TRAF4 promotes TGF-ß receptor signaling and drives breast cancer metastasis.

TGF-ß signaling is a therapeutic target in advanced cancers. We identified tumor necrosis factor receptor-associated factor 4 (TRAF4) as a key component mediating pro-oncogenic TGF-ß-induced SMAD and non-SMAD signaling. Upon TGF-ß stimulation, TRAF4 is recruited to the active TGF-ß receptor complex, where it antagonizes E3 ligase SMURF2 and facilitates the recruitment of deubiquitinase USP15 to the TGF-ß type I receptor (TßRI). Both processes contribute to TßRI stabilization on the plasma membrane and thereby enhance TGF-ß signaling. In addition, the TGF-ß receptor-TRAF4 interaction triggers Lys 63-linked TRAF4 polyubiquitylation and subsequent activation of the TGF-ß-activated kinase (TAK)1. TRAF4 is required for efficient TGF-ß-induced migration, epithelial-to-mesenchymal transition, and breast cancer metastasis. Elevated TRAF4 expression correlated with increased levels of phosphorylated SMAD2 and phosphorylated TAK1 as well as poor prognosis among breast cancer patients. Our results demonstrate that TRAF4 can regulate the TGF-ß pathway and is a key determinant in breast cancer pathogenesis.

RNF12 controls embryonic stem cell fate and morphogenesis in zebrafish embryos by targeting Smad7 for degradation.

TGF-ß members are of key importance during embryogenesis and tissue homeostasis. Smad7 is a potent antagonist of TGF-ß family/Smad-mediated responses, but the regulation of Smad7 activity is not well understood. We identified the RING domain-containing E3 ligase RNF12 as a critical component of TGF-ß signaling. Depletion of RNF12 dramatically reduced TGF-ß/Smad-induced effects in mammalian cells, whereas ectopic expression of RNF12 strongly enhanced these responses. RNF12 specifically binds to Smad7 and induces its polyubiquitination and degradation. Smad7 levels were increased in RNF12-deficient mouse embryonic stem cells, resulting in mitigation of both BMP-mediated repression of neural induction and activin-induced anterior mesoderm formation. RNF12 also antagonized Smad7 during Nodal-dependent and BMP-dependent signaling and morphogenic events in early zebrafish embryos. The gastrulation defects induced by ectopic and depleted Smad7 were rescued in part by RNF12 gain and loss of function, respectively. These findings demonstrate that RNF12 plays a critical role in TGF-ß family signaling.

SETDB1 modulates PRC2 activity at developmental genes independently of H3K9 trimethylation in mouse ES cells.

SETDB1, a histone methyltransferase responsible for methylation of histone H3 lysine 9 (H3K9), is involved in maintenance of embryonic stem (ES) cells and early embryonic development of the mouse. However, how SETDB1 regulates gene expression during development is largely unknown. Here, we characterized genome-wide SETDB1 binding and H3K9 trimethylation (H3K9me3) profiles in mouse ES cells and uncovered two distinct classes of SETDB1 binding sites, termed solo and ensemble peaks. The solo peaks were devoid of H3K9me3 and enriched near developmental regulators while the ensemble peaks were associated with H3K9me3. A subset of the SETDB1 solo peaks, particularly those near neural development-related genes, was found to be associated with Polycomb Repressive Complex 2 (PRC2) as well as PRC2-interacting proteins JARID2 and MTF2. Genetic deletion of Setdb1 reduced EZH2 binding as well as histone 3 lysine 27 (H3K27) trimethylation level at SETDB1 solo peaks and facilitated neural differentiation. Furthermore, we found that H3K27me3 inhibits SETDB1 methyltransferase activity. The currently identified reciprocal action between SETDB1 and PRC2 reveals a novel mechanism underlying ES cell pluripotency and differentiation regulation.

Fine-tuning BMP7 signalling in adipogenesis by UBE2O/E2-230K-mediated monoubiquitination of SMAD6.

SMAD6 is a crucial feedback inhibitory regulator of bone morphogenetic protein (BMP)/SMAD signalling. Although little is known regarding the post-transcriptional modification of inhibitory SMADs and the mechanism by which their function is regulated. In this study, using a whole proteomic interaction screen for SMAD6, we identified a large putative E2 ubiquitin-conjugating enzyme UBE2O (E2-230K) as a novel interacting protein of SMAD6. We showed that UBE2O functions as an E2-E3 hybrid to monoubiquitinate SMAD6 at lysine 174 and that the cysteine 885 residue of human UBE2O is necessary for SMAD6 monoubiquitination. Inactivation of the SMAD6 monoubiquitination site specially potentiates the inhibitory ability of SMAD6 against BMP7-induced SMAD1 phosphorylation and transcriptional responses. We also found that UBE2O potentiated BMP7 signalling in a SMAD6-dependent manner. Addressing the molecular mechanism by which UBE2O and monoubiquitinated SMAD6 potentiate BMP7 signalling, we demonstrated that monoubiquitinated SMAD6 impairs the binding affinity of non-modified SMAD6 to the BMP type I receptor. Moreover, UBE2O and SMAD6 cooperated in the regulation of BMP7-induced adipogenesis.

Split luciferase-based biosensors for characterizing EED binders.

The EED (embryonic ectoderm development) subunit of the Polycomb repressive complex 2 (PRC2) plays an important role in the feed forward regulation of the PRC2 enzymatic activity. We recently identified a new class of allosteric PRC2 inhibitors that bind to the H3K27me3 pocket of EED. Multiple assays were developed and used to identify and characterize this type of PRC2 inhibitors. One of them is a genetically encoded EED biosensor based on the EED[G255D] mutant and the split firefly luciferase. This EED biosensor can detect the compound binding in the transfected cells and in the in vitro biochemical assays. Compared to other commonly used cellular assays, the EED biosensor assay has the advantage of shorter compound incubation with cells. The in vitro EED biosensor is much more sensitive than other label-free biophysical assays (e.g. DSF, ITC). Based on the crystal structure, the DSF data as well as the biosensor assay data, it's most likely that compound-induced increase in the luciferase activity of the EED[G255D] biosensor results from the decreased non-productive interactions between the EED subdomain and other subdomains within the biosensor construct. This new insight of the mechanism might help to broaden the use of the split luciferase based biosensors.

Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling.

The stability of the Wnt pathway transcription factor beta-catenin is tightly regulated by the multi-subunit destruction complex. Deregulated Wnt pathway activity has been implicated in many cancers, making this pathway an attractive target for anticancer therapies. However, the development of targeted Wnt pathway inhibitors has been hampered by the limited number of pathway components that are amenable to small molecule inhibition. Here, we used a chemical genetic screen to identify a small molecule, XAV939, which selectively inhibits beta-catenin-mediated transcription. XAV939 stimulates beta-catenin degradation by stabilizing axin, the concentration-limiting component of the destruction complex. Using a quantitative chemical proteomic approach, we discovered that XAV939 stabilizes axin by inhibiting the poly-ADP-ribosylating enzymes tankyrase 1 and tankyrase 2. Both tankyrase isoforms interact with a highly conserved domain of axin and stimulate its degradation through the ubiquitin-proteasome pathway. Thus, our study provides new mechanistic insights into the regulation of axin protein homeostasis and presents new avenues for targeted Wnt pathway therapies.

Selective inhibition of Ezh2 by a small molecule inhibitor blocks tumor cells proliferation.

Ezh2 (Enhancer of zeste homolog 2) protein is the enzymatic component of the Polycomb repressive complex 2 (PRC2), which represses gene expression by methylating lysine 27 of histone H3 (H3K27) and regulates cell proliferation and differentiation during embryonic development. Recently, hot-spot mutations of Ezh2 were identified in diffused large B-cell lymphomas and follicular lymphomas. To investigate if tumor growth is dependent on the enzymatic activity of Ezh2, we developed a potent and selective small molecule inhibitor, EI1, which inhibits the enzymatic activity of Ezh2 through direct binding to the enzyme and competing with the methyl group donor S-Adenosyl methionine. EI1-treated cells exhibit genome-wide loss of H3K27 methylation and activation of PRC2 target genes. Furthermore, inhibition of Ezh2 by EI1 in diffused large B-cell lymphomas cells carrying the Y641 mutations results in decreased proliferation, cell cycle arrest, and apoptosis. These results provide strong validation of Ezh2 as a potential therapeutic target for the treatment of cancer.

Absolute quantification of histone PTM marks by MRM-based LC-MS/MS.

The N-terminal tails of core histones harbor the sites of numerous post-translational modifications (PTMs) with important roles in the regulation of chromatin structure and function. Profiling histone PTM marks provides data that help understand the epigenetics events in cells and their connections with cancer and other diseases. Our previous study demonstrated that specific derivatization of histone peptides by NHS propionate significantly improved their chromatographic performance on reversed phase columns for LC/MS analysis. As a step forward, we recently developed a multiple reaction monitoring (MRM) based LC-MS/MS method to analyze 42 targeted histone peptides. By using stable isotopic labeled peptides as internal standards that are spiked into the reconstituted solutions, this method allows to measure absolute concentration of the tryptic peptides of H3 histone proteins extracted from cancer cell lines. The method was thoroughly validated for the accuracy and reproducibility through analyzing recombinant histone proteins and cellular samples. The linear dynamic range of the MRM assays was achieved in 3 orders of magnitude from 1 nM to 1 µM for all targeted peptides. Excellent intrabatch and interbatch reproducibility (<15% CV) was obtained. This method has been used to study translocated NSD2 (a histone lysine methyltransferase that catalyzes the histone lysine 36 methylation) function with its overexpression in KMS11 multiple myeloma cells. From the results we have successfully quantitated both individual and combinatorial histone marks in parental and NSD2 selective knockout KMS11 cells.

Deep Learning for Visual Localization and Mapping: A Survey.

Deep-learning-based localization and mapping approaches have recently emerged as a new research direction and receive significant attention from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potential to track self-motion and estimate environmental models accurately and robustly for mobile agents. In this work, we provide a comprehensive survey and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising for localization and mapping, and how deep learning should be applied to solve this problem. To this end, a series of localization and mapping topics are investigated, from the learning-based visual odometry and global relocalization to mapping, and simultaneous localization and mapping (SLAM). It is our hope that this survey organically weaves together the recent works in this vein from robotics, computer vision, and machine learning communities and serves as a guideline for future researchers to apply deep learning to tackle the problem of visual localization and mapping.

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing.

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.

Structure of human SMYD2 protein reveals the basis of p53 tumor suppressor methylation.

SMYD2 belongs to a subfamily of histone lysine methyltransferase and was recently identified to methylate tumor suppressor p53 and Rb. Here we report that SMYD2 prefers to methylate p53 Lys-370 over histone substrates in vitro. Consistently, the level of endogenous p53 Lys-370 monomethylation is significantly elevated when SMYD2 is overexpressed in vivo. We have solved the high resolution crystal structures of the full-length SMYD2 protein in binary complex with its cofactor S-adenosylmethionine and in ternary complex with cofactor product S-adenosylhomocysteine and p53 substrate peptide (residues 368-375), respectively. p53 peptide binds to a deep pocket of the interface between catalytic SET(1-282) and C-terminal domain (CTD) with an unprecedented U-shaped conformation. Subtle conformational change exists around the p53 binding site between the binary and ternary structures, in particular the tetratricopeptide repeat motif of the CTD. In addition, a unique EDEE motif between the loop of anti-parallel ß7 and ß8 sheets of the SET core not only interacts with p53 substrate but also forms a hydrogen bond network with residues from CTD. These observations suggest that the tetratricopeptide repeat and EDEE motif may play an important role in determining p53 substrate binding specificity. This is further verified by the findings that deletion of the CTD domain drastically reduces the methylation activity of SMYD2 to p53 protein. Meanwhile, mutation of EDEE residues impairs both the binding and the enzymatic activity of SMYD2 to p53 Lys-370. These data together reveal the molecular basis of SMYD2 in specifically recognizing and regulating functions of p53 tumor suppressor through Lys-370 monomethylation.

Bone overgrowth-associated mutations in the LRP4 gene impair sclerostin facilitator function.

Humans lacking sclerostin display progressive bone overgrowth due to increased bone formation. Although it is well established that sclerostin is an osteocyte-secreted bone formation inhibitor, the underlying molecular mechanisms are not fully elucidated. We identified in tandem affinity purification proteomics screens LRP4 (low density lipoprotein-related protein 4) as a sclerostin interaction partner. Biochemical assays with recombinant proteins confirmed that sclerostin LRP4 interaction is direct. Interestingly, in vitro overexpression and RNAi-mediated knockdown experiments revealed that LRP4 specifically facilitates the previously described inhibitory action of sclerostin on Wnt1/ß-catenin signaling. We found the extracellular ß-propeller structured domain of LRP4 to be required for this sclerostin facilitator activity. Immunohistochemistry demonstrated that LRP4 protein is present in human and rodent osteoblasts and osteocytes, both presumed target cells of sclerostin action. Silencing of LRP4 by lentivirus-mediated shRNA delivery blocked sclerostin inhibitory action on in vitro bone mineralization. Notably, we identified two mutations in LRP4 (R1170W and W1186S) in patients suffering from bone overgrowth. We found that these mutations impair LRP4 interaction with sclerostin and its concomitant sclerostin facilitator effect. Together these data indicate that the interaction of sclerostin with LRP4 is required to mediate the inhibitory function of sclerostin on bone formation, thus identifying a novel role for LRP4 in bone.

Detecting S-adenosyl-L-methionine-induced conformational change of a histone methyltransferase using a homogeneous time-resolved fluorescence-based binding assay.

A homogeneous time-resolved fluorescence (HTRF)-based binding assay has been established to measure the binding of the histone methyltransferase (HMT) G9a to its inhibitor CJP702 (a biotin analog of the known peptide-pocket inhibitor, BIX-01294). This assay was used to characterize G9a inhibitors. As expected, the peptide-pocket inhibitors decreased the G9a-CJP702 binding signal in a concentration-dependent manner. In contrast, the S-adenosyl-L-methionine (SAM)-pocket compounds, SAM and sinefungin, significantly increased the G9a-CJP702 binding signal, whereas S-adenosyl-L-homocysteine (SAH) showed minimal effect. Enzyme kinetic studies showed that CJP702 is an uncompetitive inhibitor (vs. SAM) that has a strong preference for the E:SAM form of the enzyme. Other data presented suggest that the SAM/sinefungin-induced increase in the HTRF signal is secondary to an increased E:SAM or E:sinefungin concentration. Thus, the G9a-CJP702 binding assay not only can be used to characterize the peptide-pocket inhibitors but also can detect the subtle conformational differences induced by the binding of different SAM-pocket compounds. To our knowledge, this is the first demonstration of using an uncompetitive inhibitor as a probe to monitor the conformational change induced by compound binding with an HTRF assay.

Learning Selective Sensor Fusion for State Estimation.

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g., locations and orientations. Although deep learning (DL) approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion--a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module that can be applied to useful pairs of sensor modalities, such as monocular images and inertial measurements, depth images, and light detection and ranging (LIDAR) point clouds. Our model is a uniform framework that is not restricted to specific modality or task. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and to estimate trajectory at both scale and global pose. In particular, we propose two fusion modules--a deterministic soft fusion and a stochastic hard fusion--and offer a comprehensive study of the new strategies compared with trivial direct fusion. We extensively evaluate all fusion strategies both on public datasets and on progressively degraded datasets that present synthetic occlusions, noisy and missing data, and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself provides insights into the operation of the various models.

An effective AKT inhibitor-PARP inhibitor combination therapy for recurrent ovarian cancer.

BACKGROUND: Although the use of PARP inhibitor has received considerable amount of attention in ovarian cancer, PARP inhibitor resistance still emerges with disease progression. PI3K/AKT pathway inhibitors have been proposed to synergize with PARP inhibition to slow tumor growth, but the exact molecular mechanisms are still elusive. METHODS: Utilizing tumor samples from recurrent EOC patients with platinum resistance and prior PARP inhibitor use, Mini PDX and PDX models were established to study the anti-tumor effect of AKT inhibitor (LAE003) and LAE003/PARP inhibitor (Olaparib) in combination. Five ovarian cancer cell lines were treated with Olaparib or LAE003 or in combination in vitro. Cell viability and apoptosis rate were measured after the treatments. Combination index by the Chou-Talalay was used to evaluate in vitro combination effect of Olaparib and LAE003. The protein expression level of PARP1 and PAR was measured by Western blot in cell lines and by immunohistochemistry in PDX tumor tissues. RESULTS: Tumor cells from two out of five platinum-resistant ovarian cancer patients previously treated with PARP inhibitor were sensitive to AKT inhibition in Mini-PDX study. Inhibition of AKT further increased the response of tumor cells to Olaparib in a PDX model derived from a recurrent platinum-resistant ovarian cancer patient. Additive anti-proliferation effect of LAE003 and Olaparib was also observed in three ovarian cancer cell lines with high PARP1 protein level. Interestingly, mechanism study revealed that AKT inhibition decreased PARP enzyme activity as measured by PAR level and/or reduced PARP1 protein level in the tumor cell lines and PDX tumor tissues, which may explain the observed combined anti-tumor effect of LAE003 and Olaparib. CONCLUSION: Collectively, our results suggest that the combination of AKT inhibitor and PARP inhibitor could be a viable approach for clinical testing in recurrent ovarian cancer patients.

DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction.

Dynamical models estimate and predict the temporal evolution of physical systems. State-space models (SSMs) in particular represent the system dynamics with many desirable properties, such as being able to model uncertainty in both the model and measurements, and optimal (in the Bayesian sense) recursive formulations, e.g., the Kalman filter. However, they require significant domain knowledge to derive the parametric form and considerable hand tuning to correctly set all the parameters. Data-driven techniques, e.g., recurrent neural networks, have emerged as compelling alternatives to SSMs with wide success across a number of challenging tasks, in part due to their impressive capability to extract relevant features from rich inputs. They, however, lack interpretability and robustness to unseen conditions. Thus, data-driven models are hard to be applied in safety-critical applications, such as self-driving vehicles. In this work, we present DynaNet, a hybrid deep learning and time-varying SSM, which can be trained end-to-end. Our neural Kalman dynamical model allows us to exploit the relative merits of both SSM and deep neural networks. We demonstrate its effectiveness in the estimation and prediction on a number of physically challenging tasks, including visual odometry, sensor fusion for visual-inertial navigation, and motion prediction. In addition, we show how DynaNet can indicate failures through investigation of properties, such as the rate of innovation (Kalman gain).

Circulating Cell-Free DNA-Based Detection of Tumor Suppressor Gene Copy Number Loss and Its Clinical Implication in Metastatic Prostate Cancer.

Current liquid biopsy assays lack sufficient sensitivity to detect copy number loss, which limits the interrogation of critical tumor suppressor gene deletions during cancer progression and treatment. Here we describe a liquid biopsy assay with improved sensitivity for detection of copy number loss in blood samples with low levels of circulating tumor DNA, and demonstrate its utility by profiling PTEN, RB1, and TP53 genetic loss in metastatic prostate cancer patients.

The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications.

Natural language processing (NLP) plays a vital role in modern medical informatics. It converts narrative text or unstructured data into knowledge by analyzing and extracting concepts. A comprehensive lexical system is the foundation to the success of NLP applications and an essential component at the beginning of the NLP pipeline. The SPECIALIST Lexicon and Lexical Tools, distributed by the National Library of Medicine as one of the Unified Medical Language System Knowledge Sources, provides an underlying resource for many NLP applications. This article reports recent developments of 3 key components in the Lexicon. The core NLP operation of Unified Medical Language System concept mapping is used to illustrate the importance of these developments. Our objective is to provide generic, broad coverage and a robust lexical system for NLP applications. A novel multiword approach and other planned developments are proposed.

Spell checker for consumer language (CSpell).

Objective: Automated understanding of consumer health inquiries might be hindered by misspellings. To detect and correct various types of spelling errors in consumer health questions, we developed a distributable spell-checking tool, CSpell, that handles nonword errors, real-word errors, word boundary infractions, punctuation errors, and combinations of the above. Methods: We developed a novel approach of using dual embedding within Word2vec for context-dependent corrections. This technique was used in combination with dictionary-based corrections in a 2-stage ranking system. We also developed various splitters and handlers to correct word boundary infractions. All correction approaches are integrated to handle errors in consumer health questions. Results: Our approach achieves an F1 score of 80.93% and 69.17% for spelling error detection and correction, respectively. Discussion: The dual-embedding model shows a significant improvement (9.13%) in F1 score compared with the general practice of using cosine similarity with word vectors in Word2vec for context ranking. Our 2-stage ranking system shows a 4.94% improvement in F1 score compared with the best 1-stage ranking system. Conclusion: CSpell improves over the state of the art and provides near real-time automatic misspelling detection and correction in consumer health questions. The software and the CSpell test set are available at https://umlslex.nlm.nih.gov/cSpell.

Enhanced LexSynonym Acquisition for Effective UMLS Concept Mapping.

Concept mapping is important in natural language processing (NLP) for bioinformatics. The UMLS Metathesaurus provides a rich synonym thesaurus and is a popular resource for concept mapping. Query expansion using synonyms for subterm substitutions is an effective technique to increase recall for UMLS concept mapping. Synonyms used to substitute subterms are called element synonyms. The completeness and quality of both element synonyms and the UMLS synonym thesaurus is the key to success in such applications. The Lexical Systems Group (LSG) has developed a new system for element synonym acquisition based on new enhanced requirements and design for better performance. The results show: 1) A 36.71 times growth of synonyms in the Lexicon (lexSynonym) in the 2017 release; 2) Improvements of concept mapping for recall and F1 with similar precision using the lexSynonym.2017 as element synonyms due to the broader coverage and better quality.