Proteomics in immunity and herpes simplex encephalitis

Abstract

The genetic theory of infectious diseases has proposed that susceptibility to life-threatening infectious diseases in childhood, occurring in the course of primary infection, results mostly from individually rare but collectively diverse single-gene variants. Recent evidence of an ever-expanding spectrum of genes involved in susceptibility to infectious disease indicates that the paradigm has important implications for diagnosis and treatment. One such pathology is childhood herpes simplex encephalitis, which shows a pattern of rare but diverse disease-disposing genetic variants. The present report shows how proteomics can help to understand susceptibility to childhood herpes simplex encephalitis and other viral infections, suggests that proteomics may have a particularly important role to play, emphasizes that variation over the population is a critical issue for proteomics and notes some new challenges for proteomics and related bioinformatics tools in the context of rare but diverse genetic defects.

Figure 1

A simplified diagram of the TLR-mediated and interferon (IFN)-mediated immunity in response to viruses. TLR3 is located in the endoplasmic reticulum (ER) and in endosomes, where it recognizes double-stranded RNA produced during the replication of most viruses. Activation of TLR3 induces activation of IRF-3 and NF- κ B via the TRIF adaptor, and the production of IFN-α/β and/or - λ. UNC-93B is required for the trafficking of TLR3, TLR7, TLR8 and TLR9 from the ER to the endosomal compartment. Proteins of the TLR3 pathway for which genetic mutation have been identified and associated with susceptibility to Herpes simplex virus-1 encephalitis (TLR3, TRIF, UNC-93B, TRAF3 and TBK1) are depicted in blue.

Figure 2

Production of IFN-β by SV40-fibroblasts after poly(I:C) stimulation (25 μg/ml) for 24 hoursas assessed by ELISA. C1-C5 are the positive healthy controls and UNC93B1^-/-is the UNC-93B-deficientpatient. Mean values ± SD were calculated from three independent experiments.

Figure 3

Illustration of the potential biological significance in immunity against HSE for proteins upregulated after TLR3 activation.

Figure 4

(A) Correlation of log₂(H/L) between healthy samples (C1, C2, C3) and the healthy, non-stimulated sample (C2NS). (B) Cumulative proportion of proteins with the indicated H/L ratios for all six samples.

Figure 5

Heat maps showing alternative strategies for selection of “most significant” protein sets for subsequent functional network searches using GeneGo. (A) SILAC ratios recorded for 7 different annexins over the six simple types. The number of ratio counts for individual proteins ranged from 6 to 243 per sample. (B) Proteins retained with a Significance B < 0.05 filter applied to each sample independently. (C) Proteins retained with a Significance B* < 0.001 filter applied across all samples. Boxed regions: proteins deleted that had |log₂(S)| equal to or greater than “significant” proteins retained in other samples.

Figure 6

(A) Model of abundance changes for four networks with intrinsic abundance changes a_jk for different proteins for unit turn-on of the network. For three cell samples from healthy individuals each network is turned on to different degrees. These results in changes in the set of “most significant” proteins selected with Significance B filters (dashed lines) and their rank order for each cell sample. (B) Relationships in the 3D space of SILAC ratios [log₂(S₁), log₂(S₂), log₂(S₃)] for proteins from a single network. The red/blue spheres and axis indicate increased/decreased abundance. The relative amplitude to which the network is turned on in the different cell samples is given by the axis log₂(S₁):log₂(S₂):log₂(S₃) = 1:1:1 for equal activation in all cell samples. (C) Putative network for proteins involved in redox responses following stimulation of the healthy samples with dsRNA. log₂(S₁):log₂(S₂):log₂(S₃) = 0.42:0.91:0.13. (D) Putative network for proteins involved in nuclear processes following stimulation of the healthy samples with dsRNA. log₂(S₁):log₂(S₂):log₂(S₃) = 0.69:0.73:0.26.