tion studies (e.g. with mice or serum) are based on relatively heterogeneous biological material (various cell types, infected and non-infected cells) and are complicated by the fact that not only the direct effects of CHIKV infection are observed, but also all kinds of indirect effects such as tissue damage and the response of uninfected cells to cytokines produced elsewhere in the infected organism. In addition, the applied proteomics techniques might be a factor in the limited overlap between the different studies. For example, in the study that combined iTRAQ and 2D-DIGE, the two datasets only had a few proteins in common, despite the fact that they were generated using the same biological samples [114]. The low number of identifications generated with the 2D-DIGE approach (32 proteins) was probably the main contributor to this limited overlap.
A comparison of our results with previously reported datasets identified a few proteins that were also differentially expressed in other studies. Synthenin-1 was down regulated 2-fold at 12 h p.i. in our study and was 5-fold downregulated 2 days post infection in a human microglial cell line in the study by Abere et al.[111]. CD63 and HTRA1 were both downregulated, while HYRC and DNAJC13 were both upregulated in our study and in the study by Wikan et al., while agrin was downregulated in our study but upregulated in their study [120]. HKQ and LAMB1 were downregulated in our study and 2 days post infection in the study by Frasier et al., while SNW1 was downregulated in our study but up regulated in their study [114]. In line with our results, three other studies also observed that the majority of significantly changed proteins were downregulated [111, 114, 119].
We set out to analyze the direct effects of CHIKV-infection on cells by specifically analyzing synchronously infected cells during the first round of replication in a well- defined and characterized system. We used a SILAC-based time course so we could track abundance during the course of infection. This increases the chance of identifying dif- ferentially expressed proteins and decreases the chance of finding false positives, which are unlikely to be the same at multiple time points. A similar approach was recently used to study temporal changes during CMV infection [202]. An additional advantage of our time course study is that kinetics of the differentially regulated proteins could be compared to published half-lives. Several of the other studies analyzed samples at much later time points post infection, probably long after the onset of the translational shut- off. Therefore, the downregulation of many of the proteins identified in these studies might have merely been the result of normal turnover (in the absence of translation).
42 A + RNA virus diptych
CoNCLusioN
The data presented in this study show that the immediate impact of CHIKV infection on host cell protein abundance is rather limited. This might be because the cellular response to the infection is limited at the level of protein abundance, or because it is effectively suppressed by CHIKV, e.g. through the virus-induced translational and transcriptional shut-off, rendering the cell unable to upregulate protein expression. The previously described CHIKV nsP2-induced degradation of an RNA polymerase subunit [63] and the downregulation of most components of the POLR2 complex that we observed in our study, likely contribute importantly to the host transcriptional shut-off and suggests that also other proteome changes that we describe are relevant.
The majority of the differentially expressed proteins were downregulated during the infection, likely to manipulate the intracellular environment in a way that is beneficial for CHIKV replication. In line with this assumption, we discovered that overexpression of four of these downregulated proteins had a negative effect on the ability of CHIKV to establish a productive infection in these cells. How these four proteins affect CHIKV replication, directly or indirectly, and whether they are specifically targeted for degrada- tion by CHIKV, as previously described for Rpb1 [63], remain interesting questions for future studies.
Although CHIKV infection only led to a limited cellular response at the level of protein abundance, it remains very well possible that the cell responds to infection in ways that cannot be detected with a quantitative proteomics approach that only studies changes in total protein abundance. For example, this approach will not detect changes in subcellular localization or PTMs, such as phosphorylation, that can occur rapidly during (antiviral) signaling. Our findings advance the understanding of the response to CHIKV infection at the cellular level and this information might be used in future studies aimed at developing ‘host-directed’ antiviral strategies.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Con- sortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [203] with the dataset identifier PXD001330.
ACKNoWLEdGmENTs
The authors are grateful to Irina Albulescu for technical assistance in the BSL-3 lab, drs. Jeroen van Bergen, George Janssen and Jeroen de Keijzer for helpful discussions, prof. Andres Merits (University of Tartu, Estonia) for generously supplying CHIKV antisera, dr. Hyungshin Yim (Hanyang University, Korea) and prof. Akira Nakagawara (Chiba Cancer
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