Cutting edge: dependence of TCR antagonism on Src homology 2 domain-containing protein tyrosine phosphatase activity

The mechanism by which antagonist peptides inhibit T cell responses is unknown. Mice deficient in Src homology 2 domain-containing protein tyrosine phosphatase (SHP-1) have revealed its importance in the negative regulation of lymphocyte signaling. We investigated a possible role for SHP-1 in T cell antagonism and demonstrate, for the first time, a substantial increase in SHP-1 activity during antagonism of CD4(+) T cells. Furthermore, the removal of functional SHP-1 prevents antagonism in these cells. Our data demonstrate that T cell antagonism occurs via a negative intracellular signal that is mediated by SHP-1.     

Large-conductance cholesterol-amphotericin B channels in reconstituted lipid bilayers

The antimycotic activity of amphotericin B (AmB) depends on its ability to make complexes sterols to form ion channels that cause membrane leakage. To study this phenomenon, surface pressure (pi) as a function of surface area (A) and pi-A hysteresis were measured in monolayers of AmB-cholesterol mixtures on the water-air interface. The most stable monolayers were produced from molecules of AmB and cholesterol with 2:1 stoichiometry. At this ratio, AmB and cholesterol interact to form ion channels in lipid bilayers with millisecond dwell times and conductances of 4-400 pS. The AmB-cholesterol complexes assemble in three, four, etc., subunit aggregates to form ion channels of diverse and large-conductances. Their I-V characteristics were linear over a range of +/-200 mV. The channel currents were inhibited by the addition of tetraethylammonium (TEA), potassium channel blocker, to the cis-side of the membrane. Likewise, AmB-cholesterol complexes reconstituted in membrane-coated nanoporous silicon dioxide surfaces showed single channel behavior with large amplitudes at various voltages. Large-conductance ion channels show great promise for use in biosensors on solid supports.     

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses

R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). Availability: The package is freely available from cran.r-project.org.     

Scalable workflow for characterization of cell-cell communication in COVID-19 patients

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.