Characterizing Glycosylation of Adeno-Associated Virus Serotype 9 Capsid Proteins Generated from HEK293 Cells through Glycopeptide Mapping and Released Glycan Analysis.

Recombinant adeno-associated viral (AAV) vectors have emerged as prominent gene delivery vehicles for gene therapy. AAV capsid proteins determine tissue specificity and immunogenicity and play important roles in receptor binding, the escape of the virus from the endosome, and the transport of the viral DNA to the nuclei of target cells. Therefore, the comprehensive characterization of AAV capsid proteins is necessary for a better understanding of the vector assembly, stability, and transduction efficiency of AAV gene therapies. Glycosylation is one of the most common post-translational modifications (PTMs) and may affect the tissue tropism of AAV gene therapy. However, there are few studies on the characterization of the N- and O-glycosylation of AAV capsid proteins. In this study, we identified the N- and O-glycosylation sites and forms of AAV9 capsid proteins generated from HEK293 cells using liquid chromatography-tandem mass spectrometry (LC-MS)-based glycopeptide mapping and identified free N-glycans released from AAV9 capsid proteins by PNGase F using hydrophilic interaction (HILIC) LC-MS and HILIC LC-fluorescence detection (FLD) methods. This study demonstrates that AAV9 capsids are sprinkled with sugars, including N- and O-glycans, albeit at low levels. It may provide valuable information for a better understanding of AAV capsids in supporting AAV-based gene therapy development.

A Survey on Core Flow Cytometry Facilities: Instrument Maintenance, Usage, and Funding.

Flow cytometry is a powerful tool that finds applications in various fields such as immunology, molecular biology, cancer biology, virology, and infectious disease monitoring. A significant portion of the research in these disciplines is supported by flow cytometry shared resource laboratories (SRLs). There are several types of flow cytometers available for use in SRLs, including analyzers, sorters, imaging flow cytometers, and mass cytometers. Each type has different challenges when it comes to maintenance and life expectancy. An independent online survey was conducted to better understand instrument maintenance and turnover in flow cytometry SRLs. Questions regarding instrument uptime (availability), its usage, routine maintenance, and cost associated with it were addressed. The respondents also answered questions pertaining to the frequency of deep cleaning of the instrument and quality control. In addition, the survey queried about the source of funding used to purchase the instruments and possible reasons for a replacement. Presented herein are the results compiled from 146 core facilities that provide a look at the operation within a typical SRL, with the responses reflecting researchers' experiences with handling flow cytometers.

Peripheral squamous odontogenic tumor masquerading as oral squamous cell carcinoma in an elderly female patient: A rare entity.

Squamous odontogenic tumor is a rare epithelial odontogenic tumor. Only a few cases have been reported in the literature, out of which central cases occurring within the jawbones are more common, and very rarely peripheral cases have been reported. Here, we report a rare case of a peripheral squamous odontogenic tumor occurring in the left lower gingivobuccal sulcus region in a 63-year-old female patient and presented as a firm and fibrotic swelling clinically. Radiographic examination revealed no evidence of a central lesion in the bone. Excisional biopsy of the lesion revealed the characteristic histopathological picture of peripheral squamous odontogenic tumor (PSOT). The healing was found to be satisfactory upon periodic evaluation of the patient done to study the healing and for any recurrence of the lesion.

Machine learning approaches and their applications in drug discovery and design.

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

Prenyl Praxis: A Method for Direct Photocatalytic Defluoroprenylation.

The prenyl fragment is the quintessential constituent of terpenoid natural products, a diverse family which contains numerous members with diverse biological properties. In contrast, fluorinated and multifluorinated arenes make up an important class of anthropogenic molecules which are highly relevant to material, agricultural, and pharmaceutical industries. While allylation chemistry is well developed, effective prenylation strategies have been less forthcoming. Herein, we describe the photocatalytic defluoroprenylation, a powerful method that provides access to "hybrid molecules" that possess both the functionality of a prenyl group and fluorinated arenes. This approach involves direct prenyl group transfer under very mild conditions, displays excellent functional group tolerance, and includes relatively short reaction times (<4 h), which is the fastest photocatalytic C-F functionalization developed to date. Additionally, the strategy can be extended to include allyl and geranyl (10 carbon fragment) transfers. Another prominent finding is a reagent-dependent switch in regioselectivity of the major product from para to ortho C-F functionalization.