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.

AbstractBackgroundUsing an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD.MethodWe performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD.ResultThe SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10).ConclusionOur data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.

AbstractIntroductionCerebrovascular pathologies including cerebral amyloid angiopathy (CAA) and blood‐brain barrier (BBB) dysregulation are prominent features in the majority of Alzheimer's disease (AD) cases.MethodsWe performed neuropathologic and biochemical studies on a large, neuropathologically confirmed human AD cohort (N = 469). Amounts of endothelial tight junction proteins claudin‐5 (CLDN5) and occludin (OCLN), and major AD‐related molecules (amyloid beta [Aβ40], Aβ42, tau, p‐tau, and apolipoprotein E) in the temporal cortex were assessed by ELISA.ResultsHigher levels of soluble tau, insoluble p‐tau, and apolipoprotein E (apoE) were independently correlated with lower levels of endothelial tight junction proteins CLDN5 and OCLN in AD brains. Although high Aβ40 levels, APOE ε4, and male sex were predominantly associated with exacerbated CAA severity, those factors did not influence tight junction protein levels.DiscussionRefining the molecular mechanisms connecting tau, Aβ, and apoE with cerebrovascular pathologies is critical for greater understanding of AD pathogenesis and establishing effective therapeutic interventions for the disease.

AbstractIntroductionAbnormal gene expression patterns may contribute to the onset and progression of late‐onset Alzheimer's disease (LOAD).MethodsWe performed transcriptome‐wide meta‐analysis (N = 1440) of blood‐based microarray gene expression profiles as well as neuroimaging and cerebrospinal fluid (CSF) endophenotype analysis.ResultsWe identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid beta (Aβ) accumulation, especially in the entorhinal cortex region. cis‐expression quantitative trait loci mapping analysis of CREB5 detected five significant associations (P < 5 × 10−8), where rs56388170 (most significant) was also significantly associated with global cortical Aβ deposition measured by [18F]Florbetapir positron emission tomography and CSF Aβ1‐42.DiscussionRNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to Alzheimer's disease. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity, and learning and memory.

AbstractIntroductionMAPT H1 haplotype is implicated as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD).MethodsUsing Alzheimer's Disease Genetics Consortium (ADGC) genome‐wide association study (GWAS) data (n = 18,841), we conducted a MAPT H1/H2 haplotype–stratified association to discover MAPT haplotype–specific AD risk loci.ResultsWe identified 11 loci—5 in H2‐non‐carriers and 6 in H2‐carriers—although none of the MAPT haplotype–specific associations achieved genome‐wide significance. The most significant H2 non‐carrier–specific association was with a NECTIN2 intronic (P = 1.33E‐07) variant, and that for H2 carriers was near NKX6‐1 (P = 1.99E‐06). The GABRG2 locus had the strongest epistasis with MAPT H1/H2 variant rs8070723 (P = 3.91E‐06). Eight of the 12 genes at these loci had transcriptome‐wide significant differential expression in AD versus control temporal cortex (q < 0.05). Six genes were members of the brain transcriptional co‐expression network implicated in “synaptic transmission” (P = 9.85E‐59), which is also enriched for neuronal genes (P = 1.0E‐164), including MAPT.DiscussionThis stratified GWAS identified loci that may confer AD risk in a MAPT haplotype–specific manner. This approach may preferentially enrich for neuronal genes implicated in synaptic transmission.

Abstract OBJECTIVE Our goal is to identify new pathways perturbed in and which may lead to GRS. METHODS We collected brain transcriptome levels from healthy controls (n=5), patients with GRS (n=9), glioma without seizures (non-GRS, n=8), and idiopathic temporal lobe epilepsy (iTLE, n=7) using Illumina-based poly-A capture high-throughput RNA sequencing approach. Differential gene expression analysis was conducted to ascertain expression differences between the four groups. We identified molecular pathways enriched for differentially expressed genes (DEG) using Ingenuity Pathway Analysis. DEGs were defined as those with log2 fold change ≥ 2 and q-value < 0.01 when compared to GRS. RESULTS Principal component analysis revealed that diagnostic grouping was the main determinant of the transcriptome variance. We identified 212 DEGs in the GRS vs. non-GRS analysis. Among these, 108 genes were up-regulated and 104 genes were down-regulated. Among the genes that were down‐regulated, there was significant enrichment for those involved in acute phase response signaling and immune cell trafficking (e.g. H19, BMP5, CXCL8). Compared to iTLE samples, there were 1015 DEGs in the GRS samples. Among these 877 genes were up-regulated and 138 genes were down-regulated. There was significant enrichment for genes involved in cell-to-cell signaling among those that were either uniquely up‐regulated (e.g. NRK, RGS10, SLC4A4, and SELPLG) or down‐regulated in the GRS samples (e.g. SLC9A3 –Na+/H+ exchanger; SLC5A5-sodium-glucose co-transporter family). Further, two of the most down-regulated genes in samples with GRS, SCLC17A6- vesicular glutamate transporter 2 and FOSB, have previously been shown to be involved in glutamatergic transmission. CONCLUSIONS Our findings suggest that inflammation-related processes and alterations in cell-to cell signaling may underlie the pathogenesis of GRS. Our results also provide support for perturbations in glutamatergic transmission in GRS. Our findings require replication in larger human cohorts and functional experiments to explore the function of the genes perturbed in GRS.