Developing a Peptide That Inhibits DNA Repair by Blocking the Binding of Artemis and DNA Ligase IV to Enhance Tumor Radiosensitivity

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa cells observed with an electron microscope with dimensions 8192×8192×517. From there, a smaller region of interest (ROI) of 2000×2000×300 was cropped and manually delineated to obtain the ground truth necessary for a quantitative evaluation. A qualitative evaluation was performed on the 8192×8192 slices due to the lack of ground truth. Pairs of patches of data and labels for the classes nucleus, nuclear envelope, cell and background were generated to train U-Net architectures from scratch. Several training strategies were followed, and the results were compared against a traditional image processing algorithm. The correctness of GT, that is, the inclusion of one or more nuclei within the region of interest was also evaluated. The impact of the extent of training data was evaluated by comparing results from 36,000 pairs of data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Then, 135,000 patches from several cells from the 8192×8192 slices were generated automatically using the image processing algorithm. Finally, the two sets of 135,000 pairs were combined to train once more with 270,000 pairs. As would be expected, the accuracy and Jaccard similarity index improved as the number of pairs increased for the ROI. This was also observed qualitatively for the 8192×8192 slices. When the 8192×8192 slices were segmented with U-Nets trained with 135,000 pairs, the architecture trained with automatically generated pairs provided better results than the architecture trained with the pairs from the manually segmented ground truths. This suggests that the pairs that were extracted automatically from many cells provided a better representation of the four classes of the various cells in the 8192×8192 slice than those pairs that were manually segmented from a single cell. Finally, the two sets of 135,000 pairs were combined, and the U-Net trained with these provided the best results.

Exonucleases: Degrading DNA to Deal with Genome Damage, Cell Death, Inflammation and Cancer

Although DNA degradation might seem an unwanted event, it is essential in many cellular processes that are key to maintaining genomic stability and cell and organism homeostasis. The capacity to cut out nucleotides one at a time from the end of a DNA chain is present in enzymes called exonucleases. Exonuclease activity might come from enzymes with multiple other functions or specialized enzymes only dedicated to this function. Exonucleases are involved in central pathways of cell biology such as DNA replication, repair, and death, as well as tuning the immune response. Of note, malfunctioning of these enzymes is associated with immune disorders and cancer. In this review, we will dissect the impact of DNA degradation on the DNA damage response and its links with inflammation and cancer.

Structural analysis of the basal state of the Artemis:DNA-PKcs complex

Artemis nuclease and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) are key components in nonhomologous DNA end joining (NHEJ), the major repair mechanism for double-strand DNA breaks. Artemis activation by DNA-PKcs resolves hairpin DNA ends formed during V(D)J recombination. Artemis deficiency disrupts development of adaptive immunity and leads to radiosensitive T- B- severe combined immunodeficiency (RS-SCID). An activated state of Artemis in complex with DNA-PK was solved by cryo-EM recently, which showed Artemis bound to the DNA. Here, we report that the pre-activated form (basal state) of the Artemis:DNA-PKcs complex is stable on an agarose-acrylamide gel system, and suitable for cryo-EM structural analysis. Structures show that the Artemis catalytic domain is dynamically positioned externally to DNA-PKcs prior to ABCDE autophosphorylation and show how both the catalytic and regulatory domains of Artemis interact with the N-HEAT and FAT domains of DNA-PKcs. We define a mutually exclusive binding site for Artemis and XRCC4 on DNA-PKcs and show that an XRCC4 peptide disrupts the Artemis:DNA-PKcs complex. All of the findings are useful in explaining how a hypomorphic L3062R missense mutation of DNA-PKcs could lead to insufficient Artemis activation, hence RS-SCID. Our results provide various target site candidates to design disruptors for Artemis:DNA-PKcs complex formation.