Phosphatidylinositol-4,5-bisphosphate [PtdIns(4,5)P 2 ] is a key second messenger that regulates actin and membrane dynamics, as well as other cellular processes. Many of the effects of PtdIns(4,5)P 2 are mediated by binding to effector proteins that contain a pleckstrin homology (PH) domain. Here, we identify two novel effectors of PtdIns(4,5)P 2 in the budding yeast Saccharomyces cerevisiae: the PH domain containing protein Slm1 and its homolog Slm2. Slm1 and Slm2 serve redundant roles essential for cell growth and actin cytoskeleton polarization. Slm1 and Slm2 bind PtdIns(4,5)P 2 through their PH domains. In addition, Slm1 and Slm2 physically interact with Avo2 and Bit61, two components of the TORC2 signaling complex, which mediates Tor2 signaling to the actin cytoskeleton. Together, these interactions coordinately regulate Slm1 targeting to the plasma membrane. Our results thus identify two novel effectors of PtdIns(4,5)P 2 regulating cell growth and actin organization and suggest that Slm1 and Slm2 integrate inputs from the PtdIns(4,5)P 2 and TORC2 to modulate polarized actin assembly and growth.

The PH domain-containing proteins Slm1 and Slm2 were previously identified as effectors of the phosphatidylinositol-4,5-bisphosphate (PI4,5P 2 ) and TORC2 signaling pathways. Here, we demonstrate that Slm1 and Slm2 are also targets of sphingolipid signaling during the heat shock response. We show that upon depletion of cellular sphingolipid levels, Slm1 function becomes essential for survival under heat stress. We further demonstrate that Slm proteins are regulated by a phosphorylation/dephosphorylation cycle involving the sphingolipid-activated protein kinases Pkh1 and Pkh2 and the calcium/calmodulin-dependent protein phosphatase calcineurin. By using a combination of mass spectrometry and mutational analysis, we identified serine residue 659 in Slm1 as a site of phosphorylation. Characterization of Slm1 mutants that mimic dephosphorylated and phosphorylated states demonstrated that phosphorylation at serine 659 is vital for survival under heat stress and promotes the proper polarization of the actin cytoskeleton. Finally, we present evidence that Slm proteins are also required for the trafficking of the raft-associated arginine permease Can1 to the plasma membrane, a process that requires sphingolipid synthesis and actin polymerization. Together with previous work, our findings suggest that Slm proteins are subject to regulation by multiple signals, including PI4,5P 2 , TORC2, and sphingolipids, and may thus integrate inputs from different signaling pathways to temporally and spatially control actin polarization.

The thermodynamic properties of three halocarbon molecules relevant in atmospheric and public health applications are presented from ab initio calculations. Our technique makes use of a reaction path-like Hamiltonian to couple all the vibrational modes to a large-amplitude torsion for 1,2-difluoroethane, 1,2-dichloroethane, and 1,2-dibromoethane, each of which possesses a heavy asymmetric rotor. Optimized ab initio energies and Hessians were calculated at the CCSD(T) and MP2 levels of theory, respectively. In addition, to investigate the contribution of electronically excited states to thermodynamic properties, several excited singlet and triplet states for each of the halocarbons were computed at the CASSCF/MRCI level. Using the resulting potentials and projected frequencies, the couplings of all the vibrational modes to the large-amplitude torsion are calculated using the new STAR-P 2.4.0 software platform that automatically parallelizes our codes with distributed memory via a familiar MATLAB interface. Utilizing the efficient parallelization scheme of STAR-P, we obtain thermodynamic properties for each of the halocarbons, with temperatures ranging from 298.15 to 1000 K. We propose that the free energies, entropies, and heat capacities obtained from our methods be used to supplement theoretical and experimental values found in current thermodynamic tables.

BackgroundThere is an urgent need for a vaccine with efficacy against SARS-CoV-2. We hypothesize that peptide vaccines containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE) (figure 1). Leveraging methods initially developed for prediction of tumor-specific antigen targets, we combine computational prediction of T cell epitopes, recently published B cell epitope mapping studies, and epitope accessibility to select candidate peptide vaccines for SARS-CoV-2 (figure 2).MethodsSARS-CoV-2 HLA-I and HLA-II ligands were predicted using multiple MHC binding prediction software. T cell vaccine candidates were further refined by predicted immunogenicity, viral source protein abundance, sequence conservation, coverage of high frequency HLA alleles, and co-localization of CD4+/CD8+ T cell epitopes. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, filtering to select regions with surface accessibility, high sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. Using murine compatible T/B cell epitopes, vaccine studies were performed with downstream ELISA/ELISpot to monitor immunogenicity.ResultsWe observed distribution of HLA-I (n = 2486) and -II (n = 3138) ligands evenly across the SARS-CoV-2 proteome, with significant overlap between predicted human and murine ligands (figure 3). Applying a multivariable immunogenicity model trained from IEDB viral tetramer data (AUC 0.7 and 0.9 for HLA-I and -II, respectively), alongside filters for entropy and protein expression resulted in 292 CD8+ and 616 CD4+ epitopes (figure 4). From an initial pool of 58 B cell epitope candidates, three epitope regions were identified (figure 5). Combining B cell and T cell analyses, alongside manufacturability heuristic, we propose a set of SARS-CoV-2 vaccine peptides for use in subsequent murine studies and clinical trials (figure 6). Preliminary murine studies demonstrate evidence of T and B cell activation (figure 7).Abstract 478 Figure 1Summary of combination CD4+/CD8+ T cell and B cell SARS-CoV-2 peptide vaccine. Humoral immunity (blue dashed box) is targeted through B cell and HLA-II epitopes, aimed at viral neutralization while avoiding non-neutralizing and ADE promoting targets. Cellular immunity (red dashed box) is targeted through HLA-I and HLA-II epitopes, aimed to clear virally infected cellsAbstract 478 Figure 2Summary of B cell and CD4+/CD8+ epitope prediction workflows. Pathways are colored by B cell (blue), human T cell (black), and murine T cell (red) epitope prediction workflows. Color bars represent proportions of epitopes derived from internal proteins (ORF), nucleocapsid phosphoprotein, and surface-exposed proteins (spike, membrane, envelope)Abstract 478 Figure 3Landscape of SARS-CoV-2 MHC ligands. (A&B) Selection criteria for (A) HLA-I and (B) HLA-II SARS-CoV-2 HLA ligand candidates. Scatterplot (bottom) shows predicted (x-axis) versus IEDB (y-axis) binding affinity, with horizontal line representing 500 nM IEDB binding affinity and vertical line representing corresponding predicted binding affinity for 90% specificity in binding prediction. Histogram (top) shows all predicted SARS-CoV-2 HLA ligand candidates. (C) Landscape of predicted HLA ligands, showing nested HLA ligands comprising HLA-I and -II ligands with complete overlap (top), and LOESS fitted curve (span = 0.1) for HLA-I/II ligands by location along the SARS-CoV2 proteome (bottom). Red track represents SARS epitopes identified in literature review with sequence identity in SARS-CoV-2. Predicted HLA ligands with conserved sequences to this literature set are represented in the lollipop plot with a red stick. (D) Summary of total number of predicted HLA-I/II ligands and nested HLA ligands. (E) Summary of nested HLA ligand coverage by protein, with raw counts (left) or counts normalized by protein length (right). (F) Summary of murine/human MHC ligand overlap. (G) Distribution of population frequencies among predicted HLA-I, -II, and nested HLA ligandsAbstract 478 Figure 4Prediction of SARS-CoV-2 T cell epitopes. (Top) Summary of predicted (left) and IEDB-defined (right) SARS-CoV-2 HLA ligands, showing proportions of each derivative protein. (Middle) Funnel plot representing counts of HLA-I (red text), HLA-II (blue text), and nested HLA (violet text) ligands along with proportions of HLA-I (top bar) and HLA-II (bottom bar) alleles at each filtering step. (Bottom) Summary of CD8+ (red, top), CD4+ (blue, bottom), and nested T cell epitopes (middle) after filtering criteria in S, M, and N proteins. Y-axis and size represent the population frequency of each CD8+ and CD4+ epitopes by circles. Middle track of diamonds represents overlaps between CD8+ and CD4+ epitopes, showing the overlap with greatest population frequency (size) for each region of overlap. Color of diamonds represents the proportion of overlap between CD4+ and CD8+ epitope sequences.Abstract 478 Figure 5Selection of SARS-CoV-2 B cell epitope regions. (A) SARS-CoV-2 linear B cell epitopes curated from epitope mapping studies. X-axis represents amino acid position along the SARS-CoV-2 spike protein, with labeled start sites. (B) Schematic for filtering criteria of B cell epitope candidates. (C) Spike protein amino acid sequence, with overlay of selection features prior to filtering. Polymorphic residues are red, glycosites are blue, accessible regions highlighted in yellow. The receptor binding domain (RBD), fusion peptide (FP), and HR1/HR2 regions are outlined. (D) Spike protein functional regions (RBD, FP, HR1/2) amino acid sequences, with residues colored by how many times they occur in identified epitopes. Selected accessible sub-sequences of known antibody epitopes highlighted in purple outline. (E) S protein trimer crystal structure with glycosylation, with final linear epitope regions highlighted by colorAbstract 478 Figure 6T cell and B cell vaccine candidates. (A) 27mer vaccine peptide sets selecting for best CD4+, CD8+, CD4+/CD8+, and B cell epitopes with HLA-I, HLA-II, and total population coverage. (B) Unified list of all selected 27mer vaccine peptides. Vaccine peptides containing predicted ligands for murine MHC alleles (H2-b and H2-d haplotypes) are indicated in their respective columnsAbstract 478 Figure 7Immunogenicity of murine-compatible peptide vaccines. (A) ELISA result: peptides derived from three B cell vaccine candidate regions were coated on peptide capture plates, either in combination by overlapping core epitopes (1+2 and 3+4) or alone (5). (B) ELISpot results: splenocytes from animals vaccinated against predicted B cell epitopes (1–5) or measles peptide control (M; adapted from Obeid et al. 1995). Each point represents the average of technical triplicates, background subtracted against no-peptide control. (A&B) Colors represent adjuvant used for vaccination. P-values shown above each graph represent pair-wise Mann-Whitney u-testConclusionsA peptide vaccine targeting B cells, CD4+ T cells, and CD8+ T cells in parallel may prove an important part of a multifaceted response to the COVID-19 pandemic. Adapting methods for predicting tumor-specific antigens, we presented a set of peptide candidates with high overlap for T and B cell epitopes and broad haplotype population coverage, with validation of immunogenicity in murine vaccine studies.AcknowledgementsThe authors appreciate funding support from University of North Carolina University Cancer Research Fund (AR and BGV), the Susan G. Komen Foundation (BGV), the V Foundation for Cancer Research (BGV), and the National Institutes of Health (CCS, 1F30CA225136). We would like to thank members of the #DownWithTheCrown Slack channel for helpful discussion and feedback.