Functional characterization of the human Cdk10/Cyclin Q complex

Cyclin-dependent kinases (CDKs) are key players in cell cycle regulation and transcription. The CDK-family member Cdk10 is important for neural development and can act as a tumour suppressor, but the underlying molecular mechanisms are largely unknown. Here, we provide an in-depth analysis of Cdk10 substrate specificity and function. Using recombinant Cdk10/CycQ protein complexes, we characterize RNA pol II CTD, c-MYC and RB1 as in vitro protein substrates. Using an analogue-sensitive mutant kinase, we identify 89 different Cdk10 phosphosites in HEK cells originating from 66 different proteins. Among these, proteins involved in cell cycle, translation, stress response, growth signalling, as well as rRNA, and mRNA transcriptional regulation, are found. Of a set of pan-selective CDK- and Cdk9-specific inhibitors tested, all inhibited Cdk10/CycQ at least five times weaker than their proposed target kinases. We also identify Cdk10 as an in vitro substrate of Cdk1 and Cdk5 at multiple sites, allowing for a potential cross-talk between these CDKs. With this functional characterization, Cdk10 adopts a hybrid position in both cell cycle and transcriptional regulation

Image-based goal-oriented adaptive isogeometric analysis with application to the micro-mechanical modeling of trabecular bone

Isogeometric analysis (IGA) of geometrically complex three-dimensional objects is possible when used in combination with the Finite Cell method (FCM). In this contribution we propose a computational methodology to automatically analyze the effective elastic properties of scan-based volumetric objects of arbitrary geometric and topological complexity. The first step is the reconstruction of a smooth geometry from scan-based voxel data using a B-spline level set function. The second step is a goal-oriented adaptive isogeometric linear elastic analysis. Elements are selected for refinement using dual-weighted residual shape function indicators, and hierarchical splines are employed to construct locally refined spline spaces. The proposed methodology is studied in detail for various numerical test cases, including the computation of the effective Young's modulus of a trabecular bone micro-structure reproduced from μCT-scan data

P-TEFb Activation by RBM7 Shapes a Pro-survival Transcriptional Response to Genotoxic Stress

DNA damage response (DDR) involves dramatic transcriptional alterations, the mechanisms of which remain ill defined. Here, we show that following genotoxic stress, the RNA-binding motif protein 7 (RBM7) stimulates RNA polymerase II (Pol II) transcription and promotes cell viability by activating the positive transcription elongation factor b (P-TEFb) via its release from the inhibitory 7SK small nuclear ribonucleoprotein (7SK snRNP). This is mediated by activation of p38MAPK, which triggers enhanced binding of RBM7 with core subunits of 7SK snRNP. In turn, P-TEFb relocates to chromatin to induce transcription of short units, including key DDR genes and multiple classes of non-coding RNAs. Critically, interfering with the axis of RBM7 and P-TEFb provokes cellular hypersensitivity to DNA-damage-inducing agents due to activation of apoptosis. Our work uncovers the importance of stress-dependent stimulation of Pol II pause release, which enables a pro-survival transcriptional response that is crucial for cell fate upon genotoxic insult.Peer reviewe

Physics-based Deep Neural Network for Augmented Reality during Liver Surgery

International audienceIn this paper we present an approach combining a finite element method and a deep neural network to learn complex elastic deformations with the objective of providing augmented reality during hep-atic surgery. Derived from the U-Net architecture, our network is built entirely from physically-based simulations of a preoperative segmenta-tion of the organ. These simulations are performed using an immersed-boundary method, which offers several numerical and practical benefits, such as not requiring boundary-conforming volume elements. We perform a quantitative assessment of the method using synthetic and ex vivo patient data. Results show that the network is capable of solving the deformed state of the organ using only a sparse partial surface displacement data and achieve similar accuracy as a FEM solution, while being about 100x faster. When applied to an ex vivo liver example, we achieve the registration in only 3 ms with a mean target registration error (TRE) of 2.9 mm

FCMLab: A finite cell research toolbox for MATLAB

International audienc