Supporting Cells of the Human Olfactory Epithelium Co-Express the Lipid Scramblase TMEM16F and ACE2 and May Cause Smell Loss by SARS-CoV-2 Spike-Induced Syncytia.

BACKGROUND/AIMS: Quantitative and qualitative alterations in the sense of smell are well established symptoms of COVID-19. Some reports have shown that non-neuronal supporting (also named sustentacular) cells of the human olfactory epithelium co-express ACE2 and TMPRSS2 necessary for SARS-CoV-2 infection. In COVID-19, syncytia were found in many tissues but were not investigated in the olfactory epithelium. Some studies have shown that syncytia in some tissues are formed when SARS-CoV-2 Spike expressed at the surface of an infected cell binds to ACE2 on another cell, followed by activation of the scramblase TMEM16F (also named ANO6) which exposes phosphatidylserine to the external side of the membrane. Furthermore, niclosamide, an approved antihelminthic drug, inhibits Spike-induced syncytia by blocking TMEM16F activity. The aim of this study was to investigate if proteins involved in Spike-induced syncytia formation, i.e., ACE2 and TMEM16F, are expressed in the human olfactory epithelium. METHODS: We analysed a publicly available single-cell RNA-seq dataset from human nasal epithelium and performed immunohistochemistry in human nasal tissues from biopsies. RESULTS: We found that ACE2 and TMEM16F are co-expressed both at RNA and protein levels in non-neuronal supporting cells of the human olfactory epithelium. CONCLUSION: Our results provide the first evidence that TMEM16F is expressed in human olfactory supporting cells and indicate that syncytia formation, that could be blocked by niclosamide, is one of the pathogenic mechanisms worth investigating in COVID-19 smell loss.

Exploring Plausible Therapeutic Targets for Alzheimer's Disease using Multi-omics Approach, Machine Learning and Docking.

The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing high-throughput screening methods to identify biomarkers and variants, which help early diagnosis and treatments. OBJECTIVE: The primary purpose of this study is to develop a classification model using machine learning for predicting the deleterious effect of variants with respect to AD. METHODS: We have constructed a set of 20,401 deleterious and 37,452 control variants from Genome-Wide Association Study (GWAS) and Genotype-Tissue Expression (GTEx) portals, respectively. Recursive feature elimination using cross-validation (RFECV) followed by a forward feature selection method was utilized to select the important features and a random forest classifier was used for distinguishing between deleterious and neutral variants. RESULTS: Our method showed an accuracy of 81.21% on 10-fold cross-validation and 70.63% on a test set of 5785 variants. The same test set was used to compare the performance of CADD and FATHMM and their accuracies are in the range of 54%-62%. CONCLUSION: Our model is freely available as the Variant Effect Predictor for Alzheimer's Disease (VEPAD) at http://web.iitm.ac.in/bioinfo2/vepad/. VEPAD can be used to predict the effect of new variants associated with AD.