Biology subjects

Lloyd, D.

Publications and source records attributed to Lloyd, D..

8 recordsLinked to original sources

bioRxiv2024

LL-37 modulates IL-17A/F-mediated airway inflammation by selectively suppressing Lipocalin-2.

BackgroundLevels of the human cationic host defence peptide (CHDP) LL-37 is enhanced in the lungs during neutrophilic airway inflammation. LL-37 drives Th17 differentiation, and Th17 cells produce IL-17A and IL-17F which forms the biologically active heterodimer IL-17A/F. While IL-17 is a critical mediator of neutrophilic airway inflammation, LL-37 exhibits contradictory functions; LL-37 can both promote and mitigate neutrophil recruitment depending on the inflammatory milieu. The impact of LL-37 on IL-17-induced responses in the context of airway inflammation remains largely unknown. Therefore, we examined signaling intermediates and downstream responses mediated by the interplay of IL-17A/F and LL-37, in human bronchial epithelial cells (HBEC). As LL-37 can get citrullinated during airway inflammation, we also examined LL-37-mediated downstream response compared to that with citrullinated LL-37 (citLL- 37), in HBEC. ResultsUsing an aptamer-based proteomics approach, we identified proteins that are altered in response to IL-17A/F in HBEC. Proteins enhanced in response to IL-17A/F were primarily neutrophil chemoattractants, including chemokines and proteins associated with neutrophil migration such as lipocalin-2 (LCN-2) and Elafin. We showed that selective depletion of LCN-2 mitigated neutrophil migration, thus functionally demonstrating LCN-2 as a critical neutrophil chemoattractant. We further demonstrated that LL-37 and citLL-37 selectively suppresses IL- 17A/F-induced LCN-2 production, in bronchial epithelial cells. Mechanistic studies revealed that LL-37 and citLL-37 suppressed IL-17A/F-mediated C/EBP{beta}, a transcription factor required for LCN-2 production. In contrast, LL-37 and citLL-37 enhanced the ribonuclease Regnase-1, which is a negative regulator of IL-17 and LCN-2. In an animal model of neutrophilic airway inflammation with elevated IL-17A/F in the lungs, we demonstrated that CRAMP (mouse ortholog of LL-37) negatively correlates with LCN-2. ConclusionsOverall, our findings showed that LL-37 and citLL-37 can selectively suppress the abundance of IL-17A/F-mediated LCN-2, a protein that is critical for neutrophil migration, in bronchial epithelial cells. These results suggest that LL-37, and its modified citrullinated form, has the potential to negatively regulate IL-17-mediated neutrophil migration to control airway inflammation. To our knowledge, this is the first study to report that the immunomodulatory function of LL-37 engages an RNA binding protein, Regnase-1, indicating post-transcriptional regulation of airway inflammation by the peptide.

immunology↗

bioRxiv2023

EmbedGEM: A framework to evaluate the utility of embeddings for genetic discovery

Machine learning (ML)-derived embeddings are a compressed representation of high content data modalities. Embeddings can capture detailed information about disease states and have been qualitatively shown to be useful in genetic discovery. Despite their promise, embeddings have a major limitation: it is unclear if genetic variants associated with embeddings are relevant to the disease or trait of interest. In this work we describe EmbedGEM (Embedding Genetic Evaluation Methods), a framework to systematically evaluate the utility of embeddings in genetic discovery. EmbedGEM focuses on comparing embeddings along two axes: heritability and disease relevance. As measures of heritability, we consider the number of genome-wide significant associations and the mean{chi} 2 statistic at significant loci. For disease relevance, we compute polygenic risk scores for each embedding principal component, then evaluate their association with high-confidence disease or trait labels in a held-out evaluation patient set. While our development of EmbedGEM is motivated by embeddings, the approach is generally applicable to multivariate traits, and can readily be extended to accommodate additional metrics along the evaluation axes. We demonstrate EmbedGEMs utility by evaluating embeddings and multivariate traits in two separate datasets: i) a synthetic dataset simulated to demonstrate the ability of the framework to correctly rank traits based on their heritability and disease relevance, and ii) a real data from the UK Biobank including metabolic and liver-related traits. Importantly, we show that greater disease relevance does not automatically follow from greater heritability.

bioinformatics↗

bioRxiv2023

TaxaNorm: a novel taxa-specific normalization approach for microbiome data

BackgroundIn high-throughput sequencing studies, sequencing depth, which quantifies the total number of reads, varies across samples. Unequal sequencing depth can obscure true biological signals of interest and prevent direct comparisons between samples. To remove variability due to differential sequencing depth, taxa counts are usually normalized before downstream analysis. However, most existing normalization methods scale counts using size factors that are sample specific but not taxa specific, which can result in over- or under-correction for some taxa. ResultsWe developed TaxaNorm, a novel normalization method based on a zero-inflated negative binomial model. This method assumes the effects of sequencing depth on mean and dispersion vary across taxa. Incorporating the zero-inflation part can better capture the nature of microbiome data. We also propose two corresponding diagnosis tests on the varying sequencing depth effect for validation. We find that TaxaNorm achieves comparable performance to existing methods in most simulation scenarios in downstream analysis and reaches a higher power for some cases. Specifically, it has a well balance on power and false discoveries control. When applying the method in a real dataset, TaxaNorm has improved performance when correcting technical bias. ConclusionTaxaNorm considers correcting both sample- and taxon-specific bias by introducing an appropriate regression framework in the microbiome data, which aids in data interpretation and visualization. The TaxaNorm R package is freely available through the CRAN repository https://CRAN.R-project.org/package=TaxaNorm and the source code can be downloaded at https://github.com/wangziyue57/TaxaNorm.

bioinformatics↗

bioRxiv2023

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain bounti tissue).

neuroscience↗

bioRxiv2023

Biomechanics Characterization of Autonomic and Somatic Nerves by High Dynamic Closed-Loop MEMS force sensing

The biomechanics of peripheral nerves are determined by the blood-nerve barrier (BNB), together with the epineural barrier, extracellular matrix, and axonal composition, which maintain structural and functional stability. These elements are often ignored in the fabrication of penetrating devices, and the implant process is traumatic due to the mechanical distress, compromising the function of neuroprosthesis for sensory-motor restoration in amputees. Miniaturization of penetrating interfaces offers the unique opportunity of decoding individual nerve fibers associated to specific functions, however, a main issue for their implant is the lack of high-precision standardization of insertion forces. Current automatized electromechanical force sensors are available; however, their sensitivity and range amplitude are limited (i.e. mN), and have been tested only in-vitro. We previously developed a high-precision bi-directional micro-electromechanical force sensor, with a closed-loop mechanism (MEMS-CLFS), that while measuring with high-precision (-211.7N to 211.5N with a resolution of 4.74nN), can be used in alive animal. Our technology has an on-chip electrothermal displacement sensor with a shuttle beam displacement amplification mechanism, for large range and high-frequency resolution (dynamic range of 92.9 dB), which eliminates the adverse effect of flexural nonlinearity measurements, observed with other systems, and reduces the mechanical impact on delicate biological tissue. In this work, we use the MEMS-CLFS for in-vivo bidirectional measurement of biomechanics in somatic and autonomic nerves. Furthermore we define the mechanical implications of irrigation and collagen VI in the BNB, which is different for both autonomic and somatic nerves ([~] 8.5-8.6 fold density of collagen VI and vasculature CD31+ in the VN vs ScN). This study allowed us to create a mathematical approach to predict insertion forces. Our data highlights the necessity of nerve-customization forces to prevent injury when implanting interfaces, and describes a high precision MEMS technology and mathematical model for their measurements.

bioengineering↗

bioRxiv2021

3D UNet with GAN discriminator for robust localisation of the fetal brain and trunk in MRI with partial coverage of the fetal body

In fetal MRI, automated localisation of the fetal brain or trunk is a prerequisite for motion correction methods. However, the existing CNN-based solutions are prone to errors and may require manual editing. In this work, we propose to combine a multi-label 3D UNet with a GAN discriminator for localisation of both fetal brain and trunk in fetal MRI stacks. The proposed method is robust for datasets with both full and partial coverage of the fetal body.

bioengineering↗

bioRxiv2019

Different strategies to manage Varroa jacobsoni and Tropilaelaps mercedesae in Papua New Guinea

Apiculture in the Pacific nation of Papua New Guinea (PNG) is under significant pressure from emerging parasitic mites, Varroa jacobsoni and Tropilaelaps mercedesae. While numerous mite control products exist, beekeepers in Papua New Guinea have limited access and resources for these products and their effectiveness under local conditions is untested. Here we determined the effectiveness of two brood manipulation strategies - queen caging and queen removal - for managing V. jacobsoni and T. mercedesae in comparison to the chemical miticide Bayvarol(R). Our results found Bayvarol(R) was the most effective control strategy for V. jacobsoni, maintaining high efficacy (>90%) over four months with significantly reduced mean numbers of V. jacobsoni compared to untreated control hives. In contrast, mean numbers of T. mercedesae were significantly reduced by the brood manipulation strategies over two months, but not by Bayvarol(R) compared to the controls. These results highlight that a combination of different strategies is likely needed to effectively manage both mite pests in PNG. We discuss how these strategies could be optimised and adopted to achieve better mite management for PNG beekeepers.

ecology↗

bioRxiv2018

Minimal medical imaging can accurately reconstruct geometric bone models for musculoskeletal models

Accurate representation of subject-specific bone anatomy in lower-limb musculoskeletal models is important for human movement analyses and simulations. Mathematical methods can reconstruct geometric bone models using incomplete imaging of bone by morphing bone model templates, but the validity of these methods has not been fully explored. The purpose of this study was to determine the minimal imaging requirements for accurate reconstruction of geometric bone models. Complete geometric pelvis and femur models of 14 healthy adults were reconstructed from magnetic resonance imaging through segmentation. From each complete bone segmentation, three sets of incomplete segmentations (set 1 being the most incomplete) were created to test the effect of imaging incompleteness on reconstruction accuracy. Geometric bone models were reconstructed from complete sets, three incomplete sets, and two motion capture-based methods. Reconstructions from (in)complete sets were generated using statistical shape modelling, followed by host-mesh and local-mesh fitting through the Musculoskeletal Atlas Project Client. Reconstructions from motion capture-based methods used positional data from skin surface markers placed atop anatomic landmarks and estimated joint centre locations as target points for statistical shape modelling and linear scaling. Accuracy was evaluated with distance error (mm) and overlapping volume similarity (%) between complete bone segmentation and reconstructed bone models, and statistically compared using a repeated measure analysis of variance (p<0.05). Motion capture-based methods produced significantly higher distance error than reconstructions from (in)complete sets. Pelvis volume similarity reduced significantly with the level of incompleteness: complete set (92.70{+/-}1.92%), set 3 (85.41{+/-}1.99%), set 2 (81.22{+/-}3.03%), set 1 (62.30{+/-}6.17%), motion capture-based statistical shape modelling (41.18{+/-}9.54%), and motion capture-based linear scaling (26.80{+/-}7.19%). A similar trend was observed for femur volume similarity. Results indicate that imaging two relevant bone regions produces overlapping volume similarity > 80% compared to complete segmented bone models. These findings have implications for improving movement analysis and simulation with subject-specific musculoskeletal models.

bioengineering↗