Kockdominguez0796

CD8+ T cells are important antiviral effectors that can potentiate long-lived immunity against COVID-19, but a detailed characterization of these cells has been hampered by technical challenges. We screened 21 well-characterized, longitudinally-sampled convalescent donors that recovered from mild COVID-19 against a collection of SARS-CoV-2 tetramers, and identified one participant with an immunodominant response against Nuc 322-331 , a peptide that is conserved in all the SARS-CoV-2 variants-of-concern reported to date. We conducted 38- parameter CyTOF phenotyping on tetramer-identified Nuc 322-331 -specific CD8+ T cells, and on CD4+ and CD8+ T cells recognizing the entire nucleocapsid and spike proteins from SARS- CoV-2, and took 32 serological measurements on longitudinal specimens from this participant. We discovered a coordination of the Nuc 322-331 -specific CD8+ T response with both the CD4+ T cell and antibody pillars of adaptive immunity. Nuc 322-331 -specific CD8+ T cells were predominantly central memory T cells, but continually evolved over a ∼6-month period of convalescence. We observed a slow and progressive decrease in the activation state and polyfunctionality of the Nuc 322-331 -specific CD8+ T cells, accompanied by an increase in their lymph-node homing and homeostatic proliferation potential. These results suggest that following a typical case of mild COVID-19, SARS-CoV-2-specific CD8+ T cells not only persist but continuously differentiate in a coordinated fashion well into convalescence, into a state characteristic of long-lived, self-renewing memory.Diarrhea occurs in 2-50% of cases of COVID-19 (∼8% is average across series). The diarrhea does not appear to account for the disease mortality and its contribution to the morbidity has not been defined, even though it is a component of Long Covid or post-infectious aspects of the disease. Even less is known about the pathophysiologic mechanism of the diarrhea. To begin to understand the pathophysiology of COVID-19 diarrhea, we exposed human enteroid monolayers obtained from five healthy subjects and made from duodenum, jejunum, and proximal colon to live SARS-CoV-2 and virus like particles (VLPs) made from exosomes expressing SARS-CoV-2 structural proteins (Spike, Nucleocapsid, Membrane and Envelope). Results 1) Live virus was exposed apically for 90 min, then washed out and studied 2 and 5 days later. SARS-Cov-2 was taken up by enteroids and live virus was present in lysates and in the apical>>basolateral media of polarized enteroids 48 h after exposure. This is the first demonstration of basolateral appearikely inhibition of neutral NaCl absorption (decreased NHE3 protein and mRNA and decreased DRA mRNA).Specific lipid-protein interactions are key for cellular processes, and even more so for the replication of pathogens. learn more The COVID-19 pandemic has drastically changed our lives and cause the death of nearly three million people worldwide, as of this writing. SARS-CoV-2 is the virus that causes the disease and has been at the center of scientific research over the past year. Most of the research on the virus is focused on key players during its initial attack and entry into the cellular host; namely the S protein, its glycan shield, and its interactions with the ACE2 receptors of human cells. As cases continue to raise around the globe, and new mutants are identified, there is an urgent need to understand the mechanisms of this virus during different stages of its life cycle. Here, we consider two integral membrane proteins of SARS-CoV-2 known to be important for viral assembly and infectivity. We have used microsecond-long all-atom molecular dynamics to examine the lipid-protein and protein-protein interactions of the membrane (M) and envelope (E) structural proteins of SARS-CoV-2 in a complex membrane model. We contrast the two proposed protein complexes for each of these proteins, and quantify their effect on their local lipid environment. This ongoing work also aims to provide molecular-level understanding of the mechanisms of action of this virus to possibly aid in the design of novel treatments.As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. Our method identifies proteins of interest by a decision-tree based classifier and passes them to a kernel-density estimator (KDE) for sampling points from the density distribution.