Biology subjects

Reynolds, D.

Publications and source records attributed to Reynolds, D..

15 recordsLinked to original sources

bioRxiv2024

Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

Identifying noncoding single nucleotide variants (SNVs) in regulatory DNA linked to polygenic disease risk, the transcription factors (TFs) they bind, and the target genes they dysregulate is a goal in polygenic disease research. Massively parallel reporter gene analysis (MPRA) of 3,451 SNVs linked to risk for polygenic skin diseases characterized by disrupted epidermal homeostasis identified 355 differentially active SNVs (daSNVs). daSNV target gene analysis, combined with daSNV editing, underscored dysregulated epidermal differentiation as a pathomechanism shared across common polygenic skin diseases. CRISPR knockout screens of 1772 human TFs revealed 108 TFs essential for epidermal progenitor differentiation, uncovering novel roles for ZNF217, CXXC1, FOXJ2, IRX2 and NRF1. Population sampling CUT&RUN of 27 homeostatic TFs identified allele-specific DNA binding (ASB) differences at daSNVs enriched near epidermal homeostasis and monogenic skin disease genes, with notable representation of SP/KLF and AP-1/2 TFs. This resource implicates dysregulated differentiation in risk for diverse polygenic skin diseases.

genetics↗

bioRxiv2024

Epigenetic regulation of TERRA transcription and metacyclogenesis by base J in Leishmania major.

The hyper-modified DNA base J helps control termination of Pol II transcription at polycistronic transcription units (PTUs) in T. brucei and L. major, allowing epigenetic control of gene expression. The Telomere Repeat-containing RNA (TERRA) is synthesized in T. brucei by Pol I readthrough transcription of a telomeric PTU. While little is understood regarding TERRA synthesis and function, the hyper-modified DNA base J is highly enriched at telomeres in L. major promastigotes. We now show that TERRA is synthesized by Pol II in L. major and loss of base J leads to increased TERRA from multiple telomeric ends, presumably via readthrough transcription from an adjacent PTU. Furthermore, Pol II readthrough defects and increased TERRA correlate with increased telomeric DNA damage, increased differentiation of promastigotes to the infectious metacyclic life stage and decreased cell viability. These results help explain the essential nature of base J in Leishmania and provide insight regarding epigenetic control of RNA expression, telomere stability and parasite development during the life cycle of L. major.

molecular biology↗

bioRxiv2024

Invasion and spread of the neotropical leafhopper Curtara insularis (Hemiptera: Cicadellidae) in Africa and North America and the role of high-altitude windborne migration in invasive insects

Invasive insects threaten ecosystem stability, public health, and food security. Documenting newly invasive species and understanding how they reach into new territories, establish populations, and interact with other species remain vitally important. Here, we report on the invasion of the South American leafhopper, Curtara insularis into Africa, where it has established populations in Ghana, encroaching inland at least 350 km off the coast. Importantly, 80% of the specimens collected were intercepted between 160 and 190 m above ground. Further, the fraction of this species among all insects collected was also higher at altitude, demonstrating its propensity to engage in high-altitude windborne dispersal. Its aerial densities at altitude translate into millions of migrants/km over a year, representing massive propagule pressure. Given the predominant south-westerly winds, these sightings suggest an introduction of C. insularis into at least one of the Gulf of Guinea ports. To assess the contribution of windborne dispersal to its spread in a new territory, we examine records of C. insularis range-expansion in the USA. Reported first in 2004 from central Florida, it reached north Florida (Panhandle) by 2008-2011 and subsequently spread across the southeastern and south-central US. Its expansion fits a "diffusion-like" process with 200--300 km long "annual displacement steps"--a pattern consistent with autonomous dispersal rather than vehicular transport. Most "steps" are consistent with common wind trajectories from the nearest documented population, assuming 2--8 hours of wind-assisted flight at altitude. Curtara insularis has been intercepted at US ports and on trucks. Thus, it uses multiple dispersal modalities, yet its rapid overland spread is better explained by its massive propagule pressure linked with its high-altitude windborne dispersal. We propose that high-altitude windborne dispersal is common yet under-appreciated in invasive insect species.

ecology↗

bioRxiv2023

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plants phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

plant biology↗

bioRxiv2023

Prenatal vitamin D deficiency alters immune cell proportions of young adult offspring through alteration of long-term stem cell fates

Vitamin D deficiency is a common deficiency worldwide, particularly among women of reproductive age. During pregnancy, it increases the risk of immune-related diseases in offspring later in life. However, exactly how the body remembers exposure to an adverse environment during development is poorly understood. Herein, we explore the effects of prenatal vitamin D deficiency on immune cell proportions in offspring using vitamin D deficient mice established by dietary manipulation. We found that prenatal vitamin D deficiency alters immune cell proportions in offspring by changing the transcriptional properties of genes downstream of vitamin D receptor signaling in hematopoietic stem and progenitor cells of both the fetus and adults. Our results suggest the role of cellular differentiation properties of the hematopoiesis as the long-term memories of prenatal exposure at the adult stage. Moreover, further investigations of the associations between maternal vitamin D levels and cord blood immune cell profiles from 75 healthy pregnant women and their term babies also confirm that maternal vitamin D levels in the second trimester significantly affect immune cell proportions in the babies. This highlights the importance of providing vitamin D supplementation at specific stages of pregnancy.

developmental biology↗

bioRxiv2023

The most remarkable migrants - systematic analysis of the Western European insect flyway at a Pyrenean mountain pass

In 1950 David and Elizabeth Lack chanced upon a huge migration of insects and birds flying through the Pyrenean Pass of Bujaruelo, later describing the spectacle as combining both grandeur with novelty. The intervening years have seen many changes to land use and climate, posing the question as to the current status of this migratory phenomenon, while a lack of quantitative data has prevented insights into the ecological impact of this mass insect migration and into the factors affecting it. To address this, we revisited the site in autumn over a 4-year period and systematically monitored diurnal insect species and numbers. We document an annual mean of 17.1 million day-flying insects from 5 orders moving south, with mass migration events associated with warmer temperatures, the presence of a headwind, sunlight, low windspeed, and low rainfall. Diptera dominated the migratory assemblage and annual numbers varied by more than fourfold with larger annual migration flows associated with higher autumn temperatures in Northwest Europe. Finally, using observed environmental thresholds for migration, we estimate an annual bioflow of at least 14.6 billion day-flying insects migrating south over the whole Pyrenean Mountain range, highlighting the importance of this route for seasonal insect migrants.

zoology↗

bioRxiv2022

An optimized approach for multiplexing single-nuclear ATAC-seq using oligonucleotide conjugated antibodies

BackgroundSingle-cell technologies to analyze transcription and chromatin structure have been widely used in many research areas to reveal the functions and molecular properties of cells at single-cell resolution. Sample multiplexing techniques are valuable when performing single-cell analysis, reducing technical variation and permitting cost efficiencies. Several commercially available methods are available and have been used in many scRNA-seq studies. On the other hand, while several methods have been published, the multiplexing techniques for single nuclear Assay for Transposase-Accessible Chromatin (snATAC)-seq assays remain under development. We developed a simple nucleus hashing method using oligonucleotide conjugated antibodies recognizing nuclear pore complex proteins, NuHash, to perform snATAC-seq library preparations by multiplexing. ResultsWe performed multiplexing snATAC-seq analyses on the mixture of human and mouse cell samples (two samples, 2-plex, and four samples, 4-plex) using NuHash. The demultiplexing accuracy of NuHash was high, and only ten out of 9,144 nuclei (2-plex) and 150 of 12,208 nuclei (4-plex) had discordant classifications between NuHash demultiplexing and discrimination using reference genome alignments. We compared results between snATAC-seq and deeply sequenced bulk ATAC-seq on the same samples and found that most of the peaks detected in snATAC-seq were also detected in deeply sequenced bulk ATAC-seq. The bulk ATAC-seq signal intensity was positively correlated with the number of cell subtype clusters detected in snATAC-seq, but not the subset of peaks detected in all clusters. These subsets of snATAC-seq peaks showed different distributions over different genomic features, suggesting that the peak intensities of bulk ATAC-seq can be used to identify different types of functional loci. ConclusionsOur multiplexing method using oligo-conjugated anti-nuclear pore complex proteins, NuHash, permits high accuracy demultiplexing of samples. The NuHash protocol is straightforward, it works on frozen samples, and requires no modifications for snATAC-seq library preparation.

genomics↗

bioRxiv2021

Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection.

Complement activation has been implicated in the pathogenesis of severe SARS-CoV-2 infection. However, it remains to be determined whether increased complement activation is a broad indicator of critical illness (and thus, no different in COVID-19). It is also unclear which pathways are contributing to complement activation in COVID-19, and, if complement activation is associated with certain features of severe SARS-CoV-2 infection, such as endothelial injury and hypercoagulability. To address these questions, we investigated complement activation in the plasma from patients with COVID-19 prospectively enrolled at two tertiary care centers. We compared our patients to two non-COVID cohorts: (a) patients hospitalized with influenza, and (b) patients admitted to the intensive care unit (ICU) with acute respiratory failure requiring invasive mechanical ventilation (IMV). We demonstrate that circulating markers of complement activation (i.e., sC5b-9) are elevated in patients with COVID-19 compared to those with influenza and to patients with non-COVID-19 respiratory failure. Further, the results facilitate distinguishing those who are at higher risk of worse outcomes such as requiring ICU admission, or IMV. Moreover, the results indicate enhanced activation of the alternative complement pathway is most prevalent in patients with severe COVID-19 and is associated with markers of endothelial injury (i.e., Ang2) as well as hypercoagulability (i.e., thrombomodulin and von Willebrand factor). Our findings identify complement activation to be a distinctive feature of COVID-19, and provide specific targets that may be utilized for risk prognostication, drug discovery and personalized clinical trials. SUMMMARYComplement has been implicated in COVID-19. However, whether this is distinctive of COVID-19 remains unanswered. Ma et al report increased complement activation in COVID-19 compared to influenza and non-COVID respiratory failure, and demonstrate alternative pathway activation as a key marker of multiorgan failure and death.

immunology↗

bioRxiv2021

The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation

Viral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2. Author SummarySARS-CoV-2 is the latest pathogenic coronavirus to emerge as a public health threat. We create a database of proximal host proteins to 17 SARS-CoV-2 viral proteins. We validate that NSP1 is proximal to the EIF3 translation initiation complex and is a potent inhibitor of translation. We also identify ORF6 antagonism of RNA-mediate innate immune signaling. We produce a database of potential host targets of the viral protease NSP5, and create a fluorescence-based assay to screen cleavage of peptide sequences. We believe that this data will be useful for identifying roles for many of the uncharacterized SARS-CoV-2 proteins and provide insights into the pathogenicity of new or emerging coronaviruses.

microbiology↗

bioRxiv2020

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation

Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code in dynamic gene regulation. HIGHLIGHTSO_LIAn integrative multi-omic resource profiling chromatin and expression dynamics across keratinocyte differentiation C_LIO_LIPredictive deep learning models of chromatin dynamics reveal a high-resolution cis-regulatory DNA motif lexicon of epidermal differentiation C_LIO_LIModel interpretation enables discovery of combinatorial cis-regulatory logic of homotypic and heterotypic motif combinations C_LIO_LIMassively parallel reporter experiments validate temporal dynamics and cis-regulatory logic of the combinatorial motif lexicon C_LI

genomics↗

bioRxiv2019

Curriculum-based outdoor learning for children aged 9-11: A qualitative analysis of pupils' and teachers' views.

The relationship between child health, wellbeing and education demonstrates that healthier and happier children achieve higher educational attainment. An engaging curriculum that facilitates children in achieving their academic potential has strong implications for educational outcomes, future employment prospects and health and wellbeing during adulthood. Outdoor learning is a pedagogical approach used to enrich learning, enhance school engagement and improve pupil health and wellbeing. However, its non-traditional means of achieving curricular aims are not yet recognised beyond the early years by education inspectorates. This requires evidence into its acceptability from those at the forefront of delivery. This study aimed to explore headteachers, teachers and pupils views and experiences of an outdoor learning programme within the key stage two curriculum (ages 9-11). We examine the process of implementation to offer case study evidence through 1:1 interviews with headteachers (n=3) and teachers (n=10) and focus groups with pupils aged 9-11 (n=10) from three primary schools. Interviews and focus groups were conducted at baseline and six months into implementation. Schools introduced regular outdoor learning within the curriculum. This study found a variety of perceived benefits for pupils and schools. Pupils and teachers noticed improvements in pupils engagement with learning, concentration and behaviour, as well as positive impacts on health and wellbeing and teachers job satisfaction. Curriculum demands including testing and evidencing work were barriers to implementation, in addition to safety concerns, resources and teacher confidence. Participants supported outdoor learning as a curriculum-based programme for older primary school pupils. However, embedding outdoor learning within the curriculum requires education inspectorates to place higher value on this approach in achieving curricular aims, alongside greater acknowledgment of the wider benefits to children which current measurements do not capture.

scientific communication and education↗

bioRxiv2018

CropMonitor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

BackgroundHigh-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales.\n\nFindingsCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted.\n\nConclusionsAs an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.

plant biology↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗

bioRxiv2017

CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

plant biology↗

bioRxiv2017

Speed breeding: a powerful tool to accelerate crop research and breeding

The growing human population and a changing environment have raised significant concern for global food security, with the current improvement rate of several important crops inadequate to meet future demand [1]. This slow improvement rate is attributed partly to the long generation times of crop plants. Here we present a method called speed breeding, which greatly shortens generation time and accelerates breeding and research programs. Speed breeding can be used to achieve up to 6 generations per year for spring wheat (Triticum aestivum), durum wheat (T. durum), barley (Hordeum vulgare), chickpea (Cicer arietinum), and pea (Pisum sativum) and 4 generations for canola (Brassica napus), instead of 2-3 under normal glasshouse conditions. We demonstrate that speed breeding in fully-enclosed controlled-environment growth chambers can accelerate plant development for research purposes, including phenotyping of adult plant traits, mutant studies, and transformation. The use of supplemental lighting in a glasshouse environment allows rapid generation cycling through single seed descent and potential for adaptation to larger-scale crop improvement programs. Cost-saving through LED supplemental lighting is also outlined. We envisage great potential for integrating speed breeding with other modern crop breeding technologies, including high-throughput genotyping, genome editing, and genomic selection, accelerating the rate of crop improvement.

plant biology↗