Biology subjects

Lewis, P. A.

Publications and source records attributed to Lewis, P. A..

41 records · Page 3Linked to original sources

bioRxiv2019

Susceptibility to auditory closed-loop stimulation of sleep slow oscillations changes with age

BackgroundCortical slow oscillations (SOs) and thalamo-cortical sleep spindles hallmark slow wave sleep and facilitate sleep-dependent memory consolidation. Experiments utilising auditory closed-loop stimulation to enhance these oscillations have shown great potential in young and older subjects. However, the magnitude of responses has yet to be compared between these age groups. ObjectiveWe examined the possibility of enhancing SOs and performance on different memory tasks in a healthy older population using auditory closed-loop stimulation and contrast effects to a young adult cohort. MethodsIn a within-subject design, subjects (n = 17, 55.7 {+/-} 1.0 years, 9 female) received auditory click stimulation in synchrony with SO up-states, which was compared to a no-stimulation sham condition. Overnight memory consolidation was assessed for declarative word-pairs and procedural finger-tapping skill. Post-sleep encoding capabilities were tested with a picture recognition task. Electrophysiological effects of stimulation were compared to those reported previously in a younger cohort (n = 11, 24.2 {+/-} 0.9 years, 8 female). ResultsOvernight retention and post-sleep encoding performance of the older cohort revealed no beneficial effect of stimulation, which contrasts with the enhancing effect the same stimulation protocol had in our younger cohort. Auditory stimulation prolonged endogenous SO trains and induced sleep spindles phase-locked to SO up-states in the older population. However, responses were markedly reduced compared to younger subjects. Additionally, the temporal dynamics of stimulation effects on SOs and spindles differed between age groups. ConclusionsOur findings suggest that the susceptibility to auditory stimulation during sleep drastically changes with age and reveal the difficulties of translating a functional protocol from younger to older populations. HighlightsO_LIAuditory closed-loop stimulation induced SOs and sleep spindles in older subjects C_LIO_LIStimulation effects were reduced and overall susceptibility diminished with age C_LIO_LISlow oscillation and sleep spindle dynamics deviated from those in younger subjects C_LIO_LIStimulation shows no evidence for memory effect in older subjects C_LI

neuroscience↗

bioRxiv2019

Mutations in Auxilin cause parkinsonism via impaired clathrin-mediated trafficking at the Golgi apparatus and synapse

Parkinsons disease (PD) is a common neurodegenerative motor disorder characterized in part by neuropathological lesions in the nigrostriatal pathway. Loss of function mutations in Auxilin, the major neuronal clathrin uncoating protein, cause an aggressive form of juvenile onset PD. How mutations in Auxilin cause PD, is currently not understood. Here, we generated a novel mouse model carrying an endogenous pathogenic Auxilin mutation that phenocopies neurological features observed in patients, including motor impairments and seizures. Unbiased mapping of the Auxilin interactome identified synaptic and Golgi-resident clathrin adaptor proteins as novel interactors. Impaired clathrin-mediated trafficking in mutant Auxilin mice, both at the Golgi and the synapse, results in neuropathological lesions in the nigrostriatal pathway. Collectively, these results provide molecular mechanisms of PD pathogenesis in Auxilin mutation carriers, reinforcing a key role for clathrin-mediated trafficking in PD, and expand our understanding of the cellular function of Auxilin.

neuroscience↗

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↗

bioRxiv2019

Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

Substantial genome-wide association study (GWAS) work in Parkinsons disease (PD) has led to an increasing number of loci shown reliably and robustly to be associated with the increased risk of the disease. Prioritising causative genes and pathways from these studies has proven problematic. Here, we present a comprehensive analysis of PD GWAS data with expression and methylation quantitative trait loci (eQTL/mQTL) using Colocalisation analysis (Coloc) and transcriptome-wide association analysis (TWAS) to uncover putative gene expression and splicing mechanisms driving PD GWAS signals. Candidate genes were further characterised by determining cell-type specificity, weighted gene co-expression (WGNCA) and protein-protein interaction (PPI) networks.\n\nGene-level analysis of expression revealed 5 genes (WDR6, CD38, GPNMB, RAB29, TMEM163) that replicated using both Coloc and TWAS analyses in both GTEx and Braineac expression datasets. A further 6 genes (ZRANB3, PCGF3, NEK1, NUPL2, GALC, CTSB) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell-types compared to neurons. The WGNCA analysis showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes related with protein ubiquitination (protein ubiquitination (p=7.47e-10) and ubiquitin-dependent protein catabolic process (p = 2.57e-17) in nucleus accumbens, caudate and putamen, while TMEM163 and ZRANB3 were both important in modules indicating regulation of signalling (p=1.33e-65] and cell communication (p=7.55e-35) in the frontal cortex and caudate respectively. PPI analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known Mendelian PD and parkinsonism proteins than would be expected by chance. The proteins core proteins this network were enriched for regulation of the ERBB receptor tyrosine protein kinase signalling pathways.\n\nTogether, these results point to a number of candidate genes and pathways that are driving the associations observed in PD GWAS studies.

neuroscience↗

bioRxiv2017

Identification of Memory Reactivation during Sleep by EEG Classification

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Above chance classification was significantly higher in slow wave sleep than in stage 2, suggesting differential efficacy of TMR in these two sleep stages. Interestingly, classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be invaluable for future investigations of memory consolidation.

neuroscience↗