Background Auto-reactive CD4+ T cells play an important role in the pathogenesis of SLE and RA. Self-reactive CD4+ T cells in their abnormal interface with B cells cause tissue damage, and auto-antigen release, leading to further activation and differentiation of self-reactive CD4+ T cells. Recognition of MHC/Self-peptide plays a causative role in autoimmunity 1,2. How this leads to abnormal activation of CD4+ T cells is not clear.

Methods We optimized a,single cell resolution method – barcoded phosphoflow 3- to quantitatively measure intracellular signaling pathways in T cells that encounter MHC/Self-peptide. We unleashed this method on T cells from various genetic mouse models and combined this with a novel methodology to measure cell metabolism, SCENITH, also with single cell resolution.

Results RasGRP1 has been associated with autoimmune diseases, but the genetic basis or impact on T cells is not known. Likewise, mTORC1 signals and altered cell metabolism links to autoimmunity and the mTOR inhibitor Rapamycin reduces disease severity in autoimmune patients and mouse models. In our studies, we uncovered a novel Rasgrp1-mTORC1 pathway that is selectively triggered when T cells see MHC/Self-peptide (see figure 1). In our Rasgrp1Anaef mouse model 4,5 a single R519G point mutation results in aberrantly elevated Rasgrp1Anaef-mTORC1 signals that drive resting T cells out of their naïve state, leading to autoimmune pathology 4. Mechanistically, this elevated Rasgrp1Anaef-mTORC1 signal does not lead to altered gene expression, but instead, results in unwanted translation of mRNA targets in naïve T cells 4.

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Abstract 603 Figure 1

Conclusions Thus, Rasgrp1-mTORC1 signals are selectively triggered when T cells see self and are highest when T cells see self the strongest (auto-reactive) 4. We are investigating the fundamental properties of Rasgrp1-mTORC1 signals and effects on metabolism and protein translation. We are complementing these directions with efforts on SLE and RA patient samples and desire to expand this area through Dr. Roose’s roles in UCSF ImmunoX and AutoImmunoProfiler.

References

1. Myers DR, et al. Immunol Rev. 2019;291.

2. Myers DR, et al. Trends Immunol. 2017;38.

3. Kulhanek KR, et al. STAR Protoc. 2020;1.

4. Myers DR, et al. Cell Reports. 2019;27.

5. Daley SR, et al. eLife. 2013;2.

Acknowledgments We thank all members of the Roose lab for helpful discussions and the UCSF Parnassus Flow Cytometry Core for assistance with cell sorting (NIH P30 DK063720), the UCSF Genomics CoLab for RNA-Seq library preparation, the UCSF Center for Advanced Technology for sequencing, Max Horlbeck for assistance with bioinformatics. These studies were supported by grants from the NSF-GRFP (1650113 to DRM), the Marie Curie IOF (#PIOF-GA-2012–328666), and the NIH-NIAID (R01-AI104789 and P01-AI091580 to JPR).