List of causes of aging (beyond "Hallmarks of Aging")

DAMAGE theories vs information-theoretic/control theory/complex systems theories (stoichiometric, loss of precise control, mislocalization)

preferentially listing things others haven’t paid attention to yet: (non-enzymatic modifications)

AGING and the KINOME: Aging and Protein Kinases - PubMed (more shall be written here)


ECM: Collin Ewald | Aging & The Extracellular Matrix - YouTube

ER: (there can be much better papers on this)

extracellular matrix: and Extracellular Matrix and Ageing - PubMed

kinesin/dyenin transport: Defective recruitment of motor proteins to autophagic compartments contributes to autophagic failure in aging - PubMed and Dynein Dysfunction Disrupts Intracellular Vesicle Trafficking Bidirectionally and Perturbs Synaptic Vesicle Docking via Endocytic Disturbances: A Potential Mechanism Underlying Age-Dependent Impairment of Cognitive Function - ScienceDirect

Stoichiometry papers: (omg relocalization hypothesis is SO interesting => I don’t think it’s the rate-determiner, but SHOWS which causes we want) - along with Alexander Mendenhall’s transcriptional noise with aging

decreases in histone count -

Peter Fedichev

Failure to reposition autophagosomes and lysosomes toward the perinuclear region with age reduces the efficiency of their fusion and the subsequent degradation of the sequestered cargo. Hepatocytes from old mice display lower association of two microtubule-based minus-end-directed motor proteins, the well-characterized dynein, and the less-studied KIFC3, with autophagosomes and lysosomes, respectively

A Futile Battle? Protein Quality Control and the Stress of Aging (DILLIN)

Increasing cell size remodels the proteome and promotes senescence => more on the disorganization theory

While it has long been thought that most protein concentrations remain constant as cells grow, this paradigm had not previously been tested using a high-throughput quantitative proteomics approach. In contradiction to the previous paradigm, many protein concentrations changed with cell size. Some proteins sub-scaled with cell size, and were diluted in larger cells, while others super-scaled with cell size so that their concentrations increased as cells grew larger. This finding is reflected in the super- and sub- scaling of the mRNA transcripts for various G1/S regulators in budding yeast (24). To a large extent, these diverse protein size-scaling behaviors we observed could be predicted from a linear model based on mRNA concentration, protein half-life, and subcellular localization, which indicates the importance of both transcriptional and post-transcriptional size-scaling mechanisms.

However, this is not the case. There is a limit to the size range of efficient biosynthesis (18), and excessively large cells exhibit loss of mitochondrial potential (5), dilution of the cytoplasm (6), and reduced proliferation (19). Moreover, recent work has demonstrated the remarkable effect even small variations in cell size can have on hematopoietic stem cell proliferation (4).

One possible explanation for why there is an optimal cell size for biosynthesis would be if many key cellular proteins did not remain at constant concentration as cells grew. Then, the further cells got from their target size, the more concentrations of these proteins would change, and the more growth and metabolism would deviate from the optimum. Intriguingly, investigations of the mechanisms cells use to control their size have identified a class of proteins whose concentrations do change with cell size. In budding yeast, human, and plant cells, key cell cycle inhibitors are not synthesized in proportion to cell size so that they are diluted by cell growth, a behavior defined as sub-scaling (20-22) (Fig. 1A). Larger cells therefore have lower concentrations of cell cycle inhibitors, which promotes their division

components typically associated with cell senescence such as lysosomes, β-galactosidase, and metalloproteases were up-regulated with enlarged cell size (Lanz et al., 2021). Thus, contrary to the simple view that all proteins and organelles adapt in the same way to cell size (Levy and Heald, 2012), several processes appear to deviate from a linear scaling pattern (Cheng et al., 2021; Lanz et al., 2021; Liu et al., 2021). Furthermore, when cells reach sizes beyond their physiological range, overall proteome content no longer scales with size and the cytoplasm becomes diluted, presumably due to defective coordination of cell volume growth and biosynthesis. The consequences of cytoplasm dilution are an active area of research, with recent evidence demonstrating effects on reaction rates (Jin et al., 2022; Molines et al., 2022) and phase separation (Delarue et al., 2018) that could negatively impact cell function. New techniques enabling cellular density measurement with unprecedented precision (Miettinen et al., 2022; Oh et al., 2022),

  • “It’s the majority of the methylome that accurately predicts age, not
    just a few key genes,” said co-senior author Trey Ideker, PhD, a
    professor of medicine and chief of the Division of Medical Genetics in
    the UC San Diego School of Medicine and professor of bioengineering in
    the Jacobs School of Engineering. “The methylation state decays over
    time along the entire genome. You look in the body, into the cells, of
    young people and methylation occurs very distinctly in some spots and
    not in others. It’s very structured. Over time, though, methylation
    sites get fuzzier; the boundaries blur.”

transcriptional imbalance (lower long-read transcripts)

there are a lot of theoretically sound reasons for a bias against long mRNA (reduced polymerase processivity, bulky adducts on the DNA that knock the polymerase off, random/incidental production of interfering RNAs that squelch the long mRNA), and I’ve never heard anyone propose this before. I’d like to give this paper a deep dive, so I thought I’d review it in public on this channel. There will probably be a lot of self-replies to this thread. Feel free to join me!

It also appears that aging may cause a “cargo recognition failure,” resulting in the accumulation of damaged mitochondria in Parkinson’s disease, one of the most common neurodegenerative movement disorders (Martinez-Lopez et al., 2015).

Aged rat and fruit fly brains produce fewer messenger RNAs2,3 and cell-to-cell variation in transcription is increased in several tissues4,5,6, while gene-to-gene transcriptional coordination is decreased in aging7

“relocalization of chromatin modifiers”

as age increased, some factor beyond disease drove a predictable and incremental decline in the body’s ability to return blood cells or gait to a stable level after a disruption. When Pyrkov and his colleagues in Moscow and Buffalo, N.Y., used this predictable pace of decline to determine when resilience would disappear entirely, leading to death, they found a range of 120 to 150 years. (In 1997 Jeanne Calment, the oldest person on record to have ever lived, died in France at the age of 122.)

The researchers also found that with age, the body’s response to insults could increasingly range far from a stable normal, requiring more time for recovery. Whitson says that this result makes sense: A healthy young person can produce a rapid physiological response to adjust to fluctuations and restore a personal norm. But in an older person, she says, “everything is just a little bit dampened, a little slower to respond, and you can get overshoots,” such as when an illness brings on big swings in blood pressure.

Measurements such as blood pressure and blood cell counts have a known healthy range, however, Whitson points out, whereas step counts are highly personal. The fact that Pyrkov and his colleagues chose a variable that is so different from blood counts and still discovered the same decline over time may suggest a real pace-of-aging factor in play across different domains.

Study co-author Peter Fedichev, who trained as a physicist and co-founded Gero, says that although most biologists would view blood cell counts and step counts as “pretty different,” the fact that both sources “paint exactly the same future” suggests that this pace-of-aging component is real.

The authors pointed to social factors that reflect the findings. “We observed a steep turn at about the age of 35 to 40 years that was quite surprising,” Pyrkov says. For example, he notes, this period is often a time when an athlete’s sports career ends, “an indication that something in physiology may really be changing at this age.”

Overproduction + improper localization of proteins (like collagen or crosslinking proline oxidases) OR them not binding in the way they should (like elastin)

By focusing on elastin, the team discovered that the development of fibrosis in skin tissues was linked to a particular molecule: fibulin-5. Researchers studied mice that were genetically engineered to develop skin fibrosis and found substantially higher levels of fibulin-5 in their skin tissues than in normal mice. High levels of fibulin-5 were also found in the skin tissues of human patients with skin fibrosis. Researchers explained that elevated levels of fibulin-5 caused elastin to form in abnormally large amounts, and that higher elastin levels likely contributed to increased skin tissue inflammation and stiffening.

Researchers also demonstrated that removing fibulin-5 from the genetically engineered mice before they developed skin fibrosis helped prevent all the symptoms of skin fibrosis — including skin tissue inflammation and stiffening — from occurring.

On the molecular level, aging of elastin and elastic fibers involves enzymatic degradation (Antonicelli et al., 2007; Heinz, 2020), oxidative damage (Watanabe et al., 1996), formation of advanced glycation endproducts (AGEs) (Paul and Bailey, 1996), calcification (Urry, 1971), aspartic acid racemization (Powell et al., 1992; Sivan et al., 2012), lipid binding (Jacob et al., 1983; Robert et al., 2008), carbamylation (Gorisse et al., 2016) and mechanical fatigue (O’Rourke, 2007) (Fig. 2). With respect to the contribution of the different mechanisms to elastic-fiber aging, it has been shown that a combination of the processes of calcification, lipid binding and enzymatic degradation, which promote and enhance each other, have a strong impact on elastic fibers and affect primarily tissues rich in elastin such as the cardiorespiratory system and the eye, leading to mechanical fatigue and severe pathologies (Robert et al., 2008). Interestingly, research has shown that even a healthy lifestyle cannot fully prevent intrinsic aging processes associated with lipid accumulation, calcification and enzymatic degradation of elastin as lipids, calcium and carbohydrates are important part of the human diet and elastin accumulates more and more damage with increasing age of the individual. Therefore, even with a healthy lifestyle and the postponement of an onset of age-related disorders, human life expectancy cannot be increased endlessly, and there is an upper limit for the elastic properties of the cardiorespiratory system of about 100 years – 120 years (Robert et al., 2008).

I mapped out SO many of the critical questions here:

Information theory stuff: The fidelity of genetic information transfer with aging segregates according to biological processes (though the dataset/example they used is not the best). More examples should be made in this direction