My simple model of aging is a shifting balance between bounded, specialized repair mechanisms and unbounded, combinatorial forms of damage.
Organisms begin life as single cells. They undergo selection for a viably low level of starting damage beginning with mitosis or meiosis.
At all times, the environment and the byproducts of normal intracellular processes cause damage to cells and their surroundings. Many of these factors cause predictable forms of damage. UV light, for example, causes two specific types of DNA lesions, and cells have specialized DNA repair mechanisms to fix them. If all damage to cells and their environments was of a type and amount that was within repair capacities of organisms, then aging would not occur.
Repair is limited to specific, evolved capacities maintained by organisms to deal with the most fitness-reducing forms of damage they face. Outside these narrow capacities, repair mechanisms are typically useless. In contrast, damage factors have access to the entire energy landscape, and so they have access to an unbounded and combinatorial set of damage vectors that far outstrips the total repair capacity of the organism. We can break this down into two components.
Unusual damage: Common individual factors with mostly predictable effects can sometimes inflict unusual forms of damage, for which are uncommon enough that it is not evolutionarily tractable for organisms to evolve and maintain a repair infrastructure. Uncommong individual damage factors can likewise inflict forms of damage for which no repair mechanism exists.
Damage systems: Individual damage factors can have emergent damaging effects in combination, and the number of potential combinations means that this becomes another source of damage beyond the bounds of existing repair infrastructure. Forms of damage may accumulate at a faster rate than they can be repaired if a system of damage factors effects a positive feedback loop, inhibits repair mechanisms, or evades them.
How does life survive in this context, where damage factors always have the advantage over repair mechanisms? First of all, it frequently doesn’t. Nonviable eggs and sperm or unicellular organisms die silently. The biological machinery of reproduction, including the repair and protection mechanisms that shelter it, do not always successfully survive to the time of replication, and if they do not, that line dies out.
When life does survive to maturity, it by definition has been selected for viability—successful shelter by normal prevention and repair mechanisms from excessive unusual or combinatorial damage.
What does this model predict?
An “irreparable damage landscape” will exist beyond the boundaries of what a species’ repair mechanisms can fix; these repair mechanisms are dictated by evolutionary constraints. There will be common and uncommon forms of irreparable damage. Biomedicine can supplement normal repair mechanisms with artificial ones. There will be much that science and engineering can accomplish to push the “repair boundary” further out than what evolution alone permits.
Some specific forms of irreparable damage will be central nodes on a damage DAG, giving rise to diverse damaging downstream effects that may be the direct causes of morbidity and mortality. Tackling these central nodes will result in a higher payoff in terms of healthspan and lifespan than tackling the downstream effects. However, there is no single, fundamental cause of the damage associated with aging.
Whether or not longevity interventions are tractable will shed light on the optimizing efficiency of evolution. If evolution is efficient, then longevity interventions will come with steep tradeoffs, require complex feats of engineering, or be forced to address a multitude of minor forms of irreparable damage for which it is not worthwhile for evolution to maintain repair mechanisms. If evolution is inefficient, then we will find many significant damage nodes beyond the repair boundary that have simple solutions, yield major gains in lifespan and healthspan, and prevent or mitigate many diseases.
Longevity research is not fundamentally different from research into known diseases. Cancer, diabetes, and heart disease all have early, late, and pre-detectable stages. Longevity research is really an investigation into the pre-detectable stage of these diseases. When we try to treat late-stage cancer, we are far from longevity research. When we try to understand what activates dormant prostate cancer cells in the bone marrow, we are closer to longevity research. When we study what causes those prostate cancer cells to home to the bone marrow and how they survive there, we are closer still. When we examine what chemical reactions tend to give rise to the genetic mutations leading to prostate cancer, we are even more centrally doing longevity research. There is a temptation to assume that if we trace back the causal pathway far enough, we will find a monocause. This is not so. Many causes may input into a specific node, and a specific node may point out at many other nodes. The arrow of causality can point in two directions as well, or form loops between several nodes.
The bottleneck for longevity research will be the ability to show predictive validity of early damage factors leading to later symptoms. For example, being able to detect a cluster of genetic or chemical changes that will lead to prostate cancer with 80% confidence 5 years before it occurs. This is challenging both because increasingly early predictions have more nodes in the causal link, likely require more data to achieve a given level of predictive validity, require more interventions to correct the problem, and also because the earlier we make the prediction, the slower the feedback loop. Importantly, however, these challenges are relative. Early prediction will be harder than later prediction, but it does not mean that it will be hard. This depends on the efficiency of evolution. If it is a good optimizer for longevity, then the problem of early prediction will be hard. If it is a poor optimizer, we can hope to find some easy wins.
Diversity, noise, and ambiguity in biological systems and our methods of measurement will be key problems in longevity research.
Even if evolution is a good optimizer, we have a much greater ability to control our environments than we did in the recent past. If evolution has optimized our bodies for robustness to environmental stressors that are no longer a serious concern by making tradeoffs against longevity, then we can get alpha by re-engineering our bodies in ways that sacrifice this unnecessary robustness in exchange for optimizing for the most longevity-promoting environments we can create.
Again, longevity research in many cases will look like a specific subtype of normal disease research. My own PI and his wife, among others, examine the pre-metastatic niche. How does a local site become adapted to colonization by metastatic cancer cells? Trace this back further. What initiates that process of adaptation?
Some damage nodes will have so many common and equally important causes that it is not tractable to trace back to their causes and address them. Yet it could be worthwhile if that node also has many downstream effects.
Some biological events may cause damage in some contexts and be necessary for life in others, or both at the same time. The objective is always to repair or prevent damage with an acceptable burden of side effect damage.
All this suggests a simple strategy for doing effective longevity research: trace back diseases to their earliest causes, and see if the cause of a particular disease might be a branch point to multiple other diseases. If you have identified such a branch point, attack it.
Following up based on John’s points about turnover, we can add in a “template decay” aspect to this model. The body has many templates: DNA, of course, but also stem cells, and tissue architectures, possibly among others. Templates offer a highly but not perfectly durable repository of information or, more intuitively in some cases, structural guidance for turnover and regeneration processes.
When damage factors impact the downstream products of various templates (mRNA and proteins, differentiated cells, organs), turnover initiated by the upstream template can mitigate or eliminate the downstream impact. For example, protein damage can be repaired by degredation in proteasomes, differentiated cell dysfunction can be repaired by apoptosis, stem cell proliferation and differentiation into a replacement for the dysfunctional cell. Wounds can be regenerated as long as sufficient local tissue architecture remains intact to guide tissue reconstruction.
But templates cannot themselves be repaired unless there is a higher-order upstream template from which to initiate this repair. A key question is redundancy. DNA has no redundancy within the cell, but stem cells could potentially offer that redundancy at the level of the cell. If one stem cell dies or becomes senescent, can another stem cell copy itself and replace it? Yet we know stem cells age and lose proliferative and differentiation capacity. And this redundancy would only provide a lasting solution if there was a selecting force to regulate compatibility with host architecture instead of turning into cancer. So whatever redundancy they offer, it’s not enough for long-term stem cell health.
For an individual’s DNA sequence at least, durable storage is not a bottleneck. We are challenged to provide durable external repositories for other forms of information, such as tissue architecture and epigenetic information. We also lack adequate capacity to use any such stored information to perform “engineered turnover.”
We might categorize medicine in two categories under this paradigm:
Delay of deterioration, which would encompass much of the entire current medical paradigm. Chemo delays deterioration due to cancer. Vaccines and antibiotics prevent deterioration due to infection. Seatbelts prevent acute deterioration due to injury from car crashes. Low-dose rapamycin may be a general preventative of deterioration, as is exercise and good diet.
Template restoration, which has a few examples in current medicine, such as organ, tissue, and fecal transplants. Gene therapy is a second example. Some surgeries that reposition tissues to facilitate a new equilibrium of well-formed growth is a third example. If it becomes possible to de-age stem cells, or to replace DNA that has mutated with age with the youthful template, these interventions would also be categorizwed as template restoration.
Template replacement. An example is an artificial heart, which is based on a fundamentally different template than a biological heart, and is subject to entirely different deterioration dynamics and restoration possibilities.
The ultimate aim is to apply organized energy from outside the patient’s system, primarily in the form of biomedical interventions, to create a more stringent selection force within the patient’s body for a molecular, cellular, and tissue architecture compatible with long-term health and survival of the patient. In theory, it ought to be possible to make this selection force so stringent that there is no hard limit to the patient’s lifespan.
By this point, “there’s no such thing as a cure for cancer” is a cliche. But I don’t think this is necessarily true. If we can increase the stringency of selection for normal, healthy cells and against the formation of cancer, then we can effectively eliminate it. Our current medicine does not have the ability to target asymptomatic accumulated cellular disorder, the generator of cancer, for repair or replacement. When we can do this, we will have effectively cured cancer, along with a host of other diseases.
De-aging tissue can be conceived of as:
Delivering an intervention to host cells in situ to effect repair, such as a gene therapy that identifies and replaces mutated DNA in host cells with the original sequence.
Extracting healthy cells from the host, proliferating them in vitro, and transplanting them back into the host, either as cells or in the form of engineered tissues.
Replacing tissue with synthetic or cybernetic constructs that accomplish the same function, such as hip replacements, the destination artificial heart, and (someday soon) the bioartificial kidney.
None of this will be easy. Implanted stem cells and gene therapy can both trigger cancer at this stage in our technological development. Our bioproduction capacities are limited—many protocols are limited by our ability to culture a sufficient quantity of cells. But these are all tractable problems with short feedback loops.
As John points out, the key problem is that too many of our resources are not being applied to the right bottlenecks, and there isn’t quite enough of a cohesive blueprint or plan for how all these interventions will come together and result in longevity escape velocity. But at the same time, I tend to be pretty impressed with the research strategy of the lab directors I’ve spoken with.
I think the main key concept missing here is that turnover “fixes” basically any damage in turned-over components by default, even when there’s not a specific mechanism for that damage type. So, protein turnover “fixes” basically any damage to a protein by replacing the whole protein, cell turnover “fixes basically any damage to a cell by replacing the whole cell, etc. And since the vast majority of most multicellular organisms turns over regularly, at multiple scales (e.g. even long-lived cells have most of their individual parts turn over regularly), that means the vast majority of damage gets “fixed” even without a specific mechanism.
The key question is then: which things don’t turn over quickly, and what kind of damage do they accumulate?
It seems like a key question is something like the rate of turnover vs. the rate of damage proliferation. We also need to factor in the potential for damage to inhibit turnover (among other repair mechanisms). After all, turnover is a complex set of processes involving identification, transport, destruction, and elimination of specific structures. The mechanisms by which it is effected are subject to damage and disrepair.
We also need to consider the accuracy of regeneration after turnover. When a cell dies and is replaced, the new cell will not anchor in the exact same position in the ECM. On larger scales, this might average out, but it might also lead to larger anatomical dysfunction. Individuals that survive from meiosis into maturity may do so because they’ve been lucky in avoiding random anatomical fluctuations that kill their less fortunate brethren. If the body does not have adequate mechanisms to maintain precise numbers, arrangements, and structures of organelles, ECM, tissue, and gross anatomy over time, then turnover won’t be able to solve this problem of “structural decay.”
This is to some extent true of the proteins and cells of the body. Malformed cells and proteins are targeted for destruction. So the survivors are similarly selected for compatibility with the body’s damage detection mechanisms, even though the best way of being compatible with those mechanisms is to evade them entirely.
So we have several things to investigate:
Structures with a slower rate of turnover than of damage accumulation
Example: cortical neurons, lens proteins
Forms of damage that impair or evade turnover
Example: mutations/epigenetic changes that eliminate apoptosis receptors in cancer cells, inert protein aggregates that cannot be degraded by the proteasome and accumulate in the cell, preventing turnover of yet other proteins. As a second example of protein evasion of turnover, loss of lysine or methionine would eliminate a site for ubiquitination, impairing the marking of proteins for destruction by the proteasome.
Forms of damage that accelerate damage proliferation
Example: metastatic cancer
Forms of damage that turnover cannot fix, such as structural decay.
Example: perhaps thymic involution? I am not sure, but I am confident that this is a real phenomenon.
That said, I think it’s a very valuable insight to keep in mind that for any form of damage, we always have to ask, “why can’t turnover fix this problem?”
My simple model of aging is a shifting balance between bounded, specialized repair mechanisms and unbounded, combinatorial forms of damage.
Organisms begin life as single cells. They undergo selection for a viably low level of starting damage beginning with mitosis or meiosis.
At all times, the environment and the byproducts of normal intracellular processes cause damage to cells and their surroundings. Many of these factors cause predictable forms of damage. UV light, for example, causes two specific types of DNA lesions, and cells have specialized DNA repair mechanisms to fix them. If all damage to cells and their environments was of a type and amount that was within repair capacities of organisms, then aging would not occur.
Repair is limited to specific, evolved capacities maintained by organisms to deal with the most fitness-reducing forms of damage they face. Outside these narrow capacities, repair mechanisms are typically useless. In contrast, damage factors have access to the entire energy landscape, and so they have access to an unbounded and combinatorial set of damage vectors that far outstrips the total repair capacity of the organism. We can break this down into two components.
Unusual damage: Common individual factors with mostly predictable effects can sometimes inflict unusual forms of damage, for which are uncommon enough that it is not evolutionarily tractable for organisms to evolve and maintain a repair infrastructure. Uncommong individual damage factors can likewise inflict forms of damage for which no repair mechanism exists.
Damage systems: Individual damage factors can have emergent damaging effects in combination, and the number of potential combinations means that this becomes another source of damage beyond the bounds of existing repair infrastructure. Forms of damage may accumulate at a faster rate than they can be repaired if a system of damage factors effects a positive feedback loop, inhibits repair mechanisms, or evades them.
How does life survive in this context, where damage factors always have the advantage over repair mechanisms? First of all, it frequently doesn’t. Nonviable eggs and sperm or unicellular organisms die silently. The biological machinery of reproduction, including the repair and protection mechanisms that shelter it, do not always successfully survive to the time of replication, and if they do not, that line dies out.
When life does survive to maturity, it by definition has been selected for viability—successful shelter by normal prevention and repair mechanisms from excessive unusual or combinatorial damage.
What does this model predict?
An “irreparable damage landscape” will exist beyond the boundaries of what a species’ repair mechanisms can fix; these repair mechanisms are dictated by evolutionary constraints. There will be common and uncommon forms of irreparable damage. Biomedicine can supplement normal repair mechanisms with artificial ones. There will be much that science and engineering can accomplish to push the “repair boundary” further out than what evolution alone permits.
Some specific forms of irreparable damage will be central nodes on a damage DAG, giving rise to diverse damaging downstream effects that may be the direct causes of morbidity and mortality. Tackling these central nodes will result in a higher payoff in terms of healthspan and lifespan than tackling the downstream effects. However, there is no single, fundamental cause of the damage associated with aging.
Whether or not longevity interventions are tractable will shed light on the optimizing efficiency of evolution. If evolution is efficient, then longevity interventions will come with steep tradeoffs, require complex feats of engineering, or be forced to address a multitude of minor forms of irreparable damage for which it is not worthwhile for evolution to maintain repair mechanisms. If evolution is inefficient, then we will find many significant damage nodes beyond the repair boundary that have simple solutions, yield major gains in lifespan and healthspan, and prevent or mitigate many diseases.
Longevity research is not fundamentally different from research into known diseases. Cancer, diabetes, and heart disease all have early, late, and pre-detectable stages. Longevity research is really an investigation into the pre-detectable stage of these diseases. When we try to treat late-stage cancer, we are far from longevity research. When we try to understand what activates dormant prostate cancer cells in the bone marrow, we are closer to longevity research. When we study what causes those prostate cancer cells to home to the bone marrow and how they survive there, we are closer still. When we examine what chemical reactions tend to give rise to the genetic mutations leading to prostate cancer, we are even more centrally doing longevity research. There is a temptation to assume that if we trace back the causal pathway far enough, we will find a monocause. This is not so. Many causes may input into a specific node, and a specific node may point out at many other nodes. The arrow of causality can point in two directions as well, or form loops between several nodes.
The bottleneck for longevity research will be the ability to show predictive validity of early damage factors leading to later symptoms. For example, being able to detect a cluster of genetic or chemical changes that will lead to prostate cancer with 80% confidence 5 years before it occurs. This is challenging both because increasingly early predictions have more nodes in the causal link, likely require more data to achieve a given level of predictive validity, require more interventions to correct the problem, and also because the earlier we make the prediction, the slower the feedback loop. Importantly, however, these challenges are relative. Early prediction will be harder than later prediction, but it does not mean that it will be hard. This depends on the efficiency of evolution. If it is a good optimizer for longevity, then the problem of early prediction will be hard. If it is a poor optimizer, we can hope to find some easy wins.
Diversity, noise, and ambiguity in biological systems and our methods of measurement will be key problems in longevity research.
Even if evolution is a good optimizer, we have a much greater ability to control our environments than we did in the recent past. If evolution has optimized our bodies for robustness to environmental stressors that are no longer a serious concern by making tradeoffs against longevity, then we can get alpha by re-engineering our bodies in ways that sacrifice this unnecessary robustness in exchange for optimizing for the most longevity-promoting environments we can create.
Again, longevity research in many cases will look like a specific subtype of normal disease research. My own PI and his wife, among others, examine the pre-metastatic niche. How does a local site become adapted to colonization by metastatic cancer cells? Trace this back further. What initiates that process of adaptation?
Some damage nodes will have so many common and equally important causes that it is not tractable to trace back to their causes and address them. Yet it could be worthwhile if that node also has many downstream effects.
Some biological events may cause damage in some contexts and be necessary for life in others, or both at the same time. The objective is always to repair or prevent damage with an acceptable burden of side effect damage.
All this suggests a simple strategy for doing effective longevity research: trace back diseases to their earliest causes, and see if the cause of a particular disease might be a branch point to multiple other diseases. If you have identified such a branch point, attack it.
Following up based on John’s points about turnover, we can add in a “template decay” aspect to this model. The body has many templates: DNA, of course, but also stem cells, and tissue architectures, possibly among others. Templates offer a highly but not perfectly durable repository of information or, more intuitively in some cases, structural guidance for turnover and regeneration processes.
When damage factors impact the downstream products of various templates (mRNA and proteins, differentiated cells, organs), turnover initiated by the upstream template can mitigate or eliminate the downstream impact. For example, protein damage can be repaired by degredation in proteasomes, differentiated cell dysfunction can be repaired by apoptosis, stem cell proliferation and differentiation into a replacement for the dysfunctional cell. Wounds can be regenerated as long as sufficient local tissue architecture remains intact to guide tissue reconstruction.
But templates cannot themselves be repaired unless there is a higher-order upstream template from which to initiate this repair. A key question is redundancy. DNA has no redundancy within the cell, but stem cells could potentially offer that redundancy at the level of the cell. If one stem cell dies or becomes senescent, can another stem cell copy itself and replace it? Yet we know stem cells age and lose proliferative and differentiation capacity. And this redundancy would only provide a lasting solution if there was a selecting force to regulate compatibility with host architecture instead of turning into cancer. So whatever redundancy they offer, it’s not enough for long-term stem cell health.
For an individual’s DNA sequence at least, durable storage is not a bottleneck. We are challenged to provide durable external repositories for other forms of information, such as tissue architecture and epigenetic information. We also lack adequate capacity to use any such stored information to perform “engineered turnover.”
We might categorize medicine in two categories under this paradigm:
Delay of deterioration, which would encompass much of the entire current medical paradigm. Chemo delays deterioration due to cancer. Vaccines and antibiotics prevent deterioration due to infection. Seatbelts prevent acute deterioration due to injury from car crashes. Low-dose rapamycin may be a general preventative of deterioration, as is exercise and good diet.
Template restoration, which has a few examples in current medicine, such as organ, tissue, and fecal transplants. Gene therapy is a second example. Some surgeries that reposition tissues to facilitate a new equilibrium of well-formed growth is a third example. If it becomes possible to de-age stem cells, or to replace DNA that has mutated with age with the youthful template, these interventions would also be categorizwed as template restoration.
Template replacement. An example is an artificial heart, which is based on a fundamentally different template than a biological heart, and is subject to entirely different deterioration dynamics and restoration possibilities.
The ultimate aim is to apply organized energy from outside the patient’s system, primarily in the form of biomedical interventions, to create a more stringent selection force within the patient’s body for a molecular, cellular, and tissue architecture compatible with long-term health and survival of the patient. In theory, it ought to be possible to make this selection force so stringent that there is no hard limit to the patient’s lifespan.
By this point, “there’s no such thing as a cure for cancer” is a cliche. But I don’t think this is necessarily true. If we can increase the stringency of selection for normal, healthy cells and against the formation of cancer, then we can effectively eliminate it. Our current medicine does not have the ability to target asymptomatic accumulated cellular disorder, the generator of cancer, for repair or replacement. When we can do this, we will have effectively cured cancer, along with a host of other diseases.
De-aging tissue can be conceived of as:
Delivering an intervention to host cells in situ to effect repair, such as a gene therapy that identifies and replaces mutated DNA in host cells with the original sequence.
Extracting healthy cells from the host, proliferating them in vitro, and transplanting them back into the host, either as cells or in the form of engineered tissues.
Replacing tissue with synthetic or cybernetic constructs that accomplish the same function, such as hip replacements, the destination artificial heart, and (someday soon) the bioartificial kidney.
None of this will be easy. Implanted stem cells and gene therapy can both trigger cancer at this stage in our technological development. Our bioproduction capacities are limited—many protocols are limited by our ability to culture a sufficient quantity of cells. But these are all tractable problems with short feedback loops.
As John points out, the key problem is that too many of our resources are not being applied to the right bottlenecks, and there isn’t quite enough of a cohesive blueprint or plan for how all these interventions will come together and result in longevity escape velocity. But at the same time, I tend to be pretty impressed with the research strategy of the lab directors I’ve spoken with.
I think the main key concept missing here is that turnover “fixes” basically any damage in turned-over components by default, even when there’s not a specific mechanism for that damage type. So, protein turnover “fixes” basically any damage to a protein by replacing the whole protein, cell turnover “fixes basically any damage to a cell by replacing the whole cell, etc. And since the vast majority of most multicellular organisms turns over regularly, at multiple scales (e.g. even long-lived cells have most of their individual parts turn over regularly), that means the vast majority of damage gets “fixed” even without a specific mechanism.
The key question is then: which things don’t turn over quickly, and what kind of damage do they accumulate?
It seems like a key question is something like the rate of turnover vs. the rate of damage proliferation. We also need to factor in the potential for damage to inhibit turnover (among other repair mechanisms). After all, turnover is a complex set of processes involving identification, transport, destruction, and elimination of specific structures. The mechanisms by which it is effected are subject to damage and disrepair.
We also need to consider the accuracy of regeneration after turnover. When a cell dies and is replaced, the new cell will not anchor in the exact same position in the ECM. On larger scales, this might average out, but it might also lead to larger anatomical dysfunction. Individuals that survive from meiosis into maturity may do so because they’ve been lucky in avoiding random anatomical fluctuations that kill their less fortunate brethren. If the body does not have adequate mechanisms to maintain precise numbers, arrangements, and structures of organelles, ECM, tissue, and gross anatomy over time, then turnover won’t be able to solve this problem of “structural decay.”
This is to some extent true of the proteins and cells of the body. Malformed cells and proteins are targeted for destruction. So the survivors are similarly selected for compatibility with the body’s damage detection mechanisms, even though the best way of being compatible with those mechanisms is to evade them entirely.
So we have several things to investigate:
Structures with a slower rate of turnover than of damage accumulation
Example: cortical neurons, lens proteins
Forms of damage that impair or evade turnover
Example: mutations/epigenetic changes that eliminate apoptosis receptors in cancer cells, inert protein aggregates that cannot be degraded by the proteasome and accumulate in the cell, preventing turnover of yet other proteins. As a second example of protein evasion of turnover, loss of lysine or methionine would eliminate a site for ubiquitination, impairing the marking of proteins for destruction by the proteasome.
Forms of damage that accelerate damage proliferation
Example: metastatic cancer
Forms of damage that turnover cannot fix, such as structural decay.
Example: perhaps thymic involution? I am not sure, but I am confident that this is a real phenomenon.
That said, I think it’s a very valuable insight to keep in mind that for any form of damage, we always have to ask, “why can’t turnover fix this problem?”