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Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia

Overview of attention for article published in Theoretical Biology and Medical Modelling, June 2015
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Title
Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia
Published in
Theoretical Biology and Medical Modelling, June 2015
DOI 10.1186/s12976-015-0008-z
Pubmed ID
Authors

María Camila Álvarez-Silva, Sally Yepes, Maria Mercedes Torres, Andrés Fernando González Barrios

Abstract

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood. In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state. The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer. The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 29%
Student > Bachelor 4 19%
Student > Master 3 14%
Professor 2 10%
Student > Ph. D. Student 1 5%
Other 3 14%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 29%
Biochemistry, Genetics and Molecular Biology 3 14%
Medicine and Dentistry 3 14%
Immunology and Microbiology 1 5%
Computer Science 1 5%
Other 2 10%
Unknown 5 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 June 2015.
All research outputs
#14,229,946
of 22,813,792 outputs
Outputs from Theoretical Biology and Medical Modelling
#155
of 287 outputs
Outputs of similar age
#135,897
of 264,425 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#4
of 7 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,425 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.