Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF–target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF–target interaction database for humans—TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)—which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF–target interactions in mice, including 6552 TF–target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF–target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

Pathway-targeted cancer drugs can produce dramatic responses that are invariably limited by the emergence of drug-resistant cells. We found that many drug-treated "oncogene-addicted" cancer cells engage a positive feedback loop leading to Stat3 activation, consequently promoting cell survival and limiting overall drug response. This was observed in cancer cells driven by diverse activated kinases, including EGFR, HER2, ALK, and MET, as well as mutant KRAS. Specifically, MEK inhibition led to autocrine activation of Stat3 via the FGF receptor and JAK kinases, and pharmacological inhibition of MEK together with JAK and FGFR enhanced tumor regression. These findings suggest that inhibition of a Stat3 feedback loop may augment the response to a broad spectrum of drugs that target pathways of oncogene addiction.

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The objective of this study was to clarify whether the neutrophil-lymphocyte ratio (NLR) and the platelet-lymphocyte ratio (PLR) are significant prognostic markers in patients with resectable colorectal cancer (CRC). A total of 200 patients who underwent curative resection for CRC were enrolled. The NLR and PLR were positively correlated (p < 0.001). Both the NLR and PLR were shown to be good prognostic biomarkers of overall survival (OS) (p=0.002 and p=0.001, respectively). The PLR was an independent prognostic factor of OS based on multivariate analysis (hazard ratio, 1.971; 95% confidence interval, 1.102-3.335; p=0.021).

Broad applications of zinc finger nuclease (ZFN) technology-which allows targeted genome editing-in research, medicine, and biotechnology are hampered by the lack of a convenient, rapid, and publicly available method for the synthesis of functional ZFNs. Here we describe an efficient and easy-to-practice modular-assembly method using publicly available zinc fingers to make ZFNs that can modify the DNA sequences of predetermined genomic sites in human cells. We synthesized and tested hundreds of ZFNs to target dozens of different sites in the human CCR5 gene-a co-receptor required for HIV infection-and found that many of these nucleases induced site-specific mutations in the CCR5 sequence.

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.

The KDM5 family of histone demethylases catalyzes the demethylation of histone H3 on lysine 4 (H3K4) and is required for the survival of drug-tolerant persister cancer cells (DTPs). Here we report the discovery and characterization of the specific KDM5 inhibitor CPI-455. The crystal structure of KDM5A revealed the mechanism of inhibition of CPI-455 as well as the topological arrangements of protein domains that influence substrate binding. CPI-455 mediated KDM5 inhibition, elevated global levels of H3K4 trimethylation (H3K4me3) and decreased the number of DTPs in multiple cancer cell line models treated with standard chemotherapy or targeted agents. These findings show that pretreatment of cancer cells with a KDM5-specific inhibitor results in the ablation of a subpopulation of cancer cells that can serve as the founders for therapeutic relapse.

The basal-like subtype of breast cancer has an aggressive clinical behavior compared to that of the luminal subtype. We identified the microRNAs (miRNAs) miR-221 and miR-222 (miR-221/222) as basal-like subtype-specific miRNAs and showed that expression of miR-221/222 decreased expression of epithelial-specific genes and increased expression of mesenchymal-specific genes, and increased cell migration and invasion in a manner characteristic of the epithelial-to-mesenchymal transition (EMT). The transcription factor FOSL1 (also known as Fra-1), which is found in basal-like breast cancers but not in the luminal subtype, stimulated the transcription of miR-221/222, and the abundance of these miRNAs decreased with inhibition of the epidermal growth factor receptor (EGFR) or MEK (mitogen-activated or extracellular signal-regulated protein kinase kinase), placing miR-221/222 downstream of the RAS pathway. Furthermore, miR-221/222-mediated reduction in E-cadherin abundance depended on their targeting the 3' untranslated region of the GATA family transcriptional repressor TRPS1 (tricho-rhino-phalangeal syndrome type 1), which inhibited EMT by decreasing ZEB2 (zinc finger E-box-binding homeobox2) expression. We conclude that by promoting EMT, miR-221/222 may contribute to the more aggressive clinical behavior of basal-like breast cancers.