The recent COVID-19 pandemic is a treatment challenge in the acute infection stage but the recognition of chronic COVID-19 symptoms termed post-acute sequelae SARS-CoV-2 infection (PASC) may affect up to 30% of all infected individuals. The underlying mechanism and source of this distinct immunologic condition three months or more after initial infection remains elusive. Here, we investigated the presence of SARS-CoV-2 S1 protein in 46 individuals. We analyzed T-cell, B-cell, and monocytic subsets in both severe COVID-19 patients and in patients with post-acute sequelae of COVID-19 (PASC). The levels of both intermediate (CD14+, CD16+) and non-classical monocyte (CD14Lo, CD16+) were significantly elevated in PASC patients up to 15 months post-acute infection compared to healthy controls (P=0.002 and P=0.01, respectively). A statistically significant number of non-classical monocytes contained SARS-CoV-2 S1 protein in both severe (P=0.004) and PASC patients (P=0.02) out to 15 months post-infection. Non-classical monocytes were sorted from PASC patients using flow cytometric sorting and the SARS-CoV-2 S1 protein was confirmed by mass spectrometry. Cells from 4 out of 11 severe COVID-19 patients and 1 out of 26 also contained SARS-CoV-2 RNA. Non-classical monocytes are capable of causing inflammation throughout the body in response to fractalkine/CX3CL1 and RANTES/CCR5.
Abstract-Cancer complexity and resistance is mediated by cell-to-cell heterogeneity, which is the consequence of the enormous instability of its genetic material. It is unknown how cancer cells are able to withstand the effects of these alterations, while normal cells are typically very sensitive. We hypothesize that cancer requires specific type of stability to survive the enormous chromosomal alterations. This stability may be mediated by a group of genes, whose expression is tightly regulated to maintain viability through a process called gene dosage compensation. This mechanism could be mediated by systems-level properties of complex networks of microRNAs (miRNA) and transcription factors (TF), regulating gene expression despite changes in copy number. Therefore, we designed a biocomputational platform to automatically construct large-scale mathematical models regulating the expression of several candidate genes under dosage compensation. This platform has a broader potential application to other scientific questions involving miRNA and TF networks.
Abstract-Cancer refers to a group of diseases in which cells display uncontrolled growth. Chemotherapy options are usually conditioned by generating resistance which is explained in part by the intrinsic heterogeneity of the population in the tumor. Because the study of resistance requires biosensors able to report the response at the single cell level, we propose to use fluorescently labeled SL to estimate pathway dynamics at the single cell level and investigate its possible application as gemcitabine (GMZ) response sensor in pancreatic cancer by comparing the dynamics between GMZ resistant (Panc-1, MiaPaca-2) and sensitive (BxPC3) cell lines. We used imaging flow cytometry to extract multiple image features and classify cells using gaussian-mixture model (GMM) to generate response fingerprints upon known perturbations. The results suggest that GMZ inhibits the Sphingomyelin-Synthase of BxPC3. Also, we constructed and fitted a dynamical mathematical model to simulate these perturbations and formulate cell line models revealing a striking heterogeneity among them. Predictions of the model respect the effect of GMZ in the accumulation of ceramide in BxPC3 and glucosyl-ceramide in MiaPaca-4 were confirm experimentally. Altogether, these results indicate that fluorescent-SL analogues can be used as sensors of chemotherapy in pancreatic cancer and reveal pathway dynamics between different cell lines, and its potential usage for the development of in vitro chemosensitivy assays able to dissect pathway dynamics to overcome resistance.
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