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

Kel, Alexander; Stegmaier, Philip; Valeev, Tagir; Koschmann, Jeannette; Poroikov, Vladimir; Kel-Margoulis, Olga V.; Wingender, Edgar

doi:10.1016/j.euprot.2016.09.002

Cited by 38 publications

(48 citation statements)

References 48 publications

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“…In this work, we applied these algorithms to analyze promoters of Up-regulated genes in Nutlin-3 moderately resistant NSCLC cell lines (А427 and H292) in comparison to the Nutlin-3 sensitive cell line H1944. We focused our attention on the up-regulated genes in order to reveal the potential resistance-specific positive feedback loops (as it is described in our previous paper [10]) that are maintained mainly through gene up-regulation. For the promoter analysis, we restricted the list of up-regulated genes by two criteria: The FC>1.5…”

Section: Enriched Transcription Factor Binding Sites In Promoters Of mentioning

confidence: 99%

“…A comprehensive signal transduction network of human cells is built by the network analysis module of the BioUML platform using reactions annotated in TRANSPATH ® . The main algorithm of the master regulator search has been described earlier [10]. The goal of the algorithm is to find nodes in the global signal transduction network that may potentially regulate the activity of the set of transcription factors found at the previous step of analysis.…”

Section: Finding Master Regulators In Networkmentioning

confidence: 99%

“…The algorithm for TFBS enrichment analysis, called F-Match, has been initially described in [14]. Briefly, as it has been described in detail previously [10], the procedure finds a critical value (a threshold) for the score of each PWM in the library that maximizes the Yes/No ratio R YN as defined in Equation (1) under the constraint of statistical significance.…”

Section: Analysis Of Enriched Transcription Factor Binding Sitesmentioning

confidence: 99%

“…At the Genome Enhancer module, the analysis of gene regulatory regions of differentially expressed genes is performed using the Match™ [10] and CMA [16] tools (which in turn use the TRANSFAC ® [13], HOCOMOCO [17] and GTRD [18] databases of position weight matrices) in order to detect transcription factor binding sites (TFBSs) in the promoters and enhancers of DEGs. ChIP-seq peaks and CpG methylation sites are used in this module to help to define the enhancer and silencer regions of the differentially expressed genes.…”

Section: Computational Pipeline «From Genome To Target»mentioning

confidence: 99%

“…We revealed a number of differentially expressed genes (DEGs) between sensitive and resistant cell lines. Promoter and pathway analysis of these DEGs using the "upstream analysis" approach [10] helped us to identify potential master regulators in these cancer cell lines responsible for the elevated resistance to Nutlin-3. Among them, the most promising was mTOR as one of the most important regulators of pro-survival mechanisms in the cell.…”

Section: Introductionmentioning

confidence: 99%

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Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Boyarskikh

Pintus²,

Mandrik³

et al. 2017

Virtual Biology

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SummaryThe tumor suppressor protein p53 is one of the most important targets in various cancers including non-small cell lung cancer (NSCLC). The small molecule Nutlin-3 reactivates p53 in cancer cells by interacting with the complex between p53 and its repressor Mdm-2, which leads to increased apoptosis of cancer cells. Nevertheless, clinical and experimental studies of Nutlin-3 have shown that in some cases cancer cells are not sensitive to this compound. In this paper we studied possible mechanisms of such a resistance of cancer cells to Nutlin-3 through increased activity of specific pro-survival pathways that appeared to be more active in the resistant cells, as compared to the cells sensitive to the treatment by this compound. Using genome-wide gene expression profiling we compared several NSCLC cell lines. Nutlin-3 resistant cell lines showed a significantly different gene expression in comparison with sensitive cell lines before and after treatment by Nutlin-3.Using original bioinformatics approaches we analyzed the revealed gene expression patterns and identified complexes of transcription factors specifically regulating different expression of genes in the resistant cell lines. Analysis of the signal transduction network upstream from transcription factors allowed us to identify potential master-regulators responsible for 2 maintaining this resistant state. Among them the most promising was mTOR acting in the context of the activated PI3K pathway. With the help of chemical inhibitors, we were able to validate these findings experimentally. The Nutlin-3 resistant cell lines showed the highest sensitivity to the inhibitor of mTOR with PI3K, which lead to the rapid death of these cells.

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Section: Enriched Transcription Factor Binding Sites In Promoters Of mentioning

confidence: 99%

Section: Finding Master Regulators In Networkmentioning

confidence: 99%

Section: Analysis Of Enriched Transcription Factor Binding Sitesmentioning

confidence: 99%

Section: Computational Pipeline «From Genome To Target»mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Boyarskikh

Pintus²,

Mandrik³

et al. 2017

Virtual Biology

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Tailor‐Made Gold Nanomaterials for Applications in Soft Bioelectronics and Optoelectronics

Zhang,

Liu,

et al. 2024

Advanced Materials

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In modern nanoscience and nanotechnology, gold nanomaterials are indispensable building blocks that have demonstrated a plethora of applications in catalysis, biology, bioelectronics, and optoelectronics. Gold nanomaterials possess many appealing material properties, such as facile control over their size/shape and surface functionality, intrinsic chemical inertness yet with high biocompatibility, adjustable localized surface plasmon resonances, tunable conductivity, wide electrochemical window, etc. Such material attributes have been recently utilized for designing and fabricating soft bioelectronics and optoelectronics. This motivates to give a comprehensive overview of this burgeoning field. The discussion of representative tailor‐made gold nanomaterials, including gold nanocrystals, ultrathin gold nanowires, vertically aligned gold nanowires, hard template‐assisted gold nanowires/gold nanotubes, bimetallic/trimetallic gold nanowires, gold nanomeshes, and gold nanosheets, is begun. This is followed by the description of various fabrication methodologies for state‐of‐the‐art applications such as strain sensors, pressure sensors, electrochemical sensors, electrophysiological devices, energy‐storage devices, energy‐harvesting devices, optoelectronics, and others. Finally, the remaining challenges and opportunities are discussed.

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Search for Master Regulators in Walking Cancer Pathways

Kel

2017

Methods in Molecular Biology

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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 ).

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

Abstract: Graphical abstract

Cited by 38 publications

References 48 publications

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Tailor‐Made Gold Nanomaterials for Applications in Soft Bioelectronics and Optoelectronics

Search for Master Regulators in Walking Cancer Pathways

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