Biology subjects

Layer, M.

Publications and source records attributed to Layer, M..

3 recordsLinked to original sources

bioRxiv2023

Tetherin restricts SARS-CoV-2 replication despite antagonistic effects of Spike and ORF7a

SARS-CoV-2 infection induces interferon-stimulated genes, one of which encodes Tetherin, a transmembrane protein inhibiting the release of various enveloped viruses from infected cells. Previous studies revealed that SARS-CoV encodes two Tetherin antagonists: the Spike protein (S) inducing lysosomal degradation of Tetherin, and ORF7a altering its glycosylation. SARS-CoV-2 ORF7a has also been shown to antagonize Tetherin. Therefore, we here investigated whether SARS-CoV-2 S is also a Tetherin antagonist and compared the abilities and mechanisms of S and ORF7a in counteracting Tetherin. SARS-CoV and SARS-CoV-2 S reduced Tetherin cell surface levels in a cell type-dependent manner, possibly related to the basal protein levels of Tetherin. In HEK293T cells, under conditions of high exogenous Tetherin expression, SARS-CoV-2 S and ORF7a reduced total Tetherin levels much more efficiently than the respective counterparts derived from SARS-CoV. Nevertheless, ORF7a from both strains was able to alter Tetherin glycosylation. The ability to decrease total protein levels of Tetherin was conserved among S proteins from different SARS-CoV-2 variants (D614G, Cluster 5, , {gamma}, {delta}, o). While SARS-CoV-2 S and ORF7a both colocalized with Tetherin, only ORF7a directly interacted with the restriction factor. Despite the presence of two Tetherin antagonists, however, SARS-CoV-2 replication in Caco-2 cells was further enhanced upon Tetherin knockout. Altogether, our data show that endogenous Tetherin restricts SARS-CoV-2 replication, and that the antiviral activity of Tetherin is partially counteracted by two viral antagonists with differential and complementary modes of action, S and ORF7a. IMPORTANCEViruses have adopted multiple strategies to cope with innate antiviral immunity. They blunt signaling and encode proteins that counteract antiviral host factors. One such factor is Tetherin, that tethers nascent virions to the cell membrane and interferes with virus release. For SARS-CoV, the viral glycoprotein Spike (S) and the accessory protein ORF7a are Tetherin antagonists. For pandemic SARS-CoV-2, such activity has only been shown for ORF7a. We therefore analyzed whether SARS-CoV-2 S is a Tetherin-counteracting protein and whether there are differences in the abilities of the viral proteins to antagonize Tetherin. Of note, the efficiency of Tetherin antagonism was more pronounced for S and ORF7a from SARS-CoV-2 compared to their SARS-CoV orthologs. Still, Tetherin was able to restrict SARS-CoV-2 replication. Our results highlight the fundamental importance of the innate immune response in the context of SARS-CoV-2 control and the evolutionary pressure on pathogenic viruses to withhold efficient Tetherin antagonism.

microbiology↗

bioRxiv2021

A mean-field toolbox for spiking neuronal network model analysis

Mean-field theory of spiking neuronal networks has led to numerous advances in our analytical and intuitive understanding of the dynamics of neuronal network models during the past decades. But, the elaborate nature of many of the developed methods, as well as the difficulty of implementing them, may limit the wider neuroscientific community from taking maximal advantage of these tools. In order to make them more accessible, we implemented an extensible, easy-to-use open-source Python toolbox that collects a variety of mean-field methods for the widely used leaky integrate-and-fire neuron model. The Neuronal Network Mean-field Toolbox (NNMT) in its current state allows for estimating properties of large neuronal networks, such as firing rates, power spectra, and dynamical stability in mean-field and linear response approximation, without running simulations on high performance systems. In this article we describe how the toolbox is implemented, show how it is used to calculate neuronal network properties, and discuss different use-cases, such as extraction of network mechanisms, parameter space exploration, or hybrid modeling approaches. Although the initial version of the toolbox focuses on methods that are close to our own past and present research, its structure is designed to be open and extensible. It aims to provide a platform for collecting analytical methods for neuronal network model analysis and we discuss how interested scientists can share their own methods via this platform.

neuroscience↗

bioRxiv2020

Long-range coordination patterns in cortex change withbehavioral context

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet this massively parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

neuroscience↗