Biology subjects

Blagoev, K. B.

Publications and source records attributed to Blagoev, K. B..

4 recordsLinked to original sources

bioRxiv2025

Esc1-mediated anchoring regulates telomere clustering in response to metabolic changes.

Spatial organization of yeast telomeres is highly dynamic and regulated by growth conditions. In rich medium, the 32 telomeres group in 3 to 5 foci at the nuclear periphery. This organization is drastically rearranged in long-lived quiescent (Q) cells forming upon carbon source exhaustion: telomere foci assemble into a hypercluster containing most telomeres and located in the center of the nucleus contributing to their long-term viability. Here we explore the mechanisms regulating telomere distribution during this transition. We rule out a modification of telomere-telomere interactions via the telomeric protein Sir3 as the main factor regulating hypercluster formation. However, our physical modeling predicts that telomere anchoring antagonizes telomere clustering. Systematic deletion of all known telomere anchors identifies the inner nuclear membrane-associated protein Esc1, as a key telomeric anchor after the diauxic shift. Our data support a model where Esc1 mediated anchoring is progressively lost upon entry into quiescence through dephosphorylation of a single residue of Esc1, resulting in the formation of telomere hyperclusters in the center of the nucleus in Q cells.

cell biology↗

bioRxiv2024

Cancer Interception During Treatment: Using Growth Kinetics to Create a Continuous Variable for Assessing Disease Response

BackgroundWe applied 11 mathematical models of tumor growth to clinical trial data available from public and private sources. We have previously described the remarkable capacity for a simple biexponential model of tumor growth to fit thousands of datasets, and to correlate with overall survival. The goal of this study was to extend our analysis to additional tumor types and to evaluate whether alternate growth models could describe the time course of disease burden in the small subset of patients in whom the biexponential model failed. MethodsFor this analysis, we obtained data for tumor burden from 17,140 patients with six different tumor types. Imaging data and serum levels of tumor markers were available for 3,346 and 13,794 patients, respectively. Data from patients were first analyzed using the biexponential model to determine rates of tumor growth (g) and regression (d); for those whose data could not be described by this model, fit of their data was assessed using seven alternative models. The model that minimized the Akaike Information Criterion was selected as the best fit. Using the model that best fit an individual patients data, we estimated the rates of growth (g) and regression (d) of disease burden over time. The rates of tumor growth (g) were examined for association with a traditional endpoint (overall survival). FindingsFor each model, the number of patient datasets that fit the model were obtained. As we have previously reported, data from most patients fit a simple model of exponential growth and exponential regression (86%). Data from another 7% of patients fit an alternative model, including 3% fitting to a model of constant or linear regression and exponential growth of tumor on the surface and 3% fitting to model of exponential decay on tumor surface with asymmetric growth. As previously reported, we found that growth rate correlates well with overall survival, remarkably even when data from various histologies are considered together. For patients with multiple timepoints of tumor measurement, the growth rate could often be estimated even during the phase when only net regression of tumor quantity could be discerned. InterpretationThe validation of a simple mathematical model across different cancers and its application to existing clinical data allowed estimation of the rate of growth of a treatment resistant subpopulation of cancer cells. The quantification of available clinical data using the growth rate of tumors in individual patients and its strong correlation with overall survival makes the growth rate an excellent marker of the efficacy of therapy specific to the individual patient.

cancer biology↗

bioRxiv2023

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

During DNA repair, ATM-induced H2AX histone phosphorylation and MDC1 recruitment spread megabases beyond the damage site. The mechanism underlying this spread remains unclear. To elucidate this step of the DNA damage response, we measured ATM and MDC1 recruitment kinetics at micro-irradiation-induced complex DNA lesions. While MDC1 spreads beyond the damage-induced focus of ATM accumulation, cohesin loader NIPBL and cohesin subunit RAD21 accumulate later than MDC1. Such delayed recruitment suggests that, in complex DNA lesions, loop extrusion cannot alone explain {gamma}H2AX and MDC1 spread. To determine if diffusion of activated ATM could account for the observed behavior, we measured the exchange rate and diffusion constants of ATM and MDC1 within damaged and unperturbed chromatin. Based on these measurements, we introduced a quantitative model that describes the dynamic behavior of ATM and subsequent MDC1 spread at complex DNA lesions. Altogether, we propose activated ATM diffusion as an underlying mechanism of MDC1 spread.

cell biology↗

bioRxiv2020

Apoptotic Cells May Drive Cell Death in Hair Follicles During their Regression Cycle

Intravital microscopy in live mice have shown that the elimination of epithelial cells during hair follicle regression involves supra-basal cell differentiation and basal cell apoptosis through synergistic action of TGF-{beta} (transforming growth factor) and mesenchymal-epithelial interactions. In this process the basal epithelial cells are not internally committed to death and the mesenchymal dermal papilla (DP) plays essential role in death induction. Because the DP cells are not necessary for completion of the cycle but only for its initiation it is still an open question what is the mechanism leading to the propagation of apoptosis towards the "immortal" stem cell population. Here, we use a quantitative analysis of the length of hair follicles during their regression cycle. The data is consistent with a propagation mechanism driven by apoptotic cells inducing apoptosis in their neighboring cells. The observation that the apoptosis slows down as the apoptotic front approaches the stem cells at the end of the follicle is consistent with a gradient of a pro-survival signal sent by these stem cells. An experiment that can falsify this mechanism is proposed.

cell biology↗