Cell-based screen for altered nuclear phenotypes reveals senescence progression in polyploid cells after Aurora kinase B inhibition.

Cellular senescence is a widespread stress response and is widely considered to be an alternative cancer therapeutic goal. Unlike apoptosis, senescence is composed of a diverse set of subphenotypes, depending on which of its associated effector programs are engaged. Here we establish a simple and sensitive cell-based prosenescence screen with detailed validation assays. We characterize the screen using a focused tool compound kinase inhibitor library. We identify a series of compounds that induce different types of senescence, including a unique phenotype associated with irregularly shaped nuclei and the progressive accumulation of G1 tetraploidy in human diploid fibroblasts. Downstream analyses show that all of the compounds that induce tetraploid senescence inhibit Aurora kinase B (AURKB). AURKB is the catalytic component of the chromosome passenger complex, which is involved in correct chromosome alignment and segregation, the spindle assembly checkpoint, and cytokinesis. Although aberrant mitosis and senescence have been linked, a specific characterization of AURKB in the context of senescence is still required. This proof-of-principle study suggests that our protocol is capable of amplifying tetraploid senescence, which can be observed in only a small population of oncogenic RAS-induced senescence, and provides additional justification for AURKB as a cancer therapeutic target.This work was supported by the University of Cambridge, Cancer Research UK, Hutchison Whampoa; Cancer Research UK grants A6691 and A9892 (M.N., N.K., C.J.T., D.C.B., C.J.C., L.S.G, and M.S.); a fellowship from the Uehara Memorial Foundation (M.S.).This is the author accepted manuscript. The final version is available from the American Society for Cell Biology via http://dx.doi.org/10.1091/mbc.E15-01-000

The Joint Influence of Intra- and Inter-Team Learning Processes on Team Performance: A Constructive or Destructive Combination?

In order for teams to build a shared conception of their task, team learning is crucial. Benefits of intra-team learning have been demonstrated in numerous studies. However, teams do not operate in a vacuum, and interact with their environment to execute their tasks. Our knowledge of the added value of inter-team learning (team learning with external parties) is limited. Do both types of team learning compete over limited resources, or do they form a synergistic combination? We aim to shed light on the interplay between intra- and inter-team learning in relation to team performance, by including adaptive and transformative sub-processes of intra-team learning. A quantitative field study was conducted among 108 university teacher teams. The joint influence of intra- and inter-team learning as well as structural (task interdependence) and cultural (team efficacy) team characteristics on self-perceived and externally rated team performance were explored in a path model. The results showed that adaptive intra-team learning positively influenced self-perceived team performance, while transformative intra-team learning positively influenced externally rated team performance. Moreover, intra-team and inter-team learning were found to be both a constructive and a destructive combination. Adaptive intra-team learning combined with inter-team learning led to increased team performance, while transformative intra-team learning combined with inter-team learning hurt team performance. The findings demonstrate the importance of distinguishing between both the scope (intra- vs. inter-team) and the level (adaptive vs. transformative) of team learning in understanding team performance

Seeing Double: ASASSN-18bt Exhibits a Two-component Rise in the Early-time K2 Light

On 2018 February 4.41, the All-Sky Automated Survey for SuperNovae (ASAS-SN) discovered ASASSN-18bt in the K2 Campaign 16 field. With a redshift of z = 0.01098 and a peak apparent magnitude of B max = 14.31, ASASSN-18bt is the nearest and brightest SNe Ia yet observed by the Kepler spacecraft. Here we present the discovery of ASASSN-18bt, the K2 light curve, and prediscovery data from ASAS-SN and the Asteroid Terrestrial-impact Last Alert System. The K2 early-time light curve has an unprecedented 30-minute cadence and photometric precision for an SN Ia light curve, and it unambiguously shows a ~4 day nearly linear phase followed by a steeper rise. Thus, ASASSN-18bt joins a growing list of SNe Ia whose early light curves are not well described by a single power law. We show that a double-power-law model fits the data reasonably well, hinting that two physical processes must be responsible for the observed rise. However, we find that current models of the interaction with a nondegenerate companion predict an abrupt rise and cannot adequately explain the initial, slower linear phase. Instead, we find that existing published models with shallow 56Ni are able to span the observed behavior and, with tuning, may be able to reproduce the ASASSN-18bt light curve. Regardless, more theoretical work is needed to satisfactorily model this and other early-time SNe Ia light curves. Finally, we use Swift X-ray nondetections to constrain the presence of circumstellar material (CSM) at much larger distances and lower densities than possible with the optical light curve. For a constant-density CSM, these nondetections constrain ρ < 4.5 × 105 cm−3 at a radius of 4 × 1015 cm from the progenitor star. Assuming a wind-like environment, we place mass loss limits of for v w = 100 km s−1, ruling out some symbiotic progenitor systems. This work highlights the power of well-sampled early-time data and the need for immediate multiband, high-cadence follow-up for progress in understanding SNe Ia