DNArepairK: An Interactive Database for Exploring the Impact of Anticancer Drugs onto the Dynamics of DNA Repair Proteins

Abstract

Cells are constantly exposed to numerous mutagens that produce diverse types of DNA lesions. Eukaryotic cells have evolved an impressive array of DNA repair mechanisms that are able to detect and repair these lesions, thus preventing genomic instability. The DNA repair process is subjected to precise spatiotemporal coordination, and repair proteins are recruited to lesions in an orderly fashion, depending on their function. Here, we present DNArepairK, a unique open-access database that contains the kinetics of recruitment and removal of 70 fluorescently tagged DNA repair proteins to complex DNA damage sites in living HeLa Kyoto cells. An interactive graphical representation of the data complemented with live cell imaging movies facilitates straightforward comparisons between the dynamics of proteins contributing to different DNA repair pathways. Notably, most of the proteins included in DNArepairK are represented by their kinetics in both nontreated and PARP1/2 inhibitor-treated (talazoparib) cells, thereby providing an unprecedented overview of the effects of anticancer drugs on the regular dynamics of the DNA damage response. We believe that the exclusive dataset available in DNArepairK will be of value to scientists exploring the DNA damage response but, also, to inform and guide the development and evaluation of novel DNA repair-targeting anticancer drugs.

Diffusion of activated ATM explains γH2AX and MDC1 spread beyond the DNA damage site

“…We performed further FRAP experiments to study MDC1 mobility in the absence of damage, observing MDC1-EGFP signal recovery after bleaching a small region of the nucleus (Figure S1C). MDC1 mobility was approximately an order of magnitude slower (average D=0.055±0.033μm 2 /s) than expected for the pure diffusion of a 200kDa (0.4-0.6μm 2 /s) protein. It is known that MDC1 is weakly bound to the chromatin even in the absence of DNA damage 17,18 .…”

“…Using live-cell microscopy, we were able to measure several physical parameters that were implemented in the mathematical model (Table S1). The ATM diffusion constant was measured using FRAP of the unbound ATM (average D=0.664±0.34μm 2 /s). Complex MDC1 transport was modeled with an effective diffusion constant, which we also measured using FRAP of cells without DNA damage (average D=0.055±0.35μm 2 /s).…”

“…The ATM diffusion constant was measured using FRAP of the unbound ATM (average D=0.664±0.34μm 2 /s). Complex MDC1 transport was modeled with an effective diffusion constant, which we also measured using FRAP of cells without DNA damage (average D=0.055±0.35μm 2 /s). This effective diffusion constant captures the free diffusion as well as the process of MDC1 binding and unbinding to the undamaged chromatin.…”

“…From fitting the model to the data, we found that the αATM effective diffusion coefficient (average D=0.059 μm 2 /s) should be 10 times smaller than the diffusion coefficient of the non-activated ATM (Table S1). A possible reason for the slow diffusion of αATM is that its transport involves binding to and unbinding from the H2AX anchored within chromatin as well as its free diffusion in between these events.…”

A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning

“…In [59], the detection of cancer using images is proposed. Cells are constantly exposed to numerous mutagens that produce diverse types of DNA lesions.…”