Biology subjects

Bernardi, A.

Publications and source records attributed to Bernardi, A..

6 recordsLinked to original sources

bioRxiv2024

Deep Learning Models for Atypical Serotoninergic Cells Recognition

BackgroundThe serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of spikes. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied. New MethodsWe propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials. ResultsThe models were trained on a dataset comprising 43,327 original spike samples, alongside an extensive set of 6.7 million synthetic spike samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on "non-homogeneous" data, i.e., data not used for constructing the model, to confirm their robustness and reliability in real-world experimental conditions. Comparison with existing methodsConventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole spike reliably recognizes over 92 features of spike and activity. ConclusionsThe model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons. PACS87.19.L, 87.19.lv, 87.85.dm, 07.05.Mh, 87.85.Tu

neuroscience↗

bioRxiv2021

Engineered biological neural networks on high density CMOS micro electrode arrays

In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology in vitro. The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatiotemporal resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatiotemporal resolution of the CMOS MEA enabled us to show that we can guide action potentials using the unidirectional topology of our circular multi-node microstructure.

neuroscience↗

bioRxiv2020

DC/L-SIGN recognition of spike glycoprotein promotes SARS-CoV-2 trans-infection and can be inhibited by a glycomimetic antagonist

The efficient spread of SARS-CoV-2 resulted in a pandemic that is unique in modern history. Despite early identification of ACE2 as the receptor for viral spike protein, much remains to be understood about the molecular events behind viral dissemination. We evaluated the contribution of C-type lectin receptors (CLRS) of antigen-presenting cells, widely present in air mucosa and lung tissue. DC-SIGN, L-SIGN, Langerin and MGL bind to diverse glycans of the spike using multiple interaction areas. Using pseudovirus and cells derived from monocytes or T-lymphocytes, we demonstrate that while virus capture by the CLRs examined does not allow direct cell infection, DC/L-SIGN, among these receptors, promote virus transfer to permissive ACE2+ cells. A glycomimetic compound designed against DC-SIGN, enable inhibition of this process. Thus, we described a mechanism potentiating viral capture and spreading of infection. Early involvement of APCs opens new avenues for understanding and treating the imbalanced innate immune response observed in COVID-19 pathogenesis

immunology↗

bioRxiv2020

Development and simulation of fully glycosylated molecular models of ACE2-Fc fusion proteins and their interaction with the SARS-CoV-2 spike protein binding domain

We develop fully glycosylated computational models of ACE2-Fc fusion proteins which are promising targets for a COVID-19 therapeutic. These models are tested in their interaction with a fragment of the receptor-binding domain (RBD) of the Spike Protein S of the SARS-CoV-2 virus, via atomistic molecular dynamics simulations. We see that some ACE2 glycans interact with the S fragments, and glycans are influencing the conformation of the ACE2 receptor. Additionally, we optimize algorithms for protein glycosylation modelling in order to expedite future model development. All models and algorithms are openly available.

biophysics↗

bioRxiv2019

Development Of C-Type Lectin Oriented Surfaces For High Avidity Glycoconjugates: Towards Mimicking Multivalent Interactions On The Cell Surface

Multivalent interactions between complex carbohydrates and oligomeric C-type lectins govern a wide range of immune responses. Up to date, standard SPR (surface plasmon resonance) competitive assays have largely been to evaluate binding properties from monosaccharide units (low affinity, mM) to multivalent elemental antagonists (moderate affinity, {micro}M). Herein, we report typical case-studies of SPR competitive assays showing that they underestimate the potency of glycoclusters to inhibit the interaction between DC-SIGN and immobilized glycoconjugates. This paper describes the design and implementation of a SPR direct interaction over DC-SIGN oriented surfaces, extendable to other C-type lectin surfaces as such Langerin. This setup provides a microscopic overview of intrinsic avidity generation emanating simultaneously from multivalent glycoclusters and from DC-SIGN tetramers that are organized in nanoclusters on the cell membrane. For this purpose, covalent biospecific capture of DC-SIGN via StreptagII /StrepTactin interaction offers the preservation of tetrameric DC-SIGN and the accessibility/functionality of all active sites. From the tested glycoclusters libraries, we demonstrated that the scaffold architecture, the valency and the glycomimetic-based ligand are crucial to reach nanomolar affinities for DC-SIGN. The glycocluster 3.D illustrates the tightest binding partner in this set for a DC-SIGN surface (Kd= 18 nM). Moreover, the selectivity at monovalent scale of glycomimetic D can be easily analyzed at multivalent scale comparing its binding over different C-type lectin immobilized surfaces. This approach may give rise to novel insights into the multivalent binding mechanisms responsible to avidity and make a major contribution to the full characterization of the binding potency of promising specific and multivalent immunomodulators.

biochemistry↗

bioRxiv2016

Towards new cholera prophylactics and treatment: Crystal structures of bacterial enterotoxins in complex with GM1 mimics

Cholera is a life-threatening disease in many countries, and new drugs are clearly needed. C-glycosidic antagonists may serve such a purpose. Atomic resolution crystal structures of these compounds in complexes with the cholera toxin give unprecedented atomic details of the molecular interactions, and show how the inhibitors efficiently block the GM1 binding site. These molecules are well suited for development into low-cost prophylactic drugs, due to their relatively easy syntheses and their resistance to glycolytic enzymes. One of the compounds links two toxin B-pentamers in the crystal structure, which may yield improved inhibition through the formation of toxin aggregates. These structures can spark the improved design of GM1 mimics, either alone or as multivalent inhibitors connecting multiple GM1-binding sites. Future developments may further include compounds that link the primary and secondary binding sites. Serving as decoys, receptor mimics may lessen symptoms while avoiding the use of antibiotics.

biochemistry↗