Whole Transcriptome Sequencing Reveals Gene Expression and Splicing Differences in Brain Regions Affected by Alzheimer's Disease

Recent studies strongly indicate that aberrations in the control of gene expression might contribute to the initiation and progression of Alzheimer's disease (AD). In particular, alternative splicing has been suggested to play a role in spontaneous cases of AD. Previous transcriptome profiling of AD models and patient samples using microarrays delivered conflicting results. This study provides, for the first time, transcriptomic analysis for distinct regions of the AD brain using RNA-Seq next-generation sequencing technology. Illumina RNA-Seq analysis was used to survey transcriptome profiles from total brain, frontal and temporal lobe of healthy and AD post-mortem tissue. We quantified gene expression levels, splicing isoforms and alternative transcript start sites. Gene Ontology term enrichment analysis revealed an overrepresentation of genes associated with a neuron's cytological structure and synapse function in AD brain samples. Analysis of the temporal lobe with the Cufflinks tool revealed that transcriptional isoforms of the apolipoprotein E gene, APOE-001, -002 and -005, are under the control of different promoters in normal and AD brain tissue. We also observed differing expression levels of APOE-001 and -002 splice variants in the AD temporal lobe. Our results indicate that alternative splicing and promoter usage of the APOE gene in AD brain tissue might reflect the progression of neurodegeneration

Assessment of β-amyloid deposits in human brain: a study of the BrainNet Europe Consortium

β-Amyloid (Aβ) related pathology shows a range of lesions which differ both qualitatively and quantitatively. Pathologists, to date, mainly focused on the assessment of both of these aspects but attempts to correlate the findings with clinical phenotypes are not convincing. It has been recently proposed in the same way as ι and α synuclein related lesions, also Aβ related pathology may follow a temporal evolution, i.e. distinct phases, characterized by a step-wise involvement of different brain-regions. Twenty-six independent observers reached an 81% absolute agreement while assessing the phase of Aβ, i.e. phase 1 = deposition of Aβ exclusively in neocortex, phase 2 = additionally in allocortex, phase 3 = additionally in diencephalon, phase 4 = additionally in brainstem, and phase 5 = additionally in cerebellum. These high agreement rates were reached when at least six brain regions were evaluated. Likewise, a high agreement (93%) was reached while assessing the absence/presence of cerebral amyloid angiopathy (CAA) and the type of CAA (74%) while examining the six brain regions. Of note, most of observers failed to detect capillary CAA when it was only mild and focal and thus instead of type 1, type 2 CAA was diagnosed. In conclusion, a reliable assessment of Aβ phase and presence/absence of CAA was achieved by a total of 26 observers who examined a standardized set of blocks taken from only six anatomical regions, applying commercially available reagents and by assessing them as instructed. Thus, one may consider rating of Aβ-phases as a diagnostic tool while analyzing subjects with suspected Alzheimer’s disease (AD). Because most of these blocks are currently routinely sampled by the majority of laboratories, assessment of the Aβ phase in AD is feasible even in large scale retrospective studies

Methylation profiling of choroid plexus tumors reveals 3 clinically distinct subgroups

BACKGROUND: Choroid plexus tumors are intraventricular neoplasms derived from the choroid plexus epithelium. A better knowledge of molecular factors involved in choroid plexus tumor biology may aid in identifying patients at risk for recurrence. METHODS: Methylation profiles were examined in 29 choroid plexus papillomas (CPPs, WHO grade I), 32 atypical choroid plexus papillomas (aCPPs, WHO grade II), and 31 choroid plexus carcinomas (CPCs, WHO grade III) by Illumina Infinium HumanMethylation450 Bead Chip Array. RESULTS: Unsupervised hierarchical clustering identified 3 subgroups: methylation cluster 1 (pediatric CPP and aCPP of mainly supratentorial location), methylation cluster 2 (adult CPP and aCPP of mainly infratentorial location), and methylation cluster 3 (pediatric CPP, aCPP, and CPC of supratentorial location). In methylation cluster 3, progression-free survival (PFS) accounted for a mean of 72 months (CI, 55-89 mo), whereas only 1 of 42 tumors of methylation clusters 1 and 2 progressed (P< .001). On stratification of outcome data according to WHO grade, all CPCs clustered within cluster 3 and were associated with shorter overall survival (mean, 105 mo [CI, 81-128 mo]) and PFS (mean, 55 mo [CI, 36-73 mo]). The aCPP of methylation cluster 3 also progressed frequently (mean, 69 mo [CI, 44-93 mo]), whereas no tumor progression was observed in aCPP of methylation clusters 1 and 2 (P< .05). Only 1 of 29 CPPs recurred. CONCLUSIONS: Methylation profiling of choroid plexus tumors reveals 3 distinct subgroups (ie, pediatric low-risk choroid plexus tumors [cluster 1], adult low-risk choroid plexus tumors [cluster 2], and pediatric high-risk choroid plexus tumors [cluster 3]) and may provide useful prognostic information in addition to histopathology

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology