Biology subjects

Tomkins, J. E.

Publications and source records attributed to Tomkins, J. E..

6 recordsLinked to original sources

bioRxiv2026

Loss of catalytic activity and impaired proteostasis in guanosine nucleotide-depleted LRRK2

Coding mutations in the Leucine-rich repeat kinase 2 (LRRK2) gene represent the most common cause of familial Parkinsons disease (PD), and are frequently observed in idiopathic PD. In addition, variation around the LRRK2 locus has been shown to alter PD risk by genome-wide association studies. Disease-causing mutations cluster within the catalytic core of LRRK2 - composed of GTPase (ROC) and serine-threonine kinase domains - and lead to an increase in kinase activity, resulting in hyperphosphorylation of a subset of RAB GTPases and consequent cellular toxicity. However, the interplay between LRRK2 GTPase and kinase domains, and with the surrounding scaffold regions has remained underexplored, with implications for the prediction of on- and off-target effects associated with kinase inhibition. To address this gap, here we dissected the contributions of kinase, GTPase and scaffold domains to LRRK2 function in murine macrophages and tissues expressing endogenous levels of GTP/GDP-binding deficient Lrrk2 T1348N. Guanosine nucleotide-free Lrrk2 is devoid of both GTPase and kinase activities but maintains the scaffold shell, leading to significant reshaping of Lrrk2 interactome and engagement in novel interactions. This altered functional state leads to impaired autophagy and accumulation of enlarged lysosomes and autophagic cargo in macrophages and kidneys. Since pharmacological inhibition of LRRK2 is under clinical evaluation, our results reveal retained scaffold functions upon loss of catalytic activity that warrant careful consideration.

cell biology↗

bioRxiv2025

Genetic modulation of immune gene co-expression in the aged mouse hippocampus by the Apbb1ip locus

Ageing is a major risk factor for many neurodegenerative diseases, and the hippocampus is particularly vulnerable to the effects of ageing. To define the transcriptomic changes related to ageing and the impact of genetic variation, we analysed hippocampal gene expression data generated from a genetically diverse panel of inbred mouse strains belonging to the BXD Family. We applied a combination of differential expression, differential correlation, and weighted gene co-expression network analyses, followed by genetic mapping to delineate age-associated transcriptomic patterns and genetic modulators. This study revealed an upregulation in immune response and microglial genes. We identified a key age-associated co-expression module enriched in immune related genes, and quantitative trait locus mapping of this module uncovered a genetic regulatory locus in which Apbb1ip was the primary candidate gene. Additionally, gene level differential correlation analysis identified a substantial restructuring of the hippocampal transcriptome during ageing. Notably, Ywhab, an important signalling chaperone displayed altered gene expression correlations with >70 genes, between young and aged mice. These results provide novel insights into transcriptional dynamics of hippocampal ageing and identified the immune gene Apbb1ip as a potential modulator of immune response and microglial gene upregulation with implications for neurodegenerative disease pathogenesis. HighlightsO_LIComplementary gene expression analyses indicated an upregulation of immune responses in the ageing hippocampus of a genetically diverse mouse cohort, likely driven by microglia subtypes. C_LIO_LIA systems genetics approach revealed the Apbb1ip locus as a modulator of immune gene co-expression, highlighting a novel candidate regulator of age-associated neuroimmune dynamics. C_LIO_LISignalling chaperone Ywhab gene expression was highly differentially correlated with other transcripts during ageing which may underlie widespread age-associated molecular perturbations in the hippocampus. C_LI

bioinformatics↗

bioRxiv2023

Polyglucosan body density in the aged mouse hippocampus is controlled by a novel modifier locus on chromosome 1

In aged humans and mice, aggregates of hypobranched glycogen molecules called polyglucosan bodies (PGBs) accumulate in hippocampal astrocytes. PGBs are known to drive cognitive decline in neurological diseases but remain largely unstudied in the context of typical brain aging. Here, we show that PGBs arise in autophagy-dysregulated astrocytes of the aged C57BL/6J mouse hippocampus. To map the genetic cause of age-related PGB accumulation, we quantified PGB burden in 32 fully sequenced BXD-recombinant inbred mouse strains, which display a 400-fold variation in hippocampal PGB burden at 16-18 months of age. A major modifier locus was mapped to chromosome 1 at 72-75 Mb, which we defined as the Pgb1 locus. To evaluate candidate genes and downstream mechanisms by which Pgb1 controls the aggregation of glycogen, extensive hippocampal transcriptomic and proteomic datasets were produced for aged mice of the BXD family. We utilized these datasets to identify Smarcal1 and Usp37 as potential regulators of PGB accumulation. To assess the effect of PGB burden on age-related cognitive decline, we performed phenome-wide association scans, transcriptomic analyses as well as conditioned fear memory and Y-maze testing. Importantly, we did not find any evidence suggesting a negative impact of PGBs on cognition. Taken together, our study demonstrates that the Pgb1 locus controls glycogen aggregation in astrocytes of the aged hippocampus without affecting age-related cognitive decline.

genetics↗

bioRxiv2021

Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

systems biology↗

bioRxiv2020

PKA-mediated phosphorylation of SPG11/spatacsin regulates binding with a subset of 14-3-3 proteins

Mutations in SPG11, encoding spatacsin, constitute the major cause of autosomal recessive Hereditary Spastic Paraplegia (HSP) with thinning of the corpus callosum. Previous studies showed that spatacsin orchestrates cellular traffic events through the formation of a coat-like complex and its loss of function results in lysosomal and axonal transport impairments. However, the upstream mechanisms that regulate spatacsin trafficking are unknown. Here, using proteomics and CRISPR/Cas9-mediated tagging of endogenous spatacsin, we identified a subset of 14-3-3 proteins as physiological interactors of spatacsin. The interaction is modulated by Protein Kinase A (PKA)-dependent phosphorylation of spatacsin at Ser1955, which initiates spatacsin trafficking from the plasma membrane to the intracellular space. Our study provides novel insight in understanding spatacsin physio-pathological roles with mechanistic dissection of its associated pathways.

cell biology↗

bioRxiv2019

PINOT: An Intuitive Resource for Integrating Protein-Protein Interactions

The past decade has seen the rise of omics data, for the understanding of biological systems in health and disease. This wealth of data includes protein-protein interaction (PPI) derived from both low and high-throughput assays, which is curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.\n\nWe here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from the IMEx consortium associated repositories (members and observers) and from WormBase for constructing, respectively, human and C. elegans PPI networks.\n\nUsers submit a query containing a list of proteins of interest for which PINOT will mine PPIs. PPI data is downloaded, merged, quality checked, and confidence scored based on the number of distinct methods and publications in which each interaction has been reported. Examples of PINOT applications are provided to highlight the performance, the ease of use and the potential applications of this tool.\n\nPINOT is a tool that allows users to survey the literature, extracting PPI data for a list of proteins of interest. The comparison with analogous tools showed that PINOT was able to extract similar numbers of PPIs while incorporating a set of innovative features. PINOT processes both small and large queries, it downloads PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI annotations (i.e. removing the need of manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each of the downloaded interaction data entry and provides results in a table format that can be easily further customised and/or directly uploaded in a network visualization software.

bioinformatics↗