Biology subjects

Hassan, M.

Publications and source records attributed to Hassan, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Optimising the viscoelastic properties of hyaluronic acid hydrogels through colloidal particle interactions: a response surface methodology approach

Enhancing the viscoelastic characteristics of hydrogel systems through strategic colloidal particle interactions is paramount for their functionality in rectal gel applications. This investigation delves into the synergistic interactions between cationic nanoparticles (CNPs), anionic nanoparticles (ANPs), and composite nanoparticles (NPs) within a hyaluronic acid (HA) hydrogel matrix, employing response surface methodology (RSM) for optimisation. Critical parameters, namely the volume fraction of NPs and oscillatory amplitude, were meticulously calibrated to achieve optimal complex viscosity, as determined by advanced rheometric analysis. The findings reveal substantial effects of CNPs, ANPs, and mixed NPs on the viscoelasticity of the HA hydrogel, with complex viscosity measurements of 490.82 {+/-} 10.57, 214.70 {+/-} 8.96, and 328.46 {+/-} 6.67 mPa{middle dot}s, respectively. The hydrogel system with mixed NPs exhibited a strong concordance with empirical data (R{superscript 2} = 0.9843), validating the predictive precision of the model. Morphological assessments uncovered a highly interconnected network within the HA gel-particle composite, characterised by both densely and sparsely packed porous architectures. This study presents a robust framework for modulating viscoelastic properties in colloidal particle-gel systems, providing pivotal insights for the development of advanced rectal gel formulations.

bioengineering↗

bioRxiv2024

Parallelized Droplet Vitrification Enables Single-Run Vitrification of the Whole Rat Liver Hepatocyte Yield

Drug discovery pipelines rely on the availability of isolated primary hepatocytes for investigating potential hepatotoxicity prior to clinical application. These hepatocytes are typically isolated from livers rejected for transplantation and subsequently cryopreserved for later usage. The gold-standard cryopreservation technique, slow-freezing, is a labor-intensive process, with significant post-storage viability loss. In this work, we introduce parallelized droplet vitrification, a technique for high-volumetric, rapid vitrification of suspended cells. We show the utility of this technique through the single-run vitrification of the whole-rate liver hepatocyte yield, resulting in a 1600% increase in single-batch vitrification and a 500% increase in droplet generation rate compared to previous droplet vitrification approaches. Additionally, we showed that these implementations maintained improved post-preservation outcomes in primary rat hepatocytes.

bioengineering↗

bioRxiv2024

Enhancing Perceptual, Attentional, and Working Memory Demands through Variable Practice Schedules: Insights from High-Density EEG Multi-Scale Analyses

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We propose to harness high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway and the anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and mnemonic, as well as large-scale general processes.

neuroscience↗

bioRxiv2024

Is ChatGPT detrimental to innovation? A field experiment among university students

ChatGPT represents a momentous technological breakthrough whose implications - along with other AI innovations - are yet to fully materialize. This paper is among the first attempts to experimentally test the effect of AI applications (in the form of ChatGPT) on three dependent variables usually assumed to be AI-collaterals: innovation, readiness to exert effort, and risk behaviour. We took advantage of the delayed introduction of ChatGPT in Egypt and conducted a pre-registered field experiment with nearly 100 senior university students at a public university. Over one month during term time, participants were asked to submit three graded essay assignments. In the treatment group, students were asked to write the essays using ChatGPT whereas in the control group, such option was neither mentioned nor allowed (the experiment was fielded before ChatGPT was legally operable in Egypt). One week after all assignments were submitted, the two groups were invited to the lab to play an innovation game (deploying multiple strategies to increase the sales of a hypothetical lemonade stand), a risk game (bomb risk elicitation task), and do a real effort task. The ChatGPT group was significantly less innovative, significantly less risk averse, and exerted less effort (however not statistically significant). Our results point to possible negative effects of AI applications but need further testing and larger samples to be confirmed.

neuroscience↗

bioRxiv2024

Single-Cell Transcriptomic Profiling Unveils Critical Metabolic Alterations and Signatures in Progression of Atherosclerosis

Metabolic dysregulation is recognized as a fundamental characteristic of cardiovascular diseases (CVDs), therefore mining metabolic patterns in these diseases would help to identify the possible pathogenic mechanisms and potential intervention targets. Atherosclerosis (AS), serving as the foundational pathology in numerous CVDs, represents a paramount global health concern. However, a systematic integrated analysis of the metabolic networks of AS is still lacking. In this study, we investigated and integrated single-cell RNA sequencing datasets from calcified atherosclerotic core (AC) plaques and patient-matched proximal adjacent (PA) portions of carotid artery tissue to generate metabolic flux profiling at single-cell level. Using scFEA and scWGCNA analyses, we discerned common metabolic changes in endothelial cells (ECs), myeloid cells, and smooth muscle cells. These altered metabolic modules were predominantly enriched in glucose-related pathways and were predicted to potentially facilitate metabolic bypass. Of particular interest, we observed an enrichment of metabolites produced from glucose/sialic acid metabolism pathway in both ECs and myeloid cells. This observation may partially account for their positive involvement in plaque formation, as previously discovered. Furthermore, we predicted that metabolic genes such as HK1, ENO1, PFKL, LDHA, PGK1, and NANS may be implicated in the detected metabolic flux disorder during AS progression. Additionally, we uncovered interactions between various cell types at single-cell level using CellChat. We noted heightened interactions between endothelial/SMC as well as myeloid/ECs in AC group, with ITGB2/VCAM and CD44/CD74 receptors potentially participating in these interactions, thereby fostering the development of pro-inflammatory plaque microenvironment. In conclusion, our study unveils metabolic shifts at single-cell level and identifies key gene signatures associated with metabolic disorders and cell-cell communication in atherosclerosis.

cell biology↗

bioRxiv2023

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information.

Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default= 1.06 x 10-9 and P Empirical weight = 6.25 x 10-11). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.

genetics↗

bioRxiv2023

Delineating the heterogeneity of senescence-induced-functional alterations inhepatocytes

Background and AimCellular senescence of hepatocytes involves permanent cell cycle arrest, disrupted cellular bioenergetics, resistance to cell death, and the release of pro-inflammatory cytokines. This zombie-like state perpetuates harmful effects on tissues and holds potential implications for liver disease progression. Remarkably, senescence exhibits heterogeneity, stemming from two crucial factors: the inducing stressor and the cell type. As such, our present study endeavors to characterize stressor-specific changes in senescence phenotype, its related molecular patterns, and cellular bioenergetics in primary mouse hepatocytes (PMH) and hepatocyte-derived liver organoids (HepOrgs). MethodsPMH, isolated by collagenase-perfused mouse liver (C57B6/J; 18-23 weeks), were cultured overnight in Williams E-medium supplemented with 2% FBS, L-glutamine, and hepatocyte growth supplements. HepOrgs were developed by culturing cells in a 3D matrix for two weeks. The senescence was induced by DNA damage (doxorubicin, cisplatin, and etoposide), oxidative stress (H2O2, and ethanol), and telomere inhibition (BIBR-1532), p53 activation (nutlin-3a), DNA methyl transferase inhibition (5-azacitidine), and metabolism inhibitors (galactosamine and hydroxyurea). SA-{beta} galactosidase activity, immunofluorescence, immunoblotting, and senescence-associated secretory phenotype (SASP), and cellular bioenergetics were used to assess the senescence phenotype. ResultsEach senescence inducer triggers a unique combination of senescence markers in hepatocytes. All senescence inducers, except hydroxyurea and ethanol, increased SA-{beta} galactosidase activity, the most commonly used marker for cellular senescence. Among the SASP factors, CCL2 and IL-10 were consistently upregulated, while Plasminogen activator inhibitor-1 exhibited global downregulation across all modes of senescence. Notably, DNA damage response was activated by DNA damage inducers. Cell cycle markers were most significantly reduced by doxorubicin, cisplatin, and galactosamine. Additionally, DNA damage-induced senescence shifted cellular bioenergetics capacity from glycolysis to oxidative phosphorylation. ConclusionIn our study, we demonstrated that each senescence inducer activates a unique combination of senescence markers in PMH. Doxorubicin demonstrated the highest efficacy in inducing senescence, followed by cisplatin and H2O2, with no impact on apoptosis. Each inducer prompted DNA damage response and mitochondrial dysfunction, independent of MAPK/AKT. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/556220v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@b3ba11org.highwire.dtl.DTLVardef@17cc3e3org.highwire.dtl.DTLVardef@208fb9org.highwire.dtl.DTLVardef@1bbbbc6_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Electrophysiological signatures of anxiety in Parkinson's disease

Anxiety is a common non-motor symptom in Parkinsons disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N=18) to both PD non-anxious (PD-NA, N=50) and HC groups (N=25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and diagnosis and can ultimately support the development of new therapeutics strategies.

neuroscience↗

bioRxiv2022

A multi-omics analysis of glioma chemoresistance using a hybrid microphysiological model of glioblastoma

Chemoresistance is a major clinical challenge in the management of glioblastoma (GBM) Temozolomide (TMZ) is the chemotherapeutic drug of choice for GBM; however, the therapeutic effect of TMZ is limited due to the development of resistance. Recapitulating GBM chemoresistance in a controlled environment is thus essential in understanding the mechanism of chemoresistance. Herein, we present a hybrid microphysiological model of chemoresistant GBM-on-a-chip (HGoC) by directly co-culturing TMZ-resistant GBM spheroids with healthy neurons to mimic the microenvironment of both the tumor and the surrounding healthy tissue. We characterized the model with proteomics, lipidomics, and secretome assays. The results showed that our artificial model recapitulated the molecular signatures of recurrent GBM in humans. Both showed alterations in vesicular transport and cholesterol pathways, mitotic quiescence, and a switch in metabolism to oxidative phosphorylation associated with a transition from mesenchymal to amoeboid. This is the first report to unravel the interplay of all these molecular changes as a mechanism of chemoresistance in glioblastoma. Moreover, we have shown that the acquisition of resistance increases invasiveness and the presence of neurons decreases this property.

bioengineering↗

bioRxiv2022

Successful reproduction of a large EEG study across software packages

As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results. In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N=500) from (Williams et al. 2021) using three of the most commonly used EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting evoked-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between softwares. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.

neuroscience↗

bioRxiv2022

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

ObjectiveFunctional connectivity networks explain the different brain states during diverse motor, cognitive, and sensory functions. Extracting spatial network configurations and their temporal evolution is crucial for understanding the brain function during diverse behavioral tasks. ApproachIn this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography (MEG) data during motor and memory tasks. Main ResultsThe framework generates dominant spatial brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the DMD-based approach provides a better temporal resolution than the PCA-based approach. SignificanceWe believe that DMD has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

neuroscience↗

bioRxiv2022

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks: a simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of the connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different choices induce significant discrepancy in results and drawn conclusions, thereby hindering reproducibility of neuroimaging research. Hence, our objective in this study was to evaluate some of the parameters related to the EEG source connectivity analysis and shed light on their implications on the accuracy of the resulting networks. We simulated, using neural mass models, EEG data corresponding to two of the resting-state networks (RSNs), namely the default mode network (DMN) and the dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices, a high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed a significant variability in the performance of the tested inverse solutions and connectivity measures. In our specific simulation context, eLORETA and wMNE combined with AEC computed between orthogonalized time series exhibited the highest performance in terms of similarity between reconstructed and reference connectivity matrices. Results were similar for both DMN and DAN. We believe that this work could be useful for the field of electrophysiology connectomics, by shedding light on the challenge of analytical variability and its consequences on the reproducibility of neuroimaging studies.

neuroscience↗

bioRxiv2022

Dynamic rewiring of electrophysiological brain networks during learning

Human learning is an active and complex process. However, the brain mechanisms underlying human skill learning and the effect of learning on the communication between brain regions, at different frequency bands, are still largely unknown. Here, we tracked changes in large-scale electrophysiological networks over a 6-week training period during which participants practiced a series of motor sequences during 30 home training sessions. Our findings showed that brain networks become more flexible with learning in all the frequency bands from theta to gamma ranges. We found consistent increase of flexibility in the prefrontal and limbic areas in the theta and alpha band, and over somatomotor and visual areas in the alpha band. Specific to the beta rhythm, we revealed that higher flexibility of prefrontal regions during the early stage of learning strongly correlated with better performance measured during home training sessions. Our findings provide novel evidence that prolonged motor skill practice results in higher, frequency-specific, temporal variability in brain network structure. AUTHOR SUMMARYWe investigated the large-scale organization of electrophysiological brain networks of a cohort of 30 participants practicing a series of motor sequences during 6 weeks of training. With learning, we observed a progressive modulation of the dynamics of prefrontal and limbic regions from theta to alpha frequencies, and of centro-parietal and occipital regions within visuomotor networks in the alpha band. In addition, higher prefrontal regional flexibility during early practice correlated with learning occurring during the 6 weeks of training. This provides novel evidence of a frequency-specific reorganization of brain networks with prolonged motor skill learning and an important neural basis for non-invasive research into the role of cortical functional interactions in (visuo)motor learning.

neuroscience↗

bioRxiv2021

Impaired dynamics of prefrontal beta-band networks during cognitive action control in Parkinson's disease

Among the cognitive symptoms that are associated with Parkinsons disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 21 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the group-specific brain network states that were dominant during the task. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, HC displayed a significant functional network state that involved typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). Both group- and subject-level analyses showed that this network was less present in PD to the benefit of other networks involving lateralized temporal and insular components. The presence of this prefrontal network was associated with decreased reaction time. In the gamma band, two networks (fronto-cingulate and fronto-temporal) followed one another in HC, while 3 partially overlapping networks that included fronto-temporal, fronto-occipital and cross-hemispheric temporal connections were found in PD. At the subject-level, differences between PD and HC were less marked. Altogether, this study showed that the functional brain networks observed during CAC and their temporal changes were different in PD patients as compared to HC, and that these differences partially relate to behavioral changes. This study also highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond. HighlightsO_LICognitive action control is associated with dynamic functional networks C_LIO_LIPrefrontal and cingulate beta connectivity are prominent in healthy controls C_LIO_LIPD patients have different dynamic networks in which prefrontal nodes are absent C_LIO_LIThe occurrence of prefrontal beta networks was associated with a decreased reaction time C_LIO_LIFunctional networks in the gamma band were temporally organized in HC, but overlapping in PD patients C_LI

neuroscience↗

bioRxiv2021

Thermodynamic Constraints on Electromicrobial Protein Production

Global consumption of protein is projected to double by the middle of the 21st century1. However, protein production is one of the most energy intensive and environmentally damaging parts of the food supply system today. Electromicrobial production technologies that combine renewable electricity and CO2-fixing microbial metabolism could dramatically increase the energy efficiency of commodity chemical production2-5. Here we present a molecular-scale model that sets an upper limit on the performance of any organism performing electromicrobial protein production. We show that engineered microbes that fix CO2 and N2 using reducing equivalents produced by H2-oxidation or extracellular electron uptake could produce amino acids with energy inputs as low as 64 MJ kg-1. This work provides a roadmap for development of engineered microbes that could significantly expand access to proteins produced with a low environmental footprint.

synthetic biology↗

bioRxiv2021

Bleeding in patients and mice with Alagille syndrome: sex differences and risk factors

Spontaneous lethal bleeds are major cause of death in the pediatric liver disease Alagille syndrome (ALGS), yet risk factors and screening methods have not been established. We performed a systematic review and identified significantly more female than male patients with idiopathic intracranial hemorrhage (10:1). We investigated bleeding and vasculature in patients and a mouse model for ALGS (Jag1Ndr/Ndr mice) and asked whether phenotypes identified in mice could be detected in patients non-invasively. Jag1Ndr/Ndr mice bled spontaneously, exhibiting a thin skull and vascular defects including artery-vein crossings, tortuous vessels, capillary breakdown and CADASIL-like sparse vascular smooth muscle cell coverage which was aggravated by hypertension. Retinographs from patients confirmed tortuous blood vessels and artery-vein crossings in ALGS. In conclusion, Jag1Ndr/Ndr mice could be used to develop interventions for vascular defects in ALGS, and retinography could provide a non-invasive method for vascular analysis in these pediatric patients.

developmental biology↗

bioRxiv2020

NOTCH-YAP1/TEAD-DNMT1 axis regulates hepatocytereprogramming into intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC), a disease of poor prognosis, has increased in incidence. It is challenging to treat due to intra- and inter-tumoral heterogeneity, which in part is attributed to diverse cellular origin. Indeed, co-expression of AKT and NICD in hepatocytes (HCs) yielded ICC, with similarity to proliferative, Notch-activated, and stem cell-like subclasses of clinical ICC. NICD regulated SOX9 and YAP1 during ICC development. Yap1 deletion or TEAD inhibition impaired HC-to-biliary epithelial cell (BEC) reprogramming and ICC proliferation; Sox9 loss repressed tumor growth; and Yap1-Sox9 combined loss abolished ICC development in AKT-NICD model. DNMT1 was discovered as a novel downstream effector of YAP1-TEAD complex that directed HC-to-BEC/ICC fate-switch. DNMT1 loss prevented Notch-dependent HC-to-ICC development, and DNMT1 re-expression restored ICC development following TEAD repression. Coexpression of DNMT1 with AKT was sufficient to induce hepatic tumor development including ICC. Thus, we have identified a novel NOTCH-YAP1/TEAD-DNMT1 axis essential for HC-driven ICC development. SIGNIFICANCEWe evaluated the clinical relevance of hepatocyte-driven ICC model and revealed critical but distinct roles of YAP1 and SOX9 in AKT-NICD-driven hepatocyte-derived ICC. We also identified NOTCH-YAP1/TEAD-DNMT1 axis as a critical driver for hepatocyte-to-ICC reprogramming, which might have biological and therapeutic implications in ICC subsets.

cancer biology↗

bioRxiv2020

Dynamics of task-related electrophysiological networks: a benchmarking study

Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the random selection of methods and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We show that the independent component analysis (ICA)-based methods and especially those exploring high order statistics are the most efficient, in terms of spatiotemporal accuracy and subject-level analysis. We seek to help researchers in choosing objectively the appropriate methodology when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

neuroscience↗