Identification of FDA-approved bifonazole as a SARS-CoV-2 blocking agent following a bioreporter drug screen.

We established a split nanoluciferase complementation assay to rapidly screen for inhibitors that interfere with binding of the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein with its target receptor, angiotensin-converting enzyme 2 (ACE2). After a screen of 1,200 US Food and Drug Administration (FDA)-approved compounds, we identified bifonazole, an imidazole-based antifungal agent, as a competitive inhibitor of RBD-ACE2 binding. Mechanistically, bifonazole binds ACE2 around residue K353, which prevents association with the RBD, affecting entry and replication of spike-pseudotyped viruses as well as native SARS-CoV-2 and its variants of concern (VOCs). Intranasal administration of bifonazole reduces lethality in K18-hACE2 mice challenged with vesicular stomatitis virus (VSV)-spike by 40%, with a similar benefit after live SARS-CoV-2 challenge. Our screen identified an antiviral agent that is effective against SARS-CoV-2 and VOCs such as Omicron that employ the same receptor to infect cells and therefore has high potential to be repurposed to control, treat, or prevent coronavirus disease 2019 (COVID-19).

Immune profiling of dedifferentiated liposarcoma and identification of novel antigens for targeted immunotherapy.

Dedifferentiated liposarcoma (DDLS) is an aggressive, recurring sarcoma with limited treatments. T-cell immunotherapies selectively target malignant cells, holding promise against DDLS. The development of successful immunotherapy for DDLS requires a thorough evaluation of the tumor immune microenvironment and the identification and characterization of targetable immunogenic tumor antigens. To assess the complexity of the human DDLS tumor immune microenvironment and to identify target antigens, we used the nCounter NanoString platform, analyzing gene expression profiles across 29 DDLS and 10 healthy adipose tissue samples. Hierarchical clustering of tumors based on expression of tumor inflammation signature genes revealed two distinct groups, consisting of 15 inflamed tumors and 14 non-inflamed tumors, demonstrating tumor heterogeneity within this sarcoma subtype. Among the identified antigens, PBK and TTK exhibited substantial upregulation in mRNA expression compared to healthy adipose tissue controls, further corroborated by positive protein expression by IHC. This data shows considerable inter-tumoral heterogeneity of inflammation, which should be taken into consideration when designing an immunotherapy for DDLS, and provides a novel targetable antigen in DDLS. The results of this study lay the groundwork for the development of a novel immunotherapy for this highly aggressive sarcoma.

Comprehensive untargeted lipidomic profiling of third generation lentiviral vectors and packaging cells.

Lentiviral vectors (LV) are emerging tools for genetic therapies and novel cancer treatments. While effective, LV-based therapies have extremely large costs associated with their manufacturing and delivery. LV technology descends from human immunodeficiency virus (HIV), whose lipid envelope has been previously measured and shown to have a direct impact on its transduction efficiency. We developed a rapid, robust, and sensitive untargeted lipidomics pipeline to analyze novel LV biotherapeutic products and demonstrate its utility on HEK 293T packaging cells and concentrated culture media containing LV. The impact of 48 hours of LV production on the lipidome of HEK 293T cells was measured and compared to the expression of vesicular stomatitis virus G protein (VSV G) over the same timeframe. 151 lipids were identified in HEK 293T packaging cells, 84 of which had fold changes with FDR-corrected P < 0.05 compared to HEK 293T treated with media. It was found that fold changes with FDR-adjusted P < 0.05 after VSV G expression and LV production were highly correlated (R2 = 0.89). Concentrating LV in culture media led to the identification of 102 lipids, half of which were determined to be unique LV virion lipids after subtracting the media lipidome. Our approach can be readily used to study the lipid dynamics of large-scale LV production and be rapidly translated into targeted methods to quantify individual lipid components or applied to other viral vector platforms.

An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry 13C-Trimethylation Enhancement Using Diazomethane (13C-TrEnDi)-Modified Lipid Data.

13C-Trimethylation enhancement using diazomethane (13C-TrEnDi) is a chemical derivatization technique that uses 13C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, 13C-TrEnDi-modified PC and PE species have altered m/z and fragmentation patterns from their native counterparts. To build a database of 13C-TrEnDi-modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching 13C-TrEnDi-modified headgroups with m/z 199 (PC) and 202 (PE). Identities of 25 PC and 10 PE species were assigned after comparing to predicted masses from the Lipid Maps Structure Database with no false positive identifications observed; neutral lipids could still be annotated after derivatization. Collision energies from 16 to 52 eV were examined, resulting in three additional class-specific fragment ions emerging, as well as a combined sn-1/sn-2 fragment ion, allowing sum-composition level annotations to be assigned. Using the Lipid Blast templates, a NIST-compatible 13C-TrEnDi database was produced based on fragmentation spectra observed at 36 eV and tested on HEK 293T cell lipid extracts, identifying 47 PC and 24 PE species, representing a 1.8-fold and 2.2-fold increase in annotations, respectively. The 13C-TrEnDi database is freely available, MS vendor-independent, and widely compatible with MS data processing pipelines, increasing the throughput and accessibility of TrEnDi for lipidomics applications.

Kindlin-2 Mediates Mechanical Activation of Cardiac Myofibroblasts.

We identify the focal adhesion protein kindlin-2 as player in a novel mechanotransduction pathway that controls profibrotic cardiac fibroblast to myofibroblast activation. Kindlin-2 is co-upregulated with the myofibroblast marker α-smooth muscle actin (α-SMA) in fibrotic rat hearts and in human cardiac fibroblasts exposed to fibrosis-stiff culture substrates and pro-fibrotic TGF-ß1. Stressing fibroblasts using ferromagnetic microbeads, stretchable silicone membranes, and cell contraction agonists all result in kindlin-2 translocation to the nucleus. Overexpression of full-length kindlin-2 but not of kindlin-2 missing a putative nuclear localization sequence (∆NLS kindlin-2) results in increased α-SMA promoter activity. Downregulating kindlin-2 with siRNA leads to decreased myofibroblast contraction and reduced α-SMA expression, which is dependent on CC(A/T)-rich GG(CArG) box elements in the α-SMA promoter. Lost myofibroblast features under kindlin-2 knockdown are rescued with wild-type but not ∆NLS kindlin-2, indicating that myofibroblast control by kindlin-2 requires its nuclear translocation. Because kindlin-2 can act as a mechanotransducer regulating the transcription of α-SMA, it is a potential target to interfere with myofibroblast activation in tissue fibrosis.