Biology subjects

Jones, M. L.

Publications and source records attributed to Jones, M. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Effects of antibiotics on the abundance of antibiotic resistance determinants during and after antibiotic administration to beef cattle: A systematic review and meta-analysis of longitudinal studies

BackgroundBeef feedlots are increasing concerns that antibiotic use in beef cattle selects for antibiotic resistance, but limitations of primary studies and previous syntheses make it difficult to confirm a consistent effect. We conducted a rigorous systematic review and meta-analysis to summarise: 1) the effect during and after antibiotic administration; 2) its moderation by time since administration started/ended. MethodsEligible studies longitudinally compared beef cattle administered antibiotics to those that were not, measuring resistance determinants in faeces and/or environments. Information sources included Web of Science, CAB Abstracts, and Medline (last searches: 05/03/25). Risk of bias was assessed using RoB 2 and ROBINS-I. Meta-analysis was conducted where feasible, using individual participant data where necessary. ResultsThe 33 included studies were mostly small trials of North American feedlot cattle, all with high risks of bias. Meta-analysis of 11 studies of tylosin, ceftiofur, and chlortetracycline indicated positive effects on absolute abundance of resistance both during (SMDH = 0.4; 95% CI = 0.11 to 0.69, p = <0.01) and after (SMDH = 0.52; 95% CI = 0.33 to 0.71, p = <0.01) antibiotic administration. Log-transformed time was positively associated with effect size during (Slope = 0.63; 95% CI = 0.1 to 1.16, p = 0.02), and negatively associated after (Slope = -0.65; 95% CI = -1.24 to -0.06, p = 0.03) DiscussionAvailable evidence indicates time-dependent selection for antibiotic resistance in beef cattle, warranting further regulation to limit human health risks. Simultaneously, uncertainty about precise effect sizes warrants further research. FundingBBSRC Registrationhttps://doi.org/10.17605/OSF.IO/RXQHT

microbiology↗

bioRxiv2026

MCM10 targets CMG dimers via a conserved mechanism for synchronized helicase activation

Activation of the CMG (CDC45-MCM-GINS) helicase by MCM10 is a central unresolved step in DNA replication. DNA is melted within a dimeric CMG complex and one strand is expelled from each topologically closed MCM ring. How MCM10 drives this process is unclear owing to a lack of structural information. Here, by determining structures of yeast CMG-Mcm10 complexes, and of human CMG-Pol {varepsilon} dimers assembled by DONSON and bound to MCM10 and the helicase activator RECQL4, we reveal a conserved mechanism of CMG helicase activation in eukaryotes. MCM10 targets CMG dimers through highly conserved and species-specific interactions, that in human also involve RECQL4. Our data indicate this arrangement allows MCM10 to stimulate DNA unwinding by CMG and to utilise the associated conformational changes to drive single-stranded DNA ejection between MCM2 and MCM5. This mechanism provides an explanation for synchronized activation of two CMG helicases at origins of bidirectional replication.

molecular biology↗

bioRxiv2025

A novel chimeric coronavirus spike vaccine combining SARS-CoV-2 RBD and scaffold domains from HKU-1 elicits potent neutralising antibody responses

The receptor binding domain (RBD) of the SARS-CoV-2 spike is the major target for neutralising antibodies elicited by current vaccines. Using small domains such as the RBD as vaccine immunogens, however, may constrain the availability of CD4 T follicular helper (TFH) cells and impact immunogenicity. We engineered a novel chimeric trimeric RBD (CTR) glycoprotein, replacing the RBD of human coronavirus HKU-1 spike with SARS-CoV-2 RBD of either ancestral (WT) or Omicron BA.2 strains. This strategy maintains a native trimeric conformation of the RBD, while providing additional sources of CD4 T cell help via the HKU-1 spike scaffold. In C57BL/6 mice, CTR-BA.2 prime-boost vaccination elicited high anti-BA.2-RBD IgG and neutralising titres, matching responses in animals immunised with native SARS-CoV-2 spike proteins. GC B cells elicited by CTR-BA.2 were predominantly WT+/BA.2+ cross- reactive, and TFH cells predominantly recognised HKU-1 epitopes, demonstrating scaffold-directed T cell help. Macaques prime-boost immunised with CTR-WT similarly elicited high anti-RBD IgG, anti-spike IgG and neutralising responses, comparable to native spike-vaccinated animals. In draining lymph nodes of CTR-WT vaccinated macaques, RBD-specific GC B cells were present at elevated levels. In contrast to the murine studies, lymph node-draining TFH responses in macaques were broadly elicited against RBD, NTD/S2 or HKU-1-derived peptides. Although native SARS- CoV-2 spike was also highly immunogenic in animal models, our findings establish the chimeric glycoprotein design as a strategy to overcome the poor immunogenicity of the SARS-CoV-2 RBD by engaging CD4 TFH cells, while maintaining the ability to elicit protective neutralising responses. One sentence summaryA chimeric glycoprotein design preserves SARS-CoV-2 RBD antigenic conformation enabling elicitation of neutralising responses, while allowing recruitment of HKU-1 scaffold-directed CD4 helper responses to support the humoral response.

immunology↗

bioRxiv2025

VP-CLEM-Kit: An accessible pipeline for visual proteomics using super resolution volume correlative light and electron microscopy (SR-vCLEM)

Visual proteomics (VP) aims to allow researchers to visualise, measure and analyse proteins in the context of cell and tissue structure in health and disease. VP is becoming a reality through technological advances across several domains, including in situ structural biology and correlative light and electron microscopy (CLEM). However, widespread adoption remains limited due to the complexity and cost of the various VP approaches reported to date. Here we present the VP-CLEM-Kit, a disruptive cost-effective pipeline for super resolution volume CLEM (SR-vCLEM) that can be implemented with minimal advanced electron microscopy expertise and equipment, making it accessible to light microscopy facilities and research labs. SR-vCLEM is based on in-resin fluorescence (IRF), where fluorophores are preserved through processing into resin. The easyIRF protocol reported here reduces the requirement for complex costly sample preparation equipment and toxic chemicals compared to standard IRF protocols. easyIRF blocks are cut into ultrathin sections that are imaged using tomoSTORM, a new modular and cost-effective openFrame-based light microscope controlled by the open-source software package Micro-Manager that provides serial single molecule localisation microscopy in array tomography format. Sections are then post-stained and imaged using a tabletop scanning electron microscope controlled by open-source SBEMimage software to run in array tomography format. We demonstrate the potential of the VP-CLEM-Kit by imaging organelle reporters in human cell lines and stem cell derived neurons, fluorescently labelled protein in neurons, and immunolabelled cells in human kidney biopsy tissue from transplant patients. The VP-CLEM-Kit delivers a [~]5-fold improvement in resolution at a [~]7-fold lower cost, and thus provides new technical capability as well as a blueprint for more equitable access to advanced imaging workflows.

cell biology↗

bioRxiv2025

A protective bispecific antibody targets both Nipah virus surface glycoproteins and limits viral escape

Nipah virus (NiV) and Hendra virus (HeV) are highly pathogenic henipaviruses without approved human vaccines or therapies. Here, we report on a highly potent bispecific therapeutic that combines an anti-fusion (F) nanobody with an anti-receptor binding protein (RBP) antibody to deliver a dual-targeting biologic that is resistant to viral escape. We show that the nanobody, DS90, engages a unique, conserved site within prefusion F of NiV and HeV, and provides neutralization and complete protection from NiV disease. Bispecific engineering of DS90 with the anti-RBP mAb m102.4 results in neutralization, elimination of viral escape and superior protection from NiV disease compared to leading monovalent approaches. These findings carry implications for the development of cross-neutralizing immunotherapies that limit the emergence of henipaviral escape mutants.

microbiology↗

bioRxiv2024

Isolation and Characterization of Antibodies Against VCAM-1 Reveals Putative Role for Ig-like Domains 2 and 3 in Cell-to-Cell Interaction

Vascular cell adhesion molecule-1 (VCAM-1) plays an important role in inflammation, where it facilitates the recruitment of leukocytes to the inflamed area via leukocytes VLA-4 and endothelial cells VCAM-1 interaction. VCAM-1 expression is also upregulated in certain cancers. VCAM-1 has 7 Ig-like domains, with domains 1 and 4 shown to be critical for VLA-4 binding. However, the specific functions of individual VCAM-1 Ig-like domains remain poorly understood. In this study, we identified single-chain variable fragment (scFvs) antibodies targeting domains 2, 3, and 5 of VCAM-1, and investigated the ability of these antibodies to block VCAM-1-mediated cell adhesion to macrophages. We show that scFv antibodies against Ig-like domains 2 and 3 significantly interfere with the ability of macrophages to bind endothelial cells, suggesting that these domains also play a role in facilitating this interaction. These results emphasize the need to more carefully study the role of each domain on VCAM-1 function and highlight the potential of targeting these VCAM-1 domains for more tailored therapeutic interventions in inflammatory diseases and cancer.

molecular biology↗

bioRxiv2024

RNA Polymerase II subunits overexpressions induce genome instability and deregulate transcription

Independently of the pathways or circuits deregulated in cancer cells, these present altered transcription patterns, often also direct consequence of deregulation of transcription factors. In this sense, also the RNA Polymerase complexes responsible for transcription can be affected in cancers. We find that upregulations of RNA Polymerase II subunits, especially the largest ones, correlates with poor cancer patients outcome across a range of tumor types, presenting increased genome instability. Overexpressing the subunits RPB1, RPB3 and RPB4 in cells we find that these induce DNA damage. However, the mechanisms behind this increased genome instability are specific for each subunit, linked to the unique transcription alterations generated by the subunit overexpression. Importantly, we find significant overlap between the genes with more DNA damage in our cell line models and those more affected in cancers with subunit upregulation, indicating that upregulations could be responsible for some of the phenotypes present in these patients.

molecular biology↗

bioRxiv2023

Replaying the tape of ecology to domesticate wild microbiomes

Humanity has benefited from the domestication of nature and there is an increasing need to predict and control ecosystems. Domesticating bacterial communities would be particularly useful. Bacterial communities play key roles in global biogeochemical cycles, in industry (e.g. sewage treatment, fermented food and drink manufacturing), in agriculture (e.g. by fixing nitrogen and suppressing pathogens), and in human health and animal husbandry. There is therefore great interest in understanding bacterial community dynamics so that they can be controlled and engineered to optimise ecosystem services. We assessed the reproducibility and predictability of bacterial community dynamics by creating a frozen archive of hundreds of naturally-occuring bacterial communities that were repeatedly revived and tracked in a standardised, complex environment. Replicate communities followed reproducible trajectories and the community dynamics could be closely mapped to ecosystem functioning. However, even under standardised conditions, the communities exhibited tipping-points, where a small difference in initial community composition created divergent outcomes. We accurately predicted ecosystem outcomes based on initial bacterial community composition, and identified the conditions under which divergent ecosystem outcomes may be expected. In conclusion, we have shown the feasibility of our approach to reproducibly achieve predictable compositions and functions from wild communities. Nonetheless, the predictability of community trajectories, and therefore their utility in domestication, requires detailed knowledge of rugged compositional landscapes where ecosystem properties are not the inevitable result of prevailing environmental conditions but can be tilted toward different outcomes depending on the initial community composition.

microbiology↗

bioRxiv2023

Bridging Imaging Users to Imaging Analysis - A community survey

The "Bridging Imaging Users to Imaging Analysis" survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), Bioimaging North America (BINA), and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multi-choice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs, and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarize themselves with the fundamentals of image analysis, provide constant feedback, and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for written tutorials to acquire knowledge on image analysis. We also observed that the interest in having office hours to get an expert opinion on their image analysis methods has increased over the years. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.

scientific communication and education↗

bioRxiv2023

CLEM-Reg: An automated point cloud based registration algorithm for correlative light and volume electron microscopy

Correlative light and volume electron microscopy (vCLEM) is a powerful imaging technique that enables the visualisation of fluorescently labelled proteins within their ultrastructural context on a subcellular level. Currently, expert microscopists align vCLEM acquisitions using time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the 3D alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. These point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods, with the option to leverage alternative tools from other plugins or platforms. CLEM-Reg drastically reduces the time required to register vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to sub-micron target structures in EM on three newly acquired vCLEM benchmark datasets (fluorescence microscopy combined with FIB-SEM or SBF-SEM). CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in the trafficking of proteins between the trans-Golgi network and plasma membrane. The datasets are available in the EMPIAR and BioStudies public image archives for reuse in testing and developing multimodal registration algorithms by the wider community. A napari plugin integrating the algorithm is also provided to aid end-user adoption.

cell biology↗

bioRxiv2022

Exploring the potential of using simulation games for engaging with sheep farmers about lameness recognition

IntroductionComputer simulation games are increasingly being used in agriculture as a promising tool to study, support and influence real-life farming practices. We explored the potential of using simulation games to engage with sheep farmers on the ongoing challenge of reducing lameness. Working with UK stakeholders, we developed a game in which players are challenged with identifying all the lame sheep in a simulated flock. Here, we evaluate the games potential to act as a tool for to help assess, train and understand farmers ability to recognise the early signs of lameness. MethodsParticipants in the UK were invited to play the game in an online study, sharing with us their in-game scores alongside information relating to their real-life farming experience, how they played the game, and feedback on the game. Mixed methods were used to analyse this information in order to evaluate the game. Quantitative analyses consisted of linear modelling to test for statistical relationships between participants in-game recall (% of the total number of lame sheep that were marked as lame), and the additional information they provided. Qualitative analyses of participants feedback on the game consisted of thematic analysis and a Likert Scale questionnaire to contextualise the quantitative results and identify additional insights from the study. ResultsQuantitative analyses identified no relationships between participants (n = 63) recall scores and their real life farming experience, or the lameness signs they looked for when playing the game. The only relationship identified was a relationship between participants recall score and time spent playing the game. Qualitative analyses identified that participants did not find the game sufficiently realistic or engaging, though several enjoyed playing it and saw potential for future development. Qualitative analyses also identified several interesting and less-expected insights about real-life lameness recognition practices that participants shared after playing the game. DiscussionSimulation games have potential as a tool in livestock husbandry education and research, but achieving the desired levels of realism and/or engagingness may be an obstacle to realising this. Future research should explore this potential further, aided by larger budgets and closer collaboration with farmers, stockpeople and veterinarians.

scientific communication and education↗

bioRxiv2022

Crosshair, semi-automated targeting for electron microscopy with a motorised ultramicrotome

Volume electron microscopy (EM) is a time consuming process - often requiring weeks or months of continuous acquisition for large samples. In order to compare the ultrastructure of a number of individuals or conditions, acquisition times must therefore be reduced. For resin-embedded samples, one solution is to selectively target smaller regions of interest by trimming with an ultramicrotome. This is a difficult and labour-intensive process, requiring manual positioning of the diamond knife and sample, and much time and training to master. Here, we have developed a semi-automated workflow for targeting with a modified ultramicrotome. We adapted two recent commercial systems to add motors for each rotational axis (and also each translational axis for one system), allowing precise and automated movement. We also developed a user-friendly software to convert X-ray images of resin-embedded samples into angles and cutting depths for the ultramicrotome. This is provided as an open-source Fiji plugin called Crosshair. This workflow is demonstrated by targeting regions of interest in a series of Platynereis dumerilii samples.

cell biology↗

bioRxiv2021

Automatic instance segmentation of mitochondria in electron microscopy data

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

bioinformatics↗

bioRxiv2021

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

In this work, the unsupervised volumetric semantic segmentation of the plasma membrane of HeLa cells as observed with Serial Block Face Scanning Electron Microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8, 192 x 8, 192 pixels each. The background was used to create a distance map which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2, 000 x 2, 000 x 300 voxels that were treated as cell instances. Then, for each of these volumes the nucleus was segmented and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and Jaccard Index (JI): Nucleus: JI = 0.9665, AC= 0.9975, Cell and Nucleus JI = 0.8711, AC = 0.9655, Cell only JI = 0.8094, AC = 0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background, as in cases of tightly packed cells. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data are released openly through GitHub, Zenodo and EMPIAR.

cell biology↗

bioRxiv2020

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

cell biology↗

bioRxiv2020

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

In this work, the geometrical characteristics of two different types of cells observed with Electron Microscopy were analysed. The nuclear envelope of Wild-type HeLa cells and Chlamydia trachomatis-infected HeLa cells were automatically segmented and then modelled against a spheroid and converted to a two-dimensional surface. Geometric measurements from this surface and the volumetric nuclear envelope were extracted to compare the two types of cells. The measurements included the nuclear volume, the sphericity of the nucleus, its flatness or spikiness. In total 13 different cells were segmented: 7 Wild-type and 6 Chlamydia trachomatis-infected. The cells were statistically different in the following measurements. Wild-type HeLa cells have greater volumes than that of Chlamydia trachomatis-infected HeLa cells and they are more spherical as Jaccard index suggests. Standard deviation ({sigma}), and range of values for the nuclear envelope, which shows the distance of the highest peaks and deepest valleys from the spheroid, were also extracted from the modelling against a spheroid and these metrics were used to compare two different data sets in order to draw conclusions.

cell biology↗

bioRxiv2020

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

In this work, images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and three deep learning architectures: VGG16, ResNet18 and Inception-ResNet-v2. Three hundred slices, each 2000 x 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%).

cancer biology↗

bioRxiv2019

Productive bacterial communities exclude invaders

Experiments with artificial communities have suggested that invasion resistance in microbial communities is often a side-effect of community members contribution towards overall community productivity (broadly defined as cumulative cell density and/or growth rate). However, few experiments have investigated this in natural microbial communities. We conducted experimental invasions of two bacterial species (Pseudomonas fluorescens and Pseudomonas putida) into laboratory microcosms inoculated with 680 different mixtures of bacteria derived from naturally-occurring microbial communities collected in the field. Using 16S amplicon sequencing to characterise microcosm starting composition, and high-throughput assays of community phenotypes including productivity and invader survival, we determined that productivity is a key predictor of invasion resistance in natural microbial communities, substantially mediating the effect of composition on invasion resistance. The results suggest that similar general principles govern invasion in artificial and natural communities, and that factors affecting resident community productivity should be a focal point for future microbial invasion experiments.

ecology↗