Patients with acute myeloid leukemia (AML) often achieve remission after allogeneic hematopoietic cell transplantation (allo-HCT) but subsequently die of relapse driven by leukemia cells resistant to elimination by allogeneic T cells based on decreased major histocompatibility complex II (MHC-II) expression and apoptosis resistance. Here we demonstrate that mouse-double-minute-2 (MDM2) inhibition can counteract immune evasion of AML. MDM2 inhibition induced MHC class I and II expression in murine and human AML cells. Using xenografts of human AML and syngeneic mouse models of leukemia, we show that MDM2 inhibition enhanced cytotoxicity against leukemia cells and improved survival. MDM2 inhibition also led to increases in tumor necrosis factor-related apoptosis-inducing ligand receptor-1 and -2 (TRAIL-R1/2) on leukemia cells and higher frequencies of CD8+CD27lowPD-1lowTIM-3low T cells, with features of cytotoxicity (perforin+CD107a+TRAIL+) and longevity (bcl-2+IL-7R+). CD8+ T cells isolated from leukemia-bearing MDM2 inhibitor-treated allo-HCT recipients exhibited higher glycolytic activity and enrichment for nucleotides and their precursors compared with vehicle control subjects. T cells isolated from MDM2 inhibitor-treated AML-bearing mice eradicated leukemia in secondary AML-bearing recipients. Mechanistically, the MDM2 inhibitor-mediated effects were p53-dependent because p53 knockdown abolished TRAIL-R1/2 and MHC-II upregulation, whereas p53 binding to TRAILR1/2 promotors increased upon MDM2 inhibition. The observations in the mouse models were complemented by data from human individuals. Patient-derived AML cells exhibited increased TRAIL-R1/2 and MHC-II expression on MDM2 inhibition. In summary, we identified a targetable vulnerability of AML cells to allogeneic T-cell–mediated cytotoxicity through the restoration of p53-dependent TRAIL-R1/2 and MHC-II production via MDM2 inhibition.

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure ( https://findr.biologie.uni-freiburg.de/ ). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.

These bias and MAE estimates suggest that additional studies are needed to develop more accurate and precise wrist-based accelerometer algorithms focused on ADLs and free-living behaviors. Table 1: Bias (AG-RC) of Min in Activity Intensities and MAE by Algorithm and Activity Method Activity Sed min LPA min MVPA min Hildebrand ADL Bias *8.2 + 7.6 *-21.1 + 8.9 *12.0 + 4.7 MAE *9.3 + 5.8 *21.1 + 8.9 *12.0 + 4.7 Treadmill Bias -0.7 + 1.1 *4.1 + 2.8 *3.9 + 2.9 MAE *1.0 + 0.8 *4.1 + 2.8 *3.9 + 2.9 Montoye ADL Bias 4.9 + 7.2 *-33.3 + 7.4 *27.4 + 4.1 MAE *7.2 + 4.4 *33.3 + 7.4 *27.4 + 4.1 Treadmill Bias -0.1 + 2.4 -2.0 + 6.7 1.1 + 8.8 MAE *1.6 + 1.7 *5.4 + 3.9 *6.3 + 5.8 *significantly different from zero (p < 0.05) C-09 Energy Metabolism and Exercise 750 Chair: Craig Sale, FACSM.

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