Biology subjects

Pedersen, M.

Publications and source records attributed to Pedersen, M..

35 records · Page 2Linked to original sources

bioRxiv2023

Sculpting the tumour microenvironment by combining radiotherapy and ATR inhibition for curative-intent anti-PD-L1- and anti-NKG2A-based adjuvant immunotherapy

Despite some success in other cancer types, the results of combining radiotherapy/chemoradiotherapy and immune checkpoint blockade have been disappointing in patients with locally advanced head and neck squamous cell carcinoma (HNSCC). For such a potentially radiocurable disease, there remains an imperative to explore novel combination approaches. Here, we show that combining ATR inhibition with radiotherapy (ATRi/RT) increases the frequency of highly activated NKG2A/PD-1 double-positive T cells in patients and in animal models of HNSCC. Addition of dual anti-NKG2A and anti-PD-1/-PD-L1 blockade to ATRi/RT in the adjuvant, post-radiotherapy setting induces a robust antitumour immune response in HNSCC preclinical models. Efficacy of the combination regimen relies on CD40/CD40L costimulatory-mediated infiltration of activated/proliferative/memory CD8 and CD4 conventional T cells with persistent or new T cell receptor (TCR) signalling, respectively, as defined by tracking of T cell dynamics. In this favourable therapeutic context, TCR sequencing shows increased richness of the TCR repertoire and the emergence of numerous and large TCR clusters that share antigen specificity in response to full combination therapy. Collectively, our data point towards promising combination approaches for future clinical testing in HNSCC.

immunology↗

bioRxiv2023

Substrate availability and dietary fibre regulate metabolism of tryptophan by human gut microbes

Tryptophan is catabolized by gut microbes, resulting in a wide range of metabolites implicated in both beneficial and adverse host effects. However, it remains elusive how the gut microbial tryptophan metabolism is governed either towards indole, associated with adverse effects in chronic kidney disease, or towards indolelactic acid (ILA) and indolepropionic acid (IPA), associated with protective effects in type 2 diabetes and inflammatory bowel disease. Here, we used human fecal cultures in combination with a controlled three-species model to test competition for tryptophan, and measured the resulting metabolites both in vitro and in gnotobiotic mice colonized with the three species. We revealed that the generation of specific tryptophan-derived metabolites was not predominantly determined by the abundance of tryptophan metabolizing bacteria, but rather by substrate-dependent regulation of specific metabolic pathways. In vitro and in vivo, indole-producing Escherichia coli and ILA- and IPA-producing Clostridium sporogenes competed for tryptophan. Importantly, the fibre degrading Bacteroides thetaiotaomicron affected this competition by cross-feeding monosaccharides to E. coli, which inhibited indole production through catabolite repression, and thereby made more tryptophan available to C. sporogenes, increasing ILA and IPA production. We thus present the first mechanistic explanation for why consumption of fermentable fibres suppress indole production but promote the generation of other tryptophan metabolites associated with health benefits. We conclude that the availability of tryptophan and dietary fibre regulates gut microbiome tryptophan metabolism pathways, and consequently influences the balance between the different tryptophan catabolites generated. This balance has implications for host-microbial cross-talk affecting human health.

microbiology↗

bioRxiv2023

SNAP25 disease mutations change the energy landscape for synaptic exocytosis due to aberrant SNARE interactions

SNAP25 is one of three neuronal SNAREs driving synaptic vesicle exocytosis. We studied three mutations in SNAP25 that cause epileptic encephalopathy: V48F, and D166Y in the Synaptotagmin-1 (Syt1) binding interface, and I67N, which destabilizes the SNARE-complex. All three mutations reduced Syt1-dependent vesicle docking to SNARE-carrying liposomes and Ca2+-stimulated membrane fusion in vitro and in neurons. The V48F and D166Y mutants (with potency D166Y > V48F) led to reduced Readily Releasable Pool (RRP) size, due to increased spontaneous (mEPSC) release and decreased priming rates. These mutations lowered the energy barrier for fusion and increased the release probability, which are gain-of-function features not found in Syt1 knockout (KO) neurons; normalized mEPSC release rates were higher (potency D166Y>V48F) than in the Syt1 KO. These mutations (potency D166Y > V48F) increased spontaneous association to partner SNAREs, resulting in unregulated membrane fusion. In contrast, the I67N mutant decreased mEPSC frequency and evoked EPSC amplitudes due to an increase in the apparent height of the energy barrier for fusion, whereas the RRP size was unaffected. This could be partly compensated by positive charges lowering the energy barrier. Overall, pathogenic mutations in SNAP25 cause complex changes in the energy landscape for priming and fusion.

neuroscience↗

bioRxiv2023

Music Affects State Anxiety and Brain Connectivity

Stress, anxiety, and depressive symptoms can be reduced by listening to music, but the underlying mechanisms remain unclear. To address this gap, we measured brain connectivity while participants listened to songs of different genres: ambient, pop, and metal. Additionally, affective ratings were obtained while participants (n = 30) listened to the six different songs, and subjective ratings of state anxiety were solicited at the terminus of each song. Electroencephalography (EEG) connectivity combining weighted Phase Lag Index and graph theory was utilised to document brain activity during listening. Repeated-measures ANOVA indicated that listening to more pleasant and less arousing songs was associated with lower self-reported state anxiety levels than songs rated unpleasant and highly arousing. Of interest, EEG alpha connectivity differed across two ambient songs, particularly in the frontal lobes, despite being from the same genre and rated as highly pleasant and low in arousal. We also observed a sex effect on EEG results, where female participants (n = 18) displayed stronger connectivity than male participants (n = 12). Combined, these results suggest that ambient songs reduce state anxiety but have divergent brain responses, possibly reflecting the complex nature of music listening, including sensory processing, emotion and cognition.

neuroscience↗

bioRxiv2023

Transcriptional and epigenomic profiling identifies YAP signaling as a key regulator of intestinal epithelium maturation

During intestinal organogenesis, equipotent epithelial progenitors mature into phenotypically distinct stem cells that are responsible for life-long maintenance of the tissue. While the morphological changes associated with the transition are well-characterized, the molecular mechanisms underpinning the maturation process are not fully understood. Here, we leverage intestinal organoid cultures to profile transcriptional, chromatin accessibility, DNA methylation and 3D chromatin conformation landscapes defining fetal and adult epithelial cells. We observed prominent differences in gene expression and enhancer activity, accompanied by changes in 3D organization and local changes in DNA accessibility and methylation, between the two cellular states. Using integrative analyses, we identified sustained YAP transcriptional activity as a major gatekeeper of the immature fetal state. We found the YAP-associated transcriptional network to be regulated at various levels of chromatin organization, and likely to be coordinated by changes in extracellular matrix composition. Altogether, our work highlights the value of unbiased profiling of regulatory landscapes for the identification of key mechanisms underlying tissue maturation.

developmental biology↗

bioRxiv2023

Connectome embedding in multidimensional graph-invariant spaces

The topological organization of brain networks, or connectomes, can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to ten node-level graph theoretical invariants. Nodal invariants are intrinsic nodal properties which reflect the topological characteristics of the nodes with respect to the whole network, including segregation (e.g., clustering coefficient) and centrality (e.g., betweenness centrality) measures. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated multiple types of connectomes (structural/functional networks and binary/weighted networks) and embedded the corresponding network nodes (brain regions) into multidimensional graph spaces defined by the invariants. First, we observed that nodal invariants are correlated between them (i.e., they carry similar network information) at a whole-brain and subnetwork level. Second, we conducted a machine learning analysis to test whether brain regions embedded in multidimensional graph spaces can be accurately classified into higher order (association, subcortical and cerebellar) and lower order (visual, somatomotor, attention) areas. Brain regions of higher order and lower order brain circuits were classified with an 80-87% accuracy in a 10-dimensional (10D) space. 10D graph metrics performed better than 2D and 3D graph metrics, and non-linear Gaussian kernels performed better than linear kernels. This suggests a non-linear brain network information gain in a high-dimensional graph space. Finally, we quantified the inter-subject Euclidean distance of each brain region embedded in the multidimensional graph space. The inter-individual distance was largest for regions of the default mode and frontoparietal networks, providing a new avenue for subject-specific network coordinates in a multidimensional space. To conclude, we propose a new framework for quantifying connectome features in multidimensional spaces defined by graph invariants, providing a new avenue for subject-specific network coordinates and inter-individual distance analyses.

neuroscience↗

bioRxiv2023

Modeling the Spatiotemporal Influence of Climate on American Avian Migration

Birds have adapted to climatic and ecological cycles to inform their Spring and Fall migration timings, but anthropogenic global warming has affected these long-establish cycles. Understanding these dynamics is critical for conservation during a changing climate. Here, we employ a modeling approach to explore how climate spatiotemporally affects bird occurrence on eBird surveys. Specifically, we train an ensemble of multivariate and multi-response random forest models on North and South American climate data, then predict eBird survey occurrence rates for 41 migrating passerine bird species in a Northeastern American ecoregion from 2008-2018. In October, when many passerines have begun their southward winter migration, we achieve more accurate predictions of bird occurrence using lagged climate features alone to predict occurrence. These results suggest that analyses of machine learning model metrics may be useful for identifying spatiotemporal climatic cues that affect migratory behavior. Lastly, we explore the application and limitations of random forests for prediction of future bird occurrence using 2021-2040 climate projections.

ecology↗

bioRxiv2022

CDK4/6 inhibition and dsRNA sensor agonism co-operate to enhance anti-cancer effects through ER stress and immune modulation of tumour cells

Cytoplasmic pattern recognition receptors (PRRs) for double-stranded RNA (RIG-I/MDA5) are key mediators of anti-viral responses. PRR agonists, such as dsRNA oncolytic Reovirus type 3 Dearing (Rt3D), potently activate RNA sensors. We used an unbiased cytotoxicity screen to reveal synergistic drug-virotherapy combinations and found potent effects of Rt3D combined with the CDK4/6 inhibitor, palbociclib. The combination augmented oncolytic virus-induced endoplasmic reticulum (ER) stress/unfolded protein response (UPR) and the expression and activation/signaling of RNA sensors. Combined Rt3D-palbociclib treatment potently increased interferon production and signaling, and knockdown studies implicated key UPR proteins and the RNA sensor, RIG-I, as essential to the phenotype observed. Further experiments, using canonical RIG-I agonists and an ER stress inducer, thapsigargin, confirmed cross-talk between RNA sensing and ER stress pathways that augmented cancer cell death and interferon production. Combined Rt3D-palbociclib also increased innate immune activation within tumour cells and IFN-induced HLA expression. Analysis of the immunopeptidome revealed changes to HLA-captured peptides with Rt3D-palbociclib, including altered expression of peptides from cancer/testis antigens (CTA) and endogenous retroviral elements (ERVs). Our findings highlight cross-talk between UPR signaling and RNA-mediated PRR activation as a means of enhancing anti-cancer efficacy with potential pro-immunogenic consequences. This has implications for future clinical development of PRR agonists and oncolytic viruses, and broadens the therapeutic remit of CDK4/6 inhibitors to include roles as both ER stress and dsRNA PRR sensitizers.

cancer biology↗

bioRxiv2022

Single-cell level temporal profiling of tumour-reactive T cells under immune checkpoint blockade

The blockade of the immune checkpoints PD-1 and CTLA-4 enhances T cell response. However, it is largely unknown how antigen-reactive T cells regulate their checkpoint expression in vivo and whether and how the checkpoint blockade can change activation dynamics of tumour-reactive T cells. To address this, here we used Nr4a3-Timer-of-cell-kinetics-and-activity (Tocky), which allows analysis of temporal changes of activated T cells following TCR signalling in vivo. By analysing melanoma-bearing Nr4a3 Tocky mice, we elucidate hidden dynamics of tumour-reactive T cells in the steady-state. Checkpoint blockade depleted highly activated effector Treg, while promoting unique effector T cell populations, and thus differentially modulating activation of tumour-reactive T cell populations. Furthermore, multidimensional analysis and seamless analysis of Tocky and scRNA-seq revealed a full spectrum of T cell dynamics in response to tumour burden and treatment with checkpoint blockade. Lastly, we propose a rational design of combinatorial therapy to further enhance T cell activities.

immunology↗

bioRxiv2022

Characterization of proteome-size scaling by integrative omics reveals mechanisms of proliferation control in cancer

Almost all living cells maintain size uniformity through successive divisions. Proteins that sub- or super-scale with size act as rheostats which regulate cell progression. A comprehensive atlas of these proteins is lacking; particularly in cancer cells where both mitogen and growth signalling are dysregulated. Utilising a multi-omic strategy, that integrates quantitative single cell imaging, phosphoproteomic and transcriptomic datasets, we leverage the inherent size heterogeneity of melanoma cells to investigate how peptides, post-translational modifications, and mRNAs scale with cell size to regulate proliferation. We find melanoma cells have different mean sizes, but all retain uniformity. Across the proteome, we identify proteins and phosphorylation events that sub and super scale with cell size. In particular, G2/M, biosynthetic, and cytoskeletal regulators sub- and super-scale with size. In small cells growth and proliferation processes are tightly coupled by translation which promotes CCND1 accumulation and anabolic increases in mass. Counter intuitively, anabolic growth pathways and translational process are low in large cells, which throttles the expression of factors such as CCND1 and thereby coupling proliferation from anabolic growth. Strikingly, these cells exhibit increased growth and comparable proliferation rates. Mathematical modelling suggests that decoupling growth and proliferative signalling fosters proliferation under mitogenic inhibition. As factors which promote adhesion and actin reorganization super-scale with size or are enriched in large cells, we suggest that growth/proliferation in these cells may be decoupled by cell spreading and mechanics. This study provides one of the first demonstrations of size-scaling phenomena in cancer and how morphology determines the chemistry of the cell.

systems biology↗

bioRxiv2021

Intracranial brain stimulation modulates fMRI-based network switching

The extent to which functional MRI (fMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous electrical stimulation (es-fMRI) and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced after intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brains switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved es-fMRI connectivity. Network switching and synchrony was decreased after the first brain stimulation followed by a more consistent pattern of network switching over time. This change was commonly observed in cortical networks and adjacent to the electrode targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in epilepsy.

neuroscience↗

bioRxiv2019

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

It has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns [1]. This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of [1] was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in [1]. Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. A strong relationship was observed between temporal complexity of RSNs and fluid intelligence (peoples capacity to reason and think flexibly) through step-wise regression analysis suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. Finally, the results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales, likely due to the regulation of neural synchrony at local and global network levels.

neuroscience↗

bioRxiv2019

Reducing module size bias of participation coefficient

Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coeffcient (PC) is typically used to measure this attribute although its value also depends on the size of the module it belongs to, often leading to non-intuitive identification of highly connected nodes. Here, we develop a normalized PC that overcomes the module size bias associated with the conventional PC. Using brain, C.elegans, airport and simulated networks, we show that our measure of participation alleviates the module size bias, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.

systems biology↗

bioRxiv2019

SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinsons disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

neuroscience↗

bioRxiv2018

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Large-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics -defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates-; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

neuroscience↗

bioRxiv2018

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

ObjectiveThe process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy.\n\nMethodsWe implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other similar EEG events. We comprehensively compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only.\n\nResultsIn contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy cases, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases it revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe.\n\nConclusionsAutomatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy.\n\nSignificanceOur study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists. Our IED detection approach will be publicly available as a MATLAB package at: https://github.com/omidvarnia/Automatic_focal_spike_detection.\n\nHighlightsO_LIAutomatic spike detection increases the number of detected uniform epileptic interictal discharges and enhances statistical power of EEG-rsfMRI inter-subject variability maps,\nC_LIO_LIAutomatic spike detection can identify additional activated brain regions with presumed epileptogenic focus not seen in standard analysis based on human markup,\nC_LIO_LIAutomatic spike detection can shorten the IED identification process.\nC_LI

neuroscience↗

bioRxiv2017

On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis

Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on both temporal and topological levels.\n\nFor this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were temporally and topologically compared to IPSA.\n\nWe found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. This association peaked at a CSWA window length of [~]20 seconds, irrespective of the sampling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA).\n\nOur results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, we recommend the use of IPSA given that it does not mandate a (semi-)arbitrary choice of window length and window overlap. A MATLAB code for calculating IPSA is provided.

neuroscience↗