The Ku heterodimer, composed of Ku70 and Ku80, is best characterized for its role in repairing double-stranded DNA breaks but is also known to participate in other regulatory processes. Despite our understanding of Ku protein interplay during DNA repair, the extent of Ku’s protein interactions in other processes has never been fully determined. Using proximity-dependent biotin identification (BioID) and affinity purification coupled to mass spectrometry (AP-MS) with wild-type Ku70, we identified candidate proteins that interact with the Ku heterodimer in HEK293 cells, in the absence of exogenously induced DNA damage. BioID analysis identified approximately 250 nuclear proteins, appearing in at least two replicates, including known Ku-interacting factors such as MRE11A, WRN, and NCOA6. Meanwhile, AP-MS analysis identified approximately 50 candidate proteins. Of the novel protein interactors identified, many were involved in functions already suspected to involve Ku such as transcriptional regulation, DNA replication, and DNA repair, while several others suggest that Ku may be involved in additional functions such as RNA metabolism, chromatin-remodeling, and microtubule dynamics. Using a combination of BioID and AP-MS, this is the first report that comprehensively characterizes the Ku protein interaction landscape, revealing new cellular processes and protein complexes involving the Ku complex.

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Chromatin structures are transmitted to daughter cells through a complex system of nucleosome disassembly and re-assembly at the advancing replication forks. However, the role of replication pausing in the transmission and perturbation of chromatin structures has not been addressed. RRM3 encodes a DNA helicase, which facilitates replication at sites covered with non-histone protein complexes (tRNA genes, active gene promoters, telomeres) in Saccharomyces cerevisiae. In this report we show that the deletion of RRM3 reduces the frequency of epigenetic conversions in the subtelomeric regions of the chromosomes. This phenotype is strongly dependent on 2 histone chaperones, CAF-I and ASF1, which are involved in the reassembly of nucleosomes behind replication forks, but not on the histone chaperone HIR1. We also show that the deletion of RRM3 increases the spontaneous mutation rates in conjunction with CAF-I and ASF1, but not HIR1. Finally, we demonstrate that Rrm3p and CAF-I compete for the binding to the DNA replication clamp PCNA (Proliferating Cell Nuclear Antigen). We propose that the stalling of DNA replication predisposes to epigenetic conversions and that RRM3 and CAF-I play key roles in this process.

Since its inception, proximity-dependent biotin identification (BioID), an in vivo biochemical screening method to identify proximal protein interactors, has seen extensive developments. Improvements and variants of the original BioID technique are being reported regularly, each expanding upon the existing potential of the original technique. While this is advancing our capabilities to study protein interactions under different contexts, we have yet to explore the full potential of the existing BioID variants already at our disposal. Here, we used BioID2 in an innovative manner to identify and map domain-specific protein interactions for the human Ku70 protein. Four HEK293 cell lines were created, each stably expressing various BioID2-tagged Ku70 segments designed to collectively identify factors that interact with different regions of Ku70. Historically, although many interactions have been mapped to the C-terminus of the Ku70 protein, few have been mapped to the N-terminal von Willebrand A-like domain, a canonical protein-binding domain ideally situated as a site for protein interaction. Using this segmented approach, we were able to identify domain-specific interactors as well as evaluate advantages and drawbacks of the BioID2 technique. Our study identifies several potential new Ku70 interactors and validates RNF113A and Spindly as proteins that contact or co-localize with Ku in a Ku70 vWA domain-specific manner.

Background Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance. Methods We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan–Meier and cox-proportional hazards analyses. Results TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012). Conclusions TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.