Biology subjects

Morgan, D.

Publications and source records attributed to Morgan, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Cryo-EM Structure of a Triazole alpha-Conotoxin GI Mimetic Bound to the Muscle-Type Nicotinic Acetylcholine Receptor

Disulfide-rich peptides possess exceptional potency and selectivity but are often limited by the instability and synthetic challenges associated with native disulfide bonds. Here, we report the design, synthesis, pharmacological evaluation, and structural characterisation of triazole-based peptidomimetics of the -GI conotoxin, a selective antagonist of the muscle-type nicotinic acetylcholine receptor (nAChR). A series of 1,4- and 1,5-disubstituted triazole analogues were prepared entirely on resin using CuAAC and RuAAC chemistry to replace the native Cys3/13 disulfide bridge. Functional evaluation against human muscle nAChRs revealed that 1,5-triazole analogues retained low-nanomolar potency, with the lead mimetic exhibiting activity comparable to native -GI. Cryo-electron microscopy of the lead compound bound to the muscle-type nAChR provided the first structure of a disulfide-isostere peptidomimetic in complex with a membrane receptor. The structure demonstrates that the 1,5-triazole reproduces the native peptide fold with high fidelity while contributing receptor-facing interactions not available to the native disulfide bridge. Molecular dynamics simulations further revealed conserved hydration networks and similar conformational sampling between the native peptide and lead mimetic. Together, these findings establish triazoles as effective disulfide surrogates and provide a structural framework for the rational design of stabilised conotoxin therapeutics.

biochemistry↗

bioRxiv2025

Cell Barcoding Reveals Lineage-dependent Outcomes in hiPSC Cardiac Differentiation

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have potential applications in treating cardiovascular disease but are currently limited in their clinical translation. A primary limitation is the poor clinical scalability of hiPSC-CMs, with the heterogeneity of hiPSC cardiac differentiation significantly contributing to this limitation. We hypothesize that clinical scalability can be improved by tracking and controlling hiPSC clonal heterogeneity, a variable often overlooked in current differentiation approaches. "Fate priming", wherein clonal lineage identity determines differentiation fate, has been demonstrated in other stem cell differentiation pathways. We investigated fate priming in hiPSC cardiac differentiation using the ClonMapper cell barcoding platform to label, track, and isolate distinct hiPSC lineages from the same cell line. We show that certain hiPSC lineages preferentially differentiate into hiPSC-CMs or non-CMs. After isolating lineages with apparent fate priming, we found significant differences in cardiac differentiation outcomes between these single-clone populations and heterogeneous, multi-clone hiPSC populations. These findings indicate that lineage identity influences hiPSC cardiac differentiation outcomes. SIGNIFICANCE STATEMENTCardiovascular disease is a significant global health concern that can be addressed by engineering artificial tissues to develop new treatments for heart disease or to directly replace damaged heart tissue. Stem cells are a useful tool for engineering these tissues because of their ability to become cardiomyocytes. However, their clinical translation is limited by variability in the process of differentiating stem cells into cardiomyocytes. This article reports findings that show different lineages of genetically identical human induced pluripotent stem cells have different capacities for differentiating into cardiomyocytes, which may contribute to the variability observed.

bioengineering↗

bioRxiv2025

PD-1 blockade during T cell priming enhances long-term protection against metastatic tumors by epigenetically tuning T cell exhaustion

In cancer, CD8+ T cell responses are dominated by exhausted T cells, which can be reinvigorated using immune checkpoint blockade therapy and can control large tumors. However, it remains unclear which T cell fate best supports long-term immunity following tumor regression or clearance and a period of minimal antigen load. This question is particularly relevant following surgical tumor resection, when tuning the immune system could prevent recurrence. To determine which T cell fate provides durable protection following surgery and metastatic rechallenge, we modulated T cell priming using anti-PD-1, IFN-{beta} or agonistic anti-CD40 and assessed effects on CD8+ T cell differentiation and overall survival. IFN-{beta} and anti-CD40 promoted effector and memory-like T cell states, respectively, whereas anti-PD-1 did not markedly alter T cell differentiation, yet conferred the greatest survival benefit against metastatic tumors. Notably, anti-PD-1 induced epigenetic remodeling, which was detectable upon metastatic recall, consistent with the maintenance of a circulatory intermediate-exhausted T cell state. Thus, while effector and memory precursor-like T cells could be generated with IFN-{beta} and agonistic anti-CD40, only the intermediate-exhausted T cell state driven by anti-PD-1 supported durable anti-tumor immunity. SummaryThis study shows that PD-1 blockade during T cell priming promotes a circulatory intermediate-exhausted CD8 T cell state that uniquely supports durable anti-tumor immunity after surgical resection and metastatic challenge, outperforming effector or memory-like T cell responses generated by IFN-{beta} or CD40 agonist treatment, respectively.

immunology↗

bioRxiv2025

Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer

Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.

cancer biology↗

bioRxiv2024

Mapping cell-cell fusion at single-cell resolution

Cell-cell fusion is a tightly controlled process in the human body known to be involved in fertilization, placental development, muscle growth, bone remodeling, and viral response. Fusion between cancer cells results first in a whole-genome doubled state, which may be followed by the generation of aneuploidies; these genomic alterations are known drivers of tumor evolution. The role of cell-cell fusion in cancer progression and treatment response has been understudied due to limited experimental systems for tracking and analyzing individual fusion events. To meet this need, we developed a molecular toolkit to map the origins and outcomes of individual cell fusion events within a tumor cell population. This platform, ClonMapper Duo ( CMDuo), identifies cells that have undergone cell-cell fusion through a combination of reporter expression and engineered fluorescence-associated index sequences paired to randomly generated nucleotide barcodes. scRNA-seq of the indexed barcodes enables the mapping of each set of parental cells and fusion progeny throughout the cell population. In triple-negative breast cancer cells CMDuo uncovered subclonal transcriptomic hybridization and unveiled distinct cell-states which arise in direct consequence of homotypic cell-cell fusion. CMDuo is a platform that enables mapping of cell-cell fusion events in high-throughput single cell data and enables the study of cell fusion in disease progression and therapeutic response.

systems biology↗

bioRxiv2024

Deep learning identifies heterogeneous subpopulations in breast cancer cell lines

MotivationCells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment. ResultsWe demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.

cancer biology↗

bioRxiv2024

Directed evolution-based discovery of ligands for in vivo restimulation of CAR-T cells

Chimeric antigen receptor (CAR) T cell therapy targeting CD19 elicits remarkable clinical efficacy in B-cell malignancies, but many patients relapse due to failed expansion and/or progressive loss of CAR-T cells. We recently reported a strategy to potently restimulate CAR-T cells in vivo, enhancing their functionality by administration of a vaccine-like stimulus comprised of surrogate peptide ligands for a CAR linked to a lymph node-targeting amphiphilic PEG-lipid (termed CAR-T-vax). Here, we demonstrate a general strategy to generate and optimize peptide mimotopes enabling CAR-T-vax generation for any CAR. Using the clinical CD19 CAR (FMC63) as a test case, we employed yeast surface display to identify peptide binders to soluble IgG versions of FMC63, which were subsequently affinity matured by directed evolution. CAR-T vaccines using these optimized mimotopes triggered marked expansion and memory development of CD19 CAR-T cells in both syngeneic and humanized mouse models of B-ALL/Lymphoma, and enhanced control of disease progression. This approach thus enables vaccine boosting to be applied to any clinically relevant CAR-T cell product.

bioengineering↗

bioRxiv2024

Nutrient Sensing Receptor GPRC6A Regulates mTORC1 Signaling and Tau Biology

Tauopathies, including Alzheimers disease (AD), comprise microtubule-associated protein tau aggregates that cause neuronal cell death and clinical cognitive decline. Reducing overall tau abundance remains a central strategy for therapeutics; however, no disease-modifying treatment exists to date. One principal pathway for balancing cellular proteostasis includes the mechanistic target of rapamycin complex 1 (mTORC1) signaling. Recently, arginine emerged as one of the primary amino acids to activate mTORC1 through several intracellular arginine sensors and an extracellular arginine receptor, namely the G protein-coupled receptor (GPCR) family C, group 6, member A (GPRC6A). Human AD brains were previously reported with elevated mTORC1 signaling; however, it is unclear whether arginine sensing and signaling to mTORC1 plays a role in tauopathies. Herein, we examined arginine sensing associated with mTORC1 signaling in the human AD and animal models of tauopathy. We found that human AD brains maintained elevated levels of arginine sensors with potential uncoupling of arginine sensing pathways. Furthermore, we observed increased GPRC6A and arginine in the brain, accompanied by increased mTORC1 signaling and decreased autophagy in a mouse model of tauopathy (Tau PS19). We also discovered that both supplementing arginine and overexpressing GPRC6A in cell culture models could independently activate mTORC1 and promote tau accumulation. In addition, we found that suppressing GPRC6A signaling by either genetic reduction or pharmacological antagonism reduced tau accumulation, phosphorylation, and oligomerization. Overall, these findings uncover the crucial role of arginine sensing pathways in deregulating mTORC1 signaling in tauopathies and identify GPRC6A as a promising target for future therapeutics in tauopathies and other proteinopathies. Significance StatementTauopathies, including Alzheimers disease (AD), accumulate pathogenic tau protein inclusions that potentially contribute to the hyperactive mechanistic target of rapamycin complex 1 (mTORC1) signaling and eventually cause neuronal cell death. Here, we presented novel findings that AD and animal models of tauopathy maintained increased expression of arginine sensors and uncoupling of arginine sensing associated with mTORC1 signaling. We investigated the role of a putative extracellular arginine and basic L-amino acid sensing G protein-coupled receptor (GPCR) family C, group 6, member A (GPRC6A) in activating mTORC1 and accelerating pathogenic tau phenotypes in several cell models. Additionally, we showed that genetic repression or antagonism of GPRC6A signaling provides a novel therapeutic target for tauopathies and other proteinopathies.

neuroscience↗

bioRxiv2024

Sub-clinical glutamate receptor antagonist combinations prevent progressive demyelination.

Multiple sclerosis (MS) affects almost 3 million people globally who suffer demyelination as a series of relapses and remissions that tend towards progressive deterioration over time. The proximate cause is auto-immune attack by the adaptive immune system; therapies directed against this are effective during the relapsing-remitting phase but are less effective or ineffective during progression where other injury mechanisms may be significant. LPS- and cuprizone-induced experimental demyelination share features of progressive demyelination in MS but the underlying mechanisms are not well understood. We show here that these demyelination models can be reproduced ex vivo using short protocols, revealing that combined antagonism of two types of glutamate receptor, NMDA and AMPA, using clinically approved antagonists at sub-clinical doses, can protect against these forms of demyelination. Combined low dose therapy was subsequently shown to be effective in vivo against dietary cuprizone and experimental autoimmune-encephalomyelitis (EAE) models of demyelination and, in particular, protected the smaller myelinated axons that are the main substrate of the function loss in progressive MS.

neuroscience↗

bioRxiv2024

Functional Characterisation of the ATOH1 Molecular Subtype Indicates a Pro-Metastatic Role in Small Cell Lung Cancer

Molecular subtypes of Small Cell Lung Cancer (SCLC) have been described based on differential expression of transcription factors (TFs) ASCL1, NEUROD1, POU2F3 and immune-related genes. We previously reported an additional subtype based on expression of the neurogenic TF ATOH1 within our SCLC Circulating tumour cell- Derived eXplant (CDX) model biobank. Here we show that ATOH1 protein was detected in 7/81 preclinical models and 16/102 clinical samples of SCLC. In CDX models, ATOH1 directly regulated neurogenesis and differentiation programs consistent with roles in normal tissues. In ex vivo cultures of ATOH1-positive CDX, ATOH1 was required for cell survival. In vivo, ATOH1 depletion slowed tumour growth and suppressed liver metastasis. Our data validate ATOH1 as a bona fide oncogenic driver of SCLC with tumour cell survival and pro-metastatic functions. Further investigation to explore ATOH1 driven vulnerabilities for targeted treatment with predictive biomarkers is warranted.

cancer biology↗

bioRxiv2023

MetaQuad: Shared Informative Variants Discovery in Metagenomic Samples

MotivationStrain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge. ResultsMetaQuad employs a density-based clustering technique to efficiently differentiate shared variants from non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false-positive SNPs without greatly affecting the true-positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7,591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased six weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments. AvailabilityMetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad. Contactjwkho@hku.hk Supplementary informationSupplementary data are available at XXXX online.

genomics↗

bioRxiv2023

Antibodies generated in vitro and in vivo elucidate design of a thermostable ADDomer COVID-19 nasal nanoparticle vaccine

COVID-19 continues to damage populations, communities and economies worldwide. Vaccines have reduced COVID-19-related hospitalisations and deaths, primarily in developed countries. Persisting infection rates, and highly transmissible SARS-CoV-2 Variants of Concern (VOCs) causing repeat and breakthrough infections, underscore the ongoing need for new treatments to achieve a global solution. Based on ADDomer, a self-assembling protein nanoparticle scaffold, we created ADDoCoV, a thermostable COVID-19 candidate vaccine displaying multiple copies of a SARS-CoV-2 receptor binding motif (RBM)-derived epitope. In vitro generated neutralising nanobodies combined with molecular dynamics (MD) simulations and electron cryo-microscopy (cryo-EM) established authenticity and accessibility of the epitopes displayed. A Gigabody comprising multimerized nanobodies prevented SARS-CoV-2 virion attachment with picomolar EC50. Antibodies generated by immunising mice cross-reacted with VOCs including Delta and Omicron. Our study elucidates nasal administration of ADDomer-based nanoparticles for active and passive immunisation against SARS-CoV-2 and provides a blueprint for designing nanoparticle reagents to combat respiratory viral infections.

biochemistry↗

bioRxiv2022

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗

bioRxiv2022

Using methylation data to improve transcription factor binding prediction

Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) is commonly used to estimate transcription factor (TF) binding "motif" locations. However, the PWM-based scores assigned to these inferred locations are associated with only modest accuracy when benchmarked against in vivo TF binding. One reason for this limited performance may be because PWMs do not incorporate information regarding the epigenetic context necessary for TF binding. To investigate this, we developed a framework to score inferred TF binding locations using CpG methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores. In addition, our analysis shows that, while most TFs do not bind to methylated promoter regions, there are several exceptions to this rule, indicating that the role of methylation in TF binding may be cell-type and context specific. Finally, we also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context specific TF binding using methylation data, a crucial initial step for understanding the impact of methylation on gene regulatory processes.

bioinformatics↗

bioRxiv2021

Activating P2Y1 receptors improves function in arteries with repressed autophagy

ObjectiveThe importance of endothelial cell (EC) autophagy to vascular homeostasis is evolving. Earlier we reported that purinergic 2Y1 receptor (P2Y1-R) activation rejuvenates shearstress induced nitric oxide (NO) generation in bovine aortic endothelial cells that is otherwise compromised after pharmacological and genetic autophagy repression. Here we determined the translational and functional relevance of these findings. Approach and ResultsFirst we assessed translational relevance using older humans and mice that exhibit blunted EC autophagy at rest together with impaired arterial function vs. appropriate controls. Rhythmic handgrip exercise elevated radial artery shear rate similarly in adult and older males for 60-min. Compared to baseline, autophagy initiation, p-eNOSS1177 activation, and NO generation, occurred in radial artery ECs from adult but not older subjects. Regarding mice, indexes of autophagy and p-eNOSS1177 activation were robust in ECs from adult but not older mice in response to 60-min treadmill-running. Next we questioned whether an inability to initiate EC autophagy precipitates arterial dysfunction. Age-associated reductions in intraluminal flow-mediated vasodilation observed in older vs. adult mice were recapitulated in arteries from adult mice by : (i) NO synthase inhibition; (ii) autophagy impairment using 3-methyladenine (3-MA); (iii) EC Atg3 depletion (iecAtg3KO mice); (iv) P2Y1-R blockade; and (v) germline depletion of P2Y1-Rs. Importantly, P2Y1-R activation using 2-methylthio-ADP (2-Me-ADP) improved vasodilatory capacity in arteries from : (i) adult mice treated with 3-MA; (ii) adult iecAtg3KO mice; and (iii) older animals with repressed EC autophagy. ConclusionsArterial dysfunction concurrent with pharmacological, genetic, and age-associated EC autophagy compromise is improved by activating P2Y1-Rs.

physiology↗

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2019

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.\n\nTo deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.\n\nData available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/\n\nAuthor SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

systems biology↗

bioRxiv2019

A configural model of expert judgement as a preliminary epidemiological study of injury problems: An application to drowning.

Robust epidemiological studies identifying determinants of negative health outcomes require significant research effort. Expert judgement is proposed as an efficient alternative or preliminary research design for risk factor identification associated with unintentional injury. This proposition was tested in a multi-factorial balanced experimental design using specialist judges (N=18), lifeguards and surfers, to assess the risk contribution to drowning for swimming ability, surf bathing experience, and wave height. All factors provided unique contributions to drowning risk (p<.001). An interaction (p=.02) indicated that occasional surf bathers face a proportionally increased risk of drowning at increased wave heights relative to experienced surf bathers. Although findings were limited by strict criteria, and no gold standard comparison data were available, the study provides new evidence on causal risk factors for a drowning scenario. Countermeasures based on these factors are proposed. Further application of the method may assist in developing new interventions to reduce unintentional injury.

epidemiology↗