The present study examines whether there is a mechanism beyond the current concept of post-translational modifications to regulate the function of a protein. A small gas molecule, hydrogen sulfide (H2S), was found to bind at active-site copper of Cu/Zn-SOD using a series of methods including radiolabeled binding assay, X-ray absorption near-edge structure (XANES), and crystallography. Such an H2S binding enhanced the electrostatic forces to guide the negatively charged substrate superoxide radicals to the catalytic copper ion, changed the geometry and energy of the frontier molecular orbitals of the active site, and subsequently facilitated the transfer of an electron from the superoxide radical to the catalytic copper ion and the breakage of the copper-His61 bridge. The physiological relevance of such an H2S effect was also examined in both in vitro and in vivo models where the cardioprotective effects of H2S were dependent on Cu/Zn-SOD.
Abstract Slingshot proteins form a small group of dual‐specific phosphatases that modulate cytoskeleton dynamics through dephosphorylation of cofilin and Lim kinases (LIMK). Small chemical compounds with Slingshot‐inhibiting activities have therapeutic potential against cancers or infectious diseases. However, only a few Slingshot inhibitors have been investigated and reported, and their cellular activities have not been examined. In this study, we identified two rhodanine‐scaffold‐based para ‐substituted benzoic acid derivatives as competitive Slingshot inhibitors. The top compound, ( Z )‐4‐((4‐((4‐oxo‐2‐thioxo‐3‐( o ‐tolyl)thiazolidin‐5‐ylidene)methyl)phenoxy)methyl)benzoic acid ( D3 ) had an inhibition constant ( K i ) of around 4 μ m and displayed selectivity over a panel of other phosphatases. Moreover, compound D3 inhibited cell migration and cofilin dephosphorylation after nerve growth factor (NGF) or angiotensin II stimulation. Therefore, our newly identified Slingshot inhibitors provide a starting point for developing Slingshot‐targeted therapies.
High-quality motion estimation is essential for ultrasound elastography (USE). Traditional motion estimation algorithms based on speckle tracking such as normalized cross correlation (NCC) or regularization such as global ultrasound elastography (GLUE) are time-consuming. In order to reduce the computational cost and ensure the accuracy of motion estimation, many convolutional neural networks have been introduced into USE. Most of these networks such as radio-frequency modified pyramid, warping and cost volume network (RFMPWC-Net) are supervised and need many ground truths as labels in network training. However, the ground truths are laborious to collect for USE. In this study, we proposed a MaskFlownet-based unsupervised convolutional neural network (MF-UCNN) for fast and high-quality motion estimation in USE. The inputs to MF-UCNN are the concatenation of RF, envelope, and B-mode images before and after deformation, while the outputs are the axial and lateral displacement fields. The similarity between the predeformed image and the warped image (i.e., the postdeformed image compensated by the estimated displacement fields) and the smoothness of the estimated displacement fields were incorporated in the loss function. The network was compared with modified pyramid, warping and cost volume network (MPWC-Net)++, RFMPWC-Net, GLUE, and NCC. Results of simulations, breast phantom, and in vivo experiments show that MF-UCNN obtains higher signal-to-noise ratio (SNR) and higher contrast-to-noise ratio (CNR). MF-UCNN achieves high-quality motion estimation with significantly reduced computation time. It is unsupervised and does not need any ground truths as labels in the training, and, thus, has great potential for motion estimation in USE.
Registration of multiple stained images is a fundamental task in histological image analysis. In supervised methods, obtaining ground-truth data with known correspondences is laborious and time-consuming. Thus, unsupervised methods are expected. Unsupervised methods ease the burden of manual annotation but often at the cost of inferior results. In addition, registration of histological images suffers from appearance variance due to multiple staining, repetitive texture, and section missing during making tissue sections. To deal with these challenges, we propose an unsupervised structural feature guided convolutional neural network (SFG). Structural features are robust to multiple staining. The combination of low-resolution rough structural features and high-resolution fine structural features can overcome repetitive texture and section missing, respectively. SFG consists of two components of structural consistency constraints according to the formations of structural features, i.e., dense structural component and sparse structural component. The dense structural component uses structural feature maps of the whole image as structural consistency constraints, which represent local contextual information. The sparse structural component utilizes the distance of automatically obtained matched key points as structural consistency constraints because the matched key points in an image pair emphasize the matching of significant structures, which imply global information. In addition, a multi-scale strategy is used in both dense and sparse structural components to make full use of the structural information at low resolution and high resolution to overcome repetitive texture and section missing. The proposed method was evaluated on a public histological dataset (ANHIR) and ranked first as of Jan 18th, 2022.
Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduced to guide unsupervised deep learning (DL) based registration methods to handle such a registration task. This paper proposes an iterative keypoint correspondence-guided (IKCG) unsupervised network for non-rigid histological image registration. Fixed deep features and learnable deep features are introduced as keypoint descriptors to automatically establish keypoint correspondences, the distance between which is used as a loss function to train the registration network. Fixed deep features extracted from DL networks that are pre-trained on natural image datasets are more discriminative than handcrafted ones, benefiting from the deep and hierarchical nature of DL networks. The intermediate layer outputs of the registration networks trained on histological image datasets are extracted as learnable deep features, which reveal unique information for histological images. An iterative training strategy is adopted to train the registration network and optimize learnable deep features jointly. Benefiting from the excellent matching ability of learnable deep features optimized with the iterative training strategy, the proposed method can solve the local non-rigid large displacement problem, an inevitable problem usually caused by misoperation, such as tears in producing tissue slices. The proposed method is evaluated on the Automatic Non-rigid Histology Image Registration (ANHIR) website and AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) website. It ranked 1st on both websites as of August 6th, 2024.
Inhibitions and antagonists of L-type Ca2+ channels are important to both research and therapeutics. Here, we report C-terminus mediated inhibition (CMI) for CaV1.3 that multiple motifs coordinate to tune down Ca2+ current and Ca2+ influx toward the lower limits determined by end-stage CDI (Ca2+-dependent inactivation). Among IQV (preIQ3-IQ domain), PCRD and DCRD (proximal or distal C-terminal regulatory domain), spatial closeness of any two modules, e.g., by constitutive fusion, facilitates the trio to form the complex, compete against calmodulin, and alter the gating. Acute CMI by rapamycin-inducible heterodimerization helps reconcile the concurrent activation/inactivation attenuations to ensure Ca2+ influx is reduced, in that Ca2+ current activated by depolarization is potently (~65%) inhibited at the peak (full activation), but not later on (end-stage inactivation, ~300 ms). Meanwhile, CMI provides a new paradigm to develop CaV1 inhibitors, the therapeutic potential of which is implied by computational modeling of CaV1.3 dysregulations related to Parkinson’s disease.
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