Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

EZH2 Supports Osteoclast Differentiation and Bone Resorption Via Epigenetic and Cytoplasmic Targets

Key osteoclast (OCL) regulatory gene promoters in bone marrow–derived monocytes harbor bivalent histone modifications that combine activating Histone 3 lysine 4 tri-methyl (H3K4me3) and repressive H3K27me3 marks, which upon RANKL stimulation resolve into repressive or activating architecture. Enhancer of zeste homologue 2 (EZH2) is the histone methyltransferase component of the polycomb repressive complex 2, which catalyzes H3K27me3 modifications. Immunofluorescence microscopy reveals that EZH2 localization during murine osteoclastogenesis is dynamically regulated. Using EZH2 knockdown and small molecule EZH2 inhibitor GSK126, we show that EZH2 plays a critical epigenetic role in OCL precursors (OCLp) during the first 24 hours of RANKL activation. RANKL triggers EZH2 translocation into the nucleus where it represses OCL-negative regulators MafB, Irf8, and Arg1. Consistent with its cytoplasmic localization in OCLp, EZH2 methyltransferase activity is required during early RANKL signaling for phosphorylation of AKT, resulting in downstream activation of the mTOR complex, which is essential for induction of OCL differentiation. Inhibition of RANKL-induced pmTOR-pS6RP signaling by GSK126 altered the translation ratio of the C/EBPβ-LAP and C/EBPβ-LIP isoforms and reduced nuclear translocation of the inhibitory C/EBPβ-LIP, which is necessary for transcriptional repression of the OCL negative-regulatory transcription factor MafB. EZH2 in multinucleated OCL is primarily cytoplasmic and mature OCL cultured on bone segments in the presence of GSK126 exhibit defective cytoskeletal architecture and reduced resorptive activity. Here we present new evidence that EZH2 plays epigenetic and cytoplasmic roles during OCL differentiation by suppressing MafB transcription and regulating early phases of PI3K-AKT–mTOR-mediated RANKL signaling, respectively. Consistent with its cytoplasmic localization, EZH2 is required for cytoskeletal dynamics during resorption by mature OCL. Thus, EZH2 exhibits complex roles in supporting osteoclast differentiation and function. © 2019 American Society for Bone and Mineral Research

GFI1-Dependent Repression of SGPP1 Increases Multiple Myeloma Cell Survival

Multiple myeloma (MM) remains incurable for most patients due to the emergence of drug resistant clones. Here we report a p53-independent mechanism responsible for Growth Factor Independence-1 (GFI1) support of MM cell survival by its modulation of sphingolipid metabolism to increase the sphingosine-1-phosphate (S1P) level regardless of the p53 status. We found that expression of enzymes that control S1P biosynthesis, SphK1, dephosphorylation, and SGPP1 were differentially correlated with GFI1 levels in MM cells. We detected GFI1 occupancy on the SGGP1 gene in MM cells in a predicted enhancer region at the 5’ end of intron 1, which correlated with decreased SGGP1 expression and increased S1P levels in GFI1 overexpressing cells, regardless of their p53 status. The high S1P:Ceramide intracellular ratio in MM cells protected c-Myc protein stability in a PP2A-dependent manner. The decreased MM viability by SphK1 inhibition was dependent on the induction of autophagy in both p53WT and p53mut MM. An autophagic blockade prevented GFI1 support for viability only in p53mut MM, demonstrating that GFI1 increases MM cell survival via both p53WT inhibition and upregulation of S1P independently. Therefore, GFI1 may be a key therapeutic target for all types of MM that may significantly benefit patients that are highly resistant to current therapies

A combined computational and experimental approach reveals the structure of a C/EBP–Spi1 interaction required for IL1B gene transcription

We previously reported that transcription of the human IL1B gene, encoding the proinflammatory cytokine interleukin 1, depends on long-distance chromatin looping that is stabilized by a mutual interaction between the DNA-binding domains (DBDs) of two transcription factors: Spi1 proto-oncogene at the promoter and CCAAT enhancer– binding protein (C/EBP) at a far-upstream enhancer. We have also reported that the C-terminal tail sequence beyond the C/EBP leucine zipper is critical for its association with Spi1 via an exposed residue (Arg-232) located within a pocket at one end of the Spi1 DNA-recognition helix. Here, combining in vitro interaction studies with computational docking and molecular dynamics of existing X-ray structures for the Spi1 and C/EBP DBDs, along with the C/EBP C-terminal tail sequence, we found that the tail sequence is intimately associated with Arg-232 of Spi1. The Arg-232 pocket was computationally screened for small-molecule binding aimed at IL1B transcription inhibition, yielding L-arginine, a known anti-inflammatory amino acid, revealing a potential for disrupting the C/EBP–Spi1 interaction. As evaluated by ChIP, cultured lipopolysaccharide (LPS)-activated THP-1 cells incubated with L-arginine had significantly decreased IL1B transcription and reduced C/EBP’s association with Spi1 on the IL1B promoter. No significant change was observed in direct binding of either Spi1 or C/EBP to cognate DNA and in transcription of the C/EBP-dependent IL6 gene in the same cells. These results support the notion that disordered sequences extending from a leucine zipper can mediate protein–protein interactions and can serve as drug-gable targets for regulating gene promoter activity

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics

Abstract Background Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format. Methods We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images. Results Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images. Conclusions The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests