David Mark Eckley

In this study, we describe a morphological biomarker that detects multiple discrete sub-populations (or “age states”) at several chronological ages in a population of nematodes (C. elegans). We determined the frequencies of three healthy adult states and the timing of the transitions between them across the lifespan. We used short-lived and long-lived strains to confirm the general applicability of the state classifier and to monitor state progression. This exploration revealed healthy and unhealthy states, the former being favored in long-lived strains and the latter showing delayed onset. Short-lived strains rapidly transitioned through the putative healthy state. We previously found that age-matched animals in different age states have distinct transcriptome profiles. We isolated animals at the beginning and end of each identified state and performed microarray analysis (Principal component analysis, relative sample to sample distance measurements and gene set enrichment analysis). In some comparisons, chronologically identical individuals were farther apart than morphologically identical individuals isolated on different days. The age state biomarker allowed assessment of aging in a novel manner, complementary to chronological age progression. We found hsp70 and some small heat shock protein genes are expressed later in adulthood, consistent with the proteostasis collapse model.

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. Significance In this paper we describe a possible pathogenesis for the accelerated aging disease Cockayne syndrome that entails defective transcription through DNA secondary structures leading to activation of the DNA damage response enzyme poly-ADP-ribose polymerase 1 and downstream mitochondrial derangement. These findings are important because they signify a possible new role of transcription in the resolution of DNA structures that form spontaneously and suggest a possible pathogenesis for this accelerated aging disease.

Dynactin is an essential part of the cytoplasmic dynein motor that enhances motor processivity and serves as an adaptor that allows dynein to bind cargoes. Much is known about dynactin's interaction with dynein and microtubules, but how it associates with its diverse complement of subcellular binding partners remains mysterious. It has been suggested that cargo specification involves a group of subunits referred to as the “pointed-end complex.” We used chemical cross-linking, RNA interference, and protein overexpression to characterize interactions within the pointed-end complex and explore how it contributes to dynactin's interactions with endomembranes. The Arp11 subunit, which caps one end of dynactin's Arp1 filament, and p62, which binds Arp11 and Arp1, are necessary for dynactin stability. These subunits also allow dynactin to bind the nuclear envelope prior to mitosis. p27 and p25, by contrast, are peripheral components that can be removed without any obvious impact on dynactin integrity. Dynactin lacking these subunits shows reduced membrane binding. Depletion of p27 and p25 results in impaired early and recycling endosome movement, but late endosome movement is unaffected, and mitotic spindles appear normal. We conclude that the pointed-end complex is a bipartite structural domain that stabilizes dynactin and supports its binding to different subcellular structures.

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions. The first transition occurs at day 5 post-hatching, where a significant percentage of the population exists in both state I and state II. The temperature dependence of the timing of this transition (Q ₁₀ ~ 1.17) is too low to be explained by a stepwise process with an enzymatic or chemical rate-limiting step, potentially implicating a more complex mechanism. Individual animals at day 5 were sorted into state I and state II groups using the machine classifier and analyzed by microarray expression profiling. Despite being isogenic, grown for the same amount of time, and indistinguishable by eye, these two morphological states were confirmed to be molecularly distinct by hierarchical clustering and principal component analysis of the microarray results. These molecular differences suggest that pharynx morphology reflects the aging state of the whole organism. Our expression profiling yielded a gene set that showed significant overlap with those from three previous age-related studies and identified several genes not previously implicated in aging. A highly represented group of genes unique to this study is involved in targeted ubiquitin-mediated proteolysis, including Skp1-related (SKR), F-box-containing, and BTB motif adaptors.

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient, and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev, and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation. We also explored the effect of EPP on suboptimal feature libraries, using only textural features in one case and only Haralick features in another. The effect of EPP was especially clear for these suboptimal libraries, where the transform-based representations were found to increase separability to a greater extent than scale or subband pyramids. EPP can be particularly useful in new applications where optimal features have not yet been developed.

The subependymal zone (SEZ) of the lateral ventricles is one of the areas of the adult brain where new neurons are continuously generated from neural stem cells (NSCs), via rapidly dividing precursors. This neurogenic niche is a complex cellular and extracellular microenvironment, highly vascularized compared to non-neurogenic periventricular areas, within which NSCs and precursors exhibit distinct behavior. Here, we investigate the possible mechanisms by which extracellular matrix molecules and their receptors might regulate this differential behavior. We show that NSCs and precursors proceed through mitosis in the same domains within the SEZ of adult male mice—albeit with NSCs nearer ependymal cells—and that distance from the ventricle is a stronger limiting factor for neurogenic activity than distance from blood vessels. Furthermore, we show that NSCs and precursors are embedded in a laminin-rich extracellular matrix, to which they can both contribute. Importantly, they express differential levels of extracellular matrix receptors, with NSCs expressing low levels of α6β1 integrin, syndecan-1, and lutheran, and in vivo blocking of β1 integrin selectively induced the proliferation and ectopic migration of precursors. Finally, when NSCs are activated to reconstitute the niche after depletion of precursors, expression of laminin receptors is upregulated. These results indicate that the distinct behavior of adult NSCs and precursors is not necessarily regulated via exposure to differential extracellular signals, but rather via intrinsic regulation of their interaction with their microenvironment.

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.

We describe a method for automated recognition of painters and schools of art based on their signature styles and studied the computer-based perception of visual art. Paintings of nine artists, representing three different schools of art—impressionism, surrealism and abstract expressionism—were analyzed using a large set of image features and image transforms. The computed image descriptors were assessed using Fisher scores, and the most informative features were used for the classification and similarity measurements of paintings, painters, and schools of art. Experimental results show that the classification accuracy when classifying paintings into nine painter classes is 77%, and the accuracy of associating a given painting with its school of art is 91%. An interesting feature of the proposed method is its ability to automatically associate different artists that share the same school of art in an unsupervised fashion. The source code used for the image classification and image similarity described in this article is available for free download.

The post genomic era introduced the need to define single gene functions within biological pathways. A systems biology approach can be realized by automating image acquisition and phenotype classification. While machinery for automated data acquisition have been developing rapidly in the past years, the main bottleneck remains the effectiveness of the computer vision algorithms. Here we describe a fully automated process for finding phenotype similarities within a dataset acquired from an RNAi screen. The source code for the algorithms is available for free download.

Pattern recognition techniques can potentially be used to quantitatively analyze a wide variety of biomedical images. A challenge in applying this methodology is that biomedical imaging uses many imaging modalities and subjects. Pattern recognition relies on numerical image descriptors (features) to describe image content. Thus, the application of pattern recognition to biomedical imaging requires the development of a wide variety of image features. In this study we compared the efficacy of different techniques for constructing large feature spaces. A two-stage method was employed where several types of derived images were used as inputs for a bank of feature extraction algorithms. Image pyramids, subband filters, and image transforms were used in the first-stage. The feature bank consisted of polynomial coefficients, textures, histograms and statistics as previously described [1]. The basis for comparing the performance of these feature sets was the biological imaging benchmark described in [2]. Our results show that a set of image transforms (Fourier, Wavelet, Chebyshev) performed significantly better than a set of image filters (image pyramids, sub-band filters, and spectral decompositions). The transform technique was used to analyze images of H&E-stained tissue biopsies from two cancers: lymphoma (three types of malignancies) and melanoma (benign, primary, and five secondary tumor sites). The overall classification accuracy for these cancer data sets was 97%.