The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network

Abstract

During αβ T cell development, T cell antigen receptor (TCR) engagement transduces biochemical signals through a protein-protein interaction (PPI) network that dictates dichotomous cell fate decisions. It remains unclear how signal specificity is communicated, instructing either positive selection to advance cell differentiation or death by negative selection. Early signal discrimination might occur by PPI signatures differing qualitatively (customized, unique PPI combinations for each signal), quantitatively (graded amounts of a single PPI series), or kinetically (speed of PPI pathway progression). Using a novel PPI network analysis, we found that early TCR-proximal signals distinguishing positive from negative selection appeared to be primarily quantitative in nature. Furthermore, the signal intensity of this PPI network was used to find an antigen dose that caused a classic negative selection ligand to induce positive selection of conventional αβ T cells, suggesting that the quantity of TCR triggering was sufficient to program selection outcome. Because previous work had suggested that positive selection might involve a qualitatively unique signal through CD3δ, we reexamined the block in positive selection observed in CD3δ0 mice. We found that CD3δ0 thymocytes were inhibited but capable of signaling positive selection, generating low numbers of MHC-dependent αβ T cells that expressed diverse TCR repertoires and participated in immune responses against infection. We conclude that the major role for CD3δ in positive selection is to quantitatively boost the signal for maximal generation of αβ T cells. Together, these data indicate that a quantitative network signaling mechanism through the early proximal TCR signalosome determines thymic selection outcome.

Synaptic activity induces input‐specific rearrangements in a targeted synaptic protein interaction network

“…To identify specific interactions that significantly changed following stimulation, we utilized a combination of two independent statistical tests: ANC and CNA (see methods, and (Smith et al 2016). Prior work with QMI data found that interactions identified independently by both CNA and ANC represent a high confidence group PiSCES that are both significantly different between groups and tend to be co-regulated, minimizing the rate of false positives (Neier, Smith et al in Revision) (Smith et al 2016). Following high-K + aCSF stimulation, ANC identified 15 PiSCES that were significantly different in >70% of binary comparisons of control vs. stimulated cultures (Figure 3A).…”

“…Finally, each eigenvector is correlated to each experimental variable, or ‘trait’, and modules correlated to traits at p <0.05 are considered significant (Fig 4B). CNA analysis generated two modules of co-varying interactions (Figure 4A,B, S3), further contrasting with T cell activation, which generates a single CNA module and distinguishes between signaling inputs based on quantitative differences within that module (Neier, Smith et al, in revision). Both the larger “blue” module and smaller “brown” module were dominated by PiSCES involving Homer1, mGluR5, and NMDAR; PiSCES involving Neuroligin3 or Shank, however, tended to correlate principally with either the blue or brown module, respectively (Figure S3).…”

“…In response to different stimuli, activity-dependent changes in protein-protein interactions may instruct input-specific responses by differentially modulating either specific protein components or the overall intensity of network activation (Neier, Smith et al in Revision) (Toettcher et al 2013, Wilson et al 2017). To better understand the molecular circuitry underlying this information flow, we conducted a series of experiments to model input-specific protein interaction network responses to different and opposing components of glutamatergic drive: NMDA receptor activation (a primary component of long term potentiation), and activation of type 1 mGluRs (a primary driver of long term depression).…”

“…QMI experiments detect stimulus-induced changes in a targeted protein interaction network by taking quantitative measurements of PiSCES in matched stimulated and unstimulated cells. We found that the T cell receptor signalosome responds to distinct input types by altering a common set of PiSCES, and distinguishes between discrete input types using quantitative differences in signalosome activation (Neier, Smith et al in Revision). Data from optogenetic studies of ERK signal transduction cascades also suggest that quantitative levels of ERK activation are critical in determining signaling outcome (Toettcher et al 2013, Wilson et al 2017).…”

Public and private human T cell clones respond differentially to HCMV antigen when boosted by CD3 co-potentiation

“…OT1ab.muCD8ab.JRT3 (OT-I.JRT3) 25 and T2-Kb 12 cells were reported previously. Both cell lines tested negative for mycoplasma and were grown in RPMI (Life Technologies) with 10% CosmicCalf serum (HyClone), 2 mM L-glutamine, and penicillin (100 U/ml)/streptomycin (100 g/ml) (Life Technologies) at 37°C, 5% CO2.…”