Specific mutations in H5N1 mainly impact the magnitude and velocity of the host response in mice

Tchitchek, Nicolas; Eisfeld, Amie J; Tisoncik-Go, Jennifer; Josset, Laurence; Gralinski, Lisa E; Bécavin, Christophe; Tilton, Susan C; Webb-Robertson, Bobbie-Jo; Ferris, Martin T; Totura, Allison L; Li, Chengjun; Neumann, Gabriele; Metz, Thomas O; Smith, Richard D; Waters, Katrina M; Baric, Ralph; Kawaoka, Yoshihiro; Katze, Michael G

To determine the effects of increasing dosage on the kinetics of the host response to VN1203-WT, we used Multidimensional Scaling (MDS) [28, 29] as a visualization method and introduced two new criteria quantifying magnitude and velocity changes in the signature ( Figure 3C and D). Each dot in the MDS representations is the transcriptomic or proteomic profile of a biological sample, and pairwise distances between dots are proportional to the transcriptomic or proteomic distances (Euclidean distances) between the samples. The MDS representation, calculated using the sets of transcripts and proteins associated with the VN1203-WT kinetics, highlighted interesting differences between the dosage conditions. While all samples from different dosages were close to the mocks at 1 dpi, the late timepoints followed a similar curved trajectory. Different magnitudes, defined as the distance of one biological condition to the mock-infected condition, and velocities, defined as the speed of the host response moving from one time point to the next time point, were observed for the three dosage conditions for both the transcriptomic and proteomic levels. For example, infection with the highest dose of VN1203-WT virus (10 4 PFU) induced a For each eigenprotein identified in the kinetics of the host response to H5N1 VN1203 wild-type, the number of correlating transcripts and proteins are indicated. For each set of proteins the top 5 associated over-represented biological functions, canonical pathways, and upstream regulators are indicated. Ingenuity pathway analysis was used to determine the top 5 bio function categories, canonical pathways, and upstream regulators. The functions annotation p-value range represents the range of p-values for the functions annotations associated with each bio function category. The p-value of overlap associated for each upstream regulator indicates the significance of the overlap between the genes targeted by the upstream regulator in the IPKB database and the experimental dataset. *Upstream regulators with an apteryx signify a chemical reagent or chemical drug. similar change in host response at 4 dpi as VN1203-WT infection with the lowest dose (10 2 PFU) observed 3 days later at 7 dpi ( Figure 3C ). Compared to the transcriptomic profiles, the MDS representation of the proteomic profiles showed more noise in the data ( Figure 3D ). For instance, we observed that the variations within the groups were as high as the variations between groups. The proteomic samples collected at 1 and 2 days post-infection samples were closely clustered with the mocks. Although we were unable to differentiate these samples, we observed a similar effect of dose on the trajectories at 4 and 7 dpi, with the highest dose reaching the end of the trajectory more quickly compared to the lower doses. As such, the biological interpretations derived from the proteomic analysis have to be taken into careful considerations. In order to quantify these magnitude and velocity effects in the kinetics of the host response, we defined two criteria, the Magnitude Coefficient (MC) and the Velocity Coefficient (VC). The MC quantifies the magnitude effect as the transcriptomic distance from one biological condition to the matched mock-infected condition. The VC quantifies the velocity effect as the speed of the transcriptomic host response moving from one time point to the next in the succession of infection. Both the MC and VC were calculated based on the centroi