Biology subjects

Stone, E.

Publications and source records attributed to Stone, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Transcriptomic Characterization of Terminal Complement Complex-bound Cells in Human Choroid Using Single-Cell RNA Sequencing

Age-related macular degeneration (AMD) is among the leading causes of blindness worldwide. Early AMD is characterized by dysfunction in the choroid, including early dropout of endothelial cells and increased deposition of the complement cascade's membrane attack complex (MAC) in the choriocapillaris. In this study, we used a single-cell RNA sequencing-based approach with barcoded antibodies to measure abundance of the MAC and other surface proteins at single cell resolution on RPE/choroid samples from four aged human donor eyes. We included antibodies to detect the MAC, CD34, CD45, and complement regulators CD55 and CD59, in addition to control antibodies. Our analysis of these data revealed cell clusters with expected gene expression profiles and antibody-based detection of CD34 and CD45 congruent with transcriptome-based cell identity. We also detected surface complement regulators CD55 and CD59 across a wide variety of cell types. Across endothelial cells, surface CD55 and CD59 appeared more abundant on venous clusters, and abundance of each was correlated with expression of a third complement regulator, clusterin (CLU). The MAC was detected on a variety of cell types, but was most abundant on the surface of cells in the macrophage family, smooth muscle cells, and pericytes. We confirmed these findings by identifying MAC deposition on choriocapillaris pericytes using immunohistochemistry for MAC, endothelial, and pericyte markers. Ultimately, these data showcase a valuable new approach to analyze gene expression and surface complement in human donor eyes, and provide novel insight into patterns of MAC deposition and complement protection in the aging human choroid.

cell biology↗

bioRxiv2026

Epstein-Barr virus transformation creates a methionine-dependent ferroptosis vulnerability in B cells

Epstein-Barr virus (EBV) causes over 200,000 cancers annually, including immunoblastic lymphomas in immunosuppressed hosts. Most transformed cells arrest, yet survive when deprived of the essential amino acid methionine. We instead find that EBV transformed lymphoblastoid cell lines (LCLs), which model the EBV latency III program-driven B-cell lymphoproliferative diseases of immunosuppressed hosts, rapidly die upon methionine restriction. Methionine restriction elevated LCL lipid reactive oxygen species and triggered ferroptosis. Whereas methionine restriction hypomethylates the EBV genome and triggers viral reactivation in latency I Burkitt cells by lowering the cellular methylation potential, the LCL latency III program instead redirected methionine toward redox defense, without altering the SAM/SAH ratio. Stable-isotope tracing revealed that latency III strongly induces transsulfuration, synthesizing cysteine de novo to support glutathione pools. The EBV oncoprotein LMP2A, which mimics B-cell receptor signaling, supported newly infected human B cell cystathionine-{beta}-synthase and cystathionine-{gamma}-lyase expression and methionine dependence, phenocopied by immunoglobulin crosslinking. In vivo, dietary methionine restriction impaired LCL xenograft outgrowth and depleted tumor cystine. Combined methioninase and cyst(e)inase administration blocked both cysteine sources, collapsed tumor glutathione levels, and triggered ferroptosis. Our results define methionine metabolism as a targetable ferroptosis vulnerability of EBV-transformed B cells. HighlightsO_LIMethionine restriction triggers EBV-transformed lymphoblastoid B cell ferroptosis C_LIO_LIEBV latency III induces transsulfuration to sustain LCL cysteine and glutathione C_LIO_LIMethioninase or dietary methionine restriction strongly impair LCL growth in vivo C_LIO_LIMethioninase plus cyst(e)inase collapses xenograft GSH levels and drives ferroptosis C_LI

biochemistry↗

bioRxiv2026

Sparse Distributed Archetypes Reveal Compressible Network Motifs Underlying Naturalistic Cognition

Naturalistic cognition emerges from coordinated interactions among distributed brain systems operating across multiple representational scales. Characterizing this organization remains challenging because cognitively relevant information is embedded within high-dimensional neural activity. Here, we apply Multisubject Archetypal Analysis (MS-AA) [1] to naturalistic fMRI data collected during intact narrative listening, word-scrambled audio, and rest to investigate how condition-relevant information is distributed across archetypal representations. We examine both spatial and temporal formulations of MS-AA as complementary views of naturalistic brain activity. Across analyses, decoding performance consistently followed the hierarchy intact > word-scrambled > rest, indicating that archetypal representations preserve meaningful condition-related structure. Top-m decoding analyses further revealed that this information is highly compressible: relatively small subsets of archetypes frequently recovered substantial fractions of full-model decoding performance. Spatial AA exhibited a stable sparse-decoding regime that persisted across representational scales. Across a broad range of matched component ratios, approximately 5-15 archetypes consistently captured disproportionate amounts of condition-relevant information. These same sparse subsets also organized subjects into condition-aligned clusters more strongly than expected from random archetype subsets, with the strongest joint decoding-clustering effects occurring repeatedly within an intermediate representational regime (K {approx}50 - 88). Network over-representation analyses revealed that informative archetypes were not isolated canonical networks but distributed mixtures of interacting systems. Across the highest-performing decoding- clustering configurations, default mode and frontoparietal systems were consistently overrepresented relative to network size, whereas visual and limbic systems were underrepresented. Together, these findings suggest that the archetypal motifs most informative for distinguishing cognitive states are sparse, distributed subnetworks enriched for higher-order association systems. More broadly, the results demonstrate that MS-AA provides a useful framework for studying the compressibility, geometry, and multiscale organization of cognitive brain states.

neuroscience↗

bioRxiv2026

Pyridoxine supplementation confers protection against SGPL1R222Q variant sphingosine phosphate lyase insufficiency syndrome

Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) is a rare condition causing nephrotic syndrome, neuropathy, and other manifestations. SPLIS is caused by mutations in SGPL1, which encodes sphingosine-1-phosphate lyase (SPL), a pyridoxal 5-phosphate (PLP)-dependent enzyme needed to degrade the bioactive sphingolipid sphingosine-1-phosphate (S1P). Supplementation with the PLP precursor pyridoxine benefits some individuals with PLP-dependent enzymopathies. We sought to establish whether pyridoxine has therapeutic activity in SPLIS. Neurological improvement, plasma S1P normalization, and increased SPL activity in patient-derived fibroblasts were observed after pyridoxine supplementation in a patient with R222Q-variant SPLIS. Additionally, PLP dose-dependently augmented recombinant R222Q-variant SPL activity. To further explore pyridoxines effects, gene editing was employed to create an R222Q-variant SPLIS mouse model. SPLR222Q mice fed pyridoxine-enriched chow lacked obvious phenotypes. However, SPL inactivation, S1P accumulation, wasting, anemia, proteinuria, and glomerulosclerosis developed in SPLR222Q but not WT mice fed chow with reduced pyridoxine. Ultrastructural analysis and super-resolution microscopy showed podocyte loss and foot process effacement. Transcriptional profiling revealed a pattern of cytokine upregulation and extracellular matrix remodeling. Inhibiting S1P production prevented nephrosis in SPLR222Q mice fed chow lacking pyridoxine. Our findings establish a novel SPLIS mouse model that recapitulates R222Q-variant SPLIS, demonstrates its responsiveness to pyridoxine, and implicates S1P in its pathophysiology.

molecular biology↗

bioRxiv2026

Dynamic Antigen Expression and Intrinsic CTL Resistance in HIV Reservoir Clones

Reservoirs of clonally expanded CD4+ T-cells harboring rebound-competent HIV proviruses persist lifelong during ART. Latency is considered the principal barrier to viral eradication and has resisted pharmacological reversal, yet it appears that sustained immune pressure may still erode reservoirs. Recent advances have yielded glimpses into these exceptionally rare reservoir-harboring cells, implicating intrinsic pro-survival properties in their persistence. Here, we isolate and characterize populations of authentic reservoir clones (ARCs) that robustly proliferate and accumulate while producing infectious virus, without overtly succumbing to viral cytopathic effects. At any given moment, only small fractions of ARCs expressed HIV proteins, a state remarkably unperturbed by potent TCR or mitogenic stimulation. Nevertheless, sustained co-culture with cytotoxic T-lymphocytes (CTL) revealed extensive time-integrated antigenic vulnerability, culling clonal expansion of some ARCs by >90%. Notably, a regulatory T-cell ARC displayed pronounced cell-intrinsic resistance to CTL - a longstanding hypothesis we now directly demonstrate - linked to low oxidative stress and reversed with desferoxamine, a hypoxic stress inducer and FDA-approved therapeutic. Overall, we provide novel insights into the vulnerabilities of reservoir clones to potent, sustained CTL pressure and highlight intrinsic resistance pathways as actionable therapeutic targets, opening opportunities for advancing immune-based HIV cure strategies.

immunology↗

bioRxiv2025

PRISM-Seq: An Ultra-sensitive Sequencing Approach For Mapping Lentiviral Integration Sites

Retroviral integration into host genomes is central to both HIV-1 persistence and the safety and function of lentiviral vectors used in gene and cell therapies. However, existing integration site assays remain limited by sensitivity, input requirements, and analytical complexity, and none have been validated at the single-molecule detection limit. Here, we introduce PRISM-seq, an ultra-sensitive workflow for genome-wide recovery of lentiviral-host junctions, paired with BulkIntSiteR, an open-source, fully automated pipeline for integration site annotation. We show that PRISM-seq accurately identifies proviral insertions across diverse genomic contexts, including euchromatin, heterochromatin, and repeat-rich centromeric regions, and detects high-confidence integration events down to a single input template molecule. By systematically characterizing assay- and amplification-associated noise, we developed a five-step quality control framework that removes PCR- and sequencing-derived artifacts. PRISM-seq also enables quantitative clonal tracking through replicate-based sampling and achieves performance comparable to or exceeding high-input assays at substantially reduced cost.

bioinformatics↗

bioRxiv2025

Robust scoring system to evaluate animal well-being in murine orthotopic breast cancer and ovarian cancer models

Orthotopic models are critical to the development of new cancer therapies, as they have the greatest potential to mimic human cancer progression. However, orthotopic models are often underutilized due to practical challenges. These challenges include the need for specialized surgeries and the presence of complicating symptoms, such as ascites fluid accumulation in ovarian cancer and liver disease models, as well as ulceration of breast tumors growing in the mammary fat pad. An adaptable, quantitative animal health assessment system that utilizes simple, non-invasive, visual measurements can allow researchers to use these models to their full potential while safeguarding humane endpoints. The goal of this work was to establish such health scoring systems for translationally-relevant ovarian (ID8 Defb29-Vegf-A) and breast cancer (triple-negative model 4T1) models. We developed a tractable health assessment system by monitoring a combination of activity and responsiveness, coat condition, respiratory function, changes in body weight, posture, gait, tumor size (where measurable), and extent of ulceration, and scoring each of these parameters using a simple numerical scale. The scores at which euthanasia or other interventions were required were estimated and then adjusted based on clinical assessment by a team of researchers and veterinarians. These semi-quantitative health scoring systems were applied to survival experiments, in which mice were euthanized at a predetermined score or upon meeting a superseding health criterion, to confirm their applicability and effectiveness in maintaining humane endpoints. The scoring systems presented are intended to build on previously-established generic tumor model scoring systems that do not accommodate for the particular phenotypes present in orthotopic breast and ovarian cancer models.

cancer biology↗

bioRxiv2025

Haplotype-resolved genomes of diverse oat crown rust isolates reveal both global dispersion of long-lived clonal haplotypes and limited recombination between haplotypes.

Genetic diversity of pathogen populations plays an important role in host adaptation. While single high quality genome references are a valuable resource, a compilation of genome references from individuals increases the breadth of genetic variation within a species that is captured. Puccinia coronata f. sp. avenae (Pca), a fungal pathogen causing crown rust of oat, demonstrates rapid virulence evolution and adaptation to newly released cultivars. To broaden the geographic and temporal distribution of available Pca genomes, we generated nuclear haplotype-resolved genome references for ten isolates from Europe, Africa and the Middle East and compared these with existing references for USA and Australian isolates. Of the full collection of 52 haplotypes, 40 were unique. Importantly, the presence of a nearly identical haplotype in a UK isolate collected in 1984 and in USA isolates from 1990 and 2017 supports the existence of long-lived clonal haplotypes in the global population that have been exchanged between lineages. Taken together with the identification of infrequent recombination between haplotypes from geographically dispersed isolates, this evidence reflects a globally mobile population of Pca that is mostly comprised of persistent clonal lineages with some influence from rare recombination events. Analysis of the core and non-core proteome suggests that while the core proteome is enriched for predicted secreted and effector proteins, sequence and expression variation are most prevalent in non-core orthogroups. We anticipate that this expanded collection of haplotypes will facilitate the development of new surveillance technologies and identification of virulence loci.

genomics↗

bioRxiv2025

AGP-Net: A Universal Network for Gene Expression Prediction of Spatial Transcriptomics

In the era of high-throughput biology, molecular phenotypes have proven effective in predicting disease states and future trajectories. Transcriptomics, in particular, has enabled the dissection of complex diseases with heterogeneous genetic and environmental aetiology, both aiding diagnosis and augmenting treatment. As improving technology has led to measurements of gene expression at increasing granularity, it has become progressively feasible to resolve disease traits that present locally or with spatial heterogeneity. Principal among these are cancers in which tumour gene expression, while itself heterogenous, exhibits a distinct signature from that of the surrounding tissue. Identifying this signature through molecular phenotyping facilitates specific cancer diagnosis and treatment. Here, we introduce AGP-Net, a multi-modal foundation framework capable of predicting gene expression from histopathology images. Rather than produce an aggregated estimate of expression for each gene, AGP-Net disaggregates images into spots and attempts to resolve the variation in gene expression across them, thereby providing coarse spatial transcriptomic predictions across the tumour slice and surrounding region. The challenge in doing so is due to data sparsity relative to the dimensionality of the problem: the number of genes and their contextual heterogeneity within and between tissue and cancer types makes it difficult to train a model on the limited data available. The innovation of AGP-Net lies in borrowing strength across similar genes as defined by their textual language descriptions. Our AGP-Net supports datasets with varying gene coverage and facilitates the prediction of gene expression for previously unseen genes based on their textual descriptions. Trained on millions of spots from diverse dataset sources, AGP-Net establishes state-of-the-art performance in zero-shot spatial gene expression prediction, demonstrating its adaptability to generalize across novel scenarios.

pathology↗

bioRxiv2024

Enzyme-constrained Metabolic Model of Treponema pallidum Identified Glycerol-3-phosphate Dehydrogenase as an Alternate Electron Sink

Treponema pallidum, the causative agent of syphilis, poses a significant global health threat. Its strict intracellular lifestyle and challenges in in vitro cultivation have impeded detailed metabolic characterization. In this study, we present iTP251, the first genome-scale metabolic model of T. pallidum, reconstructed and extensively curated to capture its unique metabolic features. These refinements included the curation of key reactions such as pyrophosphate-dependent phosphorylation and pathways for nucleotide synthesis, amino acid synthesis, and cofactor metabolism. The model demonstrated high predictive accuracy, validated by a MEMOTE score of 92%. To further enhance its predictive capabilities, we developed ec-iTP251, an enzyme-constrained version of iTP251, incorporating enzyme turnover rate and molecular weight information for all reactions having gene-protein-reaction associations. Ec-iTP251 provides detailed insights into protein allocation across carbon sources, showing strong agreement with proteomics data (Pearsons correlation of 0.88) in the central carbon pathway. Moreover, the thermodynamic analysis revealed that lactate uptake serves as an additional ATP-generating strategy to utilize unused proteomes, albeit at the cost of reducing the driving force of the central carbon pathway by 27%. Subsequent analysis identified glycerol-3-phosphate dehydrogenase as an alternative electron sink, compensating for the absence of a conventional electron transport chain while maintaining cellular redox balance. These findings highlight T. pallidums metabolic adaptations for survival and redox balance in intracellular environments, providing a foundation for future research into its unique bioenergetics. IMPORTANCEThis study advances our understanding of Treponema pallidum, the syphilis-causing pathogen, through the reconstruction of iTP251, the first genome-scale metabolic model for this organism, and its enzyme-constrained version, ec-iTP251. The work addresses challenges of studying T. pallidum due to its strict intracellular nature and difficulties in in vitro cultivation. Validated with strong agreement to proteomics data, the model demonstrates high predictive reliability. Key insights include unique metabolic adaptations such as lactate uptake for ATP production and alternative redox-balancing mechanisms. These findings provide a robust framework for future studies aimed at unraveling the pathogens survival strategies and identifying potential metabolic vulnerabilities.

systems biology↗

bioRxiv2024

Characterisation of virulence of Puccinia coronata f. sp. avenae in Australia in the 2023 growing season

Crown rust caused by the basidiomycete fungus Puccinia coronata f. sp. avenae (Pca) results in significant crop losses worldwide. Genetic solutions to protect against this disease require disease resistance gene discovery and introduction of resistance genes into elite germplasm by breeders. To inform disease resistance breeding activities, it is paramount to monitor changes of virulence in the Pca population and link those to genotypes of the pathogen. In 2023, a collection of 37 Pca isolates from diverse regions in Australia were gathered and their infection types across a commonly used set of oat differential lines determined to assign virulence pathotypes. We compared those virulence phenotypes to data collected in previous collections. While some of the virulence phenotypes had been reported in 2022, our analysis detected new virulence or an increase in frequency for virulence on some resistance genes. Notably, some of the frequency increases in virulence were recorded in Western Australia (WA), a region of interest due to its role in oat production for milling.

plant biology↗

bioRxiv2024

Incorporating gene expression and environment improves genomic prediction of wheat traits

The adoption of novel molecular strategies such as genomic selection (GS) in crop breeding have been key to maintaining rates of genetic gain through increased efficiency and shortening the cycle of evaluation relative to conventional selection. In the search for improved methodologies that incorporate novel sources of variation for the assessment of genetic merit, GS remains a focus of crop breeding research globally. Here we explored the role transcrip-tome data could play in enhancing GS using wheat as a test case. Across 286 wheat lines, we integrated phenotype and multi-omic data from controlled environment and field experiments including ca. 40K single nucleotide polymorphisms (SNP), abundance data for ca. 50K transcripts as well as meta-data (e.g. categorical environments) predicted individual genetic merit for two agronomic traits, flowering time and height. We combined phenotype and multi-omic data from both controlled environments and field experiments. This included ca. 40K single nucleotide polymorphisms (SNPs), ca. 50K transcript abundance data, and metadata (such as categorical environmental conditions). Using this integrated data, we predicted individual genetic merit for two agronomic traits: flowering time and height. We evaluated the performance of different model scenarios based on linear (GBLUP) and Gaussian/nonlinear (RKHS) regression in the Bayesian analytical frame-work. These models explored the relative contributions of different combinations of explanatory variables; additive genomic (G), transcriptomic (T) and environment (E), with and without considering non-additive epistasis and the GxE random effects. In controlled environments, where traits were measured under contrasting daylength regimes (long and short days), transcriptome abundance outperformed other explanatory variables when considered independently, while the model combining SNP, environment and G x E marginally outperformed the transcriptome. The best performing model for prediction of both flowering and height combined all data types, G x E and epistasis, where the GBLUP framework showed slightly better performance overall compared with RKHS across all tests. Under field conditions, we similarly found that models combining all variables were superior, with the GBLUP and RKSH methods performing equally well. However, the relative contribution of the transcriptome was reduced. Our results show there is a predictive advantage to direct inclusion of the transcriptome for genomic evaluation in wheat breeding. However, the complexity and cost of generating large scale transcriptome data are likely to limit its feasibility for commercial breeding. We demonstrate that combining less costly environmental covariates with conventional genomic data provide a practical alternative with similar gains to the transcriptome when environments are well characterised. HighlightsO_LIIncorporating transcriptome and environment in genomic prediction; C_LIO_LIModel comparisons and Bayesian inference. C_LIO_LIDifferential random effects of transcriptome, SNP and environment. C_LI

genomics↗

bioRxiv2024

A high-resolution haplotype pangenome uncovers somatic hybridization, recombination and intercontinental migration in oat crown rust

Basidiomycetes like rust fungi have complex reproductive cycles and dikaryotic life stages which influence their population structure and evolution. Puccinia coronata f. sp. avenae (Pca), the causal agent of oat crown rust, is a pathogen of global economic importance. To investigate the genetic diversity of the species, as well as the role of mating type system and nuclear exchange (somatic hybridization) in host adaptation of Pca we acquired whole genome sequencing data of Taiwanese and Australian isolates, adding to existing data for USA and South African populations. An atlas of 30 chromosome-level, fully-phased nuclear haplotypes from six USA isolates and nine Australian isolates was generated to capture the genomic composition of key oat crown rust lineages. This study provides evidence of nuclear exchange and recombination of haplotypes in both the USA and Australian Pca populations as mechanisms for the introduction of genetic diversity. The limitations of assuming clonal evolutionary history from virulence phenotyping is demonstrated by the detection of either sexual or cryptic genetic recombination in the Pca Australian population. Overall, the characterization of intercontinental migration of Pca at the haplotype level provides further impetus for molecular monitoring of rust pathogen populations on a global scale.

genomics↗

bioRxiv2023

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. In this study, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm data to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We then apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve our understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

bioinformatics↗

bioRxiv2023

Virulence patterns of oat crown rust in Australia - season 2022

Puccinia coronata f. sp. avenae (Pca) is an important foliar pathogen of oat which causes crown rust disease. The virulence profile of 48 Pca isolates derived from different locations in Australia was characterised using a collection of oat lines often utilised in rust surveys in the USA and Australia. This analysis indicates that Pca populations in Eastern Australia are broadly virulent, in contrast to the population in Western Australia (WA). Several oat lines/Pc genes are effective against all rust samples collected from WA, suggesting they may provide useful resistance in this region if deployed in combination. We identified 19 lines from the USA oat differential set that display disease resistance to Pca in WA, some in agreement with previous rust survey reports. We adopted the 10-letter nomenclature system to define oat crown rust races in Australia and compare the frequency of those virulence traits to published data from the USA. Based on this nomenclature, 42 unique races were detected among the 48 isolates, reflecting the high diversity of virulence phenotypes for Pca in Australia. Nevertheless, the Pca population in the USA is substantially more broadly virulent than that of Australia. Close examination of resistance profiles for the oat differential set lines after infection with Pca supports hypotheses of allelism or redundancy among Pc genes or the presence of several resistance genes in some oat differential lines. These findings illustrate the need to deconvolute the oat differential set using molecular tools.

plant biology↗

bioRxiv2023

Spatial Transcriptomics Analysis of Gene Expression Prediction using Exemplar Guided Graph Neural Network

Spatial transcriptomics (ST) is essential for understanding diseases and developing novel treatments. It measures the gene expression of each fine-grained area (i.e., different windows) in the tissue slide with low throughput. This paper proposes an exemplar guided graph network dubbed EGGN to accurately and efficiently predict gene expression from each window of a tissue slide image. We apply exemplar learning to dynamically boost gene expression prediction from nearest/similar exemplars of a given tissue slide image window. Our framework has three main components connected in a sequence: i) an extractor to structure a feature space for exemplar retrievals; ii) a graph construction strategy to connect windows and exemplars as a graph; iii) a graph convolutional network backbone to process window and exemplar features, and a graph exemplar bridging block to adaptively revise the window features using its exemplars. Finally, we complete the gene expression prediction task with a simple attention-based prediction block. Experiments on standard benchmark datasets indicate the superiority of our approach when compared with past state-of-the-art methods. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/534914v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@6507f5org.highwire.dtl.DTLVardef@ed7ea5org.highwire.dtl.DTLVardef@159fd5org.highwire.dtl.DTLVardef@1b09270_HPS_FORMAT_FIGEXP M_FIG C_FIG In this paper, we aim to predict gene expression of each window in a tissue slide image. Given a tissue slide image, we encode the windows to feature space, retrieve its exemplars from the reference dataset, construct a graph, and then dynamically predict gene expression of each window with our exemplar guided graph network. HighlightsO_LIWe propose an exemplar guided graph network to accurately predict gene expression from a slide image window. C_LIO_LIWe design a graph construction strategy to connect windows and exemplars for performing exemplar learning of gene expression prediction. C_LIO_LIWe propose a graph exemplar bridging block to revise the window feature by using its nearest exemplars. C_LIO_LIExperiments on two standard benchmark datasets demonstrate our superiority when compared with state-of-the-art approaches. C_LI

bioinformatics↗

bioRxiv2023

Wireless Electrical-Molecular Quantum Signalling for Cancer Cell Induced Death

Quantum biological tunnelling for electron transfer (QBET) is involved in controlling cellular behaviour. Control of electrical-molecular communication could revolutionise the development of disruptive technologies for understanding and modulating electrically induced molecular signalling. Current communication technology is not appropriate for interfacing with cells at a spatial/temporal level equivalent to the native biological signalling. This limits our ability to tune cell function by controlling single molecular events. Here, we merge wireless nano-electrochemical tools with cancer cells. Gold-bipolar nanoelectrodes functionalised with redox active species were developed as electric field stimulated bio-actuators, that we term bio-nanoantennae. We show that a remote electrical input regulates electron transport between the redox molecules on the bio-nanoantennae in a selective manner. The wireless modulation of electron transport results in QBET triggering apoptosis in patient-derived cancer cells, representing electrical-induced induced controlled molecular signalling. Transcriptomics data highlight the electric field-induced nanoantenna targets the cancer cells in a unique manner. The insight concerning action and functional nanomaterials opens a plethora of applications in healthcare. This approach may lead to new quantum-based medical diagnostics and treatments, as well as a fundamental understanding of biological physics.

biophysics↗

bioRxiv2021

Inferring species compositions of complex fungal communities from long- and short-read sequence data

BackgroundThe kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. ResultsWe compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. ConclusionOverall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

bioinformatics↗