Important high-insight QUOTES

#1

Taken together, the data presented here suggest that intervention with AAV-Kl may be more effective in slowing the progression of sarcopenia at an earlier timepoint, rather than rescuing advanced pathology, at which time the transcriptomic response to intervention appears to be more stochastic. An interesting area of future investigation includes the determination of whether network entropy and PPI network architecture may be predictive of the efficacy of therapies designed to counteract the effect of time on skeletal muscle health and function. As an extension of this work, it would also be interesting in future studies to determine whether upregulation of Klotho at a younger age could attenuate functional declines into old, and possibly even oldest-old, age

(from The biphasic and age-dependent impact of klotho on hallmarks of aging and skeletal muscle function | eLife )

^ALSO TRUE FOR SENOLYTICS - THEY WORK BETTER IN YOUNGER PEOPLE (BOTH THE BLUNT-FORCE D+Q AND THE OSIRIN BIOTECH TYPE). Also POSSIBLY for follistatin gene therapy

slowing aging is MUCH easier than reversal, and the earlier, the better (when aging increases the entropy of the cell, the “repair/age-reversal signals” don’t know where to be precisely targeted anymore).

endolysosomal system has been well studied in non-neuronal cells and broadly classified into several subclasses, including the EE (early endosome), RE (recycling endosome), LE (late endosome), and lys (lysosome). The EE is the major sorting station for endocytosed receptors. Recycling cargos can return to the surface from the EE or from the RE. Degradative cargos fail to enter recycling pathways in the EE and are progressively sorted into intraluminal vesicles starting in the EE. The EE matures to a LE by the timed exchange of specific effectors, proteins that associate with the cytosolic leaflet of the endosome membrane, such as Rab7. LEs contain many intraluminal vesicles and are often categorized by EM as “multivesicular bodies.” LEs undergo homotypic fusions with each other as well as fuse with lysosomes for cargo degradation; and in some cases, they fuse with the plasma membrane

#2

Global mean DNA methylation (i.e. average methylation level of 3,089,098 common CpGs) remained relatively stable with age in NMRs (mean: 0.4597, std: 0.014) and remained slightly demethylated (i.e. less than 0.5 for all samples) (Fig. 1c). In contrast, the methylome entropy (mean Shannon entropy of CpG methylation levels) showed a distinct U-shaped pattern: a sharp decrease at the youngest ages (< 1 month) followed by a gradual age-related increase during the whole lifespan (Fig. 1d, p = 0.00019). A density plot of methylation levels suggested that the age-related entropy change may be driven by the decrease in extreme values (i.e., methylation levels 0 or 1) and the slight increase of the values close to 0.5 (Fig. 1e). Indeed, the proportion of sites with methylation levels 0 and 1 significantly decreased with age and the methylation levels close to 0.5 (i.e., between 0.45 and 0.55) significantly increased for NMRs older than 1 month (Fig. 1f). Altogether, except for the youngest ages that correspond to development, we observed an information loss of the NMR methylome during aging (as measured by Shannon entropy), suggesting a possibility that NMRs age.

We also examined mean methylation levels of NMR gene promoters. Mean promoter methylation across all 17,823 NMR genes covered by our data significantly increased with age (Supplementary Fig. 1a). The correlation of mean promoter methylation and age ranged from −0.5518 to 0.7821 for the 320 genes that remained significant after Bonferroni correction (Supplementary Fig. 1b, Supplementary Data 2). Tert (telomerase reverse transcriptase) promoter showed the strongest correlation with age (r = 0.7821, p = 2.67e-23), and 8 other gene promoters showed correlation coefficients greater than 0.7 (Supplementary Fig. 1c).

#3

After this procedure, epigenetic changes during the whole lifespan showed similar relative age dynamics in the three species. We observed that the trend lines of decreasing and increasing sites crossed each other at 11.87, 1.66, and 70.69 years, respectively, for the NMR, mouse and human (Fig. 4a–c). It may indicate that a 11.87 year old NMR roughly correspondent to a 1.66 year old mouse or a 70.69-year-old human in terms of age-related epigenetic changes. The relative age of the cross-over was 0.383, 0.414, and 0.577, respectively, and it occurred at the methylation levels of 0.508, 0.447, and 0.352. Entropy is often linked to the aging process, and the methylation entropy is highest at 0.5 methylation. In line with this, the trend lines of decreasing and increasing sites converged around the 0.5 methylation level. Overall, multiple lines of evidence revealed clear epigenetic aging patterns in NMRs, which generally recapitulated the well-characterized aging patterns observed in mice and humans.

“the beneficial effects of OSK-induced reprogramming in axon regeneration require the DNA demethylases TET1 and TET2”

#4

Even the best-studied genomes (those of humans, mice, nem-
atodes, fruit flies and budding yeast), have numerous genes of

unknown function. Notably, many of more recently functionally
characterized genes code for repair proteins115. Virtually all enzymes
exhibit side activities that often represent <0.1 % of the classical
activity and even sometimes <10−6

for the most specific enzymes.
The products generated by side activities have been neglected
until recently when it was realized that a new category of enzymes,
metabolite repair and clearance enzymes serve to destroy the most
important side products and avoid their accumulation, which might
otherwise be toxic, causing disease.

An example of a side activity is the production of l-2-hydroxy-
glutarate by l-malate dehydrogenase and lactate dehydrogenase, two

abundant enzymes. Their apparently tiny side activity (<10−6

com-
pared to the regular activity) leads to the daily production of grams

of l-2-hydroxyglutarate in humans. An FAD-linked mitochondrial

enzyme reconverts l-2-hydroxyglutarate to α-ketoglutarate, avoid-
ing its accumulation, which is toxic particularly to the brain. The

metabolic disease l-2-hydroxyglutaric aciduria, which is due to
inactivating mutations in the repair enzyme, leads to progressive
neurodegeneration and increased incidence of brain tumors.
In glycolysis, there seems to be at least as many distinct repair
reactions as the 11 classical reactions of glycolysis116. This huge

diversity of side products related to glycolysis suggests that hun-
dreds and probably thousands of different side products may be

formed in cells. It is likely that only some of them are eliminated by

repair and clearance enzymes. But at least for those that are elimi-
nated, it is possible to evaluate their potential toxicity in cell-based

experiments. Such experiments have shown that some of the side
products are indeed extremely toxic.

Notably, damaged molecules introduced through diet may also
contribute to cumulative molecular damage and influence the

aging process through diet, despite the majority of biomacromol-
ecules in the diet being digested14. In one study, species-specific

culture media and diets were employed that incorporated molecu-
lar extracts of young and old organisms14. In each model organism

tested (budding yeast, fruit flies and mice), the ‘old’ diet or medium
shortened the lifespan of one or both sexes compared to the control
that used the ‘young’ diet or medium. This finding suggests that
age-associated cumulative damage is deleterious, is causally linked
with aging and may affect lifespan through diet. It also suggests
that age-accelerating environmental exposures might be identified
through their effects on damage accumulation.

More acetylated states of chromatin are highly decondensed and dynamic, whereas more methylated states of chromatin are highly condensed and static [

39

]. The balance between acetylation and methylation states of chromatin dictates not only chromatin plasticity and accessibility to the DNA sequence, but also the entropic forces exerted on the nuclear membrane [

40

].

#5 
"When responding to a pro-inflammatory signal, elderly neutrophils zigzag through the tissue towards the site of injury like an emergency crew with a faulty GPS, causing collateral damage as they go. Looking at migratory patterns of neutrophils and their impact on tissues, Lord's team in Birmingham have found that ‘even in healthy elders, the amount of damage caused by neutrophils just wandering around the body looking for infections is double that in a young person’. So when in life does this become a problem? ‘We can pick it up in most 40- and 50-year-olds,’ says Lord. ‘But by the time you're 60 or 70 years old it's really bad. You struggle to find a neutrophil that is moving in the right direction efficiently.’

Blundering neutrophils are slow to reach their target, which is one reason why wounds heal so much more slowly as we age than they did when we were kids scuffing knees and elbows in the playground and quickly forming scabs. And they're one reason, too, why old people respond so poorly to infection. In severe infections such as pneumonia, old people's neutrophils are even more disorientated than usual, and the collateral damage they do in migrating to the site is vastly increased – up to five times higher than in a young person – and a potent cause of general frailty. Young people's neutrophils are also somewhat disorientated in cases of severe infection, but whereas they quickly return to pre-pneumonia levels of efficiency, the neutrophils of old people can't reset their GPS, leaving them vulnerable to repeat bouts of infection.

The problem with aged neutrophils, however, goes beyond faulty migration and difficulty killing off pathogens: in older people these cells are often extremely sluggish to respond to pro-inflammatory signals in the first place. Why? It turns out this is because of the chronic underlying inflammation which means the neutrophils are already activated and are unable to ‘hear’ the new signals clearly above the background noise. This discovery gave Lord and her colleagues an idea. Her lab had worked out the pathway between signal and response and knew there were drugs that could target this communication channel. The drugs in question were statins"
#6

Among the very large number of proteins and pathways influenced by diet, the major findings were that low energy intake increased the abundance of spliceosome proteins, while protein intake was positively correlated with the abundance of mitochondrial proteins, leading to oxidative stress. Overall metformin, rapamycin, and resveratrol reduced these proteomic responses to diet indicating their role in suppressing protein synthesis

#7

A LOT of them here (discussing michael levin)

#8

How has the field changed since you started?

To put it in perspective, When I started my Masters in 2006, there was already an increasing interest in researching aging, leading to more investment, funding, and conferences in the field.

Advancements in technology, such as CRISPR, advanced omics, AI, and novel drugs, have also enabled more efficient aging research. Also, the rapid development of the internet and applications such as Twitter have facilitated collaborations among scientists and enable them to interact more efficiently than before.

We also observed that many scientists have started to question previous assumptions, results, and theories on aging, which were previously published, and this is followed by heated debates. As the field of aging grows and we collect more observations, it is likely we will disprove more previous theories on aging.

Unfortunately, the field has surprisingly become narrower. A handful of concepts such as diet, exercise, rapamycin, mtUPR, and a few others have become dominant – so dominant that they are over-studied, over-published, and importantly, over-funded. This poses dual disadvantages by inhibiting other promising ideas to be studied/funded/published and by the fact that it is likely that the current dominant ideas will not break the glass ceiling of longevity.

#9

Alternatively, cysteine could be implemented in non-critical locations within a protein as a sacrificial means to scavenge free radicals and prevent damage to more critical domains. It

#10

In children, bones contain only red marrow. However, as
the skeleton matures, fat-storing yellow marrow displaces red
marrow in the shafts of the long bones ofthe limbs. In adults,
red marrow remains chiefly in the ribs, the vertebrae, the pelvic
bones, and the skull. It is in the red marrow that stem cells are
produced, so the transformation of red marrow into yellow
marrow explains the decline in stem cell production with age.

#11

as soon as people, as soon as people would recognize that mice and humans are different in terms of dynamic properties, mice are essentially, mice do not have dynamic stability. I have another graph here on the right. Here in the population using the wearable devices, we measured people with long autocorrelation times, with long recovery, and you can see that the fraction of people who have long recovery time is going up exponentially and doubles every eight years.

the below was a GOLDMINE

🗓 [LBF] Peter Fedichev (Co-founder @ Gero) Q&A on February 8, 2024 @ 10:00 AM | Read Meeting Report

#12

we highlight essential insights: CpG islands and related chromatin states, such as transcriptional start sites (TSS1) and flanking promoter states (PromF4, PromF5), show enrichment for CpGs negatively associated with maximum life span (18, 45). In essence, species with longer life spans exhibit lower methylation levels at CpG islands compared to shorter-lived species.

#13

A new generation of explainable DNAm biomarkers suggest they have both stochastic and non-stochastic components 32, as well as causal components18 that may be either damaging or adaptive in the aging process. It has been shown that Gen1 DNAm biomarkers that predict chronological age preferentially capture stochastic epigenetic noise, while Gen2+ DNAm biomarkers capture a larger proportion of non-stochastic aging processes.32 However, this analysis was done on cross-sectional data,