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Bell, D.

Publications and source records attributed to Bell, D..

12 recordsLinked to original sources

Streamflow drought limits fish production across river ecosystems

River flows sustain biodiversity, ecosystem function, and human well-being, yet climate change and water extraction are driving frequent and severe low-flow conditions (i.e., streamflow droughts). Here, we show that declining streamflow constrains fish production by limiting juvenile recruitment, the dominant demographic pathway regulating population dynamics. Across populations, declining flows nonlinearly reduce carrying capacity and production, with severe drought reducing production by up to 90%. Flow-production relationships vary among rivers, revealing both drought-sensitive and drought-resilient responses shaped by ecological and hydrological context. In some systems, maintaining higher flows during average years yields greater ecological benefits than equivalent drought-year interventions. These results demonstrate that streamflow drought directly limits biological production and highlight how adaptive water management can be implemented to sustain freshwater ecosystems under increasing hydroclimatic variability.

ecology↗

Selective targeting of IL-1RAP-dependent eosinophilic inflammation in allergic fungal airway disease

It is estimated that in excess of 10 million people around the globe are affected by severe asthma and fungal sensitisation (SAFS) or allergic bronchopulmonary aspergillosis (ABPA), severe asthma endotypes driven by hypersensitivity to environmentally ubiquitous fungal pathogens, primarily Aspergillus fumigatus. Here, we sought to define the immunological pathways underlying these allergic fungal airway diseases. To do so, we exploit the chronic exposure repeat challenge model using live A. fumigatus conidia to systematically define the key immunological pathways driving airway inflammation in allergic fungal airway disease. In response to daily intranasal challenge, we observed increased absolute numbers of neutrophils and eosinophils in bronchoalveolar lavage fluid (BALF), characteristic of human allergic fungal airway disease, with significant depletion of the alveolar macrophage population. Transcriptomic analysis of BALF cells identified increased expression of IL-1 family cytokines and receptors including IL-1{beta}, IL-1RL1, IL-1R2 and the IL-18 binding protein. Complementary proteomic analysis of BALF revealed increased levels of cell death related proteins calprotectin and IL-1 Receptor Accessory Protein (IL-1RAP). Targeting IL-1RAP, using knockout mice, led to selective reduction in eosinophilia, IL-5 and IL-13 in the airways without impairment of fungal killing. This study identifies a role for IL-1RAP in the generation of eosinophilia independent of neutrophil influx and highlights its potential as a novel immunotherapeutic target for the treatment of allergic fungal airway disease. Author SummaryAllergic bronchopulmonary aspergillosis (ABPA) is a form of lung disease primarily affecting those with asthma or cystic fibrosis induced by the ubiquitous pathogen Aspergillus fumigatus. Understanding the immunological mechanisms contributing to this airway disease is important for the development of novel treatments to improve quality of life and preserve lung function. In this study, we show that an Aspergillus fumigatus repeat challenge model phenocopies immunological features of allergic fungal airway disease and is hallmarked by increased IL-1 family signalling. We identify the IL-1RAP as a contributor to eosinophilia and the release of Type 2 cytokines during allergic fungal airway disease. This work represents major step in describing the molecular mechanisms of airway eosinophilia in response to fungal exposure and lays the groundwork for further dissection of inflammatory pathways to target with immunotherapeutic approaches.

immunology↗

Discovering plasticity rules that organize and maintain neural circuits

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Ojas rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

neuroscience↗

Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma

Oncogene amplification on extrachromosomal DNA (ecDNA) is strongly associated with treatment resistance and shorter survival for patients with cancer, including patients with glioblastoma. The non-chromosomal inheritance of ecDNA during cell division is a major contributor to intratumoral genetic heterogeneity. At present, the spatial dynamics of ecDNA, and the impact on tumor evolutionary trajectories, are not well understood. Here, we investigate the spatial-temporal evolution of ecDNA and its clinical impact by analyzing tumor samples from 94 treatment-naive human IDH-wildtype glioblastoma patients. We developed a spatial-temporal computational model of ecDNA positive tumors ( SPECIES) that integrates whole-genome sequencing, multi-region DNA FISH, and nascent RNAscope, to provide unique insight into the spatial dynamics of ecDNA evolution. Random segregation in combination with positive selection of ecDNAs induce large, predictable spatial patterns of cell-to-cell ecDNA copy number variation that are highly dependent on the oncogene encoded on the circular DNA. EGFR ecDNAs often reach high mean copy number (mean of 50 copies per tumor cell), are under strong positive selection (mean selection coefficient, s > 2) and do not co-amplify other oncogenes on the same ecDNA particles. In contrast, PDGFRA ecDNAs have lower mean copy number (mean of 15 copies per cell), are under weaker positive selection and frequently co-amplify other oncogenes on the same ecDNA. Evolutionary modeling suggests that EGFR ecDNAs often accumulate prior to clonal expansion. EGFR structural variants, including vIII and c-terminal deletions are under strong positive selection, are found exclusively on ecDNA, and are intermixed with wild-type EGFR ecDNAs. Simulations show EGFRvIII ecDNA likely arises after ecDNA formation in a cell with high wild-type EGFR copy number (> 10) before the onset of the most recent clonal expansion. This remains true even in cases of co-selection and co-amplification of multiple oncogenic ecDNA species in a subset of patients. Overall, our results suggest a potential time window in which early ecDNA detection may provide an opportunity for more effective intervention. HighlightsO_LIecDNA is the most common mechanism of focal oncogene amplification in IDHwt glioblastoma. C_LIO_LIEGFR and its variants on ecDNA are particularly potent, likely arising early in tumor development, providing a strong oncogenic stimulus to drive tumorigenesis. C_LIO_LIWild-type and variant EGFR ecDNA heteroplasmy (co-occurrence) is common with EGFRvIII or c-terminal deletions being derived from EGFR wild-type ecDNA prior to the most recent clonal expansion. C_LIO_LITumors with ecDNA amplified EGFR versus PDGFRA exhibit different evolutionary trajectories. C_LIO_LISPECIES model can infer spatial evolutionary dynamics of ecDNA in cancer. C_LIO_LIA delay between ecDNA accumulation and subsequent oncogenic mutation may give a therapeutic window for early intervention. C_LI

cancer biology↗

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into many-row (observation), many-column (species) datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These novel community datasets let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers within a joint species distribution model. With this approach, we generate distribution maps for 76 arthro-pod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualise the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this sideways biodiversity modelling method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres.

ecology↗

Unsupervised Restoration of a Complex Learned Behavior After Large-Scale Neuronal Perturbation

Reliable execution of behaviors requires that brain circuits correct for variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in a brain region involved in song production in adult songbirds with stereotypical songs triggered severe degradation of their songs. The song fully recovered within two weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic inputs to unmanipulated neurons in the same region. A model inspired by the behavioral and electrophysiological findings suggests that a combination of unsupervised single-cell and population-level homeostatic plasticity rules can support the observed functional restoration after large-scale disruption of networks implementing sequential dynamics. In the model the sequence is restored through a parallel homeostatic process, rather than regrown serially, and predicts that sequences should recover in a saltatory fashion. Correspondingly, we observed such recovery in the songs of manipulated animals, with syllables that rapidly alternate between abnormal and normal durations from rendition to rendition until eventually they permanently settled into their original length. These observations indicate the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.

neuroscience↗

Precision motor timing via scalar input fluctuations

Complex motor skills like playing piano require precise timing over long periods, without errors accumulating between subprocesses like the left and right hand movements. While biological models can produce motor-like sequences, how the brain quenches timing errors is not well understood. Motivated by songbirds, where the left and right brain nuclei governing song sequences do not connect but may receive low-dimensional thalamic input, we present a model where timing errors in an autonomous sequence generator are continually corrected by one-dimensional input fluctuations. We show in a spiking neural network model how such input can rapidly correct temporal offsets in a propagating spike pulse, recapitulating the precise timing seen in songbird brains. In a reduced, more general model, we show that such timing correction emerges when the spatial profile of the input over the sequence sufficiently reflects its temporal fluctuations, yielding time-locking attractors that slow advanced sequences and hasten lagging ones, up to the input timescale. Unlike models without fluctuating input, our model predicts anti-correlated durations of adjacent segments of the output sequence, which we verify in recorded zebra finch songs. This work provides a bioplausible picture of how temporal precision could arise in extended motor sequences and generally how low-dimensional input could continuously coordinate time-varying output signals. SignificanceComplex motor skills like playing piano require precision timing over long periods, often among multiple components like left and right muscle groups. Although brain-like network models can produce motor-like outputs, timing regulation is not well understood. We introduce a model, inspired by songbird brains, where imprecise timing in a cortical-like system is corrected by a single thalamic input regulating the sequential propagation, or tempo, of cortical activity. This model illuminates a relation between the inputs spatial structure and temporal variation that lets lagging activity hasten and advanced activity slow, which makes a prediction about output timing that we verify in real birdsong. This work reveals a simple, neuroplausible mechanism that may play a role in precision cortical or motor timing.

neuroscience↗

High Content and High Throughout Phenotypic Assay for the Hourly Resolution of the Malaria Parasite Erythrocytic Cycle

Over the last 20 years increased funding for malaria research has resulted in very significant technical advances to study the biology of Plasmodium species. High throughput phenotypic assays have been developed to screen millions of compounds and identify small molecules with antiparasitic activity. At the same time, advances in malaria genetic have greatly facilitated the generation of genetically modified parasites, and whole genome genetic screens are now feasible in Plasmodium species. Finally, there has been an increased interest to study malaria parasites at the population level, in particular in the area of drug resistance. Drug resistant field isolates have been collected around the world, and drug resistant strains are routinely generated in the lab to study the mechanisms of drug resistance. As a result, one of the current bottlenecks in malaria research is our ability to quickly characterize the phenotype associated with compound treatment or genetic modification, or to quickly compare differences in intracellular development between strains. Here, we present a high content/high throughput phenotypic assay that combines highly selective RNA, DNA, and RBC membrane dyes to provide hourly resolution of the full erythrocytic cycle for both P. falciparum and P. knowlesi. A flow cytometry assay allows the analysis of samples in a 384-well format and a quick way to determine the parasite developmental stage. On the other hand, the fluorescence microscopy format allows for a detailed visualization of parasite morphology. Finally, using open source software we have developed protocols for the automated cluster analysis of microscopy images. This assay can be applied to any Plasmodium species, requires very little amount of sample, is performed with fixed cells, and is easily scalable. Overall, we believe this assay will be a great tool for the malaria community to study Plasmodium species.

microbiology↗

Bombs and cocaine: detecting nefarious nitrogen sources using remote sensing and machine learning

Plants are treated with synthetic or organic nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. Here we show that hyperspectral sensing, multispectral imaging and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. Overall this suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in the reflectance spectrum detectable via machine learning of multispectral data but not the naked eye. Our results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture.

plant biology↗

Environmental DNA for the enumeration and management of Pacific salmon

Pacific salmon are a keystone resource in Alaska, generating annual revenues of well over [~]US$500 million/yr. Due to their anadromous life history, adult spawners distribute amongst thousands of streams, posing a huge management challenge. Currently, spawners are enumerated at just a few streams because of reliance on human counters and, rarely, sonar. The ability to detect organisms by shed tissue (environmental DNA, eDNA) promises a more efficient counting method. However, although eDNA correlates generally with local fish abundances, we do not know if eDNA can accurately enumerate salmon. Here we show that daily, and near-daily, flow-corrected eDNA rate closely tracks daily numbers of returning sockeye and coho spawners and outmigrating sockeye smolts. eDNA thus promises accurate and efficient enumeration, but to deliver the most robust numbers will need higher-resolution stream-flow data, at-least-daily sampling, and a focus on species with simple life histories, since shedding rate varies amongst jacks, juveniles, and adults.

ecology↗

Cell-free prototyping strategies for enhancing the sustainable production of polyhydroxyalkanoates bioplastics

The polyhydroxyalkanoates are a group of microbially-produced biopolymers that have been proposed as sustainable alternatives to several oil-derived plastics. However, polyhydroxyalkanoates are currently more expensive to produce than oil-derived plastics and therefore, more efficient production processes would be desirable. Cell-free transcription-translation-based metabolic engineering strategies have been previously used to optimise several different biosynthetic pathways but not the polyhydroxyalkanoates biosynthetic pathways. Here we have developed several Escherichia coli cell-free transcription-translation-based systems for in vitro prototyping of polyhydroxyalkanoates biosynthetic operons, and also for screening relevant metabolite recycling enzymes. These cell-free transcription-translation reactions were customised through the addition of whey permeate, an industrial waste that has been previously used as a low-cost feedstock for optimising in vivo polyhydroxyalkanoates production. We found that the inclusion of an optimal concentration of whey permeate enhanced relative cell-free GFPmut3b production by ~20% compared to control reactions that did not include whey permeate. An analysis of pH in our cell-free reactions suggests that the observed increase in GFPmut3b production was likely through enhanced ATP generation, as a consequence of the glycolytic processing of lactose present in whey permeate. We also found that whey permeate enhanced cell-free reactions produced ~3M (R)-3HB-CoA, whilst, coupled cell-free biotransformation/transcription-translation reactions produced a ten-fold greater yield of (R)-3HB-CoA. These reactions were also used to characterise a Clostridium propionicum propionyl CoA transferase enzyme that can recycle Acetyl-CoA. Together our data demonstrate that cell-free approaches can be used to complement in vivo workflows for identifying additional strategies for optimising polyhydroxyalkanoates production.

synthetic biology↗

A cell-free synthetic biochemistry platform for raspberry ketone production

Cell-free synthetic biochemistry provides a green solution to replace traditional petroleum or agricultural based methods for production of fine chemicals. 4-(4-hydroxyphenyl)-butan-2-one, also known as raspberry ketone, is the major fragrance component of raspberry fruit and is utilised as a natural additive in the food and sports industry. Current industrial processing standards involve chemical extraction with a yield of 1-4 mg per kilo of fruit. As such its market price can fluctuate up to $20,000 per kg. Metabolic engineering approaches to synthesise this molecule by microbial fermentation have only resulted in low yields of up to 5 mg L-1. In contrast, cell-free synthetic biochemistry offers an intriguing compromise to the engineering constraints provided by the living cell. Using purified enzymes or a two-step semisynthetic route, an optimised pathway was formed for raspberry ketone synthesis leading up to 100% yield conversion. The semi-synthetic route is potentially scalable and cost-efficient for industrial synthesis of raspberry ketone.

synthetic biology↗