Biology subjects

Wiley, J. C.

Publications and source records attributed to Wiley, J. C..

5 recordsLinked to original sources

bioRxiv2025

Mapping cross-domain drivers of Alzheimer's disease risk through integrated network analysis

IntroductionAlzheimers Disease (AD) is a complex neurodegenerative disorder with numerous known risk factors. Identification of which genetic factors are causal drivers is difficult due to the long disease prodrome in an inaccessible organ. The application of integrative, systems-level approaches are crucial for addressing this complexity. MethodsSixteen biological domain specific interaction networks were derived from the top AD risk- enriched proteins within each domain. Weighted key driver analysis identified influential hub nodes within each network. ResultsDistinct processes and drivers were identified within each domains network. Domains including Structural Stabilization, Endolysosome, and Lipid Metabolism were especially influential. Integrating key drivers across domains identified consistent drivers such as CTNNB1, ACSL1, and ALDH3A2, suggesting fundamental roles contributing to AD risk. DiscussionThis highly integrative network-based approach identified context-dependent drivers and enabled the inference of interactions between domains. The identified drivers suggest potential targets for future therapeutic development.

systems biology↗

bioRxiv2025

Integrated phenotypic and proteomic screening identifies top-tier Alzheimer's disease therapeutic targets

IntroductionAlzheimers disease (AD) is a complex neurodegenerative disorder. Hundreds of therapeutic targets have been nominated through genetic and multi-omic studies, but effective prioritization remains a major bottleneck. MethodsWe applied an integrative screening framework to assess 29 candidate targets from risk-enriched biological domains. Using disease-relevant murine BV2 microglial cell lines with stable Psen2 knockdown, we performed siRNA-mediated perturbations followed by cellular phenotypic assays and quantitative proteomics. ResultsTwenty-five candidate targets significantly altered at least one phenotype, with stronger effects in Psen2 knockdown cells. Integrated proteomic analyses identified several perturbations that reversed AD-associated molecular patterns. Five targets--Ap2a2, Pdhb, Pdha1, Dlat, and Psmc3-- impacted both phenotypes and related proteomic responses. DiscussionWe established a scalable platform for target functional validation that bridges unbiased systems-level assessments of AD risk with experimental evidence. The ESSJ TREAT-AD center will prioritize further resource development for these validated targets.

systems biology↗

bioRxiv2025

Kat5 cKO Biological Domain Signatures Align with Human Alzheimer Disease

BACKGROUNDAlzheimers disease (AD) is associated with amyloid plaques and can be caused by autosomal dominant mutations in APP or PSEN1/2, which form an enzyme substrate complex. Decreases in catalysis of AD mutant APP and PSEN1 supports the hypothesis that membrane delimitation of KAT5 could contribute to AD. METHODSWe compare the hippocampal transcriptome profiles of the Kat5 brain-specific knockout mouse to multiple AD datasets through alignment with the TREAT-AD AD biological domains. We examine KAT5 subcellular localization in human WT and AD neurons. RESULTSThe Kat5 KO mouse demonstrates downregulation of synaptic genes, metabolic pathways, and upregulation of DNA replication and repair, cell cycle and immune response genes. We see similar profiles in Kat5 and comparative AD datasets. KAT5 is restricted to the cytosol in human AD neurons. DISCUSSIONThis analysis supports the hypothesis that KAT5 nuclear signaling down stream of APP cleavage plays a pivotal role in neuronal homeostasis and immune regulation.

neuroscience↗

bioRxiv2023

Characterization of covalent inhibitors that disrupt the interaction between the tandem SH2 domains of SYK and FCER1G phospho-ITAM

RNA sequencing and genetic data support spleen tyrosine kinase (SYK) and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) as putative targets to be modulated for Alzheimers disease (AD) therapy. FCER1G is a component of Fc receptor complexes that contain an immunoreceptor tyrosine-based activation motif (ITAM). SYK interacts with the Fc receptor by binding to doubly phosphorylated ITAM (p-ITAM) via its two tandem SH2 domains (SYK-tSH2). Interaction of the FCER1G p-ITAM with SYK-tSH2 enables SYK activation via phosphorylation. Since SYK activation is reported to exacerbate AD pathology, we hypothesized that disruption of this interaction would be beneficial for AD patients. Herein, we developed biochemical and biophysical assays to enable the discovery of small molecules that perturb the interaction between the FCER1G p-ITAM and SYK-tSH2. We identified two distinct chemotypes using a high-throughput screen (HTS) and orthogonally assessed their binding. Both chemotypes covalently modify SYK-tSH2 and inhibit its interaction with FCER1G p-ITAM.

neuroscience↗

bioRxiv2020

Multi-Tissue Neocortical Transcriptome-Wide Associations Study Implicates 8 Genes Across 6 Genomic Loci in Alzheimer's Disease

BackgroundAlzheimers disease (AD), an incurable neurodegenerative disease, currently affecting 1.75% of the United States population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD-risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION Transcriptome Wide Association Study (TWAS) pipeline to ingest expression from multiple neocortical regions, provide a set of 6780 gene weights which are abstracatable across the neocortex, and leverage these to find 8 genes from six loci with associated AD risk validated through summary mendelian randomization (SMR) utilizing IGAP summary statistics. MethodA combined dataset of 2003 genotypes clustered to Central European (CEU) ancestry was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles across 6 Neo-cortical tissues (TCX=248, FP=50, IFG=41, STG=34, PHG=34, DLPFC=461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a genes surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and provide support for cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues. ResultsCis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N=6775 (99.92%), Bonferroni: N=6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05); APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, KNOP1. ConclusionWe provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

genomics↗