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How Scientists Are Measuring Aging at the Cellular Level

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“We illustrate our strategy in brain and liver tissue, demonstrating how cell-type specific epigenetic clocks from these tissues can improve tissue-specific estimation of chronological and biological age.”

Aging affects everyone differently. There are two types of aging: chronological aging, which refers to the number of years a person has lived, and biological aging, which reflects how well the body is functioning based on cellular changes. A recent study published as the cover for Volume 16, Issue 22 of Aging reports a new discovery that could revolutionize the way we understand aging and its impact on health. 

Understanding Biological Age

Biological age reflects how well the body is aging and can vary based on lifestyle, genetics, and environmental factors. Traditionally, scientists estimate it using epigenetic clocks, which measure DNA methylation, chemical changes that occur over time. Until recently, these clocks could only provide general estimates by analyzing entire tissues, meaning they could not distinguish how different cell types aged within those tissues. A recent study titled “Cell-type Specific Epigenetic Clocks to Quantify Biological Age at Cell-Type Resolution” aims to change that.

The Study: Measuring Aging More Precisely

To explore how different cell types age, researchers from the Chinese Academy of Sciences and Monash University analyzed publicly available DNA methylation data from brain and liver tissues using advanced computer models. The samples included healthy individuals and those with diseases like Alzheimer’s and non-alcoholic fatty liver disease. 

In addition to brain and liver samples, the study included data from other tissues such as the prostate, colon, kidney, and skin. This broader dataset ensured that the findings applied to a wide range of conditions.

The Challenge: Understanding Aging at a Cellular Level

One of the biggest challenges in estimating biological age has been the inability to distinguish between different cell types within a tissue. Traditional methods analyze a tissue as a whole, averaging the age of all existing cells. This can hide the fact that some cells age faster than others, making it difficult to identify early signs of disease.

In organs like the brain and liver, different cell types—such as neurons and glial cells in the brain, or hepatocytes in the liver—age at different rates. Without a method to study cell types individually, it has been challenging to identifying which cells are most affected by aging and how they contribute to diseases like Alzheimer’s and liver diseases.

Aging is also influenced by two factors: intrinsic aging, which refers to changes within the cells themselves, and extrinsic aging, which occurs due to changes in cell composition within a tissue. Traditional methods struggle to separate these aspects, limiting their usefulness in developing targeted anti-aging treatments.

The Results: Key Findings in Alzheimer’s and Liver Disease

The study found that different types of cells within the same tissue age at different rates. In Alzheimer’s disease, neurons and glial cells in the brain showed signs of accelerated aging, with glial cells in the temporal lobe being the most affected. This suggests that glial cells could play a crucial role in the progression of neurodegeneration. Similarly, in liver conditions such as fatty liver disease and obesity, hepatocyte-specific clocks detected signs of accelerated aging that were not as easily identified previously.

By applying their approach to brain and liver tissues, the researchers demonstrated that cell-type specific epigenetic clocks can improve tissue-specific estimation of biological age, as well as chronological age.

The Breakthrough: Cell-Type Specific Epigenetic Clocks

Before this study, biological age could only be estimated at the tissue level, providing a general picture but not showing how individual cell types were changing over time. With the development of cell-type specific epigenetic clocks, researchers can now measure aging within specific types of cells, such as neurons in the brain and hepatocytes in the liver. Also, by distinguishing intrinsic aging from changes in cell composition, this new method offers new insights into how diseases develop and progress.

The Impact: What This Means for Healthcare

The implications of this research are significant. Measuring the biological age of individual cell types can lead to earlier diagnosis of age-related diseases, more effective treatments, and personalized healthcare plans. It could also help scientists track the effectiveness of anti-aging therapies and lifestyle changes more accurately, giving individuals better tools to manage their health.

This research also offers valuable insights into age-related conditions like Alzheimer’s and liver diseases, by pinpointing which cells experience the most stress and deterioration, allowing researchers to focus their efforts on the most affected cell types.

​​Future Prospects and Conclusion

Looking ahead, researchers plan to use this method to study other tissues and cell types, further advancing the field of precision medicine. As more data becomes available, cell-type specific epigenetic clocks could become essential tools for tracking aging at an individual level.

This study represents an exciting step forward in the science of aging. By measuring aging at the cellular level, scientists are moving closer to a future where aging can be better understood and managed.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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Accelerated Aging in Young People with Sickle Cell Disease

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“[…] adolescents and adults with SCD still experience higher rates of aging-related morbidity and early mortality.”

Imagine being 15 years old but having a body that shows signs of aging as if you are decades older. For some young people with sickle cell disease (SCD), this is a reality. A new study published in Volume 16, Issue 21 of Aging shows that SCD causes the body to age much faster than normal. The research not only explains why this happens but also points to new ways to help people with the disease live healthier, longer lives.

What Is Sickle Cell Disease?

SCD is a genetic condition that changes the shape of red blood cells. Instead of being round, like a doughnut, the cells become curved like a sickle (a farming tool). These misshapen cells struggle to move through blood vessels, often blocking blood flow and leading to pain, organ damage, and other health problems. Even with modern treatments, they can experience complications like those seen in older adults, such as weaker bones, frailty, and organ failure. In the study “Adolescents and young adults with sickle cell disease exhibit accelerated aging with elevated T-cell p16INK4a expression,” researchers wanted to understand why this happens and what it means for people with the disease.

The Study: Link Between Sickle Cell Disease and Aging

To understand the connection between SCD and accelerated aging, researchers from the University of North Carolina at Chapel Hill and their collaborators focused on a protein called p16INK4a, or simply p16. This protein builds up in cells as people age. High levels of p16 indicate that a person’s cells are aging faster than normal.

They measured p16 levels in 18 young people with SCD, aged 15 to 27, and compared them to 27 healthy individuals of the same age. 

The Challenge: More Than a Genetic Disorder

Individuals with SCD often experience chronic inflammation, anemia, and physical stress due to their condition. These factors affect their immediate health but also trigger cellular changes that mimic aging, making it vital to explore potential therapies. 

The Results: Sickle Cell Disease Patients Aged 43 Years Faster

The results were startling. Young people with SCD had significantly higher levels of p16 than their healthy peers, indicating that their bodies were biologically much older. On average, their p16 levels suggested an additional 43 years of biological aging. Even the youngest participant, a 15-year-old with SCD, had more p16 than anyone in the non-SCD group.

The Breakthrough: Targeting Cellular Aging for Better Outcomes

The study reveals why young people with SCD face age-related health problems much earlier than their peers. These findings highlight the urgent need for treatments targeting cellular aging. One promising area of research involves senolytics, drugs designed to remove senescent (“old”) cells from the body. By slowing the aging process, senolytics could significantly improve both the quality and length of life for SCD patients. Additionally, measuring p16 levels may serve as a valuable tool to identify high-risk patients and enable more personalized treatment strategies.

The Impact: Why These Findings Matter

These findings elucidate how SCD accelerates biological aging, significantly impacting quality of life and reducing healthy years. Understanding the role of cellular aging allows to redefine SCD care, moving from symptom management to addressing the causes of accelerated aging. 

The impact of this study also extends to other chronic diseases by emphasizing the importance of targeting cellular aging markers. By focusing on cellular senescence, this research lays the groundwork for therapies that improve both lifespan and healthspan—the years of life spent in good health.

​​Future for Sickle Cell Disease Research

While this study is a crucial first step, further research is needed to confirm these findings and explore potential therapies. Larger studies with more diverse groups of SCD patients, as well as long-term follow-ups, will help deepen our understanding of how aging affects the disease and the effectiveness of new treatments like senolytics. Additionally, researchers are also investigating other markers of aging.

Conclusion

This study highlights the long-term impact of SCD on young patients, shedding light on how accelerated aging contributes to their health challenges. For many, these findings represent a future with better and more efficient treatments. By addressing the causes of accelerated aging, innovative therapies could significantly enhance the lives of individuals with SCD, potentially leading to healthier and longer lives.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

Click here to subscribe to Aging publication updates.

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The Hidden Link Between Sleep and Dementia: How Better Rest Can Improve Lives

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“Sleep problems in dementia patients are not only common but also contribute to a faster progression of cognitive decline and increased burden on caregivers.”

Sleep is essential for everyone, but for those living with dementia, it is vital for better health and quality of life. Addressing sleep problems in dementia care is a crucial step toward improving life for both patients and caregivers.

Dementia and Sleep

Sleep is critical for brain health and well-being, but it is often a struggle for people with dementia. Dementia, a condition that affects memory, thinking, and daily life, is frequently complicated by other health issues like heart disease, diabetes, and anxiety. On top of these challenges, sleep problems such as insomnia and sleep apnea are common, making life even harder for patients and their caregivers. 

Addressing sleep issues is key to improving the lives of people with dementia and easing the burden on their support systems. Recognizing this need, researchers Upasana Mukherjee, Ujala Sehar, Malcolm Brownell, and P. Hemachandra Reddy from Texas Tech University Health Sciences Center conducted an extensive review. Published in Aging, Volume 16, Issue 21, their work aims to update healthcare professionals on these issues and promote new practices in dementia care.

The Study: Update on Sleep and Dementia’s Connection

“Sleep deprivation in dementia comorbidities: focus on cardiovascular disease, diabetes, anxiety/depression and thyroid disorders” is a comprehensive review that explores the connections between sleep disturbances, dementia, and related conditions like heart disease, diabetes, and anxiety.

The review emphasized how untreated sleep issues can worsen cognitive decline, demonstrating that sleep health is not just a symptom of dementia but an integral part of its progression.

The Challenge: Why Sleep Problems are Overlooked but Critical

People with dementia often face significant sleep disruptions. They might wake up multiple times during the night, feel excessively sleepy during the day, or move around at night. This lack of restorative sleep worsens memory loss and confusion. For example, untreated sleep apnea reduces oxygen flow to the brain, further harming cognitive function. Meanwhile, caregivers experience immense stress and burnout from managing sleepless nights and restless behavior.

Despite these profound effects, many dementia treatment strategies fail to adequately address sleep issues, treating them as secondary problems rather than main components of care. Understanding the relationship between sleep and dementia is critical for designing effective interventions.

The Breakthrough: How Improving Sleep Can Transform Dementia Care

The study highlighted that sleep problems are deeply linked to the progression of dementia rather than being merely side effects. Conditions like cardiovascular disease and diabetes often worsen these disturbances, creating a cycle where poor health accelerates cognitive decline.

The findings showed that improving sleep quality can bring significant benefits. One solution is addressing sleep apnea, which not only improves sleep quality but also enhances brain function and lowers the risk of related health issues such as heart disease. Non-drug therapies such as structured bedtime routines, light therapy, and anxiety management have shown promise in improving sleep for dementia patients. Cognitive-behavioral therapy for insomnia has been especially effective in managing chronic sleep issues. These interventions not only improve brain health but also reduce caregiver stress, promoting a healthier and more supportive environment for everyone involved.

The Future of Dementia Care

Integrating sleep care into dementia treatment is the way forward. Addressing sleep disturbances together with other health conditions like diabetes and anxiety can have a profound impact. Personalized approaches, such as setting up calming bedtime routines and improving sleep environments, can make a real difference. Future research should focus on refining these strategies and equipping caregivers with better tools to manage sleep challenges. 

Conclusion

Sleep disturbances are more than just a symptom of dementia. They are a major factor driving this condition’s progression and affecting quality of life. By prioritizing sleep health in dementia care, memory loss can be slower, day-to-day well-being can be improved, and burden on caregivers can be reduced. Holistic care approaches that address both sleep and overall health hold the key to improving quality of life for dementia patients and their families.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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How AI and Longevity Biotechnology are Revolutionizing Healthcare for Healthier, Longer Lives

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“The integration of artificial intelligence (AI), biomarkers, ageing biology, and longevity medicine stands as a cornerstone for extending human healthy lifespan.”

Imagine a future where we not only live longer but stay healthy throughout those extra years. Thanks to recent breakthroughs in biotechnology and artificial intelligence (AI) in healthcare, this vision is closer to becoming a reality.

Advancements in Aging Research

Aging research has made significant progress in recent years by combining disciplines like biology, technology, and medicine to tackle the challenges of extending healthspans and reducing age-related diseases. While people today live longer than ever before, extending our “healthspan”—the years we stay active and illness-free—remains challenging. AI and health biomarkers (biological indicators of our body’s condition) are now key tools in the pursuit of longer, healthier lives.

In a recent paper, led by corresponding authors Yu-Xuan Lyu from Southern University of Science and Technology Shenzhen; Alex Zhavoronkov from Insilico Medicine AI Limited, Masdar City, Abu Dhabi; Morten Scheibye-Knudsen and Daniela Bakula from the Center for Healthy Aging, University of Copenhagen, along with numerous other collaborators, the transformative potential of AI in aging research was explored. The research paper, titled “Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity,” was published as the cover paper in Aging’s Volume 16, Issue 20.

The Study: A New AI-Powered Approach to Aging

The work summarizes insights from the 2023 Aging Research and Drug Discovery Meeting. Researchers from renowned institutions explored how AI, biomarkers, and clinical applications can work together to enhance longevity. This fusion, termed “longevity biotechnology,” promises to transform healthcare from reactive treatments to proactive, preventive measures focused on staying healthy as we age.

The Challenge: Targeting Multiple Health Conditions with Longevity Biotechnology

Traditional aging research often targets single diseases, but most elderly individuals experience multiple chronic conditions. Addressing this complex challenge requires identifying biological markers that indicate aging and predicting health risks before diseases manifest.

The Breakthrough: AI in Biomarker Discovery for Aging

The study highlights how AI can accelerate the discovery of biomarkers, allowing scientists to understand aging at the cellular level. By using machine learning to identify unique patterns, researchers can estimate biological age, discover potential treatments, and evaluate the impact of lifestyle changes on health. This personalized approach enables healthcare providers to create prevention and treatment plans suited to each person’s unique health needs.

The Future of Healthcare: Preventive, AI-Driven Longevity Treatments

Currently, healthcare often focuses on managing diseases as they arise. However, these AI-driven tools could bring about a shift to preventive healthcare. Instead of waiting for age-related illnesses, clinicians could use AI insights to address aging’s root causes, improving health before issues arise.

While the promise of AI in healthcare is significant, the research team emphasizes that further investment is needed to make these AI-driven approaches accessible and accurate. With continued advancements, longevity biotechnology could become a standard part of healthcare, offering a new way to maintain vitality and well-being as we age.

Conclusion

Longevity biotechnology represents a groundbreaking shift, with AI and biomarkers helping us envision a future of healthier, longer lives. This approach brings us closer to understanding and managing the aging process, making extended healthspans a real possibility.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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Exploring Baseline Variations and Mechanical Loading-Induced Bone Formation in Young-Adult and Aging Mice through Proteomics

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Bone mass declines with age, and the anabolic effects of skeletal loading decrease. While much research has focused on gene transcription, how bone ages and loses its mechanoresponsiveness at the protein level remains unclear.

Researchers Christopher J. Chermside-Scabbo, John T. Shuster, Petra Erdmann-Gilmore, Eric Tycksen, Qiang Zhang, R. Reid Townsend, Matthew J. Silva from Washington University School of Medicine and Washington University in St. Louis, MO, share their findings which underscore the need for complementary protein-level assays in skeletal biology research.

On October 12, 2024, their research paper was published as the cover of Aging (listed by MEDLINE/PubMed as “Aging (Albany NY)” and “Aging-US” by Web of Science), Volume 16, Issue 19, entitled, “A proteomics approach to study mouse long bones: examining baseline differences and mechanical loading-induced bone formation in young-adult and old mice.”

THE STUDY

In this study, the tibias of young-adult and old mice were analyzed using proteomics and RNA-seq techniques, while the femurs were examined for age-related changes in bone structure. A total of 1,903 proteins and 16,273 genes were detected through these analyses. Multidimensional scaling demonstrated a clear separation between the young-adult and old samples at both the protein and RNA levels. Furthermore, 93% of the detected proteins were also identifiable by RNA-seq, and the abundance of these shared targets showed a moderately positive correlation. Additionally, differential expression analysis revealed 183 age-related differentially expressed proteins and 2,290 differentially expressed genes between young-adult and old bone samples.

Proteomic and RNA-seq analyses were conducted on paired tibias from young-adult and old mice to study age-related differences and the effects of mechanical loading on bone formation. The results showed distinct differences in protein and gene expression between the two age groups. Many of the significantly upregulated and downregulated proteins and genes in old bone have been associated with bone phenotypes in genome-wide association studies (GWAS). The study also identified age-related differentially expressed proteins and genes involved in bone phenotypes and aging processes. Integrated analysis with GWAS data revealed eight targets that may be relevant to human disease, including Asrgl1 and Timp2. Furthermore, co-expression analysis identified an age-related module indicating baseline differences in TGF-beta and Wnt signaling. Baseline age-related differences in ECM/MMPs and TGF-beta signaling were detected in both the proteome and transcriptome. Following mechanical loading, the proteome showed distinct pathway, protein class, and process enrichments, with temporal differences observed between young-adult and old mice.

Overall, the findings provide valuable insights into the molecular mechanisms underlying age-related changes and the response to mechanical loading in mouse long bones.

DISCUSSION

This study aimed to compare the proteome and transcriptome of tibias from young-adult and old mice under baseline conditions and analyze changes in the bone proteome in response to mechanical loading. The researchers successfully developed a proteomics method to detect protein-level changes in cortical bone and used it to perform proteomic and RNA-seq analyses on tibias from both young-adult and old mice. They observed a moderately positive correlation between the proteome and transcriptome in bone tissue. Age-related differences were detected at both the protein and RNA levels, with altered TGF-beta signaling and changes in extracellular matrix (ECM) and matrix metalloproteinases (MMPs) protein and transcript levels in old bones. The researchers identified Tgfb2 as the most reduced Tgfb transcript in old bone, predominantly expressed by osteocytes. Proteomic analysis of the loading response showed modest changes compared to age-related differences, with fewer protein-level changes in old bones. The findings suggest that proteomics is a valuable tool for studying bone biology and can provide insights into protein-specific changes in aging.

The data obtained from the analysis were subjected to various statistical and data exploration techniques. Differential expression analysis was performed to compare protein abundance between different groups. Total RNA was extracted from the bones using TRIzol, and its integrity and concentration were measured. The bones were also processed for paraffin sectioning and RNA in situ hybridization.

Overall, the study involved the collection and analysis of bone samples from female mice to investigate age-related changes and loading responses in the skeletal system.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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How Single Housing Impacts Growth and Lifespan in African Turquoise Killifish

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“[…] our results suggest that sharing housing with others in early life might influence whole-life attributes, potentially leading to specific life history traits beyond the typical relationship between the growth rate and lifespan.”

In this research, Chika Takahashi, Emiko Okabe, Masanori Nono, Saya Kishimoto, Hideaki Matsui, Tohru Ishitani, Takuya Yamamoto, Masaharu Uno, and Eisuke Nishida from the RIKEN Center for Biosystems Dynamics Research (BDR) in Hyogo, Japan; Brain Research Institute, Niigata University in Niigata, Japan; Research Institute for Microbial Diseases at Osaka University in Osaka, Japan; Kyoto University in Kyoto, Japan; and RIKEN Center for Advanced Intelligence Project (AIP), explored the effects of housing density during the juvenile stage on whole-life traits, including growth, fecundity, and lifespan, in African turquoise killifish. Their research paper was published on the cover of Aging (listed by MEDLINE/PubMed as Aging (Albany NY) and as Aging-US by Web of Science), Volume 16, Issue 18, entitled, “Single housing of juveniles accelerates early-stage growth but extends adult lifespan in African turquoise killifish.”

THE STUDY

A study on African turquoise killifish examined the impact of housing density on juvenile growth. Newly hatched fish were kept in different densities ranging from 1 to 40 fish per tank. It was found that lower housing densities resulted in faster growth, with fish in single housing growing significantly larger than those in group housing. Additionally, single-housed fish reached sexual maturity earlier compared to group-housed fish at higher densities. Comparisons between group-housed and single-housed fish showed that housing conditions in the juvenile stage did not affect the appearance changes during sexual maturation. 

As the fish progressed to middle-aged adults, the rate of increase in body length slowed down, while body weight continued to increase. Differences in body weight between group-housed and single-housed fish persisted into old age, suggesting potential differences in body composition. Surprisingly, single-housed fish had a longer mean adult lifespan compared to group-housed fish, contradicting the commonly held belief that faster growth leads to shorter lifespan. Lower housing densities during the juvenile stage were also found to extend adult lifespan, further challenging the inverse correlation between growth rate and lifespan. These findings suggest that lower housing densities promote accelerated growth in the juvenile stage of African turquoise killifish.

The study also found that single-housed fish had a longer adult lifespan compared to group-housed fish. This led to the suspicion that the egg-laying period of single-housed fish might also be longer. To investigate this, the researchers conducted weekly monitoring of the number of eggs laid until the old adult stage. In group-housed fish, the number of eggs laid was high for the first two weeks, followed by a medium level for the subsequent five weeks, and then decreased. In contrast, single-housed fish showed a medium level of egg-laying for the first nine weeks, followed by a decrease. The cumulative number of live embryos was found to be lower in single-housed fish compared to group-housed fish. These findings suggest that while the number of eggs laid is not very high, single-housed fish have a longer egg-laying period than group-housed fish.

To investigate the potential reasons behind the reduction in offspring number and longer egg-laying period in single-housed fish, the researchers conducted RNA sequencing analysis of testes or ovaries at four life stages. These stages included the onset of sexual maturity, young adult, mature adult, and middle-aged adult. Interestingly, the analysis revealed that single-housed fish showed higher similarity to group-housed fish at earlier life stages compared to group-housed fish at the same life stage. For instance, in the testes, single-housed fish at stage II exhibited the highest similarity to group-housed fish at stage I. Similarly, in the ovaries, single-housed fish at stage II and III showed higher similarity to group-housed fish at stage I. These findings suggest that the rate of gonadal transcriptional change with life stage progression is slower in single-housed fish compared to group-housed fish.

The researchers identified differentially expressed genes (DEGs) between stage I and stage IV in group- and single-housed fish. In the testes, ribosome-related genes and cilium-related genes were highly enriched in DEGs with higher expression in stage I compared to stage IV, suggesting a link between life stage progression, testes development, and spermatogenesis. In the ovaries, growth-related genes and translation-related genes were highly enriched in DEGs with higher expression in stage I compared to stage IV, indicating a link between life stage progression, ovarian development, oogenesis, and aging. Comparing group-housed and single-housed fish at different stages, there were differences in the PC1 values, suggesting that single-housed fish exhibited slower progression of gametogenesis and gonadal maturation relative to life stage progression compared to group-housed fish.

To further investigate this, the researchers focused on specific genes related to spermatogenic differentiation, oocyte development, oocyte construction, and female gonad development. The expression of these genes showed slower changes with life stage progression in single-housed fish compared to group-housed fish in both the testes and ovaries. This suggests that single-housed fish may have slower rates of gametogenesis and gonadal maturation, leading to a lower proportion of mature sperm and oocytes in their gonads. Overall, the results indicate that, at the transcriptional level, the progression of gonadal maturation and ovarian aging is slower in single-housed fish compared to group-housed fish. This slower progression may explain the medium fecundity and extended egg-laying period observed in single-housed fish.

The liver was chosen for analysis as it plays a central role in organismal metabolic processes. Gene expression profiles of the livers were compared between group- and single-housed fish at two different ages: 7 weeks post-hatching (wph) and 14 wph. Surprisingly, despite the 2-week age difference, the correlation coefficients showed that group- and single-housed fish at 14 wph were highly similar. The researchers identified 1588 age-related differentially expressed genes (DEGs) between the two age groups. Hierarchical clustering based on the expression changes of these age-related genes demonstrated that the expression profiles of group- and single-housed fish were similar at 14 wph.

IN CONCLUSION

In summary, juvenile single housing in African turquoise killifish promotes faster growth, longer egg-laying periods, and extended lifespans compared to group housing. These findings challenge traditional assumptions about the relationship between growth and lifespan and shed light on the impact of early-life environmental conditions on overall life history.

Overall, the experiments involved maintaining and rearing the fish, measuring their body length and weight, analyzing RNA sequencing data, measuring lifespan, and counting the number of eggs laid. Statistical analysis was conducted to assess significant differences between groups.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

Click here to subscribe to Aging publication updates.

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The Cell Rejuvenation Atlas: Unveiling Rejuvenation Strategies through Network Biology

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Researchers introduce SINGULAR, a cell rejuvenation atlas that provides a unified analysis framework to study the effects of rejuvenation strategies at the single-cell level.

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Researchers Javier Arcos Hodar, Sascha Jung, Mohamed Soudy, Sybille Barvaux, and Antonio del Sol from CIC bioGUNE-BRTA and University of Luxembourg introduce SINGULAR, a cell rejuvenation atlas that provides a unified analysis framework to study the effects of rejuvenation strategies at the single-cell level. On September 9, 2024, their research paper was published on the cover of Aging (listed by MEDLINE/PubMed as “Aging (Albany NY)” and “Aging-US” by Web of Science), Volume 16, Issue 17, entitled, “The cell rejuvenation atlas: leveraging network biology to identify master regulators of rejuvenation strategies.”

THE RESEARCH

Various strategies, including lifestyle changes, gene therapies, and surgical procedures, have shown promise in improving aging markers and increasing lifespan in model organisms. These interventions often have limitations, however, such as not achieving comprehensive functional improvement across tissues or facing challenges in clinical translation. To address these limitations, the researchers characterized and compared rejuvenation interventions at different biological levels. The paper introduces SINGULAR, a cell rejuvenation atlas that provides a unified analysis framework to study the effects of rejuvenation strategies at the single-cell level. By examining gene regulatory networks, intracellular signaling, cell-cell communication, and cellular processes, the atlas identifies master regulators and common targets across immune cells. SINGULAR has the potential to inform future advancements in human age reversal and aid in the selection of drugs that mimic the effects of rejuvenation interventions.

RESULTS

The authors propose a unified multiscale analysis pipeline for characterizing and comparing the effects of rejuvenation interventions. This process begins by filtering low-quality cells, normalizing expression profiles, and identifying optimal cell clustering. The data is then analyzed at various biological levels, including differential gene expression, transcriptional regulatory networks, signaling cascades, and intercellular communication.

Nine previously published single-cell RNA-seq datasets from different rejuvenation interventions were collected and analyzed, revealing technical variability that highlights the need for a standardized data processing pipeline. The analysis showed heterogeneous gene expression responses across different cell types and organs. Systemic interventions had consistent effects on multiple organs, while metformin had minimal impact. Interestingly, exercise produced the largest transcriptional effects in the liver, artery, and spinal cord, even though it primarily targets muscles.

Transcriptional regulatory networks (TRNs) were reconstructed to explore the regulatory mechanisms behind these gene expression changes. The TRNs, which averaged 72 genes, were highly hierarchical, indicating the presence of ‘master regulators’ that explain significant portions of gene expression changes.

To demonstrate the practical application of SINGULAR, the study investigated the identification of drugs that could target transcription factor (TF) master regulators and key signaling molecules. Drug-target relationships from DrugBank were analyzed to find drugs that could activate master regulators or mimic the effects of rejuvenation interventions. Interestingly, only 17 out of 239 TFs could be activated by drugs, primarily nuclear receptors, with notable exceptions like AP-1 complex proteins and Trp53. Some of these drugs, such as Curcumin and Vitamin D3, have shown rejuvenating effects on lifespan in model organisms. Key signaling molecules were found to be more druggable, with several drugs targeting specific molecules, though none targeted both genes.

The study aimed to identify master regulators and their downstream effects in rejuvenation interventions. By simulating the activation of transcription factors (TFs) within the network, the researchers quantified the number of genes regulated by each TF. They discovered 493 TFs with non-zero activity across various conditions, though most acted as master regulators in only a few cases. The study also highlighted key differences between TFs involved in aging-related activity changes and those regulating rejuvenation. Notably, the AP-1 complex, consisting of Fos and Jun, emerged as a common master regulator across multiple interventions. The researchers also identified TFs linked to aging and validated their potential rejuvenating effects experimentally. They also explored crosstalk between TFs and signaling pathways, finding negative enrichment of aging gene sets in several integrated networks. Overall, the findings offer valuable insights into the regulatory mechanisms and potential rejuvenating effects of master regulators and signaling molecules involved in rejuvenation interventions.

CONCLUSION

In conclusion, this study employed a unified analysis pipeline, SINGULAR, to compare the effects and mediators of various rejuvenation interventions. Key master regulators, including Arntl, AP-1 complex proteins, NFE2L2, and MAF, were identified as playing crucial roles in rejuvenation. The analysis revealed distinct differences between aging-related transcriptional changes and rejuvenation regulators. Immune and skin cell types were highlighted as potential intervention targets, with the possibility of additive or synergistic effects by targeting non-overlapping master regulators. Some limitations were noted, such as biases in cell type comparisons, reliance on ligand-receptor interactions for cell-cell communication analysis, and the risk of false negatives in differential expression testing. Despite these limitations, SINGULAR offers valuable insights into rejuvenation mechanisms and the identification of agents for anti-aging strategies. It provides a robust framework for understanding the mechanisms behind various interventions and offers a wide range of potential target genes for a comprehensive anti-aging approach.

Click here to read the full research paper in Aging.

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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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