Biology subjects

Payne, S.

Publications and source records attributed to Payne, S..

10 recordsLinked to original sources

bioRxiv2026

Hypoxia differentially affects coronary vessel formation during heart development

BACKGROUNDThe coronary vessel system is a dense and diverse network of arteries, veins and capillaries formed by endothelial cells from a variety of sources. While hypoxia is a known stimulus for angiogenic sprouting generally, the exact mechanisms by which hypoxia, and resultant increased VEGFA, influences vessel growth in the heart are not clearly delineated. METHODSWe used a genetic model to mimic hypoxia through ectopic stabilisation of myocardial HIF. This enabled us to study the consequences of hypoxia without vascular depletion. Changes in coronary ECs in these hearts relative to littermate controls were assessed by single cell RNA-sequencing, and by examining the activity of enhancer:reporter transgenes active in different coronary vessel beds downstream of distinct vascular regulatory pathways. RESULTSAnalysis of hypoxia-mimic hearts found increased angiogenic gene expression alongside expanded activity of the VEGFA-MEF2-driven angiogenic regulatory pathway in a pattern that indicated increased endocardial-derived angiogenic sprouting. Conversely, regulatory pathways specifically active in the sinus venosus (SV)-derived plexus showed little variance in response to stabilized HIF, and sprouting from the SV was not expanded. Although hypoxia and increased VEGFA levels have been previously linked to increased arterial differentiation, we saw little change in initial arterial EC differentiation in the experimental hearts. However, mature coronary arterial formation was delayed. CONCLUSIONSThese observations further emphasize a direct and specific link between hypoxia and endocardial coronary vessel sprouting and suggest a role of hypoxia/VEGFA in guiding coronary arterial coalescence.

developmental biology↗

bioRxiv2026

A transcriptional code controlling fluid shear stress-induced gene expression

The formation and health of the vascular system is dependent on fluid shear stress (FSS), a hemodynamic force exerted onto endothelium by flowing blood. FSS strongly induces the endothelial expression of Kruppel-like factor 2 (KLF2), an atheroprotective TF essential for vascular development and homeostasis. Despite its early and crucial role in the cascade of cardiovascular events triggered by FSS, the transcriptional mechanisms by which FSS regulates KLF2 expression remain unclear, although they are known to involve the widely expressed MEF2 proteins. Here, we identified and characterized two FSS-dependent enhancers for KLF2 which collectively recapitulate endogenous endothelial KLF2 expression, and determined the TFs contributing to their regulation. This analysis identified an essential and precisely spaced MEF2-TBP double motif also shared by the FSS-sensitive KLF2 promoter. MEF-TBP double motifs are extremely rare across the genome but were also found within regulatory elements of three other FSS-induced KLF genes, including KLF4. Although normally part of the basal transcriptional machinery, TBP specifically bound all KLF elements at the MEF-TBP double motifs in a FSS-dependent manner. Collectively, this work demonstrates a specific and targetable requirement for combined MEF2-TBP binding during FSS-induced gene activation. Significance statementBlood flow induces a force known as fluid shear stress (FSS) which is required for vascular development and for the health of the mature arterial system. One of the first endothelial responses to FSS is the induction of Kruppel-like transcription factors (KLFs). However, the mechanisms by which FSS activates KLF gene expression are incompletely understood. In this paper, we characterized all regulatory elements involved in driving FSS-induced expression of KLF2. This identified an essential MEF2-TBP double motif that was extremely rare across the genome, yet found within regulatory elements for multiple FSS-responsive KLF genes including KLF2 and KLF4. This MEF2- and TBP-bound motif therefore enables blood flow to specifically activate the cascade of cardiovascular responses necessary for atheroprotective gene expression.

developmental biology↗

bioRxiv2025

Aer is a bidirectional redox sensor mediating negative chemotaxis to antibiotic-induced ROS in E. coli

The Aer chemoreceptor in E. coli is known to perform oxygen- or aero-taxis by sensing metabolic flux through ETC via an FAD cofactor bound to its PAS domain. We show in this study that Aer also senses oxidative stress and performs FAD-dependent negative chemotaxis in response to the known ROS H2O2. The ability of Aer to detect both oxidizing and reducing intracellular environments redefines its functional range and establishes Aer as a bidirectional redox sensor. We show in addition that the ability of bacterial swarms to move away from bactericidal antibiotics is Aer-dependent and is abolished by expression of the catalase-peroxidase enzyme KatG, which scavenges intracellular ROS. Our study thus provides independent behavioral evidence that certain antibiotics generate intracellular ROS, while also offering a sensitive assay for detecting these reactive species. ImportanceMotile bacteria rely on aerotaxis to seek environments that maximize energy production. We show that in E. coli, Aer mediates not only positive chemotaxis toward favorable redox conditions but also negative chemotaxis in response to oxidative stress. The ability of Aer to detect both oxidizing and reducing cellular environments reveals an unexpected sensory versatility, shared to varying degrees by other E. coli chemoreceptors. We show that E. coli uses Aer to actively move away from ROS-generating antibiotics, revealing a previously unrecognized behavioral mechanism for bacterial survival under oxidative stress.

microbiology↗

bioRxiv2024

Early Prediction of Ischaemic Stroke Outcomes: A Novel Computational Approach

Malignant stroke can lead to a death rate as high as 80%. Although early interventions can improve patient outcomes, they also lead to side effects. Therefore, the early prediction of stroke outcomes is crucial for clinical strategies. Imaging markers such as brain swelling volume and midline shift have been critical predictors in various stroke scoring systems. However, these markers can only become visible on brain images days after stroke onset, which delays clinical decisions. A primary challenge in predicting these markers is that brain swelling is a biomechanical process that relies on anatomical features, such as lesion size and location. To tackle this problem, we propose a novel computational pipeline to predict brain swelling after stroke. We first provide a mathematical model of the brain by using a five-compartment poroelastic theory. It allows us to generate high-quality stroke cases with varied 3D brain and lesion anatomy, which are then used to train and validate a deep neural network (DNN). Our in silico experimentation with 3,000 cases shows that anatomical features of stroke brains are well-learned by the DNN, with minimal errors in brain swelling prediction found in the hold-out testing cases. In addition, we used the DNN to process clinical imaging data of 60 stroke patients. The results show that the markers generated from the DNN can predict 3-month stroke outcomes with an AUC of around 0.7. It indicates that the proposed computational pipeline can potentially advance the time point for clinical decisions. Significance StatementStroke is the second leading cause of death in the world, and malignant stroke can lead to a death rate of 80%. Early interventions can improve patient outcomes but can also cause side effects. Therefore, it is crucial to predict stroke outcomes at an early stage. Radiological markers such as brain swelling volume and midline shift have been used in various stroke scoring systems. However, these markers can only become visible after days to stroke onset, which delays clinical decisions. To tackle this issue, we propose a novel computational pipeline to predict brain swelling after stroke onset. The proposed pipeline is found to predict brain swelling accurately and can potentially assist early clinical decision-making.

bioengineering↗

bioRxiv2024

AI-assisted In-silico Trial for the Optimization of Osmotherapy following Ischaemic Stroke

Over the past few decades, osmotherapy has commonly been employed to reduce intracranial pressure in post-stroke oedema. However, evaluating the effectiveness of osmotherapy has been challenging due to the difficulties in clinical intracranial pressure measurement. As a result, there are no established guidelines regarding the selection of administration protocol parameters. Considering that the infusion of osmotic agents can also give rise to various side effects, the effectiveness of osmotherapy has remained a subject of debate. In previous studies, we proposed the first mathematical model for the investigation of osmotherapy and validated the model with clinical intracranial pressure data. The physiological parameters vary among patients and such variations can result in the failure of osmotherapy. Here, we propose an AI-assisted in-silico trial for further investigation of the optimisation of administration protocols. The proposed deep neural network predicts intracranial pressure evolution over osmotherapy episodes. The effects of the parameters and the choice of dose of osmotic agents are investigated using the model. In addition, clinical stratifications of patients are related to a brain model for the first time for the optimisation of treatment of different patient groups. This provides an alternative approach to tackle clinical challenges with in-silico trials supported by both mathematical/physical laws and patient-specific biomedical information.

bioengineering↗

bioRxiv2023

Deep learning to extract the meteorological by-catch of wildlife cameras

Microclimate - proximal climatic variation at scales of meters and minutes - can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature-centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, with deep learning tools to rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, yet ecologically relevant, meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africas Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. Our best model achieves 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. Unlike previous studies we test model performance in remote and novel locations, distinguishing overcast and sunny conditions in Svalbard, Norway with 79.3% accuracy (93.9% consensus accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabeled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

ecology↗

bioRxiv2021

Spatially Interacting Phosphorylation Sites and Mutations in Cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1,255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1,000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

bioinformatics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗

bioRxiv2019

Conservation of an Ancient Oxidation Response That Controls Archaeal Epigenetic Traits Through Chromatin Protein Networks

Epigenetic variants of the archaeon Sulfolobus solfataricus called SARC have evolved heritable traits including extreme acid resistance, enhanced genome integrity and a conserved \"SARC\" transcriptome related to acid resistance. These traits appear to result from altered chromatin protein function related to the heritable hypomethylation of chromatin proteins Cren7 and Sso7D. To clarify how this might occur, ChIPseq and Affinity Purification Mass Spectrometry (AP-MS) were used to compare Cren7 and Sso7D genome binding sites and protein networks between lineages (wild type and SARC) and culture pH (pH 1 and 3). All SARC transcriptome loci were bound by these chromatin proteins but with invariant patterns indicating binding alone was insufficient to mediate the SARC traits. In contrast, chromosome association varied at other loci. Quantitative AP-MS was then used to identify protein interaction networks and these included transcription and DNA repair proteins implicated in the evolved heritable traits that varied in abundance between SARC and wild type strains. Protein networks included most of the S-adenosylmethionine (SAM) synthesis pathway including serine hydroxymethyltransferase (SHMT), whose abundance varied widely with culture pH. Because epigenetic marks are coupled to SAM pools and oxidative stress in eukaryotes, occurrence of a similar process was investigated here. Archaeal SAM pools were depleted by treatment with SAM pathway inhibitors, acid or oxidative stress and, like eukaryotes, levels were raised by vitamin B12 and methionine supplementation. We propose that in archaea, oxidation-induced SAM pool depletion acting through an SHMT sensor, drove chromatin protein hypomethylation and thereby protein network changes that established the evolved SARC epigenetic traits.\n\nSignificance StatementArchaea and eukaryotes share many molecular processes, including chromatin-mediated epigenetic inheritance of traits. As with eukaryotes, archaeal protein complexes were formed between trait-related proteins and chromatin proteins, subject to chromatin protein methylation state. Oxidation-induced depletion of S-adenosylmethionine (SAM) pools likely resulted in chromatin protein hypomethylation. Subsequent chromatin enrichment of serine hydroxymethyltransferase as a response to oxidative stress could modulate methylation at specific genomic loci. The interplay between archaeal metabolism and chromatin appear consistent with patterns observed in eukaryotes and indicate the existence of an ancient oxidation signal transduction pathway controlling epigenetics.

microbiology↗

bioRxiv2018

Proteomics of natural bacterial isolates powered by deep learning-based de novo identification.

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing. Significance StatementProteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.

bioinformatics↗