Genomic epidemiology reveals multiple introductions of Zika virus into the United States

Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital abnormalities. In July 2016, mosquito-borne ZIKV transmission was reported in the continental United States; since then, hundreds of locally acquired infections have been reported in Florida. To gain insights into the timing, source, and likely route(s) of ZIKV introduction, we tracked the virus from its first detection in Florida by sequencing ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least 4 introductions, but potentially as many as 40, contributed to the outbreak in Florida and that local transmission is likely to have started in the spring of 2016-several months before its initial detection. By analysing surveillance and genetic data, we show that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions were linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. Our study provides an understanding of how ZIKV initiates transmission in new regions

De novo transcriptome reconstruction and annotation of the Egyptian rousette bat

Background The Egyptian Rousette bat (Rousettus aegyptiacus), a common fruit bat species found throughout Africa and the Middle East, was recently identified as a natural reservoir host of Marburg virus. With Ebola virus, Marburg virus is a member of the family Filoviridae that causes severe hemorrhagic fever disease in humans and nonhuman primates, but results in little to no pathological consequences in bats. Understanding host-pathogen interactions within reservoir host species and how it differs from hosts that experience severe disease is an important aspect of evaluating viral pathogenesis and developing novel therapeutics and methods of prevention. Results Progress in studying bat reservoir host responses to virus infection is hampered by the lack of host-specific reagents required for immunological studies. In order to establish a basis for the design of reagents, we sequenced, assembled, and annotated the R. aegyptiacus transcriptome. We performed de novo transcriptome assembly using deep RNA sequencing data from 11 distinct tissues from one male and one female bat. We observed high similarity between this transcriptome and those available from other bat species. Gene expression analysis demonstrated clustering of expression profiles by tissue, where we also identified enrichment of tissue-specific gene ontology terms. In addition, we identified and experimentally validated the expression of novel coding transcripts that may be specific to this species. Conclusion We comprehensively characterized the R. aegyptiacus transcriptome de novo. This transcriptome will be an important resource for understanding bat immunology, physiology, disease pathogenesis, and virus transmission

Virus genomes reveal factors that spread and sustained the Ebola epidemic.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics

T cell receptor signaling can directly enhance the avidity of CD28 ligand binding.

T cell activation takes place in the context of a spatial and kinetic reorganization of cell surface proteins and signaling molecules at the contact site with an antigen presenting cell, termed the immunological synapse. Coordination of the activation, recruitment, and signaling from T cell receptor (TCR) in conjunction with adhesion and costimulatory receptors regulates both the initiation and duration of signaling that is required for T cell activation. The costimulatory receptor, CD28, is an essential signaling molecule that determines the quality and quantity of T cell immune responses. Although the functional consequences of CD28 engagement are well described, the molecular mechanisms that regulate CD28 function are largely unknown. Using a micropipet adhesion frequency assay, we show that TCR signaling enhances the direct binding between CD28 and its ligand, CD80. Although CD28 is expressed as a homodimer, soluble recombinant CD28 can only bind ligand monovalently. Our data suggest that the increase in CD28-CD28 binding is mediated through a change in CD28 valency. Molecular dynamic simulations and in vitro mutagenesis indicate that mutations at the base of the CD28 homodimer interface, distal to the ligand-binding site, can induce a change in the orientation of the dimer that allows for bivalent ligand binding. When expressed in T cells, this mutation allows for high avidity CD28-CD80 interactions without TCR signaling. Molecular dynamic simulations also suggest that wild type CD28 can stably adopt a bivalent conformation. These results support a model whereby inside-out signaling from the TCR can enhance CD28 ligand interactions by inducing a change in the CD28 dimer interface to allow for bivalent ligand binding and ultimately the transduction of CD28 costimulatory signals that are required for T cell activation

WT CD28 can adopt a stable conformation that would allow bivalent binding.

<p>MD simulations were run with WT CD28 starting from a rotated dimer orientation predicted from the simulations starting from the CTLA-4 dimer orientation. (<b>A</b>) The RMSD with respect to the initial conformation for three independent trajectories indicate that these dimer conformers are very stable. (<b>B</b>) When CD80 molecules were docked onto the conformations, there was no surface buried between the ligands, indicating that all conformations were bivalent. A representative bivalent conformation of CD28 (green) with docked CD80 molecules (cyan) is shown to illustrate the CD28 dimer (<b>C</b>) and in a rotated view to show the orientation of the docked CD80 molecules (<b>D</b>).</p

TCR signaling can enhance the avidity of CD28 ligand binding.

<p>(<b>A</b>) Images from a cell adhesion assay. Top, a resting T cell and CD80-coated bead brought into contact using micropipets. Middle, the same T cell 30s later after the cell and bead were separated. No adhesion was observed. Bottom, image of a T cell bound to an anti-CD3 coated bead, which is out of the plane of focus, but clearly distal to the site of interaction of the T cell with the CD80-bead. This image was taken shortly after the T cell was pulled away from the CD80-bead. In this case, the T cell-CD80-bead adhesion has caused the T cell to dislodge from the micropipet. (<b>B</b>) Cell frequency adhesion of in vitro primed and resting WT DO11.10 T cells to control beads (None) or beads coated with CD80 (CD80-Fc) in the absence (Unstim, empty bars) and after (Anti-CD3; filled bars) stimulation. No increase in adhesion to CD80 was detected when cells were stimulated with beads coated with anti-MHC class I (not shown). (<b>C</b> and <b>D</b>) WT and CD28-deficient T cells were primed with CD80/CD86-negative APC to reduce upregulation of CTLA-4. (<b>C</b>) Flow cytometry of CTLA-4 expression on WT (thin line) and CD28-deficient (thick line) T cells after in vitro priming. Isotype control (filled) and WT T cells primed in the presence of CD28 costimulation (dashed line) are included as negative and positive controls. (<b>D</b>) Cell frequency adhesion of WT and CD28-deficient (CD28KO) T cells that were primed with CD80/CD86-negative cells, to CD80-beads in the absence (Unstim) and after (Anti-CD3) stimulation. (<b>E</b>) Adhesion frequency of WT and CTLA-4-deficient (CTLA-4KO) T cells to control beads (None) or beads coated with increasing concentrations of CD80 in the absence (Unstim) and after (Anti-CD3) stimulation. Cell adhesion data are presented as mean ± SD of the adhesion frequency for individual cells (25 impingements each; n = 9–10, except for 62.5 ng/ml CD80-Fc in panel E, n = 6; p values for t tests between samples are shown; ns, not significant).</p

WT CD28 adopts a bivalent binding conformation when starting from the CTLA-4 dimer orientation.

<p>MD simulations were run with WT CD28 starting from a CTLA-4 dimer orientation. (<b>A</b>) The RMSD of three independent trajectories with respect to the initial conformation are shown over time. There is considerable rearrangement of the subunits at the beginning of the simulations, reflected by an increase in the RMSD. The conformational fluctuations stabilize after the first 100 ns of simulation, although the trajectories adopt different conformations. This instability was not inherent to this dimer orientation, as the RMSD of MD simulations of CTLA-4 remained below 4Å (data not shown). (<b>B</b>) CD80 molecules were docked onto the simulated CD28 dimers to obtain the corresponding CD28-CD80 complexes. The surface area buried between the docked ligands was calculated at various times along each of the three independent trajectories to estimate the fraction of bivalent-competent conformations; the average over all three trajectories was 79%. (<b>C</b>) A representative conformation of CD28 (green) showing docked CD80 ligands (cyan) illustrates the potential for bivalent ligand binding.</p

K118I/K120P CD28 is polarized toward CD80-positive cells in the absence of TCR signaling.

<p>(<b>A</b> and <b>B</b>) CD28-deficient, DO11.10 CD4 T cells were retrovirally transduced with WT or K118I/K120P CD28 fused to YFP, stained with anti-CD28 and analyzed by flow cytometry. Two color display (<b>A</b>) shows total transduced protein expression as detected by YFP (FL1, x-axis) and cell surface expression of CD28 as detected by anti-CD28 staining (FL3, y-axis). Single color display (<b>B</b>) shows relative cell surface expression of WT CD28 (thick line) and K118I/K120P (KK/IP) CD28 (thin line). (<b>C</b> and <b>D</b>) Representative images of WT and K118I/K120P CD28-YFP localization in cell:cell conjugates with CD80-negative (<b>C</b>) and CD80-positive (<b>D</b>) APC in the presence (+Ag) and absence (-Ag) of TCR signaling. DIC (digital image correlation) and fluorescent images are shown. (<b>E</b>) Individual conjugates were visually scored for CD28 polarization toward the interacting APC and the percentage of conjugates displaying polarized CD28 is shown (n = 25–28). (<b>F</b>) The efficiency of CD28 recruitment to CD80-positive APC was calculated by determining the ratio of YFP fluorescence within the T cell:APC contact site to the YFP fluorescence in the T cell plasma membrane distal to the contact site. Values for individual cells, population medians and statistical analysis (non-parametric Kruskal-Wallis ANOVA with Dunn’s multiple comparison) are shown (ns, not significant; * p<0.05; ***p<0.001).</p