Abhishek Dixit

Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood

BackgroundDynamic changes to the epigenome play a critical role in establishing and maintaining cellular phenotype during differentiation, but little is known about the normal methylomic differences that occur between functionally distinct areas of the brain. We characterized intra- and inter-individual methylomic variation across whole blood and multiple regions of the brain from multiple donors.ResultsDistinct tissue-specific patterns of DNA methylation were identified, with a highly significant over-representation of tissue-specific differentially methylated regions (TS-DMRs) observed at intragenic CpG islands and low CG density promoters. A large proportion of TS-DMRs were located near genes that are differentially expressed across brain regions. TS-DMRs were significantly enriched near genes involved in functional pathways related to neurodevelopment and neuronal differentiation, including BDNF, BMP4, CACNA1A, CACA1AF, EOMES, NGFR, NUMBL, PCDH9, SLIT1, SLITRK1 and SHANK3. Although between-tissue variation in DNA methylation was found to greatly exceed between-individual differences within any one tissue, we found that some inter-individual variation was reflected across brain and blood, indicating that peripheral tissues may have some utility in epidemiological studies of complex neurobiological phenotypes.ConclusionsThis study reinforces the importance of DNA methylation in regulating cellular phenotype across tissues, and highlights genomic patterns of epigenetic variation across functionally distinct regions of the brain, providing a resource for the epigenetics and neuroscience research communities.

Male-Biased Autosomal Effect of 16p13.11 Copy Number Variation in Neurodevelopmental Disorders

Copy number variants (CNVs) at chromosome 16p13.11 have been associated with a range of neurodevelopmental disorders including autism, ADHD, intellectual disability and schizophrenia. Significant sex differences in prevalence, course and severity have been described for a number of these conditions but the biological and environmental factors underlying such sex-specific features remain unclear. We tested the burden and the possible sex-biased effect of CNVs at 16p13.11 in a sample of 10,397 individuals with a range of neurodevelopmental conditions, clinically referred for array comparative genomic hybridisation (aCGH); cases were compared with 11,277 controls. In order to identify candidate phenotype-associated genes, we performed an interval-based analysis and investigated the presence of ohnologs at 16p13.11; finally, we searched the DECIPHER database for previously identified 16p13.11 copy number variants. In the clinical referral series, we identified 46 cases with CNVs of variable size at 16p13.11, including 28 duplications and 18 deletions. Patients were referred for various phenotypes, including developmental delay, autism, speech delay, learning difficulties, behavioural problems, epilepsy, microcephaly and physical dysmorphisms. CNVs at 16p13.11 were also present in 17 controls. Association analysis revealed an excess of CNVs in cases compared with controls (OR = 2.59; p = 0.0005), and a sex-biased effect, with a significant enrichment of CNVs only in the male subgroup of cases (OR = 5.62; p = 0.0002), but not in females (OR = 1.19, p = 0.673). The same pattern of results was also observed in the DECIPHER sample. Interval-based analysis showed a significant enrichment of case CNVs containing interval II (OR = 2.59; p = 0.0005), located in the 0.83 Mb genomic region between 15.49–16.32 Mb, and encompassing the four ohnologs NDE1, MYH11, ABCC1 and ABCC6. Our data confirm that duplications and deletions at 16p13.11 represent incompletely penetrant pathogenic mutations that predispose to a range of neurodevelopmental disorders, and suggest a sex-limited effect on the penetrance of the pathological phenotypes at the 16p13.11 locus.

Microdeletions of ELP4 Are Associated with Language Impairment, Autism Spectrum Disorder, and Mental Retardation.

Copy‐number variations (CNVs) are important in the aetiology of neurodevelopmental disorders and show broad phenotypic manifestations. We compared the presence of small CNVs disrupting the ELP4‐PAX6 locus in 4,092 UK individuals with a range of neurodevelopmental conditions, clinically referred for array comparative genomic hybridization, with WTCCC controls (n = 4,783). The phenotypic analysis was then extended using the DECIPHER database. We followed up association using an autism patient cohort (n = 3,143) compared with six additional control groups (n = 6,469). In the clinical discovery series, we identified eight cases with ELP4 deletions, and one with a partial duplication of ELP4 and PAX6. These cases were referred for neurological phenotypes including language impairment, developmental delay, autism, and epilepsy. Six further cases with a primary diagnosis of autism spectrum disorder (ASD) and similar secondary phenotypes were identified with ELP4 deletions, as well as another six (out of nine) with neurodevelopmental phenotypes from DECIPHER. CNVs at ELP4 were only present in 1/11,252 controls. We found a significant excess of CNVs in discovery cases compared with controls, P = 7.5 × 10−3, as well as for autism, P = 2.7 × 10−3. Our results suggest that ELP4 deletions are highly likely to be pathogenic, predisposing to a range of neurodevelopmental phenotypes from ASD to language impairment and epilepsy.

Evaluation and cross-comparison of lexical entities of biological interest (LexEBI).

Motivation Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result This study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. Conclusion LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources.

BBGRE: brain and body genetic resource exchange.

Studies of copy number variation (genomic imbalance) are providing insight into both complex and Mendelian genetic disorders. Array comparative genomic hybridization (array CGH), a tool for detecting copy number variants at a resolution previously unattainable in clinical diagnostics, is increasingly used as a first-line test at clinical genetics laboratories. Many copy number variants are of unknown significance; correlation and comparison with other patients will therefore be essential for interpretation. We present a resource for clinicians and researchers to identify specific copy number variants and associated phenotypes in patients from a single catchment area, tested using array CGH at the SE Thames Regional Genetics Centre, London. User-friendly searching is available, with links to external resources, providing a powerful tool for the elucidation of gene function. We hope to promote research by facilitating interactions between researchers and patients. The BBGRE (Brain and Body Genetic Resource Exchange) resource can be accessed at the following website: http://bbgre.org Database URL: http://bbgre.org

EMPIRICAL FEATURE SELECTION FOR BIOMARKER AND DISEASE SIGNATURE DISCOVERY: DKK1 OVEREXPRESSION IN VITRO NOMINATES PROTEINS ASSOCIATED WITH ALZHEIMER’S IN VIVO

realized power spectral analysis using Fast Fourier Transform to obtain the absolute and relative powers and mean frequencies. Results:We analyzed the brain network constructed from the EEG records, obtaining impairment in functional connectivity related to literature reports. We found increased the absolute and relative power in the theta band. Conclusions: We can see in AD patients impairment of functional connectivity and the EEG records the theta band is predominant. This is the beginning of a bigger study of functional connectivity in Mexican patients with dementia.

Blood-based biomarker discovery using aptamer capture (SOMAscan) platform technology

Background:The use of biomarkers to identify individuals at risk for developing late-onset Alzheimer’s disease (LOAD) is of interest for the design of therapeutic prevention or delay of onset clinical trials. A biomarker risk assignment algorithm (BRAA) based on APOE and TOMM40 ’523 genotypes and age is being used to enrich an international phase 3, double-blind, randomized, placebo-controlled clinical trial. This presentation reports preliminary data on the performance of the BRAA, specifically precision of the BRAA as a function of the experimental variation of the genotype assays, predictive characteristics of the algorithm to identify MCI due to AD, and comparative data for CSF and imaging (fMRI) based biomarkers.Methods: A simulation study was performed to determine how the experimental variation of the APOE and TOMM40 ’523 assays impacts the risk assignment by the BRAA. Performance of the BRAA (odds ratio, improvement in net reclassification rate vs. versions of the algorithm based only on age and/or APOE genotype) was calculated in a retrospective analysis of the Alzheimer’s Disease Neuroimaging Initiative data (n 1⁄4 660). Its performance (sensitivity and specificity) was compared to data from literature reports for proposed CSF and fMRI biomarkers. Results: The simulation study shows the expected precision of the BRAA to be>98%, based on the observed experimental variation of the TOMM40 ’523 and APOE assays. The odds ratio for using the algorithm to predict MCI or LOAD ranges from 3 to 5, and comparison of the full algorithm to a version based on APOE and age alone shows a significant (p < 0.0001) improvement in the net reclassification rate. The performance of this informative genotype BRAA compares favorably (PPV, NPV 70-80%) with CSF and imaging (fMRI) biomarkers. Conclusions: The performance characteristics of the biomarker risk algorithm support its use as a pharmacogenetic enrichment tool for stratification of individuals at high or low risk for developingMCI due to AD in a phase 3 clinical trial. The data from this prospective trial will be used to support qualification of the BRAA by regulatory agencies.