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Biology subjects

Bell, S. M.

Publications and source records attributed to Bell, S. M..

10 recordsLinked to original sources

Two functionally distinct amyloid-β intermediates link aggregation pathways to pathology in Alzheimer's disease

Neurotoxic intermediates formed during amyloid-{beta}(A{beta}) aggregation are thought to drive Alzheimer's disease, yet their rarity and transient nature have obscured their identity and formation pathways in the human brain. Here, we combine super-resolution microscopy and single-molecule functional imaging with kinetic modelling to classify A{beta} intermediates according to their membrane-disrupting activity and connect their damaging effects to the aggregation pathways that generate them. Applying this framework to recombinant A{beta}42, an Alzheimer's knock-in mouse model and human Alzheimer's brain tissue, we find two functionally distinct intermediate populations present across all systems. They form through separate aggregation routes and differ in their neurotoxicity, abundance and susceptibility to inhibition. Both damage neurons and glia, but only one converts into plaques. Suppressing these intermediates pharmacologically in mice reduces plaque burden, attenuates astrogliosis and improves cognition. Our findings connect model systems with human pathology and identify intermediate formation pathways as actionable therapeutic targets across protein-misfolding disorders.

biophysics↗

baltic: the Backronymed Adaptable Lightweight Tree vIsualisation Code

For ten years, baltic (Backronymed Adaptable Lightweight Tree vIsualization Code) has been used to make annotated phylogeny figures in molecular epidemiology, including during the West African Ebola epidemic, the Zika epidemic in the Americas, and the SARS-CoV-2 pandemic, as well as other fields. At its core, baltic is a Python library used for the efficient parsing, traversal, manipulation, and visualisation of phylogenetic trees. It is a small library with few dependencies that reads tree formats common in phylodynamic analyses and gives the user leverage to interact with a lightweight tree data structure to produce publication-ready figures with matplotlib. We present baltic v1.0, its first formally released and documented version. baltic reads and writes BEAST Nexus, Newick, and Nextstrain/Auspice JSON, and can process large BEAST posterior tree files in parallel to extract user-defined posterior statistics. The same objects are used for tree manipulation and for plotting in a single script. New to this release: a set of rooting methods (midpoint rooting, rerooting on any branch, and root-to-tip regression); support for reticulate evolution, with reassortment and recombination edges; and composite figures that combine a tree with other data, such as Muller plots, skygrid plots, tanglegrams, and plots connecting trees to maps. The release includes a documentation site with an API reference, tutorials, and a matplotlib-style gallery of worked examples.

bioinformatics↗

PRDM3/16 Regulate Chromatin Accessibility Required for NKX2-1 Mediated Alveolar Epithelial Differentiation and Function

Differential chromatin accessibility accompanies and mediates transcriptional control of diverse cell fates and their differentiation during embryogenesis. While the critical role of NKX2-1 and its transcriptional targets in lung morphogenesis and pulmonary epithelial cell differentiation is increasingly known, mechanisms by which chromatin accessibility alters the epigenetic landscape and how NKX2-1 interacts with other co-activators required for alveolar epithelial cell differentiation and function are not well understood. Here, we demonstrate that the paired domain zinc finger transcriptional regulators PRDM3 and PRDM16 regulate chromatin accessibility to mediate cell differentiation decisions during lung morphogenesis. Combined deletion of Prdm3 and Prdm16 in early lung endoderm caused perinatal lethality due to respiratory failure from loss of AT2 cell function. Prdm3/16 deletion led to the accumulation of partially differentiated AT1 cells and loss of AT2 cells. Combination of single cell RNA-seq, bulk ATAC-seq, and CUT&RUN demonstrated that PRDM3 and PRDM16 enhanced chromatin accessibility at NKX2-1 transcriptional targets in peripheral epithelial cells, all three factors binding together at a multitude of cell-type specific cis-active DNA elements. Network analysis demonstrated that PRDM3/16 regulated genes critical for perinatal AT2 cell differentiation, surfactant homeostasis, and innate host defense. Lineage specific deletion of PRDM3/16 in AT2 cells led to lineage infidelity, with PRDM3/16 null cells acquiring partial AT1 fate. Together, these data demonstrate that NKX2-1-dependent regulation of alveolar epithelial cell differentiation is mediated by epigenomic modulation via PRDM3/16. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=130 HEIGHT=200 SRC="FIGDIR/small/570481v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@500f6org.highwire.dtl.DTLVardef@821daaorg.highwire.dtl.DTLVardef@69ba87org.highwire.dtl.DTLVardef@154f754_HPS_FORMAT_FIGEXP M_FIG C_FIG Model of the role of PRMD3/16 in alveolar developmentPRMD3/16 participate in cell fate specification in the lung by modulating chromatin accessibility (top row) and by partnering with NKX2-1 and partner transcription factors to drive gene expression (second row) via a gene regulatory network required for terminal cell differentiation and surfactant expression in AT2 cells (third row). Loss of PRDM3/16 activity in lung endoderm leads to reduced AT2 quorum, failure of AT2 surfactant function, and transition to an immature AT1 phenotype (bottom panel).

developmental biology↗

CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

cell biology↗

Deficits in mitochondrial function and glucose metabolism seen in sporadic and familial Alzheimers disease derived Astrocytes are ameliorated by increasing hexokinase 1 expression.

BackgroundAstrocytes have multiple roles including providing neurons with metabolic substrates and maintaining neurotransmitter synaptic homeostasis. Astrocyte glucose metabolism plays a key role in learning and memory with astrocytic glycogen a key substrate supporting memory encoding. The neuronal support provided by astrocytes has a high metabolic demand. Deficits in astrocytic mitochondrial metabolic functioning and glycolysis could impair neuronal function. Changes to cellular metabolism are seen early in Alzheimers disease (AD). Understanding cellular metabolism changes in AD astrocytes could be exploited as a new biomarker or synergistic therapeutic agent when combined with anti-amyloid treatments in AD. MethodsIn this project, we characterised mitochondrial and glycolytic function in astrocytes derived from patients with sporadic (n=6) and familial (PSEN1, n=3) forms of AD. Astrocytes were derived using direct reprogramming methods. Astrocyte metabolic outputs: ATP, and extracellular lactate levels were measured using luminescent and fluorescent protocols. Mitochondrial respiration and glycolytic function were measured using a Seahorse XF Analyzer. Hexokinase deficits identified where corrected by transfecting astrocytes with an adenovirus viral vector containing the hexokinase 1 gene. ResultsThere was a reduction of total cellular ATP of 20% (p=0.05 in sAD astrocytes) and of 48% (p<0.01) in fAD. A 44% reduction (p<0.05), and 80% reduction in mitochondrial spare capacity was seen in sAD and fAD astrocytes respectively. Reactive oxygen species (ROS) were increased in both AD astrocyte types (p=0.05). Mitochondrial complex I and II was significantly increased in sAD (p<0.05) but not in fAD. Astrocyte glycolytic reserve and extracellular lactate was significantly reduced when compared to controls in both sAD and fAD (p<0.05). We identified a deficit in the glycolytic pathway enzyme hexokinase, and correcting this deficit restored most of the metabolic phenotype in sAD but not fAD astrocytes. ConclusionAD astrocytes have abnormalities in functional capacity of mitochondria and the process of glycolysis. These functional deficits can be improved by correcting hexokinase expression deficits with adenoviral vectors. This suggests that hexokinase 1 deficiency could potentially be exploited as a new therapeutic target for AD.

neuroscience↗

MuscleNET: Mapping Electromyography to kinematic and dynamic biomechanical variables by machine learning

ObjectiveThis paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque. ApproachThe regression models, collectively known as MuscleNET, take one of four forms: ANN (Forward Artificial Neural Network), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), and RCNN (Recurrent Convolutional Neural Network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning models input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data. Main resultsResults were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals. SignificanceAll forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.

systems biology↗

Identification of a polymorphism in the N gene of SARS-CoV-2 that adversely impacts detection by a widely-used RT-PCR assay

We identify a mutation in the N gene of SARS-CoV-2 that adversely affects annealing of a commonly used RT-PCR primer; epidemiologic evidence suggests the virus retains pathogenicity and competence for spread. This reinforces the importance of using multiple targets, preferably in at least 2 genes, for robust SARS-CoV-2 detection. Article Summary LineA SARS-CoV-2 variant that occurs worldwide and has spread in California significantly affects diagnostic sensitivity of an N gene assay, highlighting the need to employ multiple viral targets for detection.

molecular biology↗

Dengue antigenic relationships predict evolutionary dynamics

Dengue virus (DENV) exists as four genetically distinct serotypes, each of which is historically assumed to be antigenically uniform. However, recent analyses suggest that antigenic heterogeneity may exist within each serotype, but its source, extent and impact remain unclear. Here, we construct a sequence-based model to directly map antigenic change to underlying genetic divergence. We identify 49 specific substitutions and four colinear substitution clusters that contribute to dengue antigenic diversity. We report moderate antigenic diversity within each serotype, resulting in variation in genotype-specific patterns of heterotypic cross-neutralization. We also quantify the impact of this antigenic heterogeneity on real-world DENV population dynamics. We find that antigenic fitness mediates fluctuations in DENV clade frequencies, although this appears to be primarily explained by coarser serotype-level antigenic differences. These results provide a more nuanced understanding of dengue antigenic evolution, with important ramifications for vaccine design and epidemic preparedness.\n\nAuthor SummaryDengue virus (DENV), the causative agent of dengue hemorrhagic fever, exists as four genetically distinct serotypes, DENV1 to DENV4. These serotypes are antigenically distinct: symptomatic reinfection with a homotypic virus is very rare, while reinfection with a heterotypic virus is sometimes associated with severe disease. Until recently, it has been assumed that viruses within each serotype are antigenically uniform. However, specific genotypes within each serotype have been anecdotally associated with varying severity of patient outcomes and epidemic magnitude. One hypothesis is that each serotype contains overlooked, meaningful antigenic diversity. While antigenic cartography conducted on neutralization titers suggests that heterogeneity may exist within each serotype, its source, extent and impact is unclear. Here, we analyze a previously published titer dataset to quantify and characterize the extent of DENV intraserotype antigenic diversity. We map antigenic changes to specific mutations in E, the dengue envelope protein, and interpolate across the alignment to estimate the antigenic distance between pairs of viruses based on their genetic differences. We identify 49 specific substitutions and four colinear substitution clusters that contribute to dengue antigenic evolution. We find that DENV antigenic divergence is tightly coupled to DENV genetic divergence, and is likely a gradual, ongoing process. We report modest but significant antigenic diversity within each serotype of DENV, which may have important ramifications for vaccine design. To understand the impact of this antigenic heterogeneity on real-world DENV population dynamics, we also quantify the extent to which population immunity--accumulated through recent circulation of antigenically similar genotypes--determines the success and decline of DENV clades in a hyperendemic population. We find that antigenic fitness is a key determinant of DENV population turnover, although this appears to be driven by coarser serotype-level antigenic differences. By leveraging both molecular data and real-world population dynamics, these results provide a more nuanced understanding of dengue antigenic evolution, with important ramifications for improving vaccine design and epidemic preparedness.

evolutionary biology↗

Nextstrain: real-time tracking of pathogen evolution

SummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike.\n\nAvailability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

evolutionary biology↗

Modern-Day SIV viral diversity generated by extensive recombination and cross-species transmission

Cross-species transmission (CST) has led to many devastating epidemics, but is still a poorly understood phenomenon. HIV-1 and HIV-2 (human immunodeficiency virus 1 and 2), which have collectively caused over 35 million deaths, are the result of multiple CSTs from chimpanzees, gorillas, and sooty mangabeys. While the immediate history of HIV is known, there are over 45 lentiviruses that infect specific species of primates, and patterns of host switching are not well characterized. We thus took a phylogenetic approach to better understand the natural history of SIV recombination and CST. We modeled host species as a discrete character trait on the viral phylogeny and inferred historical host switches and the pairwise transmission rates between each pair of 24 primate hosts. We identify 14 novel, well-supported, ancient cross-species transmission events. We also find that lentiviral lineages vary widely in their ability to infect new host species: SIVcol (from colobus monkeys) is evolutionarily isolated, while SIVagms (from African green monkeys) frequently move between host subspecies. We also examine the origins of SIVcpz (the predecessor of HIV-1) in greater detail than previous studies, and find that there are still large portions of the genome with unknown origins. Observed patterns of CST are likely driven by a combination of ecological circumstance and innate immune factors.

evolutionary biology↗