Canonical aging papers (more worth reading than others)

Especially for new people (lots of people are still confused as to what’s important or what to focus attention on - if we figure out how to get people to focus their attention better, it really drastically increases our ability to solve the coordination problem). There are more papers than time available to read, so choose the ones that summarize the most information with the least filler possible (review papers are often best for this, and are often better than the seminal papers in the field [which often lack insight as to physical mechanism]). The best papers take a big-picture or comparative view (you can use scoping to force a big-picture view even with individual post-translational modifications on specific proteins) (I find the one mentioned here interesting enough to want to understand more - though it uses a not-fully-grounded model)

Also, Redirecting... posts A LOT of good papers and has far higher breadth than most aging researchers

MY FAVORITE ITEMS ON PUBMED (530 items) - Favorites - My NCBI Collection (more than anything else)


Physical Cell Biology

Comparative Biology of Aging

  • The Long Life of Birds: The Rat-Pigeon Comparison Revisited

Lipid/membrane Repair

Translation/Transcription (as naked mole rats show, many protein aggregates form DURING the process of translation/transcription b/c it is when they are in their most vulnerable state, so fix the upstream process)



Biophysical Profiling

** Metabolomics **

  • Undulating changes in human plasma proteome across lifespan are linked to disease
  • Tony Wyss-Corey papers


  • Lopes et al. - 2020 - Gene size matters What determines gene length in the human genome
  • Title: Aging is associated with a systemic length-driven transcriptome imbalance
  • Bartas et al. - 2020 - The changes in the p53 protein across the animal kingdom pointing to its involvement in longevity
  • Mariadassou, Pellay - 2014 - Identification of amino acids in mitochondrially encoded proteins that correlate with lifespan
  • Studer, Robinson-Rechavi - 2009 - How confident can we be that orthologs are similar, but paralogs differ
  • Peto’s Paradox: evolution’s prescription for cancer prevention
  • Relaxed Selection Limits Lifespan by Increasing Mutation Load - PubMed


Protein/Lipid/Nucleic Acid Aggregation

Neurons (really the most important celltype, so maintain them well)


Long-Lived Structural Proteins

Theoretical Proteostasis

Small Molecules
Terpenoids as Potential Geroprotectors.


*** Th**e landscape of the alternatively spliced transcriptome remains stable during aging across different species and tissues

Structural Biology


An Overview of the Role of Lipofuscin in Age-Related Neurodegeneration.

Surprisingly Crazy Interventions
(look at chimeric studies, look at T-cell and mitochondrial transfer)


Tangential Insight-Dense Papers

** Chemical-level **

  • Chemical biology of mutagenesis and DNA repair: cellular responses to DNA alkylation
  • Unraveling Oxidative Stress Resistance: Molecular Properties Govern Proteome Vulnerability
    Glucose, glycation and aging

Structural Biology

  • The nuclear pore complex – structure and function at a glance

Brain Aging

Cell Death/Hypoxia

I would also look up reperfusion injury and H2S suspended animation - has LOTS of relevance

** General Neuro**


CLASSIFY LATER (unsure of importance)

Wang-michelitsch, Michelitsch - 2015 - Potential of longevity hidden in structural complexity
Recent progress in the biology and physiology of sirtuins.


  • Neuronal intrinsic mechanisms of axon regeneration.

Neuronal intrinsic mechanisms of axon regeneration.

Alternative biochemistry

  • Did Evolution Select a Nonrandom “Alphabet” of Amino Acids?
  • Adding new chemistries to the genetic code.

The emerging role of alternative splicing in senescence and aging.


Also you will have to google “aging + EVERY biological compartment”


aging + golgi
aging+ endoplasmic reticulum
aging + spliceosome
aging + cytoskeleton
aging + nuclear membrane
aging + vimetin
aging + nuclear pore protein
aging + early endosome
aging + late endosome
aging + epitranscriptome
aging + chromatin
aging + histones
aging + nucleosome

[there will almost certainly be a review paper on this]

classify later

pulsed mitochondrial ros

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also spatial-omics!

[a lot of next-gen stuff which will be helpful for seeing mechanistic effects of interventions!!]

chemical biology/nonenzymatic modifications - Discovering the landscape of protein modifications

SENS papers are well above-average relevance but I de-emphasize them here to reduce redundancy/increase uniqueness of my content

PROTACs and other protein degraders

^matters so much more than others => Michael D Forrest papers have MORE style than ANYONE’s

Distinct signaling by insulin and IGF-1 receptors and their extra- and intracellular domains (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 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 ( => 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,*