DNA Damage Processing is Perturbed in Both Proliferative and Non-Proliferative Cells of Increased Chronological Cellular Age

Sabin, Rebecca; Pucci, Gaia; Anderson, Rhona M.

doi:10.1667/rr15348.1

Cited by 4 publications

(3 citation statements)

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“…Having worked extensively on fibroblasts, we noted a decline on DSB repair as cells age (29). Sabin et al (30) demonstrated that this reduced efficiency is present in both proliferative and nonproliferative cells, thus not merely linked to cell cycle delay/exit. Indeed, Collin et al (31) evidenced that during cell aging there is a RB-mediated transcriptional repression of DNA repair genes.…”

Section: Introductionmentioning

confidence: 80%

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Effects of p53 and ATRX inhibition on telomeric recombination in aging fibroblasts

Udroiu,

Marinaccio,

Sgura

2024

Front. Oncol.

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In order to avoid replicative senescence, tumor cells must acquire a telomere maintenance mechanism. Beside telomerase activation, a minority of tumors employs a recombinational mechanism called Alternative Lengthening of Telomeres (ALT). Several studies have investigated the potential ALT stimulation by inactivation of ATRX in tumor cells, obtaining contrasting results. Differently, since ALT can be viewed as a mechanism to overcome telomere shortening-mediated replicative senescence, we have investigated the effects of the inhibition of ATRX and p53 in aging primary fibroblasts. We observed that senescence leads to a phenotype that seems permissive for ALT activity, i.e. high levels of ALT-associated PML bodies (APB), telomeric damage and telomeric cohesion. On the other hand, RAD51 is highly repressed and thus telomeric recombination, upon which the ALT machinery relies, is almost absent. Silencing of ATRX greatly increases telomeric recombination in young cells, but is not able to overcome senescence-induced repression of homologous recombination. Conversely, inhibition of both p53 and ATRX leads to a phenotype reminiscent of some aspects of ALT activity, with a further increase of APB, a decrease of telomere shortening (and increased proliferation) and, above all, an increase of telomeric recombination.

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Section: Introductionmentioning

confidence: 80%

“…Sabin et al. ( 30 ) demonstrated that this reduced efficiency is present in both proliferative and non-proliferative cells, thus not merely linked to cell cycle delay/exit. Indeed, Collin et al.…”

Section: Introductionmentioning

confidence: 99%

Effects of p53 and ATRX inhibition on telomeric recombination in aging fibroblasts

Udroiu,

Marinaccio,

Sgura

2024

Front. Oncol.

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“…The chromatin modulator p-53 binding protein-1 (53BP1), another DDR factor, binds to chromatin at the site of damage and also forms higher order structures [12][13][14]. Detection of γ-H2AX and 53BP1 repair hubs via fluorescence microscopy remains one of the most accurate measures of the total DSB number [15][16][17][18][19][20][21]. Methods to quantify foci from fluorescence microscopy images currently consist of manual counting and automated analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification of 53BP1 Foci

Benítez-Jones,

Keegan,

Jamshahi

et al. 2024

Preprint

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Background53BP1 foci are reflective of DNA double-strand break formation and have been used as radiation markers. Manual focus counting, while prone to bias and time constraints, remains the most accurate mode of detecting 53BP1 foci. Several studies have pursued automated focus detection to replace manual methods. Deep learning, spatial 3D images, and segmentation techniques are main components of the highest performing automated methods. While these approaches have achieved promising results regarding accurate focus detection and cell classification, they are not compatible with time-sensitive large-scale applications due to their demand for long run times, advanced microscopy, and computational resources. Further, segmentation of overlapping foci in 2D images has the potential to represent focus morphologies inaccurately.ResultsTo overcome these limitations, we developed a novel method to classify 2D fluorescence microscopy images of 53BP1 foci. Our approach consisted of three key features: (1) general 53BP1 focus classes, (2) varied parameter space composed of properties from individual foci and their respective Fourier transform, and (3) widely-available machine learning classifiers. We identified four main focus classes, which consisted of blurred foci and three levels of overlapping foci. Our parameter space for the training focus library, composed of foci formed by fluorescently-tagged BP1-2, showed a wide correlation range between variables which was validated using a publicly-available library of immunostained 53BP1 foci. Random forest achieved one of the highest and most stable performances for binary and multiclass problems, followed by a support vector machine and k-nearest neighbors. Specific metrics impacted the classification of blurred and low overlap foci for both train and test sets.ConclusionsOur method classified 53BP1 foci across separate fluorescent markers, resolutions, and damage-inducing methods, using off-the-shelf machine learning classifiers, a diverse parameter space, and well-defined focus classes.

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