The checkpoint Saccharomyces cerevisiae Rad9 protein contains a tandem tudor domain that recognizes DNA.

International audienceDNA damage checkpoints are signal transduction pathways that are activated after genotoxic insults to protect genomic integrity. At the site of DNA damage, 'mediator' proteins are in charge of recruiting 'signal transducers' to molecules 'sensing' the damage. Budding yeast Rad9, fission yeast Crb2 and metazoan 53BP1 are presented as mediators involved in the activation of checkpoint kinases. Here we show that, despite low sequence conservation, Rad9 exhibits a tandem tudor domain structurally close to those found in human/mouse 53BP1 and fission yeast Crb2. Moreover, this region is important for the resistance of Saccharomyces cerevisiae to different genotoxic stresses. It does not mediate direct binding to a histone H3 peptide dimethylated on K79, nor to a histone H4 peptide dimethylated on lysine 20, as was demonstrated for 53BP1. However, the tandem tudor region of Rad9 directly interacts with single-stranded DNA and double-stranded DNAs of various lengths and sequences through a positively charged region absent from 53BP1 and Crb2 but present in several yeast Rad9 homologs. Our results argue that the tandem tudor domains of Rad9, Crb2 and 53BP1 mediate chromatin binding next to double-strand breaks. However, their modes of chromatin recognition are different, suggesting that the corresponding interactions are differently regulated

BRCA2-HSF2BP oligomeric ring disassembly by BRME1 promotes homologous recombination

In meiotic homologous recombination (HR), BRCA2 facilitates loading of the recombinases RAD51 and DMC1 at the sites of double-strand breaks (DSBs). The HSF2BP-BRME1 complex interacts with BRCA2. Its absence causes a severe reduction in recombinase loading at meiotic DSB. We previously showed that, in somatic cancer cells ectopically producing HSF2BP, DNA damage can trigger HSF2BP-dependent degradation of BRCA2, which prevents HR. Here, we report that, upon binding to BRCA2, HSF2BP forms octameric rings that are able to interlock into a large ring-shaped 24-nucleotide oligomer. Addition of BRME1 leads to dissociation of both of these ring structures and cancels the disruptive effect of HSF2BP on cancer cell resistance to DNA damage. It also prevents BRCA2 degradation during interstrand DNA crosslink repair in Xenopus egg extracts. We propose that, during meiosis, the control of HSF2BP-BRCA2 oligomerization by BRME1 ensures timely assembly of the ring complex that concentrates BRCA2 and controls its turnover, thus promoting HR.</p

BRCA2-HSF2BP oligomeric ring disassembly by BRME1 promotes homologous recombination

In meiotic homologous recombination (HR), BRCA2 facilitates loading of the recombinases RAD51 and DMC1 at the sites of double-strand breaks (DSBs). The HSF2BP-BRME1 complex interacts with BRCA2. Its absence causes a severe reduction in recombinase loading at meiotic DSB. We previously showed that, in somatic cancer cells ectopically producing HSF2BP, DNA damage can trigger HSF2BP-dependent degradation of BRCA2, which prevents HR. Here, we report that, upon binding to BRCA2, HSF2BP forms octameric rings that are able to interlock into a large ring-shaped 24-nucleotide oligomer. Addition of BRME1 leads to dissociation of both of these ring structures and cancels the disruptive effect of HSF2BP on cancer cell resistance to DNA damage. It also prevents BRCA2 degradation during interstrand DNA crosslink repair in Xenopus egg extracts. We propose that, during meiosis, the control of HSF2BP-BRCA2 oligomerization by BRME1 ensures timely assembly of the ring complex that concentrates BRCA2 and controls its turnover, thus promoting HR

Strategic and practical guidelines for successful structured illumination microscopy

Linear 2D- or 3D-structured illumination microscopy (SIM or3D-SIM, respectively) enables multicolor volumetric imaging of fixed and live specimens with subdiffraction resolution in all spatial dimensions. However, the reliance of SIM on algorithmic post-processing renders it particularly sensitive to artifacts that may reduce resolution, compromise data and its interpretations, and drain resources in terms of money and time spent. Here we present a protocol that allows users to generate high-quality SIM data while accounting and correcting for common artifacts. The protocol details preparation of calibration bead slides designed for SIM-based experiments, the acquisition of calibration data, the documentation of typically encountered SIM artifacts and corrective measures that should be taken to reduce them. It also includes a conceptual overview and checklist for experimental design and calibration decisions, and is applicable to any commercially available or custom platform. This protocol, plus accompanying guidelines, allows researchers from students to imaging professionals to create an optimal SIM imaging environment regardless of specimen type or structure of interest. The calibration sample preparation and system calibration protocol can be executed within 1-2 d

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE

Fine grained parallel computation in the cloud

The divergence of priorities between high performance computing (HPC) and cloud infrastructure has made scaling tightly coupled parallel applications in the cloud less viable than their supercomputer counterparts. As a result, many potential benefits -- elasticity, job virtualization, and cost-effectiveness -- of cloud computing remain underutilized by the HPC community. Through analysis and benchmarking of cloud compute services we see that network overhead on cloud applications leads to scalability issues in fine grained parallel computations. Our approaches seek to improve scalability of a parallel runtime system and develop new runtime system methods to hide latency within cloud scale applications. To improve scalability of communication within a node, we developed a new concurrent lock-free queue that supports a large memory bound and efficiently reclaims memory without a need for an external memory reclamation scheme. Additionally, we implement a replication system for parallel compute objects to reduce latency of requests.U of I Onlyundergraduate senior thesis not recommended for open acces

Fine grained parallel computation in the cloud

The divergence of priorities between high performance computing (HPC) and cloud infrastructure has made scaling tightly coupled parallel applications in the cloud less viable than their supercomputer counterparts. As a result, many potential benefits -- elasticity, job virtualization, and cost-effectiveness -- of cloud computing remain underutilized by the HPC community. Through analysis and benchmarking of cloud compute services we see that network overhead on cloud applications leads to scalability issues in fine grained parallel computations. Our approaches seek to improve scalability of a parallel runtime system and develop new runtime system methods to hide latency within cloud scale applications. To improve scalability of communication within a node, we developed a new concurrent lock-free queue that supports a large memory bound and efficiently reclaims memory without a need for an external memory reclamation scheme. Additionally, we implement a replication system for parallel compute objects to reduce latency of requests.U of I Onlyundergraduate senior thesis not recommended for open acces