Biology subjects

Jones, M.

Publications and source records attributed to Jones, M..

At least 91 records · Page 5Linked to original sources

bioRxiv2020

The impact of life stage and pigment source on the evolution of novel warning signal traits

Our understanding of how novel color traits evolve in aposematic taxa is based largely on studies of reproductive stages and organisms with endogenously produced pigmentation. In these systems, genetic drift is often required for novel alleles to overcome strong purifying selection stemming from frequency-dependent predation and positive assortative mating. Here we show that the importance of these mechanisms can differ if selective processes are considered in larval stage instead. By integrating population genomic data, predation experiments and phenotypic measurements of larvae and their host plants, we show that novel white alleles in Neodiprion lecontei (pine sawfly) larvae spread via selection rather than drift. The cost of being rare was not offset by an enhanced aposematic display or immune function. Instead, bottom-up selection via host plants may drive divergence among populations as white larvae were disproportionately abundant on a pine species with a reduced carotenoid content relative to other pine hosts.

evolutionary biology↗

bioRxiv2020

Hybrid assembly of an agricultural slurry virome reveals a diverse and stable community with the potential to alter the metabolism and virulence of veterinary pathogens

BackgroundViruses are the most abundant biological entities on Earth, known to be crucial components of microbial ecosystems. However, there is little information on the viral community within agricultural waste. There are currently ~2.7 million dairy cattle in the UK producing 7-8% of their own bodyweight in manure daily, and 28 million tonnes annually. To avoid pollution of UK freshwaters, manure must be stored and spread in accordance with guidelines set by DEFRA. Manures are used as fertiliser, and widely spread over crop fields, yet little is known about their microbial composition. We analysed the virome of agricultural slurry over a five-month period using short and long-read sequencing. ResultsHybrid sequencing uncovered more high-quality viral genomes than long or short-reads alone; yielding 7,682 vOTUs, 174 of which were complete viral genomes. The slurry virome was highly diverse and dominated by lytic bacteriophage, the majority of which represent novel genera (~98%). Despite constant influx and efflux of slurry, the composition and diversity of the slurry virome was extremely stable over time, with 55% of vOTUs detected in all samples over a five-month period. Functional annotation revealed a diverse and abundant range of auxiliary metabolic genes and novel features present in the community. Including the agriculturally relevant virulence factor VapE, which was widely distributed across different phage genera that were predicted to infect several hosts. Furthermore, we identified an abundance of phage-encoded diversity-generating retroelements, which were previously thought to be rare on lytic viral genomes. Additionally, we identified a group of crAssphages, including lineages that were previously thought only to be found in the human gut. ConclusionsThe cattle slurry virome is complex, diverse and dominated by novel genera, many of which are not recovered using long or short-reads alone. Phages were found to encode a wide range of AMGs that are not constrained to particular groups or predicted hosts, including virulence determinants and putative ARGs. The application of agricultural slurry to land may therefore be a driver of bacterial virulence and antimicrobial resistance in the environment.

microbiology↗

bioRxiv2020

Increased microenvironment stiffness leads to altered aldehyde metabolism and DNA damage in mammary epithelial cells through a RhoA-dependent mechanism

Microenvironmental stiffness regulates the behaviour of both normal and cancer cells. In breast tissue, high mammographic density (HMD), which reflects greater organisation and stiffness of the periductal collagen, represents a significant risk factor for cancer. However, the mechanistic link between extracellular matrix (ECM) stiffness and increased risk of breast tumour initiation remains unclear. In particular, how increased ECM stiffness might promote genomic damage, leading to the acquisition of transforming mutations, remains to be determined. Here we determine that ECM stiffness induces changes in mammary epithelial cell (MEC) metabolism that drive genomic damage. Using a 3D-culture model, we demonstrate that genome-wide transcriptional changes in response to increased ECM stiffness impair the ability of MECs to remove reactive aldehyde species, resulting in greater accumulation of DNA damage in a RhoA-dependent manner. Together, our results provide a mechanistic link between increased ECM stiffness and the genomic damage required for breast cancer initiation.

developmental biology↗

bioRxiv2020

The Coronavirus Network Explorer: Mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

Building on recent work that identified human host proteins that interact with SARS-CoV-2 viral proteins in the context of an affinity-purification mass spectrometry screen, we use a machine learning-based approach to connect the viral proteins to relevant biological functions and diseases in a large-scale knowledge graph derived from the biomedical literature. Our aim is to explore how SARS-CoV-2 could interfere with various host cell functions, and also to identify additional drug targets amongst the host genes that could potentially be modulated against COVID-19. Results are presented in the form of interactive network visualizations, that allow exploration of underlying experimental evidence. A selection of networks is discussed in the context of recent clinical observations.

systems biology↗

bioRxiv2020

Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

The timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common developmental time to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

plant biology↗

bioRxiv2020

The risk for insufficient chill accumulation: a climate change perspective for apple and cherry production in the United States

Winter chill accumulation is critical for the productivity and profitability of perennial tree fruit systems. Several studies have quantified the impacts of global warming on chill accumulation in the warmer production regions of the world, where insufficient chill events occur and their frequency is increasing. In contrast, we focus on a region with relatively cold winters-the Pacific Northwest United States (PNW)-where insufficient chill events are currently absent, and quantify the potential for introduction of these risks under climate change. Results identified spatial variation within the PNW, with chill accumulation projected to increase in some areas but decrease in others. There was also spatiotemporal variation in the driving factors of changes to chill accumulation. Even with decreases in chill accumulations, there are likely minimal issues with insufficient chill accumulation. However, delayed chill accumulation in combination with advances in the onset of heat accumulation can potentially shift the region from one where spring phenology is primarily forcing-driven to one where the dynamic interplay between chilling and forcing processes become important. These interactions might create production risks for varieties with high chill requirements, post mid-21st-century under high emissions scenarios. Future work should focus on understanding, modeling, and projecting responses across these overlapping chilling and forcing processes. Additionally, given significant spatial differences across a relatively small geographic range, it is also critical to understand and model these dynamics at a local landscape resolution for regions such as the PNW.

plant biology↗

bioRxiv2020

Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration

Cell migration in 3D micro-environments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to forward movement of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling completely, our ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach has also highlighted the unanticipated potential of targeting CDK1 to abrogate cell movement, a prediction we confirmed experimentally. Moreover, we have made a novel prediction regarding the potential existence of a set point in local stiffness gradients that promotes polarisation and rapid rear retraction. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems.

cell biology↗

bioRxiv2020

Sleep-wake Characteristics in a Mouse Model of Severe Traumatic Brain Injury: Relation to Post-Traumatic Epilepsy.

Study objectivesTraumatic brain injury (TBI) results in sequelae that include post-traumatic epilepsy (PTE) and sleep-wake disturbances. Here we sought to determine whether sleep characteristics could predict development of PTE in a model of severe TBI. MethodsFollowing controlled cortical impact (CCI), sham injury (craniotomy only) or no craniotomy (NC), CD-1 mice were implanted with epidural electroencephalography (EEG) and nuchal electromyography (EMG) electrodes. Acute (1st week) and chronic (months 1, 2 and 3 after injury) 1-week long video-EEG/EMG recordings were examined for epileptiform activity. We analyzed sleep-wake patterns manually and extracted high amplitude interictal events from EEG using an automated method. Sleep spindles and EEG delta power were derived from non-rapid eye movement (NREM) sleep epochs. Brain CTs (computerized tomography) were performed to quantify the extent of brain lesions in cohorts of sham and CCI. ResultsPosttraumatic seizures were seen with CCI, whereas interictal epileptiform activity as well as sleep-wake disruptions (shorter wake or NREM bout lengths, shorter duration or lower power for spindles, and increased NREM EEG delta power) were seen in CCI and sham groups. No sleep feature predicted PTE. Follow up brain CTs showed a small lesion in the sham injury group suggesting a milder form of TBI that may account for their interictal activity and sleep changes. ConclusionsIn our model, interictal epileptiform activity and sleep disruptions resulted from CCI and sham and thus, sham injury was not an optimal negative control. Further work is necessary to determine the relationship between sleep-wake disturbances and PTE. Statement of significanceTraumatic brain injury (TBI) results in sequelae such as post-traumatic seizures and sleep-wake disturbances but it is difficult to predict which individuals will develop these symptoms. Our study is novel in that we characterized epileptiform activity and multiple sleep characteristics in a mouse model of severe TBI (Controlled cortical impact-CCI) and explored whether any specific sleep disturbance can predict post-traumatic epilepsy. Specifically, post-traumatic seizures were seen after CCI only whereas epileptiform activity other than seizures as well as sleep-wake disruptions in mice that received a TBI and their sham injury controls. CT imaging showed that the sham injury group also had small brain lesions suggesting that a more optimal control in TBI research is to perform no craniotomy. No single sleep characteristic was predictive of post-traumatic epilepsy although NREM delta power was different in chronic recordings between TBI mice that developed seizures and those that did not. These studies are relevant to further research in TBI models, to develop a sleep biomarker for PTE. The work is also relevant to humans with TBI as monitoring sleep phenotypes may predict risk, but may also help develop therapies to prevent post-traumatic epilepsy.

neuroscience↗

bioRxiv2020

A Mesoscale Finite Element Modelling Approach for Understanding Brain Morphology and Material Heterogeneity Effects in Chronic Traumatic Encephalopathy

Chronic Traumatic Encephalopathy (CTE) affects a significant portion of athletes in contact sports but is difficult to quantify using clinical examinations and modelling approaches. We use an in silico approach to quantify CTE biomechanics using mesoscale Finite Element (FE) analysis that bridges with macroscale whole head FE analysis. The sulci geometry produces complex stress waves that interact with each another to create increased shear stresses at the sulci depth that are significantly larger than in analyses without sulci (from 0.5 kPa to 18.0 kPa). Also, Peak sulci stresses are located where CTE has been experimentally observed in the literature. Highlights3 to 5 bullet points 85 characters max O_LISulci introduce stress localizations at their depth in the gray matter C_LIO_LISulci stress fields interact to produce stress concentration sites in white matter C_LIO_LIDifferentiating brain tissue properties did not significantly affect peak stresses C_LI

biophysics↗

bioRxiv2020

Basal media optimization for the micropropagation and callogenesis of Cannabis sativa L.

Micropropagation of Cannabis sativa is an emerging area for germplasm storage and large-scale production of clean plants. Existing protocols use a limited number of genotypes and are often not reproducible. Previous studies reported MS + 0.5 M TDZ to be optimal for Cannabis nodal micropropagation, yet our preliminary studies using nodal explants suggested this media may not be optimal. It resulted in excessive callus formation, hyperhydricity, low multiplication rates, and high mortality rates. Following an initial screen of four commonly used basal salt mixtures (MS, B5, BABI, and DKW), we determined that DKW produced the healthiest plants. In a second experiment, the multiplication rate and canopy area of explants grown on MS + 0.5 M TDZ and DKW + 0.5 M TDZ were compared using five drug-type cultivars to determine if the preference for DKW was genotype-dependent. Four cultivars had significantly higher multiplication rates on DKW + 0.5 M TDZ with the combined average being 1.5x higher than explants grown on MS + 0.5 M TDZ. The canopy area was also significantly larger on DKW + 0.5 M TDZ for four cultivars with the combined average being twice as large as the explants grown on MS + 0.5 M TDZ. In the third experiment, callogenesis was compared using a range of 2,4-D concentrations (0-30 M) on both MS and DKW and similarly, callus growth was superior on DKW. This study presents the largest comparison of basal salt compositions on the micropropagation of five commercially grown Cannabis cultivars to date.

plant biology↗

bioRxiv2019

Convergent evolution of seasonal camouflage in response to reduced snow cover across the snowshoe hare range

Determining how different populations adapt to similar environments is fundamental to understanding the limits of adaptation under changing environments. Snowshoe hares (Lepus americanus) typically molt into white winter coats to remain camouflaged against snow. In some warmer climates, hares have evolved brown winter camouflage - an adaptation that may spread under climate change. We used extensive range-wide genomic data to 1) resolve broad-scale patterns of population structure and gene flow and 2) investigate the factors shaping the origins and distribution of winter-brown camouflage variation. In coastal Pacific Northwest (PNW) populations, winter-brown camouflage is known to be determined by a recessive haplotype at the Agouti pigmentation gene. Our phylogeographic analyses revealed deep structure and limited gene flow between PNW and more northern Boreal populations, where winter-brown camouflage is rare along the range edge. Genome sequencing of a winter-brown snowshoe hare from Alaska shows that it lacks the winter-brown PNW haplotype, reflecting a history of convergent phenotypic evolution. However, the PNW haplotype does occur at low frequency in a winter-white population from Montana, consistent with the spread of a locally deleterious recessive variant that is masked from selection when rare. Simulations show that if annual snow cover dramatically declined in the same population, then the predicted selective increase in frequency of the now beneficial winter-brown Agouti allele is likely to be extremely slow due to the same masking effect. Our findings underscore how allelic dominance can shape the geographic extent and rate of convergent adaptation in response to rapidly changing environments.

evolutionary biology↗

bioRxiv2019

Artificial selection finds new hypotheses for the mechanism of Wolbachia-mediated dengue blocking in mosquitoes

Wolbachia is an intracellular bacterium that blocks virus replication in insects and has been introduced into the mosquito, Aedes aegypti for the biocontrol of arboviruses including dengue, Zika and chikungunya. Despite ongoing research, the mechanism of Wolbachia-mediated virus blocking remains unclear. We recently used experimental evolution to reveal that Wolbachia-mediated dengue blocking could be selected upon in the A. aegypti host and showed evidence that strong levels of blocking could be maintained by natural selection. In this study, we investigate the genetic variation associated with blocking and use these analyses to generate testable hypotheses surrounding the mechanism of Wolbachia-mediated dengue blocking. From our results, we hypothesise that Wolbachia may block virus replication by increasing the regeneration rate of mosquito cells via the Notch signalling pathway. We also propose that Wolbachia modulates the hosts transcriptional pausing pathway either to prime the hosts anti-viral response or to directly inhibit viral replication.

evolutionary biology↗

bioRxiv2019

Systematic bias assessment in solid tissue 10x scRNA-seq workflows

Single-cell and single-nucleus RNA sequencing have been widely adopted in studies of heterogeneous tissues to estimate their cellular composition and obtain transcriptional profiles of individual cells. However, the current fragmentary understanding of artefacts introduced by sample preparation protocols impedes the selection of optimal workflows and compromises data interpretation. To bridge this gap, we compared performance of several workflows applied to adult mouse kidneys. Our study encompasses two tissue dissociation protocols, two cell preservation methods, bulk tissue RNA sequencing, single-cell and three single-nucleus RNA sequencing workflows for the 10x Genomics Chromium platform. These experiments enable a systematic comparison of recovered cell types and their transcriptional profiles across the workflows and highlight protocol-specific biases important for the experimental design and data interpretation.

genomics↗

bioRxiv2019

Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations

Landscape genomics studies focus on identifying candidate genes under selection via spatial variation in abiotic environmental variables, but rarely by biotic factors such as disease. The Tasmanian devil (Sarcophilus harrisii) is found only on the environmentally heterogeneous island of Tasmania and is threatened with extinction by a nearly 100% fatal, transmissible cancer, devil facial tumor disease (DFTD). Devils persist in regions of long-term infection despite epidemiological model predictions of species extinction, suggesting possible adaptation to DFTD. Here, we test the extent to which spatial variation and genetic diversity are associated with the abiotic environment and/or by DFTD. We employ genetic-environment association (GEAs) analyses using a RAD-capture panel consisting of 6,886 SNPs from 3,286 individuals sampled pre- and post-disease arrival. Pre-disease, we find significant correlations of allele frequencies with environmental variables, including 349 unique loci linked to 64 genes, suggesting local adaptation to abiotic environment. Following DFTD arrival, however, we detected few of the pre-disease candidate loci, but instead frequencies of candidate loci linked to 14 genes correlated with disease prevalence. Loss of apparent signal of abiotic local adaptation following disease arrival suggests swamping by the strong selection imposed by DFTD. Further support for this result comes from the fact that post-disease candidate loci are in linkage disequilibrium with genes putatively involved in immune response, tumor suppression and apoptosis. This suggests the strength GEA associations of loci with the abiotic environment are swamped resulting from the rapid onset of a biotic selective pressure.

evolutionary biology↗

bioRxiv2019

Targeting DNA damage response and Replication Stress in Pancreatic Cancer

Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. We interrogated the transcriptome, genome, proteome and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting the DNA damage response (DDR) and replication stress. We show that patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, co-segregates with response to platinum and PARP inhibitor therapy in vitro and in vivo. We generated a novel signature of replication stress with potential clinical utility in predicting response to ATR and WEE1 inhibitor treatment. Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR proficient PC, and post-platinum therapy.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC=\"FIGDIR/small/713545v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@106f2b8org.highwire.dtl.DTLVardef@1294d22org.highwire.dtl.DTLVardef@86f5d3org.highwire.dtl.DTLVardef@751e13_HPS_FORMAT_FIGEXP M_FIG C_FIG STATEMENT OF SIGNIFICANCEWe define therapeutic strategies that target subgroups of PC using novel signatures of DNA damage response deficiency and replication stress. This potentially offers patients with DNA repair defects therapeutic options outside standard of care platinum chemotherapy and is being tested in clinical trials on the Precision-Panc platform.

cancer biology↗

bioRxiv2019

A polygenic and phenotypic risk prediction for Polycystic Ovary Syndrome evaluated by Phenome-wide association studies

PurposeAs many as 75% of patients with Polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.\n\nMethods and FindingsLeveraging the electronic health records (EHRs) of 124,852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: morbid obesity, type 2 diabetes, hypercholesterolemia, disorders of lipid metabolism, hypertension and sleep apnea reaching phenome-wide significance.\n\nConclusionsOur study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

bioinformatics↗

bioRxiv2019

Survey of intertidal ecosystem reveals a legacy of potentially toxic elements from industrial activity in the Skeena Estuary, British Columbia, Canada

Relationships between concentrations of Potentially Toxic Elements (PTEs) in estuarine sediments and their impact benthic invertebrate communities are poorly understood. We sampled and analysed PTEs in sediments and benthic invertebrates from five sites surrounding the Skeena Estuary, including sites adjacent to an abandoned cannery and a decommissioned papermill. There was no indication that sediments of the salmon cannery are polluted, but acidic sediments adjacent to the papermill contained elevated concentrations of Cd, Cr, Hg and Pb. Benthic invertebrate community assemblages confirm that sediments have recovered from prior disturbances associated with discharge of papermill sludge. Oregon pill bugs (Gnorimosphaeroma oregonensis), observed at all five sites, feed on the fibers associated with the papermill discharge. Thus, G. oregonensis are useful biomonitors for quantifying the impact of the decommissioned papermill, and similar industrial development projects, on intertidal ecosystems along the north coast of British Columbia, Canada.

ecology↗

bioRxiv2019

Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference

Adaptive behavior in even the simplest decision-making tasks requires predicting future events in an environment that is generally nonstationary. As an inductive problem, this prediction requires a commitment to the statistical process underlying environmental change. This challenge can be formalized in a Bayesian framework as a question of choosing a generative model for the task dynamics. Previous learning models assume, implicitly or explicitly, that nonstationarity follows either a continuous diffusion process or a discrete changepoint process. Each approach is slow to adapt when its assumptions are violated. A new mixture of Bayesian experts framework proposes separable brain systems approximating inference under different assumptions regarding the statistical structure of the environment. This model explains data from a laboratory foraging task, in which rats experienced a change in reward contingencies after pharmacological disruption of dorsolateral (DLS) or dorsomedial striatum (DMS). The data and model suggest DLS learns under a diffusion prior whereas DMS learns under a changepoint prior. The combination of these two systems offers a new explanation for how the brain handles inference in an uncertain environment.\n\nOne Sentence SummaryAdaptive foraging behavior can be explained by separable brain systems approximating Bayesian inference under different assumptions about dynamics of the environment.

neuroscience↗