SENS papers are well above-average relevance but I de-emphasize them here to reduce redundancy/increase uniqueness of my content
^matters so much more than others
https://www.biorxiv.org/content/10.1101/2021.10.28.466310v2.full => Michael D Forrest papers have MORE style than ANYONE’s
https://www.pnas.org/doi/10.1073/pnas.2019474118
Distinct signaling by insulin and IGF-1 receptors and their extra- and intracellular domains
https://www.frontiersin.org/articles/10.3389/fmolb.2019.00048/full#B204 (THIS MORE THAN OTHER PROTEASOME PAPERS FOR AGING)
lol welp - Chapter 1 - Motivation — Computational Longevity does it SO much better than i could
As usual, it took an outsider to have the best big-picture POV on longevity
An Overview of Chemical Processes That Damage Cellular DNA: Spontaneous Hydrolysis, Alkylation, and Reactions with Radicals
https://www.biorxiv.org/content/10.1101/2022.02.22.481548v2 Chronic Glucocorticoid Stress Reveals Increased Energy Expenditure and Accelerated
Aging as Cellular Features of Allostatic Load (Sturm is on the paper)! Finally an ACTUAL metric of how stress ages
A Quantitative Proteome Map of the Human Body - PMC (note, GTEx data is postmortem and this can skew results b/c some parts of the transcriptome/proteome DO get differentially translated post-mortem)
31512061 (biorxiv.org). => this is a very very messy paper (as in, the separation of CpG sites into each colored module is not super-clear and many of the relative associations overlap hard w/the others ) - it is also going to get outdated quickly simply b/c current-gen epigenetic clocks will [so this is not canon] but I am going to still delve much deeper in this than most
there is an associated trudiagnostic youtube video
If anything, the colors separate datapoints most in the Mean DNAm in young adult blood metric (with the very light-blue modules having the lowest DNAm and focused in CpG islands, while the very red modules have highest DNAm and focused on OpenSea regions). Aren’t OpenSea regions regions of locally lower CpG given that they’re “more out in the sea/ocean?” [they are, and mean DNAm for green-yellow goes from 39% to 34.4% - this was described as “large decrease”]. Some of green-yelow is reset during “initiation phase” (prior to dedifferentiation in maturation phase"
“green yellow and green” most responsive to reprogramming and relevant to disease risk… green is in shore, greenyellow in opensea…
Light-blue (already low on DNAm) decreases DNAm most w/maturation phrase reprogramming. Lightred is already high in DNAm and increases in DNAm change/day in maturaiton phrase
maturation phase does least to change the “most hardcore” dark-magenta and dark-red CpG sites… and the pink ones (more in EpiTOC2). The “hardcore” colors don’t change much with age. “dark magenta” is hard to tease out from pink/purple - you have to *do work to notice it… dark-magenta starts out with low DNAm…
pink is also “hardcore” in that it starts out with low methylation but has the highest natural DNAm change/year… And is located in CpG islands.
The green-yellow moedule (the MOST discussed, and plurality present in Hannum/Zhang/PhenoAge) is minimally present in mammalian brain. It is also mostly in OpenSea regions… (OpenSea ones) like red…
“yellow is hypermethylated and decreases w/age” “light blue is hypomethylated and increases DNAm with age”
“red and orange” is less age-associated but not mortality associated.
“ugh do they use navy for dark blue?”
match up with Duncan Sproul…
navy and pink start hypomethylated and gain methylation w/age AND resistant to reprogramming (this is contrasted with most sites being more methylated and losing methylation with age). light blue too but is more responsive to reprogramming. They are mostly in CpG islands AND EpiToc2 is enriched specifically in these sites… Keep in mind these associations are messy and many datapoints within each model have trends opposite to the average trend of each module.
^I suspect this paper will get outdated quickly enough to not be canonical though (such is the field of epigenetic progress, but Levine IS working on an updated clock)
Morphoceuticals: perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer, and aging
Léo Pio-Lopez1 and Michael Levin1,2,*
Rejuvenation Science News (RSN)
Dmitry Dzhagarov
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Longevity, Centenarians and Modified Cellular Proteodynamics
(February 2023) https://doi.org/10.3390/ijms24032888
We have shown before that at least one intracellular proteolytic system seems to be at least as abundant in the peripheral blood lymphocytes of centenarians as in the same cells of young individuals (with the cells of the elderly population showing a significant dip compared to both young and centenarian cohorts).
Despite scarce published data, in this review, we tried to answer the question how do different types of cells of longevous people—nonagenarians to (semi)supercentenarians—maintain the quality and quantity of their structural and functional proteins? Specifically, we asked if more robust proteodynamics participate in longevity. We hypothesized that at least some factors controlling the maintenance of cellular proteomes in centenarians will remain at the “young” level (just performing better than in the average elderly). In our quest, we considered multiple aspects of cellular protein maintenance (proteodynamics), including the quality of transcribed DNA, its epigenetic changes, fidelity and quantitative features of transcription of both mRNA and noncoding RNAs, the process of translation, posttranslational modifications leading to maturation and functionalization of nascent proteins, and, finally, multiple facets of the process of elimination of misfolded, aggregated, and otherwise dysfunctional proteins (autophagy). We also included the status of mitochondria, especially production of ATP necessary for protein synthesis and maintenance.
We found that with the exception of the latter and of chaperone function, practically all of the considered aspects did show better performance in centenarians than in the average elderly, and most of them approached the levels/activities seen in the cells of young individuals.
Most translationally relevant for AI alignment tbh
[MAYBE]. This is by an extremely independent researcher too, who puts everything into a theoretically biophysics basis (even if there is not much detail).