Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Narang, Vipin; Ramli, Muhamad Azfar; Singhal, Amit; Kumar, Pavanish; Libero, Gennaro De; Monterola, Christopher

doi:10.1371/journal.pcbi.1004504

Cited by 32 publications

(33 citation statements)

References 60 publications

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“…When considering the statistical analysis for genes coding for TFs, significant variations occurred at 7 and 24 hours, with 66% and 62% variation, respectively. These results support the notion that the adaptation process between both conditions requires dramatic changes in the topology of the regulatory core of the GRN [ 45 – 47 ]. Interestingly, these results appeared to disagree with the original analyses [ 41 ], in which the largest difference on gene expression levels was reported at 4 and 7 hours, with 3.2% and 2.5% of the genes changing their expression, respectively.…”

Section: Resultssupporting

confidence: 87%

Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

et al. 2016

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Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto).

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Section: Resultssupporting

confidence: 87%

Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

et al. 2016

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“…A gene regulatory network (GRN) depicts how some genes encoding regulatory molecules, such as transcription factors or microRNAs, regulate the expression of other genes [ 30 ]. As the base network for the Etv5 analysis, we used a regulatory network based on biological interactions between transcription factors and their targets.…”

Section: Resultsmentioning

confidence: 99%

Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma

et al. 2018

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Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated mouse optic glioma models relative to non-neoplastic (normal healthy) optic nerves, as well as in the cognate human tumors (pilocytic astrocytoma) relative to normal human brain. Importantly, differential Etv5 and Etv5 network expression was not directly the result of Nf1 gene dysfunction in specific cell types, but rather reflects a property of the tumor as an aggregate tissue. Moreover, this differential Etv5 expression was independently validated at the RNA and protein levels. Taken together, the combined use of network analysis, differential RNA expression findings, and experimental validation highlights the potential of the computational network approach to provide new insights into tumor biology.

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“…As mentioned before, correlations between transcription rates would alter the picture regarding the processing of fluctuations [18]. Likewise, correlations between topological and transcriptional parameters, like those observed in [49], would the effects of heterogeneity, thereby significantly affecting crosstalk patterns.…”

Section: Methodological Choicesmentioning

confidence: 94%

Competing endogenous RNA crosstalk at system level

2019

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microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network’s topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.

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Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Cited by 32 publications

References 60 publications

Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma

Competing endogenous RNA crosstalk at system level

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