113 Conformational dynamics of human 8-oxoguanine-DNA glycosylase

DNA continuously undergoes oxidation damage from both exogenous and endogenous sources, including ionizing radiation, ultraviolet light, and products of metabolism. Replication of damaged DNA sometimes gives rise to mutations which can contribute to disease and aging. One of the most mutagenic lesions caused by DNA oxidation is 7,8-dihydro-8-oxoguanine (oxoG), which, if not repaired, results in G?→?T transversions. In human cells, oxoG is repaired through excision by 8-oxoguanine-DNA glycosylase hOGG1. In addition to its glycosylase activity, hOGG1 possesses an AP-lyase activity, which catalyzes the elimination of the 3’-phosphate (β-elimination) at the nascent, or preformed abasic (AP) site. The glycosidic bond breakage is initiated by a nucleophilic attack at C1’ by the Lys-249 residue resulting in a covalent enzyme–DNA-Schiff base intermediate, which then rearranges, and undergoes elimination. The 3-D structure of hOGG1shows that DNA binding is accompanied with drastic conformational changes, including DNA kinking, eversion of oxoGua from the double helix, and insertion of few amino acid residues into DNA. Previously (Kuznetsov et al., 2005, 2007), we have studied the stopped-flow kinetics of oxoG and AP site lesions processing by hOGG1. The character of tryptophan and 2-aminopurine fluorescence traces revealed that both the protein and the damaged DNA undergo extensive conformational changes in the course of DNA substrate binding- and -cleavage. To understand better, the mechanism by which hOGG1 recognizes DNA lesions, we have examined the influence of amino acid substitutions on conformational dynamics of hOGG1 and DNA during specific site recognition and conversion. Fluorescence kinetics of enzyme mutant forms F45?W, F319?W, Y203?W, Y203A, H270?W, K249Q demonstrated the multistep character of catalytic process and made clear the role of these amino acids for hOGG1 catalysis.     

Literature review on the role of dietary protein and amino acids in cognitive functioning and cognitive decline

As the population of elderly people is growing rapidly, the number of individuals with dementia and cognitive impairment is also increasing. One of the preventive measures against cognitive decline is diet and different dietary factors have already been investigated. This review provides an overview of studies on dietary protein and cognitive functioning and cognitive decline. Also studies on the individual amino acids that are related to brain function, tryptophan and tyrosine, are discussed. Overall, the role of dietary protein intake on cognitive functioning as well as cognitive decline has hardly been studied; we found eight observational studies and three intervention studies. More studies investigated the role of tryptophan (14 studies) and tyrosine (nine studies) in relation to cognitive functioning, but all these studies were performed in young adult populations and mostly under special conditions. Research in elderly populations, in particular, is warranted. Also more research is needed to come to definitive conclusions and specific recommendations regarding protein intake or intake of specific amino acids for maintaining optimal cognitive functioning.     

Simple algorithms for the estimation of the initial number of individuals in commingled skeletal remains

The determination of the number of individuals represented within commingled remains is based on two types of estimators, those assessing the minimum number of individuals and those assessing the most likely number of individuals. Much as the latter produce improved results, they still exhibit significant drawbacks, which are related to the misidentification of the number of pairs between the existing bilateral elements. This article addresses these problems through the use of two computer algorithms. One algorithm produces a number of potential pairs between bilateral elements and the other estimates the number of individuals in a commingled sample by incorporating the percentages of lost and altered bones into the analysis. These algorithms were validated using hypothetical and actual skeletal samples, and are more effective in comparison to any conventional estimators, particularly in cases, where the elements are poorly preserved.     

High-Throughput Screening for the Identification of New Therapeutic Options for Metastatic Pheochromocytoma and Paraganglioma

Drug repurposing or repositioning is an important part of drug discovery that has been growing in the last few years for the development of therapeutic options in oncology. We applied this paradigm in a screening of a library of about 3,800 compounds (including FDA-approved drugs and pharmacologically active compounds) employing a model of metastatic pheochromocytoma, the most common tumor of the adrenal medulla in children and adults. The collection of approved drugs was screened in quantitative mode, testing the compounds in compound-titration series (dose-response curves). Analysis of the dose-response screening data facilitated the selection of 50 molecules with potential bioactivity in pheochromocytoma cells. These drugs were classified based on molecular/cellular targets and signaling pathways affected, and selected drugs were further validated in a proliferation assay and by flow cytometric cell death analysis. Using meta-analysis information from molecular targets of the top drugs identified by our screening with gene expression data from human and murine microarrays, we identified potential drugs to be used as single drugs or in combination. An example of a combination with a synergistic effect is presented. Our study exemplifies a promising model to identify potential drugs from a group of clinically approved compounds that can more rapidly be implemented into clinical trials in patients with metastatic pheochromocytoma or paraganglioma.     

Sister chromatids fail to separate during an induced endoreplication cycle in Drosophila embryos

When mitosis is bypassed, as in some cancer cells or in natural endocycles, sister chromosomes remain paired and produce four-stranded diplochromosomes or polytene chromosomes. Cyclin/Cdk1 inactivation blocks entry into mitosis and can reset G2 cells to G1, allowing another round of replication. Reciprocally, persistent expression of Cyclin A/Cdk1 or Cyclin E/Cdk2 blocks Drosophila endocycles. Inactivation of Cyclin A/Cdk1 by mutation or overexpression of the Cyclin/Cdk1 inhibitor, Roughex (Rux), converts the 16(th) embryonic mitotic cycle to an endocycle; however, we show that Rux expression fails to convert earlier cell cycles unless Cyclin E is also downregulated. Following induction of a Rux transgene in Cyclin E mutant embryos during G2 of cell cycle 14 (G2(14)), Cyclins A, B, and B3 disappeared and cells reentered S phase. This rereplication produced diplochromosomes that segregated abnormally at a subsequent mitosis. Thus, like the yeast CKIs Rum1 and Sic1, Drosophila Rux can reset G2 cells to G1. The observed cyclin destruction suggests that cell cycle resetting by Rux was associated with activation of the anaphase-promoting complex (APC), while the presence of diplochromosomes implies that this activation of APC outside of mitosis was not sufficient to trigger sister disjunction.     

Causal graph-based analysis of genome-wide association data in rheumatoid arthritis

Background

GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.

Results

Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.

Conclusions

Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.

Reviewers

This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.     

Wave speed in excitable random networks with spatially constrained connections

Very fast oscillations (VFO) in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG), and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA) on random (Erdös-Rényi) networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic) PDE is suggested, which provides adequate wave speed that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.     

A chemocentric approach to the identification of cancer targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided.     

Automatic detection of melanoma progression by histological analysis of secondary sites

We present results from machine classification of melanoma biopsies sectioned and stained with hematoxylin/eosin (H&E) on tissue microarrays (TMA). The four stages of melanoma progression were represented by seven tissue types, including benign nevus, primary tumors with radial and vertical growth patterns (stage I) and four secondary metastatic tumors: subcutaneous (stage II), lymph node (stage III), gastrointestinal and soft tissue (stage IV). Our experiment setup comprised 14,208 image samples based on 164 TMA cores. In our experiments, we constructed an HE color space by digitally deconvolving the RGB images into separate H (hematoxylin) and E (eosin) channels. We also compared three different classifiers: Weighted Neighbor Distance (WND), Radial Basis Functions (RBF), and k-Nearest Neighbors (kNN). We found that the HE color space consistently outperformed other color spaces with all three classifiers, while the different classifiers did not have as large of an effect on accuracy. This showed that a more physiologically relevant representation of color can have a larger effect on correct image interpretation than downstream processing steps. We were able to correctly classify individual fields of view with an average of 96% accuracy when randomly splitting the dataset into training and test fields. We also obtained a classification accuracy of 100% when testing entire cores that were not previously used in training (four random trials with one test core for each of 7 classes, 28 tests total). Because each core corresponded to a different patient, this test more closely mimics a clinically relevant setting where new patients are evaluated based on training with previous cases. The analysis method used in this study contains no parameters or adjustments that are specific to melanoma morphology, suggesting it can be used for analyzing other tissues and phenotypes, as well as potentially different image modalities and contrast techniques.