Summary DNA double strand breaks (DSBs) initiate extensive local and global alterations in chromatin structure, many of which depend on the ATM kinase. Histone H2A ubiquitylation (uH2A) on chromatin surrounding DSBs is one example, thought to be important for recruitment of repair proteins. uH2A is also implicated in transcriptional repression; an intriguing yet untested hypothesis is that this function is conserved in the context of DSBs. Using a novel reporter that allows for visualization of repair protein recruitment and local transcription in single cells, we describe an ATM-dependent transcriptional silencing program in cis to DSBs. ATM prevents RNA polymerase II elongation dependent chromatin decondensation at regions distal to DSBs. Silencing is partially dependent on E3 ubiquitin ligases RNF8 and RNF168, while reversal of silencing relies on the uH2A deubiquitylating enzyme USP16. These findings give insight into the role of post-translational modifications in mediating cross talk between diverse processes occurring on chromatin.

The pathogenic sequelae of BRCA1 mutation in human and mouse cells are mitigated by concomitant deletion of 53BP1, which binds histone H4 dimethylated at Lys20 (H4K20me2) to promote nonhomologous end-joining, suggesting a balance between BRCA1 and 53BP1 regulates DNA double-strand break (DSB) repair mechanism choice. Here, we document that acetylation is a key determinant of this balance. TIP60 acetyltransferase deficiency reduced BRCA1 at DSB chromatin with commensurate increases in 53BP1, while HDAC inhibition yielded the opposite effect. TIP60 -dependent H4 acetylation diminished 53BP1 binding to H4K20me2 in part through disruption of a salt bridge between H4K16 and Glu1551 in the 53BP1 Tudor domain. Moreover, TIP60 deficiency impaired HR and conferred sensitivity to PARP inhibition in a 53BP1-dependent manner. These findings demonstrate that acetylation in cis to H4K20me2 regulates relative BRCA1 and 53BP1 DSB chromatin occupancy to direct DNA repair mechanism.

Identifying infectious causes of subacute or chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed. OBJECTIVE To present a case series of patients with diagnostically challenging subacute or chronic meningitis using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious. DESIGN, SETTING, AND PARTICIPANTS In this case series, mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples were used to develop and implement a weighted scoring metric based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results. Total RNA was extracted for mNGS from the CSF of 7 participants with subacute or chronic meningitis who were recruited between September 2013 and March 2017 as part of a multicenter study of mNGS pathogen discovery among patients with suspected neuroinflammatory conditions. The neurologic infections identified by mNGS in these 7 participants represented a diverse array of pathogens. The patients were referred from the University of California, San Francisco Medical Center (n = 2), Zuckerberg San Francisco General Hospital and Trauma Center (n = 2), Cleveland Clinic (n = 1), University of Washington (n = 1), and Kaiser Permanente (n = 1). A weighted z score was used to filter out environmental contaminants and facilitate efficient data triage and analysis. MAIN OUTCOMES AND MEASURES Pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results. RESULTS The 7 participants ranged in age from 10 to 55 years, and 3 (43%) were female. A parasitic worm (Taenia solium, in 2 participants), a virus (HIV-1), and 4 fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis) were identified among the 7 participants by using mNGS. Evaluating mNGS data with a weighted z score-based scoring algorithm reduced the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed, effectively separating bona fide pathogen sequences from spurious environmental sequences so that, in each case, the causative pathogen was found within the top 2 scoring microbes identified using the algorithm. CONCLUSIONS AND RELEVANCE Diverse microbial pathogens were identified by mNGS in the CSF of patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for 1 year, the first reported case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of C dubliniensis meningitis. Prioritizing metagenomic data with a scoring algorithm greatly clarified data interpretation and highlighted the problem of attributing biological significance to organisms present in contr...