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A Ketosis-Related Compound Alleviates Arthritis in Rats

In Aging Cell, researchers have published details on the pathway by which a compound commonly found in ketone bodies ameliorates osteoarthritis in a rat model.

Chondrocytes are crucial in arthritis

The cartilage holding joints together is built and maintained by chondrocytes, which synthesize such necessary proteins as collagen and aggrecan [1]. Unfortunately, the cellular senescence that accompanies aging prevents these cells from proliferating and doing their jobs properly [2].

Metabolism has a substantial effect on chondrocyte function [3]. A review of human clinical trials has found that the ketone bodies that occur with the consumption of a ketogenic diet can alleviate osteoarthritis [4]; however, that previous work could not elaborate on the biochemical reasons why.

These researchers have noted that β-hydroxybutyrate (βOHB), a major component of ketone bodies [5], is substantially diminished in the knee joint fluid of osteoarthritis patients [6]. As βOHB has been found to suppress inflammation [7, 8] and affects fundamental AMPK metabolism [9], the researchers suspected that βOHB might play a causal role in suppressing osteoarthritis.

Less inflammation, better function

For the first experiment, the researchers subjected rats to a surgical procedure that induces arthritis or to a sham surgery, and then subdivided those two groups into standard-diet and ketosis-inducing diet groups. βOHB was very significantly elevated in rats fed a keto diet, whether they had induced arthritis or not. Of the rats fed a standard diet, the induced arthritis group had slightly less βOHB than the sham group.

The keto diet reduced inflammatory factors in the arthritis-induced group. Compared to arthritic rats fed a standard diet, the ketosis group had less TNF-α, IL-6, and PGE2 while scoring better on the OARSI, a test that assesses function in osteoarthritis.

In another experiment, groups of rats were directly injected with βOHB in various concentrations instead of being fed a keto diet. The effects were substantial and dose-dependent. Once more, TNF-α, IL-6, and PGE2 were all substantially reduced, and OARSI scores were improved.

Cellular effects

The hydrogen peroxide derivative TBHP induces senescence and oxidative stress in chondrocytes and is commonly used to model arthritis on a cellular level. As such, it is highly toxic to these cells, regularly causing death by apoptosis. However, βOHB was able to protect chondrocytes from moderate doses of TBHP, neutralizing these toxic effects. The TBHP-induced senescence was substantially reduced, the secretions of senescent cells were similarly reduced, and the affected chrondrocytes were once able to produce cartilage-forming proteins. Apoptosis, too, was reduced, and an investigation found that the key proteins involved in apoptosis were strongly upregulated with TBHP but reduced with βOHB.

Previous work had found that mitochondrial maintenance is key in chondrocyte function and senescence prevention [10]. A main indicator of mitochondrial function that had nearly vanished in cells given TBHP had it restored by βOHB. Similarly, mitochondria given TBHP were often found in fragments, but administering βOHB prevented this fragmentation. While it had no significant effects on cells that were not subjected to TBHP, βOHB improved multiple metrics in the mitochondria of the cells that were, including ATP production and respiration.

This was found to be due to an increase in mitophagy, the process of cells consuming their own damaged mitochondria. Cells that were additionally given other compounds that interfered with the PINK1 mitophagy pathway could no longer be aided by βOHB. The researchers also found that this treatment requires HCAR2, a receptor of βOHB, to function properly, and that the AMPK pathway is crucial to its function; interference in either of these places also prevented βOHB from having its effects.

These results, while promising and positive, came from experiments done on a rat model and on cells, and they may or may not apply to patients in the clinic. Trials would have to be conducted to determine if direct administration of βOHB has any positive effects on people suffering from arthritis.

We would like to ask you a small favor. We are a non-profit foundation, and unlike some other organizations, we have no shareholders and no products to sell you. We are committed to responsible journalism, free from commercial or political influence, that allows you to make informed decisions about your future health.

All our news and educational content is free for everyone to read, but it does mean that we rely on the help of people like you. Every contribution, no matter if it’s big or small, supports independent journalism and sustains our future. You can support us by making a donation or in other ways at no cost to you.

Literature

[1] Zhang, Y., Jin, W., Chen, J., Wei, S., Cai, W., Zhong, Y., … & Peng, H. (2023). Gastrodin alleviates rat chondrocyte senescence and mitochondrial dysfunction through Sirt3. International Immunopharmacology, 118, 110022.

[2] Chen, H., Wu, J., Wang, Z., Wu, Y., Wu, T., Wu, Y., … & Shang, S. (2021). Trends and patterns of knee osteoarthritis in China: a longitudinal study of 17.7 million adults from 2008 to 2017. International Journal of Environmental Research and Public Health, 18(16), 8864.

[3] Zheng, L., Zhang, Z., Sheng, P., & Mobasheri, A. (2021). The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis. Ageing research reviews, 66, 101249.

[4] Abboud, M., AlAnouti, F., Georgaki, E., & Papandreou, D. (2021). Effect of ketogenic diet on quality of life in adults with chronic disease: A systematic review of randomized controlled trials. Nutrients, 13(12), 4463.

[5] Sharma, R., & Ramanathan, A. (2020). The aging metabolome—biomarkers to hub metabolites. Proteomics, 20(5-6), 1800407.

[6] Mickiewicz, B., Kelly, J. J., Ludwig, T. E., Weljie, A. M., Wiley, J. P., Schmidt, T. A., & Vogel, H. J. (2015). Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. Journal of Orthopaedic Research®, 33(11), 1631-1638.

[7] Fu, S. P., Li, S. N., Wang, J. F., Li, Y., Xie, S. S., Xue, W. J., … & Liu, J. X. (2014). BHBA suppresses LPS‐induced inflammation in BV‐2 cells by inhibiting NF‐κB activation. Mediators of inflammation, 2014(1), 983401.

[8] Youm, Y. H., Nguyen, K. Y., Grant, R. W., Goldberg, E. L., Bodogai, M., Kim, D., … & Dixit, V. D. (2015). The ketone metabolite β-hydroxybutyrate blocks NLRP3 inflammasome–mediated inflammatory disease. Nature medicine, 21(3), 263-269.

[9] Carretta, M. D., Barría, Y., Borquez, K., Urra, B., Rivera, A., Alarcón, P., … & Burgos, R. A. (2020). β-hydroxybutyrate and hydroxycarboxylic acid receptor 2 agonists activate the AKT, ERK and AMPK pathways, which are involved in bovine neutrophil chemotaxis. Scientific Reports, 10(1), 12491.

[10] Shang, J., Lin, N., Peng, R., Jiang, N., Wu, B., Xing, B., … & Lu, H. (2023). Inhibition of Klf10 attenuates oxidative stress-induced senescence of chondrocytes via modulating mitophagy. Molecules, 28(3), 924.

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Michael Antonov: from Oculus to Longevity Biotech

After Oculus was sold to Facebook for two billion dollars a decade ago, Michael Antonov, one of the founders, could have become a major tech investor, a popular podcaster with his own mega-theory of everything, a hedonist, or all of the above. Instead, he gravitated towards the emerging field of longevity biotech, where the uncertainties are as big as the potential.

Michael runs a sprawling investment fund, Formic Ventures, that focuses primarily on longevity biotech. He also co-founded his own company, Deep Origin, which has a soberingly realistic approach to foundation AI models in biology.

How did you end up in longevity?

I got into this space about 10 years ago. This was after I exited Oculus to Facebook, and I was still working at Oculus for a handful of years while looking for other interesting things to do.

Once, I was presenting at the same event as Aubrey de Grey. I was talking about virtual reality, and he was talking about longevity. To me, that sounded very interesting, so we ended up talking a bit afterwards. I realized there’s been a lot of progress in genomics and in biology in general.

So, I was looking for the most meaningful thing I could do in my life, and, at that point, it was clear where VR was going. Yes, it would take a lot of engineering, and many smart people were working on it, but I didn’t feel like it was critical for me to keep contributing there.

Aging, on the other hand, felt like the biggest challenge facing mankind. I had some resources for investing from the Oculus exit, and I felt I could learn things.

I got very curious about the science of aging, where it was at the moment, and what might be possible in the future. This led me to participate in the community. I was going to a lot of mixer events, conferences, and also started learning science. I ended up taking several years’ worth of biology, biochemistry, and related courses at the Berkeley Extension because I realized I wanted to know how we work internally.

Eventually, I decided that I wanted to invest in this space. I was looking at tools that speed up research and longevity-related therapeutic. One approach to improving healthspan is by developing drugs, and in the aging field, even though we want to target aging, we usually have to make a drug for a specific disease.

At some point, I met Alex Zhavoronkov, who was instrumental in helping me find my way in the ecosystem. We became good friends. I ended up investing in his InSilico Medicine, and we still connect a lot.

A few years later, I ended up starting Formic Ventures and looking at what companies I could invest in. Finally, I realized I wanted to get more into scientific tools, and that is what made me start Deep Origin.

So, the first thing you built in this space was an investment company. When I looked into it, I saw that you had invested in about 40 companies, which is an unusually wide net. Can you tell me more about the company and its philosophy?

Generally speaking, I looked at companies which could make an impact and took an approach that is different, let’s say, from the typical way of doing things. Fundamentally, that is because I want to make a difference rather than just money.

It’s not focused purely on profit, but, of course, the question of whether the company can be successful and profitable is an important one. Also, there were companies where I was willing to take larger risks because I really liked the team and the direction.

I’ll give you two examples. One of the companies I invested in is Turn Bio. They work on cellular reprogramming, and they were in this space significantly before Altos, and before this field became so hot. I still believe in their approach. At the point when I first invested, it was high risk, but it felt like there was meaningful data and direction.

If you believe that epigenetic reprogramming is possible, it seemed like a worthwhile goal to move forward. That’s one example of a company which is aligned with my vision – a unique approach (at that time) plus a meaningful impact. And when it succeeds, it certainly has a big potential.

Another good example is Nanotics. They use nanoparticles to take cytokines out of the circulation, which is very different from putting the drug in. So, it’s a different modality, high-risk novel approach, but it’s a kind of thinking that we need in this space.

Looking broadly at the Formic profolio, not all of my investments are in the longevity space, although about 70% of them are. I have also invested in some companies created by “the Oculus mafia,” meaning people who we started the company together with.

Do you think there’s any place for VR in the longevity field?

Not a lot, it’s mostly unrelated. However, VR is very good at training, and there have been some companies that utilize this in the medical space. As an example, Osso VR trains surgeons, and they have shown that the results of the training for the same time period were measurably better. VR can also be good for scientific visualization. But it’s not directly advancing science, it’s just a different way to interact with the world. For instance, you can look at the drug in 3D space and how it docks. Chemists may be seeing things a bit clearer by looking at them from different angles.

From your experience and the breadth of your investments, where do we stand now with longevity biotech? How optimistic are you? How do you judge the trends in the last couple of years?

I think the good part is that there has been progressively more capital in the space over the last five to six years. There are more funds, more people involved in it and believe in it, it’s a more active space. That’s a holistically good thing.

Specifically, biotech has been in a little bit of a trough. There was this market crash last year, and it hasn’t fully recovered. So, it’s still harder now to raise money. It certainly affects current longevity companies. Otherwise, I think it’s a positively developing industry.

Personally, I probably have grown less optimistic than I was eight years ago. That’s because we all know drug development takes a decade, but we don’t feel it in our bones. As we come into the industry, and look at the exciting research developments at conferences, the progress can feel quicker than it really is. We are making progress, just not as fast as I’d like.

I really want to think about how we as an industry find ways to speed it up. Is it scientific tooling, such as modeling of biology that will truly make a difference – which is why we created Deep Origin? Is it robotic automated labs? I don’t know.

Can we grow organs on chips and convince the FDA that those results are at least as compelling as long-term trials? We need some solution that would fundamentally speed up the progress.

I think the final wildcard is AI. There may not be quite enough data and proof for how much difference AI will make, but what is possible is also unknown, and there’s a lot of positive excitement. It may well be that with the help of AI we will make big leaps, but it’s unclear at the moment.

What do you think about AI’s role in drug discovery? How big of a gamechanger can it be?

AI is very helpful in drug discovery. It’s actually an area where we work. In Deep Origin, we have a very strong physics and AI team. We specifically work on physics-based technologies, on things like molecular dynamics, docking, energy fields, and AI training of custom models to do it even better and faster, or in broader sets of molecules.

AI helps a lot in structural biology. This is where you see that AlphaFold can predict some parts of protein interactions, there’s more and more structure. In general, AI is able to predict phenotypes and chemical outcomes, like drug properties, toxicity aspects, and what not.

To what level is that good is really a function of the data we have and, in some cases, of previous models we’re picking from. As a result, it’s a very powerful tool to speed everything up, but there are enough gaps in data and other technologies for it not to be completely lifechanging. That’s where we are today.

Everyone hopes that the next generation of AI will be just magical. At every step, something gets better, but it’s not magical. We still have lots of biological problems.

Zooming in on this data problem: specifically for big foundation models of biology, which is something Deep Origin is also working on, how serious is it, and what can we do to make things better?

It’s a significant problem. The amount of data you need is, in some sense, problem-specific. If you’re looking at a high-level patient phenotype, you need one kind of data, but when you’re looking at molecular structures, you might need biochemistry, crystallography, or other data. Those are different classes of data, and the volume of it that you will need will also be different.

We need a lot of lab automation at scale. That would be instrumental for insights into deep biology.

We also need better data integration. Often data is in different institutes, it’s siloed in, not accessible to researchers. You need to apply, maybe to form partnerships. By the nature of it, that means data is not as widely shared as we’d like it to be, which is one of the reasons the field is not moving as fast as we want it to.

We do need orders of magnitude more data to truly model biology. All kinds of data – molecular structure, tissue microscopy, multi-omics of different cell and tissue types taken from people of different ages. All those things, we don’t have enough of. We really need to build up the dataset, as a world community, and be able to share it. That would enable simulations and better predictions.

I understand that one of the things Deep Origin is trying to do is to automate the process of drug discovery.

Deep Origin’s vision is to enable finding cures faster through deeper understanding of biology. To put it in one line, our mission is to organize, model and simulate biology. That’s what we do.

The name Deep Origin speaks about going to the origins of life, which are atoms and molecules. You have to go up from atoms and molecules – how do they come together? Understanding the structure and so on.

We are building a platform that has two branches. On one hand, it supports data collection, management, and processing from the wet lab through analysis. That is, how do you record your experiment, how do you analyze it, how do you get data out of it?

The second one, beyond that, is simulation. Simulation is, basically, if we have this data, or if we have some knowledge, what would happen under certain conditions? How would proteins interact? How would physics work? How can you actually simulate a cell?

Ultimately, the way these two things are connected is that even if you have a good simulation stack, you need lots of data to validate it. Both are meaningful. So far, what we have built is a suite of tools which we provide to biotechs and license to pharma.

Right now, some of the tools we built solve very specific problems. For example, we have docking and virtual screening solutions. If you have a protein you want to drug, we can provide a state-of-the-art solution for that narrow problem.

But we’re actually looking at how multiple dimensions of research come together. If you’re going to design a novel drug molecule, you may also need to run biological screens. In which database will they be stored? How are they linked? How are aspects of biology represented? We want to support this whole process.

Yes, we also want to automate workflows for bioinformatics processes, and we will connect to labs, but before you can do advanced analysis or AI, you need to collect the data and the data needs to be in a certain understandable format.

We actually want to make parts of our platform open going further, so that users can have more standards and easier ways to access the data. The main question is how we get consistent quality data so that researchers can answer questions, and how do you run simulations that help them dig even deeper.

Tell me about your work on foundation models for biology.

We actually have foundation models for some of our docking and chemical properties predictions, for some structure work. That said, “foundation model” is a very overloaded term. Probably every time that someone tells me they’re starting a company or raising money to build a foundation model for biology, my question is, what does it do?

You can have an LLM model which is trained on text and works on text. Alternatively, you can train a model on structures, which is what AlphaFold 3 did.

I’m not an AI expert, we have really strong AI people on our team, like Garik Petrosyan, but you’re basically combining multiple training sets together for different purposes. You have a neural net to predict protein and maybe DNA structure, you have sequence, and you train them together in a way that some of the knowledge is transferable.

So, if you now want to predict something new, to adapt your model to a new domain, you do some extra training. You may not have had enough data to get a good output on this new dataset, but because you’re combining it with a bigger model, you’re now able to get high quality results, because some internal learning from these other learning modalities is being applied in your new case.

In my understanding, foundation models are a generalization of that. You’re picking a set of subdomains, and they are able to predict a set of other things. But I do not yet believe in a universal biological foundation model that will solve everything for you.

You can probably try to train it, and it will make a useful suggestion, or it will hallucinate. Imagine if you merge LLM and structural biology. Now, you can ask it questions about pathways, which is text, and you can also ask it, what would a protein look like in a given context? And it might be able to give you both a text and an image as an answer.

But the question is, will it be a good protein for the given task? Most likely, the answer is going to be “no” because the model didn’t have enough similar proteins or enough precision in training. It may be a good, smart guess but you will need to keep working on it. So, foundation models by themselves are not the complete answer.

Our approach is different. We have some foundation parts in AI, but we’re combining them with physics-based tools. If you look at molecular simulations that people have been doing for the last 20, 30 years, they use energy fields, and we’ve gotten better and better at it. But we are not perfect, it just takes too long to compute. That’s a big challenge.

But now, you can start combining it with AI. You can apply those coarse-grain approaches to it. You can train specific neural nets which will do a given task, like binding a molecule to a pocket, very well.

Your typical off-the-shelf foundation model that you find on the Internet will probably not do a good job at that because it hasn’t been trained specifically on that problem, but if you’re making a drug, you need a specific, high-quality answer.

We are combining those physics-based tools with AI, with generalized LLMs and other things, into a solution to a given problem set. It can’t answer every question in the world, but for a class of structural biology or drug discovery problems, it will use state-of-the-art tools to give you the world’s best answer (we believe) for a given set of narrow problems.

What do you think is the main bottleneck for this approach specifically and foundation models in general?

There are several. If we look at a general molecular interaction problem, such as folding, how things combine, how they bind, how reactions are happening, one problem is that we don’t always have data on energy fields.

For instance, we don’t have very good datasets of energy fields for RNA (we have much better ones for proteins). When we don’t have this, we can’t run simulations as well.

The second challenge is that some things are either too hard to compute or we simply lack knowledge. This applies, for instance, to some quantum effects. For example, if you want to break bonds, you need to do quantum chemistry, which is hard.

And the third class of problems is not having enough data – basically, how many outcomes of a given experiment have you measured? The way we approach it is, first, we try to collect the best data we can out of open or collaborative sources for our physics models. Like I said, we are trying to augment our physics with AI, so you can sometimes run simulations faster and get better data from those simulations.

So, not all the data needs to come from experiments, some of it can come from simulations, but we also try to collect data when we can. We have a small wet lab, which we do experiments in, but, in general, we are looking at a more hybrid approach. We’re not a brute force data company, we’re more “physics combined with AI” company.

Can you give me a specific example of how this hybrid physics/AI approach works?

Let’s take a look at a hard problem. There’s a technology called molecular dynamics. We have our own version of it which we think is particularly good but it’s a well-studied field. It models molecules and simulates them over time – for example, how a full protein folds and unfolds, what shapes it takes.

It’s a very useful technique. The problem is it takes too long, because you have to do computations of many atoms and all their relationships for each femtosecond, and then you need millions and millions of these time steps to get a picture of that millisecond where useful biological activity takes place.

The problem is that there’s just not enough computing power, but if you can get enough of it, you get very useful answers for some cases.

Imagine running this simulation a number of times, but instead of always trying to do big tasks, you train your neural net. Then, this neural net can give you suggested answers for certain problems faster than if you actually ran an expensive simulation each time.

So, now, for a set of problems, you’ve made it faster. We’re looking for these kinds of patterns. I’m simplifying a bit, but you can imagine doing it for every bit of biology.

You also have a foundation that funds academic research.

Yes, The Antonov Foundation makes grants in areas I care about. A meaningful part is supporting longevity research by good scientists. So, I have donated to Buck, to SENS, I’ve supported some of Vera Gorbunova’s projects. We’re talking about people whom I know who are doing interesting things.

We’ve looked at projects with promising outcomes and provided them with some capital. It’s really impact-focused.

When you are looking at the whole longevity field and your place in it, what are the main hurdles we will have to overcome? What needs to change ASAP?

More flexibility in regulation on the FDA side would help. That’s definitely a very expensive process, and it needs to be more streamlined. There have been some good steps already, for instance, with drugs for very specialized groups, which can be fast-tracked. But in my view, it’s not enough. As Milton Friedman suggested, we need to put in conscious effort to always fight the tendency to overregulate. Here, some political push is required.

In addition to that, I’d love to see more standardization and automation in biological data and processes. There are too many standards and vendors, which makes it harder to do reproducible experiments. We need a bigger push on the industry level to open data structures, representations, and protocols for experiments.

This brings me to the question I honestly forgot to ask: how does the reproducibility problem reflect on AI in biology in terms of data acquisition?

This is a big issue because, typically, models are trained on some external data sets, and those were possibly collected under heterogeneous conditions. This makes the whole thing less reliable; it needs to be somehow adjusted for. If we really want high-quality predictability, we need highly reproducible robotic labs with well-controlled conditions at scale.

Not a lot of companies today have it. Some pharma does. InSilico has an interesting new lab, but that’s not a universally adopted practice yet. We need much more coherent standardized protocols for really high-quality AI predictions.

You come from the tech industry. It seems that tech people’s interest in longevity is booming and that their outlook on longevity is more positive and optimistic than that of the general public. What do you think of this budding synergy?

I agree that the interest is growing and that tech investors, and maybe especially crypto investors, tend to be more optimistic. On the other hand, sometimes, they don’t realize what they’re getting into, but this is a good thing because often you have to believe that something is possible to keep pursuing it. In the end, because of their support, we are making more progress.

Look at Altos Labs as an example. A number of wealthy people came together to support this initiative. So, overall, more resources and optimism are going into longevity. It’s good, it’s happening, but it’s not a linear process, it feels very stochastic.

About them not realizing what they’re getting into – do you mean that people who made their money in tech industry, where timelines are different, might not have enough stamina to invest in longevity biotech and wait for a decade or more to see the return?

Some will have what it takes, and others won’t. Some people will get discouraged, maybe after a couple of bad bets, but many are on a mission, because this is something that matters. They see that having money doesn’t do that much for their lives, and they want to help the world. So, this is going to continue. As for me, I’m in for the long run.

I assume you don’t regret your decision to go into longevity head-first?

No, although I wish it was moving faster. I wish it was easier, but it is a fruitful and necessary endeavor, and, in general, the field is on an upswing. We’re growing.

We would like to ask you a small favor. We are a non-profit foundation, and unlike some other organizations, we have no shareholders and no products to sell you. We are committed to responsible journalism, free from commercial or political influence, that allows you to make informed decisions about your future health.

All our news and educational content is free for everyone to read, but it does mean that we rely on the help of people like you. Every contribution, no matter if it’s big or small, supports independent journalism and sustains our future. You can support us by making a donation or in other ways at no cost to you.

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Immune Peptide Might Keep White Blood Cells Contained

In npj Aging, researchers have described how immune cell infiltration in inflammaging can be reduced with an immune-related peptide in a mouse model.

Immune cell infiltration

In a previous paper, these researchers have reported that aging affects the movement of white blood cells (leukocytes) in a way that harms the immune system’s protective abilities [1]. That paper named cellular senescence and its secreted inflammatory molecules as causative factors that lead to more permeable vascular walls, thus causing white blood cells to more readily infiltrate into the peritoneal cavity of the abdomen.

While the dynamics of some white blood cells, such as neutrophils, have been better documented [2], the behavior of T cells and B cells has been less clear. Previous work has found that PEPITEM, a peptide related to immune system function, stimulates the production of spingosine-1-phosphate (S1P), which inhibits the transmission of white blood cells into tissues [3]. Therefore, these researchers employed a mouse model of peritonitis, the inflammation of the abdominal lining, in order to determine how PEPITEM influences white blood cells in differently aged animals.

Not all cells behave the same

The researchers administered zymosan to populations of 3-month-old and 21-month mice in order to induce peritoneal inflammation, and in some groups, they administered PEPITEM to combat it. As expected, in both young and old mice, the number of activated T cells, as measured by the CD45 marker, was increased with zymosan and decreased with PEPITEM. Naive and central memory T cells showed the same behaviors in young and old animals: greatly increased with zymosan and ameliorated with PEPITEM.

However, this did not occur among all cell populations. Cells that were positive for both KLRG1, which is a marker of terminal cell differentiation, and the T cell activator CD3 did not appear in the younger mice but appeared in the older ones. These cells were suppressed by PEPITEM.

Effector memory cells, on the other hand, were only suppressed by PEPITEM in younger mice, but not older mice. In younger mice, CD19+ B cells were increased with inflammation and suppressed by PEPITEM, but their levels were unaffected either way in older animals. Most critically, and perhaps most promising as a treatment, B cells that had markers specific to age were increased in both younger and older animals in response to zymosan and were suppressed by PEPITEM.

Taken together, these data suggest that PEPITEM can control the magnitude of an inflammatory response even in the ageing micro-environment, where low-grade chronic inflammatory phenotypes normally prevail and hinder efficient resolution.

Human cells

The researchers then tested two different groups of white blood cells: some were taken from donors under 40, while others were taken from donors over 65. Both of these populations adhered to endothelial cells that had been stimulated by cytokines, which is related to their migration and infiltration through the blood vessel walls. However, younger cells responded to adiponectin, and older cells did not; both populations, however, responded to PEPITEM.

The researchers note that due to differences in how males and females respond to inflammation, their research was exclusively on males. Further work, including both sexes and human trials, will need to be done to determine if PEPITEM can be used to combat the immune cell infiltration that accompanies inflammaging.

We would like to ask you a small favor. We are a non-profit foundation, and unlike some other organizations, we have no shareholders and no products to sell you. We are committed to responsible journalism, free from commercial or political influence, that allows you to make informed decisions about your future health.

All our news and educational content is free for everyone to read, but it does mean that we rely on the help of people like you. Every contribution, no matter if it’s big or small, supports independent journalism and sustains our future. You can support us by making a donation or in other ways at no cost to you.

Literature

[1] Hopkin, S., Lord, J. M., & Chimen, M. (2021). Dysregulation of leukocyte trafficking in ageing: Causal factors and possible corrective therapies. Pharmacological Research, 163, 105323.

[2] Arnardottir, H. H., Dalli, J., Colas, R. A., Shinohara, M., & Serhan, C. N. (2014). Aging delays resolution of acute inflammation in mice: reprogramming the host response with novel nano-proresolving medicines. The Journal of Immunology, 193(8), 4235-4244.

[3] Chimen, M., McGettrick, H. M., Apta, B., Kuravi, S. J., Yates, C. M., Kennedy, A., … & Rainger, G. E. (2015). Homeostatic regulation of T cell trafficking by a B cell–derived peptide is impaired in autoimmune and chronic inflammatory disease. Nature medicine, 21(5), 467-475.