Single-nucleotide polymorphisms are associated with cognitive decline at Alzheimer's disease conversion within mild cognitive impairment patients

INTRODUCTION: The growing public threat of Alzheimer's disease (AD) has raised the urgency to quantify the degree of cognitive decline during the conversion process of mild cognitive impairment (MCI) to AD and its underlying genetic pathway. The aim of this article was to test genetic common variants associated with accelerated cognitive decline after the conversion of MCI to AD. METHODS: In 583 subjects with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI; ADNI-1, ADNI-Go, and ADNI-2), 245 MCI participants converted to AD at follow-up. We tested the interaction effects between individual single-nucleotide polymorphisms and AD diagnosis trajectory on the longitudinal Alzheimer's Disease Assessment Scale-Cognition scores. RESULTS: Our findings reveal six genes, including BDH1, ST6GAL1, RAB20, PDS5B, ADARB2, and SPSB1, which are directly or indirectly related to MCI conversion to AD. DISCUSSION: This genome-wide association study sheds light on a genetic mechanism of longitudinal cognitive changes during the transition period from MCI to AD

Predicting Alzheimer’s Disease Using Combined Imaging-Whole Genome SNP Data

The growing public threat of Alzheimer’s disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment

Steel-based applications in earthquake-prone areas

Steel-Earth project aims at distributing among technicians, engineers, design companies and standardization bodies the results of three past RFCS projects (Steel-Retro [3], Opus [2] and PrecaSteel [1]), providing useful tools for the design and for the retrofit of existing buildings. Technical documents and practical applications to case studies, regarding design of steel and composite steel/concrete buildings and innovative steel-based techniques for the retrofit of existing r.c. and masonry constructions, have been elaborated and collected into a volume distributed during the final workshop of the dissemination project. Pre-normative and background documents concerning the design of steel and composite structures and the rehabilitation of existing constructions have been prepared. A lot of attention has been paid to the analysis of the influence of overstrength factors on the seismic design of steel and composite structures. The prepared documents have been distributed to the attending people and to the members of WG 2 (CEN/TC 250/SC 8/WG 2 “Steel and Composite Structures”) during the final workshop of the project. Technical sheets, working examples and background documents have been translated into several languages (German, French, Italian, Romanian and Greek) and are free available on the website of the project (https://www.steelconstruct.com/site/), where information regarding Steel-Earth are also presented.11 Workshops in Italy, Greece, Germany, Belgium, Portugal, Spain and Romania and 5 conferences in Emilia-Romagna have been organized, as well as 2 practical courses for engineers and academic people in Pavia (Italy). Flash-drives with the technical documents and applications elaborated in Steel-Earth have been distributed to the attending people

DNA copy number loss and allelic imbalance at 2p16 in lung cancer associated with asbestos exposure

Five to seven percent of lung tumours are estimated to occur because of occupational asbestos exposure. Using cDNA microarrays, we have earlier detected asbestos exposure-related genomic regions in lung cancer. The region at 2p was one of those that differed most between asbestos-exposed and non-exposed patients. Now, we evaluated genomic alterations at 2p22.1-p16.1 as a possible marker for asbestos exposure. Lung tumours from 205 patients with pulmonary asbestos fibre counts from 0 to 570 million fibres per gram of dry lung, were studied by fluorescence in situ hybridisation (FISH) for DNA copy number alterations (CNA). The prevalence of loss at 2p16, shown by three different FISH probes, was significantly increased in lung tumours of asbestos-exposed patients compared with non-exposed (P=0.05). In addition, a low copy number loss at 2p16 associated significantly with high-level asbestos exposure (P=0.02). Furthermore, 27 of the tumours were studied for allelic imbalances (AI) at 2p22.1–p16.1 using 14 microsatellite markers and also AI at 2p16 was related to asbestos exposure (P=0.003). Our results suggest that alterations at 2p16 combined with other markers could be useful in diagnosing asbestos-related lung cancer

Collagen mRNA levels changes during colorectal cancer carcinogenesis

<p>Abstract</p> <p>Background</p> <p>Invasive growth of epithelial cancers is a complex multi-step process which involves dissolution of the basement membrane. Type IV collagen is a major component in most basement membranes. Type VII collagen is related to anchoring fibrils and is found primarily in the basement membrane zone of stratified epithelia. Immunohistochemical studies have previously reported changes in steady-state levels of different α(IV) chains in several epithelial cancer types. In the present study we aimed to quantitatively determine the mRNA levels of <it>type IV collagen (α1/α4/α6) </it>and <it>type VII collagen (α1) </it>during colorectal cancer carcinogenesis.</p> <p>Methods</p> <p>Using quantitative RT-PCR, we have determined the mRNA levels for <it>α1(IV), α4(IV), α6(IV), and α1(VII) </it>in colorectal cancer tissue (n = 33), adenomas (n = 29) and in normal tissue from the same individuals. In addition, corresponding tissue was examined from healthy volunteers (n = 20). mRNA levels were normalized to <it>β-actin</it>. Immunohistochemical analysis of the distributions of type IV and type VII collagens were performed on normal and affected tissues from colorectal cancer patients.</p> <p>Results</p> <p>The <it>α1(IV) </it>and <it>α1(VII) </it>mRNA levels were statistically significantly higher in colorectal cancer tissue (p < 0.001) as compared to corresponding tissue from healthy controls. This is an early event as tissue from adenomas also displayed a higher level. There were small changes in the levels of <it>α4(IV)</it>. The level of <it>α6(IV) </it>was 5-fold lower in colorectal cancer tissue as compared to healthy individuals (p < 0.01). The localisation of type IV and type VII collagen was visualized by immunohistochemical staining.</p> <p>Conclusion</p> <p>Our results suggest that the down-regulation of <it>α6(IV</it>) mRNA coincides with the acquisition of invasive growth properties, whereas <it>α1(IV) </it>and <it>α1(VII) </it>mRNAs were up-regulated already in dysplastic tissue. There are no differences in collagen expression between tissues from healthy individuals and normal tissues from affected individuals.</p

The Ubiquitin Peptidase UCHL1 Induces G0/G1 Cell Cycle Arrest and Apoptosis Through Stabilizing p53 and Is Frequently Silenced in Breast Cancer

Background: Breast cancer (BrCa) is a complex disease driven by aberrant gene alterations and environmental factors. Recent studies reveal that abnormal epigenetic gene regulation also plays an important role in its pathogenesis. Ubiquitin carboxyl- terminal esterase L1 (UCHL1) is a tumor suppressor silenced by promoter methylation in multiple cancers, but its role and alterations in breast tumorigenesis remain unclear. Methodology/Principal Findings: We found that UCHL1 was frequently downregulated or silenced in breast cancer cell lines and tumor tissues, but readily expressed in normal breast tissues and mammary epithelial cells. Promoter methylation of UCHL1 was detected in 9 of 10 breast cancer cell lines (90%) and 53 of 66 (80%) primary tumors, but rarely in normal breast tissues, which was statistically correlated with advanced clinical stage and progesterone receptor status. Pharmacologic demethylation reactivated UCHL1 expression along with concomitant promoter demethylation. Ectopic expression of UCHL1 significantly suppressed the colony formation and proliferation of breast tumor cells, through inducing G0/G1 cell cycle arrest and apoptosis. Subcellular localization study showed that UCHL1 increased cytoplasmic abundance of p53. We further found that UCHL1 induced p53 accumulation and reduced MDM2 protein level, and subsequently upregulated the expression of p21, as well as cleavage of caspase3 and PARP, but not in catalytic mutant UCHL1 C90Sexpressed cells

Impact on Disease Development, Genomic Location and Biological Function of Copy Number Alterations in Non-Small Cell Lung Cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells

Linking the Epigenome to the Genome: Correlation of Different Features to DNA Methylation of CpG Islands

DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/

Large-scale integration of cancer microarray data identifies a robust common cancer signature

<p>Abstract</p> <p>Background</p> <p>There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.</p> <p>Results</p> <p>In this study, we collect and integrate ~ 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the <it>T</it>op-<it>S</it>coring <it>P</it>air of <it>G</it>roups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.</p> <p>Conclusion</p> <p>By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.</p