Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Farooqui, Anam; Tazyeen, Safia; Ahmed, Mohd Murshad; Alam, Aftab; Ali, Shahnawaz; Malik, Md. Zubbair; Ishrat, Romana

doi:10.1038/s41598-018-28375-0

Cited by 30 publications

(16 citation statements)

References 62 publications

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“…Since ovarian cancer PPI interaction network follows hierarchical scale free nature, the emergence of modules in the network is significant and therefore both these sparsely distributed few leading hubs regulate and modules, which is organize the network. Application of Newmann and Girvan’s standard community finding algorithm, the modular structure and their organization at different levels of organization were established (see Methods) [14, 17, 32]. Applying the present algorithm, the ovarian cancer network is established to be hierarchically organized through five various levels of organization shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

et al. 2019

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BackgroundIdentification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance.MethodsThe protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network.ResultsThe PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest.ConclusionsThe popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

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Section: Resultsmentioning

confidence: 99%

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

et al. 2019

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“…Turner syndrome patients surviving to adulthood are most probably mosaic cases [115] where some cells have two X chromosomes or an X and a Y chromosome. Network analysis found that KDM6A is a key regulator in Turner syndrome [116]. KDM6A is a potential candidate gene for premature ovarian failure in Turner syndrome [117,118] because of its role in fertility and pluripotency (Section 2.3), and may be involved in gonadal dysgenesis [119].…”

Section: Kdm6a In Genetic Conditionsmentioning

confidence: 99%

Lysine demethylases KDM6A and UTY: The X and Y of histone demethylation

Gažová

Lengeling

Summers

2019

Molecular Genetics and Metabolism

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Histone demethylases remove transcriptional repressive marks from histones in the nucleus. KDM6A (also known as UTX) is a lysine demethylase which acts on the trimethylated lysine at position 27 in histone 3. The KDM6A gene is located on the X chromosome but escapes X inactivation even though it is not located in the pseudoautosomal region. There is a homologue of KDM6A on the Y chromosome, known as UTY. UTY was thought to have lost its demethylase activity and to represent a non-functional remnant of the ancestral KDM6A gene. However, results with knockout mice suggest that the gene is expressed and the protein performs some function within the cell. Female mice with homozygous deletion of Kdm6a do not survive, but hemizygous males are viable, attributed to the presence of the Uty gene. KDM6A is mutated in the human condition Kabuki syndrome type 2 (OMIM 300867) and in many cases of cancer. The amino acid sequence of KDM6A has been conserved across animal phyla, although it is only found on the X chromosome in eutherian mammals. In this review, we reanalyse existing data from various sources (protein sequence comparison, evolutionary genetics, transcription factor binding and gene expression analysis) to determine the function, expression and evolution of KDM6A and UTY and show that UTY has a functional role similar to KDM6A in metabolism and development.

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“…GCN hubs) with biological functions/processes in cells and tissues 25 , 26 . Actually, targeted hub attacks in protein-protein and gene-gene networks have been used to disclose relevant functional genes in health and disease 26 – 28 . Therefore, GCN hubs are relevant both for network topology and cell functioning.…”

Section: Resultsmentioning

confidence: 99%

Minipuberty and Sexual Dimorphism in the Infant Human Thymus

Moreira-Filho

Bando

Bertonha

et al. 2018

Sci Rep

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AIRE expression in thymus is downregulated by estrogen after puberty, what probably renders women more susceptible to autoimmune disorders. Here we investigated the effects of minipuberty on male and female infant human thymic tissue in order to verify if this initial transient increase in sex hormones - along the first six months of life - could affect thymic transcriptional network regulation and AIRE expression. Gene co-expression network analysis for differentially expressed genes and miRNA-target analysis revealed sex differences in thymic tissue during minipuberty, but such differences were not detected in the thymic tissue of infants aged 7–18 months, i.e. the non-puberty group. AIRE expression was essentially the same in both sexes in minipuberty and in non-puberty groups, as assessed by genomic and immunohistochemical assays. However, AIRE-interactors networks showed several differences in all groups regarding gene-gene expression correlation. Therefore, minipuberty and genomic mechanisms interact in shaping thymic sexual dimorphism along the first six months of life.

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Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Cited by 30 publications

References 62 publications

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

Lysine demethylases KDM6A and UTY: The X and Y of histone demethylation

Minipuberty and Sexual Dimorphism in the Infant Human Thymus

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