Biology subjects

Rosado, J.

Publications and source records attributed to Rosado, J..

3 recordsLinked to original sources

bioRxiv2026

Site-Specific Entry Factors Define Cellular Susceptibility to SARS-CoV-2 in Human Tissues

SARS-CoV-2 primarily infects the respiratory epithelium through entry factors ACE2 and TMPRSS2. However, alternative viral entry receptors and/or modulators of viral entry factors expression may contribute to infection in respiratory as well as non-respiratory tissues, including the kidneys and colon. Using single-cell analyses, we identified novel candidates for spike-mediated viral entry in epithelial cells from diverse fresh human tissues exposed to a labeled spike-pseudotyped virus. Systematic viral tracking revealed tissue-specific membrane receptors that contribute to viral entry, functionally validated in human tissues and partially predicted by molecular modeling. We found ADAM17 and IL1R1, together with canonical molecules, to significantly facilitate viral entry in the lung parenchyma, while entry into the renal cortex was less dependable on canonical factors, but strongly modulated by JAK1/2 engagement. These findings uncover distinct viral entry mechanisms across tissues and suggest novel organ-specific therapeutic targets for patients at risk.

microbiology↗

bioRxiv2021

Novel Human Lung Tissue Model for the Study of SARS-CoV-2 Entry, Inflammation and New Therapeutics

The development of physiological models that reproduce SARS-CoV-2 infection in primary human cells will be instrumental to identify host-pathogen interactions and potential therapeutics. Here, using cell suspensions directly from primary human lung tissues (HLT), we have developed a rapid platform for the identification of viral targets and the expression of viral entry factors, as well as for the screening of viral entry inhibitors and anti-inflammatory compounds. The direct use of HLT cells, without long-term cell culture and in vitro differentiation approaches, preserves main immune and structural cell populations, including the most susceptible cell targets for SARS-CoV-2; alveolar type II (AT-II) cells, while maintaining the expression of proteins involved in viral infection, such as ACE2, TMPRSS2, CD147 and AXL. Further, antiviral testing of 39 drug candidates reveals a highly reproducible method, suitable for different SARS-CoV-2 variants, and provides the identification of new compounds missed by conventional systems, such as VeroE6. Using this method, we also show that interferons do not modulate ACE2 expression, and that stimulation of local inflammatory responses can be modulated by different compounds with antiviral activity. Overall, we present a relevant and rapid method for the study of SARS-CoV-2. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/440731v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@96abf6org.highwire.dtl.DTLVardef@1f3cfe3org.highwire.dtl.DTLVardef@1d55fdborg.highwire.dtl.DTLVardef@1884cd6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsEx vivo physiological systems for the study of SARS-CoV-2-host interactions are scarce. Here, we establish a method using primary human lung tissue (HLT) cells for the rapid analysis of cell tropism and identification of therapeutics. O_LIHLT cells preserve main cell subpopulations, including alveolar type-2 cells, and expression of SARS-CoV-2 entry factors ACE2, CD147, TMPRSS2 and AXL. C_LIO_LIHLT cells are readily susceptible to SARS-CoV-2 infection without the need of cell isolation or further cell differentiation. C_LIO_LIAntiviral testing in HLT cells allows the rapid identification of new drug candidates against SARS-CoV-2 variants, missed by conventional systems. C_LIO_LILocal inflammation is supported in HLT cells and offers the identification of relevant anti-inflammatory compounds for SARS-CoV-2 infection. C_LI

microbiology↗

bioRxiv2020

Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique widely used in research and clinical applications. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research and provide a framework between the physical input parameters and the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields in whole-brain volume conductor models. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. Here, we develop a multi-scale Neuron Modeling for TMS toolbox (NeMo-TMS) that enables researchers to easily generate accurate neuron models from morphological reconstructions, couple them to the external electric fields induced by TMS, and to simulate the cellular and subcellular responses of the neurons. Both single-pulse and rTMS protocols can be simulated and results visualized in 3D. We openly share our toolbox and provide example scripts and datasets for the user to explore. NeMo-TMS toolbox (https://github.com/OpitzLab/NeMo-TMS) allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

neuroscience↗