Mois : février 2025

Researchers publishing in Cellular Signaling have explained how the protein AP2A1 affects stress fibers that change with cellular senescence.

Stress fibers

Stress fibers naturally hold cells into their proper shape. They are made out of the common protein actin and linked together by α-actinin and a form of myosin that is not directly related to muscles. Mesenchymal cells, which are found in the extracellular matrix (ECM), use these stress fibers to pull the ECM into the necessary shapes [2]. These miniature mechanical processes affect how cells develop [3] and have even been implicated in cancer [4].

These fibers change with senescence [5], and these researchers have recently discovered how considerable the changes are: out of 135 proteins, the researchers found that 63 of them were upregulated as cells become senescent [6]. Many of these proteins have been thoroughly researched, but despite being generally known in its biological functions and implicated in multiple diseases [7], AP2A1 had not previously been investigated in the context of senescence.

This work began by allowing human fibroblasts to divide 30 times, which is when they were considered aged and approached replicative senescence, with the typical changes in morphology and senescence-related biomarkers. Fibroblasts passaged for 10 and 20 times were referred to as young and adult, respectively.

Part of why senescent cells look different

Consistent with the protein upregulation that these researchers previously found, the stress fibers in the aged fibroblasts were thicker than their young and adult counterparts. The natural turnover rate of these fibers was also significantly lower. These cells also kept, rather than recycled, the structural protein integrin β1, which is used to bolster fiber thickness. Additionally, the researchers confirmed that these senescent cells lacked the motility that the younger cells had.

Senescent fibroblasts were found to adhere differently, and more firmly, to the ECM than their younger counterparts. In younger cells, two proteins related to focal adhesion, vinculin and paxillin, were located at the edges, as were stress fibers; meanwhile, in older cells, these cellular features were located more centrally.

AP2A1 was found to increase with age in both proteomic and gene expression analyses. While in younger cells, this protein is diffusely spread throughout the cellular structure, it is aligned along the fibers of senescent cells. AP2A1 is known to affect endocytosis, the process that transports materials into the cell, and this process was found to also be increased with senescence. The movement of AP2A1 within the cell was found to be slowed down with age as well.

These age-related changes were confirmed to be associated with multiple forms of senescence. In addition to repeated replication, cells can be driven senescent by radiation or chemicals. Using either of these approaches led to the same increases in AP2A1, and associated changes in morphology, as replicative senescence did.

A two-way street

While senescence clearly affects AP2A1, the researchers also wanted to know whether this relationship also works in reverse. Using silencing RNA (siRNA), the researchers stopped the aged cells in their culture from expressing AP2A1. Unsurprisingly, the modified cells were smaller and had fewer stress fibers, but most critically, they also had reduced levels of senescence biomarkers, including p53, p21, and the well-known SA-β-gal. Cellular proliferation, which declines with senescence, was enhanced by this removal.

Overexpression, on the other hand, appeared to lead to senescence. Young cells that were induced to express more AP2A1 had the characteristic increases in stress fibers and overall size, and their senescence biomarkers were increased as well.

The researchers believe that these facts make AP2A1 a good target for further study. Still, this is a cellular study in fibroblasts, and its findings have not been confirmed in animal models. As this protein appears to be a fundamental building block of cellular function, broad reductions may have significant side effects in living animals. However, if it can be more precisely targeted, this protein may be of key interest to research groups looking how to mitigate the increase in senescence that comes with aging.

Literature

[1] Burridge, K., & Wittchen, E. S. (2013). The tension mounts: stress fibers as force-generating mechanotransducers. Journal of Cell Biology, 200(1), 9-19.

[2] Burridge, K., & Guilluy, C. (2016). Focal adhesions, stress fibers and mechanical tension. Experimental cell research, 343(1), 14-20.

[3] Ridley, A. J., Schwartz, M. A., Burridge, K., Firtel, R. A., Ginsberg, M. H., Borisy, G., … & Horwitz, A. R. (2003). Cell migration: integrating signals from front to back. Science, 302(5651), 1704-1709.

[4] Tojkander, S., Gateva, G., & Lappalainen, P. (2012). Actin stress fibers–assembly, dynamics and biological roles. Journal of cell science, 125(8), 1855-1864.

[5] Chen, Q. M., Tu, V. C., Catania, J., Burton, M., Toussaint, O., & Dilley, T. (2000). Involvement of Rb family proteins, focal adhesion proteins and protein synthesis in senescent morphogenesis induced by hydrogen peroxide. Journal of cell science, 113(22), 4087-4097.

[6] Liu, S., Matsui, T. S., Kang, N., & Deguchi, S. (2022). Analysis of senescence-responsive stress fiber proteome reveals reorganization of stress fibers mediated by elongation factor eEF2 in HFF-1 cells. Molecular biology of the cell, 33(1), ar10.

[7] Wang, C., Zhao, D., Shah, S. Z. A., Yang, W., Li, C., & Yang, L. (2017). Proteome analysis of potential synaptic vesicle cycle biomarkers in the cerebrospinal fluid of patients with sporadic Creutzfeldt–Jakob disease. Molecular Neurobiology, 54, 5177-5191.

Some longevity biotech companies fit neatly into one of the big buckets we have in this field, like senolytics or cellular reprogramming. Others, such as Junevity, a small spin-out from the University of California, San Francisco, dare to walk an unbeaten path. Junevity pursues the ambitious goal of fixing age-related transcriptional dysregulation using short interfering RNAs, also known as silencing RNAs (siRNAs), a powerful mechanism of regulating gene expression.

The company recently closed a $10 million funding round and is charging ahead with drug candidates against obesity and type 2 diabetes. We decided it was a good time to have a chat with Junevity’s co-founder and CSO, Dr. Janine Sengstack.

Everyone has a personal story of how they got into our field, that moment when you decide to study or even realize you’re specifically studying the biology of aging. What’s your story?

Ever since I was a little kid – since middle school, really – my goal has been to help people live longer, healthier lives. Originally, I wanted to do medicine because in middle school, a whole bunch of people important to me died of various diseases. I thought, “This is terrible, I want to go into healthcare and make a difference in people’s lives.”

As I progressed through education and started my undergrad at Cal Poly in San Luis Obispo, I realized what I’m super passionate about is doing the science behind the medicine and coming up with novel therapeutics to help people live healthier lives. That’s really where the passion for discovering new science and coming up with new ideas got fostered.

For aging biology specifically, I have a lot of great older role models in my life. I want to be like them when I grow up. Some of them include my mom’s hiking group friends who are in their 80s and still climbing mountains.

I can relate. I live in Seattle, where hiking is the favorite pastime, and those 70–80-year-olds breezing past me on trails are both inspiring and mildly annoying.

Right! They’re inspirational. My German grandma is 91 and still living by herself, super sharp, has so much energy. That inspired me to want to be like them when I grow up but also to study aging and help people live these longer, healthier lives.

I did a summer rotation with Dr. Hao Li at UCSF as part of a summer internship program. I had a phenomenal time working with him in his aging biology lab and quickly became enthralled with that science. I also really loved working with him as a mentor and the lab in general. Just a few months later, I applied for grad school thinking I would love to work in and do my PhD in Hao Li’s lab at UCSF, and that’s what I ended up doing.

We did what Hao and I called a high-risk, high-reward PhD project where we wanted to find brand new transcription factors to target to take cells from a diseased, old state and bring them back to a healthy state. We were inspired by the partial reprogramming work with the Yamanaka factors because it shows the power of targeting transcription factors to undo time in a way. That’s very powerful, but we wanted to focus on not de-differentiating cells – we want them to stay the same cell type, just bring them back to a healthier version of themselves.

I guess it all starts with the understanding that aging is a massive, very heterogeneous dysregulation of things, including transcriptional signatures. Due to the complexity of this, we probably need AI’s help, and that’s what you are using to discover those most upstream factors, correct?

Yes, that’s exactly right. We have this underlying hypothesis that in aging and in many complex diseases related to aging, transcriptional dysregulation plays a major role. There are so many changes across different pathways like inflammation, mitochondrial function.

But we don’t want to target one mutated gene, which is what a lot of traditional drug discovery does. We want to focus on the underlying gene expression changes and then, like you said, use AI and machine learning tools to predict that upstream regulator of those genes to then repress and bring cells back to a healthy state.

Are you also trying to develop a mechanistic understanding of the relationships you find?

Yes, absolutely. We think a lot about first predicting the factor, but then a very important part of our platform is the validation and follow-up analysis of each factor and what it does in the cells at a mechanistic level. Let’s say we knock down a transcription factor; what changes in the gene expression patterns downstream? We could do RNA sequencing in cells that have been treated to knock down that factor and look at specific pathways.

In our collaboration with Novo Nordisk last year, we were looking at specific readouts in metabolism. We did some exploration of metabolic rate in cells and could look at the mechanism there and see if perturbing this factor changes the energy expenditure of these cells, for example.

You have said in your presentations that one of the differences between your way of doing things and that of Yamanaka is that overexpressing transcription factors might be unsafe, in part because they bind off-target, while silencing them is safer, and that’s what you do using siRNAs, right?

Yes, that’s right: overexpressing transcription factors has more inherent risk because if you’re adding more to a system, they can bind to the wrong things. It’s also harder to dose therapeutically. In our case, we’re focused on siRNA for downregulation of transcription factors – that is safer from a cell biology perspective but also a well-established modality now. I would not have said that ten years ago. We are fortunate that the timing is perfect for us to go after using siRNA.

How does RNA silencing work?

SiRNAs are about 20 base pairs long, double-stranded RNA molecules. Other scientists in the last 20 years or so have figured out a lot of the really challenging biology and therapeutics development of siRNA, so we are thankful for how much work has gone into developing this modality. That involves backbone chemistry modifications to the siRNA so that it avoids nucleases and doesn’t get degraded.

Right, you basically use various tricks to reinforce those RNAs to make them longer-lived.

Yes, exactly, and you can do specific tissue targeting, which is important for a small molecule that can go anywhere. The most well-established way to do that is with what’s called a GalNAc conjugation: a sugar-amino type of attachment. That makes it only go to hepatocytes, so it’s super specific to liver targeting. We’re starting with that because it’s very established and very safe.

The beauty of siRNA is: repression is safer, you can do very specific targeting, and the effects are very durable. Sometimes, you can have duration up to six months. This ability to have long-lasting effects is impressive because that helps patient compliance. You don’t have to take a pill every single day, you might just get an injection every six months.

Yes, we usually think about RNA as something easily degradable, but siRNAs bind to a protein complex called RISC, and in this form, they can persist for weeks and months.

Yes, they just sit there, attacking and degrading those target mRNAs, correct.

I understand that methylation clocks didn’t show rejuvenation in the skin cells that you worked on during your PhD.

We’re not working with methylation clocks in our current research, but it’s correct: in those fibroblasts, we saw rejuvenation, but the methylation clocks didn’t show it. My speculation would be that we pushed them back towards something like a middle-passage state and not a stem cell state, but I didn’t look into it super deeply. It was more like a supplemental figure.

This caught my attention because even partial reprogramming with Yamanaka factors causes epigenetic rejuvenation.

Yes, that’s interesting. Again, my guess would be that it’s because our technology doesn’t move cells towards a stem-like cell state but rather makes them a younger, healthier version of themselves. It’s just a different mechanism of rejuvenation.

So, you started with fibroblasts, and you were able to essentially rejuvenate them. After something like that, how do you move towards more concrete indications where you want to spin out and become a company?

That is something we thought very deeply about for a long time. My PhD work developing the RESET platform proved that we can use computational and experimental methods to take cells from an older state and bring them back to a younger state by targeting different transcription factors. We used that underlying proof point to then further develop the RESET platform at Junevity to focus on targeting specific diseases related to healthspan and lifespan.

We want to help people live longer, healthier lives, but we don’t want to do a 30-year clinical trial and see if they live longer. We need to be more focused on very practical, measurable, established clinical endpoints as a company. We think that’s the most likely way to get approvals, move things forward, and make a big impact.

That’s why we focused our platform on specific indications that are deeply related to aging. If you have type 2 diabetes, your lifespan tends to be shorter, and your healthspan is certainly shorter. Obesity, too. We’re considering other indications like osteoarthritis – clearly aging-related, healthspan-related, but having a distinct clinical endpoint is still a key part of that.

Currently, your two main indications are obesity and type 2 diabetes, right? Can you tell me a little bit about how your candidates actually work on them?

I can tell you some things, but not too many. We identified our target transcription factors by looking at very large-scale human datasets of people with diabetes and obesity – over 500 patients – and looking at their transcriptomics data in the liver, at the underlying gene expression changes, and then what transcription factor is likely regulating those things.

Having identified our factor, we could create an siRNA for it. We’re very excited about it because we do see significant improvements in insulin sensitivity, which is a really big deal in the diabetes field. A lot of patients are taking several drugs – there’s a lot of amazing improvements in the space, but they still need many different drugs and have complicated dosing regimens.

Some drugs, like pioglitazone, do work for insulin sensitivity but have a negative side effect of weight gain. If you have type 2 diabetes, you really don’t want to gain more weight. We’re very excited that we have similar insulin sensitivity improvement as pioglitazone but without the weight gain.

You have very promising results from your preclinical studies for obesity, right?

Yes. The obesity space has grown dramatically with the advent of GLP-1 receptor agonists. Those drugs are very successful, and it shows that the market is huge. Many public company CEOs have been saying they don’t think this is the top of the obesity market – it’s just getting started, and there’s going to be a lot of next iterations and learnings on new approaches.

A downside of the current drugs is muscle loss because they function as a caloric restriction model. You basically just don’t eat, and you lose fat, which is great, but you also lose muscle, which is less great, especially in older patients where muscle retention really matters.

We’re excited because our candidate leads to fat loss but no muscle loss, and only a little bit reduced caloric intake. Those things together are very promising from a monotherapy direction but also as a combinatorial therapy possibility in the obesity space.

Today, longevity companies have no choice but to work with particular indications instead of targeting aging itself. Do you envision a better paradigm for aging research in the future?

I would love to do more preventative medicine as a general goal for the world. That would be phenomenal. To some degree, the GLP-1 class of drugs is doing that by helping pre-diabetics stop from becoming diabetics. So, there’s some of that happening.

I imagine that the future will involve multiple different drugs for different aspects of what’s going wrong in a person as they age. You might have one that helps with your knee cartilage, one that helps with your liver if you have pre-diabetes. Something that helps slow heart aging and something that helps slow brain aging. It’s unlikely we’ll have one drug to solve all the different problems, but I imagine there will be layers of different tissue- and cell-specific therapies that can help us across many different parts of aging biology.

A lot of this age-related dysregulation, including in transcriptional pathways, is adaptive. Basically, there’s damage and there is a compensatory reaction to it, and we do not want to hit those adaptive pathways. Is this a concern for you?

We definitely think a lot about if something is protective or maladaptive. We were literally just talking about this a few days ago at our company. Something like cell division, when you need to repair a tissue: in the short term, it’s a good idea to divide and fill up the space, but then prolonged cell division is a bad idea because then you can get cancer. There’s a temporal balance where short-term activation of something is beneficial, but long-term activation is actually bad.

You’re right – in some cases, for instance, a DNA damage repair enzyme gets upregulated as you age because you have more DNA damage to fix. We try our best to be as thorough as we can in the computational selection process, but then, of course, we do many experiments in cells and animal models to answer precisely this question: “Does repressing this factor reset the cells back to a healthy state, or was it actually something protective?”

I understand you jumped into the world of biotech straight from your graduation. What was it like?

Some four years into my PhD, Professor Hao and I realized that the science was going great, and we wanted to move it towards patients as fast as we could. I thought about that a lot and realized that if it stays in academia, it might take too long to move towards a therapeutic. It’s something that’s very near and dear to my heart.

Hao and I talked about it and decided we wanted to spin it out and create a company. I have great friends and mentors in the biotech and tech space – my husband was a founder, two of my best friends are CEOs of tech companies and biotech companies. So, I have a great network of other founders that gave me advice and helped me.

During grad school, I did a six-month program called Nucleate, which was incredibly helpful. It taught grad students what starting a company would look like: offering workshops, connecting us with founders, and giving us practice pitching and fundraising. Through that process, I went from “Do I want to start a company?” to “I definitely want to start a company.”

I really wanted co-founders because flying solo would be very difficult. I’m thrilled to have my co-founders Rob Cahill and John Hoekman. Rob has great business experience, has started his own tech company, got it acquired, ran a hundred-person team at a public company. During that time, he also shifted to studying aging biology and became super passionate about it, got a bioinformatics degree and was really wanting to start a longevity company. We met at the perfect time.

Then John, he took his PhD work from an idea all the way through FDA approval, went public, and raised several hundred million dollars. He’s one of the only people I’ve ever met that actually took PhD work all the way to FDA approval.

Still, you’re a pretty small company at this point. The funding you have received gives you some runway, but I can’t help but wonder how it feels to be a small longevity biotech startup. A bit scary, maybe?

I’m having so much fun. It is just the best. Not scary at all. I get great joy from the people that I work with and the science that we’re tackling. It’s challenging, of course, but that’s part of the fun – we really care about solving these difficult questions. I just get happy when I go to work every day.

What is your impression of the longevity biotech space in the last couple of years, how would you describe the climate?

I would say there is more enthusiasm around longevity science over the past couple of years, which is great. It’s been building through a variety of successes along the way – partial reprogramming, parabiosis, some of the small molecules that have shown lifespan extension in animal models. Those things happening give more credibility and believability to the notion that we can affect healthspan and lifespan, that you don’t have to start suffering as you age; there are ways to make things better.

Our approach, and what some other companies are also doing, is less like “we’re going to make everyone live forever” and more “here are some specific things that big pharma is used to seeing”: clear endpoints of specific indications that will make people live healthier lives. That makes it more digestible and approachable from a traditional biotech background.

How do we even define a longevity company? Sometimes, it feels like people simply either want to designate themselves as such or they don’t. For instance, Altos Labs is fighting this label tooth and nail, although many would say it’s clearly a longevity company.

I guess it would be the underlying mission: that our long-term goals are really to help people live healthier, longer lives. We’re approaching it from a specific indication angle, because that’s the most practical way to move forward in the near-ish term, but our mission in terms of selecting indications, in terms of how we approach our science, always comes back to “Will treating this help people have a healthier lifespan and healthspan?”

It actually feeds well into the notion that it’s more about ideology than technology. If you think of yourself as a longevity company, you probably are one.

Yes, something like that.

Do you have any other indications in mind?

We think our technology has great potential across many aging-related indications. Something in the neuro space would be very exciting to tackle, or in muscle wasting or bone health. They’re all critical things that deteriorate with age and have specific indications associated with them. They also have transcriptional dysregulation as an underlying core reason that we think our approach could be well applied to.

Recent research has addressed menopause-related molecular processes that impact the high prevalence of knee osteoarthritis in post-menopausal women. Restoration of female sex hormones in a post-menopausal mouse model improved joint health [1].

A centuries-old observation

The authors start the article with a quote from the English physician John Haygarth from 1805:

the nodosities [irregularities] of the joint are almost peculiar to women and begin when the menses naturally cease.

While this 200-year-old observation has been confirmed by contemporary medicine, an inadequate amount of attention is still given to knee osteoarthritis in post-menopausal females, even though age and sex are risk factors for this condition and knee osteoarthritis is more prevalent and severe in post-menopausal women than men [2, 3, 4]. The researchers of this study aimed to fill this gap and focused explicitly on post-menopausal models.

Mimicking human menopause

The researchers mention a few ways to mimic menopause in mice, which do not have the same menopausal transition as humans. One of them is the surgical removal of the ovaries (ovariectomy), although this model has many drawbacks. Human females normally transition into menopause through a period of perimenopause, during which regular cycles become irregular and finally cease. However, ovariectomy results in a sharp cessation of cycles and the abrupt disruption of ovarian sex hormones, including those that do not change during normal menopause.

Due to those shortcomings, ovariectomy is not the best model for investigating the effects of the menopause transition on the trajectory of knee osteoarthritis. Therefore, the researchers used a different model that chemically induced menopause. They injected mice with the ovarian toxin 4-vinylcyclohexene diepoxide (VCD). This approach is not an exact representation of menopause and might cause some unwanted changes in biology. However, it seems to have more benefits than ovariectomy; for example, the animals undergo perimenopause and have intact ovaries.

The authors modified previous protocols and treated 14- to 16-month-old female C57BL/6N mice with VCD. These middle-aged mice roughly correspond to 47- to 52-year-old humans, an average perimenopause age.

As expected, animals treated with VCD experienced perimenopause and the menopausal transition, including body temperature, weight, and hormonal changes that mirrored that of humans. The researchers called the VCD-treated group the ‘menopause group’ and control mice the ‘non-menopause group.’

Declining joint health

The researchers assessed the impact of menopause on cartilage, the layer of bone below the cartilage in a joint (subchondral bone), and the membranous structure located on the inner surface of joint capsules (synovium). Cartilage, subchondral bone, and synovium health didn’t differ between these groups early in the experiment, 11 days after VCD injections.

However, when cartilage integrity was scored in the menopausal group during perimenopause and the menopausal transition, the researchers noted “progressively increased degeneration,” which was not observed in the non-menopause group. Synovium health worsened in both the menopause and non-menopause groups, but the menopause group had worse scores compared to the non-menopause group. The impact of menopause on subchondral bone depended on the bone region, with some areas showing no differences between groups and others showing a decrease in bone volume and density in the menopause group.

Modeling the molecular changes

The researchers aimed to understand the molecular mechanisms behind their observation. They used mass spectrometry to identify proteins present in cartilage samples of mice at mid-perimenopause, the start of menopause, and late menopause. After identifying what proteins changed, the researchers identified pathways impacted by the menopausal transition and integrated them into a network to identify changes in pathways over time.

Their analysis revealed cellular signaling changes followed by extracellular matrix (ECM) changes, such as changes in collagen expression, in the menopause group. Conducted experiments also suggested an increased susceptibility to collagen degradation caused by menopause.

Further analysis of menopause-associated protein changes showed that, besides changes to the ECM, cellular senescence and actin cytoskeleton stress were also impacted by the menopausal transition.

Then, the researchers used a simulation system to estimate how altering sex hormone levels and administering senolytics would impact health. In this simulation, administering 17β-estradiol plus progesterone eliminated ‘cellular senescence’ and ‘ECM disassembly’ processes and improved other measurements. A senolytic, dasatinib, had a similar effect.

Testing the predictions

The researchers tested their simulation’s results in vivo. They induced menopause in mice and treated them daily from mid-perimenopause to the start of menopause with either 17β-estradiol, progesterone, 17β-estradiol plus progesterone, or dasatinib.

Cartilage integrity was improved in mice treated with 17β-estradiol and 17β-estradiol plus progesterone compared to controls. However, synovium and subchondral bone tissue were not affected by the treatments.

While analyzing a few mice from the groups for side effects, the researchers noticed abnormalities and excessive growth of tissues in the intestine of some of the animals treated with either 17β-estradiol or progesterone, but further research is needed to confirm that it was indeed caused by the treatment since the sample size was too small to be conclusive.

To learn about the functionally relevant impact of the treatments, the researchers tested behavioral outcomes. The differences were seen only in step length (increased in menopausal mice) and stride length (decreased in menopausal mice). Both progesterone and 17β-estradiol plus progesterone treatments restored those measurements to non-menopausal levels.

Improved chondrocytes health

Encouraging results in mouse models led the researchers to further experimentation on available human material. They isolated cells responsible for cartilage formation (chondrocytes) from post-menopausal patients undergoing knee surgery.

Culturing human-derived chondrocytes with 17β-estradiol, progesterone,17β-estradiol plus progesterone, and dasatinib resulted in a decreased proportion of cells expressing senescence markers, an increase in the cells expressing proliferation markers, and a reduction in the senescence-associated secretory phenotype (SASP).

Additionally, progesterone, 17β-estradiol plus progesterone, and dasatinib led to a modest increase in the expression of transcription factors essential in regulating key genes related to cartilage formation and development (chondrogenicity). 17β-estradiol plus progesterone improved the health of chondrocytes and positively regulated the expression of different types of collagen.

The researchers concluded that “these findings support our network medicine analyses suggesting that restoration of progesterone signaling alters the senescent phenotype of aged, post-menopausal chondrocytes.”

Restoring health with hormones

This study suggests a link between changes in sex hormone signaling during the menopausal transition and knee osteoarthritis development in post-menopausal females. Restoration of those hormones improves cartilage and chondrocyte health. Future studies are required to address whether such a therapy could help women with knee osteoarthritis.

Literature

[1] Gilmer, G., Iijima, H., Hettinger, Z. R., Jackson, N., Bergmann, J., Bean, A. C., Shahshahan, N., Creed, E., Kopchak, R., Wang, K., Houston, H., Franks, J. M., Calderon, M. J., St Croix, C., Thurston, R. C., Evans, C. H., & Ambrosio, F. (2025). Menopause-induced 17β-estradiol and progesterone loss increases senescence markers, matrix disassembly and degeneration in mouse cartilage. Nature aging, 5(1), 65–86.

[2] Prieto-Alhambra, D., Judge, A., Javaid, M. K., Cooper, C., Diez-Perez, A., & Arden, N. K. (2014). Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: influences of age, gender and osteoarthritis affecting other joints. Annals of the rheumatic diseases, 73(9), 1659–1664.

[3] Hame, S. L., & Alexander, R. A. (2013). Knee osteoarthritis in women. Current reviews in musculoskeletal medicine, 6(2), 182–187.

[4] Srikanth, V. K., Fryer, J. L., Zhai, G., Winzenberg, T. M., Hosmer, D., & Jones, G. (2005). A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis and cartilage, 13(9), 769–781.