IntegromeDB: an integrated system and biological search engine

Baitaluk, Michael; Kozhenkov, Sergey; Dubinina, Yulia; Ponomarenko, Julia

doi:10.1186/1471-2164-13-35

Cited by 10 publications

(10 citation statements)

References 28 publications

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“…The underlying hierarchical tree of TF-gene networks that controls specific expression patterns during cortical development ( Figure 3C) [12] was elucidated. Transcription modules [12,[49][50][51] co-regulated by common TFs were found, consistent with recent data [52] on evolutionary changes in promoter and enhancer activity during human corticogenesis (see also [53,54]).…”

Section: The Tf-gene Network Model and Autism Spectrum Disordersupporting

confidence: 90%

Climbing Brain Levels of Organisation from Genes to Consciousness

Changeux

2017

Trends in Cognitive Sciences

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Given the tremendous complexity of brain organisation, here I propose a strategy that dynamically links stages of brain organisation from genes to consciousness, at four privileged structural levels: genes; transcription factors (TFs)-gene networks; synaptic epigenesis; and long-range connectivity. These structures are viewed as nested and reciprocally inter-regulated, with a hierarchical organisation that proceeds on different timescales during the course of evolution and development. Interlevel bridging mechanisms include intrinsic variation-selection mechanisms, which offer a community of bottom-up and top-down models linking genes to consciousness in a stepwise manner.

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Section: The Tf-gene Network Model and Autism Spectrum Disordersupporting

confidence: 90%

Climbing Brain Levels of Organisation from Genes to Consciousness

Changeux

2017

Trends in Cognitive Sciences

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“…Experimentally validated microRNA-mRNA interactions are available from TarBase (Vergoulis et al, 2012), while predicted interactions can be accessed at TargetScan and PicTar (Lewis et al, 2005; Krek et al, 2005). To examine the signaling network in a unified fashion, recently a few integrated resources, like IntegromeDB, TranscriptomeBrowser 3.0 and SignaLink 2.0, have been developed allowing the examination of all layers from signaling pathways to microRNAs through transcription factors (Korcsmáros et al, 2010; Baitaluk et al, 2012; Lepoivre et al, 2012; Fazekas et al, 2013). …”

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

820

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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“…Biological Networks is a software suite that enables integrative analysis of interaction networks, signaling pathways, gene regulatory modules, and genomic sequences. 58,59 Uniprot accession IDs were uploaded into Biological Networks for database searching and network analysis using the shortest paths algorithm, with minimum probabilities for STRING functional relations (coexpression and cocitation) set to 0.9 (high stringency). Betweenness-centrality clustering was used to identify subnetworks, with a minimum and maximum number of nodes per cluster set to 10 and 20, respectively, and a minimum P value of 0.01.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

Identification of secreted proteins that reflect autophagy dynamics within tumor cells

et al. 2014

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Abbreviations: ATG5, autophagy-related 5; ATG7, autophagy-related 7; BECN1, Beclin 1, autophagy-related; AV, autophagic vacuole; CXCL8, chemokine (C-X-C motif) ligand 8; DKK3, dickkopf WNT signaling pathway inhibitor 3; EGF, epidermal growth factor; IF, interstitial fluid; IL1B, interleukin 1, b; LC3/MAP1LC3, microtubule-associated protein 1 light chain 3; LIF, leukemia inhibitory factor; M, media; PtdIns3K, phosphatidylinositol 3-kinase; SAM, significance analysis of microarrays.Macroautophagy, a catabolic process of cellular self-digestion, is an important tumor cell survival mechanism and a potential target in antineoplastic therapies. Recent discoveries have implicated autophagy in the cellular secretory process, but potential roles of autophagy-mediated secretion in modifying the tumor microenvironment are poorly understood. Furthermore, efforts to inhibit autophagy in clinical trials have been hampered by suboptimal methods to quantitatively measure tumor autophagy levels. Here, we leveraged the autophagy-based involvement in cellular secretion to identify shed proteins associated with autophagy levels in melanoma. The secretome of low-autophagy WM793 melanoma cells was compared to its highly autophagic metastatic derivative, 1205Lu in physiological 3-dimensional cell culture using quantitative proteomics. These comparisons identified candidate autophagy biomarkers IL1B (interleukin 1, b), CXCL8 (chemokine (C-X-C motif) ligand 8), LIF (leukemia inhibitory factor), FAM3C (family with sequence similarity 3, member C), and DKK3 (dickkopf WNT signaling pathway inhibitor 3) with known roles in inflammation and tumorigenesis, and these proteins were subsequently shown to be elevated in supernatants of an independent panel of high-autophagy melanoma cell lines. Secretion levels of these proteins increased when lowautophagy melanoma cells were treated with the autophagy-inducing tat-BECN1 (Beclin 1) peptide and decreased when ATG7 (autophagy-related 7) was silenced in high-autophagy cells, thereby supporting a mechanistic link between these secreted proteins and autophagy. In addition, serum from metastatic melanoma patients with high tumor autophagy levels exhibited higher levels of these proteins than serum from patients with low-autophagy tumors. These results suggest that autophagy-related secretion affects the tumor microenvironment and measurement of autophagyassociated secreted proteins in plasma and possibly in tumors can serve as surrogates for intracellular autophagy dynamics in tumor cells.

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IntegromeDB: an integrated system and biological search engine

Cited by 10 publications

References 28 publications

Climbing Brain Levels of Organisation from Genes to Consciousness

Climbing Brain Levels of Organisation from Genes to Consciousness

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Identification of secreted proteins that reflect autophagy dynamics within tumor cells

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