hnRNP K: An HDM2 Target and Transcriptional Coactivator of p53 in Response to DNA Damage

SummaryIn response to DNA damage, mammalian cells trigger the p53-dependent transcriptional induction of factors that regulate DNA repair, cell-cycle progression, or cell survival. Through differential proteomics, we identify heterogeneous nuclear ribonucleoprotein K (hnRNP K) as being rapidly induced by DNA damage in a manner that requires the DNA-damage signaling kinases ATM or ATR. Induction of hnRNP K ensues through the inhibition of its ubiquitin-dependent proteasomal degradation mediated by the ubiquitin E3 ligase HDM2/MDM2. Strikingly, hnRNP K depletion abrogates transcriptional induction of p53 target genes and causes defects in DNA-damage-induced cell-cycle-checkpoint arrests. Furthermore, in response to DNA damage, p53 and hnRNP K are recruited to the promoters of p53-responsive genes in a mutually dependent manner. These findings establish hnRNP K as a new HDM2 target and show that, by serving as a cofactor for p53, hnRNP K plays key roles in coordinating transcriptional responses to DNA damage

Extracting 3D parametric curves from 2D images of helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

An automated cell-counting algorithm for fluorescently-stained cells in migration assays

A cell-counting algorithm, developed in Matlab®, was created to efficiently count migrated fluorescently-stained cells on membranes from migration assays. At each concentration of cells used (10,000, and 100,000 cells), images were acquired at 2.5 ×, 5 ×, and 10 × objective magnifications. Automated cell counts strongly correlated to manual counts (r2 = 0.99, P < 0.0001 for a total of 47 images), with no difference in the measurements between methods under all conditions. We conclude that our automated method is accurate, more efficient, and void of variability and potential observer bias normally associated with manual counting

Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks

Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available. Both individual sources and mixing filters need to be estimated. In addition, a mixture may contain non-stationary noise which is unseen in the training set. We propose a synthesizing-decomposition (S-D) approach to solve the single-channel separation and deconvolution problem. In synthesizing, a generative model for sources is built using a generative adversarial network (GAN). In decomposition, both mixing filters and sources are optimized to minimize the reconstruction error of the mixture. The proposed S-D approach achieves a peak-to-noise-ratio (PSNR) of 18.9 dB and 15.4 dB in image inpainting and completion, outperforming a baseline convolutional neural network PSNR of 15.3 dB and 12.2 dB, respectively and achieves a PSNR of 13.2 dB in source separation together with deconvolution, outperforming a convolutive non-negative matrix factorization (NMF) baseline of 10.1 dB.Comment: 7 pages. Accepted by IJCAI 201

Monoclonal antibodies to a proenkephalin A fusion peptide synthesized in Escherichia coli recognize novel proenkephalin A precursor forms

Monoclonal antibodies have been generated to a chimeric peptide comprised of Escherichia coli beta-galactosidase fused to the amino acid sequence 69-207 of human preproenkephalin A. Two monoclonal antibodies, PE-1 and PE-2, were identified by their ability to recognize the same segment of proenkephalin A fused to the cII gene product of the E. coli bacteriophage lambda. The binding domains of PE-1 and PE-2 have been broadly located, with respect to the primary translation product, within the amino acid sequences 152-207 and 84-131, respectively. Immunoblot analysis of total bovine adrenomedullary chromaffin granule lysate reveals PE-1 and PE-2 immunoreactive forms of observed molecular mass 35, 33, 29, 24, 22, and 15 kDa, and an 18-kDa PE-1 immunoreactive form. Separation of granule membranes from their contents reveals differential membrane association of these high molecular weight polypeptides. There is preliminary evidence that PE-1 may be detecting a subset of polypeptides where shortening from the NH2 terminus has occurred. We postulate that the 35-kDa form represents the intact bovine enkephalin precursor of predicted molecular mass 27.3 kDa. This experimental approach should be generally applicable to the generation of antibodies which will recognize intact peptide precursors together with their post-translational cleavage products

Monoclonal antibodies to a proenkephalin A fusion peptide synthesized in Escherichia coli recognize novel proenkephalin A precursor forms

Monoclonal antibodies have been generated to a chimeric peptide comprised of Escherichia coli beta-galactosidase fused to the amino acid sequence 69-207 of human preproenkephalin A. Two monoclonal antibodies, PE-1 and PE-2, were identified by their ability to recognize the same segment of proenkephalin A fused to the cII gene product of the E. coli bacteriophage lambda. The binding domains of PE-1 and PE-2 have been broadly located, with respect to the primary translation product, within the amino acid sequences 152-207 and 84-131, respectively. Immunoblot analysis of total bovine adrenomedullary chromaffin granule lysate reveals PE-1 and PE-2 immunoreactive forms of observed molecular mass 35, 33, 29, 24, 22, and 15 kDa, and an 18-kDa PE-1 immunoreactive form. Separation of granule membranes from their contents reveals differential membrane association of these high molecular weight polypeptides. There is preliminary evidence that PE-1 may be detecting a subset of polypeptides where shortening from the NH2 terminus has occurred. We postulate that the 35-kDa form represents the intact bovine enkephalin precursor of predicted molecular mass 27.3 kDa. This experimental approach should be generally applicable to the generation of antibodies which will recognize intact peptide precursors together with their post-translational cleavage products

Ariel - Volume 4 Number 6

Editors David A. Jacoby Eugenia Miller Tom Williams Associate Editors Paul Bialas Terry Burt Michael Leo Gail Tenikat Editor Emeritus and Business Manager Richard J. Bonnano Movie Editor Robert Breckenridge Staff Richard Blutstein Mary F. Buechler J.D. Kanofsky Rocket Weber David Maye