An understanding of the anatomic distributions of major neurodegenerative disease lesions is important to appreciate the differential clinical profiles of these disorders and to serve as neuropathological standards for emerging molecular neuroimaging methods. To address these issues, here we present a comparative survey of the topographical distribution of the defining molecular neuropathological lesions among 10 neurodegenerative diseases from a large and uniformly assessed brain collection. Ratings of pathological severity in 16 brain regions from 671 cases with diverse neurodegenerative diseases are summarized and analyzed. These include: 1) amyloid-β and tau lesions in Alzheimer’s disease; 2) tau lesions in three other tauopathies including Pick’s disease, progressive supranuclear palsy and corticobasal degeneration; 3) α-synuclein inclusion ratings in four synucleinopathies including Parkinson’s disease, Parkinson’s disease with dementia, dementia with Lewy bodies, and multiple system atrophy; and 4) TDP-43 lesions in two TDP-43 proteinopathies, including frontotemporal lobar degeneration associated with TDP-43 and amyotrophic lateral sclerosis. The data presented graphically and topographically confirm and extend previous pathological anatomic descriptions and statistical comparisons highlight the lesion distributions that either overlap or distinguish the diseases in each molecular disease category.
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RNA is often altered post-transcriptionally by the covalent modification of particular nucleotides; these modifications are known to modulate the structure and activity of their host RNAs. The recent discovery that an RNA methyl-6 adenosine demethylase (FTO) is a risk gene in obesity has brought to light the significance of RNA modifications to human biology. These noncanonical nucleotides, when converted to cDNA in the course of RNA sequencing, can produce sequence patterns that are distinguishable from simple base-calling errors. To determine whether these modifications can be detected in RNA sequencing data, we developed a method that can not only locate these modifications transcriptome-wide with single nucleotide resolution, but can also differentiate between different classes of modifications. Using small RNA-seq data we were able to detect 92% of all known human tRNA modification sites that are predicted to affect RT activity. We also found that different modifications produce distinct patterns of cDNA sequence, allowing us to differentiate between two classes of adenosine and two classes of guanine modifications with 98% and 79% accuracy, respectively. To show the robustness of this method to sample preparation and sequencing methods, as well as to organismal diversity, we applied it to a publicly available yeast data set and achieved similar levels of accuracy. We also experimentally validated two novel and one known 3-methylcytosine (3mC) sites predicted by HAMR in human tRNAs. Researchers can now use our method to identify and characterize RNA modifications using only RNA-seq data, both retrospectively and when asking questions specifically about modified RNA.
Eleven susceptibility loci for late-onset Alzheimer’s disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer’s disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10(-8)) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.
High-density SNP genotyping technology provides a low-cost, effective tool for conducting Genome Wide Association (GWA) studies. The wide adoption of GWA studies has indeed led to discoveries of disease- or trait-associated SNPs, some of which were subsequently shown to be causal. However, the nearly universal shortcoming of many GWA studies–missing heritability–has prompted great interest in searching for other types of genetic variation, such as copy number variation (CNV). Certain CNVs have been reported to alter disease susceptibility. Algorithms and tools have been developed to identify CNVs using SNP array hybridization intensity data. Such an approach provides an additional source of data with almost no extra cost. In this unit, we demonstrate the steps for calling CNVs from Illumina SNP array data using PennCNV and performing association analysis using R and PLINK.
SUMMARY: We report our new DRAW+SneakPeek software for DNA-seq analysis. DNA resequencing analysis workflow (DRAW) automates the workflow of processing raw sequence reads including quality control, read alignment and variant calling on high-performance computing facilities such as Amazon elastic compute cloud. SneakPeek provides an effective interface for reviewing dozens of quality metrics reported by DRAW, so users can assess the quality of data and diagnose problems in their sequencing procedures. Both DRAW and SneakPeek are freely available under the MIT license, and are available as Amazon machine images to be used directly on Amazon cloud with minimal installation.
AVAILABILITY: DRAW+SneakPeek is released under the MIT license and is available for academic and nonprofit use for free. The information about source code, Amazon machine images and instructions on how to install and run DRAW+SneakPeek locally and on Amazon elastic compute cloud is available at the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (http://www.niagads.org/) and Wang lab Web site (http://wanglab.pcbi.upenn.edu/).
The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.
Cellular senescence is accompanied by dramatic changes in chromatin structure and gene expression. Using Saccharomyces cerevisiae mutants lacking telomerase (tlc1Δ) to model senescence, we found that with critical telomere shortening, the telomere-binding protein Rap1 (repressor activator protein 1) relocalizes to the upstream promoter regions of hundreds of new target genes. The set of new Rap1 targets at senescence (NRTS) is preferentially activated at senescence, and experimental manipulations of Rap1 levels indicate that it contributes directly to NRTS activation. A notable subset of NRTS includes the core histone-encoding genes; we found that Rap1 contributes to their repression and that histone protein levels decline at senescence. Rap1 and histones also display a target site-specific antagonism that leads to diminished nucleosome occupancy at the promoters of up-regulated NRTS. This antagonism apparently impacts the rate of senescence because underexpression of Rap1 or overexpression of the core histones delays senescence. Rap1 relocalization is not a simple consequence of lost telomere-binding sites, but rather depends on the Mec1 checkpoint kinase. Rap1 relocalization is thus a novel mechanism connecting DNA damage responses (DDRs) at telomeres to global changes in chromatin and gene expression while driving the pace of senescence.
Enhancer elements are essential for tissue-specific gene regulation during mammalian development. Although these regulatory elements are often distant from their target genes, they affect gene expression by recruiting transcription factors to specific promoter regions. Because of this long-range action, the annotation of enhancer element-target promoter pairs remains elusive. Here, we developed a novel analysis methodology that takes advantage of Hi-C data to comprehensively identify these interactions throughout the human genome. To do this, we used a geometric distribution-based model to identify DNA-DNA interaction hotspots that contact gene promoters with high confidence. We observed that these promoter-interacting hotspots significantly overlap with known enhancer-associated histone modifications and DNase I hypersensitive sites. Thus, we defined thousands of candidate enhancer elements by incorporating these features, and found that they have a significant propensity to be bound by p300, an enhancer binding transcription factor. Furthermore, we revealed that their target genes are significantly bound by RNA Polymerase II and demonstrate tissue-specific expression. Finally, we uncovered that these elements are generally found within 1 Mb of their targets, and often regulate multiple genes. In total, our study presents a novel high-throughput workflow for confident, genome-wide discovery of enhancer-target promoter pairs, which will significantly improve our understanding of these regulatory interactions.
IMPORTANCE: Genetic variants associated with susceptibility to late-onset Alzheimer disease are known for individuals of European ancestry, but whether the same or different variants account for the genetic risk of Alzheimer disease in African American individuals is unknown. Identification of disease-associated variants helps identify targets for genetic testing, prevention, and treatment.
OBJECTIVE: To identify genetic loci associated with late-onset Alzheimer disease in African Americans.
DESIGN, SETTING, AND PARTICIPANTS: The Alzheimer Disease Genetics Consortium (ADGC) assembled multiple data sets representing a total of 5896 African Americans (1968 case participants, 3928 control participants) 60 years or older that were collected between 1989 and 2011 at multiple sites. The association of Alzheimer disease with genotyped and imputed single-nucleotide polymorphisms (SNPs) was assessed in case-control and in family-based data sets. Results from individual data sets were combined to perform an inverse variance-weighted meta-analysis, first with genome-wide analyses and subsequently with gene-based tests for previously reported loci.
MAIN OUTCOMES AND MEASURES: Presence of Alzheimer disease according to standardized criteria.
RESULTS: Genome-wide significance in fully adjusted models (sex, age, APOE genotype, population stratification) was observed for a SNP in ABCA7 (rs115550680, allele = G; frequency, 0.09 cases and 0.06 controls; odds ratio [OR], 1.79 [95% CI, 1.47-2.12]; P = 2.2 × 10(-9)), which is in linkage disequilibrium with SNPs previously associated with Alzheimer disease in Europeans (0.8 < D' < 0.9). The effect size for the SNP in ABCA7 was comparable with that of the APOE ϵ4-determining SNP rs429358 (allele = C; frequency, 0.30 cases and 0.18 controls; OR, 2.31 [95% CI, 2.19-2.42]; P = 5.5 × 10(-47)). Several loci previously associated with Alzheimer disease but not reaching significance in genome-wide analyses were replicated in gene-based analyses accounting for linkage disequilibrium between markers and correcting for number of tests performed per gene (CR1, BIN1, EPHA1, CD33; 0.0005 < empirical P < .001).
CONCLUSIONS AND RELEVANCE: In this meta-analysis of data from African American participants, Alzheimer disease was significantly associated with variants in ABCA7 and with other genes that have been associated with Alzheimer disease in individuals of European ancestry. Replication and functional validation of this finding is needed before this information is used in clinical settings.
Survival in infants younger than 1 year who have acute lymphoblastic leukemia (ALL) is inferior whether MLL is rearranged (R) or germline (G). MLL translocations confer chemotherapy resistance, and infants experience excess complications. We characterized in vitro sensitivity to the pan-antiapoptotic BCL-2 family inhibitor obatoclax mesylate in diagnostic leukemia cells from 54 infants with ALL/bilineal acute leukemia because of the role of prosurvival BCL-2 proteins in resistance, their imbalanced expression in infant ALL, and evidence of obatoclax activity with a favorable toxicity profile in early adult leukemia trials. Overall, half maximal effective concentrations (EC50s) were lower than 176 nM (the maximal plasma concentration [Cmax] with recommended adult dose) in 76% of samples, whether in MLL-AF4, MLL-ENL, or other MLL-R or MLL-G subsets, and regardless of patients’ poor prognostic features. However, MLL status and partner genes correlated with EC50. Combined approaches including flow cytometry, Western blot, obatoclax treatment with death pathway inhibition, microarray analyses, and/or electron microscopy indicated a unique killing mechanism involving apoptosis, necroptosis, and autophagy in MLL-AF4 ALL cell lines and primary MLL-R and MLL-G infant ALL cells. This in vitro obatoclax activity and its multiple killing mechanisms across molecular cytogenetic subsets provide a rationale to incorporate a similarly acting compound into combination strategies to combat infant ALL.