Biology subjects

McClure, P.

Publications and source records attributed to McClure, P..

6 recordsLinked to original sources

bioRxiv2024

Wisteria floribunda agglutinin enhances Zaire ebolavirus entry through interactions at specific N-linked glycosylation sites on the virus glycoprotein complex

Entry of Zaire ebolavirus (EBOV) into a host cell is a complex process requiring interactions between the viral glycoproteins (GP) and cellular factors. These entry factors are cell-specific and can include cell surface lectins and phosphatidylserine receptors. NPC1 is critical to the late stage of the entry process. Entry has been demonstrated to be enhanced by interactions between the virion and surface-expressed lectins, which interact with carbohydrate moieties attached to the GP. In addition, soluble lectins, including mannose binding lectin (MBL), can enhance entry in vitro. However, the mechanism of lectin-mediated enhancement remains to be defined. This study investigated the potential of three plant lectins, Wisteria floribunda agglutinin (WFA), soybean agglutinin (SBA) and Galanthus nivalis agglutinin (GNA), which possess different carbohydrate binding specificities, to enhance EBOV entry by binding to the GP. WFA was observed to potently enhance entry of lentiviral pseudotype viruses (PVs) expressing the GP of three Ebolavirus species (Zaire, Sudan [SUDV] and Reston [RESTV]), with the greatest impact on EBOV. SBA had a modest enhancing effect on entry that was specific to EBOV, while GNA had no impact on entry of any of the Ebolavirus species. None of the lectins enhanced entry of control PVs expressing the surface proteins of other RNA viruses tested. WFA was demonstrated to bind directly with the EBOV-GP via the glycans, and mutational analysis implicated N238 as contributing to the interaction. Furthermore, enhancement was observed in both human and bat cell lines indicating a highly conserved mechanism of action. We conclude that binding of WFA to EBOV GP through interactions including the glycan at N238 results in GP alterations that enhance entry, providing evidence of a mechanism for lectin-mediated virus entry enhancement. Targeting lectin-ligand interactions presents a potential strategy for restricting Ebolavirus entry.

microbiology↗

bioRxiv2024

ARTIC RSV amplicon sequencing reveals global RSV genotype dynamics.

Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections (LTRIs) in young children and adults over 65, contributing significantly to global healthcare burdens. With the recent approval of multiple pharmacological interventions for RSV, there is an increased demand for efficient, high-throughput sequencing methods to monitor RSV genetic diversity and any potential impact these interventions may have. Here we introduce two novel amplicon-based sequencing schemes designed for RSV A and B, optimised for integration with widespread existing ARTIC sequencing workflows. We demonstrate that these primer schemes can produce high quality genomes from RSV samples across the globe, with eight laboratories in five countries generating complete genomes on both Nanopore and Illumina sequencing platforms. The ability to effectively multiplex these RSV A and B primer schemes, enables streamlined, high-throughput sequencing without prior subtyping. Furthermore, these results provide a snapshot of the circulating diversity of RSV. Phylogenetic analysis of the 882 samples sequenced for this study suggests only minimal geographic clustering of RSV sequences, underscoring the global nature of RSV spread. It also highlights the distinct lineage dynamics seen between RSV A and B. This study represents an advancement in RSV genomics, providing robust tools for global sequencing efforts aimed at tracking RSV evolution and assessing the efficacy of new therapeutic interventions both rapidly and at scale.

genomics↗

bioRxiv2021

Validating the Representational Space of DeepReinforcement Learning Models of Behavior withNeural Data

Deep Reinforcement Learning (Deep RL) agents have in recent years emerged as successful models of animal behavior in a variety of complex learning tasks, as exemplified by Song et al. [2017]. As agents are typically trained to mimic an animal subject, the emphasis in past studies on behavior as a means of evaluating the fitness of models to experimental data is only natural. But the true power of Deep RL agents lies in their ability to learn neural computations and codes that generate a particular behavior--factors that are also of great relevance and interest to computational neuroscience. On that basis, we believe that model evaluation should include an examination of neural representations and validation against neural recordings from animal subjects. In this paper, we introduce a procedure to test hypotheses about the relationship between internal representations of Deep RL agents and those in animal neural recordings. Taking a sequential learning task as a running example, we apply our method and show that the geometry of representations learnt by artificial agents is similar to that of the biological subjects, and that such similarities are driven by shared information in some latent space. Our method is applicable to any Deep RL agent that learns a Markov Decision Process, and as such enables researchers to assess the suitability of more advanced Deep Learning modules, or map hierarchies of representations to different parts of a circuit in the brain, and help shed light on their function. To demonstrate that point, we conduct an ablation study to deduce that, in the sequential task under consideration, temporal information plays a key role in molding a correct representation of the task.

neuroscience↗

bioRxiv2020

Insufficient Eye Tracking Data Leads to Errors in Evaluating Typical and Atypical Fixation Preferences

Eye tracking provides insights into social processing and its deficits in disorders such as autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic stimuli. However, reliance on manual stimuli segmentation severely limits scalability. We assessed how the amount of available data impacts individual reliability of fixation preference for different facial features, and the effect of this reliability on between-group differences. We trained an artificial neural network to segment 22 Hollywood movie clips (7410 frames). We then analyzed fixation preferences in typically developing participants and participants with ASD as we incrementally introduced movie data for analysis. Although fixations were initially variable, results stabilized as more data was added. Additionally, while those with ASD displayed significantly fewer face-centered fixations (p<.001), they did not differ in eye or mouth fixations. Our results highlight the validity of treating fixation preferences as a stable individual trait, and the risk of misinterpretation with insufficient data.

neuroscience↗

bioRxiv2017

Deep Neural Networks In Computational Neuroscience

The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioural responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term \"neural network\" suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g. visual object and auditory speech recognition) to cognitive tasks (e.g. machine translation), and on to motor control (e.g. playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviours, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the result of four directly manipulable elements: input statistics, network structure, functional objective, and learning algorithm. With full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data, DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.

neuroscience↗

bioRxiv2017

Recurrent Convolutional Neural Networks: A Better Model Of Biological Object Recognition Under Occlusion

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and nonhuman primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognising objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognise objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

neuroscience↗