Each protein also has to be analyzed in and of itself, b/c upstream of each protein contains numerous alternative splicing variants, and proteins with more splicing variants should presumably be more susceptible to mis-translation than proteins with fewer splicing variants [splicesome function also decreases with age—see william mair on this, so we need a whole discussion on splicesomes, especially as to how they’re important for important protein complexes on the ETC].
Proteins also have different variants between species (eg bowhead whales and kakapos have hypofunctioning p53). They have different half-lives in the cell—some of them have rapid turnover, and some of them (especially the neuronal proteins are extremely long-lived proteins). The extremely long-lived proteins (like nuclear pore complexes or others at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500981/ ) do not go through “degradation/recycling” as frequently as short-lived proteins, so it may be that their rate of damage is not necessarily reduced AS MUCH by increases in autophagy [THIS HAS TO BE MAPPED—there is a lot of waste that continues to accumulate in the cell when it can’t be dumped out into the bloodstream/kidneys, and glomeular filtration rate declines with age].
We have to map out which proteins are **CONTROLLERS** of the aging rate, such as protein-repair enzymes [ https://www.sciencedirect.com/science/article/abs/pii/S0098299712001276 ], DNA damage sensing/repair enzymes, Nrf2/antioxidant response elements, and stabilizing proteins like histones [loss of the histone subunits often accelerates aging of the genome by exposing more DNA as unstable euchromatin where it is in more positions to be damaged]. [note i dont include mTOR complex here b/c mTOR reduction is easy but also b/c mTOR doesn’t inherently *damage* the cell]
That article listing long-lived proteins is a handy one. I’m highly suspicious of a lot of those; radioisotope methods are gold-standard but a large chunk of the listed results are based on racemization and other chemical characteristics, which I don’t trust nearly as much. That lack of trust is based more on priors than deep knowledge at this point, though; I’ll have to dig more into it in the future.
I don’t think “controllers of the aging rate” are quite the right place to focus; there’s too many of them. The things I’ve been calling “root causes” should be less numerous, and “controllers of the aging rate” would be exactly the things which are upstream of those root causes—i.e. things which cause the root causes to accumulate faster/slower over the course of life. (Side note: I think using the phrase “root cause” has been throwing a lot of people off; I’m considering switching to “mediator of history”, i.e. things which mediate the effect of the aged organism’s history on its current state.)
If you had the perfect bioinformatics database + genomically-obsessed autist, it would be easier to deal with larger quantities of genes. Like, the human genome has 20k genes, and let’s say 1% are super-relevant for aging or brain preservation—that would be 2k genes, and that would be super-easy for an autistically-obsessed person to manage
I mean, sure, if we had a really fast car we could drive from New York to Orlando by going through Seattle. But (a) we don’t have that amazing database, and (b) it’s probably easier to be more efficient than to build the perfect bioinformatics database. With a focus on very-slow-turnover factors, the problem is unlikely to involve even 200 genes, let alone 2k.
You personally might very well be able to identify the full list of root causes, to a reasonably-high degree of certainty, without any tools beyond what you have now, by being more strategic—focusing effort on exactly the questions which matter.
Each protein also has to be analyzed in and of itself, b/c upstream of each protein contains numerous alternative splicing variants, and proteins with more splicing variants should presumably be more susceptible to mis-translation than proteins with fewer splicing variants [splicesome function also decreases with age—see william mair on this, so we need a whole discussion on splicesomes, especially as to how they’re important for important protein complexes on the ETC].
Proteins also have different variants between species (eg bowhead whales and kakapos have hypofunctioning p53). They have different half-lives in the cell—some of them have rapid turnover, and some of them (especially the neuronal proteins are extremely long-lived proteins). The extremely long-lived proteins (like nuclear pore complexes or others at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5500981/ ) do not go through “degradation/recycling” as frequently as short-lived proteins, so it may be that their rate of damage is not necessarily reduced AS MUCH by increases in autophagy [THIS HAS TO BE MAPPED—there is a lot of waste that continues to accumulate in the cell when it can’t be dumped out into the bloodstream/kidneys, and glomeular filtration rate declines with age].
We have to map out which proteins are **CONTROLLERS** of the aging rate, such as protein-repair enzymes [ https://www.sciencedirect.com/science/article/abs/pii/S0098299712001276 ], DNA damage sensing/repair enzymes, Nrf2/antioxidant response elements, and stabilizing proteins like histones [loss of the histone subunits often accelerates aging of the genome by exposing more DNA as unstable euchromatin where it is in more positions to be damaged]. [note i dont include mTOR complex here b/c mTOR reduction is easy but also b/c mTOR doesn’t inherently *damage* the cell]
That article listing long-lived proteins is a handy one. I’m highly suspicious of a lot of those; radioisotope methods are gold-standard but a large chunk of the listed results are based on racemization and other chemical characteristics, which I don’t trust nearly as much. That lack of trust is based more on priors than deep knowledge at this point, though; I’ll have to dig more into it in the future.
I don’t think “controllers of the aging rate” are quite the right place to focus; there’s too many of them. The things I’ve been calling “root causes” should be less numerous, and “controllers of the aging rate” would be exactly the things which are upstream of those root causes—i.e. things which cause the root causes to accumulate faster/slower over the course of life. (Side note: I think using the phrase “root cause” has been throwing a lot of people off; I’m considering switching to “mediator of history”, i.e. things which mediate the effect of the aged organism’s history on its current state.)
If you had the perfect bioinformatics database + genomically-obsessed autist, it would be easier to deal with larger quantities of genes. Like, the human genome has 20k genes, and let’s say 1% are super-relevant for aging or brain preservation—that would be 2k genes, and that would be super-easy for an autistically-obsessed person to manage
Alternatively, aging (like most non-discrete phenotypes) may be omnigenic.
I mean, sure, if we had a really fast car we could drive from New York to Orlando by going through Seattle. But (a) we don’t have that amazing database, and (b) it’s probably easier to be more efficient than to build the perfect bioinformatics database. With a focus on very-slow-turnover factors, the problem is unlikely to involve even 200 genes, let alone 2k.
You personally might very well be able to identify the full list of root causes, to a reasonably-high degree of certainty, without any tools beyond what you have now, by being more strategic—focusing effort on exactly the questions which matter.
https://www.pnas.org/content/116/44/22173
https://www.biorxiv.org/content/10.1101/577478v1.full