Biology subjects

Schneider, M.

Publications and source records attributed to Schneider, M..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Rapid and reversible optogenetic silencing of synaptic transmission by clustering of synaptic vesicles

Silencing neurons acutely and specifically informs about their functional roles in circuits and behavior. Existing optogenetic silencers include ion pumps or channels, and tools that damage the neurotransmitter release machinery. While the former hyperpolarize the cell and can alter ionic gradients, the latter allow only slow recovery, requiring de novo synthesis. Thus, there is a need for other strategies combining fast activation and reversibility. Here, we use light-evoked homo-oligomerization of cryptochrome CRY2 to silence synaptic transmission, by clustering synaptic vesicles (SVs). We benchmark this tool, optoSynC, by electrophysiology and locomotion in Caenorhabditis elegans. optoSynC clusters SVs within 25 s, causing approximation, observable by electron microscopy. Locomotion silencing is rapid (tauon [~]15 s) and recovers quickly (tauoff [~]10 min) after light-off. Further, optoSynC can inhibit exocytosis for several hours, at very low light intensities. optoSynC is a highly efficient, non-ionic optogenetic silencer that may further allow to manipulate different SV pools.

neuroscience↗

bioRxiv2022

Competition for cysteine acylation by C16:0 and C18:0 derived lipids is a global phenomenon in the proteome

S-acylation is a reversible posttranslational protein modification consisting of attachment of a fatty acid to a cysteine via a thioester bond. Research over the last few years has shown that a variety of different fatty acids, such as C16:0, C18:0 or C18:1, are used in cells to S-acylate proteins. We recently showed that GNAI proteins can be acylated on a single residue, Cys3, with either C16:0 or C18:1 and that the relative proportion of acylation with these fatty acids depends on the level of the respective fatty acid in the cells environment. This has functional consequences for GNAI proteins, with the identity of the acylating fatty acid affecting the subcellular localization of GNAIs. Unclear is whether this competitive acylation is specific to GNAI proteins or a more general phenomenon in the proteome. We perform here a proteome screen to identify proteins acylated with different fatty acids. We identify 218 proteins acylated with C16:0 and 308 proteins acylated with C18-lipids, thereby uncovering novel targets of acylation. We find that most proteins that can be acylated by palmitic acid (C16:0) can also be acylated with C18-fatty acids. For proteins with more than one acylation site, we find that this competitive acylation occurs on each individual cysteine residue. This raises the possibility that the function of many different proteins can be regulated by the lipid environment via differential S-acylation.

molecular biology↗

bioRxiv2022

Fc-engineered antibodies leverage neutrophils to drive control of Mycobacterium tuberculosis

Novel vaccination and therapeutic strategies are urgently needed to mitigate the tuberculosis (TB) epidemic. While extensive efforts have focused on potentiating cell-mediated immunity to control Mycobacterium tuberculosis (Mtb) infection, less effort has been invested in exploiting the humoral immune system to combat Mtb. Emerging data point to a role for antibodies in microbial control of Mtb, however the precise mechanism(s) of this control remain incompletely understood. Here we took an antibody Fc-engineering approach to determine whether Fc-modifications could improve the ability of antibodies to restrict Mtb, and to define Fc-mediated mechanism(s) antibodies leverage for this restriction. Using an antibody specific to the capsular polysaccharide -glucan, we engineer a panel of Fc variants to augment or dampen select antibody effector functions, rationally building antibodies with enhanced capacity to promote Mtb restriction in a human whole blood model of infection. Surprisingly, restrictive Fc-engineered antibodies drive Mtb control in a neutrophil, not monocyte, dependent manner. Using single cell RNA sequencing, we show that restrictive antibodies promote neutrophil survival and expression of cell intrinsic antimicrobial programs. These data provide a roadmap for exploiting Fc-engineered antibodies as a novel class of TB therapeutics able to harness the protective functions of neutrophils to achieve disease control.

immunology↗

bioRxiv2022

IRON REGULATORY PROTEIN (IRP)-MEDIATED IRON HOMEOSTASIS IS CRITICAL FOR NEUTROPHIL DEVELOPMENT AND DIFFERENTIATION IN THE BONE MARROW

Iron is mostly devoted to the hemoglobinization of erythrocytes for oxygen transport. Yet, emerging evidence points to a broader role for the metal in hematopoiesis, including the formation of the immune system. Iron availability in mammalian cells is controlled by iron-regulatory proteins (IRP)-1 and -2. We report that global disruption of both IRP1 and IRP2 in adult mice impairs neutrophil development and differentiation in the bone marrow, yielding immature neutrophils with abnormally high glycolytic and autophagic activity, resulting in neutropenia. IRPs promote neutrophil differentiation in a cell intrinsic manner by securing cellular iron supply together with transcriptional control of neutropoiesis to facilitate differentiation to fully mature neutrophils. Unlike neutrophils, monocyte count was not affected by IRP and iron deficiency, suggesting a lineage-specific effect of iron on myeloid output. This study unveils the previously unrecognized importance of IRPs and iron metabolism in the formation of a major branch of the innate immune system.

immunology↗

bioRxiv2022

Transcriptome profiling of barley and tomato shoot and root meristems unravels physiological variations underlying photoperiodic sensitivity

The average sowing date of crops in temperate climate zones has been shifted forwards by several days, resulting in a changed photoperiod regime at the emergence stage. In the present study, we performed a global transcriptome profiling of plant development genes in the seedling stage of root and shoot apical meristems of a photoperiod-sensitive species (barley) and a photoperiod insensitive species (tomato) in short-day conditions (8h). Variant expression indicated differences in physiological development under this short day-length regime between species and tissues. The barley tissue transcriptome revealed reduced differentiation compared to tomato. In addition, decreased photosynthetic activity was observed in barley, indicating a slower physiological development of shoot meristems than in tomatoes. The photomorphogenesis controlling cryptochrome gene cry1, with an effect on physiological differentiation, showed an underexpression in barley compared to tomato shoot meristems. This might lead to a cascade of suspended sink-source activities, which ultimately delay organ development and differentiation in barley shoot meristems under short photoperiods.

developmental biology↗

bioRxiv2021

Effects of Hydroxychloroquine and Azithromycin on iPSC-derived Cardiomyocytes: Considerations for the Treatment of COVID-19 Patients

Despite known adverse effects of hydroxychloroquine (HCQ) and azithromycin (AZM) on cardiac function, HCQ and AZM have been used as combination therapy in the treatment of COVID-19 patients. Recent clinical data indicate higher complication rates with HCQ/AZM combination treatment in comparison to monotherapy. Here, we used human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to systematically investigate the effects of HCQ and AZM individually and in combination. The clinically observed QT prolongation caused by treatment with HCQ could be recapitulated in iPSC-CMs based on prolonged field potential duration (FPDc). Interestingly, HCQ-induced FPDc prolongation was strongly enhanced by combined treatment with AZM, although AZM alone slightly shortened FPDc in iPSC-CMs. Furthermore, combined treatment with AZM and HCQ leads to higher cardiotoxicity, more severe structural disarrangement, and more pronounced contractile and electrophysiological dysfunctions, compared to respective mono-treatments. First mechanistic insights underlying the synergistic effects of AZM and HCQ on iPSC-CM functionality are provided based on increased Cx43- and Nav1.5-protein levels. Taken together, our results highlight that combined treatment with HCQ and AZM strongly enhances the adverse effects on cardiomyocytes, providing mechanistic evidence for the high mortality in patients receiving HCQ/AZM combination treatment.

pharmacology and toxicology↗

bioRxiv2021

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet ("Structure ENSEmble NETworks") framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology. Author SummaryRegulation and signal transduction processes in proteins are often correlated to structural changes induced by ligand binding, which can lead to suppression or enhancement of protein function. A common method to investigate such changes are numerical simulations of protein dynamics. We developed the analysis software SenseNet for predicting how protein dynamics and function is affected by e.g. ligand binding events based on molecular dynamics simulations. Our model estimates which structural elements of the protein confer the most information about their local environment, reasoning that these elements are essential for signal propagation. Applying this method on the PDZ2 domain of the hPTP1e protein, we were able to accurately predict structure elements with known signaling roles as determined by previous experiments. Integrating these experimental data with the consensus of other computational models and our predictions, we find two separate pathways which may transmit information through the PDZ2 protein structure. In addition to deepening our insight into the behavior of this particular protein, these results demonstrate the usefulness of our methods for other systems, such as potential drug targets. To make this analysis available to a broad audience, we implemented it as a plugin for the popular network analysis software Cytoscape.

bioinformatics↗

bioRxiv2021

Biological complexity facilitates tuning of the neuronal parameter space

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability. Significance statementOver the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands of searching for viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with fewer ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for this improved distribution of valid model parameters, we propose that this may generalise to evolutionary selection in other complex biological systems. In briefStudying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but because of their complexity. HighlightsO_LI15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour. C_LIO_LIBut knocking out ion channels can be compensated only in the full model. C_LIO_LIRandom sampling leads to ~ 6% solutions in full but only ~ 1% in reduced model. C_LIO_LILaw of large numbers generalises our observations to other complex biological systems. C_LI

neuroscience↗

bioRxiv2021

NRF1 Association with AUTS2-Polycomb Mediates Specific Gene Activation in the Brain

The heterogeneous complexes comprising the family of Polycomb Repressive Complex 1 (PRC1) are instrumental to establishing facultative heterochromatin that is repressive to transcription. Yet, two PRC1 species, PRC1.3 and PRC1.5, are known to comprise novel components, AUTS2, P300, and CK2 that convert this repressive function to that of transcription activation. Here, we report that patients harboring mutations in the HX repeat domain of AUTS2 exhibit defects in AUTS2 and P300 interaction as well as a developmental disorder reflective of Rubinstein-Taybi syndrome, which is mostly associated with a heterozygous pathogenic variant in CREBBP/EP300. As well, the absence of AUTS2 gives rise to a mis-regulation of a subset of developmental genes and curtails motor neuron differentiation from embryonic stem cells in the context of a well-defined system. Moreover, the transcription factor, Nuclear Respiratory Factor 1 (NRF1) exhibits a novel and integral role in this aspect of the neurodevelopmental process, being required for PRC1.3 recruitment to chromatin.

biochemistry↗

bioRxiv2021

Langerhans islets induce anti-tumor immunity at the expense of glycemic control and predict chemotherapy response in pancreatic cancer

Induction of anti-tumor immunity in pancreatic ductal adenocarcinoma (PDA) is an unresolved challenge. Systematic investigation of the microenvironment of primary pancreatic tumors revealed a role of endocrine Langerhans islets in the coordination of immune activation. We found that intratumoral {beta}-cells, regulated via STAT3, secrete C-C motif chemokine ligand 27 (CCL27) and thereby promote a TH1 phenotype in the microenvironment resulting in an enhanced T cell infiltration and prolonged patient survival. The local effect can be abrogated in a patient-based human explant model by inhibition of the CCL27 receptor CCR10. This defense mechanism is paralleled by an impaired metabolic function of Langerhans islets with reduced insulin levels resulting in a dysregulation of glycemic control in patients. Based on these findings, screening of PDA cases (n= 2264) led to the identification of type 2 diabetes mellitus (T2DM) and extractable glycated haemoglobin (HbA1c) levels as response markers for neoadjuvant chemotherapy with fluorouracil, leucovorin, irinotecan and oxaliplatin (FOLFIRINOX). Collectively these data provide insights into the interconnection of T2DM and PDA, and link declining glycemic control to therapeutic efficacy, which can be utilized as a tool for clinical decision-making and improve patient management.

cancer biology↗

bioRxiv2021

AutoSmarTrace: Automated Chain Tracing and Flexibility Analysis of Biological Filaments

Single-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed "AutoSmarTrace", the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy (AFM). We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with the ground truth. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. While trained on AFM-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary shadowing electron microscopy and fluorescence imaging. Statement of SignificanceMachine learning presents powerful capabilities to the analysis of large data sets. Here, we apply this approach to the determination of bending flexibility - described through persistence length - from single-molecule images of biological filaments. We present AutoSmarTrace, a tool for automated tracing and analysis of chain flexibility. Built on a neural network trained via machine learning, we show that AutoSmarTrace can determine persistence lengths from AFM images of a variety of biological macromolecules including collagen and DNA. While trained on AFM-like images, the algorithm works well to identify filaments in other types of images. This technique can free researchers from tedious tracing of chains in images, removing user bias and standardizing determination of chain mechanical parameters from single-molecule conformational images.

biophysics↗

bioRxiv2020

A general theory of coherence between brain areas

What does neuronal coherence tell us about neuronal communication? Does coherence between field potentials (e.g. LFP, EEG, MEG) reflect spiking entrainment or coupling between oscillators? Is it a mechanism for communication between brain areas, or a byproduct of interareal connectivity and spectral power? We hypothesized that interareal coherence is explained by the fact that outputs from one cortical area give rise to synaptic inputs in the same brain area, and correlated synaptic inputs in another area. Our mathematical analysis demonstrates that coherence between a sending and receiving area is precisely predicted from only two parameters: Interareal connectivity and oscillatory synchronization in the sending area. This model predicts narrow-band coherence even in case of a flat transfer function and in the absence of spiking entrainment in a downstream area, and reproduces frequency-specific Granger-causality patterns between brain areas (gamma feedforward, beta feedback). In general, we find that Granger-causality between field potentials is dominated by oscillatory synchronization in the sending area, whereas spiking entrainment is determined by the resonant properties of the receiver. Our model accurately reproduces LFP-LFP beta-coherence between macaque areas 7B and F5 in the absence of beta phase-locking within area F5. Together, these findings uncover a precise mechanistic model of interareal coherence as a (by)product of connectivity and power.

neuroscience↗

bioRxiv2020

TLR9-activated B cells improve their regulatory function by endogenously produced catecholamines

The sympathetic nervous system (SNS) contributes to immune balance by promoting anti-inflammatory B cells. However, whether B cells possess a self-regulating mechanism by which they modulate regulatory B cell (Breg) function is not well understood. In this study, we investigated the ability of B cells to synthesize catecholamines upon stimulation with different B cell activators. We found, that expression of the enzymes required to generate catecholamines, is upregulated by TLR9. TLR-9-specific expression of tyrosine hydroxylase (TH) correlated with upregulation of adrenergic receptors, enhanced IL-10 production, and with an overexpression of the co-inhibitory ligands PD-L1 and FasL. Moreover, concomitant stimulation of {beta}1-3-adrenergic receptors together with a BCR/TLR9 stimulus enhances the anti-inflammatory potential of Bregs to suppress CD4 T cells, a crucial population in the pathogenesis of autoimmune diseases, like rheumatoid arthritis. In conclusion, our data show that B cells possess autonomous mechanisms to modulate their regulatory function. These findings help to better understand the function of Bregs in autoimmune diseases and the interplay of sympathetic nervous system and B cell function.

immunology↗

bioRxiv2020

Exploring the conformational space of a receptor for drugdesign: an ER α case study

MotivationProtein flexibility is challenging for both experimentalists and modellers, especially in the field of drug design. Estrogen Receptor alpha (ER) is an extensively studied Nuclear Receptor (NR) and a well-known therapeutic target with an important role in development and physiology. It is also a frequent off-target in standard toxicity tests for endocrine disruption. Here, we aim to evaluate the degree to which the conformational space and macromolecular flexibility of this well-characterized drug target can be described. Our approach exploits hundreds of crystallographic structures by means of molecular dynamics simulations and of virtual screening. ResultsThe analysis of hundreds of crystal structures confirms the presence of two main conformational states, known as agonist and antagonist, that mainly differ by the orientation of the C-terminal helix H12 which serves to close the binding pocket. ER also shows some loop flexibility, as well as variable side-chain orientations in its active site. We scrutinized the extent to which standard molecular dynamics simulations or crystallographic refinement as ensemble recapitulate most of the variability features seen by the array of available crystal structure. In parallel, we investigated on the kind and extent of flexibility that is required to achieve convincing docking for all high-affinity ER ligands present in BindingDB. Using either only one conformation with a few side-chains set flexible, or static structure ensembles in parallel during docking led to good quality and similar pose predictions. These results suggest that the several hundreds of crystal structures already known can properly describe the whole conformational universe of ERs ligand binding domain. This opens the road for better drug design and affinity computation. Contactlabesse@cbs.cnrs.fr

bioinformatics↗

bioRxiv2019

Global transcriptome profiling uncovers footprints of root and shoot development in crop models barley and tomato

Land plants establish their forms at two development hotspots- root and shoots apices. In this study, we dissected and compared the global transcriptome of these developmental zones in crop models barley and tomato. We employed a state of the art transcriptome analysis technique for deep profiling of expressed genes. This analysis resulted in highly reproducible quantitative expression profiles of 19,441 and 23,388 genes in barley and tomato, respectively. In barley, 16,834 genes were expressed both in root and shoot apices, whereas 1,081 genes were specific to root apex and 1,526 genes were active in shoot apex. With significant variations 20,154 genes were expressed in root and shoot apices of tomato of which 1,858 and 1,376 genes showed root and shoot specificities. Systematic analyses of these genes revealed distinct commonalties and divergence among the active genes for root and shoot development. A deeper insight in these data uncover tissue- and species specific genes, unique footprints of gene ontologies and divergence of auxins pathway genes in root and shoot apices of barley and tomato. These data provide a primary resource to understand intra- and inter-species genetic networks of root and shoot development as well as the evolution of genes in crop plants.

genomics↗

bioRxiv2019

Combining Crowdsourcing and Deep Learning to Assess Public Opinion on CRISPR-Cas9

The discovery of the CRISPR-Cas9-based gene editing method has opened un-precedented new potential for biological and medical engineering, sparking a growing public debate on both the potential and dangers of CRISPR applications. Given the speed of technology development, and the almost instantaneous global spread of news, its important to follow evolving debates without much delay and in sufficient detail, as certain events may have a major long-term impact on public opinion and later influence policy decisions. Social media networks such as Twitter have shown to be major drivers of news dissemination and public discourse. They provide a vast amount of semi-structured data in almost real-time and give direct access to the content of the conversations. Such data can now be mined and analyzed quickly because of recent developments in machine learning and natural language processing. Here, we used BERT, an attention-based transformer model, in combination with statistical methods to analyse the entirety of all tweets ever published on CRISPR since the publication of the first gene editing application in 2013. We show that the mean sentiment of tweets was initially very positive, but began to decrease over time, and that this decline was driven by rare peaks of strong negative sentiments. Due to the high temporal resolution of the data, we were able to associate these peaks with specific events, and to observe how trending topics changed over time. Overall, this type of analysis can provide valuable and complementary insights into ongoing public debates, extending the traditional empirical bioethics toolset.

scientific communication and education↗

bioRxiv2019

A general principle of dendritic constancy – a neuron’s size and shape invariant excitability

Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neurons size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neurons response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neurons excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.\n\nIn briefWe show that realistic neuron models essentially collapse to point neurons when stimulated by randomly distributed inputs instead of by single synapses or current injection in the soma.\n\nHighlightsO_LIA simple equation that predicts voltage in response to distributed synaptic inputs.\nC_LIO_LIResponses to distributed and clustered inputs are largely independent of dendritic length.\nC_LIO_LISpike rates in various Hodgkin Huxley (HH) like or Leaky Integrate-and-Fire (LIF) models are largely independent of morphology.\nC_LIO_LIPrecise spike timing (firing pattern) depends on dendritic morphology.\nC_LIO_LINeuroMorpho.Org database-wide analysis of the relation between dendritic morphology and electrophysiology.\nC_LIO_LIOur equations set precise input-output relations in realistic dendrite models.\nC_LI

neuroscience↗

bioRxiv2019

Motion displaces population receptive fields in the direction opposite to motion

Motion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.

neuroscience↗