A Rich-Club Organization in Brain Ischemia Protein Interaction Network

Alawieh, Ali; Sabra, Zahraa; Sabra, Mohammed; Tomlinson, Stephen; Zaraket, Fadi A.

doi:10.1038/srep13513

Cited by 9 publications

(5 citation statements)

References 53 publications

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“…We also observed that this prediction performance was significantly stronger in Women compared to Men (r = 0.29 in Women and r = − 0.03 in Men of Sample 1 ). Again, a possible cause might be the involvement of ovarian hormones, targeting specifically the most densely interconnected hub structures of the connectome (Alawieh et al 2015) as well as influencing level of Extraversion (Jokela et al 2009; Ziomkiewicz et al 2012). However, more studies are needed to prove this interaction between Extraversion, estrogen, and the topographical properties of whole-brain functional connectivity.…”

Section: Discussionmentioning

confidence: 99%

Predicting personality from network-based resting-state functional connectivity

et al. 2018

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Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender.

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Section: Discussionmentioning

confidence: 99%

Predicting personality from network-based resting-state functional connectivity

et al. 2018

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“…When a PPI network related to a disease exhibits a rich-club formation, it evinces the existence of a pathological powerhouse within the network, composed of the most influential components which have to their credit of providing the network robustness and stability ( Alawieh et al, 2015 ). We evaluated the existence of a rich-club structural ordering in the SB network using brainGraph package in R and the network was found to exhibit a rich club structural ordering as indicated by an increasing rich-club coefficient ϕ( k ) as k increased ( Figure 8A ).…”

Section: Resultsmentioning

confidence: 99%

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

et al. 2021

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Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

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“…To further understand the properties of the constructed PKCζ interactome network, a rich-club analysis was conducted. A rich club is a set of high-degree nodes that are more densely interconnected than predicted by the node degrees alone [ 29 ]. The rich-club nodes may form a hub that is used by the other components in the network to influence each other.…”

Section: Resultsmentioning

confidence: 99%

“…The power-law degree distributions and adjacency matrices of the networks were generated using MATLAB. The rich-club coefficient is computed as described by others [ 28 , 29 ]. Briefly, we generated 1000 comparable random networks with equal size and the same degree distribution by rewiring to calculate the normalized rich-club coefficient.…”

Section: Methodsmentioning

confidence: 99%

Profiling the interactome of protein kinase C ζ by proteomics and bioinformatics

Hou

Liu

et al. 2018

Proteome Sci

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BackgroundProtein kinase C ζ (PKCζ), an isoform of the atypical protein kinase C, is a pivotal regulator in cancer. However, the molecular and cellular mechanisms whereby PKCζ regulates tumorigenesis and metastasis are still not fully understood. In this study, proteomics and bioinformatics analyses were performed to establish a protein-protein interaction (PPI) network associated with PKCζ, laying a stepping stone to further understand the diverse biological roles of PKCζ.MethodsProtein complexes associated with PKCζ were purified by co-immunoprecipitation from breast cancer cell MDA-MB-231 and identified by LC-MS/MS. Two biological replicates and two technical replicates were analyzed. The observed proteins were filtered using the CRAPome database to eliminate the potential false positives. The proteomics identification results were combined with PPI database search to construct the interactome network. Gene ontology (GO) and pathway analysis were performed by PANTHER database and DAVID. Next, the interaction between PKCζ and protein phosphatase 2 catalytic subunit alpha (PPP2CA) was validated by co-immunoprecipitation, Western blotting and immunofluorescence. Furthermore, the TCGA database and the COSMIC database were used to analyze the expressions of these two proteins in clinical samples.ResultsThe PKCζ centered PPI network containing 178 nodes and 1225 connections was built. Network analysis showed that the identified proteins were significantly associated with several key signaling pathways regulating cancer related cellular processes.ConclusionsThrough combining the proteomics and bioinformatics analyses, a PKCζ centered PPI network was constructed, providing a more complete picture regarding the biological roles of PKCζ in both cancer regulation and other aspects of cellular biology.Electronic supplementary materialThe online version of this article (10.1186/s12953-018-0134-8) contains supplementary material, which is available to authorized users.

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A Rich-Club Organization in Brain Ischemia Protein Interaction Network

Cited by 9 publications

References 53 publications

Predicting personality from network-based resting-state functional connectivity

Predicting personality from network-based resting-state functional connectivity

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Profiling the interactome of protein kinase C ζ by proteomics and bioinformatics

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