RAD54L2-mediated DNA damage avoidance pathway specifically preserves genome integrity in response to topoisomerase 2 poisons.

Type II topoisomerases (TOP2) form transient TOP2 cleavage complexes (TOP2ccs) during their catalytic cycle to relieve topological stress. TOP2ccs are covalently linked TOP2-DNA intermediates that are reversible but can be trapped by TOP2 poisons. Trapped TOP2ccs block transactions on DNA and generate genotoxic stress, which are the mechanisms of action of TOP2 poisons. How cells avoid TOP2cc accumulation remains largely unknown. In this study, we uncovered RAD54 like 2 (RAD54L2) as a key factor that mediates a TOP2-specific DNA damage avoidance pathway. RAD54L2 deficiency conferred unique sensitivity to treatment with TOP2 poisons. RAD54L2 interacted with TOP2A/TOP2B and ZATT/ZNF451 and promoted the turnover of TOP2 from DNA with or without TOP2 poisons. Additionally, inhibition of proteasome activity enhanced the chromatin binding of RAD54L2, which in turn led to the removal of TOP2 from chromatin. In conclusion, we propose that RAD54L2-mediated TOP2 turnover is critically important for the avoidance of potential TOP2-linked DNA damage under physiological conditions and in response to TOP2 poisons.

Time series modelling methods to forecast the volume of self-assessment tax returns in the UK.

Her Majesty's Revenue and Customs (HMRC) has the ambitious target of making tax digital for all its customers and collecting tax in a more efficient, effective and accurate manner for both the government and UK taxpayers. Self-assessment tax returns, the biggest key business event for HMRC, is also one of the most popular digital services with over 90% of the approximately 12 million taxpayers in self assessment filing their return online each year. The majority of returns are filed in January immediately prior to the self-assessment deadline (31st January), putting significant pressure not only on the self-assessment digital service but also on all other HMRC digital services. Hence, understanding and predicting demand for the system is vital to provide a robust and responsive service. We therefore developed mathematical models with Bayesian inference techniques to forecast volumes of Self-assessment (SA) returns submitted online during January, providing accurate hourly predictions of traffic on the digital system in the run up to the SA deadline. Because none of the models being considered is believed to be the true model, we use an ensemble modelling technique that combines forecasts from different models to develop a less risky demand forecast.