Myristoylated rhinovirus VP4 protein activates TLR2-dependent proinflammatory gene expression.

Asthma exacerbations are often caused by rhinovirus (RV). We and others have shown that Toll-like receptor 2 (TLR2), a membrane surface receptor that recognizes bacterial lipopeptides and lipoteichoic acid, is required and sufficient for RV-induced proinflammatory responses in vitro and in vivo. We hypothesized that viral protein-4 (VP4), an internal capsid protein that is myristoylated upon viral replication and externalized upon viral binding, is a ligand for TLR2. Recombinant VP4 and myristoylated VP4 (MyrVP4) were purified by Ni-affinity chromatography. MyrVP4 was also purified from RV-A1B-infected HeLa cells by urea solubilization and anti-VP4 affinity chromatography. Finally, synthetic MyrVP4 was produced by chemical peptide synthesis. MyrVP4-TLR2 interactions were assessed by confocal fluorescence microscopy, fluorescence resonance energy transfer (FRET), and monitoring VP4-induced cytokine mRNA expression in the presence of anti-TLR2 and anti-VP4. MyrVP4 and TLR2 colocalized in TLR2-expressing HEK-293 cells, mouse bone marrow-derived macrophages, human bronchoalveolar macrophages, and human airway epithelial cells. Colocalization was absent in TLR2-null HEK-293 cells and blocked by anti-TLR2 and anti-VP4. Cy3-labeled MyrVP4 and Cy5-labeled anti-TLR2 showed an average fractional FRET efficiency of 0.24 ± 0.05, and Cy5-labeled anti-TLR2 increased and unlabeled MyrVP4 decreased FRET efficiency. MyrVP4-induced chemokine mRNA expression was higher than that elicited by VP4 alone and was attenuated by anti-TLR2 and anti-VP4. Cytokine expression was similarly increased by MyrVP4 purified from RV-infected HeLa cells and synthetic MyrVP4. We conclude that, during RV infection, MyrVP4 and TLR2 interact to generate a proinflammatory response.

IFN-γ Blocks Development of an Asthma Phenotype in Rhinovirus-Infected Baby Mice by Inhibiting Type 2 Innate Lymphoid Cells.

Early-life wheezing-associated infections with rhinovirus (RV) have been associated with asthma development in children. We have shown that RV infection of 6-day-old mice induces mucous metaplasia and airways hyperresponsiveness, which is dependent on IL-13, IL-25, and type 2 innate lymphoid cells (ILC2s). Infection of immature mice fails to induce lung IFN-γ expression, in contrast to mature 8-week-old mice with a robust IFN-γ response, consistent with the notion that deficient IFN-γ production in immature mice permits RV-induced type 2 immune responses. We therefore examined the effects of intranasal IFN-γ administration on RV-induced ILC2 expansion and IL-13 expression in 6-day-old BALB/c and IL-13 reporter mice. Airway responses were assessed by histology, immunofluorescence microscopy, quantitative polymerase chain reaction, ELISA, and flow cytometry. Lung ILC2s were also treated with IFN-γ ex vivo. We found that, compared with untreated RV-infected immature mice, IFN-γ treatment attenuated RV-induced IL-13 and Muc5ac mRNA expression and mucous metaplasia. IFN-γ also reduced ILC2 expansion and the percentage of IL-13-secreting ILC2s. IFN-γ had no effect on the mRNA or protein expression of IL-25, IL-33, or thymic stromal lymphoprotein. Finally, IFN-γ treatment of sorted ILC2s reduced IL-5, IL-13, IL-17RB, ST2, and GATA-3 mRNA expression. We conclude that, in immature mice, IFN-γ inhibits ILC2 expansion and IL-13 expression in vivo and ex vivo, thereby attenuating RV-induced mucous metaplasia. These findings demonstrate the antagonistic function of IFN-γ on ILC2 expansion and gene expression, the absence of which may contribute to the development of an asthma-like phenotype after early-life RV infection.

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis.

The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.