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Metastatic castration resistant prostate cancers (mCRPC) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ART). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically-approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins (RIME), we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein-protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found CREB5 was associated with TGFβ and Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has created a global health and economic emergency. SARS-CoV-2 viruses hijack human proteins to promote their spread and virulence including the interactions involving the viral envelope (E) protein and human proteins. To understand the structural basis for SARS-CoV-2 viral-host recognition, we used cryo-electron microscopy to determine a structure for the human cell junction protein PALS1 and SARS-CoV-2 E protein complex. The structure shows that the E protein C-terminal DLLV motif recognizes a pocket formed exclusively by hydrophobic residues from the PDZ and SH3 domains in PALS1. Our structural analysis provides an explanation for the observation that the viral E protein recruits PALS1 from lung epithelial cell junctions resulting in vascular leakage, lung damage, viral spread, and virulence. In addition, our structure provides novel targets for peptide- and small-molecule inhibitors that could block the PALS1-E interactions to reduce the E-mediated damage to vascular structures.

A self-supervised workflow uses a 2D class average to progressively train a convolutional neural network for automated particle picking in cryo-EM., High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.

ObjectiveTo understand better the public perception and comprehension with medical technology such as artificial intelligence and robotic surgery. Additionally, to identify sensitivity to, and comfort with, the use of AI and robotics in medicine a in order to ensure acceptability and quality of counseling and to guide future development.Subjects and MethodsA survey was conducted on a convenience sample of visitors to the Minnesota State Fair (n = 264). The survey investigated participant beliefs on the capabilities of AI and robotics in medicine and their comfort with such technology. Participants were randomized to receive one of two similar surveys. In the first a diagnosis was made by a physician and in the second by an AI application in order to compare confidence in human and computer-based diagnosis.ResultsThe median age of participants was 45 (IQR 28-59), 58% were female (n=154) vs. 42% male (n=110), 69% had completed at least a bachelor’s degree, 88% were Caucasian (n=233) vs. 12% ethnic minorities (n=31) and were from 12 states in the US with most from the Upper Midwest. Participants had nearly equal trust in AI vs. physician diagnoses, however, they were significantly more likely to trust an AI diagnosis of cancer over a doctor’s diagnosis when responding to the version of the survey that suggested an AI could make medical diagnosis (p = 9.32e-06). Though 55% of respondents (n=145) reported they were uncomfortable with automated robotic surgery the majority of the individuals surveyed (88%) mistakenly believed that partially autonomous surgery was already being performed. Almost all (94%) stated they would be willing to pay for an AI to review their medical imaging, if available.ConclusionMost participants express confidence in AI providing medical diagnoses, sometimes even over human physicians. Participants generally expressed concern with surgical AI, but mistakenly believe it is already happening. As AI applications make their way into medical practice, health care providers should be cognizant of patient misconceptions and the sensitivity that patients have to how such technology is represented.

KAMO and Blend provide particularly effective tools to manage automatically the merging of large numbers of datasets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options such as use of more accurate cell distance metrics and use of reflection-intensity correlation coefficients to infer “distances” among sets of reflec- tions. This increases the sensitivity to differences in unit cell parameters and allows for clustering to assemble nearly complete datasets on the basis of intensity or ampli- tude differences. If datasets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step we use either the simple cell vector distance of the original Blend, or we use the more sensi- tive NCDist. This step tends to find clusters of sufficient size so that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections having a common hkl to merge clusters in a way sensitive to structural differences that may not have perturbed the cell parameters sufficiently to make meaningful clusters.Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into cate- gories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent from one another, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy is to use unit-cell clustering until merged data is sufficiently complete then to use intensity-based clustering. We have demonstrated that, using this strategy, we are able to accurately cluster datasets from crystals that have subtle differences.