The pro-inflammatory state of macrophages, underpinned by their metabolic condition, is essentially affecting their capacity of combating tumor cells. Here we find, via a pooled metabolic gene knockout CRISPR screen that KEAP1 and ACOD1 are strong regulators of the pro-inflammatory state in macrophages. We show that ACOD1 knockout macrophages, generated in our induced pluripotent stem cell-derived CAR-macrophage (CAR-iMAC) platform, are strongly and persistently polarized toward the pro-inflammatory state, which manifests in increased reactive oxygen species (ROS) production, more potent phagocytosis and enhanced cytotoxic functions against cancer cells in vitro. In ovarian or pancreatic cancer mouse models, ACOD1-depleted CAR-iMACs exhibit enhanced capacity in repressing tumors, leading to increased survival. In addition, combining ACOD1-depleted CAR-iMACs with immune checkpoint inhibitors (ICI), such as anti-CD47 or anti-PD1 antibodies, result in even stronger tumor suppressing effect. Mechanistically, the depletion of ACOD1 reduces levels of the immuno-metabolite itaconate, allowing KEAP1 to prevent NRF2 from entering the nucleus to activate an anti-inflammatory program. This study thus lays down the proof of principle for targeting ACOD1 in myeloid cells for cancer immunotherapy and introduces metabolically engineered human iPSC-derived CAR-iMACs cells with enhanced polarization and anti-tumor functions in adoptive cell transfer therapies.

A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org.

RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.

Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases.

In this Special Issue (SI), with a theme of “Integrative Genomics and Systems Medicine in Cancer”, we have collected a total of 12 research and review articles from researchers in the field of genomics and systems medicine