Citations

AbstractIntron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome‐wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership‐Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon‐level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron‐retained mRNAs were identified using mass spectrometry. Further, we established AD‐specific intron expression Quantitative Trait Loci, and identified splicing‐related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms.

Abstract Background: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power but often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. Results: In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that the association signals of a subset of top disease risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Conclusions: Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordance came largely from quality differences and if the variants from one batch had better quality scores.

There is considerable interest in harnessing innate immunity to treat Alzheimer’s disease (AD). Here, we explore whether a decoy receptor strategy using the ectodomain of select TLRs has therapeutic potential in AD. AAV-mediated expression of human TLR5 ectodomain (sTLR5) alone or fused to human IgG4 Fc (sTLR5Fc) results in robust attenuation of amyloid β (Aβ) accumulation in a mouse model of Alzheimer-type Aβ pathology. sTLR5Fc binds to oligomeric and fibrillar Aβ with high affinity, forms complexes with Aβ, and blocks Aβ toxicity. Oligomeric and fibrillar Aβ modulates flagellin-mediated activation of human TLR5 but does not, by itself, activate TLR5 signaling. Genetic analysis shows that rare protein coding variants in human TLR5 may be associated with a reduced risk of AD. Further, transcriptome analysis shows altered TLR gene expression in human AD. Collectively, our data suggest that TLR5 decoy receptor–based biologics represent a novel and safe Aβ-selective class of biotherapy in AD.