α-Lactosylceramide Protects Against iNKT-Mediated Murine Airway Hyperreactivity and Liver Injury Through Competitive Inhibition of Cd1d Binding.

Invariant natural killer T (iNKT) cells, which are activated by T cell receptor (TCR)-dependent recognition of lipid-based antigens presented by the CD1d molecule, have been shown to participate in the pathogenesis of many diseases, including asthma and liver injury. Previous studies have shown the inhibition of iNKT cell activation using lipid antagonists can attenuate iNKT cell-induced disease pathogenesis. Hence, the development of iNKT cell-targeted glycolipids can facilitate the discovery of new therapeutics. In this study, we synthesized and evaluated α-lactosylceramide (α-LacCer), an α-galactosylceramide (α-GalCer) analog with lactose substitution for the galactose head and a shortened acyl chain in the ceramide tail, toward iNKT cell activation. We demonstrated that α-LacCer was a weak inducer for both mouse and human iNKT cell activation and cytokine production, and the iNKT induction by α-LacCer was CD1d-dependent. However, when co-administered with α-GalCer, α-LacCer inhibited α-GalCer-induced IL-4 and IFN-γ production from iNKT cells. Consequently, α-LacCer also ameliorated both α-GalCer and GSL-1-induced airway hyperreactivity and α-GalCer-induced neutrophilia when co-administered in vivo. Furthermore, we were able to inhibit the increases of ConA-induced AST, ALT and IFN-γ serum levels through α-LacCer pre-treatment, suggesting α-LacCer could protect against ConA-induced liver injury. Mechanistically, we discerned that α-LacCer suppressed α-GalCer-stimulated cytokine production through competing for CD1d binding. Since iNKT cells play a critical role in the development of AHR and liver injury, the inhibition of iNKT cell activation by α-LacCer present a possible new approach in treating iNKT cell-mediated diseases

Pulmonary IL- 33 orchestrates innate immune cells to mediate respiratory syncytial virus- evoked airway hyperreactivity and eosinophilia

BackgroundRespiratory syncytial virus (RSV) infection is epidemiologically linked to asthma. During RSV infection, IL- 33 is elevated and promotes immune cell activation, leading to the development of asthma. However, which immune cells are responsible for triggering airway hyperreactivity (AHR), inflammation and eosinophilia remained to be clarified. We aimed to elucidate the individual roles of IL- 33- activated innate immune cells, including ILC2s and ST2+ myeloid cells, in RSV infection- triggered pathophysiology.MethodsThe role of IL- 33/ILC2 axis in RSV- induced AHR inflammation and eosinophilia were evaluated in the IL- 33- deficient and YetCre- 13 Rosa- DTA mice. Myeloid- specific, IL- 33- deficient or ST2- deficient mice were employed to examine the role of IL- 33 and ST2 signaling in myeloid cells.ResultsWe found that IL- 33- activated ILC2s were crucial for the development of AHR and airway inflammation, during RSV infection. ILC2- derived IL- 13 was sufficient for RSV- driven AHR, since reconstitution of wild- type ILC2 rescued RSV- driven AHR in IL- 13- deficient mice. Meanwhile, myeloid cell- derived IL- 33 was required for airway inflammation, ST2+ myeloid cells contributed to exacerbation of airway inflammation, suggesting the importance of IL- 33 signaling in these cells. Local and peripheral eosinophilia is linked to both ILC2 and myeloid IL- 33 signaling.ConclusionsThis study highlights the importance of IL- 33- activated ILC2s in mediating RSV- triggered AHR and eosinophilia. In addition, IL- 33 signaling in myeloid cells is crucial for airway inflammation.Respiratory syncytial virus induces ILC2 to produce IL- 5 and IL- 13 through IL- 33, which is crucial for the development of airway hyperreactivity and airway inflammation. Myeloid cell- derived IL- 33 and suppression of tumorigenicity 2- positive myeloid cells contribute to cytokine production and cellular inflammation in airway. Both ILC2 and myeloid cell IL- 33 signaling contribute to local and peripheral eosinophilia.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154896/1/all14091.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154896/2/all14091-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154896/3/all14091_am.pd

Proteomic analysis on host responses to Chikungunya virus infection / Christina Thio Li Ping

Chikungunya virus (CHIKV) is an arthropod-borne virus that has caused multiple unprecedented outbreaks in both tropical and temperate countries over the past five decades. There is no commercial vaccine or antiviral drug to date, due in part to the lack of knowledge and understanding of the biology and pathogenesis of this virus. Thus, there is an increasing need for researchers to focus their research efforts in this area of virology. The current study employed proteomics to investigate alterations of the whole cell proteome and secretome of WRL-68 cells during early CHIKV infection, with the main aim being to identify the key proteins modulated in response to infection. Two-dimensional gel electrophoresis (2-DGE) was used to compare the whole cell proteome and secretome profiles between mock control cells and cells infected at the optimised multiplicity of infection (MOI) of 5.0 at 24 hours post-infection. Protein spots that were found to be differentially expressed were identified by MALDITOF/ TOF mass spectrometry (MS) analysis, and three selected proteins were validated by Western blot. The functional association between these proteins were determined by STRING network analysis, and the mRNA expression level of selected proteins was investigated via real-time quantitative PCR. Overall, 50 and 25 protein spots from the whole cell proteome and secretome samples, respectively, were found to be differentially expressed (fold-change > 1.3, p < 0.05) and were successfully identified. The mRNA expression of 15 whole cell proteins was found to correlate with the corresponding protein expression. On the contrary, only one of the 15 selected proteins from the secretome sample showed positive correlation with its transcript expression level. By combining the proteomics and bioinformatics data from STRING network analysis, it was deduced that CHIKV disrupt the overall host cell metabolic machinery and ubiquitin-proteasome pathway (UPP). Suppression of the host immune response was also observed through the inhibition of immune-related protein secretion, mainly ii cathepsin D, cathepsin L1, C3 protein and β-2 microglobulin. Several gene expressionrelated proteins were also down-regulated, including the mRNA processing factor, hnRNP E1, and translational factors, namely elongation factor-2, eukaryotic initiation factor eIF-2BA and eIF3 subunit H. Meanwhile, up-regulation of hnRNP C1/C2 suggests that this protein may be beneficial to CHIKV. Cell cycle regulation via cyclindependent kinase 1 (CDK1) activity may also play an important role during early CHIKV infection. CDK1 was down-regulated, whereas several other proteins (such as SET protein) that indirectly regulate the activity of CDK1, were altered in favour of the inhibition of CDK1 activity. In conclusion, CHIKV infection in the human liver cells induced a widespread alteration of the whole cell proteome and secretome. Nevertheless functional characterisations of these proteins are entailed to provide more insights into the actual mechanisms at play during early infection

Differential Proteome Analysis of Chikungunya Virus Infection on Host Cells

<div><p>Background</p><p>Chikungunya virus (CHIKV) is an emerging mosquito-borne alphavirus that has caused multiple unprecedented and re-emerging outbreaks in both tropical and temperate countries. Despite ongoing research efforts, the underlying factors involved in facilitating CHIKV replication during early infection remains ill-characterized. The present study serves to identify host proteins modulated in response to early CHIKV infection using a proteomics approach.</p> <p>Methodology and Principal Findings</p><p>The whole cell proteome profiles of CHIKV-infected and mock control WRL-68 cells were compared and analyzed using two-dimensional gel electrophoresis (2-DGE). Fifty-three spots were found to be differentially modulated and 50 were successfully identified by MALDI-TOF/TOF. Eight were significantly up-regulated and 42 were down-regulated. The mRNA expressions of 15 genes were also found to correlate with the corresponding protein expression. STRING network analysis identified several biological processes to be affected, including mRNA processing, translation, energy production and cellular metabolism, ubiquitin-proteasome pathway (UPP) and cell cycle regulation.</p> <p>Conclusion/Significance</p><p>This study constitutes a first attempt to investigate alteration of the host cellular proteome during early CHIKV infection. Our proteomics data showed that during early infection, CHIKV affected the expression of proteins that are involved in mRNA processing, host metabolic machinery, UPP, and cyclin-dependent kinase 1 (CDK1) regulation (in favour of virus survival, replication and transmission). While results from this study complement the proteomics results obtained from previous late host response studies, functional characterization of these proteins is warranted to reinforce our understanding of their roles during early CHIKV infection in humans.</p> </div

Comparison of real-time qPCR and proteomics results for selected genes.

*<p><b>Bold</b> indicates RNA expression changes which are in concordance with protein expression changes in terms of directionality, and are determined to be statistically significant (<i>p</i><0.05); NSD indicates no significant differences in the RNA expression.</p>**<p>More than one protein spot was identified.</p

Reference map of the whole cell proteome of WRL-68 cells.

<p>Forty µg of protein sample were focused on 13 cm, pH 3–10 linear IPG drystrips, followed by second dimension SDS-PAGE separation on 12.5% polyacrylamide gel which was silver stained. Five biological replicates (n = 5) for each group (Mock control and CHIKV-infected) were analyzed using ImageMaster™ 2D Platinum v7.0 software. Fifty-three spots were determined to be differentially expressed (Fold-change >1.3, <i>p</i><0.05). The position of each spot is indicated by circles on the proteome map. The uppercase ‘U’ and ‘D’ denote up-regulated and down-regulated spots, respectively.</p

GO enrichment analysis of the biological processes involved in the STRING protein network.

a<p>The significance of the GO biological process derived from the cytosolic protein network was determined by FDR correction (<i>p</i><0.05).</p

STRING interaction network showing association between differentially expressed proteins.

<p>Interaction map was generated using default settings (Medium confidence of 0.4 and 7 criteria for linkage: neighbourhood, gene fusion, co-occurrence, co-expression, experimental evidences, existing databases and text mining). Twenty additional interplay proteins were also added to each network. The protein names and gene symbols used in this network are listed in Supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061444#pone.0061444.s003" target="_blank">Table S2</a>.</p

List of differentially altered proteins in WLR-68 cells in response to CHIKV infection.

a<p>MW and pI refer to the molecular weight and isoelectric point of the protein.</p>b<p>The mean % spot volume (n = 5) was used for the analysis of fold difference between mock control and CHIKV-infected protein spots. SD represents standard deviation of five biological replicates.</p>c<p>Positive fold-change values represent up-regulation whereas negative fold-change values signify down-regulation of identified proteins.</p

Optimization of the MOI and incubation time-point for early CHIKV infection study.

<p>(A) Morphological examination of WRL-68 cells infected at the MOI of 0.5, 1.0, 5.0 and 10.0 at 24 and 48 h incubation revealed a MOI and time-dependent induction of CPE by CHIKV. All images were captured at 100X magnification. (B) Flow cytometric quantification of percentage of cell death by AV/PI double staining of cells. Error bars indicate standard deviation of three biological replicates. (C) Flow cytometric quantification of percentage of infection by immunostaining of cells with anti-CHIK E2 mAB 3E4 (1∶100 dilution). Error bars indicate standard deviation of three biological replicates. (D) Confirmation of infection via indirect immunofluorescence assay at the optimized MOI of 5.0 at 24 h p.i. Mock cells served as negative control. All images were captured at100X magnification.</p