Prediction of EVT6-NTRK3-Dependent Papillary Thyroid Cancer Using Minor Expression Profile.

Solid tumors resulting from oncogenic stimulation of neurotrophin receptors (TRK) by chimeric proteins are a group of rare tumors of various localization that respond to therapy with targeted drugs entrectinib and larotrectinib. The standard method for detecting chimeric TRK genes in tumor samples today is considered to be next generation sequencing with the determination of the prime structure of the chimeric transcripts. We hypothesized that expression of the chimeric tyrosine kinase proteins in tumors can determine the specific transcriptomic profile of tumor cells. We detected differentially expressed genes allowing distinguishing between TRK-dependent tumors papillary thyroid cancer (TC) from other molecular variants of tumors of this type. Using PCR with reverse transcription (RT-PCR), we identified 7 samples of papillary TC carrying a EVT6-NTRK3 rearrangement (7/215, 3.26%). Using machine learning and the data extracted from TCGA, we developed of a recognition function for predicting the presence of rearrangement in NTRK genes based on the expression of 10 key genes: AUTS2, DTNA, ERBB4, HDAC1, IGF1, KDR, NTRK1, PASK, PPP2R5B, and PRSS1. The recognition function was used to analyze the expression data of the above genes in 7 TRK-dependent and 10 TRK-independent thyroid tumors obtained by RT-PCR. On the test samples from TCGA, the sensitivity was 72.7%, the specificity - 99.6%. On our independent validation samples tested by RT-PCR, sensitivity was 100%, specificity - 70%. We proposed an mRNA profile of ten genes that can classify TC in relation to the presence of driver NTRK-chimeric TRK genes with acceptable sensitivity and specificity.

[Master regulators associated with poor prognosis in glioblastoma multiforme]. / Master-reguliatory, sviazannye s plokhim prognozom pri glioblastome.

Glioblastoma multiforme (GBM) is a highly malignant brain tumor with average survival time of 15 months. Less than 2% of the patients survive beyond 36 months. To understand the molecular mechanism responsible for poor prognosis, we analyzed GBM samples of TCGA microarray (n=560) data. We have identified 720 genes that have a significant impact upon survival based on univariate cox regression. We applied the Genome Enhancer pipeline to analyze potential mechanisms of regulation of activity of these genes and to build gene regulatory networks. We identified 12 transcription factors enriched in the promoters of these genes including the key molecule of GBM - STAT3. We found that STAT3 had significant differential expression across extreme survivor groups (short-term survivors- survival 36 months) and also had a significant impact on survival. In the next step, we identified master regulators in the signal transduction network that regulate the activity of these transcription factors. Master regulators are filtered based on their differential expression across extreme survivors groups and impact on survival. This work validates our earlier report on master regulators IGFBP2, PDGFA, OSMR, and AEBP1 driving short survival. Additionally, we propose CD14, CD44, DUSP6, GRB10, IL1RAP, FGFR3, and POSTN as master regulators driving poor survival. These master regulators are proposed as promising therapeutic targets to counter poor prognosis in GBM. Finally, the algorithm has prioritized several drugs for the further study as potential remedies to conquer the aggressive forms of GBM and to extend survival of the patients.

Towards a European health research and innovation cloud (HRIC).

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Corrigendum to "Data on master regulators and transcription factor binding sites found by upstream analysis of multi-omics data on methotrexate resistance of colon cancer" [Data Brief. 10 (2016) 499-504].

[This corrects the article DOI: 10.1016/j.dib.2016.11.096.].

c-Jun N-terminal kinase 2 promotes enterocyte survival and goblet cell differentiation in the inflamed intestine.

c-Jun N-terminal kinases (JNKs) contribute to immune signaling but their functional role during intestinal mucosal inflammation has remained ill defined. Using genetic mouse models, we characterized the role of JNK1 and JNK2 during homeostasis and acute colitis. Epithelial apoptosis, regeneration, differentiation, and barrier function were analyzed in intestinal epithelium-specific (ΔIEC) or complete JNK1 and bone marrow chimeric or complete JNK2 deficient mice as well as double-knockout animals (JNK1ΔIECJNK2-/-) during homeostasis and acute dextran sulfate sodium (DSS)-induced colitis. Results were confirmed using human HT-29 cells and wild-type or JNK2-deficient mouse intestinal organoid cultures. We show that nonhematopoietic JNK2 but not JNK1 expression confers protection from DSS-induced intestinal inflammation reducing epithelial barrier dysfunction and enterocyte apoptosis. JNK2 additionally enhanced Atonal homolog 1 expression, goblet cell and enteroendocrine cell differentiation, and mucus production under inflammatory conditions. Our results identify a protective role of epithelial JNK2 signaling to maintain mucosal barrier function, epithelial cell integrity, and mucus layer production in the event of inflammatory tissue damage.

Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved in the detection of experimentally validated PPIs, the noise in the data is still an important issue to overcome. In the last decade several in silico PPI prediction methods using both structural and genomic information were developed for this purpose. Here we introduce a unique validation approach aimed to collect reliable non interacting proteins (NIPs). Thereafter the most relevant protein/protein-pair related features were selected. Finally, the prepared dataset was used for PPI classification, leveraging the prediction capabilities of well-established machine learning methods. Our best classification procedure displayed specificity and sensitivity values of 96.33% and 98.02%, respectively, surpassing the prediction capabilities of other methods, including those trained on gold standard datasets. We showed that the PPI/NIP predictive performances can be considerably improved by focusing on data preparation.

Prediction of protein-DNA interactions of transcription factors linking proteomics and transcriptomics data.

We compared positional weight matrix-based prediction methods for transcription factor (TF) binding sites using selected fraction of ChIP-seq data with the help of partial AUC measure (limited to false positive rate 0.1, that is the most relevant for the application of the TF search in the genome scale). Comparison of three prediction methods-additive, multiplicative and information-vector based (MATCH) showed an advantage of the MATCH method for majority of transcription factors tested. We demonstrated that application of TF site identifying methods can help to connect the proteomics and phosphoproteomics world of signaling networks to gene regulation and transcriptomics world.

[THE BIOMARKERS FOR TIMELY DIAGNOSTICS OF COLORECTAL CANCER].

The colorectal cancer (CC) is one of the most widespread type of cancer all over the world. It is confirmed that the screening procedures intended for timely detection of CC and adenomatous polyps, significantly decrease mortality. The colonoscopy and analysis offeces for occult blood are widely applied as screening procedures. However, they have a number of shortcomings. The studies of the last decade revealed number of genetic and epigenetic markers potentially permitting revealing patients with CC at early stages of development of disease. The article analyzes CC-specific microRNA and their possible interactions with different transcriptional factors. These factors, being integrated into the structure of so called network s with direct signal propagation, ensure special stability of all regulatory system. The derangement of functioning of these networks quite often results in pathological alterations.

Equal impact of diffusion and DNA binding rates on the potential spatial distribution of nuclear factor κB transcription factor inside the nucleus.

There are two physical processes that influence the spatial distribution of transcription factor molecules entering the nucleus of a eukaryotic cell, the binding to genomic DNA and the diffusion throughout the nuclear volume. Comparison of the DNA-protein association rate constant and the protein diffusion constant may determine which one is the limiting factor. If the process is diffusion-limited, transcription factor molecules are captured by DNA before their even distribution in the nuclear volume. Otherwise, if the reaction rate is limiting, these molecules diffuse evenly and then find their binding sites. Using well-studied human NF-κB dimer as an example, we calculated its diffusion constant using the Debye-Smoluchowski equation. The value of diffusion constant was about 10(-15) cm(3)/s, and it was comparable to the NF-κB association rate constant for DNA binding known from previous studies. Thus, both diffusion and DNA binding play an equally important role in NF-κB spatial distribution. The importance of genome 3D-structure in gene expression regulation and possible dependence of gene expression on the local concentration of open chromatin can be hypothesized from our theoretical estimate.

ROS-dependent activation of JNK converts p53 into an efficient inhibitor of oncogenes leading to robust apoptosis.

Rescue of the p53 tumor suppressor is an attractive cancer therapy approach. However, pharmacologically activated p53 can induce diverse responses ranging from cell death to growth arrest and DNA repair, which limits the efficient application of p53-reactivating drugs in clinic. Elucidation of the molecular mechanisms defining the biological outcome upon p53 activation remains a grand challenge in the p53 field. Here, we report that concurrent pharmacological activation of p53 and inhibition of thioredoxin reductase followed by generation of reactive oxygen species (ROS), result in the synthetic lethality in cancer cells. ROS promote the activation of c-Jun N-terminal kinase (JNK) and DNA damage response, which establishes a positive feedback loop with p53. This converts the p53-induced growth arrest/senescence to apoptosis. We identified several survival oncogenes inhibited by p53 in JNK-dependent manner, including Mcl1, PI3K, eIF4E, as well as p53 inhibitors Wip1 and MdmX. Further, we show that Wip1 is one of the crucial executors downstream of JNK whose ablation confers the enhanced and sustained p53 transcriptional response contributing to cell death. Our study provides novel insights for manipulating p53 response in a controlled way. Further, our results may enable new pharmacological strategy to exploit abnormally high ROS level, often linked with higher aggressiveness in cancer, to selectively kill cancer cells upon pharmacological reactivation of p53.

Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis.

The tumor-suppressor p53 can induce various biological responses. Yet, it is not clear whether it is p53 in vivo promoter selectivity that triggers different transcription programs leading to different outcomes. Our analysis of genome-wide chromatin occupancy by p53 using chromatin immunoprecipitation (ChIP)-seq revealed 'p53 default program', that is, the pattern of major p53-bound sites that is similar upon p53 activation by nutlin3a, reactivation of p53 and induction of tumor cell apoptosis (RITA) or 5-fluorouracil in breast cancer cells, despite different biological outcomes. Parallel analysis of gene expression allowed identification of 280 novel p53 target genes, including p53-repressed AURKA. We identified Sp1 as one of the p53 modulators, which confer specificity to p53-mediated transcriptional response upon RITA. Further, we found that STAT3 antagonizes p53-mediated repression of a subset of genes, including AURKA.

3DTF: a web server for predicting transcription factor PWMs using 3D structure-based energy calculations.

We present the webserver 3D transcription factor (3DTF) to compute position-specific weight matrices (PWMs) of transcription factors using a knowledge-based statistical potential derived from crystallographic data on protein-DNA complexes. Analysis of available structures that can be used to construct PWMs shows that there are hundreds of 3D structures from which PWMs could be derived, as well as thousands of proteins homologous to these. Therefore, we created 3DTF, which delivers binding matrices given the experimental or modeled protein-DNA complex. The webserver can be used by biologists to derive novel PWMs for transcription factors lacking known binding sites and is freely accessible at http://www.gene-regulation.com/pub/programs/3dtf/.

[Comparative analysis of the DXPas34 regulatory region in rodents].

Mouse X chromosome inactivation center contains the DXPas34 minisatellite locus which plays an important role in expression regulation of the Tsix and Xist genes, involved into female dosage compensation. Comparative analysis of the DXPas34 locus from mouse, rat, and four common vole species revealed similar organization of this region in the form of tandem repeat blocks. A search for functionally important elements in this locus showed that all the species examined carried the conservative motif monomers, which could be involved in regulation of X inactivation.

[Molecular genetic characterization of the regulatory region of the Xist gene in the common vole Microtus rossiaemeridionalis].

Two conserved regions were discovered as a result of interspecific comparison of the 5'-region of the Xist gene, which is the key gene in the process of X-chromosome inactivation in mammalian females. The first region corresponds to the minimal promoter, and the second spans between -480 bp and -400 bp from the start of Xist transcription. Footprinting experiments revealed protected regions corresponding to the potential binding sites for TBP, SP1, API, SRY, ER, and some other transcription factors. They also demonstrated the interaction with the minimal promoter of the human recombinant transcription factor SP1 in vitro and of the transcription factor CTCF in vivo. Experiments with reporter constructs showed that repressors of Xist transcription were located between -100 bp and -200 bp and between -300 bp and -400 bp and activators of Xist transcription were located between -200 bp and -300 bp and between -400 bp and -500 bp.

In silico method for identification of promising anticancer drug targets.

In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known as potential drugs for therapy of malignant diseases; for some other targets we identified hits in the commercially available sample databases.

ExPlain: finding upstream drug targets in disease gene regulatory networks.

Different signal transduction pathways leading to the activation of transcription factors (TFs) converge at key molecules that master the regulation of many cellular processes. Such crossroads of signalling networks often appear as "Achilles Heels" causing a disease when not functioning properly. Novel computational tools are needed for analysis of the gene expression data in the context of signal transduction and gene regulatory pathways and for identification of the key nodes in the networks. An integrated computational system, ExPlain (www.biobase.de) was developed for causal interpretation of gene expression data and identification of key signalling molecules. The system utilizes data from two databases (TRANSFAC and TRANSPATH) and integrates two programs: (1) Composite Module Analyst (CMA) analyses 5'-upstream regions of co-expressed genes and applies a genetic algorithm to reveal composite modules (CMs) consisting of co-occurring single TF binding sites and composite elements; (2) ArrayAnalyzer is a fast network search engine that analyses signal transduction networks controlling the activities of the corresponding TFs and seeks key molecules responsible for the observed concerted gene activation. ExPlain system was applied to microarray data on inflammatory bowel diseases (IBD). The results obtained suggest a number of highly interesting biological hypotheses about molecular mechanisms of pathological genetic disregulation.

Composite Module Analyst: identification of transcription factor binding site combinations using genetic algorithm.

Composite Module Analyst (CMA) is a novel software tool aiming to identify promoter-enhancer models based on the composition of transcription factor (TF) binding sites and their pairs. CMA is closely interconnected with the TRANSFAC database. In particular, CMA uses the positional weight matrix (PWM) library collected in TRANSFAC and therefore provides the possibility to search for a large variety of different TF binding sites. We model the structure of the long gene regulatory regions by a Boolean function that joins several local modules, each consisting of co-localized TF binding sites. Having as an input a set of co-regulated genes, CMA builds the promoter model and optimizes the parameters of the model automatically by applying a genetic-regression algorithm. We use a multicomponent fitness function of the algorithm which includes several statistical criteria in a weighted linear function. We show examples of successful application of CMA to a microarray data on transcription profiling of TNF-alpha stimulated primary human endothelial cells. The CMA web server is freely accessible at http://www.gene-regulation.com/pub/programs/cma/CMA.html. An advanced version of CMA is also a part of the commercial system ExPlaintrade mark (www.biobase.de) designed for causal analysis of gene expression data.

Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations.

MOTIVATION: Functionally related genes involved in the same molecular-genetic, biochemical or physiological process are often regulated coordinately. Such regulation is provided by precisely organized binding of a multiplicity of special proteins [transcription factors (TFs)] to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. RESULTS: Here we present a new approach for defining promoter models based on the composition of TF binding sites and their pairs. We utilize a multicomponent fitness function for selection of the promoter model that fits best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated and permutated data. AVAILABILITY: The CMA program is freely available for non-commercial users. URL http://www.gene-regulation.com/pub/programs.html#CMAnalyst. It is also a part of the commercial system ExPlain (www.biobase.de) designed for causal analysis of gene expression data..

TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes.

The TRANSFAC database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match and Patch provides increased functionality for TRANSFAC. The list of databases which are linked to the common GENE table of TRANSFAC and TRANSCompel has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD and TRANSPRO. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC 7.0 and TRANSCompel 7.0, are accessible under http://www.gene-regulation.com/pub/databases.html.

P-Match: transcription factor binding site search by combining patterns and weight matrices.

P-Match is a new tool for identifying transcription factor (TF) binding sites in DNA sequences. It combines pattern matching and weight matrix approaches thus providing higher accuracy of recognition than each of the methods alone. P-Match is closely interconnected with the TRANSFAC database. In particular, P-Match uses the matrix library as well as sets of aligned known TF-binding sites collected in TRANSFAC and therefore provides the possibility to search for a large variety of different TF binding sites. Using results of extensive tests of recognition accuracy, we selected three sets of optimized cut-off values that minimize either false negatives or false positives, or the sum of both errors. Comparison with the weight matrix approaches such as Matchtrade mark tool shows that P-Match generally provides superior recognition accuracy in the area of low false negative errors (high sensitivity). As familiar to the user of Matchtrade mark, P-Match also allows to save user-specific profiles that include selected subsets of matrices with corresponding TF-binding sites or user-defined cut-off values. Furthermore, a number of tissue-specific profiles are provided that were compiled by the TRANSFAC team. A public version of the P-Match tool is available at http://www.gene-regulation.com/cgi-bin/pub/programs/pmatch/bin/p-match.cgi.