Clonal Selection and Population Dynamics of Vγ2/Vδ2 T Cells in Macaca Fascicularis

HIV infection increases the susceptibility to new M. tuberculosis (Mtb) infections, the risk of reactivating latent infections and the risk of rapid TB progression. γδ T cells, in particular the Vγ2Jγ1.2 subset, are thought to be part of the innate immune response to both HIV and Mtb. Importantly, both HIV and Mtb perturb gd T cells homeostasis, causing a profound and highly specific depletion of the Vγ2Jγ1.2 subset

Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics.

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier Rodó should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

A Flexible Model of HIV-1 Latency Permitting Evaluation of Many Primary CD4 T-Cell Reservoirs

Latently infected cells form the major obstacle to HIV eradication. Studies of HIV latency have been generally hindered by the lack of a robust and rapidly deployable cell model that involves primary human CD4 T lymphocytes. Latently infected cell lines have proven useful, but it is unclear how closely these proliferating cells recapitulate the conditions of viral latency in non-dividing CD4 T lymphocytes in vivo. Current primary lymphocyte models more closely reflect the in vivo state of HIV latency, but they are limited by protracted culture periods and often low cell yields. Additionally, these models are always established in a single latently infected cell type that may not reflect the heterogeneous nature of the latent reservoir. Here we describe a rapid, sensitive, and quantitative primary cell model of HIV-1 latency with replication competent proviruses and multiple reporters to enhance the flexibility of the system. In this model, post-integration HIV-1 latency can be established in all populations of CD4 T cells, and reactivation of latent provirus assessed within 7 days. The kinetics and magnitude of reactivation were evaluated after stimulation with various cytokines, small molecules, and T-cell receptor agonists. Reactivation of latent HIV proviruses was readily detected in the presence of strong activators of NF-kB. Latently infected transitional memory CD4 T cells proved more responsive to these T-cell activators than latently infected central memory cells. These findings reveal potentially important biological differences within the latently infected pool of memor

Gamma Interferon Secretion by Human Vγ2Vδ2 T Cells after Stimulation with Antibody against the T-Cell Receptor plus the Toll-Like Receptor 2 Agonist Pam(3)Cys

Circulating Vγ2Vδ2 T-cell populations in healthy human beings are poised for rapid responses to bacterial or viral pathogens. We asked whether Vγ2Vδ2 T cells use the Toll-like receptor (TLR) family to recognize pathogen-associated molecular pattern molecules and to regulate cell functions. Analysis of expanded Vγ2Vδ2 T-cell lines showed the abundant presence of TLR2 mRNA, implying that these receptors are important for cell differentiation or function. However, multiple efforts to detect TLR2 protein on the cell surface or in cytoplasmic compartments gave inconsistent results. Functional assays confirmed that human Vγ2Vδ2 T cells could respond to the TLR2 agonist (S)-(2,3-bis(palmitoyloxy)-(2RS)-propyl)-N-palmitoyl-(R)-Cys-(S)-Ser(S)-Lys(4)-OH trihydrochloride (Pam(3)Cys), but the response required coincident stimulation through the γδ T-cell receptor (TCR). Dually stimulated cells produced higher levels of cytoplasmic or cell-free gamma interferon and showed increased expression of the lysosome-associated membrane protein CD107a on the cell surface. A functional TLR2 that requires coincident TCR stimulation may increase the initial potency of Vγ2Vδ2 T-cell responses at the site of infection and promote the rapid development of subsequent acquired antipathogen immunity

Summary of synergistic activity of inducer combinations.

<p>Values are the calculated synergistic index of the inducers when used in combination versus when used as single agents <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030176#pone.0030176-Blazkova1" target="_blank">[44]</a>. Each value represents the highest synergistic index value obtained for a given donor and time period of simulation over a range of six dose combinations (prostratin) or four dose combinations (HMBA). Combinations of the following dose concentrations were used: prostratin (0.1, 1, 10 µM); HMBA (0.5, 5 mM); TSA (0.1, 1 µM); SAHA (1, 10 µM); VPA (0.1, 1 mM). The index of synergism was calculated with the following formula: the luciferase value from cells after stimulation with the indicated combination of inducers divided by the sum of the luciferase values from cells after stimulation with each inducer separately. Background luciferase values from unstimulated samples were subtracted prior to synergistic index calculation. Combinations of given inducers that gave a synergistic index >1 are considered synergistic and shown in bolded text.</p

Kinetics of HIV-1 reactivation.

<p>(A) Latently infected cells were generated as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030176#pone-0030176-g001" target="_blank">Figure 1</a> with NL4-3 Luciferase virus or NL4-3 mCherry:Luc virus. Cells were either cultured in the presence of media alone or stimulated with 200 nM PMA, 200 nM PMA and 1.5 µM ionomycin, 10 µg/ml PHA, 10 µg/ml PHA with 100 units/ml IL-2, 10 ng/ml TNF-α, anti-CD3+anti-CD28 beads (ratio 1∶1), 100 units/ml IL-2, 62.5 ng/ml IL-7, or 12.5 ng/ml IL-15. Cells were harvested after 48 h of stimulation. RLU shown were normalized based on total protein present in the various cell lysates. All stimulations were performed in triplicate with error bars representing +/− SD. Results are representative of experiments performed with cells from four independent donors. (B) Latently infected cells from the same individual donor were stimulated with anti-CD3+anti-CD28 beads (ratio 1∶1), 200 nM PMA with 1.5 µM ionomycin, or 10 µg/ml PHA with 100 units/ml IL-2 and harvested at the indicated times post-stimulation. Results are representative of kinetic experiments performed with cells isolated from three independent donors.</p

Multiplex screening of inducing compounds on the reactivation of HIV-1 latency.

<p>(A) Latently infected cells were stimulated with 10-fold increasing concentrations of SAHA, TSA, HMBA, VPA, or prostratin. Cells were treated with 200 nM PMA+1.5 µM ionomycin as a positive control. The highest and lowest concentrations of the 10-fold dilution series are indicated for each compound tested. Stimulations were performed in triplicate reactions and error bars represent +/− SD. (B) Cells were treated for 24 or 48 h with the indicated concentration of compounds. Results are representative of independent experiments performed with at least three independent donors.</p