Biology subjects

Harrison, P. J.

Publications and source records attributed to Harrison, P. J..

15 recordsLinked to original sources

bioRxiv2025

Structure of a pH-sensitive pentameric ligand-gated ion channel from the Sarcoptes scabies mite

Scabies is a skin infestation caused by the Sarcoptes scabiei mite. It produces a substantial global health burden, which is exacerbated by emerging resistance to prominent treatments, such as ivermectin. An anionic pentameric ligand-gated ion channel (pLGIC) identified in the mite, termed as SsCl, shows unique pH-sensitivity and is significantly modulated by ivermectin. Here, we use cryo-EM and electrophysiology to explore the pH-sensing mechanisms of SsCl and the impact of ivermectin on channel activity. Structures of SsCl were resolved under various pH conditions to capture a closed (pH 6.5) and desensitized (pH 9) state, alongside ivermectin-bound conformations. The desensitized structure adopts an unexpected "hourglass" conformation, suggesting the gating mechanisms may be more related to cation-selective channels. Structure-based analysis and mutagenesis highlighted extracellular histidine and glutamate residues which act as pH-sensors, revealing the protonation-dependent gating mechanism. Ivermectin binds at transmembrane subunit interfaces, stabilizing an open-pore conformation through hydrophobic interactions. Ivermectin bound-structures reveal a pH-dependent modulation, enhancing open-state prevalence at pH 9 and enabling atypical activation at pH 6.5, consistent with electrophysiological data. These structural and functional insights elucidate SsCls unique pH sensitivity and ivermectins mode of action, providing a foundation for designing next-generation therapeutics targeting this pathogen-related ion channel.

biochemistry↗

bioRxiv2024

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the Single Particle Analysis (SPA) workflow into a reasonably standardised procedure. Significant investment of development time have been made particularly in sample preparation, microscope data collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practises developed at national facilities has also begun to establish a degree of standardisation to data structures coming from the SPA workflow. There is opportunity to capitalise on this moment in the fields maturation, to capture metadata from SPA experiments and correlate this with experimental outcomes, which is presented here in a set of programmes called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as for defining methods for metadata capture to assist with archiving of cryoEM SPA data. We also envisage this tool to be useful to microscope operators and facilities looking to rapidly generate reports on SPA data collection and screening sessions. SynopsisEMinsight is a Python-based tool for systematically mining metadata from single particle analysis cryoEM experiments. The capture and analysis of metadata facilitates assessment of instrument performance, provides concise reporting of experiment performance and sample quality by analysing preprocessing results, and gathers metadata for deposition. We envisage this approach to benefit the microscope operator, facility managers, database developers and users.

biophysics↗

bioRxiv2023

Data Driven Taxonomy for Antipsychotic Medication: A New Classification System

BackgroundThere are over 25 licensed antipsychotic medications with diverse pharmacological and clinical profiles. Antipsychotics are commonly described as either typical or atypical, but this does not accurately reflect pharmacological profiles. There is thus a need for a data driven antipsychotic classification scheme suitable for clinicians and researchers which maps onto both pharmacological and clinical effects. MethodWe analysed affinities of 27 antipsychotics for 42 receptors from 3,325 receptor binding studies. We used a clustering algorithm to group antipsychotics based on their pattern of receptor affinity. Using a machine learning model, we examined the ability of this grouping to predict antipsychotic-induced side effects quantified according to an umbrella review of clinical trial and treatment guideline data. ResultsClustering resulted in four groups of antipsychotics. The predominant receptor affinity and effect/side effect fingerprints of these four groups were defined, as follows: Group 1 - Muscarinic (M3-M5) receptor antagonism; Cholinergic and metabolic side effects. Group 2 - Dopamine (D2) partial agonism and adrenergic antagonism; Globally low side effect burden. Group 3 - Serotonergic and dopaminergic antagonism; Globally moderate side effect burden. Group 4 - Dopaminergic antagonism; Extrapyramidal and motor side effects. Groups 1 and 4 were more efficacious than clusters 2 and 3. The novel classification was superior to existing approaches when predicting side effects. ConclusionsA receptor affinity-based grouping not only reflects compound pharmacology but also detects meaningful clinical differences to a greater extent than existing approaches. The approach has the potential to benefit both patients and researchers by guiding treatment and informing drug development.

pharmacology and toxicology↗

bioRxiv2022

Is brightfield all you need for mechanism of action prediction?

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.

bioinformatics↗

bioRxiv2022

Combining molecular and cell painting image data for mechanism of action prediction

The mechanism of action (MoA) of a compound describes the biological interaction through which it produces a pharmacological effect. Multiple data sources can be used for the purpose of predicting MoA, including compound structural information, and various assays, such as those based on cell morphology, transcriptomics and metabolomics. In the present study we explored the benefits and potential additive/synergistic effects of combining structural information, in the form of Morgan fingerprints, and morphological information, in the form of five-channel Cell Painting image data. For a set of 10 well represented MoA classes, we compared the performance of deep learning models trained on the two datasets separately versus a model trained on both datasets simultaneously. On a held-out test set we obtained a macro-averaged F1 score of 0.58 when training on only the structural data, 0.81 when training on only the image data, and 0.92 when training on both together. Thus indicating clear additive/synergistic effects and highlighting the benefit of integrating multiple data sources for MoA prediction.

bioinformatics↗

bioRxiv2021

Glutamatergic dysfunction leads to a hyper-dopaminergic phenotype through deficits in short-term habituation: a mechanism for aberrant salience.

Psychosis in disorders like schizophrenia is commonly associated with aberrant salience and elevated striatal dopamine. However, the underlying cause(s) of this hyper-dopaminergic state remain elusive. Various lines of evidence point to glutamatergic dysfunction and impairments in synaptic plasticity in the aetiology of schizophrenia, including deficits associated with the GluA1 AMPAR subunit. GluA1 knockout (Gria1-/-) mice provide a model of impaired synaptic plasticity in schizophrenia and exhibit a selective deficit in a form of short-term memory which underlies short-term habituation. As such, these mice are unable to reduce attention to recently presented stimuli. In this study we used fast-scan cyclic voltammetry to measure phasic dopamine responses in the nucleus accumbens of Gria1-/- mice to determine whether this behavioral phenotype might be a key driver of a hyper-dopaminergic state. There was no effect of GluA1 deletion on electrically-evoked dopamine responses in anaesthetized mice, demonstrating normal endogenous release properties of dopamine neurons in Gria1-/- mice. Furthermore, dopamine signals were initially similar in Gria1-/- mice compared to controls in response to both sucrose rewards and neutral light stimuli. They were also equally sensitive to changes in the magnitude of delivered rewards. In contrast, however, these stimulus-evoked dopamine signals failed to habituate with repeated presentations in Gria1-/- mice, resulting in a task-relevant, hyper-dopaminergic phenotype. Thus, here we show that GluA1 dysfunction, resulting in impaired short-term habituation, is a key driver of enhanced striatal dopamine responses, which may be an important contributor to aberrant salience and psychosis in psychiatric disorders like schizophrenia.

neuroscience↗

bioRxiv2021

Cryptic SARS-CoV2-spike-with-sugar interactions revealed by 'universal' saturation transfer analysis

Many host pathogen interactions such as human viruses (including non-SARS-coronaviruses) rely on attachment to host cell-surface glycans. There are conflicting reports about whether the Spike protein of SARS-CoV-2 binds to sialic acid commonly found on host cell-surface N-linked glycans. In the absence of a biochemical assay, the ability to analyze the binding of glycans to heavily- modified proteins and resolve this issue is limited. Classical Saturation Transfer Difference (STD) NMR can be confounded by overlapping sugar resonances that compound with known experimental constraints. Here we present universal saturation transfer analysis (uSTA), an NMR method that builds on existing approaches to provide a general and automated workflow for studying protein-ligand interactions. uSTA reveals that B-origin-lineage-SARS-CoV-2 spike trimer binds sialoside sugars in an end on manner and modelling guided by uSTA localises binding to the spike N-terminal domain (NTD). The sialylated-polylactosamine motif is found on tetraantennary human N-linked-glycoproteins in deeper lung and may have played a role in zoonosis. Provocatively, sialic acid binding is abolished by mutations in some subsequent SARS- CoV-2 variants-of-concern. A very high resolution cryo-EM structure confirms the NTD location and end on mode; it rationalises the effect of NTD mutations and the structure-activity relationship of sialic acid analogues. uSTA is demonstrated to be a robust, rapid and quantitative tool for analysis of binding, even in the most demanding systems. Extended AbstractThe surface proteins found on both pathogens and host cells mediate entry (and exit) and influence disease progression and transmission. Both types can bear host-generated post- translational modifications such as glycosylation that are essential for function but can confound biophysical methods used for dissecting key interactions. Several human viruses (including non- SARS-coronaviruses) attach to host cell-surface N-linked glycans that include forms of sialic acid (sialosides). There remains, however, conflicting evidence as to if or how SARS-associated coronaviruses might use such a mechanism. Here, we demonstrate quantitative extension of saturation transfer protein NMR methods to a complete mathematical model of the magnetization transfer caused by interactions between protein and ligand. The method couples objective resonance-identification via a deconvolution algorithm with Bloch-McConnell analysis to enable a structural, kinetic and thermodynamic analysis of ligand binding beyond previously-perceived limits of exchange rates, concentration or system. Using an automated and openly available workflow this universal saturation transfer analysis (uSTA) can be readily-applied in a range of even heavily-modified systems in a general manner to now obtain quantitative binding interaction parameters (KD, kEx). uSTA proved critical in mapping direct interactions between natural sialoside sugar ligands and relevant virus-surface attachment glycoproteins - SARS-CoV-2-spike and influenza-H1N1-haemagglutinin variants - by quantitating ligand signal in spectral regions otherwise occluded by resonances from mobile protein glycans (that also include sialosides). In B- origin-lineage-SARS-CoV-2 spike trimer end on-binding to sialoside sugars was revealed contrasting with extended surface-binding for heparin sugar ligands; uSTA-derived constraints used in structural modelling suggested sialoside-glycan binding sites in a beta-sheet-rich region of spike N-terminal domain (NTD). Consistent with this, uSTA-glycan binding was minimally- perturbed by antibodies that neutralize the ACE2-binding domain (RBD) but strongly disrupted in spike from the B1.1.7/alpha and B1.351/beta variants-of-concern, which possess hotspot mutations in the NTD. Sialoside binding in B-origin-lineage-NTD was unequivocally pinpointed by cryo-EM to a site that is created from residues that are notably deleted in variants (e.g. H69,V70,Y145 in alpha). An analysis of beneficial genetic variances in cohorts of patients from early 2020 suggests a model in which this site in the NTD of B-origin-lineage-SARS-CoV-2 (but not in alpha/beta-variants) may have exploited a specific sialylated-polylactosamine motif found on tetraantennary human N-linked-glycoproteins in deeper lung. Together these confirm a novel binding mode mediated by the unusual NTD of SARS-CoV-2 and suggest how it may drive virulence and/or zoonosis via modulation of glycan attachment. Since cell-surface glycans are widely relevant to biology and pathology, uSTA can now provide ready, quantitative, widespread analysis of complex, host-derived and post-translationally modified proteins with putative ligands relevant to disease even in previously confounding complex systems.

biophysics↗

bioRxiv2021

Learning to see colours: generating biologically relevant fluorescent labels from bright-field images

Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images would get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images to enable virtual staining for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.

bioinformatics↗

bioRxiv2020

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is online and based on data content rather than a priori metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope. Key PointsO_LIWe propose a pipeline model for building intelligent pipelines for streams, accounting for actual information content in data rather than a priori metadata, and present the HASTE Toolkit, a cloud-native software toolkit for supporting rapid development according to the proposed model. C_LIO_LIWe demonstrate how the HASTE Toolkit enables intelligent resource optimization in two image analysis case studies based on a) high-content imaging and b) transmission electron microscopy. C_LIO_LIWe highlight the challenges of storage, processing and transfer in streamed high volume, high velocity scientific data for both cloud and cloud-edge use cases. C_LI

bioinformatics↗

bioRxiv2020

Nanopore direct RNA sequencing detects differential expression between human cell populations

Accurately quantifying gene and isoform expression changes is essential to understanding cell functions, differentiation and disease. Therefore, a crucial requirement of RNA sequencing is identifying differential expression. The recent development of long-read direct RNA (dRNA) sequencing has the potential to overcome many limitations of short and long-read sequencing methods that require RNA fragmentation, cDNA synthesis or PCR. dRNA sequences native RNA and can encompass an entire RNA in a single read. However, its ability to identify differential gene and isoform expression in complex organisms is poorly characterised. Using a mixture of synthetic controls and human SH-SY5Y cell differentiation into neuron-like cells, we show that dRNA sequencing accurately quantifies RNA expression and identifies differential expression of genes and isoforms. We generated [~]4 million dRNA reads with a median length of 991 nt. On average, reads covered 74% of SH-SY5Y transcripts and 29% were full-length. Measurement of expression and fold changes between synthetic control RNAs confirmed accurate quantification of genes and isoforms. Differential expression of 231 genes, 291 isoforms, plus 27 isoform switches were detected between undifferentiated and differentiated SH-SY5Y cells and samples clustered by differentiation state at the gene and isoform level. Genes upregulated in neuron-like cells were associated with neurogenesis. We further identified >30,000 expressed transcripts including thousands of novel splice isoforms and transcriptional units. Our results establish the ability of dRNA sequencing to identify biologically relevant differences in gene and isoform expression and perform the key capabilities of expression profiling methodologies.

genomics↗

bioRxiv2020

Structural basis for control of antibiotic production by bacterial hormones

Actinobacteria produce numerous antibiotics and other specialised metabolites with important applications in medicine and agriculture. Diffusible hormones frequently control the production of such metabolites by binding TetR family transcriptional repressors (TFTRs), but the molecular basis for this remains unclear. The production of methylenomycin antibiotics in Streptomyces coelicolor A3(2) is initiated by binding of 2-alkyl-4-hydroxymethylfuran-3-carboxylic acid (AHFCA) hormones to the TFTR MmfR. Here, we report the X-ray crystal structure of an MmfR-AHFCA complex, establishing the structural basis for hormone recognition. We also elucidate the mechanism for DNA release upon hormone binding by single particle cryo-electron microscopy of an MmfR-operator complex. Electrophoretic mobility shift assays with MmfR mutants and synthetic AHFCA analogues illuminate the role played by individual amino acid residues and hormone functional groups in ligand recognition and DNA release. These findings will facilitate the exploitation of Actinobacterial hormones and their associated TFTRs in synthetic biology and novel antibiotic discovery.

biochemistry↗

bioRxiv2020

Deep learning models for lipid-nanoparticle-based drug delivery

Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. The resulting data has high dimensionality and high (but sparse) information content, making it challenging and costly to store and process. Early prediction of experimental outcome enables intelligent data management and decision making. We start from time-lapse data of HepG2 cells exposed to lipid-nanoparticles loaded with mRNA for expression of green fluorescent protein (GFP). We hypothesize that it is possible to predict if a cell will express GFP or not based on cell morphology at time-points prior to GFP expression. Here we present results on per-cell classification (GFP expression/no GFP expression) and regression (level of GFP expression) using three different approaches. In the first approach we use a convolutional neural network extracting per-cell features at each time point. We then utilize the same features combined with: a long-short-term memory (LSTM) network encoding temporal dynamics (approach 2); and time-series feature extraction using the python package tsfresh followed by principal component analysis and gradient boosting machines (approach 3), to reach a final classification or regression result. Application of the three approaches to a previously unanalyzed test set of cells showed good predictive performance of all three approaches but that accounting for the temporal dynamics via LSTMs or tsfresh led to significantly improved performance. The predictions made by the LSTM and tsfresh applications were not significantly different. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging.

cell biology↗

bioRxiv2019

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here we report the results of a GWAS of mood instability (MI) as a trait in a large population cohort (UK Biobank, n=363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with MI were identified with a heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia (SZ), anxiety and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified total 244 significant genes and 6 enriched gene sets. Tissue expression analysis from the SNP level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. Additionally, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues, and gene sets contributing to MI as a normal trait and will inform future work on the biology of mood and psychotic disorders, and to point the way towards potential for new stratified medicine approaches and the identification of novel trans-diagnostic drug targets.

genetics↗

bioRxiv2018

Transfer learning with deep convolutional neural network for classifying cellular morphological changes

Quantification and identification of cellular phenotypes from high content microscopy images have proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several non-trivial and independent analysis steps. Recently convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study we applied the pre-trained deep convolutional neural networks ResNet50, InceptionV3 and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pre-trained on ImageNet enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95 and 97% based on \"leave-one-compound-out\" cross-validation. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labelled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.

bioinformatics↗

bioRxiv2017

Genome-wide analysis of risk-taking behaviour and cross-disorder genetic correlations in 116,255 individuals from the UK Biobank cohort

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/no to the question \"Would you consider yourself a risk-taker?\" Risk-takers (compared to controls) were more likely to be men, smokers and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

genetics↗