Like an airplane blueprint, the goal is to show how all the components connect—a system-level point of view. Much research has already been published on individual components and their local connections—anything from the elastin → wrinkles connection to the thymic involution → T-cell ratio connection to the stress → sirtuins → heterochromatin → genomic instability pathway. A blueprint should summarize the key parameters of each local component and its connections to other components, in a manner suitable for tracing whole chains of cause-and-effect from one end to the other.
aren’t there better methods of characterizing these connecting components than a textbook? Textbooks are super-linear and ill-suited for complex demands where you want to do things like “cite/search for all examples of H3K27 trimethylation affecting aging in each and every studied model organism”. They’re not great for characterizing all the numerous rare-gene variants and SNPs that may help (such as, say, the SNP substitution in bowhead whale and kakapo p53 and how this single SNP *mechanistically* affects interactions between p53 and all the other downstream effects of p53 - such as whether it increases/decreases/etc). There are many databases of aging already (esp those compiled by JP de Magalhaes and the ones recently outputted by the Glen-Corey lab and Nathan Batisty senescent cell database) but the giant databases return giant lists of genes/associations and effect sizes but also contain no insight in them.
The aging field moves fast and there are already zillions of previous textbooks that people don’t read anymore simply b/c they expect a lot of redundancy on top of what they already know.
In particular I’d like a database that lists prominent anomalies/observations (eg naked mole rat enhanced proteasome function or naked mole rat enhanced translational fidelity or “naked mole rat extreme cancer resistance which is removed if you ablate [certain gene]”) which then could be made searchable in a format that allows people to search for their intuitions
Part of the point here is that the models should be firmly nailed down. The field moves fast in large part because old (and many current) models/theories were quite speculative; they were not nailed down.
This would not be a tool for model-making (like a database would be). It would just be a model, along with the evidence/math to validate each component of that model, end-to-end.
That said, sure, a linear presentation is not great.
IDEALLY, such a model would allow people to creative putative links to hand-annotate (with a dropdown menu) all the papers in support and against support of the model. https://genomics.senescence.info/genes/ exists but it isn’t great for mechanism as it’s just a long list of genes that seems to have been insight-free scraped. A lot of the aging-related genes people have studied in-depth that have shown the strongest associations for healthy aging (eg foxo3a/IGF1) sure *help* and then there are IGF mutants [oftentimes they dont directly increase repair] but I don’t feel that they’re as *fundamental* as, say, variations in proteasome function or catalase or splicesome/cell cycle checkpoint/DNA repair genes.
Ok so here’s a model I’m thinking. Let’s focus on the proteasome alone for instance, which basically recycles proteins. It pulls a protein through the 19S subunit into the 20S barrel that has the “fingers” that can deaminate the amino acids of each protein, one by one.
We know that reduction in proteasome function is one of the factors associated with aging, esp b/c damage in proteasome function accelerates the damage of *all other proteins* [INCLUDING transcription factors/control factors for ALL the other genes of the organism] so it acts as an important CONTROL POINT in our complex system (we also know that proteasome function declines with age). We also know that increases in certain beta3/https://en.wikipedia.org/wiki/PSMB3 subunits of 20S proteasome function help increase lifespan/healthspan (ASK THE QUESTION THEN: why uniquely beta3 more so than the other elements of the proteasome?). Proteins only work in complexes, and this often requires a precise stoichiometry of proteins in order to fit—otherwise you may have too much of one protein in a complex, which [may or may not] cause issues. Perhaps the existence of some subunits help recruit some *other* subunits that are complementary, while it may do negative interference on its own synthesis [there’s often an upstream factor telling the cell not to synthesize more of a protein].
I know one prof studies Rpn13 in particular.
The proteasome has a 20S and two 19S regulatory subunits. The 19S subunit consists of 19 individual proteins. Disruptions in synthesizing *any* of the subunits or any of the proteins could make the protein synthesis go wrong.
We need to know:
is reduction in proteasome function primarily due to reduced proteasome synthesis [either through reduced transcription, splicosome errors, reduced translation, improper stoichiometry, or mislocalization] or damaged proteasomes that continue to stay in the cell and wreak havoc?
Can proteasomes recognize and degrade proteins with amino acids that have common sites of damage? (many of them known as non-enzymatic modifications)?
the pdb parameters of proteasomes (as well as the rough turnover rates of each of their subunits)
what are the active sites of proteasomes, and what amino acids do they primarily consist of? (in particular, do they consist of easily damaged amino acids like cysteines or lysines?)
What are the precise mechanisms in which the active sites of proteasomes get damaged?
How does a cell “clear out” damaged proteasomes? What happens to damaged proteasomes during mitosis?
If a cell accumulates damaged proteasomes, how much do these damaged proteasomes reduce the synthesis and function of other properlyly functioning proteasomes in the cell. Will the ubiquitin system improperly target some proteins into proteasomes that have ceased to exist?
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.
aren’t there better methods of characterizing these connecting components than a textbook? Textbooks are super-linear and ill-suited for complex demands where you want to do things like “cite/search for all examples of H3K27 trimethylation affecting aging in each and every studied model organism”. They’re not great for characterizing all the numerous rare-gene variants and SNPs that may help (such as, say, the SNP substitution in bowhead whale and kakapo p53 and how this single SNP *mechanistically* affects interactions between p53 and all the other downstream effects of p53 - such as whether it increases/decreases/etc). There are many databases of aging already (esp those compiled by JP de Magalhaes and the ones recently outputted by the Glen-Corey lab and Nathan Batisty senescent cell database) but the giant databases return giant lists of genes/associations and effect sizes but also contain no insight in them.
The aging field moves fast and there are already zillions of previous textbooks that people don’t read anymore simply b/c they expect a lot of redundancy on top of what they already know.
In particular I’d like a database that lists prominent anomalies/observations (eg naked mole rat enhanced proteasome function or naked mole rat enhanced translational fidelity or “naked mole rat extreme cancer resistance which is removed if you ablate [certain gene]”) which then could be made searchable in a format that allows people to search for their intuitions
Anyone who cares about this should friend/follow https://blog.singularitynet.io/the-impact-of-extracellular-matrix-proteins-cross-linking-on-the-aging-process-b7553d375744
Part of the point here is that the models should be firmly nailed down. The field moves fast in large part because old (and many current) models/theories were quite speculative; they were not nailed down.
This would not be a tool for model-making (like a database would be). It would just be a model, along with the evidence/math to validate each component of that model, end-to-end.
That said, sure, a linear presentation is not great.
IDEALLY, such a model would allow people to creative putative links to hand-annotate (with a dropdown menu) all the papers in support and against support of the model. https://genomics.senescence.info/genes/ exists but it isn’t great for mechanism as it’s just a long list of genes that seems to have been insight-free scraped. A lot of the aging-related genes people have studied in-depth that have shown the strongest associations for healthy aging (eg foxo3a/IGF1) sure *help* and then there are IGF mutants [oftentimes they dont directly increase repair] but I don’t feel that they’re as *fundamental* as, say, variations in proteasome function or catalase or splicesome/cell cycle checkpoint/DNA repair genes.
I think you could enter such information into Wikidata.
https://us02web.zoom.us/rec/play/75Msd7isrjg3GtOUtgSDB_V-W9S1LKys0icd-6YLnkm0WyEEYVShZLpEN_MTL-zvaHImV7BO5gtNyDo?startTime=1589388441000
Ok so here’s a model I’m thinking. Let’s focus on the proteasome alone for instance, which basically recycles proteins. It pulls a protein through the 19S subunit into the 20S barrel that has the “fingers” that can deaminate the amino acids of each protein, one by one.
We know that reduction in proteasome function is one of the factors associated with aging, esp b/c damage in proteasome function accelerates the damage of *all other proteins* [INCLUDING transcription factors/control factors for ALL the other genes of the organism] so it acts as an important CONTROL POINT in our complex system (we also know that proteasome function declines with age). We also know that increases in certain beta3/https://en.wikipedia.org/wiki/PSMB3 subunits of 20S proteasome function help increase lifespan/healthspan (ASK THE QUESTION THEN: why uniquely beta3 more so than the other elements of the proteasome?). Proteins only work in complexes, and this often requires a precise stoichiometry of proteins in order to fit—otherwise you may have too much of one protein in a complex, which [may or may not] cause issues. Perhaps the existence of some subunits help recruit some *other* subunits that are complementary, while it may do negative interference on its own synthesis [there’s often an upstream factor telling the cell not to synthesize more of a protein].
I know one prof studies Rpn13 in particular.
The proteasome has a 20S and two 19S regulatory subunits. The 19S subunit consists of 19 individual proteins. Disruptions in synthesizing *any* of the subunits or any of the proteins could make the protein synthesis go wrong.
We need to know:
is reduction in proteasome function primarily due to reduced proteasome synthesis [either through reduced transcription, splicosome errors, reduced translation, improper stoichiometry, or mislocalization] or damaged proteasomes that continue to stay in the cell and wreak havoc?
Can proteasomes recognize and degrade proteins with amino acids that have common sites of damage? (many of them known as non-enzymatic modifications)?
the pdb parameters of proteasomes (as well as the rough turnover rates of each of their subunits)
what are the active sites of proteasomes, and what amino acids do they primarily consist of? (in particular, do they consist of easily damaged amino acids like cysteines or lysines?)
What are the precise mechanisms in which the active sites of proteasomes get damaged?
How does a cell “clear out” damaged proteasomes? What happens to damaged proteasomes during mitosis?
If a cell accumulates damaged proteasomes, how much do these damaged proteasomes reduce the synthesis and function of other properlyly functioning proteasomes in the cell. Will the ubiquitin system improperly target some proteins into proteasomes that have ceased to exist?
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