Identification of unique rectal cancer-specific subtypes

BACKGROUND

Existing colorectal cancer subtyping methods were generated without much consideration of potential differences in expression profiles between colon and rectal tissues. Moreover, locally advanced rectal cancers at resection often have received neoadjuvant chemoradiotherapy which likely has a significant impact on gene expression.

METHODS

We collected mRNA expression profiles for rectal and colon cancer samples (n = 2121). We observed that (i) Consensus Molecular Subtyping (CMS) had a different prognosis in treatment-naïve rectal vs. colon cancers, and (ii) that neoadjuvant chemoradiotherapy exposure produced a strong shift in CMS subtypes in rectal cancers. We therefore clustered 182 untreated rectal cancers to find rectal cancer-specific subtypes (RSSs).

RESULTS

We identified three robust subtypes. We observed that RSS1 had better, and RSS2 had worse disease-free survival. RSS1 showed high expression of MYC target genes and low activity of angiogenesis genes. RSS2 exhibited low regulatory T cell abundance, strong EMT and angiogenesis signalling, and high activation of TGF-β, NF-κB, and TNF-α signalling. RSS3 was characterised by the deactivation of EGFR, MAPK and WNT pathways.

CONCLUSIONS

We conclude that RSS subtyping allows for more accurate prognosis predictions in rectal cancers than CMS subtyping and provides new insight into targetable disease pathways within these subtypes.

Complexation of histone deacetylase inhibitor belinostat to Cu(II) prevents premature metabolic inactivation in vitro and demonstrates potent anti-cancer activity in vitro and ex vivo in colon cancer

PURPOSE

The histone deacetylase inhibitor (HDACi), belinostat, has had limited therapeutic impact in solid tumors, such as colon cancer, due to its poor metabolic stability. Here we evaluated a novel belinostat prodrug, copper-bis-belinostat (Cubisbel), in vitro and ex vivo, designed to overcome the pharmacokinetic challenges of belinostat.

METHODS

The in vitro metabolism of each HDACi was evaluated in human liver microsomes (HLMs) using mass spectrometry. Next, the effect of belinostat and Cubisbel on cell growth, HDAC activity, apoptosis and cell cycle was assessed in three colon cancer cell lines. Gene expression alterations induced by both HDACis were determined using RNA-Seq, followed by in silico analysis to identify master regulators (MRs) of differentially expressed genes (DEGs). The effect of both HDACis on the viability of colon cancer patient-derived tumor organoids (PDTOs) was also examined.

RESULTS

Belinostat and Cubisbel significantly reduced colon cancer cell growth mediated through HDAC inhibition and apoptosis induction. Interestingly, the in vitro half-life of Cubisbel was significantly longer than belinostat. Belinostat and its Cu derivative commonly dysregulated numerous signalling and metabolic pathways while genes downregulated by Cubisbel were potentially controlled by VEGFA, ERBB2 and DUSP2 MRs. Treatment of colon cancer PDTOs with the HDACis resulted in a significant reduction in cell viability and downregulation of stem cell and proliferation markers.

CONCLUSIONS

Complexation of belinostat to Cu(II) does not alter the HDAC activity of belinostat, but instead significantly enhances its metabolic stability in vitro and targets anti-cancer pathways by perturbing key MRs in colon cancer. Complexation of HDACis to a metal ion might improve the efficacy of clinically used HDACis in patients with colon cancer.

Comparative analysis of deeply phenotyped GBM cohorts of 'short-term' and 'long-term' survivors

BACKGROUND

Glioblastoma (GBM) is an aggressive brain cancer that typically results in death in the first 15 months after diagnosis. There have been limited advances in finding new treatments for GBM. In this study, we investigated molecular differences between patients with extremely short (≤ 9 months, Short term survivors, STS) and long survival (≥ 36 months, Long term survivors, LTS).

METHODS

Patients were selected from an in-house cohort (GLIOTRAIN-cohort), using defined inclusion criteria (Karnofsky score > 70; age < 70 years old; Stupp protocol as first line treatment, IDH wild type), and a multi-omic analysis of LTS and STS GBM samples was performed.

RESULTS

Transcriptomic analysis of tumour samples identified cilium gene signatures as enriched in LTS. Moreover, Immunohistochemical analysis confirmed the presence of cilia in the tumours of LTS. Notably, reverse phase protein array analysis (RPPA) demonstrated increased phosphorylated GAB1 (Y627), SRC (Y527), BCL2 (S70) and RAF (S338) protein expression in STS compared to LTS. Next, we identified 25 unique master regulators (MR) and 13 transcription factors (TFs) belonging to ontologies of integrin signalling and cell cycle to be upregulated in STS.

CONCLUSION

Overall, comparison of STS and LTS GBM patients, identifies novel biomarkers and potential actionable therapeutic targets for the management of GBM.

BioUML-towards a universal research platform

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

Master Transcription Regulators and Transcription Factors Regulate Immune-Associated Differences Between Patients of African and European Ancestry With Colorectal Cancer

Background and Aims

Individuals of African (AFR) ancestry have a higher incidence of colorectal cancer (CRC) than those of European (EUR) ancestry and exhibit significant health disparities. Previous studies have noted differences in the tumor microenvironment between AFR and EUR patients with CRC. However, the molecular regulatory processes that underpin these immune differences remain largely unknown.

Methods

Multiomics analysis was carried out for 55 AFR and 456 EUR patients with microsatellite-stable CRC using The Cancer Genome Atlas. We evaluated the tumor microenvironment by using gene expression and methylation data, transcription factor, and master transcriptional regulator analysis to identify the cell signaling pathways mediating the observed phenotypic differences.

Results

We demonstrate that downregulated genes in AFR patients with CRC showed enrichment for canonical pathways, including chemokine signaling. Moreover, evaluation of the tumor microenvironment showed that cytotoxic lymphocytes and neutrophil cell populations are significantly decreased in AFR compared with EUR patients, suggesting AFR patients have an attenuated immune response. We further demonstrate that molecules called “master transcriptional regulators” (MTRs) play a critical role in regulating the expression of genes impacting key immune processes through an intricate signal transduction network mediated by disease-associated transcription factors (TFs). Furthermore, a core set of these MTRs and TFs showed a positive correlation with levels of cytotoxic lymphocytes and neutrophils across both AFR and EUR patients with CRC, thus suggesting their role in driving the immune infiltrate differences between the two ancestral groups.

Conclusion

Our study provides an insight into the intricate regulatory landscape of MTRs and TFs that orchestrate the differences in the tumor microenvironment between patients with CRC of AFR and EUR ancestry.

An integrative proteomics method identifies a regulator of translation during stem cell maintenance and differentiation

Detailed characterization of cell type transitions is essential for cell biology in general and particularly for the development of stem cell-based therapies in regenerative medicine. To systematically study such transitions, we introduce a method that simultaneously measures protein expression and thermal stability changes in cells and provide the web-based visualization tool ProteoTracker. We apply our method to study differences between human pluripotent stem cells and several cell types including their parental cell line and differentiated progeny. We detect alterations of protein properties in numerous cellular pathways and components including ribosome biogenesis and demonstrate that modulation of ribosome maturation through SBDS protein can be helpful for manipulating cell stemness in vitro. Using our integrative proteomics approach and the web-based tool, we uncover a molecular basis for the uncoupling of robust transcription from parsimonious translation in stem cells and propose a method for maintaining pluripotency in vitro.

Transcriptome analysis reveals intrinsic pro-inflammatory signaling in healthy African American skin

Differences in morphology and physiology of darkly pigmented compared to lightly pigmented skin are well recognized. There are also disparities in prevalence and clinical features for many inflammatory skin diseases including atopic dermatitis and psoriasis; however, the underlying mechanisms are largely unknown. We compared the baseline gene expression in full thickness skin biopsies from healthy individuals self-reporting as African American (AA) or White Non-Hispanic (WNH). Extensively validated RNA-Seq analysis identified 570 differentially expressed genes (DEG) in AA skin including immunoglobulins and their receptors such as FCER1G; pro-inflammatory genes such as TNFα, IL-32; EDC (epidermal differentiation cluster) and keratin genes. DEGs were functionally enriched for inflammatory responses, keratinization, cornified envelope formation. RNA-seq analysis of 3D human skin equivalents (HSE) made from AA and WNH primary keratinocytes revealed 360 DEGs (some shared with skin) which were enriched by similar functions. AA HSE appeared more responsive to TNFα pro-inflammatory effects. Finally, AA-specific DEGs in skin and HSE significantly overlapped with molecular signatures of skin in AD and psoriasis patients. Overall, these findings suggest the existence of intrinsic pro-inflammatory circuits in AA keratinocytes/skin that may account for disease disparities and will help to build a foundation for the development of targeted skin disease prevention.

IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme

Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.

[Master regulators associated with poor prognosis in glioblastoma multiforme]

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.

GTRD: an integrated view of transcription regulation

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

Inflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics

Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied-particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.

BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data

BioUML (homepage: http://www.biouml.org, main public server: https://ict.biouml.org) is a web-based integrated environment (platform) for systems biology and the analysis of biomedical data generated by omics technologies. The BioUML vision is to provide a computational platform to build virtual cell, virtual physiological human and virtual patient. BioUML spans a comprehensive range of capabilities, including access to biological databases, powerful tools for systems biology (visual modelling, simulation, parameters fitting and analyses), a genome browser, scripting (R, JavaScript) and a workflow engine. Due to integration with the Galaxy platform and R/Bioconductor, BioUML provides powerful possibilities for the analyses of omics data. The plug-in-based architecture allows the user to add new functionalities using plug-ins. To facilitate a user focus on a particular task or database, we have developed several predefined perspectives that display only those web interface elements that are needed for a specific task. To support collaborative work on scientific projects, there is a central authentication and authorization system (https://bio-store.org). The diagram editor enables several remote users to simultaneously edit diagrams.

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.

Role of Phagocytosis in the Pro-Inflammatory Response in LDL-Induced Foam Cell Formation; a Transcriptome Analysis

Excessive accumulation of lipid inclusions in the arterial wall cells (foam cell formation) caused by modified low-density lipoprotein (LDL) is the earliest and most noticeable manifestation of atherosclerosis. The mechanisms of foam cell formation are not fully understood and can involve altered lipid uptake, impaired lipid metabolism, or both. Recently, we have identified the top 10 master regulators that were involved in the accumulation of cholesterol in cultured macrophages induced by the incubation with modified LDL. It was found that most of the identified master regulators were related to the regulation of the inflammatory immune response, but not to lipid metabolism. A possible explanation for this unexpected result is a stimulation of the phagocytic activity of macrophages by modified LDL particle associates that have a relatively large size. In the current study, we investigated gene regulation in macrophages using transcriptome analysis to test the hypothesis that the primary event occurring upon the interaction of modified LDL and macrophages is the stimulation of phagocytosis, which subsequently triggers the pro-inflammatory immune response. We identified genes that were up- or downregulated following the exposure of cultured cells to modified LDL or latex beads (inert phagocytosis stimulators). Most of the identified master regulators were involved in the innate immune response, and some of them were encoding major pro-inflammatory proteins. The obtained results indicated that pro-inflammatory response to phagocytosis stimulation precedes the accumulation of intracellular lipids and possibly contributes to the formation of foam cells. In this way, the currently recognized hypothesis that the accumulation of lipids triggers the pro-inflammatory response was not confirmed. Comparative analysis of master regulators revealed similarities in the genetic regulation of the interaction of macrophages with naturally occurring LDL and desialylated LDL. Oxidized and desialylated LDL affected a different spectrum of genes than naturally occurring LDL. These observations suggest that desialylation is the most important modification of LDL occurring in vivo. Thus, modified LDL caused the gene regulation characteristic of the stimulation of phagocytosis. Additionally, the knock-down effect of five master regulators, such as IL15, EIF2AK3, F2RL1, TSPYL2, and ANXA1, on intracellular lipid accumulation was tested. We knocked down these genes in primary macrophages derived from human monocytes. The addition of atherogenic naturally occurring LDL caused a significant accumulation of cholesterol in the control cells. The knock-down of the EIF2AK3 and IL15 genes completely prevented cholesterol accumulation in cultured macrophages. The knock-down of the ANXA1 gene caused a further decrease in cholesterol content in cultured macrophages. At the same time, knock-down of F2RL1 and TSPYL2 did not cause an effect. The results obtained allowed us to explain in which way the inflammatory response and the accumulation of cholesterol are related confirming our hypothesis of atherogenesis development based on the following viewpoints: LDL particles undergo atherogenic modifications that, in turn, accompanied by the formation of self-associates; large LDL associates stimulate phagocytosis; as a result of phagocytosis stimulation, pro-inflammatory molecules are secreted; these molecules cause or at least contribute to the accumulation of intracellular cholesterol. Therefore, it became obvious that the primary event in this sequence is not the accumulation of cholesterol but an inflammatory response.

Modified LDL Particles Activate Inflammatory Pathways in Monocyte-derived Macrophages: Transcriptome Analysis

Background:

A hallmark of atherosclerosis is its complex pathogenesis, which is dependent on altered cholesterol metabolism and inflammation. Both arms of pathogenesis involve myeloid cells. Monocytes migrating into the arterial walls interact with modified low-density lipoprotein (LDL) parti-cles, accumulate cholesterol and convert into foam cells, which promote plaque formation and also con-tribute to inflammation by producing pro-inflammatory cytokines. A number of studies characterized transcriptomics of macrophages following interaction with modified LDL, and revealed alteration of the expression of genes responsible for inflammatory response and cholesterol metabolism. However, it is still unclear how these two processes are related to each other to contribute to atherosclerotic lesion formation.

Methods:

We attempted to identify the main mater regulator genes in macrophages treated with athero-genic modified LDL using a bioinformatics approach.

Results:

We found that most of the identified genes were involved in inflammation, and none of them was implicated in cholesterol metabolism. Among the key identified genes were interleukin (IL)-7, IL-7 receptor, IL-15 and CXCL8.

Conclusion:

Our results indicate that activation of the inflammatory pathway is the primary response of the immune cells to modified LDL, while the lipid metabolism genes may be a secondary response trig-gered by inflammatory signalling

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.

BRCA-analyzer: Automatic workflow for processing NGS reads of BRCA1 and BRCA2 genes

The use of targeted next-generation sequencing (NGS) provides great new opportunities for molecular and medical genetics. However, in order to take advantage of these opportunities, we need to have reliable tools for extracting the necessary information from the huge amount of data generated by NGS. Here we present our automatic multithreaded workflow for processing NGS data of BRCA1 and BRCA2 genes obtained with NGS technology named BRCA-analyzer. Optimizing it on the sequencing data of 899 samples from 693 patients, we were able to find the most reliable tools and adjust their parameters in such a way that all pathogenic variants found were confirmed by Sanger's sequencing. For 82 and 24 DNA samples from blood and formalin-fixed paraffin-embedded blocks, NGS libraries were prepared with GeneRead BRCA panel v2 (Qiagen). The reads obtained were processed with BRCA-analyzer and Qiagen GeneRead Data analysis workflow. In total 27 pathogenic variants were found and confirmed by Sanger's sequencing, with all of them determined with BRCA-analyzer. Qiagen GeneRead Data analysis discarded 5 true pathogenic variants due to their location in homopolymeric sequence stretches. For other 793 samples, libraries were prepared by the in-house method, and NGS data were analyzed by BRCA-analyzer in comparison to another free automatic amplicon NGS workflow Canary. From total 137 pathogenic variations, BRCA-analyzer found 135 and Canary 123. Mutations were missed by BRCA-analyzer due to the trimming primer sequences from reads before mapping to be fixed in the next version. On the freely available NGS data, we showed that BRCA-analyzer could also be used for hybrid capture gene panels, although it needs more extensive testing on such library preparation methods. Thus, BRCA-analyzer is an automatic workflow for processing NGS data of BRCA1/2 genes with variant filters adapted to amplicon-based targeted NGS data. BRCA-analyzer can be used to identify germline as well as somatic mutations. BRCA-analyzer is freely available at https://github.com/aakechin/BRCA-analyzer.

Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

BACKGROUND

Small molecule Nutlin-3 reactivates p53 in cancer cells by interacting with the complex between p53 and its repressor Mdm-2 and causing an increase in cancer cell apoptosis. Therefore, Nutlin-3 has potent anticancer properties. Clinical and experimental studies of Nutlin-3 showed that some cancer cells may lose sensitivity to this compound. Here we analyze possible mechanisms for insensitivity of cancer cells to Nutlin-3.

METHODS

We applied upstream analysis approach implemented in geneXplain platform ( genexplain.com ) using TRANSFAC® database of transcription factors and their binding sites in genome and using TRANSPATH® database of signal transduction network with associated software such as Match™ and Composite Module Analyst (CMA).

RESULTS

Using genome-wide gene expression profiling we compared several lung cancer cell lines and showed that expression programs executed in Nutlin-3 insensitive cell lines significantly differ from that of Nutlin-3 sensitive cell lines. Using artificial intelligence approach embed in CMA software, we identified a set of transcription factors cooperatively binding to the promoters of genes up-regulated in the Nutlin-3 insensitive cell lines. Graph analysis of signal transduction network upstream of these transcription factors allowed us to identify potential master-regulators responsible for maintaining such low sensitivity to Nutlin-3 with the most promising candidate mTOR, which acts in the context of activated PI3K pathway. These finding were validated experimentally using an array of chemical inhibitors.

CONCLUSIONS

We showed that the Nutlin-3 insensitive cell lines are actually highly sensitive to the dual PI3K/mTOR inhibitor NVP-BEZ235, while no responding to either PI3K -specific LY294002 nor Bcl-XL specific 2,3-DCPE compounds.

Nucleotide patterns aiding in prediction of eukaryotic promoters

Computational analysis of promoters is hindered by the complexity of their architecture. In less studied genomes with complex organization, false positive promoter predictions are common. Accurate identification of transcription start sites and core promoter regions remains an unsolved problem. In this paper, we present a comprehensive analysis of genomic features associated with promoters and show that probabilistic integrative algorithms-driven models allow accurate classification of DNA sequence into “promoters” and “non-promoters” even in absence of the full-length cDNA sequences. These models may be built upon the maps of the distributions of sequence polymorphisms, RNA sequencing reads on genomic DNA, methylated nucleotides, transcription factor binding sites, as well as relative frequencies of nucleotides and their combinations. Positional clustering of binding sites shows that the cells of Oryza sativa utilize three distinct classes of transcription factors: those that bind preferentially to the [-500,0] region (188 “promoter-specific” transcription factors), those that bind preferentially to the [0,500] region (282 “5′ UTR-specific” TFs), and 207 of the “promiscuous” transcription factors with little or no location preference with respect to TSS. For the most informative motifs, their positional preferences are conserved between dicots and monocots.

cutPrimers: A New Tool for Accurate Cutting of Primers from Reads of Targeted Next Generation Sequencing

Cutting of primers from reads is an important step of processing targeted amplicon-based next generation sequencing data. Existing tools are adapted for cutting of one or several primer/adapter sequences from reads and removing all of their occurrences. Also most of the existing tools use kmers and may cut only part of primers or primers with studied sequence of gene. Because of this, use of such programs leads to incorrect trimming, reduction of coverage, and increase in the number of false-positive and/or false-negative results. We have developed a new tool named cutPrimers for accurate cutting of any number of primers from reads. Using sequencing reads that were obtained during study of BRCA1/2 genes, we compared it with cutadapt, AlienTrimmer, and BBDuk. All of them trimmed reads in such a way that coverage of at least two amplicons decreased to unacceptable level (<30 reads) comparing with reads trimmed with cutPrimers. At the same time, Trimmomatic and AlienTrimmer cut all occurrences of primer sequences, so the length of the remaining reads was less than prospective.

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.

GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments

GTRD—Gene Transcription Regulation Database (http://gtrd.biouml.org)—is a database of transcription factor binding sites (TFBSs) identified by ChIP-seq experiments for human and mouse. Raw ChIP-seq data were obtained from ENCODE and SRA and uniformly processed: (i) reads were aligned using Bowtie2; (ii) ChIP-seq peaks were called using peak callers MACS, SISSRs, GEM and PICS; (iii) peaks for the same factor and peak callers, but different experiment conditions (cell line, treatment, etc.), were merged into clusters; (iv) such clusters for different peak callers were merged into metaclusters that were considered as non-redundant sets of TFBSs. In addition to information on location in genome, the sets contain structured information about cell lines and experimental conditions extracted from descriptions of corresponding ChIP-seq experiments. A web interface to access GTRD was developed using the BioUML platform. It provides: (i) browsing and displaying information; (ii) advanced search possibilities, e.g. search of TFBSs near the specified gene or search of all genes potentially regulated by a specified transcription factor; (iii) integrated genome browser that provides visualization of the GTRD data: read alignments, peaks, clusters, metaclusters and information about gene structures from the Ensembl database and binding sites predicted using position weight matrices from the HOCOMOCO database.

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.

Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed.

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.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

A discriminative approach for unsupervised clustering of DNA sequence motifs

Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities.

Transcription factors play a central role in the regulation of gene expression. Their interaction with specific elements in the DNA mediates dynamic changes in transcriptional activity. Databases store a growing number of known DNA sequence patterns, also denoted as DNA sequence motifs that are recognized by transcription factors. Such databases can be searched to find a match for a newly discovered pattern and that way identify the potential binding factor. It is also of interest to cluster motifs in order to examine which transcription factors have similar binding properties and, thus, may promiscuously bind to each other's sites, or how many distinct specificities have been described. To gain deeper insight into the similarities between DNA sequence motifs, we analyzed a comprehensive set of known motifs. For this purpose we devised a network-based approach that enabled us to identify clusters of related motifs that largely coincided with grouping of related TFs on the basis of protein similarity. On the basis of these results, we were able to predict whether two motifs belong to the same subgroup and constructed a novel, fully-automated method for motif clustering, which enables users to assess the similarity of a newly found motif with all known motifs in the collection.

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/.

Mild mitochondrial uncoupling does not affect mitochondrial biogenesis but downregulates pyruvate carboxylase in adipocytes: role for triglyceride content reduction

In adipocytes, mitochondrial uncoupling is known to trigger a triglyceride loss comparable with the one induced by TNFα, a proinflammatory cytokine. However, the impact of a mitochondrial uncoupling on the abundance/composition of mitochondria and its connection with triglyceride content in adipocytes is largely unknown. In this work, the effects of a mild mitochondrial uncoupling triggered by FCCP were investigated on the mitochondrial population of 3T3-L1 adipocytes by both quantitative and qualitative approaches. We found that mild mitochondrial uncoupling does not stimulate mitochondrial biogenesis in adipocytes but induces an adaptive cell response characterized by quantitative modifications of mitochondrial protein content. Superoxide anion radical level was increased in mitochondria of both TNFα- and FCCP-treated adipocytes, whereas mitochondrial DNA copy number was significantly higher only in TNFα-treated cells. Subproteomic analysis revealed that the abundance of pyruvate carboxylase was reduced significantly in mitochondria of TNFα- and FCCP-treated adipocytes. Functional study showed that overexpression of this major enzyme of lipid metabolism is able to prevent the triglyceride content reduction in adipocytes exposed to mitochondrial uncoupling or TNFα. These results suggest a new mechanism by which the effects of mitochondrial uncoupling might limit triglyceride accumulation in adipocytes.

Inhibition of glycolytic enzymes mediated by pharmacologically activated p53: targeting Warburg effect to fight cancer

Unique sensitivity of tumor cells to the inhibition of glycolysis is a good target for anticancer therapy. Here, we demonstrate that the pharmacologically activated tumor suppressor p53 mediates the inhibition of glycolytic enzymes in cancer cells in vitro and in vivo. We showed that p53 binds to the promoters of metabolic genes and represses their expression, including glucose transporters SLC2A12 (GLUT12) and SLC2A1 (GLUT1). Furthermore, p53-mediated repression of transcription factors c-Myc and HIF1α, key drivers of ATP-generating pathways in tumors, contributed to ATP production block. Inhibition of c-Myc by p53 mediated the ablation of several glycolytic genes in normoxia, whereas in hypoxia down-regulation of HIF1α contributed to this effect. We identified Sp1 as a transcription cofactor cooperating with p53 in the ablation of metabolic genes. Using different approaches, we demonstrated that glycolysis block contributes to the robust induction of apoptosis by p53 in cancer cells. Taken together, our data suggest that tumor-specific reinstatement of p53 function targets the "Achilles heel" of cancer cells (i.e. their dependence on glycolysis), which could contribute to the tumor-selective killing of cancer cells by pharmacologically activated p53.

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.

Advanced computational biology methods identify molecular switches for malignancy in an EGF mouse model of liver cancer

The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs' transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regulated transcription factors to display specificity to either the pre-tumor or the tumor state. Subsequent search for signal transduction key nodes upstream of the identified transcription factors and their targets suggested the insulin-like growth factor pathway to render the tumor cells independent of EGF receptor activity. Notably, expression of IGF2 in addition to many components of this pathway was highly upregulated in tumors. Together, we propose a switch in autocrine signaling to foster tumor growth that was initially triggered by EGF and demonstrate the knowledge gain form promoter analysis combined with upstream key node identification.

Identification of key mechanisms controlling gene expression in Leishmania infected macrophages using genome-wide promoter analysis

The present study describes the in silico prediction of the regulatory network of Leishmania infected human macrophages. The construction of the gene regulatory network requires the identification of Transcription Factor Binding Sites (TFBSs) in the regulatory regions (promoters, enhancers) of genes that are regulated upon Leishmania infection. The promoters of human, mouse, rat, dog and chimpanzee genes were first identified in the whole genomes using available experimental data on full length cDNA sequences or deep CAGE tag data (DBTSS, FANTOM3, FANTOM4), mRNA models (ENSEMBL), or using hand annotated data (EPD, TRANSFAC). A phylogenetic footprinting analysis and a MATCH analysis of the promoter sequences were then performed to predict TFBS. Then, an SQL database that integrates all results of promoter analysis as well as other genome annotation information obtained from ENSEMBL, TRANSFAC, TRED (Transcription Regulatory Element Database), ORegAnno and the ENCODE project, was established. Finally publicly available expression data from human Leishmania infected macrophages were analyzed using the genome-wide information on predicted TFBS with the computer system ExPlain™. The gene regulatory network was constructed and activated signal transduction pathways were revealed. The Irak1 pathway was identified as a key pathway regulating gene expression changes in Leishmania infected macrophages.

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Background

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.

Results

We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.

Conclusions

It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.

Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies

Background

Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.

Results

The position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.

Conclusions

A novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.

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.

Ablation of key oncogenic pathways by RITA-reactivated p53 is required for efficient apoptosis

Targeting "oncogene addiction" is a promising strategy for anticancer therapy. We report a potent inhibition of crucial oncogenes by p53 upon reactivation by small-molecule RITA in vitro and in vivo. RITA-activated p53 unleashes the transcriptional repression of antiapoptotic proteins Mcl-1, Bcl-2, MAP4, and survivin; blocks the Akt pathway on several levels; and downregulates c-Myc, cyclin E, and beta-catenin. p53 ablates c-Myc expression via several mechanisms at the transcriptional and posttranscriptional level. We show that the threshold for p53-mediated transrepression of survival genes is higher than for transactivation of proapoptotic targets. Inhibition of oncogenes by p53 reduces the cell's ability to buffer proapoptotic signals and elicits robust apoptosis. Our study highlights the role of transcriptional repression for p53-mediated tumor suppression.

Building a knowledge base for systems pathology

Translating the exponentially growing amount of omics data into knowledge usable for a personalized medicine approach poses a formidable challenge. In this article-taking diabetes as a use case-we present strategies for developing data repositories into computer-accessible knowledge sources that can be used for a systemic view on the molecular causes of diseases, thus laying the foundation for systems pathology.

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.

Short sequence motifs, overrepresented in mammalian conserved non-coding sequences

Background

A substantial fraction of non-coding DNA sequences of multicellular eukaryotes is under selective constraint. In particular, ~5% of the human genome consists of conserved non-coding sequences (CNSs). CNSs differ from other genomic sequences in their nucleotide composition and must play important functional roles, which mostly remain obscure.

Results

We investigated relative abundances of short sequence motifs in all human CNSs present in the human/mouse whole-genome alignments vs. three background sets of sequences: (i) weakly conserved or unconserved non-coding sequences (non-CNSs); (ii) near-promoter sequences (located between nucleotides -500 and -1500, relative to a start of transcription); and (iii) random sequences with the same nucleotide composition as that of CNSs. When compared to non-CNSs and near-promoter sequences, CNSs possess an excess of AT-rich motifs, often containing runs of identical nucleotides. In contrast, when compared to random sequences, CNSs contain an excess of GC-rich motifs which, however, lack CpG dinucleotides. Thus, abundance of short sequence motifs in human CNSs, taken as a whole, is mostly determined by their overall compositional properties and not by overrepresentation of any specific short motifs. These properties are: (i) high AT-content of CNSs, (ii) a tendency, probably due to context-dependent mutation, of A's and T's to clump, (iii) presence of short GC-rich regions, and (iv) avoidance of CpG contexts, due to their hypermutability. Only a small number of short motifs, overrepresented in all human CNSs are similar to binding sites of transcription factors from the FOX family.

Conclusion

Human CNSs as a whole appear to be too broad a class of sequences to possess strong footprints of any short sequence-specific functions. Such footprints should be studied at the level of functional subclasses of CNSs, such as those which flank genes with a particular pattern of expression. Overall properties of CNSs are affected by patterns in mutation, suggesting that selection which causes their conservation is not always very strong.

CYCLONET--an integrated database on cell cycle regulation and carcinogenesis

Computational modelling of mammalian cell cycle regulation is a challenging task, which requires comprehensive knowledge on many interrelated processes in the cell. We have developed a web-based integrated database on cell cycle regulation in mammals in normal and pathological states (Cyclonet database). It integrates data obtained by ‘omics’ sciences and chemoinformatics on the basis of systems biology approach. Cyclonet is a specialized resource, which enables researchers working in the field of anticancer drug discovery to analyze the wealth of currently available information in a systematic way. Cyclonet contains information on relevant genes and molecules; diagrams and models of cell cycle regulation and results of their simulation; microarray data on cell cycle and on various types of cancer, information on drug targets and their ligands, as well as extensive bibliography on modelling of cell cycle and cancer-related gene expression data. The Cyclonet database is also accessible through the BioUML workbench, which allows flexible querying, analyzing and editing the data by means of visual modelling. Cyclonet aims to predict promising anticancer targets and their agents by application of Prediction of Activity Spectra for Substances. The Cyclonet database is available at .

Activation of TLX3 and NKX2-5 in t(5;14)(q35;q32) T-cell acute lymphoblastic leukemia by remote 3'-BCL11B enhancers and coregulation by PU.1 and HMGA1

In T-cell acute lymphoblastic leukemia, alternative t(5;14)(q35;q32.2) forms effect dysregulation of either TLX3 or NKX2-5 homeobox genes at 5q35 by juxtaposition with 14q32.2 breakpoints dispersed across the BCL11B downstream genomic desert. Leukemic gene dysregulation by t(5;14) was investigated by DNA inhibitory treatments with 26-mer double-stranded DNA oligonucleotides directed against candidate enhancers at, or near, orphan T-cell DNase I hypersensitive sites located between 3'-BCL11B and VRK1. NKX2-5 down-regulation in t(5;14) PEER cells was almost entirely restricted to DNA inhibitory treatment targeting enhancers within the distal breakpoint cluster region and was dose and sequence dependent, whereas enhancers near 3'-BCL11B regulated that gene only. Chromatin immunoprecipitation assays showed that the four most effectual NKX2-5 ectopic enhancers were hyperacetylated. These enhancers clustered approximately 1 Mbp downstream of BCL11B, within a region displaying multiple regulatory stigmata, including a TCRA enhancer motif, deep sequence conservation, and tight nuclear matrix attachment relaxed by trichostatin A treatment. Intriguingly, although TLX3/NKX2-5 promoter/exon 1 regions were hypoacetylated, their expression was trichostatin A sensitive, implying extrinsic regulation by factor(s) under acetylation control. Knockdown of PU.1, known to be trichostatin A responsive and which potentially binds TLX3/NKX2-5 promoters, effected down-regulation of both homeobox genes. Moreover, genomic analysis showed preferential enrichment near ectopic enhancers of binding sites for the PU.1 cofactor HMGA1, the knockdown of which also inhibited NKX2-5. We suggest that HMGA1 and PU.1 coregulate ectopic homeobox gene expression in t(5;14) T-cell acute lymphoblastic leukemia by interactions mediated at the nuclear matrix. Our data document homeobox gene dysregulation by a novel regulatory region at 3'-BCL11B responsive to histone deacetylase inhibition and highlight a novel class of potential therapeutic target amid noncoding DNA.

Non-random genomic divergence in repetitive sequences of human and chimpanzee in genes of different functional categories

Sequencing of the human and chimpanzee genomes has revealed approximately 99% similarity in the coding sequence between both the species, which in no way parallels the observable phenotypic differences. Contribution of the non-coding sequences which comprise a bulk of the genome, in functional divergence between human and chimpanzee, is largely understudied. In this context, we have compared extents of divergence in the non-coding repetitive DNA in a data set of well-classified neuronal and housekeeping genes between human and chimpanzee. The coding regions of these genes have earlier been extensively compared between the two species. It was revealed that the neurodevelopmental genes show accelerated evolution compared to neurophysiology and housekeeping genes in human. In this study, comparative analysis in terms of repeat spectrum, divergence in dinucleotide content density, JC divergence and its partitioning in repeats versus unique regions and transcription factor binding sites indicate different extents of functional constraints associated with the non-coding repeat regions. The constraints are also different when the upstream and downstream genic regions are compared across the functional categories. The neurodevelopmental genes seem to diverge more in the genic regions, whereas the neurophysiology genes show higher divergence in the upstream 2 kb region. Most of the divergence observed in the housekeeping genes is contributed by repeats. We also observe an accumulation of function-specific transcription factor profiles in the human lineage. Interestingly, a major fraction of the regulatory sites in these regions is differently partitioned in the repetitive sequences which in turn is dependant upon the relative distribution of the repeats across the functional categories. Thus, differential distribution of repeats across the various functional categories could have substantial effects on genome wide regulation and structure. The insights obtained from this study further add a new facet to the contribution of non-coding factors especially repeats in divergence of human and chimpanzee.

Aberrant intestinal expression and allelic variants of mucin genes associated with inflammatory bowel disease

Loss of intestinal mucosa integrity is an important factor in the pathogenesis of inflammatory bowel disease (IBD). The aim of this study was to characterize expression changes and allelic variants of genes related to intestinal epithelial barrier function in this disease. Therefore, ileal and colonic mucosal biopsies from nonaffected regions of patients with ulcerative colitis (UC) and Crohn's disease (CD), as well as non-IBD probands, were subjected to Affymetrix DNA-microarray analysis. Real-time reverse transcription polymerase chain reaction was used for verification in larger IBD sample numbers. Disturbed mRNA expression was identified for several mucin genes in both disease groups and tissues. A significant downregulation in the colon was obtained for MUC2 in CD and MUC12 in CD and UC. Expression analysis of all dysregulated mucins in a broad human tissue panel revealed dominant epithelial tissue-specific transcription. In silico analysis of the regulatory regions of these mucins indicated nuclear factor kappaB (NFkappaB) binding sites in each promoter. Furthermore, NFkappaB was overrepresented in mucin promoters and a component of a specific combination of transcription factors (composite module). In vivo stimulation experiments in the adenocarcinoma cell line LS174T showed inducible mucin expression by the cytokines tumor necrosis factor-alpha and transforming growth factor-beta, which could be blocked by NFkappaB signaling inhibitors. Allelic discrimination screening obtained statistically significant associations for the MUC2-V116M (P = 0.003) polymorphism with CD and for MUC4-A585S (P = 0.025), as well as MUC13-R502S (P = 0.0003) with UC. These data suggest that the disturbed expression of mucin genes and the connection to the NFkappaB pathway may influence the integrity of the intestine and therefore contribute to the pathophysiology of IBD.