Protective Complexity
What is the general concept of significantly delaying the aging rate?
The aging process may be a movement in a non-orthogonal hyperspace of exponentially expanding coordinates in time. Future biology must find a way to stabilize the distance between the current state of the living system and the Embryonic Reset origin (Figure 1). Nature provides an example by addressing all coordinates during the original Embryonic/Cloning Reset. It reverts epigenetic age, reimposes a fresh banding pattern on chromatin, produces brand-new defect-free proteins, creates a brand-new cell-to-cell communication dynamic, and resets development equations (1)–(19) to the time point 0. As a result, optimal biology in terms of Gompertz function is reached at 10–11 years of age, with the annual probability of mortality H(t) ~ 0.0001 in modern society. Puberty shifts H(t) to ~ 0.001 at 20 years. It is interesting that annual mortality drops between 0 and 11 years. This trend shows a complex interplay between the developmental and entropic components of the aging process. The entropic component has kept rising linearly since the early embryonic period. The developmental component is on its way to the optimal vector of A(t)/R(t) ratios in the critical pathways. The global optimum, considering all the processes mentioned above, is reached around 11 years. Keeping parameters of the living system at this optimal level is a distant grand goal of anti-aging research.
The combinatorial therapy of aging must address all essential pathways simultaneously through a limited number of factors, measured in dozens. A very cumbersome method will rob life of its meaning. Figure 5 below shows the number of chemical agents consumed by the patients registered in the NACC (National Alzheimer’s Coordination Center) database.
Figure 5: The number of visits (N) in the NACC database (ordinate) as a function of the exposing ligands co-administrated to the same individual during a visit (abscissa).
Figure 5 shows that record polypharmacy reaches ~ 40 in NACC but may be higher in the databases with a greater membership. Improving therapeutic windows would make every component less toxic and more active. These theoretical anti-aging molecules can be co-formulated for delivery via optimal routes: transdermal ointments and cosmetic creams, inhalations, oral pills, and food supplements.
This longevity exposure addressing all dimensions can include chemical rejuvenation-by-reprogramming (RIR) cocktails; anti-cancer morphogenic cocktails; proteosome activating and autophagy/mitophagy activating cocktails; anti-inflammation and senolytic cocktails; embryonic stem cell exosome cocktails and pro-survival miRNA components; stimulators of brain energy metabolism; stem cell optimization cocktails, inducing quiescence and simultaneously increasing circulating titers; immuno-stimulating cocktails; anti-glycation, anti-crosslink, and anti-lipofuscin agents.
The concept of protective complexity.
There are >1063 drug-like molecules available based on carbon, oxygen, nitrogen, phosphorus, and sulfur connectivity (Sadubekov et al., Bohacek et al., page 43, footnote). Other estimates are more modest, but none are below 1023. Lead-like compounds target a specific structural class of targets with shape complementarity to fit a specific binding site. On average, 106 lead-like precursors are tested to produce a drug of median quality (Sadubekov et al., Figure 2, 4). Perhaps 1012 lead-like HTS candidates will be required to create a drug of superior quality. Among the currently used ~ 5000 regulator-approved and OTC compounds, ~ 50 were shown to be life-extending in model systems (https://genomics.senescence.info/drugs/). Thus, 1014 HTS hits are required to produce a longevity pharmaceutical of superior quality and 108 HTS hits for a longevity compound of median quality. Returning to the analysis of Bohacek et al., about 1040 superior longevity drugs exist in the organic connectivity universe. The number of 2-40 component cocktails is practically infinite.
Naturally, physical or even computational methods of today are insufficient for traversing this chemical space. Its information content may perhaps exceed the information content of life and reverse its entropy, producing the states of “alternative youth,” a path of pharmacologically controlled development moving orbitally to the Embryonic Reset point in the aging hyperspace and not exponentially escaping in infinity, as is the case today (Figure 1).
One can define “superior quality” as selectivity to the intended targets vs. inertness to other binding sites. An organism can be saturated with a greater benign polypharmacy of “superior quality agents” vs. “median quality agents” without the risks of toxicity.
We define “protective complexity” as the state of saturation of an organism with the corrective pharmacological information mobilizing enough of its endogenous regenerative potential to change the escape dynamic of biological entropy into an orbital, tangential trajectory around the Embryonic Reset in the aging hyperspace.
By blocking enough pro-aging pathways and activating enough anti-aging pathways, this systemic state will be reached early or later. Already, cocktails of 7-9 molecules cause complete reprogramming of the adult cells into embryonic cells, producing normal embryos (“chemical cloning”). These cocktails of recently developed low-molecular weight compounds perform the task of Yamanaka/Thompson factors which were fine-tuned by 4 billion years of evolution, but with the same result (Wang et al., Zhao et al., Takeda et al., Chen et al., Silva et al., Tan et al., Mitchell et al.). This efficiency after only two decades of artificial evolution, suggests that the small molecular rejuvenators can be developed rapidly.
Effectively traversing the combinatorial space.
The pathways controlling longevity are numerous but contribute with unequal weights. It is important to map the existing pool of pro-longevity molecules to the respective pathways and form a drug-target network (Yildirim et al.). The drug-target network can be expanded to the drug-pathway network and target-target network (Jadamba et al.). The initial pool of pro-longevity pharmaceuticals would map possible molecular interactors among the well-known chronic disease targets and novel players. The technical feasibility of this feat arrived with the development of protein folding algorithms like AlphaFold2, modeling the folding of all protein components, not just those amenable to X-ray crystallography or NMR-structural analysis (Jumper et al.; De Brevern). AlphaFold2 predictions accelerate, but do not replace experimental structure determination (Terwilliger et al.). Some AlphaFold2 predictions match experimental maps remarkably closely. In other cases, even very high-confidence predictions differ from experimental maps on a global scale through distortion and domain orientation and on a local scale in backbone and side-chain conformation (Terwilliger et al.). Eventually, the AI methods of protein folding prediction will reach perfection, and we will be able to attribute each small molecule to multiple binding sites in the entirety of the proteome, transcriptome, and lipidome.
What is needed is a truly diverse, unbiased library of several hundred (or even more) longevity ligands with correctly quantitated effects on human lifespan. This library will be mapped by docking to the combination of PDB/AlphaFold2 and/or other structural models of biomolecules, and the longevity effects will be mapped to the full variety of biological structures.
This strategy opens the path to short-circuiting biological evolution by the protective complexity of the agents changing the activity of the biological sites in the same fashion as natural evolution changes them by point mutations of the residues and naturally controls the lifespan.
Indeed, the sequences of short-lived and long-lived similar organisms are divergent, and these sites of divergences can be tracked to facilitate the development of the longevity network (Li et al., Kuningas et al., Tian et al.). Perhaps mapping the sequence variations between long-lived and short-lived primates onto the sequence variations between ordinary humans and centenarians may be a robust method to extract the pathways and molecular players of longevity (Farre et al.). Because the aging trajectories of different organisms are very specific and only the big picture is somewhat similar, perhaps human-only variation is one truly reliable criterion for the identification of longevity genes (Budovsky et al., Smulders et al., Revelas et al.). But human-only associations are poorly reproducible (Novelli et al.). This can be explained if the individual effects of single genes are small, but they accrue in groups that are co-evolving (Sebastiani et al.). Sebastiani et al. conclude that the genetic factor is significant in the human population, and the supercentenarians demonstrate compression of morbidity. The chronic diseases that mark the end of everybody’s life develop over a shorter, compressed period at the very end of a long lifespan. I analyzed the list of GWAS longevity variants and found that the list is strongly co-enriched with the lists associated with major human chronic diseases. The delayed onset of morbidity in the super-centenarians can be explained by the longevity variants in the disease-related genes and by the greater stability of the regulatory contours to runaway destabilization in the super-centenarians.
The longevity mapping will contract the prospective space of small molecular ligands. The candidate longevity molecules will be limited by the requirement to bind the structures that contribute most weight in the lifespan modification, and the in-silico molecules can be created purposefully to target these longevity components (Ferreira et al.). Such ligands can be planned in silico, simultaneously with the optimal synthetic route, and in silico tested against the given site, and only the best candidates will be synthesized for screening (Van Hilten et al., Coley et al., Trobe et al.). Eventually, the longevity map will be improved in all aspects: the reliability of the structural representation of target biomolecules, the inclusion of targets, the quality of docking, the presence of small ligand binding, and the clustering of these binders in the selected pathways.
In the end, by controlling the conformations and availability of active sites, the chemical cocktails saturating the recipient organisms will play the same role as the groups of mutations in the coding and regulatory regions naturally defining longevity.
The constant accumulation of mutations and macromolecular defects in the somatic cells will be compensated and bypassed by the saturating chemoinformatic field of protective complexity. The possibility of such deep compensation is suggested by the existence of artificial recombinant organisms, lacking 50% of the genome (analogue of age-related defects) but comparably viable vs. the natural prototypes (Venter et al., Moger-Reischer et al.). The Mycoplasma system took 2000 generations to compensate for viability defects due to the loss of 50% of the genome. In the Protective Complexity approach, the role of the corrective evolution will be taken by the corrective flow of chemical information, constantly directing the aging process not away from the origin but tangentially vs. the Embryonic Reset point. Alternative Youth will orbit Original Youth in this spatial representation.
The results of Lemon et al., Spindler et al., and Liao et al.
The report by Lemon et al. (2005) states that reactive oxygen species, inflammatory processes, insulin resistance, and mitochondrial dysfunction are the main dimensions of aging. According to the report, all mechanisms are emphasized in transgenic growth hormone mice (TGM), which display a syndrome resembling accelerated aging, in agreement with Bartke et al. describing growth hormone deletion in long-living murine strains.
The authors formulated a complex dietary supplement containing 31 ingredients known to ameliorate the above-mentioned aging mechanisms. Lemon et al. (2005) showed that this supplement completely abolished the severe age-related cognitive decline expressed by untreated TGM. The lifespans of both TGM and normal mice are extended by this supplement. Treated TGM showed a 28% increase (p <.00008) in mean longevity. An 11% increase in mean longevity was also significant (p <.002093) for treated normal mice compared to untreated normal mice. Similar reports are presented in Lemon et al. (2016), Jong et al. (2012), Aksenov et al. (2012), Perez et al. (2017), Hutton et al. (2018), Heath et al. (2007), and Simon et al. (2024), which mention a randomized, controlled clinical trial demonstrating improved owner-assessed cognitive function in senior dogs receiving a senolytic and NAD+ precursor combination. Further affirming evidence is in Patit et al. (effect of nutritional interventions on longevity of senior cats), Schauf et al. (healthy aging is associated with preserved or enhanced nutrient and mineral apparent digestibility in dogs and cats fed commercially relevant extruded diets), and Tran et al. (a multi-ingredient athletic supplement disproportionately enhances hind leg musculature, jumping performance, and spontaneous locomotion in crickets).
A common plot in these reports is that a plurality of actives delaying more than one route of aging either increases lifespan or reverses strong correlates of lifespan, such as cognitive status or general energy level. More evidence for mammals is provided on the “Human Data” page.
The negative results are published in Spindler et al., who treated long-lived male F1 mice with combinations of dietary supplements and natural product extracts and determined their effects on lifespan and health span. According to Spindler et al., nutraceutical, vitamin, or mineral combinations reported to extend the lifespan or health span of healthy or enfeebled rodents were tested, as were combinations of botanicals and nutraceuticals implicated in enhanced longevity by a longitudinal study of human aging. A cross-section of commercial nutraceutical combinations sold as potential health enhancers was also tested, including Bone Restore®, Juvenon®, Life Extension Mix®, Ortho Core®, Ortho Mind®, Super K w k2®, and Ultra K2®. A more complex mixture of vitamins, minerals, botanical extracts, and other nutraceuticals was compounded and tested. No significant increase in murine lifespan was found for any supplement mixture. The super-diverse mixture of all supplements significantly decreased lifespan.
To understand the difference between the pro and contra branches of data, I explored the role of murine strain in the longevity effects. The composition of Lemon et al. was included in the tests by Spindler et al. (Table 1). In the other groups tested by Spindler et al., lifespans were significantly extended by treatments. For example, a 40% caloric restricted group and two groups treated with β-adrenergic receptor blockers achieved 23% and 9% lifespan extensions, respectively. Despite being the longest living heterogenous murine strain (Spindler et al.), F1 mice are still amenable to the lifespan changes caused by stronger factors. The data obtained on OTC supplements represents combinations of biologically weak agents, no wonder they are sold without prescription.
In the experiments of Lemon et al., the “normal mice” (not TGM) were C57BL/6J male and 3 SJL female hybrids, a different strain with a different lifespan than F1. Compared to F1, this strain is relatively short-lived, even in its genetically unmodified form (see Figure 1 of Lemon et al., 2005). The TGM mice of Lemon et al. incorporated the rat GH gene into one chromosome, chronically elevating plasma GH levels more than 100-fold. Growth hormone pathways are known to be a strong regulator of lifespan in mice, with dwarf GH-negative Ames mice being the longest-living. The introduction of extra GH reduced TGM’s 50% survival to 300 days, making it to be 50% of C57BL/6J male lifespan and 3 SJL female hybrid lifespan and only 30% of F1 lifespan.
Murine strains are known to be extremely diverse biologically (Liao et al.). The work of Liao et al. is a true eye opener.
According to Liao et al., genetic variation in the murine lifespan response to dietary restriction may result in life extension or in life shortening. Caloric restriction is believed to be a golden standard for lifespan extension in rodents. Nevertheless, Figure 1 of Liao et al. shows ~ 3-fold variation in the pure-bred strain lifespans and a random effect of caloric restriction, tending to decrease lifespan in most strains, the decreases exceeding the increases. Similarly, intractable variation is present in human data, with the same factors often producing polarly opposite outcomes in different populations. This inconsistency as a function of group composition is important not only practically (as a frustrating source of false positives and negatives), but also theoretically, perhaps suggesting a random coil model of systemic responses (Figure 6 below).
Trying to translate these results to human populations, I will draw several conclusions:
- A combination of protective factors is more effective for the high early mortality pockets of any population.
- Too great a diversity of exposure may overload hepatic function, even when individual components are harmless. The experiments of Snider et al. show that a too-complex composition began to inhibit liver function and led to premature mortality in the F1 mice.
- Caution is required in the extent of the introduced polypharmacy, which is benign in other aspects.
The late mortality edge of the mammalian population (including humans) consists of individuals already optimized for longevity. In this group, the response to combinational treatments may be lower or absent. This is observed for most other factors: the lifespan of humans after ~ 105 years is barely modifiable, but at 60–70 it is easily modifiable. The planning of longevity interventions must account for the differences between the improved survival of prematurely aging or endangered individuals caused by combinational treatments (while the aging rate remains the same) and the bona fide changes in the aging rate.
Lifespan can respond to any factor in both directions, depending on the genetic make-up of a biological system. This becomes easier to understand by representing each gene’s effect as a vector linked to the preexisting vector structure. The result of adding a component to the vector sum may either increase or decrease the final vector. Apparently, the effects of individual genes combine as components of a random coil. Perhaps, the true units of longevity are the Coils/Arrays/Domains of individual effects. The effects of longevity treatments add segments to the random coil. A path to radical extension of lifespan is to increase the lengths of segments and to straighten the topology. The resulting end-to-end distance in the random coil (lifespan) will increase when the angles between the segments are constrained to avoid certain ranges, for example, all angles < 90 degrees. Figure 6 below shows how the addition of the same vector to the sum of pre-existing vectors may increase or decrease the vector sum.
Figure 6: Vector representation of lifespan. In the case of (A), the addition of the treatment vector decreases the lifespan previously defined by a combination of gene effects 1 and 2. In the case of (B), the addition of the same treatment, but in a different context, increases the lifespan.
The objective of the next section is to explore the high-throughput screening of strong anti-aging therapeutics, capable of altering aging rates.
Anti-aging, high-throughput screening.
There is no perfect method for anti-aging screening. Confluent yeast colony lifespans can be expanded by small molecular compounds (Chen et al.), but the relevance of these changes to the human lifespan is peripheral. Zooplankton can be monitored by AI, and the age-related patterns can be reversed by compounds (Cho et al.), but again, the relevance of these effects to humans is unclear. Long-living genetically heterogenous murine strains like F1 may be a more relevant tool, and dog, cat, and cattle populations are even closer tools to generate data relevant to humans. However, these systems are time-consuming, with the outcome developing for years.
Ideally, researchers must have a tool for economically exploring billions of chemical scaffolds per year. The most relevant anti-aging changes are expected in reversals of senescence in complex non-dividing adult cells, limiting the lifespan. Such cells are neurons, nephrons, cardiomyocytes, quiescent stem cells, and endocrine gland cells. As a result of the rejuvenating effect, the parameters of these primary human cells must reverse in time; for example, a senescent transcription signature must return to the signature of early adulthood. Each type of primary cell can be grown in vitro by differentiating the iSPCs. The aging of dividing cells (fibroblasts, epithelial, and endothelial cells) can be monitored relatively easily as replicative senescence, which takes place within 40–60 cell divisions. But how can one age the long-living, post-mitotic human cells?
- Long-term culture: Extended in vitro culture of iPSC derivatives can lead to cellular phenotypes that more closely resemble aged cells. Most suitable for murine cells.
- Age-inducing stressors: These can be applied to iPSC-derived neuronal cultures.
- Overexpression of Progerin: This can increase aging indicators such as ROS accumulation, γH2AX foci, morphological changes and a loss of H3K9me3 in neurons.
- Telomere shortening: This can be done using chemical compounds.
- Culture in aged extracellular matrix (ECM): This can be used to induce an aging phenotype in wild-type iPSC-derived cells.
- CRISPR/Cas9 technology: This can be used to induce an aging phenotype in wild-type iPSC-derived cells.
The most suitable for high-throughput screening is the growth of the iPSC-derived post-mitotic cells on the layer of aged extracellular matrix. This path is close to the natural aging process and still not very time-consuming. Neuronal cell cultures form signaling networks in vivo (Callaud et al., Soriano), and signaling by the young and old neurons will likely differ in pattern and will respond to a rejuvenator. Perhaps growing neurons in the presence of microglia and endothelial cells can produce an even more realistic model. The older and younger cells can be visually differentiated by morphology (Chang et al., Bhanu et al.), with the help of artificial intelligence (AI) trained on billions of images of young and old cells. The accuracy of image analysis has dramatically improved with the development of AI technology. AI can evaluate disease-specific phenotypes of iPSC-derived cells from label-free microscopic images (Kusumoto et al.). Similar image recognition algorithms exist for astrocytes (Yakovlev et al.). AI can also recognize the morphological changes associated with ECM-induced aging (Koga et al.). The test cells can harbor an OSKM cassette as an internal positive control to induce RIR and document the morphological changes.
A cassette expressing progerin would produce a dose-dependent aging-like process, well correlating with human longevity (see “human data”) and also amenable to automatic computerized morphological analysis.
Rapid reversals of aging morphology in the presence of single-tested drugs or drug combinations and the return of aging morphology in the absence of such agents would mean the discovery of a potent rejuvenator(s). After a period of recuperation, the same cells can serve as reporters for multiple agents. Such screens can be easily started with multiple post-mitotic primary human cell cultures, derived from the iSPC extracted from diverse populational backgrounds, to avoid the pitfalls of murine experiments (discussion of Figure 6).
Adipocytes from multiple individuals can be reprogrammed and converted into self-renewing iSPC cultures. The stem cell cultures can be propagated in 20–50 small bioreactors, each for a participating individual. The iSPC from each bioreactor can be withdrawn in equal numbers, mixed, and differentiated in genetically heterogeneous cultures of primary cells plated on the aging ECM monolayer (Ozcebe et al.). The positives of the HTS need to be verified in an in-vivo mammalian system.
The abundance of peripheral leucocyte, serum, urine, saliva, and exhaled air biomarkers of age allow for the design of biological clocks responding to treatment with a rapid reversal of the clock. For example, a 2-week treatment in a randomized group of experimental mice compares blood samples before and after. After a 2-week rest, the groups are re-randomized, and the next treatment is tested, etc. Considering a 24-month lifespan, at least 24 agents can be tested for putative biomarker clock reversals in the same murine group.
Use of novel anti-aging scaffold data to launch the evolutional CADD (Computational Accelerated Drug Design) process.
The HTS screening program will identify a training set of pro-longevity molecules, and this training set will be used to launch a computationally accelerated drug design (CADD) process. By migrating drug selection in silico from the physical realm, the pace of discovery can accelerate by orders of magnitude, identifying progressively more potent scaffolds. By casting away reliable negatives (a huge majority of all candidates), time and effort become available for more thorough tests of putative positives. Any in-silico process depends on training principles. For example, relying on pharmacophores would lead to the re-discovery of the same pharmacophores in different combinations.
We are also interested in such discovery principles that can anticipate novel scaffolds based on an extensive training set (several thousands of actives, covering most of the Longevity Network). An information-rich space is achievable by the in-silico adaptation of the Molecular Bio-spectra by Fliri et al. The original method involved expressing ~ 100 enzymes and receptors and measuring the affinities of a candidate molecule to each member of this panel. A very specific affinity profile would characterize the biological activity of the candidate molecule. Despite very different connectivity and composition, molecules can produce similar affinity profiles and therefore demonstrate analogous biological mechanisms. A similar principle underpins the time-tested NCI-60 system (https://ioa.cancer.gov/oncologydrugscompare). In this approach, a profile of 60 cancer cell lines is inhibited by different molecules, and the extent of inhibition in each cell line forms a pattern.
Clustering by these inhibition patterns defines the mechanisms or targets of anti-cancer activity. The original method of Fliri et al. also defines a small ligand by a profile of 3D interactors, whereas the ligand and 3D panel can both be in silico. For example, the 3D panel can constitute a set of known PDB files from the Protein Data Bank (https://www.rcsb.org/search), and the virtual molecule can be proposed by an algorithm.
Instead of measuring the binding constants of the physical molecule to physical interactors, in the virtual fingerprint method, one computes the docking affinity of the proposed structure to the virtual panel of interactors.
In modern computers, docking is a long process considering multiple poses for the ligand and protein chains. However, the supercomputers of the future and simplified docking would result in a few microseconds per profile. This profile will be compared to the profiles of known lifespan extenders, and if it falls in the same cluster in the principal component space, then the proposed molecule is likely a ligand to one of the active sites of the Longevity Network.
Instead of synthesizing and screening combinatorial libraries or natural products, the method starts with the proposal of a biologically active molecule. Its physical embodiments can be later synthesized and tested in cell culture. The strength of the rejuvenating effect can be related to the characteristics of the interaction profile across the panel of virtual partners. For example, average computed affinity and variations of the computed affinity between virtual interactors are features of a classifier linking the rejuvenation effect to the virtual profile. Figure 8 illustrates this methodology.
Figure 8: Comparison of a hypothetical virtual molecular fingerprint for a rejuvenator (upper graph) and for an inactive candidate molecule (bottom graph). The numbers on the abscissa are the positions of virtual binding targets (PDB files) in the profile. The numbers on the ordinate are natural logarithms of the computed binding affinity of the tested small molecular structure to virtual binding targets. In both cases, the candidates are matched by molecular docking to an array of 30 PDB structures of aging-related protein products. The profile includes the PDB files for known aging-related targets (p53, TERT, p21, p16, ARF, HIF, ERBB2, MYC, AT2, PAPPA, mTOR, FOXO3, SIRT1, SIRT6, etc.). The number 30 is selected for the convenience of a presentation; a more detailed profile including 100–200 members is preferred. The structures 3, 6, 9, 11, 15, 18, 22, 26, and 28 demonstrate high affinity for the rejuvenator and low affinity for a random, unrelated molecule. Different rejuvenators produce different profiles, forming positive subsets that are distinguishable from each other and from unrelated inactive molecules. A new virtually proposed small molecule producing a profile strongly correlating to that of a known rejuvenator is likely to be a rejuvenator too, despite a different scaffold.
Each class of rejuvenators in this method will be expanded manyfold through the virtual affinity profile. Similar virtual affinity profiles mean that the compared molecules bind a similar set of biological structures but can have distinct connectivity. The database DrugAge (https://genomics.senescence.info/drugs/) includes > 400 approved and investigational lifespan-modifying pharmaceuticals, tested in multiple animal species.
Each agent would form its own virtual bio spectral signature, encoding its affinity profile and biodistribution. A novel in-silico-generated molecule would also produce its own bio spectrum to compare with the known set. The virtual bio spectral data for the anti-aging compound will be contrasted with the representations of random drug-like chemicals. Random inactive compounds should produce dissimilar profiles, and the cut-off proximity metric should be accepted at the level that rejects ~ 106 of random compounds. A proposed in-silico ligand that still produces a similar bio spectrum within the cutoff distance from the known positives is a relevant candidate for chemical synthesis and testing.
Li et al. (2018) describe deep neural network-based predictions of protein interactions using primary sequences. Such methods can be supplemented with correlations of phylogenetic trees for tight protein complexes, the analysis of short sequences on the contact interfaces of interactors, co-localization on the chromosomes, and co-expression in the same differential expression signatures. The combination of predictive methods and more rare direct observations of protein complexes (two-hybrid systems, co-immunoprecipitation, affinity precipitation, denaturing gel electrophoresis analysis, etc.) suggests potential interfaces to be disrupted by in silico-generated ligands (Wells et al.). This is a challenging approach, but it is also promising to deliver multiple novel scaffolds through structure-based ligand development.
The presence of toxophores in the proposed in-silico rejuvenators can be detected by the existing QSAR/toxophore catalog tools. However, adding a bio-spectra environment subjected to AI analysis can weed out toxicity even further. Known rejected pharmaceuticals that revealed surprises in later development (Phase I-III trials) will be compared with the approved drugs, and the bio spectra profile of toxicity will be derived for each case. The bio-spectrum of each putative virtual candidate for synthesis will be analyzed by the same AI algorithm that differentiates future trial failures and future passers. Only non-toxic virtual candidates that exceed biosafety of known drugs will be allowed to become physically embodied.
Finally, simple 3- to 4-ccomponent cocktails of strong rejuvenators will be included in the longevity testing industry of the future. Synthetic lethality (Kaelin Jr. et al.) synergistically expands the therapeutic window for treating cancer while sparing normal cells. Likewise, synthetic rejuvenation may also be synergistic when the factors combine. One example is OSKM. None of these factors are sufficient to induce a full embryonic reset in differentiated cells, but together, or at least in triple combinations (OSK/OSM), the Yamanaka factors break a qualitative barrier and turn on the self-renewal software of the cell.
With the CADD processes proposed above, millions of individual rejuvenators can be ranked and combined for synergy testing. At a certain point, complex organisms will start responding to such treatments with Alternative Youth, a mixed state of fundamental rejuvenation and continuous aging. If OSKM and/or known chemical reprogramming cocktails can accomplish this feat, it is easy to believe that the hyperspace of our informational wiring can support tangential, non-escaping trajectories in the movement relative to the Embryonic Reset origin. After all, in termites, where females live 30 years and workers live 2 weeks (> 600-fold lifespan difference while harboring the same genomes!!), a certain combinatorial set of factors brings these differences in motion. Can we, as humans, gradually evolve a similar set of potent chemical factors? Figure 9 shows the scheme of this artificial chemical evolution.
Figure 9: The scheme of computationally driven chemical evolution of rejuvenating ligands and combinations. The initial training set allows multiple prediction rules, identifying pharmacophores, bio spectra fingerprints, and high affinities between known longevity targets and the discovered ligands. Based on these prediction rules, a virtual molecule is proposed and analyzed. If the score of the virtual molecule exceeds any existing scores, it progresses to the biosafety filter. If the predicted toxicity is below the lowest prior values, the molecule is synthesized and tested. The testing follows morphological changes in neuronal organoids growing on old ECM, shifts of biomarkers in experimental mouse populations, resumption of division in senescent cells, resumption of youthful contractile activity and morphology in cardiomyocytes, and extension of lifespan in short-lived zooplankton species of multiple zoological types (protozoans, crustaceans, worms, rotifers, polyps). Placing the bulk of effort in the virtual domain saves the resources and time for elaborate testing of the proposed compounds. Once the molecule is certified as a success, it joins the training set and changes the prediction rules. The next molecule is designed based on more stringent prediction rules and exceeds the previous in activity and biosafety. Gradually, the system of drug design accumulates more and more potent rejuvenators with favorable safety profiles that are amenable to forming highly complex combinations. Such combinations may lead to unknown biological effects, producing the proposed states of Alternative Youth.
Conversion of Runaway Aging into Orbital Aging by Cyclic Partial Reprogramming.
Chemically induced cyclic partial reprogramming is the current best hope for radical aging delay. The method relies on the reactivation of embryonic reprogramming machinery in adult somatic cells and inducing RIR (reprogramming-induced rejuvenation). Indeed, the markers in the reprogrammed cells suggest a rewinding of the biological clock back to its origin. The protein complexes blocking inappropriate access to the genome in heterochromatin define epigenetic information patterns (Lee et al.). Epigenetic reprogramming induces pluripotency by inducing the embryonic factor machinery that completes the natural rejuvenation cycle (Kerepesi et al.). The first step in this process is remodeling the chromatin complexes and enhancer demethylation (Bogdanovic et al.). The clearing of accumulated chromatin entropy results in a child who is biologically younger than parents, despite the latter experiencing aging in every cell before conception. Cloning of explicitly old somatic cells, converting them into normal youthful progeny by the egg’s magic is an even more striking example. Accumulated somatic mutations do not seem to matter. As a result of chromatin reorganization, the expression patterns become more distinct in younger individuals, with each cell type strongly maintaining its identity. This identity is partially lost in aging cells that tend to de-differentiate (Donertaz et al., Yang et al., Izgi et al.).
If rigidity of epigenetic patterns correlates with youth and longevity, why does partial OSKM or chemical reprogramming facilitate rejuvenation? It performs the same role as the native ova-induced reprogramming by accomplishing the initial chaotization, dissociation of sub-optimal components of heterochromatin, and re-association in correct conformations. A mechanical analogy of such a process is shaking up a basket of grain or a box of pencils. The initial effect is the increase in distances between the elements, ending in a more stable and compact pattern. When applied to chromatin, we define it as “youth.”. Tempering of steel is another analogy: first softening, then relaxation of tensions, and finally freezing the right structure at the cooling stage.
The evidence that longevity is proportional to the rigidity of the epigenetic pattern is abundant and diverse. For example, the cells of longer-living species are more resistant to the loss of identity than the cells of shorter-living species (Fu et al., Appleton et al., Tan et al.). The short-lived dividing cells (epithelial, hepatocytes) are easier to reprogram than post-mitotic lineages (cardiomyocytes); aged cells are easier to reprogram than young cells of the same species; and progeroid cells are more amenable than normal cells (Singh et al., Ocampo et al.).
In the experiments of Macip et al., systemically delivered adeno-associated viruses encoded an inducible OSK system that extended the median remaining lifespan by 109% over WT controls and enhanced several health parameters in 124-week-old male mice. Considering that the normal lifespan of a WT heterogenous murine strain is 2.5 years (140 weeks), the increment is 17 weeks, or 12% of the total lifespan, far less than achieved in other transgenes (Table 1 below). Similar scopes of effects are shown in Ocampo et al. and Alle et al. In all cases, the OSKM or OSK are delivered in a cyclic doxycycline-inducible mode. Alle et al. demonstrate an extension of the median lifespan from 45 to 60 weeks in a life-long reprogramming exposure to OSKM, but the effect is achieved in a short-lived progerin-expressing strain. The list can continue. Researchers are not in a rush to discuss the causes of death for the experimental animals. Despite the intention to maintain the rejuvenated state through a continuous reset of chromatin structure, the aging dynamic ultimately prevails.
Table 1: Murine gene knockout or knock-in experiments demonstrating the pathways regulating the aging rate in mammals Tg: transgene; -/-: homozygous knockout; +/-: heterozygous or partial knockout.
| Source | Gene Alias | Effect |
| Schriner SE et al. Extension of murine life span by overexpression of catalase targeted to mitochondria. Science. 2005 Jun 24;308(5730):1909 | mCAT | >25% |
| Ran, Q., et al. 2007. Reduction in glutathione peroxidase 4 increases life span through increased sensitivity to apoptosis. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 62(9), pp.932-942. | GPX4 | >.6% |
| Ladiges W et al.. Lifespan extension in genetically modified mice. Aging cell. 2009 Aug;8(4):346-52. | αMUPA Tg (PLAU) p66shc−/− Irs2+/− brain Irs1−/− Surf1−/− AC5−/− PappA−/− UCP2 Tg brain MT Tg heart GHr/BP−/− Igf1r+/− FIRKO Klotho Tg Mit CAT Tg MT Tg heart UCP2 Tg brain | 20% 28% 18% 8% (M), 32% (F) 19%(M), 24% (F) 32% 38% 12% (M), 18% (F) 14% (M) 38% (M), 55%(F) 9% (M), 20% (F) 30% (M), 30% (F) 16%(M), 33% (F) 11% (M), 11% (F) 30% (M), 19% (F) 27% (M), 24% (F) 14% (M) 12%(M), 20% (F) |
| Hofmann JW et al. Reduced expression of MYC increases longevity and enhances healthspan. Cell. 2015 Jan 29;160(3):477-88. | Myc+/– | 9% (M), 30% (F) |
| Florian MC et al. Inhibition of Cdc42 activity extends lifespan and decreases circulating inflammatory cytokines in aged female C57BL/6 mice. Aging cell. 2020 Sep;19(9):e13208. | Cdc42 inhibition | 24% |
| Cañadas-Lozano D. et al. Blockade of the NLRP3 inflammasome improves metabolic health and lifespan in obese mice. GeroScience. 2020 Apr;42(2):715-25. | NLRP3−/− | 14% |
| Roichman A. et al. Restoration of energy homeostasis by SIRT6 extends healthy lifespan. Nature communications. 2021 May 28;12(1):1-8. | SIRT6 | 12% (M), 20% (F) |
| Pyo JO et al. Overexpression of Atg5 in mice activates autophagy and extends lifespan. Nature communications. 2013 Aug 13;4(1):1-9. | Atg5 tg | 10-15% |
| Nojima A. et al. Haploinsufficiency of akt1 prolongs the lifespan of mice. PloS one. 2013 Jul 30;8(7):e69178. | Akt1 +/– | 12% |
| Junnila RK.et al. Disruption of the GH receptor gene in adult mice increases maximal lifespan in females. Endocrinology. 2016 Dec 1;157(12):4502-13. | GH releasing hormone (GHR) −/− | 15% (F) |
| Sun LY et al. Growth hormone-releasing hormone disruption extends lifespan and regulates response to caloric restriction in mice. Elife. 2013 Oct 29;2:e01098. | GHRH −/− | 2% (M) 27% (F) |
| Wu CY, et al. A persistent level of Cisd2 extends healthy lifespan and delays aging in mice. Human molecular genetics. 2012 Sep 15;21(18):3956-68. | Cisd2 tg | 6% (M) 28% (F) |
| Canaan A. et al. Extended lifespan and reduced adiposity in mice lacking the FAT10 gene. Proceedings of the National Academy of Sciences. 2014 Apr 8;111(14):5313-8. | FAT10−/− | 20% |
| Liu X. et al. Evolutionary conservation of the clk-1-dependent mechanism of longevity: loss of mclk1 increases cellular fitness and lifespan in mice. Genes & development. 2005 Oct 15;19(20):2424-34. | Murine clk1+/− | 15-30% |
Aging prevails because it is a multigenic and multidimensional process. As soon as an artificially rejuvenated cell begins to interact with the systemic milieu, it experiences re-equilibration, which may proceed slowly, as the results of Alle et al. suggest. In this experiment, one-time exposure to doxycycline-induced OSKM sufficed to extend the lifespan and health span of progeroid mice. The exposure took place early in life but still impacted later ages. The experiment shows that the rejuvenating effects of chromatin remodeling do not equilibrate in a short timeframe and can accumulate with multiple cycles, with perhaps a decreasing payoff per new cycle. The overall dynamic is still in favor of senescence. Instead of accumulating continuously by the Gompertz model, senescence experiences periods of short resets and then resumes the forward progression. Figure 10 illustrates the dynamic.
Figure 10: Qualitative presentation of three alternative time courses of senescence. The upper line is unmodified senescence, advancing exponentially following Gompertz Law. The middle line is the senescence profile in the presence of cyclical partial reprogramming. Activation of OSKM expression by the doxycycline inducer temporarily resets senescence, but it eventually proceeds forward due to the multiplicity of driving mechanisms. The bottom line describes the time profile of senescence in cyclic partial reprogramming when additional dimensions of the process are addressed between the resets. For example, the introduction of a component that “freezes” the currently achieved chromatin state while allowing the OSKM machinery to degrade in the interval between the DOX cycles may cause a slower compensatory onset of senescence and a greater effect. Typically, p53 pathway suppression precedes reprogramming, but its enhancement after the reprogramming cycle may delay the subsequent chromatin entropy relapses.
Combining pharmacological induction of morphogens (p53, p16, Rb/p19-ARF) with pharmacological induction of the telomerase pathway in the intervals between the reprogramming resets can further delay the accumulation of senescence. Telomerase is necessary for the maintenance of the pluripotent stem cell state (Dogan et al.). Ectopic expression of telomerase in cancer-resistant mice increases lifespan by 40% but increases cancer incidence in the non-resistant strains (Mendelsohn et al.). Transient chemical induction of TERT increases health span without an apparent increase in cancer incidence. Ectopic expression of TERT using adeno-associated virus serotype 9 (AAV9)-based gene therapy in adult mice increases both health and life span without increasing cancer incidence.
Berhanardes de Jesus et al. report the effects of telomerase gene therapy in adult (1 year of age) and old (2 years of age) mice. The treatment of 1‐ and 2‐year-old mice with an adenoassociated virus (AAV) of wide tropism expressing mouse TERT had remarkable beneficial effects on health and fitness. The experimental animals did not develop excessive cancer compared to the control animals. Finally, telomerase-treated mice, both at 1 year and at 2 years of age, had an increase in median lifespan of 24 and 13%, respectively. These beneficial effects were not observed with a catalytically inactive TERT, demonstrating that they require telomerase activity. In a human RCT, Salvador et al. followed telomere length (TL) in a randomized, double-blind, placebo-controlled study of TA-65 over a 1-year period (TA-65 is a telomerase activator, cycloastragenol). The study was conducted on 117 relatively healthy cytomegalovirus-positive subjects aged 53–87 years old. Subjects taking the low dose of TA-65 (250 U) significantly increased TL over the 12-month period (530 ± 180 bp; p = 0.005), whereas subjects in the placebo group significantly lost TL (290 ± 100 bp; p = 0.01). The high dose of TA-65 (1000 U) showed a trend of improvements in TL compared with that of the placebo group; however, the improvements did not reach statistical significance. It is possible that the high dose of the inducer is suboptimal and becomes inhibitive; also, the review of the data shows variations in the telomere length in the placebo as well.
Whittemore et al. show that the lifespan of multiple species does not correlate with telomere length but strongly correlates with the rate of telomere shortening (Figure 2 of Whittemore), with humans demonstrating one of the slowest rates of telomere decline in the animal kingdom. The entire telomerase pathway is promising as a target of rejuvenating activity between the cycles (Martinez et al.). The page “Aging Hyperspace,” already covers the combined lifespan effect of p53/p16/p21/p19-ARF induction (which increases lifespan by 10%), further augmented by TERT pathway induction (another 40% increase in lifespan). But placing the pulses of TERT/p53/p16/p21/p19-ARF small-molecule induction between the OSKM/OSK/OSM pulses may produce the dynamic at the bottom of Figure 10 (De Felice et al., Nguen et al., Tsoukalas et al.). The p53 and telomerase pathways are linked, and in murine experiments, co-activation of both produces ~ 50% of lifespan extension. Screening the sets of telomerase and of p53/p21/Rb/p16/ARF ligands as potential synergists in the context of OSKM cycles is an interesting path of research.
This TERT/p53 pathway synergy is just one example, and other pairs of tumor suppressor/oncogene activators are likely to exist to extend lifespans and rejuvenate a cell when induced by small molecules identified in the CADD-assisted screens as described above. To avoid overloading organisms with too many compounds, the rejuvenators supplementing cyclic partial reprogramming can be provided sequentially, in agreement with the recent data for aging yeast (Zhou et al.).
Orbital aging is a state of equidistance from the Embryonic Reset point. A static state is impossible due to the accumulation of molecular defects and run-away regulatory dynamics. In traditional aging, both factors lead to a runaway regime, with the distance to origin growing exponentially in time. At critical distances, viability is compromised, and the system becomes too frail to exist. Here, I hypothesize that maintaining a zero balance of senescence and restoration by continuously intensifying the pressure of rejuvenators will force the system to orbit the reset state at the same distance. Both senescence and rejuvenation are autocatalytic, with the previous state defining the next state. Activating the rejuvenating pathways enough to overcome accumulating entropy while controlling cancer would maintain this artificially stable regime.
“Lotions of youth” – rejuvenators delivered through the skin.
Partial reprogramming is not the only pathway to the rejuvenation of adult somatic cells. Complete or partial knockout of genes controlling senescence would return the cells to a more youthful state, and knockout of entire groups of genes can produce dramatic qualitative effects. Nature utilizes several long-range instruments for distant control of gene expression: hormones, peptides, and miRNA (micro-RNA). Micro-RNAs are short, hairpin-shaped constructs targeting the messenger RNA of the respective gene targets (Diener et al.). In plants, the mechanism of targeting is duplex formation and mobilization of RNAse activity. In mammals, partial complementarity allows multiple miRNAs to control multiple genes in a cooperative manner.
Most importantly, miRNAs carry rejuvenating signaling that can be amplified by combining multiple effects in a single package. Yu et al. report how embryonic stem cell-derived extracellular vesicles rejuvenate senescent cells and antagonize aging in mice. According to Yu et al., miR-15b-5p and miR-290a-5p were highly enriched in ESC-EVs (exosomes, lipid vesicles produced by the embryonic stem cells) and induced rejuvenation by silencing the Ccn2-mediated AKT/mTOR pathway. The rejuvenating effect of ESC-EVs was further investigated in vivo by injection into aged mice. The results showed that ESC-EVs successfully ameliorated the pathological age-related phenotypes and rescued the transcriptome profile of aged mice. Figure 8 in the report of Yu et al. is the most striking. Multiple markers of aging were reversed post-injection, such as senescence-associated (SA)-β-galactosidase levels in various organs, transcriptome signatures, changes in aging-related gene expression, and morphological changes in the liver and kidney. All markers display impressive reversals, producing patterns more resembling a younger state but still distinct from natural youth. Embryonic stem cell exosomes were injected every other day for 8 weeks. But why did the experiment end? It would be great to see the lifespan effect of this method.
Grigorian Shamagian et al. report rejuvenating effects of young extracellular vesicles in aged rats and in cellular models of human senescence. In this case, the source of the vesicles was cardiosphere-derived cells (CDCs). The CDCs were generated from neonatal rat hearts, and the secreted CDC-EVs (extracellular vesicles) were purified. CDC-EVs were then tested in naturally aged rats using monthly injections over a 4-month period. Figure 1 shows a different set of aging biomarkers (γH2AX+ staining, serum globulin level, alkaline phosphatase level, age-related transcription signature) completely or partially reversing. Cardiac functioning parameters also followed (Figure 2). Next, Figure 4 in Grigorian Shamagian et al. shows an impressive reversal of age-related fibrosis in multiple organs and dramatic effects on premature mortality. Kaplan-Meier survival curves show that after 65 days, 91.7% of rats survived in the CDC-EV group compared with 57.1% in the PBS control group; the latency to death was nearly three times longer in vesicle-treated rats compared with placebo. The rats were twenty-two-month-old Fisher 344 rats (male and female), with a natural lifespan of 21–26 months, and the observed median lifespan increment of ~ 15-20% is significant. More reports of similar nature are in Nguen et al. (protection of skin against photoaging), Jia et al. (reversal of bone degeneration), and hundreds of publications on this topic. A review by Fusco et al. mentions 40 human trials exploring extracellular stem cell-generated vesicles for treatment of complicated COVID-19, macular holes, skin diseases, wound healing, immunological rejection, neurological diseases, and cancers. In all cases, the treatments are non-toxic, and the effects are promising. The promise of randomized human trials adds veracity to the stunning effects observed in rodents.
Micro-RNAs are believed to be the active age-dependent factor in the extracellular vesicles, while the role of proteins in the positive and negative effects is also considered. For example, the extracellular vesicles from old organisms can accelerate the aging of the recipients by incorporating prion-like particles or senescence-promoting micro-RNA.
The extracellular vesicles (EV) can be manipulated to include additional components (Nguen et al.), and these components may include stable micro-RNA analogues copied from the sequences of the most active natural prototypes (Samad et al.). The artificially engineered cargos of the EV can include blends of miRNA, miRNA inhibitors, and ASO (antisense silencing oligonucleotides). The ASOs are the oldest tools for controlling a gene’s expression, not as effective as a gene’s ablation at the genetic level but often sufficient. The ASOs are too delivered in liposomes, and in such form, they can penetrate skin and enter the bloodstream.
Toyofuku et al. describe the non-invasive transdermal delivery of antisense oligonucleotides with biocompatible ionic liquids. Microneedles remain an option, although less desirable. Puttaraju et al. report a systematic screening process that identified therapeutic antisense oligonucleotides for Hutchinson-Gilford progeria syndrome (childhood premature aging, HPGS). HGPS is caused by a point mutation in the LMNA gene that encodes for the intermediate filament proteins lamin A and lamin C by alternative pre-mRNA splicing. Defective splicing leads to the formation of progerin. Its incorporation in the nuclear membrane instead of healthy analogues leads to pathological nuclear-cytosol traffic, DNA damage, and disrupted epigenetic patterns—the process that sufficiently overlaps with natural aging to serve as its model. The researchers screened different ASO constructs and achieved selective silencing of the mutant progerin component while retaining the expression of its healthy cousins, lamin A and lamin C. The aging-inducing progerin was reduced by 90% in multiple tissues. Figure 5 in Puttaraju et al. shows the increase in lifespan from 225 days to 325 days in the treated males vs. the scrambled control.
HGPS in humans is the most vicious among premature aging syndromes. The lifespan of affected patients is ~ 15 years, with an aging rate 10-fold higher than normal. The incorporation of defective lamin (progerin) in the inner membrane of the nuclear envelope changes the shape of the nucleus, disrupts DNA repair and recombination, and disrupts communications with the cytosol. The change in pore size and pore deformation would hinder the export of longer mRNA transcripts and the import of transcription factors typically shuttling between the nucleus and cytosol. Lamins form a 3D network in nucleoplasm, imported in organizing chromatin. The network is disrupted by the presence of mutant lamins. The informational flows of the cell and its information matrix rigidity are severely compromised. Yet despite such fundamental damage, the children with HPGS are born normal and begin to show their first symptoms at 1 year of age. What exactly staves off the progeric damage for about 8% of the available lifespan?
The logical answer is the intensity of the inherent rejuvenation program, which keeps compensating for the inherent information loss in HPGS progeria during the prenatal and early postnatal periods. As soon as the intensity of rejuvenation activity declines by 1 year, it can no longer compensate for the loss of information due to interrupted communication across nuclear pores. The source of corrective information is the morphogenic field of a rapidly growing and developing child (from 3 to 12 kg by 1 year), which includes the intense production of anti-aging miRNAs. The corrective morphogenic fields in the embryo are even more intense, staving off all premature aging syndromes, and none of them appear at birth. The teleological character of embryonic development requires more stringent control and strength of regulation at the foundational early stages to ensure correct downstream results – this is why the embryonic stem cell exosomes are the most potent and this why embryonic tissues are the most responsive to regulation.
Many genetic diseases based on mutations in single genes (inclusion body syndromes, neurodegenerative diseases) “patiently” wait for a certain degree of general senescence to start. What delays the onset? The inherent regenerative potential is the same power that staves off the worst of all—the HPGS. Haithcock et al. inquired whether similar changes in nuclear architecture occur during the normal aging process and discovered that major changes in nuclear architecture accompany Caenorhabditis elegans aging. The authors found that nuclear architecture in most nonneuronal cell types undergoes progressive and stochastic age-dependent alterations, such as changes in nuclear shape and loss of peripheral heterochromatin. The author confirmed that reducing the level of lamin and lamin-associated LEM domain proteins leads to a shorter lifespan. Why are neurons exempt from these processes? As we proposed earlier in this blog and in the book, neurons themselves, together with the gonads, organ stem cell niches, macrophages, and some T-cells, are the seats of the adult rejuvenation program, gradually retreating under the pressure of aging but holding the line until it does not. The inherent rejuvenation domain is interlinked by positive feedback, and the same is true for the pure aging domain, while both parts are mutually inhibitive (negative feedback). This leads to an interesting mathematical relationship, observed in living systems (see the book). Certain critical parameters may exist that alter the entire customary dynamic of the aging process, changing it from inexorably progressing to stationary and oscillating around an equilibrium point. Moving forward along the aging coordinate would mean the conventional dying from natural causes of old age. Moving backward along the same coordinate would mean reaching the parameters eroding differentiation pattern, a return to pluripotency incompatible with complex life. Optimal stimulation of the inherent rejuvenation domain would lead to a stationary state, or “orbital” regime proposed earlier.
Cao et al. report that in normal human fibroblasts, progressive telomere damage during cellular senescence plays a causative role in activating progerin production. Progressive telomere damage was also found to lead to extensive changes in alternative splicing in multiple other genes. Interestingly, elevated progerin production was not seen during cellular senescence, which does not entail telomere shortening. Telomere loss is typically a problem for dividing cell populations that may become the foci of aging initiation through the increased expression of progerin and associated autocatalytic senescence, spreading to other systems. Removal of such later-life splice variants by antisense and synthetic miRNA agents is almost certainly going to cause aging delays.
Perhaps the detailed study of the most effective secreted vesicle systems and reconstruction of the same therapeutic effect by artificial miRNA delivered via liposomes is the first step in taking control of nature’s other rejuvenating mechanism for differentiated cells. The miRNAs ubiquitously present in the most effective combinations (“conserved profile”) are likely the carriers of the anti-aging activities and can lead to a set of targets. The miRNAs repeatedly reappearing in the analysis of rejuvenating preparations will be artificially recreated in a more stable form, capable of enduring transdermal permeation and then capable of penetrating cell membranes, as shown in Toyofuku et al.
The presence of rejuvenating signals in complex combinations, in a more stable chemical form, and in higher concentrations will produce deeper changes than reported so far, which may necessitate the development of morphogenic cocktails, preventing cancer and teratomas. Eventually, the rejuvenating formulations delivered through the skin will take the form of aromatic ointments for daily use after a morning workout or after a shower.
References
Sadybekov, Anastasiia V., and Vsevolod Katritch. “Computational approaches streamlining drug discovery.” Nature 616, no. 7958 (2023): 673-685.
Bohacek, Regine S., Colin McMartin, and Wayne C. Guida. “The art and practice of structure‐based drug design: a molecular modeling perspective.” Medicinal research reviews 16, no. 1 (1996): 3-50.
Wang, Jinlin, Shicheng Sun, and Hongkui Deng. “Chemical reprogramming for cell fate manipulation: methods, applications, and perspectives.” Cell Stem Cell 30, no. 9 (2023): 1130-1147.
Zhao, Yang, Ting Zhao, Jingyang Guan, Xu Zhang, Yao Fu, Junqing Ye, Jialiang Zhu et al. “A XEN-like state bridges somatic cells to pluripotency during chemical reprogramming.” Cell 163, no. 7 (2015): 1678-1691.
Takeda, Yukimasa, Yoshinori Harada, Toshikazu Yoshikawa, and Ping Dai. “Chemical compound-based direct reprogramming for future clinical applications.” Bioscience Reports 38, no. 3 (2018): BSR20171650.
Chen, Zi-yang, Si-jia Ji, Chen-wen Huang, Wan-zhi Tu, Xin-yue Ren, Ren Guo, and Xin Xie. “In situ reprogramming of cardiac fibroblasts into cardiomyocytes in mouse heart with chemicals.” Acta Pharmacologica Sinica (2024): 1-10.
Silva, Jose CR, Huanhuan Li, Jiahui Huang, Wei Guan, Jinyi Wu, Haiping Luo, Litao Chang et al. “Chemically induced cell plasticity enables the generation of high-fidelity embryo model.” bioRxiv (2024): 2024-06.
Tan, Zijian, Shangyao Qin, Hong Liu, Xiao Huang, Yingyan Pu, Cheng He, Yimin Yuan, and Zhida Su. “Small molecules reprogram reactive astrocytes into neuronal cells in the injured adult spinal cord.” Journal of Advanced Research 59 (2024): 111-127.
Mitchell, Wayne, Ludger JE Goeminne, Alexander Tyshkovskiy, Sirui Zhang, Julie Y. Chen, Joao A. Paulo, Kerry A. Pierce et al. “Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation.” Elife 12 (2024): RP90579.
Yildirim, Muhammed A., Kwang-Il Goh, Michael E. Cusick, Albert-Laszlo Barabasi, and Marc Vidal. “Drug-target network.” Nature biotechnology 25, no. 10 (2007): 1119-1126.
Jadamba, Erkhembayar, and Miyoung Shin. “A systematic framework for drug repositioning from integrated omics and drug phenotype profiles using pathway‐drug network.” BioMed research international 2016, no. 1 (2016): 7147039.
Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool et al. “Highly accurate protein structure prediction with AlphaFold.” nature 596, no. 7873 (2021): 583-589.
de Brevern, Alexandre G. “An agnostic analysis of the human AlphaFold2 proteome using local protein conformations.” Biochimie 207 (2023): 11-19.
Terwilliger, Thomas C., Dorothee Liebschner, Tristan I. Croll, Christopher J. Williams, Airlie J. McCoy, Billy K. Poon, Pavel V. Afonine et al. “AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.” Nature Methods 21, no. 1 (2024): 110-116.
Li, Yang, and João Pedro de Magalhães. “Accelerated protein evolution analysis reveals genes and pathways associated with the evolution of mammalian longevity.” Age 35 (2013): 301-314.
Kuningas, Maris, Simon P. Mooijaart, Diana Van Heemst, Bas J. Zwaan, P. Eline Slagboom, and Rudi GJ Westendorp. “Genes encoding longevity: from model organisms to humans.” Aging cell 7, no. 2 (2008): 270-280.
Tian, X., Seluanov, A. and Gorbunova, V., 2017. Molecular mechanisms determining lifespan in short-and long-lived species. Trends in Endocrinology & Metabolism, 28(10), pp.722-734.
Farré, Xavier, Ruben Molina, Fabio Barteri, Paul RHJ Timmers, Peter K. Joshi, Baldomero Oliva, Sandra Acosta, Borja Esteve-Altava, Arcadi Navarro, and Gerard Muntané. “Comparative analysis of mammal genomes unveils key genomic variability for human life span.” Molecular Biology and Evolution 38, no. 11 (2021): 4948-4961.
Smulders, Larissa, and Joris Deelen. “Genetics of human longevity: From variants to genes to pathways.” Journal of Internal Medicine 295, no. 4 (2024): 416-435.
Budovsky, Arie, Thomas Craig, Jingwei Wang, Robi Tacutu, Attila Csordas, Joana Lourenço, Vadim E. Fraifeld, and João Pedro De Magalhães. “LongevityMap: a database of human genetic variants associated with longevity.” Trends in Genetics 29, no. 10 (2013): 559-560.
Revelas, Mary, Anbupalam Thalamuthu, Christopher Oldmeadow, Tiffany-Jane Evans, Nicola J. Armstrong, John B. Kwok, Henry Brodaty et al. “Review and meta-analysis of genetic polymorphisms associated with exceptional human longevity.” Mechanisms of ageing and development 175 (2018): 24-34.
Novelli, Valeria, Chiara Viviani Anselmi, Roberta Roncarati, Guia Guffanti, Alberto Malovini, Giulio Piluso, and Annibale Alessandro Puca. “Lack of replication of genetic associations with human longevity.” Biogerontology 9 (2008): 85-92.
Sebastiani, Paola, and Thomas T. Perls. “The genetics of extreme longevity: lessons from the new England centenarian study.” Frontiers in genetics 3 (2012): 277.
Ferreira, Leonardo G., Ricardo N. Dos Santos, Glaucius Oliva, and Adriano D. Andricopulo. “Molecular docking and structure-based drug design strategies.” Molecules 20, no. 7 (2015): 13384-13421.
van Hilten, Niek, Florent Chevillard, and Peter Kolb. “Virtual compound libraries in computer-assisted drug discovery.” Journal of chemical information and modeling 59, no. 2 (2019): 644-651.
Coley, Connor W., Dale A. Thomas III, Justin AM Lummiss, Jonathan N. Jaworski, Christopher P. Breen, Victor Schultz, Travis Hart et al. “A robotic platform for flow synthesis of organic compounds informed by AI planning.” Science 365, no. 6453 (2019): eaax1566.
Trobe, Melanie, and Martin D. Burke. “The molecular industrial revolution: automated synthesis of small molecules.” Angewandte Chemie International Edition 57, no. 16 (2018): 4192-4214.
Venter, J. Craig, John I. Glass, Clyde A. Hutchison, and Sanjay Vashee. “Synthetic chromosomes, genomes, viruses, and cells.” Cell 185, no. 15 (2022): 2708-2724.
Moger-Reischer, Roy Z., John I. Glass, Kim S. Wise, Lijie Sun, DM de C. Bittencourt, Brent K. Lehmkuhl, D. R. Schoolmaster Jr, Michael Lynch, and Jay T. Lennon. “Evolution of a minimal cell.” Nature 620, no. 7972 (2023): 122-127.
Lemon, Jennifer A., Douglas R. Boreham, and C. David Rollo. “A complex dietary supplement extends longevity of mice.” The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 60, no. 3 (2005): 275-279.
Lemon, J. A., V. Aksenov, R. Samigullina, S. Aksenov, W. H. Rodgers, C. D. Rollo, and D. R. Boreham. “A multi‐ingredient dietary supplement abolishes large‐scale brain cell loss, improves sensory function, and prevents neuronal atrophy in aging mice.” Environmental and molecular mutagenesis 57, no. 5 (2016): 382-404.
Long, Jiangang, Vadim Aksenov, C. David Rollo, and Jiankang Liu. “A complex dietary supplement modulates nitrative stress in normal mice and in a new mouse model of nitrative stress and cognitive aging.” Mechanisms of ageing and development 133, no. 8 (2012): 523-529.
Aksenov, Vadim, Jiangang Long, Jiankang Liu, Henry Szechtman, Parul Khanna, Sarthak Matravadia, and C. David Rollo. “A complex dietary supplement augments spatial learning, brain mass, and mitochondrial electron transport chain activity in aging mice.” Age 35 (2013): 23-33.
Perez, S.D., Du, K., Rendeiro, C., Wang, L., Wu, Q., Rubakhin, S.S., Vazhappilly, R., Baxter, J.H., Sweedler, J.V. and Rhodes, J.S., 2017. A unique combination of micronutrients rejuvenates cognitive performance in aged mice. Behavioural Brain Research, 320, pp.97-112.
Hutton, Craig P., Jennifer A. Lemon, Boris Sakic, C. David Rollo, Douglas R. Boreham, Margaret Fahnestock, J. Martin Wojtowicz, and Suzanna Becker. “Early intervention with a multi-ingredient dietary supplement improves mood and spatial memory in a triple transgenic mouse model of Alzheimer’s disease.” Journal of Alzheimer’s Disease 64, no. 3 (2018): 835-857.
Heath, Sarah Elizabeth, Stephen Barabas, and Paul Graham Craze. “Nutritional supplementation in cases of canine cognitive dysfunction—A clinical trial.” Applied Animal Behaviour Science 105, no. 4 (2007): 284-296.
Simon, Katherine E., Katharine Russell, Alejandra Mondino, Chin-Chieh Yang, Beth C. Case, Zachary Anderson, Christine Whitley, Emily Griffith, Margaret E. Gruen, and Natasha J. Olby. “A randomized, controlled clinical trial demonstrates improved owner-assessed cognitive function in senior dogs receiving a senolytic and NAD+ precursor combination.” Scientific Reports 14, no. 1 (2024): 12399.
Patil AR, Perez-Camargo PG. Effect of Nutritional Interventions on Longevity of Senior Cats.
Schauf, Sofia, Jonathan Stockman, Richard Haydock, Ryan Eyre, Lisa Fortener, Jean Soon Park, Anne Marie Bakke, and Phillip Watson. “Healthy ageing is associated with preserved or enhanced nutrient and mineral apparent digestibility in dogs and cats fed commercially relevant extruded diets.” Animals 11, no. 7 (2021): 2127.
Tran, Jonathan, Vadim Aksenov, and C. David Rollo. “A multi‐ingredient athletic supplement disproportionately enhances hind leg musculature, jumping performance, and spontaneous locomotion in crickets (A cheta domesticus).” Entomologia Experimentalis et Applicata 166, no. 1 (2018): 63-73.
Spindler, Stephen R., Patricia L. Mote, and James M. Flegal. “Lifespan effects of simple and complex nutraceutical combinations fed isocalorically to mice.” Age 36 (2014): 705-718.
Liao, Chen‐Yu, Brad A. Rikke, Thomas E. Johnson, Vivian Diaz, and James F. Nelson. “Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening.” Aging cell 9, no. 1 (2010): 92-95.
Chen, Kenneth L., Matthew M. Crane, and Matt Kaeberlein. “Microfluidic technologies for yeast replicative lifespan studies.” Mechanisms of ageing and development 161 (2017): 262-269.
Cho, Yongmin, Rachael A. Jonas‐Closs, Lev Y. Yampolsky, Marc W. Kirschner, and Leonid Peshkin. “Intelligent high‐throughput intervention testing platform in Daphnia.” Aging Cell 21, no. 3 (2022): e13571.
Caillaud, Martial, Morgane E. Le Dréan, Adrien De-Guilhem-de-Lataillade, Catherine Le Berre-Scoul, Jérôme Montnach, Steven Nedellec, Gildas Loussouarn, Vincent Paillé, Michel Neunlist, and Hélène Boudin. “A functional network of highly pure enteric neurons in a dish.” Frontiers in Neuroscience 16 (2023): 1062253.
Soriano, Jordi. “Neuronal cultures: Exploring biophysics, complex systems, and medicine in a dish.” Biophysica 3, no. 1 (2023): 181-202.
Chang, D. T. W., and I. J. Reynolds. “Differences in mitochondrial movement and morphology in young and mature primary cortical neurons in culture.” Neuroscience 141, no. 2 (2006): 727-736.
Bhanu, M. Uday, R. K. Mandraju, C. Bhaskar, and Anand Kumar Kondapi. “Cultured cerebellar granule neurons as an in vitro aging model: topoisomerase IIβ as an additional biomarker in DNA repair and aging.” Toxicology in Vitro 24, no. 7 (2010): 1935-1945.
Kusumoto, Dai, Shinsuke Yuasa, and Keiichi Fukuda. “Induced pluripotent stem cell-based drug screening by use of artificial intelligence.” Pharmaceuticals 15, no. 5 (2022): 562.
Yakovlev, Egor V., Ivan V. Simkin, Anastasiya A. Shirokova, Nataliya A. Kolotieva, Svetlana V. Novikova, Artur D. Nasyrov, Ilya R. Denisenko et al. “Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.” Scientific Reports 14, no. 1 (2024): 9846.
Koga, Shunsuke, Akihiro Ikeda, and Dennis W. Dickson. “Deep learning‐based model for diagnosing Alzheimer’s disease and tauopathies.” Neuropathology and Applied Neurobiology 48, no. 1 (2022): e12759.
Ozcebe, S. Gulberk, Gokhan Bahcecioglu, Xiaoshan S. Yue, and Pinar Zorlutuna. “Effect of cellular and ECM aging on human iPSC-derived cardiomyocyte performance, maturity and senescence.” Biomaterials 268 (2021): 120554.
Fliri, Anton F., William T. Loging, Peter F. Thadeio, and Robert A. Volkmann. “Biospectra analysis: model proteome characterizations for linking molecular structure and biological response.” Journal of medicinal chemistry 48, no. 22 (2005): 6918-6925.
Li, Hang, Xiu-Jun Gong, Hua Yu, and Chang Zhou. “Deep neural network-based predictions of protein interactions using primary sequences.” Molecules 23, no. 8 (2018): 1923.
Wells, James A., and Christopher L. McClendon. “Reaching for high-hanging fruit in drug discovery at protein–protein interfaces.” Nature 450, no. 7172 (2007): 1001-1009.
Kaelin Jr, William G. “The concept of synthetic lethality in the context of anticancer therapy.” Nature reviews cancer 5, no. 9 (2005): 689-698.
Lee, Jong-Hyuk, Edward W. Kim, Deborah L. Croteau, and Vilhelm A. Bohr. “Heterochromatin: an epigenetic point of view in aging.” Experimental & molecular medicine 52, no. 9 (2020): 1466-1474.
Kerepesi, Csaba, Bohan Zhang, Sang-Goo Lee, Alexandre Trapp, and Vadim N. Gladyshev. “Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging.” Science advances 7, no. 26 (2021): eabg6082.
Bogdanović, Ozren, Arne H. Smits, Elisa de la Calle Mustienes, Juan J. Tena, Ethan Ford, Ruth Williams, Upeka Senanayake et al. “Active DNA demethylation at enhancers during the vertebrate phylotypic period.” Nature genetics 48, no. 4 (2016): 417-426.
Dönertaş, Handan Melike, Hamit İzgi, Altuğ Kamacıoğlu, Zhisong He, Philipp Khaitovich, and Mehmet Somel. “Gene expression reversal toward pre-adult levels in the aging human brain and age-related loss of cellular identity.” Scientific reports 7, no. 1 (2017): 5894.
Yang, Jae-Hyun, Patrick T. Griffin, Daniel L. Vera, John K. Apostolides, Motoshi Hayano, Margarita V. Meer, Elias L. Salfati et al. “Erosion of the epigenetic landscape and loss of cellular identity as a cause of aging in mammals.” BioRxiv (2019): 808642.
Izgi, Hamit, Dingding Han, Ulas Isildak, Shuyun Huang, Ece Kocabiyik, Philipp Khaitovich, Mehmet Somel, and Handan Melike Dönertaş. “Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity.” Elife 11 (2022): e68048.
Fu, Kai, Constantinos Chronis, Abdenour Soufi, Giancarlo Bonora, Miguel Edwards, Stephen T. Smale, Kenneth S. Zaret, Kathrin Plath, and Matteo Pellegrini. “Comparison of reprogramming factor targets reveals both species-specific and conserved mechanisms in early iPSC reprogramming.” BMC genomics 19 (2018): 1-13.
Appleton, Evan, Kyunghee Hong, Cristina Rodríguez-Caycedo, Yoshiaki Tanaka, Asaf Ashkenazy-Titelman, Ketaki Bhide, Cody Rasmussen-Ivey et al. “Derivation of elephant induced pluripotent stem cells.” bioRxiv (2024): 2024-03
Tan, Li, Zhonghe Ke, Gregory Tombline, Nicholas Macoretta, Kevin Hayes, Xiao Tian, Ruitu Lv et al. “Naked mole rat cells have a stable epigenome that resists iPSC reprogramming.” Stem cell reports 9, no. 5 (2017): 1721-1734.
Singh, Prim B., and Assem Zhakupova. “Age reprogramming: cell rejuvenation by partial reprogramming.” Development 149, no. 22 (2022).
Ocampo, Alejandro, Pradeep Reddy, Paloma Martinez-Redondo, Aida Platero-Luengo, Fumiyuki Hatanaka, Tomoaki Hishida, Mo Li et al. “In vivo amelioration of age-associated hallmarks by partial reprogramming.” Cell 167, no. 7 (2016): 1719-1733.
Macip, Carolina Cano, Rokib Hasan, Victoria Hoznek, Jihyun Kim, Yuancheng Ryan Lu, Louis E. Metzger IV, Saumil Sethna, and Noah Davidsohn. “Gene therapy-mediated partial reprogramming extends lifespan and reverses age-related changes in aged mice.” Cellular Reprogramming 26, no. 1 (2024): 24-32.
Alle, Q., Le Borgne, E., Bensadoun, P., Lemey, C., Béchir, N., Gabanou, M., Estermann, F., Bertrand-Gaday, C., Pessemesse, L., Toupet, K. et al. (2022). A single short reprogramming early in life initiates and propagates an epigenetically related mechanism improving fitness and promoting an increased healthy lifespan. Aging Cell 00, e13714.
Dogan, Fatma, and Nicholas R. Forsyth. “Epigenetic features in regulation of telomeres and telomerase in stem cells.” Emerging Topics in Life Sciences 5, no. 4 (2021): 497-505.
Mendelsohn, Andrew R., and James W. Larrick. “Ectopic expression of telomerase safely increases health span and life span.” Rejuvenation research 15, no. 4 (2012): 435-438.
Bernardes de Jesus, Bruno, Elsa Vera, Kerstin Schneeberger, Agueda M. Tejera, Eduard Ayuso, Fatima Bosch, and Maria A. Blasco. “Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer.” EMBO molecular medicine 4, no. 8 (2012): 691-704.
Salvador, Laura, Gunasekaran Singaravelu, Calvin B. Harley, Peter Flom, Anitha Suram, and Joseph M. Raffaele. “A natural product telomerase activator lengthens telomeres in humans: a randomized, double blind, and placebo-controlled study.” Rejuvenation research 19, no. 6 (2016): 478-484.
Whittemore, Kurt, Elsa Vera, Eva Martínez-Nevado, Carola Sanpera, and Maria A. Blasco. “Telomere shortening rate predicts species life span.” Proceedings of the National Academy of Sciences 116, no. 30 (2019): 15122-15127.
Martínez, Paula, and María A. Blasco. “Telomeric and extra-telomeric roles for telomerase and the telomere-binding proteins.” Nature Reviews Cancer 11, no. 3 (2011): 161-176.
De Felice, Bruna, Anna Annunziata, Giuseppe Fiorentino, Francesco Manfellotto, Raffaella D’Alessandro, Rita Marino, Marco Borra, and Elio Biffali. “Telomerase expression in amyotrophic lateral sclerosis (ALS) patients.” Journal of human genetics 59, no. 10 (2014): 555-561.
Nguyen, Daniel, Wenjuan Liao, Shelya X. Zeng, and Hua Lu. “Reviving the guardian of the genome: Small molecule activators of p53.” Pharmacology & therapeutics 178 (2017): 92-108.
Tsoukalas, Dimitris, Persefoni Fragkiadaki, Anca Oana Docea, Athanasios K. Alegakis, Evangelia Sarandi, Maria Thanasoula, Demetrios A. Spandidos, Aristidis Tsatsakis, Mayya Petrovna Razgonova, and Daniela Calina. “Discovery of potent telomerase activators: Unfolding new therapeutic and anti-aging perspectives.” Molecular Medicine Reports 20, no. 4 (2019): 3701-3708.
Zhou, Zhen, Yuting Liu, Yushen Feng, Stephen Klepin, Lev S. Tsimring, Lorraine Pillus, Jeff Hasty, and Nan Hao. “Engineering longevity—design of a synthetic gene oscillator to slow cellular aging.” Science 380, no. 6643 (2023): 376-381.
Diener, Caroline, Andreas Keller, and Eckart Meese. “The miRNA–target interactions: An underestimated intricacy.” Nucleic Acids Research 52, no. 4 (2024): 1544-1557.
Yu, Lu, Hang Wen, Chang Liu, Chen Wang, Huaxin Yu, Kaiyue Zhang, Qingsheng Han et al. “Embryonic stem cell-derived extracellular vesicles rejuvenate senescent cells and antagonize aging in mice.” Bioactive Materials 29 (2023): 85-97.
Grigorian Shamagian, Lilian, Russell G. Rogers, Kristin Luther, David Angert, Antonio Echavez, Weixin Liu, Ryan Middleton et al. “Rejuvenating effects of young extracellular vesicles in aged rats and in cellular models of human senescence.” Scientific Reports 13, no. 1 (2023): 12240.
Nguyen, Diem DN, Diem My Vu, Nhan Vo, Nam HB Tran, Duyen TK Ho, Thieu Nguyen, Tien Anh Nguyen, Hoai‐Nghia Nguyen, and Lan N. Tu. “Skin rejuvenation and photoaging protection using adipose‐derived stem cell extracellular vesicles loaded with exogenous cargos.” Skin Research and Technology 30, no. 2 (2024): e13599.
Fusco, Clorinda, Giusy De Rosa, Ilaria Spatocco, Elisabetta Vitiello, Claudio Procaccini, Chiara Frigè, Valeria Pellegrini et al. “Extracellular vesicles as human therapeutics: A scoping review of the literature.” Journal of Extracellular Vesicles 13, no. 5 (2024): e12433.
Jia, Zhiwei, Shunxin Zhang, and Wei Li. “Harnessing stem cell-derived extracellular vesicles for the regeneration of degenerative bone conditions.” International Journal of Nanomedicine (2023): 5561-5578.
Samad, Abdul Fatah A., and Mohd Farizal Kamaroddin. “Innovative approaches in transforming microRNAs into therapeutic tools.” Wiley Interdisciplinary Reviews: RNA 14, no. 1 (2023): e1768.
Toyofuku, Kiyohiro, Rie Wakabayashi, Noriho Kamiya, and Masahiro Goto. “Non-Invasive Transdermal Delivery of Antisense Oligonucleotides with Biocompatible Ionic Liquids.” ACS Applied Materials & Interfaces 15, no. 28 (2023): 33299-33308.
Puttaraju, Madaiah, Michaela Jackson, Stephanie Klein, Asaf Shilo, C. Frank Bennett, Leslie Gordon, Frank Rigo, and Tom Misteli. “Systematic screening identifies therapeutic antisense oligonucleotides for Hutchinson–Gilford progeria syndrome.” Nature medicine 27, no. 3 (2021): 526-535.
Haithcock, Erin, Yaron Dayani, Ester Neufeld, Adam J. Zahand, Naomi Feinstein, Anna Mattout, Yosef Gruenbaum, and Jun Liu. “Age-related changes of nuclear architecture in Caenorhabditis elegans.” Proceedings of the National Academy of Sciences 102, no. 46 (2005): 16690-16695.
Cao, Kan, Cecilia D. Blair, Dina A. Faddah, Julia E. Kieckhaefer, Michelle Olive, Michael R. Erdos, Elizabeth G. Nabel, and Francis S. Collins. “Progerin and telomere dysfunction collaborate to trigger cellular senescence in normal human fibroblasts.” The Journal of clinical investigation 121, no. 7 (2011): 2833-2844.