Davis, A. J. | Search bioRxiv

bioRxiv2021

Ablating Ku70 phosphorylation results in defective DNA damage repair and spontaneous induction of hepatocellular carcinoma

Multiple pathways mediate the repair of DNA double-strand break (DSB), with numerous mechanisms responsible for driving choice between the pathways. Previously, we reported that phosphorylation of the non-homologous end joining (NHEJ) factor, Ku70, is required for the dissociation of the Ku heterodimer from DNA ends to allow DSB repair via homologous recombination (HR). A knock-in mouse, in which phosphorylation is ablated in the three conserved sites of Ku70 (Ku703A/3A), was generated in order to test the hypothesis that Ku70 phosphorylation is required for initiation of HR and that blocking this process results in enhanced genomic instability and tumorigenesis. Here, we show that Ku703A/3A mice develop spontaneous and have accelerated chemical-induced hepatocellular carcinoma (HCC) compared to wild-type (Ku70+/+) littermates. The HCC tumors from the Ku703A/3A mice have increased {gamma}H2AX and 8-oxo-G staining, suggesting DNA repair is decreased in these mice. Spontaneous transformed cell lines from Ku703A/3A mice are more radiosensitive, have a significant decrease in DNA end resection, and are more sensitive to the DNA cross-linking agent mitomycin C compared to cells from Ku70+/+ littermates. Collectively, these findings demonstrate that phosphorylation-mediated dissociation of Ku heterodimer from DNA ends is required for efficient DNA damage repair and disruption of this process results in genomic instability and accelerated development of HCC.

cancer biology↗

bioRxiv2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

O_LIMotion-activated wildlife cameras (or "camera traps") are frequently used to remotely and non-invasively observe animals. The vast number of images collected from camera trap projects have prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. C_LIO_LIWe used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the "species model," and one that determines if an image is empty or if it contains an animal, the "empty-animal model." C_LIO_LIOur species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36-91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91-94% on out-of-sample datasets from different continents. C_LIO_LIOur software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths. C_LI

ecology↗