Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments.

MOTIVATION: Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, a number of analytical models have been developed. Simplifications are inherent in these models, which may lead to inexhaustive or inaccurate exploitation of the experimental data. An appealing alternative is offered by the simulation of biological processes in realistic environments at a particle level. However, inference of kinetic parameters using simulation-based models is still limited. RESULTS: We introduce and demonstrate a new method for the inference of kinetic parameter values from FRAP data. A small number of in silico FRAP experiments is used to construct a mapping from FRAP recovery curves to the parameters of the underlying protein kinetics. Parameter estimates from experimental data can then be computed by applying the mapping to the observed recovery curves. A bootstrap process is used to investigate identifiability of the physical parameters and determine confidence regions for their estimates. Our method circumvents the computational burden of seeking the best-fitting parameters via iterative simulation. After validation on synthetic data, the method is applied to the analysis of the nuclear proteins Cdt1, PCNA and GFPnls. Parameter estimation results from several experimental samples are in accordance with previous findings, but also allow us to discuss identifiability issues as well as cell-to-cell variability of the protein kinetics. IMPLEMENTATION: All methods were implemented in MATLAB R2011b. Monte Carlo simulations were run on the HPC cluster Brutus of ETH Zurich. CONTACT: lygeros@control.ee.ethz.ch or lygerou@med.upatras.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

easyFRAP: an interactive, easy-to-use tool for qualitative and quantitative analysis of FRAP data.

SUMMARY: We present easyFRAP, a versatile tool that assists quantitative and qualitative analysis of fluorescence recovery after photobleaching (FRAP) data. The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis. AVAILABILITY: easyFRAP is free software, available under the General Public License (GPL). Executable and source files, supplementary material and sample data sets can be downloaded at: ccl.med.upatras.gr/easyfrap.html.

Ιn vivo imaging of DNA-bound minichromosome maintenance complex in embryonic mouse cortex.

The recruitment of the minichromosome maintenance complex (MCM) on DNA replication origins is a critical process for faithful genome duplication termed licensing. Aberrant licensing has been associated with cancer and, recently, with neurodevelopmental diseases. Investigating MCM loading in complicated tissues, such as brain, remains challenging. Here, we describe an optimized approach for the qualitative and quantitative analysis of DNA-bound MCMs in the developing mouse cortex through direct imaging, offering an innovative insight into the research of origin licensing in vivo.

In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity.

DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion across a population. Disruptions in these mechanisms lead to DNA re-replication, closely connected to genomic instability and oncogenesis. Here, we present a stochastic hybrid model of DNA re-replication that accurately portrays the interplay between discrete dynamics, continuous dynamics and uncertainty. Using experimental data on the fission yeast genome, model simulations show how different regions respond to re-replication and permit insight into the key mechanisms affecting re-replication dynamics. Simulated and experimental population-level profiles exhibit a good correlation along the genome, robust to model parameters, validating our approach. At a single-cell level, copy numbers of individual loci are affected by intrinsic properties of each locus, in cis effects from adjoining loci and in trans effects from distant loci. In silico analysis and single-cell imaging reveal that cell-to-cell heterogeneity is inherent in re-replication and can lead to genome plasticity and a plethora of genotypic variations.

The Licensing Factor Cdt1 Links Cell Cycle Progression to the DNA Damage Response.

The maintenance of genome integrity is essential for cellular survival and propagation. It relies upon the accurate and timely replication of the genetic material, as well as the rapid sensing and repairing of damage to DNA. Uncontrolled DNA replication and unresolved DNA lesions contribute to genomic instability and can lead to cancer. Chromatin licensing and DNA replication factor 1 (Cdt1) is essential for loading the minichromosome maintenance 2-7 helicase complex onto chromatin exclusively during the G1 phase of the cell cycle, thus limiting DNA replication to once per cell cycle. Upon DNA damage, Cdt1 rapidly accumulates to sites of damage and is subsequently poly-ubiquitinated by the cullin 4-RING E3 ubiquitin ligase complex, in conjunction with the substrate recognition factor Cdt2 (CRL4Cdt2), and targeted for degradation. We here discuss the cellular functions of Cdt1 and how it may interlink cell cycle regulation and DNA damage response pathways, contributing to genome stability.

Direct binding of Cdt2 to PCNA is important for targeting the CRL4Cdt2 E3 ligase activity to Cdt1.

The CRL4Cdt2 ubiquitin ligase complex is an essential regulator of cell-cycle progression and genome stability, ubiquitinating substrates such as p21, Set8, and Cdt1, via a display of substrate degrons on proliferating cell nuclear antigens (PCNAs). Here, we examine the hierarchy of the ligase and substrate recruitment kinetics onto PCNA at sites of DNA replication. We demonstrate that the C-terminal end of Cdt2 bears a PCNA interaction protein motif (PIP box, Cdt2PIP), which is necessary and sufficient for the binding of Cdt2 to PCNA. Cdt2PIP binds PCNA directly with high affinity, two orders of magnitude tighter than the PIP box of Cdt1. X-ray crystallographic structures of PCNA bound to Cdt2PIP and Cdt1PIP show that the peptides occupy all three binding sites of the trimeric PCNA ring. Mutating Cdt2PIP weakens the interaction with PCNA, rendering CRL4Cdt2 less effective in Cdt1 ubiquitination and leading to defects in Cdt1 degradation. The molecular mechanism we present suggests a new paradigm for bringing substrates to the CRL4-type ligase, where the substrate receptor and substrates bind to a common multivalent docking platform to enable subsequent ubiquitination.

Analysis of Protein Kinetics Using Fluorescence Recovery After Photobleaching (FRAP).

Fluorescence recovery after photobleaching (FRAP) is a cutting-edge live-cell functional imaging technique that enables the exploration of protein dynamics in individual cells and thus permits the elucidation of protein mobility, function, and interactions at a single-cell level. During a typical FRAP experiment, fluorescent molecules in a defined region of interest within the cell are bleached by a short and powerful laser pulse, while the recovery of the fluorescence in the region is monitored over time by time-lapse microscopy. FRAP experimental setup and image acquisition involve a number of steps that need to be carefully executed to avoid technical artifacts. Equally important is the subsequent computational analysis of FRAP raw data, to derive quantitative information on protein diffusion and binding parameters. Here we present an integrated in vivo and in silico protocol for the analysis of protein kinetics using FRAP. We focus on the most commonly encountered challenges and technical or computational pitfalls and their troubleshooting so that valid and robust insight into protein dynamics within living cells is gained.