Biology subjects

Noonan, J.

Publications and source records attributed to Noonan, J..

6 recordsLinked to original sources

bioRxiv2026

Phosphoproteomics profiling of the monocytes uncovers the mechanosensory pathways regulating monocytes responses to pathologic level of shear stress

Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in older adults and currently has no approved pharmacological therapy to prevent or slow disease progression. Aortic valve replacement, either surgically or by transcatheter aortic valve implantation (TAVI), therefore remains the only effective treatment for severe CAVD. Monocytes play an important role in driving inflammation and disease progression in CAVD. As the disease progresses and the aortic valve becomes increasingly stenotic, circulating monocytes passing through the narrowed valve are repeatedly exposed to pathological haemodynamic forces, including markedly elevated shear stress, before their recruitment into the valve tissue. However, how exposure to these mechanical forces reprograms circulating monocytes influences their subsequent responses within the valve remain poorly understood. Methods and Results: In this study, we used an engineered model of aortic valve stenosis combined with phosphoproteomic profiling to investigate how prolonged exposure to pathological shear stress reprograms circulating monocytes and influences their subsequent phenotype and function following recruitment into the valve matrix. We further used TAVI as a unique clinical model to determine whether restoration of physiological haemodynamics in patients with severe aortic stenosis reverses the shear-induced monocyte phenotype observed in our experimental model. We found that prolonged exposure of monocytes to pathological shear stress induced extensive remodelling of the proteomic and phosphoproteomic landscape, with enrichment of pathways associated with cellular metabolism, cytoskeletal organisation, and inflammatory signalling. Kinase activity analysis further identified increased protein kinase C (PKC) activity accompanied by suppression of AKT signalling. Consistent with these molecular changes, pathological shear stress increased actin polymerisation, mitochondrial membrane potential, intracellular calcium signalling, and PKC activity. Using a bioengineered extracellular matrix model that recapitulates the glycosaminoglycan-rich microenvironment of the stenotic aortic valve we found that prior exposure to pathological shear stress altered monocyte-to-macrophage differentiation, reducing oxidised LDL uptake and foam-cell formation while promoting co-expression of CD80 and CD163, indicative of a mixed macrophage phenotype. Finally, using a myeloid-specific Piezo1 knockout mouse model, we identified Piezo1 as a key mechanosensor linking pathological shear stress to PKC activation, mitochondrial responses, and persistent changes in monocyte phenotype following migration into the extracellular matrix. Conclusions: These findings establish a mechanistic link between abnormal valve haemodynamics, metabolic and inflammatory reprogramming of circulating monocytes, extracellular matrix remodelling, and immune dysregulation during CAVD progression, highlighting monocyte mechanotransduction and downstream PKC/Piezo1 signalling as potential therapeutic targets.

cell biology↗

bioRxiv2026

Autoimmune-like CD8⁺ T Cell Responses Drive Atherosclerotic Plaque Instability and Predict Cardiovascular Events

Background and aimsAtherosclerotic plaque rupture is a major cause of myocardial infarction and stroke. However, the precise drivers of plaque destabilisation remain elusive. We hypothesised that antigen-driven, autoimmune-like T cell responses are central to the destabilisation and rupture of atherosclerotic plaques. MethodsTo dissect T cell responses specifically in unstable compared to stable plaques, we leveraged near-infrared autofluorescence (NIRAF) imaging-guided dissection of human carotid plaques. We also used our tandem stenosis model reflecting plaque instability as seen in patients to differentiate between unstable and stable plaques in mice. To explore T cell involvement, we studied T cell differentiation states and T cell receptor (TCR) repertoires by single-cell multi-omics. Then, testing if antigen-driven CD8+ T cell responses drive plaque instability in mice, we applied a combination of AAV8-PCSK9-induced atherosclerosis, tandem stenosis and TCR transgenic mice. Finally, we leveraged data from the AtheroExpress Biobank Study to link T cell immunity to histology-defined instability and cardiovascular outcomes. ResultsT cell responses in unstable versus stable atherosclerosis were distinct. Unstable human plaques contained highly expanded, autoimmune-like CD8 T cells with markedly increased cytotoxic signatures, reduced exhaustion and distinct clonal repertoires compared to stable regions. Most plaque CD8 T cells exhibited a pronounced tissue-resident transcriptional program. Moreover, the transcriptional signature of these plaque resident T cells was distinct from multiple other human tissues. Autoimmune-like cytotoxic and tissue-resident CD8+ T cell responses were also evident in murine atherosclerosis, where restricting the activation of antigen-driven CD8+ T cells prevented plaque destabilisation. Importantly, analysis of carotid endarterectomy samples from >1000 patients identified that intraplaque cytotoxic CD8 T cell gene signatures strongly correlated with histological instability and predicted future strokes. ConclusionsIntegrated human, murine and clinical analyses demonstrate that autoimmune-like, cytotoxic CD8 T cell responses are central drivers of plaque instability and major cardiovascular events. Targeting pathogenic CD8 T cell responses may thus offer a compelling immunomodulatory strategy to stabilise plaques and reduce the risks of stroke and myocardial infarction. Graphical AbstractO_ST_ABSKey QuestionC_ST_ABSRupture of unstable atherosclerotic plaques is a typical cause of myocardial infarction and stroke. To understand the underlying cause and to prevent plaque rupture, we addressed the central hypothesis that autoimmune-like T cell responses drive plaque destabilisation and rupture. Key FindingsCD8+ T cells are clonally expanded with increased cytotoxic signatures in unstable versus stable plaques (mice and humans) and require antigen recognition to drive plaque instability. Cytotoxic CD8+ T cell signatures in excised plaques correlate with increased future cardiovascular events. Take Home MessageAutoimmune-like adaptive immune reactions, dominated by CD8+ T cells, are a major driver of plaque instability/rupture. Therefore, targeting pathogenic CD8 T cell responses offers a compelling immunomodulatory strategy to stabilise plaques and reduce the risk of myocardial infarction and stroke. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/720043v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@bd83dcorg.highwire.dtl.DTLVardef@1bee3borg.highwire.dtl.DTLVardef@1b56bc0org.highwire.dtl.DTLVardef@1b5176f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Antibody responses against bacterial glycans affinity mature and diversify in germinal centers.

Anti-carbohydrate antibodies (Abs) play crucial roles in pathogen control, but their generation remains poorly understood. By studying responses to Streptococcus pyogenes in humans, we reveal that the glycan-targeted response shifts from IgM towards IgG and IgA memory with age and antigen exposure across blood, spleen, and tonsils. Both natural colonization and controlled human infection with S. pyogenes increased class-switched B cells, with evidence of within-clone switching. Glycan-specific B cells readily participated in germinal center (GC) responses and showed robust somatic hypermutation despite a molecular signature consistent with receiving reduced T cell help. We conclude that mucosal pathogen encounters elicit glycan responses that class-switch, evolve and diversify through the GC. These findings reveal how age and infection history can influence the quality, quantity, and isotype use of glycan-specific B cells, with implications for the design and schedule of glycan-containing vaccines.

immunology↗

bioRxiv2022

Enhanced survival and low proliferation marks multifunctional virus specific memory CD4 T cells

Cytokine production by memory T cells is a key mechanism of T cell mediated protection. However, we have a limited understanding of the survival and secondary responses of memory T cells with cytokine producing capacities. We interrogate antigen-specific CD4 T cells using a mouse influenza A virus infection model. CD4 T cells with the capacity to produce cytokines survive better than non-cytokine+ cells, displaying a low fold contraction and expressing high levels of pro-survival molecules, CD127 and Bcl2. Transcriptomic analysis reveals a heterogenous population of memory CD4 T cells with three clusters of cytokine+ cells. These clusters match flow cytometry data revealing an enhanced survival signature in cells capable of producing multiple cytokines. These multifunctional cells are, however, less likely to proliferate during and following primary and secondary infections. Despite this, multifunctional memory T cells form a substantial fraction of the secondary memory pool, indicating that survival rather than proliferation may dictate which populations survive within the memory pool. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/520219v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@93e62forg.highwire.dtl.DTLVardef@1ed2444org.highwire.dtl.DTLVardef@1e7f79borg.highwire.dtl.DTLVardef@30e31d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

immunology↗

bioRxiv2018

The marked diversity of unique cortical enhancers enables the generation of neuron-specific tools by Enhancer-Driven Gene Expression (EDGE)

Understanding neural circuit function requires individually addressing their component parts: specific neuronal cell types. However, not only do the precise genetic mechanisms specifying neuronal cell types remain obscure, access to these neuronal cell types by transgenic techniques also remains elusive. While most genes are expressed in the brain, the vast majority are expressed in many different kinds of neurons, suggesting that promoters alone are not sufficiently specific to distinguish cell types. However, there are orders of magnitude more distal genetic cis-regulatory elements controlling transcription (i.e. enhancers), so we screened for enhancer activity in microdissected samples of mouse cortical subregions. This identified thousands of novel putative enhancers, many unique to particular cortical subregions. Pronuclear injection of expression constructs containing such region-specific enhancers resulted in transgenic lines driving expression in distinct sets of cells specifically in the targeted cortical subregions, even though the parent genes promoter was relatively nonspecific. These data showcase the promise of utilizing the genetic mechanisms underlying the specification of diverse neuronal cell types for the development of genetic tools potentially capable of targeting any neuronal circuit of interest, an approach we call Enhancer-Driven Gene Expression (EDGE).\n\nHighlightsO_LIEnhancer ChIP-seq of cortical subregions reveals 59372 putative enhancers.\nC_LIO_LI3740 of these are specific to particular cortical subregions.\nC_LIO_LIThis reflects the remarkable anatomical diversity of the adult cortex.\nC_LIO_LIUnique enhancers provide a means to make targeted cell-type specific genetic tools.\nC_LI

neuroscience↗

bioRxiv2017

Exploring Single-Cell Data with Multitasking Deep Neural Networks

Biomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.\n\nA well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

bioinformatics↗