A multi-omics dataset for the analysis of Frontotemporal Dementia genetic subtypes

Menden, Kevin; Francescatto, Margherita; Nyima, Tenzin; Blauwendraat, Cornelis; Dhingra, Ashutosh; Castillo-Lizardo, Melissa; Fernandes, Noémia; Kaurani, Lalit; Kronenberg‐Versteeg, Deborah; Atarsu, Burcu; Sadikoglou, Eldem; Borroni, Barbara; Rodriguez-Nieto, Salvador; Simón‐Sánchez, Javier; Fischer, André; Craig, David W.; Neumann, Manuela; Bonn, Stefan; Rizzu, Patrizia; Heutink, Peter

doi:10.1101/2020.12.01.405894

Cited by 11 publications

(12 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OPEN ACCESS pathogenesis of NBDs [119][120][121]. We discuss how coupling of NBD GWAS data with epigenetic information from different cell types can elucidate functional effects of noncoding variants, thus uncovering novel disease mechanisms.…”

Section: Trends In Geneticsmentioning

confidence: 99%

Uncovering the impact of noncoding variants in neurodegenerative brain diseases

et al. 2022

View full text Add to dashboard Cite

Neurodegenerative brain diseases (NBDs) are characterized by cognitive decline and movement impairments caused by neuronal loss in different brain regions. A large fraction of the genetic heritability of NBDs is not explained by the current known mutations. Genome-wide association studies identified novel diseaserisk loci, adding to the genetic basis of NBDs. Many of the associated variants reside in noncoding regions with distinct molecular functions. Genetic variation in these regions can alter functions and contribute to disease pathogenesis. Here, we discuss noncoding variants associated with NBDs. Methods for better functional interpretation of noncoding variation will expand our knowledge of the genetic architecture of NBDs and broaden the routes for therapeutic strategies. Noncoding elements in brain neurodegenerationNeurodegenerative brain diseases (NBDs) are neuropathologically characterized by protein aggregates that prompt progressive deterioration and loss of synaptic function. Pathological hallmarks differ between diseases. The clinical presentation depends on the affected brain regions and includes a range of cognitive and movement impairments (Box 1). Most studies so far have focused on the coding part to identify the genetic cause of NBDs. However, only a part of the heritability of NBDs is explained by coding mutations in genes causally linked with each disease [1-4]. Genome-wide association studies (GWAS) (see Glossary) have identified common genetic variants associated with various NBDs. Approximately 16-36% and 28% of the heritability of Parkinson's disease (PD) and of Alzheimer's disease (AD), respectively, can be explained by GWAS variants [5,6]. A considerable fraction of the variants is located in noncoding genome regions ). Functional modeling of variants has revealed disease-related mechanisms and explained part of the missing genetic etiology.

show abstract

Section: Trends In Geneticsmentioning

confidence: 99%

Uncovering the impact of noncoding variants in neurodegenerative brain diseases

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Whereas PGRN has anti-inflammatory properties, granulins display pro-inflammatory properties (reviewed in DOI 10.1007/s12035-012-8380-8). A clear connection between decreased GRN expression and increased neuronal inflammation processes such as neutrophil migration and response to interleukins was seen in post mortem brain tissue of GRN mediated FTD and not in MAPT mediated FTD 20 . Upregulated processes include NF-kappa-B (NFkB) signaling, as well as genes involved in tumour necrosis factor (TNF) production.…”

Section: Discussionmentioning

confidence: 90%

Evidence forGRNas part of a neuroinflammatory mechanism connecting common neurodegenerative diseases

Nalls

Blauwendraat

Sargent

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

SUMMARYBackgroundPrevious research using genome wide association studies (GWAS) has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases.MethodsWe utilized GWAS, expression quantitative trait locus (eQTL) mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data driven process.FindingseQTL analyses combined with GWAS identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex.InterpretationGRN expression mediates neuroinflammation function related to general neurodegeneration. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J. Fox Foundation.

show abstract

“…Brain Bank, and MRC King College London, as described by Menden et al 22 . For FTLD cohort 3 (FTLD3), data made available by Weber et al 15 was retrieved from GEO (accession code GSE75704).…”

Section: Methodsmentioning

confidence: 99%

“…All cases were characterized by age, gender, disease history (including disease onset and duration) as well as neuropathological findings. For FTLD cohort 2 (FTLD2, N=48), all post-mortem tissues were obtained under a Material Transfer Agreement from the Netherlands Brain Bank, and MRC Kings College London, as described by Menden et al [53]. For FTLD cohort 3 (FTLD3, N=163, after quality control), data made available by Weber et al [83] was retrieved from GEO (accession code GSE75704).…”

Section: Methodsmentioning

confidence: 99%

Brain DNA methylomic analysis of frontotemporal lobar degeneration revealsOTUD4in shared dysregulated signatures across pathological subtypes

Fodder

Murthy

Rizzu

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Frontotemporal lobar degeneration (FTLD) is an umbrella term describing the neuropathology of a clinically, genetically and pathologically heterogeneous group of diseases, including frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP). Among the major FTLD pathological subgroups, FTLD with TDP-43 positive inclusions (FTLD-TDP) and FTLD with tau positive inclusions (FTLD-tau) are the most common, representing about 90% of the cases. Although alterations in DNA methylation have been consistently associated with neurodegenerative diseases, namely Alzheimer’s disease, little is known for FTLD and its heterogeneous subgroups and subtypes. The main goal of this study was to investigate DNA methylation variation in FTLD-TDP and FTLD-tau. We used frontal cortex genome-wide DNA methylation profiles from three FTLD cohorts (228 individuals), generated using the Illumina 450K or EPIC arrays. We performed epigenomewide association studies (EWAS) for each cohort followed by meta-analysis to identify shared differential methylated loci across FTLD subgroups/subtypes. Additionally, we used weighted gene correlation network analysis to identify co-methylation signatures associated with FTLD and other disease-related traits. Wherever possible, we also incorporated relevant gene/protein expression data. The EWAS meta-analysis revealed four differentially methylated loci in FTLD, some of which showed altered gene and protein expression in FTLD. Two of the meta-analysis hits,OTUD4andCEBPZ, were found to be co-methylated within signatures strongly associated with FTLD. These signatures were enriched for genes implicated in the ubiquitin system, RNA/stress granule formation and glutamatergic synaptic signalling. Altogether, our findings identified novel FTLD-associated loci, and support a role for DNA methylation as a mechanism involved in the dysregulation of biological processes relevant to FTLD, highlighting novel potential avenues for therapeutic development.

show abstract

A multi-omics dataset for the analysis of Frontotemporal Dementia genetic subtypes

Cited by 11 publications

References 78 publications

Uncovering the impact of noncoding variants in neurodegenerative brain diseases

Uncovering the impact of noncoding variants in neurodegenerative brain diseases

Evidence forGRNas part of a neuroinflammatory mechanism connecting common neurodegenerative diseases

Brain DNA methylomic analysis of frontotemporal lobar degeneration revealsOTUD4in shared dysregulated signatures across pathological subtypes

Contact Info

Product

Resources

About