Faculty Opinions recommendation of Structural and functional protein network analyses predict novel signaling functions for rhodopsin.

Author(s):  
Kenneth Minneman
2011 ◽  
Vol 7 (1) ◽  
pp. 551 ◽  
Author(s):  
Christina Kiel ◽  
Andreas Vogt ◽  
Anne Campagna ◽  
Andrew Chatr‐aryamontri ◽  
Magdalena Swiatek‐de Lange ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarath Babu Nukala ◽  
Olga Tura-Ceide ◽  
Giancarlo Aldini ◽  
Valérie F. E. D. Smolders ◽  
Isabel Blanco ◽  
...  

AbstractChronic thromboembolic pulmonary hypertension (CTEPH) is a vascular disease characterized by the presence of organized thromboembolic material in pulmonary arteries leading to increased vascular resistance, heart failure and death. Dysfunction of endothelial cells is involved in CTEPH. The present study describes for the first time the molecular processes underlying endothelial dysfunction in the development of the CTEPH. The advanced analytical approach and the protein network analyses of patient derived CTEPH endothelial cells allowed the quantitation of 3258 proteins. The 673 differentially regulated proteins were associated with functional and disease protein network modules. The protein network analyses resulted in the characterization of dysregulated pathways associated with endothelial dysfunction, such as mitochondrial dysfunction, oxidative phosphorylation, sirtuin signaling, inflammatory response, oxidative stress and fatty acid metabolism related pathways. In addition, the quantification of advanced oxidation protein products, total protein carbonyl content, and intracellular reactive oxygen species resulted increased attesting the dysregulation of oxidative stress response. In conclusion this is the first quantitative study to highlight the involvement of endothelial dysfunction in CTEPH using patient samples and by network medicine approach.


Author(s):  
Ashish Runthala

AbstractProtein structural information is essential for the detailed mapping of a functional protein network. For a higher modelling accuracy and quicker implementation, template based algorithms have been extensively deployed and redefined. The methods only assess the predicted structure against its native state/template, and do not estimate the accuracy for each modelling step. A divergence measure is postulated to estimate the modelling accuracy against its theoretical optimal benchmark. By freezing the domain boundaries, the divergence measures are predicted for the most crucial steps of a modelling algorithm. To precisely refine the score using weighting constants, big data analysis could further be deployed.


PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13552 ◽  
Author(s):  
Benedetta Accordi ◽  
Virginia Espina ◽  
Marco Giordan ◽  
Amy VanMeter ◽  
Gloria Milani ◽  
...  

PLoS ONE ◽  
2009 ◽  
Vol 4 (6) ◽  
pp. e6017 ◽  
Author(s):  
Kakajan Komurov ◽  
Mehmet H. Gunes ◽  
Michael A. White

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.


2019 ◽  
Author(s):  
Julian Burger ◽  
Margaret S. Stroebe ◽  
Pasqualina Perrig-Chiello ◽  
Henk A.W. Schut ◽  
Stefanie Spahni ◽  
...  

Background: Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation. Methods: We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals. Results: Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were linked primarily to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals. Limitations: Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples. Conclusions: The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.


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