Weekly symptom profiles of nonhospitalized individuals infected with SARS-CoV-2 during the Omicron outbreak in Hong Kong: A retrospective observational study from a telemedicine center.

Omicron BA.2.2 is the dominant variant in the Hong Kong outbreak since December 31, 2021. There is no study reporting the weekly symptom profile after infection. In this retrospective study, participants who tested positive for SARS-CoV-2 after December 31, 2021, and registered in the telemedicine system between March 14 and May 6, 2022, were analyzed. Among registered 12 950 self-quarantined COVID-19-positive patients, 11 776 symptomatic patients were included for weekly symptom profile analysis. A total of 4718 (40.1%) patients reported symptoms in the first week after a positive test, 2501 (21.2%) in the second week, 1498 (12.7%) in the third week, 1048 (8.9%) in the fourth week, and 2011 (17.1%) in over 4 weeks. Cough was the most common symptom in all participants. Patients in the first week had higher odds of reporting fever (0.206, 95% confidence interval [CI]: 0.161-0.263, p < 0.001) and sore throat (0.228, 95% CI: 0.208-0.252, p < 0.001). Patients in over 4 weeks had higher odds of reporting fatigue (1.263, 95% CI: 1.139-1.402, p < 0.001). Further, having at least two vaccine doses linked to lower odds of having fever (0.675, 95% CI: 0.562-0.811, p < 0.001), but not associated with the presence of cough and fatigue. Diabetic patients had higher odds of reporting diarrhea (1.637, 95% CI: 1.351-1.982, p < 0.001). Symptoms from Omicron infection may last for more than 4 weeks and symptom profiles vary from week to week. Vaccination and comorbidity affect the symptom profiles.

Control of SARS-CoV-2 infection by MT1-MMP-mediated shedding of ACE2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Angiotensin-converting enzyme 2 (ACE2) is an entry receptor for SARS-CoV-2. The full-length membrane form of ACE2 (memACE2) undergoes ectodomain shedding to generate a shed soluble form (solACE2) that mediates SARS-CoV-2 entry via receptor-mediated endocytosis. Currently, it is not known how the physiological regulation of ACE2 shedding contributes to the etiology of COVID-19 in vivo. The present study identifies Membrane-type 1 Matrix Metalloproteinase (MT1-MMP) as a critical host protease for solACE2-mediated SARS-CoV-2 infection. SARS-CoV-2 infection leads to increased activation of MT1-MMP that is colocalized with ACE2 in human lung epithelium. Mechanistically, MT1-MMP directly cleaves memACE2 at M706-S to release solACE218-706 that binds to the SARS-CoV-2 spike proteins (S), thus facilitating cell entry of SARS-CoV-2. Human solACE218-706 enables SARS-CoV-2 infection in both non-permissive cells and naturally insusceptible C57BL/6 mice. Inhibition of MT1-MMP activities suppresses solACE2-directed entry of SARS-CoV-2 in human organoids and aged mice. Both solACE2 and circulating MT1-MMP are positively correlated in plasma of aged mice and humans. Our findings provide in vivo evidence demonstrating the contribution of ACE2 shedding to the etiology of COVID-19.

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.

Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.