👤 Rik Vandenberghe

Cerebrospinal fluid amyloid beta 42, total tau, and phosphorylated tau 181 are well accepted markers of Alzheimer's disease. These biomarkers better reflect disease pathogenesis compared to clinical diagnosis. Here, we perform a genome wide association study meta-analysis including 18,948 individuals of European ancestry and identify 12 genome-wide significant loci across all three biomarkers, eight of them novel. We replicate the association of biomarkers with APOE, CR1, GMNC/CCDC50 and C16orf95/MAP1LC3B. Novel loci include BIN1 for amyloid beta and GNA12, MS4A6A, SLCO1A2 with both total tau and phosphorylated tau 181, as well as additional loci on chr. 8, near ANGPT1 and chr. 9 near SMARCA2. We also demonstrate that these variants have significant association with Alzheimer's disease risk, disease progression and/or brain amyloidosis. The associated genes are implicated in lipid metabolism independent of APOE, coupled with autophagy and brain volume regulation driven by total tau and phosphorylated tau 181 dysregulation. Show less

Dementia with Lewy bodies (DLB) and Parkinson's disease (PD) are clinically, pathologically and etiologically disorders embedded in the Lewy body disease (LBD) continuum, characterized by neuronal α-synuclein pathology. Rare homozygous and compound heterozygous premature termination codon (PTC) mutations in the Vacuolar Protein Sorting 13 homolog C gene (VPS13C) are associated with early-onset recessive PD. We observed in two siblings with early-onset age (< 45) and autopsy confirmed DLB, compound heterozygous missense mutations in VPS13C, p.Trp395Cys and p.Ala444Pro, inherited from their healthy parents in a recessive manner. In lymphoblast cells of the index patient, the missense mutations reduced VPS13C expression by 90% (p = 0.0002). Subsequent, we performed targeted resequencing of VPS13C in 844 LBD patients and 664 control persons. Using the optimized sequence kernel association test, we obtained a significant association (p = 0.0233) of rare VPS13C genetic variants (minor allele frequency ≤ 1%) with LBD. Among the LBD patients, we identified one patient with homozygous missense mutations and three with compound heterozygous missense mutations in trans position, indicative for recessive inheritance. In four patients with compound heterozygous mutations, we were unable to determine trans position. The frequency of LBD patient carriers of proven recessive compound heterozygous missense mutations is 0.59% (5/844). In autopsy brain tissue of two unrelated LBD patients, the recessive compound heterozygous missense mutations reduced VPS13C expression. Overexpressing of wild type or mutant VPS13C in HeLa or SH-SY5Y cells, demonstrated that the mutations p.Trp395Cys or p.Ala444Pro, abolish the endosomal/lysosomal localization of VPS13C. Overall, our data indicate that rare missense mutations in VPS13C are associated with LBD and recessive compound heterozygous missense mutations might have variable effects on the expression and functioning of VPS13C. We conclude that comparable to the recessive inherited PTC mutations in VPS13C, combinations of rare recessive compound heterozygous missense mutations reduce VPS13C expression and contribute to increased risk of LBD. Show less

RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL. Show less