Resistance to tamoxifen in breast cancer patients is a serious therapeutic problem and major efforts are underway to understand underlying mechanisms. Resistance can be either intrinsic or acquired. We derived a series of subcloned MCF7 cell lines that were either highly sensitive or naturally resistant to tamoxifen and studied the factors that lead to drug resistance. Gene-expression studies revealed a signature of 67 genes that differentially respond to tamoxifen in sensitive vs. resistant subclones, which also predicts disease-free survival in tamoxifen-treated patients. High-throughput cell-based screens, in which >500 human kinases were independently ectopically expressed, identified 31 kinases that conferred drug resistance on sensitive cells. One of these, HSPB8, was also in the expression signature and, by itself, predicted poor clinical outcome in one cohort of patients. Further studies revealed that HSPB8 protected MCF7 cells from tamoxifen and blocked autophagy. Moreover, silencing HSBP8 induced autophagy and caused cell death. Tamoxifen itself induced autophagy in sensitive cells but not in resistant ones, and tamoxifen-resistant cells were sensitive to the induction of autophagy by other drugs. These results may point to an important role for autophagy in the sensitivity to tamoxifen.functional screen | estrogen receptor T he two thirds of women with estrogen receptor-(ER) or progesterone receptor-positive breast cancers are excellent candidates for antihormone therapy. Selective ER modulators (SERMs), like tamoxifen, block ER activation and have impacted both therapy and survival. However, the success of tamoxifen therapy is limited by intrinsic and acquired drug resistance. Several pathways have been implicated in antiestrogen resistance, including: the PI3K/AKT/mTOR (mammalian target of rapamycin) pathway, which is implicated in cell survival; the EGFR family; and the RAS/RAF/MEK1/2/ERK1/2 family, which regulate cell proliferation (1, 2). Loss of ER expression or function may also be an important mechanism of de novo resistance to tamoxifen, either through relatively rare ER mutations or changes in coactivators and corepressors (3).Several groups have used gene-expression analysis to identify genes regulated through ER (4) that are affected by SERMs in breast cancer cells (5, 6). Others have used tumor samples to develop gene signatures that can predict clinical responses to tamoxifen (7-10). Genetic strategies have also been used to identify genes that drive tamoxifen resistance. Receptor tyrosine kinases and MAPK signaling were detected using expression of pooled cDNA libraries in ZR-75-1, an approach often biased toward the most abundantly expressed genes and which requires recovery of hits by PCR (11). The analysis of antiestrogen-sensitive and -resistant MCF7 cells by SNP and comparative genomic hybridization pointed to changes in protein abundance rather than somatic genomic changes (12). An RNA interference screen of kinases identified CDK10, CRK7, and MAP2K7, whose knockdown cause tamoxif...

The long-ranged attractions between hydrophobic amorphous fluoropolymer surfaces are measured in water with and without dissolved air. An atomic force microscope is used to obtain more than 500 measured jump-in distances, which yields statistically reliable results. It is found that the range of the attraction and its variability is generally significantly decreased in deaerated water as compared to normal, aerated water. However, the range and strength of the attraction in deaerated water remain significantly greater than the van der Waals attraction for this system. The experimental observations are consistent with (1) nanobubbles being primarily responsible for the long-ranged attraction in normal water, (2) nanobubbles not being present in deaerated water when the surfaces are not in contact, and (3) the attraction in the absence of nanobubbles being most probably due to the approach to the separation-induced spinodal cavitation of the type identified by Bérard et al. [J. Chem. Phys. 1993, 98, 7236]. It is argued that the measurements in deaerated water reveal the bare or pristine hydrophobic attraction unobscured by nanobubbles.

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

On 20 March 2020, in the midst of the COVID-19 pandemic, the Singapore government released a new app called TraceTogether. Developed by the Ministry of Health, SG United, and GovTech Singapore, the app uses the Bluetooth capability of smartphones to store information about other smartphones that have come into close proximity with your own. These data facilitate the government’s process of “contact tracing” through which they track those who have potentially come into contact with the virus and place them in quarantine. This essay attempts to understand what kinds of citizens and civic behavior might be brought into being by this technology. By examining the workings and affordances of the TraceTogether app in detail, the authors argue that its peer-to-peer and open-source technology features mobilize the rhetorics and ideals of citizens science and democratic participation. However, by deploying these within a context that centralizes data, the app turns ideals born of dissent and protest on their head, using them to build trust not within a community but rather in government power and control. Rather than building social trust, TraceTogether becomes a technological substitute for it. The significant public support for TraceTogether shows both the possibilities and limitations of citizen science in less liberal political contexts and circumstances.