Cockayne syndrome group A and B proteins converge on transcription-linked resolution of non-B DNA.

Cockayne syndrome is a neurodegenerative accelerated aging disorder caused by mutations in the CSA or CSB genes. Although the pathogenesis of Cockayne syndrome has remained elusive, recent work implicates mitochondrial dysfunction in the disease progression. Here, we present evidence that loss of CSA or CSB in a neuroblastoma cell line converges on mitochondrial dysfunction caused by defects in ribosomal DNA transcription and activation of the DNA damage sensor poly-ADP ribose polymerase 1 (PARP1). Indeed, inhibition of ribosomal DNA transcription leads to mitochondrial dysfunction in a number of cell lines. Furthermore, machine-learning algorithms predict that diseases with defects in ribosomal DNA (rDNA) transcription have mitochondrial dysfunction, and, accordingly, this is found when factors involved in rDNA transcription are knocked down. Mechanistically, loss of CSA or CSB leads to polymerase stalling at non-B DNA in a neuroblastoma cell line, in particular at G-quadruplex structures, and recombinant CSB can melt G-quadruplex structures. Indeed, stabilization of G-quadruplex structures activates PARP1 and leads to accelerated aging in Caenorhabditis elegans In conclusion, this work supports a role for impaired ribosomal DNA transcription in Cockayne syndrome and suggests that transcription-coupled resolution of secondary structures may be a mechanism to repress spurious activation of a DNA damage response.

Automatic classification of lymphoma images with transform-based global features.

We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach was employed in this study. At the outer level, raw pixels were transformed with a set of transforms into spectral planes. Simple (Fourier, Chebyshev, and wavelets) and compound transforms (Chebyshev of Fourier and wavelets of Fourier) were computed. Raw pixels and spectral planes were then routed to the second stage (the inner level). At the inner level, the set of multipurpose global features was computed on each spectral plane by the same feature bank. All computed features were fused into a single feature vector. The specimens were stained with hematoxylin (H) and eosin (E) stains. Several color spaces were used: RGB, gray, CIE-L*a*b*, and also the specific stain-attributed H&E space, and experiments on image classification were carried out for these sets. The best signal (98%-99% on earlier unseen images) was found for the HE, H, and E channels of the H&E data set.