Biology subjects

Walsh, R.

Publications and source records attributed to Walsh, R..

12 recordsLinked to original sources

bioRxiv2025

Enhanced yield and subtype identity of hPSC-derived midbrain dopamine neuron by modulation of WNT and FGF18 signaling

While clinical trials are ongoing using human pluripotent stem cell-derived midbrain dopamine (mDA) neuron precursor grafts in Parkinsons disease (PD), current protocols to derive mDA neurons remain suboptimal. In particular, the yield of TH+ mDA neurons after in vivo grafting and the expression of some mDA neuron and subtype-specific markers can be further improved. For example, characterization of mDA grafts by single cell transcriptomics has yielded only a small proportion of mDA neurons and a considerable fraction of contaminating cell populations. Here we present an optimized mDA neuron differentiation strategy that builds on our clinical grade ("Boost") protocol but includes the addition of FGF18 and IWP2 treatment ("Boost+") at the mDA neurogenesis stage. We demonstrate that Boost+ mDA neurons show higher expression of EN1, PITX3 and ALDH1A1. Improvements in both mDA neurons yield and transcriptional similarity to primary mDA neurons is observed both in vitro and in grafts. Furthermore, grafts are enriched in authentic A9 mDA neurons by single nucSeq. Functional studies in vitro demonstrate increased dopamine production and release and improved electrophysiological properties. In vivo analyses show increased percentages of TH+ mDA neurons resulting in efficient rescue of amphetamine induced rotation behavior in the 6-OHDA rat model and rescue of some motor deficits in non-drug induced assays, including the ladder rung assay that is not improved by Boost mDA neurons. The Boost+ conditions present an optimized protocol with advantages for disease modeling and mDA neuron grafting paradigms.

cell biology↗

bioRxiv2024

A GABA-A receptor response to CBD following status epilepticus in the medial entorhinal cortex of the rat

Cannabidiol is a non-psychoactive phytocannabinoid that has been implicated as a potential therapeutic in numerous neurological diseases. Perhaps the most widespread therapeutic use of CBD has been in the form of Epidiolex, which is used to treat seizures associated with Lennox-Gastaut syndrome, Dravet syndrome and tuberous sclerosis. Whilst the effectiveness of CBD in seizure reduction is clear, its mechanism of action is complex, and reflects the wide range of pharmacodynamic targets that includes receptors, ion channels and enzymes. This study investigated the effects of action of cannabidiol (CBD) on GABAergic transmission in layer II of the medial entorhinal cortex in animals that had previously undergone a period of status epilepticus (SE). Spontaneous post-synaptic currents were recorded from medial entorhinal cortex layer II pyramidal cells in animals 1-7 after SE and in age matched controls as well as in tissue resected from children with temporal lobe epilepsy (TLE). CBD enhanced GABAAR-mediated inhibition by increasing decay times and inhibitory charge transfer across the postsynaptic membrane in status epilepticus (SE) but did not alter GABAergic transmission in age-matched control rats. The SE-induced effects of CBD were blocked by ligands acting as inverse agonists at the benzodiazepine site of the GABAAR receptor and the effects of CBD were additive to low-doses of benzodiazepine and barbiturate agonists, consistent with allosteric actions on the GABAAR. Similar effects were observed in both SE rat and human layer II neurons. Overall, these data suggest CBD may act as a positive allosteric modulator (PAM) at postsynaptic GABAARs and this action appears to develop following SE. Key pointsO_LICBD increases inhibition only post status epilepticus. C_LIO_LIThis effect is blocked by benzodiazepine site inverse agonists C_LIO_LICBD is additive to low-dose zolpidem, suggesting an allosteric effect on the GABAAR C_LIO_LICBD was similarly effective in ex vivo human epileptic tissue C_LIO_LIThese results suggest CBD is a positive allosteric modulator at the GABAAR C_LI

neuroscience↗

bioRxiv2024

Spatially resolved niche and tumor microenvironmental alterations in gastric cancer peritoneal metastases

Peritoneal metastases (PM) in gastric cancer (GC) portend a poor prognosis, yet our understanding of tumor microenvironmental (TME) characteristics associated with GCPM remain limited. Here, we analyzed intrinsic genomic alterations and transcriptomic programs predictive of GCPM in a prospective cohort of 248 patients, identifying CDH1, PIGR, and ELF3 mutations as predictors. By inspecting the spatial dynamics of the TME, we find that tumor compartment infiltration of pro-tumorigenic cell types such as inflammatory cancer-associated fibroblasts (CAFs) predict peritoneal recurrence. Next, in a cross-sectional study of 205 samples from 55 patients, distinct pathways and immune compositions in GCPM relative to liver metastases highlight the TMEs significance in transcoelomic metastases. Notably, several putative therapeutic targets exhibited distinct expression patterns between PTs and PMs. We also observed increased immune infiltration in GCPMs treated with systemic immunotherapy and intraperitoneal chemotherapy. Our findings highlight transcriptomic variations and niche reprogramming in the GCPM peritoneal environment, revealing roles of myeloid dendritic cells, effector memory CD8+ T cells, and CAFs in metastatic progression. Statement of significanceComprehensive molecular profiling of gastric cancer primary and peritoneal tumors unveils crucial insights into the distinct molecular and immune landscape of peritoneal metastases. Identifying predictive markers and therapeutic targets emphasizes the significance of tumor microenvironment alterations in guiding future therapies for gastric cancer peritoneal metastasis.

cancer biology↗

bioRxiv2023

Variant annotation across homologous proteins (Paralogue Annotation) identifies disease-causing missense variants with high precision, and is widely applicable across protein families

BackgroundDistinguishing pathogenic variants from those that are rare but benign remains a key challenge in clinical genetics, especially for variants not previously observed and characterised in humans. In vitro and in vivo functional characterisation are typically resource intensive, and model systems may not accurately predict influence on human disease. Many in silico tools have been developed to predict which variants are disease-causing, but typically lack precision. Here we demonstrate the applicability of a framework, called Paralogue Annotation, that draws on information from previously-characterised variants in homologous proteins to predict whether variants in a gene of interest are likely disease causing. MethodsWe assessed the performance of Paralogue Annotation through three orthogonal approaches: (1) comparison to established in silico variant prediction tools using 47,016 missense variants from ClinVar across 3,524 genes representing a broad range of diverse protein classes, by calculating precision and sensitivity; (2) evaluation against large-scale functional assays of variant effect; and (3) comparing odd ratios calculated from case-control association tests for inherited cardiac arrhythmia syndromes, and neurodevelopmental disorders with epilepsy, stratifying variants by Paralogue Annotation. ResultsParalogue Annotation correctly annotates 4,328 ClinVar pathogenic variants, with 245 false positives, yielding a precision of 0.95. This increases to 0.99 with more stringent annotation parameters (requiring greater conservation of amino acids between annotated orthologues) at the expense of sensitivity. Compared to established tools, Paralogue Annotation has higher precision for the identification of pathogenic variants, albeit with lower sensitivity across diverse test sets. Extending the technique by transferring annotations between homologous protein domains, rather than full-length protein paralogues, increases sensitivity. Rare variants predicted pathogenic by Paralogue Annotation were more strongly disease-associated (increased odds ratio) than unstratified rare variants for six out of eight genes tested with case-control cohort approaches. ConclusionsWe demonstrate that Paralogue Annotation has high precision for predicting pathogenic missense variants, providing a useful line of evidence for clinical variant interpretation that is complementary to other approaches in use. As the number of characterised variants increases in reference datasets such as ClinVar, Paralogue Annotation will further increase in sensitivity and applicability.

bioinformatics↗

bioRxiv2023

Generation of human cerebral organoids with a structured outer subventricular zone

Mammalian outer radial glia (oRG) emerge as cortical progenitor cells that directly support the development of an enlarged outer subventricular zone (oSVZ) and, in turn, the expansion of the neocortex. The in vitro generation of oRG is essential to model and investigate the underlying mechanisms of human neocortical development and expansion. By activating the STAT3 pathway using LIF, which is not produced in guided cortical organoids, we developed a cerebral organoid differentiation method from human pluripotent stem cells (hPSCs) that recapitulates the expansion of a progenitor pool into the oSVZ. The structured oSVZ is composed of progenitor cells expressing specific oRG markers such as GFAP, LIFR, HOPX, which closely matches human oRG in vivo. In this microenvironment, cortical neurons showed faster maturation with enhanced metabolic and functional activity. Incorporation of hPSC-derived brain vascular LIF- producing pericytes in cerebral organoids mimicked the effects of LIF treatment. These data indicate that the cellular complexity of the cortical microenvironment, including cell-types of the brain vasculature, favors the appearance of oRG and provides a platform to routinely study oRG in hPSC-derived brain organoids.

developmental biology↗

bioRxiv2020

Optimizing model comparison for enzymatic mechanism analysis

Characterization of enzyme inhibition in drug development is usually limited to a basic analysis with the classical inhibition models or simply the use of IC50 values. However, a better understanding of enzyme physiology and regulation is seen as key to unraveling and treating the processes associated with stubborn disease targets like Alzheimers disease. Recently it has been shown that enzyme regulation, through substrate, inhibitor or activator interactions can be modeled using the summation of binding curves. Here we examine the use of the modular equation permutations, that can be produced through binding curve summation, to fit and evaluate the interactions of abietic acid with protein tyrosine phosphatase nonreceptor type 11. This new sort of analysis will allow for improved insight into the physiological role enzymes play and the consequence their modulation may have in disease progression.

biochemistry↗

bioRxiv2020

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

BackgroundAccurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning tools are useful for genome-wide variant prioritisation but remain imprecise. Since the relationship between molecular consequence and likelihood of pathogenicity varies between genes with distinct molecular mechanisms, we hypothesised that a disease-specific classifier may outperform existing genome-wide tools. MethodsWe present a novel disease-specific variant classification tool, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias, trained with variants of known clinical effect. To benchmark against state-of-the-art genome-wide pathogenicity classification tools, we assessed classification of hold-out test variants using both overall performance metrics, and metrics of high-confidence (>90%) classifications relevant to variant interpretation. We further evaluated the prioritisation of variants associated with disease and patient clinical outcomes, providing validations that are robust to potential mis-classification in gold-standard reference datasets. ResultsCardioBoost has higher discriminating power than published genome-wide variant classification tools in distinguishing between pathogenic and benign variants based on overall classification performance measures with the highest area under the Precision-Recall Curve as 91% for cardiomyopathies and as 96% for inherited arrhythmias. When assessed at high-confidence (>90%) classification thresholds, prediction accuracy is improved by at least 120% over existing tools for both cardiomyopathies and arrhythmias, with significantly improved sensitivity and specificity. Finally, CardioBoost improves prioritisation of variants significantly associated with disease, and stratifies survival of patients with cardiomyopathies, confirming biologically relevant variant classification. ConclusionsWe demonstrate that a disease-specific variant pathogenicity prediction tool outperforms state-of-the-art genome-wide tools for the classification of rare missense variants of uncertain significance for inherited cardiac conditions. To facilitate evaluation of CardioBoost, we provide pre-computed pathogenicity scores for all possible rare missense variants in genes associated with cardiomyopathies and arrhythmias (https://www.cardiodb.org/cardioboost/). Our results also highlight the need to develop and evaluate variant classification tools focused on specific diseases and clinical application contexts. Our proposed model for assessing variants in known disease genes, and the use of application-specific evaluations, is broadly applicable to improve variant interpretation across a wide range of Mendelian diseases.

genetics↗

bioRxiv2019

Genomics of Egyptian Healthy Volunteers: The EHVol Study

Comprehensive genomic databases offer unprecedented opportunities towards effective tailored strategies for the prevention and treatment of disease. The integration of genomic and phenotypic data from diverse ethnic populations is also key to advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases, however, are not representative of the global human population, making variant interpretation challenging and uncertain, especially in underrepresented populations such as the North African population. To address this, a study of 391 Egyptian healthy volunteers (EHVols) was initiated as a milestone towards establishing the 1000 Egyptian Genomes project.

genomics↗

bioRxiv2018

Response to Shah et al: Using high-resolution variant frequencies empowers clinical genome interpretation and enables investigation of genetic architecture

Main text Main text Supplemental Data description Declaration of Interests Web Resources References Recent work by Shah and colleagues1 demonstrated that many variants in the ClinVar database2 are misclassified, and that disease-specific allele frequency (AF) thresholds can identify wrongly classified alleles by flagging variants that are too prevalent in the population to be causative of rare penetrant disease. While we agree with the main conclusions of this work, the authors compare their AF filtering approach to our recently published method3, concluding that the method we advanced \"may be prone to removing potentially pathogenic variants\". This is incorrect. Here we demonstrate that our approach is robust, and further illustrate the power of di ...

genomics↗

bioRxiv2018

Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: The case of hypertrophic cardiomyopathy

BackgroundInternational guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework.\n\nResultsWe compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF[≥]0.95, and therefore clinically actionable, were identified in 5 established HCM genes. Applying this approach leads to an estimated 14-20% increase in cases with actionable HCM variants.\n\nConclusionsWhen found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a \"likely pathogenic\" classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent, unbiased and equitable approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

genetics↗

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗

bioRxiv2017

Marginal Returns And Levels Of Research Grant Support Among Scientists Supported By The National Institutes Of Health

The current era of worsening hypercompetition in biomedical research has drawn attention to the possibility of decreasing marginal returns from research funding. Recent work has described decreasing marginal returns as a function of annual dollars granted to individual scientists. However, different fields of research incur varying cost structures. Therefore, we developed a Grant Support Index (GSI) that focuses on grant activity code, as opposed to field of study or cost. In a cohort of over 71,000 unique scientists funded by NIH between 1996 and 2014 we analyzed the association of grant support (as measured by annual GSI) with 3 bibliometric outcomes, maximum Relative Citation Ratio (which arguably reflects a scientists most influential work), median Relative Citation Ratio, and annual weighted Relative Citation Ratio (which is more dependent on publication counts). We found that for all 3 measures marginal returns decline as annual GSI increases. Thus, we confirm prior findings of decreasing marginal returns with higher levels of research funding support.

scientific communication and education↗