Biology subjects

Williams, G.

Publications and source records attributed to Williams, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Feasibility of intranasal delivery of thin-film freeze-dried, mucoadhesive AS01B-adjuvanted vaccine powders

Intranasal vaccination by directly applying a vaccine dry powder is appealing. However, a method that can be used to transform a vaccine from a liquid to a dry powder and a device that can be used to administer the powder to the desired region(s) of the nasal cavity are critical for a successful intranasal vaccination. In the present study, using a model vaccine that contains the liposomal AS01B as an adjuvant and ovalbumin (OVA) as a model antigen, it was shown that thin-film freeze-drying can be applied to convert the liquid vaccine containing sucrose at a sucrose to lipid ratio of 15:1 (w/w), in the presence or absence of carboxymethyl cellulose sodium salt (CMC) as a mucoadhesive agent, into dry powders. Ultimately, the thin-film freeze-dried AS01B/OVA vaccine powder containing 1.9% w/w of CMC (i.e., TFF AS01B/OVA/CMC1.9% powder) was selected for additional evaluation because the TFF AS01B/OVA/CMC1.9% powder was mucoadhesive and maintained the integrity of the antigen and the physical properties of the vaccine. Compared to the TFF AS01B/OVA powder that did not contain CMC, the TFF AS01B/OVA/CMC1.9% powder had a lower moisture content and a higher glass transition temperature and was more porous. In addition, the TFF AS01B/OVA/CMC1.9% thin films were relatively thicker than the TFF AS01B/OVA thin films without CMC. When sprayed with the Unit Dose System Powder (UDSP) nasal device, the TFF AS01B/OVA powder and the TFF AS01B/OVA/CMC1.9% powder generated similar particle size distribution curves, spray patterns, and plume geometries. Importantly, after the TFF AS01B/OVA/CMC1.9% powder was sprayed with the UDSP nasal device, the integrity of the OVA antigen and the AS01B liposomal adjuvant did not change. Finally, a Taguchi L8 orthogonal array was applied to identify the optimal parameters for using the UDSP device to deliver the TFF AS01B/OVA/CMC1.9% vaccine powder to the middle and lower turbinate and the nasopharynx regions in both adult and child nasal casts. Results from this study showed that it is feasible to apply the TFF technology to transform a nasal vaccine candidate from liquid to a dry powder and then use the UDSP nasal device to deliver the TFF vaccine powder to the desired regions in the nasal cavity for intranasal vaccination.

pharmacology and toxicology↗

bioRxiv2022

Neurodegenerative disease associated pathways in brain of the triple transgenic Alzheimer's model are reversed in vivo following two weeks peripheral administration of fasudil

The pan ROCK inhibitor fasudil acts as a vasodilator and has been used as a medication post cerebral stroke for the past 27 years in Japan and China. More recently, on the basis of the involvement of ROCK inhibition on synaptic function, neuronal survival and processes associated with neuroinflammation, it has been suggested that the drug may be repurposed for neurodegenerative diseases. Indeed, fasudil has demonstrated preclinical efficacy in many neurodegenerative disease models. To facilitate an understanding of the wider biological processes at play due to ROCK inhibition in the context of neurodegeneration we performed a global gene expression analysis on the brains of Alzheimers disease model mice treated with fasudil via peripheral i.p injection. Our results show that fasudil tends to drive gene expression in a reverse sense to that seen in postmortem neurodegenerative disease brains. The results are most striking in terms of pathway enrichment analysis where pathways regulated in Alzheimers disease and by fasudil treatment are overwhelmingly regulated in opposite directions. Thus, our results bolster the repurposing potential of fasudil by demonstrating an anti-neurodegenerative phenotype in a disease context and highlight the potential of in vivo transcriptional profiling of drug activity.

neuroscience↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2022

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems -- including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be due to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

neuroscience↗

bioRxiv2022

Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature

PROTACs (PROteolysis TArgeting Chimeras) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances in targeted protein degradation technology have been remarkable with several molecules moving into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step towards delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design. HighlightsO_LIMorphological profiling detects various PROTACs phenotypic signatures C_LIO_LIPhenotypic signatures can be attributed to diverse biological responses C_LIO_LIChemical clustering from phenotypic signatures separates on drug selection C_LIO_LITrained in-silico machine learning models to predict PROTACs mitochondrial toxicity C_LI

pharmacology and toxicology↗

bioRxiv2021

Transcription Profile And Pathway Analysis Of The Endocannabinoid Receptor Inverse Agonist AM630 In The Core And Infiltrative Boundary Of Human Glioblastoma Cells

BackgroundWe have previously reported that the endocannabinoid receptor inverse agonist AM630 is a potent inhibitor of isocitrade dehydrogenase-1 wild-type glioblastoma (GBM) core tumor cell proliferation. To uncover the mechanism behind the anti-tumour effects we have performed a transcriptional analysis of AM630 activity both in the tumour core cells (U87) and the invasive margin cells (GIN-8), the latter representing a better proxy of post-surgical residual disease. ResultsThe core and invasive margin cells exhibited markedly different gene expression profiles and only the core cells had high expression of a potential AM630 target, the CB1 receptor. Both cell types had moderate expression of the HTR2B serotonin receptor, a reported AM630 target. We found that the AM630 driven transcriptional response was substantially higher in the central cells than in the invasive margin cells, with the former driving the up regulation of immune response and the down regulation of cell cycle and metastatic pathways and correlating with transcriptional responses driven by established anti-neoplastics as well as serotonin receptor antagonists. ConclusionOur results highlight the different responsiveness of the core and invasive margin cells. Taken together, whilst our findings identify AM630 as an anti-neoplastic drug, showing a high correlation with known anti-proliferative drugs, we find distinct drug sensitivies of the infiltrative margin relative to contrast-enhanced core regions of GBM upon which failed molecular targeted therapies to date have been predicated.

cancer biology↗

bioRxiv2021

Label-free prediction of Cell Painting from brightfield images

Cell Painting is a high-content image-based assay which can reveal rich cellular morphology and is applied in drug discovery to predict bioactivity, assess toxicity and understand diverse mechanisms of action of chemical and genetic perturbations. In this study, we investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from paired brightfield z-stacks using deep learning models. We train and validate the models with a dataset representing 1000s of pan-assay interference compounds sampled from 17 unique batches. The model predictions are evaluated using a test set from two additional batches, treated with compounds comprised from a publicly available phenotypic set. In addition to pixel-level evaluation, we process the label-free Cell Painting images with a segmentation-based feature-extraction pipeline to understand whether the generated images are useful in downstream analysis. The mean Pearson correlation coefficient (PCC) of the images across all five channels is 0.84. Without actually incorporating these features into the model training we achieved a mean correlation of 0.45 from the features extracted from the images. Additionally we identified 30 features which correlated greater than 0.8 to the ground truth. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. Additionally, we provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitation of the approach in morphological profiling. Our findings demonstrate that label-free Cell Painting has potential above the improved visualization of cellular components, and it can be used for downstream analysis. The findings also suggest that label-free Cell Painting could allow for repurposing the imaging channels for other non-generic fluorescent stains of more targeted biological interest, thus increasing the information content of the assay.

bioinformatics↗

bioRxiv2021

Functional connectomics spanning multiple areas of mouse visual cortex

To understand the brain we must relate neurons functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1-3 and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6 and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

neuroscience↗

bioRxiv2021

Transcriptional analysis of peripheral memory T cells reveals Parkinson's disease-specific gene signatures

Parkinsons disease (PD) is a multi-stage neurodegenerative disorder with largely unknown etiology. Recent findings have identified PD-associated autoimmune features including roles for T cells. To further characterize the role of T cells in PD, we performed RNA sequencing on PBMC and peripheral CD4 and CD8 memory T cell subsets derived from PD patients and age-matched healthy controls. When the groups were stratified by their T cell responsiveness to alpha-synuclein (-syn) as a proxy for ongoing inflammatory autoimmune response, the study revealed a broad differential gene expression profile in memory T cell subsets and a specific PD associated gene signature. We identified a significant enrichment of transcriptomic signatures previously associated with PD, including for oxidative stress, phosphorylation, autophagy of mitochondria, cholesterol metabolism and inflammation, and the chemokine signaling proteins CX3CR1, CCR5 and CCR1. In addition, we identified genes in these peripheral cells that have previously been shown to be involved in PD pathogenesis and expressed in neurons, such as LRRK2, LAMP3, and aquaporin. Together, these findings suggest that features of circulating T cells with -syn-specific responses in PD patients provide insights into the interactive processes that occur during PD pathogenesis and suggest potential intervention targets.

immunology↗

bioRxiv2021

Network dynamics scale with levels of awareness

Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of brain dynamics pertaining to small world topology (quantified by sample entropy; dSW-E) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.

neuroscience↗

bioRxiv2021

Connectivity and Synaptic Physiology in the Mouse and Human Cortex

To elucidate cortical microcircuit structure and synaptic properties we present a unique, extensive, and public synaptic physiology dataset and analysis platform. Through its application, we reveal principles that relate cell type to synapse properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Despite these associations, synaptic properties are heterogeneous in most subclass to subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, while the strength axis accounts for significant heterogeneity within the subclass. In human cortex, excitatory to excitatory synapse dynamics are distinct from those in mouse and short-term plasticity varies with depth across layers 2 and 3. With a novel connectivity analysis that enables fair comparisons between circuit elements, we find that intralaminar connection probability among cell subclasses exhibits a strong layer dependence.These and other findings combined with the analysis platform create new opportunities for the neuroscience community to advance our understanding of cortical microcircuits.

neuroscience↗

bioRxiv2021

Resting-state based prediction of task-related activation in patients with disorders of consciousness

BackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning. MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients. FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92). InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patients response to motor commands.

neuroscience↗

bioRxiv2021

Utilisation of semiconductor sequencing for detection of actionable fusions in solid tumors

Oncogenic fusions represent compelling druggable targets in solid tumours highlighted by the recent site agnostic FDA approval of larotrectinib for NTRK rearrangements. However screening for fusions in routinely processed tissue samples is constrained due to degradation of nucleic acid as a result of formalin fixation., To investigate the clinical utility of semiconductor sequencing optimised for detection of actionable fusion transcripts in formalin fixed samples, we have undertaken an analysis of test trending data generated by a clinically validated next generation sequencing platform designed to capture 867 of the most clinically relevant druggable driver-partner oncogenic fusions. Here we showacross a real-life cohort of 1112 patients with solid tumours that actionable fusions occur at high frequency (7.4%) with linkage to a wide range of targeted therapy protocols including seven fusion-drug matches with FDA/EMA approval and/or NCCN/ESMO recommendations and 80 clinical trials. The majority of actionable fusions identified were independent of tumour type in keeping with signalling via evolutionary conserved Wnt/{beta}-catenin, RAS/RAF/MEK/ERK, PI3K/AKT/MTOR, PLCy/PKC and JAK/STAT pathways. Taken together our data indicates that semiconductor sequencing for detection of actionable fusions can be integrated into routine diagnostic pathology workflows enabling the identification of personalised treatment options that have potential to improve clinical cancer management across many tumour types.

cancer biology↗

bioRxiv2021

Ecological habitat partitioning and feeding specialisations of coastal minke whales (Balaenoptera acutorostrata) using a designated MPA in northeast Scotland

In the design of protected areas for cetaceans, spatial maps rarely take account of the life-history and behaviour of protected species relevant to their spatial ambit, which may be important when modelling population trends or assessing susceptibility to anthropogenic threats. In the present study, we examined the distribution and feeding behaviours of minke whales by age-class (adults versus juveniles) from long-term studies in the Moray Firth in northeast Scotland, where a Marine Protected Area (MPA) has recently been designated. Data were collected from dedicated boat surveys between 2000 and 2019, during which 657 encounters with 774 whales of confirmed age-class (444 juveniles and 330 adults) were recorded from 50,041 km of survey effort, resulting in 224 individual follows. Feeding/foraging whales were documented in 84% of the encounters. Adults and juveniles were occasionally seen together, but their distributions were not statistically correlated, and GIS revealed spatial separation by age-class[-]with juveniles preferring shallow, inshore waters with sandy-gravel sediments and adults preferring deeper, offshore waters with steep benthic slope. Whilst adult minkes employed a range of "active" prey-entrapment specialisations, showing seasonal flexibility in their targeted prey with interindividual variation, juveniles almost exclusively used "passive" (low energy) feeding methods, targeting low-density patches of inshore prey. These findings corroborate the need to incorporate demographic/behavioural data into spatial models when identifying priority areas for protected cetaceans and may be important to adaptive management objectives for the species in the Moray Firth MPA.

ecology↗

bioRxiv2021

Utilisation of semiconductor sequencing for the detection of predictive biomarkers in glioblastoma

The standard treatment for glioblastoma involves a combination of surgery, radiation and chemotherapy but have limited impact on survival. The exponential increase in targeted agents directed at pivotal oncogenic pathways now provide new therapeutic opportunities for this tumour type. However, lack of comprehensive precision oncology testing at diagnosis means such therapeutic opportunities are potentially overlooked. To investigate the role of semiconductor sequencing for detection of predictive biomarkers in routine glioblastoma samples we have undertaken analysis of test trending data generated by a clinically validated next generation sequencing platform designed to capture 764 of the leading anti-cancer targeted agents/combinations and immunotherapies via analysis of actionable genomic variants distributed across 505 genes. Analysis was performed across a cohort of 55 glioblastoma patients. Analysis of trending data has revealed a complex and rich actionable mutational landscape in which 166 actionable mutations were detected across 36 genes linked to 17 off label targeted therapy protocols and 111 clinical trials. The majority of patients harboured three or more actionable mutations affecting key cancer related regulatory networks including the PI3K/AKT/MTOR and RAS/RAF/MEK/MAPK signalling pathways, DNA-damage repair pathways and cell cycle checkpoints. Linkage with immunotherapy and PARP inhibitors was identified in 44% of glioblastoma patients as a consequence of alterations in DNA-damage repair genes. Taken together our data indicates that precision oncology testing utilising semiconductor sequencing can be used to identify a broad therapeutic armamentarium of targeted therapies and immunotherapies that can be potentially employed for the improved clinical management of glioblastoma patients.

pathology↗

bioRxiv2020

Uncovering an allosteric mode of action for a selective inhibitor of human Bloom syndrome protein

BLM (Bloom syndrome protein) is a RECQ-family helicase involved in the dissolution of complex DNA structures and repair intermediates. Synthetic lethality analysis implicates BLM as a promising target in a range of cancers with defects in the DNA damage response, however selective small molecule inhibitors of defined mechanism are currently lacking. Here we identify and characterise a specific inhibitor of BLMs ATPase-coupled DNA helicase activity, by allosteric trapping of a DNA-bound translocation intermediate. Crystallographic structures of BLM-DNA-ADP-inhibitor complexes identify a hitherto unknown interdomain interface, whose opening and closing are integral to translocation of ssDNA, and which provides a highly selective pocket for drug discovery. Comparison with structures of other RECQ helicases provides a model for branch migration of Holliday junctions by BLM.

biochemistry↗

bioRxiv2020

Drug connectivity mapping and functional analysis reveals therapeutic small molecules that differentially modulate myelination

Oligodendrocytes are the myelin forming cells of the central nervous system (CNS) and are generated from oligodendrocyte progenitor cells (OPCs). Disruption or loss of oligodendrocytes and myelin has devastating effects on CNS function and integrity, which occurs in diverse neurological disorders, including Multiple Sclerosis (MS), Alzheimers disease (AD) and neuropsychiatric disorders. Hence, there is a need to develop new therapies that promote oligodendrocyte regeneration and myelin repair. A promising approach is drug repurposing, but most agents have potentially contrasting biological actions depending on the cellular context and their dose-dependent effects on intracellular regulatory pathways. Here, we have used a combined drug connectivity systems biology and neurobiological approach to identify compounds that exert positive and negative effects on oligodendroglia, depending on concentration. Notably, LY294002, a potent inhibitor of PI3K/Akt signalling, was the most highly ranked small molecule for both pro- and anti-oligodendroglial effects. We validated these in silico findings in multiple in vivo and ex vivo neurobiological models and demonstrate that low and high doses of LY294002 have a profoundly bipartite effect on the generation of OPCs and their differentiation into myelinating oligodendrocytes. Finally, we employed transcriptional profiling and signalling pathway activity assays to determine cell-specific mechanisms of action of LY294002 on oligodendrocytes and resolve optimal in vivo conditions required to promote myelin repair. These results demonstrate the power of multifactorial neurobiological and in silico strategies in determining the therapeutic potential of small molecules in neurodegenerative disorders. One-sentence summaryDrug discovery and CNS myelination

systems biology↗

bioRxiv2020

Recurrent plasticity mechanisms for perceptual decisions in the human brain

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet, evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer-scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high field (7T) functional imaging at sub-millimetre resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths. Our results provide evidence for recurrent plasticity, by contrast to sensory encoding vs. feedback mechanisms. We demonstrate that learning alters orientation-specific representations in superficial rather than middle V1 layers, suggesting changes in read-out rather than input signals. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings propose finer-scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro-circuits of experience-dependent plasticity.

neuroscience↗