Biology subjects

Jablonski, S.

Publications and source records attributed to Jablonski, S..

3 recordsLinked to original sources

bioRxiv2021

Identification of Potent Small Molecule Inhibitors of SARS-CoV-2 Entry

The severe acute respiratory syndrome coronavirus 2 responsible for COVID-19 remains a persistent threat to mankind, especially for the immunocompromised and elderly for which the vaccine may have limited effectiveness. Entry of SARS-CoV-2 requires a high affinity interaction of the viral spike protein with the cellular receptor angiotensin-converting enzyme 2. Novel mutations on the spike protein correlate with the high transmissibility of new variants of SARS-CoV-2, highlighting the need for small molecule inhibitors of virus entry into target cells. We report the identification of such inhibitors through a robust high-throughput screen testing 15,000 small molecules from unique libraries. Several leads were validated in a suite of mechanistic assays, including whole cell SARS-CoV-2 infectivity assays. The main lead compound, Calpeptin, was further characterized using SARS-CoV-1 and the novel SARS-CoV-2 variant entry assays, SARS-CoV-2 protease assays and molecular docking. This study reveals Calpeptin as a potent and specific inhibitor of SARS-CoV-2 and some variants.

microbiology↗

bioRxiv2021

Fibroblast activation protein regulates natural killer cell migration, extravasation and tumor infiltration

Natural killer (NK) cells play a critical role in physiologic and pathologic conditions such as pregnancy, infection, autoimmune disease and cancer. In cancer, numerous strategies have been designed to exploit the cytolytic properties of NK cells, with variable success. A major hurdle to NK-cell focused therapies is NK cell recruitment and infiltration into tumors. While the chemotaxis pathways regulating NK recruitment to different tissues are well delineated, the mechanisms human NK cells employ to physically migrate are ill-defined. We show for the first time that human NK cells express fibroblast activation protein (FAP), a cell surface protease previously thought to be primarily expressed by activated fibroblasts. FAP degrades the extracellular matrix to facilitate cell migration and tissue remodeling. We used novel in vivo zebrafish and in vitro 3D culture models to demonstrate that FAP knock out and pharmacologic inhibition restrict NK cell migration, extravasation, and invasion through tissue matrix. Notably, forced overexpression of FAP promotes NK cell invasion through matrix in both transwell and tumor spheroid assays, ultimately increasing tumor cell lysis. Additionally, FAP overexpression enhances NK cells invasion into a human tumor in immunodeficient mice. These findings demonstrate the necessity of FAP in NK cell migration and present a new approach to modulate NK cell trafficking and enhance cell-based therapy in solid tumors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/429622v3_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@d6d9e5org.highwire.dtl.DTLVardef@17b6790org.highwire.dtl.DTLVardef@cc581eorg.highwire.dtl.DTLVardef@1ad2129_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2018

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology↗