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
https://www.molbiolcell.org/doi/10.1091/mbc.E21-12-0627
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!Aging is associated with a systemic length-associated transcriptome imbalance | Nature Aging
https://www.nature.com/articles/s41588-022-01279-6
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
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.
https://www.sciencedirect.com/science/article/pii/S1568163721000027
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).
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
Reduction, proliferation, anddifferentiation defects ofstem cells overtime: aconsequence ofselective accumulation ofold centrioles inthestem cells?
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.
https://www.sciencedirect.com/science/article/abs/pii/S1568163724001284?dgcid=author
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).
- Large, multi-subunit soluble complexes (e.g., mitochondrial ETC) – multiple vulnerable interfaces.
- 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.