TFClass: expanding the classification of human transcription factors to their mammalian orthologs

TFClass is a resource that classifies eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs), available online at http://tfclass.bioinf.med.uni-goettingen.de. The classification scheme of TFClass was originally derived for human TFs and is expanded here to the whole taxonomic class of mammalia. Combining information from different resources, checking manually the retrieved mammalian TFs sequences and applying extensive phylogenetic analyses, >39 000 TFs from up to 41 mammalian species were assigned to the Superclasses, Classes, Families and Subfamilies of TFClass. As a result, TFClass now provides the corresponding sequence collection in FASTA format, sequence logos and phylogenetic trees at different classification levels, predicted TF binding sites for human, mouse, dog and cow genomes as well as links to several external databases. In particular, all those TFs that are also documented in the TRANSFAC^® database (FACTOR table) have been linked and can be freely accessed. TRANSFAC^® FACTOR can also be queried through an own search interface.

Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer

BACKGROUND

The search for molecular biomarkers of early-onset colorectal cancer (CRC) is an important but still quite challenging and unsolved task. Detection of CpG methylation in human DNA obtained from blood or stool has been proposed as a promising approach to a noninvasive early diagnosis of CRC. Thousands of abnormally methylated CpG positions in CRC genomes are often located in non-coding parts of genes. Novel bioinformatic methods are thus urgently needed for multi-omics data analysis to reveal causative biomarkers with a potential driver role in early stages of cancer.

METHODS

We have developed a method for finding potential causal relationships between epigenetic changes (DNA methylations) in gene regulatory regions that affect transcription factor binding sites (TFBS) and gene expression changes. This method also considers the topology of the involved signal transduction pathways and searches for positive feedback loops that may cause the carcinogenic aberrations in gene expression. We call this method "Walking pathways", since it searches for potential rewiring mechanisms in cancer pathways due to dynamic changes in the DNA methylation status of important gene regulatory regions ("epigenomic walking").

RESULTS

In this paper, we analysed an extensive collection of full genome gene-expression data (RNA-seq) and DNA methylation data of genomic CpG islands (using Illumina methylation arrays) generated from a sample of tumor and normal gut epithelial tissues of 300 patients with colorectal cancer (at different stages of the disease) (data generated in the EU-supported SysCol project). Identification of potential epigenetic biomarkers of DNA methylation was performed using the fully automatic multi-omics analysis web service "My Genome Enhancer" (MGE) (my-genome-enhancer.com). MGE uses the database on gene regulation TRANSFAC®, the signal transduction pathways database TRANSPATH®, and software that employs AI (artificial intelligence) methods for the analysis of cancer-specific enhancers.

CONCLUSIONS

The identified biomarkers underwent experimental testing on an independent set of blood samples from patients with colorectal cancer. As a result, using advanced methods of statistics and machine learning, a minimum set of 6 biomarkers was selected, which together achieve the best cancer detection potential. The markers include hypermethylated positions in regulatory regions of the following genes: CALCA, ENO1, MYC, PDX1, TCF7, ZNF43.

Emergence of linezolid- and vancomycin-resistant Enterococcus faecium in a department for hematologic stem cell transplantation

Background

Prevalence of vancomycin-resistant enterococci has increased in Germany. Here, we report the cluster of linezolid- and vancomycin-resistant Enterococcus faecium (LVRE) in a German department for hematologic stem cell transplantation (HSCT).

Methods

In this retrospective analysis we included all patients with LVRE in a university-based department for HSCT in 2014 and 2015. Patients chart reviews were used to investigate the epidemiology and clinical outcome. Available LVRE isolates underwent detailed microbiological characterization and genotyping by pulsed-field gel electrophoresis (PFGE).

Results

In total, 20 patients with LVRE were identified within the observed time period. All except two patients underwent allogeneic HSCT. Surveillance culture results from incoming patients and chart review revealed that 10 of 20 patients were colonized at hospital admission. Eight of 10 patients with in-hospital acquired LVRE had previous linezolid treatment. Analysis of spatio-temporal patterns showed no evidence for LVRE patient-to-patient or environment-to-patient transmission within the HSCT department. In five cases (25 %) LVRE bloodstream infection occurred. Nine LVRE isolates could be saved for characterization. Eight isolates carried vanA, one isolate vanB. PFGE analysis showed that four different LVRE clones were responsible for the cluster. One single genotype was present in six LVRE isolates whereupon the corresponding patients were all referred from the same hospital to the HSCT department.

Conclusions

This is the first report demonstrating the emergence of LVRE in a German HSCT department. (L)VRE screening at patients’ admission and appropriate infection control strategies were sufficient to prevent any transmission. Further studies in this predisposed patient collective are warranted.

Establishing and validating regulatory regions for variant annotation and expression analysis

BACKGROUND

The regulatory effect of inherited or de novo genetic variants occurring in promoters as well as in transcribed or even coding gene regions is gaining greater recognition as a contributing factor to disease processes in addition to mutations affecting protein functionality. Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis.

RESULTS

In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding.

CONCLUSIONS

We have confirmed the validity of TRANSFAC's approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants.

TRANSPATH--a high quality database focused on signal transduction

TRANSPATH can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyser. Therefore, three modules have been created: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence faciliates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes. More information is available at www.biobase.de/pages/products/databases.html.

Molecular mechanistic associations of human diseases

Background

The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes.

Results

Using about 10000 manually collected causal disease/gene associations, we developed a statistical approach to infer meaningful associations between human morbidities. The derived method clustered cardiometabolic and endocrine disorders, immune system-related diseases, solid tissue neoplasms and neurodegenerative pathologies into prominent disease groups. Analysis of biological functions confirmed characteristic features of corresponding disease clusters. Inference of disease associations was further employed as a starting point for prediction of disease genes. Efforts were made to underpin the validity of results by relevant literature evidence. Interestingly, many inferred disease relationships correspond to known clinical associations and comorbidities, and several predicted disease genes were subjects of therapeutic target research.

Conclusions

Causal molecular mechanisms present a unifying principle to derive methods for disease classification, analysis of clinical disorder associations, and prediction of disease genes. According to the definition of causal disease genes applied in this study, these results are not restricted to genetic disease/gene relationships. This may be particularly useful for the study of long-term or chronic illnesses, where pathological derangement due to environmental or as part of sequel conditions is of importance and may not be fully explained by genetic background.

TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations

TRANSPATH is a database about signal transduction events. It provides information about signaling molecules, their reactions and the pathways these reactions constitute. The representation of signaling molecules is organized in a number of orthogonal hierarchies reflecting the classification of the molecules, their species-specific or generic features, and their post-translational modifications. Reactions are similarly hierarchically organized in a three-layer architecture, differentiating between reactions that are evidenced by individual publications, generalizations of these reactions to construct species-independent 'reference pathways' and the 'semantic projections' of these pathways. A number of search and browse options allow easy access to the database contents, which can be visualized with the tool PathwayBuildertrade mark. The module PathoSign adds data about pathologically relevant mutations in signaling components, including their genotypes and phenotypes. TRANSPATH and PathoSign can be used as encyclopaedia, in the educational process, for vizualization and modeling of signal transduction networks and for the analysis of gene expression data. TRANSPATH Public 6.0 is freely accessible for users from non-profit organizations under http://www.gene-regulation.com/pub/databases.html.

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.

Consistent re-modeling of signaling pathways and its implementation in the TRANSPATH database

The data model of the signaling pathways database TRANSPATH has been re-engineered to a three-layer model comprising experimental evidences and summarized pathway information, both in a mechanistically detailed manner, and a "semantic" projection for the abstract overview. Each molecule is described in the context of a certain reaction in the multidimensional space of posttranslational modification, molecular family relationships, and the biological species of its origin. The new model makes the data better suitable for reconstructing signaling pathways and networks and mapping expression data, for instance from microarray experiments, onto regulatory networks.

TRANSPATH: an integrated database on signal transduction and a tool for array analysis

TRANSPATH is a database system about gene regulatory networks that combines encyclopedic information on signal transduction with tools for visualization and analysis. The integration with TRANSFAC, a database about transcription factors and their DNA binding sites, provides the possibility to obtain complete signaling pathways from ligand to target genes and their products, which may themselves be involved in regulatory action. As of July 2002, the TRANSPATH Professional release 3.2 contains about 9800 molecules, >1800 genes and >11 400 reactions collected from approximately 5000 references. With the ArrayAnalyzer, an integrated tool has been developed for evaluation of microarray data. It uses the TRANSPATH data set to identify key regulators in pathways connected with up- or down-regulated genes of the respective array. The key molecules and their surrounding networks can be viewed with the PathwayBuilder, a tool that offers four different modes of visualization. More information on TRANSPATH is available at http://www.biobase.de/pages/products/databases.html.

The TRANSPATH signal transduction database: a knowledge base on signal transduction networks

TRANSPATH is an information system on gene-regulatory pathways, and an extension module to the TRANSFAC database system (Wingender et al., Nucleic Acids Res., 28, 316-319, 2000). It focuses on pathways involved in the regulation of transcription factors in different species, mainly human, mouse and rat. Elements of the relevant signal transduction pathways like complexes, signaling molecules, and their states are stored together with information about their interaction in an object-oriented database. The database interface provides clickable maps and automatically generated pathway cascades as additional ways to explore the data. All information is validated with references to the original publications. Also, references to other databases are provided (TRANSFAC, SWISS-PROT, EMBL, PubMed and others).

AVAILABILITY

The database is available over (http://transpath.gbf.de) for interactive perusal. As an exchange format for the data, eXtensible Markup Language (XML) flatfiles and a Document Type Definition (DTD) are provided.

The TRANSFAC system on gene expression regulation

The TRANSFAC database on transcription factors and their DNA-binding sites and profiles (http://www.gene-regulation.de/) has been quantitatively extended and supplemented by a number of modules. These modules give information about pathologically relevant mutations in regulatory regions and transcription factor genes (PathoDB), scaffold/matrix attached regions (S/MARt DB), signal transduction (TRANSPATH) and gene expression sources (CYTOMER). Altogether, these distinct database modules constitute the TRANSFAC system. They are accompanied by a number of program routines for identifying potential transcription factor binding sites or for localizing individual components in the regulatory network of a cell.

Role of nitric oxide and phosphodiesterase isoenzyme II for reduction of endothelial hyperpermeability

Regulation of endothelial permeability is poorly understood. Previous studies have shown that endothelial cells contain phosphodiesterase (PDE) isoenzymes II-IV and that simultaneous adenylate cyclase activation and/or PDE inhibition blocked endothelial hyperpermeability (J.Clin.Invest. 91: 1421-1428, 1993). We now focused on a possible role for guanosine 3',5'-cyclic monophosphate (cGMP)-dependent mechanisms and studied H2O2-exposed porcine pulmonary artery endothelial cell monolayers. Pretreatment of cells with different nitric oxide (NO) donors or atrial natriuretic peptide (ANP) increased endothelial cGMP-content severalfold and blocked H2O2-related effects on permeability; opposite results were obtained with a NO synthase inhibitor. Determination of cGMP degradation in nitroprusside-exposed endothelial cells identified PDE II as the major cGMP metabolizing pathway, whereas PDE III and IV contributed little or nothing. Inhibition of PDE II reduced H2O2-related endothelial hyperpermeability, an effect that could be enhanced synergistically by simultaneous guanylate cyclase activation. In summary, these studies indicate that cGMP-dependent mechanisms (NO donors, ANP, and dibutyryl-cGMP) blocked H2O2-related increases in endothelial permeability. The major cGMP degrading pathway in endothelial cells was PDE II, thereby substituting the missing PDE V in these cells. Simultaneous guanylate cyclase activation and/or PDE II inhibition may be a valuable approach to treat endothelial hyperpermeability.