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Triggering lysosome‐regulated immunogenic cell death (ICD, e.g., pyroptosis and necroptosis) with nanomedicines is an emerging approach for turning an “immune‐cold” tumor “hot”—a key challenge faced by cancer immunotherapies. Proton sponge such as high‐molecular‐weight branched polyethylenimine (PEI) is excellent at rupturing lysosomes, but its therapeutic application is hindered by uncontrollable toxicity due to fixed charge density and poor understanding of resulted cell death mechanism. Here, a series of proton sponge nano‐assemblies (PSNAs) with self‐assembly controllable surface charge density and cell cytotoxicity are created. Such PSNAs are constructed via low‐molecular‐weight branched PEI covalently bound to self‐assembling peptides carrying tetraphenylethene pyridinium (PyTPE, an aggregation‐induced emission‐based luminogen). Assembly of PEI assisted by the self‐assembling peptide‐PyTPE leads to enhanced surface positive charges and cell cytotoxicity of PSNA. The self‐assembly tendency of PSNAs is further optimized by tuning hydrophilic and hydrophobic components within the peptide, thus resulting in the PSNA with the highest fluorescence, positive surface charge density, cell uptake, and cancer cell cytotoxicity. Systematic cell death mechanistic studies reveal that the lysosome rupturing‐regulated pyroptosis and necroptosis are at least two causes of cell death. Tumor cells undergoing PSNA‐triggered ICD activate immune cells, suggesting the great potential of PSNAs to trigger anticancer immunity.

 

Genomic safe harbors are regions of the genome that can maintain transgene expression without disrupting the function of host cells. Genomic safe harbors play an increasingly important role in improving the efficiency and safety of genome engineering. However, limited safe harbors have been identified.

Here, we develop a framework to facilitate searches for genomic safe harbors by integrating information from polymorphic mobile element insertions that naturally occur in human populations, epigenomic signatures, and 3D chromatin organization. By applying our framework to polymorphic mobile element insertions identified in the 1000 Genomes project and the Genotype-Tissue Expression (GTEx) project, we identify 19 candidate safe harbors in blood cells and 5 in brain cells. For three candidate sites in blood, we demonstrate the stable expression of transgene without disrupting nearby genes in host erythroid cells. We also develop a computer program, Genomics and Epigenetic Guided Safe Harbor mapper (GEG-SH mapper), for knowledge-based tissue-specific genomic safe harbor selection.

Our study provides a new knowledge-based framework to identify tissue-specific genomic safe harbors. In combination with the fast-growing genome engineering technologies, our approach has the potential to improve the overall safety and efficiency of gene and cell-based therapy in the near future.

 

Summary High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area: confidence set estimation for multiple components of the model parameters, and model specifications tests. First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters becomes asymptotically negligible. The new construction enables us to estimate a valid confidence region by empirical likelihood ratio. Second, we propose a test statistic as the maximum of the marginal empirical likelihood ratios to quantify data evidence against the model specification. Our theory establishes the validity of the proposed empirical likelihood approaches, accommodating over-identification and exponentially growing data dimensionality. Numerical studies demonstrate promising performance and potential practical benefits of the new methods.