A computational framework to in silico screen for drug-induced hepatocellular toxicity.

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted compounds with high DILI risk can induce abnormal morphological changes in the endoplasmic reticulum and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI risk.

Systematic investigation of chemo-immunotherapy synergism to shift anti-PD-1 resistance in cancer.

Chemo-immunotherapy combinations have been regarded as one of the most practical ways to improve immunotherapy response in cancer patients. In this study, we integrate the transcriptomics data from anti-PD-1-treated tumors and compound-treated cancer cell lines to systematically screen for chemo-immunotherapy synergisms in silico. Through analyzing anti-PD-1 induced expression changes in patient tumors, we develop a shift ability score to measure if a chemotherapy or a small molecule inhibitor treatment can shift anti-PD-1 resistance in tumor cells. By applying shift ability analysis to 41,321 compounds and 16,853 shRNA treated cancer cell lines transcriptomic data, we characterize the landscape of chemo-immunotherapy synergism and experimentally validated a mitochondrial RNA-dependent mechanism for drug-induced immune activation in tumor. Our study represents an effort to mechanistically characterize chemo-immunotherapy synergism and will facilitate future pre-clinical and clinical studies.

Schwann cell promotes macrophage recruitment through IL-17B/IL-17RB pathway in injured peripheral nerves.

Macrophage recruitment to the injured nerve initiates a cascade of events, including myelin debris clearance and nerve trophic factor secretion, which contribute to proper nerve tissue repair. However, the mechanism of macrophage recruitment is still unclear. Here, by comparing wild-type with Mlkl-/- and Sarm1-/- mice, two mouse strains with impaired myelin debris clearance after peripheral nerve injury, we identify interleukin-17B (IL-17B) as a key regulator of macrophage recruitment. Schwann-cell-secreted IL-17B acts in an autocrine manner and binds to IL-17 receptor B to promote macrophage recruitment, and global or Schwann-cell-specific IL-17B deletion reduces macrophage infiltration, myelin clearance, and axon regeneration. We also show that the IL-17B signaling pathway is defective in the injured central nerves. These results reveal an important role for Schwann cell autocrine signaling during Wallerian degeneration and point to potential mechanistic targets for accelerating myelin clearance and improving demyelinating disease.

Systematic investigation of chemo-immunotherapy synergism to shift anti-PD-1 resistance in cancer.

Chemo-immunotherapy combinations have been regarded as one of the most practical ways to improve immunotherapy response in cancer patients. In this study, we integrated the transcriptomics data from immunotherapy-treated tumors and compound-treated cell lines to systematically identify chemo-immunotherapy synergisms and their underlying mechanisms. Through analyzing anti-PD-1 treatment induced expression changes in patient tumors, we developed a shift ability score that can measure whether a chemotherapy treatment shifts anti-PD-1 response. By applying the shift ability analysis on 41,321 compounds and 16,853 shRNA treated cancer cell line expression profiles, we characterized a systematic landscape of chemo-immunotherapy synergism and prioritized 17 potential synergy targets. Further investigation of the treatment induced transcriptomic data revealed that a mitophagy-dsRNA-MAVS-dependent activation of type I IFN signaling may be a novel mechanism for chemo-immunotherapy synergism. Our study represents the first comprehensive effort to mechanistically characterize chemo-immunotherapy synergism and will facilitate future pre-clinical and clinical studies.

Genome-wide gain-of-function screening characterized lncRNA regulators for tumor immune response.

The majority of lncRNAs' roles in tumor immunology remain elusive. This project performed a CRISPR activation screening of 9744 lncRNAs in melanoma cells cocultured with human CD8+ T cells. We identified 16 lncRNAs potentially regulating tumor immune response. Further integrative analysis using tumor immunogenomics data revealed that IL10RB-DT and LINC01198 are significantly correlated with tumor immune response and survival in melanoma and breast cancer. Specifically, IL10RB-DT suppresses CD8+ T cells activation via inhibiting IFN-γ-JAK-STAT1 signaling and antigen presentation in melanoma and breast cancer cells. On the other hand, LINC01198's up-regulation sensitizes the killing of tumor cells by CD8+ T cells. Mechanistically, LINC01198 interacts and activates NF-κB component p65 to trigger the type I and type II interferon responses in melanoma and breast cancer cells. Our study systematically characterized novel lncRNAs involved in tumor immune response.

A Computational Framework to Characterize the Cancer Drug Induced Effect on Aging Using Transcriptomic Data.

Cancer treatments such as chemotherapies may change or accelerate aging trajectories in cancer patients. Emerging evidence has shown that "omics" data can be used to study molecular changes of the aging process. Here, we integrated the drug-induced and normal aging transcriptomic data to computationally characterize the potential cancer drug-induced aging process in patients. Our analyses demonstrated that the aging-associated gene expression in the GTEx dataset can recapitulate the well-established aging hallmarks. We next characterized the drug-induced transcriptomic changes of 28 FDA approved cancer drugs in brain, kidney, muscle, and adipose tissues. Further drug-aging interaction analysis identified 34 potential drug regulated aging events. Those events include aging accelerating effects of vandetanib (Caprelsa®) and dasatinib (Sprycel®) in brain and muscle, respectively. Our result also demonstrated aging protective effect of vorinostat (Zolinza®), everolimus (Afinitor®), and bosutinib (Bosulif®) in brain.

Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens.

Background: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens. Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.

Targeting castration-resistant prostate cancer with a novel RORγ antagonist elaiophylin.

Prostate cancer (PCa) patients who progress to metastatic castration-resistant PCa (mCRPC) mostly have poor outcomes due to the lack of effective therapies. Our recent study established the orphan nuclear receptor RORγ as a novel therapeutic target for CRPC. Here, we reveal that elaiophylin (Elai), an antibiotic from Actinomycete streptomyces, is a novel RORγ antagonist and showed potent antitumor activity against CRPC in vitro and in vivo. We demonstrated that Elai selectively binded to RORγ protein and potently blocked RORγ transcriptional regulation activities. Structure-activity relationship studies showed that Elai occupied the binding pocket with several key interactions. Furthermore, Elai markedly reduced the recruitment of RORγ to its genomic DNA response element (RORE), suppressed the expression of RORγ target genes AR and AR variants, and significantly inhibited PCa cell growth. Importantly, Elai strongly suppressed tumor growth in both cell line based and patient-derived PCa xenograft models. Taken together, these results suggest that Elai is novel therapeutic RORγ inhibitor that can be used as a drug candidate for the treatment of human CRPC.