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

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.

Epigenome-Wide Changes in the Cell Layers of the Vein Wall When Exposing the Venous Endothelium to Oscillatory Shear Stress

Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from non-varicose vein segments left after surgery of 3 patients by growing the cells in selective media after magnetic immunosorting. Endothelial cells were either exposed to oscillatory shear stress or left at the static condition. Then, other cell types were treated with preconditioned media from the adjacent layer's cells. DNA isolated from the harvested cells was subjected to epigenome-wide study using Illumina microarrays followed by data analysis with GenomeStudio (Illumina), Excel (Microsoft), and Genome Enhancer (geneXplain) software packages. Differential (hypo-/hyper-) methylation was revealed for each cell layer's DNA. The most targetable master regulators controlling the activity of certain transcription factors regulating the genes near the differentially methylated sites appeared to be the following: (1) HGS, PDGFB, and AR for endothelial cells; (2) HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 for smooth muscle cells; and (3) WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN for fibroblasts. Some of the identified master regulators may serve as promising druggable targets for treating varicose veins in the future.

Bioelectronic cell-based device provides a strategy for the treatment of the experimental model of multiple sclerosis

Wireless powered optogenetic cell-based implant provides a strategy to deliver subcutaneously therapeutic proteins. Immortalize Human Mesenchymal Stem Cells (hMSC-TERT) expressing the bacteriophytochrome diguanylate cyclase (DGCL) were validated for optogenetic controlled interferon-β delivery (Optoferon cells) in a bioelectronic cell-based implant. Optoferon cells transcriptomic profiling was used to elaborate an in-silico model of the recombinant interferon-β production. Wireless optoelectronic device integration was developed using additive manufacturing and injection molding. Implant cell-based optoelectronic interface manufacturing was established to integrate industrial flexible compact low-resistance screen-printed Near Field Communication (NFC) coil antenna. Optogenetic cell-based implant biocompatibility, and device performances were evaluated in the Experimental Autoimmune Encephalomyelitis (EAE) mouse model of multiple sclerosis.

Motif and conserved module analysis in DNA (promoters, enhancers) and RNA (lncRNA, mRNA) using AlModules

Nucleic acid motifs consist of conserved and variable nucleotide regions. For functional action, several motifs are combined to modules. The tool AIModules allows identification of such motifs including combinations of them and conservation in several nucleic acid stretches. AIModules recognizes conserved motifs and combinations of motifs (modules) allowing a number of interesting biological applications such as analysis of promoter and transcription factor binding sites (TFBS), identification of conserved modules shared between several gene families, e.g. promoter regions, but also analysis of shared and conserved other DNA motifs such as enhancers and silencers, in mRNA (motifs or regulatory elements e.g. for polyadenylation) and lncRNAs. The tool AIModules presented here is an integrated solution for motif analysis, offered as a Web service as well as downloadable software. Several nucleotide sequences are queried for TFBSs using predefined matrices from the JASPAR DB or by using one's own matrices for diverse types of DNA or RNA motif discovery. Furthermore, AIModules can find TFBSs common to two or more sequences. Demanding high or low conservation, AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternative available software. The application also searches RNA motifs such as polyadenylation site or RNA-protein binding motifs as well as DNA motifs such as enhancers as well as user-specified motif combinations ( https://bioinfo-wuerz.de/aimodules/ ; alternative entry pages: https://aimodules.heinzelab.de or https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/aimodules ). The application is free and open source whether used online, on-site, or locally.

ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer

NTRK gene fusions are drivers of tumorigenesis events that specific Trk-inhibitors can target. Current knowledge of the downstream pathways activated has been previously limited to the pathways of regulator proteins phosphorylated directly by Trk receptors. Here, we aimed to detect genes whose expression is increased in response to the activation of these pathways. We identified and analyzed differentially expressed genes in thyroid cancer samples with NTRK1 or NTRK3 gene fusions, and without any NTRK fusions, versus normal thyroid gland tissues, using data from the Cancer Genome Atlas, the DESeq2 tool, and the Genome Enhancer and geneXplain platforms. Searching for the genes activated only in samples with an NTRK fusion as opposed to those without NTRK fusions, we identified 29 genes involved in nervous system development, including AUTS2, DTNA, ERBB4, FLRT2, FLRT3, RPH3A, and SCN4A. We found that genes regulating the expression of the upregulated genes (i.e., upstream regulators) were enriched in the “signaling by ERBB4” pathway. ERBB4 was also one of three genes encoding master regulators whose expression was increased only in samples with an NTRK fusion. Moreover, the algorithm searching for positive feedback loops for gene promoters and transcription factors (a so-called “walking pathways” algorithm) identified the ErbB4 protein as the key master regulator. ERBB4 upregulation (p-value = 0.004) was confirmed in an independent sample of ETV6-NTRK3-positive FFPE specimens. Thus, ErbB4 is the potential key regulator of the pathways activated by NTRK gene fusions in thyroid cancer. These results are preliminary and require additional biochemical validation.

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.

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.

Signaling Pathways Potentially Responsible for Foam Cell Formation: Cholesterol Accumulation or Inflammatory Response-What is First?

Accumulation of lipid-laden (foam) cells in the arterial wall is known to be the earliest step in the pathogenesis of atherosclerosis. There is almost no doubt that atherogenic modified low-density lipoproteins (LDL) are the main sources of accumulating lipids in foam cells. Atherogenic modified LDL are taken up by arterial cells, such as macrophages, pericytes, and smooth muscle cells in an unregulated manner bypassing the LDL receptor. The present study was conducted to reveal possible common mechanisms in the interaction of macrophages with associates of modified LDL and non-lipid latex particles of a similar size. To determine regulatory pathways that are potentially responsible for cholesterol accumulation in human macrophages after the exposure to naturally occurring atherogenic or artificially modified LDL, we used transcriptome analysis. Previous studies of our group demonstrated that any type of LDL modification facilitates the self-association of lipoprotein particles. The size of such self-associates hinders their interaction with a specific LDL receptor. As a result, self-associates are taken up by nonspecific phagocytosis bypassing the LDL receptor. That is why we used latex beads as a stimulator of macrophage phagocytotic activity. We revealed at least 12 signaling pathways that were regulated by the interaction of macrophages with the multiple-modified atherogenic naturally occurring LDL and with latex beads in a similar manner. Therefore, modified LDL was shown to stimulate phagocytosis through the upregulation of certain genes. We have identified at least three genes (F2RL1, EIF2AK3, and IL15) encoding inflammatory molecules and associated with signaling pathways that were upregulated in response to the interaction of modified LDL with macrophages. Knockdown of two of these genes, EIF2AK3 and IL15, completely suppressed cholesterol accumulation in macrophages. Correspondingly, the upregulation of EIF2AK3 and IL15 promoted cholesterol accumulation. These data confirmed our hypothesis of the following chain of events in atherosclerosis: LDL particles undergo atherogenic modification; this is 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. This chain of events may explain the relationship between cholesterol accumulation and inflammation. The primary sequence of events in this chain is related to inflammatory response rather than cholesterol accumulation.

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.

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.

Search for Master Regulators in Walking Cancer Pathways

In this chapter, we present an approach that allows a causal analysis of multiple "-omics" data with the help of an "upstream analysis" strategy. The goal of this approach is to identify master regulators in gene regulatory networks as potential drug targets for a pathological process. The data analysis strategy includes a state-of-the-art promoter analysis for potential transcription factor (TF)-binding sites using the TRANSFAC^® database combined with an analysis of the upstream signal transduction pathways that control the activity of these TFs. When applied to genes that are associated with a switch to a pathological process, the approach identifies potential key molecules (master regulators) that may exert major control over and maintenance of transient stability of the pathological state. We demonstrate this approach on examples of analysis of multi-omics data sets that contain transcriptomics and epigenomics data in cancer. The results of this analysis helped us to better understand the molecular mechanisms of cancer development and cancer drug resistance. Such an approach promises to be very effective for rapid and accurate identification of cancer drug targets with true potential. The upstream analysis approach is implemented as an automatic workflow in the geneXplain platform ( www.genexplain.com ) using the open-source BioUML framework ( www.biouml.org ).

Data on master regulators and transcription factor binding sites found by upstream analysis of multi-omics data on methotrexate resistance of colon cancer

Computational analysis of master regulators through the search for transcription factor binding sites followed by analysis of signal transduction networks of a cell is a new approach of causal analysis of multi-omics data. This paper contains results on analysis of multi-omics data that include transcriptomics, proteomics and epigenomics data of methotrexate (MTX) resistant colon cancer cell line. The data were used for analysis of mechanisms of resistance and for prediction of potential drug targets and promising compounds for reverting the MTX resistance of these cancer cells. We present all results of the analysis including the lists of identified transcription factors and their binding sites in genome and the list of predicted master regulators - potential drug targets. This data was generated in the study recently published in the article "Multi-omics "Upstream Analysis" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer" (Kel et al., 2016) [4]. These data are of interest for researchers from the field of multi-omics data analysis and for biologists who are interested in identification of novel drug targets against NTX resistance.

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.

Multi-omics "upstream analysis" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer

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Upstream analysis strategy for multi-omics data is proposed.

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Drug targets are predicted by search for TFBS and analysis of signaling network.

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Methotrexate resistance data include transcriptomics, proteomics and epigenomics.

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Predicted targets are: TGFalpha, IGFBP7, alpha9-integrin.

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Predicted drugs are: zardaverine, divalproex and human metabolite nicotinamide N-oxide.

We present an “upstream analysis” strategy for causal analysis of multiple “-omics” data. It analyzes promoters using the TRANSFAC database, combines it with an analysis of the upstream signal transduction pathways and identifies master regulators as potential drug targets for a pathological process. We applied this approach to a complex multi-omics data set that contains transcriptomics, proteomics and epigenomics data. We identified the following potential drug targets against induced resistance of cancer cells towards chemotherapy by methotrexate (MTX): TGFalpha, IGFBP7, alpha9-integrin, and the following chemical compounds: zardaverine and divalproex as well as human metabolites such as nicotinamide N-oxide.

Characterization and identification of cis-regulatory elements in Arabidopsis based on single-nucleotide polymorphism information

Identifying regulatory elements and revealing their role in gene expression regulation remains a central goal of plant genome research. We exploited the detailed genomic sequencing information of a large number of Arabidopsis (Arabidopsis thaliana) accessions to characterize known and to identify novel cis-regulatory elements in gene promoter regions of Arabidopsis by relying on conservation as the hallmark signal of functional relevance. Based on the genomic layout and the obtained density profiles of single-nucleotide polymorphisms (SNPs) in sequence regions upstream of transcription start sites, the average length of promoter regions in Arabidopsis could be established at 500 bp. Genes associated with high degrees of variability of their respective upstream regions are preferentially involved in environmental response and signaling processes, while low levels of promoter SNP density are common among housekeeping genes. Known cis-elements were found to exhibit a decreased SNP density than sequence regions not associated with known motifs. For 15 known cis-element motifs, strong positional preferences relative to the transcription start site were detected based on their promoter SNP density profiles. Five novel candidate cis-element motifs were identified as consensus motifs of 17 sequence hexamers exhibiting increased sequence conservation combined with evidence of positional preferences, annotation information, and functional relevance for inducing correlated gene expression. Our study demonstrates that the currently available resolution of SNP data offers novel ways for the identification of functional genomic elements and the characterization of gene promoter sequences.

MatrixCatch--a novel tool for the recognition of composite regulatory elements in promoters

BACKGROUND

Accurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.

RESULTS

We developed a novel method to identify composite regulatory elements in promoters using a library of known examples. In depth investigation of regularities encoded in known composite elements allowed us to introduce a new characteristic measure and to improve the specificity compared with other methods. Tests on an established benchmark and real genomic data show that our method outperforms other available methods based either on known examples or statistical evaluations. In addition to better recognition, a practical advantage of this method is first the ability to detect a high number of different types of composite elements, and second direct biological interpretation of the identified results. The program is available at http://gnaweb.helmholtz-hzi.de/cgi-bin/MCatch/MatrixCatch.pl and includes an option to extend the provided library by user supplied data.

CONCLUSIONS

The novel algorithm for the identification of composite regulatory elements presented in this paper was proved to be superior to existing methods. Its application to tissue specific promoters identified several highly specific composite elements with relevance to their biological function. This approach together with other methods will further advance the understanding of transcriptional regulation of genes.

Genome-wide transcriptional profiles during temperature and oxidative stress reveal coordinated expression patterns and overlapping regulons in rice

Genome wide transcriptional changes by cold stress, heat stress and oxidative stress in rice seedlings were analyzed. Heat stress resulted in predominant changes in transcripts of heat shock protein and heat shock transcription factor genes, as well as genes associated with synthesis of scavengers of reactive oxygen species and genes that control the level of sugars, metabolites and auxins. Cold stress treatment caused differential expression of transcripts of various transcription factors including desiccation response element binding proteins and different kinases. Transcripts of genes that are part of calcium signaling, reactive oxygen scavenging and diverse metabolic reactions were differentially expressed during cold stress. Oxidative stress induced by hydrogen peroxide treatment, resulted in significant up-regulation in transcript levels of genes related to redox homeostasis and down-regulation of transporter proteins. ROS homeostasis appeared to play central role in response to temperature extremes. The key transcription factors that may underlie the concerted transcriptional changes of specific components in various signal transduction networks involved are highlighted. Co-ordinated expression pattern and promoter architectures based analysis (promoter models and overrepresented transcription factor binding sites) suggested potential regulons involved in stress responses. A considerable overlap was noted at the level of transcription as well as in regulatory modules of differentially expressed genes.

Inferring the regulatory network behind a gene expression experiment

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

Computational identification of transcriptional regulators in human endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes.

Assessing computational methods of cis-regulatory module prediction

Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods.

Transcriptional regulation involves multiple transcription factors binding to DNA sequences. A limited repertoire of transcription factors performs this complex regulatory step through various spatial and temporal interactions between themselves and their binding sites. These transcription factor binding interactions are clustered as distinct modules: cis-regulatory modules (CRMs). Computational methods attempting to identify instances of CRMs in the genome face a challenging problem because a majority of these interactions between transcription factors remain unknown. To investigate the reliability and accuracy of these methods, we chose twelve representative methods and applied them to predict CRMs on both the fly and human genomes. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. We provide a guide for users and key considerations for developers. We also expect that, along with new technology generating new types of genomic data, future CRM prediction methods will be able to reveal transcription binding interactions in three-dimensional space.

Genome-wide discovery of human heart enhancers

The various organogenic programs deployed during embryonic development rely on the precise expression of a multitude of genes in time and space. Identifying the cis-regulatory elements responsible for this tightly orchestrated regulation of gene expression is an essential step in understanding the genetic pathways involved in development. We describe a strategy to systematically identify tissue-specific cis-regulatory elements that share combinations of sequence motifs. Using heart development as an experimental framework, we employed a combination of Gibbs sampling and linear regression to build a classifier that identifies heart enhancers based on the presence and/or absence of various sequence features, including known and putative transcription factor (TF) binding specificities. In distinguishing heart enhancers from a large pool of random noncoding sequences, the performance of our classifier is vastly superior to four commonly used methods, with an accuracy reaching 92% in cross-validation. Furthermore, most of the binding specificities learned by our method resemble the specificities of TFs widely recognized as key players in heart development and differentiation, such as SRF, MEF2, ETS1, SMAD, and GATA. Using our classifier as a predictor, a genome-wide scan identified over 40,000 novel human heart enhancers. Although the classifier used no gene expression information, these novel enhancers are strongly associated with genes expressed in the heart. Finally, in vivo tests of our predictions in mouse and zebrafish achieved a validation rate of 62%, significantly higher than what is expected by chance. These results support the existence of underlying cis-regulatory codes dictating tissue-specific transcription in mammalian genomes and validate our enhancer classifier strategy as a method to uncover these regulatory codes.

Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower

The molecular mechanisms by which floral homeotic genes act as major developmental switches to specify the identity of floral organs are still largely unknown. Floral homeotic genes encode transcription factors of the MADS-box family, which are supposed to assemble in a combinatorial fashion into organ-specific multimeric protein complexes. Major mediators of protein interactions are MADS-domain proteins of the SEPALLATA subfamily, which play a crucial role in the development of all types of floral organs. In order to characterize the roles of the SEPALLATA3 transcription factor complexes at the molecular level, we analyzed genome-wide the direct targets of SEPALLATA3. We used chromatin immunoprecipitation followed by ultrahigh-throughput sequencing or hybridization to whole-genome tiling arrays to obtain genome-wide DNA-binding patterns of SEPALLATA3. The results demonstrate that SEPALLATA3 binds to thousands of sites in the genome. Most potential target sites that were strongly bound in wild-type inflorescences are also bound in the floral homeotic agamous mutant, which displays only the perianth organs, sepals, and petals. Characterization of the target genes shows that SEPALLATA3 integrates and modulates different growth-related and hormonal pathways in a combinatorial fashion with other MADS-box proteins and possibly with non-MADS transcription factors. In particular, the results suggest multiple links between SEPALLATA3 and auxin signaling pathways. Our gene expression analyses link the genomic binding site data with the phenotype of plants expressing a dominant repressor version of SEPALLATA3, suggesting that it modulates auxin response to facilitate floral organ outgrowth and morphogenesis. Furthermore, the binding of the SEPALLATA3 protein to cis-regulatory elements of other MADS-box genes and expression analyses reveal that this protein is a key component in the regulatory transcriptional network underlying the formation of floral organs.

Most regulatory genes encode transcription factors, which modulate gene expression by binding to regulatory sequences of their target genes. In plants in particular, which genes are directly controlled by these transcription factors, and the molecular mechanisms of target gene recognition in vivo, are still largely unexplored. One of the best-understood developmental processes in plants is flower development. In different combinations, transcription factors of the MADS-box family control the identities of the different types of floral organs: sepals, petals, stamens, and carpels. Here, we present the first genome-wide analysis of binding sites of a MADS-box transcription factor in plants. We show that the MADS-domain protein SEPALLATA3 (SEP3) binds to the regulatory regions of thousands of potential target genes, many of which are also transcription factors. We provide insight into mechanisms of DNA recognition by SEP3, and suggest roles for other transcription factor families in SEP3 target gene regulation. In addition to effects on genes involved in floral organ identity, our data suggest that SEP3 binds to, and modulates, the transcription of target genes involved in hormonal signaling pathways.

The key floral regulator SEPALLATA3 binds to the promoters of a large number of potential direct target genes to integrate different growth-related and hormonal pathways in flower development.

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.

HIF-1alpha is up-regulated in activated mast cells by a process that involves calcineurin and NFAT

Mast cells play important roles in many pathological conditions where local hypoxia is observed, including asthma, rheumatic diseases, and certain types of cancer. Here, we investigated how expression of the hypoxia-inducible factor 1, alpha subunit gene (HIF1A), is regulated in mast cells. The product of HIF1A is hypoxia-inducible factor 1alpha (HIF-1alpha), is a major nuclear transcription factor modulating gene expression in response to hypoxic conditions. We observed that under hypoxic conditions, exposure of mast cells to ionomycin and substance P resulted in significant up-regulation of HIF1A expression as compared with resting mast cells incubated under identical conditions. The ionomycin-mediated increase in HIF-1alpha protein levels was sensitive to the transcription inhibitor actinomycin D and to inhibitors of calcineurin, cyclosporin A (CsA), and FK506. The increased HIF-1alpha protein level was paralleled by a severalfold increase in HIF-1alpha mRNA that could be also inhibited with actinomycin D and CsA. The HIF1A promoter activity was significantly increased in ionomycin-activated mast cells, and the promoter activity could be inhibited by CsA and FK506. Furthermore, in situ mutagenesis experiments showed that the ionomycin-mediated HIF1A promoter activity depends on a conservative NFAT-binding site. Thus, accumulation of HIF-1alpha in activated mast cells requires up-regulation of HIF1A gene transcription and depends on the calcineurin-NFAT signaling pathway.

Prediction of Cis-Regulatory Elements Controlling Genes Differentially Expressed by Retinal and Choroidal Vascular Endothelial Cells

Cultured endothelial cells of the human retina and choroid demonstrate distinct patterns of gene expression. We hypothesized that differential gene expression reflected differences in the interactions of transcription factors and respective cis-regulatory motifs(s) in these two endothelial cell subpopulations, recognizing that motifs often exist as modules. We tested this hypothesis in silico by using TRANSFAC Professional and CisModule to identify cis-regulatory motifs and modules in genes that were differentially expressed by human retinal versus choroidal endothelial cells, as identified by analysis of a microarray data set. Motifs corresponding to eight transcription factors were significantly (p < 0.05) differentially abundant in genes that were relatively highly expressed in retinal (i.e., glucocorticoid receptor, high mobility group AT-hook 1, heat shock transcription factor 1, p53, vitamin D receptor) or choroidal (i.e., transcription factor E2F, Yin Yang 1, zinc finger 5) endothelial cells. Predicted cis-regulatory modules were quite different for these two groups of genes. Our findings raise the possibility of exploiting specific cis-regulatory motifs to target therapy at the ocular endothelial cells subtypes responsible for neovascular age-related macular degeneration or proliferative diabetic retinopathy.

Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation

BACKGROUND

The regulation of genes in multicellular organisms is generally achieved through the combinatorial activity of different transcription factors. However, the quantitative mechanisms of how a combination of transcription factors controls the expression of their target genes remain unknown.

RESULTS

By using the information on the yeast transcription network and high-resolution time-series data, the combinatorial expression profiles of regulators that best correlate with the expression of their target genes are identified. We demonstrate that a number of factors, particularly time-shifts among the different regulators as well as conversion efficiencies of transcription factor mRNAs into functional binding regulators, play a key role in the quantification of target gene expression. By quantifying and integrating these factors, we have found a highly significant correlation between the combinatorial time-series expression profile of regulators and their target gene expression in 67.1% of the 161 known yeast three-regulator motifs and in 32.9% of 544 two-regulator motifs. For network motifs involved in the cell cycle, these percentages are much higher. Furthermore, the results have been verified with a high consistency in a second independent set of time-series data. Additional support comes from the finding that a high percentage of motifs again show a significant correlation in time-series data from stress-response studies.

CONCLUSION

Our data strongly support the concept that dynamic cumulative regulation is a major principle of quantitative transcriptional control. The proposed concept might also apply to other organisms and could be relevant for a wide range of biotechnological applications in which quantitative gene regulation plays a role.

ChIPCodis: mining complex regulatory systems in yeast by concurrent enrichment analysis of chip-on-chip data

MOTIVATION

Eukaryotic genes are often regulated by multiple transcription factors (TFs). Depending on the interactions among different TFs the expression of a gene can be tuned to respond to diverse environmental conditions. Chip-on-chip experiments provide a snapshot of which TF are in vivo bound to which genes in a particular condition, and have been applied to characterize the regulatory code of yeast under several experimental settings. ChIPCodis mines this data to provide new insights about how the expression of a particular group of genes is regulated. For a given list of yeast genes ChIPCodis determines which combinations of TFs are significantly over-represented in a series of environmental conditions.

AVAILABILITY

http://chipcodis.dacya.ucm.es

Assessment of composite motif discovery methods

Background

Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery – discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery.

Results

We have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise.

Conclusion

Although some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context

An application of machine learning algorithms enables prediction of the functional context of transcription factor binding sites in the human genome.

We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4α binding sites and significantly improved an overall recognition of faithful HNF4α targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.

ClusterDraw web server: a tool to identify and visualize clusters of binding motifs for transcription factors

ClusterDraw is a program aimed to identification of binding sites and binding-site clusters. Major difference of the ClusterDraw from existing tools is its ability to scan a wide range of parameter values and weigh statistical significance of all possible clusters, smaller than a selected size. The program produces graphs along with decorated FASTA files. ClusterDraw web server is available at the following URL: http://flydev.berkeley.edu/cgi-bin/cld/submit.cgi

A distance difference matrix approach to identifying transcription factors that regulate differential gene expression

We introduce a method that considers target genes of a transcription factor, and searches for transcription factor binding sites (TFBSs) of secondary factors responsible for differential responses among these targets. Based on the distance difference matrix concept, the method simultaneously integrates statistical overrepresentation and co-occurrence of TFBSs. Our approach is validated on datasets of differentially regulated human genes and is shown to be highly effective in detecting TFBSs responsible for the observed differential gene expression.

CoMoDis: composite motif discovery in mammalian genomes

Specificity of mammalian gene regulatory regions is achieved to a large extent through the combinatorial binding of sets of transcription factors to distinct binding sites, discrete combinations of which are often referred to as regulatory modules. Identification and subsequent characterization of gene regulatory modules will be a key step in assembling transcriptional regulatory networks from gene expression profiling data, with the ultimate goal of unravelling the regulatory codes that govern gene expression in various cell types. Here we describe the new bioinformatics tool, Composite Motif Discovery (CoMoDis), which streamlines computational identification of novel regulatory modules starting from a single seed motif. Seed motifs represent binding sites conserved across mammalian species. CoMoDis facilitates novel motif discovery by automating the extraction of DNA sequences flanking seed motifs and streamlining downstream motif discovery using a variety of tools, including several that utilize phylogenetic conservation criteria. CoMoDis is available at .