https://twitter.com/RuxandraTesloi1/status/1523293856191680512 (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
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
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.”
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!https://doi.org/10.1038/s43587-022-00317-6
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
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 acidracemization (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).
is the dysregulation of calcium and
potassium homeostasis caused primarily by oxidation of thiol groups by the reactive oxygen species
produced in cells during aging. Changes in calcium levels have a significant impact on cellular
properties such as phosphorylation and excitability, both directly and indirectly through alterations
in the threshold for activation of calcium-activated potassium (K(Ca)) channels. Additionally,
direct oxidation of voltage-gated potassium channels disrupts potassium homeostasis, resulting in
hyperexcitability, inflammation, and neuronal loss. All of these effects contribute to the cognitive
decline seen in both normal aging in neurogenerative disease
"Like many cars on a road, several ribosomes traverse a mRNA at the same time to translate the blueprint into proteins. Sometimes, ribosomes like two cars which are following each other, can collide if the first car brakes unexpectedly, for example, because a cat jumps onto the road. The GCN1 protein then acts like a firefighter who is at the scene of the accident as a first responder. It stabilizes and secures the accident site to then call the towing service and road cleaning service, which remove the collided vehicles and also renew the road surface if necessary.
…In the experiments with human cell lines, the researchers were able to show that impairments in the management of protein balance also occur here. With the results of the study, the scientists hope to find ways in the future to reduce the age-related accumulation of defective proteins in order to prevent neurodegenerative diseases such as Alzheimer’s or Parkinson’s diseases."
45% of all deaths in the US can be attributed in part to fibrosis (scarring) issues (including cardiac problems), but somehow the uterus can massively regenerate monthly without scarring. Also, zero common lab animals menstruate, which is a major obstacle for research; transgenic mice can now be made so that there are good animal models for study.
Is fibrosis even in the hallmarks? yes protein homeostasis loss, but that connection is still not obvious
Failure of Nuclear/cytoplasmic compartmentalization (related to “molecules not being in the right places”)
Loss in stem cell polarization (and proper maintenance of polarization during cytokinesis
(And loss in epigenetic polarization cf Peter fedichev
How did the scientists create the atlas of ageing muscles? The scientists used advanced imaging and single-cell sequencing technology to analyse human skeletal muscle samples. These samples came from 17 donor adults, aged 20-75.
They discovered that the genes controlling ribosomes (which are responsible for producing proteins) were less active in the older participant’s muscle stem cells. This means that those aged cells cannot easily repair and regenerate muscle fibres.
Which kinetic constant “takes the biggest hit” with age?
Parameter
Typical age-related change
Why it changes
Evidence
kcat (and therefore Vmax)
30 – 70 % decline for many soluble and mitochondrial enzymes; theoretical models allow up to 10-fold drop over the lifespan
Oxidative carbonylation, advanced glycation, nitration and cross-linking distort catalytic residues or prevent domain motions that gate the chemistry step; crowding/viscosity also slows the catalytic conformational change
Membrane-hypothesis meta-analysis and kinetic modelling predict 10× lower kcat in very old cells (PubMed); experimental work on complexes III & IV shows ~50 % Vmax loss with age (Direct MS); many studies (e.g., creatine-kinase, aldolase, LDH) report ≥40 % fall in turnover without large Km shifts (PMC)
KM
Usually unchanged or ↑ 10 – 30 % (lower affinity)
Modest distortion of the binding pocket or loss of critical water molecules; sometimes crowding decreases apparent KM by favouring complex formation, so the net effect averages small
Mitochondrial complexes III & IV show ~1.3–2× higher KM in old rats but the larger hit is still Vmax (Direct MS)
KI (inhibitor) / KA (activator)
Variable, usually < 20 % change and highly enzyme-specific
Ageing seldom targets the allosteric/regulatory site directly; any shift comes from the same oxidative or glycation damage but shows no systematic direction
Few broad surveys exist; most reports find smaller, mixed-direction shifts than for kcat or Vmax
Bottom line:
Across dozens of tissues and enzymes, the catalytic turnover constant kcat is the parameter most consistently and markedly depressed by ageing, dragging Vmax down with it. Substrate affinity (KM) often drifts upward but to a lesser degree, and regulatory constants (KI, KA) show no uniform trend.
Mechanistically, that makes sense: the hardest thing for an oxidised, partially cross-linked protein is not grabbing the substrate—it’s executing the precision proton or electron transfers that constitute chemistry every few micro- to milliseconds.
1. Membrane-bound vs. soluble enzymes
Lipid-embedded catalysts take the bigger kinetic hit.
Synaptosomal vs. cytosolic brain enzymes. A rat-brain survey that assayed paired glycolytic enzymes in both fractions found that the membrane-anchored copies lost 35 – 60 % of Vmax/kcat by 24 months, whereas the soluble pool of the same enzymes fell only 10 – 25 %. Authors attributed the extra drop to lipid-peroxidation by-products forming Schiff bases with Lys/His at the bilayer interface. (PubMed)
Why: bilayer peroxidation and carbonyl adducts rigidify trans-membrane helices, and oxidative cross-linking between membrane proteins slows the “breathing” motions that gate chemistry. Soluble enzymes still accumulate carbonyls/glycation, but they are spared the lipid-radical barrage.
Rule of thumb: expect roughly 1½- to 2-fold larger kcat decline for long-lived membrane enzymes than for their soluble homologues.
2. Classic antioxidant enzymes
Enzyme
Sub-cellular niche
Typical kcat / Vmax shift with age
Mechanistic notes
Key evidence
Catalase
Peroxisome ↔ cytosol “spill-over”
↓ 20 – 50 % in most mammals (liver, kidney, muscle). Some long-lived dwarf strains up-regulate CAT and show no decline, illustrating compensation by expression.
Carbonylation of His^75 and Tyr^358 slows H₂O₂ entry into the heme cavity; crowding in enlarged, lipofuscin-rich peroxisomes raises diffusional barrier.
Mouse models: 38–50 % lower CAT protein in GH-over-expressing short-lived mice; dwarfs showed ↑22 % activity with age. (PubMed)
Superoxide dismutases (MnSOD, Cu/Zn-SOD)
Mitochondrial matrix, cytosol
Mixed: total activity often rises 10–40 % as transcription compensates, but single-molecule efficiency (kcat/KM) falls 10 – 30 % because nitration of Tyr^34 (MnSOD) or glycation of Lys^122 (Cu/Zn-SOD) slows electron transfer.
Some tissues (hippocampus) actually lose activity, suggesting region-specific failure.
Reviews of SOD and degenerative disease; human CSF survey saw progressive drop in specific activity despite higher mass. (PubMed, JNS Journal)
Thioredoxin reductase (TrxR) / Peroxiredoxins
Cytosol & mitochondria
↓ 25 – 50 % kcat; TrxR nitrated at Cys-Sec dyad, Prx over-oxidised to sulfinic/-sulfonic forms that are catalytically dead until sulfiredoxin repairs them.
Loss of NADPH recycling capacity leaves Trx in oxidised state, feeding forward to further protein-S-S accumulation.
Aged mouse heart: TrxR activity down 40 %; nitration identified as cause. Prx hyper-oxidation accumulates with age. (PMC, PMC)
Glutathione system (GSH ↔ GSSG)
Cytosol, mitochondria, ER
Glutathione reductase: ↓ 15 – 35 % kcat; Glutathione peroxidase: loss is tissue-specific (0–40 %). Ageing lowers NADPH and depletes total GSH, so the effective catalytic flux through the cycle can drop >50 % even if native kcat is partly preserved.
Elderly humans show slower fractional synthesis rate (FSR) of GSH and higher GSSG, consistent with impaired GR + weaker NADPH supply.
Human isotope studies and red-blood-cell kinetic work. (PMC)
3. Take-home hierarchy of vulnerability
Membrane-embedded enzymes and transporters – worst hit (oxidised bilayer + carbonyl cross-links).
Homodimeric soluble antioxidants (SODs, catalase) – moderate decline, often masked by compensatory over-expression.
Small redox-cycle enzymes with repair pathways (Prx + sulfiredoxin) – activity falls sharply only if repair is overwhelmed or NADPH runs low.
Across these classes, the most consistent kinetic casualty of ageing is still the catalytic turnover step (kcat), with KM and allosteric constants drifting less and without a uniform direction.
Why this matters for models that “perturb kinetic parameters”
When you simulate age as a uniform ±20 % tweak to every parameter, you under-estimate real ageing for many membrane proteins (which may deserve −40 % … −70 % kcat) and over-estimate it for many regulatory KI/KA values that hardly budge. Folding a realistic hierarchy of vulnerability into the parameter-scan greatly improves the model’s ability to replicate observed metabolic flux collapse in senescent cells.
How ageing alters k cat / V max in the next set of enzyme families
Enzyme/system
Cellular location
Typical loss of catalytic turnover (old vs. young adult)
Principal lesions
Representative evidence
ATP-synthase (Complex V)
Inner-mitochondrial membrane
30 – 55 % ↓ Vmax / kcat in 24- to 30-month rodent heart & brain submitochondrial particles; starts after mid-life and parallels cardiolipin oxidation (PubMed, ScienceDirect)
Peroxidised cardiolipin distorts Fo c-ring; carbonylation of β-subunit blocks rotary hinge; mis-assembly of α3β3 stack
TCA-cycle enzymes
Matrix (soluble except Sdh)
• Aconitase 1: 50 – 70 % ↓ kcat by late life (high-sensitivity 4Fe-4S cluster) (PMC) • α-KG-dehydrogenase: 30 – 60 % ↓; acts as ROS source and target (ScienceDirect, PubMed) • Succinate dehydrogenase (Complex II): 25 – 40 % ↓; partly membrane-embedded • Citrate synthase / malate dehydrogenase: 0 – 20 % ↓ (more robust active sites)
Cluster oxidation, thiamine-dependent decarboxylase nitration, lipid-induced crowding for Sdh
Proteasome catalytic core (20 S β1/β2/β5)
Cytosol & nucleus
30 – 60 % ↓ chymotrypsin-like (β5) activity in old retina, muscle, liver and whole mouse (PMC, ScienceDirect)
4-HNE & glycation adducts on Thr-1, gating defects from 19 S oxidation, ATP drop hindering 26 S assembly
Lysosomal hydrolases
Lumen (acidic)
Enzyme mass often rises but effective kcat drops 20 – 40 % because lumenal pH drifts 0.5–0.7 units upward and lipofuscin blocks substrates; cathepsin D activity falls ≈40 % in aged myocardium while β-glucuronidase can climb (ScienceDirect, PMC)
Proton-pump failure, iron-catalysed cross-links, crowding by indigestible lipofuscin
High- vs. low-turnover proteins
—
Fast-turnover (<3 d half-life) enzymes (e.g., glycolytic triose-phosphate-isomerase) show only 5–15 % activity loss because damaged copies are constantly replaced. Long-lived (>30 d) membrane or extracellular-matrix proteins accumulate oxidative lesions, losing 30–70 % kcat unless specialised repair exists. (Nature, PMC)
Damage dilution by synthesis vs. “sit-and-take-it” exposure; synthesis itself slows with age
1 ATP-synthase (Complex V)
The rotary F1β catalytic sites are shielded, but rotation depends on the lipid-embedded c-ring and γ-shaft. Cardiolipin peroxidation stiffens the ring, and carbonyl bridges on β-subunit Gly-Ala hinges lengthen the power-stroke dwell time—halving k cat. A few c-subunit lipids have half-lives of weeks, so damage accumulates faster than turnover.
2 TCA cycle
Aconitase’s Fe–S cluster is a ROS “magnet”; once oxidised to [3Fe-4S], catalysis stalls until cluster is rebuilt (slow in aged matrix). α-KG-DH contains a lipoamide arm whose thioester is glycated and nitrated, throttling decarboxylation. Succinate-DH straddles the membrane and is doubly hit by lipid radicals and matrix ROS, whereas soluble citrate-synthase retains most activity.
3 Proteasome & lysosome
Proteasome β5 (Thr-1 active-site) is highly nucleophilic; 4-HNE and AGEs form Michael adducts that cut k cat. Oxidised 19 S caps also dissociate, so fewer 26 S particles are competent.
Lysosomal hydrolases depend on pH ≈ 4.7; ageing vacuoles rise to 5.2–5.5, cutting catalytic rates ∼2-fold even where enzyme protein increases. Lipofuscin acts as a physical inhibitor and generator of intralysosomal ROS that inactivate nearby enzymes.
4 Protein half-life matters
An enzyme with a 6 h half-life is replaced ~3 000 times during a human life; even if each copy suffers oxidative hits, the damaged fraction never climbs high. By contrast, ATP-synthase c-subunits, nuclear-pore scaffolds or lens-crystallins (half-lives > months) see cumulative lesions pile up, driving much larger drops in k cat.
Practical modelling tip
When you perturb kinetic parameters to mimic ageing:
Membrane and long-lived complexes: reduce k cat by 40–70 %, perhaps raise K M 10–20 %.
Soluble, high-turnover enzymes: apply only 5–20 % k cat loss unless the active site contains labile Fe-S or lipoamide groups (use aconitase/α-KG-DH values above).
Proteostasis machinery (proteasome, lysosome): cut k cat 30–60 % and drop V max further if ATP supply or vacuolar pH is modelled.
This hierarchy better recreates the experimentally observed collapse of fluxes in aged cells than a blanket ±20 % tweak to every kinetic constant.
