South Australian Medical and Health Research Institute

Adelaide, Australia

South Australian Medical and Health Research Institute

Adelaide, Australia

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Zhou T.,Flinders University | Souzeau E.,Flinders University | Sharma S.,Flinders University | Landers J.,Flinders University | And 19 more authors.
PLoS ONE | Year: 2017

Purpose To identify biological processes associated with POAG and its subtypes, high-tension (HTG) and normal-tension glaucoma (NTG), by analyzing rare potentially damaging genetic variants. Methods A total of 122 and 65 unrelated HTG and NTG participants, respectively, with early onset advanced POAG, 103 non-glaucoma controls and 993 unscreened ethnicity-matched controls were included in this study. Study participants without myocilin disease-causing variants and non-glaucoma controls were subjected to whole exome sequencing on an Illumina HiSeq2000. Exomes of participants were sequenced on an Illumina HiSeq2000. Qualifying variants were rare in the general population (MAF < 0.001) and potentially functionally damaging (nonsense, frameshift, splice or predicted pathogenic using SIFT or Polyphen2 software). Genes showing enrichment of qualifying variants in cases were selected for pathway and network analysis using InnateDB. POAG cases showed enrichment of rare variants in camera-type eye development genes (p = 1.40×10-7, corrected p = 3.28×10-4). Implicated eye development genes were related to neuronal or retinal development. HTG cases were significantly enriched for key regulators in the unfolded protein response (UPR) (p = 7.72×10-5, corrected p = 0.013). The UPR is known to be involved in myocilin-related glaucoma; our results suggest the UPR has a role in non-myocilin causes of HTG. NTG cases showed enrichment in ion channel transport processes (p = 1.05×10-4, corrected p = 0.027) including calcium, chloride and phospholipid transporters involved in plasma membrane homeostasis. Network analysis also revealed enrichment of the MHC Class I antigen presentation pathway in HTG, and the EGFR1 and cell-cycle pathways in both HTG and NTG. © 2017 Zhou et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Muetze T.,South Australian Medical and Health Research Institute | Goenawan I.H.,South Australian Medical and Health Research Institute | Wiencko H.L.,Teagasc | Bernal-Llinares M.,South Australian Medical and Health Research Institute | And 3 more authors.
F1000Research | Year: 2016

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. © 2016 Muetze T et al.


Muetze T.,South Australian Medical and Health Research Institute | Goenawan I.H.,South Australian Medical and Health Research Institute | Wiencko H.L.,Teagasc | Bernal-Llinares M.,South Australian Medical and Health Research Institute | And 3 more authors.
F1000Research | Year: 2016

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene list of interest, integrate contextual information, such as gene expression data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. © 2016 Muetze T et al.


PubMed | South Australian Medical and Health Research Institute, Teagasc and Flinders University
Type: | Journal: F1000Research | Year: 2016

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).


Goenawan I.H.,South Australian Medical and Health Research Institute | Bryan K.,South Australian Medical and Health Research Institute | Lynn D.J.,South Australian Medical and Health Research Institute | Lynn D.J.,Flinders University
Bioinformatics | Year: 2016

The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. Availability and Implementation: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). © 2016 The Author 2016. Published by Oxford University Press.


PubMed | South Australian Medical and Health Research Institute and Flinders University
Type: Journal Article | Journal: Bioinformatics (Oxford, England) | Year: 2016

: The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most rewired nodes across many network states.DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet).david.lynn@sahmri.comSupplementary data are available at Bioinformatics online.

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