Biology subjects

Morrison, A.

Publications and source records attributed to Morrison, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Intestinal uptake regulates T cell responses to dietary antigens

Dietary proteins induce antigen-specific immune responses, leading to oral tolerance or food allergies. While mouse models of allergic sensitization rely on antigen context and adjuvant signaling, exposure to purified proteins is usually sufficient for tolerance development in studies with soluble model antigens. In previous work, we discovered that the maize protein zein induces robust antigen-specific intestinal Tregs following normal chow exposure, but unexpectedly does not elicit a T cell response when delivered as a purified protein. Here, we investigated the mechanisms underlying these differential T cell responses, considering both biochemical properties of zein and its food matrix, as well as intestinal antigen processing. We focused on three zein preparations that induced different frequencies of antigen-specific intestinal T cells. While in vitro studies showed comparable presentation by dendritic cells, in vivo studies revealed stark differences in intestinal uptake. We identified differences in protein solubility and active sampling mechanisms as cooperative drivers of intestinal zein uptake. Different zein preparations could be sampled by either goblet cells, a route previously described for the model antigen OVA, or M cells, a route hypothesized for dietary proteins and established for transport of pathogenic bacteria. These findings also extend to peanuts, where the allergen Ara h 1 was selectively transported by goblet or M cells depending on food preparation, establishing examples of naturally occurring dietary M cell ligands. Finally, solubilizing zein increased intestinal uptake, and correspondingly feeding a diet with solubilized zein led to more intestinal zein-specific T cells compared to feeding the insoluble form. Overall, these findings suggest that intestinal uptake of dietary antigens is a regulated and context-dependent determinant of antigen-specific T cell induction, with implications for development of tolerance-restoring immunotherapies.

immunology↗

bioRxiv2026

The Fontan EV Score: A Circulating Extracellular Vesicle-Based Risk Stratification Tool for Fontan-Associated Liver Disease

BackgroundFontan-associated liver disease (FALD) is a universal complication of the Fontan palliation characterized by chronic congestion and progressive hepatic fibrosis. Current diagnostics rely on invasive biopsies or non-specific biochemical and imaging biomarkers that fail to capture early fibrogenesis, creating a critical need for non-invasive biomarkers to stratify disease severity. MethodsWe utilized a translational ovine Fontan model (n = 19) to investigate circulating serum extracellular vesicles (sEVs) as reporters of hepatic pathology. Longitudinal serum samples paired with liver elastography were collected, and sEVs were subjected to multi-omic profiling including small RNA sequencing and proteomics. Regularized regression was used to identify transcriptomic predictors, which were integrated with time post-surgery into an ordinal logistic regression framework to construct the Fontan EV Score (FES). Model performance was evaluated on a held-out test cohort and benchmarked against established serological fibrosis indices. To validate the biological relevance of the FES panel, TGF-{beta}-treated human liver organoids were generated and scored miRNA expression was assessed. ResultsThe sEV proteome exhibited robust separation by surgical physiology, while the small RNA cargo was primarily stratified by fibrotic status. Bioinformatic analysis confirmed a high hepatic origin for these transcripts and identified enrichment of inflammatory pathways including Toll-like receptor and Interleukin-17 cascades in fibrotic subjects. The FES, incorporating time post-surgery and eleven small RNA biomarkers, demonstrated high predictive accuracy in the independent testing cohort with an AUC of 0.876 for moderate and 0.963 for severe fibrosis, substantially outperforming APRI (AUC = 0.618) and FIB-4 (AUC = 0.731). In TGF-{beta}-treated human liver organoids, several scoring miRNAs, including miR-125a-5p and miR-193b-5p, were directionally responsive to profibrotic stimulation. ConclusionsCirculating sEVs carry a liver-associated molecular cargo that can be leveraged for the non-invasive prediction of FALD severity. The FES provides a biologically validated scoring system that substantially outperforms existing serological indices and offers a new avenue for early detection and risk stratification of FALD Novelty and SignificanceO_ST_ABSWhat is Known?C_ST_ABSO_LIFontan-associated liver disease (FALD) is a nearly universal consequence of the Fontan circulation, driven by chronic venous hypertension and reduced cardiac output. C_LIO_LICurrent surveillance tools, including transaminases, composite serological indices (APRI, FIB-4), and elastography, have limited sensitivity and specificity for detecting and staging hepatic fibrosis in the Fontan population. C_LIO_LICirculating small extracellular vesicles (sEVs) carry tissue-derived molecular cargo and have shown diagnostic potential in other liver diseases, but their utility in FALD has not been explored. C_LI What New Information Does This Article Contribute?O_LIMulti-omic profiling of circulating sEVs in a translational ovine Fontan model reveals that the small RNA cargo is stratified by fibrotic status and enriched for inflammatory pathways associated with hepatic stellate cell activation. C_LIO_LIThe Fontan EV Score (FES), integrating time post-surgery with eleven circulating small RNA biomarkers, predicts FALD severity with substantially greater accuracy than APRI and FIB-4. C_LIO_LITGF-{beta}-treated human liver organoids confirm that several FES-associated miRNAs are directly responsive to profibrotic stimulation, providing biological validation independent of Fontan hemodynamics. C_LI This study demonstrates that circulating sEVs function as non-invasive reporters of hepatic fibrogenesis in the Fontan circulation and introduces the first EV-based scoring system for FALD risk stratification. The FES achieved an AUC of 0.876 for moderate and 0.963 for severe fibrosis in an independent test cohort, outperforming established serological indices that were originally developed for viral hepatitis but which perform poorly in congestive hepatopathy. By combining molecular biomarker discovery with in vitro functional validation, this work establishes a foundation for developing targeted, non-invasive diagnostics to guide surveillance and clinical decision-making in the growing Fontan patient population.

bioinformatics↗

bioRxiv2026

Unveiling the Hidden Rules: Enhancing NMD Prediction for Protein-Truncating Variants

Nonsense-mediated decay (NMD) is a conserved RNA quality-control pathway that degrades transcripts containing premature termination codons. Because roughly a third of pathogenic variants in ClinVar can lead to truncated protein synthesis, predicting whether such transcripts undergo NMD is central to interpreting variant effects, yet the canonical 50-55 nucleotide rule explains only about half of observed outcome variability. Using paired whole-genome and RNA-sequencing from 10,306 individual samples in the Trans-Omics for Precision Medicine (TOPMed) program, we quantified NMD efficiency for 5,749 germline truncating variants via allele-specific expression and trained a gradient-boosting classifier, TrunCat, that distinguished NMD-sensitive from NMD-escape transcripts with [~]78% ROC-AUC (Receiver Operating Characteristic - Area Under the Curve). A reduced model using the ten features with the highest mean SHAP (SHapley Additive exPlanations) value as a measure of each features average contribution to predictions nearly matched this performance. Applied across large variant databases and a rare-disease cohort, the model produced NMD outcome predictions, with variants of uncertain significance showing higher predicted escape than pathogenic ones. This framework confirms the canonical rule, identifies non-canonical determinants, and offers a scalable resource for interpreting protein-truncating variants.

genomics↗

bioRxiv2025

A NEST-based framework unlocks massively parallel simulation of networks of multicompartment neurons with customizable subcellular dynamics.

While the implementation of learning and memory in the brain is governed in large part by subcellular mechanims in the dendrites of neurons, large-scale network simulations featuring such processes remain challenging to achieve. This can be attributed to a lack of appropriate software tools, as neuroscientific simulation software focuses on the one hand on highly detailed models, and on the other hand on massive networks featuring point-neurons. Here, we fill this gap by implementing a framework for the massively parallel simulation of simplified dendrite models with customizable subcellular dynamics. To achieve this, we leverage the NEural Simulation Tool (NEST), the neuroscientific reference with respect to efficient massively parallel simulations of point neuron networks. By co-opting the already existing model descriptions language NESTML, we generate C++ code implementing user-configurable subcellular dynamics. Through benchmarking and profiling, we show that the generated models run efficiently, leading to scalable NEST network simulations. We demonstrate relevant functionalities by showing that a key sensory computation - the association of top-down context arriving at distal dendrites in layer 1 and feedforward sensory input arriving perisomatically - can be achieved in a single shot fashion through apical calcium dynamics. Our work thus unlocks the study of how dendritic processes shape learning, and in particular of how brain-wide communication through long-range, layer 1-targetting connections steers perisomatic plasticity.

neuroscience↗

bioRxiv2024

Optimisation of single-nuclei isolation and RNA sequencing of parasitic nematodes

Single-cell/nuclei transcriptomics has revolutionised our understanding of cell, tissue, and organismal biology during health and disease of humans and model organisms. However, for non-model species, reassessment and refinement of conventional methods to address novel technical and biological challenges are needed. Here, we evaluate three established nuclei isolation protocols and, building on experiences, optimised single nuclei isolation for parasitic nematodes (SNIP). We test the versatility of SNIP across diverse species and life stages, validating it with snRNA-seq to reveal sex, stage, and tissue-specific gene expression. Optimised protocols will advance the understanding of parasitic nematodes, from fundamental biology to infection and disease.

molecular biology↗

bioRxiv2024

Chromosomal genome assembly resolves drug resistance loci in the parasitic nematode Teladorsagia circumcincta

The parasitic nematode Teladorsagia circumcincta is one of the most important pathogens of sheep and goat farming in temperate climates worldwide and can rapidly evolve resistance to drugs used to control it. To understand the genetics of drug resistance, we have generated a highly contiguous genome assembly for the UK T. circumcincta isolate, MTci2. Assembly using PacBio long-reads and Hi-C long-molecule scaffolding together with manual curation resulted in a 573 Mb assembly (N50 = 84 Mb, n = 1,286) with five autosomal and one sex-linked chromosomal-scale scaffolds consistent with its karyotype. The genome resource was further improved via annotation of 22,948 genes, with manual curation of over 3,200 of these, resulting in a robust and near complete resource (96.3% complete protein BUSCOs) to support basic and applied research on this important veterinary pathogen. Genome-wide analyses of drug resistance, combining evidence from three distinct experiments, identified selection around known candidate genes for benzimidazole, levamisole and ivermectin resistance, as well as novel regions associated with ivermectin and moxidectin resistance. These insights into contemporary and historic genetic selection further emphasise the importance of contiguous genome assemblies in interpreting genome-wide genetic variation associated with drug resistance and identified key loci to prioritise in developing diagnostic markers of anthelmintic resistance to support parasite control. AUTHOR SUMMARYUnderstanding the genetics of anthelmintic resistance is a critical part of the sustainable control of parasitic worms. Here, we have generated a chromosome-scale genome assembly for the gastrointestinal nematode, Teladorsagia circumcincta, one of the most important pathogens of sheep in temperate climates worldwide. This genome and its annotation offers a substantial improvement over existing genetic resources, and will enable new insight into the biology for this parasite and new opportunities to characterise therapeutic targets such as drug and vaccine candidates. We used this resource to map genetic variation associated with resistance to multiple anthelmintic drug classes used as the primary means of parasite control, confirming known candidate genes and variants (i.e., beta-tubulin isotypes 1 and 2 associated with benzimidazole resistance, and acr-8 associated with levamisole resistance, and pgp-9 copy number variation, of which overexpression is associated broadly with anthelmintic resistance) and revealing new regions of the genome associated with drug treatment responses (i.e. 34-38 Mb on chromosome 5 associated with ivermectin resistance). Our results highlight the importance of a contiguous genome assembly and the use of complementary experimental approaches to reveal the impact of drug-mediated selection, both historically and directly in response to treatment, on genome-wide genetic variation.

genomics↗

bioRxiv2024

Humidity determines penetrance of a latitudinal gradient in genetic selection on the microbiota by Drosophila melanogaster

The fruit fly Drosophila melanogaster is a model for understanding how hosts and their microbial partners interact as the host adapts to wild environments. These interactions are readily interrogated because of the low taxonomic and numeric complexity of the flies bacterial communities. Previous work has established that host genotype, the environment, diet, and interspecies microbial interactions can all influence host fitness and microbiota composition, but the specific processes and characters mediating these processes are incompletely understood. Here, we compared the variation in microbiota composition between wild-derived fly populations when flies could choose between the microorganisms in their diets and when flies were reared under environmental perturbation (different humidities). We also compared the colonization of the resident and transient microorganisms. We show that the ability to choose between microorganisms in the diet and the environmental condition of the flies can influence the relative abundance of the microbiota. There were also key differences in the abundances of the resident and transient microbiota. However, the microbiota only differed between populations when the flies were reared at humidities at or above 50% relative humidity. We also show that elevated humidity determined the penetrance of a gradient in host genetic selection on the microbiota that is associated with the latitude the flies were collected from. Finally, we show that the treatment-dependent variation in microbiota composition is associated with variation in host stress survival. Together, these findings emphasize that host genetic selection on the microbiota composition of a model animal host can be patterned with the source geography, and that such variation has the potential to influence their survival in the wild. ImportanceThe fruit fly Drosophila melanogaster is a model for understanding how hosts and their microbial partners interact as hosts adapt in wild environments. Our understanding of what causes geographic variation in the fruit fly microbiota remains incomplete. Previous work has shown that the D. melanogaster microbiota has relatively low numerical and taxonomic complexity. Variation in the fly microbiota composition can be attributed to environmental characters and host genetic variation, and variation in microbiota composition can be patterned with the source location of the flies. In this work we explored three possible causes of patterned variation in microbiota composition. We show that host feeding choices, the host niche colonized by the bacteria, and a single environmental character can all contribute to variation in microbiota composition. We also show that penetrance of latitudinally-patterned host genetic selection is only observed at elevated humidities. Together, these results identify several factors that influence microbiota composition in wild fly genotypes and emphasize the interplay between environmental and host genetic factors in determining the microbiota composition of these model hosts.

microbiology↗

bioRxiv2023

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

genetics↗

bioRxiv2023

Deep learning-based codon optimization with large-scale synonymous variant datasets enables generalized tunable protein expression

Increasing recombinant protein expression is of broad interest in industrial biotechnology, synthetic biology, and basic research. Codon optimization is an important step in heterologous gene expression that can have dramatic effects on protein expression level. Several codon optimization strategies have been developed to enhance expression, but these are largely based on bulk usage of highly frequent codons in the host genome, and can produce unreliable results. Here, we develop deep contextual language models that learn the codon usage rules from natural protein coding sequences across members of the Enterobacterales order. We then fine-tune these models with over 150,000 functional expression measurements of synonymous coding sequences from three proteins to predict expression in E. coli. We find that our models recapitulate natural context-specific patterns of codon usage and can accurately predict expression levels across synonymous sequences. Finally, we show that expression predictions can generalize across proteins unseen during training, allowing for in silico design of gene sequences for optimal expression. Our approach provides a novel and reliable method for tuning gene expression with many potential applications in biotechnology and biomanufacturing.

synthetic biology↗

bioRxiv2023

Towards reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning

To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatiotemporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable. Here we illustrate the importance of these aspects by providing a thorough investigation of a recent model proposed by Cone and Shouval (2021). We re-implement the modular columnar architecture and reward-based learning rule in the open-source NEST simulator, and successfully replicate the main findings of the original study. Building on these, we perform an in-depth analysis of the models robustness to parameter settings and underlying assumptions, highlighting its strengths and weaknesses. We demonstrate a limitation of the model consisting in the hard-wiring of the sequence order in the connectivity patterns, and suggest possible solutions. Finally, we show that the core functionality of the model is retained under more biologically-plausible constraints.

neuroscience↗

bioRxiv2022

Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we first demonstrate that thin dendritic branches are well suited to implementing contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to thin dendrites can solve linearly non-separable learning problems with a Hebbian, error-modulated learning rule. Finally, we demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

neuroscience↗

bioRxiv2022

Multiscale cosimulation design template implemented for a neuroscience application

Integration of information across heterogeneous sources creates added scientific value. It is, however, a challenge to progress, often a barrier, to interoperate data, tools and models across spatial and temporal scales. Here we present a design template for coupling simulators operating at different scales and enabling co-simulation. We illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level to address mechanistic questions, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validate them against multiscale experiments, thereby largely widening the explanatory power of computational models.

neuroscience↗

bioRxiv2022

Cross-comparison of state of the art neuromorphological simulators on modern CPUs and GPUs using the Brain Scaffold Builder

A variety of software simulators exist for neuronal networks, and a subset of these tools allow the scientist to model neurons in high morphological detail. The scalability of such simulation tools over a wide range in neuronal networks sizes and cell complexities is predominantly limited by effective allocation of components of such simulations over computational nodes, and the overhead in communication between them. In order to have more scalable simulation software, it is therefore important to develop a robust benchmarking strategy that allows insight into specific computational bottlenecks for models of realistic size and complexity. In this study, we demonstrate the use of the Brain Scaffold Builder (BSB; De Schepper et al., 2021) as a framework for performing such benchmarks. We perform a comparison between the well-known neuromorphological simulator NEURON (Carnevale and Hines, 2006), and Arbor (Abi Akar et al., 2019), a new simulation library developed within the framework of the Human Brain Project. The BSB can construct identical neuromorphological and network setups of highly spatially and biophysically detailed networks for each simulator. This ensures good coverage of feature support in each simulator, and realistic workloads. After validating the outputs of the BSB generated models, we execute the simulations on a variety of hardware configurations consisting of two types of nodes (GPU and CPU). We investigate performance of two different network models, one suited for a single machine, and one for distributed simulation. We investigate performance across different mechanisms, mechanism classes, mechanism combinations, and cell types. Our benchmarks show that, depending on the distribution scheme deployed by Arbor, a speed-up with respect to NEURON of between 60 and 400 can be achieved. Additionally Arbor can be up to two orders of magnitude more energy efficient.

neuroscience↗

bioRxiv2022

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies ([lsim] 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models ({gtrsim} 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ( spikes) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficacy of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework. Author summaryUnderstanding the brains function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanitys great undertakings. In experimental and clinical work probing the brains activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

neuroscience↗

bioRxiv2022

Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

neuroscience↗

bioRxiv2021

Genotype and Th2 cells control monocyte to tissue resident macrophage differentiation during nematode infection of the pleural cavity

The recent revolution in tissue-resident macrophage biology has resulted largely from murine studies performed in the C57BL/6 strain. Here, we provide a comprehensive analysis of immune cells in the pleural cavity using both C57BL/6 and BALB/c mice. Unlike C57BL/6 mice, naive tissue-resident Large Cavity Macrophages (LCM) of BALB/c mice failed to fully implement the tissue residency program. Following infection with a pleural-dwelling nematode these pre-existing differences were accentuated with LCM expansion occurring in C57BL/6 but not BALB/c mice. While infection drove monocyte recruitment in both strains, only in C57BL/6 mice were monocytes able to efficiently integrate into the resident pool. Monocyte to macrophage conversion required both T cells and IL-4R signalling. Host genetics are therefore a key influence on tissue resident macrophage biology, and during nematode infection Th2 cells control the differentiation pathway of tissue resident macrophages. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/472661v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@bba5e1org.highwire.dtl.DTLVardef@1876aa4org.highwire.dtl.DTLVardef@16a6b1corg.highwire.dtl.DTLVardef@19e18b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Genomic landscape of drug response reveals novel mediators of anthelmintic resistance

Understanding the genetic basis of anthelmintic drug resistance in parasitic nematodes is key to improving the efficacy and sustainability of parasite control. Here, we use a genetic cross in a natural host-parasite system to simultaneously map resistance loci for the three major classes of anthelmintics. This approach identifies novel alleles for resistance to benzimidazoles and levamisole and implicates the transcription factor, cky-1, in ivermectin resistance. This gene is within a locus under selection in ivermectin resistant populations worldwide; functional validation using knockout experiments supports a role for cky-1 overexpression in ivermectin resistance. Our work demonstrates the feasibility of high-resolution forward genetics in a parasitic nematode, and identifies variants for the development of molecular diagnostics to combat drug resistance in the field. One-Sentence SummaryGenetic mapping of known and novel anthelmintic resistance-associated alleles in a multi-drug resistant parasitic nematode

genomics↗

bioRxiv2021

Transcriptomic analyses implicate neuronal plasticity and chloride homeostasis in ivermectin resistance and recovery in a parasitic nematode

The antiparasitic drug ivermectin plays an essential role in human and animal health globally. However, ivermectin resistance is widespread in veterinary helminths and there are growing concerns of sub-optimal responses to treatment in related helminths of humans. Despite decades of research, the genetic mechanisms underlying ivermectin resistance are poorly understood in parasitic helminths. This reflects significant uncertainty regarding the mode of action of ivermectin in parasitic helminths, and the genetic complexity of these organisms; parasitic helminths have large, rapidly evolving genomes and differences in evolutionary history and genetic background can confound comparisons between resistant and susceptible populations. We undertook a controlled genetic cross of a multi-drug resistant and a susceptible reference isolate of Haemonchus contortus, an economically important gastrointestinal nematode of sheep, and ivermectin-selected the F2 population for comparison with an untreated F2 control. RNA-seq analyses of male and female adults of all populations identified high transcriptomic differentiation between parental isolates, which was significantly reduced in the F2, allowing differences associated specifically with ivermectin resistance to be identified. In all resistant populations, there was constitutive upregulation of a single gene, HCON_00155390:cky-1, a putative pharyngeal-expressed transcription factor, in a narrow locus on chromosome V previously shown to be under ivermectin selection. In addition, we detected sex-specific differences in gene expression between resistant and susceptible populations, including constitutive upregulation of a P-glycoprotein, HCON_00162780:pgp-11, in resistant males only. After ivermectin selection, we identified differential expression of genes with roles in neuronal function and chloride homeostasis, which is consistent with an adaptive response to ivermectin-induced hyperpolarisation of neuromuscular cells. Overall, we show the utility of a genetic cross to identify differences in gene expression that are specific to ivermectin selection and provide a framework to better understand ivermectin resistance and recovery in parasitic helminths. Author SummaryParasitic helminths (worms) infect people and animals throughout the world and are largely controlled with mass administration of anthelmintic drugs. There are a very limited number of anthelmintics available and parasitic helminths can rapidly develop resistance to these drugs. Ivermectin is a widely used anthelmintic in both humans and animals, but resistance is now widespread in the veterinary field. We crossed ivermectin resistant and ivermectin susceptible parasitic helminths and treated them with ivermectin or left them as untreated controls. This provided resistant and susceptible populations with a similar genetic background with which to study differences in gene expression associated with ivermectin resistance. We identified upregulation of a gene with no previous association with drug resistance (HCON_00155390:cky-1) in male and female worms in all resistant populations. This gene is thought to be expressed in the helminth pharynx (mouthpart) and, in mammals, plays a role in controlling nerve function and protecting nerves from damage. This is consistent with the known effects of ivermectin in inhibiting helminth feeding through pharyngeal paralysis and implicates a novel mechanism that allows resistant worms to survive treatment.

genomics↗