Biology subjects

Cary, G. A.

Publications and source records attributed to Cary, G. A..

9 recordsLinked to original sources

bioRxiv2026

Single-subject Proteomic Signatures in Alzheimers Disease Reflect Clinical Phenotypes and Distinguish Asymptomatic from Symptomatic Cases

INTRODUCTIONAlzheimers Disease (AD) exhibits considerable inter-individual variability in clinical presentation, neuropathological burden, and underlying molecular processes. Conventional cohort-based analyses of omics molecular data often mask individual-level heterogeneity, limiting insights into the precision therapeutic strategies. To address this challenge, we developed INDIGO (INdividual-level DIfferential GenOmics), a computational framework that quantifies molecular deviations for each individual relative to healthy controls, enabling subject-specific profiling of diseaseassociated alterations in proteomic data, with a framework that is readily applicable to other omics modalities. METHODSWe applied INDIGO to dorsolateral prefrontal cortex (DLPFC) proteomic data from ROSMAP cohort (N = 610). Protein level deviations were aggregated into gene set activity scores for KEGG pathways and curated AD Biodomain annotations. Functional alterations across AD and Asymptomatic AD (AsymAD) individuals were evaluated and correlated with clinical metrics including APOE4 genotype, Braak stage, CERAD and MMSE scores. Graph-based clustering was used to identify molecularly distinct subgroups based on shared patterns of functional dysregulation. RESULTSLimited overlap was observed between cohort-level differential expression analysis and INDIGO single-subject analyses. Individual deviations in various processes, including metabolic, immune and epigenetic pathways, exhibited sex- and disease stage-specific patterns. Amyloid clearance and immune activation were strongly associated with APOE4 dosage, higher amyloid and tau burden and cognitive decline, whereas upregulation of mitochondrial and synaptic modules correlated positively with preserved cognitive function. By linking individuals through concordant directional proteomic changes, we identified molecularly coherent subgroups that transcend conventional diagnostic boundaries and include both AD and AsymAD subjects. Each subgroup displayed distinct functional signatures and defined by a unique set of key regulatory proteins. DISCUSSIONThese results demonstrate that single-subject omics profiling can resolve individual molecular signatures aligned with clinical and neuropathological variation in AD. By linking molecular heterogeneity with disease phenotypes, INDIGO provides a scalable framework for precision modeling and novel therapeutic target discovery.

neuroscience↗

bioRxiv2025

Trem2*R47H and reduced TREM2 expression both mimic human Alzheimer's disease signatures in mice

INTRODUCTIONTREM2 loss of function variants are associated with late-onset Alzheimers disease (LOAD). We molecularly assessed mice with the missense variant, R47H (Trem2*R47HHSS), and mice with additional cryptic splicing and reduced Trem2 expression (Trem2*R47H) while comparing relevance to human LOAD. METHODSThe aberrant splice acceptor site in the Trem2*R47H mouse was humanized resulting in the Trem2*R47H humanized splice site (Trem2*R47HHSS) mouse. RNA sequencing was performed on mouse brain tissue and signatures were compared to human postmortem brain expression in LOAD cohorts. RESULTSTrem2*R47H mice had alternative splicing leading to reduced Trem2 expression; Trem2*R47HHSS mice expressed Trem2 at wild-type transcript and protein levels. Both models correlated with similar LOAD-associated signatures, and had similar effects on immune response, synapse, and vasculature biodomains. The Trem2*R47H model additionally affected extracellular matrix and myelination signatures. DISCUSSIONWe demonstrated that Trem2*R47H and Trem2*R47HHSS mice are complementary models for the study of molecular contributions to LOAD pathology.

neuroscience↗

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↗

bioRxiv2024

Deep conservation of cis-regulatory elements and chromatin organization in echinoderms uncover ancestral regulatory features of animal genomes

Despite the growing abundance of sequenced animal genomes, we only have detailed knowledge of regulatory organization for a handful of lineages, particularly flies and vertebrates. These two groups of taxa show contrasting trends in the molecular mechanisms of 3D chromatin organization and long-term evolutionary dynamics of cis-regulatory element (CREs) conservation. To help us identify shared versus derived features that could be responsible for the evolution of these different regulatory architectures in animals, we studied the evolution and organization of the regulatory genome of echinoderms, a lineage whose phylogenetic position and relatively slow molecular evolution has proven particularly useful for evolutionary studies. First, using PacBio and HiC data, we generated new reference genome assemblies for two species belonging to two different echinoderm classes: the purple sea urchin Strongylocentrotus purpuratus and the bat sea star Patiria miniata. Second, we characterized their 3D chromatin architecture, identifying TAD-like domains in echinoderms that, like in flies, do not seem to be associated with CTCF motif orientation. Third, we systematically profiled CREs during sea star and sea urchin development using ATAC-seq, comparing their regulatory logic and dynamics over multiple developmental stages. Finally, we investigated sea urchin and sea star CRE evolution across multiple evolutionary distances and timescales, from closely related species to other echinoderm classes and deuterostome lineages. This showed the presence of several thousand elements conserved for hundreds of millions of years, revealing a vertebrate-like pattern of CRE evolution that probably constitutes an ancestral property of the regulatory evolution of animals.

evolutionary biology↗

bioRxiv2024

An explainable graph neural network approach for integrating multi-omics data with prior knowledge to identify biomarkers from interacting biological domains.

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional omics data and set transformers to integrate low-dimensional representations of omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimers disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights functional AD biomarkers.

bioinformatics↗

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

Systematic comparison of developmental GRNs explains how novelty is incorporated in early development

The impressive array of morphological diversity among animal taxa represents the product of millions of years of evolution. Morphology is the output of development, therefore phenotypic evolution arises from changes to the topology of the gene regulatory networks (GRNs) that control the highly coordinated process of embryogenesis1. While genetic variation can arise anywhere in the genome and affect any part of an individual GRN, the need to form a viable embryo provides a constraint on the types of variation that pass the filter of selection. A particular challenge in understanding the origins of animal diversity lies in determining how GRNs incorporate novelty while preserving the overall stability of the network, and hence, embryonic viability. Here we assemble a comprehensive GRN, consisting of 42 genes (nodes) and 84 interactions (edges), for the model of endomesoderm specification in the sea star from zygote through gastrulation that corresponds to the GRN for sea urchin development of equivalent territories and stages2. Using these detailed models we make the first systems-level comparison of early development and examine how novelty is incorporated into GRNs. We show how the GRN is resilient to the introduction of a transcription factor, pmar1, the inclusion of which leads to a switch between two stable modes of Delta-Notch signaling. Signaling pathways can function in multiple modes and we propose that GRN changes that lead to switches between modes may be a common evolutionary mechanism for changes in embryogenesis. Our data additionally proposes a model in which evolutionarily conserved network motifs, or kernels, may function throughout development to stabilize these signaling transitions.

developmental biology↗