,760 --> 00:13:42,280 you in vivo to pull apart the stem cells that didn't divide so 237 00:13:42,280 --> 00:13:45,240 much with the passage of time. You can even see dormant cells 238 00:13:45,240 --> 00:13:48,000 that barely divide during the lifetime of a mouse. 239 00:13:48,000 --> 00:13:51,640 And actually, interestingly, these cells retain a young 240 00:13:51,680 --> 00:13:54,320 phenotype. And then the stem cells that 241 00:13:54,320 --> 00:13:57,840 have divided more, and now you can expose to inflammation. 242 00:13:58,320 --> 00:14:01,080 And the interesting thing is that even though all these stem 243 00:14:01,080 --> 00:14:04,440 cells are exposed to inflammation, it's the ones that 244 00:14:04,440 --> 00:14:07,320 divide that lose the functional potency. 245 00:14:07,920 --> 00:14:12,360 And the ones that can still stay dormant, they still retain their 246 00:14:12,360 --> 00:14:17,200 functional potency. So importantly, this kind of 247 00:14:17,280 --> 00:14:20,920 brings into question this concept of self renewal, this 248 00:14:20,920 --> 00:14:24,920 idea that a stem cell can make a perfect stem cell door to cell. 249 00:14:25,720 --> 00:14:29,080 And I think one of the take home messages from our work is stem 250 00:14:29,080 --> 00:14:33,880 cells can divide to make more stem cells, but very, very 251 00:14:33,880 --> 00:14:37,600 rarely do those stem cells have the same functional potency as 252 00:14:37,600 --> 00:14:40,560 the parent. So you end up in a situation 253 00:14:40,560 --> 00:14:44,840 where each round of inflammation draws some of the stem cell pool 254 00:14:44,960 --> 00:14:49,880 into cycle, it loses functional potency and it never gets 255 00:14:49,880 --> 00:14:53,160 regained by those cells. So it's irreversible. 256 00:14:54,040 --> 00:14:59,480 And what that means is that now later on week, a month, a year 257 00:14:59,480 --> 00:15:02,640 later, if you have another, another stimulus that draws more 258 00:15:02,640 --> 00:15:06,000 of the stem cell pool into cycle and they lose their functional 259 00:15:06,000 --> 00:15:11,000 potency, you can now have an additive or cumulative depletion 260 00:15:11,000 --> 00:15:13,800 of the stem cell pool. And this to me is a really 261 00:15:13,800 --> 00:15:18,280 attractive mechanism to a kind of explain clonal succession 262 00:15:18,520 --> 00:15:22,000 with aging, but also clonal contraction during aging. 263 00:15:22,440 --> 00:15:26,920 But this is happening in the HSC cells, so it's been driven by 264 00:15:26,920 --> 00:15:28,760 the environment of an inflammation. 265 00:15:29,440 --> 00:15:32,960 How much of this increased vulnerability comes from the? 266 00:15:33,560 --> 00:15:35,280 Yeah. So what I was talking about 267 00:15:35,280 --> 00:15:38,560 there was really with the focus on the hematopoietic stem cell 268 00:15:38,560 --> 00:15:41,680 compartment. So trying to enumerate the 269 00:15:41,680 --> 00:15:46,320 number of hematopoietic stem cells actually in these mice 270 00:15:46,320 --> 00:15:50,040 that we've exposed to multiple rounds of inflammation where 271 00:15:50,040 --> 00:15:51,760 we've depleted the stem cell pool. 272 00:15:52,400 --> 00:15:55,760 Actually, hematopoiesis looks relatively normal in that 273 00:15:55,760 --> 00:15:57,520 setting. And I think that's also what you 274 00:15:57,520 --> 00:16:00,240 would predict based on what we know of how things work in 275 00:16:00,240 --> 00:16:01,920 humans. Yeah, so we do. 276 00:16:01,920 --> 00:16:05,000 We are exposed to multiple rounds of infection, sometimes 277 00:16:05,000 --> 00:16:09,240 pretty severe infection, and we always kind of bounce back to 278 00:16:09,240 --> 00:16:13,160 these levels where we're able to maintain homeostatic production 279 00:16:13,160 --> 00:16:16,120 of blood cells. And interestingly, there are 280 00:16:16,120 --> 00:16:20,720 studies of very old humans, so centenarians who've had the a 281 00:16:20,720 --> 00:16:23,040 massive contraction of their stem cell pool. 282 00:16:23,040 --> 00:16:25,640 Maybe they've got one or two stem cells that are supporting 283 00:16:25,640 --> 00:16:29,720 hematopoiesis, but they're still able to maintain normal levels 284 00:16:29,760 --> 00:16:32,360 of peripheral blood cells. And I think that's kind of an 285 00:16:32,360 --> 00:16:35,160 important concept because although the stem cell pool 286 00:16:35,320 --> 00:16:39,760 contracts, we still have a system that that has evolved in 287 00:16:39,760 --> 00:16:42,840 order to be able to sustain blood production during life. 288 00:16:42,840 --> 00:16:46,040 So these mice generally their blood cell systems look pretty 289 00:16:46,040 --> 00:16:49,200 normal. I think that what's interesting 290 00:16:49,200 --> 00:16:52,920 is when you start to dial in things like these mutations that 291 00:16:52,920 --> 00:16:55,200 you see in in clonal hematopoiesis. 292 00:16:55,200 --> 00:16:58,360 So for example, the group of Grand Chalon and Catherine King 293 00:16:58,360 --> 00:17:01,480 have done very elegant studies looking at DNMT 3, a loss of 294 00:17:01,480 --> 00:17:05,200 function and then driving, adding on top of that bacterial 295 00:17:05,200 --> 00:17:07,800 infection. And you can see this expansion 296 00:17:07,880 --> 00:17:10,720 of the mutant clones in that particular setting. 297 00:17:10,880 --> 00:17:15,040 And in the mice, I don't think that there's a strong phenotype 298 00:17:15,160 --> 00:17:17,760 illustrated at the level that the mature blood cells. 299 00:17:17,760 --> 00:17:22,040 Yeah, and maybe that relates to, you know, missing ingredients in 300 00:17:22,040 --> 00:17:25,720 terms of our mouse models that are important in humans. 301 00:17:25,880 --> 00:17:29,920 So most of the time our mice are kept in specified pathogen free 302 00:17:29,920 --> 00:17:33,120 conditions where they're not readily changed, challenged by 303 00:17:33,120 --> 00:17:35,640 pathogens. Maybe if we depleted the bloods, 304 00:17:35,920 --> 00:17:40,760 the stem cell pool sufficiently, then added on top some kind of 305 00:17:40,760 --> 00:17:44,160 infection, perhaps those mice would have a shorter lifespan or 306 00:17:44,160 --> 00:17:46,680 not be able to respond so much to those challenges. 307 00:17:47,040 --> 00:17:50,080 But yeah, in, in, in that setting, again, we've been very 308 00:17:50,080 --> 00:17:54,200 much focused on the stem cell pool rather than any age 309 00:17:54,200 --> 00:17:57,160 associated phenotypes in terms of mature blood cells. 310 00:17:57,400 --> 00:17:59,520 You mentioned clonal hematopoiesis. 311 00:18:00,440 --> 00:18:03,960 How does inflammation influence the clonal expansion? 312 00:18:05,280 --> 00:18:06,880 Yeah. Again, I think that that's a 313 00:18:06,880 --> 00:18:09,600 really interesting and important question. 314 00:18:10,040 --> 00:18:14,160 I, I think that we're only now starting to delve into that to 315 00:18:14,160 --> 00:18:16,920 some extent. So I mentioned before the work 316 00:18:16,920 --> 00:18:19,120 of people like Grant Challon and Catherine King. 317 00:18:19,160 --> 00:18:22,080 I think there's also been very exciting data recently from the 318 00:18:22,080 --> 00:18:26,640 likes of her Parish Bias and John Dick looking at human 319 00:18:26,640 --> 00:18:30,880 samples, where indeed it does look like exposures to 320 00:18:30,880 --> 00:18:35,440 inflammatory stimuli and maybe infectious stimuli provide a 321 00:18:35,440 --> 00:18:41,120 selection advantage for for example DNMT 3A mutant clones. 322 00:18:41,840 --> 00:18:46,040 So I think it isn't 100% clear why that is the case, but I 323 00:18:46,040 --> 00:18:50,480 think it data does seem to indicate that with each exposure 324 00:18:50,480 --> 00:18:56,520 to inflammation and infection, you program the epigenome 325 00:18:56,800 --> 00:19:00,480 somewhat along the lines of trained immunity where if you 326 00:19:01,560 --> 00:19:04,880 execute a specific transcriptional program over and 327 00:19:04,880 --> 00:19:08,400 over again, there's a residual memory of this in the stem 328 00:19:08,400 --> 00:19:11,720 cells. And I guess the idea is that if 329 00:19:11,720 --> 00:19:14,560 you now go in again with the same kind of stimulus like an 330 00:19:14,560 --> 00:19:18,960 infection that the cells will be able to more rapidly respond or 331 00:19:19,040 --> 00:19:23,200 be able to respond with a a greater magnitude effect that 332 00:19:23,200 --> 00:19:25,160 same stimulus or something similar. 333 00:19:25,640 --> 00:19:28,600 Or perhaps there's a little bit of a cost towards this because 334 00:19:28,720 --> 00:19:31,520 hematopoietic stem cells have to be good all rounders that are 335 00:19:31,520 --> 00:19:33,840 able to produce multiple different cell types and carry 336 00:19:33,840 --> 00:19:37,480 different functions. So maybe if you program them in 337 00:19:37,480 --> 00:19:41,960 a particular direction, then maybe they don't perform so well 338 00:19:42,200 --> 00:19:45,720 as a stem cell. Now if you look at the DNNT 3A 339 00:19:45,720 --> 00:19:51,240 setting, I think what's clear is that now these cells have less 340 00:19:51,240 --> 00:19:54,760 of this priming effect when you give the inflammatory stemics. 341 00:19:54,760 --> 00:19:58,000 So they have less of a epigenetic memory of that 342 00:19:58,000 --> 00:20:01,600 exposure. And it's tempting to speculate 343 00:20:01,600 --> 00:20:05,280 that that might actually facilitate these cells still 344 00:20:05,280 --> 00:20:07,520 maintaining their stem cell attributes. 345 00:20:08,080 --> 00:20:12,240 And maybe being able to be resistant had this inflammatory 346 00:20:12,240 --> 00:20:14,960 challenge. So I think it's a little bit 347 00:20:14,960 --> 00:20:20,000 different the the idea that, for example, a stem cell would get a 348 00:20:20,000 --> 00:20:23,720 pro proliferative mutation and now this mutation would give a 349 00:20:23,720 --> 00:20:27,960 positive selection advantage and allow it to expand more rapidly 350 00:20:28,400 --> 00:20:30,120 than the rest of the stem cell pool. 351 00:20:30,480 --> 00:20:33,560 But rather this idea that there's a constant ongoing 352 00:20:33,560 --> 00:20:37,880 attrition of the stem cell compartment and maybe things 353 00:20:37,880 --> 00:20:42,440 like the DNMT 3A mutations, T mutations might make the cells 354 00:20:42,440 --> 00:20:46,160 more resistant to that. So they're fitter in that 355 00:20:46,160 --> 00:20:49,760 context. So do you think any of these 356 00:20:49,840 --> 00:20:53,920 features can be targeted for delaying or mitigating? 357 00:20:53,920 --> 00:20:58,000 Aging, yeah, potentially. I mean, I think that I'm not a 358 00:20:58,000 --> 00:21:02,880 big fan of this idea of rejuvenation. 359 00:21:04,080 --> 00:21:07,560 I mean, it would be nice, yeah. But I think it's kind of hard to 360 00:21:07,560 --> 00:21:09,840 think about exactly how that would work. 361 00:21:10,360 --> 00:21:13,800 So even if you look in these extreme settings where people 362 00:21:13,800 --> 00:21:16,880 formally demonstrate this. So I'm talking about things like 363 00:21:16,880 --> 00:21:20,320 if you put reprogramming factors into cells that you've taken 364 00:21:20,320 --> 00:21:23,640 from an elderly Organism, or actually if you generate A 365 00:21:23,640 --> 00:21:26,280 transgenic mouse where you can transiently induce the 366 00:21:26,280 --> 00:21:31,360 reprogramming factors. And now you partially reset the 367 00:21:31,360 --> 00:21:36,360 epigenome and the mice by and large look younger and actually 368 00:21:36,360 --> 00:21:39,880 have improved health metrics. So first of all, it it's 369 00:21:39,920 --> 00:21:42,640 obviously very difficult to think about exactly how you're 370 00:21:42,640 --> 00:21:44,920 going to facilitate that in the setting of a human. 371 00:21:45,560 --> 00:21:49,280 But even when we start to think about fundamental mechanisms 372 00:21:49,280 --> 00:21:52,200 that are taking place there, I don't think you're completely 373 00:21:52,200 --> 00:21:55,520 resetting the clock on the changes that occurred during 374 00:21:55,520 --> 00:21:58,800 aging. So obvious example, the somatic 375 00:21:58,800 --> 00:22:01,680 mutations that have been acquired, they're still going to 376 00:22:01,680 --> 00:22:05,640 be there. So I refer back now to the 377 00:22:05,640 --> 00:22:08,680 comment that I made towards the beginning of the podcast where I 378 00:22:08,680 --> 00:22:12,720 was talking about aging probably being multiple different things 379 00:22:12,720 --> 00:22:14,520 and multiple different destinations. 380 00:22:15,000 --> 00:22:18,880 Now if you ask me, do I think you can correct some of these 381 00:22:18,880 --> 00:22:22,960 clinically relevant phenotypes, the answer to that will be yes. 382 00:22:23,720 --> 00:22:27,800 And I think that it's not going to be some kind of panacea for 383 00:22:27,800 --> 00:22:31,240 all aging phenotypes, but maybe in a more targeted fashion we 384 00:22:31,240 --> 00:22:34,920 could think about addressing things like immune dysfunction, 385 00:22:34,960 --> 00:22:37,520 so compromised adaptive immune system. 386 00:22:37,600 --> 00:22:41,560 There are some of these hand hematopoietic rejuvenation 387 00:22:41,560 --> 00:22:43,080 strategies that have been put out there. 388 00:22:43,080 --> 00:22:45,760 So things like dietary restriction would be one of 389 00:22:45,760 --> 00:22:48,680 them. Mtor inhibition, this idea of 390 00:22:48,680 --> 00:22:51,960 depleting myeloid biased aged stem cells. 391 00:22:51,960 --> 00:22:54,080 These are all things that have been put forward. 392 00:22:54,160 --> 00:22:57,920 I think it'll be interesting to see how robustly, robustly those 393 00:22:57,920 --> 00:23:00,840 kind of findings can be recapitulated across broad 394 00:23:00,840 --> 00:23:04,520 different animal models. I think that they're clearly 395 00:23:04,560 --> 00:23:08,960 benefits to things like dietary restriction, but rejuvenation? 396 00:23:08,960 --> 00:23:12,440 I'm not so sure that that's a reality, but I will be very 397 00:23:12,440 --> 00:23:16,080 happy to be proved wrong. Is there anything else that you 398 00:23:16,080 --> 00:23:17,440 would like to add for the audience? 399 00:23:17,880 --> 00:23:20,320 So I think that one of the things that's important if you 400 00:23:20,320 --> 00:23:26,160 want to do research in this area is, is to really kind of take 401 00:23:26,160 --> 00:23:29,240 take notice of the literature that's out there, but really 402 00:23:29,240 --> 00:23:32,840 kind of take a step back and think about broadly, if we're 403 00:23:32,840 --> 00:23:37,080 looking at aging, if we if we kind of think about what we 404 00:23:37,080 --> 00:23:41,080 know, what's obvious about kind of human aging now that people 405 00:23:41,080 --> 00:23:45,520 develop very kind of vastly different phenotypes at 406 00:23:45,520 --> 00:23:48,960 different rates. That's kind of an important 407 00:23:48,960 --> 00:23:52,600 thing, I think. And to purely study aging in the 408 00:23:52,600 --> 00:23:57,200 context of old versus young. But this idea that we'll be able 409 00:23:57,200 --> 00:24:01,160 to identify mechanisms that define what happens to every 410 00:24:01,160 --> 00:24:03,840 individual, that seems kind of unlikely. 411 00:24:04,400 --> 00:24:07,440 Maybe we need to carry out the entire analysis and design our 412 00:24:07,440 --> 00:24:10,480 experiments in in slightly different ways to try and 413 00:24:11,600 --> 00:24:15,240 somehow understand this diversification process. 414 00:24:15,240 --> 00:24:20,720 Because I think if we can try to understand the basic mechanism 415 00:24:20,720 --> 00:24:25,000 that underlies this collapse of the system, then we can actually 416 00:24:25,240 --> 00:24:29,480 do meaningful research to think about whether it is possible to 417 00:24:29,560 --> 00:24:33,760 delay that or reverse some of the features, or whether that 418 00:24:33,760 --> 00:24:36,520 doesn't make any sense at all. And we need to think about 419 00:24:36,520 --> 00:24:38,640 things like cell replacement therapies. 420 00:24:38,640 --> 00:24:42,480 Is the opportunity to keep the system functioning for as long 421 00:24:42,480 --> 00:24:45,280 as we need it to? So if I understand well, there's 422 00:24:46,080 --> 00:24:48,800 this diversity that we knew already. 423 00:24:48,800 --> 00:24:52,440 We know that, you know, during a lifetime very different things 424 00:24:52,440 --> 00:24:56,000 happen, one person compared to another and that causes aging. 425 00:24:56,480 --> 00:24:59,800 But you're saying that there's also this huge diversity in the 426 00:24:59,800 --> 00:25:03,880 internal regular processes in the cells. 427 00:25:04,040 --> 00:25:08,880 So the diversity comes from everywhere in the aging process, 428 00:25:08,880 --> 00:25:11,680 not only from outside of the body, but also from our 429 00:25:11,680 --> 00:25:13,800 biological processes. They're different from one 430 00:25:13,800 --> 00:25:16,240 person to the other. So it's really hard to tackle. 431 00:25:17,200 --> 00:25:19,480 Yeah, process. Although I mean, one of the 432 00:25:19,480 --> 00:25:22,680 things that we often kind of debate within the group is we're 433 00:25:22,960 --> 00:25:25,200 spending a lot of time and effort and trying to 434 00:25:25,200 --> 00:25:31,040 characterize this stochasticity in terms of the diversification 435 00:25:31,040 --> 00:25:33,280 of the function of the hematopoietic system. 436 00:25:33,360 --> 00:25:36,680 And there I think the, the really exciting thing is not 437 00:25:36,680 --> 00:25:39,800 just kind of characterize the level of an entire system, but 438 00:25:39,800 --> 00:25:44,760 be able to dial into individual cells and clonal systems that 439 00:25:44,760 --> 00:25:48,000 occur within the hematopoietic compartment to try to understand 440 00:25:48,560 --> 00:25:53,240 what's happening there. And so, yes, that there is this 441 00:25:53,240 --> 00:25:57,800 kind of really complex pattern of diversification, but the 442 00:25:57,800 --> 00:26:01,520 debate that we have a lot in the laboratory is whether some kind 443 00:26:01,520 --> 00:26:05,960 of extrinsic pressure can actually drive you towards a 444 00:26:05,960 --> 00:26:10,320 more coherent system or whether it will actually, just as you 445 00:26:10,320 --> 00:26:13,120 were saying, make the diversification even broader. 446 00:26:13,120 --> 00:26:16,280 So I think that this is kind of next set of experiments that 447 00:26:16,280 --> 00:26:21,040 we'd like to approach. But yeah, the problem is 448 00:26:21,040 --> 00:26:23,200 studying aging. It takes a long time. 449 00:26:23,200 --> 00:26:27,080 Well, thank you very much for this fascinating conversation. 450 00:26:27,080 --> 00:26:28,560 Thank you for being with us today. 451 00:26:29,480 --> 00:26:33,280 Thank you, I've enjoyed it. Thank you to the audience for 452 00:26:33,280 --> 00:26:35,680 listening. If you enjoyed this episode, 453 00:26:35,720 --> 00:26:38,760 please like it and subscribe to our channel. 454 00:26:38,880 --> 00:26:41,720 Stay tuned for more episodes of EHM Blog.