The Ku-binding motif is a conserved module for recruitment and stimulation of non-homologous end-joining proteins

The Ku-binding motif (KBM) is a short peptide module first identified in APLF that we now show is also present in Werner syndrome protein (WRN) and in Modulator of retrovirus infection homologue (MRI). We also identify a related but functionally distinct motif in XLF, WRN, MRI and PAXX, which we denote the XLF-like motif. We show that WRN possesses two KBMs; one at the N terminus next to the exonuclease domain and one at the C terminus next to an XLF-like motif. We reveal that the WRN C-terminal KBM and XLF-like motif function cooperatively to bind Ku complexes and that the N-terminal KBM mediates Ku-dependent stimulation of WRN exonuclease activity. We also show that WRN accelerates DSB repair by a mechanism requiring both KBMs, demonstrating the importance of WRN interaction with Ku. These data define a conserved family of KBMs that function as molecular tethers to recruit and/or stimulate enzymes during NHEJ

XLF and APLF bind Ku80 at two remote sites to ensure DNA repair by non-homologous end joining

International audienceThe Ku70-Ku80 (Ku) heterodimer binds rapidly and tightly to the ends of DNA double-strand breaks and recruits factors of the non-homologous end-joining (NHEJ) repair pathway through molecular interactions that remain unclear. We have determined crystal structures of the Ku-binding motifs (KBM) of the NHEJ proteins APLF (A-KBM) and XLF (X-KBM) bound to a Ku-DNA complex. The two KBM motifs bind remote sites of the Ku80 alpha/beta domain. The X-KBM occupies an internal pocket formed by an unprecedented large outward rotation of the Ku80 alpha/beta domain. We observe independent recruitment of the APLF-interacting protein XRCC4 and of XLF to laser-irradiated sites via binding of A- and X-KBMs, respectively, to Ku80. Finally, we show that mutation of the X-KBM and A-KBM binding sites in Ku80 compromises both the efficiency and accuracy of end joining and cellular radiosensitivity. A- and X-KBMs may represent two initial anchor points to build the intricate interaction network required for NHEJ

Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments

Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plan-tower model PMS3003) for measuring PM2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM2.5 (mean +/- SD) of 9 +/- 9 and 10 +/- 3 mu g m(-3), respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM2.5 of 36 +/- 17 and 116 +/- 57 mu g m(-3) during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental beta attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM2.5 increased. The precision of reference instruments (T640: +/- 0.5 mu g m(-3) for 1 h; SHARP: +/- 2 mu g m(-3) for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and beta-attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27% for 1 h to 9% for 24 h) than in Durham (from 201% to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35% to 11 %); and (3) the higher T640-PMS3003 correlations (R-2 >= 0.63) than SHARP-PMS3003 (R-2 >= 0.25). A major RH influence was found in RTP (1 h RH = 64 +/- 22 %) due to the relatively high precision of the T640 measurements that can explain up to similar to 30% of the variance in 1 min to 6 h PMS3003 PM2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM2.5 concentrations within similar to 10% of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM2.5 exceeded similar to 125 mu g m(-3) and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization