Construction and analysis of protein-protein interaction network of non-alcoholic fatty liver disease

Amanatidou, Athina I.; Dedoussis, George

doi:10.1016/j.compbiomed.2021.104243

Cited by 15 publications

(6 citation statements)

References 92 publications

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“…However, little is known about its regulation during stress responses, especially in pathological states. It has been reported that the expression of Tcea1 could be regulated by YEATS4, a NAFLD-related proteins, in HCC cell lines 40 , 41 . We therefore speculate that the downregulation of Tcea1 in NAFLD may be regulated by YEATS4, which should be investigate in future study.…”

Section: Discussionmentioning

confidence: 99%

Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism

et al. 2022

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Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease, which progression is tightly regulated by transcription factors (TFs), nuclear receptors, and cellular enzymes. In this study, a label-free quantitative proteomic approach was used to determine the effect of the high-fat diet on the proteomics profile of liver tissue and to identify novel NAFLD related TFs. Mice were fed with HFD for 16 weeks to establish a NAFLD mouse model. Mice fed with normal chow diet were taken as controls. Liver samples were collected from each group for proteomics analysis. A total of 2298 proteins were quantified, among which 106 proteins were downregulated, while 256 proteins were upregulated in HFD-fed mice compared with the controls with fold change more than 1.5 and p value less than 0.05. Bioinformatic analysis revealed that metabolic-related functions and pathways were most significantly enriched. A subgroup of 11 TFs were observed to share interactions with metabolic-related enzymes and kinases by protein-protein interaction analysis. Among them, 7 TFs were selected for verification, and 3 TFs were finally validated, including Rbbp4, Tcea1, and ILF2. Downregulating each of the 3 TFs could significantly promote lipid accumulation in AML12 hepatocytes, by regulating the expression of fatty acid synthesis- or β-oxidation-related genes. In contrast, overexpression of Tcea1, Rbbp4, and ILF2, respectively, could ameliorate hepatocyte steatosis. These findings propose novel lipid metabolism related TFs, which might have potential roles in preventing NAFLD.

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Section: Discussionmentioning

confidence: 99%

Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism

et al. 2022

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“…Assembly and merging are the main algorithms for horizontal integrations ( De Las Rivas, Alonso-López and Arroyo, 2018 ; Amanatidou and Dedoussis, 2021 ). Two PPI networks are assembled by alignment algorithms.…”

Section: Methods Based On Text Miningmentioning

confidence: 99%

“…This has led to the development of methods and tools that allow visualization. The integration of PPI networks and their visualizations in a multi-omics context has helped in the modeling of complex systems such as Parkinson's disease (Tomkins and Manzoni, 2021), identifying central proteins in diseases (Narayanan et al, 2011;Deng, Xu and Wang, 2019), understanding protein clusters linked to cellular function (Zhao, Wang and Wu, 2017;Amanatidou and Dedoussis, 2021), understanding mechanisms of action (Jia et al, 2021;Yuan et al, 2021), and drug repositioning (Lee and Yoon, 2018;Soleimani Zakeri, Pashazadeh and MotieGhader, 2021). Larger and complex networks are more difficult to visualize.…”

Section: Visualization Of Protein-protein Networkmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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“…As a result of the developed modularity measure and functional cohesion, protein communities are identified in this model. In [182] protein-protein interaction network of non-alcoholic fatty liver disease is analyzed. Using experimentally validated interactors as well as the interaction network of human proteins associated with Non-Alcoholic Fatty Liver Disease (NAFLD), the authors identified novel associations between human proteins that may be implicated in non-alcoholic fatty liver disease.…”

Section: Analyzing Biological Networkmentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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Construction and analysis of protein-protein interaction network of non-alcoholic fatty liver disease

Cited by 15 publications

References 92 publications

Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism

Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Community Detection Algorithms in Healthcare Applications: A Systematic Review

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