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Jumonji C-domain-containing protein 6 (JMJD6), an iron (Fe 2+ ) and α-ketoglutarate (α-KG)-dependent oxygenase, is expressed at high levels, correlated with poor prognosis, and considered as a therapeutic target in multiple cancer types. However, specific JMJD6 inhibitors that are potent in suppressing tumorigenesis have not been reported so far. We herein report that iJMJD6, a specific small-molecule inhibitor of JMJD6 with favorable physiochemical properties, inhibits the enzymatic activity of JMJD6 protein both in vitro and in cultured cells. iJMJD6 is effective in suppressing cell proliferation, migration, and invasion in multiple types of cancer cells in a JMJD6-dependent manner, while it exhibits minimal toxicity in normal cells. Mechanistically, iJMJD6 represses the expression of oncogenes, including Myc and CCND1, in accordance with JMJD6 function in promoting the transcription of these genes. iJMJD6 exhibits suitable pharmacokinetic properties and suppresses tumor growth in multiple cancer cell line– and patient-derived xenograft models safely. Furthermore, combination therapy with iJMJD6 and BET protein inhibitor (BETi) JQ1 or estrogen receptor antagonist fulvestrant exhibits synergistic effects in suppressing tumor growth. Taken together, we demonstrate that inhibition of JMJD6 enzymatic activity by using iJMJD6 is effective in suppressing oncogene expression and cancer development, providing a therapeutic avenue for treating cancers that are dependent on JMJD6 in the clinic.

While protein arginine methyltransferases (PRMTs) and PRMT-catalyzed protein methylation have been well-known to be involved in a myriad of biological processes, their functions and the underlying molecular mechanisms in cancers, particularly in estrogen receptor alpha (ERα)-positive breast cancers, remain incompletely understood. Here we focused on investigating PRMT4 (also called coactivator associated arginine methyltransferase 1, CARM1) in ERα-positive breast cancers due to its high expression and the associated poor prognosis. Methods: ChIP-seq and RNA-seq were employed to identify the chromatin-binding landscape and transcriptional targets of CARM1, respectively, in the presence of estrogen in ERα-positive MCF7 breast cancer cells. High-resolution mass spectrometry analysis of enriched peptides from anti-monomethyl- and anti-asymmetric dimethyl-arginine antibodies in SILAC labeled wild-type and CARM1 knockout cells were performed to globally map CARM1 methylation substrates. Cell viability was measured by MTS and colony formation assay, and cell cycle was measured by FACS analysis. Cell migration and invasion capacities were examined by wound-healing and trans-well assay, respectively. Xenograft assay was used to analyze tumor growth in vivo. Results: CARM1 was found to be predominantly and specifically recruited to ERα-bound active enhancers and essential for the transcriptional activation of cognate estrogen-induced genes in response to estrogen treatment. Global mapping of CARM1 substrates revealed that CARM1 methylated a large cohort of proteins with diverse biological functions, including regulation of intracellular estrogen receptor-mediated signaling, chromatin organization and chromatin remodeling. A large number of CARM1 substrates were found to be exclusively hypermethylated by CARM1 on a cluster of arginine residues. Exemplified by MED12, hypermethylation of these proteins by CARM1 served as a molecular beacon for recruiting coactivator protein, tudor-domain-containing protein 3 (TDRD3), to CARM1-bound active enhancers to activate estrogen/ERα-target genes. In consistent with its critical role in estrogen/ERα-induced gene transcriptional activation, CARM1 was found to promote cell proliferation of ERα-positive breast cancer cells in vitro and tumor growth in mice. Conclusions: our study uncovered a “hypermethylation” strategy utilized by enhancer-bound CARM1 in gene transcriptional regulation, and suggested that CARM1 can server as a therapeutic target for breast cancer treatment.

[Image: see text] The 2-oxoglutarate (2OG)-dependent oxygenase JMJD6 is emerging as a potential anticancer target, but its inhibitors have not been reported so far. In this study, we reported an in silico protocol to discover JMJD6 inhibitors targeting the druggable 2OG-binding site. Following this protocol, one compound, which we named as WL12, was found to be able to inhibit JMJD6 enzymatic activity and JMJD6-dependent cell proliferation. To our best knowledge, this is the first case in drug discovery targeting JMJD6.

Yin Yang 1 (YY1) is a multifunctional transcription factor shown to be critical in a variety of biological processes. Although it is regulated by multiple types of post-translational modifications (PTMs), whether YY1 is methylated, which enzyme methylates YY1 and hence the functional significance of YY1 methylation remains completely unknown. Here we reported the first methyltransferase, SET7/9 (KMT7), capable of methylating YY1 at two highly conserved lysine (K) residues, K173 and K411, located in two distinct domains, one in the central glycine-rich region and the other in the very carboxyl-terminus. Functional studies revealed that SET7/9-mediated YY1 methylation regulated YY1 DNA-binding activity both in vitro and at specific genomic loci in cultured cells. Consistently, SET7/9-mediated YY1 methylation was shown to involve in YY1-regulated gene transcription and cell proliferation. Our findings revealed a novel regulatory strategy, methylation by lysine methyltransferase, imposed on YY1 protein and linked YY1 methylation with its biological functions.

Whereas the actions of enhancers in gene transcriptional regulation are well established, roles of JmjC domain-containing proteins in mediating enhancer activation remain poorly understood. Here we report that recruitment of the JmjC domain-containing protein 6 (JMJD6) to estrogen receptor alpha (ERα)-bound active enhancers is required for RNA polymerase II recruitment and enhancer RNA production on enhancers, resulting in transcriptional pause release of cognate estrogen target genes. JMJD6 was found to interact with MED12 in the mediator complex to regulate its recruitment. Unexpectedly, JMJD6 is necessary for MED12 to interact with CARM1, which methylates MED12 at multiple arginine sites and regulates its chromatin binding. Consistent with its role in transcriptional activation, JMJD6 is required for estrogen/ERα-induced breast cancer cell growth and tumorigenesis. Our data have uncovered a critical regulator of estrogen/ERα-induced enhancer, coding gene activation and breast cancer cell potency, providing a potential therapeutic target of ER-positive breast cancers.

The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane. The model fully considers the sequence-order effects between residues a certain distance apart in the sequence by AC combined with eight representative descriptors for both common proteins and membrane proteins. As a result of jackknife cross-validation tests, the method for submitochondria location prediction yields the accuracies of 91.8%, 96.4% and 66.1% for inner membrane, matrix, and outer membrane, respectively. The total accuracy is 89.7%. When predicting membrane protein types in mitochondria inner membrane, the method achieves the prediction performance with the accuracies of 98.4%, 64.3% and 86.7% for multi-pass inner membrane, single-pass inner membrane, and matrix side inner membrane, where the total accuracy is 93.6%. The overall performance of our method is better than the achievements of the previous studies. So our method can be an effective supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://chemlab.scu.edu.cn/Predict_subMITO/index.htm.

The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information; PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.

Membrane transporters are critical in living cells. Therefore, the discrimination of the types of membrane proteins based on their functions is of great importance both for helping genome annotation and providing a supplementary role to experimental researchers to gain insight into membrane proteins' function. There are a lot of computational methods to facilitate the identification of the functional types of membrane proteins. However, in these methods, the local sequence environment was not integrated into the constructed model. In this study, we described a new strategy to predict the functional types of membrane proteins using a model based on auto covariance and position-specific scoring matrix. The novelty of the presented approach is considering the distribution of different positions of functional conservation sites in protein sequences. Thereby, this model adequately takes into account the long-range correlation between such sites during sequential evolution. Fivefold cross-validation test shows that this method greatly improves the prediction accuracy and achieves an acceptable prediction accuracy of 87.51%. The result indicates that the current approach might be an effective tool for predicting the functional types of membrane proteins only using the primary sequences. The code and dataset used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/predict_membrane.zip.