Cytokine systems approach demonstrates differences in innate and pro-inflammatory host responses between genetically distinct MERS-CoV isolates

Selinger, Christian; Tisoncik-Go, Jennifer; Menachery, Vineet D; Agnihothram, Sudhakar; Law, G Lynn; Chang, Jean; Kelly, Sara M; Sova, Pavel; Baric, Ralph S; Katze, Michael G

To further assess the magnitude impact of MERS-CoV infection in Calu-3 2B4 cells, we integrated viral genomic RNA levels into a topological assessment of host response differences. MERS-CoV genomic RNA was measured by qRT-PCR using primer (KL200F) located in the leader region (nt 9-28) and primer (KL201) located in the ORF1 region of the CoV genome ( Figure 1A and Additional file 1: Table S5 ). Additionally, we measured viral titers by plaque assay and found MERS-CoV SA 1 had more robust infectious viral particle production than MERS-CoV Eng 1, with significantly different viral titers at 18 and 24 hpi, despite similar viral genomic RNA levels at these late time points (Additional file 2: Table S4 ). In measuring viral genomic RNA and infectious viral particle production, we assessed two different stages in the viral life cycle and the data support differences in the kinetics of replication for the two MERS-CoV strains. Since our analysis focuses on the host response to infection and its origin in pathogen recognition and activation of innate immune responses, we computationally integrated viral genomic RNA with host mRNA to better assess the differences in host response kinetics. Towards this end, we used a filtered clustering method [17] that reduced the space of 93 samples to a clustering graph of 18 nodes. First, the viral genomic RNA levels were used as a filter to bin the samples into overlapping subsets with similar viral load (i.e., viral gRNA). Second, within each subset, samples that showed a high degree of interconnection were retained. Thus, there was high similarity in gene expression levels within each subset. For a graphical representation, each set of retained samples formed a node and nodes were considered as adjacent whenever they had at least one sample in common (Methods and Figure 1B Taken together, these whole transcriptome topological analyses showed robust differences in both kinetics and magnitude of the host response between MERS-CoV SA 1 and MERS-CoV Eng 1, despite similar viral replication. Another example where viral replication may not be the best readout of host response differences is icSARS-CoV. The deletion of the entire ORF6 gene of icSARS-CoV (icSARS-CoV ΔORF6) does not impact viral replication compared to wild-type icSARS-CoV, yet substantial differences in the host response between the two viruses are detected in Calu-3 2B4 cells [18] . Table S5 ). The error bars represent the standard deviation among triplicate samples. B. Clustering graph for samples according to their Euclidean distances in gene expression obtained by an integration of viral genomic RNA levels. Samples with similar viral genomic RNA levels were grouped together. Using statistical criteria on the single linkage heights between all samples, we extracted the highly interconnected part (with smaller linkage heights). These samples were compared to highly interconnected samples of a second group of samples with overlapping viral genomic RNA levels of the first group and so forth. Whenever two highly interconnected parts had at least one sample in common we defined the two groups as adjacent.