Biology subjects

Annett, A.

Publications and source records attributed to Annett, A..

4 recordsLinked to original sources

bioRxiv2026

USP15 REGULATES NEUROINFLAMMATION AND DRIVES PATHOGENESIS IN SYNUCLEINOPATHIES

Neuroinflammation strongly contributes to the pathogenesis of neurological and neurodegenerative diseases, including Parkinson's disease. We show that ablation of Usp15 in astrocytes and in microglia protects against lethal neuroinflammation in vivo. In a mouse model of synucleinopathy, Usp15 deletion diminishes alpha-syn deposits in the brain, slows disease progression, and increases survival time. The neuroprotective effect of Usp15 is associated with differential expression of inflammatory pathways in situ including interferon stimulated genes. These USP15-dependent effects in vivo are recapitulated in vitro in primary human microglia and astrocytes. We detect high USP15 expression in microglia from Parkinson's patients with strong co-expression with LRRK2 and SNCA. In humans, we detect a strong cis-acting eQTL directing high USP15 expression in CD14+ myeloid cells. The allele driving this eQTL is itself associated with increased disease risk, linking myeloid USP15 expression, elevated USP15 plasma levels in Parkinson's patients, to genetic susceptibility.

neuroscience↗

bioRxiv2026

Dual Role of Ninjurin-1 in Myeloid Cell Adhesion and Inflammation in Relapse-Remitting EAE

Nerve Injury-Induced Protein 1 (Ninjurin-1) is an adhesion molecule implicated in inflammation and tissue injury, yet its role in neuroinflammatory diseases such as multiple sclerosis (MS) remains poorly defined. Here, we identify Ninjurin-1 as a key mediator of immune activation and CNS infiltration in relapsing-remitting experimental autoimmune encephalomyelitis (RR-EAE), a model of relapsing-remitting MS (RRMS). Using flow cytometry, gene-expression profiling, and in vivo peptide blockade, we show that Ninjurin-1 is markedly upregulated on CNS-infiltrating myeloid cells during disease progression. Ninjurin-1 myeloid cells display a dual function, as both an adhesion molecule and a marker of inflammatory activation, characterized by increased antigen presentation, cytokine production, and transcriptional enrichment for genes regulating adhesion, migration, and innate immune signaling. Importantly, therapeutic blockade of Ninjurin-1 significantly reduced clinical severity, CNS immune infiltration, and demyelination in RR-EAE. These findings uncover a previously unrecognized role for Ninjurin-1 in myeloid-driven neuroinflammation and highlight its potential as a therapeutic target for relapsing-remitting MS.

neuroscience↗

bioRxiv2021

scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods. MotivationscWGBS library preparation in a one-cell-per-library format presents practical and economical constraints to the number of cells that can be analyzed in a research project. In addition, most of the current scWGBS methods suffer from low read alignment rates. We present a scWGBS protocol which mitigates these issues, empowering single-cell DNA methylation analysis at an increased scale.

genomics↗

bioRxiv2021

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3). The resultant model had an 81% accuracy to distinguish between DA1 and DA3, with unsupervised hierarchical clustering revealing additional subgroups indicative of the immune axis involved or state of disease flare. These subgroups correlated with clinical variables, suggesting that the gene sets identified may further the understanding of gene networks that act in concert to drive disease progression. This included roles for genes i) induced by interferons (IFI35 and OTOF), ii) key to SLE cell types (KLRB1 encoding CD161), or iii) with roles in autophagy and NF-{kappa}B pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification.

bioinformatics↗