Reddy, Joseph S.

AbstractBackgroundCerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, effects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer’s disease (AD) patients. In a previous genome‐wide association study (GWAS) of 853 neuropathologically confirmed AD cases, we identified a LINC‐PINT splice variant that associated with lower CAA levels amongst individuals that did not have a copy of the APOEe4 AD risk allele (Reddy. J et al 2021). In this study, we perform GWAS to identify further genetic risk factors for CAA and AD neuropathology (Braak stage, Thal phase) in additional AD cases, and participants lacking significant AD pathology (non‐AD).MethodWe expanded the original AD cohort (N = 853) with 400 additional AD cases genotyped using the Infinium Omni2.5+Exome array. We also identified 110 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, with existing CAA scores and genome‐wide genotypes. We performed QC and imputation of these genotypes to TOPMed. We will conduct GWAS in AD only (N = 1,363), non‐AD only, as well as the combined cohort (N = 1,865). We will test imputed variant dosages for association with square root transformed CAA, Braak and Thal, using linear regression in PLINK, adjusting for relevant covariates. We will perform interaction analysis of CAA‐associated variants with APOEe4 and sex; and pursue stratified analyses. Summary statistics will be utilized to perform SNP‐based gene set enrichment analysis to identify biological pathways implicated in CAA.ResultWe expect that the increased power of this expanded dataset will lead to the identification of additional novel genetic risk factors for CAA and AD neuropathology. Genetic interaction analysis with sex and APOEe4 may further elucidate the role of these risk factors. The addition of 1,102 participants will enable assessment of the LINC‐PINT locus for association with CAA in an independent dataset.ConclusionWe expect this study will provide further insights into the genetic architecture underlying risk for CAA, both in the context of significant AD pathology, and without. Characterization of genetic variants in the context of neuropathology may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, and AD.

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

AbstractBackgroundContext‐specific networks can capture functionally important genes that may not be differentially expressed. Leveraging seven different gene expression datasets of Alzheimer’s disease (AD) and healthy control brains, we constructed a novel pipeline that includes such network analysis, augmented by in vivo validations and in silico drug repurposing to facilitate prioritization of AD therapeutic targets.MethodWe utilized publicly available gene expression data from 733 AD and 454 control brains across 7 datasets and the NetDecoder (PMID: 26975659) network biology platform to build context‐specific networks. Based on the known protein‐protein interaction and gene expression correlations, information flow from the top differentially expressed genes (DEG) to transcriptional regulator genes was calculated using a process‐guided flow algorithm. Genes with the greatest informational flow changes across AD cohorts were systematically prioritized. We next validated these genes in a medium‐throughput screen of a tau‐transgenic Drosophila model (PMID: 11408621). Further, we identified existing small‐molecule drugs to target the candidate genes utilizing curated databases.ResultsA total of 14 context‐specific networks were constructed across 7 datasets. We found AD‐related genes, such as APP, to have high flow differences between AD and control networks and high connectivity. Notably, many such genes were not DEGs between AD and control, suggesting that the information flow captures novel aspects of AD pathophysiology. We nominated 224 unique genes with the greatest flow differences for functional validation. We screened 111 of the 224 candidate genes with readily available, high‐confidence orthologs in Drosophila. We identified genes that suppress (N = 68) or enhance (N = 27) tau‐related neurodegeneration in this model. We confirmed the effects of previously nominated genes, such as HDAC1, which enhances tau‐neurodegeneration if overexpressed and suppresses it if knocked down. We also identified and validated additional therapeutic targets. Lastly, we applied a network‐proximity‐based algorithm and identified small molecule drugs for the candidate genes.ConclusionWe present a novel pipeline that integrates analytic approaches, experimental validation, and drug repurposing to facilitate therapeutic discovery in AD. We applied this pipeline and nominated previously known and novel genes implicated in AD pathogenesis. Our pipeline demonstrates a cross‐species, systems‐level approach to therapeutic nomination in AD and related neurodegenerative diseases.

AbstractBackgroundAlzheimer’s disease (AD) is neuropathologically characterized by amyloid‐β (Aβ) plaques and tau neurofibrillary tangles often quantified by Thal phase and Braak stage, respectively. Aβ also frequently deposits in the cerebrovasculature with severity categorized by a cerebral amyloid angiopathy (CAA) score. These and related measures often show high variability within AD suggesting distinct underlying mechanisms, the identification of which may reveal important biological insights into AD pathophysiology. We hypothesize that, within the AD brain, neuropathology and levels of core AD‐related proteins are influenced by variations in DNA methylation (DNAm). To test this, we performed epigenome‐wide association studies (EWAS) using DNAm clusters from the temporal cortex (TCX) and cerebellum (CER) with AD‐related neuropathologic measures (Braak, Thal, and CAA) and brain biochemical levels of five proteins (apoE, Aβ40, Aβ42, tau, and p‐tau).MethodsDNAm from neuropathologically‐confirmed AD cases available through the Mayo Clinic brain bank was measured by reduced representation bisulfite sequencing (RRBS) from 471 brain samples, 200 of which had both TCX and CER measures. Neuropathology was scored by an experienced neuropathologist. TCX levels of five AD‐related proteins from three tissue fractions (buffer‐, detergent‐, and in‐soluble) were measured by ELISA (Liu 2020). CpG methylation (CpGm) was binned by a 15‐state chromatin model (Kundaje 2015) and averaged. Averaged CpGm clusters were tested for their association with each endophenotype through multi‐variable linear regression. The influence of CpGm clusters on expression levels of nearby genes was also investigated through multi‐variable regression.ResultsOur innovative binning method demonstrated expected and biologically relevant CpGm patterns. We identified multiple epigenome‐wide significant CpGm clusters unique to each AD‐related endophenotype and brain region. Comparisons between the TCX and CER further revealed brain region‐specific enrichment of distinct CpGm clusters. Significant CpGm clusters were further characterized by their influence on gene expression.ConclusionsAlthough all neuropathologic and biochemical endophenotypes tested are core to AD pathophysiology, our results suggest each has a distinct epigenetic architecture underlying their variability in the AD brain. By discovering brain region‐specific DNAm changes that contribute to each neuropathologic or biochemical outcome, we can identify core components of complex mechanisms that individually and jointly contribute to AD.

Abstract Animal studies implicate one-carbon metabolism and DNA methylation genes in hepatocellular carcinoma (HCC) development in the setting of metabolic perturbations. Using human samples, we investigated the associations between common and rare variants in these closely related biochemical pathways and risk for metabolic HCC development in a multicenter international study. We performed targeted exome sequencing of 64 genes among 556 metabolic HCC cases and 643 cancer-free controls with metabolic conditions. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for multiple comparisons. Gene-burden tests were used for rare variant associations. Analyses were performed in the overall sample and among non-Hispanic whites. The results show that among non-Hispanic whites, presence of rare functional variants in ABCC2 was associated with 7-fold higher risk of metabolic HCC (OR = 6.92, 95% CI: 2.38–20.15, P = 0.0004), and this association remained significant when analyses were restricted to functional rare variants observed in ≥2 participants (cases 3.2% versus controls 0.0%, P = 1.02 × 10−5). In the overall multiethnic sample, presence of rare functional variants in ABCC2 was nominally associated with metabolic HCC (OR = 3.60, 95% CI: 1.52–8.58, P = 0.004), with similar nominal association when analyses were restricted to functional rare variants observed in ≥2 participants (cases 2.9% versus controls 0.2%, P = 0.006). A common variant in PNPLA3 (rs738409[G]) was associated with higher HCC risk in the overall sample (P = 6.36 × 10−6) and in non-Hispanic whites (P = 0.0002). Our findings indicate that rare functional variants in ABCC2 are associated with susceptibility to metabolic HCC in non-Hispanic whites. PNPLA3-rs738409 is also associated with metabolic HCC risk.

We previously demonstrated that increased expression of the SERPINA5 gene is associated with hippocampal vulnerability in Alzheimer’s disease (AD) brains. SERPINA5 was further demonstrated to be a novel tau-binding partner that colocalizes within neurofibrillary tangles. Our goal was to determine whether genetic variants in the SERPINA5 gene contributed to clinicopathologic phenotypes in AD. To screen for SERPINA5 variants, we sequenced 103 autopsy-confirmed young-onset AD cases with a positive family history of cognitive decline. To further assess the frequency of a rare missense variant, SERPINA5 p.E228Q, we screened an additional 1114 neuropathologically diagnosed AD cases. To provide neuropathologic context in AD, we immunohistochemically evaluated SERPINA5 and tau in a SERPINA5 p.E228Q variant carrier and a matched noncarrier. In the initial SERPINA5 screen, we observed 1 individual with a rare missense variant (rs140138746) that resulted in an amino acid change (p.E228Q). In our AD validation cohort, we identified an additional 5 carriers of this variant, resulting in an allelic frequency of 0.0021. There was no significant difference between SERPINA5 p.E228Q carriers and noncarriers in terms of demographic or clinicopathologic characteristics. Although not significant, on average SERPINA5 p.E228Q carriers were 5 years younger at age of disease onset than noncarriers (median: 66 [60–73] vs 71 [63–77] years, P = .351). In addition, SERPINA5 p.E228Q carriers exhibited a longer disease duration than noncarriers that approached significance (median: 12 [10–15]) vs 9 [6–12] years, P = .079). More severe neuronal loss was observed in the locus coeruleus, hippocampus, and amygdala of the SERPINA5 p.E228Q carrier compared to noncarrier, although no significant difference in SERPINA5-immunopositive lesions was observed. Throughout the AD brain in either carrier or noncarrier, areas with early pretangle pathology or burnt-out ghost tangle accumulation did not reveal SERPINA5-immunopositive neurons. Mature tangles and newly formed ghost tangles appeared to correspond well with SERPINA5-immunopositive tangle-bearing neurons. SERPINA5 gene expression was previously associated with disease phenotype; however, our findings suggest that SERPINA5 genetic variants may not be a contributing factor to clinicopathologic differences in AD. SERPINA5-immunopositive neurons appear to undergo a pathologic process that corresponded with specific levels of tangle maturity.

AbstractBackgroundGene expression changes have been identified in post‐mortem brain tissue of AD cases when compared with controls, implicating various genes and pathways. Such findings provide important insights into molecular dysregulation associated with this disease and nominate therapeutic targets. Here we investigate brain gene and co‐expression network changes in a cohort of AD cases to identify associations with degree of AD neuropathology including Braak stage (Tau), Thal phase (Amyloid beta) and brain tissue levels of five AD‐related proteins (apoE, Aβ40, Aβ42, tau, and p‐tau).MethodGene expression measures were collected from temporal cortex (TCX) tissue of 477 AD cases, available through the Mayo Clinic Brain Bank, using RNA sequencing (Illumina TruSeq mRNA) performed on the HiSeq4000. Following quality control 456 cases remained for analysis. Braak and Thal were provided by an experienced neuropathologist and TCX levels of five AD‐related proteins from three fractions (buffer‐soluble, detergent‐soluble, and insoluble) were measured by ELISA (Liu et al. 2020). Weighted gene coexpression network analysis (WGCNA) R package was used to build co‐expression networks. Genes and networks were tested for association with neuropathology and AD‐related protein levels, adjusting for relevant covariates.ResultA total of 57,353 genes were profiled, of which 19,044 were reliably expressed above background levels and formed 20 co‐expression modules. 36 genes were associated with Braak stage, 10 with Thal phase and up to 9,000 with the AD‐related protein fractions. Brain levels of pTau had the greatest number of associations, with an overrepresentation of genes involved in synaptic signalling and immune response gene ontology biological processes. 16 of the coexpression modules associated with at least one trait after Bonferroni correction for number of tests.ConclusionWe have identified a number of genes and networks associated with different aspects of AD neuropathology and associated proteins, further lending support to the involvement of synaptic and immune biology in AD. However it is not clear whether observed changes cause disease or are a consequence of the neurodegenerative process. Comparison of these findings across different proteins and fractions is expected to reveal common and distinct vulnerabilities associated with amyloid and tau, and different stages of protein aggregation.

AbstractBackgroundIn the Alzheimer’s disease sequencing project (ADSP) whole exome dataset, only the well‐known TREM2.pR47H variant shows genome‐wide significance by conventional single variant analysis. To identify novel AD genes related to TREM2, we used gene‐based polygenic score analysis (PGSA) to evaluate an immune, temporal cortical co‐expression network with 229 genes including TREM2.MethodThe most recent whole exome variant call files from the ADSP (NG00067.v7) were subjected to stringent quality control. Post‐QC samples were divided into discovery (4174 samples sequenced at Broad), test (3299 samples sequenced at WashU), and follow‐up (2273 samples sequenced at Baylor) sets. The pruned variants (r2 < 0.2) with a minor allele count over 20 in each gene were analyzed by Broad/WashU PGSA thereby obtaining P‐values (gPbr.wa) for each gene in WashU samples. To assess significance in independent Baylor samples, cumulative Broad/Baylor PGSA was performed using a novel approach in which variants were stratified by both gene (gPbr.wa) and variant (Broad.P).ResultAfter excluding TREM2, the five variants with gPbr.wa ≤ 0.08 and Broad.P ≤ 0.03 showed significant association in Broad/Baylor PGSA. One of these variants showed no evidence of association with AD in the combined dataset. Polygenic scores for the other four variants, which were in three genes, showed significant association in Baylor samples, and significant, essentially identical association in WashU samples. Polygenic scores for nine additional variants in these genes (0.03> Broad.P ≤ 0.13) also showed significant, essentially identical association in Baylor and WashU samples. The association of these 13 variants with AD was driven primarily by six deleterious, protein‐altering variants with minor allele frequencies less than 1%, but the other 7 variants also contributed.ConclusionUsing gene‐based polygenic score analysis to analyze an immune co‐expression network, we identify three novel immune genes with actionable nonsynonymous and stopgain variants that show significant, replicable association with AD in the most recent release of the ADSP whole exome dataset.