Tanja Hann

Most drugs for autoimmune and inflammatory disease work by blocking a cytokine everywhere, all the time. That is effective, but it broadly dampens immunity and raises infection risk. A more precise strategy is to target the immune response only when it is active. Resting and stimulated T cells run very different genetic programs, so a gene that controls a disease-relevant cytokine only in the stimulated state is a far cleaner target: interfering with it should calm an over-active response while sparing resting immune function. I built a reproducible pipeline that finds these context-specific regulators and then asks the practical question that decides whether a target matters: can it actually be drugged, and how? Starting from the genome-scale CD4+ T-cell Perturb-seq screen of Zhu, Dann et al. 2025 (every gene knocked down, one at a time, read out at rest and under stimulation), I tested a perturbation × condition interaction for each gene across 30 cytokines, rather than comparing the two states separately. A gene counts only if its knockdown moves a cytokine under stimulation, does essentially nothing at rest, and passes that interaction test genome-wide. I scored each hit for selectivity (rewarding focused single-cytokine regulators over whole-cell master switches) and applied a cross-donor reproducibility and on-target filter. This generated a high-confidence shortlist of 128 context-specific regulators, both suppressors (anti-inflammatory leads) and inducers (leads for boosting immunity, for example in cancer). The part I'm most excited about is the druggability layer, which draws directly on my background in RNA therapeutics. Target discovery has historically branded much of the genome "undruggable" because it lacks a small-molecule pocket or a cell-surface handle. But those genes are often perfectly targetable at the RNA level with ASOs or siRNA. So I scored all 128 genes on two independent axes: protein-level druggability (small molecule / antibody) and RNA-level druggability (siRNA / ASO), the RNA axis informed by transcript accessibility, predicted stability, and subcellular localization, the same considerations that govern real oligonucleotide design. Plotting both axes turns a binary "druggable/undruggable" call into a map with a concrete modality for nearly every gene: of 128, only a handful score poorly on both, and a distinct RNA-preferred group emerges where an oligonucleotide strategy is the natural path. Why it matters: the output is a ranked, modality-annotated shortlist of new candidate targets for tuning specific cytokines in activated T cells. It is a resource others can build on, and a pipeline retargetable to any cytokine, cell type, or disease. By treating the RNA axis as a first-class route rather than a fallback, it deliberately keeps in play the regulators traditional discovery throws away, which is exactly where modern RNA medicine is strongest.