The Kaposi's sarcoma-associated herpesvirus (KSHV) protein kinase, encoded by ORF36, functions to phosphorylate cellular and viral targets important in the KSHV lifecycle and to activate the anti-viral prodrug ganciclovir. Unlike the vast majority of mapped KSHV genes, no viral transcript has been identified with ORF36 positioned as the 5′-proximal gene. Here we report that ORF36 is robustly translated as a downstream cistron from the ORF35–37 polycistronic transcript in a cap-dependent manner. We identified two short, upstream open reading frames (uORFs) within the 5′ UTR of the polycistronic mRNA. While both uORFs function as negative regulators of ORF35, unexpectedly, the second allows for the translation of the downstream ORF36 gene by a termination-reinitiation mechanism. Positional conservation of uORFs within a number of related viruses suggests that this may be a common γ-herpesviral adaptation of a host translational regulatory mechanism.

In human and murine studies, IFN-γ is a critical mediator immunity to influenza. IFN-γ production is critical for viral clearance and the development of adaptive immune responses, yet excessive production of IFN-γ and other cytokines as part of a cytokine storm is associated with poor outcomes of influenza infection in humans. As NK cells are the main population of lung innate immune cells capable of producing IFN-γ early in infection, we set out to identify the drivers of the human NK cell IFN-γ response to influenza A viruses. We found that influenza triggers NK cells to secrete IFN-γ in the absence of T cells and in a manner dependent upon signaling from both cytokines and receptor-ligand interactions. Further, we discovered that the pandemic A/California/07/2009 (H1N1) strain elicits a seven-fold greater IFN-γ response than other strains tested, including a seasonal A/Victoria/361/2011 (H3N2) strain. These differential responses were independent of memory NK cells. Instead, we discovered that the A/Victoria/361/2011 influenza strain suppresses the NK cell IFN-γ response by downregulating NK-activating ligands CD112 and CD54 and by repressing the type I IFN response in a viral replication-dependent manner. In contrast, the A/California/07/2009 strain fails to repress the type I IFN response or to downregulate CD54 and CD112 to the same extent, which leads to the enhanced NK cell IFN-γ response. Our results indicate that influenza implements a strain-specific mechanism governing NK cell production of IFN-γ and identifies a previously unrecognized influenza innate immune evasion strategy.

bThe Kaposi's sarcoma-associated herpesvirus (KSHV) ORF36 protein kinase is translated as a downstream gene from the ORF35-37 polycistronic mRNA via a unique mechanism involving short upstream open reading frames (uORFs) located in the 5= untranslated region. Here, we confirm that ORF35-37 is functionally dicistronic during infection and demonstrate that mutation of the dominant uORF restricts KSHV replication. Leaky scanning past the uORFs facilitates ORF35 expression, while a reinitiation mechanism after translation of the uORFs enables ORF36 expression.

Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. Results Differential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the R package and workflow for both strategies on a pregnancy dataset. Conclusion Our approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.