Unraveling Histone Loss in Aging and Senescence

Dubey, Sushil Kumar; Dubey, Rashmi; Kleinman, Mark Ellsworth

doi:10.3390/cells13040320

Cited by 4 publications

(1 citation statement)

References 205 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was also observed in the skin in a model of atopic dermatitis(Pellefigues et al ., 2019). By contrast, basophils from aged mice were characterized by an increased expression of histone variants ( H3f3b, Hist1h2bc, Hist1h4i ), known to increase during cellular senescence(Dubey, Dubey and Kleinman, 2024) as well as genes coding proteins with anti-inflammatory properties such as thymosin β4 ( Tmsb4x ), peroxiredoxin 5 ( Prdx5 ) (Philp and Kleinman, 2010; Knoops et al ., 2011) or IκBα ( Nfkbia ) (Pellefigues, Mehta, et al ., 2021). They also expressed more coactosin-like protein ( Ctl1 ), a chaperone stabilizing lipoxygenase activity (Basavarajappa et al ., 2014), CD16 ( Fcgr3 ), known to be expressed by a minor subset of basophils in human blood(Vivanco Gonzalez et al ., 2020), and Il18rap , coding for a chain of the IL-18 receptor.…”

Section: Resultsmentioning

confidence: 99%

Basophils drive the resolution and promote wound healing in adult and aged mice

Bex,

Peter,

Saji

et al. 2024

Preprint

View full text Add to dashboard Cite

An active resolution is critical to control the duration of inflammation and limit its pathological consequences. Defects in resolution during wound healing allow the emergence of chronic wounds, a common complication in the elderly. Here, we show that basophils infiltrate the periphery of mouse skin wounds for at least three weeks, during both the inflammation and resolution phases of wound healing. Depletion of basophils induces an increased secretion of inflammatory molecules, accumulation and activation of pro-inflammatory leukocytes, and delays the wound healing response. Basophils particularly promote epidermal differentiation towards homeostasis in the wounds. Basophil-derived IL-4 and M-CSF drive partly their immunoregulatory and healing properties. Unexpectedly, aged mice basophils infiltrate more potently the wounds to promote inflammation resolution, showing a transcriptomic signature biased towards tissue remodeling. Thus, basophils are not pro-inflammatory but pro-resolution cells during an essential biological process such as skin wound healing. Unraveling basophil pro-resolution properties may reveal new strategies to fight chronic wounds in the elderly.SUMMARYThe resolution of inflammation is essential for homeostasis and becomes defective with age. Here we show basophil immunoregulatory properties promote resolution during skin wound healing, in both adult and aged mice.

show abstract

Section: Resultsmentioning

confidence: 99%

Basophils drive the resolution and promote wound healing in adult and aged mice

Bex,

Peter,

Saji

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Integration of multi-modal datasets to estimate human aging

Ribeiro,

Moraes,

Moreno

et al. 2024

Mach Learn

View full text Add to dashboard Cite

Aging involves complex biological processes leading to the decline of living organisms. As population lifespan increases worldwide, the importance of identifying factors underlying healthy aging has become critical. Integration of multi-modal datasets is a powerful approach for the analysis of complex biological systems, with the potential to uncover novel aging biomarkers. In this study, we leveraged publicly available epigenomic, transcriptomic and telomere length data along with histological images from the Genotype-Tissue Expression project to build tissue-specific regression models for age prediction. Using data from two tissues, lung and ovary, we aimed to compare model performance across data modalities, as well as to assess the improvement resulting from integrating multiple data types. Our results demostrate that methylation outperformed the other data modalities, with a mean absolute error of 3.36 and 4.36 in the test sets for lung and ovary, respectively. These models achieved lower error rates when compared with established state-of-the-art tissue-agnostic methylation models, emphasizing the importance of a tissue-specific approach. Additionally, this work has shown how the application of Hierarchical Image Pyramid Transformers for feature extraction significantly enhances age modeling using histological images. Finally, we evaluated the benefits of integrating multiple data modalities into a single model. Combining methylation data with other data modalities only marginally improved performance likely due to the limited number of available samples. Combining gene expression with histological features yielded more accurate age predictions compared with the individual performance of these data types. Given these results, this study shows how machine learning applications can be extended to/in multi-modal aging research. Code used is available at https://github.com/zroger49/multi_modal_age_prediction.

show abstract