PRDX-1 Supports the Survival and Antitumor Activity of Primary and CAR-Modified NK Cells under Oxidative Stress.

Oxidative stress, caused by the imbalance between reactive species generation and the dysfunctional capacity of antioxidant defenses, is one of the characteristic features of cancer. Here, we quantified hydrogen peroxide in the tumor microenvironment (TME) and demonstrated that hydrogen peroxide concentrations are elevated in tumor interstitial fluid isolated from murine breast cancers in vivo, when compared with blood or normal subcutaneous fluid. Therefore, we investigated the effects of increased hydrogen peroxide concentration on immune cell functions. NK cells were more susceptible to hydrogen peroxide than T cells or B cells, and by comparing T, B, and NK cells' sensitivities to redox stress and their antioxidant capacities, we identified peroxiredoxin-1 (PRDX1) as a lacking element of NK cells' antioxidative defense. We observed that priming with IL15 protected NK cells' functions in the presence of high hydrogen peroxide and simultaneously upregulated PRDX1 expression. However, the effect of IL15 on PRDX1 expression was transient and strictly dependent on the presence of the cytokine. Therefore, we genetically modified NK cells to stably overexpress PRDX1, which led to increased survival and NK cell activity in redox stress conditions. Finally, we generated PD-L1-CAR NK cells overexpressing PRDX1 that displayed potent antitumor activity against breast cancer cells under oxidative stress. These results demonstrate that hydrogen peroxide, at concentrations detected in the TME, suppresses NK cell function and that genetic modification strategies can improve CAR NK cells' resistance and potency against solid tumors.

Correction to: Spatial chromatin architecture alteration by structural variations in human genomes at the population scale.

Following publication of the original article [1], it was noticed that the incorrect Fig. 2 and Fig. 3. were processed during production. It was also noticed that Fig. 4a was processed with a superfluous "1e7" symbol in the upper right corner.

Spatial chromatin architecture alteration by structural variations in human genomes at the population scale.

BACKGROUND: The number of reported examples of chromatin architecture alterations involved in the regulation of gene transcription and in disease is increasing. However, no genome-wide testing has been performed to assess the abundance of these events and their importance relative to other factors affecting genome regulation. This is particularly interesting given that a vast majority of genetic variations identified in association studies are located outside coding sequences. This study attempts to address this lack by analyzing the impact on chromatin spatial organization of genetic variants identified in individuals from 26 human populations and in genome-wide association studies. RESULTS: We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design an algorithm to model chromatin conformational changes caused by structural variations. We show that differential gene transcription is closely linked to the variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across populations, but enriched with disease-associated SNPs. Moreover, we find boundaries of topological domains as relatively frequent targets of duplications, which suggest that these duplications can be an important evolutionary mechanism of genome spatial organization. CONCLUSIONS: This study assesses the critical impact of genetic variants on the higher-order organization of chromatin folding and provides insight into the mechanisms regulating gene transcription at the population scale, of which local arrangement of chromatin loops seems to be the most significant. It provides the first insight into the variability of the human 3D genome at the population scale.

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulations in order to explain the extensive long-range chromatin looping observed in ChIA-PET experiments for lymphoblastoid cells. Random Forest, Gradient Boosting Machine (GBM), and Deep Learning models were constructed and evaluated, when predicting high-resolution interactions within Topologically Associating Domains (TADs). The predicted interactions are consistent with the experimental long-read ChIA-PET interactions mediated by CTCF and RNAPOL2 for GM12878 cell line. The contribution of sequence information and chromatin state defined by epigenomic features to the prediction task is analyzed and reported, when using them separately and combined. Furthermore, we design three-dimensional models of chromatin contact domains (CCDs) using real (ChIA-PET) and predicted looping interactions. Initial results show a similarity between both types of 3D computational models (constructed from experimental or predicted interactions). This observation confirms the association between genome sequence, epigenomic and transcription factor profiles, and three-dimensional interactions.