Elongation Factor TFIIS Prevents Transcription Stress and R-Loop Accumulation to Maintain Genome Stability

Although correlations between RNA polymerase II (RNAPII) transcription stress, R-loops, and genome instability have been established, the mechanisms underlying these connections remain poorly understood. Here, we used a mutant version of the transcription elongation factor TFIIS (TFIISmut), aiming to specifically induce increased levels of RNAPII pausing, arrest, and/or backtracking in human cells. Indeed, TFIISmut expression results in slower elongation rates, relative depletion of polymerases from the end of genes, and increased levels of stopped RNAPII; it affects mRNA splicing and termination as well. Remarkably, TFIISmut expression also dramatically increases R-loops, which may form at the anterior end of backtracked RNAPII and trigger genome instability, including DNA strand breaks. These results shed light on the relationship between transcription stress and R-loops and suggest that different classes of R-loops may exist, potentially with distinct consequences for genome stability.Cancer Research UK FC001166UK Medical Research Council FC001166Wellcome Trust FC001166European Research Council 693327, ERC2014 AdG669898Ministerio de Economía y Competitividad BFU2013-42918-P, BFU2016-75058-

Scaling and Resilience in Numerical Algorithms for Exascale Computing

The first Petascale supercomputer, the IBM Roadrunner, went online in 2008. Ten years later, the community is now looking ahead to a new generation of Exascale machines. During the decade that has passed, several hundred Petascale capable machines have been installed worldwide, yet despite the abundance of machines, applications that scale to their full size remain rare. Large clusters now routinely have 50.000+ cores, some have several million. This extreme level of parallelism, that has allowed a theoretical compute capacity in excess of a million billion operations per second, turns out to be difficult to use in many applications of practical interest. Processors often end up spending more time waiting for synchronization, communication, and other coordinating operations to complete, rather than actually computing. Component reliability is another challenge facing HPC developers. If even a single processor fail, among many thousands, the user is forced to restart traditional applications, wasting valuable compute time. These issues collectively manifest themselves as low parallel efficiency, resulting in waste of energy and computational resources. Future performance improvements are expected to continue to come in large part due to increased parallelism. One may therefore speculate that the difficulties currently faced, when scaling applications to Petascale machines, will progressively worsen, making it difficult for scientists to harness the full potential of Exascale computing. The thesis comprises two parts. Each part consists of several chapters discussing modifications of numerical algorithms to make them better suited for future Exascale machines. In the first part, the use of Parareal for Parallel-in-Time integration techniques for scalable numerical solution of partial differential equations is considered. We propose a new adaptive scheduler that optimize the parallel efficiency by minimizing the time-subdomain length without making communication of time-subdomains too costly. In conjunction with an appropriate preconditioner, we demonstrate that it is possible to obtain time-parallel speedup on the nonlinear shallow water equation, beyond what is possible using conventional spatial domain-decomposition techniques alone. The part is concluded with the proposal of a new method for constructing Parallel-in-Time integration schemes better suited for convection dominated problems. In the second part, new ways of mitigating the impact of hardware failures are developed and presented. The topic is introduced with the creation of a new fault-tolerant variant of Parareal. In the chapter that follows, a C++ Library for multi-level checkpointing is presented. The library uses lightweight in-memory checkpoints, protected trough the use of erasure codes, to mitigate the impact of failures by decreasing the overhead of checkpointing and minimizing the compute work lost. Erasure codes have the unfortunate property that if more data blocks are lost than parity codes created, the data is effectively considered unrecoverable. The final chapter contains a preliminary study on partial information recovery for incomplete checksums. Under the assumption that some meta knowledge exists on the structure of the data encoded, we show that the data lost may be recovered, at least partially. This result is of interest not only in HPC but also in data centers where erasure codes are widely used to protect data efficiently

Altered RECQL5 expression in urothelial bladder carcinoma increases cellular proliferation and makes RECQL5 helicase activity a novel target for chemotherapy.

RECQ helicases are a family of enzymes with both over lapping and unique functions. Functional autosomal recessive loss of three members of the family BLM, WRN and RECQL4, results in hereditary human syndromes characterized by cancer predisposition and premature aging, but despite the finding that RECQL5 deficient mice are cancer prone, no such link has been made to human RECQL5. Here we demonstrate that human urothelial carcinoma of the bladder (UCC) has increased expression of RECQL5 compared to normal bladder tissue and that increasing RECQL5 expression can drive proliferation of normal bladder cells and is associated with poor prognosis. Further, by expressing a helicase dead RECQL5 and by depleting bladder cancer cells of RECQL5 we show that inhibition of RECQL5 activity has potential as a new target for treatment of UCC